#include "ggml.h"
#include "ggml-impl.h"
#include "ggml-backend.h"
#include "ggml-backend-impl.h"
#include "ggml-alloc.h"
#include "ggml-cpp.h"
#include <algorithm>
#include <cassert>
#include <cmath>
#include <cstddef>
#include <cstdint>
#include <cstring>
#include <map>
#include <memory>
#include <string>
#include <tuple>
#include <utility>
#include <vector>
struct ggml_backend_meta_device;
struct ggml_backend_meta_buffer_type;
struct ggml_backend_meta_buffer;
struct ggml_backend_meta;
const char * ggml_backend_meta_split_axis_name(enum ggml_backend_meta_split_axis split_axis) {
switch (split_axis) {
case GGML_BACKEND_SPLIT_AXIS_0:
return "0";
case GGML_BACKEND_SPLIT_AXIS_1:
return "1";
case GGML_BACKEND_SPLIT_AXIS_2:
return "2";
case GGML_BACKEND_SPLIT_AXIS_3:
return "3";
case GGML_BACKEND_SPLIT_AXIS_MIRRORED:
return "MIRRORED";
case GGML_BACKEND_SPLIT_AXIS_PARTIAL:
return "PARTIAL";
case GGML_BACKEND_SPLIT_AXIS_NONE:
return "NONE";
case GGML_BACKEND_SPLIT_AXIS_UNKNOWN:
return "UNKNOWN";
default:
GGML_ABORT("fatal error");
}
}
//
// meta backend device
//
struct ggml_backend_meta_device_context {
std::vector<ggml_backend_dev_t> simple_devs;
ggml_backend_meta_get_split_state_t get_split_state;
void * get_split_state_ud;
std::string name;
std::string description;
ggml_backend_meta_device_context(
std::vector<ggml_backend_dev_t> simple_devs, ggml_backend_meta_get_split_state_t get_split_state, void * get_split_state_ud) :
simple_devs(std::move(simple_devs)), get_split_state(get_split_state), get_split_state_ud(get_split_state_ud) {
name = std::string("Meta(");
description = std::string("Meta(");
for (size_t i = 0; i < simple_devs.size(); i++) {
if (i > 0) {
name += ",";
description += ",";
}
name += ggml_backend_dev_name (simple_devs[i]);
description += ggml_backend_dev_description(simple_devs[i]);
}
name += ")";
description += ")";
}
bool operator<(const ggml_backend_meta_device_context & other) const {
return std::tie(simple_devs, get_split_state, get_split_state_ud)
< std::tie(other.simple_devs, other.get_split_state, other.get_split_state_ud);
}
};
static bool ggml_backend_dev_is_meta(ggml_backend_dev_t dev);
static const char * ggml_backend_meta_device_get_name(ggml_backend_dev_t dev) {
GGML_ASSERT(ggml_backend_dev_is_meta(dev));
const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) dev->context;
return meta_dev_ctx->name.c_str();
}
static const char * ggml_backend_meta_device_get_description(ggml_backend_dev_t dev) {
GGML_ASSERT(ggml_backend_dev_is_meta(dev));
const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) dev->context;
return meta_dev_ctx->description.c_str();
}
static void ggml_backend_meta_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
GGML_ASSERT(ggml_backend_dev_is_meta(dev));
const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) dev->context;
*free = 0;
*total = 0;
for (ggml_backend_dev_t dev : meta_dev_ctx->simple_devs) {
size_t tmp_free, tmp_total;
ggml_backend_dev_memory(dev, &tmp_free, &tmp_total);
*free += tmp_free;
*total += tmp_total;
}
}
static enum ggml_backend_dev_type ggml_backend_meta_device_get_type(ggml_backend_dev_t dev) {
return GGML_BACKEND_DEVICE_TYPE_META;
GGML_UNUSED(dev);
}
static void ggml_backend_meta_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) {
GGML_ASSERT(ggml_backend_dev_is_meta(dev));
const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) dev->context;
// TODO replace placeholders
props->name = ggml_backend_meta_device_get_name(dev);
props->description = ggml_backend_meta_device_get_description(dev);
props->type = ggml_backend_meta_device_get_type(dev);
props->device_id = 0;
ggml_backend_meta_device_get_memory(dev, &props->memory_free, &props->memory_total);
props->caps = {
/* .async = */ true,
/* .host_buffer = */ false, // Not implemented.
/* .buffer_from_host_ptr = */ false, // Not implemented.
/* .events = */ false, // Not implemented.
};
for (ggml_backend_dev_t simple_dev : meta_dev_ctx->simple_devs) {
ggml_backend_dev_props tmp_props;
ggml_backend_dev_get_props(simple_dev, &tmp_props);
props->caps.async = props->caps.async && tmp_props.caps.async;
props->caps.host_buffer = props->caps.host_buffer && tmp_props.caps.host_buffer;
props->caps.buffer_from_host_ptr = props->caps.buffer_from_host_ptr && tmp_props.caps.buffer_from_host_ptr;
props->caps.events = props->caps.events && tmp_props.caps.events;
}
}
static ggml_backend_t ggml_backend_meta_device_init_backend(ggml_backend_dev_t dev, const char * params);
static ggml_backend_buffer_type_t ggml_backend_meta_device_get_buffer_type(ggml_backend_dev_t dev);
static ggml_backend_buffer_type_t ggml_backend_meta_device_get_host_buffer_type(ggml_backend_dev_t dev);
static bool ggml_backend_meta_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
GGML_ASSERT(ggml_backend_dev_is_meta(dev));
const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) dev->context;
return std::all_of(meta_dev_ctx->simple_devs.begin(), meta_dev_ctx->simple_devs.end(),
[op](ggml_backend_dev_t simple_dev) { return ggml_backend_dev_supports_op(simple_dev, op); });
}
static bool ggml_backend_meta_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
GGML_ASSERT(ggml_backend_dev_is_meta(dev));
ggml_backend_dev_t dev_buft = ggml_backend_buft_get_device(buft);
if (!ggml_backend_dev_is_meta(dev_buft)) {
return false;
}
const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) dev->context;
const ggml_backend_meta_device_context * meta_buft_dev_ctx = (const ggml_backend_meta_device_context *) dev_buft->context;
if (meta_dev_ctx->simple_devs.size() != meta_buft_dev_ctx->simple_devs.size()) {
return false;
}
for (size_t i = 0; i < meta_dev_ctx->simple_devs.size(); i++) {
if (meta_dev_ctx->simple_devs[i] != meta_buft_dev_ctx->simple_devs[i]) {
return false;
}
}
return true;
}
static const ggml_backend_device_i ggml_backend_meta_device_iface = {
/* .get_name = */ ggml_backend_meta_device_get_name,
/* .get_description = */ ggml_backend_meta_device_get_description,
/* .get_memory = */ ggml_backend_meta_device_get_memory,
/* .get_type = */ ggml_backend_meta_device_get_type,
/* .get_props = */ ggml_backend_meta_device_get_props,
/* .init_backend = */ ggml_backend_meta_device_init_backend,
/* .get_buffer_type = */ ggml_backend_meta_device_get_buffer_type,
/* .get_host_buffer_type = */ ggml_backend_meta_device_get_host_buffer_type,
/* .buffer_from_host_ptr = */ nullptr,
/* .supports_op = */ ggml_backend_meta_device_supports_op,
/* .supports_buft = */ ggml_backend_meta_device_supports_buft,
/* .offload_op = */ nullptr,
/* .event_new = */ nullptr,
/* .event_free = */ nullptr,
/* .event_synchronize = */ nullptr,
};
static bool ggml_backend_dev_is_meta(ggml_backend_dev_t dev) {
return dev != nullptr && dev->iface.get_name == ggml_backend_meta_device_iface.get_name;
}
static size_t ggml_backend_meta_dev_n_devs(ggml_backend_dev_t meta_dev) {
GGML_ASSERT(ggml_backend_dev_is_meta(meta_dev));
const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) meta_dev->context;
return meta_dev_ctx->simple_devs.size();
}
static ggml_backend_dev_t ggml_backend_meta_dev_simple_dev(ggml_backend_dev_t meta_dev, size_t index) {
GGML_ASSERT(ggml_backend_dev_is_meta(meta_dev));
const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) meta_dev->context;
GGML_ASSERT(index < meta_dev_ctx->simple_devs.size());
return meta_dev_ctx->simple_devs[index];
}
ggml_backend_dev_t ggml_backend_meta_device(
ggml_backend_dev_t * devs, size_t n_devs, ggml_backend_meta_get_split_state_t get_split_state, void * get_split_state_ud) {
GGML_ASSERT(n_devs <= GGML_BACKEND_META_MAX_DEVICES);
// TODO: this is not thread-safe - needs to be fixed
static std::vector<std::unique_ptr<ggml_backend_meta_device_context>> ctxs;
static std::map<ggml_backend_meta_device_context, struct ggml_backend_device> meta_devs;
std::vector<ggml_backend_dev_t> simple_devs;
simple_devs.reserve(n_devs);
for (size_t i = 0; i < n_devs; i++) {
simple_devs.push_back(devs[i]);
}
ggml_backend_meta_device_context ctx(simple_devs, get_split_state, get_split_state_ud);
{
auto it = meta_devs.find(ctx);
if (it != meta_devs.end()) {
return &it->second;
}
}
ctxs.push_back(std::make_unique<ggml_backend_meta_device_context>(ctx));
struct ggml_backend_device meta_dev = {
/*iface =*/ ggml_backend_meta_device_iface,
/*reg =*/ nullptr,
/*ctx =*/ ctxs.back().get(),
};
auto result = meta_devs.emplace(*ctxs.back(), meta_dev);
return &result.first->second;
}
//
// meta backend buffer type
//
struct ggml_backend_meta_buffer_type_context {
std::vector<ggml_backend_buffer_type_t> simple_bufts;
std::string name;
ggml_backend_meta_buffer_type_context(std::vector<ggml_backend_buffer_type_t> simple_bufts) : simple_bufts(std::move(simple_bufts)) {
name = "Meta(";
for (size_t i = 0; i < simple_bufts.size(); i++) {
if (i > 0) {
name += ",";
}
name += ggml_backend_buft_name(simple_bufts[i]);
}
name += ")";
}
bool operator<(const ggml_backend_meta_buffer_type_context & other) const {
return simple_bufts < other.simple_bufts;
}
};
static size_t ggml_backend_meta_buft_n_bufts(ggml_backend_buffer_type_t meta_buft) {
GGML_ASSERT(ggml_backend_buft_is_meta(meta_buft));
const ggml_backend_meta_buffer_type_context * meta_buft_ctx = (const ggml_backend_meta_buffer_type_context *) meta_buft->context;
return meta_buft_ctx->simple_bufts.size();
}
static const char * ggml_backend_meta_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
GGML_ASSERT(ggml_backend_buft_is_meta(buft));
const ggml_backend_meta_buffer_type_context * meta_buft_ctx = (const ggml_backend_meta_buffer_type_context *) buft->context;
return meta_buft_ctx->name.c_str();
}
static ggml_backend_buffer_type_t ggml_backend_meta_buft_simple_buft(ggml_backend_buffer_type_t meta_buft, size_t index) {
GGML_ASSERT(ggml_backend_buft_is_meta(meta_buft));
const ggml_backend_meta_buffer_type_context * meta_buft_ctx = (const ggml_backend_meta_buffer_type_context *) meta_buft->context;
GGML_ASSERT(index < meta_buft_ctx->simple_bufts.size());
return meta_buft_ctx->simple_bufts[index];
}
static ggml_backend_buffer_t ggml_backend_meta_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size);
static size_t ggml_backend_meta_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
const size_t n_simple_bufts = ggml_backend_meta_buft_n_bufts(buft);
size_t max_alignment = 1;
for (size_t i = 0; i < n_simple_bufts; i++) {
const size_t alignment = ggml_backend_buft_get_alignment(ggml_backend_meta_buft_simple_buft(buft, i));
max_alignment = std::max(max_alignment, alignment);
GGML_ASSERT(max_alignment % alignment == 0);
}
return max_alignment;
}
static size_t ggml_backend_meta_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
const size_t n_simple_bufts = ggml_backend_meta_buft_n_bufts(buft);
size_t max_size = SIZE_MAX;
for (size_t i = 0; i < n_simple_bufts; i++) {
max_size = std::min(max_size, ggml_backend_buft_get_max_size(ggml_backend_meta_buft_simple_buft(buft, i)));
}
return max_size;
}
static size_t ggml_backend_meta_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
const size_t n_simple_bufts = ggml_backend_meta_buft_n_bufts(buft);
size_t max_alloc_size = 0;
for (size_t i = 0; i < n_simple_bufts; i++) {
const size_t alloc_size = ggml_backend_buft_get_alloc_size(ggml_backend_meta_buft_simple_buft(buft, i), tensor);
max_alloc_size = std::max(max_alloc_size, alloc_size);
}
return max_alloc_size;
}
static bool ggml_backend_meta_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
const size_t n_simple_bufts = ggml_backend_meta_buft_n_bufts(buft);
for (size_t i = 0; i < n_simple_bufts; i++) {
if (!ggml_backend_buft_is_host(ggml_backend_meta_buft_simple_buft(buft, i))) {
return false;
}
}
return true;
}
static const struct ggml_backend_buffer_type_i ggml_backend_meta_buffer_type_iface = {
/* .get_name = */ ggml_backend_meta_buffer_type_get_name,
/* .alloc_buffer = */ ggml_backend_meta_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_meta_buffer_type_get_alignment,
/* .get_max_size = */ ggml_backend_meta_buffer_type_get_max_size,
/* .get_alloc_size = */ ggml_backend_meta_buffer_type_get_alloc_size,
/* .is_host = */ ggml_backend_meta_buffer_type_is_host,
};
bool ggml_backend_buft_is_meta(ggml_backend_buffer_type_t buft) {
return buft != nullptr && buft->iface.get_name == ggml_backend_meta_buffer_type_iface.get_name;
}
static ggml_backend_buffer_type_t ggml_backend_meta_device_get_buffer_type(ggml_backend_dev_t dev) {
static std::map<ggml_backend_dev_t, struct ggml_backend_buffer_type> meta_bufts;
GGML_ASSERT(ggml_backend_dev_is_meta(dev));
{
auto it = meta_bufts.find(dev);
if (it != meta_bufts.end()) {
return &it->second;
}
}
const size_t n_devs = ggml_backend_meta_dev_n_devs(dev);
std::vector<ggml_backend_buffer_type_t> simple_bufts;
simple_bufts.reserve(n_devs);
for (size_t i = 0; i < n_devs; i++) {
simple_bufts.push_back(ggml_backend_dev_buffer_type(ggml_backend_meta_dev_simple_dev(dev, i)));
}
ggml_backend_meta_buffer_type_context * buft_ctx = new ggml_backend_meta_buffer_type_context(simple_bufts);
struct ggml_backend_buffer_type meta_buft = {
/*iface =*/ ggml_backend_meta_buffer_type_iface,
/*device =*/ dev,
/*ctx =*/ buft_ctx,
};
auto result = meta_bufts.emplace(dev, meta_buft);
return &result.first->second;
}
static ggml_backend_buffer_type_t ggml_backend_meta_device_get_host_buffer_type(ggml_backend_dev_t dev) {
GGML_ASSERT(ggml_backend_dev_is_meta(dev));
const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) dev->context;
ggml_backend_buffer_type_t host_buft = nullptr;
for (ggml_backend_dev_t simple_dev : meta_dev_ctx->simple_devs) {
ggml_backend_buffer_type_t simple_host_buft = ggml_backend_dev_host_buffer_type(simple_dev);
if (simple_host_buft == nullptr) {
return nullptr;
}
if (host_buft == nullptr) {
host_buft = simple_host_buft;
} else if (host_buft != simple_host_buft) {
// if different simple devices have different host buffer types,
// we cannot provide a single host buffer type for the meta device
return nullptr;
}
}
return host_buft;
}
//
// meta backend buffer
//
struct ggml_backend_meta_buffer_context {
static constexpr size_t nbtc = GGML_TENSOR_SIZE - sizeof(ggml_tensor::padding);
std::map<std::pair<const ggml_tensor *, bool>, std::pair<ggml_backend_meta_split_state, char[nbtc]>> split_state_cache;
std::map< const ggml_tensor *, std::vector<ggml_tensor *>> simple_tensors;
struct buffer_config {
ggml_context * ctx;
ggml_backend_buffer_t buf;
buffer_config(ggml_context * ctx, ggml_backend_buffer_t buf) : ctx(ctx), buf(buf) {}
};
std::vector<buffer_config> buf_configs;
int debug;
ggml_backend_meta_buffer_context() {
const char * GGML_META_DEBUG = getenv("GGML_META_DEBUG");
debug = GGML_META_DEBUG ? atoi(GGML_META_DEBUG) : 0;
}
};
static void ggml_backend_meta_buffer_free_buffer(ggml_backend_buffer_t buffer) {
GGML_ASSERT(ggml_backend_buffer_is_meta(buffer));
ggml_backend_meta_buffer_context * buf_ctx = (ggml_backend_meta_buffer_context *) buffer->context;
for (auto & [ctx, buf] : buf_ctx->buf_configs) {
ggml_backend_buffer_free(buf);
ggml_free(ctx);
}
delete buf_ctx;
}
static size_t ggml_backend_meta_buffer_n_bufs(ggml_backend_buffer_t meta_buf) {
GGML_ASSERT(ggml_backend_buffer_is_meta(meta_buf));
ggml_backend_meta_buffer_context * buf_ctx = (ggml_backend_meta_buffer_context *) meta_buf->context;
return buf_ctx->buf_configs.size();
}
static ggml_backend_buffer_t ggml_backend_meta_buffer_simple_buffer(ggml_backend_buffer_t meta_buf, size_t index) {
GGML_ASSERT(ggml_backend_buffer_is_meta(meta_buf));
ggml_backend_meta_buffer_context * buf_ctx = (ggml_backend_meta_buffer_context *) meta_buf->context;
GGML_ASSERT(index < buf_ctx->buf_configs.size());
return buf_ctx->buf_configs[index].buf;
}
static struct ggml_tensor * ggml_backend_meta_buffer_simple_tensor(const struct ggml_tensor * tensor, size_t index) {
GGML_ASSERT(ggml_backend_buffer_is_meta(tensor->buffer));
ggml_backend_meta_buffer_context * buf_ctx = (ggml_backend_meta_buffer_context *) tensor->buffer->context;
GGML_ASSERT(index < buf_ctx->buf_configs.size());
auto it = buf_ctx->simple_tensors.find(tensor);
if (it == buf_ctx->simple_tensors.end()) {
return nullptr;
}
return it->second[index];
}
static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(const struct ggml_tensor * tensor, bool assume_sync) {
const size_t n_bufs = ggml_backend_meta_buffer_n_bufs(tensor->buffer);
ggml_backend_meta_buffer_context * buf_ctx = (ggml_backend_meta_buffer_context *) tensor->buffer->context;
auto split_states_equal = [&](const ggml_backend_meta_split_state & a, const ggml_backend_meta_split_state & b) -> bool {
if (a.axis != b.axis) {
return false;
}
for (size_t j = 0; j < n_bufs; j++) {
int64_t sum_a = 0;
for (size_t s = 0; s < a.n_segments; s++) {
sum_a += a.ne[s*n_bufs + j];
}
int64_t sum_b = 0;
for (size_t s = 0; s < b.n_segments; s++) {
sum_b += b.ne[s*n_bufs + j];
}
if (sum_a != sum_b) {
return false;
}
}
return true;
};
auto handle_generic = [&](const std::vector<ggml_backend_meta_split_state> & src_ss, bool scalar_only) -> ggml_backend_meta_split_state {
ggml_backend_meta_split_state ret = {GGML_BACKEND_SPLIT_AXIS_NONE, {0}, 1};
for (size_t i = 0; i < GGML_MAX_SRC; i++) {
if (tensor->src[i] == nullptr || tensor->src[i] == tensor) {
continue;
}
if (ret.axis == GGML_BACKEND_SPLIT_AXIS_NONE) {
ret = src_ss[i];
} else if (!split_states_equal(src_ss[i], ret)) {
ret = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
break;
}
}
if (ret.axis == GGML_BACKEND_SPLIT_AXIS_NONE) {
ret = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
}
if (scalar_only && ret.axis >= 0 && ret.axis < GGML_MAX_DIMS) {
ret = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
}
GGML_ASSERT(ret.axis != GGML_BACKEND_SPLIT_AXIS_UNKNOWN);
return ret;
};
// Some ops process data on a per-row bases:
auto handle_per_row = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
GGML_ASSERT(src_ss[0].axis != GGML_BACKEND_SPLIT_AXIS_0);
return src_ss[0];
};
// Some ops broadcast the src1 data across src0:
auto handle_bin_bcast = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
if (src_ss[0].axis >= 0 && src_ss[0].axis < GGML_MAX_DIMS &&
tensor->src[1]->ne[src_ss[0].axis] == 1 && src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
return src_ss[0];
}
if (src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && (src_ss[0].axis == src_ss[1].axis ||
(src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && (src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_PARTIAL)))) {
return src_ss[0]; // GGML_OP_ADD_ID
}
GGML_ASSERT(tensor->src[2] == nullptr || src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
return handle_generic(src_ss, /*scalar_only =*/ false);
};
auto handle_concat = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
const ggml_backend_meta_split_axis concat_axis = ggml_backend_meta_split_axis(ggml_get_op_params_i32(tensor, 0));
if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[1].axis >= 0 && src_ss[1].axis < GGML_MAX_DIMS) {
GGML_ASSERT(concat_axis != src_ss[1].axis);
return src_ss[1];
}
if (src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[0].axis >= 0 && src_ss[0].axis < GGML_MAX_DIMS) {
GGML_ASSERT(concat_axis != src_ss[0].axis);
return src_ss[0];
}
if (src_ss[0].axis == src_ss[1].axis && src_ss[0].axis != concat_axis) {
return src_ss[0];
}
return handle_generic(src_ss, /*scalar_only =*/ true);
};
auto handle_mul_mat = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
return {GGML_BACKEND_SPLIT_AXIS_MIRRORED, {0}, 1};
}
if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_1 && src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
ggml_backend_meta_split_state ret = src_ss[0];
ret.axis = GGML_BACKEND_SPLIT_AXIS_0;
ret.n_segments = 1;
return ret;
}
if (src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_1 && src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
ggml_backend_meta_split_state ret = src_ss[1];
ret.n_segments = 1;
return ret;
}
if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_0 && src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_0) {
GGML_ASSERT(split_states_equal(src_ss[0], src_ss[1]));
return {assume_sync ? GGML_BACKEND_SPLIT_AXIS_MIRRORED : GGML_BACKEND_SPLIT_AXIS_PARTIAL, {0}, 1};
}
GGML_ABORT("fatal error");
//return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
};
auto handle_cpy = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
if (src_ss[0].axis >= 0 && src_ss[0].axis < GGML_MAX_DIMS) {
int64_t ne_split_src = tensor->src[0]->ne[0];
for (int dim = 1; dim <= src_ss[0].axis; dim++) {
ne_split_src *= tensor->src[0]->ne[dim];
}
int64_t ne_split_dst = 1;
for (int dim = 0; dim < GGML_MAX_DIMS; dim++) {
ne_split_dst *= tensor->ne[dim];
if (ne_split_dst == ne_split_src) {
return {ggml_backend_meta_split_axis(dim), {0}, 1};
}
}
}
return handle_generic(src_ss, /*scalar_only =*/ false);
};
auto handle_reshape = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
switch (src_ss[0].axis) {
case GGML_BACKEND_SPLIT_AXIS_0:
case GGML_BACKEND_SPLIT_AXIS_1:
case GGML_BACKEND_SPLIT_AXIS_2:
case GGML_BACKEND_SPLIT_AXIS_3: {
GGML_ASSERT(!ggml_is_permuted(tensor) && !ggml_is_permuted(tensor->src[0]));
if (src_ss[0].axis == ggml_n_dims(tensor->src[0]) - 1) {
return {ggml_backend_meta_split_axis(ggml_n_dims(tensor) - 1), {0}, 1};
}
std::vector<int64_t> base_ne_in;
base_ne_in.reserve(GGML_MAX_DIMS - src_ss[0].axis);
{
base_ne_in.push_back(1);
int dim = 0;
for (; dim <= src_ss[0].axis; dim++) {
base_ne_in[0] *= tensor->src[0]->ne[dim];
}
for (; dim <= GGML_MAX_DIMS; dim++) {
base_ne_in.push_back(base_ne_in.back() * tensor->src[0]->ne[dim]);
}
}
int64_t base_ne_out = 1;
for (int dim = 0; dim < GGML_MAX_DIMS; dim++) {
const int64_t base_ne_out_next = base_ne_out *= tensor->ne[dim];
for (const int64_t & bni : base_ne_in) {
if (bni == base_ne_out_next) {
return {ggml_backend_meta_split_axis(dim), {0}, 1};
}
}
if (base_ne_out_next > base_ne_in[0]) {
GGML_ASSERT(dim + 1 < GGML_MAX_DIMS);
return {ggml_backend_meta_split_axis(dim + 1), {0}, 1};
}
base_ne_out = base_ne_out_next;
}
GGML_ABORT("shape mismatch for %s", ggml_op_name(tensor->op));
}
case GGML_BACKEND_SPLIT_AXIS_MIRRORED:
case GGML_BACKEND_SPLIT_AXIS_PARTIAL: {
return src_ss[0];
}
default: {
GGML_ABORT("fatal error");
//return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
}
}
};
auto handle_view = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
if (ggml_is_contiguous(tensor) && ggml_is_contiguous(tensor->src[0])) {
return handle_reshape(src_ss);
}
const int axis = src_ss[0].axis;
{
bool all_strides_the_same = true;
for (int dim = 0; dim < GGML_MAX_DIMS; dim++) {
if (tensor->ne[dim] == 1 && tensor->src[0]->ne[dim] == 1) {
continue;
}
if (tensor->nb[dim] != tensor->src[0]->nb[dim]) {
all_strides_the_same = false;
break;
}
}
if (all_strides_the_same) {
return src_ss[0];
}
}
if (!ggml_is_permuted(tensor) && !ggml_is_permuted(tensor->src[0]) && axis >= 0 && axis < GGML_MAX_DIMS-1) {
for (int dim = 0; dim < GGML_MAX_DIMS-1; dim++) {
if (tensor->nb[dim+1] == tensor->src[0]->nb[axis+1]) {
return {ggml_backend_meta_split_axis(dim), {0}, 1};
}
}
GGML_ABORT("fatal error");
}
if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED || src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_PARTIAL) {
return src_ss[0];
}
GGML_ABORT("view of permuted tensor not implemented");
//return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
};
auto handle_permute = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
switch (src_ss[0].axis) {
case GGML_BACKEND_SPLIT_AXIS_0:
case GGML_BACKEND_SPLIT_AXIS_1:
case GGML_BACKEND_SPLIT_AXIS_2:
case GGML_BACKEND_SPLIT_AXIS_3: {
return {ggml_backend_meta_split_axis(tensor->op_params[src_ss[0].axis]), {0}, 1};
}
case GGML_BACKEND_SPLIT_AXIS_MIRRORED:
case GGML_BACKEND_SPLIT_AXIS_PARTIAL: {
return src_ss[0];
}
default: {
GGML_ABORT("fatal error");
//return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
}
}
};
auto handle_transpose = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
switch (src_ss[0].axis) {
case GGML_BACKEND_SPLIT_AXIS_0:
case GGML_BACKEND_SPLIT_AXIS_1: {
return {ggml_backend_meta_split_axis(int(src_ss[0].axis) ^ 1), {0}, 1};
}
case GGML_BACKEND_SPLIT_AXIS_2:
case GGML_BACKEND_SPLIT_AXIS_3:
case GGML_BACKEND_SPLIT_AXIS_MIRRORED:
case GGML_BACKEND_SPLIT_AXIS_PARTIAL: {
return src_ss[0];
}
default: {
GGML_ABORT("fatal error");
//return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
}
}
};
auto handle_get_rows = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_0 && src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
return src_ss[0];
}
return handle_generic(src_ss, /*scalar_only =*/ true);
};
auto handle_set_rows = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
GGML_ASSERT(src_ss[0].axis != GGML_BACKEND_SPLIT_AXIS_1);
GGML_ASSERT(src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
GGML_ASSERT(split_states_equal(src_ss[0], src_ss[2]));
return src_ss[0];
};
auto handle_rope = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
GGML_ASSERT(src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
return src_ss[0];
};
auto handle_pad = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
if (src_ss[0].axis >= 0 && src_ss[0].axis < GGML_MAX_DIMS) {
GGML_ASSERT(tensor->op_params[2*src_ss[0].axis + 0] == 0);
GGML_ASSERT(tensor->op_params[2*src_ss[0].axis + 1] == 0);
}
return src_ss[0];
};
auto handle_flash_attn_ext = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
GGML_ASSERT( src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_2);
GGML_ASSERT( src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_2);
GGML_ASSERT( src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_2);
GGML_ASSERT(tensor->src[4] == nullptr || src_ss[3].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
GGML_ASSERT(tensor->src[4] == nullptr || src_ss[4].axis == GGML_BACKEND_SPLIT_AXIS_0);
return {GGML_BACKEND_SPLIT_AXIS_1, {0}, 1};
};
auto handle_ssm_conv = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
if (src_ss[0].axis == src_ss[1].axis) {
if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_0) {
return {GGML_BACKEND_SPLIT_AXIS_1, {0}, 1};
}
if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_1) {
return {GGML_BACKEND_SPLIT_AXIS_0, {0}, 1};
}
}
return handle_generic(src_ss, /*scalar_only =*/ false);
};
auto handle_gated_delta_net = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED &&
src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[3].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED &&
src_ss[4].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
return src_ss[0];
}
GGML_ASSERT(src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_1);
GGML_ASSERT(src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_1);
GGML_ASSERT(src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_1);
GGML_ASSERT(src_ss[3].axis == GGML_BACKEND_SPLIT_AXIS_1);
GGML_ASSERT(src_ss[4].axis == GGML_BACKEND_SPLIT_AXIS_1);
GGML_ASSERT(src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_2);
return {GGML_BACKEND_SPLIT_AXIS_0, {0}, 1};
};
auto calculate_split_state = [&]() -> ggml_backend_meta_split_state {
if (ggml_nelements(tensor) == 0) {
return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
}
if (ggml_backend_buffer_get_usage(tensor->buffer) != GGML_BACKEND_BUFFER_USAGE_COMPUTE && tensor->view_src == nullptr) {
ggml_backend_dev_t dev = ggml_backend_buft_get_device(ggml_backend_buffer_get_type(tensor->buffer));
const ggml_backend_meta_device_context * dev_ctx = (const ggml_backend_meta_device_context *) dev->context;
ggml_backend_meta_split_state ret = dev_ctx->get_split_state(tensor, dev_ctx->get_split_state_ud);
if (ret.axis >= 0 && ret.axis <= GGML_MAX_DIMS) {
const int64_t granularity = ret.axis == GGML_BACKEND_SPLIT_AXIS_0 ? ggml_blck_size(tensor->type) : 1;
int64_t ne_sum = 0;
for (size_t sj = 0; sj < ret.n_segments*n_bufs; sj++) {
GGML_ASSERT(ret.ne[sj] % granularity == 0);
ne_sum += ret.ne[sj];
}
GGML_ASSERT(ne_sum == tensor->ne[ret.axis]);
}
return ret;
}
std::vector<ggml_backend_meta_split_state> src_ss(GGML_MAX_SRC, {GGML_BACKEND_SPLIT_AXIS_NONE, {0}, 1});
for (size_t i = 0; i < GGML_MAX_SRC; i++) {
if (tensor->src[i] == nullptr || tensor->src[i] == tensor) {
src_ss[i] = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
continue;
}
src_ss[i] = ggml_backend_meta_get_split_state(tensor->src[i], /*assume_sync =*/ true);
GGML_ASSERT(src_ss[i].axis != GGML_BACKEND_SPLIT_AXIS_UNKNOWN);
}
ggml_backend_meta_split_state split_state;
switch (tensor->op) {
case GGML_OP_NONE: {
split_state = {GGML_BACKEND_SPLIT_AXIS_MIRRORED, {0}, 1};
} break;
case GGML_OP_DUP: {
split_state = handle_generic(src_ss, /*scalar_only =*/ true);
} break;
case GGML_OP_ADD:
case GGML_OP_ADD_ID: {
split_state = handle_bin_bcast(src_ss);
} break;
case GGML_OP_ADD1:
case GGML_OP_ACC: {
split_state = handle_generic(src_ss, /*scalar_only =*/ true);
} break;
case GGML_OP_SUB:
case GGML_OP_MUL:
case GGML_OP_DIV: {
split_state = handle_bin_bcast(src_ss);
} break;
case GGML_OP_SQR:
case GGML_OP_SQRT:
case GGML_OP_LOG:
case GGML_OP_SIN:
case GGML_OP_COS: {
split_state = handle_generic(src_ss, /*scalar_only =*/ false);
} break;
case GGML_OP_SUM: {
split_state = handle_generic(src_ss, /*scalar_only =*/ true);
} break;
case GGML_OP_SUM_ROWS:
case GGML_OP_CUMSUM:
case GGML_OP_MEAN:
case GGML_OP_ARGMAX:
case GGML_OP_COUNT_EQUAL: {
split_state = handle_per_row(src_ss);
} break;
case GGML_OP_REPEAT:
case GGML_OP_REPEAT_BACK: {
split_state = handle_generic(src_ss, /*scalar_only =*/ false);
} break;
case GGML_OP_CONCAT: {
split_state = handle_concat(src_ss);
} break;
case GGML_OP_SILU_BACK: {
split_state = handle_generic(src_ss, /*scalar_only =*/ false);
} break;
case GGML_OP_NORM:
case GGML_OP_RMS_NORM:
case GGML_OP_RMS_NORM_BACK:
case GGML_OP_GROUP_NORM:
case GGML_OP_L2_NORM: {
split_state = handle_per_row(src_ss);
} break;
case GGML_OP_MUL_MAT:
case GGML_OP_MUL_MAT_ID: {
split_state = handle_mul_mat(src_ss);
} break;
case GGML_OP_OUT_PROD: {
split_state = handle_generic(src_ss, /*scalar_only =*/ true);
} break;
case GGML_OP_SCALE: {
split_state = handle_generic(src_ss, /*scalar_only =*/ false);
} break;
case GGML_OP_SET: {
split_state = handle_generic(src_ss, /*scalar_only =*/ true);
} break;
case GGML_OP_CPY: {
split_state = handle_cpy(src_ss);
} break;
case GGML_OP_CONT:
case GGML_OP_RESHAPE: {
split_state = handle_reshape(src_ss);
} break;
case GGML_OP_VIEW: {
split_state = handle_view(src_ss);
} break;
case GGML_OP_PERMUTE: {
split_state = handle_permute(src_ss);
} break;
case GGML_OP_TRANSPOSE: {
split_state = handle_transpose(src_ss);
} break;
case GGML_OP_GET_ROWS: {
split_state = handle_get_rows(src_ss);
} break;
case GGML_OP_GET_ROWS_BACK: {
split_state = handle_generic(src_ss, /*scalar_only =*/ true);
} break;
case GGML_OP_SET_ROWS: {
split_state = handle_set_rows(src_ss);
} break;
case GGML_OP_DIAG:
case GGML_OP_DIAG_MASK_INF:
case GGML_OP_DIAG_MASK_ZERO: {
split_state = handle_generic(src_ss, /*scalar_only =*/ true);
} break;
case GGML_OP_SOFT_MAX:
case GGML_OP_SOFT_MAX_BACK: {
split_state = handle_generic(src_ss, /*scalar_only =*/ false);
} break;
case GGML_OP_ROPE: {
split_state = handle_rope(src_ss);
} break;
case GGML_OP_ROPE_BACK: {
split_state = handle_generic(src_ss, /*scalar_only =*/ true);
} break;
case GGML_OP_CLAMP: {
split_state = handle_generic(src_ss, /*scalar_only =*/ false);
} break;
case GGML_OP_CONV_TRANSPOSE_1D:
case GGML_OP_IM2COL:
case GGML_OP_IM2COL_BACK:
case GGML_OP_IM2COL_3D:
case GGML_OP_CONV_2D:
case GGML_OP_CONV_3D:
case GGML_OP_CONV_2D_DW:
case GGML_OP_CONV_TRANSPOSE_2D:
case GGML_OP_POOL_1D:
case GGML_OP_POOL_2D:
case GGML_OP_POOL_2D_BACK:
case GGML_OP_UPSCALE: {
split_state = handle_generic(src_ss, /*scalar_only =*/ true);
} break;
case GGML_OP_PAD: {
split_state = handle_pad(src_ss);
} break;
case GGML_OP_PAD_REFLECT_1D:
case GGML_OP_ROLL:
case GGML_OP_ARANGE:
case GGML_OP_TIMESTEP_EMBEDDING: {
split_state = handle_generic(src_ss, /*scalar_only =*/ true);
} break;
case GGML_OP_ARGSORT:
case GGML_OP_TOP_K: {
split_state = handle_per_row(src_ss);
} break;
case GGML_OP_LEAKY_RELU: {
split_state = handle_generic(src_ss, /*scalar_only =*/ false);
} break;
case GGML_OP_TRI: {
split_state = handle_generic(src_ss, /*scalar_only =*/ true);
} break;
case GGML_OP_FILL: {
split_state = handle_generic(src_ss, /*scalar_only =*/ false);
} break;
case GGML_OP_FLASH_ATTN_EXT: {
split_state = handle_flash_attn_ext(src_ss);
} break;
case GGML_OP_FLASH_ATTN_BACK: {
split_state = handle_generic(src_ss, /*scalar_only =*/ true);
} break;
case GGML_OP_SSM_CONV: {
split_state = handle_ssm_conv(src_ss);
} break;
case GGML_OP_SSM_SCAN:
case GGML_OP_WIN_PART:
case GGML_OP_WIN_UNPART:
case GGML_OP_GET_REL_POS:
case GGML_OP_ADD_REL_POS:
case GGML_OP_RWKV_WKV6:
case GGML_OP_GATED_LINEAR_ATTN:
case GGML_OP_RWKV_WKV7:
case GGML_OP_SOLVE_TRI: {
split_state = handle_generic(src_ss, /*scalar_only =*/ true);
} break;
case GGML_OP_GATED_DELTA_NET: {
split_state = handle_gated_delta_net(src_ss);
} break;
case GGML_OP_UNARY: {
split_state = handle_generic(src_ss, /*scalar_only =*/ false);
} break;
case GGML_OP_MAP_CUSTOM1:
case GGML_OP_MAP_CUSTOM2:
case GGML_OP_MAP_CUSTOM3:
case GGML_OP_CUSTOM: {
split_state = handle_generic(src_ss, /*scalar_only =*/ true);
} break;
case GGML_OP_CROSS_ENTROPY_LOSS:
case GGML_OP_CROSS_ENTROPY_LOSS_BACK: {
split_state = handle_per_row(src_ss);
} break;
case GGML_OP_OPT_STEP_ADAMW:
case GGML_OP_OPT_STEP_SGD:
case GGML_OP_GLU: {
split_state = handle_generic(src_ss, /*scalar_only =*/ false);
} break;
default: {
GGML_ABORT("ggml op not implemented: %s", ggml_op_name(tensor->op));
split_state = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
} break;
}
if (split_state.axis >= 0 && split_state.axis < GGML_MAX_DIMS) {
bool first_src_split_by_axis = true;
const size_t n_bufs = ggml_backend_meta_buffer_n_bufs(tensor->buffer);
for (size_t i = 0; i < GGML_MAX_SRC; i++) {
if (tensor->src[i] == nullptr || src_ss[i].axis < 0 || src_ss[i].axis >= GGML_MAX_DIMS) {
continue;
}
if (first_src_split_by_axis) {
for (size_t j = 0; j < n_bufs; j++) {
// Take over ratio from src:
for (size_t s = 0; s < src_ss[i].n_segments; s++) {
split_state.ne[s*n_bufs + j] = 0;
}
for (size_t s = 0; s < src_ss[i].n_segments; s++) {
split_state.ne[j] += src_ss[i].ne[s*n_bufs + j];
}
split_state.ne[j] *= tensor->ne[split_state.axis];
if (split_state.ne[j] != 0 || tensor->src[i]->ne[src_ss[i].axis] != 0) {
GGML_ASSERT(split_state.ne[j] % tensor->src[i]->ne[src_ss[i].axis] == 0);
split_state.ne[j] /= tensor->src[i]->ne[src_ss[i].axis];
}
}
} else {
for (size_t j = 0; j < n_bufs; j++) {
int64_t sum = 0;
for (size_t s = 0; s < src_ss[i].n_segments; s++) {
sum += src_ss[i].ne[s*n_bufs + j];
}
// Assert that ratio is consistent:
GGML_ASSERT(split_state.ne[j] * tensor->src[i]->ne[src_ss[i].axis]
== sum * tensor->ne[split_state.axis]);
}
}
first_src_split_by_axis = false;
}
GGML_ASSERT(!first_src_split_by_axis);
}
return split_state;
};
const std::pair key = std::make_pair(tensor, assume_sync);
auto it = buf_ctx->split_state_cache.find(key);
if (it != buf_ctx->split_state_cache.end() && memcmp(it->second.second, (const char *) tensor, sizeof(it->second.second)) != 0) {
buf_ctx->split_state_cache.clear();
it = buf_ctx->split_state_cache.end();
}
if (it == buf_ctx->split_state_cache.end()) {
buf_ctx->split_state_cache[key].first = calculate_split_state();
memcpy(buf_ctx->split_state_cache[key].second, tensor, sizeof(buf_ctx->split_state_cache[key].second));
if (buf_ctx->debug > 0) {
std::string srcs_info;
for (size_t i = 0; i < GGML_MAX_SRC; i++) {
if (tensor->src[i] == nullptr) {
continue;
}
if (!srcs_info.empty()) {
srcs_info += ", ";
}
const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor->src[0], true);
const char * axis_name = ggml_backend_meta_split_axis_name(split_state.axis);
std::string ne_info;
for (size_t j = 0; j < n_bufs; j++) {
if (!ne_info.empty()) {
ne_info += ", ";
}
ne_info += std::to_string(split_state.ne[j]);
}
srcs_info += std::string(tensor->src[i]->name) + "[" + ggml_op_name(tensor->src[i]->op) + ", " + axis_name + ", {" + ne_info + "}]";
}
std::string ne_info;
for (size_t j = 0; j < n_bufs; j++) {
if (!ne_info.empty()) {
ne_info += ", ";
}
ne_info += std::to_string(buf_ctx->split_state_cache[key].first.ne[j]);
}
GGML_LOG_DEBUG("SPLIT_STATE: {%s} -> %s[%s, %s, {%s}]\n", srcs_info.c_str(), tensor->name, ggml_op_name(tensor->op),
ggml_backend_meta_split_axis_name(buf_ctx->split_state_cache[key].first.axis), ne_info.c_str());
}
}
ggml_backend_meta_split_state ret = buf_ctx->split_state_cache[key].first;
GGML_ASSERT(ret.axis != GGML_BACKEND_SPLIT_AXIS_NONE);
#ifndef NDEBUG
if (ret.axis >= 0 && ret.axis < GGML_MAX_DIMS) {
int64_t ne_ret = 0;
for (size_t sj = 0; sj < ret.n_segments*n_bufs; sj++) {
ne_ret += ret.ne[sj];
}
assert(ne_ret == tensor->ne[int(ret.axis)]);
}
#endif // NDEBUG
return ret;
}
static void * ggml_backend_meta_buffer_get_base(ggml_backend_buffer_t buffer) {
GGML_UNUSED(buffer);
return (void *) 0x1000000000000000; // FIXME
}
static enum ggml_status ggml_backend_meta_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
GGML_ASSERT(ggml_backend_buffer_is_meta(buffer));
ggml_backend_meta_buffer_context * buf_ctx = (ggml_backend_meta_buffer_context *) buffer->context;
const size_t n_simple_bufs = ggml_backend_meta_buffer_n_bufs(buffer);
const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ true);
GGML_ASSERT(ggml_nelements(tensor) == 0 || split_state.axis != GGML_BACKEND_SPLIT_AXIS_UNKNOWN);
GGML_ASSERT(split_state.n_segments <= 16);
int split_dim = split_state.axis;
int64_t ne[GGML_MAX_DIMS];
size_t nb[GGML_MAX_DIMS];
for (size_t k = 0; k < GGML_MAX_DIMS; k++) {
ne[k] = tensor->ne[k];
nb[k] = tensor->nb[k];
}
std::vector<ggml_tensor *> simple_tensors;
simple_tensors.reserve(n_simple_bufs);
for (size_t j = 0; j < n_simple_bufs; j++) {
ggml_context * simple_ctx = buf_ctx->buf_configs[j].ctx;
ggml_backend_buffer_t simple_buf = buf_ctx->buf_configs[j].buf;
if (split_dim >= 0 && split_dim < GGML_MAX_DIMS) {
// TODO: the following assert fails for llama-parallel even though the results are correct:
// GGML_ASSERT(ggml_is_contiguously_allocated(tensor));
ne[split_dim] = 0;
for (size_t s = 0; s < split_state.n_segments; s++) {
ne[split_dim] += split_state.ne[s*n_simple_bufs + j];
}
for (int i = 0; i < GGML_MAX_DIMS; i++) {
if (tensor->nb[i] > tensor->nb[split_dim]) {
nb[i] = tensor->nb[i] * ne[split_dim]/tensor->ne[split_dim];
}
}
}
ggml_tensor * t_ij = ggml_new_tensor(simple_ctx, tensor->type, GGML_MAX_DIMS, ne);
t_ij->op = tensor->op;
for (int i = 0; i < GGML_MAX_DIMS; i++) {
t_ij->nb[i] = nb[i];
}
t_ij->flags = tensor->flags;
memcpy(t_ij->op_params, tensor->op_params, sizeof(tensor->op_params));
ggml_set_name(t_ij, tensor->name);
t_ij->buffer = simple_buf;
t_ij->view_src = tensor->view_src;
t_ij->view_offs = tensor->view_offs;
if (t_ij->view_src != nullptr && ggml_backend_buffer_is_meta(t_ij->view_src->buffer)) {
t_ij->view_src = ggml_backend_meta_buffer_simple_tensor(tensor->view_src, j);
if (t_ij->view_offs > 0 && split_dim >= 0 && split_dim < GGML_MAX_DIMS) {
GGML_ASSERT(tensor->ne[split_dim] != 0);
const int split_dim_view_src = ggml_backend_meta_get_split_state(tensor->view_src, /*assume_sync =*/ true).axis;
GGML_ASSERT(split_dim_view_src >= 0 && split_dim_view_src < GGML_MAX_DIMS);
// The offset can be internal to the data split, in those cases the view offset should not be scaled.
// If however, the offset is larger than the data split then it needs to be scaled proportionally.
bool split_internal_offset = t_ij->view_offs <= tensor->view_src->nb[split_dim_view_src];
for (int i = 0; i < GGML_MAX_DIMS; i++) {
const size_t dim_size = tensor->ne[i] * tensor->nb[i];
if (tensor->view_offs <= dim_size && dim_size < tensor->nb[split_dim]) {
split_internal_offset = true;
break;
}
}
if (!split_internal_offset) {
t_ij->view_offs = t_ij->view_offs * ne[split_dim]/tensor->ne[split_dim];
}
}
}
if (t_ij->view_src != nullptr) {
t_ij->data = (char *) t_ij->view_src->data + t_ij->view_offs;
} else if (simple_buf != nullptr) {
t_ij->data = (char *) ggml_backend_buffer_get_base(simple_buf)
+ size_t(tensor->data) - size_t(ggml_backend_buffer_get_base(buffer));
}
t_ij->extra = tensor->extra;
for (int i = 0; i < GGML_MAX_SRC; i++) {
t_ij->src[i] = tensor->src[i];
if (tensor->src[i] == tensor) {
t_ij->src[i] = t_ij;
} else if (t_ij->src[i] != nullptr && ggml_backend_buffer_is_meta(t_ij->src[i]->buffer)) {
t_ij->src[i] = ggml_backend_meta_buffer_simple_tensor(tensor->src[i], j);
}
}
simple_tensors.push_back(t_ij);
}
// If one of the sources has a zero-sized slice, disable the computation:
for (int i = 0; i < GGML_MAX_SRC; i++) {
if (tensor->src[i] == nullptr || !ggml_backend_buffer_is_meta(tensor->src[i]->buffer)) {
continue;
}
const ggml_backend_meta_split_state split_state_src = ggml_backend_meta_get_split_state(tensor->src[i], /*assume_sync =*/ true);
if (split_state_src.axis < 0 || split_state_src.axis >= GGML_MAX_DIMS) {
continue;
}
for (size_t j = 0; j < n_simple_bufs; j++) {
int64_t ne_sum = 0;
for (size_t s = 0; s < split_state_src.n_segments; s++) {
ne_sum += split_state_src.ne[s*n_simple_bufs + j];
}
if (ne_sum == 0) {
simple_tensors[j]->flags &= ~GGML_TENSOR_FLAG_COMPUTE;
}
}
}
buf_ctx->simple_tensors[tensor] = simple_tensors;
return GGML_STATUS_SUCCESS;
}
static void ggml_backend_meta_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
const size_t n_bufs = ggml_backend_meta_buffer_n_bufs(buffer);
GGML_ASSERT(ggml_is_contiguous(tensor));
const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ false);
if (split_state.n_segments != 1) {
GGML_ASSERT(split_state.axis >= 0 && split_state.axis < GGML_MAX_DIMS);
GGML_ASSERT(tensor->ne[3] == 1);
size_t offset_data = 0;
std::vector<size_t> simple_offsets(n_bufs, 0);
if (split_state.axis == GGML_BACKEND_SPLIT_AXIS_0) {
GGML_ASSERT(tensor->ne[2] == 1);
const size_t row_stride = tensor->nb[1];
GGML_ASSERT(offset % row_stride == 0);
GGML_ASSERT(size % row_stride == 0);
const int64_t r_start = offset / row_stride;
const int64_t r_count = size / row_stride;
GGML_ASSERT(r_start + r_count <= tensor->ne[1]);
const int64_t blck_size = ggml_blck_size(tensor->type);
for (size_t s = 0; s < split_state.n_segments; s++) {
for (size_t j = 0; j < n_bufs; j++) {
ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
GGML_ASSERT(split_state.ne[s*n_bufs + j] % blck_size == 0);
const size_t nbytes = split_state.ne[s*n_bufs + j]/blck_size * tensor->nb[0];
ggml_backend_tensor_set_2d(simple_tensor, (const char *) data + offset_data,
simple_offsets[j] + r_start * simple_tensor->nb[1], nbytes,
r_count, simple_tensor->nb[1], tensor->nb[1]);
offset_data += nbytes;
simple_offsets[j] += nbytes;
}
}
GGML_ASSERT(offset_data*r_count == size);
return;
}
GGML_ASSERT(split_state.axis == GGML_BACKEND_SPLIT_AXIS_1);
const size_t row_stride = tensor->nb[2];
GGML_ASSERT(offset % row_stride == 0);
GGML_ASSERT(size % row_stride == 0);
const int64_t r_start = offset / row_stride;
const int64_t r_count = size / row_stride;
GGML_ASSERT(r_start + r_count <= tensor->ne[2]);
for (size_t s = 0; s < split_state.n_segments; s++) {
for (size_t j = 0; j < n_bufs; j++) {
ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
const size_t nbytes = split_state.ne[s*n_bufs + j] * tensor->nb[1];
ggml_backend_tensor_set_2d(simple_tensor, (const char *) data + offset_data,
simple_offsets[j] + r_start * simple_tensor->nb[2], nbytes,
r_count, simple_tensor->nb[2], tensor->nb[2]);
offset_data += nbytes;
simple_offsets[j] += nbytes;
}
}
GGML_ASSERT(offset_data*r_count == size);
return;
}
switch (split_state.axis) {
case GGML_BACKEND_SPLIT_AXIS_0:
case GGML_BACKEND_SPLIT_AXIS_1:
case GGML_BACKEND_SPLIT_AXIS_2: {
// Exploit that tensors are contiguous to splice it with simple tensors as "chunks".
const size_t chunk_size_full = tensor->nb[split_state.axis + 1];
GGML_ASSERT(offset % chunk_size_full == 0);
GGML_ASSERT(size % chunk_size_full == 0);
const int64_t i_start = offset /chunk_size_full;
const int64_t i_stop = (offset + size)/chunk_size_full;
size_t offset_j = 0;
for (size_t j = 0; j < n_bufs; j++) {
ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
const size_t chunk_size_j = simple_tensor->nb[split_state.axis + 1];
const size_t simple_offset = i_start * chunk_size_j;
ggml_backend_tensor_set_2d(simple_tensor, (const char *) data + offset_j, simple_offset, chunk_size_j, i_stop - i_start, chunk_size_j, chunk_size_full);
offset_j += chunk_size_j;
}
GGML_ASSERT(offset_j == chunk_size_full);
} break;
case GGML_BACKEND_SPLIT_AXIS_MIRRORED: {
for (size_t j = 0; j < n_bufs; j++) {
ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
ggml_backend_tensor_set(simple_tensor, data, offset, size);
}
} break;
case GGML_BACKEND_SPLIT_AXIS_PARTIAL: {
GGML_ASSERT(tensor->type == GGML_TYPE_F32);
const int64_t ne = ggml_nelements(tensor);
std::vector<float> tmp;
tmp.reserve(ne);
for (int64_t i = 0; i < ne; i++) {
tmp.push_back(((const float *) data)[i] / n_bufs);
}
for (size_t j = 0; j < n_bufs; j++) {
ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
ggml_backend_tensor_set(simple_tensor, tmp.data(), offset, size);
}
} break;
default: {
GGML_ABORT("fatal error");
}
}
}
static void ggml_backend_meta_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
const size_t n_bufs = ggml_backend_meta_buffer_n_bufs(buffer);
GGML_ASSERT(ggml_is_contiguous(tensor));
const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ false);
if (split_state.n_segments != 1) {
GGML_ASSERT(split_state.axis >= 0 && split_state.axis < GGML_MAX_DIMS);
GGML_ASSERT(tensor->ne[3] == 1);
size_t offset_data = 0;
std::vector<size_t> simple_offsets(n_bufs, 0);
if (split_state.axis == GGML_BACKEND_SPLIT_AXIS_0) {
GGML_ASSERT(tensor->ne[2] == 1);
const size_t row_stride = tensor->nb[1];
GGML_ASSERT(offset % row_stride == 0);
GGML_ASSERT(size % row_stride == 0);
const int64_t r_start = offset / row_stride;
const int64_t r_count = size / row_stride;
GGML_ASSERT(r_start + r_count <= tensor->ne[1]);
const int64_t blck_size = ggml_blck_size(tensor->type);
for (size_t s = 0; s < split_state.n_segments; s++) {
for (size_t j = 0; j < n_bufs; j++) {
const ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
GGML_ASSERT(split_state.ne[s*n_bufs + j] % blck_size == 0);
const size_t nbytes = split_state.ne[s*n_bufs + j]/blck_size * tensor->nb[0];
ggml_backend_tensor_get_2d(simple_tensor, (char *) data + offset_data,
simple_offsets[j] + r_start * simple_tensor->nb[1], nbytes,
r_count, simple_tensor->nb[1], tensor->nb[1]);
offset_data += nbytes;
simple_offsets[j] += nbytes;
}
}
GGML_ASSERT(offset_data*r_count == size);
return;
}
GGML_ASSERT(split_state.axis == GGML_BACKEND_SPLIT_AXIS_1);
const size_t row_stride = tensor->nb[2];
GGML_ASSERT(offset % row_stride == 0);
GGML_ASSERT(size % row_stride == 0);
const int64_t r_start = offset / row_stride;
const int64_t r_count = size / row_stride;
GGML_ASSERT(r_start + r_count <= tensor->ne[2]);
for (size_t s = 0; s < split_state.n_segments; s++) {
for (size_t j = 0; j < n_bufs; j++) {
const ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
const size_t nbytes = split_state.ne[s*n_bufs + j] * tensor->nb[1];
ggml_backend_tensor_get_2d(simple_tensor, (char *) data + offset_data,
simple_offsets[j] + r_start * simple_tensor->nb[2], nbytes,
r_count, simple_tensor->nb[2], tensor->nb[2]);
offset_data += nbytes;
simple_offsets[j] += nbytes;
}
}
GGML_ASSERT(offset_data*r_count == size);
return;
}
switch (split_state.axis) {
case GGML_BACKEND_SPLIT_AXIS_0:
case GGML_BACKEND_SPLIT_AXIS_1:
case GGML_BACKEND_SPLIT_AXIS_2: {
// Exploit that tensors are contiguous to splice it with simple tensors as "chunks".
const size_t chunk_size_full = tensor->nb[split_state.axis + 1];
GGML_ASSERT(offset % chunk_size_full == 0);
GGML_ASSERT(size % chunk_size_full == 0);
const int64_t i_start = offset /chunk_size_full;
const int64_t i_stop = (offset + size)/chunk_size_full;
size_t offset_j = 0;
for (size_t j = 0; j < n_bufs; j++){
const ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
const size_t chunk_size_j = simple_tensor->nb[split_state.axis + 1];
const size_t simple_offset = i_start * chunk_size_j;
ggml_backend_tensor_get_2d(simple_tensor, (char *) data + offset_j, simple_offset, chunk_size_j, i_stop - i_start, chunk_size_j, chunk_size_full);
offset_j += chunk_size_j;
}
GGML_ASSERT(offset_j == chunk_size_full);
} break;
case GGML_BACKEND_SPLIT_AXIS_MIRRORED: {
// TODO other simple backend may be better
const ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, 0);
ggml_backend_tensor_get(simple_tensor, data, offset, size);
} break;
default: {
GGML_ABORT("fatal error");
}
}
}
static void ggml_backend_meta_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
const size_t n_buffers = ggml_backend_meta_buffer_n_bufs(buffer);
for (size_t i = 0; i < n_buffers; i++) {
ggml_backend_buffer_clear(ggml_backend_meta_buffer_simple_buffer(buffer, i), value);
}
}
static void ggml_backend_meta_buffer_reset(ggml_backend_buffer_t buffer) {
const size_t n_buffers = ggml_backend_meta_buffer_n_bufs(buffer);
for (size_t i = 0; i < n_buffers; i++) {
ggml_backend_buffer_reset(ggml_backend_meta_buffer_simple_buffer(buffer, i));
}
}
static const ggml_backend_buffer_i ggml_backend_meta_buffer_iface = {
/* .free_buffer = */ ggml_backend_meta_buffer_free_buffer,
/* .get_base = */ ggml_backend_meta_buffer_get_base,
/* .init_tensor = */ ggml_backend_meta_buffer_init_tensor,
/* .memset_tensor = */ nullptr, // TODO implement
/* .set_tensor = */ ggml_backend_meta_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_meta_buffer_get_tensor,
/* .set_tensor_2d = */ nullptr,
/* .get_tensor_2d = */ nullptr,
/* .cpy_tensor = */ nullptr,
/* .clear = */ ggml_backend_meta_buffer_clear,
/* .reset = */ ggml_backend_meta_buffer_reset,
};
bool ggml_backend_buffer_is_meta(ggml_backend_buffer_t buf) {
return buf != nullptr && buf->iface.free_buffer == ggml_backend_meta_buffer_iface.free_buffer;
}
static ggml_backend_buffer_t ggml_backend_meta_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
const size_t n_simple_bufts = ggml_backend_meta_buft_n_bufts(buft);
ggml_init_params params = {
/*.mem_size =*/ 1024*1024*1024, // FIXME
/*.mem_buffer =*/ nullptr,
/*.no_alloc =*/ true,
};
ggml_backend_meta_buffer_context * buf_ctx = new ggml_backend_meta_buffer_context();
size_t max_size = 0;
buf_ctx->buf_configs.reserve(n_simple_bufts);
for (size_t i = 0; i < n_simple_bufts; i++) {
ggml_backend_buffer_t simple_buf = ggml_backend_buft_alloc_buffer(ggml_backend_meta_buft_simple_buft(buft, i), size);
max_size = std::max(max_size, ggml_backend_buffer_get_size(simple_buf));
buf_ctx->buf_configs.emplace_back(ggml_init(params), simple_buf);
}
return ggml_backend_buffer_init(buft, ggml_backend_meta_buffer_iface, buf_ctx, max_size);
}
struct ggml_backend_buffer * ggml_backend_meta_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) {
const size_t n_simple_bufts = ggml_backend_meta_buft_n_bufts(buft);
ggml_init_params params = {
/*.mem_size =*/ 1024*1024*1024, // FIXME
/*.mem_buffer =*/ nullptr,
/*.no_alloc =*/ true,
};
ggml_backend_meta_buffer_context * meta_buf_ctx = new ggml_backend_meta_buffer_context();
meta_buf_ctx->buf_configs.reserve(n_simple_bufts);
for (size_t i = 0; i < n_simple_bufts; i++) {
meta_buf_ctx->buf_configs.emplace_back(ggml_init(params), nullptr);
}
ggml_backend_buffer_t meta_buf = ggml_backend_buffer_init(buft, ggml_backend_meta_buffer_iface, meta_buf_ctx, 0);
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
t->buffer = meta_buf;
ggml_backend_meta_buffer_init_tensor(meta_buf, t);
t->data = (void *) 0x2000000000000000; // FIXME
}
for (size_t i = 0; i < n_simple_bufts; i++) {
meta_buf_ctx->buf_configs[i].buf = ggml_backend_alloc_ctx_tensors_from_buft(
meta_buf_ctx->buf_configs[i].ctx, ggml_backend_meta_buft_simple_buft(buft, i));
meta_buf->size = std::max(meta_buf->size, ggml_backend_buffer_get_size(meta_buf_ctx->buf_configs[i].buf));
}
return meta_buf;
}
//
// meta backend
//
static ggml_guid_t ggml_backend_meta_guid() {
static ggml_guid guid = {0xf1, 0x0e, 0x34, 0xcf, 0x9c, 0x6f, 0x43, 0xcb, 0x96, 0x92, 0xbe, 0x8e, 0xbb, 0x71, 0x3f, 0xda};
return &guid;
}
struct ggml_backend_meta_context {
struct cgraph_config {
ggml_cgraph * cgraph_main = nullptr;
int offset = 0; // Node offset vs. original graph
std::vector<ggml_cgraph *> cgraphs_aux;
};
struct backend_config {
ggml_backend_t backend;
std::vector<cgraph_config> cgraphs;
std::vector<ggml_tensor *> nodes;
std::vector<ggml_backend_buffer_ptr> bufs;
backend_config(ggml_backend_t backend, const size_t n_reduce_steps) : backend(backend) {
bufs.resize(n_reduce_steps);
}
};
std::string name;
std::vector<backend_config> backend_configs;
ggml_context_ptr ctx;
std::vector<ggml_cgraph *> cgraphs_aux;
std::vector<ggml_tensor *> nodes_aux;
size_t n_reduce_steps;
int max_nnodes = 0;
size_t max_tmp_size = 0;
size_t max_subgraphs = 0;
size_t n_subgraphs = 0;
uint64_t uid = 0;
void * comm_ctx = nullptr;
ggml_backend_comm_allreduce_tensor_t comm_allreduce = nullptr;
ggml_backend_meta_context(ggml_backend_dev_t meta_dev, const char * params) {
const size_t n_devs = ggml_backend_meta_dev_n_devs(meta_dev);
n_reduce_steps = std::ceil(std::log2(n_devs));
name = "Meta(";
std::vector<ggml_backend_t> simple_backends;
backend_configs.reserve(n_devs);
simple_backends.reserve(n_devs);
for (size_t i = 0; i < n_devs; i++) {
ggml_backend_dev_t simple_dev = ggml_backend_meta_dev_simple_dev(meta_dev, i);
if (i > 0) {
name += ",";
}
name += ggml_backend_dev_name(simple_dev);
simple_backends.push_back(ggml_backend_dev_init(simple_dev, params));
backend_configs.emplace_back(simple_backends.back(), n_reduce_steps);
}
name += ")";
if (n_devs > 1) {
ggml_backend_comm_init_t comm_init = (ggml_backend_comm_init_t) ggml_backend_reg_get_proc_address(
ggml_backend_dev_backend_reg(ggml_backend_get_device(simple_backends[0])), "ggml_backend_comm_init");
if (comm_init != nullptr) {
comm_ctx = comm_init(simple_backends.data(), simple_backends.size());
}
}
if (comm_ctx != nullptr) {
comm_allreduce = (ggml_backend_comm_allreduce_tensor_t)
ggml_backend_reg_get_proc_address(ggml_backend_dev_backend_reg(
ggml_backend_get_device(simple_backends[0])), "ggml_backend_comm_allreduce_tensor");
GGML_ASSERT(comm_allreduce != nullptr);
}
}
~ggml_backend_meta_context() {
if (comm_ctx != nullptr) {
ggml_backend_comm_free_t comm_free = (ggml_backend_comm_free_t) ggml_backend_reg_get_proc_address(
ggml_backend_dev_backend_reg(ggml_backend_get_device(backend_configs[0].backend)), "ggml_backend_comm_free");
GGML_ASSERT(comm_free != nullptr);
comm_free(comm_ctx);
}
for (auto & bc : backend_configs) {
ggml_backend_free(bc.backend);
}
}
};
static const char * ggml_backend_meta_get_name(ggml_backend_t backend) {
GGML_ASSERT(ggml_backend_is_meta(backend));
const ggml_backend_meta_context * backend_ctx = (const ggml_backend_meta_context *) backend->context;
return backend_ctx->name.c_str();
}
static void ggml_backend_meta_free(ggml_backend_t backend) {
GGML_ASSERT(ggml_backend_is_meta(backend));
ggml_backend_meta_context * backend_ctx = (ggml_backend_meta_context *) backend->context;
delete backend_ctx;
delete backend;
}
static void ggml_backend_meta_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
const size_t n_backends = ggml_backend_meta_n_backends(backend);
GGML_ASSERT(offset == 0);
GGML_ASSERT(ggml_is_contiguous(tensor));
const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ false);
GGML_ASSERT(split_state.n_segments == 1);
switch (split_state.axis) {
case GGML_BACKEND_SPLIT_AXIS_0:
case GGML_BACKEND_SPLIT_AXIS_1:
case GGML_BACKEND_SPLIT_AXIS_2: {
// Exploit that tensors are contiguous to splice it with simple tensors as "chunks".
const size_t chunk_size_full = tensor->nb[split_state.axis + 1];
GGML_ASSERT(offset % chunk_size_full == 0);
GGML_ASSERT(size % chunk_size_full == 0);
const int64_t i_start = offset /chunk_size_full;
const int64_t i_stop = (offset + size)/chunk_size_full;
size_t offset_j = 0;
for (size_t j = 0; j < n_backends; j++){
ggml_backend_t simple_backend = ggml_backend_meta_simple_backend(backend, j);
ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
const size_t chunk_size_j = simple_tensor->nb[split_state.axis + 1];
ggml_backend_tensor_set_2d_async(simple_backend, simple_tensor, (const char *) data + offset_j, offset, chunk_size_j,
i_stop - i_start, chunk_size_j, chunk_size_full);
offset_j += chunk_size_j;
}
GGML_ASSERT(offset_j == chunk_size_full);
} break;
case GGML_BACKEND_SPLIT_AXIS_MIRRORED: {
for (size_t j = 0; j < n_backends; j++) {
ggml_backend_tensor_set_async(
ggml_backend_meta_simple_backend(backend, j), ggml_backend_meta_buffer_simple_tensor(tensor, j), data, offset, size);
}
} break;
default: {
GGML_ABORT("fatal error");
}
}
}
static void ggml_backend_meta_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
const size_t n_backends = ggml_backend_meta_n_backends(backend);
GGML_ASSERT(offset == 0);
GGML_ASSERT(ggml_is_contiguous(tensor));
const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ false);
GGML_ASSERT(split_state.n_segments == 1);
switch (split_state.axis) {
case GGML_BACKEND_SPLIT_AXIS_0:
case GGML_BACKEND_SPLIT_AXIS_1:
case GGML_BACKEND_SPLIT_AXIS_2: {
// Exploit that tensors are contiguous to splice it with simple tensors as "chunks".
const size_t chunk_size_full = tensor->nb[split_state.axis + 1];
GGML_ASSERT(offset % chunk_size_full == 0);
GGML_ASSERT(size % chunk_size_full == 0);
const int64_t i_start = offset /chunk_size_full;
const int64_t i_stop = (offset + size)/chunk_size_full;
size_t offset_j = 0;
for (size_t j = 0; j < n_backends; j++){
ggml_backend_t simple_backend = ggml_backend_meta_simple_backend(backend, j);
const ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
const size_t chunk_size_j = simple_tensor->nb[split_state.axis + 1];
ggml_backend_tensor_get_2d_async(simple_backend, simple_tensor, (char *) data + offset_j, offset, chunk_size_j,
i_stop - i_start, chunk_size_j, chunk_size_full);
offset_j += chunk_size_j;
}
GGML_ASSERT(offset_j == chunk_size_full);
} break;
case GGML_BACKEND_SPLIT_AXIS_MIRRORED: {
// TODO other simple backend may be better
ggml_backend_t simple_backend = ggml_backend_meta_simple_backend(backend, 0);
const ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, 0);
ggml_backend_tensor_get_async(simple_backend, simple_tensor, data, offset, size);
} break;
default: {
GGML_ABORT("fatal error");
}
}
}
static void ggml_backend_meta_synchronize(ggml_backend_t backend) {
const size_t n_backends = ggml_backend_meta_n_backends(backend);
for (size_t i = 0; i < n_backends; i++) {
ggml_backend_synchronize(ggml_backend_meta_simple_backend(backend, i));
}
}
static enum ggml_status ggml_backend_meta_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
GGML_ASSERT(cgraph->grads == nullptr);
const size_t n_backends = ggml_backend_meta_n_backends(backend);
ggml_backend_meta_context * backend_ctx = (ggml_backend_meta_context *) backend->context;
// If the previous cgraph had a defined UID it can be used to skip rebuilding the subgraphs per simple backend.
const bool needs_rebuild = (cgraph->uid == 0) || (cgraph->uid != backend_ctx->uid);
bool max_nnodes_raised = false;
if (cgraph->n_nodes > backend_ctx->max_nnodes) {
for (size_t j = 0; j < n_backends; j++) {
auto & bcj = backend_ctx->backend_configs[j];
bcj.nodes.resize(cgraph->n_nodes);
bcj.cgraphs.resize(cgraph->n_nodes);
}
backend_ctx->max_nnodes = cgraph->n_nodes;
max_nnodes_raised = true;
assert(needs_rebuild);
}
if (needs_rebuild) {
size_t n_subgraphs = 0;
size_t max_tmp_size = 0;
for (size_t j = 0; j < n_backends; j++) {
auto & bcj = backend_ctx->backend_configs[j];
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
if (node->view_src != nullptr && node->view_src->op == GGML_OP_NONE && ggml_backend_buffer_is_host(node->view_src->buffer)) {
// FIXME s_copy_main is on the CPU and its view seems to be incorrectly added to the graph nodes.
// For regular usage this doesn't matter since it's a noop but trying to call ggml_backend_meta_buffer_simple_tensor results in a crash.
bcj.nodes[i] = node;
continue;
}
bcj.nodes[i] = ggml_backend_meta_buffer_simple_tensor(node, j);
GGML_ASSERT(bcj.nodes[i]);
}
}
{
// For MoE models it may make sense to delay the AllReduce in order to reduce I/O:
auto get_i_delayed = [&](const int i) -> int {
int id = i; // i_delayed
int idr = i; // i_delayed return, last safe return value
ggml_tensor * node = cgraph->nodes[id];
int32_t n_used = ggml_node_get_use_count(cgraph, id);
// Skip MIRRORED nodes that don't consume node
auto skip_unrelated = [&]() {
while (id + 1 < cgraph->n_nodes) {
ggml_tensor * next = cgraph->nodes[id+1];
if (ggml_backend_meta_get_split_state(next, false).axis != GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
break;
}
bool safe = true;
for (int s = 0; s < GGML_MAX_SRC; s++) {
if (next->src[s] == nullptr) {
continue;
}
if (next->src[s] == node) {
safe = false;
break;
}
if (ggml_backend_meta_get_split_state(next->src[s], false).axis != GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
safe = false;
break;
}
}
if (!safe) {
break;
}
id++;
}
};
skip_unrelated();
if (id + 1 >= cgraph->n_nodes) {
return idr;
}
{
ggml_tensor * next = cgraph->nodes[id+1];
if (next->op == GGML_OP_ADD_ID && next->src[0] == node &&
ggml_backend_meta_get_split_state(next->src[1], false).axis == GGML_BACKEND_SPLIT_AXIS_PARTIAL &&
ggml_backend_meta_get_split_state(next->src[2], false).axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
node = next;
id++;
idr = id;
n_used = ggml_node_get_use_count(cgraph, id);
}
}
// Chain of MULs with MIRRORED src[1]
while (true) {
skip_unrelated();
if (id + 1 >= cgraph->n_nodes) {
return idr;
}
ggml_tensor * next = cgraph->nodes[id+1];
if (next->op == GGML_OP_MUL && next->src[0] == node &&
ggml_backend_meta_get_split_state(next->src[1], false).axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
node = next;
id++;
idr = id;
n_used = ggml_node_get_use_count(cgraph, id);
} else {
break;
}
}
if (n_used != node->ne[1] || id + 2*n_used-1 >= cgraph->n_nodes) {
return idr;
}
for (int32_t k = 0; k < n_used; k++) {
ggml_tensor * next = cgraph->nodes[id+1];
if (next->op != GGML_OP_VIEW || next->view_src != node || next->view_offs != k*node->nb[1] ||
next->ne[0] != node->ne[0] || next->ne[1] != node->ne[2] || next->nb[1] != node->nb[2] ||
ggml_node_get_use_count(cgraph, id+1) != 1) {
return idr;
}
id++;
}
{
ggml_tensor * next = cgraph->nodes[id+1];
if (next->op != GGML_OP_ADD || next->src[0] != cgraph->nodes[id - (n_used-1)] ||
next->src[1] != cgraph->nodes[id - (n_used-2)] || ggml_node_get_use_count(cgraph, id+1) != 1) {
return idr;
}
id++;
}
for (int32_t k = 0; k < n_used - 2; k++) {
ggml_tensor * next = cgraph->nodes[id+1];
if (next->op != GGML_OP_ADD || next->src[0] != cgraph->nodes[id] ||
next->src[1] != cgraph->nodes[id - (n_used-2)] || ggml_node_get_use_count(cgraph, id+1) != 1) {
return idr;
}
id++;
}
idr = id;
return idr;
};
int i_start = 0;
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
if (node->view_src != nullptr && node->view_src->op == GGML_OP_NONE && ggml_backend_buffer_is_host(node->view_src->buffer)) {
continue;
}
const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(node, /*assume_sync =*/ false);
if (split_state.axis == GGML_BACKEND_SPLIT_AXIS_PARTIAL) {
max_tmp_size = std::max(max_tmp_size, ggml_nbytes(node));
}
const bool new_subgraph = i + 1 == cgraph->n_nodes || split_state.axis == GGML_BACKEND_SPLIT_AXIS_PARTIAL;
if (!new_subgraph) {
continue;
}
const int i_delayed = get_i_delayed(i);
// If we can delay the AllReduce we need to consider the interaction with zero-sized tensor slices.
// A backend with such a slice would normally have valid data after participating in the AllReduce with a node that has
// its compute flag disabled and thus gets its data zeroed out.
// If the AllReduce is delayed then the nodes until that point also need to have their compute flag disabled.
if (i_delayed > i) {
for (size_t j = 0; j < n_backends; j++) {
auto & bcj = backend_ctx->backend_configs[j];
if ((bcj.nodes[i]->flags & GGML_TENSOR_FLAG_COMPUTE) == 0) {
for (int ii = i + 1; ii <= i_delayed; ii++) {
bcj.nodes[ii]->flags &= ~GGML_TENSOR_FLAG_COMPUTE;
}
}
}
}
i = i_delayed;
for (size_t j = 0; j < n_backends; j++) {
auto & bcj = backend_ctx->backend_configs[j];
bcj.cgraphs[n_subgraphs].offset = i_start;
}
n_subgraphs++;
i_start = i + 1;
}
GGML_ASSERT(i_start == cgraph->n_nodes);
}
backend_ctx->uid = cgraph->uid;
backend_ctx->n_subgraphs = n_subgraphs;
if (max_tmp_size > backend_ctx->max_tmp_size) {
for (size_t j = 0; j < n_backends; j++) {
auto & bcj = backend_ctx->backend_configs[j];
for (size_t i = 0; i < backend_ctx->n_reduce_steps; i++) {
bcj.bufs[i].reset(ggml_backend_alloc_buffer(bcj.backend, max_tmp_size));
}
}
backend_ctx->max_tmp_size = max_tmp_size;
}
if (max_nnodes_raised || n_subgraphs > backend_ctx->max_subgraphs) {
backend_ctx->max_subgraphs = std::max(backend_ctx->max_subgraphs, n_subgraphs);
const size_t n_nodes_per_device = 3 * backend_ctx->n_reduce_steps; // tmp + ADD (+zeroing) graph per step and device
const size_t n_cgraphs_per_device = 2 * backend_ctx->n_reduce_steps; // ADD ( + zeroing) graph per step and device
const size_t mem_per_device_graphs_main = backend_ctx->max_subgraphs*ggml_graph_overhead_custom(backend_ctx->max_nnodes, cgraph->grads);
const size_t mem_per_device_graphs_aux = n_cgraphs_per_device*backend_ctx->max_subgraphs*ggml_graph_overhead_custom(1, cgraph->grads);
const size_t mem_per_device_nodes_aux = n_nodes_per_device*backend_ctx->max_subgraphs*ggml_tensor_overhead();
ggml_init_params params = {
/*.mem_size =*/ n_backends * (mem_per_device_graphs_main + mem_per_device_graphs_aux + mem_per_device_nodes_aux),
/*.mem_buffer =*/ nullptr,
/*.no_alloc =*/ true,
};
backend_ctx->ctx.reset(ggml_init(params));
for (size_t j = 0; j < n_backends; j++) {
auto & bcj = backend_ctx->backend_configs[j];
for (size_t i = 0; i < n_subgraphs; i++) {
bcj.cgraphs[i].cgraph_main = ggml_new_graph_custom(backend_ctx->ctx.get(), cgraph->n_nodes, /*grads =*/ false);
}
}
backend_ctx->cgraphs_aux.resize(n_backends*n_cgraphs_per_device*backend_ctx->max_subgraphs);
for (size_t k = 0; k < backend_ctx->cgraphs_aux.size(); k++) {
backend_ctx->cgraphs_aux[k] = ggml_new_graph_custom(backend_ctx->ctx.get(), 1, cgraph->grads);
}
backend_ctx->nodes_aux.resize(n_backends*n_nodes_per_device*backend_ctx->max_subgraphs);
for (size_t k = 0; k < backend_ctx->nodes_aux.size(); k++) {
backend_ctx->nodes_aux[k] = ggml_new_tensor_1d(backend_ctx->ctx.get(), GGML_TYPE_F32, 1);
}
}
for (size_t j = 0; j < n_backends; j++) {
auto & bcj = backend_ctx->backend_configs[j];
for (size_t i_graph = 0; i_graph < n_subgraphs; i_graph++) {
ggml_cgraph * cgraph_ij = bcj.cgraphs[i_graph].cgraph_main;
const size_t i_node_start = bcj.cgraphs[i_graph].offset;
const size_t i_node_stop = i_graph + 1 < n_subgraphs ? bcj.cgraphs[i_graph + 1].offset : cgraph->n_nodes;
cgraph_ij->n_nodes = i_node_stop - i_node_start;
ggml_hash_set_reset(&cgraph_ij->visited_hash_set);
for (size_t i_node = i_node_start; i_node < i_node_stop; i_node++) {
ggml_tensor * node_ij = bcj.nodes[i_node];
cgraph_ij->nodes[i_node - i_node_start] = node_ij;
const size_t hash_pos_orig = ggml_hash_find(&cgraph->visited_hash_set, cgraph->nodes[i_node]);
const size_t hash_pos_ij = ggml_hash_insert(&cgraph_ij->visited_hash_set, node_ij);
cgraph_ij->use_counts[hash_pos_ij] = cgraph->use_counts[hash_pos_orig];
}
cgraph_ij->uid = ggml_graph_next_uid();
}
}
}
size_t iga = 0; // i graph aux
size_t ina = 0; // i node aux
auto get_node_aux = [&](ggml_tensor * t) -> ggml_tensor * {
ggml_tensor * ret = backend_ctx->nodes_aux[ina++];
memset(ret, 0, sizeof(ggml_tensor));
ret->op = GGML_OP_NONE;
ret->type = t->type;
for (size_t k = 0; k < GGML_MAX_DIMS; k++) {
ret->ne[k] = t->ne[k];
ret->nb[k] = t->nb[k];
}
return ret;
};
auto set_tmp_data = [&](ggml_tensor * tensor, const size_t j, const size_t i_buf) {
auto & bcj = backend_ctx->backend_configs[j];
ggml_backend_buffer_ptr & buf_ptr = bcj.bufs[i_buf];
if (!buf_ptr || ggml_backend_buffer_get_size(buf_ptr.get()) < backend_ctx->max_tmp_size) {
buf_ptr.reset(ggml_backend_alloc_buffer(bcj.backend, backend_ctx->max_tmp_size));
}
tensor->buffer = buf_ptr.get();
tensor->data = ggml_backend_buffer_get_base(buf_ptr.get());
};
// FIXME usage_counts
auto get_cgraph_aux = [&]() -> ggml_cgraph * {
ggml_cgraph * ret = backend_ctx->cgraphs_aux[iga++];
return ret;
};
// Preferentially use backend-specific allreduce_tensor_async (e.g. NCCL for CUDA), use a generic fallback if unavailable:
auto allreduce_fallback = [&](size_t i) -> ggml_status {
std::vector<ggml_cgraph *> step_cgraphs(n_backends, nullptr);
// Zero out nodes that were disabled due to having a zero-sized slice:
for (size_t j = 0; j < n_backends; j++) {
auto & bcj = backend_ctx->backend_configs[j];
ggml_tensor * node = bcj.cgraphs[i].cgraph_main->nodes[bcj.cgraphs[i].cgraph_main->n_nodes - 1];
if (node->flags & GGML_TENSOR_FLAG_COMPUTE) {
continue;
}
ggml_tensor * node_zero = get_node_aux(node);
node_zero->op = GGML_OP_SCALE; // FIXME 0.0f * NaN == NaN
node_zero->src[0] = node;
ggml_set_op_params_f32(node_zero, 0, 0.0f);
node_zero->data = node->data;
node_zero->flags |= GGML_TENSOR_FLAG_COMPUTE;
step_cgraphs[j] = get_cgraph_aux();
step_cgraphs[j]->nodes[0] = node_zero;
step_cgraphs[j]->n_nodes = 1;
const ggml_status status = ggml_backend_graph_compute_async(bcj.backend, step_cgraphs[j]);
if (status != GGML_STATUS_SUCCESS) {
return status;
}
}
std::fill(step_cgraphs.begin(), step_cgraphs.end(), nullptr);
auto push_data = [&](const size_t j_src, const size_t j_dst, const size_t i_buf) {
assert(step_cgraphs[j_dst] == nullptr);
auto & bcj_src = backend_ctx->backend_configs[j_src];
auto & bcj_dst = backend_ctx->backend_configs[j_dst];
ggml_tensor * node_src = bcj_src.cgraphs[i].cgraph_main->nodes[bcj_src.cgraphs[i].cgraph_main->n_nodes - 1];
ggml_tensor * node_dst = bcj_dst.cgraphs[i].cgraph_main->nodes[bcj_dst.cgraphs[i].cgraph_main->n_nodes - 1];
GGML_ASSERT(ggml_is_contiguous(node_src));
GGML_ASSERT(ggml_is_contiguous(node_dst));
ggml_tensor * node_tmp = get_node_aux(node_dst);
set_tmp_data(node_tmp, j_dst, i_buf);
ggml_backend_tensor_copy_async(bcj_src.backend, bcj_dst.backend, node_src, node_tmp);
ggml_tensor * node_red = get_node_aux(node_dst);
node_red->view_src = node_dst->view_src == nullptr ? node_dst : node_dst->view_src;
node_red->view_offs = node_dst->view_offs;
node_red->op = GGML_OP_ADD;
node_red->src[0] = node_dst;
node_red->src[1] = node_tmp;
node_red->flags |= GGML_TENSOR_FLAG_COMPUTE;
ggml_backend_view_init(node_red);
ggml_cgraph * cgraph_aux = get_cgraph_aux();
cgraph_aux->nodes[0] = node_red;
cgraph_aux->n_nodes = 1;
step_cgraphs[j_dst] = cgraph_aux;
};
size_t offset_j = n_backends/2;
while ((offset_j & (offset_j - 1)) != 0) {
offset_j--;
}
const size_t offset_j_max = offset_j;
size_t i_buf = 0;
// If n_backends is not a power of 2, fold in the excess prior to butterfly reduction:
for (size_t j_src = 2*offset_j_max; j_src < n_backends; j_src++) {
const size_t j_dst = j_src - 2*offset_j_max;
push_data(j_src, j_dst, i_buf);
const ggml_status status = ggml_backend_graph_compute_async(backend_ctx->backend_configs[j_dst].backend, step_cgraphs[j_dst]);
if (status != GGML_STATUS_SUCCESS) {
return status;
}
i_buf = 1;
}
// Butterfly reduction:
for (; offset_j >= 1; offset_j /= 2) {
std::fill(step_cgraphs.begin(), step_cgraphs.end(), nullptr);
for (size_t j = 0; j < 2*offset_j_max; j++) {
const size_t j_other = j ^ offset_j;
if (j_other >= n_backends) {
continue;
}
push_data(j, j_other, i_buf);
}
for (size_t j = 0; j < 2*offset_j_max; j++) {
if (step_cgraphs[j] == nullptr) {
continue;
}
auto & bcj = backend_ctx->backend_configs[j];
const ggml_status status = ggml_backend_graph_compute_async(bcj.backend, step_cgraphs[j]);
if (status != GGML_STATUS_SUCCESS) {
return status;
}
}
i_buf++;
}
assert(i_buf == backend_ctx->n_reduce_steps);
// If n_backends is not a power of 2, copy back the reduced tensors to the excess:
for (size_t j = 2*offset_j_max; j < n_backends; j++) {
auto & bcj_src = backend_ctx->backend_configs[j - 2*offset_j_max];
auto & bcj_dst = backend_ctx->backend_configs[j];
ggml_tensor * node_src = bcj_src.cgraphs[i].cgraph_main->nodes[bcj_src.cgraphs[i].cgraph_main->n_nodes - 1];
ggml_tensor * node_dst = bcj_dst.cgraphs[i].cgraph_main->nodes[bcj_dst.cgraphs[i].cgraph_main->n_nodes - 1];
ggml_backend_tensor_copy_async(bcj_src.backend, bcj_dst.backend, node_src, node_dst);
}
return GGML_STATUS_SUCCESS;
};
for (size_t i = 0; i < backend_ctx->n_subgraphs; i++) {
for (size_t j = 0; j < n_backends; j++) {
auto & bcj = backend_ctx->backend_configs[j];
const ggml_status status = ggml_backend_graph_compute_async(bcj.backend, bcj.cgraphs[i].cgraph_main);
if (status != GGML_STATUS_SUCCESS) {
return status;
}
}
if (n_backends > 1 && i < backend_ctx->n_subgraphs - 1) {
bool backend_allreduce_success = false;
if (backend_ctx->comm_ctx) {
std::vector<ggml_tensor *> nodes;
nodes.reserve(n_backends);
for (size_t j = 0; j < n_backends; j++) {
auto & bcj = backend_ctx->backend_configs[j];
ggml_cgraph * cgraph_ij = bcj.cgraphs[i].cgraph_main;
nodes.push_back(cgraph_ij->nodes[cgraph_ij->n_nodes-1]);
}
backend_allreduce_success = backend_ctx->comm_allreduce(backend_ctx->comm_ctx, nodes.data());
}
if (!backend_allreduce_success) {
const ggml_status status = allreduce_fallback(i);
if (status != GGML_STATUS_SUCCESS) {
return status;
}
}
}
}
return GGML_STATUS_SUCCESS;
}
static const ggml_backend_i ggml_backend_meta_i = {
/* .get_name = */ ggml_backend_meta_get_name,
/* .free = */ ggml_backend_meta_free,
/* .set_tensor_async = */ ggml_backend_meta_set_tensor_async,
/* .get_tensor_async = */ ggml_backend_meta_get_tensor_async,
/* .set_tensor_2d_async = */ nullptr,
/* .get_tensor_2d_async = */ nullptr,
/* .cpy_tensor_async = */ nullptr,
/* .synchronize = */ ggml_backend_meta_synchronize,
/* .graph_plan_create = */ nullptr,
/* .graph_plan_free = */ nullptr,
/* .graph_plan_update = */ nullptr,
/* .graph_plan_compute = */ nullptr,
/* .graph_compute = */ ggml_backend_meta_graph_compute,
/* .event_record = */ nullptr,
/* .event_wait = */ nullptr,
/* .graph_optimize = */ nullptr,
};
bool ggml_backend_is_meta(ggml_backend_t backend) {
return backend != nullptr && backend->iface.get_name == ggml_backend_meta_i.get_name;
}
static ggml_backend_t ggml_backend_meta_device_init_backend(ggml_backend_dev_t dev, const char * params) {
ggml_backend_meta_context * backend_ctx = new ggml_backend_meta_context(dev, params);
ggml_backend_t backend = new struct ggml_backend;
backend->guid = ggml_backend_meta_guid();
backend->iface = ggml_backend_meta_i;
backend->device = dev;
backend->context = backend_ctx;
return backend;
}
size_t ggml_backend_meta_n_backends(ggml_backend_t meta_backend) {
GGML_ASSERT(ggml_backend_is_meta(meta_backend));
const ggml_backend_meta_context * backend_ctx = (const ggml_backend_meta_context *) meta_backend->context;
return backend_ctx->backend_configs.size();
}
ggml_backend_t ggml_backend_meta_simple_backend(ggml_backend_t meta_backend, size_t index) {
GGML_ASSERT(ggml_backend_is_meta(meta_backend));
const ggml_backend_meta_context * backend_ctx = (const ggml_backend_meta_context *) meta_backend->context;
return backend_ctx->backend_configs[index].backend;
}
