#include "llama-memory-recurrent.h"
#include "ggml-backend.h"
#include "llama-impl.h"
#include "llama-io.h"
#include "llama-batch.h"
#include "llama-model.h"
#include <algorithm>
#include <cassert>
#include <cstring>
#include <limits>
#include <map>
#include <stdexcept>
//
// llama_memory_recurrent
//
llama_memory_recurrent::llama_memory_recurrent(
const llama_model & model,
ggml_type type_r,
ggml_type type_s,
bool offload,
uint32_t mem_size,
uint32_t n_seq_max,
uint32_t n_rs_seq,
const layer_filter_cb & filter) : hparams(model.hparams), n_seq_max(n_seq_max) {
const int32_t n_layer = hparams.n_layer();
head = 0;
size = mem_size;
used = 0;
this->n_rs_seq = n_rs_seq;
rs_idx.assign(n_seq_max, 0);
cells.clear();
cells.resize(mem_size);
// define a comparator for the buft -> ctx map to ensure that the order is well-defined:
struct ggml_backend_buft_comparator {
bool operator()(const ggml_backend_buffer_type_t & lhs, const ggml_backend_buffer_type_t & rhs) const {
return strcmp(ggml_backend_buft_name(lhs), ggml_backend_buft_name(rhs)) < 0;
}
};
std::map<ggml_backend_buffer_type_t, ggml_context_ptr, ggml_backend_buft_comparator> ctx_map;
// create a context for each buffer type
auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
auto it = ctx_map.find(buft);
if (it == ctx_map.end()) {
ggml_init_params params = {
/*.mem_size =*/ size_t(2u*n_layer*ggml_tensor_overhead()),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
ggml_context * ctx = ggml_init(params);
if (!ctx) {
return nullptr;
}
ctx_map.emplace(buft, ctx);
return ctx;
}
return it->second.get();
};
r_l.resize(n_layer);
s_l.resize(n_layer);
for (int i = 0; i < n_layer; i++) {
if (filter && !filter(i)) {
LLAMA_LOG_DEBUG("%s: layer %3d: skipped\n", __func__, i);
continue;
}
const char * dev_name = "CPU";
ggml_backend_buffer_type_t buft = ggml_backend_cpu_buffer_type();
if (offload) {
auto * dev = model.dev_layer(i);
buft = ggml_backend_dev_buffer_type(dev);
dev_name = ggml_backend_dev_name(dev);
}
LLAMA_LOG_DEBUG("%s, layer %3d: dev = %s\n", __func__, i, dev_name);
ggml_context * ctx = ctx_for_buft(buft);
if (!ctx) {
throw std::runtime_error("failed to create ggml context for rs cache");
}
const uint32_t n_rows = mem_size * (1 + n_rs_seq);
ggml_tensor * r = ggml_new_tensor_2d(ctx, type_r, hparams.n_embd_r(), n_rows);
ggml_tensor * s = ggml_new_tensor_2d(ctx, type_s, hparams.n_embd_s(), n_rows);
ggml_format_name(r, "cache_r_l%d", i);
ggml_format_name(s, "cache_s_l%d", i);
r_l[i] = r;
s_l[i] = s;
}
// allocate tensors and initialize the buffers to avoid NaNs in the padding
for (auto & [buft, ctx] : ctx_map) {
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx.get(), buft);
if (!buf) {
throw std::runtime_error("failed to allocate buffer for rs cache");
}
ggml_backend_buffer_clear(buf, 0);
LLAMA_LOG_INFO("%s: %10s RS buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0);
ctxs_bufs.emplace_back(std::move(ctx), buf);
}
{
const size_t memory_size_r = size_r_bytes();
const size_t memory_size_s = size_s_bytes();
LLAMA_LOG_INFO("%s: size = %7.2f MiB (%6u cells, %3d layers, %2u seqs %2u rs_seq), R (%s): %7.2f MiB, S (%s): %7.2f MiB\n", __func__,
(float)(memory_size_r + memory_size_s) / (1024.0f * 1024.0f), mem_size, n_layer, n_seq_max, n_rs_seq,
ggml_type_name(type_r), (float)memory_size_r / (1024.0f * 1024.0f),
ggml_type_name(type_s), (float)memory_size_s / (1024.0f * 1024.0f));
}
}
void llama_memory_recurrent::clear(bool data) {
for (int32_t i = 0; i < (int32_t) size; ++i) {
cells[i].pos = -1;
cells[i].seq_id.clear();
cells[i].src = -1;
cells[i].tail = -1;
}
head = 0;
used = 0;
if (data) {
for (auto & [_, buf] : ctxs_bufs) {
ggml_backend_buffer_clear(buf.get(), 0);
}
}
std::fill(rs_idx.begin(), rs_idx.end(), 0);
}
bool llama_memory_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
uint32_t new_head = size;
if (p0 < 0) {
p0 = 0;
}
if (p1 < 0) {
p1 = std::numeric_limits<llama_pos>::max();
}
if ((uint32_t) seq_id >= this->n_seq_max) {
LLAMA_LOG_ERROR("%s: invalid seq_id (%d) - larger than n_seq_max (%d)\n", __func__, seq_id, this->n_seq_max);
return false;
}
const bool rm_all = p0 == 0 && p1 == std::numeric_limits<llama_pos>::max();
if (rm_all) {
set_rs_idx(seq_id, 0);
}
// models like Mamba or RWKV can't have a state partially erased at the end
// of the sequence because their state isn't preserved for previous tokens
if (seq_id >= (int64_t) size) {
// could be fatal
return false;
}
if (0 <= seq_id) {
int32_t & tail_id = cells[seq_id].tail;
if (tail_id >= 0) {
auto & cell = cells[tail_id];
// partial rollback via per-token snapshot index (bounded by n_rs_seq)
if (0 < p0 && p0 <= cell.pos && p1 > cell.pos) {
const llama_pos rollback = cell.pos - (p0 - 1);
// pending rollback is single-use
const bool pending = rs_idx[seq_id] != 0;
if (!pending && rollback >= 1 && rollback <= (llama_pos) n_rs_seq) {
set_rs_idx(seq_id, (uint32_t) rollback);
cell.pos = p0 - 1;
return true;
}
return false;
}
// invalidate tails which will be cleared
if (p0 <= cell.pos && cell.pos < p1) {
tail_id = -1;
}
}
} else {
// seq_id is negative, then the range should include everything or nothing
if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits<llama_pos>::max())) {
//printf("[DEBUG] inside `llama_memory_recurrent::seq_rm`: `seq_id` is negative, so returning false\n");
return false;
}
}
for (uint32_t i = 0; i < size; ++i) {
if (cells[i].pos >= p0 && cells[i].pos < p1) {
if (seq_id < 0) {
cells[i].seq_id.clear();
} else if (cells[i].has_seq_id(seq_id)) {
cells[i].seq_id.erase(seq_id);
} else {
continue;
}
if (cells[i].is_empty()) {
// keep count of the number of used cells
if (cells[i].pos >= 0) {
used--;
}
cells[i].pos = -1;
cells[i].src = -1;
if (new_head == size) {
new_head = i;
}
}
}
}
// If we freed up a slot, set head to it so searching can start there.
if (new_head != size && new_head < head) {
head = new_head;
}
return true;
}
void llama_memory_recurrent::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
if (seq_id_src == seq_id_dst) {
return;
}
if (p0 < 0) {
p0 = 0;
}
if (p1 < 0) {
p1 = std::numeric_limits<llama_pos>::max();
}
if ((uint32_t) seq_id_dst < size && (uint32_t) seq_id_src < size) {
auto & tail_src = cells[seq_id_src];
auto & tail_dst = cells[seq_id_dst];
if (tail_dst.tail >= 0) {
// clear destination seq_id if it wasn't empty
auto & cell_dst = cells[tail_dst.tail];
cell_dst.seq_id.erase(seq_id_dst);
tail_dst.tail = -1;
if (cell_dst.seq_id.empty()) {
cell_dst.pos = -1;
cell_dst.src = -1;
used -= 1;
}
}
if (tail_src.tail >= 0) {
auto & cell_src = cells[tail_src.tail];
cell_src.seq_id.insert(seq_id_dst);
tail_dst.tail = tail_src.tail;
}
}
}
void llama_memory_recurrent::seq_keep(llama_seq_id seq_id) {
uint32_t new_head = size;
for (uint32_t i = 0; i < size; ++i) {
if ((llama_seq_id) i != seq_id) {
cells[i].tail = -1;
}
if (!cells[i].has_seq_id(seq_id)) {
if (cells[i].pos >= 0) {
used--;
}
cells[i].pos = -1;
cells[i].src = -1;
cells[i].seq_id.clear();
if (new_head == size){
new_head = i;
}
} else {
cells[i].seq_id.clear();
cells[i].seq_id.insert(seq_id);
}
}
// If we freed up a slot, set head to it so searching can start there.
if (new_head != size && new_head < head) {
head = new_head;
}
}
void llama_memory_recurrent::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
if (shift == 0) {
return;
}
if (p0 < 0) {
p0 = 0;
}
if (p1 < 0) {
p1 = std::numeric_limits<llama_pos>::max();
}
// If there is no range then return early to avoid looping over the
if (p0 == p1) {
return;
}
// for Mamba-like or RWKV models, only the pos needs to be shifted
if (0 <= seq_id && seq_id < (int64_t) size) {
const int32_t tail_id = cells[seq_id].tail;
if (tail_id >= 0) {
auto & cell = cells[tail_id];
if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
cell.pos += shift;
}
}
}
}
void llama_memory_recurrent::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
if (d == 1) {
return;
}
if (p0 < 0) {
p0 = 0;
}
if (p1 < 0) {
p1 = std::numeric_limits<llama_pos>::max();
}
// If there is no range then return early to avoid looping over the cache.
if (p0 == p1) {
return;
}
// for Mamba-like or RWKV models, only the pos needs to be changed
if (0 <= seq_id && seq_id < (int64_t) size) {
const int32_t tail_id = cells[seq_id].tail;
if (tail_id >= 0) {
auto & cell = cells[tail_id];
if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
cell.pos /= d;
}
}
}
}
llama_pos llama_memory_recurrent::seq_pos_min(llama_seq_id seq_id) const {
llama_pos result = std::numeric_limits<llama_pos>::max();
for (uint32_t i = 0; i < size; ++i) {
if (cells[i].has_seq_id(seq_id)) {
result = std::min(result, cells[i].pos);
}
}
if (result == std::numeric_limits<llama_pos>::max()) {
result = -1;
}
return result;
}
llama_pos llama_memory_recurrent::seq_pos_max(llama_seq_id seq_id) const {
llama_pos result = -1;
for (uint32_t i = 0; i < size; ++i) {
if (cells[i].has_seq_id(seq_id)) {
result = std::max(result, cells[i].pos);
}
}
return result;
}
void llama_memory_recurrent::set_rs_idx(llama_seq_id seq_id, uint32_t idx) {
if (seq_id < 0) {
std::fill(rs_idx.begin(), rs_idx.end(), 0);
return;
}
assert(n_seq_max == rs_idx.size());
GGML_ASSERT((uint32_t) seq_id < n_seq_max);
GGML_ASSERT(idx <= n_rs_seq);
rs_idx[seq_id] = idx;
}
std::map<ggml_backend_buffer_type_t, size_t> llama_memory_recurrent::memory_breakdown() const {
std::map<ggml_backend_buffer_type_t, size_t> ret;
for (const auto & [_, buf] : ctxs_bufs) {
ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get());
}
return ret;
}
llama_memory_context_ptr llama_memory_recurrent::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
do {
balloc.split_reset();
std::vector<llama_ubatch> ubatches;
while (true) {
llama_ubatch ubatch;
if (embd_all) {
// if all tokens are output, split by sequence
ubatch = balloc.split_seq(n_ubatch);
} else {
// TODO: non-sequential equal split can be done if using unified KV cache
// for simplicity, we always use sequential equal split for now
// [TAG_RECURRENT_ROLLBACK_SPLITS]
// the trailing (1 + n_rs_seq) tokens of each seq must stay in the same ubatch
// so that the rollback snapshots remain valid
ubatch = balloc.split_equal(n_ubatch, true, n_rs_seq > 0 ? n_rs_seq + 1 : 0);
}
if (ubatch.n_tokens == 0) {
break;
}
ubatches.push_back(std::move(ubatch)); // NOLINT
}
if (balloc.get_n_used() < balloc.get_n_tokens()) {
// failed to find a suitable split
break;
}
if (!prepare(ubatches)) {
break;
}
return std::make_unique<llama_memory_recurrent_context>(this, std::move(ubatches));
} while (false);
return std::make_unique<llama_memory_recurrent_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
llama_memory_context_ptr llama_memory_recurrent::init_full() {
return std::make_unique<llama_memory_recurrent_context>(this);
}
llama_memory_context_ptr llama_memory_recurrent::init_update(llama_context * lctx, bool optimize) {
GGML_UNUSED(lctx);
GGML_UNUSED(optimize);
return std::make_unique<llama_memory_recurrent_context>(LLAMA_MEMORY_STATUS_NO_UPDATE);
}
bool llama_memory_recurrent::prepare(const std::vector<llama_ubatch> & ubatches) {
// simply remember the full state because it is very small for this type of cache
// TODO: optimize
auto org_cells = cells;
auto org_used = used;
auto org_head = head;
bool success = true;
for (const auto & ubatch : ubatches) {
if (!find_slot(ubatch)) {
success = false;
break;
}
}
// restore the original state
cells = std::move(org_cells);
used = org_used;
head = org_head;
return success;
}
bool llama_memory_recurrent::find_slot(const llama_ubatch & ubatch) {
const uint32_t n_seq_tokens = ubatch.n_seq_tokens;
const uint32_t n_seqs = ubatch.n_seqs;
// if we have enough unused cells before the current head ->
// better to start searching from the beginning of the cache, hoping to fill it
if (head > used + 2*n_seqs) {
head = 0;
}
// For recurrent state architectures (like Mamba or RWKV),
// each cache cell can store the state for a whole sequence.
// A slot should be always be contiguous.
// can only process batches with an equal number of new tokens in each sequence
GGML_ASSERT(ubatch.equal_seqs());
int32_t min = size - 1;
int32_t max = 0;
// everything should fit if all seq_ids are smaller than the max
for (uint32_t s = 0; s < n_seqs; ++s) {
const uint32_t i = s*n_seq_tokens; // first token of sequence set s
const uint32_t n_seq_id = ubatch.n_seq_id[i];
for (uint32_t j = 0; j < n_seq_id; ++j) {
const llama_seq_id seq_id = ubatch.seq_id[i][j];
if (seq_id < 0 || (uint32_t) seq_id >= size) {
// too big seq_id
// TODO: would it be possible to resize the cache instead?
LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%u Try using a bigger --parallel value\n", __func__, seq_id, n_seq_max);
return false;
}
if (j > 0) {
auto & seq = cells[seq_id];
if (seq.tail >= 0) {
auto & cell = cells[seq.tail];
// clear cells from seq_ids that become shared
// (should not normally happen, but let's handle it anyway)
cell.seq_id.erase(seq_id);
seq.tail = -1;
if (cell.seq_id.empty()) {
cell.pos = -1;
cell.src = -1;
used -= 1;
}
}
}
}
}
#ifndef NDEBUG
{
std::vector<int32_t> tails_verif;
tails_verif.assign(size, -1);
for (uint32_t i = 0; i < size; ++i) {
auto & cell = cells[i];
for (llama_seq_id seq_id : cell.seq_id) {
if (tails_verif[seq_id] != -1) {
LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tails_verif[seq_id]);
}
tails_verif[seq_id] = i;
}
}
for (uint32_t i = 0; i < size; ++i) {
if (tails_verif[i] != cells[i].tail) {
LLAMA_LOG_ERROR("%s: wrong tail for seq_id %d, (%d instead of %d)\n", __func__, i, cells[i].tail, tails_verif[i]);
}
}
}
#endif
// find next empty cell
uint32_t next_empty_cell = head;
for (uint32_t i = 0; i < size; ++i) {
if (next_empty_cell >= size) { next_empty_cell -= size; }
auto & cell = cells[next_empty_cell];
if (cell.is_empty()) { break; }
next_empty_cell += 1;
}
// find usable cell range
for (uint32_t s = 0; s < n_seqs; ++s) {
const uint32_t i = s*n_seq_tokens;
const llama_seq_id seq_id = ubatch.seq_id[i][0];
auto & seq_meta = cells[seq_id];
bool has_cell = false;
if (seq_meta.tail >= 0) {
auto & cell = cells[seq_meta.tail];
GGML_ASSERT(cell.has_seq_id(seq_id));
// does this seq_id "own" the cell?
if (cell.seq_id.size() == 1) { has_cell = true; }
}
if (!has_cell) {
auto & empty_cell = cells[next_empty_cell];
GGML_ASSERT(empty_cell.is_empty());
// copy old tail into the empty cell
if (seq_meta.tail >= 0) {
auto & orig_cell = cells[seq_meta.tail];
empty_cell.pos = orig_cell.pos;
empty_cell.src = orig_cell.src;
orig_cell.seq_id.erase(seq_id);
empty_cell.seq_id.insert(seq_id); // will be overwritten
GGML_ASSERT(!orig_cell.is_empty()); // has at least one remaining seq_id
}
seq_meta.tail = next_empty_cell;
// find next empty cell
if (s + 1 < n_seqs) {
for (uint32_t j = 0; j < size; ++j) {
next_empty_cell += 1;
if (next_empty_cell >= size) { next_empty_cell -= size; }
auto & cell = cells[next_empty_cell];
if (cell.is_empty()) { break; }
}
}
}
if (min > seq_meta.tail) { min = seq_meta.tail; }
if (max < seq_meta.tail) { max = seq_meta.tail; }
}
// gather and re-order
for (uint32_t s = 0; s < n_seqs; ++s) {
const uint32_t i = s*n_seq_tokens;
const int32_t dst_id = s + min;
const int32_t src_id = cells[ubatch.seq_id[i][0]].tail;
if (dst_id != src_id) {
auto & dst_cell = cells[dst_id];
auto & src_cell = cells[src_id];
std::swap(dst_cell.pos, src_cell.pos);
std::swap(dst_cell.src, src_cell.src);
std::swap(dst_cell.seq_id, src_cell.seq_id);
// swap tails
for (uint32_t j = 0; j < size; ++j) {
int32_t & tail = cells[j].tail;
if (tail == src_id) {
tail = dst_id;
} else if (tail == dst_id) {
tail = src_id;
}
}
}
}
// update the pos of the used seqs
for (uint32_t s = 0; s < n_seqs; ++s) {
const uint32_t i = s*n_seq_tokens;
const llama_pos last_pos = ubatch.pos[i + n_seq_tokens - 1];
const int32_t cell_id = s + min;
auto & cell = cells[cell_id];
if (cell.pos >= 0 && last_pos != cell.pos + (llama_pos) n_seq_tokens) {
// What should happen when the pos backtracks or skips a value?
// Clearing the state mid-batch would require special-casing which isn't done.
LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d with %u new tokens\n",
__func__, last_pos, cell.pos, ubatch.seq_id[i][0], n_seq_tokens);
}
cell.pos = last_pos;
cell.seq_id.clear();
for (int32_t j = 0; j < ubatch.n_seq_id[i]; ++j) {
const llama_seq_id seq_id = ubatch.seq_id[i][j];
cell.seq_id.insert(seq_id);
cells[seq_id].tail = cell_id;
}
}
// Find first cell without src refs, to use as the zero-ed state
{
// TODO: bake-in src refcounts in the cell metadata
std::vector<int32_t> refcounts(size, 0);
for (size_t i = 0; i < size; ++i) {
const int32_t src = cells[i].src;
if (src >= 0) {
refcounts[src] += 1;
}
}
rs_z = -1;
for (int i = min; i <= max; ++i) {
if (refcounts[i] == 0) {
rs_z = i;
break;
}
}
for (int i = min; i <= max; ++i) {
if (cells[i].src < 0) {
GGML_ASSERT(rs_z >= 0);
cells[i].src0 = rs_z;
} else {
// Stage the source ids for all used cells to allow correct seq_* behavior
// and still make these values available when setting the inputs
cells[i].src0 = cells[i].src;
}
cells[i].src = i; // avoid moving or clearing twice
}
}
// allow getting the range of used cells, from head to head + n
head = min;
n = max - min + 1;
used = std::count_if(cells.begin(), cells.end(),
[](const mem_cell & cell){ return !cell.is_empty(); });
// sanity check
return n >= n_seqs;
}
bool llama_memory_recurrent::get_can_shift() const {
// shifting the pos is trivial for recurrent models
return true;
}
size_t llama_memory_recurrent::total_size() const {
size_t size = 0;
for (const auto & [_, buf] : ctxs_bufs) {
size += ggml_backend_buffer_get_size(buf.get());
}
return size;
}
size_t llama_memory_recurrent::size_r_bytes() const {
size_t size_r_bytes = 0;
for (const auto & r : r_l) {
if (r != nullptr) {
size_r_bytes += ggml_nbytes(r);
}
}
return size_r_bytes;
}
size_t llama_memory_recurrent::size_s_bytes() const {
size_t size_s_bytes = 0;
for (const auto & s : s_l) {
if (s != nullptr) {
size_s_bytes += ggml_nbytes(s);
}
}
return size_s_bytes;
}
void llama_memory_recurrent::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
GGML_UNUSED(flags);
std::vector<std::pair<uint32_t, uint32_t>> cell_ranges; // ranges, from inclusive, to exclusive
std::vector<std::pair<uint32_t, uint32_t>> cell_ranges_data; // logical source row ranges
uint32_t cell_count = 0;
// Count the number of cells with the specified seq_id
// Find all the ranges of cells with this seq id (or all, when -1)
uint32_t cell_range_begin = size;
for (uint32_t i = 0; i < size; ++i) {
const auto & cell = cells[i];
// TODO: fix incosistent handling of `seq_id < 0` and `seq_id == -1` in the codebase [TAG_LLAMA_SEQ_ID_NEG]
if ((seq_id == -1 && !cell.is_empty()) || cell.has_seq_id(seq_id)) {
++cell_count;
uint32_t rs_idx_cur = 0;
if (n_rs_seq != 0) {
if (seq_id != -1) {
GGML_ASSERT(seq_id >= 0 && (size_t) seq_id < rs_idx.size());
rs_idx_cur = rs_idx[seq_id];
} else {
bool has_rs_idx = false;
for (const llama_seq_id cell_seq_id : cell.seq_id) {
GGML_ASSERT(cell_seq_id >= 0 && (size_t) cell_seq_id < rs_idx.size());
const uint32_t seq_rs_idx = rs_idx[cell_seq_id];
if (!has_rs_idx) {
rs_idx_cur = seq_rs_idx;
has_rs_idx = true;
} else if (rs_idx_cur != seq_rs_idx) {
GGML_ABORT("cannot write shared recurrent state with different rollback indices");
}
}
}
}
const uint32_t cell_id = rs_idx_cur * size + (cell.src >= 0 ? cell.src : (int32_t) i);
if (cell_ranges_data.empty() || cell_ranges_data.back().second != cell_id) {
cell_ranges_data.emplace_back(cell_id, cell_id + 1);
} else {
cell_ranges_data.back().second++;
}
if (cell_range_begin == size) {
cell_range_begin = i;
}
} else {
if (cell_range_begin != size) {
cell_ranges.emplace_back(cell_range_begin, i);
cell_range_begin = size;
}
}
}
if (cell_range_begin != size) {
cell_ranges.emplace_back(cell_range_begin, size);
}
if ((flags & LLAMA_STATE_SEQ_FLAGS_ON_DEVICE) && cell_ranges.size() > 1) {
GGML_ABORT("cannot save/load multiple ranges of cells to/from device memory\n");
}
// DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count
uint32_t cell_count_check = 0;
for (const auto & range : cell_ranges) {
cell_count_check += range.second - range.first;
}
GGML_ASSERT(cell_count == cell_count_check);
cell_count_check = 0;
for (const auto & range : cell_ranges_data) {
cell_count_check += range.second - range.first;
}
GGML_ASSERT(cell_count == cell_count_check);
io.write(&cell_count, sizeof(cell_count));
state_write_meta(io, cell_ranges, seq_id);
state_write_data(io, cell_ranges_data);
}
void llama_memory_recurrent::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
GGML_UNUSED(flags);
uint32_t cell_count;
io.read(&cell_count, sizeof(cell_count));
bool res = true;
res = res && state_read_meta(io, cell_count, seq_id);
try {
res = res && state_read_data(io, cell_count);
} catch (...) {
res = false;
}
if (!res) {
// TODO: fix incosistent handling of `seq_id < 0` and `seq_id == -1` in the codebase [TAG_LLAMA_SEQ_ID_NEG]
if (seq_id == -1) {
clear(true);
} else {
seq_rm(seq_id, -1, -1);
}
throw std::runtime_error("failed to restore kv cache");
}
if (n_rs_seq != 0) {
set_rs_idx(seq_id, 0);
}
}
void llama_memory_recurrent::state_write_meta(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, llama_seq_id seq_id) const {
for (const auto & range : cell_ranges) {
for (uint32_t i = range.first; i < range.second; ++i) {
const auto & cell = cells[i];
const llama_pos pos = cell.pos;
const uint32_t n_seq_id = seq_id == -1 ? cell.seq_id.size() : 0;
io.write(&pos, sizeof(pos));
io.write(&n_seq_id, sizeof(n_seq_id));
if (n_seq_id) {
for (auto seq_id : cell.seq_id) {
io.write(&seq_id, sizeof(seq_id));
}
}
}
}
}
void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) const {
const uint32_t s_trans = 0;
const uint32_t n_layer = hparams.n_layer();
io.write(&s_trans, sizeof(s_trans));
io.write(&n_layer, sizeof(n_layer));
// Iterate and write all the R tensors first, each row is a cell
// Get whole range at a time
for (uint32_t il = 0; il < n_layer; ++il) {
// skip null layers (read_data will handle this by checking "r_l" and "s_l" for null)
if (r_l[il] == nullptr) continue;
// Write R tensor type
const int32_t r_type_i = (int32_t)r_l[il]->type;
io.write(&r_type_i, sizeof(r_type_i));
// Write row size of R tensor
const uint64_t r_size_row = ggml_row_size(r_l[il]->type, hparams.n_embd_r());
io.write(&r_size_row, sizeof(r_size_row));
// Write each logical cell row range. With pending recurrent rollback,
// the logical current state may live in a rollback snapshot plane.
for (const auto & range : cell_ranges) {
const size_t range_size = range.second - range.first;
const size_t buf_size = range_size * r_size_row;
io.write_tensor(r_l[il], range.first * r_size_row, buf_size);
}
}
if (!s_trans) {
for (uint32_t il = 0; il < n_layer; ++il) {
// skip null layers (read_data will handle this by checking "r_l" and "s_l" for null)
if (s_l[il] == nullptr) continue;
// Write S tensor type
const int32_t s_type_i = (int32_t)s_l[il]->type;
io.write(&s_type_i, sizeof(s_type_i));
// Write row size of S tensor
const uint64_t s_size_row = ggml_row_size(s_l[il]->type, hparams.n_embd_s());
io.write(&s_size_row, sizeof(s_size_row));
// Write each logical cell row range. With pending recurrent rollback,
// the logical current state may live in a rollback snapshot plane.
for (const auto & range : cell_ranges) {
const size_t range_size = range.second - range.first;
const size_t buf_size = range_size * s_size_row;
io.write_tensor(s_l[il], range.first * s_size_row, buf_size);
}
}
} else {
// When S tensor is transposed, we also need the element size and get the element ranges from each row
const uint32_t mem_size = size;
for (uint32_t il = 0; il < n_layer; ++il) {
// skip null layers (read_data will handle this by checking "r_l" and "s_l" for null)
if (s_l[il] == nullptr) continue;
const uint32_t n_embd_s = hparams.n_embd_s();
// Write S tensor type
const int32_t s_type_i = (int32_t)s_l[il]->type;
io.write(&s_type_i, sizeof(s_type_i));
// Write element size
const uint32_t s_size_el = ggml_type_size(s_l[il]->type);
io.write(&s_size_el, sizeof(s_size_el));
// Write GQA embedding size
io.write(&n_embd_s, sizeof(n_embd_s));
// For each row, we get the element values of each logical cell
for (uint32_t j = 0; j < n_embd_s; ++j) {
for (const auto & range : cell_ranges) {
const size_t range_size = range.second - range.first;
const size_t src_offset = (range.first + j * mem_size) * s_size_el;
const size_t buf_size = range_size * s_size_el;
io.write_tensor(s_l[il], src_offset, buf_size);
}
}
}
}
}
bool llama_memory_recurrent::state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id) {
if (dest_seq_id != -1) {
// single sequence
seq_rm(dest_seq_id, -1, -1);
if (cell_count == 0) {
return true;
}
llama_batch_allocr balloc(hparams.n_pos_per_embd());
llama_ubatch ubatch = balloc.ubatch_reserve(cell_count, 1);
for (uint32_t i = 0; i < cell_count; ++i) {
llama_pos pos;
uint32_t n_seq_id;
io.read(&pos, sizeof(pos));
io.read(&n_seq_id, sizeof(n_seq_id));
if (n_seq_id != 0) {
LLAMA_LOG_ERROR("%s: invalid seq_id-agnostic kv cell\n", __func__);
return false;
}
ubatch.pos[i] = pos;
}
ubatch.n_seq_id[0] = 1;
ubatch.seq_id[0] = &dest_seq_id;
if (!find_slot(ubatch)) {
LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
return false;
}
// DEBUG CHECK: kv.head should be our first cell, kv.head + cell_count - 1 should be our last cell (verify seq_id and pos values)
// Assume that this is one contiguous block of cells
GGML_ASSERT(head + cell_count <= size);
GGML_ASSERT(cells[head].pos == ubatch.pos[0]);
GGML_ASSERT(cells[head + cell_count - 1].pos == ubatch.pos[cell_count - 1]);
GGML_ASSERT(cells[head].has_seq_id(dest_seq_id));
GGML_ASSERT(cells[head + cell_count - 1].has_seq_id(dest_seq_id));
} else {
// whole KV cache restore
if (cell_count > size) {
LLAMA_LOG_ERROR("%s: not enough cells in kv cache\n", __func__);
return false;
}
clear(true);
for (uint32_t i = 0; i < cell_count; ++i) {
auto & cell = cells[i];
llama_pos pos;
uint32_t n_seq_id;
io.read(&pos, sizeof(pos));
io.read(&n_seq_id, sizeof(n_seq_id));
cell.pos = pos;
for (uint32_t j = 0; j < n_seq_id; ++j) {
llama_seq_id seq_id;
io.read(&seq_id, sizeof(seq_id));
if (seq_id < 0 || (uint32_t) seq_id >= this->n_seq_max) {
LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, %u)\n", __func__, seq_id, this->n_seq_max);
return false;
}
cell.seq_id.insert(seq_id);
int32_t & tail = cells[seq_id].tail;
if (tail != -1) {
LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tail);
return false;
}
tail = i;
}
}
head = 0;
used = cell_count;
}
for (uint32_t i = 0; i < cell_count; ++i) {
uint32_t cell_id = head + i;
// make sure the recurrent states will keep their restored state
cells[cell_id].src = cell_id;
}
return true;
}
bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell_count) {
uint32_t s_trans;
uint32_t n_layer;
io.read(&s_trans, sizeof(s_trans));
io.read(&n_layer, sizeof(n_layer));
if (n_layer != hparams.n_layer()) {
LLAMA_LOG_ERROR("%s: mismatched layer count (%u instead of %u)\n", __func__, n_layer, hparams.n_layer());
return false;
}
if (cell_count > size) {
LLAMA_LOG_ERROR("%s: not enough cells in kv cache to restore state (%u > %u)\n", __func__, cell_count, size);
return false;
}
if (false != (bool) s_trans) {
LLAMA_LOG_ERROR("%s: incompatible s transposition\n", __func__);
return false;
}
// For each layer, read the keys for each cell, one row is one cell, read as one contiguous block
for (uint32_t il = 0; il < n_layer; ++il) {
// skip null layers
if (r_l[il] == nullptr) continue;
// Read type of key
int32_t r_type_i_ref;
io.read(&r_type_i_ref, sizeof(r_type_i_ref));
const int32_t r_type_i = (int32_t) r_l[il]->type;
if (r_type_i != r_type_i_ref) {
LLAMA_LOG_ERROR("%s: mismatched r type (%d != %d, layer %d)\n", __func__, r_type_i, r_type_i_ref, il);
return false;
}
// Read row size of key
uint64_t r_size_row_ref;
io.read(&r_size_row_ref, sizeof(r_size_row_ref));
const size_t r_size_row = ggml_row_size(r_l[il]->type, hparams.n_embd_r());
if (r_size_row != r_size_row_ref) {
LLAMA_LOG_ERROR("%s: mismatched r row size (%zu != %zu, layer %d)\n", __func__, r_size_row, (size_t) r_size_row_ref, il);
return false;
}
if (cell_count) {
// Read and set the keys for the whole cell range
io.read_tensor(r_l[il], head * r_size_row, cell_count * r_size_row);
}
}
if (!s_trans) {
for (uint32_t il = 0; il < n_layer; ++il) {
// skip null layers
if (s_l[il] == nullptr) continue;
// Read type of value
int32_t s_type_i_ref;
io.read(&s_type_i_ref, sizeof(s_type_i_ref));
const int32_t s_type_i = (int32_t)s_l[il]->type;
if (s_type_i != s_type_i_ref) {
LLAMA_LOG_ERROR("%s: mismatched s type (%d != %d, layer %d)\n", __func__, s_type_i, s_type_i_ref, il);
return false;
}
// Read row size of value
uint64_t s_size_row_ref;
io.read(&s_size_row_ref, sizeof(s_size_row_ref));
const size_t s_size_row = ggml_row_size(s_l[il]->type, hparams.n_embd_s());
if (s_size_row != s_size_row_ref) {
LLAMA_LOG_ERROR("%s: mismatched s row size (%zu != %zu, layer %d)\n", __func__, s_size_row, (size_t) s_size_row_ref, il);
return false;
}
if (cell_count) {
// Read and set the values for the whole cell range
io.read_tensor(s_l[il], head * s_size_row, cell_count * s_size_row);
}
}
} else {
// For each layer, read the values for each cell (transposed)
for (uint32_t il = 0; il < n_layer; ++il) {
// skip null layers
if (s_l[il] == nullptr) continue;
const uint32_t n_embd_s = hparams.n_embd_s();
// Read type of value
int32_t s_type_i_ref;
io.read(&s_type_i_ref, sizeof(s_type_i_ref));
const int32_t s_type_i = (int32_t)s_l[il]->type;
if (s_type_i != s_type_i_ref) {
LLAMA_LOG_ERROR("%s: mismatched s type (%d != %d, layer %d)\n", __func__, s_type_i, s_type_i_ref, il);
return false;
}
// Read element size of value
uint32_t s_size_el_ref;
io.read(&s_size_el_ref, sizeof(s_size_el_ref));
const size_t s_size_el = ggml_type_size(s_l[il]->type);
if (s_size_el != s_size_el_ref) {
LLAMA_LOG_ERROR("%s: mismatched s element size (%zu != %zu, layer %d)\n", __func__, s_size_el, (size_t) s_size_el_ref, il);
return false;
}
// Read state embedding size
uint32_t n_embd_s_ref;
io.read(&n_embd_s_ref, sizeof(n_embd_s_ref));
if (n_embd_s != n_embd_s_ref) {
LLAMA_LOG_ERROR("%s: mismatched s embedding size (%u != %u, layer %d)\n", __func__, n_embd_s, n_embd_s_ref, il);
return false;
}
if (cell_count) {
// For each row in the transposed matrix, read the values for the whole cell range
for (uint32_t j = 0; j < n_embd_s; ++j) {
const size_t dst_offset = (head + j * size) * s_size_el;
io.read_tensor(s_l[il], dst_offset, cell_count * s_size_el);
}
}
}
}
return true;
}
//
// llama_memory_recurrent_context
//
llama_memory_recurrent_context::llama_memory_recurrent_context(llama_memory_status status) : status(status) {}
llama_memory_recurrent_context::llama_memory_recurrent_context(
llama_memory_recurrent * mem) : status(LLAMA_MEMORY_STATUS_SUCCESS), mem(mem), is_full(true) {
}
llama_memory_recurrent_context::llama_memory_recurrent_context(
llama_memory_recurrent * mem,
std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), mem(mem), ubatches(std::move(ubatches)) {}
llama_memory_recurrent_context::~llama_memory_recurrent_context() = default;
bool llama_memory_recurrent_context::next() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
if (++i_next >= ubatches.size()) {
return false;
}
return true;
}
bool llama_memory_recurrent_context::apply() {
assert(!llama_memory_status_is_fail(status));
// no ubatches -> this is an update
if (ubatches.empty()) {
// recurrent cache never performs updates
assert(status == LLAMA_MEMORY_STATUS_NO_UPDATE);
return true;
}
mem->find_slot(ubatches[i_next]);
return true;
}
llama_memory_status llama_memory_recurrent_context::get_status() const {
return status;
}
const llama_ubatch & llama_memory_recurrent_context::get_ubatch() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return ubatches[i_next];
}
uint32_t llama_memory_recurrent_context::get_n_rs() const {
return is_full ? mem->size : mem->n;
}
uint32_t llama_memory_recurrent_context::get_head() const {
return is_full ? 0 : mem->head;
}
int32_t llama_memory_recurrent_context::get_rs_z() const {
return is_full ? 0 : mem->rs_z;
}
uint32_t llama_memory_recurrent_context::get_size() const {
return mem->size;
}
ggml_tensor * llama_memory_recurrent_context::get_r_l(int32_t il) const {
return mem->r_l[il];
}
ggml_tensor * llama_memory_recurrent_context::get_s_l(int32_t il) const {
return mem->s_l[il];
}
int32_t llama_memory_recurrent_context::s_copy(int i) const {
const uint32_t cell_idx = i + mem->head;
const int32_t src0 = mem->cells[cell_idx].src0;
if (mem->n_rs_seq == 0) {
return src0;
}
uint32_t idx = 0;
if (!mem->cells[cell_idx].seq_id.empty()) {
const llama_seq_id seq = *mem->cells[cell_idx].seq_id.begin();
if (seq >= 0 && (size_t) seq < mem->rs_idx.size()) {
idx = mem->rs_idx[seq];
// reset rollback idx
mem->rs_idx[seq] = 0;
}
}
return (int32_t)(idx * mem->size) + src0;
}
bool llama_memory_recurrent_context::s_copy_main_in_place(uint32_t n_seqs) const {
if (is_full) {
return false;
}
for (uint32_t i = 0; i < n_seqs; ++i) {
const uint32_t cell_idx = i + mem->head;
if (cell_idx >= mem->size || mem->cells[cell_idx].src0 != (int32_t) cell_idx) {
return false;
}
}
return true;
}