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提交 afd4b73c 编写于 作者: L liuruilong
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run mobilenet ssd and genet

上级 c5a91cbb
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Showing with 796 addition and 250 deletion
metal/paddle-mobile-demo/paddle-mobile-demo/Net/MobileNetSSD.swift
浏览文件 @ afd4b73c
......@@ -68,6 +68,7 @@ class MobileNet_ssd_hand: Net{
let output: [Float32] = result.map { $0.floatValue }
return output
}
......
metal/paddle-mobile-demo/paddle-mobile-demo/ViewController.swift
浏览文件 @ afd4b73c
......@@ -19,17 +19,17 @@ import MetalPerformanceShaders
let threadSupport = [1]
let modelHelperMap: [SupportModel : Net] = [.mobilenet_ssd : MobileNet_ssd_hand.init(), .genet : Genet.init()]
let modelHelperMap: [SupportModel : Net] = [ .mobilenet : MobileNet.init(), .mobilenet_ssd : MobileNet_ssd_hand.init(), .genet : Genet.init()]
//, .genet : Genet.init()
//let modelHelperMap: [SupportModel : Net] = [.mobilenet : MobileNet.init(), .mobilenet_ssd : MobileNet_ssd_hand.init()]
enum SupportModel: String{
// case mobilenet = "mobilenet"
case mobilenet = "mobilenet"
case mobilenet_ssd = "mobilenetssd"
case genet = "genet"
static func supportedModels() -> [SupportModel] {
//.mobilenet,
return [.mobilenet_ssd ,.genet]
//
return [.mobilenet, .mobilenet_ssd ,.genet]
}
}
......
metal/paddle-mobile/paddle-mobile.xcodeproj/project.pbxproj
浏览文件 @ afd4b73c
......@@ -41,7 +41,6 @@
FC0E2DBE20EE460D009C1FAC /* BatchNormKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC0E2DBD20EE460D009C1FAC /* BatchNormKernel.swift */; };
FC0E2DC020EE461F009C1FAC /* ElementwiseAddKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC0E2DBF20EE461F009C1FAC /* ElementwiseAddKernel.swift */; };
FC1B16B320EC9A4F00678B91 /* Kernels.metal in Sources */ = {isa = PBXBuildFile; fileRef = FC1B16B220EC9A4F00678B91 /* Kernels.metal */; };
FC1B186620ECF1C600678B91 /* ResizeKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC1B186520ECF1C600678B91 /* ResizeKernel.swift */; };
FC3602CC2108819F00FACB58 /* PaddleMobileUnitTest.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC3602CB2108819F00FACB58 /* PaddleMobileUnitTest.swift */; };
FC4CB74920F0B954007C0C6D /* ConvKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FC4CB74820F0B954007C0C6D /* ConvKernel.metal */; };
FC4CB74B20F12C30007C0C6D /* ProgramOptimize.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC4CB74A20F12C30007C0C6D /* ProgramOptimize.swift */; };
......@@ -133,7 +132,6 @@
FC0E2DBD20EE460D009C1FAC /* BatchNormKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = BatchNormKernel.swift; sourceTree = "<group>"; };
FC0E2DBF20EE461F009C1FAC /* ElementwiseAddKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ElementwiseAddKernel.swift; sourceTree = "<group>"; };
FC1B16B220EC9A4F00678B91 /* Kernels.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = Kernels.metal; sourceTree = "<group>"; };
FC1B186520ECF1C600678B91 /* ResizeKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ResizeKernel.swift; sourceTree = "<group>"; };
FC27990D21341016000B6BAD /* BoxCoder.metal */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.metal; path = BoxCoder.metal; sourceTree = "<group>"; };
FC3602CB2108819F00FACB58 /* PaddleMobileUnitTest.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = PaddleMobileUnitTest.swift; sourceTree = "<group>"; };
FC4CB74820F0B954007C0C6D /* ConvKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = ConvKernel.metal; sourceTree = "<group>"; };
......@@ -326,7 +324,6 @@
FCEB6837212F00B100D2448E /* metal */,
FCDDC6C7212FA3CA00E5EF74 /* ConvTransposeKernel.swift */,
FC0E2DBB20EE45FE009C1FAC /* ConvKernel.swift */,
FC1B186520ECF1C600678B91 /* ResizeKernel.swift */,
FC0E2DB920EE3B8D009C1FAC /* ReluKernel.swift */,
FC0E2DBD20EE460D009C1FAC /* BatchNormKernel.swift */,
FC0E2DBF20EE461F009C1FAC /* ElementwiseAddKernel.swift */,
......@@ -506,7 +503,6 @@
FC039BBB20E11CC20081E9F8 /* ProgramDesc.swift in Sources */,
FC9D037920E229E4000F735A /* OpParam.swift in Sources */,
FC3602CC2108819F00FACB58 /* PaddleMobileUnitTest.swift in Sources */,
FC1B186620ECF1C600678B91 /* ResizeKernel.swift in Sources */,
FCF2D73820E64E70007AC5F5 /* Kernel.swift in Sources */,
FCDDC6CC212FDFDB00E5EF74 /* ReluKernel.metal in Sources */,
FC0226562138F33800F395E2 /* TransposeKernel.metal in Sources */,
......
metal/paddle-mobile/paddle-mobile/Common/MetalExtension.swift
浏览文件 @ afd4b73c
......@@ -354,7 +354,7 @@ public extension MTLTexture {
}
// n c h w - dim
func toTensor(dim: (n: Int, c: Int, h: Int, w: Int), texturePrecision: ComputePrecision = .Float16) -> [Float32] {
func toTensor(dim: (n: Int, c: Int, h: Int, w: Int)) -> [Float32] {
// print("origin dim: \(dim)")
print("texture: ")
print(self)
......@@ -392,7 +392,7 @@ public extension MTLTexture {
return output
}
func realNHWC(dim: (n: Int, h: Int, w: Int, c: Int), texturePrecision: ComputePrecision = .Float16) -> [Float32] {
func realNHWC(dim: (n: Int, h: Int, w: Int, c: Int)) -> [Float32] {
// print("origin dim: \(dim)")
// print("texture: ")
// print(self)
......
metal/paddle-mobile/paddle-mobile/Executor.swift
浏览文件 @ afd4b73c
......@@ -14,7 +14,7 @@
import Foundation
let testTo = 61
let testTo = 161
var isTest = false
......@@ -128,18 +128,18 @@ public class Executor<P: PrecisionType> {
// print(stridableInput)
// let _: Flo? = input.logDesc(header: "input: ", stridable: true)
for i in 0..<self.ops.count {
let op = self.ops[i]
print(" 第 \(i) 个 op: ")
op.delogOutput()
}
// self.ops[59].delogOutput()
// for i in 0..<self.ops.count {
// let op = self.ops[i]
// print(" 第 \(i) 个 op: ")
// op.delogOutput()
// }
// self.ops[testTo - 2].delogOutput()
// self.ops[testTo - 1].delogOutput()
// self.ops[60].delogOutput()
return
// return
let afterDate = Date.init()
var resultHolder: ResultHolder<P>
if except > 0 {
resultHolder = ResultHolder<P>.init(inDim: [], inResult: [], inElapsedTime: afterDate.timeIntervalSince(beforeDate), inIntermediateResults: outputTextures)
......
metal/paddle-mobile/paddle-mobile/Loader.swift
浏览文件 @ afd4b73c
......@@ -159,7 +159,7 @@ public class Loader<P: PrecisionType> {
} catch let error {
throw error
}
tensor.convert(to: DataLayout.NHWC())
// tensor.convert(to: DataLayout.NHWC())
// tensor.initBuffer(device: device)
scope[varDesc.name] = tensor
} else {
......
metal/paddle-mobile/paddle-mobile/Operators/BoxcoderOp.swift
浏览文件 @ afd4b73c
......@@ -75,12 +75,12 @@ class BoxcoderOp<P: PrecisionType>: Operator<BoxcoderKernel<P>, BoxcoderParam<P>
// print(targetBoxArray.strideArray())
let targetBoxOriginDim = para.targetBox.originDim
let targetBoxArray = para.targetBox.metalTexture.realNHWC(dim: (n: targetBoxOriginDim[0], h: targetBoxOriginDim[1], w: targetBoxOriginDim[2], c: targetBoxOriginDim[3]), texturePrecision: computePrecision)
let targetBoxArray = para.targetBox.metalTexture.realNHWC(dim: (n: targetBoxOriginDim[0], h: targetBoxOriginDim[1], w: targetBoxOriginDim[2], c: targetBoxOriginDim[3]))
print(" target box ")
print(targetBoxArray.strideArray())
let originDim = para.output.originDim
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]), texturePrecision: computePrecision)
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
print(" output ")
print(outputArray.strideArray())
}
......
metal/paddle-mobile/paddle-mobile/Operators/ConcatOp.swift
浏览文件 @ afd4b73c
......@@ -67,10 +67,10 @@ class ConcatOp<P: PrecisionType>: Operator<ConcatKernel<P>, ConcatParam<P>>, Run
print(" \(type) output: ")
let originDim = para.output.originDim
if para.output.transpose == [0, 1, 2, 3] {
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]), texturePrecision: computePrecision)
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
print(outputArray.strideArray())
} else if para.output.transpose == [0, 2, 3, 1] {
print(para.output.metalTexture.toTensor(dim: (n: originDim[0], c: originDim[1], h: originDim[2], w: originDim[3]), texturePrecision: computePrecision).strideArray())
print(para.output.metalTexture.toTensor(dim: (n: originDim[0], c: originDim[1], h: originDim[2], w: originDim[3])).strideArray())
} else {
fatalError(" not implemet")
}
......
metal/paddle-mobile/paddle-mobile/Operators/ConvAddOp.swift
浏览文件 @ afd4b73c
......@@ -94,13 +94,15 @@ class ConvAddOp<P: PrecisionType>: Operator<ConvAddKernel<P>, ConvAddParam<P>>,
func delogOutput() {
print("op \(type): ")
print(" \(type) output: ")
print(" padding: ")
print(para.paddings)
print("stride: ")
print(para.stride)
print("dilations: ")
print(para.dilations)
print(" \(type) output: ")
print(" para input dim: ")
print(para.input.dim)
print(" para filter dim: ")
......@@ -111,6 +113,14 @@ class ConvAddOp<P: PrecisionType>: Operator<ConvAddKernel<P>, ConvAddParam<P>>,
let biase: [Float32] = para.y.buffer.array()
print(biase)
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3]), texturePrecision: computePrecision).strideArray())
print(" - filter - ")
let array: [Float32] = para.filter.buffer.array()
print(array)
print(" - y - ")
let yArray: [Float32] = para.y.buffer.array()
print(yArray)
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3])).strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/ConvBNReluOp.swift
浏览文件 @ afd4b73c
......@@ -110,7 +110,7 @@ class ConvBNReluOp<P: PrecisionType>: Operator<ConvBNReluKernel<P>, ConvBNReluPa
func delogOutput() {
print(" \(type) output: ")
print(para.output.metalTexture.toTensor(dim: (n: para.output.originDim[0], c: para.output.originDim[1], h: para.output.originDim[2], w: para.output.originDim[3]), texturePrecision: computePrecision).strideArray())
print(para.output.metalTexture.toTensor(dim: (n: para.output.originDim[0], c: para.output.originDim[1], h: para.output.originDim[2], w: para.output.originDim[3])).strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/ConvTransposeOp.swift
浏览文件 @ afd4b73c
......@@ -43,13 +43,15 @@ class ConvTransposeOp<P: PrecisionType>: Operator<ConvTransposeKernel<P>, ConvTr
}
func delogOutput() {
print(" \(type) output: ")
let originDim = para.output.originDim
if para.output.transpose == [0, 1, 2, 3] {
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]), texturePrecision: computePrecision)
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
print(outputArray.strideArray())
} else if para.output.transpose == [0, 2, 3, 1] {
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3]), texturePrecision: computePrecision).strideArray())
let output = para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3]))
print(output.strideArray())
} else {
print(" not implement")
}
......
metal/paddle-mobile/paddle-mobile/Operators/DepthwiseConvOp.swift
浏览文件 @ afd4b73c
......@@ -58,6 +58,6 @@ class DepthConvOp<P: PrecisionType>: Operator<ConvKernel<P>, ConvParam<P>>, Runa
func delogOutput() {
print(" \(type) output: ")
print(para.output.metalTexture.toTensor(dim: (n: para.output.originDim[0], c: para.output.originDim[1], h: para.output.originDim[2], w: para.output.originDim[3]), texturePrecision: computePrecision).strideArray())
print(para.output.metalTexture.toTensor(dim: (n: para.output.originDim[0], c: para.output.originDim[1], h: para.output.originDim[2], w: para.output.originDim[3])).strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/DwConvBNReluOp.swift
浏览文件 @ afd4b73c
......@@ -65,6 +65,6 @@ class DwConvBNReluOp<P: PrecisionType>: Operator<ConvBNReluKernel<P>, ConvBNRelu
func delogOutput() {
print(" \(type) output: ")
print(para.output.metalTexture.toTensor(dim: (n: para.output.originDim[0], c: para.output.originDim[1], h: para.output.originDim[2], w: para.output.originDim[3]), texturePrecision: computePrecision).strideArray())
print(para.output.metalTexture.toTensor(dim: (n: para.output.originDim[0], c: para.output.originDim[1], h: para.output.originDim[2], w: para.output.originDim[3])).strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/ElementwiseAddOp.swift
浏览文件 @ afd4b73c
......@@ -71,12 +71,15 @@ class ElementwiseAddOp<P: PrecisionType>: Operator<ElementwiseAddKernel<P>, Elem
// print(para.inputY.metalTexture.toTensor(dim: (n: para.inputY.tensorDim[0], c: para.inputY.tensorDim[1], h: para.inputY.tensorDim[2], w: para.inputY.tensorDim[3])).strideArray())
print(" \(type) output: ")
print(para.inputY)
let originDim = para.output.originDim
if para.output.transpose == [0, 1, 2, 3] {
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]), texturePrecision: computePrecision)
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
print(outputArray.strideArray())
} else if para.output.transpose == [0, 2, 3, 1] {
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3]), texturePrecision: computePrecision).strideArray())
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3])).strideArray())
} else {
print(" not implement")
}
......
metal/paddle-mobile/paddle-mobile/Operators/FeedOp.swift
浏览文件 @ afd4b73c
......@@ -61,7 +61,7 @@ class FeedOp<P: PrecisionType>: Operator<Texture2DTo2DArrayKernel<P>, FeedParam<
func delogOutput() {
print(" \(type) output: ")
print(para.output.metalTexture.toTensor(dim: (n: para.output.originDim[0], c: para.output.originDim[1], h: para.output.originDim[2], w: para.output.originDim[3]), texturePrecision: computePrecision).strideArray())
print(para.output.metalTexture.toTensor(dim: (n: para.output.originDim[0], c: para.output.originDim[1], h: para.output.originDim[2], w: para.output.originDim[3])).strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/BatchNormKernel.swift
浏览文件 @ afd4b73c
......@@ -15,53 +15,60 @@
import Foundation
class BatchNormKernel<P: PrecisionType>: Kernel, Computable {
var newScale: MTLBuffer
var newBias: MTLBuffer
var newScale: MTLBuffer
var newBias: MTLBuffer
required init(device: MTLDevice, param: BatchNormParam<P>) {
guard let newScale = device.makeBuffer(length: param.inputScale.buffer.length) else {
fatalError()
}
guard let newBias = device.makeBuffer(length: param.inputBias.buffer.length) else {
fatalError()
}
self.newScale = newScale
self.newBias = newBias
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "batchnorm")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "batchnorm_half")
} else {
fatalError()
}
let varianceBuffer : MTLBuffer = param.inputVariance.buffer
required init(device: MTLDevice, param: BatchNormParam<P>) {
guard let newScale = device.makeBuffer(length: param.inputScale.buffer.length) else {
fatalError()
}
guard let newBias = device.makeBuffer(length: param.inputBias.buffer.length) else {
fatalError()
}
self.newScale = newScale
self.newBias = newBias
super.init(device: device, inFunctionName: "batchnorm")
let varianceBuffer : MTLBuffer = param.inputVariance.buffer
var invStd: [Float32] = Array(repeating: 0, count: varianceBuffer.length)
let varianceContents = varianceBuffer.contents().assumingMemoryBound(to: P.self)
for i in 0..<(varianceBuffer.length / MemoryLayout<P>.stride) {
invStd[i] = 1 / (Float32(varianceContents[i]) + param.epsilon).squareRoot()
}
let newScaleContents = newScale.contents().assumingMemoryBound(to: P.self)
let newBiasContents = newBias.contents().assumingMemoryBound(to: P.self)
let scale : MTLBuffer = param.inputScale.buffer
let scaleContents = scale.contents().assumingMemoryBound(to: P.self)
let bias : MTLBuffer = param.inputBias.buffer
let biasContents = bias.contents().assumingMemoryBound(to: P.self)
let meanContents = param.inputMean.buffer.contents().assumingMemoryBound(to: P.self)
for i in 0..<(newScale.length / MemoryLayout<P>.stride) {
newScaleContents[i] = P(invStd[i] * Float32(scaleContents[i]))
newBiasContents[i] = P(Float32(biasContents[i]) - Float32(meanContents[i]) * invStd[i] * Float32(scaleContents[i]))
}
var invStd: [Float32] = Array(repeating: 0, count: varianceBuffer.length)
let varianceContents = varianceBuffer.contents().assumingMemoryBound(to: P.self)
for i in 0..<(varianceBuffer.length / MemoryLayout<P>.stride) {
invStd[i] = 1 / (Float32(varianceContents[i]) + param.epsilon).squareRoot()
}
func compute(commandBuffer: MTLCommandBuffer, param: BatchNormParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encoder is nil")
}
print("BatchNorm compute")
encoder.setTexture(param.input.metalTexture, index: 0)
encoder.setTexture(param.output.metalTexture, index: 1)
encoder.setBuffer(newScale, offset: 0, index: 0)
encoder.setBuffer(newBias, offset: 0, index: 1)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
let newScaleContents = newScale.contents().assumingMemoryBound(to: P.self)
let newBiasContents = newBias.contents().assumingMemoryBound(to: P.self)
let scale : MTLBuffer = param.inputScale.buffer
let scaleContents = scale.contents().assumingMemoryBound(to: P.self)
let bias : MTLBuffer = param.inputBias.buffer
let biasContents = bias.contents().assumingMemoryBound(to: P.self)
let meanContents = param.inputMean.buffer.contents().assumingMemoryBound(to: P.self)
for i in 0..<(newScale.length / MemoryLayout<P>.stride) {
newScaleContents[i] = P(invStd[i] * Float32(scaleContents[i]))
newBiasContents[i] = P(Float32(biasContents[i]) - Float32(meanContents[i]) * invStd[i] * Float32(scaleContents[i]))
}
}
func compute(commandBuffer: MTLCommandBuffer, param: BatchNormParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encoder is nil")
}
print("BatchNorm compute")
encoder.setTexture(param.input.metalTexture, index: 0)
encoder.setTexture(param.output.metalTexture, index: 1)
encoder.setBuffer(newScale, offset: 0, index: 0)
encoder.setBuffer(newBias, offset: 0, index: 1)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvAddBatchNormReluKernel.swift
浏览文件 @ afd4b73c
......@@ -49,26 +49,39 @@ class ConvAddBatchNormReluKernel<P: PrecisionType>: Kernel, Computable, Testable
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvAddBatchNormReluParam<P>) {
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: computePrecision)
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_1x1")
} else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_add_batch_norm_relu_3x3")
} else {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_3x3")
}
param.filter.initBuffer(device: device, precision: computePrecision)
param.y.initBuffer(device: device, precision: computePrecision)
param.variance.initBuffer(device: device, precision: .Float32)
param.mean.initBuffer(device: device, precision: .Float32)
param.scale.initBuffer(device: device, precision: .Float32)
param.bias.initBuffer(device: device, precision: .Float32)
if computePrecision == .Float32 {
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_1x1")
} else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_add_batch_norm_relu_3x3")
} else if param.filter.width == 3 && param.filter.height == 3 {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_3x3")
} else {
fatalError(" unsupport ")
}
} else if computePrecision == .Float16 {
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_1x1_half")
} else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_add_batch_norm_relu_3x3_half")
} else if param.filter.width == 3 && param.filter.height == 3 {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_3x3_half")
} else {
fatalError(" unsupport ")
}
} else {
fatalError()
}
let offsetX = param.filter.width/2 - Int(param.paddings[0])
let offsetY = param.filter.height/2 - Int(param.paddings[1])
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvAddKernel.swift
浏览文件 @ afd4b73c
......@@ -17,14 +17,23 @@ import Foundation
class ConvAddKernel<P: PrecisionType>: Kernel, Computable {
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvAddParam<P>) {
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: computePrecision)
param.filter.initBuffer(device: device, precision: computePrecision)
param.y.initBuffer(device: device, precision: computePrecision)
if computePrecision == .Float16 {
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_add_1x1_half")
} else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3_half")
} else {
} else if param.filter.width == 3 && param.filter.height == 3 {
super.init(device: device, inFunctionName: "conv_add_3x3_half")
} else if param.filter.width == 1 && param.filter.height == 5 {
super.init(device: device, inFunctionName: "conv_add_5x1_half")
} else if param.filter.width == 5 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_add_1x5_half")
} else {
fatalError(" unsupport yet ")
}
} else if computePrecision == .Float32 {
if param.filter.width == 1 && param.filter.height == 1 {
......@@ -35,22 +44,21 @@ class ConvAddKernel<P: PrecisionType>: Kernel, Computable {
super.init(device: device, inFunctionName: "conv_add_5x1")
} else if param.filter.width == 5 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_add_1x5")
} else {
} else if param.filter.width == 3 && param.filter.height == 3 {
super.init(device: device, inFunctionName: "conv_add_3x3")
} else {
fatalError(" unsupport yet ")
}
} else {
fatalError()
}
let offsetY = (Int(param.dilations[1]) * (param.filter.height - 1) + 1)/2 - Int(param.paddings[1])
let offsetX = (Int(param.dilations[0]) * (param.filter.width - 1) + 1)/2 - Int(param.paddings[0])
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: computePrecision)
param.filter.initBuffer(device: device, precision: computePrecision)
param.y.initBuffer(device: device, precision: computePrecision)
print(" function: \(functionName)")
print("offset x: \(offsetX)")
print("offset y: \(offsetY)")
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvBNReluKernel.swift
浏览文件 @ afd4b73c
......@@ -49,35 +49,41 @@ class ConvBNReluKernel<P: PrecisionType>: Kernel, Computable, Testable {
}
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvBNReluParam<P>) {
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: computePrecision)
param.filter.initBuffer(device: device, precision: computePrecision)
param.variance.initBuffer(device: device, precision: .Float32)
param.mean.initBuffer(device: device, precision: .Float32)
param.scale.initBuffer(device: device, precision: .Float32)
param.bias.initBuffer(device: device, precision: .Float32)
if computePrecision == .Float32 {
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_batch_norm_relu_1x1")
} else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_batch_norm_relu_3x3")
} else {
} else if param.filter.width == 3 && param.filter.height == 3 {
super.init(device: device, inFunctionName: "conv_batch_norm_relu_3x3")
} else {
fatalError(" unsupport ")
}
} else if computePrecision == .Float16 {
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_batch_norm_relu_1x1_half")
} else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_batch_norm_relu_3x3_half")
} else {
} else if param.filter.width == 3 && param.filter.height == 3 {
super.init(device: device, inFunctionName: "conv_batch_norm_relu_3x3_half")
} else {
fatalError(" unsupport ")
}
} else {
fatalError()
}
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: computePrecision)
param.filter.initBuffer(device: device, precision: computePrecision)
param.variance.initBuffer(device: device, precision: .Float32)
param.mean.initBuffer(device: device, precision: .Float32)
param.scale.initBuffer(device: device, precision: .Float32)
param.bias.initBuffer(device: device, precision: .Float32)
let offsetX = param.filter.width/2 - Int(param.paddings[0])
let offsetY = param.filter.height/2 - Int(param.paddings[1])
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvKernel.swift
浏览文件 @ afd4b73c
......@@ -27,18 +27,20 @@ public struct MetalConvParam {
class ConvKernel<P: PrecisionType>: Kernel, Computable {
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvParam<P>) {
param.filter.initBuffer(device: device, precision: ComputePrecision.Float32)
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_1x1")
} else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_3x3")
} else {
} else if param.filter.width == 3 && param.filter.height == 3 {
super.init(device: device, inFunctionName: "conv_3x3")
} else {
fatalError(" unsupport ")
}
let offsetX = param.filter.dim[2]/2 - Int(param.paddings[0])
let offsetY = param.filter.dim[1]/2 - Int(param.paddings[1])
let offsetZ = 0.0
param.filter.initBuffer(device: device, precision: ComputePrecision.Float32)
metalParam = MetalConvParam.init(offsetX: Int16(offsetX), offsetY: Int16(offsetY), offsetZ: Int16(offsetZ), strideX: UInt16(param.stride[0]), strideY: UInt16(param.stride[1]), dilationX: UInt16(param.dilations[0]), dilationY: UInt16(param.dilations[1]))
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvTransposeKernel.swift
浏览文件 @ afd4b73c
......@@ -31,7 +31,27 @@ struct MetalConvTransposeParam {
class ConvTransposeKernel<P: PrecisionType>: Kernel, Computable{
var metalParam: MetalConvTransposeParam!
required init(device: MTLDevice, param: ConvTransposeParam<P>) {
super.init(device: device, inFunctionName: "conv_transpose")
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: computePrecision)
param.filter.initBuffer(device: device, precision: computePrecision, convertToNHWC: false, withTranspose: true)
if computePrecision == .Float32 {
if param.stride == [2, 2] && param.stride == [2, 2] {
super.init(device: device, inFunctionName: "conv_transpose2x2_stride2")
} else {
fatalError(" -- conv transpose unsupported yet -- ")
}
} else if computePrecision == .Float16 {
if param.stride == [2, 2] && param.stride == [2, 2] {
super.init(device: device, inFunctionName: "conv_transpose2x2_stride2_half")
} else {
fatalError(" -- conv transpose unsupported yet -- ")
}
} else {
fatalError()
}
// let filter: [Float32] = param.filter.buffer.array()
// print(" conv transpose filter")
// print(filter)
let kernelWidth = UInt16(param.filter.width)
let kernelHeight = UInt16(param.filter.height)
......@@ -43,9 +63,7 @@ class ConvTransposeKernel<P: PrecisionType>: Kernel, Computable{
let dilationY = UInt16(param.dilations[1])
metalParam = MetalConvTransposeParam.init(kernelW: kernelWidth, kernelH: kernelHeight, strideX: strideX, strideY: strideY, paddingX: paddingX, paddingY: paddingY, dilationX: dilationX, dilationY: dilationY)
param.output.initTexture(device: device, inTranspose: param.input.transpose)
param.filter.initBuffer(device: device)
}
func compute(commandBuffer: MTLCommandBuffer, param: ConvTransposeParam<P>) throws {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ElementwiseAddKernel.swift
浏览文件 @ afd4b73c
......@@ -15,6 +15,7 @@
import Foundation
struct ElementwiseAddMetalParam {
var unsafe_one_dim: Int32 = 0
var fast: Int32 = 0
var axis: Int32 = 0
var yoff: Int32 = 0
......@@ -26,8 +27,14 @@ struct ElementwiseAddMetalParam {
class ElementwiseAddKernel<P: PrecisionType>: Kernel, Computable {
required init(device: MTLDevice, param: ElementwiseAddParam<P>) {
super.init(device: device, inFunctionName: "elementwise_add")
param.output.initTexture(device: device, inTranspose: param.inputX.transpose, computePrecision: computePrecision)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "elementwise_add")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "elementwise_add_half")
} else {
fatalError()
}
}
func compute(commandBuffer: MTLCommandBuffer, param: ElementwiseAddParam<P>) throws {
......@@ -59,6 +66,11 @@ class ElementwiseAddKernel<P: PrecisionType>: Kernel, Computable {
emp.fast = 1
}
// TODO:
if param.inputY.tensorDim.cout() == 1 {
emp.unsafe_one_dim = 1;
}
encoder.setBytes(&emp, length: MemoryLayout<ElementwiseAddMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PoolKernel.swift
浏览文件 @ afd4b73c
......@@ -27,8 +27,14 @@ struct PoolMetalParam {
class PoolKernel<P: PrecisionType>: Kernel, Computable{
required init(device: MTLDevice, param: PoolParam<P>) {
super.init(device: device, inFunctionName: "pool")
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: computePrecision)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "pool")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "pool_half")
} else {
fatalError()
}
}
func compute(commandBuffer: MTLCommandBuffer, param: PoolParam<P>) throws {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PreluKernel.swift
浏览文件 @ afd4b73c
......@@ -10,15 +10,27 @@ import Foundation
class PreluKernel<P: PrecisionType>: Kernel, Computable{
required init(device: MTLDevice, param: PreluParam<P>) {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "prelu_channel")
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "prelu_element")
} else {
super.init(device: device, inFunctionName: "prelu_other")
}
param.alpha.initBuffer(device: device, precision: computePrecision)
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: computePrecision)
if computePrecision == .Float32 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "prelu_channel")
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "prelu_element")
} else {
super.init(device: device, inFunctionName: "prelu_other")
}
} else if computePrecision == .Float16 {
if param.mode == "channel" {
super.init(device: device, inFunctionName: "prelu_channel_half")
} else if param.mode == "element" {
super.init(device: device, inFunctionName: "prelu_element_half")
} else {
super.init(device: device, inFunctionName: "prelu_other_half")
}
} else {
fatalError()
}
}
func compute(commandBuffer: MTLCommandBuffer, param: PreluParam<P>) throws {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PriorBoxKernel.swift
浏览文件 @ afd4b73c
......@@ -33,6 +33,10 @@ class PriorBoxKernel<P: PrecisionType>: Kernel, Computable{
var metalParam: PriorBoxMetalParam!
required init(device: MTLDevice, param: PriorBoxParam<P>) {
param.output.initTexture(device: device, inTranspose: [2, 0, 1, 3], computePrecision: computePrecision)
param.outputVariances.initTexture(device: device, inTranspose: [2, 0, 1, 3], computePrecision: computePrecision)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "prior_box")
} else if computePrecision == .Float16 {
......@@ -41,9 +45,6 @@ class PriorBoxKernel<P: PrecisionType>: Kernel, Computable{
fatalError()
}
param.output.initTexture(device: device, inTranspose: [2, 0, 1, 3], computePrecision: computePrecision)
param.outputVariances.initTexture(device: device, inTranspose: [2, 0, 1, 3], computePrecision: computePrecision)
let n = 1
let h = param.output.dim[1]
let w = param.output.dim[2]
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ReluKernel.swift
浏览文件 @ afd4b73c
......@@ -15,17 +15,23 @@
import Foundation
class ReluKernel<P: PrecisionType>: Kernel, Computable{
func compute(commandBuffer: MTLCommandBuffer, param: ReluParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil")
}
encoder.setTexture(param.input.metalTexture, index: 0)
encoder.setTexture(param.output.metalTexture, index: 1)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
func compute(commandBuffer: MTLCommandBuffer, param: ReluParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil")
}
required init(device: MTLDevice, param: ReluParam<P>) {
super.init(device: device, inFunctionName: "relu")
encoder.setTexture(param.input.metalTexture, index: 0)
encoder.setTexture(param.output.metalTexture, index: 1)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
required init(device: MTLDevice, param: ReluParam<P>) {
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "relu")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "relu_half")
} else {
fatalError()
}
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ResizeKernel.swift 已删除 100644 → 0
浏览文件 @ c5a91cbb
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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. */
//
//import Foundation
//import MetalPerformanceShaders
//
//
//struct ResizeParam: OpParam{
// typealias OutputType = <#type#>
//
// typealias ParamPrecisionType = <#type#>
//
// let input: MTLTexture
// let output: MTLTexture
// let expectDim: Dim
//}
//
//struct OutputDim {
// let width: UInt16
// let height: UInt16
// let strideX: UInt16
// let strideY: UInt16
//}
//
//class ResizeKernel<P: PrecisionType>: Kernel, Computable{
// var lanczos: MPSImageLanczosScale
// required init(device: MTLDevice, param: ResizeParam) {
// lanczos = MPSImageLanczosScale.init(device: device)
// super.init(device: device, inFunctionName: "resize")
// }
// func compute(commandBuffer: MTLCommandBuffer, param: ResizeParam) throws {
//// guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
//// throw PaddleMobileError.predictError(message: " encode is nil")
//// }
// lanczos.encode(commandBuffer: commandBuffer, sourceTexture: param.input, destinationTexture: param.output)
//
//// encoder.setTexture(param.input, index: 0)
//// encoder.setTexture(param.output, index: 1)
//// let strideX = param.input.width/param.expectDim[2]
//// let strideY = param.input.height/param.expectDim[1]
//// var outputDim = OutputDim.init(width: UInt16(param.expectDim[1]), height: UInt16(param.expectDim[2]), strideX: UInt16(strideX), strideY: UInt16(strideY))
//// encoder.setBytes(&outputDim, length: MemoryLayout<OutputDim>.size, index: 0)
//// encoder.dispatch(computePipline: pipline, outTexture: param.output)
//// encoder.endEncoding()
// }
//
//
//
//
//}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ConvAddMetal.metal
浏览文件 @ afd4b73c
......@@ -429,7 +429,122 @@ kernel void depthwise_conv_add_3x3_half(texture2d_array<half, access::sample> in
}
kernel void conv_add_5x1_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
const device half4 *biase [[buffer(2)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
ushort2 stride = ushort2(param.strideX, param.strideY);
const ushort2 posInInput = ushort2(gid.xy) * stride + ushort2(param.offsetX, param.offsetY);
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
const uint kernelHXW = 5;
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
float4 output = float4(0.0);
ushort dilation_y = param.dilationY;
half4 input[5];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - 2 * dilation_y), i);
input[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - dilation_y), i);
input[2] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[3] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + dilation_y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + 2 * dilation_y), i);
for (int j = 0; j < 5; ++j) {
half4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(input[j], weight_x);
half4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(input[j], weight_y);
half4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(input[j], weight_z);
half4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(float4(input[j]), float4(weight_w));
}
}
output = output + float4(biase[gid.z]);
outTexture.write(half4(output), gid.xy, gid.z);
}
kernel void conv_add_1x5_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
const device half4 *biase [[buffer(2)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
ushort2 stride = ushort2(param.strideX, param.strideY);
const ushort2 posInInput = ushort2(gid.xy) * stride + ushort2(param.offsetX, param.offsetY);
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
const uint kernelHXW = 5;
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
float4 output = float4(0.0);
ushort dilation_x = param.dilationX;
half4 input[5];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x - 2 * dilation_x, posInInput.y), i);
input[1] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y), i);
input[2] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[3] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x + 2 * dilation_x, posInInput.y), i);
for (int j = 0; j < 5; ++j) {
half4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(input[j], weight_x);
half4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(input[j], weight_y);
half4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(input[j], weight_z);
half4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(input[j], weight_w);
}
}
output = output + float4(biase[gid.z]);
outTexture.write(half4(output), gid.xy, gid.z);
}
kernel void test_conv_add_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
......@@ -502,3 +617,6 @@ kernel void test_conv_add_3x3(texture2d_array<float, access::sample> inTexture [
// output = output + biase[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ConvKernel.metal
浏览文件 @ afd4b73c
......@@ -148,4 +148,133 @@ kernel void conv_1x1(texture2d_array<float, access::sample> inTexture [[texture(
}
kernel void conv_3x3_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
ushort2 stride = ushort2(param.strideX, param.strideY);
const ushort2 posInInput = ushort2(gid.xy) * stride + ushort2(param.offsetX, param.offsetY);
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
const uint kernelHXW = 9;
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
float4 output = float4(0.0);
half4 input[9];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y - 1), i);
input[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - 1), i);
input[2] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y - 1), i);
input[3] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[5] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y), i);
input[6] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y + 1), i);
input[7] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + 1), i);
input[8] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y + 1), i);
for (int j = 0; j < 9; ++j) {
half4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(float4(input[j]), float4(weight_x));
half4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(float4(input[j]), float4(weight_y));
half4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(float4(input[j]), float4(weight_z));
half4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(float4(input[j]), float4(weight_w));
}
}
outTexture.write(half4(output), gid.xy, gid.z);
}
kernel void depthwise_conv_3x3_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half *weights [[buffer(1)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
uint output_slice = gid.z;
ushort2 stride = ushort2(param.strideX, param.strideY);
ushort2 posInInput = ushort2(gid.xy) * stride + ushort2(param.offsetX, param.offsetY);
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
const uint kernelHXW = 9;
uint weithTo = gid.z * kernelHXW * 4;
float4 output = float4(0.0);
half4 inputs[9];
inputs[0] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y - 1), output_slice);
inputs[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - 1), output_slice);
inputs[2] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y - 1), output_slice);
inputs[3] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y), output_slice);
inputs[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), output_slice);
inputs[5] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y), output_slice);
inputs[6] = inTexture.sample(sample, float2(posInInput.x - 1, posInInput.y + 1), output_slice);
inputs[7] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + 1), output_slice);
inputs[8] = inTexture.sample(sample, float2(posInInput.x + 1, posInInput.y + 1), output_slice);
for (int j = 0; j < 9; ++j) {
half4 input = inputs[j];
output.x += float(input.x) * float(weights[weithTo + 0 * kernelHXW + j]);
output.y += float(input.y) * float(weights[weithTo + 1 * kernelHXW + j]);
output.z += float(input.z) * float(weights[weithTo + 2 * kernelHXW + j]);
output.w += float(input.w) * float(weights[weithTo + 3 * kernelHXW + j]);
}
outTexture.write(half4(output), gid.xy, gid.z);
}
kernel void conv_1x1_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
ushort2 stride = ushort2(param.strideX, param.strideY);
ushort2 posInInput = ushort2(gid.xy) * stride + ushort2(param.offsetX, param.offsetY);
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
const uint kernelHXW = 1;
uint input_arr_size = inTexture.get_array_size();
uint weithTo = gid.z * kernelHXW * input_arr_size * 4;
float4 output = float4(0.0);
half4 input;
for (uint i = 0; i < input_arr_size; ++i) {
input = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
half4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + i];
output.x += dot(float4(input), float4(weight_x));
half4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + i];
output.y += dot(float4(input), float4(weight_y));
half4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + i];
output.z += dot(float4(input), float4(weight_z));
half4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + i];
output.w += dot(float4(input), float4(weight_w));
}
outTexture.write(half4(output), gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ConvTransposeKernel.metal
浏览文件 @ afd4b73c
......@@ -29,11 +29,11 @@ struct MetalConvTransposeParam{
ushort dilationY;
};
kernel void conv_transpose(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvTransposeParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
uint3 gid [[thread_position_in_grid]]){
kernel void conv_transpose2x2_stride2(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvTransposeParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
......@@ -41,48 +41,134 @@ kernel void conv_transpose(texture2d_array<float, access::sample> inTexture [[te
}
int input_array_size = inTexture.get_array_size();
uint kernel_one_output_slice = input_array_size * param.kernelW * param.kernelH;
uint kernel_stride_z = gid.z * 4 * (kernel_one_output_slice);
int kernel_index_x = gid.x % 2;
int kernel_index_y = gid.y % 2;
int kernel_index = kernel_index_y * 2 + kernel_index_x;
int kernel_to = gid.z * input_array_size * 4 * 4 + (kernel_index * input_array_size);
int input_x = gid.x / 2;
int input_y = gid.y / 2;
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
float4 output = float4(0.0);
for (int i = 0; i < input_array_size; ++i) {
float4 input = inTexture.sample(sample, float2(input_x, input_y), i);
float4 kernel_slice0 = weights[kernel_to + input_array_size * 4 * 0 + i];
float4 kernel_slice1 = weights[kernel_to + input_array_size * 4 * 1 + i];
float4 kernel_slice2 = weights[kernel_to + input_array_size * 4 * 2 + i];
float4 kernel_slice3 = weights[kernel_to + input_array_size * 4 * 3 + i];
output.x += dot(input, kernel_slice0);
output.y += dot(input, kernel_slice1);
output.z += dot(input, kernel_slice2);
output.w += dot(input, kernel_slice3);
}
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_transpose2x2_stride2_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant MetalConvTransposeParam &param [[buffer(0)]],
const device half4 *weights [[buffer(1)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
float4 output;
int input_array_size = inTexture.get_array_size();
int kernel_index_x = gid.x % 2;
int kernel_index_y = gid.y % 2;
int kernel_index = kernel_index_y * 2 + kernel_index_x;
int kernel_to = gid.z * input_array_size * 4 * 4 + (kernel_index * input_array_size);
int input_x = gid.x / 2;
int input_y = gid.y / 2;
for (int w = 0; w < param.kernelW; ++w) {
int input_x = (gid.x - w * param.dilationX + param.paddingX) / param.strideX;
if (input_x < 0 || input_x >= int(inTexture.get_width())) {
continue;
}
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
float4 output = float4(0.0);
for (int i = 0; i < input_array_size; ++i) {
for (int h = 0; h < param.kernelH; ++h) {
int input_y = (gid.y - h * param.dilationY + param.paddingY) / param.strideY;
if (input_y < 0 || input_y >= int(inTexture.get_height())) {
continue;
}
uint kernel_index = (w * param.kernelH + h) * inTexture.get_array_size();
for (int slice = 0; slice < input_array_size; ++slice) {
float4 input;
float4 kernel_slice = weights[kernel_stride_z + 0 * kernel_one_output_slice + kernel_index + slice];
float4 kernel_slice1 = weights[kernel_stride_z + 1 * kernel_one_output_slice + kernel_index + slice];
float4 kernel_slice2 = weights[kernel_stride_z + 2 * kernel_one_output_slice + kernel_index + slice];
float4 kernel_slice3 = weights[kernel_stride_z + 3 * kernel_one_output_slice + kernel_index + slice];
input = inTexture.sample(sample, float2(input_x, input_x), slice);
output.x += dot(input, kernel_slice);
output.x += dot(input, kernel_slice1);
output.x += dot(input, kernel_slice2);
output.x += dot(input, kernel_slice3);
}
}
half4 input = inTexture.sample(sample, float2(input_x, input_y), i);
half4 kernel_slice0 = weights[kernel_to + input_array_size * 4 * 0 + i];
half4 kernel_slice1 = weights[kernel_to + input_array_size * 4 * 1 + i];
half4 kernel_slice2 = weights[kernel_to + input_array_size * 4 * 2 + i];
half4 kernel_slice3 = weights[kernel_to + input_array_size * 4 * 3 + i];
output.x += dot(float4(input), float4(kernel_slice0));
output.y += dot(float4(input), float4(kernel_slice1));
output.z += dot(float4(input), float4(kernel_slice2));
output.w += dot(float4(input), float4(kernel_slice3));
}
outTexture.write(output, gid.xy, gid.z);
outTexture.write(half4(output), gid.xy, gid.z);
}
//kernel void conv_transpose(texture2d_array<float, access::sample> inTexture [[texture(0)]],
// texture2d_array<float, access::write> outTexture [[texture(1)]],
// constant MetalConvTransposeParam &param [[buffer(0)]],
// const device float4 *weights [[buffer(1)]],
// uint3 gid [[thread_position_in_grid]]){
// if (gid.x >= outTexture.get_width() ||
// gid.y >= outTexture.get_height() ||
// gid.z >= outTexture.get_array_size()) {
// return;
// }
//
// int input_array_size = inTexture.get_array_size();
//
// uint kernel_one_output_slice = input_array_size * param.kernelW * param.kernelH;
//
// uint kernel_stride_z = gid.z * 4 * (kernel_one_output_slice);
//
// constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
//
// float4 output;
//
// for (int w = 0; w < param.kernelW; ++w) {
// int top = gid.x - w * param.dilationX + param.paddingX;
// int input_x = top / param.strideX;
// if (top < 0 || input_x >= int(inTexture.get_width())) {
// continue;
// }
//
// for (int h = 0; h < param.kernelH; ++h) {
// int top_y = gid.y - h * param.dilationY + param.paddingY;
// int input_y = top_y / param.strideY;
// if (top_y < 0 || input_y >= int(inTexture.get_height())) {
// continue;
// }
//
// uint kernel_index = (w * param.kernelH + h) * inTexture.get_array_size();
//
// for (int slice = 0; slice < input_array_size; ++slice) {
//
// float4 input;
// float4 kernel_slice = weights[kernel_stride_z + 0 * kernel_one_output_slice + kernel_index + slice];
// float4 kernel_slice1 = weights[kernel_stride_z + 1 * kernel_one_output_slice + kernel_index + slice];
//
// float4 kernel_slice2 = weights[kernel_stride_z + 2 * kernel_one_output_slice + kernel_index + slice];
//
// float4 kernel_slice3 = weights[kernel_stride_z + 3 * kernel_one_output_slice + kernel_index + slice];
//
// input = inTexture.sample(sample, float2(input_x, input_y), slice);
// output.x += dot(input, kernel_slice);
// output.y += dot(input, kernel_slice1);
// output.z += dot(input, kernel_slice2);
// output.w += dot(input, kernel_slice3);
// }
// }
// }
//
// outTexture.write(output, gid.xy, gid.z);
//}
//
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/Elementwise.metal
浏览文件 @ afd4b73c
......@@ -18,6 +18,7 @@
using namespace metal;
struct ElementwiseAddParam {
int32_t unsafe_one_dim;
int32_t fast;
int32_t axis;
int32_t yoff;
......@@ -36,7 +37,10 @@ kernel void elementwise_add(texture2d_array<float, access::read> inputX [[textur
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
float4 rx, ry;
if (pm.fast == 1) {
if (pm.unsafe_one_dim == 1) {
rx = inputX.read(gid.xy, gid.z);
ry = inputY.read(uint2(0, 0), gid.z);
} else if (pm.fast == 1) {
rx = inputX.read(gid.xy, gid.z);
ry = inputY.read(gid.xy, gid.z);
} else {
......@@ -59,3 +63,39 @@ kernel void elementwise_add(texture2d_array<float, access::read> inputX [[textur
float4 r = rx + ry;
outTexture.write(r, gid.xy, gid.z);
}
kernel void elementwise_add_half(texture2d_array<half, access::read> inputX [[texture(0)]],
texture2d_array<half, access::read> inputY [[texture(1)]],
texture2d_array<half, access::write> outTexture [[texture(2)]],
constant ElementwiseAddParam &pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) return;
half4 rx, ry;
if (pm.unsafe_one_dim == 1) {
rx = inputX.read(gid.xy, gid.z);
ry = inputY.read(uint2(0, 0), gid.z);
} else if (pm.fast == 1) {
rx = inputX.read(gid.xy, gid.z);
ry = inputY.read(gid.xy, gid.z);
} else {
rx = inputX.read(gid.xy, gid.z);
int32_t x_xyzn[4] = {int32_t(gid.x), int32_t(gid.y), int32_t(gid.z), 0}, x_abcd[4], t_abcd[4];
int32_t y_abcd[4] = {1, 1, 1, 1}, y_xyzn[4];
int32_t xtrans[4] = {pm.xtrans[0], pm.xtrans[1], pm.xtrans[2], pm.xtrans[3]};
int32_t ytrans[4] = {pm.ytrans[0], pm.ytrans[1], pm.ytrans[2], pm.ytrans[3]};
for (int n = 0; n < 4; n++) {
xyzn2abcd(pm.xdim[3], x_xyzn, x_abcd);
invtrans(xtrans, x_abcd, t_abcd);
for (int k = pm.axis; k < (4 - pm.yoff); k++) {
y_abcd[k+pm.yoff] = t_abcd[k];
}
trans(ytrans, y_abcd, t_abcd);
abcd2xyzn(pm.ydim[3], t_abcd, y_xyzn);
ry[n] = inputY.read(uint2(y_xyzn[0], y_xyzn[1]), y_xyzn[2])[y_xyzn[3]];
}
}
half4 r = rx + ry;
outTexture.write(r, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/PreluKernel.metal
浏览文件 @ afd4b73c
......@@ -81,3 +81,71 @@ kernel void prelu_other(texture2d_array<float, access::sample> inTexture [[textu
outTexture.write(output, gid.xy, gid.z);
}
kernel void prelu_channel_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
const device half4 *alpha [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]){
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
half4 input = inTexture.sample(sample, float2(gid.x, gid.y), gid.z);
half4 alpha_value = alpha[gid.z];
half4 output;
output.x = input.x > 0 ? input.x : (alpha_value.x * input.x);
output.y = input.y > 0 ? input.y : (alpha_value.y * input.y);
output.z = input.z > 0 ? input.z : (alpha_value.z * input.z);
output.w = input.w > 0 ? input.w : (alpha_value.w * input.w);
outTexture.write(output, gid.xy, gid.z);
}
kernel void prelu_element_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
const device half4 *alpha [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]){
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
half4 input = inTexture.sample(sample, float2(gid.x, gid.y), gid.z);
int alpha_to = (gid.y * inTexture.get_width() + gid.x) * inTexture.get_array_size();
half4 alpha_value = alpha[alpha_to + gid.z];
half4 output;
output.x = input.x > 0 ? input.x : (alpha_value.x * input.x);
output.y = input.y > 0 ? input.y : (alpha_value.y * input.y);
output.z = input.z > 0 ? input.z : (alpha_value.z * input.z);
output.w = input.w > 0 ? input.w : (alpha_value.w * input.w);
outTexture.write(output, gid.xy, gid.z);
}
kernel void prelu_other_half(texture2d_array<half, access::sample> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
const device half *alpha [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]){
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height() ||
gid.z >= outTexture.get_array_size()) {
return;
}
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
half4 input = inTexture.sample(sample, float2(gid.x, gid.y), gid.z);
half alpha_value = alpha[0];
half4 output;
output.x = input.x > 0 ? input.x : (alpha_value * input.x);
output.y = input.y > 0 ? input.y : (alpha_value * input.y);
output.z = input.z > 0 ? input.z : (alpha_value * input.z);
output.w = input.w > 0 ? input.w : (alpha_value * input.w);
outTexture.write(output, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/PoolOp.swift
浏览文件 @ afd4b73c
......@@ -60,7 +60,7 @@ class PoolOp<P: PrecisionType>: Operator<PoolKernel<P>, PoolParam<P>>, Runable,
func delogOutput() {
print(" \(type) output: ")
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3]), texturePrecision: computePrecision).strideArray())
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3])).strideArray())
// print("pool2d delog")
......
metal/paddle-mobile/paddle-mobile/Operators/PreluOp.swift
浏览文件 @ afd4b73c
......@@ -51,13 +51,13 @@ class PreluOp<P: PrecisionType>: Operator<PreluKernel<P>, PreluParam<P>>, Runabl
func delogOutput() {
print(" \(type) input: ")
print(para.input.metalTexture.toTensor(dim: (n: para.input.originDim[0], c: para.input.originDim[1], h: para.input.originDim[2], w: para.input.originDim[3]), texturePrecision: computePrecision).strideArray())
print(para.input.metalTexture.toTensor(dim: (n: para.input.originDim[0], c: para.input.originDim[1], h: para.input.originDim[2], w: para.input.originDim[3])).strideArray())
print(" \(type) Alpha: ")
let _: Float32? = para.alpha.buffer.logDesc(header: " alpha: ", stridable: false)
print(" \(type) output: ")
print(para.output.metalTexture.toTensor(dim: (n: para.output.originDim[0], c: para.output.originDim[1], h: para.output.originDim[2], w: para.output.originDim[3]), texturePrecision: computePrecision).strideArray())
print(para.output.metalTexture.toTensor(dim: (n: para.output.originDim[0], c: para.output.originDim[1], h: para.output.originDim[2], w: para.output.originDim[3])).strideArray())
}
// print("softmax delog")
......
metal/paddle-mobile/paddle-mobile/Operators/ReluOp.swift
浏览文件 @ afd4b73c
......@@ -46,7 +46,7 @@ class ReluOp<P: PrecisionType>: Operator<ReluKernel<P>, ReluParam<P>>, Runable,
func delogOutput() {
print(" \(type) output: ")
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3]), texturePrecision: computePrecision).strideArray())
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3])).strideArray())
}
}
......
metal/paddle-mobile/paddle-mobile/Operators/ReshapeOp.swift
浏览文件 @ afd4b73c
......@@ -76,7 +76,7 @@ class ReshapeOp<P: PrecisionType>: Operator<ReshapeKernel<P>, ReshapeParam<P>>,
let originDim = para.output.originDim
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]), texturePrecision: computePrecision)
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
print(outputArray.strideArray())
}
......
metal/paddle-mobile/paddle-mobile/Operators/SoftmaxOp.swift
浏览文件 @ afd4b73c
......@@ -54,7 +54,7 @@ class SoftmaxOp<P: PrecisionType>: Operator<SoftmaxKernel<P>, SoftmaxParam<P>>,
print("softmax delog")
let originDim = para.output.originDim
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]), texturePrecision: computePrecision)
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
print(outputArray.strideArray())
}
}
metal/paddle-mobile/paddle-mobile/framework/Tensor.swift
浏览文件 @ afd4b73c
......@@ -95,7 +95,28 @@ class Tensor<P: PrecisionType>: Tensorial {
func initBuffer(device: MTLDevice, precision: ComputePrecision = .Float16) {
func initBuffer(device: MTLDevice, precision: ComputePrecision = .Float16, convertToNHWC: Bool = true, withTranspose: Bool = false) {
if convertToNHWC {
// print(layout)
convert(to: DataLayout.NHWC())
}
if withTranspose {
let transposePointer = UnsafeMutablePointer<P>.allocate(capacity: numel())
let n = dim[0]
let hwc = numel()/n
for j in 0..<hwc {
for i in 0..<n {
//data[i * hwc + j]
transposePointer[j * n + i] = data[i * hwc + j]
}
}
dim.swapeDimAt(index1: 0, index2: 3)
data.release()
data.pointer = transposePointer
}
guard let floatPointer = data.pointer as? UnsafeMutablePointer<Float32> else {
fatalError(" not support yet ")
}
......@@ -139,6 +160,8 @@ class Tensor<P: PrecisionType>: Tensorial {
for j in 0..<paddedC {
if j < C {
dstPtr[j] = tmpPointer[j]
} else {
dstPtr[j] = 0
}
}
tmpPointer += C
......@@ -152,6 +175,47 @@ class Tensor<P: PrecisionType>: Tensorial {
float32ToFloat16(input: convertedPointer, output: buffer.contents(), count: count)
}
convertedPointer.deinitialize(count: count)
convertedPointer.deallocate()
}
} else {
let C = dim[3]
let cSlices = (C + 3) / 4
let paddedC = cSlices * 4
let count = paddedC * dim[0] * dim[1] * dim[2]
if C == paddedC {
buffer = device.makeBuffer(length: count * precisionSize)
switch precision {
case .Float32:
buffer?.contents().copyMemory(from: data.pointer, byteCount: count * MemoryLayout<P>.stride)
case .Float16:
float32ToFloat16(input: floatPointer, output: buffer.contents(), count: count)
}
} else if C == 1 {
fatalError(" not support ")
} else {
buffer = device.makeBuffer(length: count * precisionSize)
let convertedPointer = UnsafeMutablePointer<Float32>.allocate(capacity: count)
var tmpPointer = floatPointer
var dstPtr = convertedPointer
for _ in 0..<dim[0] * dim[1] * dim[2] {
for j in 0..<paddedC {
if j < C {
dstPtr[j] = tmpPointer[j]
} else {
dstPtr[j] = 0
}
}
tmpPointer += C
dstPtr += paddedC
}
switch precision {
case .Float32:
buffer?.contents().copyMemory(from: convertedPointer, byteCount: count * MemoryLayout<P>.stride)
case .Float16:
float32ToFloat16(input: convertedPointer, output: buffer.contents(), count: count)
}
convertedPointer.deinitialize(count: count)
convertedPointer.deallocate()
}
......
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