onnxruntime-web/lib/wasm/jsep/webgpu/ops/slice.ts

A Javascript library for running ONNX models on browsers

// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.

import { DataType } from '../../../wasm-common';
import { TensorView } from '../../tensor-view';
import { ShapeUtil } from '../../util';
import { AttributeWithCacheKey, createAttributeWithCacheKey } from '../attribute-with-cache-key';
import { ComputeContext, ProgramInfo, ProgramUniform, TensorInfo } from '../types';

import {
  createTensorShapeVariables,
  getElementAt,
  IndicesHelper,
  inputVariable,
  outputVariable,
  ShaderHelper,
  UniformsArrayType,
} from './common';

export interface SliceAttributes extends AttributeWithCacheKey {
  readonly starts: number[];
  readonly ends: number[];
  readonly axes: number[];
}

const validateInputs = (inputs: readonly TensorView[], attributes: SliceAttributes): void => {
  if (!inputs || inputs.length < 1) {
    throw new Error('too few inputs');
  }
  if (attributes.axes.length !== 0) {
    if (attributes.axes.length !== attributes.starts.length || attributes.axes.length !== attributes.ends.length) {
      throw new Error('axes, starts and ends must have the same length');
    }
  } else if (attributes.starts.length !== attributes.ends.length) {
    throw new Error('starts and ends must have the same length');
  }
  inputs.slice(1).forEach((_, idx) => {
    if (inputs[idx + 1].dataType !== DataType.int32 && inputs[idx + 1].dataType !== DataType.int64) {
      throw new Error(`Input ${idx} must be an array of int32 or int64`);
    }
  });
};

const readInput = (inputs: readonly TensorView[], idx: number): number[] => {
  const input: number[] = [];
  if (inputs.length > idx) {
    if (inputs[idx].dataType === DataType.int64) {
      inputs[idx].getBigInt64Array().forEach((v) => input.push(Number(v)));
    } else if (inputs[idx].dataType === DataType.int32) {
      inputs[idx].getInt32Array().forEach((v) => input.push(Number(v)));
    } else {
      throw new Error(`Input ${idx} must be an array of int32 or int64`);
    }
  }
  return input;
};

const createSliceAttributesFromInputs = (
  inputs: readonly TensorView[],
  attributes: SliceAttributes,
): SliceAttributes => {
  if (inputs.length > 1) {
    const starts: number[] = readInput(inputs, 1);
    const ends: number[] = readInput(inputs, 2);
    let axes: number[] = readInput(inputs, 3);
    if (axes.length === 0) {
      axes = [...Array(inputs[0].dims.length).keys()];
    }
    return createAttributeWithCacheKey({ starts, ends, axes });
  } else {
    return attributes;
  }
};

const fixStartEndValues = (
  value: number,
  index: number,
  inputShape: readonly number[],
  axes: readonly number[],
  steps: readonly number[],
): number => {
  let newValue = value;
  if (value < 0) {
    newValue += inputShape[axes[index]];
  }
  if (steps[index] < 0) {
    return Math.max(0, Math.min(newValue, inputShape[axes[index]] - 1));
  } else {
    return Math.max(0, Math.min(newValue, inputShape[axes[index]]));
  }
};

const calculateInputIndicesImpl = (
  input: IndicesHelper,
  output: IndicesHelper,
  inputShape: readonly number[],
): string =>
  `fn calculateInputIndices(output_indices: ${output.type.indices}) -> ${input.type.indices} {
          var input_indices: ${input.type.indices};
          var carry = 0u;
          for (var i = ${inputShape.length}; i >= 0; i--) {
            let input_shape_i = ${getElementAt('uniforms.input_shape', 'i', inputShape.length)};
            let steps_i = ${getElementAt('uniforms.steps', 'i', inputShape.length)};
            let signs_i = ${getElementAt('uniforms.signs', 'i', inputShape.length)};
            let starts_i = ${getElementAt('uniforms.starts', 'i', inputShape.length)};
            var output_index = ${output.indicesGet('output_indices', 'i')};
            var input_index = output_index * steps_i + starts_i + carry;
            carry = input_index / input_shape_i;
            input_index = input_index % input_shape_i;
            if (signs_i < 0) {
              input_index = input_shape_i - input_index - 1u + starts_i;
            }
            ${input.indicesSet('input_indices', 'i', 'input_index')};
          }
          return input_indices;
      }`;

const createSliceProgramInfo = (inputs: readonly TensorView[], attributes: SliceAttributes): ProgramInfo => {
  const inputShape = inputs[0].dims;
  const inputSize = ShapeUtil.size(inputShape);
  const axes =
    attributes.axes.length > 0
      ? ShapeUtil.normalizeAxes(attributes.axes, inputShape.length)
      : [...Array(inputShape.length).keys()];
  let steps = readInput(inputs, 4);
  steps.forEach(
    (step) =>
      step !== 0 ||
      (() => {
        throw new Error('step cannot be 0');
      }),
  );
  if (steps.length === 0) {
    steps = Array(axes.length).fill(1);
  }
  const starts = attributes.starts.map((start, i) => fixStartEndValues(start, i, inputShape, axes, steps));

  const ends = attributes.ends.map((end, i) => fixStartEndValues(end, i, inputShape, axes, steps));

  if (axes.length !== starts.length || axes.length !== ends.length) {
    throw new Error('start, ends and axes should have the same number of elements');
  }

  if (axes.length !== inputShape.length) {
    for (let i = 0; i < inputShape.length; ++i) {
      if (!axes.includes(i)) {
        starts.splice(i, 0, 0);
        ends.splice(i, 0, inputShape[i]);
        steps.splice(i, 0, 1);
      }
    }
  }
  const signs = steps.map((step) => Math.sign(step));
  // Convert negative steps to positive steps and reverse starts and ends
  steps.forEach((step, i, array) => {
    if (step < 0) {
      const numSteps = (ends[i] - starts[i]) / step;
      const newEnd = starts[i];
      const newStart = newEnd + numSteps * steps[i];
      starts[i] = newStart;
      ends[i] = newEnd;
      array[i] = -step;
    }
  });
  // Output rank is expected to be less than or equal to the input rank.
  const outputShape = inputShape.slice(0);
  axes.forEach((axis, _) => {
    outputShape[axis] = Math.ceil((ends[axis] - starts[axis]) / steps[axis]);
  });
  const outputTensorInfo: TensorInfo = { dims: outputShape, dataType: inputs[0].dataType };

  const output = outputVariable('output', inputs[0].dataType, outputShape.length);
  const input = inputVariable('input', inputs[0].dataType, inputs[0].dims.length);
  const outputSize = ShapeUtil.size(outputShape);
  const uniforms: UniformsArrayType = [
    { name: 'outputSize', type: 'u32' },
    { name: 'starts', type: 'u32', length: starts.length },
    { name: 'signs', type: 'i32', length: signs.length },
    { name: 'steps', type: 'u32', length: steps.length },
  ];

  const programUniforms: ProgramUniform[] = [
    { type: DataType.uint32, data: outputSize },
    { type: DataType.uint32, data: starts },
    { type: DataType.int32, data: signs },
    { type: DataType.uint32, data: steps },
    ...createTensorShapeVariables(inputs[0].dims, outputShape),
  ];

  const getShaderSource = (shaderHelper: ShaderHelper) => `
      ${shaderHelper.registerUniforms(uniforms).declareVariables(input, output)}
        ${calculateInputIndicesImpl(input, output, inputShape)}
        ${shaderHelper.mainStart()}
          ${shaderHelper.guardAgainstOutOfBoundsWorkgroupSizes('uniforms.outputSize')}
          let output_indices = ${output.offsetToIndices('global_idx')};
          let input_indices = calculateInputIndices(output_indices);
          ${output.setByOffset('global_idx', input.getByIndices('input_indices'))}
      }`;
  return {
    name: 'Slice',
    shaderCache: { hint: `${signs.length}_${starts.length}_${steps.length}`, inputDependencies: ['rank'] },
    getShaderSource,
    getRunData: () => ({
      outputs: [outputTensorInfo],
      dispatchGroup: { x: Math.ceil(inputSize / 64 /* workgroup size */) },
      programUniforms,
    }),
  };
};

export const slice = (context: ComputeContext, attributes: SliceAttributes): void => {
  validateInputs(context.inputs, attributes);
  const updatedAttributes = createSliceAttributesFromInputs(context.inputs, attributes);
  context.compute(createSliceProgramInfo(context.inputs, updatedAttributes), { inputs: [0] });
  // if (ShapeUtil.size(program.outputs[0].dims) > 0) {
  //   context.compute(programInfoLoader, {inputs: [0]});
  // } else {
  //   // TODO: support empty output
  //   throw new Error('slice: output size is 0');
  // }
};

export const parseSliceAttributes = (attributes: Record<string, unknown>): SliceAttributes => {
  const starts = attributes.starts as number[];
  const ends = attributes.ends as number[];
  const axes = attributes.axes as number[];
  return createAttributeWithCacheKey({ starts, ends, axes });
};