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Add the linear function algorithm #417

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Jun 29, 2023
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Add the linear function algorithm #417

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95 changes: 82 additions & 13 deletions index.bs
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Expand Up @@ -2654,29 +2654,20 @@ partial interface MLGraphBuilder {

### The linear() method ### {#api-mlgraphbuilder-linear}
Calculate a linear function `y = alpha * x + beta` on the input tensor.

<script type=idl>
dictionary MLLinearOptions {
float alpha = 1;
float beta = 0;
};

partial interface MLGraphBuilder {
MLOperand linear(MLOperand x, optional MLLinearOptions options = {});
MLOperand linear(MLOperand input, optional MLLinearOptions options = {});
MLActivation linear(optional MLLinearOptions options = {});
};
</script>
<div algorithm=linear>
**Arguments:**
- *x*: an {{MLOperand}}. The input tensor.
- *options*: an optional {{MLLinearOptions}}. The optional parameters of the operation.
- *alpha*: a {{float}} scalar multiplier, default to 1.
- *beta*: a {{float}} scalar addition, default to 0.

**Returns:**
- an {{MLOperand}}. The output tensor of the same shape as *x*.
- an {{MLActivation}}. The activation function representing the linear operation.

<div class="note">
<div class="note">
The behavior of this operation can be generically emulated from the usage of
other operations as follow. However, user agents typically have a more
efficient implementation for it, therefore its usage is encouraged from the
Expand All @@ -2686,9 +2677,87 @@ partial interface MLGraphBuilder {
builder.mul(x, builder.constant(options.alpha)),
builder.constant(options.beta));
</pre>
</div>
</div>

{{MLLinearOptions}} has the following members:
<dl dfn-type=dict-member dfn-for=MLLinearOptions>
: <dfn>alpha</dfn>
::
A {{float}} scalar multiplier.
The default value is `1`.
: <dfn>beta</dfn>
::
A {{float}} scalar addition.
The default value is `0`.
</dl>

<details open>
<summary>
To <dfn>check linear options</dfn> given |options|, run the following steps:
</summary>
<div algorithm=check-linear-options class=algorithm-steps>
1. If |options| is not an [=object=] that [=implements=] {{MLLinearOptions}}, then return `false`.
1. If |options|.{{MLEluOptions/alpha}} is `undefined`, set |options|.{{MLLinearOptions/alpha}} to `1`.
1. Else if |options|.{{MLLinearOptions/alpha}} is not a [=numeric type=], then then return `false`.
1. If |options|.{{MLLinearOptions/beta}} is `undefined`, set |options|.{{MLLinearOptions/beta}} to `0`.
1. Else if |options|.{{MLLinearOptions/beta}} is not a [=numeric type=], then then return `false`.
1. Return `true`.
</div>
</details>

#### The {{MLGraphBuilder/linear(input, options)}} method #### {#api-mlgraphbuilder-linear-input-options}
<div>
**Arguments:**
- *input*: an {{MLOperand}}. The input tensor.
- *options*: an optional {{MLLinearOptions}}. The optional parameters of the operation.

**Returns:**
- an {{MLOperand}}. The output tensor of the same shape as *x*.
</div>

<details open>
<summary>
The {{MLGraphBuilder/linear(input, options)}} method steps are:
</summary>
<div algorithm=linear-input-options class=algorithm-steps>
1. Let |input| be the first argument.
1. Let |options| be the second argument.
1. If running the <a>check linear options</a> steps with |options| returns `false`, then throw a "{{TypeError}}" {{DOMException}} and abort these steps.
1. If any of the following sub-steps fail, throw an "{{OperationError}}" {{DOMException}} and stop.
1. Let |output| be the result of invoking the <a>copy MLOperand</a> steps given |input|.
1. Make a request to the underlying platform to:
1. Let |opImpl| be an [=implementation-defined=] platform operator for the linear operation, given |options|.
1. Store a reference of |opImpl| in |output|.{{MLOperand/[[operator]]}}.
1. Create an [=implementation-defined=] platform operand |outputImpl| to represent the output, given |output| and |opImpl|.
1. Store a reference to |outputImpl| in |output|.{{MLOperand/[[operand]]}}.
1. Connect |input|.{{MLOperand/[[operand]]}} as input to |opImpl|.
1. Connect |output|.{{MLOperand/[[operand]]}} as output to |opImpl|.
1. Return |output|.
</div>
</details>

#### The {{MLGraphBuilder/linear(options)}} method #### {#api-mlgraphbuilder-linear-options}
<div>
**Arguments:**
- *options*: an optional {{MLLinearOptions}}. The optional parameters of the operation.

**Returns:**
- an {{MLActivation}}. The activation function representing the linear operation.
</div>

<details open>
<summary>
The {{MLGraphBuilder/linear(options)}} method steps are:
</summary>
<div algorithm=linear-options class=algorithm-steps>
1. Let |options| be the first argument.
1. If running the <a>check linear options</a> steps with |options| returns `false`, then throw a "{{TypeError}}" {{DOMException}} and abort these steps.
1. Let |op| be the result of invoking the <a>create MLActivation</a> steps with `"linear"` and |options|.
1. If that throws an error, re-throw the error and abort these steps.
1. Return |op|.
</div>
</details>

### The lstm() method ### {#api-mlgraphbuilder-lstm}
Long Short-Term Memory [[LSTM]] recurrent network uses an input, output, forget, and cell gate to compute the output state that rolls into the output across the temporal sequence of the network.
<script type=idl>
Expand Down