ollama/llama/ggml-backend.c

/**
 * llama.cpp - commit 3f1ae2e32cde00c39b96be6d01c2997c29bae555 - do not edit this file
 *
 * MIT License
 *
 * Copyright (c) 2023-2024 The ggml authors
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

#include "ggml-backend-impl.h"
#include "ggml-alloc.h"
#include "ggml-impl.h"

#include <assert.h>
#include <limits.h>
#include <stdarg.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>


#define MAX(a, b) ((a) > (b) ? (a) : (b))

// backend buffer type

const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) {
    return buft->iface.get_name(buft);
}

GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
    return buft->iface.alloc_buffer(buft, size);
}

size_t ggml_backend_buft_get_alignment(ggml_backend_buffer_type_t buft) {
    return buft->iface.get_alignment(buft);
}

size_t ggml_backend_buft_get_max_size(ggml_backend_buffer_type_t buft) {
    // get_max_size is optional, defaults to SIZE_MAX
    if (buft->iface.get_max_size) {
        return buft->iface.get_max_size(buft);
    }
    return SIZE_MAX;
}

GGML_CALL size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) {
    // get_alloc_size is optional, defaults to ggml_nbytes
    if (buft->iface.get_alloc_size) {
        size_t size = buft->iface.get_alloc_size(buft, tensor);
        assert(size >= ggml_nbytes(tensor));
        return size;
    }
    return ggml_nbytes(tensor);
}

bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) {
    if (buft->iface.is_host) {
        return buft->iface.is_host(buft);
    }
    return false;
}

// backend buffer

GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init(
               ggml_backend_buffer_type_t      buft,
        struct ggml_backend_buffer_i           iface,
               ggml_backend_buffer_context_t   context,
               size_t                          size) {
    ggml_backend_buffer_t buffer = malloc(sizeof(struct ggml_backend_buffer));

    (*buffer) = (struct ggml_backend_buffer) {
        /* .interface = */ iface,
        /* .buft      = */ buft,
        /* .context   = */ context,
        /* .size      = */ size,
        /* .usage     = */ GGML_BACKEND_BUFFER_USAGE_ANY
    };

    return buffer;
}

const char * ggml_backend_buffer_name(ggml_backend_buffer_t buffer) {
    return buffer->iface.get_name(buffer);
}

void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) {
    if (buffer == NULL) {
        return;
    }

    if (buffer->iface.free_buffer != NULL) {
        buffer->iface.free_buffer(buffer);
    }

// TODO: this needs to be freed in cuda and hipblas backends because
// the cuda backend implementation compiled with msvc
#if !defined(GGML_USE_CUDA) && !defined(GGML_USE_HIPBLAS)
    free(buffer);
#endif
}

size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) {
    return buffer->size;
}

void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
    void * base = buffer->iface.get_base(buffer);

    GGML_ASSERT(base != NULL && "backend buffer base cannot be NULL");

    return base;
}

GGML_CALL void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
    // init_tensor is optional
    if (buffer->iface.init_tensor) {
        buffer->iface.init_tensor(buffer, tensor);
    }
}

size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer) {
    return ggml_backend_buft_get_alignment(ggml_backend_buffer_get_type(buffer));
}

size_t ggml_backend_buffer_get_max_size(ggml_backend_buffer_t buffer) {
    return ggml_backend_buft_get_max_size(ggml_backend_buffer_get_type(buffer));
}

size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
    return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_get_type(buffer), tensor);
}

void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
    buffer->iface.clear(buffer, value);
}

bool ggml_backend_buffer_is_host(ggml_backend_buffer_t buffer) {
    return ggml_backend_buft_is_host(ggml_backend_buffer_get_type(buffer));
}

void ggml_backend_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) {
    buffer->usage = usage;

    // FIXME: add a generic callback to the buffer interface
    if (ggml_backend_buffer_is_multi_buffer(buffer)) {
        ggml_backend_multi_buffer_set_usage(buffer, usage);
    }
}

enum ggml_backend_buffer_usage ggml_backend_buffer_get_usage(ggml_backend_buffer_t buffer) {
    return buffer->usage;
}

ggml_backend_buffer_type_t ggml_backend_buffer_get_type(ggml_backend_buffer_t buffer) {
    return buffer->buft;
}

void ggml_backend_buffer_reset(ggml_backend_buffer_t buffer) {
    if (buffer->iface.reset) {
        buffer->iface.reset(buffer);
    }
}

bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst) {
    ggml_backend_buffer_t dst_buf = dst->view_src ? dst->view_src->buffer : dst->buffer;
    if (dst_buf->iface.cpy_tensor) {
        return dst_buf->iface.cpy_tensor(dst_buf, src, dst);
    }
    return false;
}

// backend

ggml_guid_t ggml_backend_guid(ggml_backend_t backend) {
    if (backend == NULL) {
        return NULL;
    }
    return backend->guid;
}

const char * ggml_backend_name(ggml_backend_t backend) {
    if (backend == NULL) {
        return "NULL";
    }
    return backend->iface.get_name(backend);
}

void ggml_backend_free(ggml_backend_t backend) {
    if (backend == NULL) {
        return;
    }

    backend->iface.free(backend);
}

ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend) {
    return backend->iface.get_default_buffer_type(backend);
}

ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size) {
    return ggml_backend_buft_alloc_buffer(ggml_backend_get_default_buffer_type(backend), size);
}

size_t ggml_backend_get_alignment(ggml_backend_t backend) {
    return ggml_backend_buft_get_alignment(ggml_backend_get_default_buffer_type(backend));
}

size_t ggml_backend_get_max_size(ggml_backend_t backend) {
    return ggml_backend_buft_get_max_size(ggml_backend_get_default_buffer_type(backend));
}

void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");

    if (backend->iface.set_tensor_async == NULL) {
        ggml_backend_tensor_set(tensor, data, offset, size);
    } else {
        backend->iface.set_tensor_async(backend, tensor, data, offset, size);
    }
}

void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");

    if (backend->iface.get_tensor_async == NULL) {
        ggml_backend_tensor_get(tensor, data, offset, size);
    } else {
        backend->iface.get_tensor_async(backend, tensor, data, offset, size);
    }
}

GGML_CALL void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;

    GGML_ASSERT(buf != NULL && "tensor buffer not set");
    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");

    if (!size) {
        return;
    }

    buf->iface.set_tensor(buf, tensor, data, offset, size);
}

GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;

    GGML_ASSERT(buf != NULL && "tensor buffer not set");
    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");

    if (!size) {
        return;
    }

    buf->iface.get_tensor(buf, tensor, data, offset, size);
}

GGML_API GGML_CALL void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;

    GGML_ASSERT(buf != NULL && "tensor buffer not set");
    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
    GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");

    if (!size) {
        return;
    }

    GGML_ASSERT(buf->iface.memset_tensor != NULL && "memset not supported by backend buffer");

    buf->iface.memset_tensor(buf, tensor, value, offset, size);
}

void ggml_backend_synchronize(ggml_backend_t backend) {
    if (backend->iface.synchronize == NULL) {
        return;
    }

    backend->iface.synchronize(backend);
}

ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
    GGML_ASSERT(backend->iface.graph_plan_create != NULL);

    return backend->iface.graph_plan_create(backend, cgraph);
}

void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
    GGML_ASSERT(backend->iface.graph_plan_free != NULL);

    backend->iface.graph_plan_free(backend, plan);
}

enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
    GGML_ASSERT(backend->iface.graph_plan_compute != NULL);

    return backend->iface.graph_plan_compute(backend, plan);
}

enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
    enum ggml_status err = ggml_backend_graph_compute_async(backend, cgraph);
    ggml_backend_synchronize(backend);
    return err;
}

enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
    return backend->iface.graph_compute(backend, cgraph);
}

bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
    return backend->iface.supports_op(backend, op);
}

bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
    return backend->iface.supports_buft(backend, buft);
}

bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op) {
    if (backend->iface.offload_op != NULL) {
        return backend->iface.offload_op(backend, op);
    }
    return false;
}

// backend copy

static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
    if (a->type != b->type) {
        return false;
    }
    for (int i = 0; i < GGML_MAX_DIMS; i++) {
        if (a->ne[i] != b->ne[i]) {
            return false;
        }
        if (a->nb[i] != b->nb[i]) {
            return false;
        }
    }
    return true;
}

void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");

    if (src == dst) {
        return;
    }

    if (ggml_backend_buffer_is_host(src->buffer)) {
        ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src));
    } else if (ggml_backend_buffer_is_host(dst->buffer)) {
        ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src));
    } else if (!ggml_backend_buffer_copy_tensor(src, dst)) {
#ifndef NDEBUG
        fprintf(stderr, "%s: warning: slow copy from %s to %s\n", __func__, ggml_backend_buffer_name(src->buffer), ggml_backend_buffer_name(dst->buffer));
#endif
        size_t nbytes = ggml_nbytes(src);
        void * data = malloc(nbytes);
        ggml_backend_tensor_get(src, data, 0, nbytes);
        ggml_backend_tensor_set(dst, data, 0, nbytes);
        free(data);
    }
}

void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst) {
    GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");

    if (src == dst) {
        return;
    }

    if (backend_dst->iface.cpy_tensor_async != NULL) {
        if (backend_dst->iface.cpy_tensor_async(backend_src, backend_dst, src, dst)) {
            return;
        }
    }

    // an async copy would normally happen after all the queued operations on both backends are completed
    // to simulate the same behavior, we need to synchronize both backends first, and do a blocking copy
    ggml_backend_synchronize(backend_src);
    ggml_backend_synchronize(backend_dst);
    ggml_backend_tensor_copy(src, dst);
}

// events

ggml_backend_event_t ggml_backend_event_new(ggml_backend_t backend) {
    if (backend->iface.event_new == NULL) {
        return NULL;
    }
    return backend->iface.event_new(backend);
}

void ggml_backend_event_free(ggml_backend_event_t event) {
    if (event == NULL) {
        return;
    }
    event->backend->iface.event_free(event);
}

void ggml_backend_event_record(ggml_backend_event_t event) {
    GGML_ASSERT(event->backend->iface.event_record != NULL);

    event->backend->iface.event_record(event);
}

void ggml_backend_event_synchronize(ggml_backend_event_t event) {
    GGML_ASSERT(event->backend->iface.event_synchronize != NULL);

    event->backend->iface.event_synchronize(event);
}

void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event) {
    GGML_ASSERT(backend->iface.event_wait != NULL);

    backend->iface.event_wait(backend, event);
}

// backend registry

#define GGML_REG_MAX_BACKENDS 64

struct ggml_backend_reg {
    char name[128];
    ggml_backend_init_fn init_fn;
    ggml_backend_buffer_type_t default_buffer_type;
    void * user_data;
};

static struct ggml_backend_reg ggml_backend_registry[GGML_REG_MAX_BACKENDS];
static size_t ggml_backend_registry_count = 0;

GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data);

GGML_CALL static void ggml_backend_registry_init(void) {
    static bool initialized = false;

    if (initialized) {
        return;
    }

    initialized = true;

    ggml_backend_register("CPU", ggml_backend_reg_cpu_init, ggml_backend_cpu_buffer_type(), NULL);

    // add forward decls here to avoid including the backend headers
#ifdef GGML_USE_CUDA
    extern GGML_CALL void ggml_backend_cuda_reg_devices(void);
    ggml_backend_cuda_reg_devices();
#endif

#ifdef GGML_USE_SYCL
    extern void ggml_backend_sycl_reg_devices(void);
    ggml_backend_sycl_reg_devices();
#endif

#ifdef GGML_USE_METAL
    extern GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data);
    extern GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
    ggml_backend_register("Metal", ggml_backend_reg_metal_init, ggml_backend_metal_buffer_type(), NULL);
#endif

#ifdef GGML_USE_VULKAN
    extern GGML_CALL int ggml_backend_vk_reg_devices(void);
    ggml_backend_vk_reg_devices();
#endif

#ifdef GGML_USE_KOMPUTE
    extern GGML_CALL void ggml_backend_kompute_reg_devices(void);
    ggml_backend_kompute_reg_devices();
#endif

#ifdef GGML_USE_CANN
    extern GGML_CALL int ggml_backend_cann_reg_devices(void);
    ggml_backend_cann_reg_devices();
#endif
}

GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) {
    GGML_ASSERT(ggml_backend_registry_count < GGML_REG_MAX_BACKENDS);

    size_t id = ggml_backend_registry_count;

    ggml_backend_registry[id] = (struct ggml_backend_reg) {
        /* .name                = */ {0},
        /* .fn                  = */ init_fn,
        /* .default_buffer_type = */ default_buffer_type,
        /* .user_data           = */ user_data,
    };

    snprintf(ggml_backend_registry[id].name, sizeof(ggml_backend_registry[id].name), "%s", name);

#ifndef NDEBUG
    fprintf(stderr, "%s: registered backend %s\n", __func__, name);
#endif

    ggml_backend_registry_count++;
}

size_t ggml_backend_reg_get_count(void) {
    ggml_backend_registry_init();

    return ggml_backend_registry_count;
}

size_t ggml_backend_reg_find_by_name(const char * name) {
    ggml_backend_registry_init();

    for (size_t i = 0; i < ggml_backend_registry_count; i++) {
        // TODO: case insensitive in a portable way
        if (strcmp(ggml_backend_registry[i].name, name) == 0) {
            return i;
        }
    }

    // not found
    return SIZE_MAX;
}

// init from backend:params string
ggml_backend_t ggml_backend_reg_init_backend_from_str(const char * backend_str) {
    ggml_backend_registry_init();

    const char * params = strchr(backend_str, ':');
    char backend_name[128];
    if (params == NULL) {
        snprintf(backend_name, sizeof(backend_name), "%s", backend_str);
        params = "";
    } else {
        snprintf(backend_name, sizeof(backend_name), "%.*s", (int)(params - backend_str), backend_str);
        params++;
    }

    size_t backend_i = ggml_backend_reg_find_by_name(backend_name);

    if (backend_i == SIZE_MAX) {
        fprintf(stderr, "%s: backend %s not found\n", __func__, backend_name);
        return NULL;
    }

    return ggml_backend_reg_init_backend(backend_i, params);
}

const char * ggml_backend_reg_get_name(size_t i) {
    ggml_backend_registry_init();

    GGML_ASSERT(i < ggml_backend_registry_count);
    return ggml_backend_registry[i].name;
}

ggml_backend_t ggml_backend_reg_init_backend(size_t i, const char * params) {
    ggml_backend_registry_init();

    GGML_ASSERT(i < ggml_backend_registry_count);
    return ggml_backend_registry[i].init_fn(params, ggml_backend_registry[i].user_data);
}

ggml_backend_buffer_type_t ggml_backend_reg_get_default_buffer_type(size_t i) {
    ggml_backend_registry_init();

    GGML_ASSERT(i < ggml_backend_registry_count);
    return ggml_backend_registry[i].default_buffer_type;
}

ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size) {
    ggml_backend_registry_init();

    GGML_ASSERT(i < ggml_backend_registry_count);
    return ggml_backend_buft_alloc_buffer(ggml_backend_registry[i].default_buffer_type, size);
}

// backend CPU

static const size_t TENSOR_ALIGNMENT = 32; // required for mmap as gguf only guarantees 32-byte alignment

GGML_CALL static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) {
    return "CPU";

    GGML_UNUSED(buffer);
}

GGML_CALL static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
    uintptr_t data = (uintptr_t)buffer->context;

    // align the buffer
    if (data % TENSOR_ALIGNMENT != 0) {
        data = GGML_PAD(data, TENSOR_ALIGNMENT);
    }

    return (void *)data;
}

GGML_CALL static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
    free(buffer->context);
}

GGML_CALL static void ggml_backend_cpu_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
    memset((char *)tensor->data + offset, value, size);

    GGML_UNUSED(buffer);
}

GGML_CALL static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
    memcpy((char *)tensor->data + offset, data, size);

    GGML_UNUSED(buffer);
}

GGML_CALL static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
    memcpy(data, (const char *)tensor->data + offset, size);

    GGML_UNUSED(buffer);
}

GGML_CALL static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
    if (ggml_backend_buffer_is_host(src->buffer)) {
        memcpy(dst->data, src->data, ggml_nbytes(src));
        return true;
    }
    return false;

    GGML_UNUSED(buffer);
}

GGML_CALL static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
    memset(buffer->context, value, buffer->size);
}

static struct ggml_backend_buffer_i cpu_backend_buffer_i = {
    /* .get_name        = */ ggml_backend_cpu_buffer_name,
    /* .free_buffer     = */ ggml_backend_cpu_buffer_free_buffer,
    /* .get_base        = */ ggml_backend_cpu_buffer_get_base,
    /* .init_tensor     = */ NULL, // no initialization required
    /* .memset_tensor   = */ ggml_backend_cpu_buffer_memset_tensor,
    /* .set_tensor      = */ ggml_backend_cpu_buffer_set_tensor,
    /* .get_tensor      = */ ggml_backend_cpu_buffer_get_tensor,
    /* .cpy_tensor      = */ ggml_backend_cpu_buffer_cpy_tensor,
    /* .clear           = */ ggml_backend_cpu_buffer_clear,
    /* .reset           = */ NULL,
};

// for buffers from ptr, free is not called
static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = {
    /* .get_name        = */ ggml_backend_cpu_buffer_name,
    /* .free_buffer     = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed
    /* .get_base        = */ ggml_backend_cpu_buffer_get_base,
    /* .init_tensor     = */ NULL, // no initialization required
    /* .memset_tensor   = */ ggml_backend_cpu_buffer_memset_tensor,
    /* .set_tensor      = */ ggml_backend_cpu_buffer_set_tensor,
    /* .get_tensor      = */ ggml_backend_cpu_buffer_get_tensor,
    /* .cpy_tensor      = */ ggml_backend_cpu_buffer_cpy_tensor,
    /* .clear           = */ ggml_backend_cpu_buffer_clear,
    /* .reset           = */ NULL,
};

GGML_CALL static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
    return "CPU";

    GGML_UNUSED(buft);
}

GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
    size += TENSOR_ALIGNMENT;   // malloc may return an address that is not aligned
    void * data = malloc(size); // TODO: use GGML_ALIGNED_MALLOC (move to ggml-impl.h)
    if (data == NULL) {
        fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size);
        return NULL;
    }

    return ggml_backend_buffer_init(buft, cpu_backend_buffer_i, data, size);
}

GGML_CALL static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
    return TENSOR_ALIGNMENT;

    GGML_UNUSED(buft);
}

GGML_CALL static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
    return true;

    GGML_UNUSED(buft);
}

GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
    static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = {
        /* .iface = */ {
            /* .get_name         = */ ggml_backend_cpu_buffer_type_get_name,
            /* .alloc_buffer     = */ ggml_backend_cpu_buffer_type_alloc_buffer,
            /* .get_alignment    = */ ggml_backend_cpu_buffer_type_get_alignment,
            /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
            /* .get_alloc_size   = */ NULL, // defaults to ggml_nbytes
            /* .is_host          = */ ggml_backend_cpu_buffer_type_is_host,
        },
        /* .context = */ NULL,
    };

    return &ggml_backend_cpu_buffer_type;
}

#ifdef GGML_USE_CPU_HBM

// buffer type HBM

#include <hbwmalloc.h>

GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
    return "CPU_HBM";

    GGML_UNUSED(buft);
}

GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) {
    return "CPU_HBM";

    GGML_UNUSED(buf);
}

GGML_CALL static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) {
    hbw_free(buffer->context);
}

GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
    //void * ptr = hbw_malloc(size);
    void * ptr;
    int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size);
    if (result != 0) {
        fprintf(stderr, "failed to allocate HBM buffer of size %zu\n", size);
        return NULL;
    }

    ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
    buffer->buft = buft;
    buffer->iface.get_name = ggml_backend_cpu_hbm_buffer_get_name;
    buffer->iface.free_buffer = ggml_backend_cpu_hbm_buffer_free_buffer;

    return buffer;
}

ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) {
    static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_hbm = {
        /* .iface    = */ {
            /* .get_name         = */ ggml_backend_cpu_hbm_buffer_type_get_name,
            /* .alloc_buffer     = */ ggml_backend_cpu_hbm_buffer_type_alloc_buffer,
            /* .get_alignment    = */ ggml_backend_cpu_buffer_type_get_alignment,
            /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
            /* .get_alloc_size   = */ NULL, // defaults to ggml_nbytes
            /* .is_host          = */ ggml_backend_cpu_buffer_type_is_host,
        },
        /* .context  = */ NULL,
    };

    return &ggml_backend_cpu_buffer_type_hbm;
}
#endif

struct ggml_backend_cpu_context {
    int                 n_threads;
    ggml_threadpool_t   threadpool;

    void *              work_data;
    size_t              work_size;

    ggml_abort_callback abort_callback;
    void *              abort_callback_data;
};

GGML_CALL static const char * ggml_backend_cpu_name(ggml_backend_t backend) {
    return "CPU";

    GGML_UNUSED(backend);
}

GGML_CALL static void ggml_backend_cpu_free(ggml_backend_t backend) {
    struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
    free(cpu_ctx->work_data);
    free(cpu_ctx);
    free(backend);
}

GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) {
    return ggml_backend_cpu_buffer_type();

    GGML_UNUSED(backend);
}

struct ggml_backend_plan_cpu {
    struct ggml_cplan cplan;
    struct ggml_cgraph cgraph;
};

GGML_CALL static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) {
    struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;

    struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu));

    cpu_plan->cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads, cpu_ctx->threadpool);
    cpu_plan->cgraph = *cgraph; // FIXME: deep copy

    if (cpu_plan->cplan.work_size > 0) {
        cpu_plan->cplan.work_data = malloc(cpu_plan->cplan.work_size);
        if (cpu_plan->cplan.work_data == NULL) {
            free(cpu_plan);
            return NULL;
        }
    }

    cpu_plan->cplan.abort_callback      = cpu_ctx->abort_callback;
    cpu_plan->cplan.abort_callback_data = cpu_ctx->abort_callback_data;

    return cpu_plan;
}

GGML_CALL static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
    struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;

    free(cpu_plan->cplan.work_data);
    free(cpu_plan);

    GGML_UNUSED(backend);
}

GGML_CALL static enum ggml_status ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
    struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;

    return ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan);

    GGML_UNUSED(backend);
}

GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
    struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;

    struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads, cpu_ctx->threadpool);

    if (cpu_ctx->work_size < cplan.work_size) {
        free(cpu_ctx->work_data);
        cpu_ctx->work_data = malloc(cplan.work_size);
        if (cpu_ctx->work_data == NULL) {
            cpu_ctx->work_size = 0;
            return GGML_STATUS_ALLOC_FAILED;
        }
        cpu_ctx->work_size = cplan.work_size;
    }
    cplan.work_data = cpu_ctx->work_data;

    cplan.abort_callback      = cpu_ctx->abort_callback;
    cplan.abort_callback_data = cpu_ctx->abort_callback_data;

    return ggml_graph_compute(cgraph, &cplan);
}

GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
    switch (op->op) {
        case GGML_OP_CPY:
            return
                op->type != GGML_TYPE_IQ2_XXS &&
                op->type != GGML_TYPE_IQ2_XS  &&
                op->type != GGML_TYPE_IQ1_S   &&
                op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float
        case GGML_OP_MUL_MAT:
            return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type;
        case GGML_OP_ROPE_BACK:
            return op->src[2] == NULL && (op->op_params[2] & 4) == 0;
        case GGML_OP_IM2COL_BACK:
            return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32;
        default:
            return true;
    }

    GGML_UNUSED(backend);
}

GGML_CALL static bool ggml_backend_cpu_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
    return ggml_backend_buft_is_host(buft);

    GGML_UNUSED(backend);
}

static struct ggml_backend_i cpu_backend_i = {
    /* .get_name                = */ ggml_backend_cpu_name,
    /* .free                    = */ ggml_backend_cpu_free,
    /* .get_default_buffer_type = */ ggml_backend_cpu_get_default_buffer_type,
    /* .set_tensor_async        = */ NULL,
    /* .get_tensor_async        = */ NULL,
    /* .cpy_tensor_async        = */ NULL,
    /* .synchronize             = */ NULL,
    /* .graph_plan_create       = */ ggml_backend_cpu_graph_plan_create,
    /* .graph_plan_free         = */ ggml_backend_cpu_graph_plan_free,
    /* .graph_plan_update       = */ NULL,
    /* .graph_plan_compute      = */ ggml_backend_cpu_graph_plan_compute,
    /* .graph_compute           = */ ggml_backend_cpu_graph_compute,
    /* .supports_op             = */ ggml_backend_cpu_supports_op,
    /* .supports_buft           = */ ggml_backend_cpu_supports_buft,
    /* .offload_op              = */ NULL,
    /* .event_new               = */ NULL,
    /* .event_free              = */ NULL,
    /* .event_record            = */ NULL,
    /* .event_wait              = */ NULL,
    /* .event_synchronize       = */ NULL,
};

static ggml_guid_t ggml_backend_cpu_guid(void) {
    static ggml_guid guid = { 0xaa, 0x67, 0xc7, 0x43, 0x96, 0xe6, 0xa3, 0x8a, 0xe3, 0xaf, 0xea, 0x92, 0x36, 0xbc, 0xfc, 0x89 };
    return &guid;
}

ggml_backend_t ggml_backend_cpu_init(void) {
    struct ggml_backend_cpu_context * ctx = malloc(sizeof(struct ggml_backend_cpu_context));
    if (ctx == NULL) {
        return NULL;
    }

    ctx->n_threads           = GGML_DEFAULT_N_THREADS;
    ctx->threadpool          = NULL;
    ctx->work_data           = NULL;
    ctx->work_size           = 0;
    ctx->abort_callback      = NULL;
    ctx->abort_callback_data = NULL;

    ggml_backend_t cpu_backend = malloc(sizeof(struct ggml_backend));
    if (cpu_backend == NULL) {
        free(ctx);
        return NULL;
    }

    *cpu_backend = (struct ggml_backend) {
        /* .guid      = */ ggml_backend_cpu_guid(),
        /* .interface = */ cpu_backend_i,
        /* .context   = */ ctx
    };
    return cpu_backend;
}

GGML_CALL bool ggml_backend_is_cpu(ggml_backend_t backend) {
    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cpu_guid());
}

void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) {
    GGML_ASSERT(ggml_backend_is_cpu(backend_cpu));

    struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context;
    ctx->n_threads = n_threads;
}

void ggml_backend_cpu_set_threadpool(ggml_backend_t backend_cpu, ggml_threadpool_t threadpool) {
    GGML_ASSERT(ggml_backend_is_cpu(backend_cpu));

    struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context;

    if (ctx->threadpool && ctx->threadpool != threadpool) {
        // already had a different threadpool, pause/suspend it before switching
        ggml_threadpool_pause(ctx->threadpool);
    }
    ctx->threadpool = threadpool;
}

void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data) {
    GGML_ASSERT(ggml_backend_is_cpu(backend_cpu));

    struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context;
    ctx->abort_callback = abort_callback;
    ctx->abort_callback_data = abort_callback_data;
}

GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) {
    GGML_ASSERT((uintptr_t)ptr % TENSOR_ALIGNMENT == 0 && "buffer pointer must be aligned");
    return ggml_backend_buffer_init(ggml_backend_cpu_buffer_type(), cpu_backend_buffer_i_from_ptr, ptr, size);
}

GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data) {
    return ggml_backend_cpu_init();

    GGML_UNUSED(params);
    GGML_UNUSED(user_data);
}

// multi-buffer buffer

struct ggml_backend_multi_buffer_context {
    ggml_backend_buffer_t * buffers;
    size_t n_buffers;
};

typedef struct ggml_backend_multi_buffer_context * ggml_backend_multi_buffer_context_t;

GGML_CALL static const char * ggml_backend_multi_buffer_get_name(ggml_backend_buffer_t buffer) {
    ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context;

    return ctx->buffers[0]->iface.get_name(ctx->buffers[0]);
}

GGML_CALL static void ggml_backend_multi_buffer_free_buffer(ggml_backend_buffer_t buffer) {
    ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context;
    for (size_t i = 0; i < ctx->n_buffers; i++) {
        ggml_backend_buffer_free(ctx->buffers[i]);
    }

    free(ctx->buffers);
    free(ctx);
}

GGML_CALL static void ggml_backend_multi_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
    ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context;
    for (size_t i = 0; i < ctx->n_buffers; i++) {
        ggml_backend_buffer_clear(ctx->buffers[i], value);
    }
}

static struct ggml_backend_buffer_i ggml_backend_multi_buffer_context_interface(void) {
    static struct ggml_backend_buffer_i multi_backend_buffer_i = {
        /* .get_name        = */ ggml_backend_multi_buffer_get_name,
        /* .free_buffer     = */ ggml_backend_multi_buffer_free_buffer,
        /* .get_base        = */ NULL,
        /* .init_tensor     = */ NULL,
        /* .memset_tensor   = */ NULL,
        /* .set_tensor      = */ NULL,
        /* .get_tensor      = */ NULL,
        /* .cpy_tensor      = */ NULL,
        /* .clear           = */ ggml_backend_multi_buffer_clear,
        /* .reset           = */ NULL,
    };

    return multi_backend_buffer_i;
}

GGML_CALL ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer_t * buffers, size_t n_buffers) {
    ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) malloc(sizeof(struct ggml_backend_multi_buffer_context));
    ctx->n_buffers = n_buffers;
    ctx->buffers = (ggml_backend_buffer_t *) malloc(n_buffers * sizeof(ggml_backend_buffer_t));

    GGML_ASSERT(ctx->buffers != NULL);

    size_t total_size = 0;
    for (size_t i = 0; i < n_buffers; i++) {
        ctx->buffers[i] = buffers[i];
        total_size += ggml_backend_buffer_get_size(buffers[i]);
    }

    return ggml_backend_buffer_init(buffers[0]->buft, ggml_backend_multi_buffer_context_interface(), ctx, total_size);
}

GGML_CALL bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer) {
    return buffer->iface.get_name == ggml_backend_multi_buffer_get_name;
}

GGML_CALL void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) {
    GGML_ASSERT(ggml_backend_buffer_is_multi_buffer(buffer));
    ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context;
    for (size_t i = 0; i < ctx->n_buffers; i++) {
        ggml_backend_buffer_set_usage(ctx->buffers[i], usage);
    }
}

// creates a copy of the tensor with the same memory layout
static struct ggml_tensor * ggml_dup_tensor_layout(struct ggml_context * ctx, const struct ggml_tensor * tensor) {
    struct ggml_tensor * dup = ggml_dup_tensor(ctx, tensor);
    for (int i = 0; i < GGML_MAX_DIMS; i++) {
        dup->nb[i] = tensor->nb[i];
    }
    return dup;
}

static bool ggml_is_view_op(enum ggml_op op) {
    return op == GGML_OP_VIEW || op == GGML_OP_RESHAPE || op == GGML_OP_PERMUTE || op == GGML_OP_TRANSPOSE;
}

// scheduler

#ifndef GGML_SCHED_MAX_BACKENDS
#define GGML_SCHED_MAX_BACKENDS 16
#endif

#ifndef GGML_SCHED_MAX_SPLIT_INPUTS
#define GGML_SCHED_MAX_SPLIT_INPUTS GGML_MAX_SRC
#endif

#ifndef GGML_SCHED_MAX_COPIES
#define GGML_SCHED_MAX_COPIES 4
#endif

struct ggml_backend_sched_split {
    int backend_id;
    int i_start;
    int i_end;
    struct ggml_tensor * inputs[GGML_SCHED_MAX_SPLIT_INPUTS];
    int n_inputs;
    // graph view of this split
    struct ggml_cgraph graph;
};

struct ggml_backend_sched {
    bool is_reset; // true if the scheduler has been reset since the last graph split
    bool is_alloc;

    int n_backends;

    ggml_backend_t backends[GGML_SCHED_MAX_BACKENDS];
    ggml_backend_buffer_type_t bufts[GGML_SCHED_MAX_BACKENDS];
    ggml_gallocr_t galloc;

    // hash map of the nodes in the graph
    struct ggml_hash_set  hash_set;
    int                 * hv_tensor_backend_ids; // [hash_set.size]
    struct ggml_tensor ** hv_tensor_copies;      // [hash_set.size][n_backends][n_copies]

    int * node_backend_ids; // [graph_size]
    int * leaf_backend_ids; // [graph_size]

    int * prev_node_backend_ids; // [graph_size]
    int * prev_leaf_backend_ids; // [graph_size]

    // copy of the graph with modified inputs
    struct ggml_cgraph graph;

    // graph splits
    struct ggml_backend_sched_split * splits;
    int n_splits;
    int splits_capacity;

    // pipeline parallelism support
    int n_copies;
    int cur_copy;
    ggml_backend_event_t events[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES];
    struct ggml_tensor * graph_inputs[GGML_SCHED_MAX_SPLIT_INPUTS];
    int n_graph_inputs;

    struct ggml_context * ctx;

    ggml_backend_sched_eval_callback callback_eval;
    void * callback_eval_user_data;

    char * context_buffer;
    size_t context_buffer_size;

    bool debug;
};

#define hash_id(tensor) ggml_hash_find_or_insert(&sched->hash_set, tensor)
#define tensor_backend_id(tensor) sched->hv_tensor_backend_ids[hash_id(tensor)]
#define tensor_id_copy(id, backend_id, copy_id) sched->hv_tensor_copies[(id) * sched->n_backends * sched->n_copies + (backend_id) * sched->n_copies + (copy_id)]
#define tensor_copy(tensor, backend_id, copy_id) tensor_id_copy(hash_id(tensor), backend_id, copy_id)

// returns the priority of the backend, lower id is higher priority
static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backend_t backend) {
    for (int i = 0; i < sched->n_backends; i++) {
        if (sched->backends[i] == backend) {
            return i;
        }
    }
    return -1;
}

static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, const struct ggml_tensor * tensor, const struct ggml_tensor * op) {
    ggml_backend_buffer_t buffer = tensor->buffer;
    if (buffer == NULL) {
        return -1;
    }

    // find highest prio backend that supports the buffer type and the op
    for (int i = 0; i < sched->n_backends; i++) {
        if (ggml_backend_supports_buft(sched->backends[i], buffer->buft) &&
            ggml_backend_supports_op(sched->backends[i], op)) {
            return i;
        }
    }

#ifndef NDEBUG
    fprintf(stderr, "%s: warning: no backend supports op %s with a weight with buffer type %s used in tensor %s, the weight will need to be copied\n",
        __func__, ggml_op_desc(tensor), ggml_backend_buffer_name(buffer), tensor->name);
#endif

    return -1;
}

#if 0
#define GGML_SCHED_MAX_SPLITS_DEBUG 4096
static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_SCHED_MAX_SPLITS_DEBUG*GGML_SCHED_MAX_SPLIT_INPUTS][128]; // debug only
#define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__)
#define GET_CAUSE(node) causes[hash_id(node)]
#else
#define SET_CAUSE(node, ...)
#define GET_CAUSE(node) ""
#endif

// returns the backend that should be used for the node based on the current locations
static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * tensor) {
    // TODO: use supports_op to check if the backend supports the op

    // assign pre-allocated nodes to their backend
    int cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor, tensor);
    if (cur_backend_id != -1) {
        SET_CAUSE(tensor, "1.dst");
        return cur_backend_id;
    }

    // view_src
    if (tensor->view_src != NULL) {
        cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src, tensor);
        if (cur_backend_id != -1) {
            SET_CAUSE(tensor, "1.vsrc");
            return cur_backend_id;
        }
    }

    if (tensor->buffer || (tensor->view_src && tensor->view_src->buffer)) {
        // since the tensor is pre-allocated, it cannot be moved to another backend
        GGML_ABORT("pre-allocated tensor in a backend that cannot run the operation");
    }

    // graph input
    if (tensor->flags & GGML_TENSOR_FLAG_INPUT) {
        cur_backend_id = sched->n_backends - 1; // last backend (assumed CPU)
        SET_CAUSE(tensor, "1.inp");
        return cur_backend_id;
    }

    // operations with weights are preferably run on the same backend as the weights
    for (int i = 0; i < GGML_MAX_SRC; i++) {
        const struct ggml_tensor * src = tensor->src[i];
        if (src == NULL) {
            continue;
        }
        if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
            int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor);
            // check if a backend with higher prio wants to offload the op
            if (src_backend_id == sched->n_backends - 1) {
                for (int b = 0; b < src_backend_id; b++) {
                    if (ggml_backend_supports_op(sched->backends[b], tensor) && ggml_backend_offload_op(sched->backends[b], tensor)) {
                        SET_CAUSE(tensor, "1.off");
                        return b;
                    }
                }
            }
            SET_CAUSE(tensor, "1.wgt%d", i);
            return src_backend_id;
        }
    }

    return -1;
}

static char * fmt_size(size_t size) {
    static char buffer[128];
    if (size >= 1024*1024) {
        snprintf(buffer, sizeof(buffer), "%zuM", size/1024/1024);
    } else {
        snprintf(buffer, sizeof(buffer), "%zuK", size/1024);
    }
    return buffer;
}

static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
    int cur_split = 0;
    for (int i = 0; i < graph->n_nodes; i++) {
        if (cur_split < sched->n_splits && i == sched->splits[cur_split].i_start) {
            ggml_backend_t split_backend = sched->backends[sched->splits[cur_split].backend_id];
            fprintf(stderr, "\n## SPLIT #%d: %s # %d inputs: ", cur_split, ggml_backend_name(split_backend),
                sched->splits[cur_split].n_inputs);
            for (int j = 0; j < sched->splits[cur_split].n_inputs; j++) {
                fprintf(stderr, "[%s (%5.5s)] ", sched->splits[cur_split].inputs[j]->name,
                    fmt_size(ggml_nbytes(sched->splits[cur_split].inputs[j])));
            }
            fprintf(stderr, "\n");
            cur_split++;
        }
        struct ggml_tensor * node = graph->nodes[i];
        if (ggml_is_view_op(node->op)) {
            continue;
        }
        ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node);
        fprintf(stderr, "node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name,
            fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node));
        for (int j = 0; j < GGML_MAX_SRC; j++) {
            struct ggml_tensor * src = node->src[j];
            if (src == NULL) {
                continue;
            }
            ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src);
            fprintf(stderr, " %20.20s (%5.5s) [%5.5s %8.8s]", src->name,
                fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src));
        }
        fprintf(stderr, "\n");
    }
}

static bool ggml_backend_sched_buffer_supported(ggml_backend_sched_t sched, struct ggml_tensor * t, int backend_id) {
    ggml_backend_buffer_t buf = t->view_src ? t->view_src->buffer : t->buffer;
    ggml_backend_buffer_type_t buft = NULL;

    if (buf) {
        // the tensor is already allocated
        buft = buf->buft;
    } else {
        // see if the tensor already has a backend assigned, and use the buffer type of that backend
        int tensor_backend_id = tensor_backend_id(t);
        if (tensor_backend_id == -1 && t->view_src) {
            tensor_backend_id = tensor_backend_id(t->view_src);
        }
        if (tensor_backend_id != -1) {
            buft = sched->bufts[tensor_backend_id];
        }
    }

    return buft != NULL && ggml_backend_supports_buft(sched->backends[backend_id], buft);
}

static void ggml_backend_sched_set_if_supported(ggml_backend_sched_t sched, struct ggml_tensor * node, int cur_backend_id, int * node_backend_id) {
    if (ggml_backend_supports_op(sched->backends[cur_backend_id], node)) {
        *node_backend_id = cur_backend_id;
        SET_CAUSE(node, "2.sup");
    }
}

// assigns backends to ops and splits the graph into subgraphs that can be computed on the same backend
static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
    // reset splits
    sched->n_splits = 0;
    sched->n_graph_inputs = 0;
    sched->is_reset = false;

    struct ggml_init_params params = {
        /* .mem_size =   */ sched->context_buffer_size,
        /* .mem_buffer = */ sched->context_buffer,
        /* .no_alloc =   */ true
    };

    ggml_free(sched->ctx);

    sched->ctx = ggml_init(params);
    if (sched->ctx == NULL) {
        GGML_ABORT("%s: failed to initialize context\n", __func__);
    }

    // pass 1: assign backends to ops with pre-allocated inputs
    for (int i = 0; i < graph->n_leafs; i++) {
        struct ggml_tensor * leaf = graph->leafs[i];
        int * leaf_backend_id = &tensor_backend_id(leaf);
        // do not overwrite user assignments
        if (*leaf_backend_id == -1) {
            *leaf_backend_id = ggml_backend_sched_backend_id_from_cur(sched, leaf);
        }
    }

    for (int i = 0; i < graph->n_nodes; i++) {
        struct ggml_tensor * node = graph->nodes[i];
        int * node_backend_id = &tensor_backend_id(node);
        // do not overwrite user assignments
        if (*node_backend_id == -1) {
            *node_backend_id = ggml_backend_sched_backend_id_from_cur(sched, node);

#if 0
            // src
            if (node->op == GGML_OP_NONE) {
                continue;
            }

            for (int j = 0; j < GGML_MAX_SRC; j++) {
                struct ggml_tensor * src = node->src[j];
                if (src == NULL) {
                    continue;
                }
                int * src_backend_id = &tensor_backend_id(src);
                if (*src_backend_id == -1) {
                    *src_backend_id = ggml_backend_sched_backend_id_from_cur(sched, src);
                }
            }
#endif
        }
    }

    // pass 2: expand current backend assignments
    // assign the same backend to adjacent nodes
    // expand gpu backends (i.e. non last prio) up and down, ignoring cpu (the lowest priority backend)
    // thus, cpu will never be used unless weights are on cpu, or there are no gpu ops between cpu ops
    // ops unsupported by the backend being expanded will be left unassigned so that they can be assigned later when the locations of its inputs are known
    // expand gpu down
    {
        int cur_backend_id = -1;
        for (int i = 0; i < graph->n_nodes; i++) {
            struct ggml_tensor * node = graph->nodes[i];
            if (ggml_is_view_op(node->op)) {
                continue;
            }
            int * node_backend_id = &tensor_backend_id(node);
            if (*node_backend_id != -1) {
                if (*node_backend_id == sched->n_backends - 1) {
                    // skip cpu (lowest prio backend)
                    cur_backend_id = -1;
                } else {
                    cur_backend_id = *node_backend_id;
                }
            } else if (cur_backend_id != -1) {
                ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id);
            }
        }
    }
    // expand gpu up
    {
        int cur_backend_id = -1;
        for (int i = graph->n_nodes - 1; i >= 0; i--) {
            struct ggml_tensor * node = graph->nodes[i];
            if (ggml_is_view_op(node->op)) {
                continue;
            }
            int * node_backend_id = &tensor_backend_id(node);
            if (*node_backend_id != -1) {
                if (*node_backend_id == sched->n_backends - 1) {
                    // skip cpu (lowest prio backend)
                    cur_backend_id = -1;
                } else {
                    cur_backend_id = *node_backend_id;
                }
            } else if (cur_backend_id != -1) {
                ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id);
            }
        }
    }
    // expand rest down
    {
        int cur_backend_id = -1;
        for (int i = 0; i < graph->n_nodes; i++) {
            struct ggml_tensor * node = graph->nodes[i];
            if (ggml_is_view_op(node->op)) {
                continue;
            }
            int * node_backend_id = &tensor_backend_id(node);
            if (*node_backend_id != -1) {
                cur_backend_id = *node_backend_id;
            } else if (cur_backend_id != -1) {
                ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id);
            }
        }
    }
    // expand rest up
    {
        int cur_backend_id = -1;
        for (int i = graph->n_nodes - 1; i >= 0; i--) {
            struct ggml_tensor * node = graph->nodes[i];
            if (ggml_is_view_op(node->op)) {
                continue;
            }
            int * node_backend_id = &tensor_backend_id(node);
            if (*node_backend_id != -1) {
                cur_backend_id = *node_backend_id;
            } else if (cur_backend_id != -1) {
                ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id);
            }
        }
    }

    // pass 3: upgrade nodes to higher prio backends with compatible buffer types
    // if the tensor is already in the same buffer type (*) as another higher priority backend, we should move it there
    // however, we also need to verify that the sources are in compatible buffer types
    // (*) the actual requirement is more relaxed, the buffer type of the backend should be supported by all the users of this tensor further down the graph
    // however, this is slow to verify, so we have a more strict requirement that the buffer type is the same
    // this is not uncommon since multiple backends can use host memory, with the same buffer type (eg. BLAS and CPU)
    // additionally, set remaining unassigned nodes to the backend with the most supported inputs
    // only nodes that could not be assigned during expansion due to the backend not supporting the op should be unassigned at this point
    for (int i = 0; i < graph->n_nodes; i++) {
        struct ggml_tensor * node = graph->nodes[i];
        if (ggml_is_view_op(node->op)) {
            continue;
        }
        int * node_backend_id = &tensor_backend_id(node);
        if (*node_backend_id == -1) {
            // unassigned node: find the backend with the most supported inputs
            int n_supported_best = -1;
            for (int b = 0; b < sched->n_backends; b++) {
                if (ggml_backend_supports_op(sched->backends[b], node)) {
                    int n_supported = 0;
                    for (int j = 0; j < GGML_MAX_SRC; j++) {
                        struct ggml_tensor * src = node->src[j];
                        if (src == NULL) {
                            continue;
                        }
                        if ((tensor_backend_id(src) != -1 || tensor_backend_id(src->view_src) != -1) && ggml_backend_sched_buffer_supported(sched, src, b)) {
                            n_supported++;
                        }
                    }
                    if (n_supported > n_supported_best) {
                        n_supported_best = n_supported;
                        *node_backend_id = b;
                        SET_CAUSE(node, "3.best");
                    }
                }
            }
        } else {
            // assigned node: upgrade to higher prio backend if possible
            for (int b = 0; b < *node_backend_id; b++) {
                if (sched->bufts[b] == sched->bufts[*node_backend_id] && ggml_backend_supports_op(sched->backends[b], node)) {
                    bool supported = true;
                    for (int j = 0; j < GGML_MAX_SRC; j++) {
                        struct ggml_tensor * src = node->src[j];
                        if (src == NULL) {
                            continue;
                        }
                        if (!ggml_backend_sched_buffer_supported(sched, src, b)) {
                            supported = false;
                            break;
                        }
                    }
                    if (supported) {
                        *node_backend_id = b;
                        SET_CAUSE(node, "3.upg");
                        break;
                    }
                }
            }
        }
    }

    // pass 4: assign backends to remaining src from dst and view_src
    for (int i = 0; i < graph->n_nodes; i++) {
        struct ggml_tensor * node = graph->nodes[i];
        int * cur_backend_id = &tensor_backend_id(node);
        if (node->view_src != NULL && *cur_backend_id == -1) {
            *cur_backend_id = tensor_backend_id(node->view_src);
            SET_CAUSE(node, "4.vsrc");
        }
        for (int j = 0; j < GGML_MAX_SRC; j++) {
            struct ggml_tensor * src = node->src[j];
            if (src == NULL) {
                continue;
            }
            int * src_backend_id = &tensor_backend_id(src);
            if (*src_backend_id == -1) {
                if (src->view_src != NULL) {
                    // views are always on the same backend as the source
                    *src_backend_id = tensor_backend_id(src->view_src);
                    SET_CAUSE(src, "4.vsrc");
                } else {
                    *src_backend_id = *cur_backend_id;
                    SET_CAUSE(src, "4.cur");
                }
            }
        }
    }

    // pass 5: split graph, find tensors that need to be copied
    {
        int i_split = 0;
        struct ggml_backend_sched_split * split = &sched->splits[0];
        // find the backend of the first split, skipping view ops
        int i = 0;
        for (; i < graph->n_nodes; i++) {
            struct ggml_tensor * node = graph->nodes[i];
            if (!ggml_is_view_op(node->op)) {
                split->backend_id = tensor_backend_id(node);
                break;
            }
        }
        split->i_start = 0;
        split->n_inputs = 0;
        int cur_backend_id = split->backend_id;
        for (; i < graph->n_nodes; i++) {
            struct ggml_tensor * node = graph->nodes[i];

            if (ggml_is_view_op(node->op)) {
                continue;
            }

            const int node_backend_id = tensor_backend_id(node);

            assert(node_backend_id != -1); // all nodes should be assigned by now

            // check if we should start a new split based on the sources of the current node
            bool need_new_split = false;
            if (node_backend_id == cur_backend_id && split->n_inputs > 0) {
                for (int j = 0; j < GGML_MAX_SRC; j++) {
                    struct ggml_tensor * src = node->src[j];
                    if (src == NULL) {
                        continue;
                    }
                    // check if a weight is on a different backend
                    // by starting a new split, the memory of the previously offloaded weights can be reused
                    if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
                        int src_backend_id = tensor_backend_id(src);
                        if (src_backend_id != cur_backend_id) {
                            need_new_split = true;
                            break;
                        }
                    }
                    // check if the split has too many inputs
                    // FIXME: count the number of inputs instead of only checking when full
                    if (split->n_inputs == GGML_SCHED_MAX_SPLIT_INPUTS) {
                        const size_t id = hash_id(src);
                        int src_backend_id = sched->hv_tensor_backend_ids[id];
                        bool supported = ggml_backend_sched_buffer_supported(sched, src, cur_backend_id);
                        if (src_backend_id != cur_backend_id && tensor_id_copy(id, cur_backend_id, 0) == NULL && !supported) {
                            //printf("starting new split because of too many inputs: node %s, input %s\n", node->name, src->name);
                            need_new_split = true;
                            break;
                        }
                    }
                }
            }

            if (node_backend_id != cur_backend_id || need_new_split) {
                split->i_end = i;
                i_split++;
                if (i_split >= sched->splits_capacity) {
                    sched->splits_capacity *= 2;
                    sched->splits = realloc(sched->splits, sched->splits_capacity * sizeof(struct ggml_backend_sched_split));
                    GGML_ASSERT(sched->splits != NULL);
                }
                split = &sched->splits[i_split];
                split->backend_id = node_backend_id;
                split->i_start = i;
                split->n_inputs = 0;
                cur_backend_id = node_backend_id;
            }

            // find inputs that are not on the same backend
            for (int j = 0; j < GGML_MAX_SRC; j++) {
                struct ggml_tensor * src = node->src[j];
                if (src == NULL) {
                    continue;
                }

                size_t src_id = hash_id(src);
                const int src_backend_id = sched->hv_tensor_backend_ids[src_id];
                assert(src_backend_id != -1); // all inputs should be assigned by now

                if (src->flags & GGML_TENSOR_FLAG_INPUT && sched->n_copies > 1) {
                    if (tensor_id_copy(src_id, src_backend_id, 0) == NULL) {
                        ggml_backend_t backend = sched->backends[src_backend_id];
                        for (int c = 0; c < sched->n_copies; c++) {
                            struct ggml_tensor * tensor_copy;
                            if (c == sched->cur_copy) {
                                tensor_copy = src; // use the original tensor as the current copy
                            } else {
                                tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
                                ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c);
                            }
                            if (sched->n_copies > 1) {
                                ggml_set_input(tensor_copy);
                                ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor
                            }
                            tensor_id_copy(src_id, src_backend_id, c) = tensor_copy;
                            SET_CAUSE(tensor_copy, "4.cpy");
                        }
                        int n_graph_inputs = sched->n_graph_inputs++;
                        GGML_ASSERT(n_graph_inputs < GGML_SCHED_MAX_SPLIT_INPUTS);
                        sched->graph_inputs[n_graph_inputs] = src;
                    }
                }

                if (src_backend_id != cur_backend_id && !ggml_backend_sched_buffer_supported(sched, src, cur_backend_id)) {
                    // create a copy of the input in the split's backend
                    if (tensor_id_copy(src_id, cur_backend_id, 0) == NULL) {
                        ggml_backend_t backend = sched->backends[cur_backend_id];
                        for (int c = 0; c < sched->n_copies; c++) {
                            struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
                            ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c);
                            if (sched->n_copies > 1) {
                                ggml_set_input(tensor_copy);
                                ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor
                            }
                            tensor_id_copy(src_id, cur_backend_id, c) = tensor_copy;
                            SET_CAUSE(tensor_copy, "4.cpy");
                        }
                        int n_inputs = split->n_inputs++;
                        GGML_ASSERT(n_inputs < GGML_SCHED_MAX_SPLIT_INPUTS);
                        split->inputs[n_inputs] = src;
                    }
                    node->src[j] = tensor_id_copy(src_id, cur_backend_id, sched->cur_copy);
                }
            }
        }
        split->i_end = graph->n_nodes;
        sched->n_splits = i_split + 1;
    }

    if (sched->debug) {
        ggml_backend_sched_print_assignments(sched, graph);
    }

    // swap node_backend_ids and leaf _backend_ids with prevs
    {
        int * tmp = sched->node_backend_ids;
        sched->node_backend_ids = sched->prev_node_backend_ids;
        sched->prev_node_backend_ids = tmp;

        tmp = sched->leaf_backend_ids;
        sched->leaf_backend_ids = sched->prev_leaf_backend_ids;
        sched->prev_leaf_backend_ids = tmp;
    }

    int graph_size = MAX(graph->n_nodes, graph->n_leafs) + sched->n_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2*sched->n_copies;
    if (sched->graph.size < graph_size) {
        sched->graph.size = graph_size;
        sched->graph.nodes = realloc(sched->graph.nodes, graph_size * sizeof(struct ggml_tensor *));
        sched->graph.leafs = realloc(sched->graph.leafs, graph_size * sizeof(struct ggml_tensor *));
        GGML_ASSERT(sched->graph.nodes != NULL);
        GGML_ASSERT(sched->graph.leafs != NULL);
    }
    sched->graph.n_nodes = 0;
    sched->graph.n_leafs = 0;

    struct ggml_cgraph * graph_copy = &sched->graph;

    for (int i = 0; i < sched->n_splits; i++) {
        struct ggml_backend_sched_split * split = &sched->splits[i];
        split->graph = ggml_graph_view(graph, split->i_start, split->i_end);

        // add inputs to the graph copy so that they are allocated by ggml-alloc at the start of the split
        for (int j = 0; j < split->n_inputs; j++) {
            assert(graph_copy->size > (graph_copy->n_nodes + 1));

            struct ggml_tensor * input = split->inputs[j];
            const size_t input_id = hash_id(input);
            struct ggml_tensor * input_cpy = tensor_id_copy(input_id, split->backend_id, sched->cur_copy);

            // add a dependency to the input source so that it is not freed before the copy is done
            struct ggml_tensor * input_dep = ggml_view_tensor(sched->ctx, input);
            input_dep->src[0] = input;
            sched->node_backend_ids[graph_copy->n_nodes] = sched->hv_tensor_backend_ids[input_id];
            graph_copy->nodes[graph_copy->n_nodes++] = input_dep;

            // add a dependency to the input copy so that it is allocated at the start of the split
            sched->node_backend_ids[graph_copy->n_nodes] = split->backend_id;
            graph_copy->nodes[graph_copy->n_nodes++] = input_cpy;
        }

        for (int j = split->i_start; j < split->i_end; j++) {
            assert(graph_copy->size > graph_copy->n_nodes);
            sched->node_backend_ids[graph_copy->n_nodes] = tensor_backend_id(graph->nodes[j]);
            graph_copy->nodes[graph_copy->n_nodes++] = graph->nodes[j];
        }
    }

    if (sched->n_copies > 1) {
        // add input copies as leafs so that they are allocated first
        for (int i = 0; i < sched->n_graph_inputs; i++) {
            struct ggml_tensor * input = sched->graph_inputs[i];
            size_t id = hash_id(input);
            int backend_id = tensor_backend_id(input);
            for (int c = 0; c < sched->n_copies; c++) {
                struct ggml_tensor * input_cpy = tensor_id_copy(id, backend_id, c);
                sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
                assert(graph_copy->size > graph_copy->n_leafs);
                graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
            }
        }

        for (int i = 0; i < sched->n_splits; i++) {
            struct ggml_backend_sched_split * split = &sched->splits[i];
            int backend_id = split->backend_id;
            for (int j = 0; j < split->n_inputs; j++) {
                struct ggml_tensor * input = split->inputs[j];
                size_t id = hash_id(input);
                for (int c = 0; c < sched->n_copies; c++) {
                    struct ggml_tensor * input_cpy = tensor_id_copy(id, backend_id, c);
                    sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
                    assert(graph_copy->size > graph_copy->n_leafs);
                    graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
                }
            }
        }
    }

    // add leafs from the original graph
    for (int i = 0; i < graph->n_leafs; i++) {
        struct ggml_tensor * leaf = graph->leafs[i];
        sched->leaf_backend_ids[graph_copy->n_leafs] = tensor_backend_id(leaf);
        assert(graph_copy->size > graph_copy->n_leafs);
        graph_copy->leafs[graph_copy->n_leafs++] = leaf;
    }
}

static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
    bool backend_ids_changed = false;
    for (int i = 0; i < sched->graph.n_nodes; i++) {
        if (sched->node_backend_ids[i] != sched->prev_node_backend_ids[i] &&
            sched->bufts[sched->node_backend_ids[i]] != sched->bufts[sched->prev_node_backend_ids[i]]) {
            backend_ids_changed = true;
            break;
        }
    }
    if (!backend_ids_changed) {
        for (int i = 0; i < sched->graph.n_leafs; i++) {
            if (sched->leaf_backend_ids[i] != sched->prev_leaf_backend_ids[i] &&
                sched->bufts[sched->leaf_backend_ids[i]] != sched->bufts[sched->prev_leaf_backend_ids[i]]) {
                backend_ids_changed = true;
                break;
            }
        }
    }

    // allocate graph
    if (backend_ids_changed || !ggml_gallocr_alloc_graph(sched->galloc, &sched->graph)) {
        // the re-allocation may cause the split inputs to be moved to a different address
        ggml_backend_sched_synchronize(sched);
#ifndef NDEBUG
        fprintf(stderr, "%s: failed to allocate graph, reserving (backend_ids_changed = %d)\n", __func__, backend_ids_changed);
#endif
        ggml_gallocr_reserve_n(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids);
        if (!ggml_gallocr_alloc_graph(sched->galloc, &sched->graph)) {
            fprintf(stderr, "%s: failed to allocate graph\n", __func__);
            return false;
        }
    }

    return true;
}

static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
    struct ggml_backend_sched_split * splits = sched->splits;

    for (int i = 0; i < sched->n_splits; i++) {
        struct ggml_backend_sched_split * split = &splits[i];
        int split_backend_id = split->backend_id;
        ggml_backend_t split_backend = sched->backends[split_backend_id];

        // copy the input tensors to the split backend
        for (int j = 0; j < split->n_inputs; j++) {
            ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[j]);
            struct ggml_tensor * input = split->inputs[j];
            struct ggml_tensor * input_cpy = tensor_copy(input, split_backend_id, sched->cur_copy);

            if (input->flags & GGML_TENSOR_FLAG_INPUT) {
                // inputs from the user must be copied immediately to prevent the user overwriting the data before the copy is done
                if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
                    ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
                } else {
                    ggml_backend_synchronize(split_backend);
                }
                ggml_backend_tensor_copy(input, input_cpy);
            } else {
                // wait for the split backend to finish using the input before overwriting it
                if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
                    ggml_backend_event_wait(split_backend, sched->events[split_backend_id][sched->cur_copy]);
                } else {
                    ggml_backend_synchronize(split_backend);
                }
                // try async copy, but if not possible, we can still use a sync copy without synchronizing the dst backend, since we handle the synchronization here with multiple copies and events
                // TODO: add public function to facilitate this, since applications do not have direct access to the backend interface
                if (!split_backend->iface.cpy_tensor_async || !split_backend->iface.cpy_tensor_async(input_backend, split_backend, input, input_cpy)) {
                    ggml_backend_synchronize(input_backend);
                    if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
                        ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
                    } else {
                        ggml_backend_synchronize(split_backend);
                    }
                    ggml_backend_tensor_copy(input, input_cpy);
                }
            }
        }

        if (!sched->callback_eval) {
            enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &split->graph);
            if (ec != GGML_STATUS_SUCCESS) {
                return ec;
            }
        } else {
            // similar to ggml_backend_compare_graph_backend
            for (int j0 = 0; j0 < split->graph.n_nodes; j0++) {
                struct ggml_tensor * t = split->graph.nodes[j0];

                // check if the user needs data from this node
                bool need = sched->callback_eval(t, true, sched->callback_eval_user_data);

                int j1 = j0;

                // determine the range [j0, j1] of nodes that can be computed together
                while (!need && j1 < split->graph.n_nodes - 1) {
                    t = split->graph.nodes[++j1];
                    need = sched->callback_eval(t, true, sched->callback_eval_user_data);
                }

                struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1);

                enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &gv);
                if (ec != GGML_STATUS_SUCCESS) {
                    return ec;
                }

                // TODO: pass backend to the callback, then the user can decide if they want to synchronize
                ggml_backend_synchronize(split_backend);

                if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) {
                    break;
                }

                j0 = j1;
            }
        }

        // record the event of this copy
        if (split->n_inputs > 0) {
            if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
                ggml_backend_event_record(sched->events[split_backend_id][sched->cur_copy]);
            }
        }
    }

    sched->cur_copy = (sched->cur_copy + 1) % sched->n_copies;

    return GGML_STATUS_SUCCESS;
}

ggml_backend_sched_t ggml_backend_sched_new(
        ggml_backend_t * backends,
        ggml_backend_buffer_type_t * bufts,
        int n_backends,
        size_t graph_size,
        bool parallel) {
    GGML_ASSERT(n_backends > 0);
    GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS);
    GGML_ASSERT(ggml_backend_is_cpu(backends[n_backends - 1])); // last backend must be CPU

    struct ggml_backend_sched * sched = calloc(1, sizeof(struct ggml_backend_sched));

    sched->debug = getenv("GGML_SCHED_DEBUG") != NULL;
    sched->n_backends = n_backends;
    sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1;

    // initialize hash table
    // FIXME: needs to be size*2 to account for leafs (do it in graph_split instead)
    sched->hash_set    = ggml_hash_set_new(graph_size);
    sched->hv_tensor_backend_ids = malloc(sched->hash_set.size * sizeof(sched->hv_tensor_backend_ids[0]));
    sched->hv_tensor_copies      = malloc(sched->hash_set.size * sched->n_backends * sched->n_copies * sizeof(struct ggml_tensor *));

    const size_t ggml_sched_max_splits = graph_size; // at most there is one split for each node in the graph
    const size_t nodes_size = graph_size + ggml_sched_max_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2;
    sched->node_backend_ids = calloc(nodes_size, sizeof(sched->node_backend_ids[0]));
    sched->leaf_backend_ids = calloc(nodes_size, sizeof(sched->leaf_backend_ids[0]));
    sched->prev_node_backend_ids = calloc(nodes_size, sizeof(sched->prev_node_backend_ids[0]));
    sched->prev_leaf_backend_ids = calloc(nodes_size, sizeof(sched->prev_leaf_backend_ids[0]));

    sched->context_buffer_size = ggml_sched_max_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + ggml_graph_overhead_custom(graph_size, false);
    sched->context_buffer = malloc(sched->context_buffer_size);

    const int initial_splits_capacity = 16;
    sched->splits = calloc(initial_splits_capacity, sizeof(sched->splits[0]));
    sched->splits_capacity = initial_splits_capacity;

    for (int b = 0; b < n_backends; b++) {
        sched->backends[b] = backends[b];
        sched->bufts[b] = bufts ? bufts[b] : ggml_backend_get_default_buffer_type(backends[b]);
        GGML_ASSERT(ggml_backend_supports_buft(backends[b], sched->bufts[b]));
        if (sched->n_copies > 1) {
            for (int c = 0; c < sched->n_copies; c++) {
                sched->events[b][c] = ggml_backend_event_new(backends[b]);
            }
        }
    }

    sched->galloc = ggml_gallocr_new_n(sched->bufts, n_backends);

    ggml_backend_sched_reset(sched);

    return sched;
}

void ggml_backend_sched_free(ggml_backend_sched_t sched) {
    if (sched == NULL) {
        return;
    }
    for (int b = 0; b < sched->n_backends; b++) {
        for (int c = 0; c < sched->n_copies; c++) {
            ggml_backend_event_free(sched->events[b][c]);
        }
    }
    ggml_gallocr_free(sched->galloc);
    ggml_free(sched->ctx);
    ggml_hash_set_free(&sched->hash_set);
    free(sched->splits);
    free(sched->hv_tensor_backend_ids);
    free(sched->hv_tensor_copies);
    free(sched->node_backend_ids);
    free(sched->leaf_backend_ids);
    free(sched->prev_node_backend_ids);
    free(sched->prev_leaf_backend_ids);
    free(sched->context_buffer);
    free(sched->graph.nodes);
    free(sched->graph.leafs);
    free(sched);
}

void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
    // reset state for the next run
    if (!sched->is_reset) {
        ggml_hash_set_reset(&sched->hash_set);
        memset(sched->hv_tensor_backend_ids, -1, sched->hash_set.size * sizeof(sched->hv_tensor_backend_ids[0]));
        memset(sched->hv_tensor_copies,       0, sched->hash_set.size * sched->n_backends * sched->n_copies * sizeof(struct ggml_tensor *));
        sched->is_reset = true;
    }
    sched->is_alloc = false;
}

bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) {
    GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs);

    ggml_backend_sched_split_graph(sched, measure_graph);

    if (!ggml_gallocr_reserve_n(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids)) {
        return false;
    }

    ggml_backend_sched_reset(sched);
    ggml_backend_sched_synchronize(sched);

    return true;
}

bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
    GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + graph->n_leafs);

    ggml_backend_sched_split_graph(sched, graph);


    if (!ggml_backend_sched_alloc_splits(sched)) {
        return false;
    }

    sched->is_alloc = true;

    return true;
}

enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
    enum ggml_status err = ggml_backend_sched_graph_compute_async(sched, graph);
    ggml_backend_sched_synchronize(sched);
    return err;
}

enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
    if (!sched->is_reset && !sched->is_alloc) {
        ggml_backend_sched_reset(sched);
    }

    if (!sched->is_alloc) {
        if (!ggml_backend_sched_alloc_graph(sched, graph)) {
            return GGML_STATUS_ALLOC_FAILED;
        }
    }

    return ggml_backend_sched_compute_splits(sched);
}

void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) {
    for (int i = 0; i < sched->n_backends; i++) {
        ggml_backend_synchronize(sched->backends[i]);
    }
}

void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) {
    sched->callback_eval = callback;
    sched->callback_eval_user_data = user_data;
}

int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched) {
    return sched->n_splits;
}

int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched) {
    return sched->n_copies;
}

int ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched) {
    return sched->n_backends;
}

ggml_backend_t ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i) {
    GGML_ASSERT(i >= 0 && i < sched->n_backends);
    return sched->backends[i];
}

size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) {
    int backend_index = ggml_backend_sched_backend_id(sched, backend);
    GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);

    return ggml_gallocr_get_buffer_size(sched->galloc, backend_index);
}

void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
    int backend_index = ggml_backend_sched_backend_id(sched, backend);
    GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
    tensor_backend_id(node) = backend_index;
    SET_CAUSE(node, "usr");
    sched->is_reset = false;
}

ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) {
    int backend_index = tensor_backend_id(node);
    if (backend_index == -1) {
        return NULL;
    }
    return sched->backends[backend_index];
}

// utils

void ggml_backend_view_init(struct ggml_tensor * tensor) {
    GGML_ASSERT(tensor->buffer == NULL);
    GGML_ASSERT(tensor->view_src != NULL);
    GGML_ASSERT(tensor->view_src->buffer != NULL);
    GGML_ASSERT(tensor->view_src->data != NULL);

    tensor->buffer = tensor->view_src->buffer;
    tensor->data = (char *)tensor->view_src->data + tensor->view_offs;
    ggml_backend_buffer_init_tensor(tensor->buffer, tensor);
}

void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr) {
    GGML_ASSERT(tensor->buffer == NULL);
    GGML_ASSERT(tensor->data == NULL);
    GGML_ASSERT(tensor->view_src == NULL);
    GGML_ASSERT(addr >= ggml_backend_buffer_get_base(buffer));
    GGML_ASSERT((char *)addr + ggml_backend_buffer_get_alloc_size(buffer, tensor) <=
                (char *)ggml_backend_buffer_get_base(buffer) + ggml_backend_buffer_get_size(buffer));

    tensor->buffer = buffer;
    tensor->data = addr;
    ggml_backend_buffer_init_tensor(buffer, tensor);
}

static struct ggml_tensor * graph_copy_dup_tensor(struct ggml_hash_set hash_set, struct ggml_tensor ** node_copies,
    struct ggml_context * ctx_allocated, struct ggml_context * ctx_unallocated, struct ggml_tensor * src) {

    GGML_ASSERT(src != NULL);
    GGML_ASSERT(src->data && "graph must be allocated");

    size_t id = ggml_hash_insert(&hash_set, src);
    if (id == GGML_HASHSET_ALREADY_EXISTS) {
        return node_copies[ggml_hash_find(&hash_set, src)];
    }

    struct ggml_tensor * dst = ggml_dup_tensor_layout(src->data && !src->view_src ? ctx_allocated : ctx_unallocated, src);
    if (src->view_src != NULL) {
        dst->view_src = graph_copy_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, src->view_src);
        dst->view_offs = src->view_offs;
    }
    dst->op = src->op;
    memcpy(dst->op_params, src->op_params, sizeof(dst->op_params));
    ggml_set_name(dst, src->name);

    // copy src
    for (int i = 0; i < GGML_MAX_SRC; i++) {
        struct ggml_tensor * s = src->src[i];
        if (s == NULL) {
            continue;
        }
        dst->src[i] = graph_copy_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, s);
    }

    node_copies[id] = dst;
    return dst;
}

static void graph_copy_init_tensor(struct ggml_hash_set * hash_set, struct ggml_tensor ** node_copies, bool * node_init, struct ggml_tensor * src) {
    size_t id = ggml_hash_find(hash_set, src);
    if (node_init[id]) {
        return;
    }
    node_init[id] = true;

    struct ggml_tensor * dst = node_copies[id];
    if (dst->view_src != NULL) {
        graph_copy_init_tensor(hash_set, node_copies, node_init, src->view_src);
        ggml_backend_view_init(dst);
    }
    else {
        ggml_backend_tensor_copy(src, dst);
    }

    // init src
    for (int i = 0; i < GGML_MAX_SRC; i++) {
        struct ggml_tensor * s = src->src[i];
        if (s == NULL) {
            continue;
        }
        graph_copy_init_tensor(hash_set, node_copies, node_init, s);
    }
}

struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph) {
    struct ggml_hash_set hash_set = ggml_hash_set_new(graph->visited_hash_set.size);
    struct ggml_tensor ** node_copies = calloc(hash_set.size, sizeof(node_copies[0])); // NOLINT
    bool * node_init = calloc(hash_set.size, sizeof(node_init[0]));

    struct ggml_init_params params = {
        /* .mem_size   = */ ggml_tensor_overhead()*hash_set.size + ggml_graph_overhead_custom(graph->size, false),
        /* .mem_buffer = */ NULL,
        /* .no_alloc   = */ true
    };

    struct ggml_context * ctx_allocated = ggml_init(params);
    struct ggml_context * ctx_unallocated = ggml_init(params);

    if (ctx_allocated == NULL || ctx_unallocated == NULL) {
        fprintf(stderr, "failed to allocate context for graph copy\n");
        ggml_hash_set_free(&hash_set);
        free(node_copies);
        free(node_init);
        ggml_free(ctx_allocated);
        ggml_free(ctx_unallocated);
        return (struct ggml_backend_graph_copy) {
            /* .buffer           = */ NULL,
            /* .ctx_allocated    = */ NULL,
            /* .ctx_unallocated  = */ NULL,
            /* .graph            = */ NULL,
        };
    }

    // dup nodes
    for (int i = 0; i < graph->n_nodes; i++) {
        struct ggml_tensor * node = graph->nodes[i];
        graph_copy_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, node);
    }

    // allocate nodes
    ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx_allocated, backend);
    if (buffer == NULL) {
        fprintf(stderr, "failed to allocate buffer for graph copy\n");
        ggml_hash_set_free(&hash_set);
        free(node_copies);
        free(node_init);
        ggml_free(ctx_allocated);
        ggml_free(ctx_unallocated);
        return (struct ggml_backend_graph_copy) {
            /* .buffer           = */ NULL,
            /* .ctx_allocated    = */ NULL,
            /* .ctx_unallocated  = */ NULL,
            /* .graph            = */ NULL,
        };
    }

    //printf("copy buffer size: %zu MB\n", ggml_backend_buffer_get_size(buffer) / 1024 / 1024);

    // copy data and init views
    for (int i = 0; i < graph->n_nodes; i++) {
        struct ggml_tensor * node = graph->nodes[i];
        graph_copy_init_tensor(&hash_set, node_copies, node_init, node);
    }

    // build graph copy
    struct ggml_cgraph * graph_copy = ggml_new_graph_custom(ctx_allocated, graph->size, false);
    for (int i = 0; i < graph->n_nodes; i++) {
        struct ggml_tensor * node = graph->nodes[i];
        struct ggml_tensor * node_copy = node_copies[ggml_hash_find(&hash_set, node)];
        graph_copy->nodes[i] = node_copy;
    }
    graph_copy->n_nodes = graph->n_nodes;

    ggml_hash_set_free(&hash_set);
    free(node_copies);
    free(node_init);

    return (struct ggml_backend_graph_copy) {
        /* .buffer           = */ buffer,
        /* .ctx_allocated    = */ ctx_allocated,
        /* .ctx_unallocated  = */ ctx_unallocated,
        /* .graph            = */ graph_copy,
    };
}

void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy) {
    ggml_backend_buffer_free(copy.buffer);
    ggml_free(copy.ctx_allocated);
    ggml_free(copy.ctx_unallocated);
}

bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data) {
    struct ggml_backend_graph_copy copy = ggml_backend_graph_copy(backend2, graph);
    if (copy.buffer == NULL) {
        return false;
    }

    struct ggml_cgraph * g1 = graph;
    struct ggml_cgraph * g2 = copy.graph;

    assert(g1->n_nodes == g2->n_nodes);

    for (int i = 0; i < g1->n_nodes; i++) {
        //printf("eval %d/%d\n", i, g1->n_nodes);
        struct ggml_tensor * t1 = g1->nodes[i];
        struct ggml_tensor * t2 = g2->nodes[i];

        assert(t1->op == t2->op && ggml_are_same_layout(t1, t2));

        struct ggml_cgraph g1v = ggml_graph_view(g1, i, i + 1);
        struct ggml_cgraph g2v = ggml_graph_view(g2, i, i + 1);

        ggml_backend_graph_compute(backend1, &g1v);
        ggml_backend_graph_compute(backend2, &g2v);

        if (ggml_is_view_op(t1->op)) {
            continue;
        }

        // compare results, calculate rms etc
        if (!callback(i, t1, t2, user_data)) {
            break;
        }
    }

    ggml_backend_graph_copy_free(copy);

    return true;
}