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wasm: Binary Operators, MatMul operator, shape_utils, and broadcast_u…
…tils (#47) * Binary ops for Wasm backend * MatMul op for Wasm backend
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,90 @@ | ||
| // Copyright (c) Microsoft Corporation. All rights reserved. | ||
| // Licensed under the MIT license. | ||
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| import {BinaryOp} from '../../../ops/binary-op'; | ||
| import {Tensor} from '../../../tensor'; | ||
| import {BroadcastUtil} from '../../../util'; | ||
| import {WasmBinding} from '../../../wasm-binding'; | ||
| import {WasmInferenceHandler} from '../inference-handler'; | ||
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| export class WasmBinaryOp extends BinaryOp { | ||
| constructor(typeConstraint: ReadonlyArray<Tensor.DataType>, opType: string, resultType?: Tensor.DataType) { | ||
| super(typeConstraint, opType, resultType); | ||
| } | ||
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|
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| run(inferenceHandler: WasmInferenceHandler, inputs: Tensor[]): Tensor[] { | ||
| const outputShape = BroadcastUtil.calcShape(inputs[0].dims, inputs[1].dims, false); | ||
| if (!outputShape) { | ||
| throw new Error('not broadcastable'); | ||
| } | ||
| let fun = ''; | ||
| // TODO: Explore better ways to deal with types than current `binaryOpType` approach | ||
| let binaryOpType = ''; | ||
| switch (this.opType) { | ||
| case 'Add': | ||
| if (inputs[0].type === 'float32') { | ||
| fun = '_add_f32'; | ||
| binaryOpType = 'float32InFloat32Out'; | ||
| } | ||
| break; | ||
| case 'Sub': | ||
| if (inputs[0].type === 'float32') { | ||
| fun = '_sub_f32'; | ||
| binaryOpType = 'float32InFloat32Out'; | ||
| } | ||
| break; | ||
| case 'Mul': | ||
| if (inputs[0].type === 'float32') { | ||
| fun = '_mul_f32'; | ||
| binaryOpType = 'float32InFloat32Out'; | ||
| } | ||
| break; | ||
| case 'Div': | ||
| if (inputs[0].type === 'float32') { | ||
| fun = '_div_f32'; | ||
| binaryOpType = 'float32InFloat32Out'; | ||
| } | ||
| break; | ||
| case 'PRelu': | ||
| if (inputs[0].type === 'float32') { | ||
| fun = '_prelu_f32'; | ||
| binaryOpType = 'float32InFloat32Out'; | ||
| } | ||
| break; | ||
| case 'Xor': | ||
| fun = '_xor_u8'; | ||
| binaryOpType = 'boolInBoolOut'; | ||
| break; | ||
| case 'Or': | ||
| fun = '_or_u8'; | ||
| binaryOpType = 'boolInBoolOut'; | ||
| break; | ||
| case 'And': | ||
| fun = '_and_u8'; | ||
| binaryOpType = 'boolInBoolOut'; | ||
| break; | ||
| default: | ||
| throw Error(`unsupported binary op by the Wasm backend`); | ||
| } | ||
| let result: Tensor; | ||
| if (binaryOpType === 'float32InFloat32Out') { | ||
| result = new Tensor(outputShape, 'float32'); | ||
| WasmBinding.getInstance().ccall( | ||
| fun, [inputs[0].floatData, 'float32ptr'], [inputs[0].dims.length, 'int32'], [inputs[0].dims, 'int32ptr'], | ||
| [inputs[1].floatData, 'float32ptr'], [inputs[1].dims.length, 'int32'], [inputs[1].dims, 'int32ptr'], | ||
| [result.floatData, 'float32ptr', 'out'], [result.floatData.length, 'int32'], [outputShape.length, 'int32'], | ||
| [outputShape, 'int32ptr']); | ||
| } else if (binaryOpType === 'boolInBoolOut') { | ||
| result = new Tensor(outputShape, 'bool'); | ||
| WasmBinding.getInstance().ccall( | ||
| fun, [inputs[0].integerData as Uint8Array, 'boolptr'], [inputs[0].dims.length, 'int32'], | ||
| [inputs[0].dims, 'int32ptr'], [inputs[1].integerData as Uint8Array, 'boolptr'], | ||
| [inputs[1].dims.length, 'int32'], [inputs[1].dims, 'int32ptr'], | ||
| [result.integerData as Uint8Array, 'boolptr', 'out'], [result.integerData.length, 'int32'], | ||
| [outputShape.length, 'int32'], [outputShape, 'int32ptr']); | ||
| } else { | ||
| throw new Error(`Unsupported binary op format. Probably unsupported data types.`); | ||
| } | ||
| return [result]; | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,47 @@ | ||
| // Copyright (c) Microsoft Corporation. All rights reserved. | ||
| // Licensed under the MIT license. | ||
|
|
||
| import {MatMul} from '../../../ops/matmul'; | ||
| import {Tensor} from '../../../tensor'; | ||
| import {BroadcastUtil, MatMulUtil, ShapeUtil} from '../../../util'; | ||
| import {WasmBinding} from '../../../wasm-binding'; | ||
| import {WasmInferenceHandler} from '../inference-handler'; | ||
|
|
||
| export class WasmMatMul extends MatMul { | ||
| run(inferenceHandler: WasmInferenceHandler, inputs: Tensor[]): Tensor[] { | ||
| let dimsA: number[]; | ||
| let dimsB: number[]; | ||
| [dimsA, dimsB] = MatMulUtil.preprocessInputShapes(inputs[0].dims.slice(), inputs[1].dims.slice()); | ||
| const outputShape = BroadcastUtil.calcShape(dimsA, dimsB, true); | ||
| if (!outputShape) { | ||
| // the inputs cannot broadcast or cannot multiply | ||
| throw new Error(`input dimensions do not match the requirement`); | ||
| } | ||
|
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| const outputSize = ShapeUtil.size(outputShape); | ||
| const resultData = new Float32Array(outputSize); | ||
| WasmBinding.getInstance().ccall( | ||
| '_matmul_f32', [inputs[0].floatData, 'float32ptr'], [inputs[0].dims, 'int32ptr'], | ||
| [inputs[0].dims.length, 'int32'], [inputs[1].floatData, 'float32ptr'], [inputs[1].dims, 'int32ptr'], | ||
| [inputs[1].dims.length, 'int32'], [resultData, 'float32ptr', 'out'], [resultData.length, 'int32'], | ||
| [outputShape, 'int32ptr'], [outputShape.length, 'int32']); | ||
| MatMulUtil.postprocessOutputShape(outputShape as number[], inputs[0].dims.length, inputs[1].dims.length); | ||
| const result = new Tensor(outputShape, inputs[0].type); | ||
| result.floatData.set(resultData); | ||
| return [result]; | ||
| } | ||
|
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| // overriding the checkInputTypes() in the base class because Wasm backend has special type limitations | ||
| checkInputTypes(inputs: Tensor[]): boolean { | ||
| // currently Wasm backend only supports 'float32' input type | ||
| if (inputs[0].type !== 'float32' || inputs[1].type !== 'float32') { | ||
| return false; | ||
| } | ||
|
|
||
| if (inputs[0].type !== inputs[1].type) { | ||
| return false; | ||
| } | ||
|
|
||
| return true; | ||
| } | ||
| } |
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