Merge branch 'master' into huydt/truncate-embed

Author: huydt-bti

.github/workflows/build.yml

@@ -84,7 +84,8 @@ jobs:
             -DCMAKE_BUILD_RPATH="@loader_path" \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DGGML_METAL_USE_BF16=ON \
-            -DGGML_METAL_EMBED_LIBRARY=ON \
+            -DGGML_METAL_EMBED_LIBRARY=OFF \
+            -DGGML_METAL_SHADER_DEBUG=ON \
             -DGGML_RPC=ON
           cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
 

docs/docker.md

@@ -25,6 +25,9 @@ Additionally, there the following images, similar to the above:
 - `ghcr.io/ggml-org/llama.cpp:full-intel`: Same as `full` but compiled with SYCL support. (platforms: `linux/amd64`)
 - `ghcr.io/ggml-org/llama.cpp:light-intel`: Same as `light` but compiled with SYCL support. (platforms: `linux/amd64`)
 - `ghcr.io/ggml-org/llama.cpp:server-intel`: Same as `server` but compiled with SYCL support. (platforms: `linux/amd64`)
+- `ghcr.io/ggml-org/llama.cpp:full-vulkan`: Same as `full` but compiled with Vulkan support. (platforms: `linux/amd64`)
+- `ghcr.io/ggml-org/llama.cpp:light-vulkan`: Same as `light` but compiled with Vulkan support. (platforms: `linux/amd64`)
+- `ghcr.io/ggml-org/llama.cpp:server-vulkan`: Same as `server` but compiled with Vulkan support. (platforms: `linux/amd64`)
 
 The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](../.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](../.github/workflows/docker.yml). If you need different settings (for example, a different CUDA, ROCm or MUSA library, you'll need to build the images locally for now).
 

ggml/include/ggml.h

@@ -1867,6 +1867,12 @@ extern "C" {
     enum ggml_scale_mode {
         GGML_SCALE_MODE_NEAREST  = 0,
         GGML_SCALE_MODE_BILINEAR = 1,
+
+        GGML_SCALE_MODE_COUNT
+    };
+
+    enum ggml_scale_flag {
+        GGML_SCALE_FLAG_ALIGN_CORNERS = (1 << 8)
     };
 
     // interpolate
@@ -1879,14 +1885,26 @@ extern "C" {
 
     // interpolate
     // interpolate scale to specified dimensions
-    GGML_API struct ggml_tensor * ggml_upscale_ext(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_upscale_ext(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
             int                   ne0,
             int                   ne1,
             int                   ne2,
             int                   ne3,
-            enum ggml_scale_mode  mode);
+            enum ggml_scale_mode  mode),
+        "use ggml_interpolate instead");
+
+    // Up- or downsamples the input to the specified size.
+    // 2D scale modes (eg. bilinear) are applied to the first two dimensions.
+    GGML_API struct ggml_tensor * ggml_interpolate(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2,
+            int64_t               ne3,
+            uint32_t              mode); // ggml_scale_mode [ | ggml_scale_flag...]
 
     // pad each dimension with zeros: [x, ..., x] -> [x, ..., x, 0, ..., 0]
     GGML_API struct ggml_tensor * ggml_pad(

ggml/src/ggml-cann/aclnn_ops.cpp

@@ -65,7 +65,7 @@
 #include <aclnnop/aclnn_eq_tensor.h>
 #include <aclnnop/aclnn_gt_scalar.h>
 #include <aclnnop/aclnn_pow.h>
-#include <aclnnop/aclnn_grouped_matmul_v2.h>
+#include <aclnnop/aclnn_grouped_matmul_v3.h>
 #include <aclnnop/aclnn_fused_infer_attention_score_v2.h>
 #include <float.h>
 
@@ -2654,6 +2654,67 @@ static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context& ctx, ggml_tensor*
         memcpy(ori_src0_nb, cast_nb, sizeof(ori_src0_nb));
     }
 
+#ifdef ASCEND_310P
+    ggml_tensor src0_row = *src0;
+    ggml_tensor src1_row = *src1;
+    ggml_tensor dst_row = *dst;
+
+    if (src0->type == GGML_TYPE_F16) {
+        src0_row.type = GGML_TYPE_F32;
+    }
+
+    // src0_row [D, M, 1, 1] weight without permute
+    src0_row.ne[2] = 1;
+    src0_row.ne[3] = 1;
+    src0_row.nb[0] = ori_src0_nb[0];
+    src0_row.nb[1] = ori_src0_nb[1];
+    src0_row.nb[2] = ori_src0_nb[1];
+    src0_row.nb[3] = ori_src0_nb[1];
+
+    // src1_row [D, 1, 1, 1] -> input
+    src1_row.ne[1] = 1;
+    src1_row.ne[2] = 1;
+    src1_row.ne[3] = 1;
+    src1_row.nb[2] = nb11;
+    src1_row.nb[3] = nb11;
+
+    // dst_row [M, 1, 1, 1] -> out
+    dst_row.ne[1] = 1;
+    dst_row.ne[2] = 1;
+    dst_row.ne[3] = 1;
+    dst_row.nb[2] = nb1;
+    dst_row.nb[3] = nb1;
+
+    //create weight for one row
+    for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
+        for (int64_t id = 0; id < n_ids; id++) {
+            // expert index
+            int32_t i02 = *(int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]);
+            GGML_ASSERT(i02 >= 0 && i02 < n_as);
+
+            // If B = 1 (broadcast), always use 0; otherwise, use id.
+            int64_t i11 = (ne11 == 1 ? 0 : id);
+            int64_t i12 = iid1;
+
+            int64_t i1 = id;
+            int64_t i2 = i12;
+
+            void* src0_tmp_ptr = src0_original + i02*ori_src0_nb[2];
+            void* src1_tmp_ptr = src1_original + i11*nb11 + i12*nb12;
+            void* dst_tmp_ptr  = dst_original  + i1*nb1   + i2*nb2;
+
+            src0_row.data = src0_tmp_ptr;
+            src1_row.data = src1_tmp_ptr;
+            dst_row.data = dst_tmp_ptr;
+            dst_row.src[0] = &src0_row;
+            dst_row.src[1] = &src1_row;
+
+            ggml_cann_mul_mat(ctx, &dst_row);
+        }
+    }
+    return;
+#endif
+
     std::vector<aclTensor*> src0_tensor_vec;
     std::vector<aclTensor*> src1_tensor_vec;
     std::vector<aclTensor*> dst_tensor_vec;
@@ -2701,9 +2762,9 @@ static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context& ctx, ggml_tensor*
     }
 
     size_t GROUP_SIZE = 128;
-    // GroupedMatmulV2 required tensor_list.size < 128
+    // GroupedMatmulV3 required tensor_list.size < 128
     for (size_t i = 0; i < src0_tensor_vec.size(); i += GROUP_SIZE) {
-        // split and call GroupedMatmulV2
+        // split and call GroupedMatmulV3
         size_t end = std::min(i + GROUP_SIZE, src0_tensor_vec.size());
         std::vector<aclTensor*> src0_tensor_vec_split(src0_tensor_vec.begin() + i, src0_tensor_vec.begin() + end);
         std::vector<aclTensor*> src1_tensor_vec_split(src1_tensor_vec.begin() + i, src1_tensor_vec.begin() + end);
@@ -2713,7 +2774,7 @@ static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context& ctx, ggml_tensor*
         aclTensorList* src1_tensor_list = aclCreateTensorList(src1_tensor_vec_split.data(), src1_tensor_vec_split.size());
         aclTensorList* dst_tensor_list = aclCreateTensorList(dst_tensor_vec_split.data(), dst_tensor_vec_split.size());
 
-        GGML_CANN_CALL_ACLNN_OP(ctx, GroupedMatmulV2, src1_tensor_list, src0_tensor_list,
+        GGML_CANN_CALL_ACLNN_OP(ctx, GroupedMatmulV3, src1_tensor_list, src0_tensor_list,
             nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, 0, -1, dst_tensor_list);
 
         ggml_cann_release_resources(ctx, src0_tensor_list, src1_tensor_list, dst_tensor_list);

ggml/src/ggml-cpu/ops.cpp

@@ -7276,12 +7276,13 @@ static void ggml_compute_forward_upscale_f32(
 
     GGML_TENSOR_UNARY_OP_LOCALS
 
-    const float sf0 = (float)ne0/src0->ne[0];
-    const float sf1 = (float)ne1/src0->ne[1];
-    const float sf2 = (float)ne2/src0->ne[2];
-    const float sf3 = (float)ne3/src0->ne[3];
+    float sf0 = (float)ne0/src0->ne[0];
+    float sf1 = (float)ne1/src0->ne[1];
+    float sf2 = (float)ne2/src0->ne[2];
+    float sf3 = (float)ne3/src0->ne[3];
 
-    const ggml_scale_mode mode = (ggml_scale_mode) ggml_get_op_params_i32(dst, 0);
+    const int32_t mode_flags = ggml_get_op_params_i32(dst, 0);
+    const ggml_scale_mode mode = (ggml_scale_mode) (mode_flags & 0xFF);
 
     if (mode == GGML_SCALE_MODE_NEAREST) {
         for (int64_t i3 = 0; i3 < ne3; i3++) {
@@ -7302,8 +7303,12 @@ static void ggml_compute_forward_upscale_f32(
             }
         }
     } else if (mode == GGML_SCALE_MODE_BILINEAR) {
-        // setting a pixel offset of 0 would replicate the behavior of pytorch interpolate with align_corners=True
-        const float pixel_offset = 0.5f;
+        float pixel_offset = 0.5f;
+        if (mode_flags & GGML_SCALE_FLAG_ALIGN_CORNERS) {
+            pixel_offset = 0.0f;
+            sf0 = (float)(ne0 - 1) / (src0->ne[0] - 1);
+            sf1 = (float)(ne1 - 1) / (src0->ne[1] - 1);
+        }
 
         for (int64_t i3 = 0; i3 < ne3; i3++) {
             const int64_t i03 = i3 / sf3;

ggml/src/ggml-metal/CMakeLists.txt

@@ -71,7 +71,9 @@ else()
         # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
         # note: unfortunately, we have to call it default.metallib instead of ggml.metallib
         #       ref: https://github.com/ggerganov/whisper.cpp/issues/1720
-        set(XC_FLAGS -fno-fast-math -fno-inline -g)
+        # note: adding -g causes segmentation fault during compile
+        #set(XC_FLAGS -fno-fast-math -fno-inline -g)
+        set(XC_FLAGS -fno-fast-math -fno-inline)
     else()
         set(XC_FLAGS -O3)
     endif()
@@ -90,7 +92,7 @@ else()
     add_custom_command(
         OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
         COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o - |
-            xcrun -sdk macosx metallib - -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
+                xcrun -sdk macosx metallib        - -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
         COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h
         COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal
         DEPENDS ggml-metal.metal ${METALLIB_COMMON}

ggml/src/ggml-opencl/CMakeLists.txt

@@ -65,6 +65,7 @@ set(GGML_OPENCL_KERNELS
     gemv_noshuffle_general
     gemv_noshuffle
     get_rows
+    glu
     group_norm
     im2col_f32
     im2col_f16

ggml/src/ggml-opencl/ggml-opencl.cpp

@@ -351,6 +351,7 @@ struct ggml_backend_opencl_context {
     cl_program program_gemv_noshuffle_general;
     cl_program program_gemv_noshuffle;
     cl_program program_get_rows;
+    cl_program program_glu;
     cl_program program_im2col_f16;
     cl_program program_im2col_f32;
     cl_program program_mul_mat_Ab_Bi_8x4;
@@ -401,6 +402,8 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_relu;
     cl_kernel kernel_sigmoid_f32, kernel_sigmoid_f16;
     cl_kernel kernel_clamp;
+    cl_kernel kernel_geglu, kernel_reglu, kernel_swiglu,
+              kernel_geglu_f16, kernel_reglu_f16, kernel_swiglu_f16;
     cl_kernel kernel_norm;
     cl_kernel kernel_rms_norm;
     cl_kernel kernel_group_norm;
@@ -738,6 +741,27 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // glu
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "glu.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("glu.cl");
+#endif
+        backend_ctx->program_glu =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_geglu      = clCreateKernel(backend_ctx->program_glu, "kernel_geglu", &err), err));
+        CL_CHECK((backend_ctx->kernel_reglu      = clCreateKernel(backend_ctx->program_glu, "kernel_reglu", &err), err));
+        CL_CHECK((backend_ctx->kernel_swiglu     = clCreateKernel(backend_ctx->program_glu, "kernel_swiglu", &err), err));
+        CL_CHECK((backend_ctx->kernel_geglu_f16  = clCreateKernel(backend_ctx->program_glu, "kernel_geglu_f16", &err), err));
+        CL_CHECK((backend_ctx->kernel_reglu_f16  = clCreateKernel(backend_ctx->program_glu, "kernel_reglu_f16", &err), err));
+        CL_CHECK((backend_ctx->kernel_swiglu_f16 = clCreateKernel(backend_ctx->program_glu, "kernel_swiglu_f16", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
     // get_rows
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -2242,6 +2266,15 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
                 default:
                     return false;
             }
+        case GGML_OP_GLU:
+            switch (ggml_get_glu_op(op)) {
+                case GGML_GLU_OP_GEGLU:
+                case GGML_GLU_OP_REGLU:
+                case GGML_GLU_OP_SWIGLU:
+                    return ggml_is_contiguous_1(op->src[0]) && (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16);
+                default:
+                    return false;
+            }
         case GGML_OP_CLAMP:
             return op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_SOFT_MAX:
@@ -6143,6 +6176,91 @@ static void ggml_cl_sum_rows(ggml_backend_t backend, const ggml_tensor * src0, c
     backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
 }
 
+static void ggml_cl_glu(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    GGML_ASSERT(ggml_is_contiguous_1(src0));
+
+    if (src1) {
+        GGML_ASSERT(src1);
+        GGML_ASSERT(src1->extra);
+        GGML_ASSERT(ggml_are_same_shape(src0, src1));
+    }
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+
+    cl_kernel kernel;
+    switch (ggml_get_glu_op(dst)) {
+        case GGML_GLU_OP_GEGLU:
+            if (dst->type == GGML_TYPE_F32) {
+                kernel = backend_ctx->kernel_geglu;
+            } else {
+                kernel = backend_ctx->kernel_geglu_f16;
+            }
+            break;
+        case GGML_GLU_OP_REGLU:
+            if (dst->type == GGML_TYPE_F32) {
+                kernel = backend_ctx->kernel_reglu;
+            } else {
+                kernel = backend_ctx->kernel_reglu_f16;
+            }
+            break;
+        case GGML_GLU_OP_SWIGLU:
+            if (dst->type == GGML_TYPE_F32) {
+                kernel = backend_ctx->kernel_swiglu;
+            } else {
+                kernel = backend_ctx->kernel_swiglu_f16;
+            }
+            break;
+        default:
+            GGML_ABORT("Unsupported glu op");
+    }
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    ggml_tensor_extra_cl * extra1 = src1 ? (ggml_tensor_extra_cl *)src1->extra : nullptr;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    cl_ulong offset1 = extra1 ? extra1->offset + src1->view_offs : offset0;
+
+    const int ne0       = dst->ne[0];
+
+    const cl_ulong nb01 = src0->nb[1];
+    const cl_ulong nb11 = src1 ? src1->nb[1] : nb01;
+
+    const cl_ulong nb1  = dst->nb[1];
+
+    const int swp = ((const int32_t *) dst->op_params)[1];
+    const int ne00_off = src1 ? 0 : (swp ? ne0 : 0);
+    const int ne10_off = src1 ? 0 : (swp ? 0 : ne0);
+
+    CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   src1 ? &extra1->data_device : &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_ulong), &nb01));
+    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &nb11));
+    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne0));
+    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb1));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne00_off));
+    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne10_off));
+
+    const size_t nrows = ggml_nrows(src0);
+    size_t nth = 512;
+    size_t global_work_size[] = {nrows*nth, 1, 1};
+    size_t local_work_size[] = {nth, 1, 1};
+
+    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+}
+
 //------------------------------------------------------------------------------
 // Op offloading
 //------------------------------------------------------------------------------
@@ -6244,6 +6362,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
                 default:
                     return false;
             } break;
+        case GGML_OP_GLU:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_glu;
+            break;
         case GGML_OP_CLAMP:
             if (!any_on_device) {
                 return false;