Update Slice with dynamic input & optional input steps (#1836)

* Update Slice with dynamic input & input steps

Author: BowenBao

docs/Changelog.md

@@ -846,72 +846,6 @@ This version of the operator has been available since version 1 of the default O
 <dd>Constrain input and output types to float tensors.</dd>
 </dl>
 
-### <a name="DynamicSlice-1"></a>**DynamicSlice-1**</a>
-
-  Produces a slice of the input tensor along multiple axes. Similar to numpy:
-  https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
-  Slices uses `axes`, `starts` and `ends` inputs to specify the start and end
-  dimension for each axis in the list of axes, it uses this information to
-  slice the input `data` tensor. If a negative value is passed for any of the
-  start or end indices, it represent number of elements before the end of that
-  dimension. If the value passed to start or end is larger than the `n` (the
-  number of elements in this dimension), it represents `n`. For slicing to the
-  end of a dimension with unknown size, it is recommended to pass in `INT_MAX`.
-  If `axes` are omitted, they are set to `[0, ..., ndim-1]`.
-  Example 1:
-    data = [
-        [1, 2, 3, 4],
-        [5, 6, 7, 8],
-    ]
-    axes = [0, 1]
-    starts = [1, 0]
-    ends = [2, 3]
-    result = [
-        [5, 6, 7],
-    ]
-  Example 2:
-    data = [
-        [1, 2, 3, 4],
-        [5, 6, 7, 8],
-    ]
-    starts = [0, 1]
-    ends = [-1, 1000]
-    result = [
-        [2, 3, 4],
-    ]
-
-#### Version
-
-No versioning maintained for experimental ops.
-#### Inputs (3 - 4)
-
-<dl>
-<dt><tt>data</tt> : T</dt>
-<dd>Tensor of data to extract slices from.</dd>
-<dt><tt>starts</tt> : Tind</dt>
-<dd>1-D tensor of starting indices of corresponding axis in `axes`</dd>
-<dt><tt>ends</tt> : Tind</dt>
-<dd>1-D tensor of ending indices (exclusive) of corresponding axis in axes</dd>
-<dt><tt>axes</tt> (optional) : Tind</dt>
-<dd>1-D tensor of axes that `starts` and `ends` apply to.</dd>
-</dl>
-
-#### Outputs
-
-<dl>
-<dt><tt>output</tt> : T</dt>
-<dd>Sliced data tensor.</dd>
-</dl>
-
-#### Type Constraints
-
-<dl>
-<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
-<dd>Constrain input and output types to all tensor types.</dd>
-<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
-<dd>Constrain indices to integer types</dd>
-</dl>
-
 ### <a name="Elu-1"></a>**Elu-1**</a>
 
   Elu takes one input data (Tensor<T>) and produces one output data
@@ -9554,6 +9488,78 @@ This version of the operator has been available since version 9 of the default O
 </dl>
 
 ## Version 10 of the default ONNX operator set
+### <a name="Slice-10"></a>**Slice-10**</a>
+
+  Produces a slice of the input tensor along multiple axes. Similar to numpy:
+  https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
+  Slices uses `starts`, `ends`, `axes` and `steps` inputs to specify the start and end
+  dimension and step for each axis in the list of axes, it uses this information to
+  slice the input `data` tensor. If a negative value is passed for any of the
+  start or end indices, it represent number of elements before the end of that
+  dimension. If the value passed to start or end is larger than the `n` (the
+  number of elements in this dimension), it represents `n`. For slicing to the
+  end of a dimension with unknown size, it is recommended to pass in `INT_MAX`.
+  If a negative value is passed for step, it represents slicing backward.
+  If `axes` are omitted, they are set to `[0, ..., ndim-1]`.
+  If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)`
+  Example 1:
+    data = [
+        [1, 2, 3, 4],
+        [5, 6, 7, 8],
+    ]
+    axes = [0, 1]
+    starts = [1, 0]
+    ends = [2, 3]
+    steps = [1, 2]
+    result = [
+        [5, 7],
+    ]
+  Example 2:
+    data = [
+        [1, 2, 3, 4],
+        [5, 6, 7, 8],
+    ]
+    starts = [0, 1]
+    ends = [-1, 1000]
+    result = [
+        [2, 3, 4],
+    ]
+
+#### Version
+
+This version of the operator has been available since version 10 of the default ONNX operator set.
+
+#### Inputs (3 - 5)
+
+<dl>
+<dt><tt>data</tt> : T</dt>
+<dd>Tensor of data to extract slices from.</dd>
+<dt><tt>starts</tt> : Tind</dt>
+<dd>1-D tensor of starting indices of corresponding axis in `axes`</dd>
+<dt><tt>ends</tt> : Tind</dt>
+<dd>1-D tensor of ending indices (exclusive) of corresponding axis in `axes`</dd>
+<dt><tt>axes</tt> (optional) : Tind</dt>
+<dd>1-D tensor of axes that `starts` and `ends` apply to.</dd>
+<dt><tt>steps</tt> (optional) : Tind</dt>
+<dd>1-D tensor of slice step of corresponding axis in `axes`. Default to 1. </dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>output</tt> : T</dt>
+<dd>Sliced data tensor.</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
+<dd>Constrain input and output types to all tensor types.</dd>
+<dt><tt>Tind</tt> : tensor(int32), tensor(int64)</dt>
+<dd>Constrain indices to integer types</dd>
+</dl>
+
 ### <a name="StringNormalizer-10"></a>**StringNormalizer-10**</a>
 
   StringNormalization performs string operations for basic cleaning.