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未验证 提交 75750786 编写于 作者: R Ruilong Liu 提交者： GitHub
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Merge pull request #878 from codeWorm2015/metal 

Metal
上级 ee6ef4d9 7325684a
展开全部 隐藏空白更改
内联 并排
Showing with 2376 addition and 1546 deletion
metal/paddle-mobile-demo/paddle-mobile-demo.xcodeproj/xcuserdata/liuruilong.xcuserdatad/xcschemes/xcschememanagement.plist
浏览文件 @ 75750786
...@@ -7,7 +7,7 @@ ...@@ -7,7 +7,7 @@
<key>paddle-mobile-demo.xcscheme</key> <key>paddle-mobile-demo.xcscheme</key>
<dict> <dict>
<key>orderHint</key> <key>orderHint</key>
<integer>2</integer> <integer>1</integer>
</dict> </dict>
</dict> </dict>
<key>SuppressBuildableAutocreation</key> <key>SuppressBuildableAutocreation</key>
......
metal/paddle-mobile-demo/paddle-mobile-demo/Net/MobileNetSSD.swift
浏览文件 @ 75750786
...@@ -33,7 +33,7 @@ class MobileNet_ssd_hand: Net{ ...@@ -33,7 +33,7 @@ class MobileNet_ssd_hand: Net{
return " \(res)" return " \(res)"
} }
func fetchResult(paddleMobileRes: ResultHolder<Float32>) -> [Float32]{ func fetchResult(paddleMobileRes: ResultHolder<Float32>) -> [Float32] {
guard let interRes = paddleMobileRes.intermediateResults else { guard let interRes = paddleMobileRes.intermediateResults else {
fatalError(" need have inter result ") fatalError(" need have inter result ")
...@@ -46,12 +46,11 @@ class MobileNet_ssd_hand: Net{ ...@@ -46,12 +46,11 @@ class MobileNet_ssd_hand: Net{
guard let bboxs = interRes["BBoxes"], bboxs.count > 0, let bbox = bboxs[0] as? Texture<Float32> else { guard let bboxs = interRes["BBoxes"], bboxs.count > 0, let bbox = bboxs[0] as? Texture<Float32> else {
fatalError() 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 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 var bboxArr = bbox.metalTexture.float32Array()
}
let nmsCompute = NMSCompute.init() let nmsCompute = NMSCompute.init()
nmsCompute.scoreThredshold = 0.01 nmsCompute.scoreThredshold = 0.01
nmsCompute.nmsTopK = 200 nmsCompute.nmsTopK = 200
...@@ -78,10 +77,37 @@ class MobileNet_ssd_hand: Net{ ...@@ -78,10 +77,37 @@ class MobileNet_ssd_hand: Net{
let paramPath: String let paramPath: String
let modelDir: String let modelDir: String
// let paramPointer: UnsafeMutableRawPointer
//
// let paramSize: Int
//
// let modelPointer: UnsafeMutableRawPointer
//
// let modelSize: Int
//
// /**
// * inParamPointer: 参数文件内存地址
// * inParamSize: 参数文件大小(字节数)
// * inModelPointer: 模型文件内存地址
// * inModelSize: 模型文件大小(字节数)
// */
// init(inParamPointer: UnsafeMutableRawPointer, inParamSize: Int, inModelPointer: UnsafeMutableRawPointer, inModelSize: Int) {
// paramPointer = inParamPointer
// paramSize = inParamSize
// modelPointer = inModelPointer
// modelSize = inModelSize
//// fatalError()
// }
init() { init() {
modelPath = Bundle.main.path(forResource: "ssd_hand_model", ofType: nil) ?! "model null" modelPath = Bundle.main.path(forResource: "ssd_hand_model", ofType: nil) ?! "model null"
paramPath = Bundle.main.path(forResource: "ssd_hand_params", ofType: nil) ?! "para null" paramPath = Bundle.main.path(forResource: "ssd_hand_params", ofType: nil) ?! "para null"
modelDir = "" modelDir = ""
preprocessKernel = MobilenetssdPreProccess.init(device: MetalHelper.shared.device) preprocessKernel = MobilenetssdPreProccess.init(device: MetalHelper.shared.device)
// fatalError()
} }
} }
metal/paddle-mobile-demo/paddle-mobile-demo/Net/PreProcessKernel.metal
浏览文件 @ 75750786
...@@ -85,6 +85,17 @@ kernel void genet_preprocess(texture2d<float, access::read> inTexture [[texture( ...@@ -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); 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]]) { 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() || if (gid.x >= outTexture.get_width() ||
gid.y >= outTexture.get_height()) return; gid.y >= outTexture.get_height()) return;
...@@ -95,12 +106,13 @@ kernel void scale(texture2d<float, access::sample> inTexture [[texture(0)]], tex ...@@ -95,12 +106,13 @@ kernel void scale(texture2d<float, access::sample> inTexture [[texture(0)]], tex
outTexture.write(input, gid); 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 @ 75750786
...@@ -26,7 +26,7 @@ let modelHelperMap: [SupportModel : Net] = [.mobilenet_ssd : MobileNet_ssd_hand. ...@@ -26,7 +26,7 @@ let modelHelperMap: [SupportModel : Net] = [.mobilenet_ssd : MobileNet_ssd_hand.
enum SupportModel: String{ enum SupportModel: String{
// case mobilenet = "mobilenet" // case mobilenet = "mobilenet"
case mobilenet_ssd = "mobilenetssd" case mobilenet_ssd = "mobilenetssd"
case genet = "enet" case genet = "genet"
static func supportedModels() -> [SupportModel] { static func supportedModels() -> [SupportModel] {
//.mobilenet, //.mobilenet,
return [.mobilenet_ssd ,.genet] return [.mobilenet_ssd ,.genet]
...@@ -87,6 +87,7 @@ class ViewController: UIViewController { ...@@ -87,6 +87,7 @@ class ViewController: UIViewController {
fatalError() fatalError()
} }
// print(result.resultArray)
if i == max - 1 { if i == max - 1 {
let time = Date.init().timeIntervalSince(startDate) let time = Date.init().timeIntervalSince(startDate)
DispatchQueue.main.async { DispatchQueue.main.async {
......
metal/paddle-mobile/paddle-mobile.xcodeproj/project.pbxproj
浏览文件 @ 75750786
...@@ -12,6 +12,8 @@ ...@@ -12,6 +12,8 @@
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4AF9288421357BE3005B6C3A /* Elementwise.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AF9288321357BE3005B6C3A /* Elementwise.metal */; }; 4AF9288421357BE3005B6C3A /* Elementwise.metal in Sources */ = {isa = PBXBuildFile; fileRef = 4AF9288321357BE3005B6C3A /* Elementwise.metal */; };
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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, ); }; }; 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 */; }; FC039B9720E11C9A0081E9F8 /* Extensions.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC039B9420E11C9A0081E9F8 /* Extensions.swift */; };
FC039B9820E11C9A0081E9F8 /* Errors.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC039B9520E11C9A0081E9F8 /* Errors.swift */; }; FC039B9820E11C9A0081E9F8 /* Errors.swift in Sources */ = {isa = PBXBuildFile; fileRef = FC039B9520E11C9A0081E9F8 /* Errors.swift */; };
...@@ -53,6 +55,9 @@ ...@@ -53,6 +55,9 @@
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FCA3A1652132A5EB00084FE5 /* Common.metal in Sources */ = {isa = PBXBuildFile; fileRef = FCA3A1642132A5EB00084FE5 /* Common.metal */; }; FCA3A1652132A5EB00084FE5 /* Common.metal in Sources */ = {isa = PBXBuildFile; fileRef = FCA3A1642132A5EB00084FE5 /* Common.metal */; };
FCA67B1721364EF000BD58AA /* ConvTransposeKernel.metal in Sources */ = {isa = PBXBuildFile; fileRef = FCA67B1621364EF000BD58AA /* ConvTransposeKernel.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 */; }; FCBCCC572122F41300D94F7E /* DwConvBNReluOp.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCBCCC562122F41300D94F7E /* DwConvBNReluOp.swift */; };
FCBCCC592122F42700D94F7E /* ConvBNReluOp.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCBCCC582122F42700D94F7E /* ConvBNReluOp.swift */; }; FCBCCC592122F42700D94F7E /* ConvBNReluOp.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCBCCC582122F42700D94F7E /* ConvBNReluOp.swift */; };
FCBCCC5B2122F66F00D94F7E /* ConvBNReluKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCBCCC5A2122F66F00D94F7E /* ConvBNReluKernel.swift */; }; FCBCCC5B2122F66F00D94F7E /* ConvBNReluKernel.swift in Sources */ = {isa = PBXBuildFile; fileRef = FCBCCC5A2122F66F00D94F7E /* ConvBNReluKernel.swift */; };
...@@ -97,6 +102,8 @@ ...@@ -97,6 +102,8 @@
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DD2E06330A1E7129C918DB46 /* Pods_paddle_mobile.framework */ = {isa = PBXFileReference; explicitFileType = wrapper.framework; includeInIndex = 0; path = Pods_paddle_mobile.framework; sourceTree = BUILT_PRODUCTS_DIR; }; 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>"; }; 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; }; 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>"; }; 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>"; }; FC039B6E20E11C3C0081E9F8 /* Info.plist */ = {isa = PBXFileReference; lastKnownFileType = text.plist.xml; path = Info.plist; sourceTree = "<group>"; };
...@@ -141,6 +148,9 @@ ...@@ -141,6 +148,9 @@
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FCA3A1642132A5EB00084FE5 /* Common.metal */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.metal; path = Common.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>"; }; 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>"; }; FCBCCC562122F41300D94F7E /* DwConvBNReluOp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = DwConvBNReluOp.swift; sourceTree = "<group>"; };
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FCBCCC5A2122F66F00D94F7E /* ConvBNReluKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ConvBNReluKernel.swift; sourceTree = "<group>"; }; FCBCCC5A2122F66F00D94F7E /* ConvBNReluKernel.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ConvBNReluKernel.swift; sourceTree = "<group>"; };
...@@ -372,6 +382,11 @@ ...@@ -372,6 +382,11 @@
FCA3A1622132A4AC00084FE5 /* ReshapeKernel.metal */, FCA3A1622132A4AC00084FE5 /* ReshapeKernel.metal */,
FCA3A1642132A5EB00084FE5 /* Common.metal */, FCA3A1642132A5EB00084FE5 /* Common.metal */,
FCA67B1621364EF000BD58AA /* ConvTransposeKernel.metal */, FCA67B1621364EF000BD58AA /* ConvTransposeKernel.metal */,
FCA67CD42138272900BD58AA /* ConvAddMetal.metal */,
FCA67CD6213827AC00BD58AA /* ConvAddBNReluKernel.metal */,
FCA67CD82138287B00BD58AA /* ConvBNReluKernel.metal */,
FC0226552138F33800F395E2 /* TransposeKernel.metal */,
FC0226572138F38D00F395E2 /* PoolKernel.metal */,
); );
path = metal; path = metal;
sourceTree = "<group>"; sourceTree = "<group>";
...@@ -478,6 +493,7 @@ ...@@ -478,6 +493,7 @@
files = ( files = (
FC9D038020E22FBB000F735A /* FeedOp.swift in Sources */, FC9D038020E22FBB000F735A /* FeedOp.swift in Sources */,
FC039B9F20E11CB20081E9F8 /* Tensor.swift in Sources */, FC039B9F20E11CB20081E9F8 /* Tensor.swift in Sources */,
FCA67CD7213827AC00BD58AA /* ConvAddBNReluKernel.metal in Sources */,
4AF9287921341661005B6C3A /* Softmax.metal in Sources */, 4AF9287921341661005B6C3A /* Softmax.metal in Sources */,
FC0E2DBC20EE45FE009C1FAC /* ConvKernel.swift in Sources */, FC0E2DBC20EE45FE009C1FAC /* ConvKernel.swift in Sources */,
FC039BAA20E11CBC0081E9F8 /* ElementwiseAddOp.swift in Sources */, FC039BAA20E11CBC0081E9F8 /* ElementwiseAddOp.swift in Sources */,
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FCF2D73820E64E70007AC5F5 /* Kernel.swift in Sources */, FCF2D73820E64E70007AC5F5 /* Kernel.swift in Sources */,
FCDDC6CC212FDFDB00E5EF74 /* ReluKernel.metal in Sources */, FCDDC6CC212FDFDB00E5EF74 /* ReluKernel.metal in Sources */,
FC0226562138F33800F395E2 /* TransposeKernel.metal in Sources */,
FCDDC6C6212F9FB800E5EF74 /* PreluKernel.swift in Sources */, FCDDC6C6212F9FB800E5EF74 /* PreluKernel.swift in Sources */,
FCA67CD52138272900BD58AA /* ConvAddMetal.metal in Sources */,
FCBCCC5B2122F66F00D94F7E /* ConvBNReluKernel.swift in Sources */, FCBCCC5B2122F66F00D94F7E /* ConvBNReluKernel.swift in Sources */,
FCEBC0F420F1FDD90099DBAF /* ConvAddBatchNormReluOp.swift in Sources */, FCEBC0F420F1FDD90099DBAF /* ConvAddBatchNormReluOp.swift in Sources */,
FC0E2DC020EE461F009C1FAC /* ElementwiseAddKernel.swift in Sources */, FC0E2DC020EE461F009C1FAC /* ElementwiseAddKernel.swift in Sources */,
4AF928772133F1DB005B6C3A /* BoxCoder.metal in Sources */, 4AF928772133F1DB005B6C3A /* BoxCoder.metal in Sources */,
FCEB684C212F093800D2448E /* PreluOp.swift in Sources */, FCEB684C212F093800D2448E /* PreluOp.swift in Sources */,
FCA67CD92138287B00BD58AA /* ConvBNReluKernel.metal in Sources */,
FC60DB8920E9AAA500FF203F /* MetalExtension.swift in Sources */, FC60DB8920E9AAA500FF203F /* MetalExtension.swift in Sources */,
FCEBC0F620F1FE120099DBAF /* ConvAddBatchNormReluKernel.swift in Sources */, FCEBC0F620F1FE120099DBAF /* ConvAddBatchNormReluKernel.swift in Sources */,
FCDDC6CA212FDF6800E5EF74 /* BatchNormKernel.metal in Sources */, FCDDC6CA212FDF6800E5EF74 /* BatchNormKernel.metal in Sources */,
...@@ -550,6 +569,7 @@ ...@@ -550,6 +569,7 @@
FC5163F620EF556E00636C28 /* Texture2DTo2DArrayKernel.swift in Sources */, FC5163F620EF556E00636C28 /* Texture2DTo2DArrayKernel.swift in Sources */,
FC039BC020E11CC20081E9F8 /* BlockDesc.swift in Sources */, FC039BC020E11CC20081E9F8 /* BlockDesc.swift in Sources */,
FCD04E6820F315020007374F /* PoolKernel.swift in Sources */, FCD04E6820F315020007374F /* PoolKernel.swift in Sources */,
FC0226582138F38D00F395E2 /* PoolKernel.metal in Sources */,
FC039BAD20E11CBC0081E9F8 /* ReluOp.swift in Sources */, FC039BAD20E11CBC0081E9F8 /* ReluOp.swift in Sources */,
FCBCCC572122F41300D94F7E /* DwConvBNReluOp.swift in Sources */, FCBCCC572122F41300D94F7E /* DwConvBNReluOp.swift in Sources */,
FC039BBE20E11CC20081E9F8 /* OpDesc.swift in Sources */, FC039BBE20E11CC20081E9F8 /* OpDesc.swift in Sources */,
...@@ -699,6 +719,7 @@ ...@@ -699,6 +719,7 @@
"@executable_path/Frameworks", "@executable_path/Frameworks",
"@loader_path/Frameworks", "@loader_path/Frameworks",
); );
MACH_O_TYPE = mh_dylib;
MTL_LANGUAGE_REVISION = UseDeploymentTarget; MTL_LANGUAGE_REVISION = UseDeploymentTarget;
PRODUCT_BUNDLE_IDENTIFIER = "orange.paddle-mobile"; PRODUCT_BUNDLE_IDENTIFIER = "orange.paddle-mobile";
PRODUCT_NAME = "$(TARGET_NAME:c99extidentifier)"; PRODUCT_NAME = "$(TARGET_NAME:c99extidentifier)";
...@@ -727,6 +748,7 @@ ...@@ -727,6 +748,7 @@
"@executable_path/Frameworks", "@executable_path/Frameworks",
"@loader_path/Frameworks", "@loader_path/Frameworks",
); );
MACH_O_TYPE = mh_dylib;
MTL_LANGUAGE_REVISION = UseDeploymentTarget; MTL_LANGUAGE_REVISION = UseDeploymentTarget;
PRODUCT_BUNDLE_IDENTIFIER = "orange.paddle-mobile"; PRODUCT_BUNDLE_IDENTIFIER = "orange.paddle-mobile";
PRODUCT_NAME = "$(TARGET_NAME:c99extidentifier)"; PRODUCT_NAME = "$(TARGET_NAME:c99extidentifier)";
......
metal/paddle-mobile/paddle-mobile/Common/MetalExtension.swift
浏览文件 @ 75750786
...@@ -225,16 +225,12 @@ extension MTLComputeCommandEncoder { ...@@ -225,16 +225,12 @@ extension MTLComputeCommandEncoder {
let groupDepth = slices let groupDepth = slices
let groups = MTLSize.init(width: groupWidth, height: groupHeight, depth: groupDepth) let groups = MTLSize.init(width: groupWidth, height: groupHeight, depth: groupDepth)
// print("groups: \(groups) ")
// print("threads per group: \(threadsPerGroup)")
setComputePipelineState(computePipline) setComputePipelineState(computePipline)
dispatchThreadgroups(groups, threadsPerThreadgroup: threadsPerGroup) dispatchThreadgroups(groups, threadsPerThreadgroup: threadsPerGroup)
} }
} }
public extension MTLTexture { public extension MTLTexture {
func stridableFloatArray<P>(stridable: Bool = true) -> [(index: Int, value: P)] { func stridableFloatArray<P>(stridable: Bool = true) -> [(index: Int, value: P)] {
...@@ -285,6 +281,23 @@ public extension MTLTexture { ...@@ -285,6 +281,23 @@ public extension MTLTexture {
return fArr return fArr
} }
func float32Array() -> [Float32] {
if pixelFormat == .rgba32Float {
let float32Array = floatArray { (f: Float32) -> Float32 in
return f
}
return float32Array
} else if pixelFormat == .rgba16Float {
var float16Array = floatArray { (f: Float16) -> Float16 in
return f
}
return float16To32(input: &float16Array, count: float16Array.count)
} else {
fatalError()
}
}
func logDesc<T>(header: String = "", stridable: Bool = true) -> T? { func logDesc<T>(header: String = "", stridable: Bool = true) -> T? {
print(header) print(header)
print("texture: \(self)") print("texture: \(self)")
...@@ -341,14 +354,27 @@ public extension MTLTexture { ...@@ -341,14 +354,27 @@ public extension MTLTexture {
} }
// n c h w - dim // 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("origin dim: \(dim)")
print("texture: ") print("texture: ")
print(self) print(self)
var textureArray: [Float32]
// if texturePrecision == .Float16
let textureArray = floatArray { (i : Float32) -> Float32 in if pixelFormat == .rgba32Float {
return i 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] = [] var output: [Float32] = []
for s in 0..<arrayLength { for s in 0..<arrayLength {
for c in 0..<4{ for c in 0..<4{
...@@ -366,16 +392,26 @@ public extension MTLTexture { ...@@ -366,16 +392,26 @@ public extension MTLTexture {
return output 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("origin dim: \(dim)")
// print("texture: ") // print("texture: ")
// print(self) // print(self)
let textureArray = floatArray { (i : Float32) -> Float32 in var textureArray: [Float32]
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] = [] var output: [Float32] = []
let numOfASlice = dim.h * dim.w * 4 let numOfASlice = dim.h * dim.w * 4
for h in 0..<dim.h { for h in 0..<dim.h {
for w in 0..<dim.w { for w in 0..<dim.w {
...@@ -394,7 +430,6 @@ public extension MTLTexture { ...@@ -394,7 +430,6 @@ public extension MTLTexture {
} }
} }
} }
// print(" tensor count -- \(output.count)")
return output return output
} }
......
metal/paddle-mobile/paddle-mobile/Common/PaddleMobileUnitTest.swift
浏览文件 @ 75750786
...@@ -314,7 +314,7 @@ public class PaddleMobileUnitTest { ...@@ -314,7 +314,7 @@ public class PaddleMobileUnitTest {
let offsetX = filterSize.width/2 - paddings.0 let offsetX = filterSize.width/2 - paddings.0
let offsetY = filterSize.height/2 - paddings.1 let offsetY = filterSize.height/2 - paddings.1
let metalParam = MetalConvParam.init(offsetX: Int16(offsetX), offsetY: Int16(offsetY), offsetZ: 0, strideX: UInt16(stride.0), strideY: UInt16(stride.1), paddedZ: UInt16(paddings.0)) let metalParam = MetalConvParam.init(offsetX: Int16(offsetX), offsetY: Int16(offsetY), offsetZ: 0, strideX: UInt16(stride.0), strideY: UInt16(stride.1), paddedZ: UInt16(paddings.0), dilationX: UInt16(1), dilationY: UInt16(1))
let param = ConvAddBatchNormReluTestParam.init(inInputTexture: inputeTexture, inOutputTexture: outputTexture, inMetalParam: metalParam, inFilterBuffer: filterBuffer, inBiaseBuffer: biaseBuffer, inNewScaleBuffer: newScalueBuffer, inNewBiaseBuffer: newBiaseBuffer, inFilterSize: filterSize) let param = ConvAddBatchNormReluTestParam.init(inInputTexture: inputeTexture, inOutputTexture: outputTexture, inMetalParam: metalParam, inFilterBuffer: filterBuffer, inBiaseBuffer: biaseBuffer, inNewScaleBuffer: newScalueBuffer, inNewBiaseBuffer: newBiaseBuffer, inFilterSize: filterSize)
......
metal/paddle-mobile/paddle-mobile/Common/Types.swift
浏览文件 @ 75750786
...@@ -13,6 +13,7 @@ ...@@ -13,6 +13,7 @@
limitations under the License. */ limitations under the License. */
import Foundation import Foundation
import Accelerate
public protocol SummableMultipliable: Equatable { public protocol SummableMultipliable: Equatable {
static func +(lhs: Self, rhs: Self) -> Self static func +(lhs: Self, rhs: Self) -> Self
...@@ -78,6 +79,28 @@ extension Float32: PrecisionType { ...@@ -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 // N - 0 C - 1 H - 2 W - 3
struct DataLayout { struct DataLayout {
......
metal/paddle-mobile/paddle-mobile/Executor.swift
浏览文件 @ 75750786
...@@ -14,7 +14,9 @@ ...@@ -14,7 +14,9 @@
import Foundation import Foundation
let testTo = 12 let testTo = 54
let computePrecision: ComputePrecision = .Float32
public class ResultHolder<P: PrecisionType> { public class ResultHolder<P: PrecisionType> {
public let dim: [Int] public let dim: [Int]
...@@ -62,11 +64,10 @@ public class Executor<P: PrecisionType> { ...@@ -62,11 +64,10 @@ public class Executor<P: PrecisionType> {
queue = inQueue queue = inQueue
for block in inProgram.programDesc.blocks { for block in inProgram.programDesc.blocks {
//block.ops.count //block.ops.count
for i in 0..<testTo { for i in 0..<block.ops.count {
let op = block.ops[i] let op = block.ops[i]
do { do {
let op = try OpCreator<P>.shared.creat(device: inDevice, opDesc: op, scope: inProgram.scope) let op = try OpCreator<P>.shared.creat(device: inDevice, opDesc: op, scope: inProgram.scope)
// op.inferShape()
ops.append(op) ops.append(op)
} catch let error { } catch let error {
throw error throw error
...@@ -110,10 +111,11 @@ public class Executor<P: PrecisionType> { ...@@ -110,10 +111,11 @@ public class Executor<P: PrecisionType> {
} }
buffer.addCompletedHandler { (commandbuffer) in buffer.addCompletedHandler { (commandbuffer) in
// let inputArr = resInput.floatArray(res: { (p:P) -> P in // let inputArr = resInput.floatArray(res: { (p:P) -> P in
// return p // return p
// }) // })
// print(inputArr.strideArray())
// //
// writeToLibrary(fileName: "genet_input_hand", array: inputArr) // writeToLibrary(fileName: "genet_input_hand", array: inputArr)
// print("write to library done") // print("write to library done")
...@@ -129,8 +131,8 @@ public class Executor<P: PrecisionType> { ...@@ -129,8 +131,8 @@ public class Executor<P: PrecisionType> {
print(" 第 \(i) 个 op: ") print(" 第 \(i) 个 op: ")
op.delogOutput() op.delogOutput()
} }
return // return
let afterDate = Date.init() let afterDate = Date.init()
...@@ -145,7 +147,6 @@ public class Executor<P: PrecisionType> { ...@@ -145,7 +147,6 @@ public class Executor<P: PrecisionType> {
return p return p
}), inElapsedTime: afterDate.timeIntervalSince(beforeDate)) }), inElapsedTime: afterDate.timeIntervalSince(beforeDate))
} }
completionHandle(resultHolder) completionHandle(resultHolder)
} }
......
metal/paddle-mobile/paddle-mobile/Loader.swift
浏览文件 @ 75750786
...@@ -16,168 +16,170 @@ import Foundation ...@@ -16,168 +16,170 @@ import Foundation
import SwiftProtobuf import SwiftProtobuf
public class Loader<P: PrecisionType> { public class Loader<P: PrecisionType> {
class ParaLoader { class ParaLoader {
let file: UnsafeMutablePointer<FILE> let file: UnsafeMutablePointer<FILE>
let fileSize: Int let fileSize: Int
var nowIndex: Int var nowIndex: Int
init(paramPath: String) throws { init(paramPath: String) throws {
guard let tmpFile = fopen(paramPath, "rb") else { guard let tmpFile = fopen(paramPath, "rb") else {
throw PaddleMobileError.loaderError(message: "open param file error" + paramPath) throw PaddleMobileError.loaderError(message: "open param file error" + paramPath)
} }
file = tmpFile file = tmpFile
fseek(file, 0, SEEK_END) fseek(file, 0, SEEK_END)
fileSize = ftell(file) fileSize = ftell(file)
guard fileSize > 0 else { guard fileSize > 0 else {
throw PaddleMobileError.loaderError(message: "param file size is too small") throw PaddleMobileError.loaderError(message: "param file size is too small")
} }
rewind(file) rewind(file)
nowIndex = 0 nowIndex = 0
}
func read(tensor: Tensor<P>) throws {
guard nowIndex <= fileSize else {
throw PaddleMobileError.loaderError(message: "out of the file range")
}
func pointerReader<T>(type: T.Type) -> T {
let ptr = UnsafeMutablePointer<T>.allocate(capacity: MemoryLayout<T>.size)
fread(ptr, 1, MemoryLayout<T>.size, file)
nowIndex += MemoryLayout<T>.size
let pointee = ptr.pointee
ptr.deinitialize(count: MemoryLayout<UInt32>.size)
ptr.deallocate()
return pointee
}
let _ = pointerReader(type: UInt32.self)
let lodLevel = pointerReader(type: UInt64.self)
for _ in 0..<lodLevel {
let size = pointerReader(type: UInt64.self)
for _ in 0..<Int(size/UInt64(MemoryLayout<size_t>.size)){
_ = pointerReader(type: size_t.self)
}
}
let _ = pointerReader(type: UInt32.self)
let tensorDescSize = pointerReader(type: Int32.self)
fseek(file, Int(tensorDescSize), SEEK_CUR)
nowIndex += Int(tensorDescSize)
/*
这里没有根据 Data Type 去判断, 而是从外部泛型直接指定了精度
*/
//现在模型传入模型为 Float 类型, 这块应该根据模型来
// let tmpCapacity = MemoryLayout<Float>.size * tensor.numel()
// let tmpPointer = UnsafeMutablePointer<Float>.allocate(capacity: tmpCapacity);
let bytesRead = fread(tensor.data.pointer, 1, tensor.data.size, file)
guard bytesRead == tensor.data.size else {
throw PaddleMobileError.loaderError(message: "param read size error")
}
// TODO: use script to convert
// let bytesRead = fread(tmpPointer, 1, tmpCapacity, file)
// for i in 0..<tensor.numel() {
// tensor.data[i] = P.init(inFloat: tmpPointer[i])
// }
// tmpPointer.deinitialize(count: tmpCapacity)
// tmpPointer.deallocate()
nowIndex += bytesRead
}
deinit {
fclose(file)
}
} }
public init(){}
public func load(device: MTLDevice, modelPath: String, paraPath: String) throws -> Program{ func read(tensor: Tensor<P>) throws {
guard let modelData = try? Data.init(contentsOf: URL.init(fileURLWithPath: modelPath)) else { guard nowIndex <= fileSize else {
throw PaddleMobileError.loaderError(message: "load " + modelPath + " failed !") throw PaddleMobileError.loaderError(message: "out of the file range")
}
func pointerReader<T>(type: T.Type) -> T {
let ptr = UnsafeMutablePointer<T>.allocate(capacity: MemoryLayout<T>.size)
fread(ptr, 1, MemoryLayout<T>.size, file)
nowIndex += MemoryLayout<T>.size
let pointee = ptr.pointee
ptr.deinitialize(count: MemoryLayout<UInt32>.size)
ptr.deallocate()
return pointee
}
let _ = pointerReader(type: UInt32.self)
let lodLevel = pointerReader(type: UInt64.self)
for _ in 0..<lodLevel {
let size = pointerReader(type: UInt64.self)
for _ in 0..<Int(size/UInt64(MemoryLayout<size_t>.size)){
_ = pointerReader(type: size_t.self)
} }
}
do {
let protoProgram = try PaddleMobile_Framework_Proto_ProgramDesc.init( let _ = pointerReader(type: UInt32.self)
serializedData: modelData)
let tensorDescSize = pointerReader(type: Int32.self)
let originProgramDesc = ProgramDesc.init(protoProgram: protoProgram)
let programDesc = ProgramOptimize<P>.init().optimize(originProgramDesc: originProgramDesc) fseek(file, Int(tensorDescSize), SEEK_CUR)
print(programDesc) nowIndex += Int(tensorDescSize)
guard let paraLoader = try? ParaLoader.init(paramPath: paraPath) else { /*
throw PaddleMobileError.loaderError(message: "load para error") 这里没有根据 Data Type 去判断, 而是从外部泛型直接指定了精度
} */
guard programDesc.blocks.count > 0 else { //现在模型传入模型为 Float 类型, 这块应该根据模型来
throw PaddleMobileError.loaderError(message: "count of blocks must greater than 0") // let tmpCapacity = MemoryLayout<Float>.size * tensor.numel()
} // let tmpPointer = UnsafeMutablePointer<Float>.allocate(capacity: tmpCapacity);
let bytesRead = fread(tensor.data.pointer, 1, tensor.data.size, file)
// to get feed key and fetch key
let block = programDesc.blocks[0] guard bytesRead == tensor.data.size else {
guard let firstOp = block.ops.first, let lastOp = block.ops.last else { throw PaddleMobileError.loaderError(message: "param read size error")
throw PaddleMobileError.loaderError(message: "at least two operator") }
}
guard firstOp.type == gFeedType, lastOp.type == gFetchType else { // TODO: use script to convert
throw PaddleMobileError.loaderError(message: "the first op is not feed or the last op is not fetch") // let bytesRead = fread(tmpPointer, 1, tmpCapacity, file)
// for i in 0..<tensor.numel() {
// tensor.data[i] = P.init(inFloat: tmpPointer[i])
// }
// tmpPointer.deinitialize(count: tmpCapacity)
// tmpPointer.deallocate()
nowIndex += bytesRead
}
deinit {
fclose(file)
}
}
public init(){}
public func load(device: MTLDevice, modelPath: String, paraPath: String) throws -> Program{
guard let modelData = try? Data.init(contentsOf: URL.init(fileURLWithPath: modelPath)) else {
throw PaddleMobileError.loaderError(message: "load " + modelPath + " failed !")
}
do {
let protoProgram = try PaddleMobile_Framework_Proto_ProgramDesc.init(
serializedData: modelData)
let originProgramDesc = ProgramDesc.init(protoProgram: protoProgram)
let programDesc = ProgramOptimize<P>.init().optimize(originProgramDesc: originProgramDesc)
print(programDesc)
guard let paraLoader = try? ParaLoader.init(paramPath: paraPath) else {
throw PaddleMobileError.loaderError(message: "load para error")
}
guard programDesc.blocks.count > 0 else {
throw PaddleMobileError.loaderError(message: "count of blocks must greater than 0")
}
// to get feed key and fetch key
let block = programDesc.blocks[0]
guard let firstOp = block.ops.first, let lastOp = block.ops.last else {
throw PaddleMobileError.loaderError(message: "at least two operator")
}
guard firstOp.type == gFeedType, lastOp.type == gFetchType else {
throw PaddleMobileError.loaderError(message: "the first op is not feed or the last op is not fetch")
}
guard let inputKey = opInfos[gFeedType]?.inputs.first, let outKey = opInfos[gFetchType]?.outputs.first else {
throw PaddleMobileError.loaderError(message: "the feed input key or fetch output key not found")
}
guard let feedKey = firstOp.inputs[inputKey]?.first, let fetchKey = lastOp.outputs[outKey]?.first else {
throw PaddleMobileError.loaderError(message: "feed key or fetch key not found")
}
let scope = Scope.init(inFeedKey: feedKey, inFetchKey: fetchKey)
// to load memory
for block in programDesc.blocks {
for varDesc in block.vars {
if (varDesc.type == .LodTensor) {
guard let tensorDesc = varDesc.tensorDesc else {
throw PaddleMobileError.loaderError(message: "get tensor desc failed")
} }
guard let inputKey = opInfos[gFeedType]?.inputs.first, let outKey = opInfos[gFetchType]?.outputs.first else { if (varDesc.persistable
throw PaddleMobileError.loaderError(message: "the feed input key or fetch output key not found") && varDesc.type != .FeedMiniBatch
} && varDesc.type != .FetchList) {
guard let feedKey = firstOp.inputs[inputKey]?.first, let fetchKey = lastOp.outputs[outKey]?.first else { let dimArr = tensorDesc.dims
throw PaddleMobileError.loaderError(message: "feed key or fetch key not found")
guard dimArr.count > 0 else {
throw PaddleMobileError.loaderError(message: "tensor desc dim size error")
}
let dim = Dim.init(inDim: dimArr)
let tensor = Tensor<P>.init(inDim: dim, inLayout: tensorDesc.dataLayout)
do {
try paraLoader.read(tensor: tensor)
} catch let error {
throw error
}
tensor.convert(to: DataLayout.NHWC())
// tensor.initBuffer(device: device)
scope[varDesc.name] = tensor
} else {
let dim = Dim.init(inDim: tensorDesc.dims)
scope[varDesc.name] = Texture<P>.init(device: device, inDim: dim)
} }
} else {
let scope = Scope.init(inFeedKey: feedKey, inFetchKey: fetchKey) if varDesc.name == fetchKey {
scope[varDesc.name] = ResultHolder<P>.init(inDim: [], inResult: [], inElapsedTime: 0.0)
// to load memory } else if varDesc.name == feedKey {
for block in programDesc.blocks {
for varDesc in block.vars {
if (varDesc.type == .LodTensor) {
guard let tensorDesc = varDesc.tensorDesc else {
throw PaddleMobileError.loaderError(message: "get tensor desc failed")
}
if (varDesc.persistable
&& varDesc.type != .FeedMiniBatch
&& varDesc.type != .FetchList) {
let dimArr = tensorDesc.dims
guard dimArr.count > 0 else {
throw PaddleMobileError.loaderError(message: "tensor desc dim size error")
}
let dim = Dim.init(inDim: dimArr)
let tensor = Tensor<P>.init(inDim: dim, inLayout: tensorDesc.dataLayout)
do {
try paraLoader.read(tensor: tensor)
} catch let error {
throw error
}
tensor.convert(to: DataLayout.NHWC())
// tensor.initBuffer(device: device)
scope[varDesc.name] = tensor
} else {
let dim = Dim.init(inDim: tensorDesc.dims)
scope[varDesc.name] = Texture<P>.init(device: device, inDim: dim)
}
} else {
if varDesc.name == fetchKey {
scope[varDesc.name] = ResultHolder<P>.init(inDim: [], inResult: [], inElapsedTime: 0.0)
} else if varDesc.name == feedKey {
}
}
}
} }
}
let program = Program.init(inProgramDesc: programDesc, inParamPath: paraPath, inScope: scope)
return program
} catch _ {
throw PaddleMobileError.loaderError(message: "protobuf decoder error")
} }
}
let program = Program.init(inProgramDesc: programDesc, inParamPath: paraPath, inScope: scope)
return program
} catch _ {
throw PaddleMobileError.loaderError(message: "protobuf decoder error")
} }
}
} }
metal/paddle-mobile/paddle-mobile/Operators/BoxcoderOp.swift
浏览文件 @ 75750786
...@@ -58,28 +58,31 @@ class BoxcoderOp<P: PrecisionType>: Operator<BoxcoderKernel<P>, BoxcoderParam<P> ...@@ -58,28 +58,31 @@ class BoxcoderOp<P: PrecisionType>: Operator<BoxcoderKernel<P>, BoxcoderParam<P>
} }
func delogOutput() { func delogOutput() {
print(" \(type) output: ") print(" \(type) output: ")
let priorBoxOriginDim = para.priorBox.originDim // let priorBoxOriginDim = para.priorBox.originDim
let priorBoxArray = para.priorBox.metalTexture.realNHWC(dim: (n: priorBoxOriginDim[0], h: priorBoxOriginDim[1], w: priorBoxOriginDim[2], c: priorBoxOriginDim[3])) // let priorBoxArray: [Float32] = para.priorBox.metalTexture.realNHWC(dim: (n: priorBoxOriginDim[0], h: priorBoxOriginDim[1], w: priorBoxOriginDim[2], c: priorBoxOriginDim[3]))
print(" prior box ") // print(" prior box ")
print(priorBoxArray.strideArray()) // print(priorBoxArray.strideArray())
//
let priorBoxVarOriginDim = para.priorBoxVar.originDim // let priorBoxVarOriginDim = para.priorBoxVar.originDim
let priorBoxVarArray = para.priorBoxVar.metalTexture.realNHWC(dim: (n: priorBoxVarOriginDim[0], h: priorBoxVarOriginDim[1], w: priorBoxVarOriginDim[2], c: priorBoxVarOriginDim[3])) // 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(" prior box var ")
print(priorBoxVarArray.strideArray()) // 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 targetBoxOriginDim = para.targetBox.originDim let targetBoxOriginDim = para.targetBox.originDim
let targetBoxArray = para.targetBox.metalTexture.realNHWC(dim: (n: targetBoxOriginDim[0], h: targetBoxOriginDim[1], w: targetBoxOriginDim[2], c: targetBoxOriginDim[3])) let targetBoxArray = para.targetBox.metalTexture.realNHWC(dim: (n: targetBoxOriginDim[0], h: targetBoxOriginDim[1], w: targetBoxOriginDim[2], c: targetBoxOriginDim[3]), texturePrecision: computePrecision)
print(" target box ") print(" target box ")
print(targetBoxArray.strideArray()) print(targetBoxArray.strideArray())
let originDim = para.output.originDim let originDim = para.output.originDim
let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]), texturePrecision: computePrecision)
let outputArray = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3])) print(" output ")
print(outputArray.strideArray()) print(outputArray.strideArray())
} }
} }
......
metal/paddle-mobile/paddle-mobile/Operators/ConcatOp.swift
浏览文件 @ 75750786
...@@ -66,10 +66,16 @@ class ConcatOp<P: PrecisionType>: Operator<ConcatKernel<P>, ConcatParam<P>>, Run ...@@ -66,10 +66,16 @@ class ConcatOp<P: PrecisionType>: Operator<ConcatKernel<P>, ConcatParam<P>>, Run
func delogOutput() { func delogOutput() {
print(" \(type) output: ") print(" \(type) output: ")
let originDim = para.output.originDim let originDim = para.output.originDim
let outputArray = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3])) if para.output.transpose == [0, 1, 2, 3] {
print(outputArray.strideArray()) let outputArray: [Float32] = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]), texturePrecision: computePrecision)
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(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 @ 75750786
...@@ -125,13 +125,6 @@ class ConvAddBatchNormReluOp<P: PrecisionType>: Operator<ConvAddBatchNormReluKer ...@@ -125,13 +125,6 @@ class ConvAddBatchNormReluOp<P: PrecisionType>: Operator<ConvAddBatchNormReluKer
// let _: P? = para.newBiase?.logDesc(header: "new biase: ", stridable: false) // let _: P? = para.newBiase?.logDesc(header: "new biase: ", stridable: false)
// let _: P? = para.newScale?.logDesc(header: "new scale: ", 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) // 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 @ 75750786
...@@ -46,9 +46,6 @@ class ConvAddParam<P: PrecisionType>: OpParam { ...@@ -46,9 +46,6 @@ class ConvAddParam<P: PrecisionType>: OpParam {
class ConvAddOp<P: PrecisionType>: Operator<ConvAddKernel<P>, ConvAddParam<P>>, Runable, Creator, InferShaperable, Fusion{ class ConvAddOp<P: PrecisionType>: Operator<ConvAddKernel<P>, ConvAddParam<P>>, Runable, Creator, InferShaperable, Fusion{
typealias OpType = ConvAddOp<P> typealias OpType = ConvAddOp<P>
static func fusionNode() -> Node { static func fusionNode() -> Node {
let beginNode = Node.init(inType: gConvType) let beginNode = Node.init(inType: gConvType)
_ = beginNode _ = beginNode
...@@ -64,7 +61,6 @@ class ConvAddOp<P: PrecisionType>: Operator<ConvAddKernel<P>, ConvAddParam<P>>, ...@@ -64,7 +61,6 @@ class ConvAddOp<P: PrecisionType>: Operator<ConvAddKernel<P>, ConvAddParam<P>>,
return gConvAddType return gConvAddType
} }
func inferShape() { func inferShape() {
let inDims = para.input.dim let inDims = para.input.dim
...@@ -97,7 +93,12 @@ class ConvAddOp<P: PrecisionType>: Operator<ConvAddKernel<P>, ConvAddParam<P>>, ...@@ -97,7 +93,12 @@ class ConvAddOp<P: PrecisionType>: Operator<ConvAddKernel<P>, ConvAddParam<P>>,
} }
func delogOutput() { func delogOutput() {
print("stride: ")
print(para.stride)
print("dilations: ")
print(para.dilations)
print(" \(type) output: ") 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 @ 75750786
...@@ -110,7 +110,7 @@ class ConvBNReluOp<P: PrecisionType>: Operator<ConvBNReluKernel<P>, ConvBNReluPa ...@@ -110,7 +110,7 @@ class ConvBNReluOp<P: PrecisionType>: Operator<ConvBNReluKernel<P>, ConvBNReluPa
func delogOutput() { func delogOutput() {
print(" \(type) output: ") 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 @ 75750786
...@@ -43,8 +43,15 @@ class ConvTransposeOp<P: PrecisionType>: Operator<ConvTransposeKernel<P>, ConvTr ...@@ -43,8 +43,15 @@ class ConvTransposeOp<P: PrecisionType>: Operator<ConvTransposeKernel<P>, ConvTr
} }
func delogOutput() { func delogOutput() {
print("conv transpose delog") print(" \(type) output: ")
let _: P? = para.input.metalTexture.logDesc(header: "conv transpose input: ", stridable: true) let originDim = para.output.originDim
let _: P? = para.output.metalTexture.logDesc(header: "conv transpose output: ", stridable: true) 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: 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 @ 75750786
...@@ -58,6 +58,6 @@ class DepthConvOp<P: PrecisionType>: Operator<ConvKernel<P>, ConvParam<P>>, Runa ...@@ -58,6 +58,6 @@ class DepthConvOp<P: PrecisionType>: Operator<ConvKernel<P>, ConvParam<P>>, Runa
func delogOutput() { func delogOutput() {
print(" \(type) output: ") 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 @ 75750786
...@@ -65,6 +65,6 @@ class DwConvBNReluOp<P: PrecisionType>: Operator<ConvBNReluKernel<P>, ConvBNRelu ...@@ -65,6 +65,6 @@ class DwConvBNReluOp<P: PrecisionType>: Operator<ConvBNReluKernel<P>, ConvBNRelu
func delogOutput() { func delogOutput() {
print(" \(type) output: ") 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 @ 75750786
...@@ -56,18 +56,6 @@ class ElementwiseAddOp<P: PrecisionType>: Operator<ElementwiseAddKernel<P>, Elem ...@@ -56,18 +56,6 @@ class ElementwiseAddOp<P: PrecisionType>: Operator<ElementwiseAddKernel<P>, Elem
// para.output.dim = para.input.dim // para.output.dim = para.input.dim
} }
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) 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())
}
func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws { func runImpl(device: MTLDevice, buffer: MTLCommandBuffer) throws {
do { do {
try kernel.compute(commandBuffer: buffer, param: para) try kernel.compute(commandBuffer: buffer, param: para)
...@@ -75,6 +63,24 @@ class ElementwiseAddOp<P: PrecisionType>: Operator<ElementwiseAddKernel<P>, Elem ...@@ -75,6 +63,24 @@ class ElementwiseAddOp<P: PrecisionType>: Operator<ElementwiseAddKernel<P>, Elem
throw 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) 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: 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/FeedOp.swift
浏览文件 @ 75750786
...@@ -60,9 +60,8 @@ class FeedOp<P: PrecisionType>: Operator<Texture2DTo2DArrayKernel<P>, FeedParam< ...@@ -60,9 +60,8 @@ class FeedOp<P: PrecisionType>: Operator<Texture2DTo2DArrayKernel<P>, FeedParam<
} }
func delogOutput() { func delogOutput() {
// para.input.mtlTexture.logDesc() print(" \(type) output: ")
// let _: P? = para.input.mtlTexture.logDesc(header: "feed input: ", stridable: true) 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())
// let _: P? = para.output.metalTexture.logDesc(header: "feed output: ", stridable: false)
} }
} }
metal/paddle-mobile/paddle-mobile/Operators/Kernels/Base/Kernel.swift
浏览文件 @ 75750786
...@@ -19,68 +19,76 @@ public protocol TestParam { ...@@ -19,68 +19,76 @@ public protocol TestParam {
} }
public protocol Testable { public protocol Testable {
associatedtype TestParamType: TestParam associatedtype TestParamType: TestParam
func test(commandBuffer: MTLCommandBuffer, param: TestParamType) func test(commandBuffer: MTLCommandBuffer, param: TestParamType)
init(device: MTLDevice, testParam: TestParamType) init(device: MTLDevice, testParam: TestParamType)
} }
protocol Computable { protocol Computable {
associatedtype ParamType: OpParam associatedtype ParamType: OpParam
func compute(commandBuffer: MTLCommandBuffer, param: ParamType) throws func compute(commandBuffer: MTLCommandBuffer, param: ParamType) throws
init(device: MTLDevice, param: ParamType) init(device: MTLDevice, param: ParamType)
} }
protocol KernelProtocol { protocol KernelProtocol {
var pipline: MTLComputePipelineState { get set } var pipline: MTLComputePipelineState { get set }
var functionName: String { get set } var functionName: String { get set }
} }
open class Kernel { open class Kernel {
let pipline: MTLComputePipelineState let pipline: MTLComputePipelineState
let functionName: String let functionName: String
public init(device: MTLDevice, inFunctionName: String, usePaddleMobileLib: Bool = true) { public init(device: MTLDevice, inFunctionName: String, usePaddleMobileLib: Bool = true) {
pipline = device.pipeLine(funcName: inFunctionName, inPaddleMobileLib: usePaddleMobileLib) pipline = device.pipeLine(funcName: inFunctionName, inPaddleMobileLib: usePaddleMobileLib)
functionName = inFunctionName functionName = inFunctionName
} }
} }
open class CusomKernel: Kernel { open class CusomKernel: Kernel {
public struct Shape { public struct Shape {
public let width: Int public let width: Int
public let height: Int public let height: Int
public let channel: Int public let channel: Int
public init(inWidth: Int, inHeight: Int, inChannel: Int){ public init(inWidth: Int, inHeight: Int, inChannel: Int){
width = inWidth width = inWidth
height = inHeight height = inHeight
channel = inChannel 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 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 { if computePrecision == .Float16 {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else { textureDesc.pixelFormat = .rgba16Float
throw PaddleMobileError.predictError(message: " encode is nil") } else if computePrecision == .Float32 {
} textureDesc.pixelFormat = .rgba32Float
encoder.setTexture(inputTexuture, index: 0) } else {
encoder.setTexture(outputTexture, index: 1) fatalError()
encoder.dispatch(computePipline: pipline, outTexture: outputTexture)
encoder.endEncoding()
} }
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 @ 75750786
...@@ -18,22 +18,29 @@ struct BoxcoderMetalParam { ...@@ -18,22 +18,29 @@ struct BoxcoderMetalParam {
} }
class BoxcoderKernel<P: PrecisionType>: Kernel, Computable{ class BoxcoderKernel<P: PrecisionType>: Kernel, Computable{
func compute(commandBuffer: MTLCommandBuffer, param: BoxcoderParam<P>) throws { func compute(commandBuffer: MTLCommandBuffer, param: BoxcoderParam<P>) throws {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else { guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil") 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()
} }
encoder.setTexture(param.priorBox.metalTexture, index: 0)
required init(device: MTLDevice, param: BoxcoderParam<P>) { encoder.setTexture(param.priorBoxVar.metalTexture, index: 1)
param.output.initTexture(device: device) encoder.setTexture(param.targetBox.metalTexture, index: 2)
super.init(device: device, inFunctionName: "boxcoder") 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 @ 75750786
...@@ -121,8 +121,14 @@ class ConcatKernel<P: PrecisionType>: Kernel, Computable{ ...@@ -121,8 +121,14 @@ class ConcatKernel<P: PrecisionType>: Kernel, Computable{
} }
required init(device: MTLDevice, param: ConcatParam<P>) { required init(device: MTLDevice, param: ConcatParam<P>) {
param.output.initTexture(device: device, inTranspose: param.transpose) param.output.initTexture(device: device, inTranspose: param.transpose, computePrecision: computePrecision)
super.init(device: device, inFunctionName: "concat") 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) { required init(device: MTLDevice, testParam: ConcatTestParam) {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvAddBatchNormReluKernel.swift
浏览文件 @ 75750786
...@@ -15,127 +15,155 @@ ...@@ -15,127 +15,155 @@
import Foundation import Foundation
struct ConvAddBatchNormReluTestParam: TestParam { struct ConvAddBatchNormReluTestParam: TestParam {
let inputTexture: MTLTexture let inputTexture: MTLTexture
let outputTexture: MTLTexture let outputTexture: MTLTexture
var metalParam: MetalConvParam var metalParam: MetalConvParam
let filterBuffer: MTLBuffer let filterBuffer: MTLBuffer
let biaseBuffer: MTLBuffer let biaseBuffer: MTLBuffer
let newScaleBuffer: MTLBuffer let newScaleBuffer: MTLBuffer
let newBiaseBuffer: MTLBuffer let newBiaseBuffer: MTLBuffer
let filterSize: (width: Int, height: Int, channel: Int) 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)) { init(inInputTexture: MTLTexture, inOutputTexture: MTLTexture, inMetalParam: MetalConvParam, inFilterBuffer: MTLBuffer, inBiaseBuffer: MTLBuffer, inNewScaleBuffer: MTLBuffer, inNewBiaseBuffer: MTLBuffer, inFilterSize: (width: Int, height: Int, channel: Int)) {
inputTexture = inInputTexture inputTexture = inInputTexture
outputTexture = inOutputTexture outputTexture = inOutputTexture
metalParam = inMetalParam metalParam = inMetalParam
filterBuffer = inFilterBuffer filterBuffer = inFilterBuffer
biaseBuffer = inBiaseBuffer biaseBuffer = inBiaseBuffer
newScaleBuffer = inNewScaleBuffer newScaleBuffer = inNewScaleBuffer
newBiaseBuffer = inNewBiaseBuffer newBiaseBuffer = inNewBiaseBuffer
filterSize = inFilterSize filterSize = inFilterSize
} }
} }
class ConvAddBatchNormReluKernel<P: PrecisionType>: Kernel, Computable, Testable { class ConvAddBatchNormReluKernel<P: PrecisionType>: Kernel, Computable, Testable {
required init(device: MTLDevice, testParam: ConvAddBatchNormReluTestParam) { required init(device: MTLDevice, testParam: ConvAddBatchNormReluTestParam) {
if testParam.filterSize.width == 1 && testParam.filterSize.height == 1 { if testParam.filterSize.width == 1 && testParam.filterSize.height == 1 {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_1x1") super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_1x1")
} else if testParam.filterSize.channel == 1 { } else if testParam.filterSize.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_add_batch_norm_relu_3x3") super.init(device: device, inFunctionName: "depthwise_conv_add_batch_norm_relu_3x3")
} else { } else {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_3x3") super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_3x3")
}
} }
}
var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvAddBatchNormReluParam<P>) {
var metalParam: MetalConvParam! param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: computePrecision)
required init(device: MTLDevice, param: ConvAddBatchNormReluParam<P>) { if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_1x1")
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1]) } else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_add_batch_norm_relu_3x3")
if param.filter.width == 1 && param.filter.height == 1 { } else {
super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_1x1") super.init(device: device, inFunctionName: "conv_add_batch_norm_relu_3x3")
} 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]))
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()
} }
func compute(commandBuffer: MTLCommandBuffer, param: ConvAddBatchNormReluParam<P>) throws { param.filter.initBuffer(device: device, precision: computePrecision)
guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
throw PaddleMobileError.predictError(message: " encode is nil") 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 {
newBiaseBuffer = device.makeBuffer(length: param.bias.buffer.length / 2)!
encoder.setTexture(param.input.metalTexture, index: 0) newScaleBuffer = device.makeBuffer(length: param.bias.buffer.length / 2)!
encoder.setTexture(param.output.metalTexture, index: 1)
encoder.setBytes(&metalParam, length: MemoryLayout<MetalConvParam>.size, index: 0) float32ToFloat16(input: newBiase as! UnsafeMutablePointer<Float32>, output: newBiaseBuffer.contents(), count: param.bias.buffer.length / MemoryLayout<P>.size)
encoder.setBuffer(param.filter.buffer, offset: 0, index: 1)
encoder.setBuffer(param.y.buffer, offset: 0, index: 2) float32ToFloat16(input: newScale as! UnsafeMutablePointer<Float32>, output: newScaleBuffer.contents(), count: param.scale.buffer.length / MemoryLayout<P>.size)
encoder.setBuffer(param.newScale!, offset: 0, index: 3) } else {
encoder.setBuffer(param.newBiase!, offset: 0, index: 4) fatalError(" unsupport ")
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding()
} }
public func test(commandBuffer: MTLCommandBuffer, param: ConvAddBatchNormReluTestParam) { param.newBiase = newBiaseBuffer
guard let encoder = commandBuffer.makeComputeCommandEncoder() else { param.newScale = newScaleBuffer
fatalError()
} newScale.deinitialize(count: param.scale.buffer.length)
newScale.deallocate()
encoder.setTexture(param.inputTexture, index: 0)
encoder.setTexture(param.outputTexture, index: 1) newBiase.deinitialize(count: param.bias.buffer.length)
var inMetalParam = param.metalParam newBiase.deallocate()
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) func compute(commandBuffer: MTLCommandBuffer, param: ConvAddBatchNormReluParam<P>) throws {
encoder.setBuffer(param.newScaleBuffer, offset: 0, index: 3) guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
encoder.setBuffer(param.newBiaseBuffer, offset: 0, index: 4) throw PaddleMobileError.predictError(message: " encode is nil")
encoder.dispatch(computePipline: pipline, outTexture: param.outputTexture)
encoder.endEncoding()
} }
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 @ 75750786
...@@ -17,27 +17,45 @@ import Foundation ...@@ -17,27 +17,45 @@ import Foundation
class ConvAddKernel<P: PrecisionType>: Kernel, Computable { class ConvAddKernel<P: PrecisionType>: Kernel, Computable {
var metalParam: MetalConvParam! var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvAddParam<P>) { required init(device: MTLDevice, param: ConvAddParam<P>) {
if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_add_1x1") if computePrecision == .Float16 {
} else if param.filter.channel == 1 { if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_add_3x3") 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 { } 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 = param.filter.width/2 - Int(param.paddings[0]) let offsetX = (Int(param.dilations[0]) * (param.filter.width - 1) + 1)/2 - Int(param.paddings[0])
let offsetY = param.filter.height/2 - Int(param.paddings[1])
param.filter.initBuffer(device: device, precision: Tensor.BufferPrecision.Float32) let offsetY = (Int(param.dilations[1]) * (param.filter.height - 1) + 1)/2 - Int(param.paddings[1])
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 x: \(offsetX)")
print("offset y: \(offsetY)") print("offset y: \(offsetY)")
let offsetZ = 0.0 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])) let inMetalParam = 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]))
print("metal param: ")
print(inMetalParam)
metalParam = inMetalParam
} }
func compute(commandBuffer: MTLCommandBuffer, param: ConvAddParam<P>) throws { func compute(commandBuffer: MTLCommandBuffer, param: ConvAddParam<P>) throws {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvBNReluKernel.swift
浏览文件 @ 75750786
...@@ -51,21 +51,33 @@ class ConvBNReluKernel<P: PrecisionType>: Kernel, Computable, Testable { ...@@ -51,21 +51,33 @@ class ConvBNReluKernel<P: PrecisionType>: Kernel, Computable, Testable {
var metalParam: MetalConvParam! var metalParam: MetalConvParam!
required init(device: MTLDevice, param: ConvBNReluParam<P>) { required init(device: MTLDevice, param: ConvBNReluParam<P>) {
if computePrecision == .Float32 {
if param.filter.width == 1 && param.filter.height == 1 { if param.filter.width == 1 && param.filter.height == 1 {
super.init(device: device, inFunctionName: "conv_batch_norm_relu_1x1") super.init(device: device, inFunctionName: "conv_batch_norm_relu_1x1")
} else if param.filter.channel == 1 { } else if param.filter.channel == 1 {
super.init(device: device, inFunctionName: "depthwise_conv_batch_norm_relu_3x3") 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 { } 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.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: computePrecision)
param.mean.initBuffer(device: device) param.filter.initBuffer(device: device, precision: computePrecision)
param.scale.initBuffer(device: device)
param.bias.initBuffer(device: device) 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 offsetX = param.filter.width/2 - Int(param.paddings[0])
let offsetY = param.filter.height/2 - Int(param.paddings[1]) let offsetY = param.filter.height/2 - Int(param.paddings[1])
...@@ -81,7 +93,7 @@ class ConvBNReluKernel<P: PrecisionType>: Kernel, Computable, Testable { ...@@ -81,7 +93,7 @@ class ConvBNReluKernel<P: PrecisionType>: Kernel, Computable, Testable {
let offsetZ = 0.0 let offsetZ = 0.0
print(" fuck ") print(" fuck ")
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])) 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] = [] var invs: [P] = []
let varianceContents = param.variance.buffer.contents().assumingMemoryBound(to: P.self) let varianceContents = param.variance.buffer.contents().assumingMemoryBound(to: P.self)
...@@ -102,8 +114,26 @@ class ConvBNReluKernel<P: PrecisionType>: Kernel, Computable, Testable { ...@@ -102,8 +114,26 @@ class ConvBNReluKernel<P: PrecisionType>: Kernel, Computable, Testable {
newBiase[i] = biaseContents[i] - meanContents[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) var newBiaseBuffer: MTLBuffer
param.newScale = device.makeBuffer(bytes: newScale, length: param.scale.buffer.length) var newScaleBuffer: MTLBuffer
if computePrecision == .Float32 {
newBiaseBuffer = device.makeBuffer(bytes: newBiase, length: param.bias.buffer.length)!
newScaleBuffer = device.makeBuffer(bytes: newScale, length: param.scale.buffer.length)!
} else if computePrecision == .Float16 {
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.deinitialize(count: param.scale.buffer.length)
newScale.deallocate() newScale.deallocate()
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvKernel.swift
浏览文件 @ 75750786
...@@ -21,6 +21,8 @@ public struct MetalConvParam { ...@@ -21,6 +21,8 @@ public struct MetalConvParam {
let strideX: UInt16 let strideX: UInt16
let strideY: UInt16 let strideY: UInt16
let paddedZ: UInt16 let paddedZ: UInt16
let dilationX: UInt16
let dilationY: UInt16
} }
class ConvKernel<P: PrecisionType>: Kernel, Computable { class ConvKernel<P: PrecisionType>: Kernel, Computable {
...@@ -37,9 +39,9 @@ class ConvKernel<P: PrecisionType>: Kernel, Computable { ...@@ -37,9 +39,9 @@ class ConvKernel<P: PrecisionType>: Kernel, Computable {
let offsetX = param.filter.dim[2]/2 - Int(param.paddings[0]) let offsetX = param.filter.dim[2]/2 - Int(param.paddings[0])
let offsetY = param.filter.dim[1]/2 - Int(param.paddings[1]) let offsetY = param.filter.dim[1]/2 - Int(param.paddings[1])
let offsetZ = 0.0 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])) 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]))
} }
func compute(commandBuffer: MTLCommandBuffer, param: ConvParam<P>) throws { func compute(commandBuffer: MTLCommandBuffer, param: ConvParam<P>) throws {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ConvTransposeKernel.swift
浏览文件 @ 75750786
...@@ -45,6 +45,7 @@ class ConvTransposeKernel<P: PrecisionType>: Kernel, Computable{ ...@@ -45,6 +45,7 @@ class ConvTransposeKernel<P: PrecisionType>: Kernel, Computable{
metalParam = MetalConvTransposeParam.init(kernelW: kernelWidth, kernelH: kernelHeight, strideX: strideX, strideY: strideY, paddingX: paddingX, paddingY: paddingY, dilationX: dilationX, dilationY: dilationY) metalParam = MetalConvTransposeParam.init(kernelW: kernelWidth, kernelH: kernelHeight, strideX: strideX, strideY: strideY, paddingX: paddingX, paddingY: paddingY, dilationX: dilationX, dilationY: dilationY)
param.output.initTexture(device: device, inTranspose: param.input.transpose) param.output.initTexture(device: device, inTranspose: param.input.transpose)
param.filter.initBuffer(device: device)
} }
func compute(commandBuffer: MTLCommandBuffer, param: ConvTransposeParam<P>) throws { func compute(commandBuffer: MTLCommandBuffer, param: ConvTransposeParam<P>) throws {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ElementwiseAddKernel.swift
浏览文件 @ 75750786
...@@ -55,7 +55,7 @@ class ElementwiseAddKernel<P: PrecisionType>: Kernel, Computable { ...@@ -55,7 +55,7 @@ class ElementwiseAddKernel<P: PrecisionType>: Kernel, Computable {
} }
emp.yoff = 4 - Int32(param.inputY.tensorDim.cout()) emp.yoff = 4 - Int32(param.inputY.tensorDim.cout())
if (param.inputX.dim == param.inputY.dim) && (param.inputX.transpose == param.inputY.transpose) { if (param.inputX.dim == param.inputY.dim) && (param.inputX.transpose == param.inputY.transpose) {
print("===> elementwise_add fast!!!") // print("===> elementwise_add fast!!!")
emp.fast = 1 emp.fast = 1
} }
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PoolKernel.swift
浏览文件 @ 75750786
...@@ -28,7 +28,7 @@ class PoolKernel<P: PrecisionType>: Kernel, Computable{ ...@@ -28,7 +28,7 @@ class PoolKernel<P: PrecisionType>: Kernel, Computable{
required init(device: MTLDevice, param: PoolParam<P>) { required init(device: MTLDevice, param: PoolParam<P>) {
super.init(device: device, inFunctionName: "pool") 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 { func compute(commandBuffer: MTLCommandBuffer, param: PoolParam<P>) throws {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PreluKernel.swift
浏览文件 @ 75750786
...@@ -17,8 +17,8 @@ class PreluKernel<P: PrecisionType>: Kernel, Computable{ ...@@ -17,8 +17,8 @@ class PreluKernel<P: PrecisionType>: Kernel, Computable{
} else { } else {
super.init(device: device, inFunctionName: "prelu_other") super.init(device: device, inFunctionName: "prelu_other")
} }
param.alpha.initBuffer(device: device) param.alpha.initBuffer(device: device, precision: computePrecision)
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: PreluParam<P>) throws { func compute(commandBuffer: MTLCommandBuffer, param: PreluParam<P>) throws {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/PriorBoxKernel.swift
浏览文件 @ 75750786
...@@ -33,11 +33,16 @@ class PriorBoxKernel<P: PrecisionType>: Kernel, Computable{ ...@@ -33,11 +33,16 @@ class PriorBoxKernel<P: PrecisionType>: Kernel, Computable{
var metalParam: PriorBoxMetalParam! var metalParam: PriorBoxMetalParam!
required init(device: MTLDevice, param: PriorBoxParam<P>) { required init(device: MTLDevice, param: PriorBoxParam<P>) {
super.init(device: device, inFunctionName: "prior_box") if computePrecision == .Float32 {
param.output.initTexture(device: device, inTranspose: [2, 0, 1, 3]) super.init(device: device, inFunctionName: "prior_box")
} else if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "prior_box_half")
} else {
fatalError()
}
param.output.initTexture(device: device, inTranspose: [2, 0, 1, 3], computePrecision: computePrecision)
param.outputVariances.initTexture(device: device, inTranspose: [2, 0, 1, 3]) param.outputVariances.initTexture(device: device, inTranspose: [2, 0, 1, 3], computePrecision: computePrecision)
let n = 1 let n = 1
let h = param.output.dim[1] let h = param.output.dim[1]
...@@ -79,7 +84,18 @@ class PriorBoxKernel<P: PrecisionType>: Kernel, Computable{ ...@@ -79,7 +84,18 @@ class PriorBoxKernel<P: PrecisionType>: Kernel, Computable{
} }
} }
param.newAspectRatios = outputAspectRatior if computePrecision == .Float16 {
let buffer = device.makeBuffer(length: outputAspectRatior.count * MemoryLayout<Float16>.size)
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 aspectRatiosSize = uint(outputAspectRatior.count)
let maxSizeSize: uint = uint(param.maxSizes.count) let maxSizeSize: uint = uint(param.maxSizes.count)
...@@ -102,12 +118,13 @@ class PriorBoxKernel<P: PrecisionType>: Kernel, Computable{ ...@@ -102,12 +118,13 @@ class PriorBoxKernel<P: PrecisionType>: Kernel, Computable{
encoder.setTexture(param.input.metalTexture, index: 0) encoder.setTexture(param.input.metalTexture, index: 0)
encoder.setTexture(param.output.metalTexture, index: 1) encoder.setTexture(param.output.metalTexture, index: 1)
encoder.setTexture(param.outputVariances.metalTexture, index: 2) 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.setBytes(param.variances, length: MemoryLayout<Float32>.size * param.variances.count, index: 2)
encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture) encoder.dispatch(computePipline: pipline, outTexture: param.output.metalTexture)
encoder.endEncoding() encoder.endEncoding()
} }
} }
metal/paddle-mobile/paddle-mobile/Operators/Kernels/ReshapeKernel.swift
浏览文件 @ 75750786
...@@ -15,58 +15,65 @@ ...@@ -15,58 +15,65 @@
import Foundation import Foundation
struct ReshapeMetalParam { struct ReshapeMetalParam {
var idim: (Int32, Int32, Int32, Int32) var idim: (Int32, Int32, Int32, Int32)
var itrans: (Int32, Int32, Int32, Int32) var itrans: (Int32, Int32, Int32, Int32)
var odim: (Int32, Int32, Int32, Int32) var odim: (Int32, Int32, Int32, Int32)
var otrans: (Int32, Int32, Int32, Int32) var otrans: (Int32, Int32, Int32, Int32)
} }
struct ReshapeTestParam: TestParam { struct ReshapeTestParam: TestParam {
let inputTexture: MTLTexture let inputTexture: MTLTexture
let outputTexture: MTLTexture let outputTexture: MTLTexture
let param: ReshapeMetalParam let param: ReshapeMetalParam
} }
class ReshapeKernel<P: PrecisionType>: Kernel, Computable{ class ReshapeKernel<P: PrecisionType>: Kernel, Computable{
required init(device: MTLDevice, param: ReshapeParam<P>) { required init(device: MTLDevice, param: ReshapeParam<P>) {
param.output.initTexture(device: device) param.output.initTexture(device: device, computePrecision: computePrecision)
super.init(device: device, inFunctionName: "reshape") 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") 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 { func compute(commandBuffer: MTLCommandBuffer, param: ReshapeParam<P>) throws {
throw PaddleMobileError.predictError(message: " encoder is nil") 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()
} }
func test(commandBuffer: MTLCommandBuffer, testParam: ReshapeTestParam) { encoder.setTexture(param.input.metalTexture, index: 0)
guard let encoder = commandBuffer.makeComputeCommandEncoder() else { encoder.setTexture(param.output.metalTexture, index: 1)
fatalError() let id: [Int32] = (0..<4).map { Int32(param.input.dim[$0]) }
} let it: [Int32] = param.input.transpose.map { Int32($0) }
encoder.setTexture(testParam.inputTexture, index: 0) let od: [Int32] = (0..<4).map { Int32(param.output.dim[$0]) }
encoder.setTexture(testParam.outputTexture, index: 1) let ot: [Int32] = param.output.transpose.map { Int32($0) }
var pm: ReshapeMetalParam = testParam.param var rmp = ReshapeMetalParam.init(
encoder.setBytes(&pm, length: MemoryLayout<ReshapeMetalParam>.size, index: 0) idim: (id[0], id[1], id[2], id[3]),
encoder.dispatch(computePipline: pipline, outTexture: testParam.outputTexture) itrans: (it[0], it[1], it[2], it[3]),
encoder.endEncoding() 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 @ 75750786
...@@ -38,7 +38,13 @@ class SoftmaxKernel<P: PrecisionType>: Kernel, Computable{ ...@@ -38,7 +38,13 @@ class SoftmaxKernel<P: PrecisionType>: Kernel, Computable{
} }
required init(device: MTLDevice, param: SoftmaxParam<P>) { required init(device: MTLDevice, param: SoftmaxParam<P>) {
param.output.initTexture(device: device) param.output.initTexture(device: device, computePrecision: computePrecision)
super.init(device: device, inFunctionName: "softmax") 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/Texture2DTo2DArrayKernel.swift
浏览文件 @ 75750786
...@@ -32,7 +32,14 @@ class Texture2DTo2DArrayKernel<P: PrecisionType>: Kernel, Computable{ ...@@ -32,7 +32,14 @@ class Texture2DTo2DArrayKernel<P: PrecisionType>: Kernel, Computable{
} }
required init(device: MTLDevice, param: FeedParam<P>) { required init(device: MTLDevice, param: FeedParam<P>) {
param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1]) param.output.initTexture(device: device, inTranspose: [0, 2, 3, 1], computePrecision: computePrecision)
super.init(device: device, inFunctionName: "texture2d_to_2d_array") if computePrecision == .Float16 {
super.init(device: device, inFunctionName: "texture2d_to_2d_array_half")
} else if computePrecision == .Float32 {
super.init(device: device, inFunctionName: "texture2d_to_2d_array")
} else {
fatalError()
}
} }
} }
metal/paddle-mobile/paddle-mobile/Operators/Kernels/TransposeKernel.swift
浏览文件 @ 75750786
...@@ -41,33 +41,27 @@ struct TransposeTestParam: TestParam { ...@@ -41,33 +41,27 @@ struct TransposeTestParam: TestParam {
} }
class TransposeKernel<P: PrecisionType>: Kernel, Computable, Testable { 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>) { required init(device: MTLDevice, param: TransposeParam<P>) {
param.output.initTexture(device: device, inTranspose: [0, 1, 2, 3]) param.output.initTexture(device: device, inTranspose: [0, 1, 2, 3], computePrecision: computePrecision)
super.init(device: device, inFunctionName: "transpose")
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] var invT: [Int] = [0, 1, 2, 3]
for (i, v) in param.input.transpose.enumerated() { for (i, v) in param.input.transpose.enumerated() {
invT[v] = i invT[v] = i
} }
var axis: [Int] = [0, 1, 2, 3] var axis: [Int] = [0, 1, 2, 3]
// var doNothing = false // var doNothing = false
// if param.axis.count == param.input.transpose.count { // if param.axis.count == param.input.transpose.count {
// doNothing = param.axis == param.input.transpose.map { Int32($0) } // doNothing = param.axis == param.input.transpose.map { Int32($0) }
// } // }
for i in 0..<param.axis.count { for i in 0..<param.axis.count {
...@@ -84,10 +78,30 @@ class TransposeKernel<P: PrecisionType>: Kernel, Computable, Testable { ...@@ -84,10 +78,30 @@ class TransposeKernel<P: PrecisionType>: Kernel, Computable, Testable {
} }
metalParam = tmp metalParam = tmp
} }
required init(device: MTLDevice, testParam: TransposeTestParam) { required init(device: MTLDevice, testParam: TransposeTestParam) {
super.init(device: device, inFunctionName: "transpose") if computePrecision == .Float16 {
fatalError() 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) { public func test(commandBuffer: MTLCommandBuffer, param: TransposeTestParam) {
guard let encoder = commandBuffer.makeComputeCommandEncoder() else { guard let encoder = commandBuffer.makeComputeCommandEncoder() else {
......
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/BoxCoder.metal
浏览文件 @ 75750786
...@@ -34,7 +34,6 @@ kernel void boxcoder(texture2d_array<float, access::read> priorBox [[texture(0)] ...@@ -34,7 +34,6 @@ kernel void boxcoder(texture2d_array<float, access::read> priorBox [[texture(0)]
float tw = exp(pv.z * t.z) * pw; float tw = exp(pv.z * t.z) * pw;
float th = exp(pv.w * t.w) * ph; float th = exp(pv.w * t.w) * ph;
float4 r; float4 r;
r.x = tx - tw / 2; r.x = tx - tw / 2;
r.y = ty - th / 2; r.y = ty - th / 2;
...@@ -43,3 +42,31 @@ kernel void boxcoder(texture2d_array<float, access::read> priorBox [[texture(0)] ...@@ -43,3 +42,31 @@ kernel void boxcoder(texture2d_array<float, access::read> priorBox [[texture(0)]
output.write(r, gid.xy, gid.z); 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 @ 75750786
...@@ -57,3 +57,14 @@ inline void invtrans(int32_t trans[4], int32_t ipos[4], int32_t opos[4]) { ...@@ -57,3 +57,14 @@ inline void invtrans(int32_t trans[4], int32_t ipos[4], int32_t opos[4]) {
opos[trans[i]] = ipos[i]; 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 @ 75750786
...@@ -69,3 +69,48 @@ kernel void concat(texture2d_array<float, access::read> in0 [[texture(0)]], ...@@ -69,3 +69,48 @@ kernel void concat(texture2d_array<float, access::read> in0 [[texture(0)]],
} }
out.write(r, gid.xy, gid.z); 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 @ 75750786
/* 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 @ 75750786
/* 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 @ 75750786
/* 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 @ 75750786
...@@ -44,18 +44,6 @@ kernel void resize(texture2d<half, access::read> inTexture [[texture(0)]], ...@@ -44,18 +44,6 @@ kernel void resize(texture2d<half, access::read> inTexture [[texture(0)]],
} }
//kernel void texture2d_to_2d_array(texture2d<half, access::read> inTexture [[texture(0)]],
// texture2d_array<half, access::write> outTexture [[texture(1)]],
// uint3 gid [[thread_position_in_grid]]) {
// if (gid.x >= inTexture.get_width() ||
// gid.y >= inTexture.get_height()){
// return;
// }
// const half4 input = inTexture.read(gid.xy);
// outTexture.write(input, gid.xy, 0);
//}
kernel void texture2d_to_2d_array(texture2d<float, access::read> inTexture [[texture(0)]], kernel void texture2d_to_2d_array(texture2d<float, access::read> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]], texture2d_array<float, access::write> outTexture [[texture(1)]],
uint3 gid [[thread_position_in_grid]]) { uint3 gid [[thread_position_in_grid]]) {
...@@ -67,10 +55,9 @@ kernel void texture2d_to_2d_array(texture2d<float, access::read> inTexture [[tex ...@@ -67,10 +55,9 @@ kernel void texture2d_to_2d_array(texture2d<float, access::read> inTexture [[tex
outTexture.write(input, gid.xy, 0); outTexture.write(input, gid.xy, 0);
} }
kernel void texture2d_to_2d_array_half(texture2d<half, access::read> inTexture [[texture(0)]], kernel void texture2d_to_2d_array_half(texture2d<half, access::read> inTexture [[texture(0)]],
texture2d_array<half, access::write> outTexture [[texture(1)]], texture2d_array<half, access::write> outTexture [[texture(1)]],
uint3 gid [[thread_position_in_grid]]) { uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= inTexture.get_width() || if (gid.x >= inTexture.get_width() ||
gid.y >= inTexture.get_height()){ gid.y >= inTexture.get_height()){
return; return;
...@@ -79,113 +66,4 @@ kernel void texture2d_to_2d_array_half(texture2d<half, access::read> inTexture [ ...@@ -79,113 +66,4 @@ kernel void texture2d_to_2d_array_half(texture2d<half, access::read> inTexture [
outTexture.write(input, gid.xy, 0); 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 @ 75750786
/* 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 @ 75750786
...@@ -15,8 +15,6 @@ ...@@ -15,8 +15,6 @@
#include <metal_stdlib> #include <metal_stdlib>
using namespace metal; using namespace metal;
kernel void prelu_channel(texture2d_array<float, access::sample> inTexture [[texture(0)]], kernel void prelu_channel(texture2d_array<float, access::sample> inTexture [[texture(0)]],
texture2d_array<float, access::write> outTexture [[texture(1)]], texture2d_array<float, access::write> outTexture [[texture(1)]],
const device float4 *alpha [[buffer(0)]], const device float4 *alpha [[buffer(0)]],
...@@ -82,3 +80,4 @@ kernel void prelu_other(texture2d_array<float, access::sample> inTexture [[textu ...@@ -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); output.w = input.w > 0 ? input.w : (alpha_value * input.w);
outTexture.write(output, gid.xy, gid.z); outTexture.write(output, gid.xy, gid.z);
} }
metal/paddle-mobile/paddle-mobile/Operators/Kernels/metal/PriorBoxKernel.metal
浏览文件 @ 75750786
...@@ -35,8 +35,8 @@ struct PriorBoxMetalParam { ...@@ -35,8 +35,8 @@ struct PriorBoxMetalParam {
kernel void prior_box(texture2d_array<float, access::read> inTexture [[texture(0)]], 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> outBoxTexture [[texture(1)]],
texture2d_array<float, access::write> varianceTexture [[texture(2)]], texture2d_array<float, access::write> varianceTexture [[texture(2)]],
constant PriorBoxMetalParam &param [[buffer(0)]], const device float *aspect_ratios [[buffer(0)]],
const device float *aspect_ratios [[buffer(1)]], constant PriorBoxMetalParam &param [[buffer(1)]],
const device float4 *variances [[buffer(2)]], const device float4 *variances [[buffer(2)]],
uint3 gid [[thread_position_in_grid]]) { uint3 gid [[thread_position_in_grid]]) {
if (gid.x >= outBoxTexture.get_width() || if (gid.x >= outBoxTexture.get_width() ||
...@@ -96,3 +96,68 @@ kernel void prior_box(texture2d_array<float, access::read> inTexture [[texture(0 ...@@ -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)]],
const device half *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() ||
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 @ 75750786
...@@ -102,14 +102,36 @@ kernel void reshape(texture2d_array<float, access::read> inTexture [[texture(0)] ...@@ -102,14 +102,36 @@ kernel void reshape(texture2d_array<float, access::read> inTexture [[texture(0)]
outTexture.write(r, gid.xy, gid.z); 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); kernel void reshape_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
outTexture.write(r, gid.xy, gid.z); 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 @ 75750786
...@@ -57,25 +57,44 @@ kernel void softmax(texture2d_array<float, access::read> inTexture [[texture(0)] ...@@ -57,25 +57,44 @@ kernel void softmax(texture2d_array<float, access::read> inTexture [[texture(0)]
rr = exp(rr - maxv) / sum; rr = exp(rr - maxv) / sum;
outTexture.write(rr, gid.xy, gid.z); outTexture.write(rr, gid.xy, gid.z);
} }
//
//kernel void softmax_half(texture2d_array<half, access::read> inTexture [[texture(0)]], kernel void softmax_half(texture2d_array<half, access::read> inTexture [[texture(0)]],
// texture2d_array<half, access::write> outTexture [[texture(1)]], texture2d_array<half, access::write> outTexture [[texture(1)]],
// uint3 gid [[thread_position_in_grid]]) { constant SoftmaxParam &sp [[buffer(0)]],
// if (gid.x >= outTexture.get_width() || uint3 gid [[thread_position_in_grid]]) {
// gid.y >= outTexture.get_height() || if (gid.x >= outTexture.get_width() ||
// gid.z >= outTexture.get_array_size()) return; gid.y >= outTexture.get_height() ||
// int zsize = inTexture.get_array_size(); gid.z >= outTexture.get_array_size()) return;
// half maxv = inTexture.read(uint2(0, 0), 0)[0]; // int zsize = inTexture.get_array_size();
// for (int z = 0; z < zsize; z++) { half maxv = inTexture.read(gid.xy, 0)[0];
// half4 r = inTexture.read(uint2(0, 0), z); int group = sp.K / 4;
// maxv = max(maxv, max(max(r[0], r[1]), max(r[2], r[3]))); int remain = sp.K % 4;
// } for (int z = 0; z < group; z++) {
// float sum = 0; half4 r = inTexture.read(gid.xy, z);
// for (int z = 0; z < zsize; z++) { maxv = max(maxv, max(r[0], max(r[1], max(r[2], r[3]))));
// 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); if (remain > 0) {
// } half4 r = inTexture.read(gid.xy, group);
// half4 rr = inTexture.read(gid.xy, gid.z); for (int i = 0; i < remain; i++) {
// rr = exp(rr - maxv) / sum; maxv = max(maxv, r[i]);
// outTexture.write(rr, gid.xy, gid.z); }
//} }
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 @ 75750786
/* 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 @ 75750786
...@@ -60,7 +60,7 @@ class PoolOp<P: PrecisionType>: Operator<PoolKernel<P>, PoolParam<P>>, Runable, ...@@ -60,7 +60,7 @@ class PoolOp<P: PrecisionType>: Operator<PoolKernel<P>, PoolParam<P>>, Runable,
func delogOutput() { func delogOutput() {
print(" \(type) output: ") 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") // print("pool2d delog")
......
metal/paddle-mobile/paddle-mobile/Operators/PreluOp.swift
浏览文件 @ 75750786
...@@ -51,13 +51,13 @@ class PreluOp<P: PrecisionType>: Operator<PreluKernel<P>, PreluParam<P>>, Runabl ...@@ -51,13 +51,13 @@ class PreluOp<P: PrecisionType>: Operator<PreluKernel<P>, PreluParam<P>>, Runabl
func delogOutput() { func delogOutput() {
print(" \(type) input: ") 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: ") print(" \(type) Alpha: ")
let _: Float32? = para.alpha.buffer.logDesc(header: " alpha: ", stridable: false) let _: Float32? = para.alpha.buffer.logDesc(header: " alpha: ", stridable: false)
print(" \(type) output: ") 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") // print("softmax delog")
......
metal/paddle-mobile/paddle-mobile/Operators/PriorBoxOp.swift
浏览文件 @ 75750786
...@@ -39,7 +39,7 @@ class PriorBoxParam<P: PrecisionType>: OpParam { ...@@ -39,7 +39,7 @@ class PriorBoxParam<P: PrecisionType>: OpParam {
let minSizes: [Float32] let minSizes: [Float32]
let maxSizes: [Float32] let maxSizes: [Float32]
let aspectRatios: [Float32] let aspectRatios: [Float32]
var newAspectRatios: [Float32]? var newAspectRatios: MTLBuffer?
let variances: [Float32] let variances: [Float32]
let flip: Bool let flip: Bool
let clip: Bool let clip: Bool
...@@ -69,14 +69,22 @@ class PriorBoxOp<P: PrecisionType>: Operator<PriorBoxKernel<P>, PriorBoxParam<P> ...@@ -69,14 +69,22 @@ class PriorBoxOp<P: PrecisionType>: Operator<PriorBoxKernel<P>, PriorBoxParam<P>
} }
func delogOutput() { func delogOutput() {
print(" \(type) output: ")
print(" \(type) output: ")
// output // output
let outputArray = para.output.metalTexture.floatArray { (o: Float32) -> Float32 in let outputArray = para.output.metalTexture.float32Array()
return o
}
print(outputArray) print(outputArray)
// output
// 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")
// }
// writeToLibrary(fileName: "box_out", array: outputArray) // writeToLibrary(fileName: "box_out", array: outputArray)
......
metal/paddle-mobile/paddle-mobile/Operators/ReluOp.swift
浏览文件 @ 75750786
...@@ -46,7 +46,7 @@ class ReluOp<P: PrecisionType>: Operator<ReluKernel<P>, ReluParam<P>>, Runable, ...@@ -46,7 +46,7 @@ class ReluOp<P: PrecisionType>: Operator<ReluKernel<P>, ReluParam<P>>, Runable,
func delogOutput() { func delogOutput() {
print(" \(type) output: ") 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 @ 75750786
...@@ -76,7 +76,7 @@ class ReshapeOp<P: PrecisionType>: Operator<ReshapeKernel<P>, ReshapeParam<P>>, ...@@ -76,7 +76,7 @@ class ReshapeOp<P: PrecisionType>: Operator<ReshapeKernel<P>, ReshapeParam<P>>,
let originDim = para.output.originDim 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()) print(outputArray.strideArray())
} }
......
metal/paddle-mobile/paddle-mobile/Operators/SoftmaxOp.swift
浏览文件 @ 75750786
...@@ -52,9 +52,9 @@ class SoftmaxOp<P: PrecisionType>: Operator<SoftmaxKernel<P>, SoftmaxParam<P>>, ...@@ -52,9 +52,9 @@ class SoftmaxOp<P: PrecisionType>: Operator<SoftmaxKernel<P>, SoftmaxParam<P>>,
func delogOutput() { func delogOutput() {
print("softmax delog") print("softmax delog")
let originDim = para.output.originDim 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()) print(outputArray.strideArray())
} }
} }
metal/paddle-mobile/paddle-mobile/Operators/TransposeOp.swift
浏览文件 @ 75750786
...@@ -48,9 +48,15 @@ class TransposeOp<P: PrecisionType>: Operator<TransposeKernel<P>, TransposeParam ...@@ -48,9 +48,15 @@ class TransposeOp<P: PrecisionType>: Operator<TransposeKernel<P>, TransposeParam
func delogOutput() { func delogOutput() {
print(" \(type) output: ") print(" \(type) output: ")
let originDim = para.output.tensorDim let originDim = para.output.originDim
let outputArray = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3])) if para.output.transpose == [0, 1, 2, 3] {
print(outputArray.strideArray()) let outputArray = para.output.metalTexture.realNHWC(dim: (n: originDim[0], h: originDim[1], w: originDim[2], c: originDim[3]))
print(outputArray.strideArray())
} else if para.output.transpose == [0, 2, 3, 1] {
print(para.output.metalTexture.toTensor(dim: (n: para.output.tensorDim[0], c: para.output.tensorDim[1], h: para.output.tensorDim[2], w: para.output.tensorDim[3])).strideArray())
} else {
print(" not implement")
}
} }
} }
......
metal/paddle-mobile/paddle-mobile/Program/BlockDesc.swift
浏览文件 @ 75750786
...@@ -48,8 +48,10 @@ extension BlockDesc: CustomStringConvertible, CustomDebugStringConvertible { ...@@ -48,8 +48,10 @@ extension BlockDesc: CustomStringConvertible, CustomDebugStringConvertible {
var description: String { var description: String {
var str = "" var str = ""
for op in ops { for i in 0..<ops.count {
str += op.description str += " op \(i): "
let op = ops[i]
str += op.description
} }
for varDesc in vars { for varDesc in vars {
......
metal/paddle-mobile/paddle-mobile/framework/Tensor.swift
浏览文件 @ 75750786
...@@ -12,249 +12,244 @@ ...@@ -12,249 +12,244 @@
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
import Accelerate
import Foundation import Foundation
protocol Tensorial: CustomStringConvertible, CustomDebugStringConvertible{ protocol Tensorial: CustomStringConvertible, CustomDebugStringConvertible{
var dim: Dim { get set } var dim: Dim { get set }
func numel() -> Int func numel() -> Int
var layout: DataLayout { get } var layout: DataLayout { get }
} }
extension Tensorial { extension Tensorial {
func numel() -> Int { func numel() -> Int {
return dim.numel() return dim.numel()
} }
}
public enum ComputePrecision {
case Float32, Float16
} }
class Tensor<P: PrecisionType>: Tensorial { class Tensor<P: PrecisionType>: Tensorial {
enum BufferPrecision {
case Float32, Float16 var data: Data
var dim: Dim
var buffer: MTLBuffer!
private(set) var layout: DataLayout
class Data {
init(inSize: Int, inPointer: UnsafeMutablePointer<P>) {
size = inSize
pointer = inPointer
} }
let size: Int
var data: Data var pointer: UnsafeMutablePointer<P>
var dim: Dim subscript(index: Int) -> P{
var buffer: MTLBuffer! get {
private(set) var layout: DataLayout return pointer[index]
}
class Data { set {
init(inSize: Int, inPointer: UnsafeMutablePointer<P>) { pointer[index] = newValue
size = inSize }
pointer = inPointer
}
let size: Int
var pointer: UnsafeMutablePointer<P>
subscript(index: Int) -> P{
get {
return pointer[index]
}
set {
pointer[index] = newValue
}
}
func release() {
pointer.deinitialize(count: size)
pointer.deallocate()
}
deinit {
// release()
}
} }
func release() {
required init(inDim: Dim, inLayout: DataLayout = DataLayout.NCHW()) { pointer.deinitialize(count: size)
dim = inDim pointer.deallocate()
let size = inDim.numel() * MemoryLayout<P>.size }
let pointer = UnsafeMutablePointer<P>.allocate(capacity: size) deinit {
data = Data.init(inSize: size, inPointer: pointer) // release()
layout = inLayout }
}
required init(inDim: Dim, inLayout: DataLayout = DataLayout.NCHW()) {
dim = inDim
let size = inDim.numel() * MemoryLayout<P>.size
let pointer = UnsafeMutablePointer<P>.allocate(capacity: size)
data = Data.init(inSize: size, inPointer: pointer)
layout = inLayout
}
func convert(to: DataLayout) {
guard to != layout else {
return
} }
func convert(to: DataLayout) { guard dim.cout() == 4 else {
guard to != layout else { return
return
}
guard dim.cout() == 4 else {
return
}
guard layout == DataLayout.NCHW() && to == DataLayout.NHWC() else {
// other not support
return
}
let newPointer = UnsafeMutablePointer<P>.allocate(capacity: data.size)
if layout == DataLayout.NCHW() {
NCHW2NHWC(newPtr: newPointer)
}
data.release()
data.pointer = newPointer
layout = to
} }
func float32ToFloat16(input: UnsafeMutablePointer<Float32>, output: UnsafeMutableRawPointer, count: Int) { guard layout == DataLayout.NCHW() && to == DataLayout.NHWC() else {
var float32Buffer = vImage_Buffer(data: input, height: 1, width: UInt(count), rowBytes: count * 4) // other not support
var float16buffer = vImage_Buffer(data: output, height: 1, width: UInt(count), rowBytes: count * 2) return
guard vImageConvert_PlanarFtoPlanar16F(&float32Buffer, &float16buffer, 0) == kvImageNoError else {
fatalError(" float 32 to float 16 error ! ")
}
} }
let newPointer = UnsafeMutablePointer<P>.allocate(capacity: data.size)
func initBuffer(device: MTLDevice, precision: BufferPrecision = .Float32) { if layout == DataLayout.NCHW() {
guard let floatPointer = data.pointer as? UnsafeMutablePointer<Float32> else { NCHW2NHWC(newPtr: newPointer)
fatalError(" not support yet ")
}
let precisionSize: Int
switch precision {
case .Float32:
precisionSize = 4
case .Float16:
precisionSize = 2
}
if dim.cout() == 4 {
if layout == DataLayout.NHWC() {
let C = dim[3]
let cSlices = (C + 3) / 4
let paddedC = cSlices * 4
let count = paddedC * dim[0] * dim[1] * dim[2]
if C == paddedC {
buffer = device.makeBuffer(length: count * precisionSize)
switch precision {
case .Float32:
buffer?.contents().copyMemory(from: data.pointer, byteCount: count * MemoryLayout<P>.stride)
case .Float16:
float32ToFloat16(input: floatPointer, output: buffer.contents(), count: count)
}
} else if C == 1 {
buffer = device.makeBuffer(length: numel() * precisionSize)
switch precision {
case .Float32:
buffer?.contents().copyMemory(from: data.pointer, byteCount: numel() * MemoryLayout<P>.stride)
case .Float16:
float32ToFloat16(input: floatPointer, output: buffer.contents(), count: numel())
}
} else {
buffer = device.makeBuffer(length: count * precisionSize)
let convertedPointer = UnsafeMutablePointer<Float32>.allocate(capacity: count)
var tmpPointer = floatPointer
var dstPtr = convertedPointer
for _ in 0..<dim[0] * dim[1] * dim[2] {
for j in 0..<paddedC {
if j < C {
dstPtr[j] = tmpPointer[j]
}
}
tmpPointer += C
dstPtr += paddedC
}
switch precision {
case .Float32:
buffer?.contents().copyMemory(from: convertedPointer, byteCount: count * MemoryLayout<P>.stride)
case .Float16:
float32ToFloat16(input: convertedPointer, output: buffer.contents(), count: count)
}
convertedPointer.deinitialize(count: count)
convertedPointer.deallocate()
}
}
} else if dim.cout() == 1 {
buffer = device.makeBuffer(length: numel() * precisionSize)
switch precision {
case .Float32:
buffer?.contents().copyMemory(from: data.pointer, byteCount: numel() * MemoryLayout<P>.stride)
case .Float16:
float32ToFloat16(input: floatPointer, output: buffer.contents(), count: numel())
}
} else {
fatalError(" not support !")
}
//TODO: release
data.release()
} }
var width: Int { data.release()
get { data.pointer = newPointer
if dim.cout() == 4 { layout = to
return dim[1] }
} else {
fatalError()
}
} func initBuffer(device: MTLDevice, precision: ComputePrecision = .Float16) {
guard let floatPointer = data.pointer as? UnsafeMutablePointer<Float32> else {
fatalError(" not support yet ")
} }
var height: Int { let precisionSize: Int
get { switch precision {
if dim.cout() == 4 { case .Float32:
return dim[2] precisionSize = 4
} else { case .Float16:
fatalError() precisionSize = 2
}
}
} }
var channel: Int { if dim.cout() == 4 {
get { if layout == DataLayout.NHWC() {
if dim.cout() == 4 { let C = dim[3]
return dim[3] let cSlices = (C + 3) / 4
} else { let paddedC = cSlices * 4
fatalError() let count = paddedC * dim[0] * dim[1] * dim[2]
if C == paddedC {
buffer = device.makeBuffer(length: count * precisionSize)
switch precision {
case .Float32:
buffer?.contents().copyMemory(from: data.pointer, byteCount: count * MemoryLayout<P>.stride)
case .Float16:
float32ToFloat16(input: floatPointer, output: buffer.contents(), count: count)
}
} else if C == 1 {
buffer = device.makeBuffer(length: numel() * precisionSize)
switch precision {
case .Float32:
buffer?.contents().copyMemory(from: data.pointer, byteCount: numel() * MemoryLayout<P>.stride)
case .Float16:
float32ToFloat16(input: floatPointer, output: buffer.contents(), count: numel())
}
} else {
buffer = device.makeBuffer(length: count * precisionSize)
let convertedPointer = UnsafeMutablePointer<Float32>.allocate(capacity: count)
var tmpPointer = floatPointer
var dstPtr = convertedPointer
for _ in 0..<dim[0] * dim[1] * dim[2] {
for j in 0..<paddedC {
if j < C {
dstPtr[j] = tmpPointer[j]
}
} }
} tmpPointer += C
dstPtr += paddedC
}
switch precision {
case .Float32:
buffer?.contents().copyMemory(from: convertedPointer, byteCount: count * MemoryLayout<P>.stride)
case .Float16:
float32ToFloat16(input: convertedPointer, output: buffer.contents(), count: count)
}
convertedPointer.deinitialize(count: count)
convertedPointer.deallocate()
}
}
} else if dim.cout() == 1 {
let num = ((numel() + 3) / 4) * 4
buffer = device.makeBuffer(length: num * precisionSize)
switch precision {
case .Float32:
buffer?.contents().copyMemory(from: data.pointer, byteCount: num * MemoryLayout<P>.stride)
case .Float16:
float32ToFloat16(input: floatPointer, output: buffer.contents(), count: num)
}
} else {
fatalError(" not support !")
} }
//TODO: release
data.release()
}
var width: Int {
get {
if dim.cout() == 4 {
return dim[1]
} else {
fatalError()
}
}
}
var height: Int {
get {
if dim.cout() == 4 {
return dim[2]
} else {
fatalError()
}
}
}
var channel: Int {
get {
if dim.cout() == 4 {
return dim[3]
} else {
fatalError()
}
}
}
func NCHW2NHWC(newPtr: UnsafeMutablePointer<P>) {
let N = dim[0]
let C = dim[1]
let H = dim[2]
let W = dim[3]
let HXW = H * W
let CXHXW = C * H * W
func NCHW2NHWC(newPtr: UnsafeMutablePointer<P>) { var index: Int = 0
let N = dim[0] for n in 0..<N {
let C = dim[1] for h in 0..<H{
let H = dim[2] for w in 0..<W{
let W = dim[3] for c in 0..<C{
let HXW = H * W newPtr[index] = data.pointer[n * CXHXW + c * HXW + h * W + w]
let CXHXW = C * H * W index += 1
}
var index: Int = 0 }
for n in 0..<N { }
for h in 0..<H{
for w in 0..<W{
for c in 0..<C{
newPtr[index] = data.pointer[n * CXHXW + c * HXW + h * W + w]
index += 1
}
}
}
}
dim.swapeDimAt(index1: 1, index2: 3)
} }
dim.swapeDimAt(index1: 1, index2: 3)
}
} }
extension Tensor { extension Tensor {
var debugDescription: String { var debugDescription: String {
var str = "dim: \(dim) \n" var str = "dim: \(dim) \n"
str += "MTLBuffer: \(self.buffer) \n" str += "MTLBuffer: \(self.buffer) \n"
for i in 0..<buffer.length/MemoryLayout<P>.size { for i in 0..<buffer.length/MemoryLayout<P>.size {
str += " \(buffer.contents().assumingMemoryBound(to: P.self)[i])" str += " \(buffer.contents().assumingMemoryBound(to: P.self)[i])"
}
return str
} }
return str
func logDataPointer(header: String = "") { }
print(header)
var str = "" func logDataPointer(header: String = "") {
str += "data size: \(data.size) \n" print(header)
str += "dim: \(dim) \n" var str = ""
for i in 0..<numel() { str += "data size: \(data.size) \n"
str += " \(data.pointer[i])" str += "dim: \(dim) \n"
} for i in 0..<numel() {
print(str) str += " \(data.pointer[i])"
} }
print(str)
var description: String { }
return debugDescription
} var description: String {
return debugDescription
}
} }
metal/paddle-mobile/paddle-mobile/framework/Texture.swift
浏览文件 @ 75750786
...@@ -46,7 +46,7 @@ public class Texture<P: PrecisionType>: Tensorial { ...@@ -46,7 +46,7 @@ public class Texture<P: PrecisionType>: Tensorial {
public var metalTexture: MTLTexture! public var metalTexture: MTLTexture!
var transpose: [Int] = [0, 1, 2, 3] 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 transpose = inTranspose
let newDim = transpose.map { originDim[$0] } let newDim = transpose.map { originDim[$0] }
...@@ -64,12 +64,10 @@ public class Texture<P: PrecisionType>: Tensorial { ...@@ -64,12 +64,10 @@ public class Texture<P: PrecisionType>: Tensorial {
tmpTextureDes.depth = 1 tmpTextureDes.depth = 1
tmpTextureDes.arrayLength = ((newDim[0]) * (newDim[3]) + 3) / 4 tmpTextureDes.arrayLength = ((newDim[0]) * (newDim[3]) + 3) / 4
tmpTextureDes.textureType = .type2DArray tmpTextureDes.textureType = .type2DArray
if MemoryLayout<P>.size == 1 { if computePrecision == .Float16 {
tmpTextureDes.pixelFormat = .rgba8Unorm
} else if MemoryLayout<P>.size == 2 {
tmpTextureDes.pixelFormat = .rgba16Float tmpTextureDes.pixelFormat = .rgba16Float
} else if MemoryLayout<P>.size == 4 { } else if computePrecision == .Float32 {
tmpTextureDes.pixelFormat = .rgba32Float tmpTextureDes.pixelFormat = .rgba32Float
} }
......
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