forked from mindspore-Ecosystem/mindspore
!12338 [Docs] update formulas for math and array operators
From: @david-he91 Reviewed-by: @ljl0711,@liangchenghui Signed-off-by: @liangchenghui
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6b7766b530
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@ -3275,11 +3275,9 @@ class ScatterUpdate(_ScatterOp_Dynamic):
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Using given values to update tensor value, along with the input indices.
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for each `i, ..., j` in `indices.shape`:
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.. math::
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\begin{array}{l}
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\text {for each i, ..., j in indices.shape:} \\
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input\_x[indices[i, ..., j], :] = updates[i, ..., j, :]
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\end{array}
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\text{input_x}[\text{indices}[i, ..., j], :] = \text{updates}[i, ..., j, :]
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Inputs of `input_x` and `updates` comply with the implicit type conversion rules to make the data types consistent.
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If they have different data types, lower priority data type will be converted to
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@ -3393,11 +3391,10 @@ class ScatterMax(_ScatterOp):
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Using given values to update tensor value through the max operation, along with the input indices.
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This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
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for each `i, ..., j` in `indices.shape`:
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.. math::
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\begin{array}{l}
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\text {for each i, ..., j in indices.shape:} \\
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input\_x[indices[i, ..., j], :] = max(input\_x[indices[i, ..., j], :], updates[i, ..., j, :])
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\end{array}
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\text{input_x}[\text{indices}[i, ..., j], :]
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= max(\text{input_x}[\text{indices}[i, ..., j], :], \text{updates}[i, ..., j, :])
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Inputs of `input_x` and `updates` comply with the implicit type conversion rules to make the data types consistent.
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If they have different data types, lower priority data type will be converted to
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@ -3438,11 +3435,10 @@ class ScatterMin(_ScatterOp):
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Using given values to update tensor value through the min operation, along with the input indices.
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This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
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for each `i, ..., j` in `indices.shape`:
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.. math::
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\begin{array}{l}
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\text {for each i, ..., j in indices.shape:} \\
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input\_x[indices[i, ..., j], :] = min(input\_x[indices[i, ..., j], :], updates[i, ..., j, :])
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\end{array}
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\text{input_x}[\text{indices}[i, ..., j], :]
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= min(\text{input_x}[\text{indices}[i, ..., j], :], \text{updates}[i, ..., j, :])
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Inputs of `input_x` and `updates` comply with the implicit type conversion rules to make the data types consistent.
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If they have different data types, lower priority data type will be converted to
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@ -3483,11 +3479,9 @@ class ScatterAdd(_ScatterOp_Dynamic):
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Using given values to update tensor value through the add operation, along with the input indices.
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This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
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for each `i, ..., j` in `indices.shape`:
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.. math::
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\begin{array}{l}
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\text {for each i, ..., j in indices.shape:} \\
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input\_x[indices[i, ..., j], :] \mathrel{+}= updates[i, ..., j, :]
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\end{array}
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\text{input_x}[\text{indices}[i, ..., j], :] \mathrel{+}= \text{updates}[i, ..., j, :]
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Inputs of `input_x` and `updates` comply with the implicit type conversion rules to make the data types consistent.
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If they have different data types, lower priority data type will be converted to
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@ -3535,11 +3529,9 @@ class ScatterSub(_ScatterOp):
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Using given values to update tensor value through the subtraction operation, along with the input indices.
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This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
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for each `i, ..., j` in `indices.shape`:
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.. math::
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\begin{array}{l}
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\text {for each i, ..., j in indices.shape:} \\
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input\_x[indices[i, ..., j], :] \mathrel{-}= updates[i, ..., j, :]
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\end{array}
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\text{input_x}[\text{indices}[i, ..., j], :] \mathrel{-}= \text{updates}[i, ..., j, :]
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Inputs of `input_x` and `updates` comply with the implicit type conversion rules to make the data types consistent.
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If they have different data types, lower priority data type will be converted to
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@ -3581,11 +3573,9 @@ class ScatterMul(_ScatterOp):
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Using given values to update tensor value through the mul operation, along with the input indices.
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This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
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for each `i, ..., j` in `indices.shape`:
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.. math::
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\begin{array}{l}
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\text {for each i, ..., j in indices.shape:} \\
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input\_x[indices[i, ..., j], :] \mathrel{*}= updates[i, ..., j, :]
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\end{array}
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\text{input_x}[\text{indices}[i, ..., j], :] \mathrel{*}= \text{updates}[i, ..., j, :]
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Inputs of `input_x` and `updates` comply with the implicit type conversion rules to make the data types consistent.
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If they have different data types, lower priority data type will be converted to
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@ -3626,11 +3616,9 @@ class ScatterDiv(_ScatterOp):
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Using given values to update tensor value through the div operation, along with the input indices.
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This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
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for each `i, ..., j` in `indices.shape`:
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.. math::
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\begin{array}{l}
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\text {for each i, ..., j in indices.shape:} \\
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input\_x[indices[i, ..., j], :] \mathrel{/}= updates[i, ..., j, :]
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\end{array}
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\text{input_x}[\text{indices}[i, ..., j], :] \mathrel{/}= \text{updates}[i, ..., j, :]
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Inputs of `input_x` and `updates` comply with the implicit type conversion rules to make the data types consistent.
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If they have different data types, lower priority data type will be converted to
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@ -791,9 +791,10 @@ class MatMul(PrimitiveWithCheck):
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class BatchMatMul(MatMul):
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"""
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Computes matrix multiplication between two tensors by batch
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Computes matrix multiplication between two tensors by batch.
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`result[..., :, :] = tensor(a[..., :, :]) * tensor(b[..., :, :])`.
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.. math::
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\text{output}[..., :, :] = \text{matrix}(a[..., :, :]) * \text{matrix}(b[..., :, :])
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The two input tensors must have the same rank and the rank must be not less than `3`.
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