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提交 8a3867e1 编写于 作者: L liuruilong
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add half judge

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metal/paddle-mobile-demo/paddle-mobile-demo.xcodeproj/xcuserdata/liuruilong.xcuserdatad/xcschemes/xcschememanagement.plist
浏览文件 @ 8a3867e1
......@@ -7,7 +7,7 @@
<key>paddle-mobile-demo.xcscheme</key>
<dict>
<key>orderHint</key>
<integer>2</integer>
<integer>1</integer>
</dict>
</dict>
<key>SuppressBuildableAutocreation</key>
......
metal/paddle-mobile-demo/paddle-mobile-demo/Net/MobileNetSSD.swift
浏览文件 @ 8a3867e1
......@@ -33,7 +33,7 @@ class MobileNet_ssd_hand: Net{
return " \(res)"
}
func fetchResult(paddleMobileRes: ResultHolder<Float32>) -> [Float32]{
func fetchResult(paddleMobileRes: ResultHolder<Float32>) -> [Float32] {
guard let interRes = paddleMobileRes.intermediateResults else {
fatalError(" need have inter result ")
......@@ -46,12 +46,13 @@ class MobileNet_ssd_hand: Net{
guard let bboxs = interRes["BBoxes"], bboxs.count > 0, let bbox = bboxs[0] as? Texture<Float32> else {
fatalError()
}
var scoreFormatArr: [Float32] = score.metalTexture.realNHWC(dim: (n: score.originDim[0], h: score.originDim[1], w: score.originDim[2], c: score.originDim[3]))
var bboxArr = bbox.metalTexture.floatArray { (f) -> Float32 in
return f
}
let nmsCompute = NMSCompute.init()
nmsCompute.scoreThredshold = 0.01
nmsCompute.nmsTopK = 200
......@@ -79,6 +80,7 @@ class MobileNet_ssd_hand: Net{
let modelDir: String
// let paramPointer: UnsafeMutableRawPointer
//
// let paramSize: Int
......
metal/paddle-mobile-demo/paddle-mobile-demo/Net/PreProcessKernel.metal
浏览文件 @ 8a3867e1
......@@ -85,6 +85,17 @@ kernel void genet_preprocess(texture2d<float, access::read> inTexture [[texture(
outTexture.write(float4(inColor.z, inColor.y, inColor.x, 0.0f), gid);
}
kernel void genet_preprocess_half(texture2d<half, access::read> inTexture [[texture(0)]], texture2d<half, access::write> outTexture [[texture(1)]], uint2 gid [[thread_position_in_grid]])
{
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height()) {
return;
}
const auto means = half4(128.0f, 128.0f, 128.0f, 0.0f);
const half4 inColor = (inTexture.read(gid) * 255.0 - means) * 0.017;
outTexture.write(half4(inColor.z, inColor.y, inColor.x, 0.0f), gid);
}
kernel void scale(texture2d<float, access::sample> inTexture [[texture(0)]], texture2d<float, access::write> outTexture [[texture(1)]], uint2 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height()) return;
......@@ -95,12 +106,13 @@ kernel void scale(texture2d<float, access::sample> inTexture [[texture(0)]], tex
outTexture.write(input, gid);
}
kernel void scale_half(texture2d<float, access::sample> inTexture [[texture(0)]], texture2d<half, access::write> outTexture [[texture(1)]], uint2 gid [[thread_position_in_grid]]) {
if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height()) return;
float w_stride = inTexture.get_width() / outTexture.get_width();
float h_stride = inTexture.get_height() / outTexture.get_height();
constexpr sampler sample(coord::pixel, filter::nearest, address::clamp_to_zero);
float4 input = inTexture.sample(sample, float2(gid.x * w_stride, gid.y * h_stride), 0);
outTexture.write(half4(input), gid);
}
metal/paddle-mobile-demo/paddle-mobile-demo/ViewController.swift
浏览文件 @ 8a3867e1
......@@ -79,7 +79,7 @@ class ViewController: UIViewController {
return
}
do {
let max = 10
let max = 50
let startDate = Date.init()
for i in 0..<max {
try net.predict(inTexture: inTexture) { [weak self] (result) in
......@@ -87,7 +87,7 @@ class ViewController: UIViewController {
fatalError()
}
print(result.resultArray)
// print(result.resultArray)
if i == max - 1 {
let time = Date.init().timeIntervalSince(startDate)
DispatchQueue.main.async {
......
metal/paddle-mobile/paddle-mobile.xcodeproj/project.pbxproj
浏览文件 @ 8a3867e1
......@@ -12,6 +12,8 @@
4AF928822135673D005B6C3A /* Concat.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AF928812135673D005B6C3A /* Concat.metal */; };
4AF9288421357BE3005B6C3A /* Elementwise.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AF9288321357BE3005B6C3A /* Elementwise.metal */; };
D3831F70E7E0B565B9AC22DA /* Pods_paddle_mobile.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = DD2E06330A1E7129C918DB46 /* Pods_paddle_mobile.framework */; };
FC0226562138F33800F395E2 /* TransposeKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FC0226552138F33800F395E2 /* TransposeKernel.metal */; };
FC0226582138F38D00F395E2 /* PoolKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FC0226572138F38D00F395E2 /* PoolKernel.metal */; };
FC039B6F20E11C3C0081E9F8 /* paddle_mobile.h in Headers */ = {isa = PBXBuildFile; fileRef = FC039B6D20E11C3C0081E9F8 /* paddle_mobile.h */; settings = {ATTRIBUTES = (Public, ); }; };
FC039B9720E11C9A0081E9F8 /* Extensions.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC039B9420E11C9A0081E9F8 /* Extensions.swift */; };
FC039B9820E11C9A0081E9F8 /* Errors.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC039B9520E11C9A0081E9F8 /* Errors.swift */; };
......@@ -53,6 +55,9 @@
FCA3A1632132A4AC00084FE5 /* ReshapeKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FCA3A1622132A4AC00084FE5 /* ReshapeKernel.metal */; };
FCA3A1652132A5EB00084FE5 /* Common.metal in Sources */ = {isa = PBXBuildFile; fileRef = FCA3A1642132A5EB00084FE5 /* Common.metal */; };
FCA67B1721364EF000BD58AA /* ConvTransposeKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FCA67B1621364EF000BD58AA /* ConvTransposeKernel.metal */; };
FCA67CD52138272900BD58AA /* ConvAddMetal.metal in Sources */ = {isa = PBXBuildFile; fileRef = FCA67CD42138272900BD58AA /* ConvAddMetal.metal */; };
FCA67CD7213827AC00BD58AA /* ConvAddBNReluKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FCA67CD6213827AC00BD58AA /* ConvAddBNReluKernel.metal */; };
FCA67CD92138287B00BD58AA /* ConvBNReluKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FCA67CD82138287B00BD58AA /* ConvBNReluKernel.metal */; };
FCBCCC572122F41300D94F7E /* DwConvBNReluOp.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCBCCC562122F41300D94F7E /* DwConvBNReluOp.swift */; };
FCBCCC592122F42700D94F7E /* ConvBNReluOp.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCBCCC582122F42700D94F7E /* ConvBNReluOp.swift */; };
FCBCCC5B2122F66F00D94F7E /* ConvBNReluKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCBCCC5A2122F66F00D94F7E /* ConvBNReluKernel.swift */; };
......@@ -97,6 +102,8 @@
CDF58151D902A1CBAE56A0C2 /* Pods-paddle-mobile.debug.xcconfig */ = {isa = PBXFileReference; includeInIndex = 1; lastKnownFileType = text.xcconfig; name = "Pods-paddle-mobile.debug.xcconfig"; path = "../Pods/Target Support Files/Pods-paddle-mobile/Pods-paddle-mobile.debug.xcconfig"; sourceTree = "<group>"; };
DD2E06330A1E7129C918DB46 /* Pods_paddle_mobile.framework */ = {isa = PBXFileReference; explicitFileType = wrapper.framework; includeInIndex = 0; path = Pods_paddle_mobile.framework; sourceTree = BUILT_PRODUCTS_DIR; };
E2A7957C92EDA5C3BEC0FFC2 /* Pods-paddle-mobile.release.xcconfig */ = {isa = PBXFileReference; includeInIndex = 1; lastKnownFileType = text.xcconfig; name = "Pods-paddle-mobile.release.xcconfig"; path = "../Pods/Target Support Files/Pods-paddle-mobile/Pods-paddle-mobile.release.xcconfig"; sourceTree = "<group>"; };
FC0226552138F33800F395E2 /* TransposeKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = TransposeKernel.metal; sourceTree = "<group>"; };
FC0226572138F38D00F395E2 /* PoolKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = PoolKernel.metal; sourceTree = "<group>"; };
FC039B6A20E11C3C0081E9F8 /* paddle_mobile.framework */ = {isa = PBXFileReference; explicitFileType = wrapper.framework; includeInIndex = 0; path = paddle_mobile.framework; sourceTree = BUILT_PRODUCTS_DIR; };
FC039B6D20E11C3C0081E9F8 /* paddle_mobile.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = paddle_mobile.h; sourceTree = "<group>"; };
FC039B6E20E11C3C0081E9F8 /* Info.plist */ = {isa = PBXFileReference; lastKnownFileType = text.plist.xml; path = Info.plist; sourceTree = "<group>"; };
......@@ -141,6 +148,9 @@
FCA3A1622132A4AC00084FE5 /* ReshapeKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = ReshapeKernel.metal; sourceTree = "<group>"; };
FCA3A1642132A5EB00084FE5 /* Common.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = Common.metal; sourceTree = "<group>"; };
FCA67B1621364EF000BD58AA /* ConvTransposeKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = ConvTransposeKernel.metal; sourceTree = "<group>"; };
FCA67CD42138272900BD58AA /* ConvAddMetal.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = ConvAddMetal.metal; sourceTree = "<group>"; };
FCA67CD6213827AC00BD58AA /* ConvAddBNReluKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = ConvAddBNReluKernel.metal; sourceTree = "<group>"; };
FCA67CD82138287B00BD58AA /* ConvBNReluKernel.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = ConvBNReluKernel.metal; sourceTree = "<group>"; };
FCBCCC562122F41300D94F7E /* DwConvBNReluOp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = DwConvBNReluOp.swift; sourceTree = "<group>"; };
FCBCCC582122F42700D94F7E /* ConvBNReluOp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ConvBNReluOp.swift; sourceTree = "<group>"; };
FCBCCC5A2122F66F00D94F7E /* ConvBNReluKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ConvBNReluKernel.swift; sourceTree = "<group>"; };
......@@ -372,6 +382,11 @@
FCA3A1622132A4AC00084FE5 /* ReshapeKernel.metal */,
FCA3A1642132A5EB00084FE5 /* Common.metal */,
FCA67B1621364EF000BD58AA /* ConvTransposeKernel.metal */,
FCA67CD42138272900BD58AA /* ConvAddMetal.metal */,
FCA67CD6213827AC00BD58AA /* ConvAddBNReluKernel.metal */,
FCA67CD82138287B00BD58AA /* ConvBNReluKernel.metal */,
FC0226552138F33800F395E2 /* TransposeKernel.metal */,
FC0226572138F38D00F395E2 /* PoolKernel.metal */,
);
path = metal;
sourceTree = "<group>";
......@@ -478,6 +493,7 @@
files = (
FC9D038020E22FBB000F735A /* FeedOp.swift in Sources */,
FC039B9F20E11CB20081E9F8 /* Tensor.swift in Sources */,
FCA67CD7213827AC00BD58AA /* ConvAddBNReluKernel.metal in Sources */,
4AF9287921341661005B6C3A /* Softmax.metal in Sources */,
FC0E2DBC20EE45FE009C1FAC /* ConvKernel.swift in Sources */,
FC039BAA20E11CBC0081E9F8 /* ElementwiseAddOp.swift in Sources */,
......@@ -493,12 +509,15 @@
FC1B186620ECF1C600678B91 /* ResizeKernel.swift in Sources */,
FCF2D73820E64E70007AC5F5 /* Kernel.swift in Sources */,
FCDDC6CC212FDFDB00E5EF74 /* ReluKernel.metal in Sources */,
FC0226562138F33800F395E2 /* TransposeKernel.metal in Sources */,
FCDDC6C6212F9FB800E5EF74 /* PreluKernel.swift in Sources */,
FCA67CD52138272900BD58AA /* ConvAddMetal.metal in Sources */,
FCBCCC5B2122F66F00D94F7E /* ConvBNReluKernel.swift in Sources */,
FCEBC0F420F1FDD90099DBAF /* ConvAddBatchNormReluOp.swift in Sources */,
FC0E2DC020EE461F009C1FAC /* ElementwiseAddKernel.swift in Sources */,
4AF928772133F1DB005B6C3A /* BoxCoder.metal in Sources */,
FCEB684C212F093800D2448E /* PreluOp.swift in Sources */,
FCA67CD92138287B00BD58AA /* ConvBNReluKernel.metal in Sources */,
FC60DB8920E9AAA500FF203F /* MetalExtension.swift in Sources */,
FCEBC0F620F1FE120099DBAF /* ConvAddBatchNormReluKernel.swift in Sources */,
FCDDC6CA212FDF6800E5EF74 /* BatchNormKernel.metal in Sources */,
......@@ -550,6 +569,7 @@
FC5163F620EF556E00636C28 /* Texture2DTo2DArrayKernel.swift in Sources */,
FC039BC020E11CC20081E9F8 /* BlockDesc.swift in Sources */,
FCD04E6820F315020007374F /* PoolKernel.swift in Sources */,
FC0226582138F38D00F395E2 /* PoolKernel.metal in Sources */,
FC039BAD20E11CBC0081E9F8 /* ReluOp.swift in Sources */,
FCBCCC572122F41300D94F7E /* DwConvBNReluOp.swift in Sources */,
FC039BBE20E11CC20081E9F8 /* OpDesc.swift in Sources */,
......
metal/paddle-mobile/paddle-mobile/Common/MetalExtension.swift
浏览文件 @ 8a3867e1
......@@ -341,14 +341,27 @@ public extension MTLTexture {
}
// n c h w - dim
func toTensor(dim: (n: Int, c: Int, h: Int, w: Int)) -> [Float32] {
func toTensor(dim: (n: Int, c: Int, h: Int, w: Int), texturePrecision: ComputePrecision = .Float16) -> [Float32] {
// print("origin dim: \(dim)")
print("texture: ")
print(self)
var textureArray: [Float32]
// if texturePrecision == .Float16
let textureArray = floatArray { (i : Float32) -> Float32 in
return i
if pixelFormat == .rgba32Float {
textureArray = floatArray { (i : Float32) -> Float32 in
return i
}
} else if pixelFormat == .rgba16Float {
var textureFloat16Array = floatArray { (i : Float16) -> Float16 in
return i
}
textureArray = float16To32(input: &textureFloat16Array, count: textureFloat16Array.count)
} else {
fatalError(" 目前还不支持其他类型 ")
}
var output: [Float32] = []
for s in 0..<arrayLength {
for c in 0..<4{
......@@ -366,16 +379,27 @@ public extension MTLTexture {
return output
}
func realNHWC(dim: (n: Int, h: Int, w: Int, c: Int)) -> [Float32] {
func realNHWC(dim: (n: Int, h: Int, w: Int, c: Int), texturePrecision: ComputePrecision = .Float16) -> [Float32] {
// print("origin dim: \(dim)")
// print("texture: ")
// print(self)
let textureArray = floatArray { (i : Float32) -> Float32 in
return i
var textureArray: [Float32]
// if texturePrecision == .Float16
if pixelFormat == .rgba32Float {
textureArray = floatArray { (i : Float32) -> Float32 in
return i
}
} else if pixelFormat == .rgba16Float {
var textureFloat16Array = floatArray { (i : Float16) -> Float16 in
return i
}
textureArray = float16To32(input: &textureFloat16Array, count: textureFloat16Array.count)
} else {
fatalError(" 目前还不支持其他类型 ")
}
var output: [Float32] = []
let numOfASlice = dim.h * dim.w * 4
for h in 0..<dim.h {
for w in 0..<dim.w {
......@@ -394,7 +418,6 @@ public extension MTLTexture {
}
}
}
// print(" tensor count -- \(output.count)")
return output
}
......
metal/paddle-mobile/paddle-mobile/Common/Types.swift
浏览文件 @ 8a3867e1
......@@ -13,6 +13,7 @@
limitations under the License. */
import Foundation
import Accelerate
public protocol SummableMultipliable: Equatable {
static func +(lhs: Self, rhs: Self) -> Self
......@@ -78,6 +79,28 @@ extension Float32: PrecisionType {
}
}
public func float32ToFloat16(input: UnsafeMutablePointer<Float32>, output: UnsafeMutableRawPointer, count: Int) {
var float32Buffer = vImage_Buffer(data: input, height: 1, width: UInt(count), rowBytes: count * 4)
var float16buffer = vImage_Buffer(data: output, height: 1, width: UInt(count), rowBytes: count * 2)
guard vImageConvert_PlanarFtoPlanar16F(&float32Buffer, &float16buffer, 0) == kvImageNoError else {
fatalError(" float 32 to float 16 error ! ")
}
}
public func float16To32(input: UnsafeMutablePointer<Float16>, count: Int) -> [Float32] {
var output = Array<Float>.init(repeating: 0.0, count: count)
float16to32(input: input, output: &output, count: count)
return output
}
public func float16to32(input: UnsafeMutablePointer<Float16>, output: UnsafeMutablePointer<Float32>, count: Int) {
var bufferFloat16 = vImage_Buffer(data: input, height: 1, width: UInt(count), rowBytes: count * 2)
var bufferFloat32 = vImage_Buffer(data: output, height: 1, width: UInt(count), rowBytes: count * 4)
if vImageConvert_Planar16FtoPlanarF(&bufferFloat16, &bufferFloat32, 0) != kvImageNoError {
fatalError(" convert float16 to float32 error")
}
}
// N - 0 C - 1 H - 2 W - 3
struct DataLayout {
......
metal/paddle-mobile/paddle-mobile/Executor.swift
浏览文件 @ 8a3867e1
......@@ -16,6 +16,8 @@ import Foundation
let testTo = 54
let computePrecision: ComputePrecision = .Float32
public class ResultHolder<P: PrecisionType> {
public let dim: [Int]
public let resultArr: [P]
......@@ -66,7 +68,6 @@ public class Executor<P: PrecisionType> {
let op = block.ops[i]
do {
let op = try OpCreator<P>.shared.creat(device: inDevice, opDesc: op, scope: inProgram.scope)
// op.inferShape()
ops.append(op)
} catch let error {
throw error
......
metal/paddle-mobile/paddle-mobile/Operators/BoxcoderOp.swift
浏览文件 @ 8a3867e1
......@@ -58,28 +58,26 @@ class BoxcoderOp<P: PrecisionType>: Operator<BoxcoderKernel<P>, BoxcoderParam<P>
}
func delogOutput() {
print(" \(type) output: ")
let priorBoxOriginDim = para.priorBox.originDim
let priorBoxArray = para.priorBox.metalTexture.realNHWC(dim: (n: priorBoxOriginDim[0], h: priorBoxOriginDim[1], w: priorBoxOriginDim[2], c: priorBoxOriginDim[3]))
print(" prior box ")
print(priorBoxArray.strideArray())
let priorBoxVarOriginDim = para.priorBoxVar.originDim
let priorBoxVarArray = para.priorBoxVar.metalTexture.realNHWC(dim: (n: priorBoxVarOriginDim[0], h: priorBoxVarOriginDim[1], w: priorBoxVarOriginDim[2], c: priorBoxVarOriginDim[3]))
print(" prior box var ")
print(priorBoxVarArray.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]))
print(" target box ")
print(targetBoxArray.strideArray())
// let priorBoxOriginDim = para.priorBox.originDim
// let priorBoxArray: [Float32] = para.priorBox.metalTexture.realNHWC(dim: (n: priorBoxOriginDim[0], h: priorBoxOriginDim[1], w: priorBoxOriginDim[2], c: priorBoxOriginDim[3]))
// print(" prior box ")
// print(priorBoxArray.strideArray())
//
// let priorBoxVarOriginDim = para.priorBoxVar.originDim
// let priorBoxVarArray: [Float32] = para.priorBoxVar.metalTexture.realNHWC(dim: (n: priorBoxVarOriginDim[0], h: priorBoxVarOriginDim[1], w: priorBoxVarOriginDim[2], c: priorBoxVarOriginDim[3]))
// print(" prior box var ")
// print(priorBoxVarArray.strideArray())
//
// let targetBoxOriginDim = para.targetBox.originDim
// let targetBoxArray: [Float32] = 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 = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]), texturePrecision: computePrecision)
print(outputArray.strideArray())
}
}
......
metal/paddle-mobile/paddle-mobile/Operators/ConcatOp.swift
浏览文件 @ 8a3867e1
......@@ -66,9 +66,16 @@ class ConcatOp<P: PrecisionType>: Operator<ConcatKernel<P>, ConcatParam<P>>, Run
func delogOutput() {
print(" \(type) output: ")
let originDim = para.output.originDim
let outputArray = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
print(outputArray.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())
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)
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())
} else {
fatalError(" not implemet")
}
}
}
......
metal/paddle-mobile/paddle-mobile/Operators/ConvAddBatchNormReluOp.swift
浏览文件 @ 8a3867e1
......@@ -125,13 +125,6 @@ class ConvAddBatchNormReluOp<P: PrecisionType>: Operator<ConvAddBatchNormReluKer
// let _: P? = para.newBiase?.logDesc(header: "new biase: ", stridable: false)
// let _: P? = para.newScale?.logDesc(header: "new scale: ", stridable: false)
let output = para.output.metalTexture.floatArray { (p: P) -> P in
return p
}
//
writeToLibrary(fileName: "output_112x112x32_2", array: output)
print(" write done")
// let _: P? = para.output.metalTexture.logDesc(header: "conv add batchnorm relu output: ", stridable: false)
}
}
metal/paddle-mobile/paddle-mobile/Operators/ConvAddOp.swift
浏览文件 @ 8a3867e1
......@@ -46,9 +46,6 @@ class ConvAddParam<P: PrecisionType>: OpParam {
class ConvAddOp<P: PrecisionType>: Operator<ConvAddKernel<P>, ConvAddParam<P>>, Runable, Creator, InferShaperable, Fusion{
typealias OpType = ConvAddOp<P>
static func fusionNode() -> Node {
let beginNode = Node.init(inType: gConvType)
_ = beginNode
......@@ -64,7 +61,6 @@ class ConvAddOp<P: PrecisionType>: Operator<ConvAddKernel<P>, ConvAddParam<P>>,
return gConvAddType
}
func inferShape() {
let inDims = para.input.dim
......@@ -101,10 +97,8 @@ class ConvAddOp<P: PrecisionType>: Operator<ConvAddKernel<P>, ConvAddParam<P>>,
print(para.stride)
print("dilations: ")
print(para.dilations)
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])).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]), texturePrecision: computePrecision).strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/ConvBNReluOp.swift
浏览文件 @ 8a3867e1
......@@ -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])).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]), texturePrecision: computePrecision).strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/ConvTransposeOp.swift
浏览文件 @ 8a3867e1
......@@ -46,10 +46,10 @@ class ConvTransposeOp<P: PrecisionType>: Operator<ConvTransposeKernel<P>, ConvTr
print(" \(type) output: ")
let originDim = para.output.originDim
if para.output.transpose == [0, 1, 2, 3] {
let outputArray = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]), texturePrecision: computePrecision)
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])).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]), texturePrecision: computePrecision).strideArray())
} else {
print(" not implement")
}
......
metal/paddle-mobile/paddle-mobile/Operators/DepthwiseConvOp.swift
浏览文件 @ 8a3867e1
......@@ -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])).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]), texturePrecision: computePrecision).strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/DwConvBNReluOp.swift
浏览文件 @ 8a3867e1
......@@ -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])).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]), texturePrecision: computePrecision).strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/ElementwiseAddOp.swift
浏览文件 @ 8a3867e1
......@@ -56,31 +56,30 @@ class ElementwiseAddOp<P: PrecisionType>: Operator<ElementwiseAddKernel<P>, Elem
// para.output.dim = para.input.dim
}
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
} catch let error {
throw error
}
}
func delogOutput() {
print(" \(type) inputX: ")
print(para.inputX.metalTexture.toTensor(dim: (n: para.inputX.tensorDim[0], c: para.inputX.tensorDim[1], h: para.inputX.tensorDim[2], w: para.inputX.tensorDim[3])).strideArray())
print(" \(type) inputY: ")
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) inputX: ")
// print(para.inputX.metalTexture.toTensor(dim: (n: para.inputX.tensorDim[0], c: para.inputX.tensorDim[1], h: para.inputX.tensorDim[2], w: para.inputX.tensorDim[3])).strideArray())
// print(" \(type) inputY: ")
// 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: ")
let originDim = para.output.originDim
if para.output.transpose == [0, 1, 2, 3] {
let outputArray = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]), texturePrecision: computePrecision)
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])).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]), texturePrecision: computePrecision).strideArray())
} else {
print(" not implement")
}
}
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do {
try kernel.compute(commandBuffer: buffer, param: para)
} catch let error {
throw error
}
}
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/Base/Kernel.swift
浏览文件 @ 8a3867e1
......@@ -19,68 +19,76 @@ public protocol TestParam {
}
public protocol Testable {
associatedtype TestParamType: TestParam
func test(commandBuffer: MTLCommandBuffer, param: TestParamType)
init(device: MTLDevice, testParam: TestParamType)
associatedtype TestParamType: TestParam
func test(commandBuffer: MTLCommandBuffer, param: TestParamType)
init(device: MTLDevice, testParam: TestParamType)
}
protocol Computable {
associatedtype ParamType: OpParam
func compute(commandBuffer: MTLCommandBuffer, param: ParamType) throws
init(device: MTLDevice, param: ParamType)
associatedtype ParamType: OpParam
func compute(commandBuffer: MTLCommandBuffer, param: ParamType) throws
init(device: MTLDevice, param: ParamType)
}
protocol KernelProtocol {
var pipline: MTLComputePipelineState { get set }
var functionName: String { get set }
var pipline: MTLComputePipelineState { get set }
var functionName: String { get set }
}
open class Kernel {
let pipline: MTLComputePipelineState
let functionName: String
public init(device: MTLDevice, inFunctionName: String, usePaddleMobileLib: Bool = true) {
pipline = device.pipeLine(funcName: inFunctionName, inPaddleMobileLib: usePaddleMobileLib)
functionName = inFunctionName
}
let pipline: MTLComputePipelineState
let functionName: String
public init(device: MTLDevice, inFunctionName: String, usePaddleMobileLib: Bool = true) {
pipline = device.pipeLine(funcName: inFunctionName, inPaddleMobileLib: usePaddleMobileLib)
functionName = inFunctionName
}
}
open class CusomKernel: Kernel {
public struct Shape {
public let width: Int
public let height: Int
public let channel: Int
public init(inWidth: Int, inHeight: Int, inChannel: Int){
width = inWidth
height = inHeight
channel = inChannel
}
}
public let outputTexture: MTLTexture
public init(device: MTLDevice, inFunctionName: String, outputDim: Shape, usePaddleMobileLib: Bool = false) {
let textureDesc = MTLTextureDescriptor.init()
textureDesc.textureType = .type2D
textureDesc.width = outputDim.width
textureDesc.height = outputDim.height
textureDesc.depth = (outputDim.channel + 3) / 4
textureDesc.pixelFormat = .rgba32Float
textureDesc.usage = [.shaderRead, .shaderWrite]
textureDesc.storageMode = .shared
outputTexture = device.makeTexture(descriptor: textureDesc) ?! " make texture error "
super.init(device: device, inFunctionName: inFunctionName, usePaddleMobileLib: usePaddleMobileLib)
public struct Shape {
public let width: Int
public let height: Int
public let channel: Int
public init(inWidth: Int, inHeight: Int, inChannel: Int){
width = inWidth
height = inHeight
channel = inChannel
}
}
public let outputTexture: MTLTexture
public init(device: MTLDevice, inFunctionName: String, outputDim: Shape, usePaddleMobileLib: Bool = false) {
let textureDesc = MTLTextureDescriptor.init()
textureDesc.textureType = .type2D
textureDesc.width = outputDim.width
textureDesc.height = outputDim.height
textureDesc.depth = (outputDim.channel + 3) / 4
public func compute(inputTexuture: MTLTexture, commandBuffer: MTLCommandBuffer) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil")
}
encoder.setTexture(inputTexuture, index: 0)
encoder.setTexture(outputTexture, index: 1)
encoder.dispatch(computePipline: pipline, outTexture: outputTexture)
encoder.endEncoding()
if computePrecision == .Float16 {
textureDesc.pixelFormat = .rgba16Float
} else if computePrecision == .Float32 {
textureDesc.pixelFormat = .rgba32Float
} else {
fatalError()
}
textureDesc.usage = [.shaderRead, .shaderWrite]
textureDesc.storageMode = .shared
outputTexture = device.makeTexture(descriptor: textureDesc) ?! " make texture error "
super.init(device: device, inFunctionName: inFunctionName, usePaddleMobileLib: usePaddleMobileLib)
}
public func compute(inputTexuture: MTLTexture, commandBuffer: MTLCommandBuffer) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil")
}
encoder.setTexture(inputTexuture, index: 0)
encoder.setTexture(outputTexture, index: 1)
encoder.dispatch(computePipline: pipline, outTexture: outputTexture)
encoder.endEncoding()
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/BoxcoderKernel.swift
浏览文件 @ 8a3867e1
......@@ -18,22 +18,29 @@ struct BoxcoderMetalParam {
}
class BoxcoderKernel<P: PrecisionType>: Kernel, Computable{
func compute(commandBuffer: MTLCommandBuffer, param: BoxcoderParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil")
}
encoder.setTexture(param.priorBox.metalTexture, index: 0)
encoder.setTexture(param.priorBoxVar.metalTexture, index: 1)
encoder.setTexture(param.targetBox.metalTexture, index: 2)
encoder.setTexture(param.output.metalTexture, index: 3)
var bmp = BoxcoderMetalParam.init()
encoder.setBytes(&bmp, length: MemoryLayout<BoxcoderMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
func compute(commandBuffer: MTLCommandBuffer, param: BoxcoderParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil")
}
required init(device: MTLDevice, param: BoxcoderParam<P>) {
param.output.initTexture(device: device)
super.init(device: device, inFunctionName: "boxcoder")
encoder.setTexture(param.priorBox.metalTexture, index: 0)
encoder.setTexture(param.priorBoxVar.metalTexture, index: 1)
encoder.setTexture(param.targetBox.metalTexture, index: 2)
encoder.setTexture(param.output.metalTexture, index: 3)
var bmp = BoxcoderMetalParam.init()
encoder.setBytes(&bmp, length: MemoryLayout<BoxcoderMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
required init(device: MTLDevice, param: BoxcoderParam<P>) {
param.output.initTexture(device: device, computePrecision: computePrecision)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "boxcoder")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "boxcoder_half")
} else {
fatalError()
}
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConcatKernel.swift
浏览文件 @ 8a3867e1
......@@ -121,8 +121,14 @@ class ConcatKernel<P: PrecisionType>: Kernel, Computable{
}
required init(device: MTLDevice, param: ConcatParam<P>) {
param.output.initTexture(device: device, inTranspose: param.transpose)
super.init(device: device, inFunctionName: "concat")
param.output.initTexture(device: device, inTranspose: param.transpose, computePrecision: computePrecision)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "concat")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "concat_half")
} else {
fatalError()
}
}
required init(device: MTLDevice, testParam: ConcatTestParam) {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvAddBatchNormReluKernel.swift
浏览文件 @ 8a3867e1
......@@ -15,127 +15,155 @@
import Foundation
struct ConvAddBatchNormReluTestParam: TestParam {
let inputTexture: MTLTexture
let outputTexture: MTLTexture
var metalParam: MetalConvParam
let filterBuffer: MTLBuffer
let biaseBuffer: MTLBuffer
let newScaleBuffer: MTLBuffer
let newBiaseBuffer: MTLBuffer
let filterSize: (width: Int, height: Int, channel: Int)
init(inInputTexture: MTLTexture, inOutputTexture: MTLTexture, inMetalParam: MetalConvParam, inFilterBuffer: MTLBuffer, inBiaseBuffer: MTLBuffer, inNewScaleBuffer: MTLBuffer, inNewBiaseBuffer: MTLBuffer, inFilterSize: (width: Int, height: Int, channel: Int)) {
inputTexture = inInputTexture
outputTexture = inOutputTexture
metalParam = inMetalParam
filterBuffer = inFilterBuffer
biaseBuffer = inBiaseBuffer
newScaleBuffer = inNewScaleBuffer
newBiaseBuffer = inNewBiaseBuffer
filterSize = inFilterSize
}
let inputTexture: MTLTexture
let outputTexture: MTLTexture
var metalParam: MetalConvParam
let filterBuffer: MTLBuffer
let biaseBuffer: MTLBuffer
let newScaleBuffer: MTLBuffer
let newBiaseBuffer: MTLBuffer
let filterSize: (width: Int, height: Int, channel: Int)
init(inInputTexture: MTLTexture, inOutputTexture: MTLTexture, inMetalParam: MetalConvParam, inFilterBuffer: MTLBuffer, inBiaseBuffer: MTLBuffer, inNewScaleBuffer: MTLBuffer, inNewBiaseBuffer: MTLBuffer, inFilterSize: (width: Int, height: Int, channel: Int)) {
inputTexture = inInputTexture
outputTexture = inOutputTexture
metalParam = inMetalParam
filterBuffer = inFilterBuffer
biaseBuffer = inBiaseBuffer
newScaleBuffer = inNewScaleBuffer
newBiaseBuffer = inNewBiaseBuffer
filterSize = inFilterSize
}
}
class ConvAddBatchNormReluKernel<P: PrecisionType>: Kernel, Computable, Testable {
required init(device: MTLDevice, testParam: ConvAddBatchNormReluTestParam) {
if testParam.filterSize.width == 1 && testParam.filterSize.height == 1 {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_1x1")
} else if testParam.filterSize.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")
}
required init(device: MTLDevice, testParam: ConvAddBatchNormReluTestParam) {
if testParam.filterSize.width == 1 && testParam.filterSize.height == 1 {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_1x1")
} else if testParam.filterSize.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")
}
}
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvAddBatchNormReluParam<P>) {
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvAddBatchNormReluParam<P>) {
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1])
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: Tensor.BufferPrecision.Float32)
param.y.initBuffer(device: device, precision: Tensor.BufferPrecision.Float32)
param.variance.initBuffer(device: device)
param.mean.initBuffer(device: device)
param.scale.initBuffer(device: device)
param.bias.initBuffer(device: device)
let offsetX = param.filter.width/2 - Int(param.paddings[0])
let offsetY = param.filter.height/2 - Int(param.paddings[1])
print("offset x: \(offsetX)")
print("offset y: \(offsetY)")
let offsetZ = 0.0
metalParam = MetalConvParam.init(offsetX: Int16(offsetX), offsetY: Int16(offsetY), offsetZ: Int16(offsetZ), strideX: UInt16(param.stride[0]), strideY: UInt16(param.stride[1]), paddedZ: UInt16(param.input.metalTexture.arrayLength * 4 - param.input.dim[3]), dilationX: UInt16(param.dilations[0]), dilationY: UInt16(param.dilations[1]))
var invs: [P] = []
let varianceContents = param.variance.buffer.contents().assumingMemoryBound(to: P.self)
for i in 0..<param.variance.buffer.length/MemoryLayout<P>.stride {
let inv = 1.0/pow(Float32.init(varianceContents[i]) + param.epsilon, 0.5)
invs.append(P(inv))
}
let newScale: UnsafeMutablePointer<P> = UnsafeMutablePointer<P>.allocate(capacity: param.scale.buffer.length)
let newBiase: UnsafeMutablePointer<P> = UnsafeMutablePointer<P>.allocate(capacity: param.bias.buffer.length)
let scaleContents = param.scale.buffer.contents().assumingMemoryBound(to: P.self)
let biaseContents = param.bias.buffer.contents().assumingMemoryBound(to: P.self)
let meanContents = param.mean.buffer.contents().assumingMemoryBound(to: P.self)
for i in 0..<param.scale.buffer.length/MemoryLayout<P>.stride {
newScale[i] = invs[i] * scaleContents[i]
newBiase[i] = biaseContents[i] - meanContents[i] * invs[i] * scaleContents[i]
}
param.newBiase = device.makeBuffer(bytes: newBiase, length: param.bias.buffer.length)
param.newScale = device.makeBuffer(bytes: newScale, length: param.scale.buffer.length)
newScale.deinitialize(count: param.scale.buffer.length)
newScale.deallocate()
newBiase.deinitialize(count: param.bias.buffer.length)
newBiase.deallocate()
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")
}
func compute(commandBuffer: MTLCommandBuffer, param: ConvAddBatchNormReluParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil")
}
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)
let offsetX = param.filter.width/2 - Int(param.paddings[0])
let offsetY = param.filter.height/2 - Int(param.paddings[1])
print("offset x: \(offsetX)")
print("offset y: \(offsetY)")
let offsetZ = 0.0
metalParam = MetalConvParam.init(offsetX: Int16(offsetX), offsetY: Int16(offsetY), offsetZ: Int16(offsetZ), strideX: UInt16(param.stride[0]), strideY: UInt16(param.stride[1]), paddedZ: UInt16(param.input.metalTexture.arrayLength * 4 - param.input.dim[3]), dilationX: UInt16(param.dilations[0]), dilationY: UInt16(param.dilations[1]))
var invs: [P] = []
let varianceContents = param.variance.buffer.contents().assumingMemoryBound(to: P.self)
for i in 0..<param.variance.buffer.length/MemoryLayout<P>.stride {
let inv = 1.0/pow(Float32.init(varianceContents[i]) + param.epsilon, 0.5)
invs.append(P(inv))
}
let newScale: UnsafeMutablePointer<P> = UnsafeMutablePointer<P>.allocate(capacity: param.scale.buffer.length)
let newBiase: UnsafeMutablePointer<P> = UnsafeMutablePointer<P>.allocate(capacity: param.bias.buffer.length)
let scaleContents = param.scale.buffer.contents().assumingMemoryBound(to: P.self)
let biaseContents = param.bias.buffer.contents().assumingMemoryBound(to: P.self)
let meanContents = param.mean.buffer.contents().assumingMemoryBound(to: P.self)
for i in 0..<param.scale.buffer.length/MemoryLayout<P>.stride {
newScale[i] = invs[i] * scaleContents[i]
newBiase[i] = biaseContents[i] - meanContents[i] * invs[i] * scaleContents[i]
}
// var newScaleFP16: UnsafeMutableRawPointer
//
// float32ToFloat16(input: newScale as! UnsafeMutablePointer<Float32>, output: newScaleFP16, count: param.scale.buffer.length / MemoryLayout<P>.size)
// let newBiaseFloat16 = device.makeBuffer(length: <#T##Int#>, options: <#T##MTLResourceOptions#>)
var newBiaseBuffer: MTLBuffer
var newScaleBuffer: MTLBuffer
if computePrecision == .Float16 {
newBiaseBuffer = device.makeBuffer(bytes: newBiase, length: param.bias.buffer.length)!
newScaleBuffer = device.makeBuffer(bytes: newScale, length: param.scale.buffer.length)!
} else if computePrecision == .Float32 {
encoder.setTexture(param.input.metalTexture, index: 0)
encoder.setTexture(param.output.metalTexture, index: 1)
encoder.setBytes(&metalParam, length: MemoryLayout<MetalConvParam>.size, index: 0)
encoder.setBuffer(param.filter.buffer, offset: 0, index: 1)
encoder.setBuffer(param.y.buffer, offset: 0, index: 2)
encoder.setBuffer(param.newScale!, offset: 0, index: 3)
encoder.setBuffer(param.newBiase!, offset: 0, index: 4)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
newBiaseBuffer = device.makeBuffer(length: param.bias.buffer.length / 2)!
newScaleBuffer = device.makeBuffer(length: param.bias.buffer.length / 2)!
float32ToFloat16(input: newBiase as! UnsafeMutablePointer<Float32>, output: newBiaseBuffer.contents(), count: param.bias.buffer.length / MemoryLayout<P>.size)
float32ToFloat16(input: newScale as! UnsafeMutablePointer<Float32>, output: newScaleBuffer.contents(), count: param.scale.buffer.length / MemoryLayout<P>.size)
} else {
fatalError(" unsupport ")
}
public func test(commandBuffer: MTLCommandBuffer, param: ConvAddBatchNormReluTestParam) {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
fatalError()
}
encoder.setTexture(param.inputTexture, index: 0)
encoder.setTexture(param.outputTexture, index: 1)
var inMetalParam = param.metalParam
encoder.setBytes(&inMetalParam, length: MemoryLayout<MetalConvParam>.size, index: 0)
encoder.setBuffer(param.filterBuffer, offset: 0, index: 1)
encoder.setBuffer(param.biaseBuffer, offset: 0, index: 2)
encoder.setBuffer(param.newScaleBuffer, offset: 0, index: 3)
encoder.setBuffer(param.newBiaseBuffer, offset: 0, index: 4)
encoder.dispatch(computePipline: pipline, outTexture: param.outputTexture)
encoder.endEncoding()
param.newBiase = newBiaseBuffer
param.newScale = newScaleBuffer
newScale.deinitialize(count: param.scale.buffer.length)
newScale.deallocate()
newBiase.deinitialize(count: param.bias.buffer.length)
newBiase.deallocate()
}
func compute(commandBuffer: MTLCommandBuffer, param: ConvAddBatchNormReluParam<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.setBytes(&metalParam, length: MemoryLayout<MetalConvParam>.size, index: 0)
encoder.setBuffer(param.filter.buffer, offset: 0, index: 1)
encoder.setBuffer(param.y.buffer, offset: 0, index: 2)
encoder.setBuffer(param.newScale!, offset: 0, index: 3)
encoder.setBuffer(param.newBiase!, offset: 0, index: 4)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
public func test(commandBuffer: MTLCommandBuffer, param: ConvAddBatchNormReluTestParam) {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
fatalError()
}
encoder.setTexture(param.inputTexture, index: 0)
encoder.setTexture(param.outputTexture, index: 1)
var inMetalParam = param.metalParam
encoder.setBytes(&inMetalParam, length: MemoryLayout<MetalConvParam>.size, index: 0)
encoder.setBuffer(param.filterBuffer, offset: 0, index: 1)
encoder.setBuffer(param.biaseBuffer, offset: 0, index: 2)
encoder.setBuffer(param.newScaleBuffer, offset: 0, index: 3)
encoder.setBuffer(param.newBiaseBuffer, offset: 0, index: 4)
encoder.dispatch(computePipline: pipline, outTexture: param.outputTexture)
encoder.endEncoding()
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvAddKernel.swift
浏览文件 @ 8a3867e1
......@@ -17,22 +17,35 @@ import Foundation
class ConvAddKernel<P: PrecisionType>: Kernel, Computable {
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvAddParam<P>) {
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_add_1x1")
} else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3")
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 {
super.init(device: device, inFunctionName: "conv_add_3x3_half")
}
} else if computePrecision == .Float32 {
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_add_1x1")
} else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3")
} else {
super.init(device: device, inFunctionName: "conv_add_3x3")
}
} else {
super.init(device: device, inFunctionName: "conv_add_3x3")
fatalError()
}
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1])
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: computePrecision)
let offsetX = (Int(param.dilations[0]) * (param.filter.width - 1) + 1)/2 - Int(param.paddings[0])
let offsetY = (Int(param.dilations[1]) * (param.filter.height - 1) + 1)/2 - Int(param.paddings[1])
param.filter.initBuffer(device: device, precision: Tensor.BufferPrecision.Float32)
param.y.initBuffer(device: device, precision: Tensor.BufferPrecision.Float32)
param.filter.initBuffer(device: device, precision: computePrecision)
param.y.initBuffer(device: device, precision: computePrecision)
print("offset x: \(offsetX)")
print("offset y: \(offsetY)")
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvBNReluKernel.swift
浏览文件 @ 8a3867e1
......@@ -51,21 +51,33 @@ class ConvBNReluKernel<P: PrecisionType>: Kernel, Computable, Testable {
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvBNReluParam<P>) {
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")
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 {
super.init(device: device, inFunctionName: "conv_batch_norm_relu_3x3")
}
} 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 {
super.init(device: device, inFunctionName: "conv_batch_norm_relu_3x3_half")
}
} else {
super.init(device: device, inFunctionName: "conv_batch_norm_relu_3x3")
fatalError()
}
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1])
param.filter.initBuffer(device: device, precision: Tensor.BufferPrecision.Float32)
param.variance.initBuffer(device: device)
param.mean.initBuffer(device: device)
param.scale.initBuffer(device: device)
param.bias.initBuffer(device: device)
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])
......@@ -102,8 +114,26 @@ class ConvBNReluKernel<P: PrecisionType>: Kernel, Computable, Testable {
newBiase[i] = biaseContents[i] - meanContents[i] * invs[i] * scaleContents[i]
}
param.newBiase = device.makeBuffer(bytes: newBiase, length: param.bias.buffer.length)
param.newScale = device.makeBuffer(bytes: newScale, length: param.scale.buffer.length)
var newBiaseBuffer: MTLBuffer
var newScaleBuffer: MTLBuffer
if computePrecision == .Float16 {
newBiaseBuffer = device.makeBuffer(bytes: newBiase, length: param.bias.buffer.length)!
newScaleBuffer = device.makeBuffer(bytes: newScale, length: param.scale.buffer.length)!
} else if computePrecision == .Float32 {
newBiaseBuffer = device.makeBuffer(length: param.bias.buffer.length / 2)!
newScaleBuffer = device.makeBuffer(length: param.bias.buffer.length / 2)!
float32ToFloat16(input: newBiase as! UnsafeMutablePointer<Float32>, output: newBiaseBuffer.contents(), count: param.bias.buffer.length / MemoryLayout<P>.size)
float32ToFloat16(input: newScale as! UnsafeMutablePointer<Float32>, output: newScaleBuffer.contents(), count: param.scale.buffer.length / MemoryLayout<P>.size)
} else {
fatalError(" unsupport ")
}
param.newBiase = newBiaseBuffer
param.newScale = newScaleBuffer
newScale.deinitialize(count: param.scale.buffer.length)
newScale.deallocate()
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvKernel.swift
浏览文件 @ 8a3867e1
......@@ -39,7 +39,7 @@ class ConvKernel<P: PrecisionType>: Kernel, Computable {
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: Tensor.BufferPrecision.Float32)
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]), paddedZ: UInt16(param.input.metalTexture.arrayLength * 4 - param.input.dim[3]), dilationX: UInt16(param.dilations[0]), dilationY: UInt16(param.dilations[1]))
}
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PoolKernel.swift
浏览文件 @ 8a3867e1
......@@ -28,7 +28,7 @@ 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)
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: computePrecision)
}
func compute(commandBuffer: MTLCommandBuffer, param: PoolParam<P>) throws {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PreluKernel.swift
浏览文件 @ 8a3867e1
......@@ -17,8 +17,8 @@ class PreluKernel<P: PrecisionType>: Kernel, Computable{
} else {
super.init(device: device, inFunctionName: "prelu_other")
}
param.alpha.initBuffer(device: device)
param.output.initTexture(device: device, inTranspose: param.input.transpose)
param.alpha.initBuffer(device: device, precision: computePrecision)
param.output.initTexture(device: device, inTranspose: param.input.transpose, computePrecision: computePrecision)
}
func compute(commandBuffer: MTLCommandBuffer, param: PreluParam<P>) throws {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PriorBoxKernel.swift
浏览文件 @ 8a3867e1
......@@ -33,11 +33,16 @@ class PriorBoxKernel<P: PrecisionType>: Kernel, Computable{
var metalParam: PriorBoxMetalParam!
required init(device: MTLDevice, param: PriorBoxParam<P>) {
super.init(device: device, inFunctionName: "prior_box")
param.output.initTexture(device: device, inTranspose: [2, 0, 1, 3])
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "prior_box")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "prior_box_half")
} else {
fatalError()
}
param.outputVariances.initTexture(device: device, inTranspose: [2, 0, 1, 3])
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]
......@@ -79,7 +84,18 @@ class PriorBoxKernel<P: PrecisionType>: Kernel, Computable{
}
}
param.newAspectRatios = outputAspectRatior
if computePrecision == .Float16 {
let buffer = device.makeBuffer(length: outputAspectRatior.count)
float32ToFloat16(input: &outputAspectRatior, output:(buffer?.contents())!, count: outputAspectRatior.count)
param.newAspectRatios = buffer
} else if computePrecision == .Float32 {
let buffer = device.makeBuffer(bytes: outputAspectRatior, length: outputAspectRatior.count * MemoryLayout<Float32>.size, options: [])
param.newAspectRatios = buffer
} else {
fatalError()
}
let aspectRatiosSize = uint(outputAspectRatior.count)
let maxSizeSize: uint = uint(param.maxSizes.count)
......@@ -102,12 +118,13 @@ class PriorBoxKernel<P: PrecisionType>: Kernel, Computable{
encoder.setTexture(param.input.metalTexture, index: 0)
encoder.setTexture(param.output.metalTexture, index: 1)
encoder.setTexture(param.outputVariances.metalTexture, index: 2)
encoder.setBytes(&metalParam, length: MemoryLayout<PriorBoxMetalParam>.size, index: 0)
encoder.setBytes(param.newAspectRatios!, length: MemoryLayout<Float32>.size * param.newAspectRatios!.count, index: 1)
encoder.setBuffer(param.newAspectRatios!, offset: 0, index: 0)
encoder.setBytes(&metalParam, length: MemoryLayout<PriorBoxMetalParam>.size, index: 1)
encoder.setBytes(param.variances, length: MemoryLayout<Float32>.size * param.variances.count, index: 2)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ReshapeKernel.swift
浏览文件 @ 8a3867e1
......@@ -15,58 +15,65 @@
import Foundation
struct ReshapeMetalParam {
var idim: (Int32, Int32, Int32, Int32)
var itrans: (Int32, Int32, Int32, Int32)
var odim: (Int32, Int32, Int32, Int32)
var otrans: (Int32, Int32, Int32, Int32)
var idim: (Int32, Int32, Int32, Int32)
var itrans: (Int32, Int32, Int32, Int32)
var odim: (Int32, Int32, Int32, Int32)
var otrans: (Int32, Int32, Int32, Int32)
}
struct ReshapeTestParam: TestParam {
let inputTexture: MTLTexture
let outputTexture: MTLTexture
let param: ReshapeMetalParam
let inputTexture: MTLTexture
let outputTexture: MTLTexture
let param: ReshapeMetalParam
}
class ReshapeKernel<P: PrecisionType>: Kernel, Computable{
required init(device: MTLDevice, param: ReshapeParam<P>) {
param.output.initTexture(device: device)
super.init(device: device, inFunctionName: "reshape")
required init(device: MTLDevice, param: ReshapeParam<P>) {
param.output.initTexture(device: device, computePrecision: computePrecision)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "reshape")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "reshape_half")
} else {
fatalError()
}
required init(device: MTLDevice, testParam: ReshapeTestParam) {
super.init(device: device, inFunctionName: "reshape")
}
func compute(commandBuffer: MTLCommandBuffer, param: ReshapeParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encoder is nil")
}
encoder.setTexture(param.input.metalTexture, index: 0)
encoder.setTexture(param.output.metalTexture, index: 1)
let id: [Int32] = (0..<4).map { Int32(param.input.dim[$0]) }
let it: [Int32] = param.input.transpose.map { Int32($0) }
let od: [Int32] = (0..<4).map { Int32(param.output.dim[$0]) }
let ot: [Int32] = param.output.transpose.map { Int32($0) }
var rmp = ReshapeMetalParam.init(
idim: (id[0], id[1], id[2], id[3]),
itrans: (it[0], it[1], it[2], it[3]),
odim: (od[0], od[1], od[2], od[3]),
otrans: (ot[0], ot[1], ot[2], ot[3])
)
encoder.setBytes(&rmp, length: MemoryLayout<ReshapeMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
required init(device: MTLDevice, testParam: ReshapeTestParam) {
super.init(device: device, inFunctionName: "reshape")
}
func compute(commandBuffer: MTLCommandBuffer, param: ReshapeParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encoder is nil")
}
func test(commandBuffer: MTLCommandBuffer, testParam: ReshapeTestParam) {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
fatalError()
}
encoder.setTexture(testParam.inputTexture, index: 0)
encoder.setTexture(testParam.outputTexture, index: 1)
var pm: ReshapeMetalParam = testParam.param
encoder.setBytes(&pm, length: MemoryLayout<ReshapeMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: testParam.outputTexture)
encoder.endEncoding()
encoder.setTexture(param.input.metalTexture, index: 0)
encoder.setTexture(param.output.metalTexture, index: 1)
let id: [Int32] = (0..<4).map { Int32(param.input.dim[$0]) }
let it: [Int32] = param.input.transpose.map { Int32($0) }
let od: [Int32] = (0..<4).map { Int32(param.output.dim[$0]) }
let ot: [Int32] = param.output.transpose.map { Int32($0) }
var rmp = ReshapeMetalParam.init(
idim: (id[0], id[1], id[2], id[3]),
itrans: (it[0], it[1], it[2], it[3]),
odim: (od[0], od[1], od[2], od[3]),
otrans: (ot[0], ot[1], ot[2], ot[3])
)
encoder.setBytes(&rmp, length: MemoryLayout<ReshapeMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
func test(commandBuffer: MTLCommandBuffer, testParam: ReshapeTestParam) {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
fatalError()
}
encoder.setTexture(testParam.inputTexture, index: 0)
encoder.setTexture(testParam.outputTexture, index: 1)
var pm: ReshapeMetalParam = testParam.param
encoder.setBytes(&pm, length: MemoryLayout<ReshapeMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: testParam.outputTexture)
encoder.endEncoding()
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/SoftmaxKernel.swift
浏览文件 @ 8a3867e1
......@@ -38,7 +38,13 @@ class SoftmaxKernel<P: PrecisionType>: Kernel, Computable{
}
required init(device: MTLDevice, param: SoftmaxParam<P>) {
param.output.initTexture(device: device)
super.init(device: device, inFunctionName: "softmax")
param.output.initTexture(device: device, computePrecision: computePrecision)
if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "softmax")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "softmax_half")
} else {
fatalError()
}
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/TransposeKernel.swift
浏览文件 @ 8a3867e1
......@@ -41,33 +41,27 @@ struct TransposeTestParam: TestParam {
}
class TransposeKernel<P: PrecisionType>: Kernel, Computable, Testable {
var metalParam: TransposeMetalParam!
func compute(commandBuffer: MTLCommandBuffer, param: TransposeParam<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.setBytes(&metalParam, length: MemoryLayout<TransposeMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
required init(device: MTLDevice, param: TransposeParam<P>) {
param.output.initTexture(device: device, inTranspose: [0, 1, 2, 3])
super.init(device: device, inFunctionName: "transpose")
param.output.initTexture(device: device, inTranspose: [0, 1, 2, 3], computePrecision: computePrecision)
if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "transpose_half")
} else if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "transpose")
} else {
fatalError()
}
var invT: [Int] = [0, 1, 2, 3]
for (i, v) in param.input.transpose.enumerated() {
invT[v] = i
}
var axis: [Int] = [0, 1, 2, 3]
// var doNothing = false
// if param.axis.count == param.input.transpose.count {
// doNothing = param.axis == param.input.transpose.map { Int32($0) }
// }
// var doNothing = false
// if param.axis.count == param.input.transpose.count {
// doNothing = param.axis == param.input.transpose.map { Int32($0) }
// }
for i in 0..<param.axis.count {
......@@ -84,10 +78,30 @@ class TransposeKernel<P: PrecisionType>: Kernel, Computable, Testable {
}
metalParam = tmp
}
required init(device: MTLDevice, testParam: TransposeTestParam) {
super.init(device: device, inFunctionName: "transpose")
fatalError()
if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "transpose_half")
} else if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "transpose")
} else {
fatalError()
}
}
var metalParam: TransposeMetalParam!
func compute(commandBuffer: MTLCommandBuffer, param: TransposeParam<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.setBytes(&metalParam, length: MemoryLayout<TransposeMetalParam>.size, index: 0)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
}
public func test(commandBuffer: MTLCommandBuffer, param: TransposeTestParam) {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/BoxCoder.metal
浏览文件 @ 8a3867e1
......@@ -34,7 +34,6 @@ kernel void boxcoder(texture2d_array<float, access::read> priorBox [[texture(0)]
float tw = exp(pv.z * t.z) * pw;
float th = exp(pv.w * t.w) * ph;
float4 r;
r.x = tx - tw / 2;
r.y = ty - th / 2;
......@@ -43,3 +42,31 @@ kernel void boxcoder(texture2d_array<float, access::read> priorBox [[texture(0)]
output.write(r, gid.xy, gid.z);
}
kernel void boxcoder_half(texture2d_array<half, access::read> priorBox [[texture(0)]],
texture2d_array<half, access::read> priorBoxVar [[texture(1)]],
texture2d_array<half, access::read> targetBox [[texture(2)]],
texture2d_array<half, access::write> output[[texture(3)]],
uint3 gid [[thread_position_in_grid]]) {
half4 t = targetBox.read(gid.xy, gid.z);
half4 p = priorBox.read(gid.xy, gid.z);
half4 pv = priorBoxVar.read(gid.xy, gid.z);
float px = (float(p.x) + float(p.z)) / 2;
float py = (float(p.y) + float(p.w)) / 2;
float pw = float(p.z) - float(p.x);
float ph = float(p.w) - float(p.y);
float tx = float(pv.x) * float(t.x) * pw + px;
float ty = float(pv.y) * float(t.y) * ph + py;
float tw = exp(float(pv.z) * float(t.z)) * pw;
float th = exp(float(pv.w) * float(t.w)) * ph;
float4 r;
r.x = tx - tw / 2;
r.y = ty - th / 2;
r.z = tx + tw / 2;
r.w = ty + th / 2;
output.write(half4(r), gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/Common.metal
浏览文件 @ 8a3867e1
......@@ -57,3 +57,14 @@ inline void invtrans(int32_t trans[4], int32_t ipos[4], int32_t opos[4]) {
opos[trans[i]] = ipos[i];
}
}
struct MetalConvParam {
short offsetX;
short offsetY;
short offsetZ;
ushort strideX;
ushort strideY;
ushort dilationX;
ushort dilationY;
};
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/Concat.metal
浏览文件 @ 8a3867e1
......@@ -69,3 +69,48 @@ kernel void concat(texture2d_array<float, access::read> in0 [[texture(0)]],
}
out.write(r, gid.xy, gid.z);
}
kernel void concat_half(texture2d_array<half, access::read> in0 [[texture(0)]],
texture2d_array<half, access::read> in1 [[texture(1)]],
texture2d_array<half, access::read> in2 [[texture(2)]],
texture2d_array<half, access::read> in3 [[texture(3)]],
texture2d_array<half, access::read> in4 [[texture(4)]],
texture2d_array<half, access::read> in5 [[texture(5)]],
texture2d_array<half, access::read> inx [[texture(6)]],
texture2d_array<half, access::write> out [[texture(7)]],
constant ConcatParam & pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
ConcatParam cp = pm;
int xyzn[4] = {int(gid.x), int(gid.y), int(gid.z), 0}, abcd[4], oxyzn[4];
half4 r;
for (int i = 0; i < 4; i++) {
xyzn[3] = i;
xyzn2abcd(cp.odim[3], xyzn, abcd);
int k = abcd[cp.axis] - cp.offset;
int j = 0;
if (k < 0) {
r[i] = inx.read(gid.xy, gid.z)[i];
} else {
for (; j < 6; j++) {
if (k < cp.vdim[j]) {
break;
}
k -= cp.vdim[j];
}
int ta = cp.odim[cp.axis];
abcd[cp.axis] = k;
cp.odim[cp.axis] = cp.vdim[j];
abcd2xyzn(cp.odim[3], abcd, oxyzn);
cp.odim[cp.axis] = ta;
switch (j) {
case 0: r[i] = in0.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
case 1: r[i] = in1.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
case 2: r[i] = in2.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
case 3: r[i] = in3.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
case 4: r[i] = in4.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
case 5: r[i] = in5.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2])[oxyzn[3]]; break;
}
}
}
out.write(r, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ConvAddBNReluKernel.metal 0 → 100644
浏览文件 @ 8a3867e1
/* 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. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
kernel void conv_add_batch_norm_relu_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)]],
const device half4 *biase [[buffer(2)]],
const device float4 *new_scale [[buffer(3)]],
const device float4 *new_biase [[buffer(4)]],
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;
half4 output = half4(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(input, weight_x);
half4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + i];
output.y += dot(input, weight_y);
half4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + i];
output.z += dot(input, weight_z);
half4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + i];
output.w += dot(input, weight_w);
}
output = half4(fmax((float4(output) + float4(biase[gid.z])) * new_scale[gid.z] + new_biase[gid.z], 0.0));
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_add_batch_norm_relu_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)]],
const device half4 *biase [[buffer(2)]],
const device float4 *new_scale [[buffer(3)]],
const device float4 *new_biase [[buffer(4)]],
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;
half4 output = half4(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(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 = half4(fmax((float4(output) + float4(biase[gid.z])) * new_scale[gid.z] + new_biase[gid.z], 0.0));
outTexture.write(output, gid.xy, gid.z);
}
kernel void depthwise_conv_add_batch_norm_relu_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)]],
const device half4 *biase [[buffer(2)]],
const device float4 *new_scale [[buffer(3)]],
const device float4 *new_biase [[buffer(4)]],
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;
half4 output = half4(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 += input.x * weights[weithTo + 0 * kernelHXW + j];
output.y += input.y * weights[weithTo + 1 * kernelHXW + j];
output.z += input.z * weights[weithTo + 2 * kernelHXW + j];
output.w += input.w * weights[weithTo + 3 * kernelHXW + j];
}
output = half4(fmax((float4(output) + float4(biase[gid.z])) * new_scale[gid.z] + new_biase[gid.z], 0.0));
outTexture.write(output, gid.xy, gid.z);
}
/*---------------------------------------------*/
kernel void conv_add_batch_norm_relu_1x1(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *biase [[buffer(2)]],
const device float4 *new_scale [[buffer(3)]],
const device float4 *new_biase [[buffer(4)]],
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);
float4 input;
for (uint i = 0; i < input_arr_size; ++i) {
input = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
float4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + i];
output.x += dot(input, weight_x);
float4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + i];
output.y += dot(input, weight_y);
float4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + i];
output.z += dot(input, weight_z);
float4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + i];
output.w += dot(input, weight_w);
}
output = fmax((output + biase[gid.z]) * new_scale[gid.z] + new_biase[gid.z], 0.0);
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_add_batch_norm_relu_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *biase [[buffer(2)]],
const device float4 *new_scale [[buffer(3)]],
const device float4 *new_biase [[buffer(4)]],
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);
float4 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) {
float4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(input[j], weight_x);
float4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(input[j], weight_y);
float4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(input[j], weight_z);
float4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(input[j], weight_w);
}
}
output = fmax((output + biase[gid.z]) * new_scale[gid.z] + new_biase[gid.z], 0.0);
outTexture.write(output, gid.xy, gid.z);
}
kernel void depthwise_conv_add_batch_norm_relu_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float *weights [[buffer(1)]],
const device float4 *biase [[buffer(2)]],
const device float4 *new_scale [[buffer(3)]],
const device float4 *new_biase [[buffer(4)]],
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);
float4 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) {
float4 input = inputs[j];
output.x += input.x * weights[weithTo + 0 * kernelHXW + j];
output.y += input.y * weights[weithTo + 1 * kernelHXW + j];
output.z += input.z * weights[weithTo + 2 * kernelHXW + j];
output.w += input.w * weights[weithTo + 3 * kernelHXW + j];
}
output = fmax((output + biase[gid.z]) * new_scale[gid.z] + new_biase[gid.z], 0.0);
outTexture.write(output, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ConvAddMetal.metal 0 → 100644
浏览文件 @ 8a3867e1
/* 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. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
#pragma mark - convAdd
kernel void conv_add_1x1(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *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);
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);
float4 input;
for (uint i = 0; i < input_arr_size; ++i) {
input = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
float4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + i];
output.x += dot(input, weight_x);
float4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + i];
output.y += dot(input, weight_y);
float4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + i];
output.z += dot(input, weight_z);
float4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + i];
output.w += dot(input, weight_w);
}
output = output + biase[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_add_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *biase [[buffer(2)]],
const device float4 *new_scale [[buffer(3)]],
const device float4 *new_biase [[buffer(4)]],
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);
ushort dilation_x = param.dilationX;
ushort dilation_y = param.dilationY;
float4 input[9];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y - dilation_y), i);
input[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - dilation_y), i);
input[2] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y - dilation_y), i);
input[3] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[5] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y), i);
input[6] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y + dilation_y), i);
input[7] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + dilation_y), i);
input[8] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y + dilation_y), i);
for (int j = 0; j < 9; ++j) {
float4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(input[j], weight_x);
float4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(input[j], weight_y);
float4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(input[j], weight_z);
float4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(input[j], weight_w);
}
}
output = output + biase[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
kernel void depthwise_conv_add_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float *weights [[buffer(1)]],
const device float4 *biase [[buffer(2)]],
const device float4 *new_scale [[buffer(3)]],
const device float4 *new_biase [[buffer(4)]],
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);
float4 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) {
float4 input = inputs[j];
output.x += input.x * weights[weithTo + 0 * kernelHXW + j];
output.y += input.y * weights[weithTo + 1 * kernelHXW + j];
output.z += input.z * weights[weithTo + 2 * kernelHXW + j];
output.w += input.w * weights[weithTo + 3 * kernelHXW + j];
}
output = output + biase[gid.z];
outTexture.write(output, gid.xy, gid.z);
}
#pragma mark - half
kernel void conv_add_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)]],
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);
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(input, weight_x);
half4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + i];
output.y += dot(input, weight_y);
half4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + i];
output.z += dot(input, weight_z);
half4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + i];
output.w += dot(input, weight_w);
}
output = output + float4(biase[gid.z]);
outTexture.write(half4(output), gid.xy, gid.z);
}
kernel void conv_add_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)]],
const device half4 *biase [[buffer(2)]],
const device half4 *new_scale [[buffer(3)]],
const device half4 *new_biase [[buffer(4)]],
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);
ushort dilation_x = param.dilationX;
ushort dilation_y = param.dilationY;
half4 input[9];
for (uint i = 0; i < input_arr_size; ++i) {
input[0] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y - dilation_y), i);
input[1] = inTexture.sample(sample, float2(posInInput.x, posInInput.y - dilation_y), i);
input[2] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y - dilation_y), i);
input[3] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y), i);
input[4] = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
input[5] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y), i);
input[6] = inTexture.sample(sample, float2(posInInput.x - dilation_x, posInInput.y + dilation_y), i);
input[7] = inTexture.sample(sample, float2(posInInput.x, posInInput.y + dilation_y), i);
input[8] = inTexture.sample(sample, float2(posInInput.x + dilation_x, posInInput.y + dilation_y), 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));
}
}
output = output + float4(biase[gid.z]);
outTexture.write(half4(output), gid.xy, gid.z);
}
kernel void depthwise_conv_add_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)]],
const device half4 *biase [[buffer(2)]],
const device half4 *new_scale [[buffer(3)]],
const device half4 *new_biase [[buffer(4)]],
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 += input.w * weights[weithTo + 3 * kernelHXW + j];
}
output = output + float4(biase[gid.z]);
outTexture.write(half4(output), gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ConvBNReluKernel.metal 0 → 100644
浏览文件 @ 8a3867e1
/* 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. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
#pragma mark - conv bn relu
kernel void conv_batch_norm_relu_1x1(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *new_scale [[buffer(2)]],
const device float4 *new_biase [[buffer(3)]],
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);
float4 input;
for (uint i = 0; i < input_arr_size; ++i) {
input = inTexture.sample(sample, float2(posInInput.x, posInInput.y), i);
float4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + i];
output.x += dot(input, weight_x);
float4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + i];
output.y += dot(input, weight_y);
float4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + i];
output.z += dot(input, weight_z);
float4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + i];
output.w += dot(input, weight_w);
}
output = fmax(output * new_scale[gid.z] + new_biase[gid.z], 0.0);
outTexture.write(output, gid.xy, gid.z);
}
kernel void conv_batch_norm_relu_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float4 *weights [[buffer(1)]],
const device float4 *new_scale [[buffer(2)]],
const device float4 *new_biase [[buffer(3)]],
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);
float4 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) {
float4 weight_x = weights[weithTo + 0 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.x += dot(input[j], weight_x);
float4 weight_y = weights[weithTo + 1 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.y += dot(input[j], weight_y);
float4 weight_z = weights[weithTo + 2 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.z += dot(input[j], weight_z);
float4 weight_w = weights[weithTo + 3 * kernelHXW * input_arr_size + j * input_arr_size + i];
output.w += dot(input[j], weight_w);
}
}
output = fmax(output * new_scale[gid.z] + new_biase[gid.z], 0.0);
outTexture.write(output, gid.xy, gid.z);
}
kernel void depthwise_conv_batch_norm_relu_3x3(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant MetalConvParam &param [[buffer(0)]],
const device float *weights [[buffer(1)]],
const device float4 *new_scale [[buffer(2)]],
const device float4 *new_biase [[buffer(3)]],
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);
float4 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) {
float4 input = inputs[j];
output.x += input.x * weights[weithTo + 0 * kernelHXW + j];
output.y += input.y * weights[weithTo + 1 * kernelHXW + j];
output.z += input.z * weights[weithTo + 2 * kernelHXW + j];
output.w += input.w * weights[weithTo + 3 * kernelHXW + j];
}
output = fmax(output * new_scale[gid.z] + new_biase[gid.z], 0.0);
outTexture.write(output, gid.xy, gid.z);
}
#pragma mark - half
kernel void conv_batch_norm_relu_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)]],
const device half4 *new_scale [[buffer(2)]],
const device half4 *new_biase [[buffer(3)]],
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));
}
output = fmax(output * float4(new_scale[gid.z]) + float4(new_biase[gid.z]), 0.0);
outTexture.write(half4(output), gid.xy, gid.z);
}
kernel void conv_batch_norm_relu_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)]],
const device half4 *new_scale [[buffer(2)]],
const device half4 *new_biase [[buffer(3)]],
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));
}
}
output = fmax(output * float4(new_scale[gid.z]) + float4(new_biase[gid.z]), 0.0);
outTexture.write(half4(output), gid.xy, gid.z);
}
kernel void depthwise_conv_batch_norm_relu_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)]],
const device half4 *new_scale [[buffer(2)]],
const device half4 *new_biase [[buffer(3)]],
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 += input.x * weights[weithTo + 0 * kernelHXW + j];
output.y += input.y * weights[weithTo + 1 * kernelHXW + j];
output.z += input.z * weights[weithTo + 2 * kernelHXW + j];
output.w += input.w * weights[weithTo + 3 * kernelHXW + j];
}
output = fmax(output * float4(new_scale[gid.z]) + float4(new_biase[gid.z]), 0.0);
outTexture.write(half4(output), gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/Kernels.metal
浏览文件 @ 8a3867e1
......@@ -79,113 +79,6 @@ kernel void texture2d_to_2d_array_half(texture2d<half, access::read> inTexture [
outTexture.write(input, gid.xy, 0);
}
struct PoolParam {
int ksizeX;
int ksizeY;
int strideX;
int strideY;
int paddingX;
int paddingY;
int poolType;
};
kernel void pool(texture2d_array<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant PoolParam &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;
int xmin = gid.x * pm.strideX - pm.paddingX;
int xmax = min(xmin + pm.ksizeX, int(inTexture.get_width()));
xmin = max(xmin, 0);
int ymin = gid.y * pm.strideX - pm.paddingX;
int ymax = min(ymin + pm.ksizeX, int(inTexture.get_height()));
ymin = max(ymin, 0);
float4 r = 0;
if (pm.poolType == 0) {
r = inTexture.read(uint2(xmin, ymin), gid.z);
for (int x = xmin; x < xmax; x++) {
for (int y = ymin; y < ymax; y++) {
r = fmax(r, inTexture.read(uint2(x, y), gid.z));
}
}
} else if (pm.poolType == 1) {
for (int x = xmin; x < xmax; x++) {
for (int y = ymin; y < ymax; y++) {
r += inTexture.read(uint2(x, y), gid.z);
}
}
r /= pm.ksizeX * pm.ksizeY;
}
outTexture.write(r, gid.xy, gid.z);
}
kernel void pool_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant PoolParam &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;
int xmin = gid.x * pm.strideX - pm.paddingX;
int xmax = min(xmin + pm.ksizeX, int(inTexture.get_width()));
xmin = max(xmin, 0);
int ymin = gid.y * pm.strideX - pm.paddingX;
int ymax = min(ymin + pm.ksizeX, int(inTexture.get_height()));
ymin = max(ymin, 0);
half4 r = 0;
if (pm.poolType == 0) {
r = inTexture.read(uint2(xmin, ymin), gid.z);
for (int x = xmin; x < xmax; x++) {
for (int y = ymin; y < ymax; y++) {
r = fmax(r, inTexture.read(uint2(x, y), gid.z));
}
}
} else if (pm.poolType == 1) {
for (int x = xmin; x < xmax; x++) {
for (int y = ymin; y < ymax; y++) {
r += inTexture.read(uint2(x, y), gid.z);
}
}
r /= pm.ksizeX * pm.ksizeY;
}
outTexture.write(r, gid.xy, gid.z);
}
struct TransposeParam {
int iC;
int oC;
int axis[4];
};
kernel void transpose(texture2d_array<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant TransposeParam &pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if ((pm.axis[0] == 0) && (pm.axis[1] == 1) && (pm.axis[2] == 2) && (pm.axis[3] == 3)) {
// do nothing
float4 r = inTexture.read(gid.xy, gid.z);
outTexture.write(r, gid.xy, gid.z);
} else {
float4 r;
for (int n = 0; n < 4; n++) {
int ixyzn[] = {int(gid.x), int(gid.y), int(gid.z), n};
int iabcd[4], oabcd[4], oxyzn[4];
xyzn2abcd(pm.oC, ixyzn, iabcd);
oabcd[pm.axis[0]] = iabcd[0];
oabcd[pm.axis[1]] = iabcd[1];
oabcd[pm.axis[2]] = iabcd[2];
oabcd[pm.axis[3]] = iabcd[3];
abcd2xyzn(pm.iC, oabcd, oxyzn);
float4 rt = inTexture.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2]);
r[n] = rt[oxyzn[3]];
}
outTexture.write(r, gid.xy, gid.z);
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/PoolKernel.metal 0 → 100644
浏览文件 @ 8a3867e1
/* 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. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
struct PoolParam {
int ksizeX;
int ksizeY;
int strideX;
int strideY;
int paddingX;
int paddingY;
int poolType;
};
kernel void pool(texture2d_array<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant PoolParam &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;
int xmin = gid.x * pm.strideX - pm.paddingX;
int xmax = min(xmin + pm.ksizeX, int(inTexture.get_width()));
xmin = max(xmin, 0);
int ymin = gid.y * pm.strideX - pm.paddingX;
int ymax = min(ymin + pm.ksizeX, int(inTexture.get_height()));
ymin = max(ymin, 0);
float4 r = 0;
if (pm.poolType == 0) {
r = inTexture.read(uint2(xmin, ymin), gid.z);
for (int x = xmin; x < xmax; x++) {
for (int y = ymin; y < ymax; y++) {
r = fmax(r, inTexture.read(uint2(x, y), gid.z));
}
}
} else if (pm.poolType == 1) {
for (int x = xmin; x < xmax; x++) {
for (int y = ymin; y < ymax; y++) {
r += inTexture.read(uint2(x, y), gid.z);
}
}
r /= pm.ksizeX * pm.ksizeY;
}
outTexture.write(r, gid.xy, gid.z);
}
kernel void pool_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant PoolParam &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;
int xmin = gid.x * pm.strideX - pm.paddingX;
int xmax = min(xmin + pm.ksizeX, int(inTexture.get_width()));
xmin = max(xmin, 0);
int ymin = gid.y * pm.strideX - pm.paddingX;
int ymax = min(ymin + pm.ksizeX, int(inTexture.get_height()));
ymin = max(ymin, 0);
half4 r = 0;
if (pm.poolType == 0) {
r = inTexture.read(uint2(xmin, ymin), gid.z);
for (int x = xmin; x < xmax; x++) {
for (int y = ymin; y < ymax; y++) {
r = fmax(r, inTexture.read(uint2(x, y), gid.z));
}
}
} else if (pm.poolType == 1) {
for (int x = xmin; x < xmax; x++) {
for (int y = ymin; y < ymax; y++) {
r += inTexture.read(uint2(x, y), gid.z);
}
}
r /= pm.ksizeX * pm.ksizeY;
}
outTexture.write(r, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/PreluKernel.metal
浏览文件 @ 8a3867e1
......@@ -15,8 +15,6 @@
#include <metal_stdlib>
using namespace metal;
kernel void prelu_channel(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
const device float4 *alpha [[buffer(0)]],
......@@ -82,3 +80,4 @@ kernel void prelu_other(texture2d_array<float, access::sample> inTexture [[textu
output.w = input.w > 0 ? input.w : (alpha_value * input.w);
outTexture.write(output, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/PriorBoxKernel.metal
浏览文件 @ 8a3867e1
......@@ -35,8 +35,8 @@ struct PriorBoxMetalParam {
kernel void prior_box(texture2d_array<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outBoxTexture [[texture(1)]],
texture2d_array<float, access::write> varianceTexture [[texture(2)]],
constant PriorBoxMetalParam &param [[buffer(0)]],
const device float *aspect_ratios [[buffer(1)]],
const device float *aspect_ratios [[buffer(0)]],
constant PriorBoxMetalParam &param [[buffer(1)]],
const device float4 *variances [[buffer(2)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outBoxTexture.get_width() ||
......@@ -96,3 +96,68 @@ kernel void prior_box(texture2d_array<float, access::read> inTexture [[texture(0
}
}
kernel void prior_box_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outBoxTexture [[texture(1)]],
texture2d_array<half, access::write> varianceTexture [[texture(2)]],
constant PriorBoxMetalParam &param [[buffer(0)]],
const device half *aspect_ratios [[buffer(1)]],
const device float4 *variances [[buffer(2)]],
uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outBoxTexture.get_width() ||
gid.y >= outBoxTexture.get_height() ||
gid.z >= outBoxTexture.get_array_size()) return;
float center_x = (gid.x + param.offset) * param.stepWidth;
float center_y = (gid.y + param.offset) * param.stepHeight;
float box_width, box_height;
if (gid.z < param.aspecRatiosSize) {
half ar = aspect_ratios[gid.z];
box_width = param.minSize * sqrt(ar) / 2;
box_height = param.minSize / sqrt(ar) / 2;
float4 box;
box.x = (center_x - box_width) / param.imageWidth;
box.y = (center_y - box_height) / param.imageHeight;
box.z = (center_x + box_width) / param.imageWidth;
box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = fmin(fmax(box, 0.0), 1.0);
} else {
res = box;
}
outBoxTexture.write(half4(res), gid.xy, gid.z);
} else if (gid.z >= param.aspecRatiosSize) {
if (param.maxSizeSize > 0) {
box_width = box_height = sqrt(param.minSize * param.maxSize) / 2;
float4 max_box;
max_box.x = (center_x - box_width) / param.imageWidth;
max_box.y = (center_y - box_height) / param.imageHeight;
max_box.z = (center_x + box_width) / param.imageWidth;
max_box.w = (center_y + box_height) / param.imageHeight;
float4 res;
if (param.clip) {
res = min(max(max_box, 0.0), 1.0);
} else {
res = max_box;
}
outBoxTexture.write(half4(max_box), gid.xy, gid.z);
}
}
float4 variance = variances[0];
if (gid.z < param.numPriors) {
float4 variances_output;
variances_output.x = variance.x;
variances_output.y = variance.y;
variances_output.z = variance.z;
variances_output.w = variance.w;
varianceTexture.write(half4(variances_output), gid.xy, gid.z);
}
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/ReshapeKernel.metal
浏览文件 @ 8a3867e1
......@@ -102,14 +102,36 @@ kernel void reshape(texture2d_array<float, access::read> inTexture [[texture(0)]
outTexture.write(r, gid.xy, gid.z);
}
kernel void reshape_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(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;
half4 r = inTexture.read(uint2(0, 0), gid.x);
outTexture.write(r, gid.xy, gid.z);
kernel void reshape_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant ReshapeParam &rp [[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;
int oxyzn[4] = {int(gid.x), int(gid.y), int(gid.z), 0}, oabcd[4], ixyzn[4], iabcd[4];
ReshapeParam lrp = rp;
int oC = lrp.odim[lrp.otrans[3]];
int iC = lrp.idim[lrp.itrans[3]];
int count = lrp.odim[0] * lrp.odim[1] * lrp.odim[2] * lrp.odim[3];
half4 r;
for (int n = 0; n < 4; n++) {
oxyzn[3] = n;
xyzn2abcd(oC, oxyzn, oabcd);
int tabcd[4];
invtrans(lrp.otrans, oabcd, tabcd);
int index = abcd2index(lrp.odim, tabcd);
if (index < count) {
index2abcd(lrp.idim, index, tabcd);
trans(lrp.itrans, tabcd, iabcd);
abcd2xyzn(iC, iabcd, ixyzn);
r[n] = inTexture.read(uint2(ixyzn[0], ixyzn[1]), ixyzn[2])[ixyzn[3]];
} else {
r[n] = 0;
}
}
outTexture.write(r, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/Softmax.metal
浏览文件 @ 8a3867e1
......@@ -57,25 +57,44 @@ kernel void softmax(texture2d_array<float, access::read> inTexture [[texture(0)]
rr = exp(rr - maxv) / sum;
outTexture.write(rr, gid.xy, gid.z);
}
//
//kernel void softmax_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
// texture2d_array<half, access::write> outTexture [[texture(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 zsize = inTexture.get_array_size();
// half maxv = inTexture.read(uint2(0, 0), 0)[0];
// for (int z = 0; z < zsize; z++) {
// half4 r = inTexture.read(uint2(0, 0), z);
// maxv = max(maxv, max(max(r[0], r[1]), max(r[2], r[3])));
// }
// float sum = 0;
// for (int z = 0; z < zsize; z++) {
// half4 r = inTexture.read(uint2(0, 0), z);
// sum += exp(r[0] - maxv) + exp(r[1] - maxv) + exp(r[2] - maxv) + exp(r[3] - maxv);
// }
// half4 rr = inTexture.read(gid.xy, gid.z);
// rr = exp(rr - maxv) / sum;
// outTexture.write(rr, gid.xy, gid.z);
//}
kernel void softmax_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant SoftmaxParam &sp [[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;
// int zsize = inTexture.get_array_size();
half maxv = inTexture.read(gid.xy, 0)[0];
int group = sp.K / 4;
int remain = sp.K % 4;
for (int z = 0; z < group; z++) {
half4 r = inTexture.read(gid.xy, z);
maxv = max(maxv, max(r[0], max(r[1], max(r[2], r[3]))));
}
if (remain > 0) {
half4 r = inTexture.read(gid.xy, group);
for (int i = 0; i < remain; i++) {
maxv = max(maxv, r[i]);
}
}
float4 rsum = {0, 0, 0, 0};
for (int z = 0; z < group; z++) {
half4 r = inTexture.read(gid.xy, z);
rsum += exp(float4(r) - float4(maxv));
}
float sum = rsum[0] + rsum[1] + rsum[2] + rsum[3];
if (remain > 0) {
half4 r = inTexture.read(gid.xy, group);
for (int i = 0; i < remain; i++) {
sum += exp(float(r[i]) - float(maxv));
}
}
half4 rr = inTexture.read(gid.xy, gid.z);
rr = half4(exp(float4(rr) - float(maxv)) / sum);
outTexture.write(rr, gid.xy, gid.z);
}
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/TransposeKernel.metal 0 → 100644
浏览文件 @ 8a3867e1
/* 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. */
#include <metal_stdlib>
#include "Common.metal"
using namespace metal;
struct TransposeParam {
int iC;
int oC;
int axis[4];
};
kernel void transpose(texture2d_array<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]],
constant TransposeParam &pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if ((pm.axis[0] == 0) && (pm.axis[1] == 1) && (pm.axis[2] == 2) && (pm.axis[3] == 3)) {
// do nothing
float4 r = inTexture.read(gid.xy, gid.z);
outTexture.write(r, gid.xy, gid.z);
} else {
float4 r;
for (int n = 0; n < 4; n++) {
int ixyzn[] = {int(gid.x), int(gid.y), int(gid.z), n};
int iabcd[4], oabcd[4], oxyzn[4];
xyzn2abcd(pm.oC, ixyzn, iabcd);
oabcd[pm.axis[0]] = iabcd[0];
oabcd[pm.axis[1]] = iabcd[1];
oabcd[pm.axis[2]] = iabcd[2];
oabcd[pm.axis[3]] = iabcd[3];
abcd2xyzn(pm.iC, oabcd, oxyzn);
float4 rt = inTexture.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2]);
r[n] = rt[oxyzn[3]];
}
outTexture.write(r, gid.xy, gid.z);
}
}
kernel void transpose_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]],
constant TransposeParam &pm [[buffer(0)]],
uint3 gid [[thread_position_in_grid]]) {
if ((pm.axis[0] == 0) && (pm.axis[1] == 1) && (pm.axis[2] == 2) && (pm.axis[3] == 3)) {
// do nothing
half4 r = inTexture.read(gid.xy, gid.z);
outTexture.write(r, gid.xy, gid.z);
} else {
half4 r;
for (int n = 0; n < 4; n++) {
int ixyzn[] = {int(gid.x), int(gid.y), int(gid.z), n};
int iabcd[4], oabcd[4], oxyzn[4];
xyzn2abcd(pm.oC, ixyzn, iabcd);
oabcd[pm.axis[0]] = iabcd[0];
oabcd[pm.axis[1]] = iabcd[1];
oabcd[pm.axis[2]] = iabcd[2];
oabcd[pm.axis[3]] = iabcd[3];
abcd2xyzn(pm.iC, oabcd, oxyzn);
half4 rt = inTexture.read(uint2(oxyzn[0], oxyzn[1]), oxyzn[2]);
r[n] = rt[oxyzn[3]];
}
outTexture.write(r, gid.xy, gid.z);
}
}
metal/paddle-mobile/paddle-mobile/Operators/PoolOp.swift
浏览文件 @ 8a3867e1
......@@ -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])).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]), texturePrecision: computePrecision).strideArray())
// print("pool2d delog")
......
metal/paddle-mobile/paddle-mobile/Operators/PreluOp.swift
浏览文件 @ 8a3867e1
......@@ -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])).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]), texturePrecision: computePrecision).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])).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]), texturePrecision: computePrecision).strideArray())
}
// print("softmax delog")
......
metal/paddle-mobile/paddle-mobile/Operators/PriorBoxOp.swift
浏览文件 @ 8a3867e1
......@@ -39,7 +39,7 @@ class PriorBoxParam<P: PrecisionType>: OpParam {
let minSizes: [Float32]
let maxSizes: [Float32]
let aspectRatios: [Float32]
var newAspectRatios: [Float32]?
var newAspectRatios: MTLBuffer?
let variances: [Float32]
let flip: Bool
let clip: Bool
......@@ -69,15 +69,19 @@ class PriorBoxOp<P: PrecisionType>: Operator<PriorBoxKernel<P>, PriorBoxParam<P>
}
func delogOutput() {
print(" \(type) output: ")
// output
let outputArray = para.output.metalTexture.floatArray { (o: Float32) -> Float32 in
return o
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)
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())
} else {
print(" not implement")
}
print(outputArray)
// writeToLibrary(fileName: "box_out", array: outputArray)
// output variance
......
metal/paddle-mobile/paddle-mobile/Operators/ReluOp.swift
浏览文件 @ 8a3867e1
......@@ -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])).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]), texturePrecision: computePrecision).strideArray())
}
}
......
metal/paddle-mobile/paddle-mobile/Operators/ReshapeOp.swift
浏览文件 @ 8a3867e1
......@@ -76,7 +76,7 @@ class ReshapeOp<P: PrecisionType>: Operator<ReshapeKernel<P>, ReshapeParam<P>>,
let originDim = para.output.originDim
let outputArray = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]), texturePrecision: computePrecision)
print(outputArray.strideArray())
}
......
metal/paddle-mobile/paddle-mobile/Operators/SoftmaxOp.swift
浏览文件 @ 8a3867e1
......@@ -52,9 +52,9 @@ class SoftmaxOp<P: PrecisionType>: Operator<SoftmaxKernel<P>, SoftmaxParam<P>>,
func delogOutput() {
print("softmax delog")
let originDim = para.output.originDim
let outputArray = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]), texturePrecision: computePrecision)
print(outputArray.strideArray())
}
}
metal/paddle-mobile/paddle-mobile/Operators/TransposeOp.swift
浏览文件 @ 8a3867e1
......@@ -49,7 +49,7 @@ class TransposeOp<P: PrecisionType>: Operator<TransposeKernel<P>, TransposeParam
func delogOutput() {
print(" \(type) output: ")
let originDim = para.output.tensorDim
let outputArray = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]), texturePrecision: computePrecision)
print(outputArray.strideArray())
}
}
......
metal/paddle-mobile/paddle-mobile/framework/Tensor.swift
浏览文件 @ 8a3867e1
......@@ -12,7 +12,6 @@
See the License for the specific language governing permissions and
limitations under the License. */
import Accelerate
import Foundation
protocol Tensorial: CustomStringConvertible, CustomDebugStringConvertible{
......@@ -27,10 +26,11 @@ extension Tensorial {
}
}
public enum ComputePrecision {
case Float32, Float16
}
class Tensor<P: PrecisionType>: Tensorial {
enum BufferPrecision {
case Float32, Float16
}
var data: Data
var dim: Dim
......@@ -93,15 +93,9 @@ class Tensor<P: PrecisionType>: Tensorial {
layout = to
}
func float32ToFloat16(input: UnsafeMutablePointer<Float32>, output: UnsafeMutableRawPointer, count: Int) {
var float32Buffer = vImage_Buffer(data: input, height: 1, width: UInt(count), rowBytes: count * 4)
var float16buffer = vImage_Buffer(data: output, height: 1, width: UInt(count), rowBytes: count * 2)
guard vImageConvert_PlanarFtoPlanar16F(&float32Buffer, &float16buffer, 0) == kvImageNoError else {
fatalError(" float 32 to float 16 error ! ")
}
}
func initBuffer(device: MTLDevice, precision: BufferPrecision = .Float32) {
func initBuffer(device: MTLDevice, precision: ComputePrecision = .Float16) {
guard let floatPointer = data.pointer as? UnsafeMutablePointer<Float32> else {
fatalError(" not support yet ")
}
......
metal/paddle-mobile/paddle-mobile/framework/Texture.swift
浏览文件 @ 8a3867e1
......@@ -46,7 +46,7 @@ public class Texture<P: PrecisionType>: Tensorial {
public var metalTexture: MTLTexture!
var transpose: [Int] = [0, 1, 2, 3]
func initTexture(device: MTLDevice, inTranspose: [Int] = [0, 1, 2, 3]) {
func initTexture(device: MTLDevice, inTranspose: [Int] = [0, 1, 2, 3], computePrecision: ComputePrecision = .Float16) {
transpose = inTranspose
let newDim = transpose.map { originDim[$0] }
......@@ -64,12 +64,10 @@ public class Texture<P: PrecisionType>: Tensorial {
tmpTextureDes.depth = 1
tmpTextureDes.arrayLength = ((newDim[0]) * (newDim[3]) + 3) / 4
tmpTextureDes.textureType = .type2DArray
if MemoryLayout<P>.size == 1 {
tmpTextureDes.pixelFormat = .rgba8Unorm
} else if MemoryLayout<P>.size == 2 {
if computePrecision == .Float16 {
tmpTextureDes.pixelFormat = .rgba16Float
} else if MemoryLayout<P>.size == 4 {
} else if computePrecision == .Float32 {
tmpTextureDes.pixelFormat = .rgba32Float
}
......
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