@tensorflow/tfjs-layers
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TensorFlow layers API in JavaScript
95 lines • 12 kB
JavaScript
/**
* @license
* Copyright 2023 Google LLC.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* Utility functions for `TransformerDecoder`.
*/
/* Original source: keras_nlp/layers/modeling/transformer_layer_utils.py */
import { add, expandDims, tensor, tidy } from '@tensorflow/tfjs-core';
import { ValueError } from '../../../errors';
function checkMasksShapes(inputs, paddingMask, attentionMask) {
if (paddingMask != null) {
if (paddingMask.shape.length !== 2) {
throw new ValueError('`paddingMask` should have shape ' +
`[batchSize, targetLength]. Received shape ${paddingMask.shape}.`);
}
}
if (attentionMask != null) {
if (attentionMask.shape.length !== 3) {
throw new ValueError('`attentionMask` should have shape ' +
`[batchSize, targetLength, sourceLength]. ` +
`Received shape ${attentionMask.shape}.`);
}
}
}
/**
* Compute a causal attention mask for a transformer decoder.
*
* @param batchSize batch size for the mask.
* @param inputLength the length of key/value tensors in the attention layer.
* @param outputLength the length of query tensor in the attention layer.
* @param cacheIndex the current index for cached generation. If passed, the
* query sequence will be considered to start at `cacheIndex` rather than zero.
* For example, a casual mask with `outputLength=1` and `cacheIndex=5` would
* allow the query tensor to attend to the first five positions of the
* key/value tensors.
*
* @returns a causal attention mask with shape
* `[batchSize, outputLength, inputLength]` that can be passed to a attention
* layer.
*/
export function computeCausalMask(batchSize, inputLength, outputLength, cacheIndex = 0) {
return tidy(() => {
const i = add(expandDims(Array.from({ length: outputLength }, (_, i) => i), 1), cacheIndex);
const j = tensor(Array.from({ length: inputLength }, (_, i) => i));
const mask = i.greaterEqual(j).cast('int32').expandDims(0);
return mask.broadcastTo([batchSize, outputLength, inputLength]);
});
}
/**
* Merge the padding mask with a customized attention mask.
*
* @param inputs the input sequence.
* @param paddingMask the 1D padding mask, of shape
* [batchSize, sequenceLength].
* @param attentionMask the 2D customized mask, of shape
* [batchSize, sequenceLength, sequence2_length].
* @returns
* A merged 2D mask or null. If only `paddingMask` is provided, the
* returned mask is paddingMask with one additional axis.
*/
export function mergePaddingAndAttentionMask(inputs, paddingMask, attentionMask) {
return tidy(() => {
checkMasksShapes(inputs, paddingMask, attentionMask);
let mask;
if (paddingMask != null) {
// Add an axis for broadcasting, the attention mask should be 2D
// (not including the batch axis).
mask = paddingMask.expandDims(1).cast('int32');
}
if (attentionMask != null) {
attentionMask = attentionMask.cast('int32');
if (mask == null) {
return attentionMask;
}
else {
return mask.minimum(attentionMask);
}
}
return mask;
});
}
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Received shape ${paddingMask.shape}.`\n      );\n    }\n  }\n  if (attentionMask != null) {\n    if (attentionMask.shape.length !== 3) {\n      throw new ValueError(\n        '`attentionMask` should have shape ' +\n        `[batchSize, targetLength, sourceLength]. ` +\n        `Received shape ${attentionMask.shape}.`\n      );\n    }\n  }\n}\n\n/**\n * Compute a causal attention mask for a transformer decoder.\n *\n * @param batchSize batch size for the mask.\n * @param inputLength the length of key/value tensors in the attention layer.\n * @param outputLength the length of query tensor in the attention layer.\n * @param cacheIndex the current index for cached generation. If passed, the\n *  query sequence will be considered to start at `cacheIndex` rather than zero.\n *  For example, a casual mask with `outputLength=1` and `cacheIndex=5` would\n *  allow the query tensor to attend to the first five positions of the\n *  key/value tensors.\n *\n * @returns a causal attention mask with shape\n *  `[batchSize, outputLength, inputLength]` that can be passed to a attention\n *  layer.\n */\nexport function computeCausalMask(\n    batchSize: number,\n    inputLength: number,\n    outputLength: number,\n    cacheIndex = 0\n  ): Tensor {\n  return tidy(() => {\n    const i = add(\n      expandDims(Array.from({length: outputLength}, (_, i) => i), 1),\n      cacheIndex,\n    );\n    const j = tensor(Array.from({length: inputLength}, (_, i) => i));\n    const mask = i.greaterEqual(j).cast('int32').expandDims(0);\n    return mask.broadcastTo([batchSize, outputLength, inputLength]);\n  });\n}\n\n/**\n * Merge the padding mask with a customized attention mask.\n *\n * @param inputs the input sequence.\n * @param paddingMask the 1D padding mask, of shape\n *          [batchSize, sequenceLength].\n * @param attentionMask the 2D customized mask, of shape\n *          [batchSize, sequenceLength, sequence2_length].\n * @returns\n *  A merged 2D mask or null. If only `paddingMask` is provided, the\n *  returned mask is paddingMask with one additional axis.\n */\nexport function mergePaddingAndAttentionMask(\n    inputs: Tensor, paddingMask: Tensor, attentionMask: Tensor): Tensor {\n  return tidy(() => {\n    checkMasksShapes(inputs, paddingMask, attentionMask);\n    let mask: Tensor;\n    if (paddingMask != null) {\n      // Add an axis for broadcasting, the attention mask should be 2D\n      // (not including the batch axis).\n      mask = paddingMask.expandDims(1).cast('int32');\n    }\n    if (attentionMask != null) {\n      attentionMask = attentionMask.cast('int32');\n      if (mask == null) {\n        return attentionMask;\n      } else {\n        return mask.minimum(attentionMask);\n      }\n    }\n    return mask;\n  });\n}\n"]}