#include "llama-context.h"
#include "llama-impl.h"
#include "llama-io.h"
#include "llama-mmap.h"
#include "llama-model.h"
#include "llama-kv-cache.h"
#include <cstring>
#include <stdexcept>
#include <cinttypes>
//
// llama_context
//
llama_context::llama_context(
const llama_model & model,
llama_context_params params) :
model(model) {
LLAMA_LOG_INFO("%s: constructing llama_context\n", __func__);
t_start_us = model.t_start_us;
t_load_us = model.t_load_us;
const auto & hparams = model.hparams;
cparams.n_seq_max = std::max(1u, params.n_seq_max);
if (cparams.n_seq_max > LLAMA_MAX_PARALLEL_SEQUENCES) {
throw std::runtime_error("n_seq_max must be <= " + std::to_string(LLAMA_MAX_PARALLEL_SEQUENCES));
}
cparams.n_threads = params.n_threads;
cparams.n_threads_batch = params.n_threads_batch;
cparams.yarn_ext_factor = params.yarn_ext_factor;
cparams.yarn_attn_factor = params.yarn_attn_factor;
cparams.yarn_beta_fast = params.yarn_beta_fast;
cparams.yarn_beta_slow = params.yarn_beta_slow;
cparams.defrag_thold = params.defrag_thold;
cparams.embeddings = params.embeddings;
cparams.offload_kqv = params.offload_kqv;
cparams.flash_attn = params.flash_attn;
cparams.no_perf = params.no_perf;
cparams.pooling_type = params.pooling_type;
cparams.warmup = false;
cparams.n_ctx = params.n_ctx == 0 ? hparams.n_ctx_train : params.n_ctx;
cparams.rope_freq_base = params.rope_freq_base == 0.0f ? hparams.rope_freq_base_train : params.rope_freq_base;
cparams.rope_freq_scale = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale;
cparams.n_ctx_orig_yarn = params.yarn_orig_ctx != 0 ? params.yarn_orig_ctx :
hparams.n_ctx_orig_yarn != 0 ? hparams.n_ctx_orig_yarn :
hparams.n_ctx_train;
cparams.cb_eval = params.cb_eval;
cparams.cb_eval_user_data = params.cb_eval_user_data;
auto rope_scaling_type = params.rope_scaling_type;
if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED) {
rope_scaling_type = hparams.rope_scaling_type_train;
}
if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_NONE) {
cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none
}
if (cparams.yarn_ext_factor < 0.0f) { // negative indicates 'not set'
cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f;
}
cparams.yarn_attn_factor *= hparams.rope_attn_factor;
if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
if (hparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
cparams.pooling_type = LLAMA_POOLING_TYPE_NONE;
} else {
cparams.pooling_type = hparams.pooling_type;
}
}
if (params.attention_type == LLAMA_ATTENTION_TYPE_UNSPECIFIED) {
cparams.causal_attn = hparams.causal_attn;
} else {
cparams.causal_attn = params.attention_type == LLAMA_ATTENTION_TYPE_CAUSAL;
}
// with causal attention, the batch size is limited by the context size
cparams.n_batch = cparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch;
// the batch has to be at least GGML_KQ_MASK_PAD because we will be padding the KQ_mask
// this is required by GPU kernels in order to avoid out-of-bounds accesses (e.g. ggml_flash_attn_ext)
// ref: https://github.com/ggerganov/llama.cpp/pull/5021
// TODO: this padding is not needed for the cache-less context so we should probably move it to llama_context_kv_self
if (cparams.n_batch < GGML_KQ_MASK_PAD) {
LLAMA_LOG_WARN("%s: n_batch is less than GGML_KQ_MASK_PAD - increasing to %d\n", __func__, GGML_KQ_MASK_PAD);
cparams.n_batch = GGML_KQ_MASK_PAD;
}
cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
cparams.op_offload = params.op_offload;
const uint32_t n_ctx_per_seq = cparams.n_ctx / cparams.n_seq_max;
LLAMA_LOG_INFO("%s: n_seq_max = %u\n", __func__, cparams.n_seq_max);
LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, cparams.n_ctx);
LLAMA_LOG_INFO("%s: n_ctx_per_seq = %u\n", __func__, n_ctx_per_seq);
LLAMA_LOG_INFO("%s: n_batch = %u\n", __func__, cparams.n_batch);
LLAMA_LOG_INFO("%s: n_ubatch = %u\n", __func__, cparams.n_ubatch);
LLAMA_LOG_INFO("%s: causal_attn = %d\n", __func__, cparams.causal_attn);
LLAMA_LOG_INFO("%s: flash_attn = %d\n", __func__, cparams.flash_attn);
LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base);
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale);
if (n_ctx_per_seq < hparams.n_ctx_train) {
LLAMA_LOG_WARN("%s: n_ctx_per_seq (%u) < n_ctx_train (%u) -- the full capacity of the model will not be utilized\n",
__func__, n_ctx_per_seq, hparams.n_ctx_train);
}
if (n_ctx_per_seq > hparams.n_ctx_train) {
LLAMA_LOG_WARN("%s: n_ctx_per_seq (%u) > n_ctx_train (%u) -- possible training context overflow\n",
__func__, n_ctx_per_seq, hparams.n_ctx_train);
}
if (!hparams.vocab_only) {
// GPU backends
for (auto * dev : model.devices) {
ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr);
if (backend == nullptr) {
throw std::runtime_error(format("failed to initialize %s backend", ggml_backend_dev_name(dev)));
}
backends.emplace_back(backend);
}
// add ACCEL backends (such as BLAS)
for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
ggml_backend_dev_t dev = ggml_backend_dev_get(i);
if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_ACCEL) {
ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr);
if (backend == nullptr) {
throw std::runtime_error(format("failed to initialize %s backend", ggml_backend_dev_name(dev)));
}
backends.emplace_back(backend);
}
}
// add CPU backend
backend_cpu = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr);
if (backend_cpu == nullptr) {
throw std::runtime_error("failed to initialize CPU backend");
}
backends.emplace_back(backend_cpu);
// create a list of the set_n_threads functions in the backends
for (auto & backend : backends) {
ggml_backend_dev_t dev = ggml_backend_get_device(backend.get());
ggml_backend_reg_t reg = dev ? ggml_backend_dev_backend_reg(dev) : nullptr;
if (reg) {
auto ggml_backend_set_n_threads_fn = (ggml_backend_set_n_threads_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_n_threads");
if (ggml_backend_set_n_threads_fn) {
set_n_threads_fns.emplace_back(backend.get(), ggml_backend_set_n_threads_fn);
}
}
}
llama_set_abort_callback(this, params.abort_callback, params.abort_callback_data);
// graph outputs buffer
{
// resized during inference when a batch uses more outputs
if ((uint32_t) output_reserve(params.n_seq_max) < params.n_seq_max) {
throw std::runtime_error("failed to reserve initial output buffer");
}
LLAMA_LOG_INFO("%s: %10s output buffer size = %8.2f MiB\n", __func__,
ggml_backend_buffer_name (buf_output.get()),
ggml_backend_buffer_get_size(buf_output.get()) / 1024.0 / 1024.0);
}
}
// init the memory module
if (!hparams.vocab_only) {
llama_memory_params params_mem = {
/*.type_k =*/ params.type_k,
/*.type_v =*/ params.type_v,
/*.swa_full =*/ params.swa_full,
};
memory.reset(model.create_memory(params_mem, cparams));
}
// init backends
if (!hparams.vocab_only) {
LLAMA_LOG_DEBUG("%s: enumerating backends\n", __func__);
backend_buft.clear();
backend_ptrs.clear();
for (auto & backend : backends) {
auto * buft = ggml_backend_get_default_buffer_type(backend.get());
auto backend_type = ggml_backend_dev_type(ggml_backend_get_device(backend.get()));
if (backend_type == GGML_BACKEND_DEVICE_TYPE_CPU && !model.devices.empty()) {
// use the host buffer of the first device CPU for faster transfer of the intermediate state
auto * dev = model.devices[0];
auto * host_buft = ggml_backend_dev_host_buffer_type(dev);
if (host_buft) {
buft = host_buft;
}
}
backend_buft.push_back(buft);
backend_ptrs.push_back(backend.get());
}
LLAMA_LOG_DEBUG("%s: backend_ptrs.size() = %zu\n", __func__, backend_ptrs.size());
const size_t max_nodes = this->graph_max_nodes();
LLAMA_LOG_DEBUG("%s: max_nodes = %zu\n", __func__, max_nodes);
// buffer used to store the computation graph and the tensor meta data
buf_compute_meta.resize(ggml_tensor_overhead()*max_nodes + ggml_graph_overhead_custom(max_nodes, false));
// TODO: move these checks to ggml_backend_sched
// enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary
bool pipeline_parallel =
model.n_devices() > 1 &&
model.params.n_gpu_layers > (int) model.hparams.n_layer &&
model.params.split_mode == LLAMA_SPLIT_MODE_LAYER &&
cparams.offload_kqv &&
!model.has_tensor_overrides();
// pipeline parallelism requires support for async compute and events in all devices
if (pipeline_parallel) {
for (auto & backend : backends) {
auto dev_type = ggml_backend_dev_type(ggml_backend_get_device(backend.get()));
if (dev_type == GGML_BACKEND_DEVICE_TYPE_CPU) {
// ignore CPU backend
continue;
}
auto * dev = ggml_backend_get_device(backend.get());
ggml_backend_dev_props props;
ggml_backend_dev_get_props(dev, &props);
if (!props.caps.async || !props.caps.events) {
// device does not support async compute or events
pipeline_parallel = false;
break;
}
}
}
sched.reset(ggml_backend_sched_new(backend_ptrs.data(), backend_buft.data(), backend_ptrs.size(), max_nodes, pipeline_parallel, cparams.op_offload));
if (pipeline_parallel) {
LLAMA_LOG_INFO("%s: pipeline parallelism enabled (n_copies=%d)\n", __func__, ggml_backend_sched_get_n_copies(sched.get()));
}
}
// reserve worst-case graph
if (!hparams.vocab_only && memory) {
const uint32_t n_seqs = 1; // TODO: worst-case number of sequences
const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
llama_token token = model.vocab.token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
// restore later
// TODO: something cleaner
const auto n_outputs_save = n_outputs;
LLAMA_LOG_DEBUG("%s: worst-case: n_tokens = %d, n_seqs = %d, n_outputs = %d\n", __func__, n_tokens, n_seqs, n_outputs);
int n_splits_pp = -1;
int n_nodes_pp = -1;
int n_splits_tg = -1;
int n_nodes_tg = -1;
// simulate full KV cache
llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
kv_self->set_full();
cross.v_embd.clear();
// reserve pp graph first so that buffers are only allocated once
{
llama_ubatch ubatch_pp = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
// max number of outputs
n_outputs = ubatch_pp.n_tokens;
LLAMA_LOG_DEBUG("%s: reserving graph for n_tokens = %d, n_seqs = %d\n", __func__, ubatch_pp.n_tokens, ubatch_pp.n_seqs);
auto * gf = graph_init();
graph_build(ctx_compute.get(), gf, ubatch_pp, LLM_GRAPH_TYPE_DEFAULT);
if (!ggml_backend_sched_reserve(sched.get(), gf)) {
throw std::runtime_error("failed to allocate compute pp buffers");
}
n_splits_pp = ggml_backend_sched_get_n_splits(sched.get());
n_nodes_pp = ggml_graph_n_nodes(gf);
}
// reserve with tg graph to get the number of splits and nodes
{
llama_ubatch ubatch_tg = { true, 1, 1, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
n_outputs = ubatch_tg.n_tokens;
LLAMA_LOG_DEBUG("%s: reserving graph for n_tokens = %d, n_seqs = %d\n", __func__, ubatch_tg.n_tokens, ubatch_tg.n_seqs);
auto * gf = graph_init();
graph_build(ctx_compute.get(), gf, ubatch_tg, LLM_GRAPH_TYPE_DEFAULT);
if (!ggml_backend_sched_reserve(sched.get(), gf)) {
throw std::runtime_error("failed to allocate compute tg buffers");
}
n_splits_tg = ggml_backend_sched_get_n_splits(sched.get());
n_nodes_tg = ggml_graph_n_nodes(gf);
}
// reserve again with pp graph to avoid ggml-alloc reallocations during inference
{
llama_ubatch ubatch_pp = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
n_outputs = ubatch_pp.n_tokens;
LLAMA_LOG_DEBUG("%s: reserving graph for n_tokens = %d, n_seqs = %d\n", __func__, ubatch_pp.n_tokens, ubatch_pp.n_seqs);
auto * gf = graph_init();
graph_build(ctx_compute.get(), gf, ubatch_pp, LLM_GRAPH_TYPE_DEFAULT);
if (!ggml_backend_sched_reserve(sched.get(), gf)) {
throw std::runtime_error("failed to allocate compute pp buffers");
}
}
n_outputs = n_outputs_save;
for (size_t i = 0; i < backend_ptrs.size(); ++i) {
ggml_backend_t backend = backend_ptrs[i];
ggml_backend_buffer_type_t buft = backend_buft[i];
size_t size = ggml_backend_sched_get_buffer_size(sched.get(), backend);
if (size > 1) {
LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
ggml_backend_buft_name(buft),
size / 1024.0 / 1024.0);
}
}
if (n_nodes_pp == n_nodes_tg) {
LLAMA_LOG_INFO("%s: graph nodes = %d\n", __func__, n_nodes_pp);
} else {
LLAMA_LOG_INFO("%s: graph nodes = %d (with bs=%d), %d (with bs=1)\n", __func__, n_nodes_pp, n_tokens, n_nodes_tg);
}
if (n_splits_pp == n_splits_tg) {
LLAMA_LOG_INFO("%s: graph splits = %d\n", __func__, n_splits_pp);
} else {
LLAMA_LOG_INFO("%s: graph splits = %d (with bs=%d), %d (with bs=1)\n", __func__, n_splits_pp, n_tokens, n_splits_tg);
}
}
}
llama_context::~llama_context() {
ggml_opt_free(opt_ctx);
}
void llama_context::synchronize() {
ggml_backend_sched_synchronize(sched.get());
// FIXME: if multiple single tokens are evaluated without a synchronization,
// the stats will be added to the prompt evaluation stats
// this should only happen when using batch size 1 to evaluate a batch
// add the evaluation to the stats
if (n_queued_tokens == 1) {
if (!cparams.no_perf) {
t_eval_us += ggml_time_us() - t_compute_start_us;
}
n_eval++;
} else if (n_queued_tokens > 1) {
if (!cparams.no_perf) {
t_p_eval_us += ggml_time_us() - t_compute_start_us;
}
n_p_eval += n_queued_tokens;
}
// get a more accurate load time, upon first eval
if (n_queued_tokens > 0 && !has_evaluated_once) {
t_load_us = ggml_time_us() - t_start_us;
has_evaluated_once = true;
}
n_queued_tokens = 0;
t_compute_start_us = 0;
}
const llama_model & llama_context::get_model() const {
return model;
}
const llama_cparams & llama_context::get_cparams() const {
return cparams;
}
ggml_backend_sched_t llama_context::get_sched() const {
return sched.get();
}
ggml_context * llama_context::get_ctx_compute() const {
return ctx_compute.get();
}
uint32_t llama_context::n_ctx() const {
return cparams.n_ctx;
}
uint32_t llama_context::n_ctx_per_seq() const {
return cparams.n_ctx / cparams.n_seq_max;
}
uint32_t llama_context::n_batch() const {
return cparams.n_batch;
}
uint32_t llama_context::n_ubatch() const {
return cparams.n_ubatch;
}
uint32_t llama_context::n_seq_max() const {
return cparams.n_seq_max;
}
uint32_t llama_context::n_threads() const {
return cparams.n_threads;
}
uint32_t llama_context::n_threads_batch() const {
return cparams.n_threads_batch;
}
llama_kv_cache * llama_context::get_kv_self() {
llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
return kv_self;
}
const llama_kv_cache * llama_context::get_kv_self() const {
llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
return kv_self;
}
void llama_context::kv_self_update() {
bool need_reserve = false;
llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
need_reserve = kv_self->update(*this);
// reserve a worst case graph if needed
if (need_reserve) {
LLAMA_LOG_DEBUG("%s: reserving a worst case graph\n", __func__);
// build worst-case graph
uint32_t n_seqs = 1; // TODO: worst-case number of sequences
uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
// simulate full KV cache
kv_self->set_full();
llama_token token = model.vocab.token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
llama_ubatch ubatch = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
auto * gf = graph_init();
graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT);
// initialize scheduler with the worst-case graph
ggml_backend_sched_reset(sched.get());
if (!ggml_backend_sched_reserve(sched.get(), gf)) {
LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
}
}
}
enum llama_pooling_type llama_context::pooling_type() const {
return cparams.pooling_type;
}
float * llama_context::get_logits() {
return logits;
}
float * llama_context::get_logits_ith(int32_t i) {
int32_t j = -1;
try {
if (logits == nullptr) {
throw std::runtime_error("no logits");
}
if (i < 0) {
j = n_outputs + i;
if (j < 0) {
throw std::runtime_error(format("negative index out of range [0, %d)", n_outputs));
}
} else if ((size_t) i >= output_ids.size()) {
throw std::runtime_error(format("out of range [0, %zu)", output_ids.size()));
} else {
j = output_ids[i];
}
if (j < 0) {
throw std::runtime_error(format("batch.logits[%d] != true", i));
}
if (j >= n_outputs) {
// This should not happen
throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, n_outputs));
}
return logits + j*model.vocab.n_tokens();
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: invalid logits id %d, reason: %s\n", __func__, i, err.what());
#ifndef NDEBUG
GGML_ABORT("fatal error");
#else
return nullptr;
#endif
}
}
float * llama_context::get_embeddings() {
return embd;
}
float * llama_context::get_embeddings_ith(int32_t i) {
int32_t j = -1;
try {
if (embd == nullptr) {
throw std::runtime_error("no embeddings");
}
if (i < 0) {
j = n_outputs + i;
if (j < 0) {
throw std::runtime_error(format("negative index out of range [0, %d)", n_outputs));
}
} else if ((size_t) i >= output_ids.size()) {
throw std::runtime_error(format("out of range [0, %zu)", output_ids.size()));
} else {
j = output_ids[i];
}
if (j < 0) {
throw std::runtime_error(format("batch.logits[%d] != true", i));
}
if (j >= n_outputs) {
// This should not happen
throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, n_outputs));
}
return embd + j*model.hparams.n_embd;
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: invalid embeddings id %d, reason: %s\n", __func__, i, err.what());
#ifndef NDEBUG
GGML_ABORT("fatal error");
#else
return nullptr;
#endif
}
}
float * llama_context::get_embeddings_seq(llama_seq_id seq_id) {
auto it = embd_seq.find(seq_id);
if (it == embd_seq.end()) {
return nullptr;
}
return it->second.data();
}
void llama_context::attach_threadpool(
ggml_threadpool_t threadpool,
ggml_threadpool_t threadpool_batch) {
LLAMA_LOG_DEBUG("%s: call\n", __func__);
this->threadpool = threadpool;
this->threadpool_batch = threadpool_batch ? threadpool_batch : threadpool;
}
void llama_context::detach_threadpool() {
LLAMA_LOG_DEBUG("%s: call\n", __func__);
this->threadpool = nullptr;
this->threadpool_batch = nullptr;
}
void llama_context::set_n_threads(int32_t n_threads, int32_t n_threads_batch) {
LLAMA_LOG_DEBUG("%s: n_threads = %d, n_threads_batch = %d\n", __func__, n_threads, n_threads_batch);
cparams.n_threads = n_threads;
cparams.n_threads_batch = n_threads_batch;
}
void llama_context::set_abort_callback(bool (*abort_callback)(void * data), void * abort_callback_data) {
LLAMA_LOG_DEBUG("%s: call\n", __func__);
this->abort_callback = abort_callback;
this->abort_callback_data = abort_callback_data;
for (auto & backend : backends) {
auto * reg = ggml_backend_dev_backend_reg(ggml_backend_get_device(backend.get()));
auto * set_abort_callback_fn = (ggml_backend_set_abort_callback_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_abort_callback");
if (set_abort_callback_fn) {
set_abort_callback_fn(backend.get(), this->abort_callback, this->abort_callback_data);
}
}
}
void llama_context::set_embeddings(bool value) {
LLAMA_LOG_DEBUG("%s: value = %d\n", __func__, value);
cparams.embeddings = value;
}
void llama_context::set_causal_attn(bool value) {
LLAMA_LOG_DEBUG("%s: value = %d\n", __func__, value);
cparams.causal_attn = value;
}
void llama_context::set_warmup(bool value) {
LLAMA_LOG_DEBUG("%s: value = %d\n", __func__, value);
cparams.warmup = value;
}
void llama_context::set_adapter_lora(
llama_adapter_lora * adapter,
float scale) {
LLAMA_LOG_DEBUG("%s: adapter = %p, scale = %f\n", __func__, (void *) adapter, scale);
loras[adapter] = scale;
}
bool llama_context::rm_adapter_lora(
llama_adapter_lora * adapter) {
LLAMA_LOG_DEBUG("%s: adapter = %p\n", __func__, (void *) adapter);
auto pos = loras.find(adapter);
if (pos != loras.end()) {
loras.erase(pos);
return true;
}
return false;
}
void llama_context::clear_adapter_lora() {
LLAMA_LOG_DEBUG("%s: call\n", __func__);
loras.clear();
}
bool llama_context::apply_adapter_cvec(
const float * data,
size_t len,
int32_t n_embd,
int32_t il_start,
int32_t il_end) {
LLAMA_LOG_DEBUG("%s: il_start = %d, il_end = %d\n", __func__, il_start, il_end);
return cvec.apply(model, data, len, n_embd, il_start, il_end);
}
int llama_context::encode(llama_batch & inp_batch) {
if (inp_batch.n_tokens == 0) {
LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__);
return -1;
}
// temporary allocate memory for the input batch if needed
// note: during encode, we always pass the full sequence starting from pos = 0
llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : 0);
const llama_batch & batch = batch_allocr.batch;
const int32_t n_tokens = batch.n_tokens;
const auto & hparams = model.hparams;
GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
// TODO: move the validation to the llama_batch_allocr
if (batch.token) {
for (int32_t i = 0; i < n_tokens; ++i) {
if (batch.token[i] < 0 || (uint32_t) batch.token[i] >= model.vocab.n_tokens()) {
LLAMA_LOG_ERROR("%s: invalid token[%d] = %d\n", __func__, i, batch.token[i]);
return -1;
}
if (batch.seq_id && (batch.seq_id[i][0] < 0 || batch.seq_id[i][0] >= LLAMA_MAX_PARALLEL_SEQUENCES)) {
LLAMA_LOG_ERROR("%s: invalid seq_id[%d] = %d > %d\n", __func__, i, batch.seq_id[i][0], LLAMA_MAX_PARALLEL_SEQUENCES);
throw -1;
}
}
}
// micro-batching is not possible for non-causal encoding, so we process the batch in a single shot
GGML_ASSERT(cparams.n_ubatch >= (uint32_t) n_tokens && "encoder requires n_ubatch >= n_tokens");
if (t_compute_start_us == 0) {
t_compute_start_us = ggml_time_us();
}
embd_seq.clear();
n_queued_tokens += n_tokens;
const int64_t n_embd = hparams.n_embd;
llama_sbatch sbatch = llama_sbatch(batch, n_embd, /* simple_split */ true, /* logits_all */ true);
const llama_ubatch ubatch = sbatch.split_simple(n_tokens);
// reserve output buffer
if (output_reserve(n_tokens) < n_tokens) {
LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_tokens);
return -2;
};
for (int32_t i = 0; i < n_tokens; ++i) {
output_ids[i] = i;
}
n_outputs = n_tokens;
//batch_manager->prepare(ubatch);
ggml_backend_sched_reset(sched.get());
ggml_backend_sched_set_eval_callback(sched.get(), cparams.cb_eval, cparams.cb_eval_user_data);
const auto causal_attn_org = cparams.causal_attn;
// always use non-causal attention for encoder graphs
// TODO: this is a tmp solution until we have a proper way to support enc-dec models
// ref: https://github.com/ggml-org/llama.cpp/pull/12181#issuecomment-2730451223
cparams.causal_attn = false;
auto * gf = graph_init();
auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_ENCODER);
ggml_backend_sched_alloc_graph(sched.get(), gf);
res->set_inputs(&ubatch);
cparams.causal_attn = causal_attn_org;
const auto compute_status = graph_compute(gf, n_tokens > 1);
switch (compute_status) {
case GGML_STATUS_SUCCESS:
break;
case GGML_STATUS_ABORTED:
return 2;
case GGML_STATUS_ALLOC_FAILED:
return -2;
case GGML_STATUS_FAILED:
default:
return -3;
}
auto * t_embd = res->get_embd_pooled() ? res->get_embd_pooled() : res->get_embd();
// extract embeddings
if (t_embd) {
ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(sched.get(), t_embd);
GGML_ASSERT(backend_embd != nullptr);
switch (cparams.pooling_type) {
case LLAMA_POOLING_TYPE_NONE:
{
// extract token embeddings
GGML_ASSERT(embd != nullptr);
GGML_ASSERT(n_tokens*n_embd <= (int64_t) embd_size);
ggml_backend_tensor_get_async(backend_embd, t_embd, embd, 0, n_tokens*n_embd*sizeof(float));
} break;
case LLAMA_POOLING_TYPE_MEAN:
case LLAMA_POOLING_TYPE_CLS:
case LLAMA_POOLING_TYPE_LAST:
{
// extract sequence embeddings
auto & embd_seq_out = embd_seq;
embd_seq_out.clear();
GGML_ASSERT(!ubatch.equal_seqs); // TODO: handle equal splits
for (int32_t i = 0; i < n_tokens; i++) {
const llama_seq_id seq_id = ubatch.seq_id[i][0];
if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
continue;
}
embd_seq_out[seq_id].resize(n_embd);
ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
}
} break;
case LLAMA_POOLING_TYPE_RANK:
{
// extract the rerank score - a single float per sequence
auto & embd_seq_out = embd_seq;
for (uint32_t s = 0; s < ubatch.n_seqs; ++s) {
const llama_seq_id seq_id = ubatch.seq_id[s][0];
if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
continue;
}
embd_seq_out[seq_id].resize(1);
ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (seq_id)*sizeof(float), sizeof(float));
}
} break;
case LLAMA_POOLING_TYPE_UNSPECIFIED:
{
GGML_ABORT("unknown pooling type");
}
}
}
// Reset state for the next token before backend sync, to allow the CPU activities in the reset to
// overlap with device computation.
ggml_backend_sched_reset(sched.get());
// TODO: hacky solution
if (model.arch == LLM_ARCH_T5 && t_embd) {
//cross.t_embd = t_embd;
synchronize();
cross.n_embd = t_embd->ne[0];
cross.n_enc = t_embd->ne[1];
cross.v_embd.resize(cross.n_embd*cross.n_enc);
memcpy(cross.v_embd.data(), embd, ggml_nbytes(t_embd));
// remember the sequence ids used during the encoding - needed for cross attention later
cross.seq_ids_enc.resize(n_tokens);
for (int32_t i = 0; i < n_tokens; i++) {
cross.seq_ids_enc[i].clear();
for (int s = 0; s < ubatch.n_seq_id[i]; s++) {
llama_seq_id seq_id = ubatch.seq_id[i][s];
cross.seq_ids_enc[i].insert(seq_id);
}
}
}
return 0;
}
int llama_context::decode(llama_batch & inp_batch) {
if (!memory) {
LLAMA_LOG_DEBUG("%s: cannot decode batches with this context (calling encode() instead)\n", __func__);
return encode(inp_batch);
}
if (inp_batch.n_tokens == 0) {
LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__);
return -1;
}
if (!inp_batch.pos) {
if (inp_batch.seq_id) {
LLAMA_LOG_ERROR("%s: pos == NULL, but seq_id != NULL\n", __func__);
return -1;
}
}
llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
// temporary allocate memory for the input batch if needed
llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : kv_self->seq_pos_max(0) + 1);
const llama_batch & batch = batch_allocr.batch;
const auto & vocab = model.vocab;
const auto & hparams = model.hparams;
const int32_t n_vocab = vocab.n_tokens();
const int64_t n_tokens_all = batch.n_tokens;
const int64_t n_embd = hparams.n_embd;
llama_kv_cache_guard kv_guard(kv_self);
GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
// TODO: move the validation to the llama_batch_allocr
if (batch.token) {
for (int64_t i = 0; i < n_tokens_all; ++i) {
if (batch.token[i] < 0 || (uint32_t) batch.token[i] >= model.vocab.n_tokens()) {
LLAMA_LOG_ERROR("%s: invalid token[%" PRId64 "] = %d\n", __func__, i, batch.token[i]);
return -1;
}
if (batch.seq_id && (batch.seq_id[i][0] < 0 || batch.seq_id[i][0] >= LLAMA_MAX_PARALLEL_SEQUENCES)) {
LLAMA_LOG_ERROR("%s: invalid seq_id[%" PRId64 "] = %d >= %d\n", __func__, i, batch.seq_id[i][0], LLAMA_MAX_PARALLEL_SEQUENCES);
return -1;
}
}
}
GGML_ASSERT(n_tokens_all <= cparams.n_batch);
GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens");
if (t_compute_start_us == 0) {
t_compute_start_us = ggml_time_us();
}
n_queued_tokens += n_tokens_all;
// this indicates we are doing pooled embedding, so we ignore batch.logits and output all tokens
const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE;
embd_seq.clear();
int64_t n_outputs_all = 0;
// count outputs
if (batch.logits && !embd_pooled) {
for (uint32_t i = 0; i < n_tokens_all; ++i) {
n_outputs_all += batch.logits[i] != 0;
}
} else if (embd_pooled) {
n_outputs_all = n_tokens_all;
} else {
// keep last output only
n_outputs_all = 1;
}
llama_sbatch sbatch = kv_self->sbatch_init(batch, /* logits_all */ n_outputs_all == n_tokens_all);
// reserve output buffer
if (output_reserve(n_outputs_all) < n_outputs_all) {
LLAMA_LOG_ERROR("%s: could not reserve space for batch with %" PRId64 " outputs\n", __func__, n_outputs_all);
return -2;
};
// handle any pending defrags/shifts
kv_self_update();
int64_t n_outputs_prev = 0;
while (sbatch.n_tokens > 0) {
llama_ubatch ubatch = kv_self->ubatch_next(sbatch, cparams.n_ubatch, embd_pooled);
// count the outputs in this u_batch
{
int32_t n_outputs_new = 0;
if (n_outputs_all == n_tokens_all) {
n_outputs_new = ubatch.n_tokens;
} else {
GGML_ASSERT(ubatch.output);
for (uint32_t i = 0; i < ubatch.n_tokens; i++) {
n_outputs_new += (int32_t) (ubatch.output[i] != 0);
}
}
// needs to happen before the graph is built
n_outputs = n_outputs_new;
}
// find KV slot
if (!kv_self->find_slot(ubatch)) {
return 1;
}
ggml_backend_sched_reset(sched.get());
ggml_backend_sched_set_eval_callback(sched.get(), cparams.cb_eval, cparams.cb_eval_user_data);
auto * gf = graph_init();
auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DECODER);
// LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
ggml_backend_sched_alloc_graph(sched.get(), gf);
res->set_inputs(&ubatch);
const auto compute_status = graph_compute(gf, ubatch.n_tokens > 1);
if (compute_status != GGML_STATUS_SUCCESS) {
switch (compute_status) {
case GGML_STATUS_ABORTED:
return 2;
case GGML_STATUS_ALLOC_FAILED:
return -2;
case GGML_STATUS_FAILED:
default:
return -3;
}
}
// plot the computation graph in dot format (for debugging purposes)
//if (n_past%100 == 0) {
// ggml_graph_dump_dot(gf, NULL, "llama.dot");
//}
auto * t_logits = cparams.embeddings ? nullptr : res->get_logits();
auto * t_embd = cparams.embeddings ? res->get_embd() : nullptr;
if (t_embd && res->get_embd_pooled()) {
t_embd = res->get_embd_pooled();
}
// extract logits
if (t_logits && n_outputs > 0) {
ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(sched.get(), t_logits);
GGML_ASSERT(backend_res != nullptr);
GGML_ASSERT(logits != nullptr);
float * logits_out = logits + n_outputs_prev*n_vocab;
if (n_outputs) {
GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
GGML_ASSERT((n_outputs_prev + n_outputs)*n_vocab <= (int64_t) logits_size);
ggml_backend_tensor_get_async(backend_res, t_logits, logits_out, 0, n_outputs*n_vocab*sizeof(float));
}
}
// extract embeddings
if (t_embd && n_outputs > 0) {
ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(sched.get(), t_embd);
GGML_ASSERT(backend_embd != nullptr);
switch (cparams.pooling_type) {
case LLAMA_POOLING_TYPE_NONE:
{
// extract token embeddings
GGML_ASSERT(embd != nullptr);
float * embd_out = embd + n_outputs_prev*n_embd;
if (n_outputs) {
GGML_ASSERT( n_outputs_prev + n_outputs <= n_outputs_all);
GGML_ASSERT((n_outputs_prev + n_outputs)*n_embd <= (int64_t) embd_size);
ggml_backend_tensor_get_async(backend_embd, t_embd, embd_out, 0, n_outputs*n_embd*sizeof(float));
}
} break;
case LLAMA_POOLING_TYPE_MEAN:
case LLAMA_POOLING_TYPE_CLS:
case LLAMA_POOLING_TYPE_LAST:
{
// extract sequence embeddings (cleared before processing each batch)
auto & embd_seq_out = embd_seq;
for (uint32_t s = 0; s < ubatch.n_seqs; ++s) {
const llama_seq_id seq_id = ubatch.seq_id[s][0];
if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
continue;
}
embd_seq_out[seq_id].resize(n_embd);
ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
}
} break;
case LLAMA_POOLING_TYPE_RANK:
{
// extract the rerank score - a single float per sequence
auto & embd_seq_out = embd_seq;
for (uint32_t s = 0; s < ubatch.n_seqs; ++s) {
const llama_seq_id seq_id = ubatch.seq_id[s][0];
if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
continue;
}
embd_seq_out[seq_id].resize(1);
ggml_backend_tensor_get_async(backend_embd, t_embd, embd_seq_out[seq_id].data(), (seq_id)*sizeof(float), sizeof(float));
}
} break;
case LLAMA_POOLING_TYPE_UNSPECIFIED:
{
GGML_ABORT("unknown pooling type");
}
}
}
n_outputs_prev += n_outputs;
}
// finalize the batch processing
kv_guard.commit();
// set to total number of outputs in the batch, for use in llama_get_logits_ith
n_outputs = n_outputs_all;
// set output mappings
{
bool sorted_output = true;
auto & out_ids = sbatch.out_ids;
GGML_ASSERT(out_ids.size() == (size_t) n_outputs_all);
for (int64_t i = 0; i < n_outputs_all; ++i) {
int64_t out_id = out_ids[i];
output_ids[out_id] = i;
if (out_id != i) {
sorted_output = false;
}
}
// make the outputs have the same order they had in the user-provided batch
// note: this is mostly relevant for recurrent models atm
if (!sorted_output) {
const uint32_t n_vocab = model.vocab.n_tokens();
const uint32_t n_embd = model.hparams.n_embd;
GGML_ASSERT((size_t) n_outputs == out_ids.size());
// TODO: is there something more efficient which also minimizes swaps?
// selection sort, to minimize swaps (from https://en.wikipedia.org/wiki/Selection_sort)
for (int32_t i = 0; i < n_outputs - 1; ++i) {
int32_t j_min = i;
for (int32_t j = i + 1; j < n_outputs; ++j) {
if (out_ids[j] < out_ids[j_min]) {
j_min = j;
}
}
if (j_min == i) { continue; }
std::swap(out_ids[i], out_ids[j_min]);
if (logits_size > 0) {
for (uint32_t k = 0; k < n_vocab; k++) {
std::swap(logits[i*n_vocab + k], logits[j_min*n_vocab + k]);
}
}
if (embd_size > 0) {
for (uint32_t k = 0; k < n_embd; k++) {
std::swap(embd[i*n_embd + k], embd[j_min*n_embd + k]);
}
}
}
std::fill(output_ids.begin(), output_ids.end(), -1);
for (int32_t i = 0; i < n_outputs; ++i) {
output_ids[out_ids[i]] = i;
}
}
}
// wait for the computation to finish (automatically done when obtaining the model output)
//synchronize();
// decide if we need to defrag the kv cache
if (cparams.defrag_thold > 0.0f) {
kv_self->defrag_sched(cparams.defrag_thold);
}
// Reset state for the next token before backend sync, to allow the CPU activities in the reset to
// overlap with device computation.
ggml_backend_sched_reset(sched.get());
return 0;
}
//
// output
//
int32_t llama_context::output_reserve(int32_t n_outputs) {
const auto & hparams = model.hparams;
const auto & vocab = model.vocab;
const int64_t n_outputs_max = std::max<int64_t>(n_outputs, n_seq_max());
const auto n_batch = cparams.n_batch;
const auto n_vocab = vocab.n_tokens();
const auto n_embd = hparams.n_embd;
// TODO: use a per-batch flag for logits presence instead
bool has_logits = !cparams.embeddings;
bool has_embd = cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
// TODO: hacky enc-dec support
if (model.arch == LLM_ARCH_T5) {
has_logits = true;
has_embd = true;
}
logits_size = has_logits ? n_vocab*n_outputs_max : 0;
embd_size = has_embd ? n_embd*n_outputs_max : 0;
if (output_ids.empty()) {
// init, never resized afterwards
output_ids.resize(n_batch);
}
const size_t prev_size = buf_output ? ggml_backend_buffer_get_size(buf_output.get()) : 0;
const size_t new_size = (logits_size + embd_size) * sizeof(float);
// alloc only when more than the current capacity is required
// TODO: also consider shrinking the buffer
if (!buf_output || prev_size < new_size) {
if (buf_output) {
#ifndef NDEBUG
// This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark)
LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
#endif
buf_output = nullptr;
logits = nullptr;
embd = nullptr;
}
auto * buft = ggml_backend_cpu_buffer_type();
// try to use the host buffer of the device where the output tensor is allocated for faster transfer to system memory
auto * output_dev = model.dev_output();
auto * output_dev_host_buft = output_dev ? ggml_backend_dev_host_buffer_type(output_dev) : nullptr;
if (output_dev_host_buft) {
buft = output_dev_host_buft;
}
buf_output.reset(ggml_backend_buft_alloc_buffer(buft, new_size));
if (buf_output == nullptr) {
LLAMA_LOG_ERROR("%s: failed to allocate output buffer of size %.2f MiB\n", __func__, new_size / (1024.0 * 1024.0));
return 0;
}
}
float * output_base = (float *) ggml_backend_buffer_get_base(buf_output.get());
logits = has_logits ? output_base : nullptr;
embd = has_embd ? output_base + logits_size : nullptr;
// set all ids as invalid (negative)
std::fill(output_ids.begin(), output_ids.end(), -1);
this->n_outputs = 0;
this->n_outputs_max = n_outputs_max;
return n_outputs_max;
}
//
// graph
//
int32_t llama_context::graph_max_nodes() const {
return std::max<int32_t>(65536, 5*model.n_tensors());
}
ggml_cgraph * llama_context::graph_init() {
ggml_init_params params = {
/*.mem_size =*/ buf_compute_meta.size(),
/*.mem_buffer =*/ buf_compute_meta.data(),
/*.no_alloc =*/ true,
};
ctx_compute.reset(ggml_init(params));
return ggml_new_graph_custom(ctx_compute.get(), graph_max_nodes(), false);
}
llm_graph_result_ptr llama_context::graph_build(
ggml_context * ctx,
ggml_cgraph * gf,
const llama_ubatch & ubatch,
llm_graph_type gtype) {
return model.build_graph(
{
/*.ctx =*/ ctx,
/*.arch =*/ model.arch,
/*.hparams =*/ model.hparams,
/*.cparams =*/ cparams,
/*.ubatch =*/ ubatch,
/*.sched =*/ sched.get(),
/*.backend_cpu =*/ backend_cpu,
/*.cvec =*/ &cvec,
/*.loras =*/ &loras,
/*.memory =*/ memory.get(),
/*.cross =*/ &cross,
/*.n_outputs =*/ n_outputs,
/*.cb =*/ graph_get_cb(),
}, gf, gtype);
}
ggml_status llama_context::graph_compute(
ggml_cgraph * gf,
bool batched) {
int n_threads = batched ? cparams.n_threads_batch : cparams.n_threads;
ggml_threadpool_t tp = batched ? threadpool_batch : threadpool;
if (backend_cpu != nullptr) {
auto * reg = ggml_backend_dev_backend_reg(ggml_backend_get_device(backend_cpu));
auto * set_threadpool_fn = (decltype(ggml_backend_cpu_set_threadpool) *) ggml_backend_reg_get_proc_address(reg, "ggml_backend_cpu_set_threadpool");
set_threadpool_fn(backend_cpu, tp);
}
// set the number of threads for all the backends
for (const auto & set_n_threads_fn : set_n_threads_fns) {
set_n_threads_fn.second(set_n_threads_fn.first, n_threads);
}
auto status = ggml_backend_sched_graph_compute_async(sched.get(), gf);
if (status != GGML_STATUS_SUCCESS) {
LLAMA_LOG_ERROR("%s: ggml_backend_sched_graph_compute_async failed with error %d\n", __func__, status);
}
// fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(sched));
return status;
}
llm_graph_cb llama_context::graph_get_cb() const {
return [&](const llama_ubatch & ubatch, ggml_tensor * cur, const char * name, int il) {
if (il >= 0) {
ggml_format_name(cur, "%s-%d", name, il);
} else {
ggml_set_name(cur, name);
}
if (!cparams.offload_kqv) {
if (strcmp(name, "kqv_merged_cont") == 0) {
// all nodes between the KV store and the attention output are run on the CPU
ggml_backend_sched_set_tensor_backend(sched.get(), cur, backend_cpu);
}
}
// norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends
// FIXME: fix in ggml_backend_sched
const bool full_offload = model.params.n_gpu_layers > (int) model.hparams.n_layer;
if (ubatch.n_tokens < 32 || full_offload) {
if (il != -1 && strcmp(name, "norm") == 0) {
const auto & dev_layer = model.dev_layer(il);
for (const auto & backend : backends) {
if (ggml_backend_get_device(backend.get()) == dev_layer) {
if (ggml_backend_supports_op(backend.get(), cur)) {
ggml_backend_sched_set_tensor_backend(sched.get(), cur, backend.get());
}
}
}
}
}
};
}
//
// state save/load
//
class llama_io_write_dummy : public llama_io_write_i {
public:
llama_io_write_dummy() = default;
void write(const void * /* src */, size_t size) override {
size_written += size;
}
void write_tensor(const ggml_tensor * /* tensor */, size_t /* offset */, size_t size) override {
size_written += size;
}
size_t n_bytes() override {
return size_written;
}
private:
size_t size_written = 0;
};
class llama_io_write_buffer : public llama_io_write_i {
public:
llama_io_write_buffer(
uint8_t * p, size_t len) : ptr(p), buf_size(len) {}
void write(const void * src, size_t size) override {
if (size > buf_size) {
throw std::runtime_error("unexpectedly reached end of buffer");
}
memcpy(ptr, src, size);
ptr += size;
size_written += size;
buf_size -= size;
}
void write_tensor(const ggml_tensor * tensor, size_t offset, size_t size) override {
if (size > buf_size) {
throw std::runtime_error("unexpectedly reached end of buffer");
}
ggml_backend_tensor_get(tensor, ptr, offset, size);
ptr += size;
size_written += size;
buf_size -= size;
}
size_t n_bytes() override {
return size_written;
}
private:
uint8_t * ptr;
size_t buf_size = 0;
size_t size_written = 0;
};
class llama_io_read_buffer : public llama_io_read_i {
public:
llama_io_read_buffer(const uint8_t * p, size_t len) : ptr(p), buf_size(len) {}
const uint8_t * read(size_t size) override {
const uint8_t * base_ptr = ptr;
if (size > buf_size) {
throw std::runtime_error("unexpectedly reached end of buffer");
}
ptr += size;
size_read += size;
buf_size -= size;
return base_ptr;
}
void read_to(void * dst, size_t size) override {
memcpy(dst, read(size), size);
}
size_t n_bytes() override {
return size_read;
}
private:
const uint8_t * ptr;
size_t buf_size = 0;
size_t size_read = 0;
};
class llama_io_write_file : public llama_io_write_i {
public:
llama_io_write_file(llama_file * f) : file(f) {}
void write(const void * src, size_t size) override {
file->write_raw(src, size);
size_written += size;
}
void write_tensor(const ggml_tensor * tensor, size_t offset, size_t size) override {
temp_buffer.resize(size);
ggml_backend_tensor_get(tensor, temp_buffer.data(), offset, size);
write(temp_buffer.data(), temp_buffer.size());
}
size_t n_bytes() override {
return size_written;
}
private:
llama_file * file;
size_t size_written = 0;
std::vector<uint8_t> temp_buffer;
};
class llama_io_read_file : public llama_io_read_i {
public:
llama_io_read_file(llama_file * f) : file(f) {}
void read_to(void * dst, size_t size) override {
file->read_raw(dst, size);
size_read += size;
}
const uint8_t * read(size_t size) override {
temp_buffer.resize(size);
read_to(temp_buffer.data(), size);
return temp_buffer.data();
}
size_t n_bytes() override {
return size_read;
}
private:
llama_file * file;
size_t size_read = 0;
std::vector<uint8_t> temp_buffer;
};
size_t llama_context::state_get_size() {
llama_io_write_dummy io;
try {
return state_write_data(io);
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: error getting state size: %s\n", __func__, err.what());
return 0;
}
}
size_t llama_context::state_get_data(uint8_t * dst, size_t size) {
llama_io_write_buffer io(dst, size);
try {
return state_write_data(io);
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: error saving state: %s\n", __func__, err.what());
return 0;
}
}
size_t llama_context::state_set_data(const uint8_t * src, size_t size) {
llama_io_read_buffer io(src, size);
try {
return state_read_data(io);
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: error loading state: %s\n", __func__, err.what());
return 0;
}
}
size_t llama_context::state_seq_get_size(llama_seq_id seq_id) {
llama_io_write_dummy io;
try {
return state_seq_write_data(io, seq_id);
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: error getting state size: %s\n", __func__, err.what());
return 0;
}
}
size_t llama_context::state_seq_get_data(llama_seq_id seq_id, uint8_t * dst, size_t size) {
llama_io_write_buffer io(dst, size);
try {
return state_seq_write_data(io, seq_id);
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: error saving state: %s\n", __func__, err.what());
return 0;
}
}
size_t llama_context::state_seq_set_data(llama_seq_id seq_id, const uint8_t * src, size_t size) {
llama_io_read_buffer io(src, size);
try {
return state_seq_read_data(io, seq_id);
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: error loading state: %s\n", __func__, err.what());
return 0;
}
}
bool llama_context::state_load_file(const char * filepath, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
llama_file file(filepath, "rb");
// sanity checks
{
const uint32_t magic = file.read_u32();
const uint32_t version = file.read_u32();
if (magic != LLAMA_SESSION_MAGIC || version != LLAMA_SESSION_VERSION) {
LLAMA_LOG_ERROR("%s: unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
return false;
}
}
// load the prompt
{
const uint32_t n_token_count = file.read_u32();
if (n_token_count > n_token_capacity) {
LLAMA_LOG_ERROR("%s: token count in session file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
return false;
}
file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
*n_token_count_out = n_token_count;
}
// restore the context state
{
const size_t n_state_size_cur = file.size() - file.tell();
llama_io_read_file io( &file);
const size_t n_read = state_read_data(io);
if (n_read != n_state_size_cur) {
LLAMA_LOG_ERROR("%s: did not read all of the session file data! size %zu, got %zu\n", __func__, n_state_size_cur, n_read);
return false;
}
}
return true;
}
bool llama_context::state_save_file(const char * filepath, const llama_token * tokens, size_t n_token_count) {
llama_file file(filepath, "wb");
file.write_u32(LLAMA_SESSION_MAGIC);
file.write_u32(LLAMA_SESSION_VERSION);
// save the prompt
file.write_u32((uint32_t) n_token_count);
file.write_raw(tokens, sizeof(llama_token) * n_token_count);
// save the context state using stream saving
llama_io_write_file io(&file);
state_write_data(io);
return true;
}
size_t llama_context::state_seq_load_file(llama_seq_id seq_id, const char * filepath, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
llama_file file(filepath, "rb");
// version checks
{
const uint32_t magic = file.read_u32();
const uint32_t version = file.read_u32();
if (magic != LLAMA_STATE_SEQ_MAGIC || version != LLAMA_STATE_SEQ_VERSION) {
LLAMA_LOG_ERROR("%s: unknown (magic, version) for sequence state file: %08x, %08x\n", __func__, magic, version);
return 0;
}
}
// load the prompt
{
const uint32_t n_token_count = file.read_u32();
if (n_token_count > n_token_capacity) {
LLAMA_LOG_ERROR("%s: token count in sequence state file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
return 0;
}
file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
*n_token_count_out = n_token_count;
}
// restore the context state
{
const size_t state_size = file.size() - file.tell();
llama_io_read_file io(&file);
const size_t nread = state_seq_read_data(io, seq_id);
if (!nread) {
LLAMA_LOG_ERROR("%s: failed to restore sequence state\n", __func__);
return 0;
}
GGML_ASSERT(nread <= state_size);
GGML_ASSERT(nread + sizeof(uint32_t) * 3 + sizeof(llama_token) * *n_token_count_out == file.tell());
}
return file.tell();
}
size_t llama_context::state_seq_save_file(llama_seq_id seq_id, const char * filepath, const llama_token * tokens, size_t n_token_count) {
llama_file file(filepath, "wb");
file.write_u32(LLAMA_STATE_SEQ_MAGIC);
file.write_u32(LLAMA_STATE_SEQ_VERSION);
// save the prompt
file.write_u32((uint32_t) n_token_count);
file.write_raw(tokens, sizeof(llama_token) * n_token_count);
// save the context state using stream saving
llama_io_write_file io(&file);
state_seq_write_data(io, seq_id);
const size_t res = file.tell();
GGML_ASSERT(res == sizeof(uint32_t) * 3 + sizeof(llama_token) * n_token_count + io.n_bytes());
return res;
}
size_t llama_context::state_write_data(llama_io_write_i & io) {
LLAMA_LOG_DEBUG("%s: writing state\n", __func__);
// write model info
{
LLAMA_LOG_DEBUG("%s: - writing model info\n", __func__);
const std::string arch_str = llm_arch_name(model.arch);
io.write_string(arch_str);
// TODO: add more model-specific info which should prevent loading the session file if not identical
}
// write output ids
{
LLAMA_LOG_DEBUG("%s: - writing output ids\n", __func__);
const auto n_outputs = this->n_outputs;
const auto & output_ids = this->output_ids;
std::vector<int32_t> w_output_pos;
GGML_ASSERT(n_outputs <= n_outputs_max);
w_output_pos.resize(n_outputs);
// build a more compact representation of the output ids
for (size_t i = 0; i < n_batch(); ++i) {
// map an output id to a position in the batch
int32_t pos = output_ids[i];
if (pos >= 0) {
GGML_ASSERT(pos < n_outputs);
w_output_pos[pos] = i;
}
}
io.write(&n_outputs, sizeof(n_outputs));
if (n_outputs) {
io.write(w_output_pos.data(), n_outputs * sizeof(int32_t));
}
}
// write logits
{
LLAMA_LOG_DEBUG("%s: - writing logits\n", __func__);
const uint64_t logits_size = std::min((uint64_t) this->logits_size, (uint64_t) n_outputs * model.vocab.n_tokens());
io.write(&logits_size, sizeof(logits_size));
if (logits_size) {
io.write(logits, logits_size * sizeof(float));
}
}
// write embeddings
{
LLAMA_LOG_DEBUG("%s: - writing embeddings\n", __func__);
const uint64_t embd_size = std::min((uint64_t) this->embd_size, (uint64_t) n_outputs * model.hparams.n_embd);
io.write(&embd_size, sizeof(embd_size));
if (embd_size) {
io.write(embd, embd_size * sizeof(float));
}
}
llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
if (kv_self != nullptr) {
LLAMA_LOG_DEBUG("%s: - writing KV self\n", __func__);
kv_self->state_write(io);
}
return io.n_bytes();
}
size_t llama_context::state_read_data(llama_io_read_i & io) {
LLAMA_LOG_DEBUG("%s: reading state\n", __func__);
// read model info
{
LLAMA_LOG_DEBUG("%s: - reading model info\n", __func__);
const std::string cur_arch_str = llm_arch_name(model.arch);
std::string arch_str;
io.read_string(arch_str);
if (cur_arch_str != arch_str) {
throw std::runtime_error(format("wrong model arch: '%s' instead of '%s'", arch_str.c_str(), cur_arch_str.c_str()));
}
// TODO: add more info which needs to be identical but which is not verified otherwise
}
// read output ids
{
LLAMA_LOG_DEBUG("%s: - reading output ids\n", __func__);
auto n_outputs = this->n_outputs;
io.read_to(&n_outputs, sizeof(n_outputs));
if (n_outputs > output_reserve(n_outputs)) {
throw std::runtime_error("could not reserve outputs");
}
std::vector<int32_t> output_pos;
if (n_outputs) {
output_pos.resize(n_outputs);
io.read_to(output_pos.data(), n_outputs * sizeof(int32_t));
for (int32_t i = 0; i < (int32_t) output_pos.size(); ++i) {
int32_t id = output_pos[i];
if ((uint32_t) id >= n_batch()) {
throw std::runtime_error(format("invalid output id, %d does not fit in batch size of %u", id, n_batch()));
}
this->output_ids[id] = i;
}
this->n_outputs = n_outputs;
}
}
// read logits
{
LLAMA_LOG_DEBUG("%s: - reading logits\n", __func__);
uint64_t logits_size;
io.read_to(&logits_size, sizeof(logits_size));
if (this->logits_size < logits_size) {
throw std::runtime_error("logits buffer too small");
}
if (logits_size) {
io.read_to(this->logits, logits_size * sizeof(float));
}
}
// read embeddings
{
LLAMA_LOG_DEBUG("%s: - reading embeddings\n", __func__);
uint64_t embd_size;
io.read_to(&embd_size, sizeof(embd_size));
if (this->embd_size < embd_size) {
throw std::runtime_error("embeddings buffer too small");
}
if (embd_size) {
io.read_to(this->embd, embd_size * sizeof(float));
}
}
if (memory) {
LLAMA_LOG_DEBUG("%s: - reading KV self\n", __func__);
llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
kv_self->state_read(io);
}
return io.n_bytes();
}
size_t llama_context::state_seq_write_data(llama_io_write_i & io, llama_seq_id seq_id) {
GGML_UNUSED(seq_id);
if (memory) {
llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
kv_self->state_write(io, seq_id);
}
return io.n_bytes();
}
size_t llama_context::state_seq_read_data(llama_io_read_i & io, llama_seq_id seq_id) {
GGML_UNUSED(seq_id);
if (memory) {
llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
kv_self->state_read(io, seq_id);
}
return io.n_bytes();
}
//
// perf
//
llama_perf_context_data llama_context::perf_get_data() const {
llama_perf_context_data data = {};
data.t_start_ms = 1e-3 * t_start_us;
data.t_load_ms = 1e-3 * t_load_us;
data.t_p_eval_ms = 1e-3 * t_p_eval_us;
data.t_eval_ms = 1e-3 * t_eval_us;
data.n_p_eval = std::max(1, n_p_eval);
data.n_eval = std::max(1, n_eval);
return data;
}
void llama_context::perf_reset() {
t_start_us = ggml_time_us();
t_eval_us = n_eval = 0;
t_p_eval_us = n_p_eval = 0;
}
//
// training
//
static void llama_set_param(struct ggml_tensor * tensor, llama_opt_param_filter param_filter, void * userdata) {
if (!tensor || tensor->type != GGML_TYPE_F32) {
return;
}
if (!param_filter(tensor, userdata)) {
return;
}
if (strcmp(tensor->name, "token_embd.weight") == 0) {
return; // FIXME
}
if (strcmp(tensor->name, "rope_freqs.weight") == 0) {
return; // FIXME
}
ggml_set_param(tensor);
}
void llama_context::opt_init(struct llama_model * model, struct llama_opt_params lopt_params) {
GGML_ASSERT(!opt_ctx);
model->hparams.n_ctx_train = lopt_params.n_ctx_train > 0 ? lopt_params.n_ctx_train : n_ctx();
const uint32_t n_batch = std::min(this->n_batch(), model->hparams.n_ctx_train);
const uint32_t n_ubatch = std::min(this->n_ubatch(), n_batch);
GGML_ASSERT(model->hparams.n_ctx_train % n_batch == 0);
GGML_ASSERT(n_batch % n_ubatch == 0);
ggml_opt_params opt_params = ggml_opt_default_params(sched.get(), GGML_OPT_LOSS_TYPE_CROSS_ENTROPY);
opt_params.opt_period = n_batch / n_ubatch;
opt_params.get_opt_pars = lopt_params.get_opt_pars;
opt_params.get_opt_pars_ud = lopt_params.get_opt_pars_ud;
opt_ctx = ggml_opt_init(opt_params);
llama_opt_param_filter param_filter = lopt_params.param_filter;
void * param_filter_ud = lopt_params.param_filter_ud;
//llama_set_param(model->tok_embd, param_filter, param_filter_ud); // FIXME
llama_set_param(model->type_embd, param_filter, param_filter_ud);
llama_set_param(model->pos_embd, param_filter, param_filter_ud);
llama_set_param(model->tok_norm, param_filter, param_filter_ud);
llama_set_param(model->tok_norm_b, param_filter, param_filter_ud);
llama_set_param(model->output_norm, param_filter, param_filter_ud);
llama_set_param(model->output_norm_b, param_filter, param_filter_ud);
llama_set_param(model->output, param_filter, param_filter_ud);
llama_set_param(model->output_b, param_filter, param_filter_ud);
llama_set_param(model->output_norm_enc, param_filter, param_filter_ud);
llama_set_param(model->cls, param_filter, param_filter_ud);
llama_set_param(model->cls_b, param_filter, param_filter_ud);
llama_set_param(model->cls_out, param_filter, param_filter_ud);
llama_set_param(model->cls_out_b, param_filter, param_filter_ud);
for (struct llama_layer & layer : model->layers) {
for (size_t i = 0; i < sizeof(layer)/sizeof(struct ggml_tensor *); ++i) {
llama_set_param(reinterpret_cast<struct ggml_tensor **>(&layer)[i], param_filter, param_filter_ud);
}
}
}
void llama_context::opt_epoch_iter(
ggml_opt_dataset_t dataset,
ggml_opt_result_t result,
const std::vector<llama_token> & tokens,
const std::vector<llama_token> & labels_sparse,
llama_batch & batch,
ggml_opt_epoch_callback callback,
bool train,
int64_t idata_in_loop,
int64_t ndata_in_loop,
int64_t t_loop_start) {
GGML_ASSERT(opt_ctx);
const uint32_t n_ctx = llama_model_n_ctx_train(&model);
const uint32_t n_batch = std::min(this->n_batch(), n_ctx);
const uint32_t n_ubatch = std::min(this->n_ubatch(), n_batch);
llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
kv_self->clear();
llama_kv_cache_guard kv_guard(kv_self);
for (uint32_t pos_ctx = 0; pos_ctx < n_ctx; pos_ctx += n_batch) {
batch.n_tokens = n_batch;
for (uint32_t pos_batch = 0; pos_batch < n_batch; ++pos_batch) {
batch.token [pos_batch] = tokens[pos_ctx + pos_batch];
batch.pos [pos_batch] = pos_ctx + pos_batch;
batch.n_seq_id[pos_batch] = 1;
batch.seq_id [pos_batch][0] = 0;
batch.logits [pos_batch] = true;
}
const auto n_tokens_all = batch.n_tokens;
n_queued_tokens += n_tokens_all;
// this indicates we are doing pooled embedding, so we ignore batch.logits and output all tokens
const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE;
embd_seq.clear();
int64_t n_outputs_all = n_tokens_all;
llama_sbatch sbatch = kv_self->sbatch_init(batch, /*logits_all =*/ true);
// reserve output buffer
if (output_reserve(n_outputs_all) < n_outputs_all) {
LLAMA_LOG_ERROR("%s: could not reserve space for batch with %" PRId64 " outputs\n", __func__, n_outputs_all);
GGML_ABORT("TODO: handle this error");
};
for (uint32_t pos_batch = 0; pos_batch < n_batch; pos_batch += n_ubatch) {
llama_ubatch ubatch = kv_self->ubatch_next(sbatch, cparams.n_ubatch, embd_pooled);
n_outputs = ubatch.n_tokens;
// TODO: not sure if this is needed
if (!kv_self->find_slot(ubatch)) {
LLAMA_LOG_WARN("%s: failed to find KV cache slot for ubatch of size %d\n", __func__, ubatch.n_tokens);
GGML_ABORT("TODO: handle this error");
}
auto * gf = graph_init();
auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT);
struct ggml_context * ctx_compute_opt;
{
const size_t size_gf = ggml_graph_size(gf);
const size_t size_meta = 4*size_gf*ggml_tensor_overhead() + 2*ggml_graph_overhead_custom(size_gf, /*grads = */ true);
struct ggml_init_params params = {
/*.mem_size =*/ size_meta,
/*.mem_buffer =*/ nullptr,
/*.no_alloc =*/ true,
};
ctx_compute_opt = ggml_init(params);
}
ggml_opt_prepare_alloc(opt_ctx, ctx_compute_opt, gf, res->get_tokens(), res->get_logits());
ggml_opt_alloc(opt_ctx, train);
res->set_inputs(&ubatch);
{
struct ggml_tensor * labels = ggml_opt_labels(opt_ctx);
GGML_ASSERT(labels->ne[1] == n_ubatch);
ggml_set_zero(labels);
const float onef = 1.0f;
for (uint32_t pos_ubatch = 0; pos_ubatch < n_ubatch; ++pos_ubatch) {
const uint32_t ilabel = pos_ctx + pos_batch + pos_ubatch;
GGML_ASSERT(labels_sparse[ilabel] < labels->ne[0]);
ggml_backend_tensor_set(labels, &onef, (pos_ubatch*labels->ne[0] + labels_sparse[ilabel])*sizeof(float), sizeof(float));
}
}
ggml_opt_eval(opt_ctx, result);
if (callback) {
callback(train, opt_ctx, dataset, result, idata_in_loop + (pos_ctx + pos_batch)/n_ubatch + 1, ndata_in_loop, t_loop_start);
}
ggml_free(ctx_compute_opt);
}
}
kv_guard.commit();
}
void llama_context::opt_epoch(
ggml_opt_dataset_t dataset,
ggml_opt_result_t result_train,
ggml_opt_result_t result_eval,
int64_t idata_split,
ggml_opt_epoch_callback callback_train,
ggml_opt_epoch_callback callback_eval) {
const uint32_t n_ctx = this->n_ctx();
const uint32_t n_batch = std::min(cparams.n_batch, n_ctx);
const uint32_t n_ubatch = std::min(cparams.n_ubatch, n_batch);
const int64_t ndata = ggml_opt_dataset_ndata(dataset);
GGML_ASSERT(idata_split >= 0);
GGML_ASSERT(idata_split <= ndata);
const uint32_t ubatch_per_ctx = n_ctx / n_ubatch;
struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
std::vector<llama_token> tokens(n_ctx);
std::vector<llama_token> labels_sparse(n_ctx);
int64_t idata = 0;
int64_t t_loop_start = ggml_time_us();
int64_t ndata_in_loop = idata_split*ubatch_per_ctx;
for (; idata < idata_split; ++idata) {
constexpr bool train = true;
const int64_t idata_in_loop = idata*ubatch_per_ctx;
ggml_opt_dataset_get_batch_host(dataset, tokens.data(), n_ctx*sizeof(llama_token), labels_sparse.data(), idata);
opt_epoch_iter(dataset, result_train, tokens, labels_sparse, batch,
callback_train, train, idata_in_loop, ndata_in_loop, t_loop_start);
}
t_loop_start = ggml_time_us();
ndata_in_loop = (ndata - idata_split)*ubatch_per_ctx;
for (; idata < ndata; ++idata) {
constexpr bool train = false;
const int64_t idata_in_loop = (idata - idata_split)*ubatch_per_ctx;
ggml_opt_dataset_get_batch_host(dataset, tokens.data(), n_ctx*sizeof(llama_token), labels_sparse.data(), idata);
opt_epoch_iter(dataset, result_eval, tokens, labels_sparse, batch,
callback_eval, train, idata_in_loop, ndata_in_loop, t_loop_start);
}
llama_batch_free(batch);
}
//
// interface implementation
//
llama_context_params llama_context_default_params() {
llama_context_params result = {
/*.n_ctx =*/ 512,
/*.n_batch =*/ 2048,
/*.n_ubatch =*/ 512,
/*.n_seq_max =*/ 1,
/*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
/*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS,
/*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
/*.pooling_type =*/ LLAMA_POOLING_TYPE_UNSPECIFIED,
/*.attention_type =*/ LLAMA_ATTENTION_TYPE_UNSPECIFIED,
/*.rope_freq_base =*/ 0.0f,
/*.rope_freq_scale =*/ 0.0f,
/*.yarn_ext_factor =*/ -1.0f,
/*.yarn_attn_factor =*/ 1.0f,
/*.yarn_beta_fast =*/ 32.0f,
/*.yarn_beta_slow =*/ 1.0f,
/*.yarn_orig_ctx =*/ 0,
/*.defrag_thold =*/ -1.0f,
/*.cb_eval =*/ nullptr,
/*.cb_eval_user_data =*/ nullptr,
/*.type_k =*/ GGML_TYPE_F16,
/*.type_v =*/ GGML_TYPE_F16,
/*.abort_callback =*/ nullptr,
/*.abort_callback_data =*/ nullptr,
/*.embeddings =*/ false,
/*.offload_kqv =*/ true,
/*.flash_attn =*/ false,
/*.no_perf =*/ true,
/*.op_offload =*/ true,
/*.swa_full =*/ true,
};
return result;
}
llama_context * llama_init_from_model(
llama_model * model,
llama_context_params params) {
if (!model) {
LLAMA_LOG_ERROR("%s: model cannot be NULL\n", __func__);
return nullptr;
}
if (params.n_batch == 0 && params.n_ubatch == 0) {
LLAMA_LOG_ERROR("%s: n_batch and n_ubatch cannot both be zero\n", __func__);
return nullptr;
}
if (params.n_ctx == 0 && model->hparams.n_ctx_train == 0) {
LLAMA_LOG_ERROR("%s: n_ctx and model->hparams.n_ctx_train cannot both be zero\n", __func__);
return nullptr;
}
if (params.flash_attn && model->arch == LLM_ARCH_GROK) {
LLAMA_LOG_WARN("%s: flash_attn is not compatible with Grok - forcing off\n", __func__);
params.flash_attn = false;
}
if (ggml_is_quantized(params.type_v) && !params.flash_attn) {
LLAMA_LOG_ERROR("%s: V cache quantization requires flash_attn\n", __func__);
return nullptr;
}
try {
auto * ctx = new llama_context(*model, params);
return ctx;
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: failed to initialize the context: %s\n", __func__, err.what());
}
return nullptr;
}
// deprecated
llama_context * llama_new_context_with_model(
llama_model * model,
llama_context_params params) {
return llama_init_from_model(model, params);
}
void llama_free(llama_context * ctx) {
delete ctx;
}
uint32_t llama_n_ctx(const llama_context * ctx) {
return ctx->n_ctx();
}
uint32_t llama_n_batch(const llama_context * ctx) {
return ctx->n_batch();
}
uint32_t llama_n_ubatch(const llama_context * ctx) {
return ctx->n_ubatch();
}
uint32_t llama_n_seq_max(const llama_context * ctx) {
return ctx->n_seq_max();
}
const llama_model * llama_get_model(const llama_context * ctx) {
return &ctx->get_model();
}
llama_kv_cache * llama_get_kv_self(llama_context * ctx) {
return ctx->get_kv_self();
}
void llama_kv_self_update(llama_context * ctx) {
ctx->kv_self_update();
}
enum llama_pooling_type llama_pooling_type(const llama_context * ctx) {
return ctx->pooling_type();
}
void llama_attach_threadpool(
llama_context * ctx,
ggml_threadpool_t threadpool,
ggml_threadpool_t threadpool_batch) {
ctx->attach_threadpool(threadpool, threadpool_batch);
}
void llama_detach_threadpool(llama_context * ctx) {
ctx->detach_threadpool();
}
void llama_set_n_threads(llama_context * ctx, int32_t n_threads, int32_t n_threads_batch) {
ctx->set_n_threads(n_threads, n_threads_batch);
}
int32_t llama_n_threads(llama_context * ctx) {
return ctx->n_threads();
}
int32_t llama_n_threads_batch(llama_context * ctx) {
return ctx->n_threads_batch();
}
void llama_set_abort_callback(llama_context * ctx, bool (*abort_callback)(void * data), void * abort_callback_data) {
ctx->set_abort_callback(abort_callback, abort_callback_data);
}
void llama_set_embeddings(llama_context * ctx, bool embeddings) {
ctx->set_embeddings(embeddings);
}
void llama_set_causal_attn(llama_context * ctx, bool causal_attn) {
ctx->set_causal_attn(causal_attn);
}
void llama_set_warmup(llama_context * ctx, bool warmup) {
ctx->set_warmup(warmup);
}
void llama_synchronize(llama_context * ctx) {
ctx->synchronize();
}
float * llama_get_logits(llama_context * ctx) {
ctx->synchronize();
return ctx->get_logits();
}
float * llama_get_logits_ith(llama_context * ctx, int32_t i) {
ctx->synchronize();
return ctx->get_logits_ith(i);
}
float * llama_get_embeddings(llama_context * ctx) {
ctx->synchronize();
return ctx->get_embeddings();
}
float * llama_get_embeddings_ith(llama_context * ctx, int32_t i) {
ctx->synchronize();
return ctx->get_embeddings_ith(i);
}
float * llama_get_embeddings_seq(llama_context * ctx, llama_seq_id seq_id) {
ctx->synchronize();
return ctx->get_embeddings_seq(seq_id);
}
// llama adapter API
int32_t llama_set_adapter_lora(
llama_context * ctx,
llama_adapter_lora * adapter,
float scale) {
ctx->set_adapter_lora(adapter, scale);
return 0;
}
int32_t llama_rm_adapter_lora(
llama_context * ctx,
llama_adapter_lora * adapter) {
bool res = ctx->rm_adapter_lora(adapter);
return res ? 0 : -1;
}
void llama_clear_adapter_lora(llama_context * ctx) {
ctx->clear_adapter_lora();
}
int32_t llama_apply_adapter_cvec(
llama_context * ctx,
const float * data,
size_t len,
int32_t n_embd,
int32_t il_start,
int32_t il_end) {
bool res = ctx->apply_adapter_cvec(data, len, n_embd, il_start, il_end);
return res ? 0 : -1;
}
//
// kv cache
//
// deprecated
int32_t llama_kv_self_n_tokens(const llama_context * ctx) {
const auto * kv = ctx->get_kv_self();
if (!kv) {
return 0;
}
int32_t res = 0;
for (uint32_t s = 0; s < ctx->get_cparams().n_seq_max; s++) {
const llama_pos p0 = kv->seq_pos_min(s);
const llama_pos p1 = kv->seq_pos_max(s);
if (p0 >= 0) {
res += (p1 - p0) + 1;
}
}
return res;
}
// deprecated
// note: this is the same as above - will be removed anyway, so it's ok
int32_t llama_kv_self_used_cells(const llama_context * ctx) {
const auto * kv = ctx->get_kv_self();
if (!kv) {
return 0;
}
int32_t res = 0;
for (uint32_t s = 0; s < ctx->get_cparams().n_seq_max; s++) {
const llama_pos p0 = kv->seq_pos_min(s);
const llama_pos p1 = kv->seq_pos_max(s);
if (p0 >= 0) {
res += (p1 - p0) + 1;
}
}
return res;
}
void llama_kv_self_clear(llama_context * ctx) {
auto * kv = ctx->get_kv_self();
if (!kv) {
return;
}
kv->clear();
}
bool llama_kv_self_seq_rm(
llama_context * ctx,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1) {
auto * kv = ctx->get_kv_self();
if (!kv) {
return true;
}
return kv->seq_rm(seq_id, p0, p1);
}
void llama_kv_self_seq_cp(
llama_context * ctx,
llama_seq_id seq_id_src,
llama_seq_id seq_id_dst,
llama_pos p0,
llama_pos p1) {
auto * kv = ctx->get_kv_self();
if (!kv) {
return;
}
kv->seq_cp(seq_id_src, seq_id_dst, p0, p1);
}
void llama_kv_self_seq_keep(llama_context * ctx, llama_seq_id seq_id) {
auto * kv = ctx->get_kv_self();
if (!kv) {
return;
}
kv->seq_keep(seq_id);
}
void llama_kv_self_seq_add(
llama_context * ctx,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1,
llama_pos delta) {
auto * kv = ctx->get_kv_self();
if (!kv) {
return;
}
kv->seq_add(seq_id, p0, p1, delta);
}
void llama_kv_self_seq_div(
llama_context * ctx,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1,
int d) {
auto * kv = ctx->get_kv_self();
if (!kv) {
return;
}
kv->seq_div(seq_id, p0, p1, d);
}
llama_pos llama_kv_self_seq_pos_min(llama_context * ctx, llama_seq_id seq_id) {
const auto * kv = ctx->get_kv_self();
if (!kv) {
return -1;
}
return kv->seq_pos_min(seq_id);
}
llama_pos llama_kv_self_seq_pos_max(llama_context * ctx, llama_seq_id seq_id) {
const auto * kv = ctx->get_kv_self();
if (!kv) {
return -1;
}
return kv->seq_pos_max(seq_id);
}
void llama_kv_self_defrag(llama_context * ctx) {
auto * kv = ctx->get_kv_self();
if (!kv) {
return;
}
// force defrag
kv->defrag_sched(-1.0f);
}
bool llama_kv_self_can_shift(const llama_context * ctx) {
const auto * kv = ctx->get_kv_self();
if (!kv) {
return false;
}
return kv->get_can_shift();
}
// llama state API
// deprecated
size_t llama_get_state_size(llama_context * ctx) {
return llama_state_get_size(ctx);
}
// deprecated
size_t llama_copy_state_data(llama_context * ctx, uint8_t * dst) {
return llama_state_get_data(ctx, dst, -1);
}
// deprecated
size_t llama_set_state_data(llama_context * ctx, const uint8_t * src) {
return llama_state_set_data(ctx, src, -1);
}
// deprecated
bool llama_load_session_file(llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
return llama_state_load_file(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
}
// deprecated
bool llama_save_session_file(llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
return llama_state_save_file(ctx, path_session, tokens, n_token_count);
}
// Returns the *actual* size of the state.
// Intended to be used when saving to state to a buffer.
size_t llama_state_get_size(llama_context * ctx) {
return ctx->state_get_size();
}
size_t llama_state_get_data(llama_context * ctx, uint8_t * dst, size_t size) {
ctx->synchronize();
return ctx->state_get_data(dst, size);
}
// Sets the state reading from the specified source address
size_t llama_state_set_data(llama_context * ctx, const uint8_t * src, size_t size) {
ctx->synchronize();
return ctx->state_set_data(src, size);
}
bool llama_state_load_file(llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
ctx->synchronize();
try {
return ctx->state_load_file(path_session, tokens_out, n_token_capacity, n_token_count_out);
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: error loading session file: %s\n", __func__, err.what());
return false;
}
}
bool llama_state_save_file(llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
ctx->synchronize();
try {
return ctx->state_save_file(path_session, tokens, n_token_count);
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: error saving session file: %s\n", __func__, err.what());
return false;
}
}
size_t llama_state_seq_get_size(llama_context * ctx, llama_seq_id seq_id) {
return ctx->state_seq_get_size(seq_id);
}
size_t llama_state_seq_get_data(llama_context * ctx, uint8_t * dst, size_t size, llama_seq_id seq_id) {
ctx->synchronize();
return ctx->state_seq_get_data(seq_id, dst, size);
}
size_t llama_state_seq_set_data(llama_context * ctx, const uint8_t * src, size_t size, llama_seq_id seq_id) {
ctx->synchronize();
return ctx->state_seq_set_data(seq_id, src, size);
}
size_t llama_state_seq_save_file(llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count) {
ctx->synchronize();
try {
return ctx->state_seq_save_file(seq_id, filepath, tokens, n_token_count);
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: error saving sequence state file: %s\n", __func__, err.what());
return 0;
}
}
size_t llama_state_seq_load_file(llama_context * ctx, const char * filepath, llama_seq_id dest_seq_id, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
ctx->synchronize();
try {
return ctx->state_seq_load_file(dest_seq_id, filepath, tokens_out, n_token_capacity, n_token_count_out);
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: error loading sequence state file: %s\n", __func__, err.what());
return 0;
}
}
///
int32_t llama_encode(
llama_context * ctx,
llama_batch batch) {
const int ret = ctx->encode(batch);
if (ret != 0) {
LLAMA_LOG_ERROR("%s: failed to encode, ret = %d\n", __func__, ret);
}
return ret;
}
int32_t llama_decode(
llama_context * ctx,
llama_batch batch) {
int ret = ctx->decode(batch);
// defrag and try again
// TODO: distinguish return code when we are sure that even after defrag there is no space available
if (ret == 1) {
llama_kv_self_defrag(ctx);
ret = ctx->decode(batch);
if (ret == 1) {
LLAMA_LOG_WARN("%s: failed to find KV cache slot for batch of size %d\n", __func__, batch.n_tokens);
return ret;
}
}
if (ret != 0) {
LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
}
return ret;
}
//
// perf
//
llama_perf_context_data llama_perf_context(const llama_context * ctx) {
llama_perf_context_data data = {};
if (ctx == nullptr) {
return data;
}
data = ctx->perf_get_data();
return data;
}
void llama_perf_context_print(const llama_context * ctx) {
const auto data = llama_perf_context(ctx);
const double t_end_ms = 1e-3 * ggml_time_us();
LLAMA_LOG_INFO("%s: load time = %10.2f ms\n", __func__, data.t_load_ms);
LLAMA_LOG_INFO("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, data.t_p_eval_ms, data.n_p_eval, data.t_p_eval_ms / data.n_p_eval, 1e3 / data.t_p_eval_ms * data.n_p_eval);
LLAMA_LOG_INFO("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, data.t_eval_ms, data.n_eval, data.t_eval_ms / data.n_eval, 1e3 / data.t_eval_ms * data.n_eval);
LLAMA_LOG_INFO("%s: total time = %10.2f ms / %5d tokens\n", __func__, (t_end_ms - data.t_start_ms), (data.n_p_eval + data.n_eval));
}
void llama_perf_context_reset(llama_context * ctx) {
ctx->perf_reset();
}
//
// training
//
bool llama_opt_param_filter_all(const struct ggml_tensor * tensor, void * userdata) {
GGML_UNUSED(tensor);
GGML_UNUSED(userdata);
return true;
}
void llama_opt_init(struct llama_context * ctx, struct llama_model * model, struct llama_opt_params lopt_params) {
ctx->opt_init(model, lopt_params);
}
void llama_opt_epoch(
struct llama_context * ctx,
ggml_opt_dataset_t dataset,
ggml_opt_result_t result_train,
ggml_opt_result_t result_eval,
int64_t idata_split,
ggml_opt_epoch_callback callback_train,
ggml_opt_epoch_callback callback_eval) {
ctx->opt_epoch(
dataset,
result_train,
result_eval,
idata_split,
callback_train,
callback_eval);
}