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Bugfix: avoid squeezing batch size 1 dimension in lstm and gru emulation code #913

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Merged
fdwr merged 1 commit into from
Jan 9, 2026
Merged

Bugfix: avoid squeezing batch size 1 dimension in lstm and gru emulation code #913

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14 changes: 7 additions & 7 deletions index.bs
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Original file line number Diff line number Diff line change
Expand Up @@ -5630,14 +5630,14 @@ partial dictionary MLOpSupportLimits {
null);
let currentHidden = squeeze(
builder,
builder.slice(hiddenState, [dir, 0, 0], [1, batchSize, hiddenSize]));
builder.slice(hiddenState, [dir, 0, 0], [1, batchSize, hiddenSize]), [0]);

for (let step = 0; step < steps; ++step) {
const slice =
(dir == 1 || direction == 'backward' ? steps - step - 1 : step);
const currentInput = squeeze(
builder,
builder.slice(input, [slice, 0, 0], [1, batchSize, inputSize]));
builder.slice(input, [slice, 0, 0], [1, batchSize, inputSize]), [0]);

currentHidden = builder.gruCell(
currentInput,
Expand Down Expand Up @@ -7011,17 +7011,17 @@ partial dictionary MLOpSupportLimits {

let currentHidden = squeeze(
builder,
builder.slice(hiddenState, [dir, 0, 0], [1, batchSize, hiddenSize]));
builder.slice(hiddenState, [dir, 0, 0], [1, batchSize, hiddenSize]), [0]);
let currentCell = squeeze(
builder,
builder.slice(cellState, [dir, 0, 0], [1, batchSize, hiddenSize]));
builder.slice(cellState, [dir, 0, 0], [1, batchSize, hiddenSize]), [0]);

for (let step = 0; step < steps; ++step) {
const slice =
(dir == 1 || direction == 'backward' ? steps - step - 1 : step);
const currentInput = squeeze(
builder,
builder.slice(input, [slice, 0, 0], [1, batchSize, inputSize]));
builder.slice(input, [slice, 0, 0], [1, batchSize, inputSize]), [0]);

[currentHidden, currentCell] = builder.lstmCell(
currentInput,
Expand Down Expand Up @@ -10466,7 +10466,7 @@ Operations present in other neural network inference APIs can often be emulated
axes.push(i);
});
const shape = Array.from(input.shape);
for (let axis in axes.sort().reverse())
for (let axis of axes.sort().reverse())
if (axis < shape.length && shape[axis] == 1)
shape.splice(axis, 1);
return builder.reshape(input, shape);
Expand All @@ -10485,7 +10485,7 @@ Operations present in other neural network inference APIs can often be emulated
<pre highlight="js">
function unsqueeze(builder, input, axes) {
const shape = Array.from(input.shape);
for (let axis in axes.sort())
for (let axis of axes.sort())
shape.splice(axis, 0, 1);
return builder.reshape(input, shape);
}
Expand Down