fix aicpu ut

mindspore/ops/_op_impl/aicpu/reverse_sequence.py

@@ -26,6 +26,7 @@ reverse_sequence_op_info = AiCPURegOp("ReverseSequence") \
     .dtype_format(DataType.I8_Default, DataType.I32_Default, DataType.I8_Default) \
     .dtype_format(DataType.I16_Default, DataType.I32_Default, DataType.I16_Default) \
     .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I64_Default, DataType.I32_Default) \
     .dtype_format(DataType.I64_Default, DataType.I32_Default, DataType.I64_Default) \
     .dtype_format(DataType.U8_Default, DataType.I32_Default, DataType.U8_Default) \
     .dtype_format(DataType.U16_Default, DataType.I32_Default, DataType.U16_Default) \

mindspore/ops/operations/nn_ops.py

@@ -1892,7 +1892,7 @@ class RNNTLoss(PrimitiveWithInfer):
         - **acts** (Tensor) - Tensor of shape :math:`(B, T, U, V)`. Data type should be float16 or float32.
         - **labels** (Tensor[int32]) - Tensor of shape :math:`(B, U-1)`.
         - **input_lengths** (Tensor[int32]) - Tensor of shape :math:`(B,)`.
-        - **label_lebgths** (Tensor[int32]) - Tensor of shape :math:`(B,)`.
+        - **label_lengths** (Tensor[int32]) - Tensor of shape :math:`(B,)`.
 
     Outputs:
         - **costs** (Tensor[int32]) - Tensor of shape :math:`(B,)`.

tests/st/ops/ascend/test_aicpu_ops/test_ctc_loss.py

@@ -17,7 +17,6 @@ import numpy as np
 import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
-from mindspore.common.api import ms_function
 from mindspore.ops import operations as P
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
@@ -28,16 +27,15 @@ class Net(nn.Cell):
         super(Net, self).__init__()
         self.ctc_loss = P.CTCLoss()
 
-    @ms_function
     def construct(self, inputs, labels_indices, labels_values, sequence_length):
         return self.ctc_loss(inputs, labels_indices, labels_values, sequence_length)
 
 
 def test_net_float32():
-    x = np.rand.randn(2, 2, 3).astype(np.float32)
-    labels_indices = np.array([[0, 0], [1, 0]]).astype(np.int64)
-    labels_values = np.array([2, 2]).astype(np.int32)
+    x = np.random.randn(2, 2, 3).astype(np.float32)
+    labels_indices = np.array([[0, 1], [1, 0]]).astype(np.int64)
+    labels_values = np.array([1, 2]).astype(np.int32)
     sequence_length = np.array([2, 2]).astype(np.int32)
     net = Net()
     output = net(Tensor(x), Tensor(labels_indices), Tensor(labels_values), Tensor(sequence_length))
-    print(output.asnumpy())
+    print(output)

tests/st/ops/ascend/test_aicpu_ops/test_reverse_sequence.py

@@ -40,7 +40,7 @@ def test_net_int8():
     batch_dim = 1
     net = Net(seq_dim, batch_dim)
     output = net(Tensor(x), Tensor(seq_lengths))
-    expected = np.array([1, 5, 9], [4, 2, 6], [7, 8, 3]).astype(np.int8)
+    expected = np.array([[1, 5, 9], [4, 2, 6], [7, 8, 3]]).astype(np.int8)
     assert np.array_equal(output.asnumpy(), expected)
 
 
@@ -51,5 +51,5 @@ def test_net_int32():
     batch_dim = 0
     net = Net(seq_dim, batch_dim)
     output = net(Tensor(x), Tensor(seq_lengths))
-    expected = np.array([1, 2, 3], [5, 4, 6], [9, 8, 7]).astype(np.int32)
+    expected = np.array([[1, 2, 3], [5, 4, 6], [9, 8, 7]]).astype(np.int32)
     assert np.array_equal(output.asnumpy(), expected)