vbatts/llama.cpp
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Server: fix seed for multiple slots (#6835)

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* Server: add tests for consistent results

* sampling: separate rng per sampling context
This commit is contained in:
Johannes Gäßler 2024-04-24 11:08:36 +02:00 • committed by GitHub
parent c0d1b3e03e
commit 28103f4832
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GPG key ID: B5690EEEBB952194
11 changed files with 145 additions and 30 deletions
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2
common/common.cpp
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@ -242,7 +242,9 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
invalid_param = true;
return true;
}
// This is temporary, in the future the samplign state will be moved fully to llama_sampling_context.
params.seed = std::stoul(argv[i]);
sparams.seed = std::stoul(argv[i]);
return true;
}
if (arg == "-t" || arg == "--threads") {

13
common/sampling.cpp
View file

@ -1,4 +1,6 @@
#define LLAMA_API_INTERNAL
#include "sampling.h"
#include <random>
struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params) {
struct llama_sampling_context * result = new llama_sampling_context();
@ -33,6 +35,8 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_
result->prev.resize(params.n_prev);
llama_sampling_set_rng_seed(result, params.seed);
return result;
}
@ -62,6 +66,13 @@ void llama_sampling_reset(llama_sampling_context * ctx) {
ctx->cur.clear();
}
void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed) {
if (seed == LLAMA_DEFAULT_SEED) {
seed = time(NULL);
}
ctx->rng.seed(seed);
}
void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst) {
if (dst->grammar) {
llama_grammar_free(dst->grammar);
@ -203,7 +214,7 @@ static llama_token llama_sampling_sample_impl(
sampler_queue(ctx_main, params, cur_p, min_keep);
id = llama_sample_token(ctx_main, &cur_p);
id = llama_sample_token_with_rng(ctx_main, &cur_p, ctx_sampling->rng);
//{
// const int n_top = 10;

47
common/sampling.h
View file

@ -4,9 +4,10 @@
#include "grammar-parser.h"
#include <random>
#include <string>
#include <vector>
#include <unordered_map>
#include <vector>
// sampler types
enum class llama_sampler_type : char {
@ -20,25 +21,26 @@ enum class llama_sampler_type : char {
// sampling parameters
typedef struct llama_sampling_params {
int32_t n_prev = 64; // number of previous tokens to remember
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens
int32_t top_k = 40; // <= 0 to use vocab size
float top_p = 0.95f; // 1.0 = disabled
float min_p = 0.05f; // 0.0 = disabled
float tfs_z = 1.00f; // 1.0 = disabled
float typical_p = 1.00f; // 1.0 = disabled
float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
float dynatemp_range = 0.00f; // 0.0 = disabled
float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat = 1.00f; // 1.0 = disabled
float penalty_freq = 0.00f; // 0.0 = disabled
float penalty_present = 0.00f; // 0.0 = disabled
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool penalize_nl = false; // consider newlines as a repeatable token
int32_t n_prev = 64; // number of previous tokens to remember
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens
int32_t top_k = 40; // <= 0 to use vocab size
float top_p = 0.95f; // 1.0 = disabled
float min_p = 0.05f; // 0.0 = disabled
float tfs_z = 1.00f; // 1.0 = disabled
float typical_p = 1.00f; // 1.0 = disabled
float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
float dynatemp_range = 0.00f; // 0.0 = disabled
float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat = 1.00f; // 1.0 = disabled
float penalty_freq = 0.00f; // 0.0 = disabled
float penalty_present = 0.00f; // 0.0 = disabled
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool penalize_nl = false; // consider newlines as a repeatable token
uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampling_context
std::vector<llama_sampler_type> samplers_sequence = {
llama_sampler_type::TOP_K,
@ -79,6 +81,8 @@ struct llama_sampling_context {
// TODO: replace with ring-buffer
std::vector<llama_token> prev;
std::vector<llama_token_data> cur;
std::mt19937 rng;
};
#include "common.h"
@ -93,6 +97,9 @@ void llama_sampling_free(struct llama_sampling_context * ctx);
// - reset grammar
void llama_sampling_reset(llama_sampling_context * ctx);
// Set the sampler seed
void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed);
// Copy the sampler context
void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst);