llama : add api for getting/setting the complete state: rng, logits, embedding and kv_cache (#1105)

* reserve correct size for logits

* add functions to get and set the whole llama state:

including rng, logits, embedding and kv_cache

* remove unused variables

* remove trailing whitespace

* fix comment
This commit is contained in:
xaedes 2023-04-22 08:21:32 +02:00 committed by GitHub
parent 50cb666b8a
commit b6e7f9b09e
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2 changed files with 133 additions and 1 deletions

122
llama.cpp
View file

@ -27,6 +27,7 @@
#include <thread>
#include <atomic>
#include <mutex>
#include <sstream>
#define LLAMA_USE_SCRATCH
#define LLAMA_MAX_SCRATCH_BUFFERS 16
@ -1787,7 +1788,7 @@ struct llama_context * llama_init_from_file(
if (params.logits_all) {
ctx->logits.reserve(hparams.n_ctx*hparams.n_vocab);
} else {
ctx->logits.reserve(hparams.n_ctx);
ctx->logits.reserve(hparams.n_vocab);
}
if (params.embedding){
@ -2252,3 +2253,122 @@ const char * llama_print_system_info(void) {
std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx) {
return ctx->model.tensors_by_name;
}
// Returns the size of the state
size_t llama_get_state_size(struct llama_context * ctx) {
const size_t s_bool = sizeof(int32_t);
// we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
// for reference, std::mt19937(1337) serializes to 6701 bytes.
const size_t s_rng_size = sizeof(size_t);
const size_t s_rng = 64*1024;
const size_t s_logits_capacity = sizeof(size_t);
const size_t s_logits_size = sizeof(size_t);
const size_t s_logits = ctx->logits.capacity() * sizeof(float);
const size_t s_embedding_size = sizeof(size_t);
const size_t s_embedding = ctx->embedding.size() * sizeof(float);
const size_t s_kv_size = sizeof(size_t);
const size_t s_kv_ntok = sizeof(int);
const size_t s_kv = llama_get_kv_cache_size(ctx);
const size_t s_total = (
+ s_rng_size
+ s_rng
+ s_logits_capacity
+ s_logits_size
+ s_logits
+ s_embedding_size
+ s_embedding
+ s_kv_size
+ s_kv_ntok
+ s_kv
);
return s_total;
}
// Copies the state to the specified destination address
size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dest) {
std::stringstream rng_ss;
rng_ss << ctx->rng;
const size_t rng_size = rng_ss.str().size();
char rng_buf[64*1024];
memset(&rng_buf[0], 0, 64*1024);
memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size());
const size_t logits_capacity = ctx->logits.capacity();
const size_t logits_size = ctx->logits.size();
const size_t embedding_size = ctx->embedding.size();
const size_t kv_size = llama_get_kv_cache_size(ctx);
const int kv_ntok = llama_get_kv_cache_token_count(ctx);
uint8_t * out = dest;
memcpy(out, &rng_size, sizeof(size_t)); out += sizeof(size_t);
memcpy(out, &rng_buf[0], 64*1024); out += 64*1024;
memcpy(out, &logits_capacity, sizeof(size_t)); out += sizeof(size_t);
memcpy(out, &logits_size, sizeof(size_t)); out += sizeof(size_t);
if (logits_size) {
memcpy(out, ctx->logits.data(), logits_size * sizeof(float));
}
out += logits_capacity * sizeof(float);
memcpy(out, &embedding_size, sizeof(size_t)); out += sizeof(size_t);
if (embedding_size) {
memcpy(out, ctx->embedding.data(), embedding_size * sizeof(float)); out += embedding_size * sizeof(float);
}
memcpy(out, &kv_size, sizeof(size_t)); out += sizeof(size_t);
memcpy(out, &kv_ntok, sizeof(int)); out += sizeof(int);
if (kv_size) {
memcpy(out, llama_get_kv_cache(ctx), kv_size); out += kv_size;
}
const size_t written = out - dest;
const size_t expected = llama_get_state_size(ctx);
LLAMA_ASSERT(written == expected);
return written;
}
// Sets the state reading from the specified source address
size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
size_t rng_size;
char rng_buf[64*1024];
std::stringstream rng_ss;
const uint8_t * in = src;
memcpy(&rng_size, in, sizeof(size_t)); in += sizeof(size_t);
memcpy(&rng_buf[0], in, 64*1024); in += 64*1024;
rng_ss.str(std::string(&rng_buf[0], rng_size));
rng_ss >> ctx->rng;
LLAMA_ASSERT(rng_ss.fail() == false);
size_t logits_capacity;
size_t logits_size;
size_t embedding_size;
size_t kv_size;
int kv_ntok;
memcpy(&logits_capacity, in, sizeof(size_t)); in += sizeof(size_t);
memcpy(&logits_size, in, sizeof(size_t)); in += sizeof(size_t);
LLAMA_ASSERT(ctx->logits.capacity() == logits_capacity);
if (logits_size) {
ctx->logits.resize(logits_size);
memcpy(ctx->logits.data(), in, logits_size * sizeof(float));
}
in += logits_capacity * sizeof(float);
memcpy(&embedding_size, in, sizeof(size_t)); in += sizeof(size_t);
LLAMA_ASSERT(ctx->embedding.capacity() == embedding_size);
if (embedding_size) {
memcpy(ctx->embedding.data(), in, embedding_size * sizeof(float));
in += embedding_size * sizeof(float);
}
memcpy(&kv_size, in, sizeof(size_t)); in += sizeof(size_t);
memcpy(&kv_ntok, in, sizeof(int)); in += sizeof(int);
if (kv_size) {
LLAMA_ASSERT(ctx->model.kv_self.buf.size == kv_size);
void * k_data = ctx->model.kv_self.k->data; // remember data pointers
void * v_data = ctx->model.kv_self.v->data; // because their value is stored in buf and overwritten by memcpy
memcpy(ctx->model.kv_self.buf.addr, in, kv_size);
ctx->model.kv_self.k->data = k_data; // restore correct data pointers
ctx->model.kv_self.v->data = v_data;
in += kv_size;
}
ctx->model.kv_self.n = kv_ntok;
const size_t nread = in - src;
const size_t expected = llama_get_state_size(ctx);
LLAMA_ASSERT(nread == expected);
return nread;
}

12
llama.h
View file

@ -129,6 +129,18 @@ extern "C" {
size_t n_size,
int n_token_count);
// Returns the size in bytes of the state (rng, logits, embedding and kv_cache)
LLAMA_API size_t llama_get_state_size(struct llama_context * ctx);
// Copies the state to the specified destination address.
// Destination needs to have allocated enough memory.
// Returns the number of bytes copied
LLAMA_API size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dest);
// Set the state reading from the specified address
// Returns the number of bytes read
LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src);
// Run the llama inference to obtain the logits and probabilities for the next token.
// tokens + n_tokens is the provided batch of new tokens to process
// n_past is the number of tokens to use from previous eval calls