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TdAI / llama.cpp /ggml /src /ggml-backend-meta.cpp
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#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 <set>
#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
//
// Container to hold the tensor slices per simple ggml backend buffer.
struct ggml_backend_meta_simple_tensor_container {
 std::vector<ggml_context_ptr> ctxs;
 std::map<const ggml_tensor *, std::vector<ggml_tensor *>> simple_tensors;
ggml_backend_meta_simple_tensor_container(const ggml_init_params & params, const int n_simple) {
 ctxs.reserve(n_simple);
 for (int i = 0; i < n_simple; i++) {
 ctxs.emplace_back(ggml_init(params));
 }
 }
 ggml_backend_meta_simple_tensor_container() {}
};
struct ggml_backend_meta_buffer_context {
 // FIXME
 // Most tensors can simply be stored statically in their own buffer.
 // Externally created views however also need a mapping to simple tensors but they use the buffer of the view source.
 // If external views are simply using that buffer they will slowly deplete its memory.
 // Current solution: rotating set of 2 "compute" containers to hold external views, works correctly for llama.cpp.
 // Long-term: tie the lifetime of external views to the meta backend executing the graph instead,
 // currently not possible due to graph-external operations in the backend scheduler.
 ggml_backend_meta_simple_tensor_container stc_static;
 ggml_backend_meta_simple_tensor_container stc_compute[2];
 int stc_compute_index = 0;
 int stc_compute_index_next = 0;
 std::vector<ggml_backend_buffer_ptr> bufs;
// FIXME
 // The size of the split state cache is unbounded and can theoretically grow infinitely large.
 // However, it is also expensive to build and clearing it on every rebuild in ggml_backend_meta_graph_compute is too expensive.
 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;
int debug;
ggml_backend_meta_buffer_context(
 ggml_backend_meta_simple_tensor_container & stc_static,
 ggml_backend_meta_simple_tensor_container & stc_compute_0,
 ggml_backend_meta_simple_tensor_container & stc_compute_1,
 const std::vector<ggml_backend_buffer_t> & bufs)
 : stc_static(std::move(stc_static)), stc_compute{std::move(stc_compute_0), std::move(stc_compute_1)} {
 this->bufs.reserve(bufs.size());
 for (ggml_backend_buffer_t buf : bufs) {
 this->bufs.emplace_back(buf);
 }
 const char * GGML_META_DEBUG = getenv("GGML_META_DEBUG");
 debug = GGML_META_DEBUG ? atoi(GGML_META_DEBUG) : 0;
 }
ggml_backend_meta_simple_tensor_container & get_simple_tensor_container(const ggml_tensor * tensor) {
 if (stc_static.simple_tensors.find(tensor) != stc_static.simple_tensors.end()) {
 return stc_static;
 }
 return stc_compute[stc_compute_index];
 }
};
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;
 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->bufs.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->bufs.size());
 return buf_ctx->bufs[index].get();
}
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->bufs.size());
ggml_backend_meta_simple_tensor_container & stc = buf_ctx->get_simple_tensor_container(tensor);
 auto it = stc.simple_tensors.find(tensor);
 if (it == stc.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);
static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
 ggml_backend_meta_simple_tensor_container & stc, const struct ggml_tensor * tensor, bool assume_sync) {
 // FIXME Currently this function preserves/erases the information in n_segments and nr in an inconsistent way.
 // Since the operations in question are developed specifically for llama.cpp this currently does not manifest as a bug there.
 // However, in a broader ggml context with arbitrary ggml graphs this can lead to unexpected results.
 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] * a.nr[s];
 }
 int64_t sum_b = 0;
 for (size_t s = 0; s < b.n_segments; s++) {
 sum_b += b.ne[s*n_bufs + j] * b.nr[s];
 }
 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}, 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}, 1};
 break;
 }
 }
 if (ret.axis == GGML_BACKEND_SPLIT_AXIS_NONE) {
 ret = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, {1}, 1};
 }
 if (scalar_only && ret.axis >= 0 && ret.axis < GGML_MAX_DIMS) {
 ret = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, {1}, 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}, 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.nr[0] = 1;
 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) {
 return src_ss[1];
 }
 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}, 1};
 }
 GGML_ABORT("fatal error");
 //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, {1}, 1};
 };
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(src_ss[0].n_segments == 1);
 if (src_ss[0].axis == ggml_n_dims(tensor->src[0]) - 1 && src_ss[0].nr[0] == 1) {
 return {ggml_backend_meta_split_axis(ggml_n_dims(tensor) - 1), {0}, {1}, 1};
 }
 int64_t base_ne_in = tensor->src[0]->ne[0];
 for (int dim = 1; dim <= src_ss[0].axis; dim++) {
 base_ne_in *= tensor->src[0]->ne[dim];
 }
 base_ne_in /= src_ss[0].nr[0];
 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];
 if (base_ne_out_next % base_ne_in == 0) {
 return {ggml_backend_meta_split_axis(dim), {0}, {uint32_t(base_ne_out_next/base_ne_in)}, 1};
 }
 if (base_ne_out_next > base_ne_in) {
 GGML_ASSERT(src_ss[0].n_segments == 1);
 GGML_ASSERT(src_ss[0].nr[0] == 1);
 return {ggml_backend_meta_split_axis(dim), {0}, {1}, 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}, 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) {
 return handle_reshape(src_ss);
 }
 return handle_generic(src_ss, /*scalar_only =*/ false);
 };
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}, 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}, 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: {
 GGML_ASSERT(src_ss[0].n_segments == 1 || src_ss[0].nr[0] == 1);
 return {ggml_backend_meta_split_axis(tensor->op_params[src_ss[0].axis]), {0}, {src_ss[0].nr[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}, 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: {
 GGML_ASSERT(src_ss[0].n_segments == 1 || src_ss[0].nr[0] == 1);
 return {ggml_backend_meta_split_axis(int(src_ss[0].axis) ^ 1), {0}, {src_ss[0].nr[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}, 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}, 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}, 1};
 }
 if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_1) {
 return {GGML_BACKEND_SPLIT_AXIS_0, {0}, {1}, 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);
 // state shape is [S_v, S_v, H_v, n_seqs] (s0 only); the heads dim is its own axis 2,
 // so a head-aligned split on the input cache lands on axis 2 here.
 GGML_ASSERT(src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_2 || src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_1 || src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_0);
 return {GGML_BACKEND_SPLIT_AXIS_0, {0}, {1}, 1};
 };
auto calculate_split_state = [&]() -> ggml_backend_meta_split_state {
 if (ggml_nelements(tensor) == 0) {
 return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, {1}, 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 s = 0; s < ret.n_segments; s++) {
 for (size_t j = 0; j < n_bufs; j++) {
 GGML_ASSERT(ret.ne[s*n_bufs + j] % granularity == 0);
 ne_sum += ret.ne[s*n_bufs + j] * ret.nr[s];
 }
 }
 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}, 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}, 1};
 continue;
 }
 src_ss[i] = ggml_backend_meta_get_split_state(stc, 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}, 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}, 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] * src_ss[i].nr[s];
 }
 split_state.ne[j] *= tensor->ne[split_state.axis];
 if (split_state.ne[j] != 0 || tensor->src[i]->ne[src_ss[i].axis] != 0) {
 const int64_t div = tensor->src[i]->ne[src_ss[i].axis] * split_state.nr[0];
 GGML_ASSERT(split_state.ne[j] % div == 0);
 split_state.ne[j] /= div;
 }
 }
 } else {
 GGML_ASSERT(split_state.n_segments == 1);
 for (size_t j = 0; j < n_bufs; j++) {
 // Assert that ratio is consistent:
 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] * src_ss[i].nr[s];
 }
 GGML_ASSERT(split_state.ne[j]*split_state.nr[0] * 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);
 GGML_ASSERT(split_state.n_segments == 1);
 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]) + "x" + std::to_string(split_state.nr[0]);
 }
 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 += ", ";
 }
 const ggml_backend_meta_split_state & ss = buf_ctx->split_state_cache[key].first;
 ne_info += std::to_string(ss.ne[j]) + "x" + std::to_string(ss.nr[0]);
 }
 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 s = 0; s < ret.n_segments; s++) {
 for (size_t j = 0; j < n_bufs; j++) {
 ne_ret += ret.ne[s*n_bufs + j] * ret.nr[s];
 }
 }
 assert(ne_ret == tensor->ne[int(ret.axis)]);
 }
#endif // NDEBUG
 return ret;
}
static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(const struct ggml_tensor * tensor, bool assume_sync) {
 GGML_ASSERT(ggml_backend_buffer_is_meta(tensor->buffer));
 ggml_backend_meta_buffer_context * buf_ctx = (ggml_backend_meta_buffer_context *) tensor->buffer->context;
 return ggml_backend_meta_get_split_state(buf_ctx->get_simple_tensor_container(tensor), tensor, assume_sync);
}
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_impl(ggml_backend_meta_simple_tensor_container & stc, ggml_tensor * tensor) {
 GGML_ASSERT(ggml_backend_buffer_is_meta(tensor->buffer));
 ggml_backend_meta_buffer_context * buf_ctx = (ggml_backend_meta_buffer_context *) tensor->buffer->context;
 const size_t n_simple_bufs = ggml_backend_meta_buffer_n_bufs(tensor->buffer);
const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(stc, 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 = stc.ctxs[j].get();
 ggml_backend_buffer_t simple_buf = buf_ctx->bufs[j].get();
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] * split_state.nr[s];
 }
 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(tensor->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] * split_state_src.nr[s];
 }
 if (ne_sum == 0) {
 simple_tensors[j]->flags &= ~GGML_TENSOR_FLAG_COMPUTE;
 }
 }
 }
stc.simple_tensors[tensor] = simple_tensors;
return GGML_STATUS_SUCCESS;
}
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;
 buf_ctx->stc_compute_index = buf_ctx->stc_compute_index_next;
 return ggml_backend_meta_buffer_init_tensor_impl(buf_ctx->get_simple_tensor_container(tensor), tensor);
}
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 || split_state.nr[0] != 1) {
 GGML_ASSERT(split_state.axis >= 0 && split_state.axis < GGML_MAX_DIMS);
 GGML_ASSERT(split_state.nr[0] != 0);
 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 row_start = offset / row_stride;
 const int64_t row_count = size / row_stride;
 GGML_ASSERT(row_start + row_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 r = 0; r < split_state.nr[s]; r++) {
 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] + row_start * simple_tensor->nb[1], nbytes,
 row_count, simple_tensor->nb[1], tensor->nb[1]);
 offset_data += nbytes;
 simple_offsets[j] += nbytes;
 }
 }
 }
 GGML_ASSERT(offset_data*row_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 row_start = offset / row_stride;
 const int64_t row_count = size / row_stride;
 GGML_ASSERT(row_start + row_count <= tensor->ne[2]);
for (size_t s = 0; s < split_state.n_segments; s++) {
 for (size_t r = 0; r < split_state.nr[s]; r++) {
 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] + row_start * simple_tensor->nb[2], nbytes,
 row_count, simple_tensor->nb[2], tensor->nb[2]);
 offset_data += nbytes;
 simple_offsets[j] += nbytes;
 }
 }
 }
 GGML_ASSERT(offset_data*row_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];
 if (chunk_size_j == 0) {
 continue;
 }
 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 || split_state.nr[0] != 1) {
 GGML_ASSERT(split_state.axis >= 0 && split_state.axis < GGML_MAX_DIMS);
 GGML_ASSERT(split_state.nr[0] != 0);
 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 row_start = offset / row_stride;
 const int64_t row_count = size / row_stride;
 GGML_ASSERT(row_start + row_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 r = 0; r < split_state.nr[s]; r++) {
 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] + row_start * simple_tensor->nb[1], nbytes,
 row_count, simple_tensor->nb[1], tensor->nb[1]);
 offset_data += nbytes;
 simple_offsets[j] += nbytes;
 }
 }
 }
 GGML_ASSERT(offset_data*row_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 row_start = offset / row_stride;
 const int64_t row_count = size / row_stride;
 GGML_ASSERT(row_start + row_count <= tensor->ne[2]);
for (size_t s = 0; s < split_state.n_segments; s++) {
 for (size_t r = 0; r < split_state.nr[s]; r++) {
 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] + row_start * simple_tensor->nb[2], nbytes,
 row_count, simple_tensor->nb[2], tensor->nb[2]);
 offset_data += nbytes;
 simple_offsets[j] += nbytes;
 }
 }
 }
 GGML_ASSERT(offset_data*row_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];
 if (chunk_size_j == 0) {
 continue;
 }
 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) {
 GGML_ASSERT(ggml_backend_buffer_is_meta(buffer));
 ggml_backend_meta_buffer_context * buf_ctx = (ggml_backend_meta_buffer_context *) buffer->context;
 for (size_t i = 0; i < buf_ctx->bufs.size(); 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);
const ggml_init_params params = {
 /*.mem_size =*/ 1024*1024*ggml_tensor_overhead(), // FIXME
 /*.mem_buffer =*/ nullptr,
 /*.no_alloc =*/ true,
 };
 ggml_backend_meta_simple_tensor_container stc_static;
 ggml_backend_meta_simple_tensor_container stc_compute_0(params, n_simple_bufts);
 ggml_backend_meta_simple_tensor_container stc_compute_1(params, n_simple_bufts);
size_t max_size = 0;
 std::vector<ggml_backend_buffer_t> bufs;
 bufs.reserve(n_simple_bufts);
 for (size_t i = 0; i < n_simple_bufts; i++) {
 bufs.push_back(ggml_backend_buft_alloc_buffer(ggml_backend_meta_buft_simple_buft(buft, i), size));
 GGML_ASSERT(bufs.back() != nullptr);
 max_size = std::max(max_size, ggml_backend_buffer_get_size(bufs.back()));
 }
 ggml_backend_meta_buffer_context * buf_ctx = new ggml_backend_meta_buffer_context(stc_static, stc_compute_0, stc_compute_1, bufs);
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);
constexpr size_t compute_headroom = 16; // Maximum number of views per statically allocated tensor that can be created between evals.
 const ggml_init_params params_static = {
 /*.mem_size =*/ ggml_get_mem_size(ctx),
 /*.mem_buffer =*/ nullptr,
 /*.no_alloc =*/ true,
 };
 const ggml_init_params params_compute = {
 /*.mem_size =*/ compute_headroom*ggml_get_mem_size(ctx),
 /*.mem_buffer =*/ nullptr,
 /*.no_alloc =*/ true,
 };
 ggml_backend_meta_simple_tensor_container stc_static (params_static, n_simple_bufts);
 ggml_backend_meta_simple_tensor_container stc_compute_0(params_compute, n_simple_bufts);
 ggml_backend_meta_simple_tensor_container stc_compute_1(params_compute, n_simple_bufts);
std::vector<ggml_backend_buffer_t> bufs(n_simple_bufts, nullptr);
 ggml_backend_meta_buffer_context * meta_buf_ctx = new ggml_backend_meta_buffer_context(stc_static, stc_compute_0, stc_compute_1, bufs);
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_impl(meta_buf_ctx->stc_static, t);
 t->data = (void *) 0x2000000000000000; // FIXME
 }
 for (size_t i = 0; i < n_simple_bufts; i++) {
 ggml_context * ctx = meta_buf_ctx->stc_static.ctxs[i].get();
 ggml_backend_buffer_type_t simple_buft = ggml_backend_meta_buft_simple_buft(buft, i);
// If a ggml_context only has zero-sized tensors, ggml_backend_alloc_ctx_tensors_from_buft returns NULL.
 // For those edge cases, allocate a dummy buffer instead.
 bool any_nonzero_slice = false;
 for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
 if (ggml_nelements(t) != 0) {
 any_nonzero_slice = true;
 break;
 }
 }
 if (any_nonzero_slice) {
 meta_buf_ctx->bufs[i].reset(ggml_backend_alloc_ctx_tensors_from_buft(ctx, simple_buft));
 } else {
 meta_buf_ctx->bufs[i].reset(ggml_backend_buft_alloc_buffer(simple_buft, 0));
 for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
 t->buffer = meta_buf_ctx->bufs[i].get();
 }
 }
 GGML_ASSERT(meta_buf_ctx->bufs[i]);
 meta_buf->size = std::max(meta_buf->size, ggml_backend_buffer_get_size(meta_buf_ctx->bufs[i].get()));
 }
 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);
 GGML_ASSERT(split_state.nr[0] == 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];
 if (chunk_size_j == 0) {
 continue;
 }
 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);
 GGML_ASSERT(split_state.nr[0] == 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];
 if (chunk_size_j == 0) {
 continue;
 }
 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) {
 std::set<ggml_backend_buffer_t> used_buffers;
 for (int i = 0; i < cgraph->n_leafs; i++) {
 if (ggml_backend_buffer_is_meta(cgraph->leafs[i]->buffer)) {
 used_buffers.emplace(cgraph->leafs[i]->buffer);
 }
 }
 for (int i = 0; i < cgraph->n_nodes; i++) {
 if (ggml_backend_buffer_is_meta(cgraph->nodes[i]->buffer)) {
 used_buffers.emplace(cgraph->nodes[i]->buffer);
 }
 }
 for (ggml_backend_buffer_t buf : used_buffers) {
 ggml_backend_meta_buffer_context * buf_ctx = (ggml_backend_meta_buffer_context *) buf->context;
 buf_ctx->stc_compute_index_next = buf_ctx->stc_compute_index ^ 1;
 ggml_backend_meta_simple_tensor_container & stc = buf_ctx->stc_compute[buf_ctx->stc_compute_index_next];
 for (ggml_context_ptr & ctx : stc.ctxs) {
 ggml_reset(ctx.get());
 }
 stc.simple_tensors.clear();
 }
 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();
 const 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->buffer = node->buffer;
 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;
}