Add option to force generic algorithms on x86 (#22917)

Option is named onnxruntime_FORCE_GENERIC_ALGORITHMS

Follow up to #22125.

### Description
This change adds compile-time option to disable optimized algorithms and
use generic algorithms (exclude AVX* and SSE etc in GEMM) on x86. This
new option is intended only for testing these algorithms, not for
production use.

Following build command on linux x86_64 builds onnxruntime with new
option enabled:
`./build.sh --parallel --cmake_extra_defines
onnxruntime_FORCE_GENERIC_ALGORITHMS=1`

### Motivation and Context
This change allows testing generic algorithms. This may be needed for
platforms which don't have optimized implementations available, like in
#22125.

cmake/CMakeLists.txt

@@ -252,6 +252,7 @@ cmake_dependent_option(MSVC_Z7_OVERRIDE "replacing /Zi and /ZI with /Z7 when usi
 
 option(onnxruntime_USE_AZURE "Build with azure inferencing support" OFF)
 option(onnxruntime_USE_LOCK_FREE_QUEUE "Build with lock-free task queue for threadpool." OFF)
+option(onnxruntime_FORCE_GENERIC_ALGORITHMS "Disable optimized arch-specific algorithms. Use only for testing and debugging generic algorithms." OFF)
 
 # ENABLE_TRAINING includes all training functionality
 # The following 2 entry points
@@ -971,6 +972,10 @@ if (onnxruntime_USE_LOCK_FREE_QUEUE)
     add_compile_definitions(USE_LOCK_FREE_QUEUE)
 endif()
 
+if (onnxruntime_FORCE_GENERIC_ALGORITHMS)
+    add_compile_definitions(FORCE_GENERIC_ALGORITHMS)
+endif()
+
 if (onnxruntime_ENABLE_LAZY_TENSOR)
   # To support LazyTensor, ORT needs to call Python function from C/C++.
   # so onnxruntime_ENABLE_PYTHON is required.

cmake/onnxruntime_mlas.cmake

@@ -679,6 +679,13 @@ endif()
     if(NOT ONNXRUNTIME_MLAS_MULTI_ARCH AND MLAS_SOURCE_IS_NOT_SET)
         file(GLOB_RECURSE mlas_platform_srcs
           "${MLAS_SRC_DIR}/scalar/*.cpp")
+    elseif (onnxruntime_FORCE_GENERIC_ALGORITHMS)
+        file(GLOB_RECURSE mlas_platform_srcs_generic
+          "${MLAS_SRC_DIR}/scalar/*.cpp")
+        set(mlas_platform_srcs
+            ${mlas_platform_srcs}
+            ${mlas_platform_srcs_generic}
+            )
     endif()
     target_sources(onnxruntime_mlas PRIVATE ${mlas_platform_srcs})
 endif()

onnxruntime/core/mlas/lib/mlasi.h

@@ -358,6 +358,22 @@ size_t
     bool ZeroMode
     );
 
+#ifdef FORCE_GENERIC_ALGORITHMS
+typedef
+size_t
+(MLASCALL MLAS_GEMM_FLOAT_KERNEL_GENERIC)(
+    const float* A,
+    const float* B,
+    float* C,
+    size_t CountK,
+    size_t CountM,
+    size_t CountN,
+    size_t lda,
+    size_t ldc,
+    float alpha
+    );
+#endif
+
 #else
 
 #if defined(__aarch64__) && defined(__linux__)
@@ -733,6 +749,10 @@ extern "C" {
 #if defined(MLAS_TARGET_AMD64_IX86)
     MLAS_GEMM_FLOAT_KERNEL MlasGemmFloatKernelSse;
     MLAS_GEMM_FLOAT_KERNEL MlasGemmFloatKernelAvx;
+#ifdef FORCE_GENERIC_ALGORITHMS
+    MLAS_GEMM_FLOAT_KERNEL_GENERIC MlasSgemmKernelZero;
+    MLAS_GEMM_FLOAT_KERNEL_GENERIC MlasSgemmKernelAdd;
+#endif
 #if defined(MLAS_TARGET_AMD64)
     MLAS_GEMM_FLOAT_KERNEL MlasGemmFloatKernelFma3;
     MLAS_GEMM_FLOAT_KERNEL MlasGemmFloatKernelAvx512F;

onnxruntime/core/mlas/lib/platform.cpp

@@ -286,7 +286,11 @@ Return Value:
     this->QuantizeLinearS4Kernel = MlasQuantizeLinearS4Kernel;
     this->QuantizeLinearU4Kernel = MlasQuantizeLinearU4Kernel;
 #ifndef __APPLE__
+#ifndef FORCE_GENERIC_ALGORITHMS
     this->CastF16ToF32Kernel = &MlasCastF16ToF32KernelSse;
+#else  // FORCE_GENERIC_ALGORITHMS
+    this->CastF16ToF32Kernel = nullptr;
+#endif  // FORCE_GENERIC_ALGORITHMS
 #endif  // __APPLE__
 
     this->NchwcBlockSize = 8;
@@ -308,8 +312,11 @@ Return Value:
     //
     // Check if the processor supports SSE 4.1 instructions.
     //
-
+#ifndef FORCE_GENERIC_ALGORITHMS
     if ((Cpuid1[2] & 0x80000) != 0) {
+#else  // FORCE_GENERIC_ALGORITHMS
+    if (false) {
+#endif  // FORCE_GENERIC_ALGORITHMS
         this->GemmU8S8Dispatch = &MlasGemmU8S8DispatchSse41;
     }
 
@@ -319,7 +326,11 @@ Return Value:
     // Check if the processor supports the AVX and OSXSAVE features.
     //
 
+#ifndef FORCE_GENERIC_ALGORITHMS
     if ((Cpuid1[2] & 0x18000000) == 0x18000000) {
+#else  // FORCE_GENERIC_ALGORITHMS
+    if (false) {
+#endif  // FORCE_GENERIC_ALGORITHMS
 
         //
         // Check if the operating system supports saving SSE and AVX states.

onnxruntime/core/mlas/lib/qgemm.h

@@ -867,6 +867,7 @@ MlasGemmQuantGetDispatch(
 {
     const MLAS_GEMM_QUANT_DISPATCH* GemmQuantDispatch = &MlasGemmQuantDispatchDefault;
 
+#if !defined(FORCE_GENERIC_ALGORITHMS)
 #if defined(MLAS_TARGET_AMD64_IX86)
     if (AIsSigned) {
         GemmQuantDispatch =
@@ -901,6 +902,7 @@ MlasGemmQuantGetDispatch(
             BIsSigned ? GetMlasPlatform().GemmU8S8Dispatch : GetMlasPlatform().GemmU8U8Dispatch;
     }
 #endif
+#endif // !defined(FORCE_GENERIC_ALGORITHMS)
 
     if (nullptr == GemmQuantDispatch) {
         std::stringstream ss;

onnxruntime/core/mlas/lib/sgemm.cpp

@@ -1061,7 +1061,7 @@ Return Value:
 
         size_t RowsHandled;
 
-#if defined(MLAS_TARGET_AMD64_IX86) || defined(MLAS_TARGET_POWER) || defined(MLAS_TARGET_LARCH64)
+#if (defined(MLAS_TARGET_AMD64_IX86) || defined(MLAS_TARGET_POWER) || defined(MLAS_TARGET_LARCH64)) && !defined(FORCE_GENERIC_ALGORITHMS)
         RowsHandled = GetMlasPlatform().GemmFloatKernel(A, B, C, CountK, CountM, CountN, lda, ldc, alpha, ZeroMode);
 #else
         if (ZeroMode) {
@@ -1158,6 +1158,7 @@ Return Value:
 
     if (M == 1 && TransA == CblasNoTrans && alpha == 1.0f && (beta == 0.0f || beta == 1.0f)) {
 
+#if !defined(FORCE_GENERIC_ALGORITHMS)
 #if defined(MLAS_TARGET_AMD64)
 
         MLAS_SGEMM_KERNEL_M1_ROUTINE* SgemmKernelM1Routine;
@@ -1181,6 +1182,7 @@ Return Value:
         }
 
 #endif
+#endif // !defined(FORCE_GENERIC_ALGORITHMS)
 
     }
 
@@ -1193,7 +1195,7 @@ Return Value:
 
     if (N == 1 && ldb == 1 && ldc == 1 && alpha == 1.0f && (beta == 0.0f || beta == 1.0f)) {
 
-#if defined(MLAS_TARGET_AMD64)
+#if defined(MLAS_TARGET_AMD64) && !defined(FORCE_GENERIC_ALGORITHMS)
 
         MLAS_SGEMM_KERNEL_M1_ROUTINE* SgemmKernelM1Routine;