ggml : initial zDNN backend (llama/14975)

ggml/src/ggml-zdnn/CMakeLists.txt

@@ -0,0 +1,36 @@
+if (DEFINED ZDNN_ROOT)
+    message(STATUS "zdnn: using ZDNN_ROOT override: ${ZDNN_ROOT}")
+    set(ZDNN_HINT "${ZDNN_ROOT}")
+else()
+    set(ZDNN_HINT "")
+endif()
+
+find_path(ZDNN_INCLUDE
+            NAMES zdnn.h
+            HINTS ${ZDNN_HINT} /usr /usr/local
+            PATH_SUFFIXES include)
+if (ZDNN_INCLUDE)
+    message(STATUS "zdnn: found include: ${ZDNN_INCLUDE}")
+else()
+    message(FATAL_ERROR "zdnn: include directory not found, please set ZDNN_ROOT to the proper path if necessary")
+endif()
+
+find_library(ZDNN_LIB
+                NAMES zdnn
+                HINTS ${ZDNN_HINT} /usr /usr/local
+                PATH_SUFFIXES lib lib64)
+if (ZDNN_LIB)
+    message(STATUS "zdnn: found library: ${ZDNN_LIB}")
+else()
+    message(FATAL_ERROR "zdnn: library not found, please set ZDNN_ROOT to the proper path if necessary")
+endif()
+
+file(GLOB GGML_SOURCES_ZDNN "*.c" "*.cpp")
+file(GLOB GGML_HEADERS_ZDNN "*.h" "*.hpp")
+
+ggml_add_backend_library(ggml-zdnn ${GGML_HEADERS_ZDNN} ${GGML_SOURCES_ZDNN})
+target_link_libraries(ggml-zdnn PRIVATE ${ZDNN_LIB})
+target_include_directories(ggml-zdnn PRIVATE ${ZDNN_INCLUDE})
+target_link_directories(ggml-zdnn PRIVATE ${ZDNN_LIB})
+
+target_compile_definitions(ggml-zdnn PRIVATE GGML_USE_ZDNN)

ggml/src/ggml-zdnn/ggml-zdnn-impl.h

@@ -0,0 +1,97 @@
+#ifndef GGML_ZDNN_IMPL
+#define GGML_ZDNN_IMPL
+
+#include "zdnn.h"
+#include "ggml.h"
+#include "ggml-zdnn.h"
+
+#include <vector>
+#include <memory>
+#include <vecintrin.h>
+
+#define GGML_ZDNN_NAME    "zDNN"
+#define GGML_ZDNN_VERSION ZDNN_VERNUM
+
+#define vec_neg(a)    (-(a))                // Vector Negate
+#define vec_add(a, b) ((a) + (b))           // Vector Add
+#define vec_sub(a, b) ((a) - (b))           // Vector Subtract
+#define vec_mul(a, b) ((a) * (b))           // Vector Multiply
+#define vec_div(a, b) ((a) / (b))           // Vector Divide
+#define vec_sl(a, b)  ((a) << (b))          // Vector Shift Left
+#define vec_sra(a, b) ((a) >> (b))          // Vector Shift Right
+#define vec_sr(a, b)  ((a) >> (b))          // Vector Shift Right Algebraic
+#define vec_slo(a, b) vec_slb(a, (b) << 64) // Vector Shift Left by Octet
+#define vec_sro(a, b) vec_srb(a, (b) << 64) // Vector Shift Right by Octet
+
+#ifndef vec_and
+#define vec_and(a, b) ((a) & (b)) // Vector AND
+#endif
+
+#ifndef vec_or
+#define vec_or(a, b)  ((a) | (b)) // Vector OR
+#endif
+
+#ifndef vec_xor
+#define vec_xor(a, b) ((a) ^ (b)) // Vector XOR
+#endif
+
+typedef   signed char char8x16_t  __attribute__((vector_size(16)));
+typedef unsigned char uchar8x16_t __attribute__((vector_size(16)));
+
+typedef int8_t   int8x16_t  __attribute__((vector_size(16)));
+typedef int16_t  int16x8_t  __attribute__((vector_size(16)));
+typedef int32_t  int32x4_t  __attribute__((vector_size(16)));
+typedef uint8_t  uint8x16_t __attribute__((vector_size(16)));
+typedef uint16_t uint16x8_t __attribute__((vector_size(16)));
+typedef uint32_t uint32x4_t __attribute__((vector_size(16)));
+
+typedef float float32x4_t   __attribute__((vector_size(16)));
+typedef double double64x2_t __attribute__((vector_size(16)));
+
+typedef   signed long long long64x2_t  __attribute__((vector_size(16)));
+typedef unsigned long long ulong64x2_t __attribute__((vector_size(16)));
+
+#define ZDNN_CHECK(stmt)                \
+    do {                                \
+        zdnn_status status = (stmt);    \
+        GGML_ASSERT(status == ZDNN_OK); \
+    } while (0);
+
+struct ggml_backend_zdnn_device_context {
+    int zdnn_device;
+    int zdnn_device_ref_count;
+
+    bool has_parmblkformat_0;
+    bool has_parmblkformat_1;
+
+    size_t max_size;
+
+    char name[128];
+};
+
+struct ggml_backend_zdnn_context {
+    int device;
+    ggml_cgraph * gf;
+};
+
+struct ggml_backend_zdnn_buffer {
+    void * data;
+    size_t size;
+
+    zdnn_tensor_desc pre_tfm_desc;
+    zdnn_tensor_desc tfm_desc;
+    zdnn_ztensor     ztensor;
+
+    char name[GGML_MAX_NAME];
+};
+
+struct ggml_backend_zdnn_buffer_context {
+    void * all_data;
+    size_t all_size;
+    bool owned;
+
+    int n_buffers;
+    std::vector<std::unique_ptr<ggml_backend_zdnn_buffer>> buffers;
+};
+
+#endif  // GGML_ZDNN_IMPL

ggml/src/ggml-zdnn/ggml-zdnn.cpp

@@ -0,0 +1,846 @@
+#include "zdnn.h"
+#include "ggml-zdnn.h"
+#include "ggml-zdnn-impl.h"
+
+#include "ggml-impl.h"
+#include "ggml-backend-impl.h"
+
+#include <vector>
+#include <memory>
+#include <csignal>
+#include <unistd.h>
+
+inline zdnn_data_types ggml_zdnn_type_mapping(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_F32:
+            return FP32;
+        case GGML_TYPE_F16:
+            return FP16;
+        case GGML_TYPE_BF16:
+            return BFLOAT;
+        case GGML_TYPE_I8:
+            return INT8;
+        case GGML_TYPE_I32:
+            return INT32;
+        case GGML_TYPE_Q8_0:
+            return INT8;
+        default:
+            GGML_ABORT("%s: fatal: unable to determine zTensor data type",
+                       __func__);
+            break;
+    }
+}
+
+inline void ggml_zdnn_create_tensor(zdnn_tensor_desc  & pre_tfm_desc,
+                                    zdnn_tensor_desc  & tfm_desc,
+                                    zdnn_ztensor      & ztensor,
+                              const ggml_tensor       * src,
+                              const int64_t           * ne,
+                              const zdnn_data_layouts   layout) {
+    zdnn_init_pre_transformed_desc(
+        layout,
+        ggml_zdnn_type_mapping(src->type),
+        &pre_tfm_desc,
+        ne[3], ne[2], ne[1], ne[0]
+    );
+
+    ZDNN_CHECK(zdnn_generate_transformed_desc(&pre_tfm_desc, &tfm_desc));
+    ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&pre_tfm_desc, &tfm_desc, &ztensor));
+}
+
+inline void ggml_zdnn_load_tensor(zdnn_ztensor & ztensor,
+                                          void * buffer) {
+    ZDNN_CHECK(zdnn_transform_ztensor(&ztensor, buffer));
+}
+
+inline void ggml_zdnn_init_tensor(ggml_backend_zdnn_buffer * buffer, const ggml_tensor * tensor) {
+    switch (tensor->op) {
+        case GGML_OP_MUL_MAT:
+            {
+                zdnn_init_pre_transformed_desc(
+                    ZDNN_2D,
+                    ggml_zdnn_type_mapping(tensor->type),
+                    &buffer->pre_tfm_desc,
+                    tensor->ne[1], tensor->ne[0]
+                );
+            } break;
+
+        default:
+            {
+                // For 4D tensors, GGML uses NCHW layout. However, because zDNN
+                // automatically transforms everything to NHWC, we will use it
+                // directly to avoid the performance penalty changing the
+                // layout and reshaping the tensor.
+                zdnn_init_pre_transformed_desc(
+                    ZDNN_NHWC,
+                    ggml_zdnn_type_mapping(tensor->type),
+                    &buffer->pre_tfm_desc,
+                    tensor->ne[3], tensor->ne[2], tensor->ne[1], tensor->ne[0]
+                );
+
+                // TODO: Consider adding a ggml check.
+                // TODO: If tensor = 4D, use ZDNN_NCHW by default.
+                // TODO: If tensor = 2D, use ZDNN_NHWC by default.
+            } break;
+    }
+
+    ZDNN_CHECK(zdnn_generate_transformed_desc(&buffer->pre_tfm_desc, &buffer->tfm_desc));
+    ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&buffer->pre_tfm_desc, &buffer->tfm_desc, &buffer->ztensor));
+}
+
+static void ggml_zdnn_mul_mat_op(ggml_backend_zdnn_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_TENSOR_BINARY_OP_LOCALS;
+
+    const enum ggml_type type = src0->type;
+
+    GGML_ASSERT(ne0 == ne01);
+    GGML_ASSERT(ne1 == ne11);
+    GGML_ASSERT(ne2 == ne12);
+    GGML_ASSERT(ne3 == ne13);
+
+    // we don't support permuted src0 or src1
+    GGML_ASSERT(nb00 == ggml_type_size(type));
+    GGML_ASSERT(nb10 == ggml_type_size(src1->type));
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 == sizeof(float));
+    GGML_ASSERT(nb0 <= nb1);
+    GGML_ASSERT(nb1 <= nb2);
+    GGML_ASSERT(nb2 <= nb3);
+
+    const ggml_tensor * weights = src0;
+    const ggml_tensor * inputs  = src1;
+          ggml_tensor * output  = dst;
+
+    ggml_backend_zdnn_buffer * weights_extra = (ggml_backend_zdnn_buffer *)weights->extra;
+    ggml_backend_zdnn_buffer * inputs_extra  = (ggml_backend_zdnn_buffer *)inputs->extra;
+    ggml_backend_zdnn_buffer * output_extra  = (ggml_backend_zdnn_buffer *)output->extra;
+
+    zdnn_tensor_desc ptd_bias, td_bias;
+    zdnn_ztensor zt_bias;
+
+    const int64_t weights_rows = ne01;
+    const int64_t weights_cols = ne00;
+    const int64_t inputs_rows  = ne11;
+    const int64_t inputs_cols  = ne10;
+
+    assert(inputs_cols == weights_cols);
+
+    const int64_t output_rows = ne1;
+    const int64_t output_cols = ne0;
+
+    const int64_t bias_dim  [GGML_MAX_DIMS]  = { 1, 1, 1, output_cols };
+    ggml_zdnn_create_tensor(ptd_bias, td_bias, zt_bias, output, bias_dim, ZDNN_1D);
+
+    void * bias_data = (void *)calloc(ne0, ggml_element_size(output));
+    if (weights_extra->ztensor.is_transformed == false) ggml_zdnn_load_tensor(weights_extra->ztensor, weights->data);
+    if (inputs_extra->ztensor.is_transformed == false) ggml_zdnn_load_tensor(inputs_extra->ztensor, inputs->data);
+    ggml_zdnn_load_tensor(zt_bias, bias_data);
+
+    // GGML_LOG_INFO("%s: tensor '%s' tensor dimensions: [%ld, %ld, %ld, %ld] pre_tfm_desc dimensions: [%ld, %ld, %ld, %ld]\n",
+    //               __func__, weights_extra->name,
+    //               weights->ne[3], weights->ne[2], weights->ne[1], weights->ne[0],
+    //               weights_extra->pre_tfm_desc.dim1,
+    //               weights_extra->pre_tfm_desc.dim2,
+    //               weights_extra->pre_tfm_desc.dim3,
+    //               weights_extra->pre_tfm_desc.dim4);
+
+    // GGML_LOG_INFO("%s: tensor '%s' tensor dimensions: [%ld, %ld, %ld, %ld] pre_tfm_desc dimensions: [%ld, %ld, %ld, %ld]\n",
+    //               __func__, inputs_extra->name,
+    //               inputs->ne[3], inputs->ne[2], inputs->ne[1], inputs->ne[0],
+    //               inputs_extra->pre_tfm_desc.dim1,
+    //               inputs_extra->pre_tfm_desc.dim2,
+    //               inputs_extra->pre_tfm_desc.dim3,
+    //               inputs_extra->pre_tfm_desc.dim4);
+
+    GGML_ASSERT(weights_extra->pre_tfm_desc.dim1 == weights->ne[0] && "weights_extra->pre_tfm_desc.dim1 must match weights->ne[0]");
+    GGML_ASSERT(weights_extra->pre_tfm_desc.dim2 == weights->ne[1] && "weights_extra->pre_tfm_desc.dim2 must match weights->ne[1]");
+    GGML_ASSERT(inputs_extra->pre_tfm_desc.dim1  == inputs->ne[0]  && "inputs_extra->pre_tfm_desc.dim1 must match inputs->ne[0]");
+    GGML_ASSERT(inputs_extra->pre_tfm_desc.dim2  == inputs->ne[1]  && "inputs_extra->pre_tfm_desc.dim2 must match inputs->ne[1]");
+
+    ZDNN_CHECK(zdnn_matmul_transpose_op(&inputs_extra->ztensor, &weights_extra->ztensor, &zt_bias,
+                                        false, true, MATMUL_OP_ADDITION, &output_extra->ztensor));
+    // TODO: Remove in the future as we are currently DLF16 -> FP32 then in the next op, FP32 -> DLF16 again. Inefficient.
+    ZDNN_CHECK(zdnn_transform_origtensor(&output_extra->ztensor, output->data));
+
+    ZDNN_CHECK(zdnn_free_ztensor_buffer(&zt_bias));
+    free(bias_data);
+}
+
+static void ggml_zdnn_mul_mat_dispatch(ggml_backend_zdnn_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    bool use_mul_mat_vec =
+        (src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_F16)
+        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
+        && src0->ne[0] % 2 == 0 && src1->ne[1] == 1;
+
+    bool use_mul_mat_vec_q =
+        ggml_is_quantized(src0->type)
+        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
+
+    bool use_mul_mat_q =
+        ggml_is_quantized(src0->type)
+        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
+
+    // debug helpers
+    // GGML_LOG_INFO("%s: use_mul_mat_vec   = %d\n", __func__, use_mul_mat_vec);
+    // GGML_LOG_INFO("%s: use_mul_mat_vec_q = %d\n", __func__, use_mul_mat_vec_q);
+    // GGML_LOG_INFO("%s: use_mul_mat_q     = %d\n", __func__, use_mul_mat_q);
+    // GGML_LOG_INFO("%s: src0: %8d %8d %8d %8d\n", __func__, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
+    // GGML_LOG_INFO("%s:       %8d %8d %8d %8d\n", __func__, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]);
+    // GGML_LOG_INFO("%s: src1: %8d %8d %8d %8d\n", __func__, src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]);
+    // GGML_LOG_INFO("%s:       %8d %8d %8d %8d\n", __func__, src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]);
+    // GGML_LOG_INFO("%s: src0 is contiguous %d, transposed %d, type = %s, name = %s\n", __func__, ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
+    // GGML_LOG_INFO("%s: src1 is contiguous %d, transposed %d, type = %s, name = %s\n", __func__, ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
+
+    if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16
+        && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)
+        && src1->ne[2] * src1->ne[3] > 1) {
+        // general KQ + KQV multi-batch
+        GGML_LOG_INFO("%s: using zdnn_mul_mat_batched for KQ + KQV multi-batch\n", __func__);
+        // ggml_zdnn_mul_mat_batched(ctx, src0, src1, dst);
+    } else if (use_mul_mat_vec) {
+        GGML_LOG_INFO("%s: using zdnn_op_mul_mat_vec for vector multiplication\n", __func__);
+        // ggml_zdnn_op_mul_mat(ctx, src0, src1, dst, ggml_zdnn_op_mul_mat_vec, nullptr);
+    } else if (use_mul_mat_vec_q) {
+        GGML_LOG_INFO("%s: using zdnn_op_mul_mat_vec_q for quantized vector multiplication\n", __func__);
+        // ggml_zdnn_op_mul_mat(ctx, src0, src1, dst, ggml_zdnn_op_mul_mat_vec_q, ggml_zdnn_quantize_row_q8_1);
+    } else if (use_mul_mat_q) {
+        GGML_LOG_INFO("%s: using zdnn_op_mul_mat_q for quantized matrix multiplication\n", __func__);
+        // ggml_zdnn_op_mul_mat(ctx, src0, src1, dst, ggml_zdnn_op_mul_mat_q, ggml_zdnn_quantize_mmq_q8_1);
+    } else {
+        // GGML_LOG_INFO("%s: using zdnn_op_mul_mat for general matrix multiplication\n", __func__);
+        ggml_zdnn_mul_mat_op(ctx, src0, src1, dst);
+    }
+}
+
+static bool ggml_zdnn_compute_forward(ggml_backend_zdnn_context * ctx, ggml_tensor * dst) {
+    switch (dst->op) {
+        case GGML_OP_MUL_MAT:
+            ggml_zdnn_mul_mat_dispatch(ctx, dst->src[0], dst->src[1], dst);
+            break;
+
+        default:
+            return false;
+    }
+
+    return true;
+}
+
+static enum ggml_status ggml_zdnn_graph_compute(ggml_backend_t backend, ggml_cgraph * gf) {
+    ggml_backend_zdnn_context        * ctx     = (       ggml_backend_zdnn_context *)backend->context;
+    ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *)backend->device->context;
+
+    ctx->gf = gf;
+    for (int i = 0; i < gf->n_nodes; i++) {
+        ggml_tensor * node = gf->nodes[i];
+
+        if (ggml_is_empty(node)
+            || node->op == GGML_OP_NONE
+            || node->op == GGML_OP_RESHAPE
+            || node->op == GGML_OP_VIEW
+            || node->op == GGML_OP_PERMUTE
+            || node->op == GGML_OP_TRANSPOSE) {
+            continue;
+        }
+
+        bool ok = ggml_zdnn_compute_forward(ctx, node);
+        if (!ok) {
+            GGML_LOG_ERROR("%s: unsupported op %s (%s)\n",
+                           __func__, node->name, ggml_op_name(node->op));
+        }
+
+        GGML_ASSERT(ok);
+    }
+
+    return GGML_STATUS_SUCCESS;
+}
+
+static bool ggml_zdnn_supports_op(const ggml_backend_zdnn_device_context * ctx_dev, const ggml_tensor * op) {
+    switch (op->op) {
+        case GGML_OP_NONE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_VIEW:
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_PERMUTE:
+            return true;
+
+        case GGML_OP_MUL_MAT:
+            {
+                const ggml_tensor * src0 = op->src[0];
+                const ggml_tensor * src1 = op->src[1];
+
+                const int64_t ne10 = src1->ne[0];
+                const int64_t ne0 = op->ne[0];
+                const int64_t ne1 = op->ne[1];
+
+                const int64_t max_batch = ctx_dev->max_size;
+
+                return ggml_is_matrix(src0) &&
+                       ggml_is_matrix(src1) &&
+                       ggml_is_contiguous(src0) &&
+                       ggml_is_contiguous(src1) &&
+                       src0->view_src == nullptr && src1->view_src == nullptr &&
+                       src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 &&
+                       (ne0 <= max_batch && ne1 <= max_batch && ne10 <= max_batch);
+            } break;
+
+        default:
+            return false;
+    }
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+//
+// globals
+//
+
+// initialised in ggml_backend_zdnn_reg
+static ggml_backend_reg    g_ggml_backend_zdnn_reg;
+static ggml_backend_device g_ggml_backend_zdnn_device;
+
+static ggml_backend_zdnn_device_context g_ggml_ctx_dev_main = {
+    /* .zdnn_device           = */ 0,
+    /* .zdnn_device_ref_count = */ 0,
+    /* .has_parmblkformat_0   = */ false,
+    /* .has_parmblkformat_1   = */ false,
+    /* .max_size              = */ 0,
+    /* .name                  = */ "",
+};
+
+static int ggml_backend_zdnn_device_acq(ggml_backend_zdnn_device_context * ctx) {
+    assert(ctx != NULL);
+
+    if (ctx->zdnn_device == 0) {
+        ctx->zdnn_device = 1;
+    }
+
+    if (ctx->zdnn_device >= 1) {
+        ctx->has_parmblkformat_0 = zdnn_is_nnpa_parmblk_fmt_installed(1, NNPA_PARMBLKFORMAT_0);
+        ctx->has_parmblkformat_1 = zdnn_is_nnpa_parmblk_fmt_installed(1, NNPA_PARMBLKFORMAT_1);
+        ctx->max_size = zdnn_get_nnpa_max_dim_idx_size();
+        strncpy(ctx->name, GGML_ZDNN_NAME, sizeof(ctx->name) - 1);
+    }
+
+    ctx->zdnn_device_ref_count++;
+    return ctx->zdnn_device;
+}
+
+static void ggml_backend_zdnn_device_rel(ggml_backend_zdnn_device_context * ctx) {
+    assert(ctx != NULL);
+    assert(ctx->zdnn_device_ref_count > 0);
+
+    ctx->zdnn_device_ref_count--;
+    if (ctx->zdnn_device_ref_count == 0) {
+        if (ctx->zdnn_device >= 0) {
+            ctx->zdnn_device = 0;
+        }
+    }
+}
+
+static ggml_backend_zdnn_context * ggml_zdnn_init(ggml_backend_dev_t dev) {
+    GGML_LOG_INFO("%s: allocating\n", __func__);
+    GGML_LOG_INFO("%s: found 1 device\n", __func__);
+
+    #ifdef STATIC_LIB
+    zdnn_init();
+    #endif
+
+    ggml_backend_zdnn_context * ctx = new ggml_backend_zdnn_context();
+    ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *)dev->context;
+
+    int device = 1;
+    GGML_LOG_INFO("%s: picking default device: %s\n", __func__, ctx_dev->name);
+
+    ctx->device = device;
+    GGML_LOG_INFO("%s: NNPA name: %s\n", __func__, ctx_dev->name);
+    GGML_LOG_INFO("%s: NNPA_PARMBLKFORMAT_0 = %s\n", __func__, ctx_dev->has_parmblkformat_0 ? "true" : "false");
+    GGML_LOG_INFO("%s: NNPA_PARMBLKFORMAT_1 = %s\n", __func__, ctx_dev->has_parmblkformat_1 ? "true" : "false");
+
+    ctx->gf = nullptr;
+
+    return ctx;
+}
+
+static void ggml_zdnn_free(ggml_backend_zdnn_context * ctx) {
+    GGML_LOG_INFO("%s: deallocating\n", __func__);
+    delete ctx;
+}
+
+//
+// backend interface
+//
+
+static void ggml_backend_zdnn_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_backend_zdnn_buffer_context * ctx = (ggml_backend_zdnn_buffer_context *)buffer->context;
+
+    for (int i = 0; i < ctx->n_buffers; i++) {
+        if (ctx->buffers[i]->ztensor.buffer != NULL && ctx->buffers[i]->ztensor.is_transformed) {
+            ZDNN_CHECK(zdnn_free_ztensor_buffer(&ctx->buffers[i]->ztensor));
+        }
+    }
+
+    delete ctx;
+}
+
+static void * ggml_backend_zdnn_buffer_get_base(ggml_backend_buffer_t buffer) {
+    ggml_backend_zdnn_buffer_context * ctx = (ggml_backend_zdnn_buffer_context *)buffer->context;
+    return ctx->all_data;
+}
+
+static enum ggml_status ggml_backend_zdnn_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
+    if (tensor->view_src != NULL) {
+        assert(tensor->view_src->buffer->buft == buffer->buft);
+        return GGML_STATUS_SUCCESS;
+    }
+
+    ggml_backend_zdnn_buffer_context * ctx = (ggml_backend_zdnn_buffer_context *)buffer->context;
+
+    const int64_t tsize = ggml_nbytes(tensor);
+    int buffer_idx = ctx->n_buffers;
+
+    std::unique_ptr<ggml_backend_zdnn_buffer> zdnn_buffer = std::make_unique<ggml_backend_zdnn_buffer>();
+    zdnn_buffer->data = tensor->data;
+    zdnn_buffer->size = tsize;
+    strncpy(zdnn_buffer->name, tensor->name, GGML_MAX_NAME - 1);
+
+    ggml_zdnn_init_tensor(zdnn_buffer.get(), tensor);
+    tensor->extra = zdnn_buffer.get();
+
+    ctx->buffers.push_back(std::move(zdnn_buffer));
+    ctx->n_buffers++;
+
+    // GGML_LOG_INFO("%s: initialised tensor '%s' in buffer %d, size = %8.2f MiB\n",
+    //               __func__, tensor->name, buffer_idx, tsize);
+
+    return GGML_STATUS_SUCCESS;
+}
+
+static void ggml_backend_zdnn_buffer_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
+    memset((char *)tensor->data + offset, value, size);
+
+    GGML_UNUSED(buffer);
+}
+
+static void ggml_backend_zdnn_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    memcpy((char *)tensor->data + offset, data, size);
+
+    GGML_UNUSED(buffer);
+}
+
+static void ggml_backend_zdnn_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    memcpy(data, (const char *)tensor->data + offset, size);
+
+    GGML_UNUSED(buffer);
+}
+
+static void ggml_backend_zdnn_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    ggml_backend_zdnn_buffer_context * ctx = (ggml_backend_zdnn_buffer_context *)buffer->context;
+
+    memset(ctx->all_data, value, ctx->all_size);
+}
+
+static ggml_backend_buffer_i ggml_backend_zdnn_buffer_i = {
+    /* .free_buffer   = */ ggml_backend_zdnn_buffer_free_buffer,
+    /* .get_base      = */ ggml_backend_zdnn_buffer_get_base,
+    /* .init_tensor   = */ ggml_backend_zdnn_buffer_init_tensor,
+    /* .memset_tensor = */ ggml_backend_zdnn_buffer_memset_tensor,
+    /* .set_tensor    = */ ggml_backend_zdnn_buffer_set_tensor,
+    /* .get_tensor    = */ ggml_backend_zdnn_buffer_get_tensor,
+    /* .cpy_tensor    = */ NULL,
+    /* .clear         = */ ggml_backend_zdnn_buffer_clear,
+    /* .reset         = */ NULL,
+};
+
+//
+// default buffer type
+//
+
+static const char * ggml_backend_zdnn_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
+    return GGML_ZDNN_NAME;
+
+    GGML_UNUSED(buft);
+}
+
+static ggml_backend_buffer_t ggml_backend_zdnn_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    ggml_backend_zdnn_buffer_context * ctx = new ggml_backend_zdnn_buffer_context();
+
+    const size_t size_page = sysconf(_SC_PAGESIZE);
+
+    size_t size_aligned = size;
+    if ((size_aligned % size_page) != 0) {
+        size_aligned += size_page - (size_aligned % size_page);
+    }
+
+    ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *)buft->device->context;
+
+    GGML_ASSERT(ctx_dev->zdnn_device >= 0);
+    int device = ctx_dev->zdnn_device; GGML_UNUSED(device);
+
+    ctx->all_data  = ggml_aligned_malloc(size_aligned);
+    ctx->all_size  = size_aligned;
+    ctx->owned     = true;
+    ctx->n_buffers = 1;
+
+    if (ctx->all_data != NULL) {
+        std::unique_ptr<ggml_backend_zdnn_buffer> zdnn_buffer = std::make_unique<ggml_backend_zdnn_buffer>();
+        zdnn_buffer->data = ctx->all_data;
+        zdnn_buffer->size = size_aligned;
+        ctx->buffers.push_back(std::move(zdnn_buffer));
+    }
+
+    if (size_aligned > 0 && (ctx->all_data == NULL)) {
+        GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f\n",
+                       __func__, size_aligned / 1024.0 / 1024.0);
+        delete ctx;
+        return NULL;
+    }
+
+    return ggml_backend_buffer_init(buft, ggml_backend_zdnn_buffer_i, ctx, size);
+}
+
+static size_t ggml_backend_zdnn_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    return 256;
+
+    GGML_UNUSED(buft);
+}
+
+static bool ggml_backend_zdnn_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
+    return true;
+
+    GGML_UNUSED(buft);
+}
+
+ggml_backend_buffer_type_t ggml_backend_zdnn_buffer_type(void) {
+    static ggml_backend_buffer_type ggml_backend_buffer_type_zdnn = {
+        /* .iface   = */ {
+            /* .get_name       = */ ggml_backend_zdnn_buffer_type_get_name,
+            /* .alloc_buffer   = */ ggml_backend_zdnn_buffer_type_alloc_buffer,
+            /* .get_alignment  = */ ggml_backend_zdnn_buffer_type_get_alignment,
+            /* .get_max_size   = */ NULL,
+            /* .get_alloc_size = */ NULL,  // defaults to ggml_nbytes
+            /* .is_host        = */ ggml_backend_zdnn_buffer_type_is_host,
+        },
+        /* .device  = */ &g_ggml_backend_zdnn_device,
+        /* .context = */ NULL,
+    };
+
+    return &ggml_backend_buffer_type_zdnn;
+}
+
+static const char * ggml_backend_zdnn_buffer_from_ptr_type_get_name(ggml_backend_buffer_type_t buft) {
+    return GGML_ZDNN_NAME "_Mapped";
+
+    GGML_UNUSED(buft);
+}
+
+static ggml_backend_buffer_type_t ggml_backend_zdnn_buffer_from_ptr_type(void) {
+    static ggml_backend_buffer_type ggml_backend_buffer_from_ptr_type_zdnn = {
+        /* .iface = */ {
+            /* .get_name       = */ ggml_backend_zdnn_buffer_from_ptr_type_get_name,
+            /* .alloc_buffer   = */ ggml_backend_zdnn_buffer_type_alloc_buffer,
+            /* .get_alignment  = */ ggml_backend_zdnn_buffer_type_get_alignment,
+            /* .get_max_size   = */ NULL,
+            /* .get_alloc_size = */ NULL,  // defaults to ggml_nbytes
+            /* .is_host        = */ ggml_backend_zdnn_buffer_type_is_host,
+        },
+        /* .device  = */ &g_ggml_backend_zdnn_device,
+        /* .context = */ NULL,
+    };
+
+    return &ggml_backend_buffer_from_ptr_type_zdnn;
+}
+
+//
+// backend
+//
+
+static const char * ggml_backend_zdnn_name(ggml_backend_t backend) {
+    return GGML_ZDNN_NAME;
+
+    GGML_UNUSED(backend);
+}
+
+static void ggml_backend_zdnn_free(ggml_backend_t backend) {
+    ggml_backend_zdnn_context * ctx = (ggml_backend_zdnn_context *)backend->context;
+
+    ggml_zdnn_free(ctx);
+    free(backend);
+}
+
+static enum ggml_status ggml_backend_zdnn_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+    return ggml_zdnn_graph_compute(backend, cgraph);
+}
+
+static ggml_backend_i ggml_backend_zdnn_i = {
+    /* .get_name           = */ ggml_backend_zdnn_name,
+    /* .free               = */ ggml_backend_zdnn_free,
+    /* .set_tensor_async   = */ NULL,
+    /* .get_tensor_async   = */ NULL,
+    /* .cpy_tensor_async   = */ NULL,
+    /* .synchronize        = */ NULL,
+    /* .graph_plan_create  = */ NULL,
+    /* .graph_plan_free    = */ NULL,
+    /* .graph_plan_update  = */ NULL,
+    /* .graph_plan_compute = */ NULL,
+    /* .graph_compute      = */ ggml_backend_zdnn_graph_compute,
+    /* .event_record       = */ NULL,
+    /* .event_wait         = */ NULL,
+};
+
+static ggml_guid_t ggml_backend_zdnn_guid(void) {
+    static const char * guid_str = "IBM-ZDNN-ACCELER";
+    return reinterpret_cast<ggml_guid_t>((void *)guid_str);
+}
+
+// TODO: remove in the future
+ggml_backend_t ggml_backend_zdnn_init(void) {
+    ggml_backend_dev_t dev = ggml_backend_reg_dev_get(ggml_backend_zdnn_reg(), 0);
+
+    ggml_backend_zdnn_context * ctx = ggml_zdnn_init(dev);
+    if (ctx == NULL) {
+        GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
+        return NULL;
+    }
+
+    ggml_backend_t backend = (ggml_backend_t)malloc(sizeof(ggml_backend));
+    *backend = (ggml_backend) {
+        /* .guid       = */ ggml_backend_zdnn_guid(),
+        /* .iface      = */ ggml_backend_zdnn_i,
+        /* .device     = */ dev,
+        /* .context    = */ ctx,
+    };
+
+    return backend;
+}
+
+bool ggml_backend_is_zdnn(ggml_backend_t backend) {
+    return backend != NULL &&
+           ggml_guid_matches(backend->guid, ggml_backend_zdnn_guid());
+
+    GGML_UNUSED(backend);
+}
+
+//
+// backend device
+//
+
+static const char * ggml_backend_zdnn_device_get_name(ggml_backend_dev_t dev) {
+    return GGML_ZDNN_NAME;
+
+    GGML_UNUSED(dev);
+}
+
+static const char * ggml_backend_zdnn_device_get_description(ggml_backend_dev_t dev) {
+    return "IBM Z Neural Network Processing Assist (NNPA)";
+}
+
+static void ggml_backend_zdnn_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
+    *free  = 0;
+    *total = 0;
+}
+
+static enum ggml_backend_dev_type ggml_backend_zdnn_device_get_type(ggml_backend_dev_t dev) {
+    return GGML_BACKEND_DEVICE_TYPE_ACCEL;
+
+    GGML_UNUSED(dev);
+}
+
+static void ggml_backend_zdnn_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) {
+    props->name        = ggml_backend_zdnn_device_get_name(dev);
+    props->description = ggml_backend_zdnn_device_get_description(dev);
+    props->type        = ggml_backend_zdnn_device_get_type(dev);
+    ggml_backend_zdnn_device_get_memory(dev, &props->memory_free, &props->memory_total);
+    props->caps = (ggml_backend_dev_caps) {
+        /* .async                = */ false,
+        /* .host_buffer          = */ false,
+        /* .buffer_from_host_ptr = */ true,
+        /* .events               = */ false,
+    };
+}
+
+static ggml_backend_t ggml_backend_zdnn_device_init(ggml_backend_dev_t dev, const char * params) {
+    ggml_backend_zdnn_context * ctx = ggml_zdnn_init(dev);
+    if (ctx == NULL) {
+        GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
+        return NULL;
+    }
+
+    ggml_backend_t backend = (ggml_backend *)malloc(sizeof(ggml_backend));
+    *backend = (ggml_backend) {
+        /* .guid       = */ ggml_backend_zdnn_guid(),
+        /* .iface      = */ ggml_backend_zdnn_i,
+        /* .device     = */ dev,
+        /* .context    = */ ctx,
+    };
+
+    return backend;
+
+    GGML_UNUSED(params);
+}
+
+static ggml_backend_buffer_type_t ggml_backend_zdnn_device_get_buffer_type(ggml_backend_dev_t dev) {
+    return ggml_backend_zdnn_buffer_type();
+
+    GGML_UNUSED(dev);
+}
+
+static ggml_backend_buffer_t ggml_backend_zdnn_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
+    ggml_backend_zdnn_buffer_context * ctx = new ggml_backend_zdnn_buffer_context();
+
+    ctx->all_data  = ptr;
+    ctx->all_size  = size;
+    ctx->owned     = false;
+    ctx->n_buffers = 0;
+
+    const size_t size_page = sysconf(_SC_PAGESIZE);
+
+    // page-align the data ptr
+    {
+        const uintptr_t offs = (uintptr_t) ptr % size_page;
+        ptr  = (void *)((char *)ptr - offs);
+        size += offs;
+    }
+
+    size_t size_aligned = size;
+    if ((size_aligned % size_page) != 0) {
+        size_aligned += size_page - (size_aligned % size_page);
+    }
+
+    ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *)dev->context;
+
+    GGML_ASSERT(ctx_dev->zdnn_device >= 0);
+    int device = ctx_dev->zdnn_device; GGML_UNUSED(device);
+
+    std::unique_ptr<ggml_backend_zdnn_buffer> zdnn_buffer = std::make_unique<ggml_backend_zdnn_buffer>();
+    zdnn_buffer->data = ptr;
+    zdnn_buffer->size = size;
+    ctx->buffers.push_back(std::move(zdnn_buffer));
+
+    GGML_LOG_INFO("%s: allocated buffer, size = %8.2f MiB\n",
+                  __func__, size_aligned / 1024.0 / 1024.0);
+
+    ++ctx->n_buffers;
+
+    return ggml_backend_buffer_init(ggml_backend_zdnn_buffer_from_ptr_type(), ggml_backend_zdnn_buffer_i, ctx, size);
+}
+
+static bool ggml_backend_zdnn_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
+    ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *) dev->context;
+
+    return ggml_zdnn_supports_op(ctx_dev, op);
+}
+
+static bool ggml_backend_zdnn_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
+    return
+        buft->iface.get_name == ggml_backend_zdnn_buffer_type_get_name ||
+        buft->iface.get_name == ggml_backend_zdnn_buffer_from_ptr_type_get_name;
+
+    GGML_UNUSED(dev);
+}
+
+static ggml_backend_device_i ggml_backend_zdnn_device_i = {
+    /* .get_name             = */ ggml_backend_zdnn_device_get_name,
+    /* .get_description      = */ ggml_backend_zdnn_device_get_description,
+    /* .get_memory           = */ ggml_backend_zdnn_device_get_memory,
+    /* .get_type             = */ ggml_backend_zdnn_device_get_type,
+    /* .get_props            = */ ggml_backend_zdnn_device_get_props,
+    /* .init_backend         = */ ggml_backend_zdnn_device_init,
+    /* .get_buffer_type      = */ ggml_backend_zdnn_device_get_buffer_type,
+    /* .get_host_buffer_type = */ NULL,
+    /* .buffer_from_host_ptr = */ ggml_backend_zdnn_device_buffer_from_ptr,
+    /* .supports_op          = */ ggml_backend_zdnn_device_supports_op,
+    /* .supports_buft        = */ ggml_backend_zdnn_device_supports_buft,
+    /* .offload_op           = */ NULL,
+    /* .event_new            = */ NULL,
+    /* .event_free           = */ NULL,
+    /* .event_synchronize    = */ NULL,
+};
+
+//
+// backend registry
+//
+
+static const char * ggml_backend_zdnn_reg_get_name(ggml_backend_reg_t reg) {
+    return GGML_ZDNN_NAME;
+
+    GGML_UNUSED(reg);
+}
+
+static size_t ggml_backend_zdnn_reg_device_count(ggml_backend_reg_t reg) {
+    if (!zdnn_is_nnpa_installed()) {
+        return 0;
+    }
+    return 1;
+
+    GGML_UNUSED(reg);
+}
+
+static ggml_backend_dev_t ggml_backend_zdnn_reg_device_get(ggml_backend_reg_t reg, size_t index) {
+    GGML_ASSERT(index == 0);
+
+    return &g_ggml_backend_zdnn_device;
+
+    GGML_UNUSED(reg);
+    GGML_UNUSED(index);
+}
+
+static ggml_backend_feature g_ggml_backend_zdnn_features[] = {
+    { "NNPA", zdnn_is_nnpa_installed() ? "1" : "0" },
+    { "NNPA_PARMBLKFORMAT_0", zdnn_is_nnpa_parmblk_fmt_installed(1, NNPA_PARMBLKFORMAT_0) ? "1" : "0" },
+    { "NNPA_PARMBLKFORMAT_1", zdnn_is_nnpa_parmblk_fmt_installed(1, NNPA_PARMBLKFORMAT_1) ? "1" : "0" },
+    { NULL, NULL },
+};
+
+static ggml_backend_feature * ggml_backend_zdnn_get_features(ggml_backend_reg_t reg) {
+    return g_ggml_backend_zdnn_features;
+
+    GGML_UNUSED(reg);
+}
+
+static void * ggml_backend_zdnn_get_proc_address(ggml_backend_reg_t reg, const char * name) {
+    if (strcmp(name, "ggml_backend_get_features") == 0) {
+        return (void *) ggml_backend_zdnn_get_features;
+    }
+
+    return NULL;
+
+    GGML_UNUSED(reg);
+}
+
+static ggml_backend_reg_i ggml_backend_zdnn_reg_i = {
+    /* .get_name         = */ ggml_backend_zdnn_reg_get_name,
+    /* .get_device_count = */ ggml_backend_zdnn_reg_device_count,
+    /* .get_device       = */ ggml_backend_zdnn_reg_device_get,
+    /* .get_proc_address = */ ggml_backend_zdnn_get_proc_address,
+};
+
+static void ggml_zdnn_cleanup(void) {
+    ggml_backend_zdnn_device_rel(&g_ggml_ctx_dev_main);
+}
+
+// TODO: make thread-safe
+ggml_backend_reg_t ggml_backend_zdnn_reg(void) {
+    ggml_backend_zdnn_device_acq(&g_ggml_ctx_dev_main);
+
+    // register cleanup callback
+    atexit(ggml_zdnn_cleanup);
+
+    {
+        g_ggml_backend_zdnn_reg = (ggml_backend_reg) {
+            /* .api_version = */ GGML_ZDNN_VERSION,
+            /* .iface       = */ ggml_backend_zdnn_reg_i,
+            /* .context     = */ NULL,
+        };
+
+        g_ggml_backend_zdnn_device = (ggml_backend_device) {
+            /* .iface       = */ ggml_backend_zdnn_device_i,
+            /* .reg         = */ &g_ggml_backend_zdnn_reg,
+            /* .context     = */ &g_ggml_ctx_dev_main,
+        };
+
+        return &g_ggml_backend_zdnn_reg;
+    }
+}
+
+GGML_BACKEND_DL_IMPL(ggml_backend_zdnn_reg)