apache-arrow
Version:
Apache Arrow columnar in-memory format
184 lines (182 loc) • 7.08 kB
JavaScript
"use strict";
// automatically generated by the FlatBuffers compiler, do not modify
Object.defineProperty(exports, "__esModule", { value: true });
exports.SparseTensorIndexCSF = void 0;
const flatbuffers = require("flatbuffers");
const buffer_js_1 = require("./buffer.js");
const int_js_1 = require("./int.js");
/**
* Compressed Sparse Fiber (CSF) sparse tensor index.
*/
class SparseTensorIndexCSF {
constructor() {
this.bb = null;
this.bb_pos = 0;
}
__init(i, bb) {
this.bb_pos = i;
this.bb = bb;
return this;
}
static getRootAsSparseTensorIndexCSF(bb, obj) {
return (obj || new SparseTensorIndexCSF()).__init(bb.readInt32(bb.position()) + bb.position(), bb);
}
static getSizePrefixedRootAsSparseTensorIndexCSF(bb, obj) {
bb.setPosition(bb.position() + flatbuffers.SIZE_PREFIX_LENGTH);
return (obj || new SparseTensorIndexCSF()).__init(bb.readInt32(bb.position()) + bb.position(), bb);
}
/**
* CSF is a generalization of compressed sparse row (CSR) index.
* See [smith2017knl](http://shaden.io/pub-files/smith2017knl.pdf)
*
* CSF index recursively compresses each dimension of a tensor into a set
* of prefix trees. Each path from a root to leaf forms one tensor
* non-zero index. CSF is implemented with two arrays of buffers and one
* arrays of integers.
*
* For example, let X be a 2x3x4x5 tensor and let it have the following
* 8 non-zero values:
* ```text
* X[0, 0, 0, 1] := 1
* X[0, 0, 0, 2] := 2
* X[0, 1, 0, 0] := 3
* X[0, 1, 0, 2] := 4
* X[0, 1, 1, 0] := 5
* X[1, 1, 1, 0] := 6
* X[1, 1, 1, 1] := 7
* X[1, 1, 1, 2] := 8
* ```
* As a prefix tree this would be represented as:
* ```text
* 0 1
* / \ |
* 0 1 1
* / / \ |
* 0 0 1 1
* /| /| | /| |
* 1 2 0 2 0 0 1 2
* ```
* The type of values in indptrBuffers
*/
indptrType(obj) {
const offset = this.bb.__offset(this.bb_pos, 4);
return offset ? (obj || new int_js_1.Int()).__init(this.bb.__indirect(this.bb_pos + offset), this.bb) : null;
}
/**
* indptrBuffers stores the sparsity structure.
* Each two consecutive dimensions in a tensor correspond to a buffer in
* indptrBuffers. A pair of consecutive values at `indptrBuffers[dim][i]`
* and `indptrBuffers[dim][i + 1]` signify a range of nodes in
* `indicesBuffers[dim + 1]` who are children of `indicesBuffers[dim][i]` node.
*
* For example, the indptrBuffers for the above X is:
* ```text
* indptrBuffer(X) = [
* [0, 2, 3],
* [0, 1, 3, 4],
* [0, 2, 4, 5, 8]
* ].
* ```
*/
indptrBuffers(index, obj) {
const offset = this.bb.__offset(this.bb_pos, 6);
return offset ? (obj || new buffer_js_1.Buffer()).__init(this.bb.__vector(this.bb_pos + offset) + index * 16, this.bb) : null;
}
indptrBuffersLength() {
const offset = this.bb.__offset(this.bb_pos, 6);
return offset ? this.bb.__vector_len(this.bb_pos + offset) : 0;
}
/**
* The type of values in indicesBuffers
*/
indicesType(obj) {
const offset = this.bb.__offset(this.bb_pos, 8);
return offset ? (obj || new int_js_1.Int()).__init(this.bb.__indirect(this.bb_pos + offset), this.bb) : null;
}
/**
* indicesBuffers stores values of nodes.
* Each tensor dimension corresponds to a buffer in indicesBuffers.
* For example, the indicesBuffers for the above X is:
* ```text
* indicesBuffer(X) = [
* [0, 1],
* [0, 1, 1],
* [0, 0, 1, 1],
* [1, 2, 0, 2, 0, 0, 1, 2]
* ].
* ```
*/
indicesBuffers(index, obj) {
const offset = this.bb.__offset(this.bb_pos, 10);
return offset ? (obj || new buffer_js_1.Buffer()).__init(this.bb.__vector(this.bb_pos + offset) + index * 16, this.bb) : null;
}
indicesBuffersLength() {
const offset = this.bb.__offset(this.bb_pos, 10);
return offset ? this.bb.__vector_len(this.bb_pos + offset) : 0;
}
/**
* axisOrder stores the sequence in which dimensions were traversed to
* produce the prefix tree.
* For example, the axisOrder for the above X is:
* ```text
* axisOrder(X) = [0, 1, 2, 3].
* ```
*/
axisOrder(index) {
const offset = this.bb.__offset(this.bb_pos, 12);
return offset ? this.bb.readInt32(this.bb.__vector(this.bb_pos + offset) + index * 4) : 0;
}
axisOrderLength() {
const offset = this.bb.__offset(this.bb_pos, 12);
return offset ? this.bb.__vector_len(this.bb_pos + offset) : 0;
}
axisOrderArray() {
const offset = this.bb.__offset(this.bb_pos, 12);
return offset ? new Int32Array(this.bb.bytes().buffer, this.bb.bytes().byteOffset + this.bb.__vector(this.bb_pos + offset), this.bb.__vector_len(this.bb_pos + offset)) : null;
}
static startSparseTensorIndexCSF(builder) {
builder.startObject(5);
}
static addIndptrType(builder, indptrTypeOffset) {
builder.addFieldOffset(0, indptrTypeOffset, 0);
}
static addIndptrBuffers(builder, indptrBuffersOffset) {
builder.addFieldOffset(1, indptrBuffersOffset, 0);
}
static startIndptrBuffersVector(builder, numElems) {
builder.startVector(16, numElems, 8);
}
static addIndicesType(builder, indicesTypeOffset) {
builder.addFieldOffset(2, indicesTypeOffset, 0);
}
static addIndicesBuffers(builder, indicesBuffersOffset) {
builder.addFieldOffset(3, indicesBuffersOffset, 0);
}
static startIndicesBuffersVector(builder, numElems) {
builder.startVector(16, numElems, 8);
}
static addAxisOrder(builder, axisOrderOffset) {
builder.addFieldOffset(4, axisOrderOffset, 0);
}
static createAxisOrderVector(builder, data) {
builder.startVector(4, data.length, 4);
for (let i = data.length - 1; i >= 0; i--) {
builder.addInt32(data[i]);
}
return builder.endVector();
}
static startAxisOrderVector(builder, numElems) {
builder.startVector(4, numElems, 4);
}
static endSparseTensorIndexCSF(builder) {
const offset = builder.endObject();
builder.requiredField(offset, 4); // indptrType
builder.requiredField(offset, 6); // indptrBuffers
builder.requiredField(offset, 8); // indicesType
builder.requiredField(offset, 10); // indicesBuffers
builder.requiredField(offset, 12); // axisOrder
return offset;
}
}
exports.SparseTensorIndexCSF = SparseTensorIndexCSF;
//# sourceMappingURL=sparse-tensor-index-csf.js.map