add op: ReverseSequence

mindspore/ops/_op_impl/aicpu/__init__.py

@@ -27,3 +27,4 @@ from .random_choice_with_mask import _random_choice_with_mask_aicpu
 from .ctcloss import _ctcloss_aicpu
 from .rnnt_loss import _rnnt_loss_aicpu
 from .random_categorical import _random_categorical_aicpu
+from .reverse_sequence import _reverse_sequence_aicpu

mindspore/ops/_op_impl/aicpu/reverse_sequence.py

@@ -0,0 +1,78 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""ReverseSequence op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+reverse_sequence_op_info = AiCPURegOp("ReverseSequence") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x", "required") \
+    .input(1, "seq_lengths", "required") \
+    .output(0, "y", "required") \
+    .attr("seq_dim", "int") \
+    .attr("batch_dim", "int") \
+    .dtype_format(DataType.BOOL_Default, DataType.I32_Default, DataType.BOOL_Default) \
+    .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.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) \
+    .dtype_format(DataType.U32_Default, DataType.I32_Default, DataType.U32_Default) \
+    .dtype_format(DataType.U64_Default, DataType.I32_Default, DataType.U64_Default) \
+    .dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F64_Default, DataType.I32_Default, DataType.F64_Default) \
+    .dtype_format(DataType.BOOL_NCHW, DataType.I32_NCHW, DataType.BOOL_NCHW) \
+    .dtype_format(DataType.I8_NCHW, DataType.I32_NCHW, DataType.I8_NCHW) \
+    .dtype_format(DataType.I16_NCHW, DataType.I32_NCHW, DataType.I16_NCHW) \
+    .dtype_format(DataType.I32_NCHW, DataType.I32_NCHW, DataType.I32_NCHW) \
+    .dtype_format(DataType.I64_NCHW, DataType.I32_NCHW, DataType.I64_NCHW) \
+    .dtype_format(DataType.U8_NCHW, DataType.I32_NCHW, DataType.U8_NCHW) \
+    .dtype_format(DataType.U16_NCHW, DataType.I32_NCHW, DataType.U16_NCHW) \
+    .dtype_format(DataType.U32_NCHW, DataType.I32_NCHW, DataType.U32_NCHW) \
+    .dtype_format(DataType.U64_NCHW, DataType.I32_NCHW, DataType.U64_NCHW) \
+    .dtype_format(DataType.F16_NCHW, DataType.I32_NCHW, DataType.F16_NCHW) \
+    .dtype_format(DataType.F32_NCHW, DataType.I32_NCHW, DataType.F32_NCHW) \
+    .dtype_format(DataType.F64_NCHW, DataType.I32_NCHW, DataType.F64_NCHW) \
+    .dtype_format(DataType.BOOL_Default, DataType.I64_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I8_Default, DataType.I64_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I16_Default, DataType.I64_Default, DataType.I16_Default) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.U8_Default, DataType.I64_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U16_Default, DataType.I64_Default, DataType.U16_Default) \
+    .dtype_format(DataType.U32_Default, DataType.I64_Default, DataType.U32_Default) \
+    .dtype_format(DataType.U64_Default, DataType.I64_Default, DataType.U64_Default) \
+    .dtype_format(DataType.F16_Default, DataType.I64_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I64_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F64_Default, DataType.I64_Default, DataType.F64_Default) \
+    .dtype_format(DataType.BOOL_NCHW, DataType.I64_NCHW, DataType.BOOL_NCHW) \
+    .dtype_format(DataType.I8_NCHW, DataType.I64_NCHW, DataType.I8_NCHW) \
+    .dtype_format(DataType.I16_NCHW, DataType.I64_NCHW, DataType.I16_NCHW) \
+    .dtype_format(DataType.I32_NCHW, DataType.I64_NCHW, DataType.I32_NCHW) \
+    .dtype_format(DataType.I64_NCHW, DataType.I64_NCHW, DataType.I64_NCHW) \
+    .dtype_format(DataType.U8_NCHW, DataType.I64_NCHW, DataType.U8_NCHW) \
+    .dtype_format(DataType.U16_NCHW, DataType.I64_NCHW, DataType.U16_NCHW) \
+    .dtype_format(DataType.U32_NCHW, DataType.I64_NCHW, DataType.U32_NCHW) \
+    .dtype_format(DataType.U64_NCHW, DataType.I64_NCHW, DataType.U64_NCHW) \
+    .dtype_format(DataType.F16_NCHW, DataType.I64_NCHW, DataType.F16_NCHW) \
+    .dtype_format(DataType.F32_NCHW, DataType.I64_NCHW, DataType.F32_NCHW) \
+    .dtype_format(DataType.F64_NCHW, DataType.I64_NCHW, DataType.F64_NCHW) \
+    .get_op_info()
+
+@op_info_register(reverse_sequence_op_info)
+def _reverse_sequence_aicpu():
+    """ReverseSequence AiCPU register"""
+    return

mindspore/ops/operations/__init__.py

@@ -30,7 +30,7 @@ from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack,
                         Squeeze, StridedSlice, Tile,
                         Transpose, TruncatedNormal, TupleToArray, UnsortedSegmentMin,
                         UnsortedSegmentSum, SpaceToDepth, DepthToSpace, SpaceToBatch, BatchToSpace,
-                        SpaceToBatchND, BatchToSpaceND)
+                        SpaceToBatchND, BatchToSpaceND, ReverseSequence)
 from .comm_ops import (AllGather, AllReduce, _AlltoAll, ReduceScatter, Broadcast,
                        _MirrorOperator, ReduceOp, _VirtualDataset,
                        _VirtualDiv, _GetTensorSlice,
@@ -278,6 +278,7 @@ __all__ = [
     "ApplyCenteredRMSProp",
     "SpaceToBatchND",
     "BatchToSpaceND",
+    "ReverseSequence",
     "SquareSumAll",
     "BitwiseAnd",
     "BitwiseOr",

mindspore/ops/operations/array_ops.py

@@ -2720,3 +2720,54 @@ class BatchToSpaceND(PrimitiveWithInfer):
                              f'block_shape_prod {block_shape_prod}')
         out_shape[0] = out_shape[0] // block_shape_prod
         return out_shape
+
+
+class ReverseSequence(PrimitiveWithInfer):
+    """
+    Reverses variable length slices.
+
+    Note:
+        If the specified axis is a negative number, the index is counted
+        backward from the end and starts at 1.
+
+    Raises:
+        ValueError: If axis is not an integer or not in the valid range.
+    Args:
+        seq_dim (int): The dimension which is partially reversed. Required.
+        batch_dim (int): The dimension along which reversal is performed. Default: 0
+
+    Inputs:
+        - **x** (Tensor) - The input to reverse.
+        - **seq_lengths** (int) - Must be 1-D vector with types: int32, int64
+
+    Outputs:
+        Reversed tensor with the same shape and data type as x.
+
+    Examples:
+        >>> x = Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mindspore.float32)
+        >>> seq_lengths = Tensor(np.array([1, 2, 3]))
+        >>> reverse_sequence = P.ReverseSequence(seq_dim=1)
+        >>> output = reverse_sequence(x, seq_lengths)
+    """
+
+    @prim_attr_register
+    def __init__(self, seq_dim, batch_dim=0):
+        """init ReverseSequence"""
+        self.init_prim_io_names(inputs=['x', 'seq_lengths'], outputs=['y'])
+        validator.check_value_type("seq_dim", seq_dim, [int], self.name)
+        self.seq_dim_ = seq_dim
+        validator.check_value_type("batch_dim", batch_dim, [int], self.name)
+        self.batch_dim_ = batch_dim
+
+    def infer_shape(self, x, seq_lengths):
+        validator.check("seq_dim", self.seq_dim_, "x rank", len(x), Rel.LE, self.name)
+        validator.check("batch_dim", self.batch_dim_, "x rank", len(x), Rel.LE, self.name)
+        validator.check("batch_dim", self.batch_dim_, "seq_dim", self.seq_dim_, Rel.NE, self.name)
+        validator.check("seq_lengths rank", len(seq_lengths), "expected", 1, Rel.EQ, self.name)
+        validator.check("seq_lengths vector size", seq_lengths[0],
+                        "input size along batch_dim", x[self.batch_dim_], Rel.EQ, self.name)
+        return x
+
+    def infer_dtype(self, x, seq_lengths):
+        validator.check_tensor_type_same({"seq_lengths_dtype": seq_lengths}, [mstype.int32, mstype.int64], self.name)
+        return x