vbatts/llama.cpp
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common : fix mirostat state when using multiple sequences (#3543)

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* Fix mirostat state when using multiple sequences

* Fix mirostat by completely refactoring sampling!

* Try to fix zig build.

* Export function to fetch/create default sampler states

Code formatting cleanups and add some comments

Silence a warning about id not being used when logging is disabled

* Apply some renaming suggestions.

Fix comments that were out of sync with the pull.

* Use more consistant naming convention for sampling contexts
This commit is contained in:
Kerfuffle 2023-10-11 13:35:46 -06:00 • committed by GitHub
parent 8c70a5ff25
commit 70c29da118
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GPG key ID: 4AEE18F83AFDEB23
14 changed files with 495 additions and 334 deletions
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2
common/CMakeLists.txt
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@ -5,6 +5,8 @@ set(TARGET common)
add_library(${TARGET} OBJECT
common.h
common.cpp
sampling.h
sampling.cpp
console.h
console.cpp
grammar-parser.h

228
common/common.cpp
View file

@ -107,6 +107,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
std::string arg;
gpt_params default_params;
const std::string arg_prefix = "--";
llama_sampling_params & sparams = params.sampling_params;
for (int i = 1; i < argc; i++) {
arg = argv[i];
@ -184,7 +185,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
invalid_param = true;
break;
}
params.top_k = std::stoi(argv[i]);
sparams.top_k = std::stoi(argv[i]);
} else if (arg == "-c" || arg == "--ctx-size") {
if (++i >= argc) {
invalid_param = true;
@ -216,73 +217,73 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
invalid_param = true;
break;
}
params.top_p = std::stof(argv[i]);
sparams.top_p = std::stof(argv[i]);
} else if (arg == "--temp") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.temp = std::stof(argv[i]);
sparams.temp = std::stof(argv[i]);
} else if (arg == "--tfs") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.tfs_z = std::stof(argv[i]);
sparams.tfs_z = std::stof(argv[i]);
} else if (arg == "--typical") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.typical_p = std::stof(argv[i]);
sparams.typical_p = std::stof(argv[i]);
} else if (arg == "--repeat-last-n") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.repeat_last_n = std::stoi(argv[i]);
sparams.repeat_last_n = std::stoi(argv[i]);
} else if (arg == "--repeat-penalty") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.repeat_penalty = std::stof(argv[i]);
sparams.repeat_penalty = std::stof(argv[i]);
} else if (arg == "--frequency-penalty") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.frequency_penalty = std::stof(argv[i]);
sparams.frequency_penalty = std::stof(argv[i]);
} else if (arg == "--presence-penalty") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.presence_penalty = std::stof(argv[i]);
sparams.presence_penalty = std::stof(argv[i]);
} else if (arg == "--mirostat") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.mirostat = std::stoi(argv[i]);
sparams.mirostat = std::stoi(argv[i]);
} else if (arg == "--mirostat-lr") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.mirostat_eta = std::stof(argv[i]);
sparams.mirostat_eta = std::stof(argv[i]);
} else if (arg == "--mirostat-ent") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.mirostat_tau = std::stof(argv[i]);
sparams.mirostat_tau = std::stof(argv[i]);
} else if (arg == "--cfg-negative-prompt") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.cfg_negative_prompt = argv[i];
sparams.cfg_negative_prompt = argv[i];
} else if (arg == "--cfg-negative-prompt-file") {
if (++i >= argc) {
invalid_param = true;
@ -294,16 +295,16 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
invalid_param = true;
break;
}
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.cfg_negative_prompt));
if (!params.cfg_negative_prompt.empty() && params.cfg_negative_prompt.back() == '\n') {
params.cfg_negative_prompt.pop_back();
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(sparams.cfg_negative_prompt));
if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\n') {
sparams.cfg_negative_prompt.pop_back();
}
} else if (arg == "--cfg-scale") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.cfg_scale = std::stof(argv[i]);
sparams.cfg_scale = std::stof(argv[i]);
} else if (arg == "-b" || arg == "--batch-size") {
if (++i >= argc) {
invalid_param = true;
@ -512,7 +513,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
} else if (arg == "--ignore-eos") {
params.ignore_eos = true;
} else if (arg == "--no-penalize-nl") {
params.penalize_nl = false;
sparams.penalize_nl = false;
} else if (arg == "-l" || arg == "--logit-bias") {
if (++i >= argc) {
invalid_param = true;
@ -524,7 +525,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
std::string value_str;
try {
if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
params.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
sparams.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
} else {
throw std::exception();
}
@ -627,6 +628,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
}
void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
const llama_sampling_params & sparams = params.sampling_params;
printf("usage: %s [options]\n", argv[0]);
printf("\n");
printf("options:\n");
@ -659,19 +662,19 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
printf(" -c N, --ctx-size N size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx);
printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k);
printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p);
printf(" --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z);
printf(" --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)params.typical_p);
printf(" --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", params.repeat_last_n);
printf(" --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)params.repeat_penalty);
printf(" --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)params.presence_penalty);
printf(" --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)params.frequency_penalty);
printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", sparams.top_k);
printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)sparams.top_p);
printf(" --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)sparams.tfs_z);
printf(" --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)sparams.typical_p);
printf(" --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", sparams.repeat_last_n);
printf(" --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)sparams.repeat_penalty);
printf(" --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.presence_penalty);
printf(" --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.frequency_penalty);
printf(" --mirostat N use Mirostat sampling.\n");
printf(" Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n");
printf(" (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", params.mirostat);
printf(" --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)params.mirostat_eta);
printf(" --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)params.mirostat_tau);
printf(" (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", sparams.mirostat);
printf(" --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)sparams.mirostat_eta);
printf(" --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)sparams.mirostat_tau);
printf(" -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n");
printf(" modifies the likelihood of token appearing in the completion,\n");
printf(" i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n");
@ -682,7 +685,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" negative prompt to use for guidance. (default: empty)\n");
printf(" --cfg-negative-prompt-file FNAME\n");
printf(" negative prompt file to use for guidance. (default: empty)\n");
printf(" --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale);
printf(" --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", sparams.cfg_scale);
printf(" --rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale\n");
printf(" --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: loaded from model)\n");
printf(" --rope-freq-scale N RoPE frequency linear scaling factor (default: loaded from model)\n");
@ -690,7 +693,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" --no-penalize-nl do not penalize newline token\n");
printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
printf(" not recommended: doubles context memory required and no measurable increase in quality\n");
printf(" --temp N temperature (default: %.1f)\n", (double)params.temp);
printf(" --temp N temperature (default: %.1f)\n", (double)sparams.temp);
printf(" --logits-all return logits for all tokens in the batch (default: disabled)\n");
printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
printf(" --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
@ -840,7 +843,7 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
}
if (params.ignore_eos) {
params.logit_bias[llama_token_eos(lctx)] = -INFINITY;
params.sampling_params.logit_bias[llama_token_eos(lctx)] = -INFINITY;
}
{
@ -932,127 +935,6 @@ std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_to
return result;
}
//
// Sampling utils
//
llama_token llama_sample_token(
struct llama_context * ctx,
struct llama_context * ctx_guidance,
struct llama_grammar * grammar,
const struct gpt_params & params,
const std::vector<llama_token> & last_tokens,
std::vector<llama_token_data> & candidates,
int idx) {
const int n_ctx = llama_n_ctx(ctx);
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const float temp = params.temp;
const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k;
const float top_p = params.top_p;
const float tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
const float repeat_penalty = params.repeat_penalty;
const float alpha_presence = params.presence_penalty;
const float alpha_frequency = params.frequency_penalty;
const int mirostat = params.mirostat;
const float mirostat_tau = params.mirostat_tau;
const float mirostat_eta = params.mirostat_eta;
const bool penalize_nl = params.penalize_nl;
llama_token id = 0;
float * logits = llama_get_logits_ith(ctx, idx);
// Apply params.logit_bias map
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
logits[it->first] += it->second;
}
candidates.clear();
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
if (ctx_guidance) {
llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale);
}
// apply penalties
if (!last_tokens.empty()) {
const float nl_logit = logits[llama_token_nl(ctx)];
const int last_n_repeat = std::min(std::min((int)last_tokens.size(), repeat_last_n), n_ctx);
llama_sample_repetition_penalty(ctx, &cur_p,
last_tokens.data() + last_tokens.size() - last_n_repeat,
last_n_repeat, repeat_penalty);
llama_sample_frequency_and_presence_penalties(ctx, &cur_p,
last_tokens.data() + last_tokens.size() - last_n_repeat,
last_n_repeat, alpha_frequency, alpha_presence);
if (!penalize_nl) {
for (size_t idx = 0; idx < cur_p.size; idx++) {
if (cur_p.data[idx].id == llama_token_nl(ctx)) {
cur_p.data[idx].logit = nl_logit;
break;
}
}
}
}
if (grammar != NULL) {
llama_sample_grammar(ctx, &cur_p, grammar);
}
if (temp <= 0) {
// Greedy sampling
id = llama_sample_token_greedy(ctx, &cur_p);
} else {
if (mirostat == 1) {
static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100;
llama_sample_temp(ctx, &cur_p, temp);
id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
} else if (mirostat == 2) {
static float mirostat_mu = 2.0f * mirostat_tau;
llama_sample_temp(ctx, &cur_p, temp);
id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu);
} else {
// Temperature sampling
size_t min_keep = std::max(1, params.n_probs);
llama_sample_top_k (ctx, &cur_p, top_k, min_keep);
llama_sample_tail_free (ctx, &cur_p, tfs_z, min_keep);
llama_sample_typical (ctx, &cur_p, typical_p, min_keep);
llama_sample_top_p (ctx, &cur_p, top_p, min_keep);
llama_sample_temp(ctx, &cur_p, temp);
{
const int n_top = 10;
LOG("top %d candidates:\n", n_top);
for (int i = 0; i < n_top; i++) {
const llama_token id = cur_p.data[i].id;
LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p);
}
}
id = llama_sample_token(ctx, &cur_p);
LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str());
}
}
// printf("`%d`", candidates_p.size);
if (grammar != NULL) {
llama_grammar_accept_token(ctx, grammar, id);
}
return id;
}
//
// YAML utils
//
@ -1204,6 +1086,8 @@ std::string get_sortable_timestamp() {
void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const llama_context * lctx,
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
const llama_sampling_params & sparams = params.sampling_params;
fprintf(stream, "build_commit: %s\n", BUILD_COMMIT);
fprintf(stream, "build_number: %d\n", BUILD_NUMBER);
fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false");
@ -1250,21 +1134,21 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str());
fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch);
dump_string_yaml_multiline(stream, "cfg_negative_prompt", params.cfg_negative_prompt.c_str());
fprintf(stream, "cfg_scale: %f # default: 1.0\n", params.cfg_scale);
dump_string_yaml_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str());
fprintf(stream, "cfg_scale: %f # default: 1.0\n", sparams.cfg_scale);
fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", params.frequency_penalty);
fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.frequency_penalty);
dump_string_yaml_multiline(stream, "grammar", params.grammar.c_str());
fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n");
fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false");
fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks);
const auto logit_bias_eos = params.logit_bias.find(llama_token_eos(lctx));
const bool ignore_eos = logit_bias_eos != params.logit_bias.end() && logit_bias_eos->second == -INFINITY;
const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(lctx));
const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY;
fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false");
dump_string_yaml_multiline(stream, "in_prefix", params.input_prefix.c_str());
@ -1277,7 +1161,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str());
fprintf(stream, "logit_bias:\n");
for (std::pair<llama_token, float> lb : params.logit_bias) {
for (std::pair<llama_token, float> lb : sparams.logit_bias) {
if (ignore_eos && lb.first == logit_bias_eos->first) {
continue;
}
@ -1301,30 +1185,30 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
fprintf(stream, "lora_base: %s\n", params.lora_base.c_str());
fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
fprintf(stream, "memory_f32: %s # default: false\n", !params.memory_f16 ? "true" : "false");
fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", params.mirostat);
fprintf(stream, "mirostat_ent: %f # default: 5.0\n", params.mirostat_tau);
fprintf(stream, "mirostat_lr: %f # default: 0.1\n", params.mirostat_eta);
fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat);
fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau);
fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta);
fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str());
fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());
fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", params.n_probs);
fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
fprintf(stream, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false");
fprintf(stream, "no_penalize_nl: %s # default: false\n", !params.penalize_nl ? "true" : "false");
fprintf(stream, "no_penalize_nl: %s # default: false\n", !sparams.penalize_nl ? "true" : "false");
fprintf(stream, "numa: %s # default: false\n", params.numa ? "true" : "false");
fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
fprintf(stream, "presence_penalty: %f # default: 0.0\n", params.presence_penalty);
fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.presence_penalty);
dump_string_yaml_multiline(stream, "prompt", params.prompt.c_str());
fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str());
fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false");
fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false");
dump_vector_int_yaml(stream, "prompt_tokens", prompt_tokens);
fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false");
fprintf(stream, "repeat_penalty: %f # default: 1.1\n", params.repeat_penalty);
fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.repeat_penalty);
fprintf(stream, "reverse_prompt:\n");
for (std::string ap : params.antiprompt) {
@ -1342,15 +1226,15 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
fprintf(stream, "seed: %d # default: -1 (random seed)\n", params.seed);
fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
fprintf(stream, "temp: %f # default: 0.8\n", params.temp);
fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + LLAMA_MAX_DEVICES);
dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector);
fprintf(stream, "tfs: %f # default: 1.0\n", params.tfs_z);
fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);
fprintf(stream, "threads: %d # default: %d\n", params.n_threads, std::thread::hardware_concurrency());
fprintf(stream, "top_k: %d # default: 40\n", params.top_k);
fprintf(stream, "top_p: %f # default: 0.95\n", params.top_p);
fprintf(stream, "typical_p: %f # default: 1.0\n", params.typical_p);
fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p);
fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
}

56
common/common.h
View file

@ -4,6 +4,8 @@
#include "llama.h"
#include "sampling.h"
#define LOG_NO_FILE_LINE_FUNCTION
#include "log.h"
@ -49,31 +51,12 @@ struct gpt_params {
int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
int32_t n_beams = 0; // if non-zero then use beam search of given width.
float rope_freq_base = 0.0f; // RoPE base frequency
float rope_freq_scale = 0.0f; // RoPE frequency scaling factor
// sampling parameters
int32_t top_k = 40; // <= 0 to use vocab size
float top_p = 0.95f; // 1.0 = disabled
float tfs_z = 1.00f; // 1.0 = disabled
float typical_p = 1.00f; // 1.0 = disabled
float temp = 0.80f; // 1.0 = disabled
float repeat_penalty = 1.10f; // 1.0 = disabled
int32_t repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float frequency_penalty = 0.00f; // 0.0 = disabled
float presence_penalty = 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
std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
// Classifier-Free Guidance
// https://arxiv.org/abs/2306.17806
std::string cfg_negative_prompt; // string to help guidance
float cfg_scale = 1.f; // How strong is guidance
// // sampling parameters
struct llama_sampling_params sampling_params;
std::string model = "models/7B/ggml-model-f16.gguf"; // model path
std::string model_draft = ""; // draft model for speculative decoding
@ -115,7 +98,6 @@ struct gpt_params {
bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix
bool ignore_eos = false; // ignore generated EOS tokens
bool instruct = false; // instruction mode (used for Alpaca models)
bool penalize_nl = true; // consider newlines as a repeatable token
bool logits_all = false; // return logits for all tokens in the batch
bool use_mmap = true; // use mmap for faster loads
bool use_mlock = false; // use mlock to keep model in memory
@ -180,36 +162,6 @@ std::string llama_detokenize_bpe(
llama_context * ctx,
const std::vector<llama_token> & tokens);
//
// Sampling utils
//
// this is a common sampling function used across the examples for convenience
// it can serve as a starting point for implementing your own sampling function
//
// required:
// - ctx: context to use for sampling
// - params: sampling parameters
//
// optional:
// - ctx_guidance: context to use for classifier-free guidance, ignore if NULL
// - grammar: grammar to use for sampling, ignore if NULL
// - last_tokens: needed for repetition penalty, ignore if empty
// - idx: sample from llama_get_logits_ith(ctx, idx)
//
// returns:
// - token: sampled token
// - candidates: vector of candidate tokens
//
llama_token llama_sample_token(
struct llama_context * ctx,
struct llama_context * ctx_guidance,
struct llama_grammar * grammar,
const struct gpt_params & params,
const std::vector<llama_token> & last_tokens,
std::vector<llama_token_data> & candidates,
int idx = 0);
//
// YAML utils
//

166
common/sampling.cpp Normal file
View file

@ -0,0 +1,166 @@
#include "sampling.h"
llama_sampling_context::~llama_sampling_context() {
for (auto & it : sequence_contexts) {
if (it.second.grammar != NULL) {
llama_grammar_free(it.second.grammar);
it.second.grammar = NULL;
}
}
}
llama_sampling_context llama_sampling_context_init(
const struct gpt_params & params,
llama_grammar * grammar) {
llama_sampling_context result;
result.params = params.sampling_params;
result.grammar = grammar;
return result;
}
// Note: Creates the context if it doesn't exist, so this always return something.
llama_sampler_sequence_context & llama_sampling_get_sequence_context(
llama_sampling_context & ctx_sampling,
const llama_seq_id seq) {
const auto it = ctx_sampling.sequence_contexts.find(seq);
if (it != ctx_sampling.sequence_contexts.end()) {
return it->second;
}
llama_sampler_sequence_context new_ctx = {
2.0f * ctx_sampling.params.mirostat_tau,
ctx_sampling.grammar != NULL ? llama_grammar_copy(ctx_sampling.grammar) : NULL,
};
return ctx_sampling.sequence_contexts.insert({seq, new_ctx}).first->second;
}
bool llama_sampling_context_reset(
llama_sampling_context & ctx_sampling,
const llama_seq_id seq) {
const auto it = ctx_sampling.sequence_contexts.find(seq);
if (it == ctx_sampling.sequence_contexts.end()) return false;
if (it->second.grammar != NULL) {
llama_grammar_free(it->second.grammar);
it->second.grammar = NULL;
}
ctx_sampling.sequence_contexts.erase(it);
return true;
}
llama_token llama_sampling_sample(
struct llama_context * ctx,
struct llama_context * ctx_guidance,
struct llama_sampling_context & ctx_sampling,
const std::vector<llama_token> & last_tokens,
std::vector<llama_token_data> & candidates,
const int idx,
llama_seq_id seq) {
const int n_ctx = llama_n_ctx(ctx);
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const llama_sampling_params & params = ctx_sampling.params;
const float temp = params.temp;
const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k;
const float top_p = params.top_p;
const float tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
const float repeat_penalty = params.repeat_penalty;
const float alpha_presence = params.presence_penalty;
const float alpha_frequency = params.frequency_penalty;
const int mirostat = params.mirostat;
const float mirostat_tau = params.mirostat_tau;
const float mirostat_eta = params.mirostat_eta;
const bool penalize_nl = params.penalize_nl;
llama_token id = 0;
float * logits = llama_get_logits_ith(ctx, idx);
// Apply params.logit_bias map
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
logits[it->first] += it->second;
}
candidates.clear();
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
if (ctx_guidance) {
llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale);
}
// apply penalties
if (!last_tokens.empty()) {
const float nl_logit = logits[llama_token_nl(ctx)];
const int last_n_repeat = std::min(std::min((int)last_tokens.size(), repeat_last_n), n_ctx);
llama_sample_repetition_penalty(ctx, &cur_p,
last_tokens.data() + last_tokens.size() - last_n_repeat,
last_n_repeat, repeat_penalty);
llama_sample_frequency_and_presence_penalties(ctx, &cur_p,
last_tokens.data() + last_tokens.size() - last_n_repeat,
last_n_repeat, alpha_frequency, alpha_presence);
if (!penalize_nl) {
for (size_t idx = 0; idx < cur_p.size; idx++) {
if (cur_p.data[idx].id == llama_token_nl(ctx)) {
cur_p.data[idx].logit = nl_logit;
break;
}
}
}
}
llama_sampler_sequence_context & ctx_seq = llama_sampling_get_sequence_context(ctx_sampling, seq);
if (ctx_seq.grammar != NULL) {
llama_sample_grammar(ctx, &cur_p, ctx_seq.grammar);
}
if (temp <= 0) {
// Greedy sampling
id = llama_sample_token_greedy(ctx, &cur_p);
} else {
if (mirostat == 1) {
const int mirostat_m = 100;
llama_sample_temp(ctx, &cur_p, temp);
id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &ctx_seq.mirostat_mu);
} else if (mirostat == 2) {
llama_sample_temp(ctx, &cur_p, temp);
id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &ctx_seq.mirostat_mu);
} else {
// Temperature sampling
size_t min_keep = std::max(1, params.n_probs);
llama_sample_top_k (ctx, &cur_p, top_k, min_keep);
llama_sample_tail_free (ctx, &cur_p, tfs_z, min_keep);
llama_sample_typical (ctx, &cur_p, typical_p, min_keep);
llama_sample_top_p (ctx, &cur_p, top_p, min_keep);
llama_sample_temp(ctx, &cur_p, temp);
{
const int n_top = 10;
LOG("top %d candidates:\n", n_top);
for (int i = 0; i < n_top; i++) {
const llama_token id = cur_p.data[i].id;
(void)id; // To avoid a warning that id is unused when logging is disabled.
LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p);
}
}
id = llama_sample_token(ctx, &cur_p);
LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str());
}
}
if (ctx_seq.grammar != NULL) {
llama_grammar_accept_token(ctx, ctx_seq.grammar, id);
}
return id;
}

108
common/sampling.h Normal file
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@ -0,0 +1,108 @@
#pragma once
#include "llama.h"
#include <string>
#include <vector>
#include <unordered_map>
// sampling parameters
typedef struct llama_sampling_params {
int32_t top_k = 40; // <= 0 to use vocab size
float top_p = 0.95f; // 1.0 = disabled
float tfs_z = 1.00f; // 1.0 = disabled
float typical_p = 1.00f; // 1.0 = disabled
float temp = 0.80f; // 1.0 = disabled
float repeat_penalty = 1.10f; // 1.0 = disabled
int32_t repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float frequency_penalty = 0.00f; // 0.0 = disabled
float presence_penalty = 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 = true; // consider newlines as a repeatable token
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
// Classifier-Free Guidance
// https://arxiv.org/abs/2306.17806
std::string cfg_negative_prompt; // string to help guidance
float cfg_scale = 1.f; // How strong is guidance
std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
} llama_sampling_params;
// per-sequence sampler context
typedef struct llama_sampler_sequence_context {
float mirostat_mu; // mirostat sampler state
llama_grammar * grammar;
} llama_sampler_sequence_context;
// general sampler context
typedef struct llama_sampling_context {
~llama_sampling_context();
// parameters that will be used for sampling and when creating
// new llama_sampler_sequence_context instances
llama_sampling_params params;
// map of sequence ids to sampler contexts
std::unordered_map<llama_seq_id, llama_sampler_sequence_context> sequence_contexts;
// when non-NULL, new instances of llama_sampler_sequence_context
// will get a copy of the grammar here
// note: only the pointer is stored here, it is not a copy of
// the grammar and shouldn't be freed
llama_grammar * grammar;
} llama_sampling_context;
#include "common.h"
// Create a new sampling context instance.
llama_sampling_context llama_sampling_context_init(
const struct gpt_params & params,
llama_grammar * grammar = NULL);
// Fetches the sampler context for the specified sequence id (defaults to 0).
// If the context for that sequence id doesn't already exist, it will be created with
// default values based on the parameters in the ctx_sampling argument.
llama_sampler_sequence_context & llama_sampling_get_sequence_context(
llama_sampling_context & ctx_sampling,
const llama_seq_id seq = 0);
// Reset the sampler context for the supplied sequence id (defaults to 0).
// This is necessary to reuse a sequence id or free memory used by sequences
// that are no longer required.
bool llama_sampling_context_reset(
llama_sampling_context & ctx_sampling,
const llama_seq_id seq = 0);
// this is a common sampling function used across the examples for convenience
// it can serve as a starting point for implementing your own sampling function
// Note: When using multiple sequences, it is the caller's responsibility to call
// llama_sampling_context_reset when a sequence ends
//
// required:
// - ctx: context to use for sampling
// - ctx_sampling: sampling-specific context
//
// optional:
// - ctx_guidance: context to use for classifier-free guidance, ignore if NULL
// - last_tokens: needed for repetition penalty, ignore if empty
// - idx: sample from llama_get_logits_ith(ctx, idx)
// - seq: sequence id to associate sampler state with
//
// returns:
// - token: sampled token
// - candidates: vector of candidate tokens
//
llama_token llama_sampling_sample(
struct llama_context * ctx,
struct llama_context * ctx_guidance,
struct llama_sampling_context & ctx_sampling,
const std::vector<llama_token> & last_tokens,
std::vector<llama_token_data> & candidates,
const int idx = 0,
llama_seq_id seq = 0);