Div - 1 vs 13#

Next section compares an older to a newer version of the same operator after both definition are converted into markdown text. Green means an addition to the newer version, red means a deletion. Anything else is unchanged.

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  1. Div1 → Div13 +34 -12
Div1 → Div13 RENAMED
@@ -1 +1 @@
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- Performs element-wise binary division (with Numpy-style broadcasting support).
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+ Performs element-wise binary division (with limited broadcast support).
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+ If necessary the right-hand-side argument will be broadcasted to match the
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- This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check Broadcasting in ONNX <https://github.com/onnx/onnx/blob/master/docs/Broadcasting.md>_.
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+ shape of left-hand-side argument. When broadcasting is specified, the second
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+ tensor can either be of element size 1 (including a scalar tensor and any
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+ tensor with rank equal to or smaller than the first tensor), or having its
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+ shape as a contiguous subset of the first tensor's shape. The starting of the
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+ mutually equal shape is specified by the argument "axis", and if it is not set,
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+ suffix matching is assumed. 1-dim expansion doesn't work yet.
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+
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+ For example, the following tensor shapes are supported (with broadcast=1):
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+
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+ shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
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+ shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
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+ shape(A) = (2, 3, 4, 5), shape(B) = (5,)
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+ shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
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+ shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
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+ shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0
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+
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+ Attribute broadcast=1 needs to be passed to enable broadcasting.
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+
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+ **Attributes**
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+
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+ * **axis**:
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+ If set, defines the broadcast dimensions. See doc for details.
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+ * **broadcast**:
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+ Pass 1 to enable broadcasting
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+ * **consumed_inputs**:
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+ legacy optimization attribute.
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  **Inputs**
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  * **A** (heterogeneous) - **T**:
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- First operand.
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+ First operand, should share the type with the second operand.
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  * **B** (heterogeneous) - **T**:
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- Second operand.
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+ Second operand. With broadcasting can be of smaller size than A. If
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+ broadcasting is disabled it should be of the same size.
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  **Outputs**
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  * **C** (heterogeneous) - **T**:
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- Result, has same element type as two inputs
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+ Result, has same dimensions and type as A
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  **Type Constraints**
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  * **T** in (
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- tensor(bfloat16),
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  tensor(double),
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  tensor(float),
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- tensor(float16),
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+ tensor(float16)
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- tensor(int32),
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- tensor(int64),
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- tensor(uint32),
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- tensor(uint64)
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  ):
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- Constrain input and output types to high-precision numeric tensors.? ^^^^^^^^^^^^ ^^^^^^^^^
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+ Constrain input and output types to float tensors.? ^^ ^^