MNN:Sync: Sync Internal 3.2.3

.gitignore

@@ -376,4 +376,6 @@ datasets/*
 
 # qnn 3rdParty
 source/backend/qnn/3rdParty/include
-apps/Android/MnnLlmChat/release_outputs
+project/android/.cxx
+pymnn/android/.cxx/
+pymnn/android/.cxx/abi_configuration_5u53tc49.json

README.md

@@ -11,8 +11,6 @@
 
 
 ## News 🔥
-- [2025/08/08] Now we support [gpt-oss-20b](./apps/Android/MnnLlmChat/README.md#releases).
-- [2025/08/05] MNN Chat Android is availabe in [GooglePlay](https://play.google.com/store/apps/details?id=com.alibaba.mnnllm.android.release) !
 - [2025/06/11] New App MNN TaoAvatar released, you can talk with 3DAvatar offline with LLM, ASR, TTS, A2BS and NNR models all run local on your device!! [MNN TaoAvatar](./apps/Android/Mnn3dAvatar/README.md) 
 <p align="center">
   <img width="20%" alt="Icon"  src="https://meta.alicdn.com/data/mnn/avatar/avatar_demo.gif" style="margin: 0 10px;">

docs/inference/module.md

@@ -183,6 +183,21 @@ struct Info {
 const Info* getInfo() const;
 ```
 
+### 获取设备信息
+调用`getDeviceInfo`函数可获取`Device`信息，可以参考代码：
+```cpp
+std::string soc_id, dsp_arch;
+bool success = MNN::Express::Executor::RuntimeManager::getDeviceInfo("dsp_arch", MNN_FORWARD_NN, dsp_arch);
+if(success) {
+    MNN_PRINT("Device dsp_arch: %s\n", dsp_arch.c_str());
+}
+
+success = MNN::Express::Executor::RuntimeManager::getDeviceInfo("soc_id", MNN_FORWARD_NN, soc_id);
+if(success) {
+    MNN_PRINT("Device soc_id: %s\n", soc_id.c_str());
+}
+```
+
 ### 执行推理
 调用`onForward`执行推理。
 

docs/inference/npu.md

@@ -58,6 +58,10 @@ adb push ${MNN_ROOT}/source/backend/qnn/3rdParty/lib/hexagon-v${HEXAGON_ARCH}/un
 adb shell "cd /data/local/tmp && LD_LIBRARY_PATH=/data/local/tmp ADSP_LIBRARY_PATH=/data/local/tmp ./MyExe.out"
 ```
 
+### QNN量化功能说明
+- 仅权重量化（激活是浮点）：只支持Linear权重int8、channel-wise的对称量化。
+- 激活&权重都量化：支持激活per-tensor对称量化，权重是int8/int4、channel-wise的对称量化。
+
 ## CoreML
 适用于 Mac / iOS / iPad
 

docs/tools/convert.md

@@ -388,3 +388,12 @@ npu model path:./qnn_smolvlm_model.bin
  
 ./ModuleBasic.out qnn_smolvlm_model.mnn dir 0 0 10
 ```
+### 生成多种QNN设备模型脚本
+tools/script/genQNNModelsFromMNN.py中提供了8Gen1 ~ 8Elite设备的QNN模型生成脚本
+```
+// 使用示例
+cd mnn_path
+cd build
+python3 ../tools/script/genQNNModelsFromMNN.py --config_path ../source/backend/qnn/convertor/config_example/ --graph_name visual_qnn --qnn_sdk_root_path /mnt/2Tpartition/tianbu/QNN/qairt/2.37.0.250724/ --src_model visual.mnn --executable_path ./MNN2QNNModel
+```
+后续将在qnn_models文件夹下生成8Gen1 ~ 8Elite设备的QNN模型产物。

docs/transformers/llm.md

@@ -106,6 +106,10 @@ optional arguments:
                         mnn quant bit, 4 or 8, default is 4.
   --quant_block QUANT_BLOCK
                         mnn quant block, 0 mean channle-wise, default is 128.
+  --visual_quant_bit VISUAL_QUANT_BIT
+                        mnn visual model quant bit, 4 or 8, default is setting in utils/vision.py by different vit model.
+  --visual_quant_block VISUAL_QUANT_BLOCK
+                        mnn visual model quant block, 0 mean channle-wise, default is setting in utils/vision.py by different vit model.
   --lm_quant_bit LM_QUANT_BIT
                         mnn lm_head quant bit, 4 or 8, default is `quant_bit`.
   --mnnconvert MNNCONVERT
@@ -113,10 +117,12 @@ optional arguments:
   --ppl                 Whether or not to get all logits of input tokens.
   --awq                 Whether or not to use awq quant.
   --sym                 Whether or not to using symmetric quant (without zeropoint), defualt is False.
+  --visual_sym          Whether or not to using symmetric quant (without zeropoint) for visual model, defualt is False.
   --seperate_embed      For lm and embed shared model, whether or not to sepearte embed to avoid quant, defualt is False, if True, embed weight will be seperate to embeddingbf16.bin.
   --lora_split          Whether or not export lora split, defualt is False.
 ```
 
+
 ### 权重读取
 llmexport.py 同时支持 LLM 的验证功能，有较多的依赖。在没有相应环境的情况下，MNN-LLM也提供由 safetensors 或 gguf 文件读取权重的工具，可以降低内存需求，提高转换速度。使用方法如下：
 
@@ -166,6 +172,7 @@ python3 gguf2mnn.py --gguf ~/third/llama.cpp/build/ggml-model-Q4_K.gguf --mnn_di
 ```
 -DLLM_SUPPORT_VISION=true -DMNN_BUILD_OPENCV=true -DMNN_IMGCODECS=true
 ```
+
 - 需要开启音频功能时，增加相关编译宏
 ```
 -DLLM_SUPPORT_AUDIO=true -DMNN_BUILD_AUDIO=true
@@ -195,6 +202,12 @@ cd project/android
 mkdir build_64
 ../build_64.sh -DMNN_LOW_MEMORY=true -DMNN_CPU_WEIGHT_DEQUANT_GEMM=true -DMNN_BUILD_LLM=true -DMNN_SUPPORT_TRANSFORMER_FUSE=true -DMNN_ARM82=true -DMNN_OPENCL=true -DMNN_USE_LOGCAT=true
 ```
+高通设备部分视觉模型支持NPU功能，可增加`MNN_QNN` 和`MNN_WITH_PLUGIN`的宏启用QNN功能。
+```
+cd project/android
+mkdir build_64
+../build_64.sh -DMNN_LOW_MEMORY=true -DMNN_CPU_WEIGHT_DEQUANT_GEMM=true -DMNN_BUILD_LLM=true -DMNN_SUPPORT_TRANSFORMER_FUSE=true -DMNN_ARM82=true -DMNN_OPENCL=true -DMNN_QNN=true -DMNN_WITH_PLUGIN=true -DMNN_USE_LOGCAT=true
+```
 
 #### iOS: 参考 transformers/llm/engine/ios/README.md
 ```

express/Executor.cpp

@@ -281,6 +281,17 @@ bool Executor::RuntimeManager::getInfo(Interpreter::SessionInfoCode code, void*
     return false;
 }
 
+bool Executor::RuntimeManager::getDeviceInfo(const std::string& deviceKey, const MNNForwardType type, std::string& deviceValue) {
+    auto creator = MNNGetExtraRuntimeCreator(type);
+    if (creator != nullptr) {
+        auto res = creator->onGetDeviceInfo(deviceKey, deviceValue);
+        if(res) {
+            return true;
+        }
+    }
+    return false;
+}
+
 Executor::RuntimeManager::RuntimeManager() {
     mInside = new RuntimeAttr;
     mInside->mContent.reset(new RuntimeAttr::Immutable);

include/MNN/expr/Executor.hpp

@@ -130,6 +130,7 @@ public:
         void setHint(Interpreter::HintMode mode, int* value, size_t size);
         void setHintPtr(Interpreter::HintMode mode, void* value);
         bool getInfo(Interpreter::SessionInfoCode code, void* ptr);
+        static bool getDeviceInfo(const std::string& deviceKey, const MNNForwardType type, std::string& deviceValue);
         BackendConfig* getBnConfig();
         const RuntimeAttr* getInside() const {
             return mInside;

project/android/CMakeExports.txt

@@ -25,3 +25,9 @@ set_target_properties( MNNOpenCV
                 PROPERTIES IMPORTED_LOCATION
                 ${CMAKE_CURRENT_LIST_DIR}/libs/${ANDROID_ABI}/libMNNOpenCV.so
                 )
+
+add_library(MNN_LLM  SHARED IMPORTED GLOBAL )
+set_target_properties(MNN_LLM
+            PROPERTIES IMPORTED_LOCATION
+            ${CMAKE_CURRENT_LIST_DIR}/libs/${ANDROID_ABI}/libllm.so
+)

project/android/gradle/wrapper/gradle-wrapper.properties

@@ -3,4 +3,4 @@ distributionBase=GRADLE_USER_HOME
 distributionPath=wrapper/dists
 zipStoreBase=GRADLE_USER_HOME
 zipStorePath=wrapper/dists
-distributionUrl=http\://mtl-gradle-mirror.oss-cn-hangzhou.aliyuncs.com/gradle-4.6-all.zip
+distributionUrl=http://mtl-gradle-mirror.oss-cn-hangzhou.aliyuncs.com/gradle-6.7.1-all.zip

project/android/nativepub.gradle

@@ -20,23 +20,48 @@ afterEvaluate {
 android.libraryVariants.all {variant ->
     def name = variant.buildType.name
     def zipTask = tasks.create(name: "zipNative${name.capitalize()}", type: Zip) {
-        from project.zipTree("${variant.packageLibrary.destinationDir.path}/${project.name}-${name}.aar")
+        from project.zipTree("${variant.packageLibrary.destinationDir.path}/${artifactName}-${name}.aar")
         from "$buildDir/native-packed"
-        archiveName "${project.name}-${name}-tmp.aar"
+        archiveName "${artifactName}-${name}-tmp.aar"
         destinationDir(variant.packageLibrary.destinationDir)
+        inputs.dir("$buildDir/native-packed")
+        inputs.file("${variant.packageLibrary.destinationDir.path}/${artifactName}-${name}.aar")
+        outputs.file("${variant.packageLibrary.destinationDir.path}/${artifactName}-${name}.aar")
     }
+    
+    zipTask.dependsOn("externalNativeBuild${name.capitalize()}")
+    
+    // Ensure QNN dependencies are prepared when BUILD_QNN is enabled
+    if (project.ext.has('BUILD_QNN') && project.ext.BUILD_QNN) {
+        zipTask.dependsOn('prepareQnnDeps')
+    }
+    
     zipTask.doLast {
         copy {
-            from "${variant.packageLibrary.destinationDir.path}/${project.name}-${name}-tmp.aar"
+            from "${variant.packageLibrary.destinationDir.path}/${artifactName}-${name}-tmp.aar"
             into "${variant.packageLibrary.destinationDir.path}"
             rename{
                 String fileName->
                     fileName.replace('-tmp','')
             }
         }
-        delete "${variant.packageLibrary.destinationDir.path}/${project.name}-${name}-tmp.aar"
+        delete "${variant.packageLibrary.destinationDir.path}/${artifactName}-${name}-tmp.aar"
+        
+        println "Generated final AAR: ${variant.packageLibrary.destinationDir.path}/${artifactName}-${name}.aar"
     }
-    tasks.findByName("assemble${name.capitalize()}").doLast {
-        zipTask.execute()
+    
+    def bundleTask = tasks.findByName("bundle${name.capitalize()}Aar")
+    if (bundleTask != null) {
+        zipTask.dependsOn(bundleTask)
+    }
+    
+    def assembleTask = tasks.findByName("assemble${name.capitalize()}")
+    if (assembleTask != null) {
+        assembleTask.dependsOn(zipTask)
+        zipTask.mustRunAfter(bundleTask)
+    }
+    def packageTask = tasks.findByName("package${name.capitalize()}Aar")
+    if (packageTask != null) {
+        packageTask.finalizedBy(zipTask)
     }
 }

project/android/qnnprepare.gradle

@@ -0,0 +1,174 @@
+// QNN Dependencies Preparation
+// This gradle file handles downloading and preparing QNN dependencies
+
+// QNN configuration
+ext {
+    // QNN download settings
+    QNN_LIBS_URL = 'http://meta.alicdn.com/data/mnn/libs/qnn_inc_libs.zip'
+}
+
+def qnnZipName = 'qnn_inc_libs.zip'
+def qnnZipFile = new File(buildDir, qnnZipName)
+def qnnTmpDir = new File(buildDir, 'qnn_tmp')
+
+task prepareQnnDeps {
+    group = 'build setup'
+    description = 'Download and extract QNN include/lib into source/backend/qnn/3rdParty when BUILD_QNN is enabled.'
+    onlyIf { 
+        project.ext.has('BUILD_QNN') && project.ext.BUILD_QNN
+    }
+    
+    // Define inputs and outputs for incremental build
+    inputs.property("qnn_libs_url", { QNN_LIBS_URL })
+    inputs.property("build_qnn", { project.ext.has('BUILD_QNN') && project.ext.BUILD_QNN })
+    outputs.dir(new File(project.rootDir, '../../source/backend/qnn/3rdParty'))
+    outputs.dir(new File(buildDir, 'native-packed/native/libs'))
+    
+    doLast {
+        println "BUILD_QNN is ON. Preparing QNN dependencies..."
+        
+        def url = QNN_LIBS_URL
+        println "Downloading QNN dependencies from ${url}"
+        
+        if (!qnnZipFile.exists()) {
+            println "Downloading ${qnnZipName} ..."
+            qnnZipFile.parentFile.mkdirs()
+            
+            try {
+                new java.net.URL(url).withInputStream { inputStream ->
+                    qnnZipFile.withOutputStream { outputStream ->
+                        outputStream << inputStream
+                    }
+                }
+                println "Downloaded to: ${qnnZipFile.absolutePath}"
+            } catch (Exception e) {
+                throw new RuntimeException("Failed to download QNN dependencies from ${url}. Error: ${e.message}")
+            }
+        } else {
+            println "Using cached zip: ${qnnZipFile.absolutePath}"
+        }
+
+        // Clean temp dir and unpack
+        project.delete(qnnTmpDir)
+        qnnTmpDir.mkdirs()
+        copy {
+            from zipTree(qnnZipFile)
+            into qnnTmpDir
+        }
+
+        // Find the extracted QNN directory
+        def extractedQnnDir = findQnnDirectory(qnnTmpDir)
+        if (extractedQnnDir == null) {
+            throw new RuntimeException("Failed to find QNN directory structure in ${qnnZipFile.name}")
+        }
+        
+        copyQnnFiles(extractedQnnDir)
+    }
+}
+
+def findQnnDirectory(File searchDir) {
+    // Look for directory containing both include and jniLibs (or lib) directories
+    def candidates = []
+    
+    searchDir.eachDirRecurse { dir ->
+        def hasInclude = new File(dir, 'include').exists()
+        def hasLibs = new File(dir, 'jniLibs').exists() || new File(dir, 'lib').exists()
+        
+        if (hasInclude && hasLibs) {
+            candidates.add(dir)
+        }
+    }
+    
+    if (candidates.isEmpty()) {
+        // Fallback: look for directory that contains include
+        searchDir.eachDirRecurse { dir ->
+            if (new File(dir, 'include').exists()) {
+                candidates.add(dir)
+            }
+        }
+    }
+    
+    return candidates.isEmpty() ? null : candidates[0]
+}
+
+def copyQnnFiles(File sourceDir) {
+    // Resolve destination directories
+    def qnnRoot = new File(project.rootDir, '../../source/backend/qnn/3rdParty')
+    def includeDir = new File(qnnRoot, 'include')
+    def libDir = new File(qnnRoot, 'lib')
+    
+    includeDir.mkdirs()
+    libDir.mkdirs()
+
+    // Copy include files
+    def sourceInclude = new File(sourceDir, 'include')
+    if (sourceInclude.exists()) {
+        copy {
+            from sourceInclude
+            into includeDir
+        }
+        println "QNN includes copied to: ${includeDir.absolutePath}"
+    } else {
+        throw new RuntimeException("Include directory not found in ${sourceDir.absolutePath}")
+    }
+
+    // Copy library files - try both jniLibs and lib directories
+    def sourceLibs = new File(sourceDir, 'jniLibs')
+    if (!sourceLibs.exists()) {
+        sourceLibs = new File(sourceDir, 'lib')
+    }
+    
+    if (sourceLibs.exists()) {
+        copy {
+            from sourceLibs
+            into libDir
+        }
+        println "QNN libs copied to: ${libDir.absolutePath}"
+        
+        // Also copy QNN .so files to native-packed for AAR packaging
+        copyQnnLibsToNativePacked(sourceLibs)
+    } else {
+        println "Warning: No lib/jniLibs directory found in ${sourceDir.absolutePath}"
+    }
+    
+    println "QNN dependencies preparation completed successfully."
+}
+
+def copyQnnLibsToNativePacked(File sourceLibsDir) {
+    // Create native-packed directory structure for QNN .so files
+    def nativePackedLibsDir = new File(buildDir, 'native-packed/native/libs')
+    nativePackedLibsDir.mkdirs()
+    
+    println "Copying QNN .so files to native-packed for AAR packaging..."
+    
+    // Copy all .so files from QNN libs to native-packed
+    sourceLibsDir.eachFileRecurse { file ->
+        if (file.name.endsWith('.so')) {
+            // Determine the ABI directory (arm64-v8a, armeabi-v7a, etc.)
+            def relativePath = sourceLibsDir.toPath().relativize(file.toPath())
+            def targetFile = new File(nativePackedLibsDir, relativePath.toString())
+            
+            // Create parent directories if they don't exist
+            targetFile.parentFile.mkdirs()
+            
+            // Copy the .so file
+            copy {
+                from file
+                into targetFile.parentFile
+            }
+            
+            println "Copied QNN lib: ${file.name} -> ${targetFile.absolutePath}"
+        }
+    }
+    
+    println "QNN .so files copied to native-packed directory"
+}
+
+// Ensure preparation runs before compilation when enabled
+if (project.ext.has('BUILD_QNN') && project.ext.BUILD_QNN) {
+    afterEvaluate {
+        if (tasks.findByName('preBuild')) {
+            preBuild.dependsOn prepareQnnDeps
+        }
+    }
+}

project/ios/MNN.xcodeproj/project.pbxproj

@@ -480,7 +480,6 @@
 		92FF02E023AA0B5A00AC97F6 /* MNNMatrixSub.S in Sources */ = {isa = PBXBuildFile; fileRef = 92FF016023AA0B4E00AC97F6 /* MNNMatrixSub.S */; };
 		92FF02E123AA0B5A00AC97F6 /* MNNPowC8.S in Sources */ = {isa = PBXBuildFile; fileRef = 92FF016123AA0B4E00AC97F6 /* MNNPowC8.S */; };
 		92FF02E223AA0B5A00AC97F6 /* MNNMatrixAdd.S in Sources */ = {isa = PBXBuildFile; fileRef = 92FF016223AA0B4E00AC97F6 /* MNNMatrixAdd.S */; };
-		92FF02E323AA0B5A00AC97F6 /* MNNExpC8.S in Sources */ = {isa = PBXBuildFile; fileRef = 92FF016323AA0B4E00AC97F6 /* MNNExpC8.S */; };
 		92FF02E523AA0B5A00AC97F6 /* MNNConvDwF23SourceTransUnit.S in Sources */ = {isa = PBXBuildFile; fileRef = 92FF016523AA0B4E00AC97F6 /* MNNConvDwF23SourceTransUnit.S */; };
 		92FF02E723AA0B5A00AC97F6 /* MNNDeconvRunForUnitDepthWise.S in Sources */ = {isa = PBXBuildFile; fileRef = 92FF016723AA0B4E00AC97F6 /* MNNDeconvRunForUnitDepthWise.S */; };
 		92FF02E823AA0B5A00AC97F6 /* MNNSamplerC1BilinearOpt.S in Sources */ = {isa = PBXBuildFile; fileRef = 92FF016823AA0B4E00AC97F6 /* MNNSamplerC1BilinearOpt.S */; };
@@ -520,7 +519,6 @@
 		92FF032023AA0B5A00AC97F6 /* MNNMatrixSub.S in Sources */ = {isa = PBXBuildFile; fileRef = 92FF01A123AA0B4E00AC97F6 /* MNNMatrixSub.S */; };
 		92FF032123AA0B5A00AC97F6 /* MNNPowC8.S in Sources */ = {isa = PBXBuildFile; fileRef = 92FF01A223AA0B4E00AC97F6 /* MNNPowC8.S */; };
 		92FF032223AA0B5A00AC97F6 /* MNNMatrixAdd.S in Sources */ = {isa = PBXBuildFile; fileRef = 92FF01A323AA0B4E00AC97F6 /* MNNMatrixAdd.S */; };
-		92FF032323AA0B5A00AC97F6 /* MNNExpC8.S in Sources */ = {isa = PBXBuildFile; fileRef = 92FF01A423AA0B4E00AC97F6 /* MNNExpC8.S */; };
 		92FF032523AA0B5A00AC97F6 /* MNNConvDwF23SourceTransUnit.S in Sources */ = {isa = PBXBuildFile; fileRef = 92FF01A623AA0B4E00AC97F6 /* MNNConvDwF23SourceTransUnit.S */; };
 		92FF032723AA0B5A00AC97F6 /* MNNDeconvRunForUnitDepthWise.S in Sources */ = {isa = PBXBuildFile; fileRef = 92FF01A823AA0B4E00AC97F6 /* MNNDeconvRunForUnitDepthWise.S */; };
 		92FF032823AA0B5A00AC97F6 /* MNNSamplerC1BilinearOpt.S in Sources */ = {isa = PBXBuildFile; fileRef = 92FF01A923AA0B4E00AC97F6 /* MNNSamplerC1BilinearOpt.S */; };
@@ -1315,7 +1313,6 @@
 		92FF016023AA0B4E00AC97F6 /* MNNMatrixSub.S */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.asm; path = MNNMatrixSub.S; sourceTree = "<group>"; };
 		92FF016123AA0B4E00AC97F6 /* MNNPowC8.S */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.asm; path = MNNPowC8.S; sourceTree = "<group>"; };
 		92FF016223AA0B4E00AC97F6 /* MNNMatrixAdd.S */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.asm; path = MNNMatrixAdd.S; sourceTree = "<group>"; };
-		92FF016323AA0B4E00AC97F6 /* MNNExpC8.S */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.asm; path = MNNExpC8.S; sourceTree = "<group>"; };
 		92FF016523AA0B4E00AC97F6 /* MNNConvDwF23SourceTransUnit.S */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.asm; path = MNNConvDwF23SourceTransUnit.S; sourceTree = "<group>"; };
 		92FF016723AA0B4E00AC97F6 /* MNNDeconvRunForUnitDepthWise.S */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.asm; path = MNNDeconvRunForUnitDepthWise.S; sourceTree = "<group>"; };
 		92FF016823AA0B4E00AC97F6 /* MNNSamplerC1BilinearOpt.S */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.asm; path = MNNSamplerC1BilinearOpt.S; sourceTree = "<group>"; };
@@ -1355,7 +1352,6 @@
 		92FF01A123AA0B4E00AC97F6 /* MNNMatrixSub.S */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.asm; path = MNNMatrixSub.S; sourceTree = "<group>"; };
 		92FF01A223AA0B4E00AC97F6 /* MNNPowC8.S */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.asm; path = MNNPowC8.S; sourceTree = "<group>"; };
 		92FF01A323AA0B4E00AC97F6 /* MNNMatrixAdd.S */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.asm; path = MNNMatrixAdd.S; sourceTree = "<group>"; };
-		92FF01A423AA0B4E00AC97F6 /* MNNExpC8.S */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.asm; path = MNNExpC8.S; sourceTree = "<group>"; };
 		92FF01A623AA0B4E00AC97F6 /* MNNConvDwF23SourceTransUnit.S */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.asm; path = MNNConvDwF23SourceTransUnit.S; sourceTree = "<group>"; };
 		92FF01A823AA0B4E00AC97F6 /* MNNDeconvRunForUnitDepthWise.S */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.asm; path = MNNDeconvRunForUnitDepthWise.S; sourceTree = "<group>"; };
 		92FF01A923AA0B4E00AC97F6 /* MNNSamplerC1BilinearOpt.S */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.asm; path = MNNSamplerC1BilinearOpt.S; sourceTree = "<group>"; };
@@ -2591,7 +2587,6 @@
 				92FF016023AA0B4E00AC97F6 /* MNNMatrixSub.S */,
 				92FF016123AA0B4E00AC97F6 /* MNNPowC8.S */,
 				92FF016223AA0B4E00AC97F6 /* MNNMatrixAdd.S */,
-				92FF016323AA0B4E00AC97F6 /* MNNExpC8.S */,
 				92FF016523AA0B4E00AC97F6 /* MNNConvDwF23SourceTransUnit.S */,
 				92FF016723AA0B4E00AC97F6 /* MNNDeconvRunForUnitDepthWise.S */,
 				92FF016823AA0B4E00AC97F6 /* MNNSamplerC1BilinearOpt.S */,
@@ -2677,7 +2672,6 @@
 				92FF01A123AA0B4E00AC97F6 /* MNNMatrixSub.S */,
 				92FF01A223AA0B4E00AC97F6 /* MNNPowC8.S */,
 				92FF01A323AA0B4E00AC97F6 /* MNNMatrixAdd.S */,
-				92FF01A423AA0B4E00AC97F6 /* MNNExpC8.S */,
 				92FF01A623AA0B4E00AC97F6 /* MNNConvDwF23SourceTransUnit.S */,
 				92FF01A823AA0B4E00AC97F6 /* MNNDeconvRunForUnitDepthWise.S */,
 				92FF01A923AA0B4E00AC97F6 /* MNNSamplerC1BilinearOpt.S */,
@@ -3279,7 +3273,6 @@
 				92FF02C223AA0B5A00AC97F6 /* MNNLoadU8AndSum.S in Sources */,
 				4819FB2E24C1396A0050BD09 /* GeometryLSTM.cpp in Sources */,
 				9558334B29B09A7B00488807 /* MNNGeluFP16.S in Sources */,
-				92FF02E323AA0B5A00AC97F6 /* MNNExpC8.S in Sources */,
 				92FF030223AA0B5A00AC97F6 /* MNNQuanToDestUint8.S in Sources */,
 				489D7AA12550FDC900AD896A /* MetalUnary.mm in Sources */,
 				92FF037323AA0B5A00AC97F6 /* CPUEltwiseInt8.cpp in Sources */,
@@ -3438,7 +3431,6 @@
 				92FF030723AA0B5A00AC97F6 /* MNNBlitC1ToFloatRGBA.S in Sources */,
 				92FF03A423AA0B5A00AC97F6 /* OptimizedComputer.cpp in Sources */,
 				92FF032E23AA0B5A00AC97F6 /* MNNReluWithSlopeChannel.S in Sources */,
-				92FF032323AA0B5A00AC97F6 /* MNNExpC8.S in Sources */,
 				95278CEA2B9F09C0009E9B29 /* ShapeDynamicQuant.cpp in Sources */,
 				92FF044C23AA0B7100AC97F6 /* ShapePool3D.cpp in Sources */,
 				92FF029823AA0B5A00AC97F6 /* CPUTFQuantizedConv2D.cpp in Sources */,

pymnn/CMakeLists.txt

@@ -17,6 +17,7 @@ option(PYMNN_INTERNAL_SERVING "Internal use only." OFF)
 option(PYMNN_OPENCV_API "MNN OpenCV API be exposed" ON)
 option(PYMNN_IMGCODECS "MNN IMGCODECS API be exposed" OFF)
 option(PYMNN_AUDIO_API "MNN Audio API be exposed" OFF)
+option(PYMNN_LLM_API "MNN LLM API be exposed" OFF)
 option(PYMNN_OHOS_INTERNAL "compile for harmony internal." OFF)
 option(PYMNN_LLM_COLLECTION "offline llm data collection." OFF)
 
@@ -97,6 +98,10 @@ if(PYMNN_AUDIO_API)
     target_compile_definitions(mnnpybridge PRIVATE PYMNN_AUDIO_API)
 endif()
 
+if(PYMNN_LLM_API)
+    target_compile_definitions(mnnpybridge PRIVATE PYMNN_LLM_API)
+endif()
+
 if(PYMNN_INTERNAL_SERVING)
     message(STATUS "mnnpybridge define PYMNN_INTERNAL_SERVING")
     target_compile_definitions(mnnpybridge PRIVATE PYMNN_INTERNAL_SERVING)
@@ -214,6 +219,9 @@ else()
         if(PYMNN_NUMPY_USABLE)
             target_link_libraries(mnnpybridge PRIVATE numpy_python)
         endif()
+        if(PYMNN_LLM_API)
+            target_link_libraries(mnnpybridge PRIVATE MNN_LLM)
+        endif()
     endif()
 endif()
 

pymnn/src/llm.h

@@ -1,6 +1,10 @@
 #include <sstream>
+#include <iostream>
+#ifdef BUILD_FOR_IOS
+#include "MNN/llm/llm.hpp"
+#else
 #include "llm/llm.hpp"
-
+#endif
 #ifdef PYMNN_LLM_COLLECTION
 #include "cpp/getLinearInput.hpp"
 #endif
@@ -85,18 +89,87 @@ static PyObject* PyMNNLLM_getCurrentHistory(LLM *self, PyObject *args) {
 }
 static PyObject* PyMNNLLM_response(LLM *self, PyObject *args) {
     if (self->is_embedding) {
+        MNN_PRINT("[MNNLLM] response: is_embedding\n");
         Py_RETURN_NONE;
     }
-    const char* query = NULL;
+    
+    PyObject* content = nullptr;
     int stream = 0;
-    if (!PyArg_ParseTuple(args, "s|p", &query, &stream)) {
+    int max_new_tokens = 2048;
+    
+    if (!PyArg_ParseTuple(args, "O|ii", &content, &stream, &max_new_tokens)) {
+        MNN_PRINT("[MNNLLM] response: PyArg_ParseTuple failed\n");
         Py_RETURN_NONE;
     }
+    
     std::ostringstream null_os;
-    self->llm->response(query, stream ? &std::cout : &null_os);
-    return string2Object(null_os.str());
+    std::ostream* output_stream = stream ? &std::cout : &null_os;
+    
+    if (isString(content)) {
+        std::string text = object2String(content);
+        MNN_PRINT("[MNNLLM] response: text=%s, stream=%d, max_new_tokens=%d\n", text.c_str(), stream, max_new_tokens);
+        self->llm->response(text, output_stream, nullptr, max_new_tokens);
+    } else if (isPyDict(content)) {
+        MNN::Transformer::MultimodalPrompt multimodal_input;
+        PyObject* text_obj = PyDict_GetItemString(content, "text");
+        if (text_obj && isString(text_obj)) {
+            multimodal_input.prompt_template = object2String(text_obj);
+        }
+        PyObject* images_obj = PyDict_GetItemString(content, "images");
+        if (images_obj && PyList_Check(images_obj)) {
+            Py_ssize_t img_count = PyList_Size(images_obj);
+            for (Py_ssize_t i = 0; i < img_count; i++) {
+                PyObject* img_dict = PyList_GetItem(images_obj, i);
+                if (isPyDict(img_dict)) {
+                    PyObject* data_obj = PyDict_GetItemString(img_dict, "data");
+                    PyObject* width_obj = PyDict_GetItemString(img_dict, "width");
+                    PyObject* height_obj = PyDict_GetItemString(img_dict, "height");
+                    
+                    if (data_obj && width_obj && height_obj) {
+                        MNN::Transformer::PromptImagePart image_part;
+                        image_part.image_data = toVar(data_obj);
+                        image_part.width = PyLong_AsLong(width_obj);
+                        image_part.height = PyLong_AsLong(height_obj);
+                        
+                        std::string key = "image_" + std::to_string(i);
+                        multimodal_input.images[key] = image_part;
+                    }
+                }
+            }
+        }
+        
+        PyObject* audios_obj = PyDict_GetItemString(content, "audios");
+        if (audios_obj && PyList_Check(audios_obj)) {
+            Py_ssize_t audio_count = PyList_Size(audios_obj);
+            for (Py_ssize_t i = 0; i < audio_count; i++) {
+                PyObject* audio_dict = PyList_GetItem(audios_obj, i);
+                if (isPyDict(audio_dict)) {
+                    MNN::Transformer::PromptAudioPart audio_part;
+                    
+                    PyObject* file_path_obj = PyDict_GetItemString(audio_dict, "file_path");
+                    if (file_path_obj && isString(file_path_obj)) {
+                        audio_part.file_path = object2String(file_path_obj);
+                    }
+                    
+                    PyObject* waveform_obj = PyDict_GetItemString(audio_dict, "waveform");
+                    if (waveform_obj) {
+                        audio_part.waveform = toVar(waveform_obj);
+                    }
+                    
+                    std::string key = "audio_" + std::to_string(i);
+                    multimodal_input.audios[key] = audio_part;
+                }
+            }
+        }
+        MNN_PRINT("[MNNLLM] response: multimodal, stream=%d, max_new_tokens=%d\n", stream, max_new_tokens);
+        self->llm->response(multimodal_input, output_stream, nullptr, max_new_tokens);
+    } else {
+        PyMNN_ERROR("content must be str or dict");
+    }
+    std::string response_str = null_os.str();
+    MNN_PRINT("[MNNLLM] response: %s\n", response_str.c_str());
+    return string2Object(response_str);
 }
-
 static PyObject* PyMNNLLM_tokenizer_encode(LLM *self, PyObject *args) {
     if (self->is_embedding) {
         Py_RETURN_NONE;
@@ -149,6 +222,14 @@ static PyObject* PyMNNLLM_reset(LLM *self, PyObject *args) {
     Py_RETURN_NONE;
 }
 
+static PyObject* PyMNNLLM_get_statistics(LLM *self, PyObject *args) {
+    if (self->is_embedding) {
+        Py_RETURN_NONE;
+    }
+    auto statistics = self->llm->get_statistics();
+    return string2Object(statistics);
+}
+
 #ifdef PYMNN_LLM_COLLECTION
 static PyObject* PyMNNLLM_enable_collection_mode(LLM *self, PyObject *args) {
     if (self->is_embedding) {
@@ -205,12 +286,11 @@ static PyObject* PyMNNLLM_enable_collection_mode(LLM *self, PyObject *args) {
     return toPyObj(true);  
 }
 #endif
-
 static PyMethodDef PyMNNLLM_methods[] = {
     {"load", (PyCFunction)PyMNNLLM_load, METH_VARARGS, "load model."},
     {"forward", (PyCFunction)PyMNNLLM_forward, METH_VARARGS, "forward `logits` by `input_ids`."},
     {"generate", (PyCFunction)PyMNNLLM_generate, METH_VARARGS, "generate `output_ids` by `input_ids`."},
-    {"response", (PyCFunction)PyMNNLLM_response, METH_VARARGS, "response `query` without hsitory."},
+    {"response", (PyCFunction)PyMNNLLM_response, METH_VARARGS, "response `query` - supports both text and multimodal input."},
     {"get_current_history", (PyCFunction)PyMNNLLM_getCurrentHistory, METH_VARARGS, "Get Current History."},
     {"erase_history", (PyCFunction)PyMNNLLM_eraseHistory, METH_VARARGS, "Erase History."},
     {"tokenizer_encode", (PyCFunction)PyMNNLLM_tokenizer_encode, METH_VARARGS, "tokenizer encode."},
@@ -219,6 +299,7 @@ static PyMethodDef PyMNNLLM_methods[] = {
     {"create_lora", (PyCFunction)PyMNNLLM_create_lora, METH_VARARGS, "create_lora."},
     {"set_config", (PyCFunction)PyMNNLLM_set_config, METH_VARARGS, "set_config."},
     {"reset", (PyCFunction)PyMNNLLM_reset, METH_VARARGS, "reset."},
+    {"get_statistics", (PyCFunction)PyMNNLLM_get_statistics, METH_VARARGS, "get performance statistics."},
 #ifdef PYMNN_LLM_COLLECTION
     {"enable_collection_mode", (PyCFunction)PyMNNLLM_enable_collection_mode, METH_VARARGS, "Enable data collection mode."},
 #endif
@@ -274,7 +355,7 @@ static PyObject* PyMNNLLM_create_lora(LLM *self, PyObject *args) {
         Py_RETURN_NONE;
     }
     auto lora = self->llm->create_lora(path);
-    LLM *llm = (LLM *)PyObject_Call((PyObject*)&PyMNNLLM, PyTuple_New(0), NULL);
+    LLM *llm = (LLM *)PyObject_Call((PyObject*)PyType_FindTLSType(&PyMNNLLM), PyTuple_New(0), NULL);
     if (!llm) {
         return NULL;
     }
@@ -288,10 +369,11 @@ static PyObject* PyMNNLLM_create(PyObject *self, PyObject *args) {
     }
     const char* path = NULL;
     int embedding_model = 0;
-    if (!PyArg_ParseTuple(args, "s|p", &path, &embedding_model)) {
+    if (!PyArg_ParseTuple(args, "s|i", &path, &embedding_model)) {
+        PyMNN_ERROR_LOG("Invalid arguments. Usage: create(path, embedding_model=False)");
         return NULL;
     }
-    LLM *llm = (LLM *)PyObject_Call((PyObject*)&PyMNNLLM, PyTuple_New(0), NULL);
+    LLM *llm = (LLM *)PyObject_Call((PyObject*)PyType_FindTLSType(&PyMNNLLM), PyTuple_New(0), NULL);
     if (!llm) {
         return NULL;
     }

pymnn/src/util.h

@@ -398,6 +398,9 @@ static inline bool isPySequence(PyObject* obj) {
     // use isPySequence replace PySequence_Check
     return PyTuple_Check(obj) || PyList_Check(obj) || PyBytes_Check(obj);
 }
+static inline bool isPyDict(PyObject* obj) {
+    return PyDict_Check(obj);
+}
 static inline int PySequenceSize(PyObject* obj) {
     if (PyTuple_Check(obj)) return PyTuple_Size(obj);
     if (PyList_Check(obj)) return PyList_Size(obj);

source/backend/cpu/CPUAttention.cpp

@@ -24,10 +24,12 @@
 #define FLOAT16_T float
 #endif
 
+#define MNN_FLASH_ATTENTION_BLOCK_SIZE 64
+
 namespace MNN {
 
 template <typename T>
-void CPUAttention::pack_query(Tensor* query, char* pack_q, char* sum_q, int seq_len, int h, float q_scale) {
+void CPUAttention::pack_query(Tensor* query, int8_t* pack_q, int8_t* sum_q, int seq_len, int h, float q_scale) {
     if (mUseGemmInt8) { // Shape of Query: numhead, [seqlen/eP8, headdim/lP8, eP8, lP8]
         mMinQ[h] = query->host<T>()[h * mHeadDim];
         mMaxQ[h] = query->host<T>()[h * mHeadDim];
@@ -75,21 +77,21 @@ void CPUAttention::pack_query(Tensor* query, char* pack_q, char* sum_q, int seq_
 }
 
 template <typename T>
-void CPUAttention::unpack_QK(float * unpack_qk_dst, char * pack_qk_src, int seq_len, int kv_seq_len) {
+void CPUAttention::unpack_QK(float * unpack_qk_dst, int8_t * pack_qk_src, int seq_len, int kv_seq_len) {
     float * dst = unpack_qk_dst;
     T * src = (T *)(pack_qk_src);
-    // [kv_seq_len/unit, seq_len, unit] -> [seq_len, kv_seq_len]
+    // [kv_seq_len/mPack, seq_len, mPack] -> [seq_len, kv_seq_len]
     for (int i = 0; i < seq_len; i++) {
         for (int j = 0; j < kv_seq_len; j++) {
-            int out_index = j / unit;
-            int in_index  = j % unit;
-            dst[i * kv_seq_len + j] = src[out_index * seq_len * unit + i * unit + in_index];
+            int out_index = j / mPack;
+            int in_index  = j % mPack;
+            dst[i * kv_seq_len + j] = src[out_index * seq_len * mPack + i * mPack + in_index];
         }
     }
 }
 
 template <typename T>
-static void pack_QK(char * pack_qk_dst, float * qk_src, int seq_len, int kv_seq_len, int eP, int lP, int bytes) {
+static void pack_QK(int8_t * pack_qk_dst, float * qk_src, int seq_len, int kv_seq_len, int eP, int lP, int bytes) {
     T * dst = reinterpret_cast<T*>(pack_qk_dst);
     float * src = reinterpret_cast<float*>(qk_src);
     // [seq_len, kv_seq_len] -> [seq_len/eP, kv_seq_len/lP, eP, lP]
@@ -108,38 +110,50 @@ static void pack_QK(char * pack_qk_dst, float * qk_src, int seq_len, int kv_seq_
 }
 
 template <typename T>
-static void mask_QK(float * unpack_qk, int seq_len, int kv_seq_len, float mScale, float min_val, const Tensor* mask) {
-    if (mask == nullptr) {
-        for (int i = 0; i < kv_seq_len; i++) {
+static void mask_QK(float * unpack_qk, int seq_len, int kv_seq_len, float mScale, float min_val, const Tensor* maskTensor, int offset, int startIndx, int processedKvLen) {
+    
+    int endIndx = startIndx + processedKvLen;
+    if (maskTensor == nullptr) {
+        for (int i = 0; i < processedKvLen; i++) {
             unpack_qk[i] = unpack_qk[i] * mScale;
         }
-    } else if (mask->getType() == halide_type_of<float>()) {
+        return;
+    }
+    const int8_t* mask = maskTensor->host<int8_t>();
+    halide_type_t htype = maskTensor->getType();
+    int maskSize = maskTensor->elementSize();
+    
+    if (htype == halide_type_of<float>()) {
         // float mask
-        T* fpmask_ptr = mask->host<T>();
-        if (mask->elementSize() == seq_len * kv_seq_len) {
-            // normal mask for all token
-            for (int i = 0; i < seq_len * kv_seq_len; i++) {
-                unpack_qk[i] = unpack_qk[i] * mScale + fpmask_ptr[i];
-            }
-        } else {
-            // square mask just for new generation token
-            int offset = kv_seq_len - seq_len;
+        T* fpmask_ptr = (T*)mask;
+        if (maskSize == seq_len * kv_seq_len) { // sliding attention, mask shape: [seq_len, kv_seq_len]
             for (int i = 0; i < seq_len; ++i) {
-                auto unpack_qki = unpack_qk + i * kv_seq_len;
-                auto fpmask_ptri = fpmask_ptr + i * seq_len;
-                for (int j = 0; j < offset; ++j) {
-                    unpack_qki[j] = unpack_qki[j] * mScale;
+                auto unpack_qki = unpack_qk + i * processedKvLen;
+                auto fpmask_ptri = fpmask_ptr + i * kv_seq_len;
+                for (int j = startIndx; j < endIndx; ++j) {
+                    unpack_qki[j - startIndx] = unpack_qki[j - startIndx] * mScale + fpmask_ptri[j];
                 }
-                for (int j = 0; j < seq_len; ++j) {
-                    unpack_qki[offset + j] = unpack_qki[offset + j] * mScale + fpmask_ptri[j];
+            }
+        } else { // mask shape: [seq_len, seq_len]
+            for (int i = 0; i < seq_len; ++i) {
+                auto unpack_qki = unpack_qk + i * processedKvLen;
+                auto fpmask_ptri = fpmask_ptr + i * seq_len;
+
+                auto notMaskIndx = ALIMIN(endIndx, offset);
+                auto stMaskIndx = ALIMAX(startIndx, offset);
+                for (int j = startIndx; j < notMaskIndx; ++j) {
+                    unpack_qki[j - startIndx] = unpack_qki[j - startIndx] * mScale;
+                }
+                for (int j = stMaskIndx; j < endIndx; ++j) {
+                    unpack_qki[j - startIndx] = unpack_qki[j - startIndx] * mScale + fpmask_ptri[j - offset];
                 }
             }
         }
     } else {
         // int mask
-        int* mask_ptr = mask->host<int>();
-        for (int i = 0; i < seq_len * kv_seq_len; i++) {
-            if (mask_ptr[i]) {
+        int* mask_ptr = (int*)mask;
+        for (int i = 0; i < seq_len * processedKvLen; i++) {
+            if (mask_ptr[i / processedKvLen * kv_seq_len + i % processedKvLen]) {
                 unpack_qk[i] = unpack_qk[i] * mScale;
             } else {
                 unpack_qk[i] = min_val;
@@ -148,41 +162,47 @@ static void mask_QK(float * unpack_qk, int seq_len, int kv_seq_len, float mScale
     }
 }
 
-static void softmax_QK(float* softmax_qk_addr, float* unpack_qk_addr, int seq_len, int kv_seq_len) {
-    for (int i = 0; i < seq_len; i++) {  // softmax each row
-        MNNSoftmax(softmax_qk_addr + i * kv_seq_len, unpack_qk_addr + i * kv_seq_len, kv_seq_len);
+typedef void(softmaxFunc)(float* softmaxDst, float* input, float* runningMax, float* runningSum, float* updateScale, int outside, int reduceSize);
+template <typename T>
+static void softmaxQK(float* softmax_qk_addr, float* unpack_qk_addr, float* runningMax, float* runningSum, float* diffScale, const float* sinkPtr, softmaxFunc* sffunc, int seq_len, int kv_seq_len, int headIdx, bool isLastKvBlock) {
+    
+    // not sliding attention
+    if (sinkPtr == nullptr) {
+        sffunc(softmax_qk_addr, unpack_qk_addr, runningMax, runningSum, diffScale, seq_len, kv_seq_len);
+        return;
+    }
+    
+    float sink = ((T*)sinkPtr)[headIdx];
+    if (!runningMax && !runningSum) { // Do not use flash attention
+        
+        for (int i = 0; i < seq_len; ++i) {
+            float exprOffset[4] = {1, 0, -sink, 1.f};
+            MNNExp(softmax_qk_addr + i * kv_seq_len, unpack_qk_addr + i * kv_seq_len, exprOffset, kv_seq_len);
+            for (int j = 0; j < kv_seq_len; ++j) {
+                softmax_qk_addr[i * kv_seq_len + j] /= exprOffset[3];
+            }
+        }
+        return;
     }
-}
 
-static void sink_softmax_QK(float* softmax_qk_addr, float* unpack_qk_addr, int seq_len, int kv_seq_len, float sink) {
-    // TODO: opt
-    std::vector<float> buffer(2 * (kv_seq_len + 1));
-    float* sinkSrc = buffer.data();
-    float* sinkDst = buffer.data() + kv_seq_len + 1;
-    for (int i = 0; i < seq_len; i++) {  // softmax each row
-        ::memcpy(sinkSrc, unpack_qk_addr + i * kv_seq_len, kv_seq_len * sizeof(float));
-        sinkSrc[kv_seq_len] = sink;
-        float rowMax = sink;
-        for (int j = 0; j < kv_seq_len; j++) {
-            rowMax = ALIMAX(rowMax, sinkSrc[j]);
+    // Use flash attention    
+    if (isLastKvBlock) {
+        for (int i = 0; i < seq_len; ++i) {
+            runningSum[i] += expf(sink - runningMax[i]);
         }
-        for (int j = 0; j < kv_seq_len + 1; j++) {
-            sinkSrc[j] = sinkSrc[j] - rowMax;
-        }
-        MNNSoftmax(sinkDst, sinkSrc, kv_seq_len + 1);
-        ::memcpy(softmax_qk_addr + i * kv_seq_len, sinkDst, kv_seq_len * sizeof(float));
     }
+    MNNSoftmax(softmax_qk_addr, unpack_qk_addr, runningMax, runningSum, diffScale, seq_len, kv_seq_len);
 }
 
 template <typename T>
-static void unpack_QKV(char* pack_qkv, char* unpack_qkv, int mNumHead, int mHeadDim, int unit, int seq_len) {
+static void unpack_QKV(int8_t* pack_qkv, int8_t* unpack_qkv, int mNumHead, int mHeadDim, int mPack, int seq_len) {
     auto src_ptr = reinterpret_cast<T*>(pack_qkv);
     auto dst_ptr = reinterpret_cast<T*>(unpack_qkv);
     for (int i = 0; i < seq_len; i++) {
         for (int j = 0; j < mHeadDim; j++) {
-            int a = j / unit;
-            int b = j % unit;
-            dst_ptr[i * mNumHead * mHeadDim + j] = src_ptr[a * seq_len * unit + i * unit + b];
+            int a = j / mPack;
+            int b = j % mPack;
+            dst_ptr[i * mNumHead * mHeadDim + j] = src_ptr[a * seq_len * mPack + i * mPack + b];
         }
     }
 }
@@ -191,10 +211,10 @@ ErrorCode CPUAttention::onResize(const std::vector<Tensor*>& inputs, const std::
     auto core = static_cast<CPUBackend *>(backend())->functions();
     core->MNNGetMatMulPackMode(&eP, &lP, &hP);
     mThreadNum = ((CPUBackend *)backend())->threadNumber();
-    unit  = core->pack;
+    mPack  = core->pack;
     bytes = core->bytes;
     int qkvQuantOptions = static_cast<CPUBackend *>(backend())->getRuntime()->hint().qkvQuantOption;
-    mUseGemmInt8 = (qkvQuantOptions == 4);
+    mUseGemmInt8 = (qkvQuantOptions % 8 == 4);
     if (mUseGemmInt8) {
         static_cast<CPUBackend*>(backend())->int8Functions()->MNNGetGemmUnit(&hP8, &lP8, &eP8);
     }
@@ -208,7 +228,7 @@ ErrorCode CPUAttention::onResize(const std::vector<Tensor*>& inputs, const std::
     if (mUseGemmInt8) {
         mPackQ.reset(Tensor::createDevice<int8_t>({mThreadNum, UP_DIV(seq_len, eP8), UP_DIV(mHeadDim, lP8), eP8 * lP8}));
         mSumQ.reset(Tensor::createDevice<int32_t>({mThreadNum, UP_DIV(seq_len, eP8), eP8}));
-        mPackQKV.reset(Tensor::createDevice<float>({mThreadNum, UP_DIV(mHeadDim, unit), seq_len, unit}));
+        mPackQKV.reset(Tensor::createDevice<float>({mThreadNum, UP_DIV(mHeadDim, mPack), seq_len, mPack}));
         backend()->onAcquireBuffer(mPackQ.get(), Backend::DYNAMIC);
         backend()->onAcquireBuffer(mSumQ.get(), Backend::DYNAMIC);
         backend()->onAcquireBuffer(mPackQKV.get(), Backend::DYNAMIC);
@@ -220,18 +240,40 @@ ErrorCode CPUAttention::onResize(const std::vector<Tensor*>& inputs, const std::
         mQueryScale.resize(mNumHead);
         mQueryZeroPoint.resize(mNumHead);
     } else {
-        mPackQ.reset(Tensor::createDevice<float>({mThreadNum, UP_DIV(seq_len, eP), ROUND_UP(mHeadDim, lP), eP}));
-        mPackQKV.reset(Tensor::createDevice<float>({mThreadNum, UP_DIV(mHeadDim, unit), seq_len, unit}));
+        mPackQ.reset(Tensor::createDevice<int8_t>({mThreadNum, UP_DIV(seq_len, eP), ROUND_UP(mHeadDim, lP), eP * bytes}));
+        mPackQKV.reset(Tensor::createDevice<int8_t>({mThreadNum, UP_DIV(mHeadDim, mPack), seq_len, mPack * bytes}));
         backend()->onAcquireBuffer(mPackQ.get(), Backend::DYNAMIC);
         backend()->onAcquireBuffer(mPackQKV.get(), Backend::DYNAMIC);
+        
+        // flash attention
+        if (qkvQuantOptions / 8 == 1) {
+            mRunningMax.reset(Tensor::createDevice<int8_t>({mThreadNum, seq_len * 4}));
+            mRunningSum.reset(Tensor::createDevice<int8_t>({mThreadNum, seq_len * 4}));
+            mExpfDiffMax.reset(Tensor::createDevice<int8_t>({mThreadNum, seq_len * 4}));
+            mTempOut.reset(Tensor::createDevice<int8_t>({mThreadNum, UP_DIV(mHeadDim, mPack), seq_len, mPack * bytes}));
+
+            backend()->onAcquireBuffer(mRunningMax.get(), Backend::DYNAMIC);
+            backend()->onAcquireBuffer(mRunningSum.get(), Backend::DYNAMIC);
+            backend()->onAcquireBuffer(mExpfDiffMax.get(), Backend::DYNAMIC);
+            backend()->onAcquireBuffer(mTempOut.get(), Backend::DYNAMIC);
+        }
+
         backend()->onReleaseBuffer(mPackQ.get(), Backend::DYNAMIC);
         backend()->onReleaseBuffer(mPackQKV.get(), Backend::DYNAMIC);
+
+        if (qkvQuantOptions / 8 == 1) {
+            backend()->onReleaseBuffer(mRunningMax.get(), Backend::DYNAMIC);
+            backend()->onReleaseBuffer(mRunningSum.get(), Backend::DYNAMIC);
+            backend()->onReleaseBuffer(mExpfDiffMax.get(), Backend::DYNAMIC);
+            backend()->onReleaseBuffer(mTempOut.get(), Backend::DYNAMIC);
+        }
     }
     return NO_ERROR;
 }
 
 ErrorCode CPUAttention::onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
     auto core  = static_cast<CPUBackend *>(backend())->functions();
+    auto qkvQuantOptions = static_cast<CPUBackend *>(backend())->getRuntime()->hint().qkvQuantOption;
     auto query = inputs[0];
     auto key   = inputs[1];
     auto value = inputs[2];
@@ -283,146 +325,146 @@ ErrorCode CPUAttention::onExecute(const std::vector<Tensor*>& inputs, const std:
     int max_len = mKVCacheManager->maxLength();
     bool quant_key = mKVCacheManager->config()->mQuantKey;
     bool quant_value = mKVCacheManager->config()->mQuantValue;
+
+    mBlockKV = (qkvQuantOptions / 8 == 1) ? ALIMIN(MNN_FLASH_ATTENTION_BLOCK_SIZE, kv_seq_len) : kv_seq_len;
+    int32_t units[2] = {eP, lP};
+
     // Temporary tensors for intermediate results
-    std::shared_ptr<Tensor> packQK(Tensor::createDevice<float>({mThreadNum, UP_DIV(kv_seq_len, unit), seq_len, unit}));
-    std::shared_ptr<Tensor> unpackQK(Tensor::createDevice<int32_t>({mThreadNum, seq_len, kv_seq_len}));
-    std::shared_ptr<Tensor> softmMaxQ(Tensor::createDevice<int32_t>({mThreadNum, seq_len, kv_seq_len}));
-    std::shared_ptr<Tensor> newPackQK(Tensor::createDevice<float>({mThreadNum, UP_DIV(seq_len, eP), ROUND_UP(kv_seq_len, lP), eP}));
-    std::shared_ptr<Tensor> dequantV(Tensor::createDevice<float>({mKvNumHead, UP_DIV(mHeadDim, hP), kv_seq_len, hP}));
-    backend()->onAcquireBuffer(packQK.get(), Backend::STATIC);
+    std::shared_ptr<Tensor> unpackQK(Tensor::createDevice<int32_t>({mThreadNum, seq_len, mBlockKV}));
+    std::shared_ptr<Tensor> softmMaxQ(Tensor::createDevice<int32_t>({mThreadNum, seq_len, mBlockKV}));
+    std::shared_ptr<Tensor> newPackQK(Tensor::createDevice<int8_t>({mThreadNum, UP_DIV(seq_len, eP), ROUND_UP(mBlockKV, lP), eP * bytes}));
+    std::shared_ptr<Tensor> dequantV(Tensor::createDevice<int8_t>({mKvNumHead, UP_DIV(mHeadDim, hP), kv_seq_len, hP * bytes}));
+    // mTempQKBlock.reset(Tensor::createDevice<int8_t>({mThreadNum, UP_DIV(mBlockKV, mPack), seq_len, mPack * bytes}));
+    std::shared_ptr<Tensor> tempQKBlock(Tensor::createDevice<int8_t>({mThreadNum, UP_DIV(mBlockKV, mPack), seq_len, mPack * bytes}));
     backend()->onAcquireBuffer(unpackQK.get(), Backend::STATIC);
     backend()->onAcquireBuffer(softmMaxQ.get(), Backend::STATIC);
     backend()->onAcquireBuffer(newPackQK.get(), Backend::STATIC);
+    backend()->onAcquireBuffer(tempQKBlock.get(), Backend::STATIC);
     if (quant_value) {
         backend()->onAcquireBuffer(dequantV.get(), Backend::STATIC);
         mKVCacheManager->onDequantValue(dequantV.get());
     }
     const float* sinksPtr = sinks ? sinks->host<float>() : nullptr;
     std::function<void(int)> mCompute = [=](int tId) {
-        auto pack_q      = mPackQ->host<char>() + tId * UP_DIV(seq_len, eP) * ROUND_UP(mHeadDim, lP) * eP * bytes;
-        auto pack_qk     = packQK->host<char>() + tId * UP_DIV(kv_seq_len, unit) * seq_len * unit * bytes;
-        char * sum_q     = nullptr;
-        auto unpack_qk   = unpackQK->host<float>() + tId * seq_len * kv_seq_len;
-        auto softmax_qk  = softmMaxQ->host<float>() + tId * seq_len * kv_seq_len;
-        auto new_pack_qk = newPackQK->host<char>() + tId * UP_DIV(seq_len, eP) * ROUND_UP(kv_seq_len, lP) * eP * bytes;
-        auto pack_qkv    = mPackQKV->host<char>() + tId * UP_DIV(mHeadDim, unit) * seq_len * unit * bytes;
+        auto qReordered      = mPackQ->host<int8_t>() + tId * mPackQ->stride(0);
+        auto qkPacked     = tempQKBlock->host<int8_t>() + tId * tempQKBlock->stride(0);
+        int8_t * sum_q     = nullptr;
+        auto qkFlatten   = unpackQK->host<float>() + tId * unpackQK->stride(0);
+        auto qkSoftmax  = softmMaxQ->host<float>() + tId * softmMaxQ->stride(0);
+        auto qkReordered = newPackQK->host<int8_t>() + tId * newPackQK->stride(0);
+        auto qkvPacked    = mPackQKV->host<int8_t>() + tId * mPackQKV->stride(0);
         auto QxK         = quant_key ? core->MNNPackedMatMul_int8 : core->MNNPackedMatMul;
         auto QxK_remain  = quant_key ? core->MNNPackedMatMulRemain_int8 : core->MNNPackedMatMulRemain;
+
+        // Flash Attention
+        auto runningMax = mRunningMax ? (float*)(mRunningMax->host<int8_t>() + tId * mRunningMax->stride(0)) : nullptr;
+        auto runningSum = mRunningSum ? (float*)(mRunningSum->host<int8_t>() + tId * mRunningSum->stride(0)) : nullptr;
+        auto diffScale = mExpfDiffMax ? (float*)(mExpfDiffMax->host<int8_t>() + tId * mExpfDiffMax->stride(0)) : nullptr;
+        auto outputPacked = mTempOut ? mTempOut->host<int8_t>() + tId * mTempOut->stride(0) : qkvPacked;
         int  head_index  = tId * tileCount;
+        int  kvBlocks = UP_DIV(kv_seq_len, mBlockKV);
+
         if (mUseGemmInt8) {
-            pack_q  = mPackQ->host<char>() + tId * UP_DIV(seq_len, eP8) * UP_DIV(mHeadDim, lP8) * eP8 * lP8;
-            sum_q   = mSumQ->host<char>() + tId * UP_DIV(seq_len, eP8) * eP8 * 4;
+            qReordered  = mPackQ->host<int8_t>() + tId * UP_DIV(seq_len, eP8) * UP_DIV(mHeadDim, lP8) * eP8 * lP8;
+            sum_q   = mSumQ->host<int8_t>() + tId * UP_DIV(seq_len, eP8) * eP8 * 4;
         }
         for (int h = head_index; h < head_index + tileCount && h < mNumHead; h++) {
-            int    kv_h            = h / group_size;
-            char * key_addr        = mKVCacheManager->addrOfKey(kv_h);
-            char * scale_addr      = mKVCacheManager->addrOfScale(kv_h);
-            char * zero_point_addr = mKVCacheManager->addrOfZeroPoint(kv_h);
-            char * key_sum_addr    = mKVCacheManager->addrOfKeySum(kv_h);
-            char * value_addr      = quant_value ? (dequantV->host<char>() + kv_h * UP_DIV(mHeadDim, hP) * ROUND_UP(kv_seq_len, lP) * hP * bytes) : mKVCacheManager->addrOfValue(kv_h);
-            if (bytes == 2) {
-                pack_query<FLOAT16_T>(query, pack_q, sum_q, seq_len, h, q_scale);
-            } else {
-                pack_query<float>(query, pack_q, sum_q, seq_len, h, q_scale);
+            if (runningSum && runningMax) {
+                memset(runningSum, 0, mRunningSum->stride(0));
+                if (sinksPtr == nullptr) {
+                    for (int k = 0; k < seq_len; ++k) {
+                        runningMax[k] = -std::numeric_limits<float>::infinity();
+                    }
+                } else {
+                    float sinkVal;
+                    if (bytes == 2) {
+                        sinkVal = ((FLOAT16_T*)sinksPtr)[h];
+                    } else {
+                        sinkVal =sinksPtr[h];
+                    }
+                    for (int k = 0; k < seq_len; ++k) {
+                        runningMax[k] = sinkVal;
+                    }
+                }
             }
-            // query @ key
+            int    kv_h              = h / group_size;
+            int8_t * key_addr        = mKVCacheManager->addrOfKey(kv_h);
+            int8_t * scale_addr      = mKVCacheManager->addrOfScale(kv_h);
+            int8_t * zero_point_addr = mKVCacheManager->addrOfZeroPoint(kv_h);
+            int8_t * key_sum_addr    = mKVCacheManager->addrOfKeySum(kv_h);
+            int8_t * value_addr      = quant_value ? (dequantV->host<int8_t>() + kv_h * UP_DIV(mHeadDim, hP) * ROUND_UP(kv_seq_len, lP) * hP * bytes) : mKVCacheManager->addrOfValue(kv_h);
             if (mUseGemmInt8) {
-                auto GemmInt8Kernel = static_cast<CPUBackend*>(backend())->int8Functions()->Int8GemmKernel;
-                if (bytes == 2 && unit == 8) {
-                    GemmInt8Kernel = static_cast<CPUBackend*>(backend())->int8Functions()->MNNGemmInt8AddBiasScale_Unit_FP16;
-                }
-                std::vector<float> postScale(ROUND_UP(kv_seq_len, hP8), 0.0f);
-                for (int i = 0; i < kv_seq_len; i++) {
-                    postScale[i] = ((float*)scale_addr)[i] * mQueryScale[h] * q_scale;
-                }
-                std::vector<float> weightQuantBias(ROUND_UP(kv_seq_len, hP8), 0.0f);
-                for (int i = 0; i < kv_seq_len; i++) {
-                    weightQuantBias[i] = -((float*)scale_addr)[i] * ((float*)zero_point_addr)[i] * q_scale;
-                }
-                std::vector<float> biasFloat(ROUND_UP(kv_seq_len, hP8), 0.0f);
-                for (int i = 0; i < kv_seq_len; i++) {
-                    biasFloat[i] = -mQueryScale[h] * mQueryZeroPoint[h] * ((float*)key_sum_addr)[i] * q_scale;
-                }
-                QuanPostTreatParameters post;
-                post.bias = nullptr;
-                post.biasFloat = biasFloat.data();
-                post.blockNum = 1;
-                post.inputBias = nullptr;
-                post.inputScale = nullptr;
-                post.fp32minmax = nullptr;
-                post.scale = postScale.data();
-                post.useInt8 = false;
-                post.weightKernelSum = weightQuantBias.data();
-                int N = UP_DIV(seq_len, eP8);
-                for (int i = 0; i < N; i++) {
-                    int realcount = ALIMIN(eP8, seq_len - i * eP8);
-                    post.srcKernelSum = (float*)((char*)sum_q + i * eP8 * 4);
-                    GemmInt8Kernel(
-                        (int8_t*)pack_qk + i * eP8 * unit * bytes,
-                        (int8_t*)pack_q + i * ROUND_UP(mHeadDim, lP8) * eP8,
-                        (int8_t*)key_addr,
-                        UP_DIV(mHeadDim, lP8),
-                        seq_len * unit * bytes,
-                        UP_DIV(kv_seq_len, unit),
-                        &post,
-                        realcount
-                    );
+                if (bytes == 2) {
+                    pack_query<FLOAT16_T>(query, qReordered, sum_q, seq_len, h, q_scale);
+                } else {
+                    pack_query<float>(query, qReordered, sum_q, seq_len, h, q_scale);
                 }
+            } else {
+                core->MNNAttenPackAndScaleSingleHead((float*)qReordered, (float*)(query->host<int8_t>() + h * mHeadDim * bytes), mHeadDim * mNumHead, &q_scale, units, seq_len, mHeadDim);
             }
-            else {
+            for (int i = 0; i < kvBlocks; ++i) {
+                int subKvSeqLen = ALIMIN(mBlockKV, kv_seq_len - i * mBlockKV);
+                auto keyPtr = key_addr + i * UP_DIV(mBlockKV, hP) * ROUND_UP(mHeadDim, lP) * hP * bytes;
+                auto valuePtr = value_addr + i * UP_DIV(mBlockKV, lP) * hP * lP * bytes;
+                // query @ key
+                {
+                    int loop_e = seq_len / eP;
+                    int remain = seq_len % eP;
+                    auto qStride0 = ROUND_UP(mHeadDim, lP) * eP * bytes;
+                    size_t shapeParameters[7] = {(size_t)eP * bytes, ROUND_UP((size_t)mHeadDim, lP), (size_t)subKvSeqLen, (size_t)seq_len * mPack * bytes, 0, 0, 0};
+                    for (int ei = 0 ; ei < loop_e; ei++) {
+                        QxK((float*)(qkPacked + (ei * eP * mPack) * bytes), (float*)(qReordered + ei * qStride0), (float*)keyPtr, shapeParameters, nullptr, nullptr, (float*)scale_addr, (float*)zero_point_addr);
+                    }
+                    QxK_remain((float*)(qkPacked + (loop_e * eP * mPack) * bytes), (float*)(qReordered + loop_e * qStride0), (float*)keyPtr, remain, shapeParameters, nullptr, nullptr, (float*)scale_addr, (float*)zero_point_addr);
+                }
+                // qk: [kv_seq_len/mPack, seq_len, mPack] -> [seq_len/eP, kv_seq_len, eP]
+                {
+                    if(bytes == 2) {
+                        if (seq_len == 1) {
+                            core->MNNLowpToFp32((int16_t*)qkPacked, qkFlatten, seq_len * subKvSeqLen);
+                        } else {
+                            core->MNNAttenUnpackAndConvertFp16(qkFlatten, (float*)qkPacked, subKvSeqLen, seq_len, mPack);
+                        }
+                        mask_QK<FLOAT16_T>(qkFlatten, seq_len, kv_seq_len, mScale, std::numeric_limits<float>::lowest(), mask, kv_seq_len - seq_len, i * mBlockKV, subKvSeqLen);
+                        softmaxQK<FLOAT16_T>(qkSoftmax, qkFlatten, runningMax, runningSum, diffScale, sinksPtr, core->MNNSoftmax, seq_len, subKvSeqLen, h, i == kvBlocks - 1);
+                        core->MNNAttenPackAndConvertFp32((float*)qkReordered, qkSoftmax, units, seq_len, subKvSeqLen);
+                    } else {
+                        if (seq_len > 1) {
+                            int32_t areaOffset[2] = {seq_len, seq_len};
+                            core->MNNUnpackCUnitTranspose(qkFlatten, (float*)qkPacked, seq_len, subKvSeqLen, areaOffset);
+                        } else {
+                            memcpy(qkFlatten, qkPacked, subKvSeqLen * sizeof(float));
+                        }
+                        mask_QK<float>(qkFlatten, seq_len, kv_seq_len, mScale, std::numeric_limits<float>::lowest(), mask, kv_seq_len - seq_len, i * mBlockKV, subKvSeqLen);
+                        softmaxQK<float>(qkSoftmax, qkFlatten, runningMax, runningSum, diffScale, sinksPtr, core->MNNSoftmax, seq_len, subKvSeqLen, h, i == kvBlocks - 1);
+                        packKvCache((float*)qkReordered, qkSoftmax, seq_len, subKvSeqLen, eP);
+                    }
+                }
+                // qk @ v
+                // TODO: update qkvPacked using diffScale
+                size_t shapeParameters[7] = {(size_t)eP * bytes, ROUND_UP((size_t)subKvSeqLen, lP), (size_t)mHeadDim, (size_t)seq_len * mPack * bytes, 0, 0, 0};
+                size_t bExtraStride = (UP_DIV(max_len, lP) - UP_DIV(subKvSeqLen + i * mBlockKV, lP) + UP_DIV(i * mBlockKV, lP)) * hP * lP * bytes;
+                shapeParameters[5] = quant_value ? 0 : bExtraStride;
                 int loop_e = seq_len / eP;
                 int remain = seq_len % eP;
-                auto qStride0 = ROUND_UP(mHeadDim, lP) * eP * bytes;
-                size_t shapeParameters[7] = {(size_t)eP * bytes, ROUND_UP((size_t)mHeadDim, lP), (size_t)kv_seq_len, (size_t)seq_len * unit * bytes, 0, 0, 0};
-                for (int i = 0 ; i < loop_e; i++) {
-                    QxK((float*)(pack_qk + (i * eP * unit) * bytes), (float*)(pack_q + i * qStride0), (float*)key_addr, shapeParameters, nullptr, nullptr, (float*)scale_addr, (float*)zero_point_addr);
+                auto qkStride0 = ROUND_UP(subKvSeqLen, lP) * eP * bytes;
+                for (int ei = 0 ; ei < loop_e; ei++) {
+                    core->MNNPackedMatMul((float*)(qkvPacked + (ei * eP * mPack) * bytes), (float*)(qkReordered + ei * qkStride0), (float*)valuePtr, shapeParameters, nullptr, nullptr, nullptr, nullptr);
                 }
-                QxK_remain((float*)(pack_qk + (loop_e * eP * unit) * bytes), (float*)(pack_q + loop_e * qStride0), (float*)key_addr, remain, shapeParameters, nullptr, nullptr, (float*)scale_addr, (float*)zero_point_addr);
-            }
-            // qk: [kv_seq_len/unit, seq_len, unit] -> [seq_len, kv_seq_len] -> [seq_len/eP, kv_seq_len, eP]
-            if (sinksPtr != nullptr) {
-                if(bytes == 2) {
-                    unpack_QK<FLOAT16_T>(unpack_qk, pack_qk, seq_len, kv_seq_len);
-                    mask_QK<FLOAT16_T>(unpack_qk, seq_len, kv_seq_len, mScale, std::numeric_limits<float>::lowest(), mask);
-                    sink_softmax_QK(softmax_qk, unpack_qk, seq_len, kv_seq_len, sinksPtr[h]);
-                    pack_QK<FLOAT16_T>(new_pack_qk, softmax_qk, seq_len, kv_seq_len, eP, lP, bytes);
-                } else {
-                    unpack_QK<float>(unpack_qk, pack_qk, seq_len, kv_seq_len);
-                    mask_QK<float>(unpack_qk, seq_len, kv_seq_len, mScale, std::numeric_limits<float>::lowest(), mask);
-                    sink_softmax_QK(softmax_qk, unpack_qk, seq_len, kv_seq_len, sinksPtr[h]);
-                    pack_QK<float>(new_pack_qk, softmax_qk, seq_len, kv_seq_len, eP, lP, bytes);
-                }
-            } else {
-                if(bytes == 2) {
-                    unpack_QK<FLOAT16_T>(unpack_qk, pack_qk, seq_len, kv_seq_len);
-                    mask_QK<FLOAT16_T>(unpack_qk, seq_len, kv_seq_len, mScale, std::numeric_limits<float>::lowest(), mask);
-                    softmax_QK(softmax_qk, unpack_qk, seq_len, kv_seq_len);
-                    pack_QK<FLOAT16_T>(new_pack_qk, softmax_qk, seq_len, kv_seq_len, eP, lP, bytes);
-                } else {
-                    unpack_QK<float>(unpack_qk, pack_qk, seq_len, kv_seq_len);
-                    mask_QK<float>(unpack_qk, seq_len, kv_seq_len, mScale, std::numeric_limits<float>::lowest(), mask);
-                    softmax_QK(softmax_qk, unpack_qk, seq_len, kv_seq_len);
-                    pack_QK<float>(new_pack_qk, softmax_qk, seq_len, kv_seq_len, eP, lP, bytes);
+                core->MNNPackedMatMulRemain((float*)(qkvPacked + (loop_e * eP * mPack) * bytes), (float*)(qkReordered + loop_e * qkStride0), (float*)valuePtr, remain, shapeParameters, nullptr, nullptr, nullptr, nullptr);
+
+                if (runningMax != nullptr && runningSum != nullptr && diffScale != nullptr) {
+                    core->MNNFlashAttentionUpdateBlockOutput((float*)outputPacked, (float*)qkvPacked, diffScale, runningSum, UP_DIV(mHeadDim, mPack), seq_len, mPack, i, kvBlocks, mPackQKV->stride(0) / bytes, bytes);
                 }
             }
-            // qk @ v
-            size_t shapeParameters[7] = {(size_t)eP * bytes, ROUND_UP((size_t)kv_seq_len, lP), (size_t)mHeadDim, (size_t)seq_len * unit * bytes, 0, 0, 0};
-            size_t bExtraStride = (UP_DIV(max_len, lP) - UP_DIV(kv_seq_len, lP)) * hP * lP * bytes;
-            shapeParameters[5] = quant_value ? 0 : bExtraStride;
-            int loop_e = seq_len / eP;
-            int remain = seq_len % eP;
-            auto qkStride0 = ROUND_UP(kv_seq_len, lP) * eP * bytes;
-            for (int i = 0 ; i < loop_e; i++) {
-                core->MNNPackedMatMul((float*)(pack_qkv + (i * eP * unit) * bytes), (float*)(new_pack_qk + i * qkStride0), (float*)value_addr, shapeParameters, nullptr, nullptr, nullptr, nullptr);
-            }
-            core->MNNPackedMatMulRemain((float*)(pack_qkv + (loop_e * eP * unit) * bytes), (float*)(new_pack_qk + loop_e * qkStride0), (float*)value_addr, remain, shapeParameters, nullptr, nullptr, nullptr, nullptr);
-            // unpack: [head_dim/unit, seq_len, unit] -> [seq_len, num_head, head_dim]
-            auto dst_ptr = outputs[0]->host<char>() + h * mHeadDim * bytes;
+            // unpack: [head_dim/mPack, seq_len, mPack] -> [seq_len, num_head, head_dim]
+            auto dst_ptr = outputs[0]->host<int8_t>() + h * mHeadDim * bytes;
             if (bytes == 2) {
-                unpack_QKV<int16_t>(pack_qkv, dst_ptr, mNumHead, mHeadDim, unit, seq_len);
+                unpack_QKV<int16_t>((int8_t*)outputPacked, dst_ptr, mNumHead, mHeadDim, mPack, seq_len);
             } else {
-                unpack_QKV<float>(pack_qkv, dst_ptr, mNumHead, mHeadDim, unit, seq_len);
+                unpack_QKV<float>((int8_t*)outputPacked, dst_ptr, mNumHead, mHeadDim, mPack, seq_len);
             }
+            
         }
     };
 
@@ -431,10 +473,10 @@ ErrorCode CPUAttention::onExecute(const std::vector<Tensor*>& inputs, const std:
     }
     MNN_CONCURRENCY_END();
 
-    backend()->onReleaseBuffer(packQK.get(), Backend::STATIC);
     backend()->onReleaseBuffer(unpackQK.get(), Backend::STATIC);
     backend()->onReleaseBuffer(softmMaxQ.get(), Backend::STATIC);
     backend()->onReleaseBuffer(newPackQK.get(), Backend::STATIC);
+    backend()->onReleaseBuffer(tempQKBlock.get(), Backend::STATIC);
     if (quant_value){
         backend()->onReleaseBuffer(dequantV.get(), Backend::STATIC);
     }
@@ -460,9 +502,19 @@ CPUAttention::CPUAttention(Backend *backend, bool kv_cache) : Execution(backend)
     mPackQKV.reset(Tensor::createDevice<float>({1, 1, 1, 1}));
     MNN::KVCacheManager::KVCacheConfig kvconfig;
     int qkvQuantOptions = static_cast<CPUBackend *>(backend)->getRuntime()->hint().qkvQuantOption;
-    kvconfig.mUseInt8Kernel = (qkvQuantOptions == 4);
-    kvconfig.mQuantKey   = (qkvQuantOptions == 4) || (qkvQuantOptions & 1);
-    kvconfig.mQuantValue = (qkvQuantOptions == 4) || ((qkvQuantOptions >> 1) & 1);
+    kvconfig.mUseInt8Kernel = (qkvQuantOptions % 8 == 4);
+
+    // qkvQuantOption % 8:
+    // 0: Do not quantize
+    // 1: Only quantize key, use int8 asymmetric quantization
+    // 2: Only quantize value, use fp8 quantization
+    // 3: quantize both key and value
+    // 4: quantize query, key and value, and use gemm int8 kernel to compute K*V
+
+    // qkvQuantOption / 8:
+    // 1: use flash attention
+    kvconfig.mQuantKey   = (qkvQuantOptions % 8 == 4) || (qkvQuantOptions % 8 == 1) || (qkvQuantOptions % 8 == 3);
+    kvconfig.mQuantValue = (qkvQuantOptions % 8 == 4) || (qkvQuantOptions % 8 == 2);
     kvconfig.mKVCacheDir = static_cast<CPUBackend *>(backend)->getRuntime()->hint().kvcacheDirPath;
     kvconfig.mKVCacheSizeLimit = static_cast<CPUBackend *>(backend)->getRuntime()->hint().kvcacheSizeLimit;
     kvconfig.mExpandChunk = 64;

source/backend/cpu/CPUAttention.hpp

@@ -30,15 +30,16 @@ private:
     bool mKVCache        = true;
     bool mUseGemmInt8    = false;
     int bytes = 4;
-    int mThreadNum = 1;;
-    int eP, lP, hP, unit; // float matmul packing
+    int mThreadNum = 1;
+    int mBlockKV = 512;
+    int eP, lP, hP, mPack; // float matmul packing
     int eP8, lP8, hP8;    // GemmInt8 packing
     int mNumHead, mKvNumHead, mHeadDim;
-    std::shared_ptr<Tensor> mPackQ, mPackQKV, mSumQ;
+    std::shared_ptr<Tensor> mPackQ, mPackQKV, mSumQ, mRunningMax, mRunningSum, mTempQKBlock, mTempOut, mExpfDiffMax;
     std::shared_ptr<KVCacheManager> mKVCacheManager = nullptr;
     std::vector<float> mMinQ, mMaxQ, mQueryScale, mQueryZeroPoint;
-    template <typename T> void pack_query(Tensor* query, char* pack_q, char* sum_q, int seq_len, int h, float q_scale);
-    template <typename T> void unpack_QK(float * unpack_qk_dst, char * pack_qk_src, int seq_len, int kv_seq_len);
+    template <typename T> void pack_query(Tensor* query, int8_t* pack_q, int8_t* sum_q, int seq_len, int h, float q_scale);
+    template <typename T> void unpack_QK(float * unpack_qk_dst, int8_t * pack_qk_src, int seq_len, int kv_seq_len);
     KVMeta* mMeta;
 };
 

source/backend/cpu/CPUBackend.cpp

@@ -509,21 +509,21 @@ CPUBackend::CPUBackend(const CPURuntime* runtime, BackendConfig::PrecisionMode p
         mDmaInfo.reset(new CPURuntime::DynamicAllocator);
         mDmaInfo->mDynamicAllocator.reset(mRuntime->createDynamicBufferAlloctor(0));
         mDmaInfo->mCurrentDynamicAllocator = mDmaInfo->mDynamicAllocator.get();
+        mDmaInfo->mCacheGroup.resize(MNN_CPU_MAX_BUFFER_INDEX);
+        for (int i=0; i<mDmaInfo->mCacheGroup.size(); ++i) {
+            mDmaInfo->mCacheGroup[i].reset(new CPUResizeCache);
+        }
     } else {
         mDmaInfo = dynamicAlloc;
     }
     mPrecisionMode = precision;
     mCoreFunctions = MNNGetCoreFunctions();
     mInt8CoreFunctions = MNNGetInt8CoreFunctions();
-    mCacheGroup.resize(MNN_CPU_MAX_BUFFER_INDEX);
-    for (int i=0; i<mCacheGroup.size(); ++i) {
-        mCacheGroup[i].reset(new CPUResizeCache);
-    }
-    mCache = mCacheGroup[0].get();
+    mCache = mDmaInfo->mCacheGroup[0].get();
 }
 
 CPUBackend::~CPUBackend() {
-    mCacheGroup.clear();
+    // Do nothing
 }
 void CPUBackend::_resetDynamicMemory() const {
     mRuntime->pCurrentStatus = mDmaInfo->mDynamicAllocator->apply();
@@ -560,7 +560,7 @@ bool CPUBackend::onSelectDynamicAllocator(int index, int maxIndex) {
         mRuntime->buffer(0)->release();
         mDmaInfo->mCurrentDynamicAllocator = mDmaInfo->mDynamicAllocator.get();
     }
-    mCache = mCacheGroup[index].get();
+    mCache = mDmaInfo->mCacheGroup[index].get();
     return true;
 }
 

source/backend/cpu/CPUBackend.hpp

@@ -23,12 +23,14 @@
 
 namespace MNN {
 class WorkerThread;
+class CPUResizeCache;
 class CPURuntime : public Runtime {
 public:
     struct DynamicAllocator {
         std::shared_ptr<BufferAllocator> mDynamicAllocator;
         std::shared_ptr<BufferAllocator> mDynamicAllocatorBackup;
         BufferAllocator* mCurrentDynamicAllocator = nullptr;
+        std::vector<std::shared_ptr<CPUResizeCache>> mCacheGroup;
     };
     friend class CPUBackend;
     CPURuntime(const Backend::Info& info);
@@ -82,7 +84,6 @@ struct CoreFunctions;
 struct CoreInt8Functions;
 struct MatmulRelatedFunctions;
 
-class CPUResizeCache;
 class CPUMemObj : public Backend::MemObj {
 public:
     CPUMemObj(BufferAllocator* allocator, MemChunk chunk, int size) : mAllocator(allocator), mChunk(chunk), mSize(size) {}
@@ -199,7 +200,6 @@ private:
     BackendConfig::MemoryMode mMemory;
     static std::map<OpType, CPUBackend::Creator*>* gCreator;
     CPUResizeCache* mCache;
-    std::vector<std::shared_ptr<CPUResizeCache>> mCacheGroup;
 };
 /** execution cast wrapper. insert tensor cast dynamic. */
 class CastWrapExecution : public Execution {

source/backend/cpu/CPURuntime.cpp

@@ -170,7 +170,7 @@ cpu_mask_t MNNGetCPUMask(const std::vector<int>& cpuIds) {
 
 // cpuinfo
 // Reference from: https://github.com/pytorch/cpuinfo
-#if defined(ENABLE_ARMV82) && defined(__arm__)
+#if (defined(ENABLE_ARMV82) && defined(__arm__)) || (defined(__ANDROID__) && defined(__aarch64__))
 
 /* As per include/sys/system_properties.h in Android NDK */
 #define CPUINFO_HARDWARE_VALUE_MAX 64
@@ -1180,7 +1180,7 @@ struct cpuinfo_arm_chipset cpuinfo_arm_android_decode_chipset(const struct cpuin
     // MNN_PRINT("chipset vendor, series, model is: %d, %d, %d\n", chipset.vendor, chipset.series, chipset.model);
     return chipset;
 }
-static void _getInfoARMv7(MNNCPUInfo* cpuinfo_isa) {
+static void _getInfoArm(MNNCPUInfo* cpuinfo_isa) {
     // Get White List And Black List
     struct cpuinfo_arm_linux_processor* arm_linux_processors = NULL;
     if (0 == cpuinfo_isa->groups.size()) {
@@ -1500,8 +1500,8 @@ static void _fillInfo(MNNCPUInfo* cpuinfo_isa) {
 #if defined(__aarch64__)
     _getInfoAux(cpuinfo_isa);
 #endif
-#if defined(ENABLE_ARMV82) && defined(__arm__)
-    _getInfoARMv7(cpuinfo_isa);
+#if (defined(ENABLE_ARMV82) && defined(__arm__)) || (defined(__ANDROID__) && defined(__aarch64__))
+    _getInfoArm(cpuinfo_isa);
 #endif // #ifdef arm / arm64
 #endif // #ifdef __linux__
 

source/backend/cpu/CPUSoftmax.cpp

@@ -200,7 +200,7 @@ int CPUSoftmax::_softmaxCommon(const uint8_t *srcData, uint8_t *dstData) {
             for (int v=0; v<mInside; ++v) {
                 //TODO: Fix x86 compute error and use the same function
 #ifdef MNN_USE_SSE
-                MNNSoftmax(workDst+v*mChannel, workSrc+v*mChannel, mChannel);
+                MNNSoftmax(workDst+v*mChannel, workSrc+v*mChannel, nullptr, nullptr, nullptr, 1, mChannel);
 #else
                 ___MNNSoftmax(workDst+v*mChannel, workSrc+v*mChannel, mChannel, mulFunction);
 #endif

source/backend/cpu/KVCacheManager.cpp

@@ -75,13 +75,13 @@ void KVCacheManager::resetKVCacheFileSize(size_t keySize, size_t valueSize) {
 void KVCacheManager::mmapKVCache(size_t keySize, size_t valueSize)
 {
     if (mMapKeyAddr == nullptr) {
-        mMapKeyAddr = (char *)MNNMmapFile(mKeyCacheFD, keySize);
+        mMapKeyAddr = (int8_t *)MNNMmapFile(mKeyCacheFD, keySize);
         if (mMapKeyAddr == nullptr) {
             MNN_PRINT("Failed to memory-map the kvcache!\n");
         }
     }
     if (mMapValueAddr == nullptr) {
-        mMapValueAddr = (char *)MNNMmapFile(mValueCacheFD, valueSize);
+        mMapValueAddr = (int8_t *)MNNMmapFile(mValueCacheFD, valueSize);
         if (mMapValueAddr == nullptr) {
             MNN_PRINT("Failed to memory-map the kvcache!\n");
         }
@@ -111,8 +111,8 @@ void KVCacheManager::expandKVCacheInMem(int oldMaxLength) {
         mBackend->onAcquireBuffer(new_key, Backend::STATIC);
         for (int h = 0; h < mKvNumHead; h++) {
             memcpy(
-                new_key->host<char>() + h * UP_DIV(mMaxLength, hP8) * UP_DIV(mHeadDim, lP8) * hP8 * lP8,
-                mPastKey->host<char>() + h * UP_DIV(oldMaxLength, hP8) * UP_DIV(mHeadDim, lP8) * hP8 * lP8,
+                new_key->host<int8_t>() + h * UP_DIV(mMaxLength, hP8) * UP_DIV(mHeadDim, lP8) * hP8 * lP8,
+                mPastKey->host<int8_t>() + h * UP_DIV(oldMaxLength, hP8) * UP_DIV(mHeadDim, lP8) * hP8 * lP8,
                 UP_DIV(oldMaxLength, hP8) * UP_DIV(mHeadDim, lP8) * hP8 * lP8
             );
         }
@@ -123,8 +123,8 @@ void KVCacheManager::expandKVCacheInMem(int oldMaxLength) {
         mBackend->onAcquireBuffer(new_key, Backend::STATIC);
         for (int h = 0; h < mKvNumHead; h++) {
             memcpy(
-                new_key->host<char>() + h * new_key->stride(0),
-                mPastKey->host<char>() + h * ROUND_UP(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP),
+                new_key->host<int8_t>() + h * new_key->stride(0),
+                mPastKey->host<int8_t>() + h * ROUND_UP(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP),
                 ROUND_UP(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP)
             );
         }
@@ -135,12 +135,12 @@ void KVCacheManager::expandKVCacheInMem(int oldMaxLength) {
         mBackend->onAcquireBuffer(new_key, Backend::STATIC);
         for (int h = 0; h < mKvNumHead; h++) {
             memcpy(
-                new_key->host<char>() + h * new_key->stride(0) * mBytes,
-                mPastKey->host<char>() + h * ROUND_UP(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP) * mBytes,
+                new_key->host<int8_t>() + h * new_key->stride(0) * mBytes,
+                mPastKey->host<int8_t>() + h * ROUND_UP(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP) * mBytes,
                 ROUND_UP(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP) * mBytes
             );
             if ((new_key->stride(0) - mPastKey->stride(0)) > 0) {
-                memset(new_key->host<char>() + h * new_key->stride(0) * mBytes + mPastKey->stride(0) * mBytes, 0, (new_key->stride(0) - mPastKey->stride(0)) * mBytes);
+                memset(new_key->host<int8_t>() + h * new_key->stride(0) * mBytes + mPastKey->stride(0) * mBytes, 0, (new_key->stride(0) - mPastKey->stride(0)) * mBytes);
             }
         }
         mPastKey.reset(new_key);
@@ -152,8 +152,8 @@ void KVCacheManager::expandKVCacheInMem(int oldMaxLength) {
         for (int h = 0; h < mKvNumHead; h++) {
             for (int i = 0; i < UP_DIV(mHeadDim, hP); i++) {
                 memcpy(
-                    new_value->host<char>() + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(mMaxLength, lP) * hP,
-                    mPastValue->host<char>() + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(oldMaxLength, lP) * hP,
+                    new_value->host<int8_t>() + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(mMaxLength, lP) * hP,
+                    mPastValue->host<int8_t>() + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(oldMaxLength, lP) * hP,
                     ROUND_UP(oldMaxLength, lP) * hP
                 );
             }
@@ -166,12 +166,12 @@ void KVCacheManager::expandKVCacheInMem(int oldMaxLength) {
         for (int h = 0; h < mKvNumHead; h++) {
             for (int i = 0; i < UP_DIV(mHeadDim, hP); i++) {
                 memcpy(
-                    new_value->host<char>() + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(mMaxLength, lP) * hP * mBytes,
-                    mPastValue->host<char>() + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(oldMaxLength, lP) * hP * mBytes,
+                    new_value->host<int8_t>() + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(mMaxLength, lP) * hP * mBytes,
+                    mPastValue->host<int8_t>() + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(oldMaxLength, lP) * hP * mBytes,
                     ROUND_UP(oldMaxLength, lP) * hP * mBytes
                 );
                 if ((new_value->stride(1) - mPastValue->stride(1)) > 0) {
-                    memset(new_value->host<char>() + (h * new_value->stride(0) + i * new_value->stride(1)) * mBytes + mPastValue->stride(1) * mBytes, 0, (new_value->stride(1) - mPastValue->stride(1)) * mBytes);
+                    memset(new_value->host<int8_t>() + (h * new_value->stride(0) + i * new_value->stride(1)) * mBytes + mPastValue->stride(1) * mBytes, 0, (new_value->stride(1) - mPastValue->stride(1)) * mBytes);
                 }
             }
         }
@@ -189,7 +189,7 @@ void KVCacheManager::moveKVCacheFromMemToDisk(int oldMaxLength) {
         for (int h = 0; h < mKvNumHead; h++) {
             memcpy(
                 mMapKeyAddr + h * UP_DIV(mMaxLength, hP8) * UP_DIV(mHeadDim, lP8) * hP8 * lP8,
-                mPastKey->host<char>() + h * UP_DIV(oldMaxLength, hP8) * UP_DIV(mHeadDim, lP8) * hP8 * lP8,
+                mPastKey->host<int8_t>() + h * UP_DIV(oldMaxLength, hP8) * UP_DIV(mHeadDim, lP8) * hP8 * lP8,
                 UP_DIV(oldMaxLength, hP8) * UP_DIV(mHeadDim, lP8) * hP8 * lP8
             );
         }
@@ -200,7 +200,7 @@ void KVCacheManager::moveKVCacheFromMemToDisk(int oldMaxLength) {
         for (int h = 0; h < mKvNumHead; h++) {
             memcpy(
                 mMapKeyAddr + h * UP_DIV(mMaxLength, hP) * ROUND_UP(mHeadDim, lP) * hP,
-                mPastKey->host<char>() + h * UP_DIV(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP) * hP,
+                mPastKey->host<int8_t>() + h * UP_DIV(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP) * hP,
                 UP_DIV(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP) * hP
             );
         }
@@ -214,7 +214,7 @@ void KVCacheManager::moveKVCacheFromMemToDisk(int oldMaxLength) {
         for (int h = 0; h < mKvNumHead; h++) {
             memcpy(
                 mMapKeyAddr + h * UP_DIV(mMaxLength, hP) * ROUND_UP(mHeadDim, lP) * hP * mBytes,
-                mPastKey->host<char>() + h * UP_DIV(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP) * hP * mBytes,
+                mPastKey->host<int8_t>() + h * UP_DIV(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP) * hP * mBytes,
                 UP_DIV(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP) * hP * mBytes
             );
         }
@@ -227,7 +227,7 @@ void KVCacheManager::moveKVCacheFromMemToDisk(int oldMaxLength) {
             for (int i = 0; i < UP_DIV(mHeadDim, hP); i++) {
                 memcpy(
                     mMapValueAddr + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(mMaxLength, lP) * hP,
-                    mPastValue->host<char>() + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(oldMaxLength, lP) * hP,
+                    mPastValue->host<int8_t>() + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(oldMaxLength, lP) * hP,
                     ROUND_UP(oldMaxLength, lP) * hP
                 );
             }
@@ -243,7 +243,7 @@ void KVCacheManager::moveKVCacheFromMemToDisk(int oldMaxLength) {
             for (int i = 0; i < UP_DIV(mHeadDim, hP); i++) {
                 memcpy(
                     mMapValueAddr + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(mMaxLength, lP) * hP * mBytes,
-                    mPastValue->host<char>() + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(oldMaxLength, lP) * hP * mBytes,
+                    mPastValue->host<int8_t>() + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(oldMaxLength, lP) * hP * mBytes,
                     ROUND_UP(oldMaxLength, lP) * hP * mBytes
                 );
             }
@@ -282,8 +282,8 @@ void KVCacheManager::expandKVCacheInDisk(int oldMaxLength, int oldKeySize, int o
         memset(old_value->host<uint8_t>(), 0, old_value->length(0) * old_value->stride(0) * mBytes);
     }
     mmapKVCache(oldKeySize, oldValueSize);
-    memcpy(old_key->host<char>(),   mMapKeyAddr,   oldKeySize);
-    memcpy(old_value->host<char>(), mMapValueAddr, oldValueSize);
+    memcpy(old_key->host<int8_t>(),   mMapKeyAddr,   oldKeySize);
+    memcpy(old_value->host<int8_t>(), mMapValueAddr, oldValueSize);
     // Step 2: Resize the kvcache files and remap them
     unmapKVCache(oldKeySize, oldValueSize);
     resetKVCacheFileSize(keySize, valueSize);
@@ -293,7 +293,7 @@ void KVCacheManager::expandKVCacheInDisk(int oldMaxLength, int oldKeySize, int o
         for (int h = 0; h < mKvNumHead; h++) {
             memcpy(
                 mMapKeyAddr + h * UP_DIV(mMaxLength, hP8) * UP_DIV(mHeadDim, lP8) * hP8 * lP8,
-                old_key->host<char>() + h * UP_DIV(oldMaxLength, hP8) * UP_DIV(mHeadDim, lP8) * hP8 * lP8,
+                old_key->host<int8_t>() + h * UP_DIV(oldMaxLength, hP8) * UP_DIV(mHeadDim, lP8) * hP8 * lP8,
                 UP_DIV(oldMaxLength, hP8) * UP_DIV(mHeadDim, lP8) * hP8 * lP8
             );
         }
@@ -301,7 +301,7 @@ void KVCacheManager::expandKVCacheInDisk(int oldMaxLength, int oldKeySize, int o
         for (int h = 0; h < mKvNumHead; h++) {
             memcpy(
                 mMapKeyAddr + h * UP_DIV(mMaxLength, hP) * ROUND_UP(mHeadDim, lP) * hP,
-                old_key->host<char>() + h * UP_DIV(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP) * hP,
+                old_key->host<int8_t>() + h * UP_DIV(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP) * hP,
                 UP_DIV(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP) * hP
             );
         }
@@ -309,7 +309,7 @@ void KVCacheManager::expandKVCacheInDisk(int oldMaxLength, int oldKeySize, int o
         for (int h = 0; h < mKvNumHead; h++) {
             memcpy(
                 mMapKeyAddr + h * UP_DIV(mMaxLength, hP) * ROUND_UP(mHeadDim, lP) * hP * mBytes,
-                old_key->host<char>() + h * UP_DIV(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP) * hP * mBytes,
+                old_key->host<int8_t>() + h * UP_DIV(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP) * hP * mBytes,
                 UP_DIV(oldMaxLength, hP) * ROUND_UP(mHeadDim, lP) * hP * mBytes
             );
         }
@@ -319,7 +319,7 @@ void KVCacheManager::expandKVCacheInDisk(int oldMaxLength, int oldKeySize, int o
             for (int i = 0; i < UP_DIV(mHeadDim, hP); i++) {
                 memcpy(
                     mMapValueAddr + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(mMaxLength, lP) * hP,
-                    old_value->host<char>() + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(oldMaxLength, lP) * hP,
+                    old_value->host<int8_t>() + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(oldMaxLength, lP) * hP,
                     ROUND_UP(oldMaxLength, lP) * hP
                 );
             }
@@ -329,7 +329,7 @@ void KVCacheManager::expandKVCacheInDisk(int oldMaxLength, int oldKeySize, int o
             for (int i = 0; i < UP_DIV(mHeadDim, hP); i++) {
                 memcpy(
                     mMapValueAddr + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(mMaxLength, lP) * hP * mBytes,
-                    old_value->host<char>() + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(oldMaxLength, lP) * hP * mBytes,
+                    old_value->host<int8_t>() + (h * UP_DIV(mHeadDim, hP) + i) * ROUND_UP(oldMaxLength, lP) * hP * mBytes,
                     ROUND_UP(oldMaxLength, lP) * hP * mBytes
                 );
             }
@@ -460,9 +460,9 @@ void KVCacheManager::onRealloc(const KVMeta* meta) {
             mBackend->onAcquireBuffer(new_zeroPoint, Backend::STATIC);
             mBackend->onAcquireBuffer(new_sum, Backend::STATIC);
             for (int h = 0; h < mKvNumHead; h++) {
-                memcpy(new_scale->host<char>() + h * UP_DIV(mMaxLength, hP8) * hP8 * 4, mKeyScale->host<char>() + h * UP_DIV(oldMaxLength, hP8) * hP8 * 4, UP_DIV(oldMaxLength, hP8) * hP8 * 4);
-                memcpy(new_zeroPoint->host<char>() + h * UP_DIV(mMaxLength, hP8) * hP8 * 4, mKeyZeroPoint->host<char>() + h * UP_DIV(oldMaxLength, hP8) * hP8 * 4, UP_DIV(oldMaxLength, hP8) * hP8 * 4);
-                memcpy(new_sum->host<char>() + h * UP_DIV(mMaxLength, hP8) * hP8 * 4, mKeySum->host<char>() + h * UP_DIV(oldMaxLength, hP8) * hP8 * 4, UP_DIV(oldMaxLength, hP8) * hP8 * 4);
+                memcpy(new_scale->host<int8_t>() + h * UP_DIV(mMaxLength, hP8) * hP8 * 4, mKeyScale->host<int8_t>() + h * UP_DIV(oldMaxLength, hP8) * hP8 * 4, UP_DIV(oldMaxLength, hP8) * hP8 * 4);
+                memcpy(new_zeroPoint->host<int8_t>() + h * UP_DIV(mMaxLength, hP8) * hP8 * 4, mKeyZeroPoint->host<int8_t>() + h * UP_DIV(oldMaxLength, hP8) * hP8 * 4, UP_DIV(oldMaxLength, hP8) * hP8 * 4);
+                memcpy(new_sum->host<int8_t>() + h * UP_DIV(mMaxLength, hP8) * hP8 * 4, mKeySum->host<int8_t>() + h * UP_DIV(oldMaxLength, hP8) * hP8 * 4, UP_DIV(oldMaxLength, hP8) * hP8 * 4);
             }
             mKeyScale.reset(new_scale);
             mKeyZeroPoint.reset(new_zeroPoint);
@@ -473,8 +473,8 @@ void KVCacheManager::onRealloc(const KVMeta* meta) {
             mBackend->onAcquireBuffer(new_scale, Backend::STATIC);
             mBackend->onAcquireBuffer(new_zeroPoint, Backend::STATIC);
             for (int h = 0; h < mKvNumHead; h++) {
-                memcpy(new_scale->host<char>() + h * UP_DIV(mMaxLength, hP) * hP * mBytes, mKeyScale->host<char>() + h * UP_DIV(oldMaxLength, hP) * hP * mBytes, UP_DIV(oldMaxLength, hP) * hP * mBytes);
-                memcpy(new_zeroPoint->host<char>() + h * UP_DIV(mMaxLength, hP) * hP * mBytes, mKeyZeroPoint->host<char>() + h * UP_DIV(oldMaxLength, hP) * hP * mBytes, UP_DIV(oldMaxLength, hP) * hP * mBytes);
+                memcpy(new_scale->host<int8_t>() + h * UP_DIV(mMaxLength, hP) * hP * mBytes, mKeyScale->host<int8_t>() + h * UP_DIV(oldMaxLength, hP) * hP * mBytes, UP_DIV(oldMaxLength, hP) * hP * mBytes);
+                memcpy(new_zeroPoint->host<int8_t>() + h * UP_DIV(mMaxLength, hP) * hP * mBytes, mKeyZeroPoint->host<int8_t>() + h * UP_DIV(oldMaxLength, hP) * hP * mBytes, UP_DIV(oldMaxLength, hP) * hP * mBytes);
             }
             mKeyScale.reset(new_scale);
             mKeyZeroPoint.reset(new_zeroPoint);
@@ -690,8 +690,8 @@ void KVCacheManager::onDequantValue(Tensor * dequantedValues) {
     int tileCount = UP_DIV(mKvNumHead, mThreadNum);
     std::function<void(int)> dequant = [=](int tid) {
         for (int kv_h = tid * tileCount; kv_h < (tid+1) * tileCount && kv_h < mKvNumHead; kv_h++) {
-            char * dst = dequantedValues->host<char>() + kv_h * UP_DIV(mHeadDim, hP) * mPastLength * hP * mBytes;
-            char * src = addrOfValue(kv_h);
+            int8_t * dst = dequantedValues->host<int8_t>() + kv_h * UP_DIV(mHeadDim, hP) * mPastLength * hP * mBytes;
+            int8_t * src = addrOfValue(kv_h);
             for (int i = 0; i < UP_DIV(mHeadDim, hP); i++) {
                 if (mBytes == 2) {
                     core->MNNFp8ToFp16((uint16_t*)dst, (uint8_t*)src, mPastLength * hP);

source/backend/cpu/KVCacheManager.hpp

@@ -46,8 +46,8 @@ private:
     std::shared_ptr<Tensor> mKeySum;                // numhead, [maxlen/hP8, hP8]
     file_t mKeyCacheFD   = INVALID_FILE;            // The file descriptor of keys
     file_t mValueCacheFD = INVALID_FILE;            // The file descriptor of values
-    char * mMapKeyAddr   = nullptr;                 // Memory-mapped address of keys
-    char * mMapValueAddr = nullptr;                 // Memory-mapped address of values
+    int8_t * mMapKeyAddr   = nullptr;                 // Memory-mapped address of keys
+    int8_t * mMapValueAddr = nullptr;                 // Memory-mapped address of values
     bool mKVCacheInDisk  = false;                   // Whether the kvcache is in disk or in memory now
     int  mPastLength     = 0;                       // Length of past kvcache
     int  mMaxLength      = 0;                       // Capacity of current kvcache buffer (how many kv items can be stored at most)
@@ -104,15 +104,15 @@ public:
         return mMaxLength;
     }
     uint8_t* keyAddr() {
-        char * baseAddr = mKVCacheInDisk ? mMapKeyAddr : mPastKey->host<char>();
+        int8_t * baseAddr = mKVCacheInDisk ? mMapKeyAddr : mPastKey->host<int8_t>();
         return (uint8_t*)baseAddr;
     }
     uint8_t* valudAddr() {
-        char * baseAddr = mKVCacheInDisk ? mMapValueAddr : mPastValue->host<char>();
+        int8_t * baseAddr = mKVCacheInDisk ? mMapValueAddr : mPastValue->host<int8_t>();
         return (uint8_t*)baseAddr;
     }
-    char * addrOfKey(int kv_h) {
-        char * baseAddr = mKVCacheInDisk ? mMapKeyAddr : mPastKey->host<char>();
+    int8_t * addrOfKey(int kv_h) {
+        int8_t * baseAddr = mKVCacheInDisk ? mMapKeyAddr : mPastKey->host<int8_t>();
         if (mConfig.mUseInt8Kernel) {
             return baseAddr + kv_h * UP_DIV(mMaxLength, hP8) * UP_DIV(mHeadDim, lP8) * hP8 * lP8;
         } else if (mConfig.mQuantKey) {
@@ -121,35 +121,35 @@ public:
             return baseAddr + kv_h * UP_DIV(mMaxLength, hP) * ROUND_UP(mHeadDim, lP) * hP * mBytes;
         }
     }
-    char * addrOfValue(int kv_h) {
-        char * baseAddr = mKVCacheInDisk ? mMapValueAddr : mPastValue->host<char>();
+    int8_t * addrOfValue(int kv_h) {
+        int8_t * baseAddr = mKVCacheInDisk ? mMapValueAddr : mPastValue->host<int8_t>();
         if (mConfig.mQuantValue) {
             return baseAddr + kv_h * UP_DIV(mHeadDim, hP) * ROUND_UP(mMaxLength, lP) * hP;
         } else {
             return baseAddr + kv_h * UP_DIV(mHeadDim, hP) * ROUND_UP(mMaxLength, lP) * hP * mBytes;
         }
     }
-    char * addrOfScale(int kv_h) {
+    int8_t * addrOfScale(int kv_h) {
         if (mConfig.mUseInt8Kernel) {
-            return mKeyScale->host<char>() + kv_h * UP_DIV(mMaxLength, hP8) * hP8 * 4;
+            return mKeyScale->host<int8_t>() + kv_h * UP_DIV(mMaxLength, hP8) * hP8 * 4;
         } else if (mConfig.mQuantKey) {
-            return mKeyScale->host<char>() + kv_h * UP_DIV(mMaxLength, hP) * hP * mBytes;
+            return mKeyScale->host<int8_t>() + kv_h * UP_DIV(mMaxLength, hP) * hP * mBytes;
         } else {
             return nullptr;
         }
     }
-    char * addrOfZeroPoint(int kv_h) {
+    int8_t * addrOfZeroPoint(int kv_h) {
         if (mConfig.mUseInt8Kernel) {
-            return mKeyZeroPoint->host<char>() + kv_h * UP_DIV(mMaxLength, hP8) * hP8 * 4;
+            return mKeyZeroPoint->host<int8_t>() + kv_h * UP_DIV(mMaxLength, hP8) * hP8 * 4;
         } else if (mConfig.mQuantKey) {
-            return mKeyZeroPoint->host<char>() + kv_h * UP_DIV(mMaxLength, hP) * hP * mBytes;
+            return mKeyZeroPoint->host<int8_t>() + kv_h * UP_DIV(mMaxLength, hP) * hP * mBytes;
         } else {
             return nullptr;
         }
     }
-    char * addrOfKeySum(int kv_h) {
+    int8_t * addrOfKeySum(int kv_h) {
         if (mConfig.mUseInt8Kernel) {
-            return mKeySum->host<char>() + kv_h * UP_DIV(mMaxLength, hP8) * hP8 * 4;
+            return mKeySum->host<int8_t>() + kv_h * UP_DIV(mMaxLength, hP8) * hP8 * 4;
         }else {
             return nullptr;
         }

source/backend/cpu/arm/CommonOptFunctionNeon.cpp

@@ -73,6 +73,386 @@ void MNNTranspose16Bit(int16_t* dstO, const int16_t* srcO, int32_t* dim) {
     }
 }
 
+
+#define EXP_APPROX_MIN_INPUT vdupq_n_f32(-88.0f)
+#define EXP_APPROX_MAX_INPUT vdupq_n_f32(88.0f)
+#define EXP_APPROX_LN2         vdupq_n_f32(0.69314718056f)  // ln(2)
+#define EXP_APPROX_LN2_INV     vdupq_n_f32(1.44269504089f)   // 1/ln(2)
+// Fourth-order polynomial approximation coefficients of exp(r):
+// P(x) = c4*x^4 + c3*x^3 + c2*x^2 + c1*x + c0
+#define EXP_APPROX_C4          vdupq_n_f32(0.0416624f)
+#define EXP_APPROX_C3          vdupq_n_f32(0.166665f)
+#define EXP_APPROX_C2          vdupq_n_f32(0.500000f)
+#define EXP_APPROX_C1          vdupq_n_f32(1.0f)
+#define EXP_APPROX_C0          vdupq_n_f32(1.0f)
+
+#ifndef __aarch64__
+static inline float32x4_t vrndaq_f32_compat(float32x4_t val) {
+    const float32x4_t v_zero = vdupq_n_f32(0.0f);
+
+    float32x4_t v_truncated = vcvtq_f32_s32(vcvtq_s32_f32(val));
+
+    uint32x4_t v_is_positive_frac = vcgtq_f32(val, v_truncated);
+    uint32x4_t v_is_negative_frac = vcltq_f32(val, v_truncated);
+
+    float32x4_t v_offset = vbslq_f32(v_is_positive_frac, vdupq_n_f32(1.0f), v_zero);
+    v_offset = vbslq_f32(v_is_negative_frac, vdupq_n_f32(-1.0f), v_offset);
+
+    return vaddq_f32(v_truncated, v_offset);
+}
+
+#endif
+
+static inline float32x4_t expApprox(float32x4_t x) {
+    x = vminq_f32(vmaxq_f32(x, EXP_APPROX_MIN_INPUT), EXP_APPROX_MAX_INPUT);
+    
+    float32x4_t k_float;
+    float32x4_t r;
+    float32x4_t exp_r;
+
+#if defined(__aarch64__)
+    // 1. x = k * ln(2) + r
+    k_float = vrndaq_f32(vmulq_f32(x, EXP_APPROX_LN2_INV));
+    
+    // r = x - k * ln(2)
+    r = vfmsq_f32(x, k_float, EXP_APPROX_LN2);
+
+    // 2. c0 + r*(c1 + r*(c2 + r*(c3 + r*c4)))  (Horner's method)
+    exp_r = vfmaq_f32(EXP_APPROX_C3, EXP_APPROX_C4, r); // c3 + c4*r
+    exp_r = vfmaq_f32(EXP_APPROX_C2, exp_r, r);         // c2 + r*(...)
+    exp_r = vfmaq_f32(EXP_APPROX_C1, exp_r, r);         // c1 + r*(...)
+    exp_r = vfmaq_f32(EXP_APPROX_C0, exp_r, r);         // c0 + r*(...)
+
+#else
+
+    k_float = vrndaq_f32_compat(vmulq_f32(x, EXP_APPROX_LN2_INV));
+    
+
+    r = vsubq_f32(x, vmulq_f32(k_float, EXP_APPROX_LN2));
+
+    // 2. c0 + r*(c1 + r*(c2 + r*(c3 + r*c4)))
+    exp_r = vmlaq_f32(EXP_APPROX_C3, EXP_APPROX_C4, r); // c3 + c4*r
+    exp_r = vmlaq_f32(EXP_APPROX_C2, exp_r, r);         // c2 + r*(...)
+    exp_r = vmlaq_f32(EXP_APPROX_C1, exp_r, r);         // c1 + r*(...)
+    exp_r = vmlaq_f32(EXP_APPROX_C0, exp_r, r);         // c0 + r*(...)
+
+#endif
+
+    int32x4_t k_int = vcvtq_s32_f32(k_float);
+    int32x4_t k_shifted = vshlq_n_s32(k_int, 23);
+    float32x4_t result = vreinterpretq_f32_s32(vaddq_s32(vreinterpretq_s32_f32(exp_r), k_shifted));   
+    return result;
+}
+
+void MNNExpC8(float* dst, const float* src, float* offset, const float* parameters, size_t countC8) {
+    float32x4_t maxVec = vdupq_n_f32(offset[2]);
+    float32x4_t sumVec0 = vdupq_n_f32(0);
+    float32x4_t sumVec1 = vdupq_n_f32(0);
+
+    float32x4_t c0 = vdupq_n_f32(offset[0]);
+    float32x4_t c1 = vdupq_n_f32(offset[1]);
+
+
+    for (int i = 0; i < countC8; ++i) {
+        float32x4_t srcVec0 = vld1q_f32(src);
+        float32x4_t srcVec1 = vld1q_f32(src + 4);
+        auto subVec0 = vaddq_f32(vmulq_f32(srcVec0, c0), maxVec);
+        auto subVec1 = vaddq_f32(vmulq_f32(srcVec1, c0), maxVec);
+        auto expVec0 = vaddq_f32(expApprox(subVec0), c1);
+        auto expVec1 = vaddq_f32(expApprox(subVec1), c1);
+        vst1q_f32(dst, expVec0);
+        vst1q_f32(dst + 4, expVec1);
+        sumVec0 = vaddq_f32(sumVec0, expVec0);
+        sumVec1 = vaddq_f32(sumVec1, expVec1);
+
+        src += 8;
+        dst += 8;
+    }
+
+    sumVec0 = vaddq_f32(sumVec0, sumVec1);
+    float32x2_t sumP = vpadd_f32(vget_low_f32(sumVec0), vget_high_f32(sumVec0));
+    sumP = vpadd_f32(sumP, sumP);
+    offset[3] += vget_lane_f32(sumP, 0);
+}
+
+
+void MNNExp(float* destPtr, const float* srcPtr, float* offset, size_t size) {
+    float32x4_t maxVec = vdupq_n_f32(-offset[2]);
+    float32x4_t sumVec0 = vdupq_n_f32(0);
+    float32x4_t sumVec1 = vdupq_n_f32(0);
+    if (offset[0] == 1.f && offset[1] == 0.f) {
+        while (size >= 8) {
+            float32x4_t srcVec0 = vld1q_f32(srcPtr);
+            float32x4_t srcVec1 = vld1q_f32(srcPtr + 4);
+            auto subVec0 = vsubq_f32(srcVec0, maxVec);
+            auto subVec1 = vsubq_f32(srcVec1, maxVec);
+            auto expVec0 = expApprox(subVec0);
+            auto expVec1 = expApprox(subVec1);
+            vst1q_f32(destPtr, expVec0);
+            vst1q_f32(destPtr + 4, expVec1);
+            sumVec0 = vaddq_f32(sumVec0, expVec0);
+            sumVec1 = vaddq_f32(sumVec1, expVec1);
+            srcPtr += 8;
+            destPtr += 8;
+            size -= 8;
+            
+        }
+        while (size >= 4) {
+            float32x4_t srcVec0 = vld1q_f32(srcPtr);
+            auto subVec0 = vsubq_f32(srcVec0, maxVec);
+            auto expVec0 = expApprox(subVec0);
+            sumVec0 = vaddq_f32(sumVec0, expVec0);
+            vst1q_f32(destPtr, expVec0);
+            srcPtr += 4;
+            destPtr += 4;
+            size -= 4;
+        }
+        //merge
+        sumVec0 = vaddq_f32(sumVec0, sumVec1);
+        float32x2_t sumP = vpadd_f32(vget_low_f32(sumVec0), vget_high_f32(sumVec0));
+        sumP = vpadd_f32(sumP, sumP);
+        auto newSum = vget_lane_f32(sumP, 0);
+        if (size > 0) {
+            float tmp[4];
+            memcpy(tmp, srcPtr, size * sizeof(float));
+            float32x4_t srcVec0 = vld1q_f32(tmp);
+            auto subVec0 = vsubq_f32(srcVec0, maxVec);
+            auto expVec0 = expApprox(subVec0);
+            vst1q_f32(tmp, expVec0);
+            for (int i = 0; i < size; ++i) {
+                newSum += tmp[i];
+                destPtr[i] = tmp[i];
+            }
+        }
+        offset[3] += newSum;
+    } else {
+        float32x4_t c0 = vdupq_n_f32(offset[0]);
+        float32x4_t c1 = vdupq_n_f32(offset[1]);
+        while (size >= 8) {
+            float32x4_t srcVec0 = vld1q_f32(srcPtr);
+            float32x4_t srcVec1 = vld1q_f32(srcPtr + 4);
+            auto subVec0 = vsubq_f32(vmulq_f32(srcVec0, c0), maxVec);
+            auto subVec1 = vsubq_f32(vmulq_f32(srcVec1, c0), maxVec);
+            auto expVec0 = vaddq_f32(expApprox(subVec0), c1);
+            auto expVec1 = vaddq_f32(expApprox(subVec1), c1);
+            vst1q_f32(destPtr, expVec0);
+            vst1q_f32(destPtr + 4, expVec1);
+            sumVec0 = vaddq_f32(sumVec0, expVec0);
+            sumVec1 = vaddq_f32(sumVec1, expVec1);
+            srcPtr += 8;
+            destPtr += 8;
+            size -= 8;
+            
+        }
+        while (size >= 4) {
+            float32x4_t srcVec0 = vld1q_f32(srcPtr);
+            auto subVec0 = vsubq_f32(vmulq_f32(srcVec0, c0), maxVec);
+            auto expVec0 = vaddq_f32(expApprox(subVec0), c1);
+            sumVec0 = vaddq_f32(sumVec0, expVec0);
+            vst1q_f32(destPtr, expVec0);
+            srcPtr += 4;
+            destPtr += 4;
+            size -= 4;
+        }
+        //merge
+        sumVec0 = vaddq_f32(sumVec0, sumVec1);
+        float32x2_t sumP = vpadd_f32(vget_low_f32(sumVec0), vget_high_f32(sumVec0));
+        sumP = vpadd_f32(sumP, sumP);
+        auto newSum = vget_lane_f32(sumP, 0);
+        if (size > 0) {
+            float tmp[4];
+            memcpy(tmp, srcPtr, size * sizeof(float));
+            float32x4_t srcVec0 = vld1q_f32(tmp);
+            auto subVec0 = vsubq_f32(vmulq_f32(srcVec0, c0), maxVec);
+            auto expVec0 = vaddq_f32(expApprox(subVec0), c1);
+            vst1q_f32(tmp, expVec0);
+            for (int i = 0; i < size; ++i) {
+                newSum += tmp[i];
+                destPtr[i] = tmp[i];
+            }
+        }
+        offset[3] += newSum;
+    }
+}
+
+
+static inline void transposeAndStore4x4(const float* srcRowPtrs[4], float* dstColBase, size_t dstColStride) {
+    float32x4_t row0 = vld1q_f32(srcRowPtrs[0]);
+    float32x4_t row1 = vld1q_f32(srcRowPtrs[1]);
+    float32x4_t row2 = vld1q_f32(srcRowPtrs[2]);
+    float32x4_t row3 = vld1q_f32(srcRowPtrs[3]);
+
+    // Step 1: Transpose 2x2 blocks of 2-element vectors
+    float32x4x2_t t01 = vtrnq_f32(row0, row1);
+    float32x4x2_t t23 = vtrnq_f32(row2, row3);
+
+    // Step 2: Combine the results to get the full transpose
+    float32x4_t col0 = vcombine_f32(vget_low_f32(t01.val[0]), vget_low_f32(t23.val[0]));
+    float32x4_t col1 = vcombine_f32(vget_low_f32(t01.val[1]), vget_low_f32(t23.val[1]));
+    float32x4_t col2 = vcombine_f32(vget_high_f32(t01.val[0]), vget_high_f32(t23.val[0]));
+    float32x4_t col3 = vcombine_f32(vget_high_f32(t01.val[1]), vget_high_f32(t23.val[1]));
+
+    vst1q_f32(dstColBase, col0);
+    vst1q_f32(dstColBase + dstColStride, col1);
+    vst1q_f32(dstColBase + 2 * dstColStride, col2);
+    vst1q_f32(dstColBase + 3 * dstColStride, col3);
+}
+
+
+void packKvCache(float* dst, const float* src, size_t seqLen, size_t kvSeqLen, size_t eP) {
+    if (seqLen == 0 || kvSeqLen == 0) {
+        return;
+    }
+
+    // source [seqLen, kvSeqLen]
+    // dest   [seqLen/eP, kvSeqLen, eP]
+
+    const int kTileS = 4; // Tiling size for seqLen dimension
+    const int kTileK = 4; // Tiling size for kvSeqLen dimension
+    const size_t dstSOuterStride = kvSeqLen * eP;
+
+    int s = 0;
+    for (; s + kTileS <= seqLen; s += kTileS) {
+        const int sOuter = s / eP;
+        const int sInner = s % eP;
+        if (sInner + kTileS > eP) {
+            break;
+        }
+
+        float* dstSBase = dst + sOuter * dstSOuterStride + sInner;
+        const float* srcRowPtrs[kTileS];
+        srcRowPtrs[0] = src + (s + 0) * kvSeqLen;
+        srcRowPtrs[1] = src + (s + 1) * kvSeqLen;
+        srcRowPtrs[2] = src + (s + 2) * kvSeqLen;
+        srcRowPtrs[3] = src + (s + 3) * kvSeqLen;
+
+        int k = 0;
+        for (; k + kTileK <= kvSeqLen; k += kTileK) {
+            const float* currentSrcPtrs[kTileS];
+            currentSrcPtrs[0] = srcRowPtrs[0] + k;
+            currentSrcPtrs[1] = srcRowPtrs[1] + k;
+            currentSrcPtrs[2] = srcRowPtrs[2] + k;
+            currentSrcPtrs[3] = srcRowPtrs[3] + k;
+            float* dstKBase = dstSBase + k * eP;
+            transposeAndStore4x4(currentSrcPtrs, dstKBase, eP);
+        }
+
+        for (; k < kvSeqLen; ++k) {
+            float buffer[kTileS] = {
+                srcRowPtrs[0][k],
+                srcRowPtrs[1][k],
+                srcRowPtrs[2][k],
+                srcRowPtrs[3][k]
+            };
+            vst1q_f32(dstSBase + k * eP, vld1q_f32(buffer));
+        }
+    }
+
+    for (; s < seqLen; ++s) {
+        const int sOuter = s / eP;
+        const int sInner = s % eP;
+        const float* srcRow = src + s * kvSeqLen;
+        float* dstSBase = dst + sOuter * dstSOuterStride + sInner;
+        for (int k = 0; k < kvSeqLen; ++k) {
+            dstSBase[k * eP] = srcRow[k];
+        }
+    }
+}
+
+void MNNSoftmax(float* softmaxDst, float* input, float* runningMax, float* runningSum, float* updateScale, int outside, int reduceSize) {
+    for (int k = 0; k < outside; ++k) {
+        auto source = input + k * reduceSize;
+        auto dest = softmaxDst + k * reduceSize;
+
+        // new max
+        auto srcPtr = source;
+        auto size = reduceSize;
+        float32x4_t maxVec0 = vdupq_n_f32(source[0]);
+        auto maxVec1 = maxVec0;
+
+        float oldMax = source[0];
+        if (runningMax) {
+            oldMax = runningMax[k];
+        }
+
+        while (size >= 8) {
+            float32x4_t srcVec0 = vld1q_f32(srcPtr);
+            float32x4_t srcVec1 = vld1q_f32(srcPtr + 4);
+
+            maxVec0 = vmaxq_f32(maxVec0, srcVec0);
+            maxVec1 = vmaxq_f32(maxVec1, srcVec1);
+
+            srcPtr += 8;
+            size -= 8;
+        }
+
+        while (size >= 4) {
+            float32x4_t srcVec0 = vld1q_f32(srcPtr);
+            maxVec0 = vmaxq_f32(maxVec0, srcVec0);
+            srcPtr += 4;
+            size -= 4;
+        }
+
+        maxVec0 = vmaxq_f32(maxVec0, maxVec1);
+        float32x2_t maxP = vpmax_f32(vget_low_f32(maxVec0), vget_high_f32(maxVec0));
+        maxP = vpmax_f32(maxP, maxP);
+        auto newMax = vget_lane_f32(maxP, 0);
+
+        while (size > 0) {
+            newMax = ALIMAX(newMax, srcPtr[0]);
+            srcPtr += 1;
+            size -= 1;
+        }
+
+        newMax = ALIMAX(oldMax, newMax);
+        srcPtr = source;
+        auto destPtr = dest;
+        size = reduceSize;
+
+        float exprOffset[4] = {
+            1.0f,
+            0.0f,
+            0.0f,
+            0.0f
+        };
+        exprOffset[2] = -newMax;
+
+        // expf(xi-newmax) & new sum
+        MNNExp(destPtr, srcPtr, exprOffset, size);
+
+        if (runningMax != nullptr && runningSum != nullptr && updateScale != nullptr) {
+            // update runningSum, runningMax, scale=expf(oldMax-newMax)
+            float newSum = exprOffset[3];
+            runningSum[k] = runningSum[k] * expf(oldMax - newMax) + newSum;
+            runningMax[k] = newMax;
+            updateScale[k] = expf(oldMax - newMax);
+        } else {
+            // Normalize
+            float sum = exprOffset[3];
+            float scale = 1.0f / (sum + 1e-20f);
+            int count = reduceSize;
+            auto pDest = dest;
+
+            float32x4_t scaleVec = vdupq_n_f32(scale);
+            while (count >= 4) {
+                float32x4_t data = vld1q_f32(pDest);
+                data = vmulq_f32(data, scaleVec);
+                vst1q_f32(pDest, data);
+                
+                pDest += 4;
+                count -= 4;
+            }
+
+            while (count > 0) {
+                *pDest *= scale;
+                pDest++;
+                count--;
+            }
+        }
+    }
+}
+
+
 #ifndef MNN_USE_NEON
 
 void MNNPackedSparseMatMulEpx1(float* C, const float* A, const float* B, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias, unsigned int* NNZMap, int* dataOffsetMap) {

source/backend/cpu/arm/arm32/MNNExpC8.S

@@ -1,115 +0,0 @@
-//
-//  MNNExpC8.S
-//  MNN
-//
-//  Created by MNN on 2019/01/18.
-//  Copyright © 2018, Alibaba Group Holding Limited
-//
-
-#ifdef __arm__
-#ifndef __aarch64__
-
-#include "MNNAsmGlobal.h"
-.text
-.align 5
-//void MNNExpC8(float* dest, const float* source, const float* offset, const float* parameters, size_t countC8)
-asm_function MNNExpC8
-
-//r0: dest, r1:source, r2: offset, r3:parameters, r4:countC8
-push {r4, r5, lr}
-ldr r4, [sp, #12]
-vpush {q4-q7}
-vmov.i32 q7, #0
-ldr r5, [r2, #0]
-vdup.32 q4, r5 // Alpha
-ldr r5, [r2, #4]
-vdup.32 q5, r5 // Beta
-ldr r5, [r2, #8]
-vdup.32 q6, r5 // Bias
-
-
-vld1.32 {q0, q1}, [r3]
-
-vmov.i32 q2, #87
-vcvt.f32.s32 q2, q2
-vneg.f32 q3, q2
-
-Loop:
-
-vld1.32 {q8, q9}, [r1]!
-vmul.f32 q8, q8, q4
-vmul.f32 q9, q9, q4
-vadd.f32 q8, q8, q6
-vadd.f32 q9, q9, q6
-
-vmin.f32 q8, q8, q2
-vmin.f32 q9, q9, q2
-vmax.f32 q10, q8, q3
-vmax.f32 q11, q9, q3
-
-vmul.f32 q8, q10, d0[1]
-vmul.f32 q9, q11, d0[1]
-vcvt.s32.f32 q8, q8
-vcvt.s32.f32 q9, q9
-
-vcvt.f32.s32 q12, q8
-vcvt.f32.s32 q13, q9
-
-//q10, q11: t
-vmls.f32 q10, q12, d0[0]
-vmls.f32 q11, q13, d0[0]
-
-vmul.f32 q10, q10, d1[0]
-vmul.f32 q11, q11, d1[0]
-
-.macro MLA_TWO z0 z1 z2 z3
-vdup.32 \z1, \z0
-vmla.f32 \z1, \z2, \z3
-.endm
-
-MLA_TWO d3[0], q12, q10, d3[1]
-MLA_TWO d3[0], q13, q11, d3[1]
-MLA_TWO d2[1], q14, q10, q12
-MLA_TWO d2[1], q15, q11, q13
-MLA_TWO d2[0], q12, q10, q14
-MLA_TWO d2[0], q13, q11, q15
-MLA_TWO d1[1], q14, q10, q12
-MLA_TWO d1[1], q15, q11, q13
-MLA_TWO d1[1], q12, q10, q14
-MLA_TWO d1[1], q13, q11, q15
-
-//q12, q13 is expRemain
-
-vmul.f32 q12, q12, q12
-vmul.f32 q13, q13, q13
-vmul.f32 q12, q12, q12
-vmul.f32 q13, q13, q13
-
-vshl.i32 q8, q8, #23
-vshl.i32 q9, q9, #23
-vadd.i32 q12, q12, q8
-vadd.i32 q13, q13, q9
-
-vadd.f32 q12, q12, q5
-vadd.f32 q13, q13, q5
-vadd.f32 q7, q12, q7
-
-vst1.32 {q12, q13}, [r0]!
-vadd.f32 q7, q13, q7
-
-subs r4, r4, #1
-bne Loop
-add r5, r2, #12
-vld1.32 {d0[0]}, [r5]
-vadd.f32 d14, d14, d15
-vtrn.32 d14, d15
-vadd.f32 d14, d14, d15
-vadd.f32 d0, d14, d0
-vst1.32 {d0[0]}, [r5]
-
-vpop {q4-q7}
-pop {r4, r5, pc}
-
-
-#endif
-#endif

source/backend/cpu/arm/arm32/MNNSoftmax.S

@@ -1,270 +0,0 @@
-//
-//  MNNSoftmax.S
-//  MNN
-//
-//  Created by MNN on 2021/07/05.
-//  Copyright © 2018, Alibaba Group Holding Limited
-//
-
-#ifdef __arm__
-#ifndef __aarch64__
-
-#include "MNNAsmGlobal.h"
-.text
-.align 5
-//void MNNSoftmax(float* dest, const float* source, size_t size)
-asm_function MNNSoftmax
-        push    {r4, r5, r6, r7, r8, lr}
-        bic     r3, r2, #3
-        lsrs    r4, r2, #2
-        vpush.64        {d8, d9, d10, d11, d12, d13, d14, d15}
-        sub     sp, sp, #8
-        beq     Loop_2
-        vld1.32 {d16-d17}, [r1]
-        cmp     r4, #1
-        beq     Loop_3
-        add     lr, r1, #16
-        mov     ip, #1
-Loop_4:
-        vld1.32 {d18-d19}, [lr]!
-        add     ip, ip, #1
-        cmp     r4, ip
-        vmax.f32        q8, q8, q9
-        bne     Loop_4
-Loop_3:
-        vmov.32 ip, d16[0]
-        vmov    s15, ip
-        vmov.32 ip, d16[1]
-        vmov    s12, ip
-        vmov.32 ip, d17[0]
-        vcmpe.f32       s15, s12
-        vmov    s13, ip
-        vmov.32 ip, d17[1]
-        vmrs    APSR_nzcv, FPSCR
-        vmov    s14, ip
-        vmovle.f32      s15, s12
-        vcmpe.f32       s13, s15
-        vmrs    APSR_nzcv, FPSCR
-        vmovpl.f32      s15, s13
-        vcmpe.f32       s14, s15
-        vmrs    APSR_nzcv, FPSCR
-        vmovpl.f32      s15, s14
-        cmp     r2, r3
-        bls     Loop_25
-Loop_24:
-        add     lr, r1, r3, lsl #2
-        mov     ip, r3
-Loop_11:
-        vldmia.32       lr!, {s14}
-        add     ip, ip, #1
-        vcmpe.f32       s14, s15
-        vmrs    APSR_nzcv, FPSCR
-        vmovpl.f32      s15, s14
-        cmp     r2, ip
-        bhi     Loop_11
-        cmp     r4, #0
-        beq     Loop_8
-Loop_25:
-        vmov.f32        q11, #0.0  @ v4sf
-        mov     r5, r1
-        vldr    d14, Loop_54
-        vldr    d15, Loop_54+8
-        mov     lr, r0
-        vldr    d12, Loop_54+16
-        vldr    d13, Loop_54+24
-        mov     ip, #0
-        vmov.f32        q12, #1.0e+0  @ v4sf
-        vstr.32 s15, [sp, #4]
-        vmov.f32        q5, #5.0e-1  @ v4sf
-        vldr    d8, Loop_54+32
-        vldr    d9, Loop_54+40
-        vldr    d0, Loop_54+48
-        vldr    d1, Loop_54+56
-        vldr    d2, Loop_54+64
-        vldr    d3, Loop_54+72
-        vldr    d4, Loop_54+80
-        vldr    d5, Loop_54+88
-        vldr    d30, Loop_54+96
-        vldr    d31, Loop_54+104
-        vmov.i32        q14, #8388608  @ v4si
-        vdup.32 q13, d7[1]
-Loop_12:
-        vld1.32 {d20-d21}, [r5]!
-        add     ip, ip, #1
-        vmov.i32        q3, #127  @ v4si
-        cmp     r4, ip
-        vsub.f32        q10, q10, q13
-        vmax.f32        q10, q10, q15
-        vmin.f32        q10, q10, q2
-        vmul.f32        q9, q10, q6
-        vcvt.s32.f32    q9, q9
-        vcvt.f32.s32    q8, q9
-        vadd.i32        q9, q9, q3
-        vmul.f32        q8, q8, q7
-        vmul.i32        q9, q9, q14
-        vsub.f32        q10, q10, q8
-        vmul.f32        q8, q1, q10
-        vadd.f32        q8, q8, q0
-        vmul.f32        q8, q8, q10
-        vadd.f32        q8, q8, q4
-        vmul.f32        q8, q8, q10
-        vadd.f32        q8, q8, q5
-        vmul.f32        q8, q8, q10
-        vadd.f32        q8, q8, q12
-        vmul.f32        q8, q8, q10
-        vadd.f32        q8, q8, q12
-        vmul.f32        q9, q9, q8
-        vadd.f32        q11, q9, q11
-        vst1.32 {d18-d19}, [lr]!
-        bgt     Loop_12
-        vmov.32 ip, d22[0]
-        vldr.32 s15, [sp, #4]
-        cmp     r2, r3
-        vmov    s12, ip
-        vmov.32 ip, d22[1]
-        vmov    s11, ip
-        vmov.32 ip, d23[0]
-        vadd.f32        s12, s12, s11
-        vmov    s13, ip
-        vmov.32 ip, d23[1]
-        vadd.f32        s12, s12, s13
-        vmov    s14, ip
-        vadd.f32        s12, s12, s14
-        bls     Loop_52
-Loop_26:
-        lsl     ip, r3, #2
-        add     r5, r1, r2, lsl #2
-        add     lr, r1, ip
-        movw    r7, #13877
-        movt    r7, 179
-        movw    r6, #55317
-        movt    r6, 32310
-        vldr.32 s11, Loop_54+112
-        vldr.32 s10, Loop_54+116
-        add     r1, r0, ip
-        vldr.32 s9, Loop_54+120
-        vmov.f32        s8, #5.0e-1
-        vldr.32 s5, Loop_54+124
-        vldr.32 s6, Loop_54+128
-        vldr.32 s7, Loop_54+132
-Loop_17:
-        vldmia.32       lr!, {s14}
-        vmov    s13, r6
-        vsub.f32        s14, s14, s15
-        vcmpe.f32       s14, s11
-        vmrs    APSR_nzcv, FPSCR
-        vmovle  s13, r7
-        ble     Loop_14
-        vcmpe.f32       s14, s10
-        vmrs    APSR_nzcv, FPSCR
-        bmi     Loop_53
-Loop_14:
-        vadd.f32        s12, s12, s13
-        cmp     r5, lr
-        vstmia.32       r1!, {s13}
-        bne     Loop_17
-        vmov.f32        s15, #1.0e+0
-        cmp     r4, #0
-        vdiv.f32        s14, s15, s12
-        beq     Loop_20
-Loop_23:
-        vdup.32 q9, d7[0]
-        mov     ip, r0
-        mov     r1, #0
-Loop_19:
-        vld1.32 {d16-d17}, [ip]
-        add     r1, r1, #1
-        cmp     r4, r1
-        vmul.f32        q8, q8, q9
-        vst1.32 {d16-d17}, [ip]!
-        bgt     Loop_19
-        cmp     r2, r3
-        bls     Loop_1
-        lsl     ip, r3, #2
-Loop_20:
-        add     r0, r0, ip
-Loop_21:
-        vldr.32 s15, [r0]
-        add     r3, r3, #1
-        cmp     r2, r3
-        vmul.f32        s15, s15, s14
-        vstmia.32       r0!, {s15}
-        bhi     Loop_21
-Loop_1:
-        add     sp, sp, #8
-        vldm    sp!, {d8-d15}
-        pop     {r4, r5, r6, r7, r8, pc}
-Loop_2:
-        vldr.32 s15, Loop_54+136
-        cmp     r2, r3
-        bhi     Loop_24
-Loop_8:
-        cmp     r2, r3
-        vldrhi.32       s12, Loop_54+136
-        bhi     Loop_26
-        b       Loop_1
-Loop_52:
-        vmov.f32        s15, #1.0e+0
-        cmp     r4, #0
-        vdiv.f32        s14, s15, s12
-        bne     Loop_23
-        b       Loop_1
-Loop_53:
-        vdiv.f32        s4, s14, s9
-        vmov.f32        s13, #1.0e+0
-        vcvt.s32.f32    s4, s4
-        vcvt.f32.s32    s3, s4
-        vmov    r8, s4  @ int
-        vmov.f32        s4, s7
-        vmls.f32        s14, s3, s9
-        vmov.f32        s3, s6
-        add     r8, r8, #127
-        lsl     r8, r8, #23
-        vmla.f32        s3, s14, s5
-        vmla.f32        s4, s3, s14
-        vmov.f32        s3, s8
-        vmla.f32        s3, s4, s14
-        vmov.f32        s4, s13
-        vmla.f32        s4, s3, s14
-        vmla.f32        s13, s4, s14
-        vmov    s14, r8
-        vmul.f32        s13, s13, s14
-        b       Loop_14
-Loop_54:
-        .word   1060205080
-        .word   1060205080
-        .word   1060205080
-        .word   1060205080
-        .word   1069066811
-        .word   1069066811
-        .word   1069066811
-        .word   1069066811
-        .word   1042983595
-        .word   1042983595
-        .word   1042983595
-        .word   1042983595
-        .word   1026206379
-        .word   1026206379
-        .word   1026206379
-        .word   1026206379
-        .word   1007192201
-        .word   1007192201
-        .word   1007192201
-        .word   1007192201
-        .word   1118699520
-        .word   1118699520
-        .word   1118699520
-        .word   1118699520
-        .word   -1028784128
-        .word   -1028784128
-        .word   -1028784128
-        .word   -1028784128
-        .word   -1028784128
-        .word   1118699520
-        .word   1060205080
-        .word   1007192201
-        .word   1026206379
-        .word   1042983595
-        .word   0
-#endif
-#endif

source/backend/cpu/arm/arm64/MNNExpC8.S

@@ -1,109 +0,0 @@
-//
-//  MNNExpC8.S
-//  MNN
-//
-//  Created by MNN on 2019/01/18.
-//  Copyright © 2018, Alibaba Group Holding Limited
-//
-
-#ifdef __aarch64__
-
-#include "MNNAsmGlobal.h"
-.text
-.align 5
-
-//void MNNExpC8(float* dest, const float* source, const float* offset, const float* parameters, size_t countC8)
-asm_function MNNExpC8
-
-//x0: dest, x1:source, x2: offset, x3:parameters, x4:countC8
-ldr w5, [x2, #0]
-ldr w6, [x2, #4]
-ldr w7, [x2, #8]
-
-ld1 {v0.4s, v1.4s}, [x3]
-movi v2.4s, #23
-movi v3.4s, #87
-scvtf v3.4s, v3.4s
-fneg v4.4s, v3.4s
-dup v30.4s, w5
-dup v31.4s, w6
-dup v29.4s, w7
-
-// Summer
-movi v28.4s, #0
-
-Loop:
-
-ld1 {v16.4s, v17.4s}, [x1], #32
-fmul v16.4s, v16.4s, v30.4s
-fmul v17.4s, v17.4s, v30.4s
-fadd v16.4s, v16.4s, v29.4s
-fadd v17.4s, v17.4s, v29.4s
-fmin v16.4s, v16.4s, v3.4s
-fmin v17.4s, v17.4s, v3.4s
-fmax v18.4s, v16.4s, v4.4s
-fmax v19.4s, v17.4s, v4.4s
-
-fmul v16.4s, v18.4s, v0.s[1]
-fmul v17.4s, v19.4s, v0.s[1]
-fcvtzs v16.4s, v16.4s
-fcvtzs v17.4s, v17.4s
-scvtf v20.4s, v16.4s
-scvtf v21.4s, v17.4s
-
-//v18.4s, v19.4s: t
-fmls v18.4s, v20.4s, v0.s[0]
-fmls v19.4s, v21.4s, v0.s[0]
-
-fmul v18.4s, v18.4s, v0.s[2]
-fmul v19.4s, v19.4s, v0.s[2]
-
-.macro MLA_TWO z0 z1 z2 z3
-dup \z1, \z0
-fmla \z1, \z2, \z3
-.endm
-
-MLA_TWO v1.s[2], v20.4s, v18.4s, v1.s[3]
-MLA_TWO v1.s[2], v21.4s, v19.4s, v1.s[3]
-MLA_TWO v1.s[1], v22.4s, v18.4s, v20.4s
-MLA_TWO v1.s[1], v23.4s, v19.4s, v21.4s
-MLA_TWO v1.s[0], v20.4s, v18.4s, v22.4s
-MLA_TWO v1.s[0], v21.4s, v19.4s, v23.4s
-MLA_TWO v0.s[3], v22.4s, v18.4s, v20.4s
-MLA_TWO v0.s[3], v23.4s, v19.4s, v21.4s
-MLA_TWO v0.s[3], v20.4s, v18.4s, v22.4s
-MLA_TWO v0.s[3], v21.4s, v19.4s, v23.4s
-
-//v20.4s, v21.4s is expRemain
-fmul v20.4s, v20.4s, v20.4s
-fmul v21.4s, v21.4s, v21.4s
-fmul v20.4s, v20.4s, v20.4s
-fmul v21.4s, v21.4s, v21.4s
-
-ushl v16.4s, v16.4s, v2.4s
-ushl v17.4s, v17.4s, v2.4s
-add v20.4s, v20.4s, v16.4s
-add v21.4s, v21.4s, v17.4s
-
-fadd v20.4s, v20.4s, v31.4s
-fadd v21.4s, v21.4s, v31.4s
-
-st1 {v20.4s, v21.4s}, [x0], #32
-fadd v28.4s, v28.4s, v20.4s
-fadd v28.4s, v28.4s, v21.4s
-
-subs x4, x4, #1
-bne Loop
-
-// Bias
-add x7, x2, #12
-ld1 {v27.s}[0], [x7]
-faddp v28.4s, v28.4s, v28.4s
-faddp v28.2s, v28.2s, v28.2s
-fadd v27.2s, v28.2s, v27.2s
-st1 {v27.s}[0], [x7]
-
-ret
-
-#endif
-

source/backend/cpu/arm/arm64/MNNSoftmax.S

@@ -1,204 +0,0 @@
-//
-//  MNNSoftmax.S
-//  MNN
-//
-//  Created by MNN on 2021/07/05.
-//  Copyright © 2018, Alibaba Group Holding Limited
-//
-
-#ifdef __aarch64__
-
-#include "MNNAsmGlobal.h"
-.text
-.align 5
-
-//void MNNSoftmax(float* dest, const float* source, size_t countC8)
-asm_function MNNSoftmax
-    stp x21, x22, [sp, #-32]!
-    stp x19, x20, [sp, #16]
-    sxtw    x8, w2
-    lsr     w9, w2, #2
-    and     x8, x8, #-4
-    cbz     w9, Loop_5
-    ldr     q0, [x1]
-    cmp     w9, #1
-    b.eq    Loop_4
-    add     x10, x1, #16
-    sub     x11, x9, #1
-Loop_3:
-    ldr     q1, [x10], #16
-    subs    x11, x11, #1
-    fmax    v0.4s, v0.4s, v1.4s
-    b.ne    Loop_3
-Loop_4:
-    mov     s1, v0.s[1]
-    fcmp    s0, s1
-    mov     s2, v0.s[2]
-    fcsel   s1, s0, s1, gt
-    fcmp    s1, s2
-    fcsel   s1, s1, s2, gt
-    mov     s0, v0.s[3]
-    fcmp    s1, s0
-    fcsel   s0, s1, s0, gt
-    cmp     w8, w2
-    b.lo    Loop_6
-    b       Loop_8
-Loop_5:
-    fmov    s0, wzr
-    cmp     w8, w2
-    b.hs    Loop_8
-Loop_6:
-    add     x10, x1, w8, sxtw #2
-    mov     w11, w8
-Loop_7:
-    ldr     s1, [x10], #4
-    add     w11, w11, #1
-    fcmp    s0, s1
-    fcsel   s0, s0, s1, gt
-    cmp     w11, w2
-    b.lo    Loop_7
-Loop_8:
-    cbz     w9, Loop_12
-    mov     w10, #-1028784128
-    mov     w12, #43579
-    dup     v4.4s, w10
-    mov     w10, #29208
-    mov     w11, #1118699520
-    movk    w12, #16312, lsl #16
-    movk    w10, #48945, lsl #16
-    dup     v5.4s, w11
-    mov     w11, #34953
-    dup     v6.4s, w12
-    mov     w12, #43691
-    dup     v7.4s, w10
-    mov     w10, #43691
-    movk    w11, #15368, lsl #16
-    movk    w12, #15658, lsl #16
-    movk    w10, #15914, lsl #16
-    dup     v2.4s, v0.s[0]
-    movi    v1.16b, #0
-    fmov    v3.4s, #1.0
-    dup     v16.4s, w11
-    dup     v17.4s, w12
-    dup     v18.4s, w10
-    movi    v19.4s, #63, lsl #24
-    mov     x10, x9
-    mov     x11, x1
-    mov     x12, x0
-Loop_10:
-    ldr     q20, [x11], #16
-    subs    x10, x10, #1
-    fsub    v20.4s, v20.4s, v2.4s
-    fmax    v20.4s, v20.4s, v4.4s
-    fmin    v20.4s, v20.4s, v5.4s
-    fmul    v21.4s, v20.4s, v6.4s
-    fcvtzs  v21.4s, v21.4s
-    scvtf   v22.4s, v21.4s
-    fmla    v20.4s, v22.4s, v7.4s
-    fmul    v22.4s, v20.4s, v16.4s
-    fadd    v22.4s, v22.4s, v17.4s
-    fmul    v22.4s, v20.4s, v22.4s
-    fadd    v22.4s, v22.4s, v18.4s
-    fmul    v22.4s, v20.4s, v22.4s
-    fadd    v22.4s, v22.4s, v19.4s
-    fmul    v22.4s, v20.4s, v22.4s
-    fadd    v22.4s, v22.4s, v3.4s
-    shl     v21.4s, v21.4s, #23
-    fmul    v20.4s, v20.4s, v22.4s
-    add     v21.4s, v21.4s, v3.4s
-    fadd    v20.4s, v20.4s, v3.4s
-    fmul    v20.4s, v20.4s, v21.4s
-    fadd    v1.4s, v1.4s, v20.4s
-    str     q20, [x12], #16
-    b.ne    Loop_10
-    dup     v2.4s, v1.s[1]
-    dup     v3.4s, v1.s[2]
-    fadd    v2.4s, v1.4s, v2.4s
-    fadd    v2.4s, v3.4s, v2.4s
-    dup     v1.4s, v1.s[3]
-    fadd    v1.4s, v1.4s, v2.4s
-    b       Loop_13
-Loop_12:
-    fmov    s1, wzr
-Loop_13:
-    cmp     w8, w2
-    fmov    s2, #1.0
-    b.hs    Loop_16
-    lsl     x21, x8, #2
-    mov     w12, #29208
-    mov     w14, #34953
-    mov     w15, #43691
-    mov     w19, #43691
-    mov     w10, #-1028784128
-    mov     w11, #1118699520
-    movk    w12, #16177, lsl #16
-    mov     w13, #1065353216
-    movk    w14, #15368, lsl #16
-    movk    w15, #15658, lsl #16
-    movk    w19, #15914, lsl #16
-    add     x20, x1, x21
-    add     x21, x0, x21
-    fmov    s3, #0.5
-    mov     w1, w8
-Loop_15:
-    ldr     s4, [x20], #4
-    fmov    s5, w10
-    fmov    s6, w11
-    fmov    s7, w12
-    fsub    s4, s4, s0
-    fmaxnm  s4, s4, s5
-    fminnm  s4, s4, s6
-    fdiv    s6, s4, s7
-    fcvtzs  w3, s6
-    scvtf   s6, w3
-    fmul    s6, s6, s7
-    fmov    s5, w14
-    fsub    s4, s4, s6
-    fmov    s7, w15
-    fmul    s5, s4, s5
-    fadd    s5, s5, s7
-    fmov    s6, w19
-    fmul    s5, s4, s5
-    fadd    s5, s5, s6
-    fmul    s5, s4, s5
-    fadd    s5, s5, s3
-    fmul    s5, s4, s5
-    fadd    s5, s5, s2
-    add     w3, w13, w3, lsl #23
-    fmul    s4, s4, s5
-    fmov    s7, w3
-    fadd    s4, s4, s2
-    add     w1, w1, #1
-    fmul    s4, s4, s7
-    cmp     w1, w2
-    str     s4, [x21], #4
-    fadd    s1, s1, s4
-    b.lo    Loop_15
-Loop_16:
-    fdiv    s0, s2, s1
-    cbz     w9, Loop_19
-    dup     v1.4s, v0.s[0]
-    mov     x10, x0
-Loop_18:
-    ldr     q2, [x10]
-    subs    x9, x9, #1
-    fmul    v2.4s, v1.4s, v2.4s
-    str     q2, [x10], #16
-    b.ne    Loop_18
-Loop_19:
-    cmp     w8, w2
-    b.hs    Loop_22
-    add     x9, x0, w8, sxtw #2
-    Loop_21:
-    ldr     s1, [x9]
-    add     w8, w8, #1
-    cmp     w8, w2
-    fmul    s1, s0, s1
-    str     s1, [x9], #4
-    b.lo    Loop_21
-Loop_22:
-    ldp x19, x20, [sp, #16]
-    ldp x21, x22, [sp], #32
-    ret
-#endif
-

source/backend/cpu/arm/arm64/sme2_asm/MNNGemmInt8AddBiasScaleHp128_SME2_w4_Fp32.S

@@ -379,10 +379,10 @@ LoopSzEnd_TILE_1:
     .inst 0x05b02324  // dup z4.q, z25.q[2]
     .inst 0x05f02325  // dup z5.q, z25.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -404,10 +404,10 @@ LoopSzEnd_TILE_1:
     .inst 0x05f02325  // dup z5.q, z25.q[3]
     .inst 0x05702346  // dup z6.q, z26.q[1]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -431,11 +431,11 @@ LoopSzEnd_TILE_1:
     .inst 0x05702346  // dup z6.q, z26.q[1]
     .inst 0x05b02347  // dup z7.q, z26.q[2]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -461,11 +461,11 @@ TILE1_STORE48:
     .inst 0x05b02347  // dup z7.q, z26.q[2]
     .inst 0x05f02348  // dup z8.q, z26.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -492,12 +492,12 @@ TILE1_STORE52:
     .inst 0x05b02347  // dup z7.q, z26.q[2]
     .inst 0x05f02348  // dup z8.q, z26.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -526,12 +526,12 @@ TILE1_STORE56:
     .inst 0x05f02348  // dup z8.q, z26.q[3]
     .inst 0x05702369  // dup z9.q, z27.q[1]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -562,13 +562,13 @@ TILE1_STORE60:
     .inst 0x05702369  // dup z9.q, z27.q[1]
     .inst 0x05b0236a  // dup z10.q, z27.q[2]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -601,13 +601,13 @@ TILE1_STORE64:
     .inst 0x05b0236a  // dup z10.q, z27.q[2]
     .inst 0x05f0236b  // dup z11.q, z27.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -640,15 +640,14 @@ TILE1_STORE68:
     .inst 0x05702369  // dup z9.q, z27.q[1]
     .inst 0x05b0236a  // dup z10.q, z27.q[2]
     .inst 0x05f0236b  // dup z11.q, z27.q[3]
-    .inst 0x0570238c  // dup z12.q, z28.q[1]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -667,7 +666,7 @@ TILE1_STORE68:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
+    add x9, x6, x4, lsl #4 // +16
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     b TILE1_Dz_End
@@ -685,14 +684,15 @@ TILE1_STORE72:
     .inst 0x05702369  // dup z9.q, z27.q[1]
     .inst 0x05b0236a  // dup z10.q, z27.q[2]
     .inst 0x05f0236b  // dup z11.q, z27.q[3]
+    .inst 0x0570238c  // dup z12.q, z28.q[1]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -711,7 +711,7 @@ TILE1_STORE72:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
+    add x9, x6, x4, lsl #4 // +16
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
@@ -731,14 +731,15 @@ TILE1_STORE76:
     .inst 0x05b0236a  // dup z10.q, z27.q[2]
     .inst 0x05f0236b  // dup z11.q, z27.q[3]
     .inst 0x0570238c  // dup z12.q, z28.q[1]
+    .inst 0x05b0238d  // dup z13.q, z28.q[2]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -757,8 +758,8 @@ TILE1_STORE76:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
@@ -779,14 +780,16 @@ TILE1_STORE80:
     .inst 0x05b0236a  // dup z10.q, z27.q[2]
     .inst 0x05f0236b  // dup z11.q, z27.q[3]
     .inst 0x0570238c  // dup z12.q, z28.q[1]
+    .inst 0x05b0238d  // dup z13.q, z28.q[2]
+    .inst 0x05f0238e  // dup z14.q, z28.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -805,12 +808,13 @@ TILE1_STORE80:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
     .inst 0xe400f5ed  // st1b {z13.b}, p5, [x15]
+    .inst 0xe40455ee  // st1b {z14.b}, p5, [x15, x4]
     b TILE1_Dz_End
 
 TILE1_STORE84:
@@ -827,14 +831,17 @@ TILE1_STORE84:
     .inst 0x05b0236a  // dup z10.q, z27.q[2]
     .inst 0x05f0236b  // dup z11.q, z27.q[3]
     .inst 0x0570238c  // dup z12.q, z28.q[1]
+    .inst 0x05b0238d  // dup z13.q, z28.q[2]
+    .inst 0x05f0238e  // dup z14.q, z28.q[3]
+    .inst 0x057023af  // dup z15.q, z29.q[1]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -853,12 +860,15 @@ TILE1_STORE84:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
     .inst 0xe400f5ed  // st1b {z13.b}, p5, [x15]
+    .inst 0xe40455ee  // st1b {z14.b}, p5, [x15, x4]
+    .inst 0xe400f51d  // st1b {z29.b}, p5, [x8]
     b TILE1_Dz_End
 
 TILE1_STORE88:
@@ -876,14 +886,16 @@ TILE1_STORE88:
     .inst 0x05f0236b  // dup z11.q, z27.q[3]
     .inst 0x0570238c  // dup z12.q, z28.q[1]
     .inst 0x05b0238d  // dup z13.q, z28.q[2]
+    .inst 0x05f0238e  // dup z14.q, z28.q[3]
+    .inst 0x057023af  // dup z15.q, z29.q[1]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -902,12 +914,16 @@ TILE1_STORE88:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
     .inst 0xe400f5ed  // st1b {z13.b}, p5, [x15]
+    .inst 0xe40455ee  // st1b {z14.b}, p5, [x15, x4]
+    .inst 0xe400f51d  // st1b {z29.b}, p5, [x8]
+    .inst 0xe404550f  // st1b {z15.b}, p5, [x8, x4]
     b TILE1_Dz_End
 
 TILE1_STORE92:
@@ -926,14 +942,17 @@ TILE1_STORE92:
     .inst 0x0570238c  // dup z12.q, z28.q[1]
     .inst 0x05b0238d  // dup z13.q, z28.q[2]
     .inst 0x05f0238e  // dup z14.q, z28.q[3]
+    .inst 0x057023af  // dup z15.q, z29.q[1]
+    .inst 0x05b023b0  // dup z16.q, z29.q[2]
+    .inst 0x05f023b1  // dup z17.q, z29.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -952,15 +971,18 @@ TILE1_STORE92:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
-    add x8, x23, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
+    add x11, x5, x4, lsl #3 // +22
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
     .inst 0xe400f5ed  // st1b {z13.b}, p5, [x15]
     .inst 0xe40455ee  // st1b {z14.b}, p5, [x15, x4]
     .inst 0xe400f51d  // st1b {z29.b}, p5, [x8]
+    .inst 0xe404550f  // st1b {z15.b}, p5, [x8, x4]
+    .inst 0xe400f570  // st1b {z16.b}, p5, [x11]
     b TILE1_Dz_End
 
 TILE1_STORE96:
@@ -980,14 +1002,16 @@ TILE1_STORE96:
     .inst 0x05b0238d  // dup z13.q, z28.q[2]
     .inst 0x05f0238e  // dup z14.q, z28.q[3]
     .inst 0x057023af  // dup z15.q, z29.q[1]
+    .inst 0x05b023b0  // dup z16.q, z29.q[2]
+    .inst 0x05f023b1  // dup z17.q, z29.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -1006,9 +1030,10 @@ TILE1_STORE96:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
-    add x8, x23, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
+    add x11, x5, x4, lsl #3 // +22
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
@@ -1016,6 +1041,8 @@ TILE1_STORE96:
     .inst 0xe40455ee  // st1b {z14.b}, p5, [x15, x4]
     .inst 0xe400f51d  // st1b {z29.b}, p5, [x8]
     .inst 0xe404550f  // st1b {z15.b}, p5, [x8, x4]
+    .inst 0xe400f570  // st1b {z16.b}, p5, [x11]
+    .inst 0xe4045571  // st1b {z17.b}, p5, [x11, x4]
     b TILE1_Dz_End
 
 TILE1_STORE100:
@@ -1036,14 +1063,15 @@ TILE1_STORE100:
     .inst 0x05f0238e  // dup z14.q, z28.q[3]
     .inst 0x057023af  // dup z15.q, z29.q[1]
     .inst 0x05b023b0  // dup z16.q, z29.q[2]
+    .inst 0x05f023b1  // dup z17.q, z29.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -1062,10 +1090,11 @@ TILE1_STORE100:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
-    add x8, x23, x4, lsl #3
-    add x11, x5, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
+    add x11, x5, x4, lsl #3 // +22
+    add x13, x9, x4, lsl #3 // +24
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
@@ -1074,6 +1103,8 @@ TILE1_STORE100:
     .inst 0xe400f51d  // st1b {z29.b}, p5, [x8]
     .inst 0xe404550f  // st1b {z15.b}, p5, [x8, x4]
     .inst 0xe400f570  // st1b {z16.b}, p5, [x11]
+    .inst 0xe4045571  // st1b {z17.b}, p5, [x11, x4]
+    .inst 0xe400f5be  // st1b {z30.b}, p5, [x13]
     b TILE1_Dz_End
 
 TILE1_STORE104:
@@ -1095,75 +1126,15 @@ TILE1_STORE104:
     .inst 0x057023af  // dup z15.q, z29.q[1]
     .inst 0x05b023b0  // dup z16.q, z29.q[2]
     .inst 0x05f023b1  // dup z17.q, z29.q[3]
-
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
-
-    .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
-    .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
-    .inst 0xe400f521  // st1b {z1.b}, p5, [x9]
-    .inst 0xe4045522  // st1b {z2.b}, p5, [x9, x4]
-    .inst 0xe400f5f9  // st1b {z25.b}, p5, [x15]
-    .inst 0xe40455e3  // st1b {z3.b}, p5, [x15, x4]
-    .inst 0xe400f504  // st1b {z4.b}, p5, [x8]
-    .inst 0xe4045505  // st1b {z5.b}, p5, [x8, x4]
-    .inst 0xe400f57a  // st1b {z26.b}, p5, [x11]
-    .inst 0xe4045566  // st1b {z6.b}, p5, [x11, x4]
-    .inst 0xe400f5a7  // st1b {z7.b}, p5, [x13]
-    .inst 0xe40455a8  // st1b {z8.b}, p5, [x13, x4]
-    .inst 0xe400f6fb  // st1b {z27.b}, p5, [x23]
-    .inst 0xe40456e9  // st1b {z9.b}, p5, [x23, x4]
-    .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
-    .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
-
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
-    add x8, x23, x4, lsl #3
-    add x11, x5, x4, lsl #3
-
-    .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
-    .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
-    .inst 0xe400f5ed  // st1b {z13.b}, p5, [x15]
-    .inst 0xe40455ee  // st1b {z14.b}, p5, [x15, x4]
-    .inst 0xe400f51d  // st1b {z29.b}, p5, [x8]
-    .inst 0xe404550f  // st1b {z15.b}, p5, [x8, x4]
-    .inst 0xe400f570  // st1b {z16.b}, p5, [x11]
-    .inst 0xe4045571  // st1b {z17.b}, p5, [x11, x4]
-    b TILE1_Dz_End
-
-TILE1_STORE108:
-    .inst 0x05702300  // dup z0.q, z24.q[1]
-    .inst 0x05b02301  // dup z1.q, z24.q[2]
-    .inst 0x05f02302  // dup z2.q, z24.q[3]
-    .inst 0x05702323  // dup z3.q, z25.q[1]
-    .inst 0x05b02324  // dup z4.q, z25.q[2]
-    .inst 0x05f02325  // dup z5.q, z25.q[3]
-    .inst 0x05702346  // dup z6.q, z26.q[1]
-    .inst 0x05b02347  // dup z7.q, z26.q[2]
-    .inst 0x05f02348  // dup z8.q, z26.q[3]
-    .inst 0x05702369  // dup z9.q, z27.q[1]
-    .inst 0x05b0236a  // dup z10.q, z27.q[2]
-    .inst 0x05f0236b  // dup z11.q, z27.q[3]
-    .inst 0x0570238c  // dup z12.q, z28.q[1]
-    .inst 0x05b0238d  // dup z13.q, z28.q[2]
-    .inst 0x05f0238e  // dup z14.q, z28.q[3]
-    .inst 0x057023af  // dup z15.q, z29.q[1]
-    .inst 0x05b023b0  // dup z16.q, z29.q[2]
-    .inst 0x05f023b1  // dup z17.q, z29.q[3]
     .inst 0x057023d2  // dup z18.q, z30.q[1]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -1182,11 +1153,11 @@ TILE1_STORE108:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
-    add x8, x23, x4, lsl #3
-    add x11, x5, x4, lsl #3
-    add x13, x9, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
+    add x11, x5, x4, lsl #3 // +22
+    add x13, x9, x4, lsl #3 // +24
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
@@ -1200,7 +1171,8 @@ TILE1_STORE108:
     .inst 0xe40455b2  // st1b {z18.b}, p5, [x13, x4]
     b TILE1_Dz_End
 
-    TILE1_STORE112:
+    /* oc=108 */
+    TILE1_STORE108:
     .inst 0x05702300  // dup z0.q, z24.q[1]
     .inst 0x05b02301  // dup z1.q, z24.q[2]
     .inst 0x05f02302  // dup z2.q, z24.q[3]
@@ -1222,13 +1194,13 @@ TILE1_STORE108:
     .inst 0x057023d2  // dup z18.q, z30.q[1]
     .inst 0x05b023d3  // dup z19.q, z30.q[2]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -1247,12 +1219,12 @@ TILE1_STORE108:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
-    add x8, x23, x4, lsl #3
-    add x11, x5, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
+    add x11, x5, x4, lsl #3 // +22
+    add x13, x9, x4, lsl #3 // +24
+    add x23, x15, x4, lsl #3 // +26
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
@@ -1267,6 +1239,77 @@ TILE1_STORE108:
     .inst 0xe400f6f3  // st1b {z19.b}, p5, [x23]
     b TILE1_Dz_End
 
+    /* oc=112 */
+    TILE1_STORE112:
+    .inst 0x05702300  // dup z0.q, z24.q[1]
+    .inst 0x05b02301  // dup z1.q, z24.q[2]
+    .inst 0x05f02302  // dup z2.q, z24.q[3]
+    .inst 0x05702323  // dup z3.q, z25.q[1]
+    .inst 0x05b02324  // dup z4.q, z25.q[2]
+    .inst 0x05f02325  // dup z5.q, z25.q[3]
+    .inst 0x05702346  // dup z6.q, z26.q[1]
+    .inst 0x05b02347  // dup z7.q, z26.q[2]
+    .inst 0x05f02348  // dup z8.q, z26.q[3]
+    .inst 0x05702369  // dup z9.q, z27.q[1]
+    .inst 0x05b0236a  // dup z10.q, z27.q[2]
+    .inst 0x05f0236b  // dup z11.q, z27.q[3]
+    .inst 0x0570238c  // dup z12.q, z28.q[1]
+    .inst 0x05b0238d  // dup z13.q, z28.q[2]
+    .inst 0x05f0238e  // dup z14.q, z28.q[3]
+    .inst 0x057023af  // dup z15.q, z29.q[1]
+    .inst 0x05b023b0  // dup z16.q, z29.q[2]
+    .inst 0x05f023b1  // dup z17.q, z29.q[3]
+    .inst 0x057023d2  // dup z18.q, z30.q[1]
+    .inst 0x05b023d3  // dup z19.q, z30.q[2]
+    .inst 0x05f023d4  // dup z20.q, z30.q[3]
+
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
+
+    .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
+    .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
+    .inst 0xe400f521  // st1b {z1.b}, p5, [x9]
+    .inst 0xe4045522  // st1b {z2.b}, p5, [x9, x4]
+    .inst 0xe400f5f9  // st1b {z25.b}, p5, [x15]
+    .inst 0xe40455e3  // st1b {z3.b}, p5, [x15, x4]
+    .inst 0xe400f504  // st1b {z4.b}, p5, [x8]
+    .inst 0xe4045505  // st1b {z5.b}, p5, [x8, x4]
+    .inst 0xe400f57a  // st1b {z26.b}, p5, [x11]
+    .inst 0xe4045566  // st1b {z6.b}, p5, [x11, x4]
+    .inst 0xe400f5a7  // st1b {z7.b}, p5, [x13]
+    .inst 0xe40455a8  // st1b {z8.b}, p5, [x13, x4]
+    .inst 0xe400f6fb  // st1b {z27.b}, p5, [x23]
+    .inst 0xe40456e9  // st1b {z9.b}, p5, [x23, x4]
+    .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
+    .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
+
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
+    add x11, x5, x4, lsl #3 // +22
+    add x13, x9, x4, lsl #3 // +24
+    add x23, x15, x4, lsl #3 // +26
+
+    .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
+    .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
+    .inst 0xe400f5ed  // st1b {z13.b}, p5, [x15]
+    .inst 0xe40455ee  // st1b {z14.b}, p5, [x15, x4]
+    .inst 0xe400f51d  // st1b {z29.b}, p5, [x8]
+    .inst 0xe404550f  // st1b {z15.b}, p5, [x8, x4]
+    .inst 0xe400f570  // st1b {z16.b}, p5, [x11]
+    .inst 0xe4045571  // st1b {z17.b}, p5, [x11, x4]
+    .inst 0xe400f5be  // st1b {z30.b}, p5, [x13]
+    .inst 0xe40455b2  // st1b {z18.b}, p5, [x13, x4]
+    .inst 0xe400f6f3  // st1b {z19.b}, p5, [x23]
+    .inst 0xe40456f4  // st1b {z20.b}, p5, [x23, x4]
+    b TILE1_Dz_End
+
+    /* oc=116 */
     TILE1_STORE116:
     .inst 0x05702300  // dup z0.q, z24.q[1]
     .inst 0x05b02301  // dup z1.q, z24.q[2]
@@ -1290,13 +1333,13 @@ TILE1_STORE108:
     .inst 0x05b023d3  // dup z19.q, z30.q[2]
     .inst 0x05f023d4  // dup z20.q, z30.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -1315,13 +1358,13 @@ TILE1_STORE108:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
-    add x8, x23, x4, lsl #3
-    add x11, x5, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
+    add x11, x5, x4, lsl #3 // +22
+    add x13, x9, x4, lsl #3 // +24
+    add x23, x15, x4, lsl #3 // +26
+    add x5, x8, x4, lsl #3   // +28
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
@@ -1338,6 +1381,7 @@ TILE1_STORE108:
     .inst 0xe400f4bf  // st1b {z31.b}, p5, [x5]
     b TILE1_Dz_End
 
+    /* oc=120 */
     TILE1_STORE120:
     .inst 0x05702300  // dup z0.q, z24.q[1]
     .inst 0x05b02301  // dup z1.q, z24.q[2]
@@ -1362,13 +1406,13 @@ TILE1_STORE108:
     .inst 0x05f023d4  // dup z20.q, z30.q[3]
     .inst 0x057023f5  // dup z21.q, z31.q[1]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -1387,13 +1431,13 @@ TILE1_STORE108:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
-    add x8, x23, x4, lsl #3
-    add x11, x5, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
+    add x11, x5, x4, lsl #3 // +22
+    add x13, x9, x4, lsl #3 // +24
+    add x23, x15, x4, lsl #3 // +26
+    add x5, x8, x4, lsl #3   // +28
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
@@ -1411,6 +1455,7 @@ TILE1_STORE108:
     .inst 0xe40454b5  // st1b {z21.b}, p5, [x5, x4]
     b TILE1_Dz_End
 
+    /* oc=124 */
     TILE1_STORE124:
     .inst 0x05702300  // dup z0.q, z24.q[1]
     .inst 0x05b02301  // dup z1.q, z24.q[2]
@@ -1487,6 +1532,7 @@ TILE1_STORE108:
     .inst 0xe400f596  // st1b {z22.b}, p5, [x12]
     b TILE1_Dz_End
 
+    /* oc=128 */
     TILE1_STORE128:
     .inst 0x05702300  // dup z0.q, z24.q[1]
     .inst 0x05b02301  // dup z1.q, z24.q[2]

source/backend/cpu/arm/arm64/sme2_asm/MNNGemmInt8AddBiasScaleHp128_SME2_w8_Fp32.S

@@ -372,10 +372,10 @@ LoopSzEnd_TILE_1:
     .inst 0x05b02324  // dup z4.q, z25.q[2]
     .inst 0x05f02325  // dup z5.q, z25.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -397,10 +397,10 @@ LoopSzEnd_TILE_1:
     .inst 0x05f02325  // dup z5.q, z25.q[3]
     .inst 0x05702346  // dup z6.q, z26.q[1]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -424,11 +424,11 @@ LoopSzEnd_TILE_1:
     .inst 0x05702346  // dup z6.q, z26.q[1]
     .inst 0x05b02347  // dup z7.q, z26.q[2]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -454,11 +454,11 @@ TILE1_STORE48:
     .inst 0x05b02347  // dup z7.q, z26.q[2]
     .inst 0x05f02348  // dup z8.q, z26.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -485,12 +485,12 @@ TILE1_STORE52:
     .inst 0x05b02347  // dup z7.q, z26.q[2]
     .inst 0x05f02348  // dup z8.q, z26.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -519,12 +519,12 @@ TILE1_STORE56:
     .inst 0x05f02348  // dup z8.q, z26.q[3]
     .inst 0x05702369  // dup z9.q, z27.q[1]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -555,13 +555,13 @@ TILE1_STORE60:
     .inst 0x05702369  // dup z9.q, z27.q[1]
     .inst 0x05b0236a  // dup z10.q, z27.q[2]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -594,13 +594,13 @@ TILE1_STORE64:
     .inst 0x05b0236a  // dup z10.q, z27.q[2]
     .inst 0x05f0236b  // dup z11.q, z27.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -633,15 +633,14 @@ TILE1_STORE68:
     .inst 0x05702369  // dup z9.q, z27.q[1]
     .inst 0x05b0236a  // dup z10.q, z27.q[2]
     .inst 0x05f0236b  // dup z11.q, z27.q[3]
-    .inst 0x0570238c  // dup z12.q, z28.q[1]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -660,7 +659,7 @@ TILE1_STORE68:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
+    add x9, x6, x4, lsl #4 // +16
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     b TILE1_Dz_End
@@ -678,14 +677,15 @@ TILE1_STORE72:
     .inst 0x05702369  // dup z9.q, z27.q[1]
     .inst 0x05b0236a  // dup z10.q, z27.q[2]
     .inst 0x05f0236b  // dup z11.q, z27.q[3]
+    .inst 0x0570238c  // dup z12.q, z28.q[1]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -704,7 +704,7 @@ TILE1_STORE72:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
+    add x9, x6, x4, lsl #4 // +16
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
@@ -724,14 +724,15 @@ TILE1_STORE76:
     .inst 0x05b0236a  // dup z10.q, z27.q[2]
     .inst 0x05f0236b  // dup z11.q, z27.q[3]
     .inst 0x0570238c  // dup z12.q, z28.q[1]
+    .inst 0x05b0238d  // dup z13.q, z28.q[2]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -750,8 +751,8 @@ TILE1_STORE76:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
@@ -772,14 +773,16 @@ TILE1_STORE80:
     .inst 0x05b0236a  // dup z10.q, z27.q[2]
     .inst 0x05f0236b  // dup z11.q, z27.q[3]
     .inst 0x0570238c  // dup z12.q, z28.q[1]
+    .inst 0x05b0238d  // dup z13.q, z28.q[2]
+    .inst 0x05f0238e  // dup z14.q, z28.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -798,12 +801,13 @@ TILE1_STORE80:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
     .inst 0xe400f5ed  // st1b {z13.b}, p5, [x15]
+    .inst 0xe40455ee  // st1b {z14.b}, p5, [x15, x4]
     b TILE1_Dz_End
 
 TILE1_STORE84:
@@ -820,14 +824,16 @@ TILE1_STORE84:
     .inst 0x05b0236a  // dup z10.q, z27.q[2]
     .inst 0x05f0236b  // dup z11.q, z27.q[3]
     .inst 0x0570238c  // dup z12.q, z28.q[1]
+    .inst 0x05b0238d  // dup z13.q, z28.q[2]
+    .inst 0x05f0238e  // dup z14.q, z28.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -846,12 +852,15 @@ TILE1_STORE84:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
     .inst 0xe400f5ed  // st1b {z13.b}, p5, [x15]
+    .inst 0xe40455ee  // st1b {z14.b}, p5, [x15, x4]
+    .inst 0xe400f51d  // st1b {z29.b}, p5, [x8]
     b TILE1_Dz_End
 
 TILE1_STORE88:
@@ -869,14 +878,16 @@ TILE1_STORE88:
     .inst 0x05f0236b  // dup z11.q, z27.q[3]
     .inst 0x0570238c  // dup z12.q, z28.q[1]
     .inst 0x05b0238d  // dup z13.q, z28.q[2]
+    .inst 0x05f0238e  // dup z14.q, z28.q[3]
+    .inst 0x057023af  // dup z15.q, z29.q[1]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -895,12 +906,16 @@ TILE1_STORE88:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
     .inst 0xe400f5ed  // st1b {z13.b}, p5, [x15]
+    .inst 0xe40455ee  // st1b {z14.b}, p5, [x15, x4]
+    .inst 0xe400f51d  // st1b {z29.b}, p5, [x8]
+    .inst 0xe404550f  // st1b {z15.b}, p5, [x8, x4]
     b TILE1_Dz_End
 
 TILE1_STORE92:
@@ -919,14 +934,17 @@ TILE1_STORE92:
     .inst 0x0570238c  // dup z12.q, z28.q[1]
     .inst 0x05b0238d  // dup z13.q, z28.q[2]
     .inst 0x05f0238e  // dup z14.q, z28.q[3]
+    .inst 0x057023af  // dup z15.q, z29.q[1]
+    .inst 0x05b023b0  // dup z16.q, z29.q[2]
+    .inst 0x05f023b1  // dup z17.q, z29.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -945,15 +963,18 @@ TILE1_STORE92:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
-    add x8, x23, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
+    add x11, x5, x4, lsl #3 // +22
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
     .inst 0xe400f5ed  // st1b {z13.b}, p5, [x15]
     .inst 0xe40455ee  // st1b {z14.b}, p5, [x15, x4]
     .inst 0xe400f51d  // st1b {z29.b}, p5, [x8]
+    .inst 0xe404550f  // st1b {z15.b}, p5, [x8, x4]
+    .inst 0xe400f570  // st1b {z16.b}, p5, [x11]
     b TILE1_Dz_End
 
 TILE1_STORE96:
@@ -973,14 +994,16 @@ TILE1_STORE96:
     .inst 0x05b0238d  // dup z13.q, z28.q[2]
     .inst 0x05f0238e  // dup z14.q, z28.q[3]
     .inst 0x057023af  // dup z15.q, z29.q[1]
+    .inst 0x05b023b0  // dup z16.q, z29.q[2]
+    .inst 0x05f023b1  // dup z17.q, z29.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -999,9 +1022,10 @@ TILE1_STORE96:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
-    add x8, x23, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
+    add x11, x5, x4, lsl #3 // +22
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
@@ -1009,6 +1033,8 @@ TILE1_STORE96:
     .inst 0xe40455ee  // st1b {z14.b}, p5, [x15, x4]
     .inst 0xe400f51d  // st1b {z29.b}, p5, [x8]
     .inst 0xe404550f  // st1b {z15.b}, p5, [x8, x4]
+    .inst 0xe400f570  // st1b {z16.b}, p5, [x11]
+    .inst 0xe4045571  // st1b {z17.b}, p5, [x11, x4]
     b TILE1_Dz_End
 
 TILE1_STORE100:
@@ -1029,14 +1055,15 @@ TILE1_STORE100:
     .inst 0x05f0238e  // dup z14.q, z28.q[3]
     .inst 0x057023af  // dup z15.q, z29.q[1]
     .inst 0x05b023b0  // dup z16.q, z29.q[2]
+    .inst 0x05f023b1  // dup z17.q, z29.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -1055,10 +1082,11 @@ TILE1_STORE100:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
-    add x8, x23, x4, lsl #3
-    add x11, x5, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
+    add x11, x5, x4, lsl #3 // +22
+    add x13, x9, x4, lsl #3 // +24
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
@@ -1067,6 +1095,8 @@ TILE1_STORE100:
     .inst 0xe400f51d  // st1b {z29.b}, p5, [x8]
     .inst 0xe404550f  // st1b {z15.b}, p5, [x8, x4]
     .inst 0xe400f570  // st1b {z16.b}, p5, [x11]
+    .inst 0xe4045571  // st1b {z17.b}, p5, [x11, x4]
+    .inst 0xe400f5be  // st1b {z30.b}, p5, [x13]
     b TILE1_Dz_End
 
 TILE1_STORE104:
@@ -1088,75 +1118,15 @@ TILE1_STORE104:
     .inst 0x057023af  // dup z15.q, z29.q[1]
     .inst 0x05b023b0  // dup z16.q, z29.q[2]
     .inst 0x05f023b1  // dup z17.q, z29.q[3]
-
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
-
-    .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
-    .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
-    .inst 0xe400f521  // st1b {z1.b}, p5, [x9]
-    .inst 0xe4045522  // st1b {z2.b}, p5, [x9, x4]
-    .inst 0xe400f5f9  // st1b {z25.b}, p5, [x15]
-    .inst 0xe40455e3  // st1b {z3.b}, p5, [x15, x4]
-    .inst 0xe400f504  // st1b {z4.b}, p5, [x8]
-    .inst 0xe4045505  // st1b {z5.b}, p5, [x8, x4]
-    .inst 0xe400f57a  // st1b {z26.b}, p5, [x11]
-    .inst 0xe4045566  // st1b {z6.b}, p5, [x11, x4]
-    .inst 0xe400f5a7  // st1b {z7.b}, p5, [x13]
-    .inst 0xe40455a8  // st1b {z8.b}, p5, [x13, x4]
-    .inst 0xe400f6fb  // st1b {z27.b}, p5, [x23]
-    .inst 0xe40456e9  // st1b {z9.b}, p5, [x23, x4]
-    .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
-    .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
-
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
-    add x8, x23, x4, lsl #3
-    add x11, x5, x4, lsl #3
-
-    .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
-    .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
-    .inst 0xe400f5ed  // st1b {z13.b}, p5, [x15]
-    .inst 0xe40455ee  // st1b {z14.b}, p5, [x15, x4]
-    .inst 0xe400f51d  // st1b {z29.b}, p5, [x8]
-    .inst 0xe404550f  // st1b {z15.b}, p5, [x8, x4]
-    .inst 0xe400f570  // st1b {z16.b}, p5, [x11]
-    .inst 0xe4045571  // st1b {z17.b}, p5, [x11, x4]
-    b TILE1_Dz_End
-
-TILE1_STORE108:
-    .inst 0x05702300  // dup z0.q, z24.q[1]
-    .inst 0x05b02301  // dup z1.q, z24.q[2]
-    .inst 0x05f02302  // dup z2.q, z24.q[3]
-    .inst 0x05702323  // dup z3.q, z25.q[1]
-    .inst 0x05b02324  // dup z4.q, z25.q[2]
-    .inst 0x05f02325  // dup z5.q, z25.q[3]
-    .inst 0x05702346  // dup z6.q, z26.q[1]
-    .inst 0x05b02347  // dup z7.q, z26.q[2]
-    .inst 0x05f02348  // dup z8.q, z26.q[3]
-    .inst 0x05702369  // dup z9.q, z27.q[1]
-    .inst 0x05b0236a  // dup z10.q, z27.q[2]
-    .inst 0x05f0236b  // dup z11.q, z27.q[3]
-    .inst 0x0570238c  // dup z12.q, z28.q[1]
-    .inst 0x05b0238d  // dup z13.q, z28.q[2]
-    .inst 0x05f0238e  // dup z14.q, z28.q[3]
-    .inst 0x057023af  // dup z15.q, z29.q[1]
-    .inst 0x05b023b0  // dup z16.q, z29.q[2]
-    .inst 0x05f023b1  // dup z17.q, z29.q[3]
     .inst 0x057023d2  // dup z18.q, z30.q[1]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -1175,11 +1145,11 @@ TILE1_STORE108:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
-    add x8, x23, x4, lsl #3
-    add x11, x5, x4, lsl #3
-    add x13, x9, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
+    add x11, x5, x4, lsl #3 // +22
+    add x13, x9, x4, lsl #3 // +24
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
@@ -1193,7 +1163,8 @@ TILE1_STORE108:
     .inst 0xe40455b2  // st1b {z18.b}, p5, [x13, x4]
     b TILE1_Dz_End
 
-    TILE1_STORE112:
+    /* oc=108 */
+    TILE1_STORE108:
     .inst 0x05702300  // dup z0.q, z24.q[1]
     .inst 0x05b02301  // dup z1.q, z24.q[2]
     .inst 0x05f02302  // dup z2.q, z24.q[3]
@@ -1215,13 +1186,13 @@ TILE1_STORE108:
     .inst 0x057023d2  // dup z18.q, z30.q[1]
     .inst 0x05b023d3  // dup z19.q, z30.q[2]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -1240,12 +1211,12 @@ TILE1_STORE108:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
-    add x8, x23, x4, lsl #3
-    add x11, x5, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
+    add x11, x5, x4, lsl #3 // +22
+    add x13, x9, x4, lsl #3 // +24
+    add x23, x15, x4, lsl #3 // +26
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
@@ -1260,6 +1231,77 @@ TILE1_STORE108:
     .inst 0xe400f6f3  // st1b {z19.b}, p5, [x23]
     b TILE1_Dz_End
 
+    /* oc=112 */
+    TILE1_STORE112:
+    .inst 0x05702300  // dup z0.q, z24.q[1]
+    .inst 0x05b02301  // dup z1.q, z24.q[2]
+    .inst 0x05f02302  // dup z2.q, z24.q[3]
+    .inst 0x05702323  // dup z3.q, z25.q[1]
+    .inst 0x05b02324  // dup z4.q, z25.q[2]
+    .inst 0x05f02325  // dup z5.q, z25.q[3]
+    .inst 0x05702346  // dup z6.q, z26.q[1]
+    .inst 0x05b02347  // dup z7.q, z26.q[2]
+    .inst 0x05f02348  // dup z8.q, z26.q[3]
+    .inst 0x05702369  // dup z9.q, z27.q[1]
+    .inst 0x05b0236a  // dup z10.q, z27.q[2]
+    .inst 0x05f0236b  // dup z11.q, z27.q[3]
+    .inst 0x0570238c  // dup z12.q, z28.q[1]
+    .inst 0x05b0238d  // dup z13.q, z28.q[2]
+    .inst 0x05f0238e  // dup z14.q, z28.q[3]
+    .inst 0x057023af  // dup z15.q, z29.q[1]
+    .inst 0x05b023b0  // dup z16.q, z29.q[2]
+    .inst 0x05f023b1  // dup z17.q, z29.q[3]
+    .inst 0x057023d2  // dup z18.q, z30.q[1]
+    .inst 0x05b023d3  // dup z19.q, z30.q[2]
+    .inst 0x05f023d4  // dup z20.q, z30.q[3]
+
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
+
+    .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
+    .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
+    .inst 0xe400f521  // st1b {z1.b}, p5, [x9]
+    .inst 0xe4045522  // st1b {z2.b}, p5, [x9, x4]
+    .inst 0xe400f5f9  // st1b {z25.b}, p5, [x15]
+    .inst 0xe40455e3  // st1b {z3.b}, p5, [x15, x4]
+    .inst 0xe400f504  // st1b {z4.b}, p5, [x8]
+    .inst 0xe4045505  // st1b {z5.b}, p5, [x8, x4]
+    .inst 0xe400f57a  // st1b {z26.b}, p5, [x11]
+    .inst 0xe4045566  // st1b {z6.b}, p5, [x11, x4]
+    .inst 0xe400f5a7  // st1b {z7.b}, p5, [x13]
+    .inst 0xe40455a8  // st1b {z8.b}, p5, [x13, x4]
+    .inst 0xe400f6fb  // st1b {z27.b}, p5, [x23]
+    .inst 0xe40456e9  // st1b {z9.b}, p5, [x23, x4]
+    .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
+    .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
+
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
+    add x11, x5, x4, lsl #3 // +22
+    add x13, x9, x4, lsl #3 // +24
+    add x23, x15, x4, lsl #3 // +26
+
+    .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
+    .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
+    .inst 0xe400f5ed  // st1b {z13.b}, p5, [x15]
+    .inst 0xe40455ee  // st1b {z14.b}, p5, [x15, x4]
+    .inst 0xe400f51d  // st1b {z29.b}, p5, [x8]
+    .inst 0xe404550f  // st1b {z15.b}, p5, [x8, x4]
+    .inst 0xe400f570  // st1b {z16.b}, p5, [x11]
+    .inst 0xe4045571  // st1b {z17.b}, p5, [x11, x4]
+    .inst 0xe400f5be  // st1b {z30.b}, p5, [x13]
+    .inst 0xe40455b2  // st1b {z18.b}, p5, [x13, x4]
+    .inst 0xe400f6f3  // st1b {z19.b}, p5, [x23]
+    .inst 0xe40456f4  // st1b {z20.b}, p5, [x23, x4]
+    b TILE1_Dz_End
+
+    /* oc=116 */
     TILE1_STORE116:
     .inst 0x05702300  // dup z0.q, z24.q[1]
     .inst 0x05b02301  // dup z1.q, z24.q[2]
@@ -1283,13 +1325,13 @@ TILE1_STORE108:
     .inst 0x05b023d3  // dup z19.q, z30.q[2]
     .inst 0x05f023d4  // dup z20.q, z30.q[3]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -1308,13 +1350,13 @@ TILE1_STORE108:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
-    add x8, x23, x4, lsl #3
-    add x11, x5, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
+    add x11, x5, x4, lsl #3 // +22
+    add x13, x9, x4, lsl #3 // +24
+    add x23, x15, x4, lsl #3 // +26
+    add x5, x8, x4, lsl #3   // +28
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
@@ -1331,6 +1373,7 @@ TILE1_STORE108:
     .inst 0xe400f4bf  // st1b {z31.b}, p5, [x5]
     b TILE1_Dz_End
 
+    /* oc=120 */
     TILE1_STORE120:
     .inst 0x05702300  // dup z0.q, z24.q[1]
     .inst 0x05b02301  // dup z1.q, z24.q[2]
@@ -1355,13 +1398,13 @@ TILE1_STORE108:
     .inst 0x05f023d4  // dup z20.q, z30.q[3]
     .inst 0x057023f5  // dup z21.q, z31.q[1]
 
-    add x9, x6, x4, lsl #1
-    add x15, x6, x4, lsl #2
-    add x8, x9, x4, lsl #2
-    add x11, x6, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #1 // +2
+    add x15, x6, x4, lsl #2 // +4
+    add x8, x9, x4, lsl #2 // +6
+    add x11, x6, x4, lsl #3 // +8
+    add x13, x9, x4, lsl #3 // +10
+    add x23, x15, x4, lsl #3 // +12
+    add x5, x8, x4, lsl #3   // +14
 
     .inst 0xe400f4d8  // st1b {z24.b}, p5, [x6]
     .inst 0xe40454c0  // st1b {z0.b}, p5, [x6, x4]
@@ -1380,13 +1423,13 @@ TILE1_STORE108:
     .inst 0xe400f4aa  // st1b {z10.b}, p5, [x5]
     .inst 0xe40454ab  // st1b {z11.b}, p5, [x5, x4]
 
-    add x9, x6, x4, lsl #4
-    add x15, x13, x4, lsl #3
-    add x8, x23, x4, lsl #3
-    add x11, x5, x4, lsl #3
-    add x13, x9, x4, lsl #3
-    add x23, x15, x4, lsl #3
-    add x5, x8, x4, lsl #3
+    add x9, x6, x4, lsl #4 // +16
+    add x15, x13, x4, lsl #3 // +18
+    add x8, x23, x4, lsl #3 // +20
+    add x11, x5, x4, lsl #3 // +22
+    add x13, x9, x4, lsl #3 // +24
+    add x23, x15, x4, lsl #3 // +26
+    add x5, x8, x4, lsl #3   // +28
 
     .inst 0xe400f53c  // st1b {z28.b}, p5, [x9]
     .inst 0xe404552c  // st1b {z12.b}, p5, [x9, x4]
@@ -1404,6 +1447,7 @@ TILE1_STORE108:
     .inst 0xe40454b5  // st1b {z21.b}, p5, [x5, x4]
     b TILE1_Dz_End
 
+    /* oc=124 */
     TILE1_STORE124:
     .inst 0x05702300  // dup z0.q, z24.q[1]
     .inst 0x05b02301  // dup z1.q, z24.q[2]
@@ -1480,6 +1524,7 @@ TILE1_STORE108:
     .inst 0xe400f596  // st1b {z22.b}, p5, [x12]
     b TILE1_Dz_End
 
+    /* oc=128 */
     TILE1_STORE128:
     .inst 0x05702300  // dup z0.q, z24.q[1]
     .inst 0x05b02301  // dup z1.q, z24.q[2]

source/backend/cpu/compute/CommonOptFunction.cpp

@@ -1390,6 +1390,89 @@ void MNNAccumulateSequenceNumber (float* dst, const float* src, int size) {
     *dst = sum;
 }
 
+#ifdef MNN_SUPPORT_TRANSFORMER_FUSE
+
+static void MNNFlashAttentionUpdateBlockOutput(float* dst, float* src, float* scale, float* normalizeScale, int depthQuad, int plane, int pack, int idx, int kvBlocks, int size, int bytes) {
+    // source shape:                 [headDim/pack, seqLen, pack]
+    // scale & normalizeScale shape: [seqLen]
+    // dest shape:                   [headDim/pack, seqLen, pack]
+    auto stride0 = plane * pack;
+
+    if (idx > 0) {
+        for (int j = 0; j < depthQuad; ++j) {
+            for (int i = 0; i < plane; ++i) {
+                auto dataNew = Vec::load(src + j * stride0 + i * pack);
+                auto dataOld = Vec::load(dst + j * stride0 + i * pack);
+                auto s = Vec(scale[i]);
+                dataNew = Vec::fma(dataNew, dataOld, s);
+                Vec::save(dst + j * stride0 + i * pack, dataNew);
+            }
+        }
+    } else {
+        memcpy(dst, src, size * bytes);
+    }
+    if (idx == kvBlocks - 1) { // if last subBlock, exp(xi)/sum(exp(xi))
+        for (int j = 0; j < depthQuad; ++j) {
+            for (int i = 0; i < plane; ++i) {
+                auto dataNew = Vec::load(dst + j * stride0 + i * pack);
+                auto ns = Vec(1.0f / normalizeScale[i]);
+                dataNew = dataNew * ns;
+                Vec::save(dst + j * stride0 + i * pack, dataNew);
+            }
+        }
+    }
+}
+
+static void MNNAttenPackAndScaleSingleHead(float* dst, const float* srcHeadBase, size_t srcRowStride, const float* scale, const int32_t* units, size_t seqLen, size_t headDim) {
+    const int32_t eP = units[0];
+    const int32_t lP = units[1];
+
+    if (lP != 1) {
+        MNN_ERROR("This function only supports lP=1 or 2\n");
+        return;
+    }
+
+    const float scaleVal = scale[0];
+#ifdef MNN_USE_NEON
+    const float32x4_t vScale = vdupq_n_f32(scaleVal);
+#endif
+    const size_t packedHeadDim = UP_DIV(headDim, lP);
+    const size_t dstStrideDOuter = (size_t)eP * lP;
+    const size_t dstStrideSOuter = packedHeadDim * dstStrideDOuter;
+
+    for (int s = 0; s < seqLen; ++s) {
+        const int sOuter = s / eP;
+        const int sInner = s % eP;
+        const float* srcRowPtr = srcHeadBase + s * srcRowStride;
+        float* dstBasePtr = dst + sOuter * dstStrideSOuter + sInner * lP;
+        
+        size_t d = 0;
+#ifdef MNN_USE_NEON
+        for (; d + 7 < headDim; d += 8) {
+            float32x4_t sVec0 = vld1q_f32(srcRowPtr + d);
+            float32x4_t sVec1 = vld1q_f32(srcRowPtr + d + 4);
+            sVec0 = vmulq_f32(sVec0, vScale);
+            sVec1 = vmulq_f32(sVec1, vScale);
+            
+            dstBasePtr[(d + 0) * dstStrideDOuter] = sVec0[0];
+            dstBasePtr[(d + 1) * dstStrideDOuter] = sVec0[1];
+            dstBasePtr[(d + 2) * dstStrideDOuter] = sVec0[2];
+            dstBasePtr[(d + 3) * dstStrideDOuter] = sVec0[3];
+            dstBasePtr[(d + 4) * dstStrideDOuter] = sVec1[0];
+            dstBasePtr[(d + 5) * dstStrideDOuter] = sVec1[1];
+            dstBasePtr[(d + 6) * dstStrideDOuter] = sVec1[2];
+            dstBasePtr[(d + 7) * dstStrideDOuter] = sVec1[3];
+        }
+#else
+        for (; d < headDim; ++d) {
+            dstBasePtr[d * dstStrideDOuter] = srcRowPtr[d] * scaleVal;
+        }
+#endif
+    }
+}
+#endif // MNN_SUPPORT_TRANSFORMER_FUSE
+
+
 #ifndef MNN_USE_NEON
 void MNNGetMatMulPackMode(int* eP, int *lP, int* hP) {
     *eP = 16;
@@ -2619,30 +2702,54 @@ void MNNExpC8(float* dest, const float* source, float* offset, const float* para
     offset[3] = summer;
 }
 
-void MNNSoftmax(float* dest, const float* source, size_t size) {
-    float maxValue = ALIMAX(source[0], source[1]);
-    for (int i = 2; i < size; ++i) {
-        maxValue = ALIMAX(maxValue, source[i]);
-    }
-    float xLimit = 87, param = 0.6931471805599453, sumValue = 0.f;
-    for (int i = 0; i < size; ++i) {
-        auto x         = source[i] - maxValue;
-        x = x > -xLimit ? x : -xLimit;
-        x = x < xLimit ? x : xLimit;
+void MNNSoftmax(float* softmaxDst, float* input, float* runningMax, float* runningSum, float* updateScale, int outside, int reduceSize) {
+    for (int k = 0; k < outside; ++k) {
+        auto source = input + k * reduceSize;
+        auto dest = softmaxDst + k * reduceSize;
 
-        int div        = (x / param);
-        int div2       = (div + 127) << 23;
-        auto xReamin   = x - div * param;
-        float expBasic = *(float*)(&div2);
+        float oldMax = source[0];
+        if (runningMax) {
+            oldMax = runningMax[k];
+        }
 
-        auto t         = xReamin;
-        auto expRemain = ((((1.0f / 120 * t + 1.0f / 24) * t + 1.0f / 6) * t + 0.5f) * t + 1.0f) * t + 1.0f;
-        dest[i]  = expBasic * expRemain;
-        sumValue += dest[i];
-    }
-    sumValue = 1.f / sumValue;
-    for (int i = 0; i < size; ++i) {
-        dest[i] *= sumValue;
+        // find max value of current block
+        float blockMax =source[0];
+        for (int i = 1; i < reduceSize; ++i) {
+            blockMax = ALIMAX(blockMax, source[i]);
+        }
+        float newMax = ALIMAX(oldMax, blockMax);
+        
+        // caculate block's expr(xi-newmax) and update runningMax
+        float xLimit = 87, param = 0.6931471805599453;
+        float blockSum = 0.f;
+        for (int i = 0; i < reduceSize; ++i) {
+            auto x         = source[i] - newMax;
+            x = x > -xLimit ? x : -xLimit;
+            x = x < xLimit ? x : xLimit;
+
+            int div        = (x / param);
+            int div2       = (div + 127) << 23;
+            auto xReamin   = x - div * param;
+            float expBasic = *(float*)(&div2);
+
+            auto t         = xReamin;
+            auto expRemain = ((((1.0f / 120 * t + 1.0f / 24) * t + 1.0f / 6) * t + 0.5f) * t + 1.0f) * t + 1.0f;
+            dest[i]  = expBasic * expRemain;
+            blockSum += dest[i];
+        }
+
+        if (runningMax != nullptr && runningSum != nullptr && updateScale != nullptr) {
+            // update runningSum, runningMax, scale=expf(oldMax-newMax)
+            runningSum[k] = runningSum[k] * expf(oldMax - newMax) + blockSum;
+            runningMax[k] = newMax;
+            updateScale[k] = expf(oldMax - newMax);
+        } else {
+            // Normalize
+            auto scale = 1.f / blockSum;
+            for (int i = 0; i < reduceSize; ++i) {
+                dest[i] *= scale;
+            }
+        }
     }
 }
 
@@ -2657,6 +2764,68 @@ void MNNReluInt8(int8_t* dst, const int8_t* src, size_t size, ssize_t zeroPoint)
 }
 #endif // no MNN_USE_SSE
 
+void MNNExp(float* dst, const float* src, float* offset, size_t dataSize) {
+    int countC8        = static_cast<int32_t>(dataSize) / 8;
+    int remain = static_cast<int32_t>(dataSize) % 8;
+    static const float parameters[] = {
+        (float)logf(2.0f), 1.0f / (float)logf(2.0f), 0.25f, 1.0f, 0.5f, 1.0f / 6.0f, 1.0f / 24.0f, 1.0f / 120.0f};
+    if (countC8 > 0) {
+        // Align to eight so asm is easier to write
+        MNNExpC8(dst, src, offset, parameters, countC8);
+    }
+    if (remain > 0) {
+        auto param = parameters[0];
+        float xLimit = 87;
+        float summer = offset[3];
+        auto source = src + countC8 * 8;
+        auto dest = dst + countC8 * 8;
+        for (int i = 0; i < remain; ++i) {
+            auto x         = source[i] * offset[0] + offset[2];
+            x = ALIMAX(x, -xLimit);
+            x = ALIMIN(x, xLimit);
+            int div        = (x * parameters[1]);
+            int div2       = (div + 127) << 23;
+            auto xReamin   = x - div * param;
+            float expBasic = *(float*)(&div2);
+            auto t = xReamin * 0.25f;
+            auto expRemain =
+            ((((parameters[7] * t + parameters[6]) * t + parameters[5]) * t + parameters[4]) * t + 1.0f) * t +
+                1.0f;
+            expRemain = expRemain * expRemain;
+            expRemain = expRemain * expRemain;
+            dest[i] = expBasic * expRemain + offset[1];
+            summer+= dest[i];
+        }
+        offset[3] = summer;
+    }
+}
+
+void packKvCache(float* dst, const float* src, size_t seqLen, size_t kvSeqLen, size_t eP) {
+    if (seqLen == 0 || kvSeqLen == 0) {
+        return;
+    }
+
+    const size_t dstSOuterStride = kvSeqLen * eP;
+
+    for (size_t sBase = 0; sBase < seqLen; sBase += eP) {
+        const size_t numRowsInBlock = std::min(eP, seqLen - sBase);
+        const size_t sOuter = sBase / eP;
+
+        float* dstSOuterBase = dst + sOuter * dstSOuterStride;
+
+        for (size_t k = 0; k < kvSeqLen; ++k) {
+            float* dstColStart = dstSOuterBase + k * eP;
+
+            for (size_t sInner = 0; sInner < numRowsInBlock; ++sInner) {
+                const size_t s = sBase + sInner;
+
+                const float value = src[s * kvSeqLen + k];
+                dstColStart[sInner] = value;
+            }
+        }
+    }
+}
+
 void MNNMaxFloat(float* input, float* maxBuffer, int32_t inputCountUnit) {
     for (int i = 0; i < inputCountUnit; i++) {
         for (int j = 0; j < UNIT; j++) {
@@ -3173,42 +3342,6 @@ void MNNPackTranspose(float* dst, const float* src, size_t area, size_t depth, i
     }
 }
 
-void MNNExp(float* dst, const float* src, float* offset, size_t dataSize) {
-    int countC8        = static_cast<int32_t>(dataSize) / 8;
-    int remain = static_cast<int32_t>(dataSize) % 8;
-    static const float parameters[] = {
-        (float)logf(2.0f), 1.0f / (float)logf(2.0f), 0.25f, 1.0f, 0.5f, 1.0f / 6.0f, 1.0f / 24.0f, 1.0f / 120.0f};
-    if (countC8 > 0) {
-        // Align to eight so asm is easier to write
-        MNNExpC8(dst, src, offset, parameters, countC8);
-    }
-    if (remain > 0) {
-        auto param = parameters[0];
-        float xLimit = 87;
-        float summer = offset[3];
-        auto source = src + countC8 * 8;
-        auto dest = dst + countC8 * 8;
-        for (int i = 0; i < remain; ++i) {
-            auto x         = source[i] * offset[0] + offset[2];
-            x = ALIMAX(x, -xLimit);
-            x = ALIMIN(x, xLimit);
-            int div        = (x * parameters[1]);
-            int div2       = (div + 127) << 23;
-            auto xReamin   = x - div * param;
-            float expBasic = *(float*)(&div2);
-            auto t = xReamin * 0.25f;
-            auto expRemain =
-            ((((parameters[7] * t + parameters[6]) * t + parameters[5]) * t + parameters[4]) * t + 1.0f) * t +
-                1.0f;
-            expRemain = expRemain * expRemain;
-            expRemain = expRemain * expRemain;
-            dest[i] = expBasic * expRemain + offset[1];
-            summer+= dest[i];
-        }
-        offset[3] = summer;
-    }
-}
-
 // Lambert's series with 7 divisions
 // reference from
 // https://varietyofsound.wordpress.com/2011/02/14/efficient-tanh-computation-using-lamberts-continued-fraction/
@@ -4011,6 +4144,7 @@ void MNNCoreFunctionInit() {
     gCoreFunction->chooseWinoDestUnrollTransform = WinogradFunction::chooseWinoDestUnrollTransform;
     gCoreFunction->MNNDeconvRunForLineDepthwise = MNNDeconvRunForLineDepthwise;
     gCoreFunction->MNNDeconvRunForUnitDepthWise = MNNDeconvRunForUnitDepthWise;
+    gCoreFunction->MNNSoftmax = MNNSoftmax;
 #ifdef MNN_USE_NEON
     gCoreFunction->MNNDepthwiseConvFastKernel = MNNDepthwiseConvFastKernel;
 #endif
@@ -4019,6 +4153,12 @@ void MNNCoreFunctionInit() {
 #ifdef MNN_SUPPORT_QUANT_EXTEND
     gCoreFunction->MNNSelectUnaryFunctionForInt8 = CPUUnary::selectForInt8;
 #endif
+
+#ifdef MNN_SUPPORT_TRANSFORMER_FUSE
+    gCoreFunction->MNNAttenPackAndScaleSingleHead = MNNAttenPackAndScaleSingleHead;
+    gCoreFunction->MNNFlashAttentionUpdateBlockOutput = MNNFlashAttentionUpdateBlockOutput;
+#endif
+
     gCoreFunction->MNNReluWithSlopeChannel = MNNReluWithSlopeChannel;
     gCoreFunction->MNNPoolingAvg = (decltype(gCoreFunction->MNNPoolingAvg))(poolingAvg<float, Vec4, 4>);
     // Set min value as 1 << 24

source/backend/cpu/compute/CommonOptFunction.h

@@ -32,12 +32,16 @@ void MNNAbsMaxFP32_Pack4(const float* source, float* absmax, size_t src_depth_qu
 void MNNAbsMaxFP32_Pack8(const float* source, float* absmax, size_t src_depth_quad, size_t realSize, int pack);
 void MNNQuantScaleFP32(float* absmax, float* quant_scale, float* dequant_scale, size_t thread, size_t batch);
 void MNNDynamicUpdateConvBiasScale(float* newbias, float* oldbias, float* weightKernelSum, float* inputZero, size_t ocQuad);
-#endif
+#endif // MNN_LOW_MEMORY
+
 #ifdef MNN_SME2
 void MNNPackedMatMulRemainFP32_SME2(float* C, const float* A, const float* B, size_t eSize, const size_t* parameter, const float* postParameters, const float* bias, const float* k, const float* b);
+#endif
+
+#endif // __aarch64__
+
+
 
-#endif
-#endif
 void MNNFp32ToFp8(uint8_t* dst, const float* src, size_t size);
 void MNNFp8ToFp32(float* dst, const uint8_t* src, size_t size);
 void MNNFp16ToFp8(uint8_t* dst, const uint16_t* src, size_t size);
@@ -117,7 +121,7 @@ void MNNReluWithSlopeCommon(float* dst, const float* src, size_t size, float slo
 void MNNHardSwishCommon(float* dst, const float* src, size_t size);
 void MNNGeluCommon(float* dst, const float* src, size_t size);
 void MNNGeluStandardCommon(float* dst, const float* src, size_t size);
-void MNNSoftmax(float* dest, const float* source, size_t size);
+void MNNSoftmax(float* softmaxDst, float* input, float* runningMax, float* runningSum, float* updateScale, int outside, int reduceSize);
 void MNNNorm(float* dest, const float* source, const float *gamma, const float *beta, float epsilon, size_t size, bool RMSNorm = false);
 
 // Get Pack for MatMul's e , l , h , the pack number must be 1 or 4 * n
@@ -187,6 +191,8 @@ void MNNComputeMatMulForE_1(const float* A, const float* B, float* C, const floa
 void MNNCopyC4Int16WithStride(const float* sourceF, float* destF, size_t srcStride, size_t dstStride, size_t count);
 void MNNInt8ToInt16(int16_t* dest, const int8_t* source, size_t count);
 
+void packKvCache(float* dst, const float* src, size_t seqLen, size_t kvSeqLen, size_t eP);
+
 struct SumByAxisParams {
     ssize_t kernelCountUnitDouble;
     ssize_t unitColBufferSize;
@@ -401,6 +407,13 @@ struct CoreFunctions {
     void(*MNNSumWeightInt8)(float* kernelsum, int8_t* source, size_t outside, size_t reduceAxis, size_t hP, size_t lP);
     void(*MNNSumWeightInt8SmeHp64)(float* kernelsum, int8_t* source, size_t outside, size_t reduceAxis, size_t hP, size_t lP);
 
+    // Attention
+    void(*MNNAttenUnpackAndConvertFp16)(float* dst, float* src, size_t depth, size_t planesize, int pack);
+    void(*MNNAttenPackAndConvertFp32)(float* dst, float* src, const int32_t* units, size_t depth, size_t planesize);
+    void(*MNNAttenPackAndScaleSingleHead)(float* dst, const float* srcHeadBase, size_t srcRowStride, const float* scale, const int32_t* units, size_t seqLen, size_t headDim);
+    void(*MNNFlashAttentionUpdateBlockOutput)(float* dst, float* src, float* scale, float* normalizeScale, int depthQuad, int plane, int pack, int idx, int kvBlocks, int size, int bytes);
+    void(*MNNSoftmax)(float* softmaxDst, float* input, float* runningMax, float* runningSum, float* updateScale, int outside, int reduceSize);
+
     MatmulRelatedFunctions int8MatmulRelatedFunctions;
     MatmulRelatedFunctions sme2Int8MatmulRelatedFuncionsHp32;
 };

source/backend/cpu/compute/ConvInt8TiledExecutor.cpp

@@ -805,17 +805,16 @@ ErrorCode DenseConvInt8TiledExecutor::onResize(const std::vector<Tensor*>& input
         return OUT_OF_MEMORY;
     }
     if (mOnlineReorderWeightSme && planeSize > 1) { // only prefill need
-        int dstHp = 32; // if mOnlineReorderWeightSme==true, UNIT is 64 when model loaded.
-        int weightlenNew = ROUND_UP(outC, dstHp) * mBlockNum * ROUND_UP(ic / mBlockNum, SRC_UNIT) * kernelCount * SRC_UNIT * dstHp;
+        int weightlenNew = ROUND_UP(outC, SME_DECODE_MAXHP) * mBlockNum * ROUND_UP(ic / mBlockNum, SRC_UNIT) * kernelCount;
         if (mResourceInt8->mActBits == 4) {
             weightlenNew /= 2;
         }
-        mWeight4Prefill = bufferAlloc->alloc(weightlenNew + 2 * ROUND_UP(outC, dstHp) * QUANT_INFO_BYTES);
+        mWeight4Prefill = bufferAlloc->alloc(weightlenNew + 2 * mBlockNum * ROUND_UP(outC, SME_DECODE_MAXHP) * QUANT_INFO_BYTES);
         if (mWeight4Prefill.invalid()) {
             return OUT_OF_MEMORY;
         }
         if (mInputBlockNum > 1) { // only in this case, need to use weight_kernel_sum
-            mWeightKernelSum4Prefill = bufferAlloc->alloc(ROUND_UP(outC, 32) * mBlockNum * sizeof(float));
+            mWeightKernelSum4Prefill = bufferAlloc->alloc(ROUND_UP(outC, SME_DECODE_MAXHP) * mBlockNum * sizeof(float));
             if (mWeightKernelSum4Prefill.invalid()) {
                 return OUT_OF_MEMORY;
             }

source/backend/cpu/x86_x64/AVX2Functions.cpp

@@ -62,7 +62,8 @@ bool AVX2Functions::init(int cpuFlags) {
     coreFunction->MNNComputeMatMulForH_1 = _AVX_MNNComputeMatMulForH_1;
     // Dynamic Quant
     coreFunction->MNNCountMaxMinValue = _AVX_MNNCountMinMaxValue;
-    
+
+    coreFunction->MNNSoftmax = _AVX_MNNSoftmax;
 
     // For Packed Functions
     coreFunction->pack = 8;

source/backend/cpu/x86_x64/FunctionDispatcher.cpp

@@ -24,7 +24,7 @@ struct FunctionGroup {
     int lP                                                                                       = 1;
     int hP                                                                                       = 4;
     void (*MNNExpC8)(float* dest, const float* source, float* offset, const float* parameters, size_t countC8) = _SSE_MNNExpC8;
-    void (*MNNSoftmax)(float* dest, const float* source, size_t size) = _SSE_MNNSoftmax;
+    void (*MNNSoftmax)(float* softmaxDst, float* input, float* runningMax, float* runningSum, float* updateScale, int outside, int reduceSize) = _SSE_MNNSoftmax;
     void (*MNNReluInt8)(int8_t* dst, const int8_t* src, size_t size, ssize_t zeroPoint) = _SSE_MNNReluInt8;
     void (*MNNHardSwish)(float* dst, const float* src, size_t size) = _SSE_MNNHardSwish;
     void (*MNNGelu)(float* dst, const float* src, size_t size, float* parameters) = _SSE_MNNGelu;
@@ -65,6 +65,8 @@ void MNNFunctionInit() {
         coreFunction->MNNPackForMatMul_B    = _SSE_MNNPackForMatMul_B;
         // Dynamic Quant
         coreFunction->MNNCountMaxMinValue = _SSE_MNNCountMinMaxValue;
+
+        coreFunction->MNNSoftmax = _SSE_MNNSoftmax;
     }
 #ifdef MNN_USE_AVX
     if (cpuFlags & libyuv::kCpuHasAVX2) {
@@ -205,8 +207,8 @@ void MNNInt8ToInt16(int16_t* dest, const int8_t* source, size_t count) {
     _SSE_MNNInt8ToInt16(dest, source, count);
 }
 
-void MNNSoftmax(float* dest, const float* source, size_t size) {
-    gFunc.MNNSoftmax(dest, source, size);
+void MNNSoftmax(float* softmaxDst, float* input, float* runningMax, float* runningSum, float* updateScale, int outside, int reduceSize) {
+    gFunc.MNNSoftmax(softmaxDst, input, runningMax, runningSum, updateScale, outside, reduceSize);
 }
 
 void MNNNorm(float* dest, const float* source, const float *gamma, const float *beta, float epsilon, size_t size, bool RMSNorm) {

source/backend/cpu/x86_x64/avx/FunctionSummary.hpp

@@ -53,7 +53,7 @@ void _AVX_MNNAsyQuantInfo(float* scale, float* bias, float* qscale, float* qbias
 void _AVX_MNNPackC4ForMatMul_A(float* destOrigin, float const** sourceGroup, const int32_t* info, const int32_t* el);
 
 void _AVX_MNNExpC8(float* dest, const float* source, float* offset, const float* parameters, size_t countC8);
-void _AVX_MNNSoftmax(float* dest, const float* source, size_t size);
+void _AVX_MNNSoftmax(float* softmaxDst, float* input, float* runningMax, float* runningSum, float* updateScale, int outside, int reduceSize);
 void _AVX_MNNFloat2Int8(const float* src, int8_t* dst, size_t sizeQuad, const float* scalep, ssize_t minV, ssize_t maxV, const float* zeroPoint, ssize_t quanParamVec);
 void _AVX_MNNInt8ScaleToFloat(float* dst, const int8_t* src, const float* scale, size_t sizeQuad, const float* zeroPoint, ssize_t quanParamVec);
 void _AVX_MNNLineDepthWiseInt8AddBiasScaleUnit(int8_t* dstO, const int8_t* srcO, const int8_t* weightO, const QuanPostTreatParameters* parameters, size_t width, size_t src_w_step, size_t fw, size_t fh, size_t dilateX_step, size_t dilateY_step, int8_t* idxOrder);

source/backend/cpu/x86_x64/avx/MathFunctions.cpp

@@ -117,103 +117,71 @@ void _AVX_MNNExpC8(float* dest, const float* source, float* offset, const float*
 }
 
 
-void _AVX_MNNSoftmax(float* dest, const float* source, size_t size) {
-    float tmpfloat8[8];
-    int count  = size / 8;
-    int remain = count * 8;
-    // step 1: get maxValue
-    float maxValue = source[0];
-    if (count > 0) {
-        auto maxVal = _mm256_loadu_ps(source);
-        for (int i = 1; i < count; i++) {
-            maxVal = _mm256_max_ps(maxVal, _mm256_loadu_ps(source + i * 8));
-        }
-        _mm256_storeu_ps(tmpfloat8, maxVal);
-        maxValue = tmpfloat8[0] > tmpfloat8[1] ? tmpfloat8[0] : tmpfloat8[1];
-        for (int i = 2; i < 8; i++) {
-            maxValue = maxValue > tmpfloat8[i] ? maxValue : tmpfloat8[i];
-        }
-    }
-    for (int i = remain; i < size; i++) {
-        maxValue = maxValue > source[i] ? maxValue : source[i];
-    }
+void _AVX_MNNSoftmax(float* softmaxDst, float* input, float* runningMax, float* runningSum, float* updateScale, int outside, int reduceSize) {
+    const float xLimit = 87.0f;
+    const float param = 0.6931471805599453f; // ln(2)
+    const float inv_param = 1.0f / param;
+    const int32_t exp_offset = 127;
+    const float exp_scale = 8388608.0f; // 2^23
 
-    // step 2: get exp(x - maxValue) and sum(exp(x - maxValue))
-    float sumValue = 0.f;
-    if (count > 0) {
-        auto sumVal = _mm256_set1_ps(0.f);
-        auto p0    = _mm256_set1_ps(0.6931471805599453);
-        auto p1    = _mm256_set1_ps(1.4426950408889634);
-        auto p2    = _mm256_set1_ps(1.f);
-        auto p3    = _mm256_set1_ps(1.f);
-        auto p4    = _mm256_set1_ps(0.5);
-        auto p5    = _mm256_set1_ps(0.1666666666666666);
-        auto p6    = _mm256_set1_ps(0.041666666666666664);
-        auto p7    = _mm256_set1_ps(0.008333333333333333);
-        auto xMax  = _mm256_set1_ps(87);
-        auto xMin  = _mm256_set1_ps(-87);
-        auto basic = _mm256_set1_epi32(1 << 23);
-        auto temp127 = _mm256_set1_epi32(127);
-        for (int i = 0; i < count; ++i) {
-            auto x            = _mm256_sub_ps(_mm256_loadu_ps(source + i * 8), _mm256_set1_ps(maxValue));
-            x                 = _mm256_max_ps(x, xMin);
-            x                 = _mm256_min_ps(x, xMax);
-            auto div          = _mm256_mul_ps(x, p1);
-            auto divInt       = _mm256_cvtps_epi32(div);
-            div               = _mm256_cvtepi32_ps(divInt);
-            auto div2         = _mm256_add_epi32(divInt, temp127);
-            div2 = _mm256_mullo_epi32(div2, basic);
-            auto expBasic  = _mm256_castsi256_ps(div2);
-            auto xReamin   = _mm256_sub_ps(x, _mm256_mul_ps(div, p0));
-            auto t         = xReamin;
-            auto c0        = _mm256_mul_ps(p7, t);
-            auto c1        = _mm256_add_ps(c0, p6);
-            auto c2        = _mm256_mul_ps(c1, t);
-            auto c3        = _mm256_add_ps(c2, p5);
-            auto c4        = _mm256_mul_ps(c3, t);
-            auto c5        = _mm256_add_ps(c4, p4);
-            auto c6        = _mm256_mul_ps(c5, t);
-            auto c7        = _mm256_add_ps(c6, p3);
-            auto c8        = _mm256_mul_ps(c7, t);
-            auto c9        = _mm256_add_ps(c8, p2);
-            auto expRemain = c9;
-            auto expRes    = _mm256_mul_ps(expBasic, expRemain);
-            sumVal         = _mm256_add_ps(expRes, sumVal);
-            _mm256_storeu_ps(dest + 8 * i, expRes);
-        }
-        _mm256_storeu_ps(tmpfloat8, sumVal);
-        for (int i = 0; i < 8; i++) {
-            sumValue += tmpfloat8[i];
-        }
-    }
-    auto param = 0.6931471805599453;
-    float xLimit = 87;
-    for (int i = remain; i < size; i++) {
-        auto x         = source[i] - maxValue;
-        x = x > -xLimit ? x : -xLimit;
-        x = x < xLimit ? x : xLimit;
+    for (int k = 0; k < outside; ++k) {
+        float* source = input + k * reduceSize;
+        float* dest = softmaxDst + k * reduceSize;
 
-        int div        = (x / param);
-        int div2       = (div + 127) << 23;
-        auto xReamin   = x - div * param;
-        float expBasic = *(float*)(&div2);
+        float tmpfloat8[8];
+        int count  = reduceSize/ 8;
+        int remain = count * 8;
+        // step 1: get maxValue
+        float maxValue = source[0];
 
-        auto t         = xReamin;
-        auto expRemain = ((((1.0f / 120 * t + 1.0f / 24) * t + 1.0f / 6) * t + 0.5f) * t + 1.0f) * t + 1.0f;
-        dest[i]  = expBasic * expRemain;
-        sumValue += dest[i];
-    }
-    // step 3: get x / sum and store
-    for (int i = 0; i < count; ++i) {
-        // using  1 / ((1 / x) * sum) instead x * (1 / sum) or x / sum for some bugs in intel cpu
-        auto x = _mm256_rcp_ps(_mm256_loadu_ps(dest + 8 * i));
-        auto y = _mm256_set1_ps(sumValue);
-        auto z = _mm256_rcp_ps(_mm256_mul_ps(x, y));
-        _mm256_storeu_ps(dest + 8 * i, z);
-    }
-    sumValue = 1.f / sumValue;
-    for (int i = remain; i < size; i++) {
-        dest[i] *= sumValue;
+        float oldMax = maxValue;
+        if (runningMax) {
+            oldMax = runningMax[k];
+        }
+
+        if (count > 0) {
+            auto maxVal = _mm256_loadu_ps(source);
+            for (int i = 1; i < count; i++) {
+                maxVal = _mm256_max_ps(maxVal, _mm256_loadu_ps(source + i * 8));
+            }
+            _mm256_storeu_ps(tmpfloat8, maxVal);
+            maxValue = tmpfloat8[0] > tmpfloat8[1] ? tmpfloat8[0] : tmpfloat8[1];
+            for (int i = 2; i < 8; i++) {
+                maxValue = maxValue > tmpfloat8[i] ? maxValue : tmpfloat8[i];
+            }
+        }
+        for (int i = remain; i < reduceSize; i++) {
+            maxValue = maxValue > source[i] ? maxValue : source[i];
+        }
+
+        float newMax = ALIMAX(oldMax, maxValue);
+
+        // step 2: get exp(x - newMax) and sum(exp(x - newMax))
+        float exprOffset[4] = {1.0f, 0.0f, 0.0f, 0.0f };
+        exprOffset[2] = -newMax;
+        MNNExp(dest, source, exprOffset, reduceSize);
+        float sumValue = exprOffset[3];
+
+        if (runningMax != nullptr && runningSum != nullptr && updateScale != nullptr) {
+            // === Step 3: Update running variables ===
+            float scale = expf(oldMax - newMax);
+            runningSum[k] = runningSum[k] * scale + sumValue;
+            runningMax[k] = newMax;
+            updateScale[k] = scale;
+        } else {
+            // step 3: get x / sum and store
+            for (int i = 0; i < count; ++i) {
+                // using  1 / ((1 / x) * sum) instead x * (1 / sum) or x / sum for some bugs in intel cpu
+                auto x = _mm256_rcp_ps(_mm256_loadu_ps(dest + 8 * i));
+                auto y = _mm256_set1_ps(sumValue);
+                auto z = _mm256_rcp_ps(_mm256_mul_ps(x, y));
+                _mm256_storeu_ps(dest + 8 * i, z);
+            }
+            auto scale = 1.f / sumValue;
+            for (int i = remain; i < reduceSize; i++) {
+                dest[i] *= scale;
+            }
+        }
     }
 }
 

source/backend/cpu/x86_x64/avx/PackedFunction.cpp

@@ -48,8 +48,45 @@ void _AVX_MNNConvRunForLineDepthwise(float* dst, const float* src, const float*
                                 size_t fw, size_t fh, size_t dilateX_step, size_t dilateY_step, size_t height,
                                      size_t srcHStep, size_t dstHStep, const float* bias, const float* parameters);
 void _AVX_MNNAxByClampBroadcastUnit(float* C, const float* A, const float* B, size_t width, size_t cStride, size_t aStride, size_t height, const float* parameters);
+
+#ifdef MNN_SUPPORT_TRANSFORMER_FUSE
+void _AVX_MNNFlashAttentionUpdateBlockOutput(float* dst, float* src, float* scale, float* normalizeScale, int depthQuad, int plane, int pack, int idx, int kvBlocks, int size, int bytes);
+#endif 
 }
 
+#ifdef MNN_SUPPORT_TRANSFORMER_FUSE
+void _AVX_MNNFlashAttentionUpdateBlockOutput(float* dst, float* src, float* scale, float* normalizeScale, int depthQuad, int plane, int pack, int idx, int kvBlocks, int size, int bytes) {
+    // source shape:                 [headDim/pack, seqLen, pack]
+    // scale & normalizeScale shape: [seqLen]
+    // dest shape:                   [headDim/pack, seqLen, pack]
+    auto stride0 = plane * pack;
+
+    if (idx > 0) {
+        for (int j = 0; j < depthQuad; ++j) {
+            for (int i = 0; i < plane; ++i) {
+                auto dataNew = Vec::load(src + j * stride0 + i * pack);
+                auto dataOld = Vec::load(dst + j * stride0 + i * pack);
+                auto s = Vec(scale[i]);
+                dataNew = Vec::fma(dataNew, dataOld, s);
+                Vec::save(dst + j * stride0 + i * pack, dataNew);
+            }
+        }
+    } else {
+        memcpy(dst, src, size * bytes);
+    }
+    if (idx == kvBlocks - 1) { // if last subBlock, exp(xi)/sum(exp(xi))
+        for (int j = 0; j < depthQuad; ++j) {
+            for (int i = 0; i < plane; ++i) {
+                auto dataNew = Vec::load(dst + j * stride0 + i * pack);
+                auto ns = Vec(1.0f / normalizeScale[i]);
+                dataNew = dataNew * ns;
+                Vec::save(dst + j * stride0 + i * pack, dataNew);
+            }
+        }
+    }
+}
+#endif
+
 
 void _AVX_MNNCopyC4WithStride(const float* source, float* dest, size_t srcStride, size_t dstStride, size_t count) {
     for (int i = 0; i < count; ++i) {
@@ -728,4 +765,9 @@ void _AVX_ExtraInit(void* functions) {
     // sparse conv funcs
     coreFunction->MNNGetSparseMatMulPackMode = _AVX_MNNGetSparseMatMulPackMode;
     coreFunction->MNNAdjustOptimalSparseKernel = _AVX_MNNAdjustOptimalSparseKernel;
+
+    // attention
+#ifdef MNN_SUPPORT_TRANSFORMER_FUSE
+    coreFunction->MNNFlashAttentionUpdateBlockOutput = _AVX_MNNFlashAttentionUpdateBlockOutput;
+#endif
 }

source/backend/cpu/x86_x64/avx512/PackedFunction.cpp

@@ -1064,6 +1064,39 @@ static void _AVX512_MNNAdjustOptimalSparseKernel(int& sparseBlockOC, MNN::CoreFu
     }
 }
 
+#ifdef MNN_SUPPORT_TRANSFORMER_FUSE
+void _AVX512_MNNFlashAttentionUpdateBlockOutput(float* dst, float* src, float* scale, float* normalizeScale, int depthQuad, int plane, int pack, int idx, int kvBlocks, int size, int bytes) {
+    // source shape:                 [headDim/pack, seqLen, pack]
+    // scale & normalizeScale shape: [seqLen]
+    // dest shape:                   [headDim/pack, seqLen, pack]
+    auto stride0 = plane * pack;
+
+    if (idx > 0) {
+        for (int j = 0; j < depthQuad; ++j) {
+            for (int i = 0; i < plane; ++i) {
+                auto dataNew = Vec::load(src + j * stride0 + i * pack);
+                auto dataOld = Vec::load(dst + j * stride0 + i * pack);
+                auto s = Vec(scale[i]);
+                dataNew = Vec::fma(dataNew, dataOld, s);
+                Vec::save(dst + j * stride0 + i * pack, dataNew);
+            }
+        }
+    } else {
+        memcpy(dst, src, size * bytes);
+    }
+    if (idx == kvBlocks - 1) { // if last subBlock, exp(xi)/sum(exp(xi))
+        for (int j = 0; j < depthQuad; ++j) {
+            for (int i = 0; i < plane; ++i) {
+                auto dataNew = Vec::load(dst + j * stride0 + i * pack);
+                auto ns = Vec(1.0f / normalizeScale[i]);
+                dataNew = dataNew * ns;
+                Vec::save(dst + j * stride0 + i * pack, dataNew);
+            }
+        }
+    }
+}
+#endif
+
 
 void _AVX512_ExtraInit(void* functions) {
     auto coreFunction = static_cast<MNN::CoreFunctions*>(functions);
@@ -1100,4 +1133,8 @@ void _AVX512_ExtraInit(void* functions) {
 
     coreFunction->MNNGetSparseMatMulPackMode = _AVX512_MNNGetSparseMatMulPackMode;
     coreFunction->MNNAdjustOptimalSparseKernel = _AVX512_MNNAdjustOptimalSparseKernel;
+
+#ifdef MNN_SUPPORT_TRANSFORMER_FUSE 
+    coreFunction->MNNFlashAttentionUpdateBlockOutput = _AVX512_MNNFlashAttentionUpdateBlockOutput;
+#endif
 }

source/backend/cpu/x86_x64/sse/FunctionSummary.hpp

@@ -81,7 +81,7 @@ void _SSE_MNNInt8ToInt16(int16_t* dest, const int8_t* source, size_t count);
 
 void _SSE_MNNPackForMatMul_B_BF16(float* dest, const float* source, size_t h, size_t kernelsize, size_t ic, bool transpose);
 void _SSE_MNNReluInt8(int8_t* dst, const int8_t* src, size_t size, ssize_t zeroPoint);
-void _SSE_MNNSoftmax(float* dest, const float* source, size_t size);
+void _SSE_MNNSoftmax(float* softmaxDst, float* input, float* runningMax, float* runningSum, float* updateScale, int outside, int reduceSize);
 void _SSE_ExtraInit(void* functions);
 void _SSE_MNNNorm(float *dst, const float *src, const float *gamma, const float *beta, float epsilon, size_t size, bool RMSNorm);
 void _SSE_ImageProcessInit(void* functions, int cpuFlags);

source/backend/cpu/x86_x64/sse/MathFunctions.cpp

@@ -69,100 +69,68 @@ void _SSE_MNNExpC8(float* dest, const float* source, float* offset, const float*
     offset[3] = total;
 }
 
-void _SSE_MNNSoftmax(float* dest, const float* source, size_t size) {
-    float tmpfloat4[4];
-    int count  = static_cast<int32_t>(size / 4);
-    int remain = count * 4;
-    // step 1: get maxValue
-    float maxValue = source[0];
-    if (count > 0) {
-        auto maxVal = _mm_loadu_ps(source);
-        for (int i = 1; i < count; i++) {
-            maxVal = _mm_max_ps(maxVal, _mm_loadu_ps(source + i * 4));
+void _SSE_MNNSoftmax(float* softmaxDst, float* input, float* runningMax, float* runningSum, float* updateScale, int outside, int reduceSize) {
+    const float xLimit = 87.0f;
+    const float param = 0.6931471805599453f; // ln(2)
+    const float inv_param = 1.0f / param;
+    const int32_t exp_offset = 127;
+    const float exp_scale = 8388608.0f; // 2^23
+
+    for (int k = 0; k < outside; ++k) {
+        float* source = input + k * reduceSize;
+        float* dest = softmaxDst + k * reduceSize;
+
+        float tmpfloat4[4];
+        int count  = static_cast<int32_t>(reduceSize / 4);
+        int remain = count * 4;
+        // step 1: get maxValue
+        float maxValue = source[0];
+        float oldMax = maxValue;
+        if (runningMax) {
+            oldMax = runningMax[k];
         }
-        _mm_storeu_ps(tmpfloat4, maxVal);
-        maxValue = tmpfloat4[0] > tmpfloat4[1] ? tmpfloat4[0] : tmpfloat4[1];
-        maxValue = maxValue > tmpfloat4[2] ? maxValue : tmpfloat4[2];
-        maxValue = maxValue > tmpfloat4[3] ? maxValue : tmpfloat4[3];
-    }
-    for (int i = remain; i < size; i++) {
-        maxValue = maxValue > source[i] ? maxValue : source[i];
-    }
-
-    // step 2: get exp(x - maxValue) and sum(exp(x - maxValue))
-    float sumValue = 0.f;
-    if (count > 0) {
-        auto sumVal = _mm_set1_ps(0.f);
-        auto p0    = _mm_set1_ps(0.6931471805599453);
-        auto p1    = _mm_set1_ps(1.4426950408889634);
-        auto p2    = _mm_set1_ps(1.f);
-        auto p3    = _mm_set1_ps(1.f);
-        auto p4    = _mm_set1_ps(0.5);
-        auto p5    = _mm_set1_ps(0.1666666666666666);
-        auto p6    = _mm_set1_ps(0.041666666666666664);
-        auto p7    = _mm_set1_ps(0.008333333333333333);
-        auto xMax  = _mm_set1_ps(87);
-        auto xMin  = _mm_set1_ps(-87);
-        // auto basic = _mm_set1_epi32(1 << 23);
-        for (int i = 0; i < count; ++i) {
-            auto x            = _mm_sub_ps(_mm_loadu_ps(source + i * 4), _mm_set1_ps(maxValue));
-            x                 = _mm_max_ps(x, xMin);
-            x                 = _mm_min_ps(x, xMax);
-            auto div          = _mm_mul_ps(x, p1);
-            auto divInt       = _mm_cvtps_epi32(div);
-            div               = _mm_cvtepi32_ps(divInt);
-            auto div2         = _mm_add_epi32(divInt, _mm_set1_epi32(127));
-            // div2 = _mm_mullo_epi32(div2, basic);
-            div2 = _mm_slli_epi32(div2, 23);
-            auto expBasic  = _mm_castsi128_ps(div2);
-            auto xReamin   = _mm_sub_ps(x, _mm_mul_ps(div, p0));
-            auto t         = xReamin;
-            auto c0        = _mm_mul_ps(p7, t);
-            auto c1        = _mm_add_ps(c0, p6);
-            auto c2        = _mm_mul_ps(c1, t);
-            auto c3        = _mm_add_ps(c2, p5);
-            auto c4        = _mm_mul_ps(c3, t);
-            auto c5        = _mm_add_ps(c4, p4);
-            auto c6        = _mm_mul_ps(c5, t);
-            auto c7        = _mm_add_ps(c6, p3);
-            auto c8        = _mm_mul_ps(c7, t);
-            auto c9        = _mm_add_ps(c8, p2);
-            auto expRemain = c9;
-            auto expRes    = _mm_mul_ps(expBasic, expRemain);
-            sumVal         = _mm_add_ps(expRes, sumVal);
-            _mm_storeu_ps(dest + 4 * i, expRes);
+        if (count > 0) {
+            auto maxVal = _mm_loadu_ps(source);
+            for (int i = 1; i < count; i++) {
+                maxVal = _mm_max_ps(maxVal, _mm_loadu_ps(source + i * 4));
+            }
+            _mm_storeu_ps(tmpfloat4, maxVal);
+            maxValue = tmpfloat4[0] > tmpfloat4[1] ? tmpfloat4[0] : tmpfloat4[1];
+            maxValue = maxValue > tmpfloat4[2] ? maxValue : tmpfloat4[2];
+            maxValue = maxValue > tmpfloat4[3] ? maxValue : tmpfloat4[3];
+        }
+        for (int i = remain; i < reduceSize; i++) {
+            maxValue = maxValue > source[i] ? maxValue : source[i];
         }
-        _mm_storeu_ps(tmpfloat4, sumVal);
-        sumValue = tmpfloat4[0] + tmpfloat4[1] + tmpfloat4[2] + tmpfloat4[3];
-    }
-    auto param = 0.6931471805599453;
-    float xLimit = 87;
-    for (int i = remain; i < size; i++) {
-        auto x         = source[i] - maxValue;
-        x = x > -xLimit ? x : -xLimit;
-        x = x < xLimit ? x : xLimit;
 
-        int div        = (x / param);
-        int div2       = (div + 127) << 23;
-        auto xReamin   = x - div * param;
-        float expBasic = *(float*)(&div2);
+        float newMax = ALIMAX(oldMax, maxValue);
 
-        auto t         = xReamin;
-        auto expRemain = ((((1.0f / 120 * t + 1.0f / 24) * t + 1.0f / 6) * t + 0.5f) * t + 1.0f) * t + 1.0f;
-        dest[i]  = expBasic * expRemain;
-        sumValue += dest[i];
-    }
-    // step 3: get x / sum and store
-    for (int i = 0; i < count; ++i) {
-        // using  1 / ((1 / x) * sum) instead x * (1 / sum) or x / sum for some bugs in intel cpu
-        auto x = _mm_rcp_ps(_mm_loadu_ps(dest + 4 * i));
-        auto y = _mm_set1_ps(sumValue);
-        auto z = _mm_rcp_ps(_mm_mul_ps(x, y));
-        _mm_storeu_ps(dest + 4 * i, z);
-    }
-    sumValue = 1.f / sumValue;
-    for (int i = remain; i < size; i++) {
-        dest[i] *= sumValue;
+        // step 2: get exp(x - newMax) and sum(exp(x - newMax))
+        float exprOffset[4] = {1.0f, 0.0f, 0.0f, 0.0f };
+        exprOffset[2] = -newMax;
+        MNNExp(dest, source, exprOffset, reduceSize);
+        float sumValue = exprOffset[3];
+
+        if (runningMax != nullptr && runningSum != nullptr && updateScale != nullptr) {
+            // === Step 3: Update running variables ===
+            float scale = expf(oldMax - newMax);
+            runningSum[k] = runningSum[k] * scale + sumValue;
+            runningMax[k] = newMax;
+            updateScale[k] = scale;
+        } else {
+            // step 3: get x / sum and store
+            for (int i = 0; i < count; ++i) {
+                // using  1 / ((1 / x) * sum) instead x * (1 / sum) or x / sum for some bugs in intel cpu
+                auto x = _mm_rcp_ps(_mm_loadu_ps(dest + 4 * i));
+                auto y = _mm_set1_ps(sumValue);
+                auto z = _mm_rcp_ps(_mm_mul_ps(x, y));
+                _mm_storeu_ps(dest + 4 * i, z);
+            }
+            auto scale = 1.f / sumValue;
+            for (int i = remain; i < reduceSize; i++) {
+                dest[i] *= scale;
+            }
+        }
     }
 }
 

source/backend/opencl/core/BufferConvertor.cpp

@@ -324,9 +324,9 @@ bool BufferConvertor::convertToNC4HW4Buffer(const Tensor *buffer, const OpenCLBu
     auto formattedBufferShape = tensorShapeFormat(buffer);//NHWC
     std::vector<size_t> imageShape;
     getImageShape(formattedBufferShape, type, &imageShape);
-
+    
     uint32_t gws[2] = {static_cast<uint32_t>(imageShape[0]), static_cast<uint32_t>(imageShape[1])};
-
+    
     auto runtime = mOpenCLRuntime;
     std::string kernelName;
     std::string kernelFile = "buffer_convert_buf";
@@ -360,26 +360,23 @@ bool BufferConvertor::convertToNC4HW4Buffer(const Tensor *buffer, const OpenCLBu
         default:
             break;
     }
-    if (mBufferToImageKernel.get() == nullptr || mBufferToImageKernelName != kernelName) {
-        mBufferToImageKernelName = kernelName;
-        std::set<std::string> buildOptions;
-        if(needTrans) {
-            //buildOptions.emplace("-DBUFFER_FORMAT_INP_TRANS");
-            kernelName += "_floatin";
-        }
-#ifdef MNN_LOW_MEMORY
-        if (lowMemory) {
-            if (quantBit == 8) {
-                // int8 case
-                buildOptions.emplace("-DUSE_LOW_BIT_WEIGHT_INT8");
-            } else if (quantBit == 4){
-                // int4 case
-                buildOptions.emplace("-DUSE_LOW_BIT_WEIGHT_INT4");
-            } else {/* More types to be supported. */}
-        }
-#endif
-        mBufferToImageKernel = runtime->buildKernelWithCache(kernelFile, kernelName, buildOptions, precision, buffer, image);
+    std::set<std::string> buildOptions;
+    if(needTrans) {
+        //buildOptions.emplace("-DBUFFER_FORMAT_INP_TRANS");
+        kernelName += "_floatin";
     }
+#ifdef MNN_LOW_MEMORY
+    if (lowMemory) {
+        if (quantBit == 8) {
+            // int8 case
+            buildOptions.emplace("-DUSE_LOW_BIT_WEIGHT_INT8");
+        } else if (quantBit == 4){
+            // int4 case
+            buildOptions.emplace("-DUSE_LOW_BIT_WEIGHT_INT4");
+        } else {/* More types to be supported. */}
+    }
+#endif
+    mBufferToImageKernel = runtime->buildKernelWithCache(kernelFile, kernelName, buildOptions, precision, buffer, image);
     auto kernel = mBufferToImageKernel->get();
 
     uint32_t idx = 0;

source/backend/opencl/core/BufferConvertor.hpp

@@ -43,7 +43,7 @@ public:
     explicit BufferConvertor(OpenCLRuntime *opencl_runtime) : mOpenCLRuntime(opencl_runtime) {
     }
     bool convertToNC4HW4Buffer(const Tensor *input, const OpenCLBufferFormat type, Tensor *output, int precision,
-                               bool needTrans, bool needWait = false, bool lowMemory = false, int quantBit = 0);
+                               bool needTrans, bool needWait = true, bool lowMemory = false, int quantBit = 0);
 
 private:
     OpenCLRuntime *mOpenCLRuntime;

source/backend/opencl/core/OpenCLBackend.cpp

@@ -47,8 +47,13 @@ CLRuntime::CLRuntime(const Backend::Info& info){
         mPrecision = mInfo.user->precision;
         mMemory    = mInfo.user->memory;
     }
-
-        mOpenCLRuntime.reset(new OpenCLRuntime(platform_size, platform_id, device_id, context_ptr, hint()));
+    
+    // protect
+    if(mPrecision > 2 || mPrecision < 0){
+        mPrecision = BackendConfig::Precision_High;
+    }
+    
+    mOpenCLRuntime.reset(new OpenCLRuntime(platform_size, platform_id, device_id, context_ptr, hint()));
     
     //Whether runtimeError
     mCLRuntimeError = mOpenCLRuntime->isCreateError();
@@ -206,6 +211,10 @@ Backend* CLRuntime::onCreate(const BackendConfig* config, Backend* origin) const
         precision = config->precision;
         memory = config->memory;
     }
+    // protect
+    if(precision > 2 || precision < 0){
+        precision = BackendConfig::Precision_High;
+    }
     auto backend = new OpenCLBackend(precision, memory, mInfo.gpuMode, mImagePool, mBufferPool, this);
     backend->setMetaPtr(pMeta);
     return backend;
@@ -246,6 +255,10 @@ OpenCLBackend::OpenCLBackend(BackendConfig::PrecisionMode precision, BackendConf
     } else{
         mPrecision = BackendConfig::Precision_High;
     }
+    // protect
+    if(mPrecision > 2 || mPrecision < 0){
+        mPrecision = BackendConfig::Precision_High;
+    }
     mMemory = memory;
     // set tuneLevel, memtype, record mode
     setGpuMode(gpuMode);

source/backend/qnn/backend/QNNBackend.cpp

@@ -18,6 +18,8 @@ struct QnnContext {
     Qnn_LogHandle_t logHandle = nullptr;
     Qnn_BackendHandle_t backendHandle = nullptr;
     Qnn_DeviceHandle_t deviceHandle = nullptr;
+    int soc_id;
+    int dsp_arch;
 };
 
 static QnnContext gContext;
@@ -94,6 +96,7 @@ bool PluginShapeRaw::compute(InferShapeContext* ctx) {
             auto dst = ctx->output(i);
             std::string key = prefix + std::to_string(i);
             auto attr = ctx->getAttr(key.c_str());
+
             if (nullptr == attr || nullptr == attr->tensor()) {
                 MNN_ERROR("MNN_QNN: Failed to find raw shape %s.\n", key.c_str());
                 return false;
@@ -886,7 +889,12 @@ ErrorCode QnnBackend::onResizeEnd() {
     #ifdef QNN_VERBOSE
     MNN_PRINT("start finalize\n");
     #endif
+    buildOutputDequant();
     finalizeGraph();
+    for(auto func : mReleaseFunc){
+        func();
+    }
+    mReleaseFunc.clear();
     #ifdef QNN_VERBOSE
     MNN_PRINT("end finalize\n");
     #endif
@@ -915,7 +923,14 @@ Backend::MemObj* QnnBackend::onAcquire(const Tensor* tensor, StorageType storage
     }
 
     Qnn_DataType_t tDataType;
-    MNN_ASSERT((tensor->getType().code == halide_type_float) || (tensor->getType().code == halide_type_int && tensor->getType().bits == 32));
+    Qnn_QuantizeParams_t tQuantizeParams{};
+    tQuantizeParams.encodingDefinition = QNN_DEFINITION_UNDEFINED;
+    tQuantizeParams.quantizationEncoding = QNN_QUANTIZATION_ENCODING_UNDEFINED;
+    Qnn_ScaleOffset_t tScaleOffsetEncoding;
+    tScaleOffsetEncoding.scale = 0.0f;
+    tScaleOffsetEncoding.offset = 0;
+    auto quant = TensorUtils::getDescribe(tensor)->quantAttr.get();
+    //MNN_ASSERT((tensor->getType().code == halide_type_float) || (tensor->getType().code == halide_type_int && tensor->getType().bits == 32));
     if (mUseFP16 && tensor->getType().code == halide_type_float) {
         tDataType = QNN_DATATYPE_FLOAT_16;
     } else if (tensor->getType().code == halide_type_float) {
@@ -926,13 +941,19 @@ Backend::MemObj* QnnBackend::onAcquire(const Tensor* tensor, StorageType storage
         MNN_PRINT("MNN_QNN: Not supported data type in <QnnBackend::onAcquire>.\n");
         return nullptr;
     }
-
-    Qnn_QuantizeParams_t tQuantizeParams{};
-    tQuantizeParams.encodingDefinition = QNN_DEFINITION_UNDEFINED;
-    tQuantizeParams.quantizationEncoding = QNN_QUANTIZATION_ENCODING_UNDEFINED;
-    Qnn_ScaleOffset_t tScaleOffsetEncoding;
-    tScaleOffsetEncoding.scale = 0.0f;
-    tScaleOffsetEncoding.offset = 0;
+    if(quant != nullptr && TensorUtils::getDescribe(tensor)->type == DataType_DT_INT8) {
+        tQuantizeParams.encodingDefinition = QNN_DEFINITION_DEFINED;
+        tQuantizeParams.quantizationEncoding = QNN_QUANTIZATION_ENCODING_SCALE_OFFSET;
+        tScaleOffsetEncoding.scale = quant->scale;
+        if(quant->zero != 0){
+            MNN_PRINT("MNN_QNN: Not supported asymmetric quant in <QnnBackend::onAcquire>.\n");
+            return nullptr;
+        }
+        tDataType = QNN_DATATYPE_SFIXED_POINT_8;
+        if (isOutput) {
+            tType = QNN_TENSOR_TYPE_NATIVE;
+        }
+    }
     tQuantizeParams.scaleOffsetEncoding = tScaleOffsetEncoding;
 
     std::unique_ptr<Tensor> tempTensor(new Tensor(tensor, gQnnTensorDimType, false));
@@ -947,17 +968,41 @@ Backend::MemObj* QnnBackend::onAcquire(const Tensor* tensor, StorageType storage
 
     Qnn_Tensor_t * qnnTensor = qnnTensorWrapper->getNativeTensor();
     CALL_QNN(mRuntime->mQnnInterface.tensorCreateGraphTensor(mQnnGraphHandle, qnnTensor));
+    mQNNTensorWrappers.push_back(qnnTensorWrapper);
+    mTensorMap.insert({TensorUtils::getDescribe(tensor), mTensorCounter});
 
     if (isInput) {
         mInputTensorIndexes.push_back(mTensorCounter);
         qnnTensorWrapper->alloc();
     }
     if (isOutput) {
-        mOutputTensorIndexes.push_back(mTensorCounter);
-        qnnTensorWrapper->alloc();
+        if(quant != nullptr && TensorUtils::getDescribe(tensor)->type == DataType_DT_INT8){
+            mTensorCounter += 1;
+            std::shared_ptr<Tensor> stageTensor;
+            stageTensor.reset(Tensor::create<float>(tensor->shape(), nullptr, gQnnTensorDimType));
+            tName = "QnnTensor_" + std::to_string(mTensorCounter);
+            tType = QNN_TENSOR_TYPE_APP_READ;
+            if (mUseFP16 && tensor->getType().code == halide_type_float) {
+                tDataType = QNN_DATATYPE_FLOAT_16;
+            } else if (tensor->getType().code == halide_type_float) {
+                tDataType = QNN_DATATYPE_FLOAT_32;
+            } else {
+                MNN_PRINT("MNN_QNN: Not supported data type in <QnnBackend::onAcquire>.\n");
+                return nullptr;
+            }
+            Qnn_QuantizeParams_t tQuantizeParamstmp = QNN_QUANTIZE_PARAMS_INIT;
+            std::shared_ptr<QNNTensorWrapper> qnnOutputTensorWrapper = QNNTensorWrapper::create(tName, tType, tDataType, tDims, tQuantizeParamstmp);
+            CALL_QNN(mRuntime->mQnnInterface.tensorCreateGraphTensor(mQnnGraphHandle, qnnOutputTensorWrapper->getNativeTensor()));
+            mDeQuantOutputTensorMap.insert({TensorUtils::getDescribe(tensor), {tensor, stageTensor}});
+            mQNNTensorWrappers.push_back(qnnOutputTensorWrapper);
+            mTensorMap.insert({TensorUtils::getDescribe(const_cast<const Tensor*>(stageTensor.get())), mTensorCounter});
+            mOutputTensorIndexes.push_back(mTensorCounter);
+            qnnOutputTensorWrapper->alloc();
+        } else{
+            mOutputTensorIndexes.push_back(mTensorCounter);
+            qnnTensorWrapper->alloc();
+        }
     }
-    mQNNTensorWrappers.push_back(qnnTensorWrapper);
-    mTensorMap.insert({TensorUtils::getDescribe(tensor), mTensorCounter});
 
     mTensorCounter += 1;
     #ifdef QNN_VERBOSE
@@ -1029,7 +1074,13 @@ void QnnBackend::inputIO(const Tensor* srcTensor, const Tensor* dstTensor) const
 }
 
 void QnnBackend::outputIO(const Tensor* srcTensor, const Tensor* dstTensor) const {
-    int srcIndex = getTensorIdx(srcTensor);
+    auto iter = mDeQuantOutputTensorMap.find(TensorUtils::getDescribe(srcTensor));
+    int srcIndex = -1;
+    if(iter != mDeQuantOutputTensorMap.end()){
+        srcIndex = getTensorIdx(iter->second.second.get());
+    } else{
+        srcIndex = getTensorIdx(srcTensor);
+    }
     std::shared_ptr<QNNTensorWrapper> srcQnnTensorWrapper = mQNNTensorWrappers[srcIndex];
     std::shared_ptr<Tensor> srcDataContainer = srcQnnTensorWrapper->getDataContainer();
 
@@ -1091,7 +1142,7 @@ void QnnBackend::finalizeGraph() {
         // set QNN_PROFILE_LEVEL_DETAILED
         QnnProfile_Level_t profileLevel = QNN_PROFILE_LEVEL_DETAILED;
         MNN_PRINT("[QNN Profile] Creating QNN Profile Handle with DETAILED level.\n");
-        auto profile_err = mRuntime->mQnnInterface.profileCreate(mQnnContextHandle, profileLevel, &mQnnProfileHandle);
+        auto profile_err = mRuntime->mQnnInterface.profileCreate(mRuntime->mQnnContextHandle, profileLevel, &mQnnProfileHandle);
         if (profile_err != QNN_SUCCESS || mQnnProfileHandle == nullptr) {
             MNN_ERROR("[QNN Profile] Failed to create QNN Profile Handle, error: %d\n", (int)profile_err);
             mQnnProfileHandle = nullptr;
@@ -1216,6 +1267,27 @@ void QnnBackend::clean() {
     mTensorMap.clear();
     mInputTensorIndexes.clear();
     mOutputTensorIndexes.clear();
+    mDeQuantOutputTensorMap.clear();
+}
+void QnnBackend::buildOutputDequant(){
+    Qnn_OpConfigVersion_t mOpConfigVersion = QNN_OPCONFIG_VERSION_1;
+    std::string mNodeName;
+    std::string mPackageName = "qti.aisw";
+    std::string mNodeType;
+    std::vector<Qnn_Param_t> mParams;
+    std::vector<Qnn_Tensor_t> mInputs;
+    std::vector<Qnn_Tensor_t> mOutputs;
+    for(auto iter : mDeQuantOutputTensorMap){
+        mNodeType.clear();
+        mParams.clear();
+        mInputs.clear();
+        mOutputs.clear();
+        mNodeType = "Dequantize";
+        std::string name = "Dequantize_I_" + std::to_string(getTensorIdx(iter.second.first)) + "_O_" + std::to_string(getTensorIdx(iter.second.second.get()));;
+        mInputs.push_back(*(getNativeTensor(iter.second.first))); // input
+        mOutputs.push_back(*(getNativeTensor(iter.second.second.get()))); // output
+        addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+    }
 }
 
 QnnRuntime::QnnRuntime(const Backend::Info& info, QNN_INTERFACE_VER_TYPE qnnInterface, Qnn_LogHandle_t qnnLogHandle, Qnn_BackendHandle_t qnnBackendHandle, Qnn_DeviceHandle_t qnnDeviceHandle) {
@@ -1324,12 +1396,23 @@ bool QnnRuntime::registerCustomOpPackage(QNN_INTERFACE_VER_TYPE qnnInterface, Qn
 
 class QnnRuntimeCreator : public RuntimeCreator {
 public:
-    virtual Runtime* onCreate(const Backend::Info& info) const {
+    virtual Runtime* onCreate(const Backend::Info& info) const override {
         return QnnRuntime::create(info);
     }
-    virtual bool onValid(Backend::Info& info) const {
+    virtual bool onValid(Backend::Info& info) const override {
         return true;
     }
+    virtual bool onGetDeviceInfo(const std::string& deviceKey, std::string& deviceValue) const override {
+        if(deviceKey == "soc_id" && gContext.soc_id != 0) {
+            deviceValue = std::to_string(gContext.soc_id);
+            return true;
+        }
+        if(deviceKey == "dsp_arch" && gContext.dsp_arch != 0) {
+            deviceValue = "v" + std::to_string(gContext.dsp_arch);
+            return true;
+        }
+        return false;
+    }
 };
 
 } // end namespace QNN
@@ -1401,6 +1484,8 @@ void registerQNNRuntimeCreator() {
 
     // Create Device.
     Qnn_DeviceHandle_t deviceHandle = nullptr;
+    QnnHtpDevice_Arch_t dspArch = QNN_HTP_DEVICE_ARCH_NONE;
+    uint32_t socId = 0;
     {
         // Check whether the device API is supported.
         bool supportDevice = QNN::checkCapability(qnnInterface, QNN_PROPERTY_GROUP_DEVICE);
@@ -1422,10 +1507,8 @@ void registerQNNRuntimeCreator() {
                     MNN_PRINT("[Warning]: deviceGetPlatformInfo Failed to query platform info");
                 } else {
                     QnnDevice_HardwareDeviceInfo_t* hwDeviceInfo = backendPlatformInfoPtr->v1.hwDevices;
-                    QnnHtpDevice_Arch_t arch = hwDeviceInfo->v1.deviceInfoExtension->onChipDevice.arch;
-                    uint32_t socModel = hwDeviceInfo->v1.deviceInfoExtension->onChipDevice.socModel;
-                    MNN_PRINT("Qnn Device soc_id: %d\n", socModel);
-                    MNN_PRINT("Qnn Device dsp_arch: v%d\n", arch);
+                    dspArch = hwDeviceInfo->v1.deviceInfoExtension->onChipDevice.arch;
+                    socId = hwDeviceInfo->v1.deviceInfoExtension->onChipDevice.socModel;
                 }
             }
         } else {
@@ -1478,6 +1561,8 @@ void registerQNNRuntimeCreator() {
     QNN::gContext.backendHandle = backendHandle;
     QNN::gContext.deviceHandle = deviceHandle;
     QNN::gContext.logHandle = logHandle;
+    QNN::gContext.soc_id = socId;
+    QNN::gContext.dsp_arch = dspArch;
 
     QNN::registerQNNOps();
     MNNInsertExtraRuntimeCreator(MNN_FORWARD_NN, new QNN::QnnRuntimeCreator, false);

source/backend/qnn/backend/QNNBackend.hpp

@@ -78,6 +78,10 @@ public:
     std::shared_ptr<QNNTensorWrapper> getTensorWrapper(const Tensor * tensor);
     bool useCache() const;
     bool getUseFP16() const;
+    void buildOutputDequant();
+    void pushReleaseFunc(std::function<void()> func){
+        mReleaseFunc.push_back(func);
+    }
 
 private:
     void clean();
@@ -109,8 +113,10 @@ private:
     mutable int mTensorCounter = 0;
     mutable std::vector<std::shared_ptr<QNNTensorWrapper>> mQNNTensorWrappers;
     mutable std::map<const Tensor::InsideDescribe::NativeInsideDescribe *, int> mTensorMap;
+    mutable std::map<const Tensor::InsideDescribe::NativeInsideDescribe *, std::pair<const Tensor*, std::shared_ptr<Tensor>>> mDeQuantOutputTensorMap;
     std::vector<int> mInputTensorIndexes;
     std::vector<int> mOutputTensorIndexes;
+    std::vector<std::function<void()>> mReleaseFunc;
 };
 
 

source/backend/qnn/backend/QNNUtils.cpp

@@ -134,6 +134,8 @@ void registerQNNOps() {
     #ifdef MNN_SUPPORT_TRANSFORMER_FUSE
     ___QNNAttentionCreator__OpType_Attention__();
     #endif
+    ___QNNQuantCreator__OpType_FloatToInt8__();
+    ___QNNDeQuantCreator__OpType_Int8ToFloat__();
 }
 
 Tensor::DimensionType gQnnTensorDimType = Tensor::TENSORFLOW;

source/backend/qnn/backend/QNNUtils.hpp

@@ -104,6 +104,8 @@ extern void ___QNNMatMulCreator__OpType_MatMul__();
 #ifdef MNN_SUPPORT_TRANSFORMER_FUSE
 extern void ___QNNAttentionCreator__OpType_Attention__();
 #endif
+extern void ___QNNQuantCreator__OpType_FloatToInt8__();
+extern void ___QNNDeQuantCreator__OpType_Int8ToFloat__();
 void registerQNNOps();
 
 extern Tensor::DimensionType gQnnTensorDimType;

source/backend/qnn/backend/QNNWrapper.cpp

@@ -25,7 +25,7 @@ std::shared_ptr<QNNTensorWrapper> QNNTensorWrapper::create(const std::string & n
 
 std::shared_ptr<QNNTensorWrapper> QNNTensorWrapper::createStaticTensor(const std::string & name, Qnn_DataType_t dataType, const std::vector<uint32_t> & dimensions, const void * buffer, Qnn_QuantizeParams_t quantizeParam) {
     MNN_ASSERT(!name.empty() && !dimensions.empty() && buffer);
-    MNN_ASSERT(dataType == QNN_DATATYPE_SFIXED_POINT_8 || dataType == QNN_DATATYPE_INT_32);
+    MNN_ASSERT(dataType == QNN_DATATYPE_SFIXED_POINT_8 || dataType == QNN_DATATYPE_INT_32 || dataType == QNN_DATATYPE_SFIXED_POINT_32 || dataType == QNN_DATATYPE_UFIXED_POINT_8);
 
     std::shared_ptr<QNNTensorWrapper> tensorWrapper = QNNTensorWrapper::create(name, QNN_TENSOR_TYPE_STATIC, dataType, dimensions, quantizeParam);
     uint32_t numElement = 1;
@@ -114,7 +114,8 @@ void * QNNTensorWrapper::alloc() {
         dims[i] = (int)mDimensions[i];
     }
 
-    MNN_ASSERT(mQnnTensor.v1.dataType == QNN_DATATYPE_FLOAT_32 || mQnnTensor.v1.dataType == QNN_DATATYPE_FLOAT_16 || mQnnTensor.v1.dataType == QNN_DATATYPE_INT_32 ||  mQnnTensor.v1.dataType == QNN_DATATYPE_SFIXED_POINT_8);
+    MNN_ASSERT(mQnnTensor.v1.dataType == QNN_DATATYPE_FLOAT_32 || mQnnTensor.v1.dataType == QNN_DATATYPE_FLOAT_16 || mQnnTensor.v1.dataType == QNN_DATATYPE_INT_32 ||  mQnnTensor.v1.dataType == QNN_DATATYPE_SFIXED_POINT_8 || mQnnTensor.v1.dataType == QNN_DATATYPE_SFIXED_POINT_32
+    || mQnnTensor.v1.dataType == QNN_DATATYPE_UFIXED_POINT_8);
     halide_type_t halideType;
 
     halideType.lanes = 1;
@@ -134,6 +135,15 @@ void * QNNTensorWrapper::alloc() {
         case QNN_DATATYPE_SFIXED_POINT_8:
             halideType.code = halide_type_int;
             halideType.bits = 8;
+            break;
+        case QNN_DATATYPE_SFIXED_POINT_32:
+            halideType.code = halide_type_int;
+            halideType.bits = 32;
+            break;
+        case QNN_DATATYPE_UFIXED_POINT_8:
+            halideType.code = halide_type_int;
+            halideType.bits = 8;
+            break;
         default:
             break;
     }

source/backend/qnn/convertor/QNNConvertor.cpp

@@ -269,6 +269,10 @@ std::vector<std::string> QNNTranslator::TranslateTensor(const QNNCommandTensor&
     if (isParam) {
         result.push_back(QNNTranslator::TranslateParamDataArray(dataNameSymbol, cmdT.dataType, cmdT.clientBuf));
     }
+    if(hasQuant){
+        std::vector<std::string> linesQuantScaleOffset = TranslateQuantizeScaleOffsetDataArray(tensorNameSymbol, cmdT.quantizeParams, cmdT.rank, cmdT.dimensions);
+        APPEND_VECTOR(result, linesQuantScaleOffset);
+    }
     result.push_back("  Qnn_Tensor_t " + tensorNameSymbol +   " = QNN_TENSOR_INIT;");
     result.push_back("  {");
     result.push_back("  " + tensorNameSymbol + ".version = QNN_TENSOR_VERSION_1;");
@@ -456,11 +460,122 @@ std::string QNNTranslator::TranslateParamDataArray(const std::string & dataNameS
     return result;
 }
 
+std::vector<std::string> QNNTranslator::TranslateQuantizeScaleOffsetDataArray(const std::string & tensorNameSymbol, const Qnn_QuantizeParams_t & quantizeParams, uint32_t rank, const uint32_t * dimensions){
+    std::vector<std::string> result;
+    if(quantizeParams.encodingDefinition == QNN_DEFINITION_DEFINED && quantizeParams.quantizationEncoding == QNN_QUANTIZATION_ENCODING_AXIS_SCALE_OFFSET){
+        result.push_back("  Qnn_ScaleOffset_t " + tensorNameSymbol + "_axis_scale_offset[] = {");
+        int totalnum = (quantizeParams.axisScaleOffsetEncoding.numScaleOffsets + 3) / 4;
+        for(int i = 0; i < totalnum; ++i){
+            std::string line = "    ";
+            for(int j = 0; j < 4; ++j){
+                int index = i * 4 + j;
+                if(index >= quantizeParams.axisScaleOffsetEncoding.numScaleOffsets)
+                    break;
+                line += "{.scale= " + std::to_string(quantizeParams.axisScaleOffsetEncoding.scaleOffset[index].scale) + ", .offset= " + std::to_string(quantizeParams.axisScaleOffsetEncoding.scaleOffset[index].offset) + "}, ";
+            }
+            result.push_back(line);
+        }
+        result.push_back("  };");
+    }
+    
+    if(quantizeParams.encodingDefinition == QNN_DEFINITION_DEFINED && quantizeParams.quantizationEncoding == QNN_QUANTIZATION_ENCODING_BW_AXIS_SCALE_OFFSET){
+        result.push_back("  float " + tensorNameSymbol + "_bwaxis_scale[] = {");
+        int totalnum = (quantizeParams.bwAxisScaleOffsetEncoding.numElements + 3) / 4;
+        for(int i = 0; i < totalnum; ++i){
+            std::string line = "    ";
+            for(int j = 0; j < 4; ++j){
+                int index = i * 4 + j;
+                if(index >= quantizeParams.bwAxisScaleOffsetEncoding.numElements)
+                    break;
+                line += std::to_string(quantizeParams.bwAxisScaleOffsetEncoding.scales[index]) + ", ";
+            }
+            result.push_back(line);
+        }
+        result.push_back("  };");
+        if(quantizeParams.bwAxisScaleOffsetEncoding.offsets != nullptr){
+            result.push_back("  int32_t " + tensorNameSymbol + "_bwaxis_offset[] = {");
+            for(int i = 0; i < totalnum; ++i){
+                std::string line = "    ";
+                for(int j = 0; j < 4; ++j){
+                    int index = i * 4 + j;
+                    if(index >= quantizeParams.bwAxisScaleOffsetEncoding.numElements)
+                        break;
+                    line += std::to_string(quantizeParams.bwAxisScaleOffsetEncoding.offsets[index]) + ", ";
+                }
+                result.push_back(line);
+            }
+            result.push_back("  };");
+        }
+    }
+    
+    if(quantizeParams.encodingDefinition == QNN_DEFINITION_DEFINED && quantizeParams.quantizationEncoding == QNN_QUANTIZATION_ENCODING_BLOCKWISE_EXPANSION){
+        int axis = quantizeParams.blockwiseExpansion->axis;
+        int oc = dimensions[axis];
+        int blockSize = quantizeParams.blockwiseExpansion->numBlocksPerAxis;
+        result.push_back("  Qnn_BlockwiseExpansion_t " + tensorNameSymbol + "_blockwiseExpansion = QNN_BLOCKWISE_EXPANSION_INIT;");
+        
+        result.push_back("  Qnn_ScaleOffset_t " + tensorNameSymbol + "_blockwiseExpansionScaleOffset[] = {");
+        int totalnum = (oc + 3) / 4;
+        for(int i = 0; i < totalnum; ++i){
+            std::string line = "    ";
+            for(int j = 0; j < 4; ++j){
+                int index = i * 4 + j;
+                if(index >= oc)
+                    break;
+                line += "{.scale= " + std::to_string(quantizeParams.blockwiseExpansion->scaleOffsets[index].scale) + ", .offset= " + std::to_string(quantizeParams.blockwiseExpansion->scaleOffsets[index].offset) + "}, ";
+            }
+            result.push_back(line);
+        }
+        result.push_back("  };");
+        if(quantizeParams.blockwiseExpansion->blockScaleStorageType == QNN_BLOCKWISE_EXPANSION_BITWIDTH_SCALE_STORAGE_8){
+            result.push_back("  uint8_t " + tensorNameSymbol + "_blockwiseExpansionBlockScale[] = {");
+            totalnum = (oc * blockSize + 3) / 4;
+            for(int i = 0; i < totalnum; ++i){
+                std::string line = "    ";
+                for(int j = 0; j < 4; ++j){
+                    int index = i * 4 + j;
+                    if(index >= oc * blockSize)
+                        break;
+                    line += std::to_string(quantizeParams.blockwiseExpansion->blocksScale8[index]) + ", ";
+                }
+                result.push_back(line);
+            }
+            result.push_back("  };");
+        }else{
+            result.push_back("  uint16_t " + tensorNameSymbol + "_blockwiseExpansionBlockScale[] = {");
+            totalnum = (oc * blockSize + 3) / 4;
+            for(int i = 0; i < totalnum; ++i){
+                std::string line = "    ";
+                for(int j = 0; j < 4; ++j){
+                    int index = i * 4 + j;
+                    if(index >= oc * blockSize)
+                        break;
+                    line += std::to_string(quantizeParams.blockwiseExpansion->blocksScale16[index]) + ", ";
+                }
+                result.push_back(line);
+            }
+            result.push_back("  };");
+        }
+        result.push_back("  " + tensorNameSymbol + "_blockwiseExpansion.axis = " + std::to_string(quantizeParams.blockwiseExpansion->axis) + ";");
+        result.push_back("  " + tensorNameSymbol + "_blockwiseExpansion.scaleOffsets = " + tensorNameSymbol + "_blockwiseExpansionScaleOffset;");
+        result.push_back("  " + tensorNameSymbol + "_blockwiseExpansion.numBlocksPerAxis = " + std::to_string(quantizeParams.blockwiseExpansion->numBlocksPerAxis) + ";");
+        result.push_back("  " + tensorNameSymbol + "_blockwiseExpansion.blockScaleBitwidth = " + std::to_string(quantizeParams.blockwiseExpansion->blockScaleBitwidth) + ";");
+        if(quantizeParams.blockwiseExpansion->blockScaleStorageType == QNN_BLOCKWISE_EXPANSION_BITWIDTH_SCALE_STORAGE_8){
+            result.push_back("  " + tensorNameSymbol + "_blockwiseExpansion.blockScaleStorageType = QNN_BLOCKWISE_EXPANSION_BITWIDTH_SCALE_STORAGE_8;");
+            result.push_back("  " + tensorNameSymbol + "_blockwiseExpansion.blocksScale8 = " + tensorNameSymbol + "_blockwiseExpansionBlockScale;");
+        }else{
+            result.push_back("  " + tensorNameSymbol + "_blockwiseExpansion.blockScaleStorageType = QNN_BLOCKWISE_EXPANSION_BITWIDTH_SCALE_STORAGE_16;");
+            result.push_back("  " + tensorNameSymbol + "_blockwiseExpansion.blocksScale16 = " + tensorNameSymbol + "_blockwiseExpansionBlockScale;");
+        }
+    }
+    return result;
+}
+
 // Currently, only support QNN_QUANTIZATION_ENCODING_UNDEFINED, QNN_QUANTIZATION_ENCODING_SCALE_OFFSET.
-std::vector<std::string> QNNTranslator::TranslateTensorQuantizeParams(const std::string tensorNameSymbol, const Qnn_QuantizeParams_t & quantizeParmas) {
+std::vector<std::string> QNNTranslator::TranslateTensorQuantizeParams(const std::string tensorNameSymbol, const Qnn_QuantizeParams_t & quantizeParams) {
     std::vector<std::string> result;
 
-    if (quantizeParmas.encodingDefinition == QNN_DEFINITION_UNDEFINED) {
+    if (quantizeParams.encodingDefinition == QNN_DEFINITION_UNDEFINED) {
         result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.encodingDefinition = QNN_DEFINITION_UNDEFINED;");
         result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.quantizationEncoding = QNN_QUANTIZATION_ENCODING_UNDEFINED;");
         result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.scaleOffsetEncoding.scale = 0.0f;");
@@ -468,13 +583,42 @@ std::vector<std::string> QNNTranslator::TranslateTensorQuantizeParams(const std:
         return result;
     }
 
-    if (quantizeParmas.encodingDefinition == QNN_DEFINITION_DEFINED && quantizeParmas.quantizationEncoding == QNN_QUANTIZATION_ENCODING_SCALE_OFFSET) {
+    if (quantizeParams.encodingDefinition == QNN_DEFINITION_DEFINED && quantizeParams.quantizationEncoding == QNN_QUANTIZATION_ENCODING_SCALE_OFFSET) {
         result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.encodingDefinition = QNN_DEFINITION_DEFINED;");
         result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.quantizationEncoding = QNN_QUANTIZATION_ENCODING_SCALE_OFFSET;");
-        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.scaleOffsetEncoding.scale = " + std::to_string(quantizeParmas.scaleOffsetEncoding.scale) + ";");
-        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.scaleOffsetEncoding.offset = " + std::to_string(quantizeParmas.scaleOffsetEncoding.offset) + ";");
+        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.scaleOffsetEncoding.scale = " + std::to_string(quantizeParams.scaleOffsetEncoding.scale) + ";");
+        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.scaleOffsetEncoding.offset = " + std::to_string(quantizeParams.scaleOffsetEncoding.offset) + ";");
         return result;
     }
+    
+    if(quantizeParams.encodingDefinition == QNN_DEFINITION_DEFINED && quantizeParams.quantizationEncoding == QNN_QUANTIZATION_ENCODING_AXIS_SCALE_OFFSET){
+        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.encodingDefinition = QNN_DEFINITION_DEFINED;");
+        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.quantizationEncoding = QNN_QUANTIZATION_ENCODING_AXIS_SCALE_OFFSET;");
+        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.axisScaleOffsetEncoding.axis = " + std::to_string(quantizeParams.axisScaleOffsetEncoding.axis) + ";");
+        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.axisScaleOffsetEncoding.numScaleOffsets = " + std::to_string(quantizeParams.axisScaleOffsetEncoding.numScaleOffsets) + ";");
+        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.axisScaleOffsetEncoding.scaleOffset = " + tensorNameSymbol + "_axis_scale_offset;");
+        return result;
+    }
+    
+    if(quantizeParams.encodingDefinition == QNN_DEFINITION_DEFINED && quantizeParams.quantizationEncoding == QNN_QUANTIZATION_ENCODING_BW_AXIS_SCALE_OFFSET){
+        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.encodingDefinition = QNN_DEFINITION_DEFINED;");
+        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.quantizationEncoding = QNN_QUANTIZATION_ENCODING_BW_AXIS_SCALE_OFFSET;");
+        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.bwAxisScaleOffsetEncoding.axis = " + std::to_string(quantizeParams.bwAxisScaleOffsetEncoding.axis) + ";");
+        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.bwAxisScaleOffsetEncoding.bitwidth = " + std::to_string(quantizeParams.bwAxisScaleOffsetEncoding.bitwidth) + ";");
+        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.bwAxisScaleOffsetEncoding.numElements = " + std::to_string(quantizeParams.bwAxisScaleOffsetEncoding.numElements) + ";");
+        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.bwAxisScaleOffsetEncoding.scales = " + tensorNameSymbol + "_bwaxis_scale;");
+        if(quantizeParams.bwAxisScaleOffsetEncoding.offsets != nullptr)
+            result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.bwAxisScaleOffsetEncoding.offset = " + tensorNameSymbol + "_bwaxis_offset;");
+        return result;
+    }
+    
+    if(quantizeParams.encodingDefinition == QNN_DEFINITION_DEFINED && quantizeParams.quantizationEncoding == QNN_QUANTIZATION_ENCODING_BLOCKWISE_EXPANSION){
+        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.encodingDefinition = QNN_DEFINITION_DEFINED;");
+        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.quantizationEncoding = QNN_QUANTIZATION_ENCODING_BLOCKWISE_EXPANSION;");
+        result.push_back("  " + tensorNameSymbol + ".v1.quantizeParams.blockwiseExpansion = &" + tensorNameSymbol + "_blockwiseExpansion;");
+        return result;
+    }
+
 
     MNN_ERROR("MNN_QNN: Unknown QuantizeParams.\n");
 

source/backend/qnn/convertor/QNNConvertor.hpp

@@ -69,7 +69,8 @@ private:
     static std::string MapDataType(Qnn_DataType_t dataType);
     static std::string TranslateDimensionsArray(const std::string & dimensionsNameSymbol, uint32_t rank, const uint32_t * dimensions);
     static std::string TranslateParamDataArray(const std::string & dataNameSymbol, Qnn_DataType_t dataType, const Qnn_ClientBuffer_t & clientBuf);
-    static std::vector<std::string> TranslateTensorQuantizeParams(const std::string tensorNameSymbol, const Qnn_QuantizeParams_t & quantizeParmas);
+    static std::vector<std::string> TranslateQuantizeScaleOffsetDataArray(const std::string & tensorNameSymbol, const Qnn_QuantizeParams_t & quantizeParams, uint32_t rank, const uint32_t * dimensions);
+    static std::vector<std::string> TranslateTensorQuantizeParams(const std::string tensorNameSymbol, const Qnn_QuantizeParams_t & quantizeParams);
     static std::vector<std::string> TranslateTensorClientBuf(const std::string & tensorNameSymbol, const std::string & dataNameSymbol, const std::string & sname, const Qnn_ClientBuffer_t & clientBuf, bool hasClientBuf, bool isParam);
     // Utility functions used by TranslateNode.
     static std::vector<std::string> TranslateNodeParamArray(const std::string & nodeName,const std::string & paramArraySymbol, uint32_t numOfParams, const Qnn_Param_t * params);

source/backend/qnn/execution/QNNConvDepthwise.cpp

@@ -11,6 +11,157 @@
 namespace MNN {
 namespace QNN {
 
+void QNNConvDepthwise::isWeightQuantSupported(const Tensor *input, const int oc){
+    Qnn_DataType_t dataType = mBackend->getNativeTensor(input)->v1.dataType;
+    if(mOp->main_as_Convolution2D()->quanParameter() == nullptr){
+        mWeightQuant = false;
+        return;
+    }else{
+        bool hasBais = false;
+        auto bias = mOp->main_as_Convolution2D()->bias();
+        auto biasPtr = (float*)bias->data();
+        for(int i = 0; i < oc; ++i){
+            if(biasPtr[i] != 0.0f){
+                hasBais = true;
+                break;
+            }
+        }
+        
+        std::shared_ptr<ConvolutionCommon::Int8Common> quanCommon = ConvolutionCommon::load(mOp, this->backend(), false, true);
+        if(quanCommon->asymmetric || dataType == QNN_DATATYPE_FLOAT_16 || dataType == QNN_DATATYPE_FLOAT_32){
+            // not support asymmetric and mBlockSize > 1 results incorrect now
+            mWeightQuant = false;
+            return;
+        }
+        
+        float inputScale = mBackend->getNativeTensor(input)->v1.quantizeParams.scaleOffsetEncoding.scale;
+        int inputOffset = mBackend->getNativeTensor(input)->v1.quantizeParams.scaleOffsetEncoding.offset;
+        if(inputOffset == 0){
+            mWeightQuant = true;
+        }else{
+            if(hasBais){
+                mWeightQuant = false;
+            }else{
+                mWeightQuant = true;
+            }
+        }
+    }
+}
+
+ErrorCode QNNConvDepthwise::onEncodeQuantDequantDepthConv(Tensor *input, Tensor *output, const int n, const int ic, const int oc) {
+    auto conv2D     = mOp->main_as_Convolution2D();
+    auto common     = conv2D->common();
+    Qnn_DataType_t dataType = QNN_DATATYPE_FLOAT_32;
+    if(mBackend->getUseFP16()){
+        dataType = QNN_DATATYPE_FLOAT_16;
+    }
+    
+    // create dequant input stage tensor
+    this->createStageTensor("DequantInput", dataType, getNHWCShape(input)); // mTempTensorWrappers[2]
+    this->createStageTensor("QuantOutput", dataType, getNHWCShape(output)); // mTempTensorWrappers[3]
+    
+    // add nodes
+    {
+        // dequant input
+        {
+            mParams.clear();
+            mInputs.clear();
+            mOutputs.clear();
+            mNodeType = "Dequantize";
+            std::string name = mNodeName + "_dequant_input";
+        
+            mInputs.push_back(*(mBackend->getNativeTensor(input))); // input
+            mOutputs.push_back(*(mTempTensorWrappers[2]->getNativeTensor())); // DequantInput
+            mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+        }
+        
+        if (common->relu() || common->relu6()) {
+            this->createStageTensor("ReluTensor", dataType, getNHWCShape(output)); // mTempTensorWrappers[4]
+            // Stage one
+            {
+                mParams.clear();
+                mInputs.clear();
+                mOutputs.clear();
+                mNodeType = "DepthWiseConv2d";
+                std::string name = mNodeName + "_convDepthwise";
+                mParams.push_back(*(mParamTensorWrappers[0]->getNativeParam())); // stride
+                mParams.push_back(*(mParamTensorWrappers[1]->getNativeParam())); // pad_amount
+                mParams.push_back(*(mParamTensorWrappers[2]->getNativeParam())); // dilation
+        
+                mInputs.push_back(*(mTempTensorWrappers[2]->getNativeTensor())); // DequantInput
+                mInputs.push_back(*(mTempTensorWrappers[0]->getNativeTensor())); // weight
+                mInputs.push_back(*(mTempTensorWrappers[1]->getNativeTensor())); // bias
+        
+                mOutputs.push_back(*(mTempTensorWrappers[4]->getNativeTensor())); // ReluTensor
+                mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+            }
+
+            // Stage two
+            {
+                mParams.clear();
+                mInputs.clear();
+                mOutputs.clear();
+                mNodeType = common->relu6() ? "ReluMinMax" : "Relu";
+                std::string name = mNodeName + "_relu";
+                if (common->relu6()) {
+                    mParams.push_back(*(mParamScalarWrappers[1]->getNativeParam())); // min_value
+                    mParams.push_back(*(mParamScalarWrappers[2]->getNativeParam())); // max_value
+                }
+                mInputs.push_back(*(mTempTensorWrappers[4]->getNativeTensor())); // ReluTensor
+                mOutputs.push_back(*(mTempTensorWrappers[3]->getNativeTensor())); // QuantOutput
+                mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+            }
+
+        } else {
+            mParams.clear();
+            mInputs.clear();
+            mOutputs.clear();
+            mNodeType = "DepthWiseConv2d";
+            mParams.push_back(*(mParamTensorWrappers[0]->getNativeParam())); // stride
+            mParams.push_back(*(mParamTensorWrappers[1]->getNativeParam())); // pad_amount
+            mParams.push_back(*(mParamTensorWrappers[2]->getNativeParam())); // dilation
+            
+            mInputs.push_back(*(mTempTensorWrappers[2]->getNativeTensor())); // DequantInput
+            mInputs.push_back(*(mTempTensorWrappers[0]->getNativeTensor())); // weight
+            mInputs.push_back(*(mTempTensorWrappers[1]->getNativeTensor())); // bias
+            
+            mOutputs.push_back(*(mTempTensorWrappers[3]->getNativeTensor())); // QuantOutput
+            mBackend->addNodeToGraph(mOpConfigVersion, mNodeName.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+        }
+        
+        // Quant output
+        {
+            auto QuantOutputTensor = mTempTensorWrappers[3]->getNativeTensor();
+            if(mBackend->getUseFP16()){
+                this->createStageTensor("CastOutput", QNN_DATATYPE_FLOAT_32, getNHWCShape(output));
+                
+                mParams.clear();
+                mInputs.clear();
+                mOutputs.clear();
+                mNodeType = "Cast";
+                std::string name = mNodeName + "_Cast_Output";
+                
+                mInputs.push_back(*(mTempTensorWrappers[3]->getNativeTensor())); // QuantOutput
+                mOutputs.push_back(*(mTempTensorWrappers.back()->getNativeTensor())); // CastOutput
+                mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+                QuantOutputTensor = mTempTensorWrappers.back()->getNativeTensor();
+            }
+            {
+                mParams.clear();
+                mInputs.clear();
+                mOutputs.clear();
+                mNodeType = "Quantize";
+                std::string name = mNodeName + "_Quant_Output";
+                
+                mInputs.push_back(*(QuantOutputTensor)); // stage tensor
+                mOutputs.push_back(*(mBackend->getNativeTensor(output))); // output
+                mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+            }
+        }
+    }
+    return NO_ERROR;
+}
+
 ErrorCode QNNConvDepthwise::onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
     auto conv2D     = mOp->main_as_Convolution2D();
     auto common     = conv2D->common();
@@ -35,7 +186,8 @@ ErrorCode QNNConvDepthwise::onEncode(const std::vector<Tensor *> &inputs, const
     padTop = std::get<1>(pads); padBottom = std::get<3>(pads); padLeft = std::get<0>(pads); padRight = std::get<2>(pads);
     dilationH = common->dilateY(); dilationW = common->dilateX();
 }
-
+    
+    isWeightQuantSupported(inputs[0], oc);
     // create all tensors and params
 {
     std::vector<uint32_t> strideData = {(uint32_t)strideH, (uint32_t)strideW};
@@ -49,10 +201,24 @@ ErrorCode QNNConvDepthwise::onEncode(const std::vector<Tensor *> &inputs, const
         this->createParamScalar("max_value", 6.0f);
     }
 
-    this->createWeight(dataType, oc, kernelH, kernelW);
-    this->createBias(dataType, oc);
+    this->createWeightAndBias(dataType, inputs[0], oc, kernelH, kernelW);
+    // dequant input and quant output
+    if(mWeightQuant == false && dataType != QNN_DATATYPE_FLOAT_16 && dataType != QNN_DATATYPE_FLOAT_32){
+        return this->onEncodeQuantDequantDepthConv(inputs[0], outputs[0], n, ic, oc);
+    }
+    
     if (common->relu() || common->relu6()) {
-        this->createStageTensor("ReluTensor", dataType, getNHWCShape(outputs[0]));
+        Qnn_QuantizeParams_t quantize = DEFAULT_QUANTIZE_PARAMS;
+        Qnn_ScaleOffset_t tScaleOffsetEncoding;
+        auto quant = TensorUtils::getDescribe(outputs[0])->quantAttr.get();
+        if(quant != nullptr && TensorUtils::getDescribe(outputs[0])->type == DataType_DT_INT8){
+            quantize.encodingDefinition = QNN_DEFINITION_DEFINED;
+            quantize.quantizationEncoding = QNN_QUANTIZATION_ENCODING_SCALE_OFFSET;
+            tScaleOffsetEncoding.scale = quant->scale;
+            tScaleOffsetEncoding.offset = quant->zero;
+            quantize.scaleOffsetEncoding = tScaleOffsetEncoding;
+        }
+        this->createStageTensor("ReluTensor", dataType, getNHWCShape(outputs[0]), quantize);
     }
 }
 
@@ -112,43 +278,140 @@ ErrorCode QNNConvDepthwise::onEncode(const std::vector<Tensor *> &inputs, const
 
 
 
-void QNNConvDepthwise::createWeight(Qnn_DataType_t dataType, int oc, int kernelH, int kernelW) {
-    std::vector<float> weightData;
-    const float* source = nullptr;
-    int weightElementNum = 0;
-    std::shared_ptr<ConvolutionCommon::Int8Common> quanWeight;
-    ConvolutionCommon::getConvParameters(&quanWeight, mBackend, mOp, &source, &weightElementNum);
-    // oc ic h w ---> h w ic oc
-    weightData.resize(weightElementNum);
-    convertWeight(source, (float *) weightData.data(), oc, kernelH, kernelW);
-    this->createStaticFloatTensor("weight", dataType, {(uint32_t)kernelH, (uint32_t)kernelW, 1, (uint32_t)oc}, weightData.data());
-}
+void QNNConvDepthwise::createWeightAndBias(Qnn_DataType_t dataType, const Tensor *input, int oc, int kernelH, int kernelW) {
+    if(mWeightQuant){
+        Qnn_QuantizeParams_t weightQuantize{};
+        std::shared_ptr<ConvolutionCommon::Int8Common> quanCommon = ConvolutionCommon::load(mOp, this->backend(), false, true);
 
+        // [TODO] Support asymmetric and other quantBits.
+        MNN_ASSERT(!quanCommon->asymmetric);
 
-void QNNConvDepthwise::createBias(Qnn_DataType_t dataType, int oc) {
-    int biasElementNum = oc;
-    std::vector<float> biasData;
-    biasData.resize(biasElementNum, 0);
-    auto bias = mOp->main_as_Convolution2D()->bias();
-    if (nullptr != bias) {
-        ::memcpy((void *)biasData.data(), (void *)bias->data(), biasElementNum * sizeof(float));
-    }
-    this->createStaticFloatTensor("bias", dataType, {(uint32_t)oc}, biasData.data());
-}
-
-
-// oc, h, w ---> h, w, oc
-void QNNConvDepthwise::convertWeight(const float * src, float * dst, int oc, int kernelH, int kernelW) {
-    for (int c = 0; c < oc; c++) {
-        for (int h = 0; h < kernelH; h++) {
-            for (int w = 0; w < kernelW; w++) {
-                int srcOffset = w + kernelW * (h + kernelH * c);
-                int dstOffset = c + oc * (w + kernelW * h);
-                dst[dstOffset] = src[srcOffset];
+        // create weight
+        const int8_t * source = quanCommon->weight.get();
+        std::vector<int8_t> quantWeightData(oc * kernelH * kernelW, 0);
+        if(quanCommon->canUseInt4){
+            for (int c = 0; c < oc; c++) {
+                for (int h = 0; h < kernelH; h++) {
+                    for (int w = 0; w < kernelW; w++) {
+                        int srcOffset = w + kernelW * (h + kernelH * c);
+                        int dstOffset = c + oc * (w + kernelW * h);
+                        if(srcOffset % 2 == 0){
+                            quantWeightData[dstOffset] = ((source[srcOffset / 2] >> 4) & 0x0f) - 8;
+                        }else{
+                            quantWeightData[dstOffset] = (source[srcOffset / 2] & 0x0f) - 8;
+                        }
+                    }
+                }
             }
+        }else{
+            convertWeight(source, (int8_t *) quantWeightData.data(), oc, kernelH, kernelW);
         }
+
+        mDequantAlpha = quanCommon->alpha.get();
+        if(quanCommon->canUseInt4){
+            weightQuantize.encodingDefinition = QNN_DEFINITION_DEFINED;
+            weightQuantize.quantizationEncoding = QNN_QUANTIZATION_ENCODING_BW_AXIS_SCALE_OFFSET;
+            Qnn_BwAxisScaleOffset_t weightBWAxisScaleOffsetEncoding{};
+            weightBWAxisScaleOffsetEncoding.bitwidth = 4;
+            weightBWAxisScaleOffsetEncoding.axis = 3;
+            weightBWAxisScaleOffsetEncoding.numElements = oc;
+            mScale.resize(oc);
+            std::vector<int32_t> OffsetData(oc);
+            for (int i = 0; i < oc; i++) {
+                mScale[i] = mDequantAlpha[i];
+            }
+            weightBWAxisScaleOffsetEncoding.scales = mScale.data();
+            weightQuantize.bwAxisScaleOffsetEncoding = weightBWAxisScaleOffsetEncoding;
+            
+            this->createStaticTensor("quantWeight", QNN_DATATYPE_SFIXED_POINT_8, {(uint32_t)kernelH, (uint32_t)kernelW, 1, (uint32_t)oc}, (void *) quantWeightData.data(), weightQuantize);
+            std::function<void()> mReleaseWeightScaleOffset = [&](){
+                std::vector<float>().swap(mScale);
+            };
+            mBackend->pushReleaseFunc(mReleaseWeightScaleOffset);
+        }else{
+            weightQuantize.encodingDefinition = QNN_DEFINITION_DEFINED;
+            weightQuantize.quantizationEncoding = QNN_QUANTIZATION_ENCODING_AXIS_SCALE_OFFSET;
+            Qnn_AxisScaleOffset_t weightAxisScaleOffsetEncoding{};
+            weightAxisScaleOffsetEncoding.axis = 3;
+            weightAxisScaleOffsetEncoding.numScaleOffsets = oc;
+            mScaleOffsetData.resize(oc);
+            for (int i = 0; i < oc; i++) {
+                mScaleOffsetData[i].scale = mDequantAlpha[i];
+                mScaleOffsetData[i].offset = 0;
+            }
+            weightAxisScaleOffsetEncoding.scaleOffset = mScaleOffsetData.data();
+            weightQuantize.axisScaleOffsetEncoding = weightAxisScaleOffsetEncoding;
+
+            this->createStaticTensor("quantWeight", QNN_DATATYPE_SFIXED_POINT_8, {(uint32_t)kernelH, (uint32_t)kernelW, 1, (uint32_t)oc}, (void *) quantWeightData.data(), weightQuantize);
+        
+            std::function<void()> mReleaseWeightScaleOffset = [&](){
+                std::vector<Qnn_ScaleOffset_t>().swap(mScaleOffsetData);
+            };
+            mBackend->pushReleaseFunc(mReleaseWeightScaleOffset);
+        }
+        // create bias
+        {
+            float inputScale = mBackend->getNativeTensor(input)->v1.quantizeParams.scaleOffsetEncoding.scale;
+            int inputOffset = mBackend->getNativeTensor(input)->v1.quantizeParams.scaleOffsetEncoding.offset;
+            std::vector<int> biasData;
+            biasData.resize(oc, 0);
+
+            Qnn_QuantizeParams_t biasQuantize{};
+            biasQuantize.encodingDefinition = QNN_DEFINITION_DEFINED;
+            biasQuantize.quantizationEncoding = QNN_QUANTIZATION_ENCODING_AXIS_SCALE_OFFSET;
+            Qnn_AxisScaleOffset_t biasAxisScaleOffsetEncoding{};
+            biasAxisScaleOffsetEncoding.axis = 0;
+            biasAxisScaleOffsetEncoding.numScaleOffsets = oc;
+            mBiasScaleOffsetData.resize(oc);
+
+            auto bias = mOp->main_as_Convolution2D()->bias();
+            auto biasPtr = (float*)bias->data();
+            if (nullptr != bias) {
+                for(int i = 0; i < oc; ++i){
+                    float biasScale = inputScale * mDequantAlpha[i];
+                    mBiasScaleOffsetData[i].scale = 0.f;
+                    mBiasScaleOffsetData[i].offset = 0;
+                    if(fabs(biasPtr[i]) < 0.000001 || fabs(biasScale) < 0.000001){
+                        biasData[i] = 0;
+                    } else{
+                        biasData[i] = (int)(biasPtr[i] / (biasScale));
+                    }
+                }
+            }
+            biasAxisScaleOffsetEncoding.scaleOffset = mBiasScaleOffsetData.data();
+            biasQuantize.axisScaleOffsetEncoding = biasAxisScaleOffsetEncoding;
+
+            this->createStaticTensor("bias", QNN_DATATYPE_SFIXED_POINT_32, {(uint32_t)oc}, biasData.data(), biasQuantize);
+            std::function<void()> mReleaseBiasScaleOffset = [&](){
+                std::vector<Qnn_ScaleOffset_t>().swap(mBiasScaleOffsetData);
+            };
+            mBackend->pushReleaseFunc(mReleaseBiasScaleOffset);
+        }
+    }else{
+        Qnn_DataType_t floatDatatype = QNN_DATATYPE_FLOAT_32;
+        if(mBackend->getUseFP16()){
+            floatDatatype = QNN_DATATYPE_FLOAT_16;
+        }
+        std::vector<float> weightData;
+        const float* source = nullptr;
+        int weightElementNum = 0;
+        std::shared_ptr<ConvolutionCommon::Int8Common> quanWeight;
+        ConvolutionCommon::getConvParameters(&quanWeight, mBackend, mOp, &source, &weightElementNum);
+        // oc ic h w ---> h w ic oc
+        weightData.resize(weightElementNum);
+        convertWeight(source, (float *) weightData.data(), oc, kernelH, kernelW);
+        this->createStaticFloatTensor("weight", floatDatatype, {(uint32_t)kernelH, (uint32_t)kernelW, 1, (uint32_t)oc}, weightData.data());
+        
+        // create bias
+        std::vector<float> biasData;
+        biasData.resize(oc, 0);
+        auto bias = mOp->main_as_Convolution2D()->bias();
+        if (nullptr != bias) {
+            ::memcpy((void *)biasData.data(), (void *)bias->data(), oc * sizeof(float));
+        }
+        this->createStaticFloatTensor("bias", floatDatatype, {(uint32_t)oc}, biasData.data());
     }
-} 
+}
 
 class QNNConvDepthwiseCreator : public QnnBackend::Creator {
 public:

source/backend/qnn/execution/QNNConvDepthwise.hpp

@@ -19,10 +19,28 @@ class QNNConvDepthwise : public QNNCommonExecution {
 public:
     QNNConvDepthwise(Backend *backend, const Op *op) : QNNCommonExecution(backend, op) {}
     virtual ErrorCode onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) override;
+    ErrorCode onEncodeQuantDequantDepthConv(Tensor *input, Tensor *output, const int n, const int ic, const int oc);
 private:
-    void createWeight(Qnn_DataType_t dataType, int oc, int kernelH, int kernelW);
-    void createBias(Qnn_DataType_t dataType, int oc);
-    void convertWeight(const float * src, float * dst, int oc, int kernelH, int kernelW);
+template <typename T>
+    void convertWeight(const T * src, T * dst, int oc, int kernelH, int kernelW) {
+        for (int c = 0; c < oc; c++) {
+            for (int h = 0; h < kernelH; h++) {
+                for (int w = 0; w < kernelW; w++) {
+                    int srcOffset = w + kernelW * (h + kernelH * c);
+                    int dstOffset = c + oc * (w + kernelW * h);
+                    dst[dstOffset] = src[srcOffset];
+                }
+            }
+        }
+    }
+    void isWeightQuantSupported(const Tensor *input, const int oc);
+    void createWeightAndBias(Qnn_DataType_t dataType, const Tensor *input, int oc, int kernelH, int kernelW);
+    std::vector<float> mScale;
+    std::vector<Qnn_ScaleOffset_t> mScaleOffsetData;
+    std::vector<Qnn_ScaleOffset_t> mBiasScaleOffsetData;
+    std::vector<uint8_t> mBlockScale;
+    float *mDequantAlpha = nullptr;
+    bool mWeightQuant = false;
 };
 
 } // end namespace QNN

source/backend/qnn/execution/QNNConvolution.cpp

@@ -21,6 +21,63 @@ static std::pair<int, int> closest_factors(int n) {
     }
     return {1, n};  
 }
+
+void QNNConvolution::isWeightQuantSupported(const Tensor *input, const int ic, const int oc){
+    Qnn_DataType_t dataType = mBackend->getNativeTensor(input)->v1.dataType;
+    if(mOp->main_as_Convolution2D()->quanParameter() == nullptr){
+        mWeightQuant = false;
+        return;
+    }else{
+        bool hasBias = false;
+        auto bias = mOp->main_as_Convolution2D()->bias();
+        auto biasPtr = (float*)bias->data();
+        for(int i = 0; i < oc; ++i){
+            if(biasPtr[i] != 0.0f){
+                hasBias = true;
+                break;
+            }
+        }
+        
+        std::shared_ptr<ConvolutionCommon::Int8Common> quanCommon = ConvolutionCommon::load(mOp, this->backend(), false, true);
+        int totalCount = quanCommon->alpha.size();
+        mBlockSize = totalCount / oc;
+        if(quanCommon->asymmetric){
+            // not support asymmetric and mBlockSize > 1 results incorrect now
+            mWeightQuant = false;
+            return;
+        }
+        
+        if(dataType == QNN_DATATYPE_FLOAT_16 || dataType == QNN_DATATYPE_FLOAT_32){
+            if(mIsMatMul && mBlockSize == 1){
+                mWeightQuant = true;
+            }else{
+                mWeightQuant = false;
+            }
+            return;
+        }
+        
+        float inputScale = mBackend->getNativeTensor(input)->v1.quantizeParams.scaleOffsetEncoding.scale;
+        int inputOffset = mBackend->getNativeTensor(input)->v1.quantizeParams.scaleOffsetEncoding.offset;
+        if(inputOffset == 0){
+            mWeightQuant = true;
+        }else{
+            if(hasBias){
+                mWeightQuant = false;
+            }else{
+                mWeightQuant = true;
+            }
+        }
+        
+        if(mBlockSize > 1 && mWeightQuant){
+            if(mIs1x1Conv && hasBias == false && (ic / mBlockSize) >= 16){
+                mWeightQuant = true;
+            }else{
+                mWeightQuant = false;
+            }
+        }
+    }
+}
+
 ErrorCode QNNConvolution::onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
     auto conv2D     = mOp->main_as_Convolution2D();
     auto common     = conv2D->common();
@@ -46,32 +103,16 @@ ErrorCode QNNConvolution::onEncode(const std::vector<Tensor *> &inputs, const st
         dilationH = common->dilateY(); dilationW = common->dilateX();
         group = common->group();
     }
-
-    const float * weightSource = nullptr;
-    std::shared_ptr<ConvolutionCommon::Int8Common> quanCommon;
-    if (mOp->main_as_Convolution2D()->quanParameter()) {
-        bool forceFloat = (common->kernelX() == 1 && common->kernelY() == 1) ? false : true;
-
-        quanCommon = ConvolutionCommon::load(mOp, this->backend(), forceFloat);
-        if (quanCommon->weightFloat.get() == nullptr) {
-            // [TODO] Support asymmetric and other quantBits.
-            // isQuantWeight && symmetric quantization && int8 quantization && 1x1 conv
-            if (quanCommon->asymmetric || quanCommon->canUseInt4) {
-                return NOT_SUPPORT;
-            }
-            return this->onEncodeQuant(inputs[0], outputs[0], n, ih, iw, ic, oc, quanCommon);
-        } else {
-            weightSource = quanCommon->weightFloat.get();
-        }
-    } else {
-        int weightElementNum;
-        ConvolutionCommon::getConvParameters(&quanCommon, mBackend, mOp, &weightSource, &weightElementNum);
+    mIs1x1Conv = kernelW==1 && strideH==1 && \
+                 strideW==1 && dilationH==1 && dilationW==1 && group==1 && \
+                 padTop==0 && padBottom==0 && padLeft==0 && padRight==0;
+    mIsMatMul = ih==1 && iw==1 && oh==1 && ow==1 && mIs1x1Conv;
+    isWeightQuantSupported(inputs[0], ic, oc);
+    
+    if(mIsMatMul && mWeightQuant && (dataType == QNN_DATATYPE_FLOAT_16 || dataType == QNN_DATATYPE_FLOAT_32)){
+        return onEncodeFpAIntBMatMul(inputs[0], outputs[0], n, ih, iw, ic, oc);
     }
-
-    #ifdef QNN_VERBOSE
-    MNN_PRINT("n:%d, ih:%d, iw:%d, ic:%d, oh:%d, ow:%d, oc:%d, kernelH:%d, kernelW:%d, dilationH:%d, dilationW:%d, strideH:%d, strideW:%d, group:%d, pad:%d %d %d %d\n", n, ih, iw, ic, oh, ow, oc, kernelH, kernelW, dilationH, \
-        dilationW, strideH, strideW, group, padTop, padBottom, padLeft, padRight);
-    #endif
+    
     // create all tensors and params
     {
         std::vector<uint32_t> strideData = {(uint32_t)strideH, (uint32_t)strideW};
@@ -85,12 +126,26 @@ ErrorCode QNNConvolution::onEncode(const std::vector<Tensor *> &inputs, const st
             this->createParamScalar("min_value", 0.0f);
             this->createParamScalar("max_value", 6.0f);
         }
+    }
 
-        this->createWeight(dataType, oc, ic, kernelH, kernelW, group, weightSource);
-        this->createBias(dataType, oc);
-        if (common->relu() || common->relu6()) {
-            this->createStageTensor("ReluTensor", dataType, getNHWCShape(outputs[0]));
+    this->createWeightAndBias(dataType, inputs[0], oc, ic, kernelH, kernelW, group);
+    // dequant input and quant output
+    if(mWeightQuant == false && dataType != QNN_DATATYPE_FLOAT_16 && dataType != QNN_DATATYPE_FLOAT_32){
+        return this->onEncodeQuantDequantConv(inputs[0], outputs[0], n, ic, oc);
+    }
+    
+    if (common->relu() || common->relu6()) {
+        Qnn_QuantizeParams_t quantize = DEFAULT_QUANTIZE_PARAMS;
+        Qnn_ScaleOffset_t tScaleOffsetEncoding;
+        auto quant = TensorUtils::getDescribe(outputs[0])->quantAttr.get();
+        if(quant != nullptr && TensorUtils::getDescribe(outputs[0])->type == DataType_DT_INT8){
+            quantize.encodingDefinition = QNN_DEFINITION_DEFINED;
+            quantize.quantizationEncoding = QNN_QUANTIZATION_ENCODING_SCALE_OFFSET;
+            tScaleOffsetEncoding.scale = quant->scale;
+            tScaleOffsetEncoding.offset = quant->zero;
+            quantize.scaleOffsetEncoding = tScaleOffsetEncoding;
         }
+        this->createStageTensor("ReluTensor", dataType, getNHWCShape(outputs[0]), quantize);
     }
 
     // add nodes
@@ -131,13 +186,34 @@ ErrorCode QNNConvolution::onEncode(const std::vector<Tensor *> &inputs, const st
             }
 
         } else {
-            bool isMatmul = ih==1 && iw==1 && oh==1 && ow==1 && kernelH==1 && kernelW==1 && strideH==1 && \
-                strideW==1 && dilationH==1 && dilationW==1 && group==1 && \
-                padTop==0 && padBottom==0 && padLeft==0 && padRight==0;
-            if(isMatmul && n > 1) {
+            if(mIsMatMul && n > 1) {
                 auto num = closest_factors(n);
-                this->createStageTensor("InputReshapeTensor", dataType, std::vector<int>({1, num.first, num.second, ic}));
-                this->createStageTensor("OutputReshapeTensor", dataType, std::vector<int>({1, num.first, num.second, oc}));
+                {
+                    Qnn_QuantizeParams_t quantize = DEFAULT_QUANTIZE_PARAMS;
+                    Qnn_ScaleOffset_t tScaleOffsetEncoding;
+                    auto quant = TensorUtils::getDescribe(inputs[0])->quantAttr.get();
+                    if(quant != nullptr && TensorUtils::getDescribe(inputs[0])->type == DataType_DT_INT8){
+                        quantize.encodingDefinition = QNN_DEFINITION_DEFINED;
+                        quantize.quantizationEncoding = QNN_QUANTIZATION_ENCODING_SCALE_OFFSET;
+                        tScaleOffsetEncoding.scale = quant->scale;
+                        tScaleOffsetEncoding.offset = quant->zero;
+                        quantize.scaleOffsetEncoding = tScaleOffsetEncoding;
+                    }
+                    this->createStageTensor("InputReshapeTensor", dataType, std::vector<int>({1, num.first, num.second, ic}), quantize);
+                }
+                {
+                    Qnn_QuantizeParams_t quantize = DEFAULT_QUANTIZE_PARAMS;
+                    Qnn_ScaleOffset_t tScaleOffsetEncoding;
+                    auto quant = TensorUtils::getDescribe(outputs[0])->quantAttr.get();
+                    if(quant != nullptr && TensorUtils::getDescribe(outputs[0])->type == DataType_DT_INT8){
+                        quantize.encodingDefinition = QNN_DEFINITION_DEFINED;
+                        quantize.quantizationEncoding = QNN_QUANTIZATION_ENCODING_SCALE_OFFSET;
+                        tScaleOffsetEncoding.scale = quant->scale;
+                        tScaleOffsetEncoding.offset = quant->zero;
+                        quantize.scaleOffsetEncoding = tScaleOffsetEncoding;
+                    }
+                    this->createStageTensor("OutputReshapeTensor", dataType, std::vector<int>({1, num.first, num.second, oc}), quantize);
+                }
                 #ifdef QNN_VERBOSE
                 MNN_PRINT("Matmul2Conv, start reshape batch:%d -> %dx%d\n", n, num.first, num.second);
                 #endif
@@ -208,52 +284,273 @@ ErrorCode QNNConvolution::onEncode(const std::vector<Tensor *> &inputs, const st
     return NO_ERROR;
 }
 
+ErrorCode QNNConvolution::onEncodeQuantDequantConv(Tensor *input, Tensor *output, const int n, const int ic, const int oc) {
+    auto conv2D     = mOp->main_as_Convolution2D();
+    auto common     = conv2D->common();
+    Qnn_DataType_t dataType = QNN_DATATYPE_FLOAT_32;
+    if(mBackend->getUseFP16()){
+        dataType = QNN_DATATYPE_FLOAT_16;
+    }
+    
+    // create dequant input stage tensor
+    this->createStageTensor("DequantInput", dataType, getNHWCShape(input)); // mTempTensorWrappers[2]
+    this->createStageTensor("QuantOutput", dataType, getNHWCShape(output)); // mTempTensorWrappers[3]
+    
+    // add nodes
+    {
+        // dequant input
+        {
+            mParams.clear();
+            mInputs.clear();
+            mOutputs.clear();
+            mNodeType = "Dequantize";
+            std::string name = mNodeName + "_dequant_input";
+        
+            mInputs.push_back(*(mBackend->getNativeTensor(input))); // input
+            mOutputs.push_back(*(mTempTensorWrappers[2]->getNativeTensor())); // DequantInput
+            mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+        }
+        
+        if (common->relu() || common->relu6()) {
+            this->createStageTensor("ReluTensor", dataType, getNHWCShape(output)); // mTempTensorWrappers[4]
+            // Stage one
+            {
+                mParams.clear();
+                mInputs.clear();
+                mOutputs.clear();
+                mNodeType = "Conv2d";
+                std::string name = mNodeName + "_conv";
+                mParams.push_back(*(mParamTensorWrappers[0]->getNativeParam())); // stride
+                mParams.push_back(*(mParamTensorWrappers[1]->getNativeParam())); // pad_amount
+                mParams.push_back(*(mParamTensorWrappers[2]->getNativeParam())); // dilation
+                mParams.push_back(*(mParamScalarWrappers[0]->getNativeParam())); // group
+        
+                mInputs.push_back(*(mTempTensorWrappers[2]->getNativeTensor())); // DequantInput
+                mInputs.push_back(*(mTempTensorWrappers[0]->getNativeTensor())); // weight
+                mInputs.push_back(*(mTempTensorWrappers[1]->getNativeTensor())); // bias
+        
+                mOutputs.push_back(*(mTempTensorWrappers[4]->getNativeTensor())); // ReluTensor
+                mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+            }
 
-ErrorCode QNNConvolution::onEncodeQuant(Tensor * input, Tensor * output, int n, int h, int w, int ic, int oc, std::shared_ptr<ConvolutionCommon::Int8Common> quanCommon) {
+            // Stage two
+            {
+                mParams.clear();
+                mInputs.clear();
+                mOutputs.clear();
+                mNodeType = common->relu6() ? "ReluMinMax" : "Relu";
+                std::string name = mNodeName + "_relu";
+                if (common->relu6()) {
+                    mParams.push_back(*(mParamScalarWrappers[1]->getNativeParam())); // min_value
+                    mParams.push_back(*(mParamScalarWrappers[2]->getNativeParam())); // max_value
+                }
+                mInputs.push_back(*(mTempTensorWrappers[4]->getNativeTensor())); // ReluTensor
+                mOutputs.push_back(*(mTempTensorWrappers[3]->getNativeTensor())); // QuantOutput
+                mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+            }
+
+        } else {
+            if(mIsMatMul && n > 1) {
+                auto num = closest_factors(n);
+                this->createStageTensor("InputReshapeTensor", dataType, std::vector<int>({1, num.first, num.second, ic})); // mTempTensorWrappers[4]
+                this->createStageTensor("OutputReshapeTensor", dataType, std::vector<int>({1, num.first, num.second, oc})); // mTempTensorWrappers[5]
+                #ifdef QNN_VERBOSE
+                MNN_PRINT("Matmul2Conv, start reshape batch:%d -> %dx%d\n", n, num.first, num.second);
+                #endif
+                // reshape input
+                {
+                    std::string name = mNodeName + "_input_reshape";
+                    mParams.clear();
+                    mInputs.clear();
+                    mOutputs.clear();
+                    mNodeType = "Reshape";
+
+                    mInputs.push_back(*(mTempTensorWrappers[2]->getNativeTensor())); // DequantInput
+                    mOutputs.push_back(*(mTempTensorWrappers[4]->getNativeTensor())); // InputReshapeTensor
+                    mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+                }
+                // conv2d
+                {
+                    std::string name = mNodeName;
+                    mParams.clear();
+                    mInputs.clear();
+                    mOutputs.clear();
+                    mNodeType = "Conv2d";
+
+                    mParams.push_back(*(mParamTensorWrappers[0]->getNativeParam())); // stride
+                    mParams.push_back(*(mParamTensorWrappers[1]->getNativeParam())); // pad_amount
+                    mParams.push_back(*(mParamTensorWrappers[2]->getNativeParam())); // dilation
+                    mParams.push_back(*(mParamScalarWrappers[0]->getNativeParam())); // group
+
+                    mInputs.push_back(*(mTempTensorWrappers[4]->getNativeTensor())); // InputReshapeTensor
+                    mInputs.push_back(*(mTempTensorWrappers[0]->getNativeTensor())); // weight
+                    mInputs.push_back(*(mTempTensorWrappers[1]->getNativeTensor())); // bias
+
+                    mOutputs.push_back(*(mTempTensorWrappers[5]->getNativeTensor())); // OutputReshapeTensor
+                    mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+                }
+
+                // reshape output
+                {
+                    std::string name = mNodeName + "_output_reshape";
+                    mParams.clear();
+                    mInputs.clear();
+                    mOutputs.clear();
+                    mNodeType = "Reshape";
+
+                    mInputs.push_back(*(mTempTensorWrappers[5]->getNativeTensor())); // OutputReshapeTensor
+                    mOutputs.push_back(*(mTempTensorWrappers[3]->getNativeTensor())); // QuantOutput
+                    mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+                }
+            } else{
+                mParams.clear();
+                mInputs.clear();
+                mOutputs.clear();
+                mNodeType = "Conv2d";
+                mParams.push_back(*(mParamTensorWrappers[0]->getNativeParam())); // stride
+                mParams.push_back(*(mParamTensorWrappers[1]->getNativeParam())); // pad_amount
+                mParams.push_back(*(mParamTensorWrappers[2]->getNativeParam())); // dilation
+                mParams.push_back(*(mParamScalarWrappers[0]->getNativeParam())); // group
+                
+                mInputs.push_back(*(mTempTensorWrappers[2]->getNativeTensor())); // DequantInput
+                mInputs.push_back(*(mTempTensorWrappers[0]->getNativeTensor())); // weight
+                mInputs.push_back(*(mTempTensorWrappers[1]->getNativeTensor())); // bias
+                
+                mOutputs.push_back(*(mTempTensorWrappers[3]->getNativeTensor())); // QuantOutput
+                mBackend->addNodeToGraph(mOpConfigVersion, mNodeName.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+            }
+        }
+        
+        // Quant output
+        {
+            auto QuantOutputTensor = mTempTensorWrappers[3]->getNativeTensor();
+            if(mBackend->getUseFP16()){
+                this->createStageTensor("CastOutput", QNN_DATATYPE_FLOAT_32, getNHWCShape(output));
+                
+                mParams.clear();
+                mInputs.clear();
+                mOutputs.clear();
+                mNodeType = "Cast";
+                std::string name = mNodeName + "_Cast_Output";
+                
+                mInputs.push_back(*(mTempTensorWrappers[3]->getNativeTensor())); // QuantOutput
+                mOutputs.push_back(*(mTempTensorWrappers.back()->getNativeTensor())); // CastOutput
+                mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+                QuantOutputTensor = mTempTensorWrappers.back()->getNativeTensor();
+            }
+            {
+                mParams.clear();
+                mInputs.clear();
+                mOutputs.clear();
+                mNodeType = "Quantize";
+                std::string name = mNodeName + "_Quant_Output";
+                
+                mInputs.push_back(*(QuantOutputTensor)); // stage tensor
+                mOutputs.push_back(*(mBackend->getNativeTensor(output))); // output
+                mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+            }
+        }
+    }
+    return NO_ERROR;
+}
+
+ErrorCode QNNConvolution::onEncodeFpAIntBMatMul(Tensor * input, Tensor * output, int n, int h, int w, int ic, int oc) {
     // create parameters and stage tensors
+    auto conv2D     = mOp->main_as_Convolution2D();
+    auto common     = conv2D->common();
+    Qnn_DataType_t dataType = mBackend->getNativeTensor(input)->v1.dataType;
     {
         bool transposeWeightFlag = true;
         this->createParamScalar("transpose_in1", transposeWeightFlag);
-
+        
         std::vector<uint32_t> tempInputShape = {(uint32_t) n * h * w , (uint32_t) ic};
         std::vector<uint32_t> tempOutputShape = {(uint32_t) n * h * w , (uint32_t) oc};
-        this->createStageTensor("tempInput", QNN_DATATYPE_FLOAT_16, tempInputShape);
-        this->createStageTensor("tempOutput", QNN_DATATYPE_FLOAT_16, tempOutputShape);
+        this->createStageTensor("tempInput", dataType, tempInputShape);
+        this->createStageTensor("tempOutput", dataType, tempOutputShape);
 
-        // create weight
-        const int8_t * source = quanCommon->weight.get();
-        std::vector<int8_t> quantWeightData(oc * ic, 0);
-        ::memcpy(quantWeightData.data(), source, oc * ic * sizeof(int8_t));
-
-        float * dequantAlpha = quanCommon->alpha.get();
-
-        Qnn_QuantizeParams_t weightQuantize{};
-        weightQuantize.encodingDefinition = QNN_DEFINITION_DEFINED;
-        weightQuantize.quantizationEncoding = QNN_QUANTIZATION_ENCODING_AXIS_SCALE_OFFSET;
-        Qnn_AxisScaleOffset_t weightAxisScaleOffsetEncoding{};
-        weightAxisScaleOffsetEncoding.axis = 0;
-        weightAxisScaleOffsetEncoding.numScaleOffsets = oc;
-        std::vector<Qnn_ScaleOffset_t> scaleOffsetData(oc);
-        for (int i = 0; i < oc; i++) {
-            if (quanCommon->asymmetric) {
-                scaleOffsetData[i].scale = dequantAlpha[2 * i + 1];
-                // scaleOffsetData[i].offset = (int) dequantAlpha[2 * i + 0];
-                scaleOffsetData[i].offset = 0;
-            } else {
-                scaleOffsetData[i].scale = dequantAlpha[i];
-                scaleOffsetData[i].offset = 0;
+        // create weight and bias
+        {
+            Qnn_QuantizeParams_t weightQuantize{};
+            std::shared_ptr<ConvolutionCommon::Int8Common> quanCommon = ConvolutionCommon::load(mOp, this->backend(), false, true);
+            MNN_ASSERT(!quanCommon->asymmetric);
+            const int8_t * source = quanCommon->weight.get();
+            std::vector<int8_t> quantWeightData(oc * ic, 0);
+            if(quanCommon->canUseInt4){
+                for (int o = 0; o < oc; o++) {
+                    for (int i = 0; i < ic; i++) {
+                        uint32_t srcOffset = o * ic + i;
+                        uint32_t dstOffset = srcOffset;
+                        if(srcOffset % 2 == 0){
+                            quantWeightData[dstOffset] = ((source[srcOffset / 2] >> 4) & 0x0f) - 8;
+                        }else{
+                            quantWeightData[dstOffset] = (source[srcOffset / 2] & 0x0f) - 8;
+                        }
+                    }
+                }
+            }else{
+                ::memcpy(quantWeightData.data(), source, oc * ic * sizeof(int8_t));
             }
+            mDequantAlpha = quanCommon->alpha.get();
+            int totalCount = quanCommon->alpha.size();
+            mBlockSize = totalCount / oc;
+            int blockNum = ic / mBlockSize;
+            if(quanCommon->canUseInt4){
+                weightQuantize.encodingDefinition = QNN_DEFINITION_DEFINED;
+                weightQuantize.quantizationEncoding = QNN_QUANTIZATION_ENCODING_BW_AXIS_SCALE_OFFSET;
+                Qnn_BwAxisScaleOffset_t weightBWAxisScaleOffsetEncoding{};
+                weightBWAxisScaleOffsetEncoding.bitwidth = 4;
+                weightBWAxisScaleOffsetEncoding.axis = 0;
+                weightBWAxisScaleOffsetEncoding.numElements = oc;
+                mScale.resize(oc);
+                std::vector<int32_t> OffsetData(oc);
+                for (int i = 0; i < oc; i++) {
+                    mScale[i] = mDequantAlpha[i];
+                }
+                weightBWAxisScaleOffsetEncoding.scales = mScale.data();
+                weightQuantize.bwAxisScaleOffsetEncoding = weightBWAxisScaleOffsetEncoding;
+                
+                this->createStaticTensor("quantWeight", QNN_DATATYPE_SFIXED_POINT_8, {(uint32_t)oc, (uint32_t)ic}, (void *) quantWeightData.data(), weightQuantize);
+                std::function<void()> mReleaseWeightScaleOffset = [&](){
+                    std::vector<float>().swap(mScale);
+                };
+                mBackend->pushReleaseFunc(mReleaseWeightScaleOffset);
+            }else{
+                weightQuantize.encodingDefinition = QNN_DEFINITION_DEFINED;
+                weightQuantize.quantizationEncoding = QNN_QUANTIZATION_ENCODING_AXIS_SCALE_OFFSET;
+                Qnn_AxisScaleOffset_t weightAxisScaleOffsetEncoding{};
+                weightAxisScaleOffsetEncoding.axis = 0;
+                weightAxisScaleOffsetEncoding.numScaleOffsets = oc;
+                mScaleOffsetData.resize(oc);
+                for (int i = 0; i < oc; i++) {
+                    mScaleOffsetData[i].scale = mDequantAlpha[i];
+                    mScaleOffsetData[i].offset = 0;
+                }
+                weightAxisScaleOffsetEncoding.scaleOffset = mScaleOffsetData.data();
+                weightQuantize.axisScaleOffsetEncoding = weightAxisScaleOffsetEncoding;
+                
+                this->createStaticTensor("quantWeight", QNN_DATATYPE_SFIXED_POINT_8, {(uint32_t)oc, (uint32_t)ic}, (void *) quantWeightData.data(), weightQuantize);
+                std::function<void()> mReleaseWeightScaleOffset = [&](){
+                    std::vector<Qnn_ScaleOffset_t>().swap(mScaleOffsetData);
+                };
+                mBackend->pushReleaseFunc(mReleaseWeightScaleOffset);
+            }
+            //create bias
+            this->createBias(dataType, oc, input, quanCommon);
+        }
+        
+        if (common->relu6()) {
+            this->createParamScalar("min_value", 0.0f);
+            this->createParamScalar("max_value", 6.0f);
+        }
+        if (common->relu() || common->relu6()) {
+            this->createStageTensor("ReluTensor", dataType, getNHWCShape(output));
         }
-        weightAxisScaleOffsetEncoding.scaleOffset = scaleOffsetData.data();
-        weightQuantize.axisScaleOffsetEncoding = weightAxisScaleOffsetEncoding;
-
-        this->createStaticTensor("quantWeight", QNN_DATATYPE_SFIXED_POINT_8, {(uint32_t) oc, (uint32_t) ic}, (void *) quantWeightData.data(), weightQuantize);
     }
 
     // Stage One: reshape input
     {
         mNodeType = "Reshape";
-        std::string name = mNodeName + "_reshapeOutput";
+        std::string name = mNodeName + "_reshapeInput";
         mParams.clear();
         mInputs.clear();
         mOutputs.clear();
@@ -273,6 +570,7 @@ ErrorCode QNNConvolution::onEncodeQuant(Tensor * input, Tensor * output, int n,
         mInputs.push_back(*(mTempTensorWrappers[0]->getNativeTensor())); // tempInput
         // mInputs.push_back(*(mBackend->getNativeTensor(input)));
         mInputs.push_back(*(mTempTensorWrappers[2]->getNativeTensor())); // weight
+        mInputs.push_back(*(mTempTensorWrappers[3]->getNativeTensor())); // bias
         mOutputs.push_back(*(mTempTensorWrappers[1]->getNativeTensor())); // tempOutput
         // mOutputs.push_back(*(mBackend->getNativeTensor(output)));
         mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
@@ -286,48 +584,217 @@ ErrorCode QNNConvolution::onEncodeQuant(Tensor * input, Tensor * output, int n,
         mInputs.clear();
         mOutputs.clear();
         mInputs.push_back(*(mTempTensorWrappers[1]->getNativeTensor()));
-        mOutputs.push_back(*(mBackend->getNativeTensor(output)));
+        if (common->relu() || common->relu6()){
+            mOutputs.push_back(*(mTempTensorWrappers[4]->getNativeTensor())); //ReluTensor
+        }else{
+            mOutputs.push_back(*(mBackend->getNativeTensor(output)));
+        }
         mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
     }
+    
+    // Stage Four: relu or relu6
+    if (common->relu() || common->relu6()){
+        mNodeType.clear();
+        mParams.clear();
+        mInputs.clear();
+        mOutputs.clear();
+        mNodeType = common->relu6() ? "ReluMinMax" : "Relu";
+        std::string name = mNodeName + "_relu";
+        if (common->relu6()) {
+            mParams.push_back(*(mParamScalarWrappers[1]->getNativeParam())); // min_value
+            mParams.push_back(*(mParamScalarWrappers[2]->getNativeParam())); // max_value
+        }
+        mInputs.push_back(*(mTempTensorWrappers[4]->getNativeTensor())); // ReluTensor
+        mOutputs.push_back(*(mBackend->getNativeTensor(output))); // output
+        mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+    }
+    return NO_ERROR;
+}
 
+bool QNNConvolution::createWeightAndBias(Qnn_DataType_t dataType, const Tensor *input, int oc, int ic, int kernelH, int kernelW, int group) {
+    if(mWeightQuant){
+        Qnn_QuantizeParams_t weightQuantize{};
+        std::shared_ptr<ConvolutionCommon::Int8Common> quanCommon = ConvolutionCommon::load(mOp, this->backend(), false, true);
+        if(quanCommon->asymmetric) {
+            MNN_ERROR("[Error]: Qnn weight quant only support symmetric currently\n");
+            return false;
+        }
+        const int8_t * source = quanCommon->weight.get();
+        std::vector<int8_t> quantWeightData(oc * (ic / group) * kernelH * kernelW, 0);
+        if(quanCommon->canUseInt4){
+            for (int o = 0; o < oc; o++) {
+                for (int i = 0; i < ic/group; i++) {
+                    for (int h = 0; h < kernelH; h++) {
+                        for (int w = 0; w < kernelW; w++) {
+                            uint32_t srcOffset = w + kernelW * (h + kernelH * (i + ic/group * o));
+                            uint32_t dstOffset = o + oc * (i + ic/group * (w + kernelW * h));
+                            if(srcOffset % 2 == 0){
+                                quantWeightData[dstOffset] = ((source[srcOffset / 2] >> 4) & 0x0f) - 8;
+                            }else{
+                                quantWeightData[dstOffset] = (source[srcOffset / 2] & 0x0f) - 8;
+                            }
+                        }
+                    }
+                }
+            }
+        }else{
+            convertWeight(source, (int8_t *) quantWeightData.data(), oc, ic/group, kernelH, kernelW);
+        }
+        mDequantAlpha = quanCommon->alpha.get();
+        int totalCount = quanCommon->alpha.size();
+        mBlockSize = totalCount / oc;
+        // Todo: result is wrong, need to verify
+        if(mBlockSize > 1){
+            Qnn_QuantizeParams_t weightQuantize{};
+            weightQuantize.encodingDefinition = QNN_DEFINITION_DEFINED;
+            weightQuantize.quantizationEncoding = QNN_QUANTIZATION_ENCODING_BLOCKWISE_EXPANSION;
+            
+            weightBlockwiseExpansionEncoding.axis = 3;
+            weightBlockwiseExpansionEncoding.numBlocksPerAxis = mBlockSize;
+            weightBlockwiseExpansionEncoding.blockScaleBitwidth = 4;
+            weightBlockwiseExpansionEncoding.blockScaleStorageType = QNN_BLOCKWISE_EXPANSION_BITWIDTH_SCALE_STORAGE_8;
+            mBlockScale.resize(oc * mBlockSize);
+            mScaleOffsetData.resize(oc);
+            for (int i = 0; i < oc; i++) {
+                float maxscale = -MAXFLOAT;
+                for(int j = 0; j < mBlockSize; ++j){
+                    if(mDequantAlpha[i * mBlockSize + j] > maxscale){
+                        maxscale = mDequantAlpha[i * mBlockSize + j];
+                    }
+                }
+                float blockScale = maxscale / 16.0f;
+                for(int j = 0; j < mBlockSize; ++j){
+                    int quantBlock = round(mDequantAlpha[i * mBlockSize + j] / blockScale);
+                    mBlockScale[i * mBlockSize + j] = (uint8_t)std::min(std::max(quantBlock, 1), 16);
+                }
+                mScaleOffsetData[i].scale = blockScale;
+                mScaleOffsetData[i].offset = 0;
+            }
+            weightBlockwiseExpansionEncoding.scaleOffsets = mScaleOffsetData.data();
+            weightBlockwiseExpansionEncoding.blocksScale8 = mBlockScale.data();
+            weightQuantize.blockwiseExpansion = &weightBlockwiseExpansionEncoding;
+            this->createStaticTensor("quantWeight", QNN_DATATYPE_SFIXED_POINT_8, {(uint32_t)kernelH, (uint32_t)kernelW, (uint32_t)ic / (uint32_t)group, (uint32_t)oc}, (void *) quantWeightData.data(), weightQuantize);
+            std::function<void()> mReleaseWeightScaleOffset = [&](){
+                std::vector<Qnn_ScaleOffset_t>().swap(mScaleOffsetData);
+            };
+            mBackend->pushReleaseFunc(mReleaseWeightScaleOffset);
+            std::function<void()> mReleaseBlockScale = [&](){
+                std::vector<uint8_t>().swap(mBlockScale);
+            };
+            mBackend->pushReleaseFunc(mReleaseBlockScale);
+        }else if(quanCommon->canUseInt4){
+            weightQuantize.encodingDefinition = QNN_DEFINITION_DEFINED;
+            weightQuantize.quantizationEncoding = QNN_QUANTIZATION_ENCODING_BW_AXIS_SCALE_OFFSET;
+            Qnn_BwAxisScaleOffset_t weightBWAxisScaleOffsetEncoding{};
+            weightBWAxisScaleOffsetEncoding.bitwidth = 4;
+            weightBWAxisScaleOffsetEncoding.axis = 3;
+            weightBWAxisScaleOffsetEncoding.numElements = oc;
+            mScale.resize(oc);
+            std::vector<int32_t> OffsetData(oc);
+            for (int i = 0; i < oc; i++) {
+                mScale[i] = mDequantAlpha[i];
+            }
+            weightBWAxisScaleOffsetEncoding.scales = mScale.data();
+            weightQuantize.bwAxisScaleOffsetEncoding = weightBWAxisScaleOffsetEncoding;
+            
+            this->createStaticTensor("quantWeight", QNN_DATATYPE_SFIXED_POINT_8, {(uint32_t)kernelH, (uint32_t)kernelW, (uint32_t)ic / (uint32_t)group, (uint32_t)oc}, (void *) quantWeightData.data(), weightQuantize);
+            std::function<void()> mReleaseWeightScaleOffset = [&](){
+                std::vector<float>().swap(mScale);
+            };
+            mBackend->pushReleaseFunc(mReleaseWeightScaleOffset);
+        }else{
+            weightQuantize.encodingDefinition = QNN_DEFINITION_DEFINED;
+            weightQuantize.quantizationEncoding = QNN_QUANTIZATION_ENCODING_AXIS_SCALE_OFFSET;
+            Qnn_AxisScaleOffset_t weightAxisScaleOffsetEncoding{};
+            weightAxisScaleOffsetEncoding.axis = 3;
+            weightAxisScaleOffsetEncoding.numScaleOffsets = oc;
+            mScaleOffsetData.resize(oc);
+            for (int i = 0; i < oc; i++) {
+                mScaleOffsetData[i].scale = mDequantAlpha[i];
+                mScaleOffsetData[i].offset = 0;
+            }
+            weightAxisScaleOffsetEncoding.scaleOffset = mScaleOffsetData.data();
+            weightQuantize.axisScaleOffsetEncoding = weightAxisScaleOffsetEncoding;
+
+            this->createStaticTensor("quantWeight", QNN_DATATYPE_SFIXED_POINT_8, {(uint32_t)kernelH, (uint32_t)kernelW, (uint32_t)ic / (uint32_t)group, (uint32_t)oc}, (void *) quantWeightData.data(), weightQuantize);
+            std::function<void()> mReleaseWeightScaleOffset = [&](){
+                std::vector<Qnn_ScaleOffset_t>().swap(mScaleOffsetData);
+            };
+            mBackend->pushReleaseFunc(mReleaseWeightScaleOffset);
+        }
+        this->createBias(dataType, oc, input, quanCommon);
+    } else {
+        std::vector<float> weightData;
+        const float* source = nullptr;
+        int weightElementNum = 0;
+        std::shared_ptr<ConvolutionCommon::Int8Common> quanWeight;
+        ConvolutionCommon::getConvParameters(&quanWeight, mBackend, mOp, &source, &weightElementNum);
+        // oc ic h w ---> h w ic oc
+        weightData.resize(weightElementNum);
+        convertWeight(source, (float *) weightData.data(), oc, ic/group, kernelH, kernelW);
+        Qnn_DataType_t floatDatatype = QNN_DATATYPE_FLOAT_32;
+        if(mBackend->getUseFP16()){
+            floatDatatype = QNN_DATATYPE_FLOAT_16;
+        }
+        this->createStaticFloatTensor("weight", floatDatatype, {(uint32_t)kernelH, (uint32_t)kernelW, (uint32_t)ic / (uint32_t)group, (uint32_t)oc}, weightData.data());
+        this->createBias(dataType, oc, input, nullptr);
+    }
     return NO_ERROR;
 }
 
 
-void QNNConvolution::createWeight(Qnn_DataType_t dataType, int oc, int ic, int kernelH, int kernelW, int group, const float * source) {
-    std::vector<float> weightData;
-    int weightElementNum = oc * ic / group * kernelH * kernelW;
-    // oc ic/group h w ---> h w ic/group oc
-    weightData.resize(weightElementNum);
-
-    convertWeight(source, (float *) weightData.data(), oc, ic/group, kernelH, kernelW);
-    this->createStaticFloatTensor("weight", dataType, {(uint32_t)kernelH, (uint32_t)kernelW, (uint32_t)ic / (uint32_t)group, (uint32_t)oc}, weightData.data());
-}
-
-
-void QNNConvolution::createBias(Qnn_DataType_t dataType, int oc) {
+void QNNConvolution::createBias(Qnn_DataType_t dataType, int oc, const Tensor *input, std::shared_ptr<ConvolutionCommon::Int8Common> quanCommon) {
     int biasElementNum = oc;
-    std::vector<float> biasData;
-    biasData.resize(biasElementNum, 0);
-    auto bias = mOp->main_as_Convolution2D()->bias();
-    if (nullptr != bias) {
-        ::memcpy((void *)biasData.data(), (void *)bias->data(), biasElementNum * sizeof(float));
-    }
-    this->createStaticFloatTensor("bias", dataType, {(uint32_t)oc}, biasData.data());
-}
+    if(dataType != QNN_DATATYPE_FLOAT_16 && dataType != QNN_DATATYPE_FLOAT_32 && mWeightQuant){
+        mDequantAlpha = quanCommon->alpha.get();
+        float inputScale = mBackend->getNativeTensor(input)->v1.quantizeParams.scaleOffsetEncoding.scale;
+        int inputOffset = mBackend->getNativeTensor(input)->v1.quantizeParams.scaleOffsetEncoding.offset;
+        std::vector<int> biasData;
+        biasData.resize(biasElementNum, 0);
 
-// oc ic h w ---> h w ic oc
-void QNNConvolution::convertWeight(const float * src, float * dst, int oc, int ic, int kernelH, int kernelW) {
-    for (int o = 0; o < oc; o++) {
-        for (int i = 0; i < ic; i++) {
-            for (int h = 0; h < kernelH; h++) {
-                for (int w = 0; w < kernelW; w++) {
-                    uint32_t srcOffset = w + kernelW * (h + kernelH * (i + ic * o));
-                    uint32_t dstOffset = o + oc * (i + ic * (w + kernelW * h));
-                    dst[dstOffset] = src[srcOffset];
+        Qnn_QuantizeParams_t biasQuantize{};
+        biasQuantize.encodingDefinition = QNN_DEFINITION_DEFINED;
+        biasQuantize.quantizationEncoding = QNN_QUANTIZATION_ENCODING_AXIS_SCALE_OFFSET;
+        Qnn_AxisScaleOffset_t biasAxisScaleOffsetEncoding{};
+        biasAxisScaleOffsetEncoding.axis = 0;
+        biasAxisScaleOffsetEncoding.numScaleOffsets = biasElementNum;
+        mBiasScaleOffsetData.resize(biasElementNum);
+
+        auto bias = mOp->main_as_Convolution2D()->bias();
+        auto biasPtr = (float*)bias->data();
+        if (nullptr != bias) {
+            for(int i = 0; i < biasElementNum; ++i){
+                float biasScale = inputScale * mDequantAlpha[i];
+                mBiasScaleOffsetData[i].scale = biasScale;
+                mBiasScaleOffsetData[i].offset = 0;
+                if(fabs(biasPtr[i]) < 0.000001 || fabs(biasScale) < 0.000001){
+                    biasData[i] = 0;
+                } else{
+                    biasData[i] = (int)(biasPtr[i] / biasScale);
                 }
             }
         }
+        
+        biasAxisScaleOffsetEncoding.scaleOffset = mBiasScaleOffsetData.data();
+        biasQuantize.axisScaleOffsetEncoding = biasAxisScaleOffsetEncoding;
+
+        this->createStaticTensor("bias", QNN_DATATYPE_SFIXED_POINT_32, {(uint32_t)biasElementNum}, biasData.data(), biasQuantize);
+        std::function<void()> mReleaseBiasScaleOffset = [&](){
+            std::vector<Qnn_ScaleOffset_t>().swap(mBiasScaleOffsetData);
+        };
+        mBackend->pushReleaseFunc(mReleaseBiasScaleOffset);
+    }else{
+        std::vector<float> biasData;
+        biasData.resize(biasElementNum, 0);
+        auto bias = mOp->main_as_Convolution2D()->bias();
+        if (nullptr != bias) {
+            ::memcpy((void *)biasData.data(), (void *)bias->data(), biasElementNum * sizeof(float));
+        }
+        Qnn_DataType_t floatDatatype = QNN_DATATYPE_FLOAT_32;
+        if(mBackend->getUseFP16()){
+            floatDatatype = QNN_DATATYPE_FLOAT_16;
+        }
+        this->createStaticFloatTensor("bias", floatDatatype, {(uint32_t)oc}, biasData.data());
     }
 }
 

source/backend/qnn/execution/QNNConvolution.hpp

@@ -19,12 +19,37 @@ class QNNConvolution : public QNNCommonExecution {
 public:
     QNNConvolution(Backend *backend, const Op *op) : QNNCommonExecution(backend, op) {}
     virtual ErrorCode onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) override;
-    ErrorCode onEncodeQuant(Tensor * input, Tensor * output, int n, int h, int w, int ic, int oc, std::shared_ptr<ConvolutionCommon::Int8Common> quanCommon);
+    ErrorCode onEncodeFpAIntBMatMul(Tensor * input, Tensor * output, int n, int h, int w, int ic, int oc);
+    ErrorCode onEncodeQuantDequantConv(Tensor *input, Tensor *output, const int n, const int ic, const int oc);
 
 private:
-    void createWeight(Qnn_DataType_t dataType, int oc, int ic, int kernelH, int kernelW, int group, const float * source);
-    void createBias(Qnn_DataType_t dataType, int oc);
-    void convertWeight(const float * src, float * dst, int oc, int ic, int kernelH, int kernelW);
+    template <typename T>
+    void convertWeight(const T * src, T * dst, int oc, int ic, int kernelH, int kernelW) {
+        for (int o = 0; o < oc; o++) {
+            for (int i = 0; i < ic; i++) {
+                for (int h = 0; h < kernelH; h++) {
+                    for (int w = 0; w < kernelW; w++) {
+                        uint32_t srcOffset = w + kernelW * (h + kernelH * (i + ic * o));
+                        uint32_t dstOffset = o + oc * (i + ic * (w + kernelW * h));
+                        dst[dstOffset] = src[srcOffset];
+                    }
+                }
+            }
+        }
+    }
+    void isWeightQuantSupported(const Tensor *input, const int ic, const int oc);
+    bool createWeightAndBias(Qnn_DataType_t dataType, const Tensor *input, int oc, int ic, int kernelH, int kernelW, int group);
+    void createBias(Qnn_DataType_t dataType, int oc, const Tensor *input, std::shared_ptr<ConvolutionCommon::Int8Common> quanCommon);
+    std::vector<float> mScale;
+    std::vector<Qnn_ScaleOffset_t> mScaleOffsetData;
+    std::vector<Qnn_ScaleOffset_t> mBiasScaleOffsetData;
+    std::vector<uint8_t> mBlockScale;
+    Qnn_BlockwiseExpansion_t weightBlockwiseExpansionEncoding = QNN_BLOCKWISE_EXPANSION_INIT;
+    float *mDequantAlpha = nullptr;
+    int mBlockSize = 1;
+    bool mWeightQuant = false;
+    bool mIsMatMul = false;
+    bool mIs1x1Conv = false;
 };
 
 } // end namespace QNN

source/backend/qnn/execution/QNNQuant.cpp

@@ -0,0 +1,81 @@
+//
+//  QNNQuant.cpp
+//  MNN
+//
+//  Created by MNN on b'2025/05/29'.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include "QNNQuant.hpp"
+
+namespace MNN {
+namespace QNN {
+
+ErrorCode QNNQuant::onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
+    this->createStageTensor("Cast", QNN_DATATYPE_FLOAT_32, getNHWCShape(outputs[0]));
+     // Stage one  fp16 -> fp32
+    {
+        mNodeType = "Cast";
+        std::string name = mNodeName + "_Cast";
+    
+        mInputs.push_back(*(mBackend->getNativeTensor(inputs[0]))); // input
+        mOutputs.push_back(*(mTempTensorWrappers[0]->getNativeTensor())); // stage tensor
+        mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+    }
+
+    // Stage two  fp32 -> int8
+    {
+        mNodeType.clear();
+        mParams.clear();
+        mInputs.clear();
+        mOutputs.clear();
+        mNodeType = "Quantize";
+        std::string name = mNodeName;
+    
+        mInputs.push_back(*(mTempTensorWrappers[0]->getNativeTensor())); // stage tensor
+        mOutputs.push_back(*(mBackend->getNativeTensor(outputs[0]))); // output
+        mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+    }
+    return NO_ERROR;
+}
+
+
+class QNNQuantCreator : public QnnBackend::Creator {
+public:
+    virtual QNNCommonExecution * onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs, const MNN::Op* op,
+                                Backend* backend) const override {
+        return new QNNQuant(backend, op);
+    }
+};
+
+ErrorCode QNNDeQuant::onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
+     // Stage one  int8 -> fp16
+    {
+        mNodeType.clear();
+        mParams.clear();
+        mInputs.clear();
+        mOutputs.clear();
+        mNodeType = "Dequantize";
+        std::string name = mNodeName;
+    
+        mInputs.push_back(*(mBackend->getNativeTensor(inputs[0]))); // input
+        mOutputs.push_back(*(mBackend->getNativeTensor(outputs[0]))); // output
+        mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+    }
+    return NO_ERROR;
+}
+
+
+class QNNDeQuantCreator : public QnnBackend::Creator {
+public:
+    virtual QNNCommonExecution * onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs, const MNN::Op* op,
+                                Backend* backend) const override {
+        return new QNNDeQuant(backend, op);
+    }
+};
+
+REGISTER_QNN_OP_CREATOR(QNNQuantCreator, OpType_FloatToInt8)
+REGISTER_QNN_OP_CREATOR(QNNDeQuantCreator, OpType_Int8ToFloat)
+
+} // end namespace QNN
+} // end namespace MNN

source/backend/qnn/execution/QNNQuant.hpp

@@ -0,0 +1,32 @@
+//
+//  QNNQuant.hpp
+//  MNN
+//
+//  Created by MNN on b'2025/05/29'.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#ifndef MNN_QNNQUANT_HPP
+#define MNN_QNNQUANT_HPP
+
+#include "QNNCommonExecution.hpp"
+
+namespace MNN {
+namespace QNN {
+
+class QNNQuant : public QNNCommonExecution {
+public:
+    QNNQuant(Backend *backend, const Op *op) : QNNCommonExecution(backend, op) {};
+    virtual ErrorCode onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) override;
+};
+
+class QNNDeQuant : public QNNCommonExecution {
+public:
+    QNNDeQuant(Backend *backend, const Op *op) : QNNCommonExecution(backend, op) {};
+    virtual ErrorCode onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) override;
+};
+
+} // end namespace QNN
+} // end namespace MNN
+
+#endif // end MNN_QNNQUANT_HPP

source/backend/qnn/execution/QNNScale.cpp

@@ -28,10 +28,25 @@ ErrorCode QNNScale::onEncode(const std::vector<Tensor *> &inputs, const std::vec
         MNN_ASSERT(channel == mWeightData.size());
 
         Qnn_DataType_t dataType = mBackend->getNativeTensor(inputs[0])->v1.dataType;
-
-        this->createStaticFloatTensor("weight", dataType, {(uint32_t)channel}, mWeightData.data());
-        this->createStaticFloatTensor("bias", dataType, {(uint32_t)channel}, mBiasData.data());
-        this->createStageTensor("Stage", dataType, getNHWCShape(inputs[0]));
+        mNeedQuantDequant = dataType != QNN_DATATYPE_FLOAT_16 && dataType != QNN_DATATYPE_FLOAT_32;
+        if(mNeedQuantDequant){
+            Qnn_DataType_t tempDataType = QNN_DATATYPE_FLOAT_32;
+            if(mBackend->getUseFP16()){
+                tempDataType = QNN_DATATYPE_FLOAT_16;
+            }
+            this->createStaticFloatTensor("weight", tempDataType, {(uint32_t)channel}, mWeightData.data());
+            this->createStaticFloatTensor("bias", tempDataType, {(uint32_t)channel}, mBiasData.data());
+            this->createStageTensor("Stage", tempDataType, getNHWCShape(inputs[0]));
+            this->createStageTensor("Stage_dequantize_input", tempDataType, getNHWCShape(inputs[0]));
+            this->createStageTensor("Stage_add_output", tempDataType, getNHWCShape(outputs[0]));
+            if(mBackend->getUseFP16()){
+                this->createStageTensor("Stage_cast_output", QNN_DATATYPE_FLOAT_32, getNHWCShape(outputs[0]));
+            }
+        }else{
+            this->createStaticFloatTensor("weight", dataType, {(uint32_t)channel}, mWeightData.data());
+            this->createStaticFloatTensor("bias", dataType, {(uint32_t)channel}, mBiasData.data());
+            this->createStageTensor("Stage", dataType, getNHWCShape(inputs[0]));
+        }
     }
 
     // add nodes
@@ -42,34 +57,98 @@ ErrorCode QNNScale::onEncode(const std::vector<Tensor *> &inputs, const std::vec
 }
 
 void QNNScale::mulWeight(Tensor * input) {
-    mNodeType = "ElementWiseMultiply";
-    std::string name = mNodeName + "_mul";
-    mParams.clear();
-    mInputs.clear();
-    mOutputs.clear();
-
-    mInputs.push_back(*(mBackend->getNativeTensor(input)));
-    mInputs.push_back(*(mTempTensorWrappers[0]->getNativeTensor()));
-
-    mOutputs.push_back(*(mTempTensorWrappers[2]->getNativeTensor()));
-
-    mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+    Qnn_DataType_t dataType = mBackend->getNativeTensor(input)->v1.dataType;
+    // need dequantize to float16
+    if(mNeedQuantDequant){
+        mNodeType.clear();
+        mParams.clear();
+        mInputs.clear();
+        mOutputs.clear();
+        mNodeType = "Dequantize";
+        std::string name = mNodeName + "_Dequantize";
+    
+        mInputs.push_back(*(mBackend->getNativeTensor(input))); // input
+        mOutputs.push_back(*(mTempTensorWrappers[3]->getNativeTensor())); //Stage_dequantize_input
+        mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+    }
+    {
+        mNodeType.clear();
+        mParams.clear();
+        mInputs.clear();
+        mOutputs.clear();
+        mNodeType = "ElementWiseMultiply";
+        std::string name = mNodeName + "_mul";
+        
+        if(mNeedQuantDequant){
+            mInputs.push_back(*(mTempTensorWrappers[3]->getNativeTensor())); //Stage_dequantize_input
+        }else{
+            mInputs.push_back(*(mBackend->getNativeTensor(input)));
+        }
+        mInputs.push_back(*(mTempTensorWrappers[0]->getNativeTensor()));
+        
+        mOutputs.push_back(*(mTempTensorWrappers[2]->getNativeTensor()));
+        
+        mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+    }
 }
 
 
 void QNNScale::addBias(Tensor * output) {
-    mNodeType = "ElementWiseAdd";
-    std::string name = mNodeName + "_add";
-    mParams.clear();
-    mInputs.clear();
-    mOutputs.clear();
+    Qnn_DataType_t dataType = mBackend->getNativeTensor(output)->v1.dataType;
+    {
+        mNodeType.clear();
+        mParams.clear();
+        mInputs.clear();
+        mOutputs.clear();
+        mNodeType = "ElementWiseAdd";
+        std::string name = mNodeName + "_add";
+        
+        mInputs.push_back(*(mTempTensorWrappers[2]->getNativeTensor()));
+        mInputs.push_back(*(mTempTensorWrappers[1]->getNativeTensor()));
+        
+        if(mNeedQuantDequant){
+            mOutputs.push_back(*(mTempTensorWrappers[4]->getNativeTensor())); // Stage_add_output
+        }else{
+            mOutputs.push_back(*(mBackend->getNativeTensor(output)));
+        }
+        
+        mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+    }
+    
+    // need quantize output
+    if(mNeedQuantDequant){
+        // Stage one  fp16 -> fp32
+        if(mBackend->getUseFP16()){
+           mNodeType.clear();
+           mParams.clear();
+           mInputs.clear();
+           mOutputs.clear();
+           mNodeType = "Cast";
+           std::string name = mNodeName + "_Cast";
+       
+           mInputs.push_back(*(mTempTensorWrappers[4]->getNativeTensor())); // Stage_add_output
+           mOutputs.push_back(*(mTempTensorWrappers[5]->getNativeTensor())); // Stage_cast_output
+           mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+       }
 
-    mInputs.push_back(*(mTempTensorWrappers[2]->getNativeTensor()));
-    mInputs.push_back(*(mTempTensorWrappers[1]->getNativeTensor()));
-
-    mOutputs.push_back(*(mBackend->getNativeTensor(output)));
-
-    mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+       // Stage two  fp32 -> int8
+       {
+           mNodeType.clear();
+           mParams.clear();
+           mInputs.clear();
+           mOutputs.clear();
+           mNodeType = "Quantize";
+           std::string name = mNodeName + "_Quantize";
+           
+           if(mBackend->getUseFP16()){
+               mInputs.push_back(*(mTempTensorWrappers[5]->getNativeTensor())); // Stage_cast_output
+           }else{
+               mInputs.push_back(*(mTempTensorWrappers[4]->getNativeTensor())); // Stage_add_output
+           }
+           mOutputs.push_back(*(mBackend->getNativeTensor(output))); // output
+           mBackend->addNodeToGraph(mOpConfigVersion, name.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
+       }
+    }
 }
 
 ErrorCode QNNScale::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {

source/backend/qnn/execution/QNNScale.hpp

@@ -25,6 +25,7 @@ private:
 private:
     std::vector<float> mWeightData;
     std::vector<float> mBiasData;
+    bool mNeedQuantDequant = false;
 };
 
 } // end namespace QNN

source/core/Backend.hpp

@@ -34,12 +34,17 @@ struct RuntimeHint {
     int cpuDecreaseRate = 50;
     int dynamicQuantOption = 0;
 
+    // qkvQuantOption % 8:
     // 0: Do not quantize
     // 1: Only quantize key, use int8 asymmetric quantization
     // 2: Only quantize value, use fp8 quantization
     // 3: quantize both key and value
     // 4: quantize query, key and value, and use gemm int8 kernel to compute K*V
-    int qkvQuantOption = 0;
+
+    // qkvQuantOption / 8:
+    // 1: use flash attention
+
+    int qkvQuantOption = 8;
 
     // the kvcache size limit of each layer
     // if the size of kvcache in memory exceeds the limit
@@ -403,6 +408,9 @@ public:
         info.mode = Backend::Info::DIRECT;
         return true;
     }
+    virtual bool onGetDeviceInfo(const std::string& deviceKey, std::string& deviceValue) const {
+        return false;
+    }
 protected:
     /**
      @brief deinitializer.

tools/converter/source/common/cli.cpp

@@ -129,6 +129,13 @@ static int dumpModelInfo(const char* modelName) {
     } else {
         MNN_PRINT("Model Version: %s \n", info->version.c_str());
     }
+    if (!info->metaData.empty()) {
+        MNN_PRINT("MetaData: Begin \n");
+        for (auto& iter : info->metaData) {
+            MNN_PRINT("[Meta] %s : %s\n", iter.first.c_str(), iter.second.c_str());
+        }
+        MNN_PRINT("MetaData: End \n");
+    }
     return 0;
 }
 

tools/converter/source/optimizer/PostConverter.cpp

@@ -130,7 +130,17 @@ bool CompleteSubGraph(const std::unordered_map<std::string, VARP>& inputs, const
     return true;
 }
 
-
+static bool _hasDupName(std::unique_ptr<MNN::NetT>& originNet) {
+    std::set<std::string> names;
+    for (auto& tensorName : originNet->tensorName) {
+        if (names.find(tensorName) != names.end()) {
+            MNN_ERROR("Repeat name %s\n", tensorName.c_str());
+            return true;
+        }
+        names.insert(tensorName);
+    }
+    return false;
+}
 void RunNetPass(const std::vector<std::string>& passes, std::unique_ptr<MNN::NetT>& originNet) {
     for (auto pass : passes) {
         auto convert = PostConverter::get(pass);
@@ -138,7 +148,14 @@ void RunNetPass(const std::vector<std::string>& passes, std::unique_ptr<MNN::Net
             LOG(INFO) << "Can't find pass of " << pass << "\n";
             continue;
         }
+        auto originSize = originNet->oplists.size();
         bool valid = convert->onExecute(originNet);
+#ifdef DEBUG
+        auto hasDup = _hasDupName(originNet);
+        if (originSize != originNet->oplists.size() || hasDup) {
+            MNN_PRINT("%s: %d -> %d, dup: %d\n", pass.c_str(), originSize, originNet->oplists.size(), hasDup);
+        }
+#endif
         if (!valid) {
             LOG(INFO) << "Run " << pass << "Error\n";
         }

tools/converter/source/optimizer/onnxextra/OnnxClip.cpp

@@ -19,7 +19,7 @@ static EXPRP clipConvert(EXPRP expr, bool supportRelu6) {
     auto extraParam = op->main_as_Extra();
     // auto dataType = expr->outputInfo(0)->type.code;
     auto maxValue  = std::numeric_limits<T>().max();
-    auto minValue  = std::numeric_limits<T>().min();
+    auto minValue  = std::numeric_limits<T>().lowest();
     if (nullptr != extraParam->attr()) {
         const int attrSize = extraParam->attr()->size();
         for (int i = 0; i < attrSize; ++i) {

tools/converter/source/optimizer/postconvert/RemoveCopy.cpp

@@ -12,20 +12,70 @@
 class RemoveCopy : public PostConverter {
 public:
     virtual bool onExecute(std::unique_ptr<MNN::NetT>& net) const override {
-        auto config = Global<modelConfig>::Get();
-        if (config->optimizeLevel < 1 || config->inSubGraph) {
-            return true;
+        std::set<std::string> netOutputNames;
+        for (auto& t : net->outputName) {
+            netOutputNames.insert(t);
         }
+        for (auto iter = net->oplists.begin(); iter != net->oplists.end(); iter++) {
+            auto& op          = *iter;
+            if (op->type == MNN::OpType_Input) {
+                for (auto o : op->outputIndexes) {
+                    netOutputNames.insert(net->tensorName[o]);
+                }
+            }
+        }
+        auto config = Global<modelConfig>::Get();
         for (auto iter = net->oplists.begin(); iter != net->oplists.end();) {
             auto& op          = *iter;
-            if (op->type != MNN::OpType_Identity) {
+            if (op->type != MNN::OpType_Identity || op->inputIndexes.size() != op->outputIndexes.size()) {
                 iter++;
                 continue;
             }
+            
+            bool hasOutputName = false;
+            for (auto o : op->outputIndexes) {
+                if (netOutputNames.find(net->tensorName[o]) != netOutputNames.end()) {
+                    hasOutputName = true;
+                    break;
+                }
+            }
+            bool hasOutputFromInput = false;
+            for (auto o : op->inputIndexes) {
+                if (netOutputNames.find(net->tensorName[o]) != netOutputNames.end()) {
+                    hasOutputFromInput = true;
+                    break;
+                }
+            }
+            if (hasOutputFromInput && hasOutputName) {
+                iter++;
+                continue;
+            }
+            auto originInput  = op->inputIndexes;
+            auto originOutputs = op->outputIndexes;
+            MNN_ASSERT(originInput.size() == originOutputs.size());
+            if (hasOutputName) {
+                bool valid = true;
+                for (int i=0; i<op->inputIndexes.size(); ++i) {
+                    auto o = op->outputIndexes[i];
+                    auto originInput = op->inputIndexes[i];
+                    if (netOutputNames.find(net->tensorName[o]) != netOutputNames.end()) {
+                        if (netOutputNames.find(net->tensorName[originInput]) != netOutputNames.end()) {
+                            valid = false;
+                            break;
+                        }
+                        auto originName = net->tensorName[originInput];
+                        net->tensorName[originInput] = net->tensorName[o];
+                        net->tensorName[o] = originName;
+                    }
+                }
+                if (!valid) {
+                    continue;
+                }
+            }
+
             std::map<int, int> replaceIndexes;
             for (int i=0; i<op->inputIndexes.size();++i) {
                 replaceIndexes.insert(std::make_pair(op->outputIndexes[i], op->inputIndexes[i]));
-                net->tensorName[op->inputIndexes[i]] = net->tensorName[op->outputIndexes[i]];
             }
             for (auto subIter = net->oplists.begin(); subIter != net->oplists.end(); subIter++) {
                 auto& subOp = *subIter;
@@ -35,14 +85,6 @@ public:
                     }
                 }
             }
-            for (int v=0; v<op->inputIndexes.size(); ++v) {
-                for (auto& o : net->outputName) {
-                    if (o == net->tensorName[op->inputIndexes[v]]) {
-                        o = net->tensorName[op->outputIndexes[v]];
-                        break;
-                    }
-                }
-            }
             iter = net->oplists.erase(iter);
         }
         return true;

tools/converter/source/optimizer/postconvert/RemoveTestNoUseOps.cpp

@@ -0,0 +1,149 @@
+#include "RemoveTestNoUseOps.hpp"
+bool RemoveTestNoUseOps::onExecute(std::unique_ptr<MNN::NetT>& net) const {
+    const MNN::NetT* const netPtr = net.get();
+    std::set<std::string> netOutputNames;
+    for (auto& t : net->outputName) {
+        netOutputNames.insert(t);
+    }
+    for (auto iter = net->oplists.begin(); iter != net->oplists.end(); iter++) {
+        auto& op          = *iter;
+        if (op->type == OpType_Input) {
+            for (auto o : op->outputIndexes) {
+                netOutputNames.insert(net->tensorName[o]);
+            }
+        }
+    }
+
+    std::unordered_set<int> removedInputs;
+    for (auto iter = net->oplists.begin(); iter != net->oplists.end();) {
+        auto& op          = *iter;
+        bool shouldDelete = shouldDeleteJudge(op.get(), netPtr);
+        if (!shouldDelete) {
+            iter++;
+            continue;
+        }
+        bool hasOutputName = false;
+        for (auto o : op->outputIndexes) {
+            if (netOutputNames.find(net->tensorName[o]) != netOutputNames.end()) {
+                hasOutputName = true;
+                break;
+            }
+        }
+        bool hasOutputFromInput = false;
+        for (auto o : op->inputIndexes) {
+            if (netOutputNames.find(net->tensorName[o]) != netOutputNames.end()) {
+                hasOutputFromInput = true;
+                break;
+            }
+        }
+        if (hasOutputFromInput && hasOutputName) {
+            iter++;
+            continue;
+        }
+        bool deleteOutput = shouldDeleteOutput(op.get());
+        // Find the next op
+        if (op->outputIndexes.empty() || op->inputIndexes.empty()) {
+            iter = net->oplists.erase(iter);
+            continue;
+        }
+        auto originInput  = op->inputIndexes[0];
+        auto originOutputs = op->outputIndexes;
+        if ((!deleteOutput) && hasOutputName) {
+            bool valid = true;
+            for (auto o : originOutputs) {
+                if (netOutputNames.find(net->tensorName[o]) != netOutputNames.end()) {
+                    if (netOutputNames.find(net->tensorName[originInput]) != netOutputNames.end()) {
+                        valid = false;
+                        break;
+                    }
+                    net->tensorName[originInput] = net->tensorName[o];
+                }
+            }
+            if (!valid) {
+                continue;
+            }
+        }
+        for (auto subIter = net->oplists.begin(); subIter != net->oplists.end(); subIter++) {
+            auto& subOp = *subIter;
+            if (deleteOutput) {
+                for (auto iter=subOp->inputIndexes.begin(); iter != subOp->inputIndexes.end();) {
+                    if (std::find(originOutputs.begin(), originOutputs.end(), *iter) != originOutputs.end()) {
+                        iter = subOp->inputIndexes.erase(iter);
+                        continue;
+                    }
+                    iter++;
+                }
+            } else {
+                for (int v = 0; v < subOp->inputIndexes.size(); ++v) {
+                    if (std::find(originOutputs.begin(), originOutputs.end(), subOp->inputIndexes[v]) != originOutputs.end()) {
+                        subOp->inputIndexes[v] = originInput;
+                    }
+                }
+            }
+        }
+        bool removeUselessInput = shouldRemoveUnusefulInputs(op.get());
+        if (removeUselessInput) {
+            for (int input : op->inputIndexes) {
+                removedInputs.emplace(input);
+            }
+        }
+        iter = net->oplists.erase(iter);
+    }
+
+    // Remove the op only if the reference counts of it's all outputs
+    // are reduced to be zero.
+    std::unordered_map<int, int/*reference count*/> uselessIndex;
+    for (const auto& op : net->oplists) {
+        for (int input : op->inputIndexes) {
+            auto it = uselessIndex.find(input);
+            if (it == uselessIndex.end()) {
+                uselessIndex.emplace(input, 1);
+            } else {
+                ++it->second;
+            }
+        }
+    }
+    // Set reference count 1 for all net outputs.
+    for (const auto& op : net->oplists) {
+        for (int output : op->outputIndexes) {
+            auto it = uselessIndex.find(output);
+            if (it == uselessIndex.end()) {
+                if (removedInputs.count(output)) {
+                    uselessIndex.emplace(output, 0);
+                } else {
+                    uselessIndex.emplace(output, 1);
+                }
+            }
+        }
+    }
+
+    bool needIteration = false;
+    do {
+        needIteration = false;
+        for (auto iter = net->oplists.begin(); iter != net->oplists.end();) {
+            auto& op     = *iter;
+            bool useless = true;
+            for (auto index : op->outputIndexes) {
+                if (uselessIndex.at(index) > 0) {
+                    useless = false;
+                    break;
+                }
+            }
+            if (!useless) {
+                iter++;
+                continue;
+            }
+            if (!op->inputIndexes.empty()) {
+                for (auto index : op->inputIndexes) {
+                    auto it = uselessIndex.find(index);
+                    MNN_ASSERT(it != uselessIndex.end());
+                    --it->second;
+                }
+                needIteration = true;
+            }
+            iter = net->oplists.erase(iter);
+        }
+    } while (needIteration);
+
+    return true;
+}

tools/converter/source/optimizer/postconvert/RemoveTestNoUseOps.hpp

@@ -25,135 +25,5 @@ public:
 
     virtual bool shouldDeleteOutput(const MNN::OpT* op) const = 0;
 
-    virtual bool onExecute(std::unique_ptr<MNN::NetT>& net) const override {
-        const MNN::NetT* const netPtr = net.get();
-        std::set<std::string> netOutputNames;
-        for (auto& t : net->outputName) {
-            netOutputNames.insert(t);
-        }
-        std::unordered_set<int> removedInputs;
-        for (auto iter = net->oplists.begin(); iter != net->oplists.end();) {
-            auto& op          = *iter;
-            bool shouldDelete = shouldDeleteJudge(op.get(), netPtr);
-            if (!shouldDelete) {
-                iter++;
-                continue;
-            }
-            bool hasOutputName = false;
-            for (auto o : op->outputIndexes) {
-                if (netOutputNames.find(net->tensorName[o]) != netOutputNames.end()) {
-                    hasOutputName = true;
-                    break;
-                }
-            }
-            bool hasOutputFromInput = false;
-            for (auto o : op->inputIndexes) {
-                if (netOutputNames.find(net->tensorName[o]) != netOutputNames.end()) {
-                    hasOutputFromInput = true;
-                    break;
-                }
-            }
-            if (hasOutputFromInput && hasOutputName) {
-                iter++;
-                continue;
-            }
-            bool deleteOutput = shouldDeleteOutput(op.get());
-            // Find the next op
-            if (op->outputIndexes.empty() || op->inputIndexes.empty()) {
-                iter = net->oplists.erase(iter);
-                continue;
-            }
-            auto originInput  = op->inputIndexes[0];
-            auto originOutputs = op->outputIndexes;
-            if ((!deleteOutput) && hasOutputName) {
-                for (auto o : originOutputs) {
-                    if (netOutputNames.find(net->tensorName[o]) != netOutputNames.end()) {
-                        net->tensorName[originInput] = net->tensorName[o];
-                    }
-                }
-            }
-            for (auto subIter = net->oplists.begin(); subIter != net->oplists.end(); subIter++) {
-                auto& subOp = *subIter;
-                if (deleteOutput) {
-                    for (auto iter=subOp->inputIndexes.begin(); iter != subOp->inputIndexes.end();) {
-                        if (std::find(originOutputs.begin(), originOutputs.end(), *iter) != originOutputs.end()) {
-                            iter = subOp->inputIndexes.erase(iter);
-                            continue;
-                        }
-                        iter++;
-                    }
-                } else {
-                    for (int v = 0; v < subOp->inputIndexes.size(); ++v) {
-                        if (std::find(originOutputs.begin(), originOutputs.end(), subOp->inputIndexes[v]) != originOutputs.end()) {
-                            subOp->inputIndexes[v] = originInput;
-                        }
-                    }
-                }
-            }
-            bool removeUselessInput = shouldRemoveUnusefulInputs(op.get());
-            if (removeUselessInput) {
-                for (int input : op->inputIndexes) {
-                    removedInputs.emplace(input);
-                }
-            }
-            iter = net->oplists.erase(iter);
-        }
-
-        // Remove the op only if the reference counts of it's all outputs
-        // are reduced to be zero.
-        std::unordered_map<int, int/*reference count*/> uselessIndex;
-        for (const auto& op : net->oplists) {
-            for (int input : op->inputIndexes) {
-                auto it = uselessIndex.find(input);
-                if (it == uselessIndex.end()) {
-                    uselessIndex.emplace(input, 1);
-                } else {
-                    ++it->second;
-                }
-            }
-        }
-        // Set reference count 1 for all net outputs.
-        for (const auto& op : net->oplists) {
-            for (int output : op->outputIndexes) {
-                auto it = uselessIndex.find(output);
-                if (it == uselessIndex.end()) {
-                    if (removedInputs.count(output)) {
-                        uselessIndex.emplace(output, 0);
-                    } else {
-                        uselessIndex.emplace(output, 1);
-                    }
-                }
-            }
-        }
-
-        bool needIteration = false;
-        do {
-            needIteration = false;
-            for (auto iter = net->oplists.begin(); iter != net->oplists.end();) {
-                auto& op     = *iter;
-                bool useless = true;
-                for (auto index : op->outputIndexes) {
-                    if (uselessIndex.at(index) > 0) {
-                        useless = false;
-                        break;
-                    }
-                }
-                if (!useless) {
-                    iter++;
-                    continue;
-                }
-                if (!op->inputIndexes.empty()) {
-                    for (auto index : op->inputIndexes) {
-                        auto it = uselessIndex.find(index);
-                        MNN_ASSERT(it != uselessIndex.end());
-                        --it->second;
-                    }
-                    needIteration = true;
-                }
-                iter = net->oplists.erase(iter);
-            }
-        } while (needIteration);
-
-        return true;
-    }
+    virtual bool onExecute(std::unique_ptr<MNN::NetT>& net) const override;
 };

tools/converter/source/optimizer/postconvert/RemoveUnusefulOp.cpp

@@ -33,6 +33,11 @@ public:
                 return true;
             }
         }
+        if (op->type == OpType_Identity) {
+            // Support 1->N
+            return op->inputIndexes.size() == 1 && op->outputIndexes.size() > 1;
+        }
+
         if (op->type == OpType_Cast) {
             if (op->main.AsCastParam()->dstT == op->main.AsCastParam()->srcT) {
                 return true;

tools/cpp/MNN2QNNModel.cpp

@@ -37,11 +37,21 @@ int main(int argc, const char* argv[]) {
     /**
     generate qnn .cpp and .bin
     */
-
+    std::string dstModelName = dstMNN;
+    size_t pos = dstModelName.find_last_of("/\\");
+    std::string dstModelPath;
+    if (pos == std::string::npos) {
+        // current path
+        dstModelPath = "./";
+    } else {
+        dstModelPath = dstModelName.substr(0, pos);
+    }
+    std::string qnnModelPath = dstModelPath + "/" + qnnModelName;
+    MNN_PRINT("[Temp Product]: Qnn temp product generate at %s\n", qnnModelPath.c_str());
     MNN::ScheduleConfig config;
     config.type = MNN_FORWARD_NN;
     std::shared_ptr<Executor::RuntimeManager> rtmgr(Executor::RuntimeManager::createRuntimeManager(config));
-    rtmgr->setCache(qnnModelName.c_str());
+    rtmgr->setCache(qnnModelPath.c_str());
     MNN::Express::Module::Config mConfig;
     mConfig.shapeMutable = false;
     std::shared_ptr<MNN::Express::Module> m(MNN::Express::Module::load(inputNames, outputNames, srcMNN, rtmgr, &mConfig), MNN::Express::Module::destroy);
@@ -64,7 +74,7 @@ int main(int argc, const char* argv[]) {
     }
 
     int ret = 0;
-    std::string tarBinCmd = "cd " + qnnModelName + \
+    std::string tarBinCmd = "cd " + qnnModelPath + \
         " && " + \
         "tar -cf " + qnnModelName + ".bin *.raw";
     ret = system(tarBinCmd.c_str());
@@ -74,10 +84,10 @@ int main(int argc, const char* argv[]) {
     }
 
     std::string modelLibCmd = qnnSdkPath + "/bin/x86_64-linux-clang/qnn-model-lib-generator " + \
-        "-c " + qnnModelName + "/" + qnnModelName + ".cpp " + \
-        "-b " + qnnModelName + "/" + qnnModelName + ".bin " + \
+        "-c " + qnnModelPath + "/" + qnnModelName + ".cpp " + \
+        "-b " + qnnModelPath + "/" + qnnModelName + ".bin " + \
         "-t x86_64-linux-clang " + \
-        "-o " + qnnModelName + "/lib ";
+        "-o " + qnnModelPath + "/lib ";
     ret = system(modelLibCmd.c_str());
     if(ret) {
         MNN_ERROR("[Error]: qnn-model-lib-generator error!\n");
@@ -86,12 +96,13 @@ int main(int argc, const char* argv[]) {
         MNN_PRINT("[Pass]: qnn-model-lib-generator success!\n");
     }
 
+    std::string qnnBin = dstModelPath + "/" + qnnModelName + ".bin";
     std::string binaryGenCmd = qnnSdkPath + "/bin/x86_64-linux-clang/qnn-context-binary-generator " + \
-        "--model " + qnnModelName + "/lib/x86_64-linux-clang/lib" + qnnModelName + ".so " + \
+        "--model " + qnnModelPath + "/lib/x86_64-linux-clang/lib" + qnnModelName + ".so " + \
         "--backend " + qnnSdkPath + "/lib/x86_64-linux-clang/libQnnHtp.so " + \
         "--binary_file " + qnnModelName + " " + \
         "--config_file " + qnnContextConfig + " " + \
-        "--output_dir " + qnnModelName + "/binary";
+        "--output_dir " + dstModelPath;
     ret = system(binaryGenCmd.c_str());
     if(ret) {
         MNN_ERROR("[Error]: qnn-context-binary-generator error!\n");
@@ -100,7 +111,7 @@ int main(int argc, const char* argv[]) {
         MNN_PRINT("[Pass]: qnn-context-binary-generator success!\n");
     }
 
-    
+
     std::vector<MNN::Express::Variable::Info> inputInfos(inputs.size());
     for (int i=0; i<inputInfos.size(); ++i) {
         inputInfos[i] = *inputs[i]->getInfo();
@@ -122,7 +133,8 @@ int main(int argc, const char* argv[]) {
         dstNet->oplists.emplace_back(std::move(input));
     }
 
-    std::string npuPath = std::string("/") + qnnModelName + std::string(".bin"); 
+    std::string npuPath = std::string("/") + qnnModelName + std::string(".bin");
+ 
     MNN_PRINT("npu model path:%s\n", npuPath.c_str());
     /** Fuse to Op*/
     std::unique_ptr<MNN::OpT> op(new OpT);
@@ -204,7 +216,7 @@ int main(int argc, const char* argv[]) {
     }
     for (int i=0; i<outputInfos.size(); ++i) {
         attr.reset(new MNN::AttributeT);
-        attr->key = "o_" + std::to_string(i) + "_0";
+        attr->key = "o_0_" + std::to_string(i);
         attr->tensor.reset(new BlobT);
         attr->tensor->dataType = OpCommonUtils::convertDataType(outputInfos[i].type);
         attr->tensor->dims = outputInfos[i].dim;
@@ -240,7 +252,6 @@ int main(int argc, const char* argv[]) {
     outputOs.close();
 
     MNN_PRINT("[All Pass]: npu model generator success!\n");
-    std::string qnnBin = qnnModelName + "/binary/" + qnnModelName + ".bin";
     MNN_PRINT("[Output Product]:\nNew mnn model path: %s\nNpu model path: %s\n", dstMNN, qnnBin.c_str());
     return 0;
 }

tools/script/convertOnnxTest.py

@@ -43,6 +43,6 @@ for w in gWrong:
     print(w)
 print('TEST_NAME_MODULE: 模型测试\nTEST_CASE_AMOUNT_MODULE: {\"blocked\":0,\"failed\":%d,\"passed\":%d,\"skipped\":0}\n'%(len(gWrong), total_num - len(gWrong)))
 print('TEST_CASE={\"name\":\"Onnx转换测试\",\"failed\":%d,\"passed\":%d}\n'%(len(gWrong), total_num - len(gWrong)))
-print('Total Size: ', mnnsize,' MB, convert ', correct_num," model")
+print('Total Size: ', total_size,' MB, convert ', correct_num," model")
 if len(gWrong) > 0:
     exit(1)

tools/script/genQNNModelsFromMNN.py

@@ -0,0 +1,168 @@
+import json
+import copy
+import argparse
+import os
+import subprocess
+import shutil # 导入 shutil 模块用于删除目录
+
+def generate_all_configs(config_path, graph_name, qnn_sdk_root_path, src_model, executable_path, output_dir):
+    """
+    为每个组合创建子目录，生成配置文件，并调用C++可执行文件进行模型转换。
+    """
+    # --- 0. 准备工作 ---
+    # 创建主输出目录
+    os.makedirs(output_dir, exist_ok=True)
+    print(f"所有生成的文件将被保存在主目录: '{output_dir}'")
+    
+    # 定义组合
+    combinations = [
+        [36, 'v69'],
+        [42, 'v69'],
+        [43, 'v73'],
+        [57, 'v75'],
+        [69, 'v79']
+    ]
+
+    # --- 1. 读取模板文件 ---
+    htp_template_file = os.path.join(config_path, "htp_backend_extensions.json")
+    context_template_file = os.path.join(config_path, "context_config.json")
+
+    try:
+        with open(htp_template_file, 'r', encoding='utf-8') as f:
+            base_htp_data = json.load(f)
+        print(f"成功读取模板文件 '{htp_template_file}'。")
+        
+        with open(context_template_file, 'r', encoding='utf-8') as f:
+            base_context_data = json.load(f)
+        print(f"成功读取模板文件 '{context_template_file}'。")
+    except FileNotFoundError as e:
+        print(f"错误：模板文件未找到。请确保 '{e.filename}' 存在于指定的路径中。")
+        return
+    except json.JSONDecodeError as e:
+        print(f"错误：文件格式无效。请检查 {e.doc} 是否为有效的JSON。")
+        return
+
+    # --- 2. 遍历组合，生成文件并执行命令 ---
+    for soc_id, dsp_arch in combinations:
+        print(f"\n{'='*15} 处理组合: soc_id={soc_id}, dsp_arch={dsp_arch} {'='*15}")
+        
+        # --- 新增步骤: 为当前组合创建专用的子目录 ---
+        new_graph_name = f"{graph_name}_{soc_id}_{dsp_arch}"
+        graph_specific_dir = output_dir
+
+        # --- Part A: 生成 htp_backend_extensions 文件 (路径更新) ---
+        htp_config_data = copy.deepcopy(base_htp_data)
+        try:
+            htp_config_data["graphs"][0]["graph_names"] = [new_graph_name]
+            htp_config_data["devices"][0]["soc_id"] = soc_id
+            htp_config_data["devices"][0]["dsp_arch"] = dsp_arch
+        except (IndexError, KeyError) as e:
+            print(f"处理组合时出错: '{htp_template_file}' 结构不正确。错误: {e}")
+            continue
+
+        htp_output_filename = f"htp_backend_extensions_{soc_id}_{dsp_arch}.json"
+        # 更新路径，使其指向新的子目录
+        htp_output_filepath = os.path.join(graph_specific_dir, htp_output_filename)
+        with open(htp_output_filepath, 'w', encoding='utf-8') as f:
+            json.dump(htp_config_data, f, indent=4, ensure_ascii=False)
+        print(f"-> 已生成配置文件: '{htp_output_filepath}'")
+        
+        # --- Part B: 生成 context_config 文件 (路径更新) ---
+        context_config_data = copy.deepcopy(base_context_data)
+        try:
+            # 这里的 htp_output_filename 是相对路径，这是正确的，
+            # 因为 context_config 和 htp_backend_extensions 在同一个目录中。
+            context_config_data["backend_extensions"]["config_file_path"] = htp_output_filepath
+            path_template = context_config_data["backend_extensions"]["shared_library_path"]
+            new_lib_path = path_template.replace("{QNN_SDK_ROOT}", qnn_sdk_root_path)
+            context_config_data["backend_extensions"]["shared_library_path"] = new_lib_path
+        except KeyError as e:
+            print(f"处理组合时出错: '{context_template_file}' 结构不正确，缺少键: {e}")
+            continue
+            
+        context_output_filename = f"context_config_{soc_id}_{dsp_arch}.json"
+        # 更新路径，使其指向新的子目录
+        context_output_filepath = os.path.join(graph_specific_dir, context_output_filename)
+        with open(context_output_filepath, 'w', encoding='utf-8') as f:
+            json.dump(context_config_data, f, indent=4, ensure_ascii=False)
+        print(f"-> 已生成关联文件: '{context_output_filepath}'")
+        
+        # --- Part C: 调用C++可执行命令 (路径更新) ---
+        dst_model_filename = f"{graph_name}_{soc_id}_{dsp_arch}.mnn"
+        # 更新路径，使其指向新的子目录
+        dst_model_filepath = os.path.join(graph_specific_dir, dst_model_filename)
+        
+        graph_product_dir = os.path.join(graph_specific_dir, new_graph_name)
+        os.makedirs(graph_product_dir, exist_ok=True)
+        print(f"-> 已创建/确认子目录: '{graph_product_dir}'")
+
+        command = [
+            executable_path,
+            src_model,
+            dst_model_filepath,
+            qnn_sdk_root_path,
+            new_graph_name,
+            context_output_filepath
+        ]
+        
+        print("--> 准备执行命令...")
+        print(f"    $ {' '.join(command)}")
+
+        try:
+            result = subprocess.run(command, check=True, capture_output=True, text=True)
+            print("--> 命令执行成功!")
+            # 即使成功，也打印 C++ 程序的输出，这对于查看警告等信息很有用
+            if result.stdout:
+                print("    --- C++程序输出 (stdout) ---")
+                print(result.stdout.strip())
+                print("    ------------------------------")
+
+        except FileNotFoundError:
+            print(f"!!! 命令执行失败: 可执行文件未找到 '{executable_path}'。请检查路径。")
+            break # 如果可执行文件找不到，直接退出循环
+        except subprocess.CalledProcessError as e:
+            # 这是关键的修改部分
+            print(f"!!! 命令执行失败 (返回码: {e.returncode})")
+            
+            # 检查并打印 C++ 程序在失败前产生的标准输出
+            if e.stdout:
+                print("    --- C++程序输出 (stdout) ---")
+                print(e.stdout.strip())
+                print("    ------------------------------")
+            
+            # 检查并打印 C++ 程序在失败前产生的标准错误（错误日志通常在这里）
+            if e.stderr:
+                print("    --- C++程序错误 (stderr) ---")
+                print(e.stderr.strip())
+                print("    ------------------------------")
+        except Exception as e:
+            print(f"!!! 执行期间发生未知错误: {e}")
+        
+        finally:
+            # --- 步骤 3: 清理 ---
+            # 检查目录是否存在，然后删除
+            if os.path.exists(graph_product_dir):
+                print(f"--> 清理临时文件和目录: '{graph_product_dir}'")
+                shutil.rmtree(graph_product_dir)
+            else:
+                print("--> 无需清理，临时目录未创建。")
+
+# --- 脚本执行入口 ---
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="为多个组合创建子目录，生成QNN配置文件并调用模型转换工具。",
+        formatter_class=argparse.RawTextHelpFormatter
+    )
+    # ... (argparse部分保持完全不变) ...
+    gen_group = parser.add_argument_group('文件生成参数')
+    gen_group.add_argument("--config_path", required=True, help="[必需] 包含模板文件的目录路径。")
+    gen_group.add_argument("--graph_name", required=True, help="[必需] 模型图的名称 (不含soc_id等后缀)。")
+    gen_group.add_argument("--qnn_sdk_root_path", required=True, help="[必需] QNN SDK 的根路径。")
+    
+    exec_group = parser.add_argument_group('模型转换参数')
+    exec_group.add_argument("--src_model", required=True, help="[必需] 源模型文件路径 (例如: my_model.mnn)。")
+    exec_group.add_argument("--executable_path", required=True, help="[必需] C++模型转换可执行文件的路径。")
+    exec_group.add_argument("--output_dir", default="./qnn_models", help="存放所有生成文件的输出目录 (默认: ./qnn_models)。")
+
+    args = parser.parse_args()
+    generate_all_configs(args.config_path, args.graph_name, args.qnn_sdk_root_path, args.src_model, args.executable_path, args.output_dir)

transformers/llm/engine/CMakeLists.txt

@@ -51,6 +51,21 @@ if (LLM_SUPPORT_AUDIO AND MNN_BUILD_AUDIO)
     add_definitions(-DLLM_SUPPORT_AUDIO)
 endif()
 
+IF(CMAKE_SYSTEM_NAME MATCHES "^Android" AND NOT MNN_BUILD_FOR_ANDROID_COMMAND)
+IF(NOT NATIVE_INCLUDE_OUTPUT)
+  set(NATIVE_INCLUDE_OUTPUT ".")
+ENDIF()
+add_custom_command(
+  TARGET llm
+  POST_BUILD
+  COMMAND ${CMAKE_COMMAND}
+  ARGS -E copy_directory ${CMAKE_CURRENT_LIST_DIR}/include ${NATIVE_INCLUDE_OUTPUT}
+)
+ELSE()
+INSTALL(DIRECTORY ${CMAKE_CURRENT_LIST_DIR}/include/ DESTINATION include FILES_MATCHING PATTERN *.hpp)
+ENDIF()
+
+
 add_executable(llm_demo ${CMAKE_CURRENT_LIST_DIR}/demo/llm_demo.cpp)
 add_executable(embedding_demo ${CMAKE_CURRENT_LIST_DIR}/demo/embedding_demo.cpp)
 add_executable(reranker_demo ${CMAKE_CURRENT_LIST_DIR}/demo/reranker_demo.cpp)

transformers/llm/engine/include/llm/llm.hpp

@@ -37,6 +37,23 @@ struct TimePerformance;
 using ChatMessage = std::pair<std::string, std::string>; // <role, content>
 using ChatMessages = std::vector<ChatMessage>;
 
+struct MNN_PUBLIC PromptImagePart {
+    MNN::Express::VARP image_data;
+    int width;
+    int height;
+};
+
+struct MNN_PUBLIC PromptAudioPart {
+    std::string file_path;
+    MNN::Express::VARP waveform;
+};
+
+struct MNN_PUBLIC MultimodalPrompt {
+    std::string prompt_template;
+    std::map<std::string, PromptImagePart> images;
+    std::map<std::string, PromptAudioPart> audios;
+};
+
 enum TuneType {
     // op encoder number for commit
     OP_ENCODER_NUMBER = 0,
@@ -108,11 +125,17 @@ public:
     std::string dump_config();
     bool set_config(const std::string& content);
     Llm* create_lora(const std::string& lora_path);
+    std::string get_statistics();
     // tokenier function
     bool is_stop(int token);
     std::string tokenizer_decode(int token);
     virtual std::vector<int> tokenizer_encode(const std::string& query);
     friend class Pipeline;
+    virtual std::vector<int> tokenizer_encode(const MultimodalPrompt& multimodal_input);
+    void response(const MultimodalPrompt& multimodal_input, 
+                  std::ostream* os = &std::cout, 
+                  const char* end_with = nullptr, 
+                  int max_new_tokens = -1);
     const LlmContext* getContext() const {
         return mContext.get();
     }

transformers/llm/engine/src/diskembedding.cpp

@@ -48,6 +48,7 @@ DiskEmbedding::DiskEmbedding(const std::shared_ptr<LlmConfig>& config, std::stri
         if (mQuantBit != 16) {
             if (mQuantBlock == 0) {
                 mBlockNum = 1;
+                mQuantBlock = mHiddenSize; // be used for mDequantFunc.
             } else {
                 mBlockNum = mHiddenSize / mQuantBlock;
             }

transformers/llm/engine/src/llm.cpp

@@ -9,6 +9,7 @@
 #include <fstream>
 #include <iostream>
 #include <sstream>
+#include <iomanip>
 #include <unordered_set>
 
 #include <MNN/AutoTime.hpp>
@@ -97,11 +98,44 @@ bool Llm::set_config(const std::string& content) {
     return res;
 }
 
+std::string Llm::get_statistics() {
+    auto context = getContext();
+    int prompt_len = context->prompt_len;
+    int decode_len = context->gen_seq_len;
+    int64_t vision_time = context->vision_us;
+    int64_t audio_time = context->audio_us;
+    int64_t prefill_time = context->prefill_us;
+    int64_t decode_time = context->decode_us;
+    int64_t sample_time = context->sample_us;
+    float vision_s = vision_time / 1e6;
+    float audio_s = audio_time / 1e6;
+    float prefill_s = prefill_time / 1e6;
+    float decode_s = decode_time / 1e6;
+    float sample_s = sample_time / 1e6;
+    float prefill_speed = (prefill_s > 0.0f) ? (prompt_len / prefill_s) : 0.0f;
+    float decode_speed = (decode_s > 0.0f) ? (decode_len / decode_s) : 0.0f;
+
+    std::ostringstream json_stream;
+    json_stream << "{"
+                << "\"prompt_tokens\":" << prompt_len << ","
+                << "\"decode_tokens\":" << decode_len << ","
+                << "\"vision_time\":" << std::fixed << std::setprecision(2) << vision_s << ","
+                << "\"audio_time\":" << std::fixed << std::setprecision(2) << audio_s << ","
+                << "\"prefill_time\":" << std::fixed << std::setprecision(2) << prefill_s << ","
+                << "\"decode_time\":" << std::fixed << std::setprecision(2) << decode_s << ","
+                << "\"sample_time\":" << std::fixed << std::setprecision(2) << sample_s << ","
+                << "\"prefill_speed\":" << std::fixed << std::setprecision(2) << prefill_speed << ","
+                << "\"decode_speed\":" << std::fixed << std::setprecision(2) << decode_speed
+                << "}";
+
+    return json_stream.str();
+}
+
 void Llm::setRuntimeHint(std::shared_ptr<Express::Executor::RuntimeManager> &rtg) {
     rtg->setHint(MNN::Interpreter::INIT_THREAD_NUMBER, 4);
 
     rtg->setHint(MNN::Interpreter::MEM_ALLOCATOR_TYPE, 0);
-    rtg->setHint(MNN::Interpreter::QKV_QUANT_OPTIONS, mConfig->quant_qkv());
+    rtg->setHint(MNN::Interpreter::QKV_QUANT_OPTIONS, mConfig->config_.value("quant_qkv", 8));
     rtg->setHint(MNN::Interpreter::KVCACHE_SIZE_LIMIT, mConfig->kvcache_limit());
     if (mConfig->use_cached_mmap()) {
         rtg->setHint(MNN::Interpreter::USE_CACHED_MMAP, 1);
@@ -113,6 +147,8 @@ void Llm::setRuntimeHint(std::shared_ptr<Express::Executor::RuntimeManager> &rtg
     if (mConfig->use_mmap()) {
         rtg->setExternalPath(tmpPath, MNN::Interpreter::EXTERNAL_WEIGHT_DIR);
     }
+    // set npu model dir
+    rtg->setExternalPath(mConfig->npu_model_dir(), 3);
     auto dynamicOption = mConfig->dynamic_option();
     if (mConfig->dynamic_option()) {
         rtg->setHint(MNN::Interpreter::DYNAMIC_QUANT_OPTIONS, mConfig->dynamic_option());
@@ -246,8 +282,7 @@ void Llm::load() {
     if (needHiddenState) {
         outputNames.emplace_back("hidden_states");
     }
-    // set npu model dir
-    mRuntimeManager->setExternalPath(mConfig->npu_model_dir(), 3);
+
     mRuntimeManager->setExternalFile(mConfig->llm_weight());
     mModules[0].reset(Module::load(inputNames, outputNames, model_path.c_str(), mRuntimeManager, &module_config));
     mRuntimeManager->setExternalFile("");
@@ -594,6 +629,29 @@ std::vector<int> Llm::tokenizer_encode(const std::string& user_content) {
     return mTokenizer->encode(user_content);
 }
 
+std::vector<int> Llm::tokenizer_encode(const MultimodalPrompt& multimodal_input) {
+    return mTokenizer->encode(multimodal_input.prompt_template);
+}
+
+void Llm::response(const MultimodalPrompt& multimodal_input, 
+                   std::ostream* os, const char* end_with, int max_new_tokens) {
+    auto prompt = multimodal_input.prompt_template;
+    if (mConfig->use_template()) {
+        prompt = mPrompt->applyTemplate(prompt, true);
+    }
+    
+    int prompt_len = 0;
+    int decode_len = 0;
+    int64_t vision_time = 0;
+    int64_t audio_time = 0;
+    int64_t prefill_time = 0;
+    int64_t decode_time = 0;
+    int64_t sample_time = 0;
+    
+    std::vector<int> input_ids = tokenizer_encode(multimodal_input);
+    response(input_ids, os, end_with, max_new_tokens);
+}
+
 std::vector<int> Llm::generate(MNN::Express::VARP input_embeds, int max_tokens) {
     if (max_tokens < 0) {
         max_tokens = mConfig->max_new_tokens();
@@ -621,8 +679,8 @@ std::vector<int> Llm::generate(MNN::Express::VARP input_embeds, int max_tokens)
     }
     {
         std::ofstream outFile("logits.txt");
-        auto temp = outputs[0]->readMap<float>();
-        for (size_t i = 0; i < outputs[0]->getInfo()->size; ++i) {
+        auto temp = mGenerateParam->outputs[0]->readMap<float>();
+        for (size_t i = 0; i < mGenerateParam->outputs[0]->getInfo()->size; ++i) {
             outFile << temp[i] << " "; // 每个数字后加空格
         }
         outFile.close();

transformers/llm/engine/src/llmconfig.hpp

@@ -359,10 +359,6 @@ public:
         return config_.value("memory", "low");
     }
 
-    int quant_qkv() const {
-        return config_.value("quant_qkv", 0);
-    }
-
     int kvcache_limit() const {
         return config_.value("kvcache_limit", -1);
     }

transformers/llm/engine/src/omni.cpp

@@ -9,6 +9,7 @@
 #define _USE_MATH_DEFINES
 #endif
 #include <regex>
+#include <algorithm>
 #include <MNN/AutoTime.hpp>
 #include <MNN/expr/ExecutorScope.hpp>
 #include "omni.hpp"
@@ -555,8 +556,16 @@ std::vector<int> Omni::minicpmVisionProcess(VARP image) {
 std::vector<int> Omni::visionProcess(const std::string& file) {
 #ifdef LLM_SUPPORT_VISION
     VARP image = MNN::CV::imread(file);
+    return visionProcess(image);
+#else
+    return std::vector<int>(0);
+#endif
+}
+
+std::vector<int> Omni::visionProcess(VARP image) {
+#ifdef LLM_SUPPORT_VISION
     if (image == nullptr) {
-        MNN_PRINT("Omni Can't open image: %s\n", file.c_str());
+        MNN_PRINT("Omni Can't open image\n");
         return std::vector<int>(0);
     }
     Timer _t;
@@ -573,7 +582,7 @@ std::vector<int> Omni::visionProcess(const std::string& file) {
     } else {
         imgIds = defaultVisionProcess(image);
     }
-    mContext->vision_us = _t.durationInUs();
+    mContext->vision_us += _t.durationInUs();
     // set vision number for image idx
     mVisionNum += 1;
     return imgIds;
@@ -591,9 +600,19 @@ std::vector<int> Omni::audioProcess(const std::string& file) {
         MNN_PRINT("Omni Can't open audio: %s\n", file.c_str());
         return std::vector<int>(0);
     }
-    // int sample_rate      = load_res.second;
-    int wav_len          = waveform->getInfo()->dim[0];
-    int hop_length       = 160;
+    return audioProcess(waveform);
+#else
+    return std::vector<int>(0);
+#endif
+}
+
+std::vector<int> Omni::audioProcess(MNN::Express::VARP waveform) {
+#ifdef LLM_SUPPORT_AUDIO
+    if (waveform == nullptr) {
+        MNN_PRINT("Omni Can't process audio: waveform is null\n");
+        return std::vector<int>(0);
+    }
+    
     Timer _t;
     auto input_features  = MNN::AUDIO::whisper_fbank(waveform);
     VARP audio_embedding;
@@ -727,21 +746,30 @@ void Omni::addPositionIds(int t, int h, int w) {
     }
 }
 
-std::vector<int> Omni::tokenizer_encode(const std::string& prompt) {
-    // split query
+std::vector<int> Omni::tokenizer_encode(const MultimodalPrompt& multimodal_input) {
+    std::string prompt = multimodal_input.prompt_template;
+    // MNN_PRINT("tokenizer_encode(MultimodalPrompt) prompt: %s", prompt.c_str());
     std::regex multimode_regex("<(img|audio)>(.*?)</\\1>");
     std::string::const_iterator searchStart(prompt.cbegin());
     std::smatch match;
-    std::vector<std::string> img_infos;
     std::vector<int> ids{};
-
     mPositionIds.clear();
+    
     while (std::regex_search(searchStart, prompt.cend(), match, multimode_regex)) {
-        // std::cout << "img match: " << match[1].str() << std::endl;
         auto txt_ids = mTokenizer->encode(match.prefix().str());
         addPositionIds(txt_ids.size());
         ids.insert(ids.end(), txt_ids.begin(), txt_ids.end());
-        auto mul_ids = multimodeProcess(match[1].str(), match[2].str());
+        std::string mode = match[1].str();
+        std::string content = match[2].str();
+        std::vector<int> mul_ids;
+        if (mode == "img") {
+            mul_ids = processImageContent(content, multimodal_input.images);
+            // MNN_PRINT("tokenizer_encode(MultimodalPrompt) image mul_ids size: %lu", mul_ids.size());
+        } else if (mode == "audio") {
+            mul_ids = processAudioContent(content, multimodal_input.audios);
+            // MNN_PRINT("tokenizer_encode(MultimodalPrompt) audio mul_ids size: %lu", mul_ids.size());
+        }
+        
         ids.insert(ids.end(), mul_ids.begin(), mul_ids.end());
         searchStart = match.suffix().first;
     }
@@ -753,6 +781,43 @@ std::vector<int> Omni::tokenizer_encode(const std::string& prompt) {
     return ids;
 }
 
+std::vector<int> Omni::tokenizer_encode(const std::string& prompt) {
+    MultimodalPrompt multimodal_input;
+    multimodal_input.prompt_template = prompt;
+    return tokenizer_encode(multimodal_input);
+}
+
+std::vector<int> Omni::processImageContent(const std::string& content, const std::map<std::string, PromptImagePart>& images) {
+    auto it = images.find(content);
+    if (it != images.end()) {
+        if (it->second.height > 0 && it->second.width > 0) {
+            mVisionHeight = it->second.height;
+            mVisionWidth = it->second.width;
+        }
+        // MNN_PRINT("processImageContent: using placeholder '%s' with size %dx%d", content.c_str(), mVisionWidth, mVisionHeight);
+        return visionProcess(it->second.image_data);
+    }
+    // MNN_PRINT("processImageContent: treating '%s' as file path or URL", content.c_str());
+    return multimodeProcess("img", content);
+}
+
+std::vector<int> Omni::processAudioContent(const std::string& content, const std::map<std::string, PromptAudioPart>& audios) {
+    auto it = audios.find(content);
+    if (it != audios.end()) {
+        // MNN_PRINT("processAudioContent: using placeholder '%s'", content.c_str());
+        if (it->second.waveform.get() != nullptr) {
+            return audioProcess(it->second.waveform);
+        } else if (!it->second.file_path.empty()) {
+            return audioProcess(it->second.file_path);
+        } else {
+            MNN_PRINT("processAudioContent: audio_part has no valid input\n");
+            return std::vector<int>(0);
+        }
+    }
+    // MNN_PRINT("processAudioContent: treating '%s' as file path", content.c_str());
+    return multimodeProcess("audio", content);
+}
+
 VARP Omni::embedding(const std::vector<int>& input_ids) {
     if (input_ids.size() == 1) {
         return Llm::embedding(input_ids);
@@ -845,21 +910,28 @@ VARP Omni::gen_position_ids(int seq_len) {
     auto ptr = positionIds->writeMap<int>();
     if (mContext->gen_seq_len > 0) {
         for (int i=0; i<seq_len; ++i) {
-            auto pos = mContext->gen_seq_len + mPositionIds.back() + i;
+            // auto pos = mContext->gen_seq_len + mPositionIds.back() + i;
+            auto pos = mContext->all_seq_len + i;
             ptr[i + 0] = pos;
             ptr[i + seq_len] = pos;
             ptr[i + seq_len * 2] = pos;
         }
     } else {
         for (int i = 0; i < seq_len; i++) {
-            ptr[i] = mPositionIds.mT[i];
-            ptr[i + seq_len] = mPositionIds.mH[i];
-            ptr[i + seq_len * 2] = mPositionIds.mW[i];
+            ptr[i] = mPositionIds.mT[i] + mContext->all_seq_len;
+            ptr[i + seq_len] = mPositionIds.mH[i] + mContext->all_seq_len;
+            ptr[i + seq_len * 2] = mPositionIds.mW[i] + mContext->all_seq_len;
         }
         if (mTalker) {
             mTalker->setPostionIds(mPositionIds);
         }
     }
+    // // dump position ids
+    // printf("position_ids = [");
+    // for (int i = 0; i < seq_len; i++) {
+    //     printf("%d ", ptr[i]);
+    // }
+    // printf("]\n");
     return positionIds;
 }
 

transformers/llm/engine/src/omni.hpp

@@ -105,12 +105,14 @@ public:
     virtual void load() override;
     virtual std::vector<Express::VARP> forwardRaw(Express::VARP hiddenState, Express::VARP mask, Express::VARP inputPos) override;
     virtual std::vector<int> tokenizer_encode(const std::string& query) override;
+    virtual std::vector<int> tokenizer_encode(const MultimodalPrompt& multimodal_input) override;
     virtual Express::VARP embedding(const std::vector<int>& input_ids) override;
     virtual Express::VARP gen_position_ids(int seq_len) override;
     virtual void response(const std::vector<int>& input_ids, std::ostream* os = &std::cout, const char* end_with = nullptr, int max_new_tokens = -1) override;
     virtual void setWavformCallback(std::function<bool(const float*, size_t, bool)> callback) override;
     virtual void generateWavform() override;
     // some models preprocess function
+    std::vector<int> visionProcess(VARP image);
     std::vector<int> defaultVisionProcess(VARP image);
     std::vector<int> qwen2VisionProcess(VARP image);
     std::vector<int> smolvlmVisionProcess(VARP image);
@@ -126,6 +128,9 @@ private:
     std::vector<int> multimodeProcess(const std::string& mode, std::string info);
     std::vector<int> visionProcess(const std::string& file);
     std::vector<int> audioProcess(const std::string& file);
+    std::vector<int> audioProcess(MNN::Express::VARP waveform);
+    std::vector<int> processImageContent(const std::string& content, const std::map<std::string, PromptImagePart>& images);
+    std::vector<int> processAudioContent(const std::string& content, const std::map<std::string, PromptAudioPart>& audios);
     std::shared_ptr<Module> mVisionModule, mAudioModule;
     std::vector<VARP> mVisionEmbeddings, mAudioEmbeddings;
     std::shared_ptr<Talker> mTalker;

transformers/llm/export/llmexport.py

@@ -225,6 +225,7 @@ class LlmExporter(torch.nn.Module):
             self.llm_config['jinja'] = prompt_template['jinja']
         # load modules
         ModelMapper.do_map(self, self.model, self.model_map['model'])
+
         # rebuild modules
         if self.lm_ is None:
             out_features, in_features = self.embed_.weight.shape
@@ -244,6 +245,7 @@ class LlmExporter(torch.nn.Module):
             self.embed = Embedding(embed_copy, self)
         else:
             self.embed = Embedding(self.embed_, self)
+
         # tie word embeddings
         self.tie_word_embeddings = not self.args.seperate_embed and self.lm_.weight.equal(self.embed_.weight)
         if self.tie_word_embeddings:
@@ -802,14 +804,21 @@ class LlmExporter(torch.nn.Module):
         if self.mnn_converter:
             fuse_transformer = self.visual.transformer_fuse
             native_group_conv = self.visual.group_conv_native
+            quant_bit_visual = self.visual.quant_bit
+            quant_block_visual = self.visual.quant_block
             if self.args.transformer_fuse:
                 fuse_transformer = True
             if self.args.group_conv_native:
                 native_group_conv = True
-            self.mnn_converter.export(vision_onnx, self.visual.quant_bit,
-                                      self.visual.quant_block,
+            if self.args.visual_quant_bit is not None:
+                quant_bit_visual = self.args.visual_quant_bit
+            if self.args.visual_quant_block is not None:
+                quant_block_visual = self.args.visual_quant_block
+            self.mnn_converter.export(vision_onnx, quant_bit_visual,
+                                      quant_block_visual,
                                       transformer_fuse=fuse_transformer,
-                                      group_conv_native=native_group_conv)
+                                      group_conv_native=native_group_conv,
+                                      weight_sym=self.args.visual_sym)
 
     def export_audio(self):
         if self.audio is None:
@@ -1237,6 +1246,9 @@ def export(path,
         'onnx_slim': onnx_slim,
         'quant_bit': quant_bit,
         'quant_block': quant_block,
+        'visual_quant_bit': visual_quant_bit,
+        'visual_quant_block': visual_quant_block,
+        'visual_sym': visual_sym,
         'lm_quant_bit': lm_quant_bit,
         'mnnconvert': mnnconvert,
         'ppl': ppl,
@@ -1276,6 +1288,8 @@ def main():
     parser.add_argument('--onnx_slim', action='store_true', help='Whether or not to use onnx-slim.')
     parser.add_argument('--quant_bit', type=int, default=4, help='mnn quant bit, 4 or 8, default is 4.')
     parser.add_argument('--quant_block', type=int, default=64, help='mnn quant block, 0 mean channle-wise, default is 64.')
+    parser.add_argument('--visual_quant_bit', type=int, default=None, help='mnn viusal quant bit, 4 or 8, default is setting in utils/vision.py by different vit model.')
+    parser.add_argument('--visual_quant_block', type=int, default=None, help='mnn quant block, default is setting in utils/vision.py by different vit model.')
     parser.add_argument('--lm_quant_bit', type=int, default=None, help='mnn lm_head quant bit, 4 or 8, default is `quant_bit`.')
     parser.add_argument('--mnnconvert', type=str, default='../../../build/MNNConvert', help='local mnnconvert path, if invalid, using pymnn.')
     parser.add_argument('--ppl', action='store_true', help='Whether or not to get all logits of input tokens.')
@@ -1284,9 +1298,10 @@ def main():
     parser.add_argument('--transformer_fuse', action='store_true', help='Whether or not to fuse vision transformer op.')
     parser.add_argument('--group_conv_native', action='store_true', help='Whether or not to keep native group_conv.')
     parser.add_argument('--smooth', action='store_true', help='Whether or not to use smooth quant.')
-    parser.add_argument('--sym', action='store_true', help='Whether or not to using symmetric quant (without zeropoint), defualt is False.')
-    parser.add_argument('--seperate_embed', action='store_true', help='For lm and embed shared model, whether or not to sepearte embed to avoid quant, defualt is False, if True, embed weight will be seperate to embeddingbf16.bin.')
-    parser.add_argument('--lora_split', action='store_true', help='Whether or not export lora split, defualt is False.')
+    parser.add_argument('--sym', action='store_true', help='Whether or not to using symmetric quant (without zeropoint), default is False.')
+    parser.add_argument('--visual_sym', action='store_true', help='Whether or not to using symmetric quant (without zeropoint) for visual model, default is False.')
+    parser.add_argument('--seperate_embed', action='store_true', help='For lm and embed shared model, whether or not to sepearte embed to avoid quant, default is False, if True, embed weight will be seperate to embeddingbf16.bin.')
+    parser.add_argument('--lora_split', action='store_true', help='Whether or not export lora split, default is False.')
     parser.add_argument('--calib_data', type=str, default=None, help='calibration data path, defaut is `None` mean not use calib data.')
     args = parser.parse_args()
 

transformers/llm/export/utils/mnn_converter.py

@@ -51,7 +51,7 @@ class MNNConveter:
             os.close(log_fd)
 
     @spinner_run(f'convert onnx model to ')
-    def onnx2mnn(self, onnx_path, mnn_path, args = [], transformer_fuse = True, group_conv_native = False, save_external_data = True):
+    def onnx2mnn(self, onnx_path, mnn_path, args = [], transformer_fuse = True, group_conv_native = False, weight_sym = False, save_external_data = True):
         convert_args = [
             '',
             '-f',
@@ -66,6 +66,8 @@ class MNNConveter:
             convert_args += ['--transformerFuse']
         if group_conv_native:
             convert_args += ['--groupConvNative']
+        if weight_sym:
+            convert_args += ['--weightQuantAsymmetric=0']
         if save_external_data:
             convert_args += ['--saveExternalData']
         convert_args += args
@@ -112,7 +114,7 @@ class MNNConveter:
         self.convert(convert_args)
         return mnn_path
 
-    def export(self, onnx_path, quant_bit = None, quant_block = None, transformer_fuse = True, group_conv_native = False):
+    def export(self, onnx_path, quant_bit = None, quant_block = None, transformer_fuse = True, group_conv_native = False, weight_sym = None):
         self.onnx_model_path = onnx_path
         self.mnn_name = os.path.basename(onnx_path).replace('.onnx', '.mnn')
         self.mnn_model_path = os.path.join(self.config.args.dst_path, self.mnn_name)
@@ -133,10 +135,10 @@ class MNNConveter:
                 ]
             if quant_bit == 32:
                 quant_args = []
-            self.onnx2mnn(self.onnx_model_path, self.mnn_model_path, quant_args, transformer_fuse=transformer_fuse, group_conv_native=group_conv_native)
+            self.onnx2mnn(self.onnx_model_path, self.mnn_model_path, quant_args, transformer_fuse=transformer_fuse, group_conv_native=group_conv_native, weight_sym=weight_sym)
         else:
             mnn_json = f'{self.mnn_model_path}.json'
-            self.onnx2mnn(self.onnx_model_path, self.mnn_model_path, transformer_fuse=transformer_fuse, group_conv_native=group_conv_native)
+            self.onnx2mnn(self.onnx_model_path, self.mnn_model_path, transformer_fuse=transformer_fuse, group_conv_native=group_conv_native, weight_sym=weight_sym)
             self.mnn2json(self.mnn_model_path, mnn_json)
             self.rebuild(mnn_json)
             self.json2mnn(mnn_json, self.mnn_model_path)
@@ -511,4 +513,4 @@ class MNNConveter:
         }
         if name.startswith('/expert/'):
             post_reshape['main']['dims'] = [-1, oc]
-        return [pre_reshape, pre_convert, conv_op, post_convert, post_reshape]
+        return [pre_reshape, pre_convert, conv_op, post_convert, post_reshape]

transformers/llm/export/utils/transformers.py

@@ -266,7 +266,6 @@ class Rotary(torch.nn.Module):
 
         def get_theta():
             return 1.0 / (self.rope_theta ** (torch.arange(0, self.rotary_dim, 2, dtype=torch.float32) / self.rotary_dim))
-
         # default rope type's theta
         self.theta = get_theta()
         # other type
@@ -278,7 +277,6 @@ class Rotary(torch.nn.Module):
                 rope_type = config.rope_scaling['type']
             elif 'rope_type' in config.rope_scaling:
                 rope_type = config.rope_scaling['rope_type']
-
             # gen theta for rope_type
             if rope_type == 'dynamic': # NTK
                 if 'alpha' in config.rope_scaling: # NTKAlpha in Hunyuan
@@ -286,7 +284,7 @@ class Rotary(torch.nn.Module):
                 else: # NTKScaling
                     pass
                 self.theta = get_theta()
-            elif rope_type == 'yarn': # YaRN in gpt-oss
+            elif rope_type == 'yarn':
                 self.is_scaled = True
                 self.theta, self.attention_scaling = _compute_yarn_parameters(
                     rotary_dim=self.rotary_dim,