diff --git a/imperative/python/megengine/functional/nn.py b/imperative/python/megengine/functional/nn.py
index 14e4fe83ae4c83334728b7c42ceb7a733c68f817..527f77213abd337304933045d29d53c7adc4b2c1 100644
--- a/imperative/python/megengine/functional/nn.py
+++ b/imperative/python/megengine/functional/nn.py
@@ -1454,7 +1454,7 @@ def indexing_one_hot(
     return result
 
 
-def nms(boxes: Tensor, iou_thresh: float, scores: Optional[Tensor] = None) -> Tensor:
+def nms(boxes: Tensor, scores: Tensor, iou_thresh: float) -> Tensor:
     r"""
     Performs non-maximum suppression (NMS) on the boxes according to their intersection-over-union (IoU).
 
@@ -1477,7 +1477,7 @@ def nms(boxes: Tensor, iou_thresh: float, scores: Optional[Tensor] = None) -> Te
         x[:,2:] = np.random.rand(100,2)*20 + 100
         scores = tensor(np.random.rand(100))
         inp = tensor(x)
-        result = F.nms(inp, iou_thresh=0.7, scores=scores)
+        result = F.nms(inp, scores, iou_thresh=0.7)
         print(result.numpy())
 
     Outputs:
@@ -1490,24 +1490,27 @@ def nms(boxes: Tensor, iou_thresh: float, scores: Optional[Tensor] = None) -> Te
     assert (
         boxes.ndim == 2 and boxes.shape[1] == 4
     ), "the expected shape of boxes is (N, 4)"
+    assert scores.ndim == 1, "the expected shape of scores is (N,)"
+    assert (
+        boxes.shape[0] == scores.shape[0]
+    ), "number of boxes and scores are not matched"
 
-    sorted_idx = None
-    if not scores is None:
-        assert scores.ndim == 1, "the expected shape of scores is (N,)"
-        sorted_idx = argsort(scores, descending=True)
-        boxes = boxes[sorted_idx]
+    boxes = boxes.detach()
+    scores = scores.detach()
+    sorted_idx = argsort(scores, descending=True)
+    boxes = boxes[sorted_idx]
     max_output = boxes.shape[0]
 
     op = builtin.NMSKeep(iou_thresh, max_output)
     inp = utils.convert_inputs(boxes.reshape(1, -1, 4))
     indices, count = apply(op, *inp)
     indices = indices[0][: count.item()]
-    ret = sorted_idx[indices] if sorted_idx is not None else indices
-    return ret
+    keep_inds = sorted_idx[indices]
+    return keep_inds
 
 
 def batched_nms(
-    boxes: Tensor, iou_thresh: float, idxs: Tensor, scores: Optional[Tensor] = None
+    boxes: Tensor, scores: Tensor, idxs: Tensor, iou_thresh: float,
 ) -> Tensor:
     r"""
     Performs non-maximum suppression (NMS) on the boxes according to their intersection-over-union (IoU).
@@ -1533,7 +1536,7 @@ def batched_nms(
         scores = tensor(np.random.rand(100))
         idxs =  tensor(np.random.randint(0, 10, 100))
         inp = tensor(x)
-        result = F.batched_nms(inp, iou_thresh=0.6, idxs=idxs, scores=scores)
+        result = F.batched_nms(inp, scores, idxs, iou_thresh=0.6)
         print(result.numpy())
 
     Outputs:
@@ -1546,20 +1549,24 @@ def batched_nms(
     assert (
         boxes.ndim == 2 and boxes.shape[1] == 4
     ), "the expected shape of boxes is (N, 4)"
+    assert scores.ndim == 1, "the expected shape of scores is (N,)"
+    assert idxs.ndim == 1, "the expected shape of idxs is (N,)"
+    assert boxes.shape[0] == scores.shape[0] == idxs.shape[0]
+
+    boxes = boxes.detach()
+    scores = scores.detach()
+    idxs = idxs.detach()
     max_coordinate = boxes.max()
     offsets = idxs.astype("float32") * (max_coordinate + 1)
     boxes = boxes + offsets.reshape(-1, 1).broadcast(boxes.shape[0], 4)
 
-    sorted_idx = None
-    if not scores is None:
-        assert scores.ndim == 1, "the expected shape of scores is (N,)"
-        sorted_idx = argsort(scores, descending=True)
-        boxes = boxes[sorted_idx]
+    sorted_idx = argsort(scores, descending=True)
+    boxes = boxes[sorted_idx]
     max_output = boxes.shape[0]
 
     op = builtin.NMSKeep(iou_thresh, max_output)
     inp = utils.convert_inputs(boxes.reshape(1, -1, 4))
     indices, count = apply(op, *inp)
     indices = indices[0][: count.item()]
-    ret = sorted_idx[indices] if sorted_idx is not None else indices
-    return ret
+    keep_inds = sorted_idx[indices]
+    return keep_inds
diff --git a/imperative/python/megengine/functional/tensor.py b/imperative/python/megengine/functional/tensor.py
index ef1e3a76e89512631a02543ef9ef120ccbf2bafa..e63ed3462d18c98267b4f8d4817d563f66c889d9 100644
--- a/imperative/python/megengine/functional/tensor.py
+++ b/imperative/python/megengine/functional/tensor.py
@@ -231,6 +231,9 @@ def concat(
          [ 9. 10. 11.]]
 
     """
+    if len(inps) == 1:
+        return inps[0]
+
     dtype = dtype_promotion(inps)
     device = get_device(inps)
 
diff --git a/imperative/python/test/unit/functional/test_functional.py b/imperative/python/test/unit/functional/test_functional.py
index 58a582d0542742826ca32df7067ea2bf3cffc83b..3401ad102bd2c15d8fb6b325b0e9afeb8745a7a2 100644
--- a/imperative/python/test/unit/functional/test_functional.py
+++ b/imperative/python/test/unit/functional/test_functional.py
@@ -470,7 +470,7 @@ def test_nms():
     )
     inp = tensor(x)
     scores = tensor([0.5, 0.8, 0.9, 0.6], dtype=np.float32)
-    result = F.nms(inp, iou_thresh=0.5, scores=scores)
+    result = F.nms(inp, scores=scores, iou_thresh=0.5)
     np.testing.assert_equal(result.numpy(), np.array([2, 1, 3], dtype=np.int32))
 
 
@@ -489,7 +489,7 @@ def test_batched_nms():
     inp = tensor(x)
     scores = tensor([0.6, 0.9, 0.5, 0.6, 0.8, 0.7], dtype=np.float32)
     idxs = tensor([0, 1, 0, 1, 0, 1], dtype=np.int32)
-    results = F.batched_nms(inp, iou_thresh=0.5, idxs=idxs, scores=scores)
+    results = F.batched_nms(inp, scores=scores, idxs=idxs, iou_thresh=0.5)
     np.testing.assert_equal(results.numpy(), np.array([1, 4, 5], dtype=np.int32))