llama.cpp/ggml/src/ggml-sycl/common.hpp

//
// MIT license
// Copyright (C) 2024 Intel Corporation
// SPDX-License-Identifier: MIT
//

//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//

#ifndef GGML_SYCL_COMMON_HPP
#define GGML_SYCL_COMMON_HPP

#include <cstddef>
#include <fstream>
#include <iostream>
#include <string>

#include "dpct/helper.hpp"
#include "ggml-sycl.h"
#include "presets.hpp"
#include "sycl_hw.hpp"


#if GGML_SYCL_DNNL
#include "dnnl.hpp"
#include "dnnl_sycl.hpp"
#endif

#define GGML_COMMON_DECL_SYCL
#define GGML_COMMON_IMPL_SYCL
/* suppress warning spam */
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wnested-anon-types"
#include "ggml-common.h"
#pragma clang diagnostic pop
#include "ggml-impl.h"

void* ggml_sycl_host_malloc(size_t size);
void ggml_sycl_host_free(void* ptr);


extern int g_ggml_sycl_debug;
extern int g_ggml_sycl_disable_optimize;
extern int g_ggml_sycl_prioritize_dmmv;

#if defined(__clang__) && __has_builtin(__builtin_expect)
// Hint the optimizer to pipeline the more likely following instruction in branches
#    define LIKELY(expr)   __builtin_expect(expr, true)
#    define UNLIKELY(expr) __builtin_expect(expr, false)
#else
#    define LIKELY(expr)   (expr)
#    define UNLIKELY(expr) (expr)
#endif

#define GGML_SYCL_DEBUG(...)              \
    do {                                  \
        if (UNLIKELY(g_ggml_sycl_debug))  \
            fprintf(stderr, __VA_ARGS__); \
    } while (0)

#define CHECK_TRY_ERROR(expr)                                            \
  [&]() {                                                                \
    try {                                                                \
      expr;                                                              \
      return dpct::success;                                              \
    } catch (std::exception const& e) {                                  \
      std::cerr << e.what() << "\nException caught at file:" << __FILE__ \
                << ", line:" << __LINE__ << ", func:" << __func__        \
                << std::endl;                                            \
      return dpct::default_error;                                        \
    }                                                                    \
  }()


#define __SYCL_ARCH__ DPCT_COMPATIBILITY_TEMP
#define VER_4VEC 610 // todo for hardward optimize.
#define VER_GEN9 700 // todo for hardward optimize.
#define VER_GEN12 1000000 // todo for hardward optimize.
#define VER_GEN13 (VER_GEN12 + 1030) // todo for hardward optimize.

#define GGML_SYCL_MAX_NODES 8192 // TODO: adapt to hardwares

// define for XMX in Intel GPU
// TODO: currently, it's not used for XMX really.
#if !defined(GGML_SYCL_FORCE_MMQ)
    #define SYCL_USE_XMX
#endif

// max batch size to use MMQ kernels when tensor cores are available
#define MMQ_MAX_BATCH_SIZE 32

// dmmv = dequantize_mul_mat_vec
#ifndef GGML_SYCL_DMMV_X
#define GGML_SYCL_DMMV_X 32
#endif
#ifndef GGML_SYCL_MMV_Y
#define GGML_SYCL_MMV_Y 1
#endif

typedef sycl::queue *queue_ptr;

enum ggml_sycl_backend_gpu_mode {
  SYCL_UNSET_GPU_MODE = -1,
  SYCL_SINGLE_GPU_MODE = 0,
  SYCL_MUL_GPU_MODE
};

static_assert(sizeof(sycl::half) == sizeof(ggml_fp16_t), "wrong fp16 size");

static void crash() {
  int* ptr = NULL;
  *ptr = 0;
}

[[noreturn]] static void ggml_sycl_error(
    const char* stmt,
    const char* func,
    const char* file,
    const int line,
    const char* msg) {
  fprintf(stderr, "SYCL error: %s: %s\n", stmt, msg);
  fprintf(stderr, "  in function %s at %s:%d\n", func, file, line);
  GGML_ABORT("SYCL error");
}

#define SYCL_CHECK(err)                                                                                    \
    do {                                                                                                   \
        auto err_ = (err);                                                                                 \
        if (err_ != 0)                                                                                     \
            ggml_sycl_error(#err, __func__, __FILE__, __LINE__, "Exception caught in this line of code."); \
    } while (0)

#if DPCT_COMPAT_RT_VERSION >= 11100
#define GGML_SYCL_ASSUME(x) __builtin_assume(x)
#else
#define GGML_SYCL_ASSUME(x)
#endif // DPCT_COMPAT_RT_VERSION >= 11100

#ifdef GGML_SYCL_F16
typedef sycl::half dfloat; // dequantize float
typedef sycl::half2 dfloat2;
#else
typedef float dfloat; // dequantize float
typedef sycl::float2 dfloat2;
#endif // GGML_SYCL_F16

#define MMVQ_MAX_BATCH_SIZE  8

static int g_all_sycl_device_count = -1;
static bool g_ggml_backend_sycl_buffer_type_initialized = false;

static ggml_sycl_backend_gpu_mode g_ggml_sycl_backend_gpu_mode =
    SYCL_UNSET_GPU_MODE;

static void* g_scratch_buffer = nullptr;
static size_t g_scratch_size = 0; // disabled by default
static size_t g_scratch_offset = 0;

[[noreturn]] static inline void bad_arch(const sycl::stream& stream_ct1) {
  stream_ct1 << "ERROR: ggml-sycl was compiled without support for the "
                "current GPU architecture.\n";
  // __trap();
  std::exit(1);

  (void)bad_arch; // suppress unused function warning
}

int get_current_device_id();

inline dpct::err0 ggml_sycl_set_device(const int device) try {
  int current_device_id;
  SYCL_CHECK(CHECK_TRY_ERROR(current_device_id = get_current_device_id()));

  // GGML_SYCL_DEBUG("ggml_sycl_set_device device_id=%d,
  // current_device_id=%d\n", device, current_device);
  if (device == current_device_id) {
    return 0;
  }

  return CHECK_TRY_ERROR(dpct::select_device(device));
} catch (sycl::exception const& exc) {
  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
            << ", line:" << __LINE__ << std::endl;
  crash();
  std::exit(1);
}

//////////////////////
struct optimize_feature {
    bool reorder=false;
};

struct sycl_device_info {
    int     cc;                 // compute capability
    int nsm; // number of streaming multiprocessors (CUDA) maps to the maximum
             // number of compute units on a SYCL device.
    // size_t  smpb;               // max. shared memory per block
    size_t  smpbo;              // max. shared memory per block (with opt-in)
    bool    vmm;                // virtual memory support
    size_t  total_vram;
    //sycl_hw_info hw_info;     \\ device id and aarch, currently not used
    optimize_feature opt_feature;
};


struct ggml_sycl_device_info {
    int device_count;

    sycl_device_info devices[GGML_SYCL_MAX_DEVICES] = {};

    std::array<float, GGML_SYCL_MAX_DEVICES> default_tensor_split = {};

    int max_work_group_sizes[GGML_SYCL_MAX_DEVICES] = {0};
};

const ggml_sycl_device_info & ggml_sycl_info();

struct ggml_sycl_pool {
    virtual ~ggml_sycl_pool() = default;

    virtual void * alloc(size_t size, size_t * actual_size) = 0;
    virtual void free(void * ptr, size_t size) = 0;
};

template<typename T>
struct ggml_sycl_pool_alloc {
    ggml_sycl_pool * pool = nullptr;
    T * ptr = nullptr;
    size_t actual_size = 0;

    explicit ggml_sycl_pool_alloc(ggml_sycl_pool & pool) : pool(&pool) {
    }

    ggml_sycl_pool_alloc(ggml_sycl_pool & pool, size_t size) : pool(&pool) {
        alloc(size);
    }

    ~ggml_sycl_pool_alloc() {
        if (ptr != nullptr) {
            pool->free(ptr, actual_size);
        }
    }

    T * realloc(size_t size) {
        GGML_ASSERT(pool != nullptr);
        if (ptr)
            pool->free(ptr, actual_size);
        ptr = (T *) pool->alloc(size * sizeof(T), &this->actual_size);
        return ptr;
    }

    // size is in number of elements
    T * alloc(size_t size) {
        GGML_ASSERT(pool != nullptr);
        GGML_ASSERT(ptr == nullptr);
        ptr = (T *) pool->alloc(size * sizeof(T), &this->actual_size);
        return ptr;
    }

    T * alloc(ggml_sycl_pool & pool, size_t size) {
        this->pool = &pool;
        return alloc(size);
    }

    T * get() {
        return ptr;
    }

    ggml_sycl_pool_alloc() = default;
    ggml_sycl_pool_alloc(const ggml_sycl_pool_alloc &) = delete;
    ggml_sycl_pool_alloc(ggml_sycl_pool_alloc &&) = delete;
    ggml_sycl_pool_alloc& operator=(const ggml_sycl_pool_alloc &) = delete;
    ggml_sycl_pool_alloc& operator=(ggml_sycl_pool_alloc &&) = delete;
};

// backend interface

struct ggml_tensor_extra_gpu {
  void* data_device[GGML_SYCL_MAX_DEVICES]; // 1 pointer for each device for split
                                       // tensors
  dpct::event_ptr events[GGML_SYCL_MAX_DEVICES]
                        [GGML_SYCL_MAX_STREAMS]; // events for synchronizing multiple GPUs
  optimize_feature optimized_feature;
};

void release_extra_gpu(ggml_tensor_extra_gpu * extra, std::vector<queue_ptr> streams={});

namespace sycl_ex = sycl::ext::oneapi::experimental;
struct ggml_backend_sycl_context {
    int device;
    std::string name;
    optimize_feature opt_feature;

    queue_ptr qptrs[GGML_SYCL_MAX_DEVICES][GGML_SYCL_MAX_STREAMS] = { { nullptr } };

    explicit ggml_backend_sycl_context(int device) :
        device(device),
        name(GGML_SYCL_NAME + std::to_string(device)) {
        opt_feature = ggml_sycl_info().devices[device].opt_feature;
    }

    queue_ptr stream(int device, int stream) {
        if (qptrs[device][stream] == nullptr) {
            qptrs[device][stream] = &(dpct::get_device(device).default_queue());
        }
        return qptrs[device][stream];
    }

    queue_ptr stream() {
        return stream(device, 0);
    }

#if GGML_SYCL_DNNL
    dnnl::engine make_engine(sycl::queue* q) {
        // Get the device associated with the queue
        sycl::device dev = q->get_device();
        // Get the context associated with the queue
        sycl::context ctx = q->get_context();
        const dnnl::engine eng = dnnl::sycl_interop::make_engine(dev, ctx);
        return eng;
    }

    std::unordered_map<sycl::queue*, dnnl::stream> stream_map;
    std::unordered_map<sycl::queue*, dnnl::engine> engine_map;
    dnnl::stream stream_dnnl(int device, int _stream) {
        auto q = stream(device, _stream);
        return stream_dnnl(q);
    }
    dnnl::engine engine_dnnl(sycl::queue* qptr) {
        auto it = engine_map.find(qptr);
        if (it == engine_map.end()) {
            auto eng = make_engine(qptr);
            engine_map[qptr] = eng;
            return eng;
        }
        else
        {
            return it->second;
        }
    }
    dnnl::stream stream_dnnl(sycl::queue* qptr) {
        auto it = stream_map.find(qptr);
        if (it == stream_map.end()) {
            auto eng = engine_dnnl(qptr);
            auto stream = dnnl::sycl_interop::make_stream(eng, *qptr);
            stream_map[qptr] = stream;
            return stream;
        }
        else
        {
            return it->second;
        }
    }
    dnnl::stream stream_dnnl() {
        return stream_dnnl(device, 0);
    }
    dnnl::memory get_scratchpad_mem(const dnnl::memory::desc & scratchpad_md,
                                    const dnnl::engine & eng, const queue_ptr q) {
        ggml_sycl_pool_alloc<uint8_t> * pool;
        auto it = scratchpad_map.find(q);
        if (it == scratchpad_map.end()) {
            scratchpad_map[q] = std::make_unique<ggml_sycl_pool_alloc<uint8_t>>(this->pool());
            pool = scratchpad_map[q].get();
        } else {
            pool = it->second.get();
        }

        size_t scratchpad_size = scratchpad_md.get_size();
        if (scratchpad_size > pool->actual_size) {
            pool->realloc(scratchpad_size);
        }
        void * mem_ptr = pool->get();
        return dnnl::memory(scratchpad_md, eng, mem_ptr);
    }
#endif

    // pool
    std::unique_ptr<ggml_sycl_pool> pools[GGML_SYCL_MAX_DEVICES];
    std::unordered_map<sycl::queue *, std::unique_ptr<ggml_sycl_pool_alloc<uint8_t>>> scratchpad_map;

    std::unique_ptr<ggml_sycl_pool> host_pools[GGML_SYCL_MAX_DEVICES];

    static std::unique_ptr<ggml_sycl_pool> new_pool_for_device(queue_ptr qptr, int device);

    static std::unique_ptr<ggml_sycl_pool> new_pool_for_host(queue_ptr qptr, int device);

    ggml_sycl_pool & pool(int device) {
        if (pools[device] == nullptr) {
            pools[device] = new_pool_for_device(stream(device,0), device);
        }
        return *pools[device];
    }

    ggml_sycl_pool & pool() {
        return pool(device);
    }

#ifdef GGML_SYCL_GRAPH
    std::unique_ptr<sycl_ex::command_graph<sycl_ex::graph_state::executable>> exec_graph = nullptr;
#endif

    ggml_sycl_pool & host_pool(int device) {
        if (host_pools[device] == nullptr) {
            host_pools[device] = new_pool_for_host(stream(device, 0), device);
        }
        return *host_pools[device];
    }

    ggml_sycl_pool & host_pool() { return host_pool(device); }
};

// common device functions

static __dpct_inline__ float warp_reduce_sum(float x,
    const sycl::nd_item<3>& item_ct1) {
#pragma unroll
    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        x += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), x, mask);
    }
    return x;
}

static __dpct_inline__ sycl::float2
warp_reduce_sum(sycl::float2 a, const sycl::nd_item<3>& item_ct1) {
#pragma unroll
    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        a.x() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.x(),
            mask);
        a.y() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.y(),
            mask);
    }
    return a;
}

template <int width = WARP_SIZE>
static __dpct_inline__ int warp_reduce_sum(int x) {
  return sycl::reduce_over_group(
      sycl::ext::oneapi::this_work_item::get_sub_group(), x, sycl::plus<>());
}

template <int width = WARP_SIZE>
static __dpct_inline__ float warp_reduce_sum(float x) {
#pragma unroll
  for (int offset = width / 2; offset > 0; offset >>= 1) {
    x += dpct::permute_sub_group_by_xor(
        sycl::ext::oneapi::this_work_item::get_sub_group(), x, offset, width);
  }
  return x;
}

template <int width = WARP_SIZE>
static __dpct_inline__ sycl::float2 warp_reduce_sum(sycl::float2 a) {
#pragma unroll
  for (int offset = width / 2; offset > 0; offset >>= 1) {
    a.x() += dpct::permute_sub_group_by_xor(
        sycl::ext::oneapi::this_work_item::get_sub_group(), a.x(), offset,
        width);
    a.y() += dpct::permute_sub_group_by_xor(
        sycl::ext::oneapi::this_work_item::get_sub_group(), a.y(), offset,
        width);
  }
  return a;
}

template <int width = WARP_SIZE>
static __dpct_inline__ sycl::half2 warp_reduce_sum(sycl::half2 a) {
#pragma unroll
  for (int offset = width / 2; offset > 0; offset >>= 1) {
    a = a + dpct::permute_sub_group_by_xor(
                sycl::ext::oneapi::this_work_item::get_sub_group(), a, offset,
                width);
  }
  return a;
}

static constexpr int ggml_sycl_get_physical_warp_size() {
  // todo: for old iGPU + dGPU case, need to be changed.
  return WARP_SIZE;
}

template <int width = WARP_SIZE>
static __dpct_inline__ float warp_reduce_max(float x) {
#pragma unroll
  for (int offset = width / 2; offset > 0; offset >>= 1) {
    x = sycl::fmax(x, dpct::permute_sub_group_by_xor(
                          sycl::ext::oneapi::this_work_item::get_sub_group(), x,
                          offset, width));
  }
  return x;
}

static __dpct_inline__ float warp_reduce_max(float x,
    const sycl::nd_item<3>& item_ct1) {
#pragma unroll
    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        x = sycl::fmax(x, dpct::permute_sub_group_by_xor(
            item_ct1.get_sub_group(), x, mask));
    }
    return x;
}

/* Helper for Computing the linear offset of a ggml_tensor given
per-dimension sizes, strides, and indices */
template<int N>
__dpct_inline__ size_t calculate_offset(const std::array<int, N> & strides, const std::array<int, N> & indices) {
    size_t offset = 0;
#pragma unroll
    for (int i = 0; i < N; i++) {
        auto index_i = indices[i];
        offset += strides[i] * index_i;
    }
    return offset;
}

// Helper for vec loading aligned data
template <typename Tp, int n>
inline sycl::vec<Tp, n> vec_aligned_load(const Tp* aligned_ptr) {
    return *reinterpret_cast<const sycl::vec<Tp, n>*>(aligned_ptr);
}

// Helper for accessing pointers with no warnings
template <typename Tp, int dim>
static __dpct_inline__ Tp* get_pointer(sycl::local_accessor<Tp, dim> acc) {
    return acc.template get_multi_ptr<sycl::access::decorated::no>().get();
}

int64_t downsample_sycl_global_range(int64_t accumulate_block_num, int64_t block_size);

constexpr size_t ceil_div(const size_t m, const size_t n) {
    return (m + n - 1) / n;
}

bool gpu_has_xmx(sycl::device &dev);

template <int N, class T> std::string debug_get_array_str(const std::string & prefix, const T array[N]) {
    if (LIKELY(!g_ggml_sycl_debug)) {
        return "";
    }
    std::stringstream ss;
    ss << prefix << "=[";
    for (std::size_t i = 0; i < N - 1; ++i) {
        ss << array[i] << ", ";
    }
    if constexpr (N > 0) {
        ss << array[N - 1];
    }
    ss << "]";
    return ss.str();
}

inline std::string debug_get_tensor_str(const std::string &prefix,
        const ggml_tensor *tensor, const std::string &suffix = "") {
    std::stringstream ss;
    if (LIKELY(!g_ggml_sycl_debug)) { return ss.str(); }
    ss << prefix.c_str() << "=";
    if (tensor) {
        ss << "'" << tensor->name << "':type=" << ggml_type_name(tensor->type);
        ss << debug_get_array_str<GGML_MAX_DIMS>(";ne", tensor->ne);
        ss << debug_get_array_str<GGML_MAX_DIMS>(";nb", tensor->nb);

        if (!ggml_is_contiguous(tensor)) { ss << ";strided"; }
        if (ggml_is_permuted(tensor)) { ss << ";permuted"; }
    } else {
        ss << "nullptr";
    }
    ss << suffix;
    return ss.str();
}

// Use scope_op_debug_print to log operations coming from running a model
struct scope_op_debug_print {
    // Use string_views to avoid the cost of creating a string and concatenating them
    // string_views must be alive for as long as the object is alive
    // scope_op_debug_print are used with string literals in practice which are stored in constant space so always accessible
    scope_op_debug_print(const std::string_view & func, const std::string_view & func_suffix, const ggml_tensor * dst,
                         std::size_t num_src, const std::string_view & suffix = "") :
        func(func),
        func_suffix(func_suffix) {
        if (LIKELY(!g_ggml_sycl_debug)) {
            return;
        }
        GGML_SYCL_DEBUG("[SYCL][OP] call %s%s:", func.data(), func_suffix.data());
        GGML_SYCL_DEBUG("%s", debug_get_tensor_str(" dst", dst).c_str());
        if (dst) {
            for (std::size_t i = 0; i < num_src; ++i) {
                GGML_SYCL_DEBUG("%s", debug_get_tensor_str("\tsrc" + std::to_string(i), dst->src[i]).c_str());
            }
        }
        GGML_SYCL_DEBUG("%s\n", suffix.data());
    }

    scope_op_debug_print(const std::string_view & func, const ggml_tensor * dst, std::size_t num_src,
                         const std::string_view & suffix = "") :
        scope_op_debug_print(func, "", dst, num_src, suffix) {}

    ~scope_op_debug_print() { GGML_SYCL_DEBUG("[SYCL][OP] call %s%s done\n", func.data(), func_suffix.data()); }

  private:
    std::string_view func;
    std::string_view func_suffix;
};

static __dpct_inline__ float get_alibi_slope(const float    max_bias,
                                             const uint32_t h,
                                             const uint32_t n_head_log2,
                                             const float    m0,
                                             const float    m1) {
    if (max_bias <= 0.0f) {
        return 1.0f;
    }
    const float base = h < n_head_log2 ? m0 : m1;
    const int   exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;

    return dpct::pow(base, exph);
}

#endif // GGML_SYCL_COMMON_HPP