mirror of
https://github.com/saymrwulf/onnxruntime.git
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[VSINPU] update crosscompiling patch (#22937)
### Description <!-- Describe your changes. --> Update this patch because the origin file has changed ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
This commit is contained in:
Chen Feiyue
GitHub
parent
8826e39a81
commit
487184fa42
1 changed files with 21 additions and 35 deletions
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@ -1,8 +1,8 @@
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diff --git a/cmake/onnxruntime_mlas.cmake b/cmake/onnxruntime_mlas.cmake
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index c02ac2096d..2bc51298f0 100644
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index 10c307b3b9..a52bf71c4d 100644
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--- a/cmake/onnxruntime_mlas.cmake
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+++ b/cmake/onnxruntime_mlas.cmake
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@@ -361,7 +361,7 @@ else()
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@@ -370,7 +370,7 @@ else()
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)
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set_source_files_properties(${MLAS_SRC_DIR}/sqnbitgemm_kernel_neon_int8.cpp
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PROPERTIES COMPILE_FLAGS " -march=armv8.2-a+dotprod")
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@ -12,10 +12,10 @@ index c02ac2096d..2bc51298f0 100644
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${mlas_platform_srcs}
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${MLAS_SRC_DIR}/aarch64/HalfGemmKernelNeon.S
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diff --git a/onnxruntime/core/mlas/inc/mlas.h b/onnxruntime/core/mlas/inc/mlas.h
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index e46105324a..414c46a1ce 100644
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index 28ae64c4d5..0c77e0ca78 100644
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--- a/onnxruntime/core/mlas/inc/mlas.h
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+++ b/onnxruntime/core/mlas/inc/mlas.h
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@@ -82,6 +82,9 @@ Abstract:
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@@ -83,6 +83,9 @@ Abstract:
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#if (!defined(_MSC_VER)) || (_MSC_VER >= 1930)
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#if defined(MLAS_TARGET_ARM64) || defined(MLAS_TARGET_ARM64EC)
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@ -25,7 +25,7 @@ index e46105324a..414c46a1ce 100644
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#if !defined(__APPLE__)
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// Had to temporary disable fp16 under APPLE ARM64, as compiling
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// the source files require a hardware specific compilation flag.
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@@ -90,6 +93,7 @@ Abstract:
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@@ -91,6 +94,7 @@ Abstract:
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#define MLAS_F16VEC_INTRINSICS_SUPPORTED
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@ -33,7 +33,7 @@ index e46105324a..414c46a1ce 100644
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#endif //
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#endif // ARM64
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#endif // Visual Studio 16 or earlier does not support fp16 intrinsic
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@@ -1635,6 +1639,7 @@ MlasHalfGemmConvertPackB(
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@@ -1644,6 +1648,7 @@ MlasHalfGemmConvertPackB(
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);
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#if defined(__aarch64__) && defined(__linux__)
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@ -41,7 +41,7 @@ index e46105324a..414c46a1ce 100644
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/**
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* @brief Whether current CPU supports Bfloat16(bf16) acceleration.
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*/
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@@ -1746,6 +1751,7 @@ MlasSBGemmPackBSize(size_t N, size_t K);
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@@ -1755,6 +1760,7 @@ MlasSBGemmPackBSize(size_t N, size_t K);
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void MLASCALL
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MlasSBGemmConvertPackB(size_t N, size_t K, const float* B, size_t ldb, void* PackedB);
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#endif
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@ -50,10 +50,10 @@ index e46105324a..414c46a1ce 100644
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/**
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* @brief Indirect Depthwise convolution for fp16
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diff --git a/onnxruntime/core/mlas/lib/mlasi.h b/onnxruntime/core/mlas/lib/mlasi.h
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index 4239e2ecae..3df7e5573d 100644
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index 0533a5e49b..c18bf7f90d 100644
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--- a/onnxruntime/core/mlas/lib/mlasi.h
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+++ b/onnxruntime/core/mlas/lib/mlasi.h
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@@ -361,6 +361,7 @@ size_t
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@@ -377,6 +377,7 @@ size_t
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#else
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#if defined(__aarch64__) && defined(__linux__)
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@ -61,7 +61,7 @@ index 4239e2ecae..3df7e5573d 100644
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typedef size_t(MLASCALL MLAS_SBGEMM_FLOAT_KERNEL)(
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const float* A,
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const bfloat16_t* B,
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@@ -373,6 +374,7 @@ typedef size_t(MLASCALL MLAS_SBGEMM_FLOAT_KERNEL)(
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@@ -389,6 +390,7 @@ typedef size_t(MLASCALL MLAS_SBGEMM_FLOAT_KERNEL)(
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const float* Bias
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);
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#endif
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@ -69,7 +69,7 @@ index 4239e2ecae..3df7e5573d 100644
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typedef
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size_t
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@@ -763,8 +765,10 @@ extern "C" {
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@@ -796,8 +798,10 @@ extern "C" {
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MLAS_GEMM_FLOAT_KERNEL MlasSgemmKernelZero;
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MLAS_GEMM_FLOAT_KERNEL MlasSgemmKernelAdd;
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#if defined(__aarch64__) && defined(__linux__)
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@ -80,7 +80,7 @@ index 4239e2ecae..3df7e5573d 100644
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#endif
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MLAS_GEMM_DOUBLE_KERNEL MlasDgemmKernelZero;
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MLAS_GEMM_DOUBLE_KERNEL MlasDgemmKernelAdd;
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@@ -899,8 +903,10 @@ extern "C" {
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@@ -946,8 +950,10 @@ extern "C" {
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#define MLAS_QGEMM_THREAD_COMPLEXITY 65536
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#if defined(__aarch64__) && defined(__linux__)
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@ -91,26 +91,12 @@ index 4239e2ecae..3df7e5573d 100644
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//
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// Single-threaded single precision matrix/matrix multiply operation.
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@@ -2570,4 +2576,3 @@ MlasPackInt4Elements(uint8_t* Output, UnpackedType ValueLow, UnpackedType ValueH
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static_assert(std::is_same_v<UnpackedType, uint8_t> || std::is_same_v<UnpackedType, int8_t>);
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*Output = static_cast<uint8_t>(((ValueHigh & 0xF) << 4) | (ValueLow & 0xF));
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}
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-
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diff --git a/onnxruntime/core/mlas/lib/platform.cpp b/onnxruntime/core/mlas/lib/platform.cpp
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index ed437f20f7..8c9d0a75fd 100644
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index b3c9461293..424c3b0441 100644
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--- a/onnxruntime/core/mlas/lib/platform.cpp
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+++ b/onnxruntime/core/mlas/lib/platform.cpp
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@@ -20,7 +20,7 @@ Abstract:
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#include <thread>
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#include <mutex>
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-#if defined(MLAS_TARGET_POWER)
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+#if defined(MLAS_TARGET_POWER)
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#if defined(__linux__)
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#include <sys/auxv.h>
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#elif defined(_AIX)
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@@ -536,7 +536,7 @@ Return Value:
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this->QNBitGemmDispatch = &MlasSQNBitGemmDispatchNeon;
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@@ -574,7 +574,7 @@ Return Value:
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this->ConvSymS8S8Dispatch = &MlasConvSymS8DispatchDot;
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}
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-#if defined(__linux__)
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@ -137,10 +123,10 @@ index de7fd72fad..4f75dbd6fa 100644
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+#endif
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#endif // defined(__aarch64__) && defined(__linux__)
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diff --git a/onnxruntime/core/providers/cpu/math/matmul.cc b/onnxruntime/core/providers/cpu/math/matmul.cc
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index 6a71283f9d..d8bd348854 100644
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index 2c6d23e4de..61aaacdfd6 100644
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--- a/onnxruntime/core/providers/cpu/math/matmul.cc
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+++ b/onnxruntime/core/providers/cpu/math/matmul.cc
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@@ -132,7 +132,7 @@ Status MatMul<T>::Compute(OpKernelContext* ctx) const {
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@@ -133,7 +133,7 @@ Status MatMul<T>::Compute(OpKernelContext* ctx) const {
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return Status::OK();
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}
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@ -149,7 +135,7 @@ index 6a71283f9d..d8bd348854 100644
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bool GemmPackBBfloat16(AllocatorPtr& alloc,
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const Tensor& tensor_b,
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bool trans_b,
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@@ -180,6 +180,7 @@ Status MatMul<float>::PrePack(const Tensor& tensor, int input_idx, /*out*/ Alloc
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@@ -181,6 +181,7 @@ Status MatMul<float>::PrePack(const Tensor& tensor, int input_idx, /*out*/ Alloc
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if (input_idx == 1) {
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size_t packed_b_size;
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#if defined(__aarch64__) && defined(__linux__)
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@ -157,7 +143,7 @@ index 6a71283f9d..d8bd348854 100644
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size_t dim1 = 0;
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size_t dim2 = 0;
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TensorShape b_shape = tensor.Shape();
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@@ -192,6 +193,7 @@ Status MatMul<float>::PrePack(const Tensor& tensor, int input_idx, /*out*/ Alloc
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@@ -193,6 +194,7 @@ Status MatMul<float>::PrePack(const Tensor& tensor, int input_idx, /*out*/ Alloc
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if (use_fastmath_mode_ && (trans_b_attr_ == 0) && ((dim1 * dim2) >= kFastMathModeKernelsizeThreshold)) {
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is_packed = GemmPackBBfloat16(alloc, tensor, trans_b_attr_ != 0, packed_b_, packed_b_size, b_shape_);
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} else
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@ -165,7 +151,7 @@ index 6a71283f9d..d8bd348854 100644
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#endif
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{
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is_packed = GemmPackBFp32(alloc, tensor, trans_b_attr_ != 0, packed_b_, packed_b_size, b_shape_);
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@@ -257,6 +259,7 @@ Status MatMul<float>::Compute(OpKernelContext* ctx) const {
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@@ -259,6 +261,7 @@ Status MatMul<float>::Compute(OpKernelContext* ctx) const {
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const size_t lda = helper.Lda(trans_a);
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const size_t ldb = helper.Ldb(trans_b);
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#if defined(__aarch64__) && defined(__linux__)
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@ -173,7 +159,7 @@ index 6a71283f9d..d8bd348854 100644
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if (use_fastmath_mode_ && !trans_b && ((N * K) >= kFastMathModeKernelsizeThreshold)) {
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std::vector<MLAS_SBGEMM_DATA_PARAMS> data(max_len);
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for (size_t i = 0; i < max_len; i++) {
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@@ -273,6 +276,7 @@ Status MatMul<float>::Compute(OpKernelContext* ctx) const {
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@@ -275,6 +278,7 @@ Status MatMul<float>::Compute(OpKernelContext* ctx) const {
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}
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MlasSBGemmBatch(M, N, K, max_len, data.data(), thread_pool);
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} else
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