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[WebGPU/JS] Added Pad operator support (#16928)
### Description <!-- Describe your changes. --> ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,252 @@ | ||
| // Copyright (c) Microsoft Corporation. All rights reserved. | ||
| // Licensed under the MIT License. | ||
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| import {DataType} from '../../../wasm-common'; | ||
| import {TensorView} from '../../tensor'; | ||
| import {ShapeUtil} from '../../util'; | ||
| import {AttributeWithCacheKey, createAttributeWithCacheKey} from '../attribute-with-cache-key'; | ||
| import {ComputeContext, GpuDataType, ProgramInfo, ProgramInfoLoader, ProgramMetadata} from '../types'; | ||
|
|
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| import {IndicesHelper, inputVariable, outputVariable, ShaderHelper} from './common'; | ||
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| export interface PadAttributes extends AttributeWithCacheKey { | ||
| // 0-constant, 1-reflect, 2-edge, 3-wrap | ||
| readonly mode: number; | ||
| readonly value: number; | ||
| readonly pads: number[]; | ||
| } | ||
|
|
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| const validateInputs = (inputs: readonly TensorView[]): void => { | ||
| if (!inputs || inputs.length < 1) { | ||
| throw new Error('Too few inputs'); | ||
| } | ||
| if (inputs[0].dataType !== DataType.float) { | ||
| throw new Error('Input type must be float.'); | ||
| } | ||
|
|
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| if (inputs.length >= 2) { | ||
| let validPads = inputs[0].dims.length * 2 === inputs[1].dims[0]; | ||
| if (inputs.length === 4) { | ||
| validPads = inputs[3].dims[0] * 2 === inputs[1].dims[0]; | ||
| } | ||
| if (!validPads) { | ||
| throw new Error('The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].'); | ||
| } | ||
| } | ||
| }; | ||
|
|
||
| const getPadConstant = | ||
| (output: IndicesHelper, outputDims: readonly number[], inputDims: readonly number[], | ||
| inputStrides: readonly number[], pads: number[], dataType: string, constantValue: number): string => { | ||
| const inputRank = inputDims.length; | ||
|
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| let block = ''; | ||
| for (let i = inputRank - 1; i >= 0; --i) { | ||
| block += ` | ||
| k = i32(${output.indicesGet('indices', i)}) - ${pads[i]}; | ||
| if (k < 0) { | ||
| break; | ||
| } | ||
| if (k >= ${inputDims[i]}) { | ||
| break; | ||
| } | ||
| offset += k * ${inputStrides[i]}; | ||
| `; | ||
| } | ||
|
|
||
| return ` | ||
| value = ${dataType}(${constantValue}); | ||
| for (var i = 0; i < 1; i++) { | ||
| var offset = 0; | ||
| var k = 0; | ||
| ${block} | ||
| value = x[offset]; | ||
| } | ||
| `; | ||
| }; | ||
|
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||
| const getPadReflect = | ||
| (output: IndicesHelper, outputDims: readonly number[], inputDims: readonly number[], | ||
| inputStrides: readonly number[], pads: number[]): string => { | ||
| const inputRank = inputDims.length; | ||
|
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||
| let block = ''; | ||
| for (let i = inputRank - 1; i >= 0; --i) { | ||
| block += ` | ||
| k = i32(${output.indicesGet('indices', i)}) - ${pads[i]}; | ||
| if (k < 0) { | ||
| k = -k; | ||
| } | ||
| { | ||
| let _2n_1 = ${2 * (inputDims[i] - 1)}; | ||
| k = k % _2n_1; | ||
| if(k >= ${inputDims[i]}) { | ||
| k = _2n_1 - k; | ||
| } | ||
| } | ||
| offset += k * ${inputStrides[i]}; | ||
| `; | ||
| } | ||
|
|
||
| return ` | ||
| var offset = 0; | ||
| var k = 0; | ||
| ${block} | ||
| value = x[offset]; | ||
| `; | ||
| }; | ||
|
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||
| const getPadEdge = | ||
| (output: IndicesHelper, outputDims: readonly number[], inputDims: readonly number[], | ||
| inputStrides: readonly number[], pads: number[]): string => { | ||
| const inputRank = inputDims.length; | ||
|
|
||
| let block = ''; | ||
| for (let i = inputRank - 1; i >= 0; --i) { | ||
| block += ` | ||
| k = i32(${output.indicesGet('indices', i)}) - ${pads[i]}; | ||
| if (k < 0) { | ||
| k = 0; | ||
| } | ||
| if (k >= ${inputDims[i]}) { | ||
| k = ${inputDims[i] - 1}; | ||
| } | ||
| offset += k * ${inputStrides[i]}; | ||
| `; | ||
| } | ||
|
|
||
| return ` | ||
| var offset = 0; | ||
| var k = 0; | ||
| ${block} | ||
| value = x[offset]; | ||
| `; | ||
| }; | ||
|
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| const getPadWrap = | ||
| (output: IndicesHelper, outputDims: readonly number[], inputDims: readonly number[], | ||
| inputStrides: readonly number[], pads: number[]): string => { | ||
| const inputRank = inputDims.length; | ||
|
|
||
| let block = ''; | ||
| for (let i = inputRank - 1; i >= 0; --i) { | ||
| block += ` | ||
| k = i32(${output.indicesGet('indices', i)}) - ${pads[i]}; | ||
| if (k < 0) { | ||
| k += ${inputDims[i]}; | ||
| } | ||
| if (k >= ${inputDims[i]}) { | ||
| k -= ${inputDims[i]}; | ||
| } | ||
| offset += k * ${inputStrides[i]}; | ||
| `; | ||
| } | ||
|
|
||
| return ` | ||
| var offset = 0; | ||
| var k = 0; | ||
| ${block} | ||
| value = x[offset]; | ||
| `; | ||
| }; | ||
|
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| const getPadSnippet = | ||
| (output: IndicesHelper, outputDims: readonly number[], inputDims: readonly number[], | ||
| inputStrides: readonly number[], attributes: PadAttributes, dataType: string): string => { | ||
| switch (attributes.mode) { | ||
| case 0: | ||
| return getPadConstant( | ||
| output, outputDims, inputDims, inputStrides, attributes.pads, dataType, attributes.value); | ||
| case 1: | ||
| return getPadReflect(output, outputDims, inputDims, inputStrides, attributes.pads); | ||
| case 2: | ||
| return getPadEdge(output, outputDims, inputDims, inputStrides, attributes.pads); | ||
| case 3: | ||
| return getPadWrap(output, outputDims, inputDims, inputStrides, attributes.pads); | ||
| default: | ||
| throw new Error('Invalid mode'); | ||
| } | ||
| }; | ||
|
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| const generatePadCode = | ||
| (shaderHelper: ShaderHelper, inputs: readonly TensorView[], attributes: PadAttributes, dataType: string): | ||
| string => { | ||
| const inputDims = inputs[0].dims; | ||
| const outputDims = ShapeUtil.padShape(inputDims.slice(), attributes.pads); | ||
| const outputSize = ShapeUtil.size(outputDims); | ||
| const inputStrides = ShapeUtil.computeStrides(inputDims); | ||
|
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| const output = outputVariable('output', inputs[0].dataType, outputDims); | ||
| const input = inputVariable('x', inputs[0].dataType, inputDims); | ||
|
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| const padSnippet = getPadSnippet(output, outputDims, inputDims, inputStrides, attributes, dataType); | ||
| const padCode = ` | ||
| ${shaderHelper.declareVariables(input, output)} | ||
| ${output.impl()} | ||
| ${shaderHelper.mainStart()} | ||
| ${shaderHelper.guardAgainstOutOfBoundsWorkgroupSizes(outputSize)} | ||
| let indices = ${output.offsetToIndices('global_idx')}; | ||
| var value = ${dataType}(0); | ||
| ${padSnippet} | ||
| output[global_idx] = value; | ||
| }`; | ||
| return padCode; | ||
| }; | ||
|
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| const createPadProgramInfo = | ||
| (inputs: readonly TensorView[], metadata: ProgramMetadata, attributes: PadAttributes): ProgramInfo => { | ||
| const outputShape = ShapeUtil.padShape(inputs[0].dims.slice(), attributes.pads); | ||
| return { | ||
| ...metadata, | ||
| outputs: [{dims: outputShape, dataType: inputs[0].dataType, gpuDataType: GpuDataType.default}], | ||
| getShaderSource: shaderHelper => generatePadCode(shaderHelper, inputs, attributes, 'f32'), | ||
| dispatchGroup: () => ({x: Math.ceil(ShapeUtil.size(outputShape) / 64 /* workgroup size */)}) | ||
| }; | ||
| }; | ||
|
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| const createPadAttributesFromInputs = (inputs: readonly TensorView[], attributes: PadAttributes): PadAttributes => { | ||
| if (inputs.length > 1) { | ||
| const bigInt64Pads = inputs[1].getBigInt64Array(); | ||
| const value = (inputs.length >= 3) ? inputs[2].getFloat32Array()[0] : 0.0; | ||
|
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| const inputRank = inputs[0].dims.length; | ||
| const updatePads = new Int32Array(2 * inputRank).fill(0); | ||
| if (inputs.length >= 4) { | ||
| const axes = inputs[3].getBigInt64Array(); | ||
| for (let i = 0; i < axes.length; i++) { | ||
| updatePads[Number(axes[i])] = Number(bigInt64Pads[i]); | ||
| updatePads[Number(axes[i]) + inputRank] = Number(bigInt64Pads[i + axes.length]); | ||
| } | ||
| } else { | ||
| bigInt64Pads.forEach((i, v) => updatePads[Number(i)] = (Number(v))); | ||
| } | ||
|
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| const pads: number[] = []; | ||
| updatePads.forEach(v => pads.push(v)); | ||
|
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| return createAttributeWithCacheKey({mode: attributes.mode, value, pads}); | ||
| } else { | ||
| return attributes; | ||
| } | ||
| }; | ||
|
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| const createPadProgramInfoLoader = (inputs: readonly TensorView[], attributes: PadAttributes): ProgramInfoLoader => { | ||
| const updatedAttributes = createPadAttributesFromInputs(inputs, attributes); | ||
| const metadata: | ||
| ProgramMetadata = {name: 'Pad', inputTypes: [GpuDataType.default], cacheHint: updatedAttributes.cacheKey}; | ||
| return {...metadata, get: () => createPadProgramInfo(inputs, metadata, updatedAttributes)}; | ||
| }; | ||
|
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| export const pad = (context: ComputeContext, attributes: PadAttributes): void => { | ||
| validateInputs(context.inputs); | ||
| context.compute(createPadProgramInfoLoader(context.inputs, attributes), {inputs: [0]}); | ||
| }; | ||
|
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| export const parsePadAttributes = (attributes: Record<string, unknown>): PadAttributes => { | ||
| const mode = attributes.mode as number; | ||
| const value = attributes.value as number; | ||
| const pads = attributes.pads as number[]; | ||
| return createAttributeWithCacheKey({mode, value, pads}); | ||
| }; |
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