Merge branch 'ggerganov:master' into bitnet

.github/pull_request_template.md

@@ -2,4 +2,4 @@
     - [ ] Review Complexity : Low
     - [ ] Review Complexity : Medium
     - [ ] Review Complexity : High
-- [ ] I have read the [contributing guidelines](CONTRIBUTING.md)
+- [ ] I have read the [contributing guidelines](https://github.com/ggerganov/llama.cpp/blob/master/CONTRIBUTING.md)

.github/workflows/build.yml

@@ -13,7 +13,7 @@ on:
     paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m']
   pull_request:
     types: [opened, synchronize, reopened]
-    paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m']
+    paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m']
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
@@ -684,7 +684,7 @@ jobs:
             cmake --build build --config ${{ matrix.build }} -j $(nproc)
 
   windows-latest-cmake:
-    runs-on: windows-latest
+    runs-on: windows-2019
 
     env:
       OPENBLAS_VERSION: 0.3.23
@@ -829,7 +829,7 @@ jobs:
           name: llama-bin-win-${{ matrix.build }}.zip
 
   windows-latest-cmake-cuda:
-    runs-on: windows-latest
+    runs-on: windows-2019
 
     strategy:
       matrix:
@@ -843,8 +843,9 @@ jobs:
         with:
           fetch-depth: 0
 
-      - uses: Jimver/cuda-toolkit@v0.2.11
+      - name: Install CUDA toolkit
         id: cuda-toolkit
+        uses: Jimver/cuda-toolkit@v0.2.15
         with:
           cuda: ${{ matrix.cuda }}
           method: 'network'

README.md

@@ -576,7 +576,9 @@ Building the program with BLAS support may lead to some performance improvements
   vulkaninfo
   ```
 
-  Alternatively your package manager might be able to provide the appropiate libraries. For example for Ubuntu 22.04 you can install `libvulkan-dev` instead.
+  Alternatively your package manager might be able to provide the appropriate libraries.
+  For example for Ubuntu 22.04 you can install `libvulkan-dev` instead.
+  For Fedora 40, you can install `vulkan-devel`, `glslc` and `glslang` packages.
 
   Then, build llama.cpp using the cmake command below:
 

examples/llama-bench/llama-bench.cpp

@@ -1033,6 +1033,27 @@ struct markdown_printer : public printer {
         if (field == "n_gpu_layers") {
             return 3;
         }
+        if (field == "n_threads") {
+            return 7;
+        }
+        if (field == "n_batch") {
+            return 7;
+        }
+        if (field == "n_ubatch") {
+            return 8;
+        }
+        if (field == "type_k" || field == "type_v") {
+            return 6;
+        }
+        if (field == "split_mode") {
+            return 5;
+        }
+        if (field == "flash_attn") {
+            return 2;
+        }
+        if (field == "use_mmap") {
+            return 4;
+        }
         if (field == "test") {
             return 13;
         }

ggml-alloc.c

@@ -886,7 +886,7 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
         fprintf(stderr, "%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(buft), size);
 #endif
         for (size_t i = 0; i < *n_buffers; i++) {
-            ggml_backend_buffer_free(*buffers[i]);
+            ggml_backend_buffer_free((*buffers)[i]);
         }
         free(*buffers);
         return false;

ggml-cuda/mma.cuh

@@ -1,5 +1,27 @@
 #include "common.cuh"
 
+struct mma_int_A_I16K4 {
+    static constexpr int I  = 16;
+    static constexpr int K  = 4;
+    static constexpr int ne = 2;
+
+    int x[ne] = {0};
+
+    static __device__ __forceinline__ int get_i(const int l) {
+        const int ret = (l%2) * (I/2) + threadIdx.x / K;
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  I);
+        return ret;
+    }
+
+    static __device__ __forceinline__ int get_k(const int /* l */) {
+        const int ret = threadIdx.x % K;
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  K);
+        return ret;
+    }
+};
+
 struct mma_int_A_I16K8 {
     static constexpr int I  = 16;
     static constexpr int K  = 8;
@@ -22,6 +44,28 @@ struct mma_int_A_I16K8 {
     }
 };
 
+struct mma_int_B_J8K4 {
+    static constexpr int J  = 8;
+    static constexpr int K  = 4;
+    static constexpr int ne = 1;
+
+    int x[ne] = {0};
+
+    static __device__ __forceinline__ int get_j(const int /* l */) {
+        const int ret = threadIdx.x / K;
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  J);
+        return ret;
+    }
+
+    static __device__ __forceinline__ int get_k(const int /* l */) {
+        const int ret = threadIdx.x % K;
+        GGML_CUDA_ASSUME(ret >= 0);
+        GGML_CUDA_ASSUME(ret <  K);
+        return ret;
+    }
+};
+
 struct mma_int_B_J8K8 {
     static constexpr int J  = 8;
     static constexpr int K  = 8;
@@ -65,6 +109,28 @@ struct mma_int_C_I16J8 {
         return ret;
     }
 
+    __device__ __forceinline__ void mma_K4(const mma_int_A_I16K4 & mma_A, const mma_int_B_J8K4 & mma_B) {
+#ifdef INT8_MMA_AVAILABLE
+#if __CUDA_ARCH__ >= CC_AMPERE
+        asm("mma.sync.aligned.m16n8k16.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
+            : "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3])
+            : "r"(mma_A.x[0]), "r"(mma_A.x[1]), "r"(mma_B.x[0]));
+#else
+        // On Turing m16n8k16 mma is not available, use 2x m8n8k16 mma instead:
+        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
+            : "+r"(x[0]), "+r"(x[1])
+            : "r"(mma_A.x[0]), "r"(mma_B.x[0]));
+        asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};"
+            : "+r"(x[2]), "+r"(x[3])
+            : "r"(mma_A.x[1]), "r"(mma_B.x[0]));
+#endif // __CUDA_ARCH__ >= CC_AMPERE
+#else
+        GGML_UNUSED(mma_A);
+        GGML_UNUSED(mma_B);
+        NO_DEVICE_CODE;
+#endif // INT8_MMA_AVAILABLE
+    }
+
     __device__ __forceinline__ void mma_K8(const mma_int_A_I16K8 & mma_A, const mma_int_B_J8K8 & mma_B) {
 #ifdef INT8_MMA_AVAILABLE
 #if __CUDA_ARCH__ >= CC_AMPERE

ggml-cuda/mmq.cuh

@@ -1089,7 +1089,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q4_K_q8_1_mul_mat(
+static __device__ __forceinline__ void vec_dot_q4_K_q8_1_dp4a(
     const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
     const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
 
@@ -1115,6 +1115,97 @@ static __device__ __forceinline__ void vec_dot_q4_K_q8_1_mul_mat(
     }
 }
 
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q4_K_q8_1_mma(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    typedef mma_int_A_I16K8 mma_A;
+    typedef mma_int_B_J8K8  mma_B;
+    typedef mma_int_C_I16J8 mma_C;
+
+    const int   * y_qs = (const int   *) y + 4;
+    const half2 * y_ds = (const half2 *) y;
+
+    const int i0 = threadIdx.y*mma_A::I;
+    static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
+
+    mma_A   A[2];
+    int   scA[mma_C::ne/2][2];
+    int    mA[mma_C::ne/2][2];
+    half2 dmA[mma_C::ne/2];
+#pragma unroll
+    for (int kvdr = 0; kvdr < VDR_Q4_K_Q8_1_MMQ; kvdr += 4) {
+#pragma unroll
+        for (int l = 0; l < mma_A::ne; ++l) {
+            const int i = i0 + mma_A::get_i(l);
+            const int k = k0 + mma_A::get_k(l);
+
+            A[kvdr/4].x[l] = (x_ql[i*(WARP_SIZE + 1) + k] >> kvdr) & 0x0F0F0F0F;
+        }
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int i = i0 + mma_C::get_i(2*l);
+
+            const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]) + 2 * ((k0 % 16) / 8);
+            const uint8_t *  m = sc + 8;
+
+            scA[l][kvdr/4] = sc[kvdr/4];
+            mA[l][kvdr/4]  =  m[kvdr/4];
+        }
+    }
+
+#pragma unroll
+    for (int l = 0; l < mma_C::ne/2; ++l) {
+        const int i = i0 + mma_C::get_i(2*l);
+
+        dmA[l] = x_dm[i*(WARP_SIZE/QI5_K) + i/QI5_K + k0/QI5_K];
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
+        float tmpd[mma_C::ne] = {0.0f};
+        float tmpm[mma_C::ne] = {0.0f};
+
+#pragma unroll
+        for (int kvdr = 0; kvdr < VDR_Q5_K_Q8_1_MMQ; kvdr += 4) {
+            mma_C   C;
+            mma_B   B;
+            half2 dsB[mma_C::ne/2];
+
+#pragma unroll
+            for (int l = 0; l < mma_B::ne; ++l) {
+                const int j = j0 + mma_B::get_j(l);
+                const int k = (2*k0 + 2*kvdr + mma_B::get_k(l)) % WARP_SIZE;
+
+                B.x[l] = y_qs[j*MMQ_TILE_Y_K + k];
+            }
+#pragma unroll
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
+
+                dsB[l] = y_ds[j*MMQ_TILE_Y_K + ((2*k0 + 2*kvdr)/QI8_1) % (WARP_SIZE/QI8_1)];
+            }
+
+            C.mma_K8(A[kvdr/4], B);
+
+#pragma unroll
+            for (int l = 0; l < mma_C::ne; ++l) {
+                tmpd[l] += (C.x[l]*scA[l/2][kvdr/4]) *  __low2float(dsB[l%2]);
+                tmpm[l] += mA[l/2][kvdr/4]           * __high2float(dsB[l%2]);
+            }
+        }
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne; ++l) {
+            sum[(j0/mma_B::J)*mma_C::ne + l] += __low2float(dmA[l/2])*tmpd[l] - __high2float(dmA[l/2])*tmpm[l];
+        }
+    }
+}
+
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
     const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
     int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
@@ -1188,7 +1279,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q5_K_q8_1_mul_mat(
+static __device__ __forceinline__ void vec_dot_q5_K_q8_1_dp4a(
     const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
     const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
 
@@ -1214,6 +1305,97 @@ static __device__ __forceinline__ void vec_dot_q5_K_q8_1_mul_mat(
     }
 }
 
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q5_K_q8_1_mma(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    typedef mma_int_A_I16K8 mma_A;
+    typedef mma_int_B_J8K8  mma_B;
+    typedef mma_int_C_I16J8 mma_C;
+
+    const int   * y_qs = (const int   *) y + 4;
+    const half2 * y_ds = (const half2 *) y;
+
+    const int i0 = threadIdx.y*mma_A::I;
+    static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
+
+    mma_A   A[2];
+    int   scA[mma_C::ne/2][2];
+    int    mA[mma_C::ne/2][2];
+    half2 dmA[mma_C::ne/2];
+#pragma unroll
+    for (int kvdr = 0; kvdr < VDR_Q5_K_Q8_1_MMQ; kvdr += 4) {
+#pragma unroll
+        for (int l = 0; l < mma_A::ne; ++l) {
+            const int i = i0 + mma_A::get_i(l);
+            const int k = QR5_K*k0 + QR5_K*kvdr + mma_A::get_k(l);
+
+            A[kvdr/4].x[l] = x_ql[i*(QR5_K*WARP_SIZE + 1) + k];
+        }
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int i = i0 + mma_C::get_i(2*l);
+
+            const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]) + 2 * ((k0 % 16) / 8);
+            const uint8_t *  m = sc + 8;
+
+            scA[l][kvdr/4] = sc[kvdr/4];
+            mA[l][kvdr/4]  =  m[kvdr/4];
+        }
+    }
+
+#pragma unroll
+    for (int l = 0; l < mma_C::ne/2; ++l) {
+        const int i = i0 + mma_C::get_i(2*l);
+
+        dmA[l] = x_dm[i*(WARP_SIZE/QI5_K) + i/QI5_K + k0/QI5_K];
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
+        float tmpd[mma_C::ne] = {0.0f};
+        float tmpm[mma_C::ne] = {0.0f};
+
+#pragma unroll
+        for (int kvdr = 0; kvdr < VDR_Q5_K_Q8_1_MMQ; kvdr += 4) {
+            mma_C   C;
+            mma_B   B;
+            half2 dsB[mma_C::ne/2];
+
+#pragma unroll
+            for (int l = 0; l < mma_B::ne; ++l) {
+                const int j = j0 + mma_B::get_j(l);
+                const int k = (2*k0 + 2*kvdr + mma_B::get_k(l)) % WARP_SIZE;
+
+                B.x[l] = y_qs[j*MMQ_TILE_Y_K + k];
+            }
+#pragma unroll
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
+
+                dsB[l] = y_ds[j*MMQ_TILE_Y_K + ((2*k0 + 2*kvdr)/QI8_1) % (WARP_SIZE/QI8_1)];
+            }
+
+            C.mma_K8(A[kvdr/4], B);
+
+#pragma unroll
+            for (int l = 0; l < mma_C::ne; ++l) {
+                tmpd[l] += (C.x[l]*scA[l/2][kvdr/4]) *  __low2float(dsB[l%2]);
+                tmpm[l] += mA[l/2][kvdr/4]           * __high2float(dsB[l%2]);
+            }
+        }
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne; ++l) {
+            sum[(j0/mma_B::J)*mma_C::ne + l] += __low2float(dmA[l/2])*tmpd[l] - __high2float(dmA[l/2])*tmpm[l];
+        }
+    }
+}
+
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
     const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
     int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) {
@@ -1280,7 +1462,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mul_mat(
+static __device__ __forceinline__ void vec_dot_q6_K_q8_1_dp4a(
     const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
     const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
 
@@ -1307,6 +1489,97 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mul_mat(
     }
 }
 
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    typedef mma_int_A_I16K4 mma_A;
+    typedef mma_int_B_J8K4  mma_B;
+    typedef mma_int_C_I16J8 mma_C;
+
+    const float * x_df = (const float *) x_dm;
+    const int   * y_qs = (const int   *) y + 4;
+    const float * y_df = (const float *) y;
+
+    const int i0 = threadIdx.y*mma_A::I;
+    static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y");
+
+    mma_A   A[4];
+    int   scA[mma_C::ne/2][4];
+    float  dA[mma_C::ne/2];
+#pragma unroll
+    for (int kvdr = 0; kvdr < VDR_Q6_K_Q8_1_MMQ; kvdr += 4) {
+#pragma unroll
+        for (int l = 0; l < mma_A::ne; ++l) {
+            const int i = i0 + mma_A::get_i(l);
+            const int k = QR6_K*k0 + QR6_K*kvdr + mma_A::get_k(l);
+
+            A[kvdr/2 + 0].x[l] = x_ql[i*(QR6_K*WARP_SIZE + 1) + k + 0];
+            A[kvdr/2 + 1].x[l] = x_ql[i*(QR6_K*WARP_SIZE + 1) + k + mma_A::K];
+        }
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int i = i0 + mma_C::get_i(2*l);
+
+            const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/8]);
+
+            scA[l][kvdr/2 + 0] = sc[kvdr/2 + 0];
+            scA[l][kvdr/2 + 1] = sc[kvdr/2 + 1];
+        }
+    }
+
+#pragma unroll
+    for (int l = 0; l < mma_C::ne/2; ++l) {
+        const int i = i0 + mma_C::get_i(2*l);
+
+        dA[l] = x_df[i*(WARP_SIZE/QI6_K) + i/QI6_K + k0/QI6_K];
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) {
+        float tmp[mma_C::ne] = {0.0f};
+
+#pragma unroll
+        for (int kvdr = 0; kvdr < VDR_Q6_K_Q8_1_MMQ; kvdr += 4) {
+            mma_C C[2];
+            mma_B B[2];
+            float dB[mma_C::ne/2];
+
+#pragma unroll
+            for (int l = 0; l < mma_B::ne; ++l) {
+                const int j = j0 + mma_B::get_j(l);
+                const int k = (2*k0 + 2*kvdr + mma_B::get_k(l)) % WARP_SIZE;
+
+                B[0].x[l] = y_qs[j*MMQ_TILE_Y_K + k + 0];
+                B[1].x[l] = y_qs[j*MMQ_TILE_Y_K + k + mma_B::K];
+            }
+#pragma unroll
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
+
+                dB[l] = y_df[j*MMQ_TILE_Y_K + ((2*k0 + 2*kvdr)/QI8_1) % (WARP_SIZE/QI8_1)];
+            }
+
+            C[0].mma_K4(A[kvdr/2 + 0], B[0]);
+            C[1].mma_K4(A[kvdr/2 + 1], B[1]);
+
+#pragma unroll
+            for (int l = 0; l < mma_C::ne; ++l) {
+                tmp[l] += (C[0].x[l]*scA[l/2][kvdr/2 + 0] + C[1].x[l]*scA[l/2][kvdr/2 + 1])*dB[l%2];
+            }
+        }
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne; ++l) {
+            sum[(j0/mma_B::J)*mma_C::ne + l] += tmp[l]*dA[l/2];
+        }
+    }
+}
+
 template<int mmq_x, int mmq_y, int nwarps, bool need_check>
 static __device__ __forceinline__ void mmq_write_back_dp4a(const float * __restrict__ sum, float * __restrict__ dst, const int & ne0, const int & ne1) {
 #pragma unroll
@@ -1448,24 +1721,39 @@ template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_K> {
     static constexpr int              vdr        = VDR_Q4_K_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles = load_tiles_q4_K<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q4_K_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
+#ifdef INT8_MMA_AVAILABLE
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q4_K_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
+#else
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q4_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
     static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
+#endif // INT8_MMA_AVAILABLE
 };
 
 template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_K> {
     static constexpr int              vdr        = VDR_Q5_K_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles = load_tiles_q5_K<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q5_K_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
+#ifdef INT8_MMA_AVAILABLE
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q5_K_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
+#else
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q5_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
     static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
+#endif // INT8_MMA_AVAILABLE
 };
 
 template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q6_K> {
     static constexpr int              vdr        = VDR_Q6_K_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles = load_tiles_q6_K<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q6_K_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
+#ifdef INT8_MMA_AVAILABLE
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q6_K_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
+#else
+    static constexpr vec_dot_mmq_t    vec_dot    = vec_dot_q6_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
     static constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
+#endif // INT8_MMA_AVAILABLE
 };
 
 static int mmq_need_sum(const ggml_type type_x) {

ggml-vulkan.cpp

@@ -1,5 +1,5 @@
 #include "ggml-vulkan.h"
-
+#include <vulkan/vulkan_core.h>
 #ifdef GGML_VULKAN_RUN_TESTS
 #include <chrono>
 #endif
@@ -9,12 +9,13 @@
 #include <algorithm>
 #include <cmath>
 #include <iostream>
-#include <limits>
 #include <tuple>
 #include <vector>
 #include <sstream>
 #include <utility>
 #include <memory>
+#include <limits>
+#include <map>
 
 #include "ggml.h"
 #include "ggml-backend-impl.h"
@@ -150,7 +151,7 @@ struct vk_device {
     vk_pipeline pipeline_relu_f32;
     vk_pipeline pipeline_diag_mask_inf_f32;
     vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16;
-    vk_pipeline pipeline_rope_f32, pipeline_rope_f16;
+    vk_pipeline pipeline_rope_norm_f32, pipeline_rope_norm_f16;
     vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16;
     vk_pipeline pipeline_argsort_f32;
     vk_pipeline pipeline_sum_rows_f32;
@@ -283,26 +284,15 @@ struct vk_op_diag_mask_push_constants {
 
 struct vk_op_rope_push_constants {
     uint32_t ncols;
+    uint32_t n_dims;
     float freq_scale;
     uint32_t p_delta_rows;
     float freq_base;
     float ext_factor;
     float attn_factor;
-    float corr_dims[4];
+    float corr_dims[2];
-};
-
-struct vk_op_rope_neox_push_constants {
-    uint32_t ncols;
-    uint32_t ndims;
-    float freq_scale;
-    uint32_t p_delta_rows;
-    float freq_base;
-    float ext_factor;
-    float attn_factor;
-    float corr_dims[4];
     float theta_scale;
-    float inv_ndims;
+    uint32_t has_ff;
-    uint32_t has_freq_facs;
 };
 
 struct vk_op_soft_max_push_constants {
@@ -1534,11 +1524,11 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) {
     ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_norm_f32, "rope_norm_f32", rope_norm_f32_len, rope_norm_f32_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_norm_f16, "rope_norm_f16", rope_norm_f16_len, rope_norm_f16_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 4, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
-    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 4, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
 
     ggml_vk_create_pipeline(ctx, ctx->device->pipeline_argsort_f32, "argsort_f32", argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1024, 1, 1}, {}, 1);
 
@@ -1566,8 +1556,10 @@ static void ggml_vk_print_gpu_info(size_t idx) {
     vk::PhysicalDeviceProperties2 props2;
     vk::PhysicalDeviceMaintenance3Properties props3;
     vk::PhysicalDeviceSubgroupProperties subgroup_props;
+    vk::PhysicalDeviceDriverProperties driver_props;
     props2.pNext = &props3;
     props3.pNext = &subgroup_props;
+    subgroup_props.pNext = &driver_props;
     physical_device.getProperties2(&props2);
 
     const size_t subgroup_size = subgroup_props.subgroupSize;
@@ -1611,7 +1603,7 @@ static void ggml_vk_print_gpu_info(size_t idx) {
     fp16 = fp16 && vk12_features.shaderFloat16;
 
     std::string device_name = props2.properties.deviceName.data();
-    std::cerr << GGML_VK_NAME << idx << ": " << device_name << " | uma: " << uma << " | fp16: " << fp16 << " | warp size: " << subgroup_size << std::endl;
+    std::cerr << GGML_VK_NAME << idx << ": " << device_name << " (" << driver_props.driverName << ") | uma: " << uma << " | fp16: " << fp16 << " | warp size: " << subgroup_size << std::endl;
 
     if (props2.properties.deviceType == vk::PhysicalDeviceType::eCpu) {
         std::cerr << "ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want." << std::endl;
@@ -1707,7 +1699,78 @@ void ggml_vk_instance_init() {
             vk::PhysicalDeviceProperties props = devices[i].getProperties();
 
             if (props.deviceType == vk::PhysicalDeviceType::eDiscreteGpu) {
-                vk_instance.device_indices.push_back(i);
+                // Check if there are two physical devices corresponding to the same GPU
+                auto old_device = std::find_if(
+                    vk_instance.device_indices.begin(),
+                    vk_instance.device_indices.end(),
+                    [&devices, &props](const size_t k){ return devices[k].getProperties().deviceID == props.deviceID; }
+                );
+                if (old_device == vk_instance.device_indices.end()) {
+                    vk_instance.device_indices.push_back(i);
+                } else {
+                    // There can be two physical devices corresponding to the same GPU if there are 2 different drivers
+                    // This can cause error when splitting layers aross the devices, need to keep only 1
+#ifdef GGML_VULKAN_DEBUG
+                    std::cerr << "Device " << i << " and device " << *old_device << " have the same device id" << std::endl;
+#endif
+
+                    vk::PhysicalDeviceProperties2 old_prop;
+                    vk::PhysicalDeviceDriverProperties old_driver;
+                    old_prop.pNext = &old_driver;
+                    devices[*old_device].getProperties2(&old_prop);
+
+                    vk::PhysicalDeviceProperties2 new_prop;
+                    vk::PhysicalDeviceDriverProperties new_driver;
+                    new_prop.pNext = &new_driver;
+                    devices[i].getProperties2(&new_prop);
+
+                    std::map<vk::DriverId, int> driver_priorities {};
+                    int old_priority = std::numeric_limits<int>::max();
+                    int new_priority = std::numeric_limits<int>::max();
+
+                    // Check https://registry.khronos.org/vulkan/specs/1.3-extensions/man/html/VkDriverId.html for the list of driver id
+                    // Smaller number -> higher priority
+                    switch (old_prop.properties.vendorID) {
+                        case VK_VENDOR_ID_AMD:
+                            driver_priorities[vk::DriverId::eMesaRadv] = 1;
+                            driver_priorities[vk::DriverId::eAmdOpenSource] = 2;
+                            driver_priorities[vk::DriverId::eAmdProprietary] = 3;
+                            break;
+                        case VK_VENDOR_ID_INTEL:
+                            driver_priorities[vk::DriverId::eIntelOpenSourceMESA] = 1;
+                            driver_priorities[vk::DriverId::eIntelProprietaryWindows] = 2;
+                            break;
+                        case VK_VENDOR_ID_NVIDIA:
+                            driver_priorities[vk::DriverId::eNvidiaProprietary] = 1;
+#if defined(VK_API_VERSION_1_3) && VK_HEADER_VERSION >= 235
+                            driver_priorities[vk::DriverId::eMesaNvk] = 2;
+#endif
+                            break;
+                    }
+
+                    if (driver_priorities.count(old_driver.driverID)) {
+                        old_priority = driver_priorities[old_driver.driverID];
+                    }
+                    if (driver_priorities.count(new_driver.driverID)) {
+                        new_priority = driver_priorities[new_driver.driverID];
+                    }
+
+                    if (new_priority < old_priority) {
+                        auto r = std::remove(vk_instance.device_indices.begin(), vk_instance.device_indices.end(), *old_device);
+                        vk_instance.device_indices.erase(r, vk_instance.device_indices.end());
+                        vk_instance.device_indices.push_back(i);
+
+#ifdef GGML_VULKAN_DEBUG
+                        std::cerr << "Prioritize device " << i << " driver " << new_driver.driverName << " over device " << *old_device << " driver " << old_driver.driverName << std::endl;
+#endif
+                    }
+#ifdef GGML_VULKAN_DEBUG
+                    else {
+                        std::cerr << "Prioritize device " << *old_device << " driver " << old_driver.driverName << " over device " << i << " driver " << new_driver.driverName << std::endl;
+
+                    }
+#endif
+                }
             }
         }
 
@@ -3905,10 +3968,10 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
                 }
             } else {
                 if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-                    return ctx->device->pipeline_rope_f32;
+                    return ctx->device->pipeline_rope_norm_f32;
                 }
                 if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
-                    return ctx->device->pipeline_rope_f16;
+                    return ctx->device->pipeline_rope_norm_f16;
                 }
             }
             return nullptr;
@@ -4152,24 +4215,16 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
             ggml_vk_sync_buffers(subctx);
             ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
         } else if (op == GGML_OP_ROPE) {
-            const int mode          = ((int32_t *) dst->op_params)[2];
+            // Empty src2 is possible in rope, but the shader needs a buffer
-            const bool is_neox = mode & 2;
+            vk_subbuffer subbuf_z;
-
+            if (use_src2) {
-            if (is_neox) {
+                subbuf_z = { d_Z, z_buf_offset, z_sz };
-                // Empty src2 is possible in rope, but the shader needs a buffer
-                vk_subbuffer subbuf_z;
-                if (use_src2) {
-                    subbuf_z = { d_Z, z_buf_offset, z_sz };
-                } else {
-                    subbuf_z = { d_X, 0, d_X->size };
-                }
-
-                ggml_vk_sync_buffers(subctx);
-                ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
             } else {
-                ggml_vk_sync_buffers(subctx);
+                subbuf_z = { d_X, 0, d_X->size };
-                ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
             }
+
+            ggml_vk_sync_buffers(subctx);
+            ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
         } else if (use_src2) {
             ggml_vk_sync_buffers(subctx);
             ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_Z, z_buf_offset, z_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
@@ -4391,7 +4446,7 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx,
 
 static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) {
     const int n_dims        = ((int32_t *) dst->op_params)[1];
-    const int mode          = ((int32_t *) dst->op_params)[2];
+    // const int mode          = ((int32_t *) dst->op_params)[2];
     // const int n_ctx         = ((int32_t *) dst->op_params)[3];
     const int n_ctx_orig    = ((int32_t *) dst->op_params)[4];
     const float freq_base   = ((float *)   dst->op_params)[5];
@@ -4401,28 +4456,16 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, con
     const float beta_fast   = ((float *)   dst->op_params)[9];
     const float beta_slow   = ((float *)   dst->op_params)[10];
 
-    const bool is_neox = mode & 2;
-
-#pragma message("TODO: update rope NORM mode to match NEOX mode")
-#pragma message("      https://github.com/ggerganov/llama.cpp/pull/7634")
-
     float corr_dims[2];
     ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
 
-    if (is_neox) {
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
-        const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
-        const float inv_ndims = -1.0f / n_dims;
+    ggml_vk_op_f32<vk_op_rope_push_constants>(ctx, subctx, src0, src1, src2, dst, GGML_OP_ROPE, {
-        ggml_vk_op_f32<vk_op_rope_neox_push_constants>(ctx, subctx, src0, src1, src2, dst, GGML_OP_ROPE, {
+        (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1],
-            (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1],
+        freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1]}, theta_scale,
-            freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f}, theta_scale, inv_ndims,
+        src2 != nullptr,
-            src2 != nullptr,
+    });
-        });
-    } else {
-        ggml_vk_op_f32<vk_op_rope_push_constants>(ctx, subctx, src0, src1, src2, dst, GGML_OP_ROPE, {
-            (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1],
-            freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f}
-        });
-    }
 }
 
 static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
@@ -6070,7 +6113,13 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(
     std::cerr << "ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")" << std::endl;
 #endif
     ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
-    vk_buffer dev_buffer = ggml_vk_create_buffer_device(ctx->ctx, size);
+
+    vk_buffer dev_buffer = nullptr;
+    try {
+        dev_buffer = ggml_vk_create_buffer_device(ctx->ctx, size);
+    } catch (const vk::SystemError& e) {
+        return nullptr;
+    }
 
     ggml_backend_vk_buffer_context * bufctx = new ggml_backend_vk_buffer_context(ctx->ctx, std::move(dev_buffer), ctx->name);
 
@@ -6466,7 +6515,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const
         //         return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
         //     } break;
         case GGML_OP_ROPE:
-            return true;
+            return ggml_is_contiguous(op->src[0]);
         case GGML_OP_NONE:
         case GGML_OP_RESHAPE:
         case GGML_OP_VIEW:

ggml_vk_generate_shaders.py

@@ -2400,7 +2400,7 @@ void main() {
 """
 
 # ROPE
-rope_src = """
+rope_norm_src = """
 #version 450
 
 #extension GL_EXT_shader_16bit_storage : require
@@ -2408,17 +2408,21 @@ rope_src = """
 layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in;
 
 layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer Y {int data_b[];};
+layout (binding = 1) readonly buffer Y {int data_pos[];};
-layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
+layout (binding = 2) readonly buffer Z {float data_ff[];};
+layout (binding = 3) writeonly buffer D {D_TYPE data_d[];};
 
 layout (push_constant) uniform parameter {
     uint ncols;
+    uint n_dims;
     float freq_scale;
     uint p_delta_rows;
     float freq_base;
     float ext_factor;
     float attn_factor;
-    float corr_dims[4];
+    float corr_dims[2];
+    float theta_scale;
+    uint has_ff;
 } p;
 
 float rope_yarn_ramp(const float low, const float high, const uint i0) {
@@ -2450,14 +2454,24 @@ void main() {
         return;
     }
 
+    if (col >= p.n_dims) {
+        const uint i = row*p.ncols + col;
+
+        data_d[i + 0] = data_a[i + 0];
+        data_d[i + 1] = data_a[i + 1];
+
+        return;
+    }
+
     const uint i = row*p.ncols + col;
     const uint i2 = row/p.p_delta_rows;
 
-    const int pos = data_b[i2];
+    const float theta_base = data_pos[i2] * pow(p.theta_scale, col/2.0f);
-    const float theta_base = pos * pow(p.freq_base, -float(col)/p.ncols);
+
+    const float freq_factor = p.has_ff != 0 ? data_ff[col/2] : 1.0f;
 
     float cos_theta, sin_theta;
-    rope_yarn(theta_base, col, cos_theta, sin_theta);
+    rope_yarn(theta_base / freq_factor, col, cos_theta, sin_theta);
 
     const float x0 = float(data_a[i + 0]);
     const float x1 = float(data_a[i + 1]);
@@ -2475,22 +2489,21 @@ rope_neox_src = """
 layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in;
 
 layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) readonly buffer Y {int data_b[];};
+layout (binding = 1) readonly buffer Y {int data_pos[];};
-layout (binding = 2) readonly buffer Z {float data_freq_factors[];};
+layout (binding = 2) readonly buffer Z {float data_ff[];};
 layout (binding = 3) writeonly buffer D {D_TYPE data_d[];};
 
 layout (push_constant) uniform parameter {
     uint ncols;
-    uint ndims;
+    uint n_dims;
     float freq_scale;
     uint p_delta_rows;
     float freq_base;
     float ext_factor;
     float attn_factor;
-    float corr_dims[4];
+    float corr_dims[2];
     float theta_scale;
-    float inv_ndims;
+    uint has_ff;
-    uint has_freq_facs;
 } p;
 
 float rope_yarn_ramp(const float low, const float high, const uint i0) {
@@ -2522,11 +2535,8 @@ void main() {
         return;
     }
 
-    const uint ib = col / p.ndims;
+    if (col >= p.n_dims) {
-    const uint ic = col % p.ndims;
+        const uint i = row*p.ncols + col;
-
-    if (ib > 0) {
-        const uint i = row*p.ncols + ib*p.ndims + ic;
 
         data_d[i + 0] = data_a[i + 0];
         data_d[i + 1] = data_a[i + 1];
@@ -2534,29 +2544,27 @@ void main() {
         return;
     }
 
-    const uint i  = row*p.ncols + ib*p.ndims + ic/2;
+    const uint i  = row*p.ncols + col/2;
     const uint i2 = row/p.p_delta_rows;
 
-    const int pos = data_b[i2];
+    const float theta_base = data_pos[i2] * pow(p.theta_scale, col/2.0f);
-    const float freq_factor = p.has_freq_facs != 0 ? data_freq_factors[ic/2] : 1.0f;
+
-    const float theta_base = pos*p.freq_scale*pow(p.theta_scale, col/2.0f) / freq_factor;
+    const float freq_factor = p.has_ff != 0 ? data_ff[col/2] : 1.0f;
 
     float cos_theta, sin_theta;
-    rope_yarn(theta_base, ic, cos_theta, sin_theta);
+    rope_yarn(theta_base / freq_factor, col, cos_theta, sin_theta);
 
     const float x0 = float(data_a[i + 0]);
-    const float x1 = float(data_a[i + p.ndims/2]);
+    const float x1 = float(data_a[i + p.n_dims/2]);
 
     data_d[i + 0]        = D_TYPE(x0*cos_theta - x1*sin_theta);
-    data_d[i + p.ndims/2] = D_TYPE(x0*sin_theta + x1*cos_theta);
+    data_d[i + p.n_dims/2] = D_TYPE(x0*sin_theta + x1*cos_theta);
 }
 """
 
 argsort_src = """
 #version 450
 
-#extension GL_EXT_shader_16bit_storage : require
-
 #define BLOCK_SIZE 1024
 #define ASC 0
 
@@ -3039,8 +3047,8 @@ async def main():
     tasks.append(string_to_spv("soft_max_f32", f"{soft_max_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "C_TYPE": "float", "D_TYPE": "float"}))
     tasks.append(string_to_spv("soft_max_f32_f16", f"{soft_max_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float16_t", "C_TYPE": "float16_t", "D_TYPE": "float"}))
 
-    tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("rope_norm_f32", rope_norm_src, {"A_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
+    tasks.append(string_to_spv("rope_norm_f16", rope_norm_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
 
     tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"}))
     tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))

tests/test-json-schema-to-grammar.cpp

@@ -870,7 +870,7 @@ int main() {
         }
     });
 
-    if (getenv("LLAMA_PYTHON_AVAILABLE") || (std::system("python --version") == 0)) {
+    if (getenv("LLAMA_PYTHON_AVAILABLE") || (std::system("python -c \"import sys; exit(1) if sys.version_info < (3, 8) else print('Python version is sufficient')\"") == 0)) {
         test_all("Python", [](const TestCase & tc) {
             write("test-json-schema-input.tmp", tc.schema);
             tc.verify_status(std::system(
@@ -878,7 +878,7 @@ int main() {
             tc.verify(read("test-grammar-output.tmp"));
         });
     } else {
-        fprintf(stderr, "\033[33mWARNING: Python not found, skipping Python JSON schema -> grammar tests.\n\033[0m");
+        fprintf(stderr, "\033[33mWARNING: Python not found (min version required is 3.8), skipping Python JSON schema -> grammar tests.\n\033[0m");
     }
 
     if (getenv("LLAMA_NODE_AVAILABLE") || (std::system("node --version") == 0)) {