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#include "llama-context.h"
#include "llama-arch.h"
#include "llama-impl.h"
#include "llama-batch.h"
#include "llama-io.h"
#include "llama-memory.h"
#include "llama-mmap.h"
#include "llama-model.h"
#include <cinttypes>
#include <cmath>
#include <cstring>
#include <limits>
#include <stdexcept>
//
// llama_context
//
llama_context::llama_context(
const llama_model & model,
llama_context_params params) :
model(model),
balloc(std::make_unique<llama_batch_allocr>(model.hparams.n_pos_per_embd())) {
// TODO warning when creating llama_context with awkward ctx size that is not a power of 2,
// may need to be backend-dependent
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_SEQ) {
throw std::runtime_error("n_seq_max must be <= " + std::to_string(LLAMA_MAX_SEQ));
}
cparams.n_threads = params.n_threads;
cparams.n_threads_batch = params.n_threads_batch;
cparams.yarn_ext_factor = params.yarn_ext_factor >= 0.0f ? params.yarn_ext_factor : hparams.yarn_ext_factor;
cparams.yarn_attn_factor = params.yarn_attn_factor >= 0.0f ? params.yarn_attn_factor : hparams.yarn_attn_factor;
cparams.yarn_beta_fast = params.yarn_beta_fast >= 0.0f ? params.yarn_beta_fast : hparams.yarn_beta_fast;
cparams.yarn_beta_slow = params.yarn_beta_slow >= 0.0f ? params.yarn_beta_slow : hparams.yarn_beta_slow;
cparams.embeddings = params.embeddings;
cparams.offload_kqv = params.offload_kqv;
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;
// Initialize backend samplers here so they are part of the sampling graph
// before the reserve passes run later in this function. This avoids a later
// re-reserve when graph nodes change.
if (params.samplers != nullptr && params.n_samplers > 0) {
for (size_t i = 0; i < params.n_samplers; ++i) {
const auto & config = params.samplers[i];
if (llama_sampler_chain_get(config.sampler, -1) == nullptr) {
throw std::runtime_error("the backend samplers must be of type llama_sampler_chain");
}
if (set_sampler(config.seq_id, config.sampler)) {
const int n_samplers = llama_sampler_chain_n(config.sampler);
LLAMA_LOG_INFO("%s: setting backend sampler for seq_id %d (n = %d)\n", __func__, config.seq_id, n_samplers);
}
}
}
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;
}
if (cparams.yarn_ext_factor != 0) {
static auto get_mscale = [](float scale, float mscale) {
return scale <= 1.0f ? 1.0f : (0.1f * mscale * logf(scale) + 1.0f);
};
const float factor = 1.0f / cparams.rope_freq_scale;
// ref: https://github.com/huggingface/transformers/blob/6d00f6b0a5679c36510f203e4226e36f517c3032/src/transformers/modeling_rope_utils.py#L336-L348
if (hparams.rope_yarn_log_mul != 0.0f) {
// note: here we assume `mscale == 1.0f`
// TODO: start reading the actual value of mscale and handle the case where it is not 1.0f
float mscale = 1.0f;
const float mscale_all_dims = hparams.rope_yarn_log_mul;
// [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
// special-case DEEPSEEK v2:
// https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat/blob/main/config.json#L42-L43
if (model.arch == LLM_ARCH_DEEPSEEK2 && mscale_all_dims != 1.0f) {
mscale = mscale_all_dims;
}
cparams.yarn_attn_factor = get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dims);
LLAMA_LOG_WARN("%s: setting new yarn_attn_factor = %.4f (mscale == %.1f, mscale_all_dim = %.1f)\n",
__func__, cparams.yarn_attn_factor, mscale, mscale_all_dims);
} else {
cparams.yarn_attn_factor = get_mscale(factor, 1.0f);
}
// when YARN is applied with yarn_ext_factor != 0.0f, we need to cancel this factor:
// https://github.com/ggml-org/llama.cpp/blob/a81a569577cc38b32558958b048228150be63eae/ggml/src/ggml-cpu/ops.cpp#L5541-L5544
//
// ref: https://github.com/ggml-org/llama.cpp/discussions/7416
// https://github.com/ggml-org/llama.cpp/pull/17945
cparams.yarn_attn_factor *= 1.0f / (1.0f + 0.1f * logf(factor));
}
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;
}
cparams.flash_attn = params.flash_attn_type != LLAMA_FLASH_ATTN_TYPE_DISABLED;
cparams.auto_fa = params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO;
// 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;
cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
cparams.op_offload = params.op_offload;
cparams.kv_unified = params.kv_unified;
// intialized later
cparams.pipeline_parallel = false;
{
const char * LLAMA_GRAPH_REUSE_DISABLE = getenv("LLAMA_GRAPH_REUSE_DISABLE");
graph_reuse_disable = LLAMA_GRAPH_REUSE_DISABLE ? (atoi(LLAMA_GRAPH_REUSE_DISABLE) != 0) : graph_reuse_disable;
if (graph_reuse_disable) {
LLAMA_LOG_WARN("%s: graph reuse disabled\n", __func__);
}
}
// ref: https://github.com/ggml-org/llama.cpp/pull/17046#discussion_r2503085732
cparams.n_ctx = GGML_PAD(cparams.n_ctx, 256);
if (cparams.kv_unified) {
cparams.n_ctx_seq = cparams.n_ctx;
} else {
cparams.n_ctx_seq = cparams.n_ctx / cparams.n_seq_max;
cparams.n_ctx_seq = GGML_PAD(cparams.n_ctx_seq, 256);
if (cparams.n_ctx_seq == 0) {
throw std::runtime_error("n_ctx_seq == 0");
}
if (cparams.n_ctx != cparams.n_ctx_seq * cparams.n_seq_max) {
cparams.n_ctx = cparams.n_ctx_seq * cparams.n_seq_max;
LLAMA_LOG_WARN("%s: n_ctx is not divisible by n_seq_max - rounding down to %u\n", __func__, cparams.n_ctx);
}
}
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_seq = %u\n", __func__, cparams.n_ctx_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 = %s\n", __func__, llama_flash_attn_type_name(params.flash_attn_type));
LLAMA_LOG_INFO("%s: kv_unified = %s\n", __func__, cparams.kv_unified ? "true" : "false");
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 (cparams.n_ctx_seq < hparams.n_ctx_train) {
LLAMA_LOG_WARN("%s: n_ctx_seq (%u) < n_ctx_train (%u) -- the full capacity of the model will not be utilized\n",
__func__, cparams.n_ctx_seq, hparams.n_ctx_train);
}
if (cparams.n_ctx_seq > hparams.n_ctx_train) {
LLAMA_LOG_WARN("%s: n_ctx_seq (%u) > n_ctx_train (%u) -- possible training context overflow\n",
__func__, cparams.n_ctx_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
{
if (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();
backend_buf_exp_size.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());
backend_buf_exp_size.push_back(0);
}
LLAMA_LOG_DEBUG("%s: backend_ptrs.size() = %zu\n", __func__, backend_ptrs.size());
// 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.n_gpu_layers() > model.hparams.n_layer &&
model.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
// TODO: should we ignore ACCEL types too?
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;
}
}
}
cparams.pipeline_parallel = pipeline_parallel;
if (cparams.pipeline_parallel) {
LLAMA_LOG_INFO("%s: pipeline parallelism enabled\n", __func__);
}
sched_reserve();
if (!cparams.flash_attn) {
if (ggml_is_quantized(params.type_v)) {
throw std::runtime_error("quantized V cache was requested, but this requires Flash Attention");
}
}
}
// Initialize the full vocabulary token ids for backend samplers.
{
const int n_vocab = model.vocab.n_tokens();
sampling.token_ids_full_vocab.resize(n_vocab);
for (int i = 0; i < n_vocab; ++i) {
sampling.token_ids_full_vocab[i] = i;
}
}
}
llama_context::~llama_context() {
if (!model.hparams.no_alloc) {
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];
const size_t size_exp = backend_buf_exp_size[i];
const size_t size_act = ggml_backend_sched_get_buffer_size(sched.get(), backend);
if (size_exp == size_act) {
LLAMA_LOG_DEBUG("%s: %10s compute buffer size is %8.4f MiB, matches expectation of %8.4f MiB\n",
__func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0));
} else {
LLAMA_LOG_WARN("%s: %10s compute buffer size of %8.4f MiB, does not match expectation of %8.4f MiB\n",
__func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0));
}
}
}
ggml_opt_free(opt_ctx);
}
void llama_context::sched_reserve() {
if (!sched_need_reserve) {
return;
}
sched_need_reserve = false;
LLAMA_LOG_INFO("%s: reserving ...\n", __func__);
synchronize();
const int64_t t_start_us = ggml_time_us();
const uint32_t n_seqs = cparams.n_seq_max;
const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
const size_t max_nodes = this->graph_max_nodes(n_tokens);
LLAMA_LOG_DEBUG("%s: max_nodes = %zu\n", __func__, max_nodes);
gf_res_prev.reset(new llm_graph_result(max_nodes));
gf_res_reserve.reset(new llm_graph_result(max_nodes));
sched.reset(ggml_backend_sched_new(backend_ptrs.data(), backend_buft.data(), backend_ptrs.size(), max_nodes, cparams.pipeline_parallel, cparams.op_offload));
llama_memory_context_ptr mctx;
if (memory) {
LLAMA_LOG_DEBUG("%s: reserving full memory module\n", __func__);
mctx = memory->init_full();
if (!mctx) {
throw std::runtime_error("failed to initialize memory module");
}
}
// avoid reserving graphs with zero outputs - assume one output per sequence
const int n_outputs = n_seqs;
LLAMA_LOG_DEBUG("%s: worst-case: n_tokens = %d, n_seqs = %d, n_outputs = %d\n", __func__, n_tokens, n_seqs, n_outputs);
// resolve automatic Flash Attention use
if (cparams.auto_fa) {
auto * gf = graph_reserve(1, n_seqs, n_outputs, mctx.get(), true);
if (!gf) {
throw std::runtime_error("failed to split graph for Flash Attention check");
}
const size_t prefix_len = strlen(LLAMA_TENSOR_NAME_FATTN) + 1;
bool fa_device_mismatch = false;
for (int i = 0; i < ggml_graph_n_nodes(gf); i++) {
ggml_tensor * n = ggml_graph_node(gf, i);
if (n->op != GGML_OP_FLASH_ATTN_EXT) {
continue;
}
ggml_backend_dev_t device_fa = ggml_backend_get_device(
ggml_backend_sched_get_tensor_backend(sched.get(), n));
// TODO: instead of the tensor names, use a map to keep track of which (FA) tensors belong to which layer
GGML_ASSERT(strncmp(n->name, LLAMA_TENSOR_NAME_FATTN "-", prefix_len) == 0);
const int il = std::stoi(n->name + prefix_len);
ggml_backend_dev_t device_kv = model.dev_layer(il);
if (device_fa != device_kv) {
LLAMA_LOG_WARN("%s: layer %d is assigned to device %s but the Flash Attention tensor "
"is assigned to device %s (usually due to missing support)\n",
__func__, il, ggml_backend_dev_name(device_kv), ggml_backend_dev_name(device_fa));
// FIXME: fa_device_mismatch logic is wrong for --no-kv-offload, but this is broken anyways
fa_device_mismatch = true;
break;
}
}
if (fa_device_mismatch) {
cparams.flash_attn = false;
LLAMA_LOG_WARN("%s: Flash Attention was auto, set to disabled\n", __func__);
} else {
cparams.flash_attn = true;
LLAMA_LOG_INFO("%s: Flash Attention was auto, set to enabled\n", __func__);
}
cparams.auto_fa = false;
}
// reserve worst-case graph
int n_splits_pp = -1;
int n_nodes_pp = -1;
int n_splits_tg = -1;
int n_nodes_tg = -1;
// reserve pp (prompt processing) graph first so that buffers are only allocated once
{
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get(),
model.hparams.no_alloc, model.hparams.no_alloc ? backend_buf_exp_size.data() : nullptr);
if (!gf) {
if (cparams.pipeline_parallel) {
LLAMA_LOG_WARN("%s: compute buffer allocation failed, retrying without pipeline parallelism\n", __func__);
cparams.pipeline_parallel = false;
sched.reset(ggml_backend_sched_new(backend_ptrs.data(), backend_buft.data(), backend_ptrs.size(), max_nodes, false, cparams.op_offload));
gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
}
if (!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 (token generation) graph to get the number of splits and nodes
{
auto * gf = graph_reserve(n_seqs, n_seqs, n_seqs, mctx.get(), model.hparams.no_alloc);
if (!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
{
// TODO: not sure if the following graph would be worster case for multi-stream KV caches:
//
// auto * gf = graph_reserve(n_tokens, 1, n_tokens, mctx.get());
//
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get(), model.hparams.no_alloc);
if (!gf) {
throw std::runtime_error("failed to allocate compute pp buffers");
}
}
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];
if (!model.hparams.no_alloc) {
backend_buf_exp_size[i] = ggml_backend_sched_get_buffer_size(sched.get(), backend);
}
if (backend_buf_exp_size[i] > 1) {
LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
ggml_backend_buft_name(buft),
backend_buf_exp_size[i] / 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);
}
const int64_t t_end_us = ggml_time_us();
LLAMA_LOG_INFO("%s: reserve took %.2f ms, sched copies = %d\n",
__func__, (t_end_us - t_start_us)/1000.0, ggml_backend_sched_get_n_copies(sched.get()));
}
void llama_context::synchronize() {
if (!sched) {
return;
}
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();
}
uint32_t llama_context::n_ctx() const {
return cparams.n_ctx;
}
uint32_t llama_context::n_ctx_seq() const {
return cparams.n_ctx_seq;
}
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_memory_t llama_context::get_memory() const {
return memory.get();
}
bool llama_context::memory_update(bool optimize) {
if (!memory) {
return false;
}
{
const auto mctx = memory->init_update(this, optimize);
switch (mctx->get_status()) {
case LLAMA_MEMORY_STATUS_SUCCESS:
{
// noop
} break;
case LLAMA_MEMORY_STATUS_NO_UPDATE:
{
// no updates need to be performed
return false;
}
case LLAMA_MEMORY_STATUS_FAILED_PREPARE:
case LLAMA_MEMORY_STATUS_FAILED_COMPUTE:
{
LLAMA_LOG_ERROR("%s: failed to prepare memory update\n", __func__);
return false;
}
}
// reset the previous graph result to make sure that it won't be reused
// TODO: change the mctx->apply() to return information if a graph reserve is needed
// reset the graph result only if the memory module did reset the scheduler
gf_res_prev->reset();
if (!mctx->apply()) {
LLAMA_LOG_ERROR("%s: failed to apply memory update\n", __func__);
}
}
// if the memory module did any computation, we have to reserve a new worst-case graph
{
const auto mctx = memory->init_full();
if (!mctx) {
throw std::runtime_error("failed to initialize memory context");
}
const uint32_t n_seqs = cparams.n_seq_max;
const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
if (!gf) {
LLAMA_LOG_ERROR("%s: failed to reserve graph after the memory update\n", __func__);
}
}
return true;
}
enum llama_pooling_type llama_context::pooling_type() const {
return cparams.pooling_type;
}
float * llama_context::get_logits() {
output_reorder();
return logits.data;
}
int64_t llama_context::output_resolve_row(int32_t i) const {
int64_t j = -1;
// support negative indices (last output row)
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 {
// use output_ids to translate the batch token index into a row number
// that holds this token's data.
j = output_ids[i];
}
if (j < 0) {
// the batch token was not configured to output anything
throw std::runtime_error(format("batch.logits[%d] != true", i));
}
if (j >= n_outputs) {
throw std::runtime_error(format("corrupt output buffer (j=%" PRId64 ", n_outputs=%d)", j, n_outputs));
}
return j;
}
float * llama_context::get_logits_ith(int32_t i) {
int64_t j = -1;
output_reorder();
try {
if (logits.data == nullptr) {
throw std::runtime_error("no logits");
}
// TODO: use output_resolve_row()
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=%" PRId64 ", n_outputs=%d)", j, n_outputs));
}
return logits.data + 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() {
output_reorder();
return embd.data;
}
llama_token * llama_context::get_sampled_tokens() const{
return sampling.sampled.data;
}
float * llama_context::get_embeddings_ith(int32_t i) {
int64_t j = -1;
output_reorder();
try {
if (embd.data == nullptr) {
throw std::runtime_error("no embeddings");
}
// TODO: use output_resolve_row()
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=%" PRId64 ", n_outputs=%d)", j, n_outputs));
}
const uint32_t n_embd_out = model.hparams.n_embd_out();
return embd.data + j*n_embd_out;
} 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();
}
llama_token llama_context::get_sampled_token_ith(int32_t idx) {
output_reorder();
if (!sampling.sampled.has_data()) {
return LLAMA_TOKEN_NULL;
}
try {
const int64_t row = output_resolve_row(idx);
GGML_ASSERT(row < (int64_t) sampling.sampled.size);
return sampling.sampled.data[row];
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: invalid backend sampled token id %d, reason: %s\n", __func__, idx, err.what());
return LLAMA_TOKEN_NULL;
}
}
float * llama_context::get_sampled_probs_ith(int32_t idx) {
output_reorder();
if (!sampling.probs.has_data()) {
return nullptr;
}
try {
const int64_t row = output_resolve_row(idx);
if ((size_t) row >= sampling.probs_count.size() || sampling.probs_count[row] == 0) {
return nullptr;
}
return sampling.probs.data + row*model.vocab.n_tokens();
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: invalid backend sampled probs id %d, reason: %s\n", __func__, idx, err.what());
return nullptr;
}
}
float * llama_context::get_sampled_logits_ith(int32_t idx) {
output_reorder();
if (!sampling.logits.has_data()) {
return nullptr;
}
try {
const int64_t row = output_resolve_row(idx);
if ((size_t) row >= sampling.logits_count.size() || sampling.logits_count[row] == 0) {
return nullptr;
}
return sampling.logits.data + row*model.vocab.n_tokens();
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: invalid backend sampled logits id %d, reason: %s\n", __func__, idx, err.what());
return nullptr;
}
}
const llama_token * llama_context::get_sampled_candidates_ith(int32_t idx) {
output_reorder();
try {
const int64_t row = output_resolve_row(idx);
if (sampling.candidates.has_data() &&
(size_t) row < sampling.candidates_count.size() &&
sampling.candidates_count[row] > 0) {
return sampling.candidates.data + row*model.vocab.n_tokens();
}
} catch (const std::exception & err) {
// fallback to full vocab list
}
return sampling.token_ids_full_vocab.data();
}
size_t llama_context::get_sampled_candidates_count(int32_t idx) {
output_reorder();
if (!sampling.candidates.has_data()) {
return 0;
}
try {
const int64_t row = output_resolve_row(idx);
if ((size_t) row >= sampling.candidates_count.size()) {
return 0;
}
return sampling.candidates_count[row];
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: invalid backend sampled candidates count id %d, reason: %s\n", __func__, idx, err.what());
return 0;
}
}
size_t llama_context::get_sampled_logits_count(int32_t idx) {
output_reorder();
if (!sampling.logits.has_data()) {
return model.vocab.n_tokens();
}
try {
const int64_t row = output_resolve_row(idx);
if ((size_t) row >= sampling.logits_count.size()) {
return 0;
}
return sampling.logits_count[row];
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: invalid backend sampled logits count id %d, reason: %s\n", __func__, idx, err.what());
return 0;
}
}
size_t llama_context::get_sampled_probs_count(int32_t idx) {
output_reorder();
if (!sampling.probs.has_data()) {
return 0;
}
try {
const int64_t row = output_resolve_row(idx);
if ((size_t) row >= sampling.probs_count.size()) {
return 0;
}
return sampling.probs_count[row];
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: invalid backend sampled probs count id %d, reason: %s\n", __func__, idx, err.what());
return 0;
}
}
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;
// TODO: not sure yet if we want to reserve here
//sched_need_reserve = true;
}
void llama_context::set_causal_attn(bool value) {
LLAMA_LOG_DEBUG("%s: value = %d\n", __func__, value);
if (cparams.causal_attn == value) {
return;
}
cparams.causal_attn = value;
sched_need_reserve = true;