**Arguments:**
-
input: an {{MLOperand}}. The input 2-D tensor of shape *[batchSize, inputSize]*.
- -
weight: an {{MLOperand}}. The 2-D input weight tensor of shape *[3 * hiddenSize, inputSize]*. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to *options.layout*.
- -
recurrentWeight: an {{MLOperand}}. The 2-D recurrent weight tensor of shape *[3 * hiddenSize, hiddenSize]*. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to *options.layout*.
+ -
weight: an {{MLOperand}}. The 2-D input weight tensor of shape *[3 * hiddenSize, inputSize]*. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to *options*.{{MLGruCellOptions/layout}}.
+ -
recurrentWeight: an {{MLOperand}}. The 2-D recurrent weight tensor of shape *[3 * hiddenSize, hiddenSize]*. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to *options*.{{MLGruCellOptions/layout}}.
-
hiddenState: an {{MLOperand}}. The 2-D input hidden state tensor of shape *[batchSize, hiddenSize]*.
-
hiddenSize: an {{unsigned long}} scalar. The value of the second dimension of the output tensor shape. It indicates the number of features in the hidden state.
-
options: an optional {{MLGruCellOptions}}. The optional parameters of the operation.
@@ -4816,11 +4816,11 @@ partial dictionary MLOpSupportLimits {
: scale
::
- The 1-D tensor of the scaling values whose [=list/size=] is equal to the number of channels, i.e. the size of the feature dimension of the input. For example, for an |input| tensor with {{MLInputOperandLayout/"nchw"}} layout, the [=list/size=] is equal to |input|'s [=MLOperand/shape=][1].
+ The 1-D tensor of the scaling values whose [=list/size=] is equal to the number of channels, i.e. the size of the feature dimension of the input. For example, for an *input* tensor with {{MLInputOperandLayout/"nchw"}} layout, the [=list/size=] is equal to *input*'s [=MLOperand/shape=][1].
: bias
::
- The 1-D tensor of the bias values whose [=list/size=] is equal to the size of the feature dimension of the input. For example, for an |input| tensor with {{MLInputOperandLayout/"nchw"}} layout, the [=list/size=] is equal to |input|'s [=MLOperand/shape=][1].
+ The 1-D tensor of the bias values whose [=list/size=] is equal to the size of the feature dimension of the input. For example, for an *input* tensor with {{MLInputOperandLayout/"nchw"}} layout, the [=list/size=] is equal to *input*'s [=MLOperand/shape=][1].
: epsilon
::
@@ -4971,11 +4971,11 @@ partial dictionary MLOpSupportLimits {
: scale
::
- The N-D tensor of the scaling values whose shape is determined by the |axes| member in that each value in |axes| indicates the dimension of the input tensor with scaling values. For example, for an |axes| values of [1,2,3], the shape of this tensor is the list of the corresponding sizes of the input dimension 1, 2 and 3. When this member is not present, the scaling value is assumed to be 1.
+ The N-D tensor of the scaling values whose shape is determined by the {{MLLayerNormalizationOptions/axes}} member in that each value in {{MLLayerNormalizationOptions/axes}} indicates the dimension of the input tensor with scaling values. For example, for an {{MLLayerNormalizationOptions/axes}} values of [1,2,3], the shape of this tensor is the list of the corresponding sizes of the input dimension 1, 2 and 3. When this member is not present, the scaling value is assumed to be 1.
: bias
::
- The N-D tensor of the bias values whose shape is determined by the |axes| member in that each value in |axes| indicates the dimension of the input tensor with bias values. For example, for an |axes| values of [1,2,3], the shape of this tensor is the list of the corresponding sizes of the input dimension 1, 2 and 3. When this member is not present, the bias value is assumed to be 0.
+ The N-D tensor of the bias values whose shape is determined by the {{MLLayerNormalizationOptions/axes}} member in that each value in {{MLLayerNormalizationOptions/axes}} indicates the dimension of the input tensor with bias values. For example, for an {{MLLayerNormalizationOptions/axes}} values of [1,2,3], the shape of this tensor is the list of the corresponding sizes of the input dimension 1, 2 and 3. When this member is not present, the bias value is assumed to be 0.
: axes
::
@@ -5376,13 +5376,13 @@ partial dictionary MLOpSupportLimits {
**Arguments:**
- input: an {{MLOperand}}. The input 3-D tensor of shape *[steps, batchSize, inputSize]*.
- - weight: an {{MLOperand}}. The 3-D input weight tensor of shape *[numDirections, 4 * hiddenSize, inputSize]*. The ordering of the weight vectors in the second dimension of the tensor shape is specified according to the |options|.{{MLLstmOptions/layout}}.
- - recurrentWeight: an {{MLOperand}}. The 3-D recurrent weight tensor of shape *[numDirections, 4 * hiddenSize, hiddenSize]*. The ordering of the weight vectors in the second dimension of the tensor shape is specified according to |options|.{{MLLstmOptions/layout}}.
+ - weight: an {{MLOperand}}. The 3-D input weight tensor of shape *[numDirections, 4 * hiddenSize, inputSize]*. The ordering of the weight vectors in the second dimension of the tensor shape is specified according to *options*.{{MLLstmOptions/layout}}.
+ - recurrentWeight: an {{MLOperand}}. The 3-D recurrent weight tensor of shape *[numDirections, 4 * hiddenSize, hiddenSize]*. The ordering of the weight vectors in the second dimension of the tensor shape is specified according to *options*.{{MLLstmOptions/layout}}.
- steps: an {{unsigned long}} scalar. The number of time steps in the recurrent network. The value must be greater than 0.
- hiddenSize: an {{unsigned long}} scalar. The value of the third dimension of the cell output tensor shape. It indicates the number of features in the hidden state.
- options: an optional {{MLLstmOptions}}. The optional parameters of the operation.
- **Returns:** [=sequence=]<{{MLOperand}}>. The first element is a 3-D tensor of shape *[numDirections, batchSize, hiddenSize]*, the output hidden state from the last time step of the network. The second element is a 3-D tensor of shape *[numDirections, batchSize, hiddenSize]*, the output cell state from the last time step of the network. Additionally, if |options|.{{MLLstmOptions/returnSequence}} is set to true, the third element is the 4-D output tensor of shape *[steps, numDirections, batchSize, hiddenSize]* containing every output from each time step in the temporal sequence.
+ **Returns:** [=sequence=]<{{MLOperand}}>. The first element is a 3-D tensor of shape *[numDirections, batchSize, hiddenSize]*, the output hidden state from the last time step of the network. The second element is a 3-D tensor of shape *[numDirections, batchSize, hiddenSize]*, the output cell state from the last time step of the network. Additionally, if *options*.{{MLLstmOptions/returnSequence}} is set to true, the third element is the 4-D output tensor of shape *[steps, numDirections, batchSize, hiddenSize]* containing every output from each time step in the temporal sequence.
@@ -5735,8 +5735,8 @@ partial dictionary MLOpSupportLimits {
**Arguments:**
- input: an {{MLOperand}}. The input 2-D tensor of shape *[batchSize, inputSize]*.
- - weight: an {{MLOperand}}. The 2-D input weight tensor of shape *[4 * hiddenSize, inputSize]*. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to *options.layout*.
- - recurrentWeight: an {{MLOperand}}. The 2-D recurrent weight tensor of shape *[4 * hiddenSize, hiddenSize]*. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to *options.layout*.
+ - weight: an {{MLOperand}}. The 2-D input weight tensor of shape *[4 * hiddenSize, inputSize]*. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to *options*.{{MLLstmCellOptions/layout}}.
+ - recurrentWeight: an {{MLOperand}}. The 2-D recurrent weight tensor of shape *[4 * hiddenSize, hiddenSize]*. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to *options*.{{MLLstmCellOptions/layout}}.
- hiddenState: an {{MLOperand}}. The 2-D input hidden state tensor of shape *[batchSize, hiddenSize]*.
- cellState: an {{MLOperand}}. The 2-D input cell state tensor of shape *[batchSize, hiddenSize]*.
- hiddenSize: an {{unsigned long}} scalar. The value of the second dimension of the output tensor shape. It indicates the number of features in the hidden state.
diff --git a/tools/lint.mjs b/tools/lint.mjs
index 6eedb82f..07d0e506 100755
--- a/tools/lint.mjs
+++ b/tools/lint.mjs
@@ -251,6 +251,13 @@ for (const algorithm of root.querySelectorAll('.algorithm')) {
}
}
+// Eschew vars outside of algorithms.
+const algorithmVars = new Set(root.querySelectorAll('.algorithm var'));
+for (const v of root.querySelectorAll('var').filter(v => !algorithmVars.has(v))) {
+ error(`Variable outside of algorithm: ${v.innerText}`);
+}
+
+
// Prevent accidental normative references to other specs. This reports an error
// if there is a normative reference to any spec *other* than these ones. This
// helps avoid an autolink like [=object=] adding an unexpected reference to