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common : refactor args

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ggml-ci
This commit is contained in:
Georgi Gerganov 2024-11-24 14:55:16 +02:00
parent c8880e786c
commit 7f9cc2058c
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GPG Key ID: 449E073F9DC10735
22 changed files with 330 additions and 294 deletions
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445
common/arg.cpp
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@ -233,10 +233,11 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
}
}
postprocess_cpu_params(params.cpuparams, nullptr);
postprocess_cpu_params(params.cpuparams, nullptr);
postprocess_cpu_params(params.cpuparams_batch, &params.cpuparams);
postprocess_cpu_params(params.draft_cpuparams, &params.cpuparams);
postprocess_cpu_params(params.draft_cpuparams_batch, &params.cpuparams_batch);
postprocess_cpu_params(params.speculative.cpuparams, &params.cpuparams);
postprocess_cpu_params(params.speculative.cpuparams_batch, &params.cpuparams_batch);
if (params.prompt_cache_all && (params.interactive || params.interactive_first)) {
throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
@ -251,7 +252,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
for (auto & antiprompt : params.antiprompt) {
string_process_escapes(antiprompt);
}
for (auto & seq_breaker : params.sparams.dry_sequence_breakers) {
for (auto & seq_breaker : params.sampling.dry_sequence_breakers) {
string_process_escapes(seq_breaker);
}
}
@ -329,7 +330,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
std::string sampler_type_chars;
std::string sampler_type_names;
for (const auto & sampler : params.sparams.samplers) {
for (const auto & sampler : params.sampling.samplers) {
sampler_type_chars += common_sampler_type_to_chr(sampler);
sampler_type_names += common_sampler_type_to_str(sampler) + ";";
}
@ -407,26 +408,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
}
));
add_opt(common_arg(
{"-td", "--threads-draft"}, "N",
"number of threads to use during generation (default: same as --threads)",
[](common_params & params, int value) {
params.draft_cpuparams.n_threads = value;
if (params.draft_cpuparams.n_threads <= 0) {
params.draft_cpuparams.n_threads = std::thread::hardware_concurrency();
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-tbd", "--threads-batch-draft"}, "N",
"number of threads to use during batch and prompt processing (default: same as --threads-draft)",
[](common_params & params, int value) {
params.draft_cpuparams_batch.n_threads = value;
if (params.draft_cpuparams_batch.n_threads <= 0) {
params.draft_cpuparams_batch.n_threads = std::thread::hardware_concurrency();
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-C", "--cpu-mask"}, "M",
"CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: \"\")",
@ -515,115 +496,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.cpuparams_batch.poll = value;
}
));
add_opt(common_arg(
{"-Cd", "--cpu-mask-draft"}, "M",
"Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
[](common_params & params, const std::string & mask) {
params.draft_cpuparams.mask_valid = true;
if (!parse_cpu_mask(mask, params.draft_cpuparams.cpumask)) {
throw std::invalid_argument("invalid cpumask");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-Crd", "--cpu-range-draft"}, "lo-hi",
"Ranges of CPUs for affinity. Complements --cpu-mask-draft",
[](common_params & params, const std::string & range) {
params.draft_cpuparams.mask_valid = true;
if (!parse_cpu_range(range, params.draft_cpuparams.cpumask)) {
throw std::invalid_argument("invalid range");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--cpu-strict-draft"}, "<0|1>",
"Use strict CPU placement for draft model (default: same as --cpu-strict)",
[](common_params & params, int value) {
params.draft_cpuparams.strict_cpu = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--prio-draft"}, "N",
string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.draft_cpuparams.priority),
[](common_params & params, int prio) {
if (prio < 0 || prio > 3) {
throw std::invalid_argument("invalid value");
}
params.draft_cpuparams.priority = (enum ggml_sched_priority) prio;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--poll-draft"}, "<0|1>",
"Use polling to wait for draft model work (default: same as --poll])",
[](common_params & params, int value) {
params.draft_cpuparams.poll = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-Cbd", "--cpu-mask-batch-draft"}, "M",
"Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
[](common_params & params, const std::string & mask) {
params.draft_cpuparams_batch.mask_valid = true;
if (!parse_cpu_mask(mask, params.draft_cpuparams_batch.cpumask)) {
throw std::invalid_argument("invalid cpumask");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-Crbd", "--cpu-range-batch-draft"}, "lo-hi",
"Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)",
[](common_params & params, const std::string & range) {
params.draft_cpuparams_batch.mask_valid = true;
if (!parse_cpu_range(range, params.draft_cpuparams_batch.cpumask)) {
throw std::invalid_argument("invalid cpumask");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--cpu-strict-batch-draft"}, "<0|1>",
"Use strict CPU placement for draft model (default: --cpu-strict-draft)",
[](common_params & params, int value) {
params.draft_cpuparams_batch.strict_cpu = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--prio-batch-draft"}, "N",
string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.draft_cpuparams_batch.priority),
[](common_params & params, int prio) {
if (prio < 0 || prio > 3) {
throw std::invalid_argument("invalid value");
}
params.draft_cpuparams_batch.priority = (enum ggml_sched_priority) prio;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--poll-batch-draft"}, "<0|1>",
"Use polling to wait for draft model work (default: --poll-draft)",
[](common_params & params, int value) {
params.draft_cpuparams_batch.poll = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--draft"}, "N",
string_format("number of tokens to draft for speculative decoding (default: %d)", params.n_draft),
[](common_params & params, int value) {
params.n_draft = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"--draft-min"}, "N",
string_format("minimum number of draft tokens to use for speculative decoding (default: %d)", params.n_draft_min),
[](common_params & params, int value) {
params.n_draft_min = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"-ps", "--p-split"}, "N",
string_format("speculative decoding split probability (default: %.1f)", (double)params.p_split),
[](common_params & params, const std::string & value) {
params.p_split = std::stof(value);
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-lcs", "--lookup-cache-static"}, "FNAME",
"path to static lookup cache to use for lookup decoding (not updated by generation)",
@ -708,7 +580,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),
[](common_params & params) {
params.no_perf = true;
params.sparams.no_perf = true;
params.sampling.no_perf = true;
}
).set_env("LLAMA_ARG_NO_PERF"));
add_opt(common_arg(
@ -890,155 +762,155 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
string_format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()),
[](common_params & params, const std::string & value) {
const auto sampler_names = string_split<std::string>(value, ';');
params.sparams.samplers = common_sampler_types_from_names(sampler_names, true);
params.sampling.samplers = common_sampler_types_from_names(sampler_names, true);
}
).set_sparam());
add_opt(common_arg(
{"-s", "--seed"}, "SEED",
string_format("RNG seed (default: %d, use random seed for %d)", params.sparams.seed, LLAMA_DEFAULT_SEED),
string_format("RNG seed (default: %d, use random seed for %d)", params.sampling.seed, LLAMA_DEFAULT_SEED),
[](common_params & params, const std::string & value) {
params.sparams.seed = std::stoul(value);
params.sampling.seed = std::stoul(value);
}
).set_sparam());
add_opt(common_arg(
{"--sampling-seq"}, "SEQUENCE",
string_format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()),
[](common_params & params, const std::string & value) {
params.sparams.samplers = common_sampler_types_from_chars(value);
params.sampling.samplers = common_sampler_types_from_chars(value);
}
).set_sparam());
add_opt(common_arg(
{"--ignore-eos"},
"ignore end of stream token and continue generating (implies --logit-bias EOS-inf)",
[](common_params & params) {
params.sparams.ignore_eos = true;
params.sampling.ignore_eos = true;
}
).set_sparam());
add_opt(common_arg(
{"--penalize-nl"},
string_format("penalize newline tokens (default: %s)", params.sparams.penalize_nl ? "true" : "false"),
string_format("penalize newline tokens (default: %s)", params.sampling.penalize_nl ? "true" : "false"),
[](common_params & params) {
params.sparams.penalize_nl = true;
params.sampling.penalize_nl = true;
}
).set_sparam());
add_opt(common_arg(
{"--temp"}, "N",
string_format("temperature (default: %.1f)", (double)params.sparams.temp),
string_format("temperature (default: %.1f)", (double)params.sampling.temp),
[](common_params & params, const std::string & value) {
params.sparams.temp = std::stof(value);
params.sparams.temp = std::max(params.sparams.temp, 0.0f);
params.sampling.temp = std::stof(value);
params.sampling.temp = std::max(params.sampling.temp, 0.0f);
}
).set_sparam());
add_opt(common_arg(
{"--top-k"}, "N",
string_format("top-k sampling (default: %d, 0 = disabled)", params.sparams.top_k),
string_format("top-k sampling (default: %d, 0 = disabled)", params.sampling.top_k),
[](common_params & params, int value) {
params.sparams.top_k = value;
params.sampling.top_k = value;
}
).set_sparam());
add_opt(common_arg(
{"--top-p"}, "N",
string_format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sparams.top_p),
string_format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sampling.top_p),
[](common_params & params, const std::string & value) {
params.sparams.top_p = std::stof(value);
params.sampling.top_p = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--min-p"}, "N",
string_format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sparams.min_p),
string_format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sampling.min_p),
[](common_params & params, const std::string & value) {
params.sparams.min_p = std::stof(value);
params.sampling.min_p = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--xtc-probability"}, "N",
string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sparams.xtc_probability),
string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sampling.xtc_probability),
[](common_params & params, const std::string & value) {
params.sparams.xtc_probability = std::stof(value);
params.sampling.xtc_probability = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--xtc-threshold"}, "N",
string_format("xtc threshold (default: %.1f, 1.0 = disabled)", (double)params.sparams.xtc_threshold),
string_format("xtc threshold (default: %.1f, 1.0 = disabled)", (double)params.sampling.xtc_threshold),
[](common_params & params, const std::string & value) {
params.sparams.xtc_threshold = std::stof(value);
params.sampling.xtc_threshold = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--typical"}, "N",
string_format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sparams.typ_p),
string_format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sampling.typ_p),
[](common_params & params, const std::string & value) {
params.sparams.typ_p = std::stof(value);
params.sampling.typ_p = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--repeat-last-n"}, "N",
string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sparams.penalty_last_n),
string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sampling.penalty_last_n),
[](common_params & params, int value) {
params.sparams.penalty_last_n = value;
params.sparams.n_prev = std::max(params.sparams.n_prev, params.sparams.penalty_last_n);
params.sampling.penalty_last_n = value;
params.sampling.n_prev = std::max(params.sampling.n_prev, params.sampling.penalty_last_n);
}
).set_sparam());
add_opt(common_arg(
{"--repeat-penalty"}, "N",
string_format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sparams.penalty_repeat),
string_format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sampling.penalty_repeat),
[](common_params & params, const std::string & value) {
params.sparams.penalty_repeat = std::stof(value);
params.sampling.penalty_repeat = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--presence-penalty"}, "N",
string_format("repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_present),
string_format("repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)params.sampling.penalty_present),
[](common_params & params, const std::string & value) {
params.sparams.penalty_present = std::stof(value);
params.sampling.penalty_present = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--frequency-penalty"}, "N",
string_format("repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_freq),
string_format("repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)params.sampling.penalty_freq),
[](common_params & params, const std::string & value) {
params.sparams.penalty_freq = std::stof(value);
params.sampling.penalty_freq = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--dry-multiplier"}, "N",
string_format("set DRY sampling multiplier (default: %.1f, 0.0 = disabled)", (double)params.sparams.dry_multiplier),
string_format("set DRY sampling multiplier (default: %.1f, 0.0 = disabled)", (double)params.sampling.dry_multiplier),
[](common_params & params, const std::string & value) {
params.sparams.dry_multiplier = std::stof(value);
params.sampling.dry_multiplier = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--dry-base"}, "N",
string_format("set DRY sampling base value (default: %.2f)", (double)params.sparams.dry_base),
string_format("set DRY sampling base value (default: %.2f)", (double)params.sampling.dry_base),
[](common_params & params, const std::string & value) {
float potential_base = std::stof(value);
if (potential_base >= 1.0f)
{
params.sparams.dry_base = potential_base;
params.sampling.dry_base = potential_base;
}
}
).set_sparam());
add_opt(common_arg(
{"--dry-allowed-length"}, "N",
string_format("set allowed length for DRY sampling (default: %d)", params.sparams.dry_allowed_length),
string_format("set allowed length for DRY sampling (default: %d)", params.sampling.dry_allowed_length),
[](common_params & params, int value) {
params.sparams.dry_allowed_length = value;
params.sampling.dry_allowed_length = value;
}
).set_sparam());
add_opt(common_arg(
{"--dry-penalty-last-n"}, "N",
string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sparams.dry_penalty_last_n),
string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sampling.dry_penalty_last_n),
[](common_params & params, int value) {
params.sparams.dry_penalty_last_n = value;
params.sampling.dry_penalty_last_n = value;
}
).set_sparam());
add_opt(common_arg(
{"--dry-sequence-breaker"}, "STRING",
string_format("add sequence breaker for DRY sampling, clearing out default breakers (%s) in the process; use \"none\" to not use any sequence breakers\n",
params.sparams.dry_sequence_breakers.empty() ? "none" :
std::accumulate(std::next(params.sparams.dry_sequence_breakers.begin()),
params.sparams.dry_sequence_breakers.end(),
std::string("'") + (params.sparams.dry_sequence_breakers[0] == "\n" ? "\\n" : params.sparams.dry_sequence_breakers[0]) + "'",
params.sampling.dry_sequence_breakers.empty() ? "none" :
std::accumulate(std::next(params.sampling.dry_sequence_breakers.begin()),
params.sampling.dry_sequence_breakers.end(),
std::string("'") + (params.sampling.dry_sequence_breakers[0] == "\n" ? "\\n" : params.sampling.dry_sequence_breakers[0]) + "'",
[](const std::string& a, const std::string& b) {
std::string formatted_b = (b == "\n") ? "\\n" : b;
return a + ", '" + formatted_b + "'";
@ -1047,51 +919,51 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
static bool defaults_cleared = false;
if (!defaults_cleared) {
params.sparams.dry_sequence_breakers.clear();
params.sampling.dry_sequence_breakers.clear();
defaults_cleared = true;
}
if (value == "none") {
params.sparams.dry_sequence_breakers.clear();
params.sampling.dry_sequence_breakers.clear();
} else {
params.sparams.dry_sequence_breakers.emplace_back(value);
params.sampling.dry_sequence_breakers.emplace_back(value);
}
}
).set_sparam());
add_opt(common_arg(
{"--dynatemp-range"}, "N",
string_format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sparams.dynatemp_range),
string_format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sampling.dynatemp_range),
[](common_params & params, const std::string & value) {
params.sparams.dynatemp_range = std::stof(value);
params.sampling.dynatemp_range = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--dynatemp-exp"}, "N",
string_format("dynamic temperature exponent (default: %.1f)", (double)params.sparams.dynatemp_exponent),
string_format("dynamic temperature exponent (default: %.1f)", (double)params.sampling.dynatemp_exponent),
[](common_params & params, const std::string & value) {
params.sparams.dynatemp_exponent = std::stof(value);
params.sampling.dynatemp_exponent = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--mirostat"}, "N",
string_format("use Mirostat sampling.\nTop K, Nucleus and Locally Typical samplers are ignored if used.\n"
"(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", params.sparams.mirostat),
"(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", params.sampling.mirostat),
[](common_params & params, int value) {
params.sparams.mirostat = value;
params.sampling.mirostat = value;
}
).set_sparam());
add_opt(common_arg(
{"--mirostat-lr"}, "N",
string_format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sparams.mirostat_eta),
string_format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sampling.mirostat_eta),
[](common_params & params, const std::string & value) {
params.sparams.mirostat_eta = std::stof(value);
params.sampling.mirostat_eta = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
{"--mirostat-ent"}, "N",
string_format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sparams.mirostat_tau),
string_format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sampling.mirostat_tau),
[](common_params & params, const std::string & value) {
params.sparams.mirostat_tau = std::stof(value);
params.sampling.mirostat_tau = std::stof(value);
}
).set_sparam());
add_opt(common_arg(
@ -1107,7 +979,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
try {
if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
const float bias = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
params.sparams.logit_bias.push_back({key, bias});
params.sampling.logit_bias.push_back({key, bias});
} else {
throw std::invalid_argument("invalid input format");
}
@ -1118,9 +990,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_sparam());
add_opt(common_arg(
{"--grammar"}, "GRAMMAR",
string_format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sparams.grammar.c_str()),
string_format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sampling.grammar.c_str()),
[](common_params & params, const std::string & value) {
params.sparams.grammar = value;
params.sampling.grammar = value;
}
).set_sparam());
add_opt(common_arg(
@ -1134,7 +1006,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
std::copy(
std::istreambuf_iterator<char>(file),
std::istreambuf_iterator<char>(),
std::back_inserter(params.sparams.grammar)
std::back_inserter(params.sampling.grammar)
);
}
).set_sparam());
@ -1142,7 +1014,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
{"-j", "--json-schema"}, "SCHEMA",
"JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\nFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead",
[](common_params & params, const std::string & value) {
params.sparams.grammar = json_schema_to_grammar(json::parse(value));
params.sampling.grammar = json_schema_to_grammar(json::parse(value));
}
).set_sparam());
add_opt(common_arg(
@ -1451,17 +1323,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
}
).set_env("LLAMA_ARG_N_GPU_LAYERS"));
add_opt(common_arg(
{"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N",
"number of layers to store in VRAM for the draft model",
[](common_params & params, int value) {
params.n_gpu_layers_draft = value;
if (!llama_supports_gpu_offload()) {
fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n");
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"-sm", "--split-mode"}, "{none,layer,row}",
"how to split the model across multiple GPUs, one of:\n"
@ -1600,13 +1461,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.model = value;
}
).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}).set_env("LLAMA_ARG_MODEL"));
add_opt(common_arg(
{"-md", "--model-draft"}, "FNAME",
"draft model for speculative decoding (default: unused)",
[](common_params & params, const std::string & value) {
params.model_draft = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"-mu", "--model-url"}, "MODEL_URL",
"model download url (default: unused)",
@ -2044,5 +1898,168 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
).set_env("LLAMA_LOG_TIMESTAMPS"));
// speculative parameters
add_opt(common_arg(
{"-td", "--threads-draft"}, "N",
"number of threads to use during generation (default: same as --threads)",
[](common_params & params, int value) {
params.speculative.cpuparams.n_threads = value;
if (params.speculative.cpuparams.n_threads <= 0) {
params.speculative.cpuparams.n_threads = std::thread::hardware_concurrency();
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-tbd", "--threads-batch-draft"}, "N",
"number of threads to use during batch and prompt processing (default: same as --threads-draft)",
[](common_params & params, int value) {
params.speculative.cpuparams_batch.n_threads = value;
if (params.speculative.cpuparams_batch.n_threads <= 0) {
params.speculative.cpuparams_batch.n_threads = std::thread::hardware_concurrency();
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-Cd", "--cpu-mask-draft"}, "M",
"Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
[](common_params & params, const std::string & mask) {
params.speculative.cpuparams.mask_valid = true;
if (!parse_cpu_mask(mask, params.speculative.cpuparams.cpumask)) {
throw std::invalid_argument("invalid cpumask");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-Crd", "--cpu-range-draft"}, "lo-hi",
"Ranges of CPUs for affinity. Complements --cpu-mask-draft",
[](common_params & params, const std::string & range) {
params.speculative.cpuparams.mask_valid = true;
if (!parse_cpu_range(range, params.speculative.cpuparams.cpumask)) {
throw std::invalid_argument("invalid range");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--cpu-strict-draft"}, "<0|1>",
"Use strict CPU placement for draft model (default: same as --cpu-strict)",
[](common_params & params, int value) {
params.speculative.cpuparams.strict_cpu = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--prio-draft"}, "N",
string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.speculative.cpuparams.priority),
[](common_params & params, int prio) {
if (prio < 0 || prio > 3) {
throw std::invalid_argument("invalid value");
}
params.speculative.cpuparams.priority = (enum ggml_sched_priority) prio;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--poll-draft"}, "<0|1>",
"Use polling to wait for draft model work (default: same as --poll])",
[](common_params & params, int value) {
params.speculative.cpuparams.poll = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-Cbd", "--cpu-mask-batch-draft"}, "M",
"Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
[](common_params & params, const std::string & mask) {
params.speculative.cpuparams_batch.mask_valid = true;
if (!parse_cpu_mask(mask, params.speculative.cpuparams_batch.cpumask)) {
throw std::invalid_argument("invalid cpumask");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"-Crbd", "--cpu-range-batch-draft"}, "lo-hi",
"Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)",
[](common_params & params, const std::string & range) {
params.speculative.cpuparams_batch.mask_valid = true;
if (!parse_cpu_range(range, params.speculative.cpuparams_batch.cpumask)) {
throw std::invalid_argument("invalid cpumask");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--cpu-strict-batch-draft"}, "<0|1>",
"Use strict CPU placement for draft model (default: --cpu-strict-draft)",
[](common_params & params, int value) {
params.speculative.cpuparams_batch.strict_cpu = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--prio-batch-draft"}, "N",
string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.speculative.cpuparams_batch.priority),
[](common_params & params, int prio) {
if (prio < 0 || prio > 3) {
throw std::invalid_argument("invalid value");
}
params.speculative.cpuparams_batch.priority = (enum ggml_sched_priority) prio;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--poll-batch-draft"}, "<0|1>",
"Use polling to wait for draft model work (default: --poll-draft)",
[](common_params & params, int value) {
params.speculative.cpuparams_batch.poll = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--draft-max", "--draft", "--draft-n"}, "N",
string_format("number of tokens to draft for speculative decoding (default: %d)", params.speculative.n_max),
[](common_params & params, int value) {
params.speculative.n_max = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"--draft-min", "--draft-n-min"}, "N",
string_format("minimum number of draft tokens to use for speculative decoding (default: %d)", params.speculative.n_min),
[](common_params & params, int value) {
params.speculative.n_min = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"--draft-p-split"}, "P",
string_format("speculative decoding split probability (default: %.1f)", (double)params.speculative.p_split),
[](common_params & params, const std::string & value) {
params.speculative.p_split = std::stof(value);
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--draft-p-min"}, "P",
string_format("minimum speculative decoding probability (greedy) (default: %.1f)", (double)params.speculative.p_min),
[](common_params & params, const std::string & value) {
params.speculative.p_min = std::stof(value);
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"-cd", "--ctx-size-draft"}, "N",
string_format("size of the prompt context for the draft model (default: %d, 0 = loaded from model)", params.speculative.n_ctx),
[](common_params & params, int value) {
params.speculative.n_ctx = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N",
"number of layers to store in VRAM for the draft model",
[](common_params & params, int value) {
params.speculative.n_gpu_layers = value;
if (!llama_supports_gpu_offload()) {
fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n");
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
}
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"-md", "--model-draft"}, "FNAME",
"draft model for speculative decoding (default: unused)",
[](common_params & params, const std::string & value) {
params.speculative.model = value;
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
return ctx_arg;
}

4
common/common.cpp
View File

@ -925,9 +925,9 @@ struct common_init_result common_init_from_params(common_params & params) {
common_lora_adapters_apply(lctx, iparams.lora_adapters);
}
if (params.sparams.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) {
if (params.sampling.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) {
LOG_WRN("%s: warning: model does not have an EOS token, ignoring --ignore-eos\n", __func__);
params.sparams.ignore_eos = false;
params.sampling.ignore_eos = false;
}
if (params.warmup) {

28
common/common.h
View File

@ -103,8 +103,8 @@ enum dimre_method {
DIMRE_METHOD_MEAN,
};
// sampler parameters
struct common_sampler_params {
// sampling parameters
struct common_params_sampling {
uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
int32_t n_prev = 64; // number of previous tokens to remember
@ -155,20 +155,30 @@ struct common_sampler_params {
std::string print() const;
};
struct common_params_speculative {
int32_t n_ctx = 4096; // draft context size
int32_t n_max = 5; // maximum number of tokens to draft during speculative decoding
int32_t n_min = 0; // minimum number of draft tokens to use for speculative decoding
int32_t n_gpu_layers = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
float p_split = 0.1f; // speculative decoding split probability
float p_min = 0.9f; // minimum speculative decoding probability (greedy)
struct cpu_params cpuparams;
struct cpu_params cpuparams_batch;
std::string model = ""; // draft model for speculative decoding // NOLINT
};
struct common_params {
int32_t n_predict = -1; // new tokens to predict
int32_t n_ctx = 4096; // context size
int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_draft = 5; // number of tokens to draft during speculative decoding
int32_t n_draft_min = 0; // minimum number of draft tokens to use for speculative decoding
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
int32_t n_parallel = 1; // number of parallel sequences to decode
int32_t n_sequences = 1; // number of sequences to decode
float p_split = 0.1f; // speculative decoding split probability
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
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[128] = {0}; // how split tensors should be distributed across GPUs
int32_t grp_attn_n = 1; // group-attention factor
@ -185,8 +195,6 @@ struct common_params {
struct cpu_params cpuparams;
struct cpu_params cpuparams_batch;
struct cpu_params draft_cpuparams;
struct cpu_params draft_cpuparams_batch;
ggml_backend_sched_eval_callback cb_eval = nullptr;
void * cb_eval_user_data = nullptr;
@ -198,10 +206,10 @@ struct common_params {
enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
enum llama_attention_type attention_type = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings
struct common_sampler_params sparams;
struct common_params_sampling sampling;
struct common_params_speculative speculative;
std::string model = ""; // model path // NOLINT
std::string model_draft = ""; // draft model for speculative decoding // NOLINT
std::string model_alias = "unknown"; // model alias // NOLINT
std::string model_url = ""; // model url to download // NOLINT
std::string hf_token = ""; // HF token // NOLINT

6
common/sampling.cpp
View File

@ -99,7 +99,7 @@ struct ring_buffer {
};
struct common_sampler {
common_sampler_params params;
common_params_sampling params;
struct llama_sampler * grmr;
struct llama_sampler * chain;
@ -125,7 +125,7 @@ struct common_sampler {
}
};
std::string common_sampler_params::print() const {
std::string common_params_sampling::print() const {
char result[1024];
snprintf(result, sizeof(result),
@ -141,7 +141,7 @@ std::string common_sampler_params::print() const {
return std::string(result);
}
struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_sampler_params & params) {
struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params) {
llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
lparams.no_perf = params.no_perf;

2
common/sampling.h
View File

@ -36,7 +36,7 @@ struct common_sampler;
// llama_sampler API overloads
struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_sampler_params & params);
struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params);
void common_sampler_free(struct common_sampler * gsmpl);

22
common/speculative.cpp
View File

@ -29,32 +29,32 @@ struct common_speculative * common_speculative_init(
// TODO: optimize or pass from outside?
#if 0
{
common_sampler_params sparams;
sparams.no_perf = false;
common_params_sampling params;
params.no_perf = false;
sparams.top_k = 40;
sparams.top_p = 0.9;
params.top_k = 40;
params.top_p = 0.9;
sparams.samplers = {
params.samplers = {
COMMON_SAMPLER_TYPE_TOP_K,
COMMON_SAMPLER_TYPE_TOP_P,
COMMON_SAMPLER_TYPE_INFILL,
};
result->smpl = common_sampler_init(llama_get_model(ctx_dft), sparams);
result->smpl = common_sampler_init(llama_get_model(ctx_dft), params);
}
#else
{
common_sampler_params sparams;
sparams.no_perf = false;
common_params_sampling params;
params.no_perf = false;
sparams.top_k = 10;
params.top_k = 10;
sparams.samplers = {
params.samplers = {
COMMON_SAMPLER_TYPE_TOP_K,
};
result->smpl = common_sampler_init(llama_get_model(ctx_dft), sparams);
result->smpl = common_sampler_init(llama_get_model(ctx_dft), params);
}
#endif

8
examples/batched/batched.cpp
View File

@ -68,10 +68,10 @@ int main(int argc, char ** argv) {
llama_sampler * smpl = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sparams.top_k));
llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sparams.top_p, params.sparams.min_keep));
llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sparams.temp));
llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sparams.seed));
llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k));
llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep));
llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp));
llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed));
if (ctx == NULL) {
LOG_ERR("%s: error: failed to create the llama_context\n" , __func__);

2
examples/infill/infill.cpp
View File

@ -73,7 +73,7 @@ int main(int argc, char ** argv) {
common_init();
auto & sparams = params.sparams;
auto & sparams = params.sampling;
console::init(params.simple_io, params.use_color);
atexit([]() { console::cleanup(); });

2
examples/llava/llava-cli.cpp
View File

@ -191,7 +191,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
LOG("\n");
struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sparams);
struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling);
if (!smpl) {
LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
exit(1);

2
examples/llava/minicpmv-cli.cpp
View File

@ -237,7 +237,7 @@ static struct common_sampler * llama_init(struct llava_context * ctx_llava, comm
LOG_INF("\n");
struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sparams);
struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling);
return smpl;
}

2
examples/lookahead/lookahead.cpp
View File

@ -115,7 +115,7 @@ int main(int argc, char ** argv) {
llama_batch batch = llama_batch_init(params.n_ctx, 0, W + G + 1);
// target model sampling context
struct common_sampler * smpl = common_sampler_init(model, params.sparams);
struct common_sampler * smpl = common_sampler_init(model, params.sampling);
// verification n-grams
std::vector<ngram_data> ngrams_cur(G);

3
examples/lookup/lookup-stats.cpp
View File

@ -21,7 +21,7 @@ int main(int argc, char ** argv){
common_init();
const int n_draft = params.n_draft;
const int n_draft = params.speculative.n_max;
// init llama.cpp
llama_backend_init();
@ -40,6 +40,7 @@ int main(int argc, char ** argv){
common_ngram_cache ngram_cache_context;
common_ngram_cache ngram_cache_dynamic;
common_ngram_cache ngram_cache_static;
int64_t t_draft_flat_us = 0;
int64_t t_draft_us = 0;

4
examples/lookup/lookup.cpp
View File

@ -22,7 +22,7 @@ int main(int argc, char ** argv){
common_init();
// max. number of additional tokens to draft if match is found
const int n_draft = params.n_draft;
const int n_draft = params.speculative.n_max;
const bool dump_kv_cache = params.dump_kv_cache;
@ -102,7 +102,7 @@ int main(int argc, char ** argv){
bool has_eos = false;
struct common_sampler * smpl = common_sampler_init(model, params.sparams);
struct common_sampler * smpl = common_sampler_init(model, params.sampling);
std::vector<llama_token> draft;

2
examples/main/main.cpp
View File

@ -100,7 +100,7 @@ int main(int argc, char ** argv) {
common_init();
auto & sparams = params.sparams;
auto & sparams = params.sampling;
// save choice to use color for later
// (note for later: this is a slightly awkward choice)

2
examples/parallel/parallel.cpp
View File

@ -160,7 +160,7 @@ int main(int argc, char ** argv) {
for (size_t i = 0; i < clients.size(); ++i) {
auto & client = clients[i];
client.id = i;
client.smpl = common_sampler_init(model, params.sparams);
client.smpl = common_sampler_init(model, params.sampling);
}
std::vector<llama_token> tokens_system;

4
examples/retrieval/retrieval.cpp
View File

@ -282,8 +282,8 @@ int main(int argc, char ** argv) {
return a.second > b.second;
});
LOG("Top %d similar chunks:\n", params.sparams.top_k);
for (int i = 0; i < std::min(params.sparams.top_k, (int) chunks.size()); i++) {
LOG("Top %d similar chunks:\n", params.sampling.top_k);
for (int i = 0; i < std::min(params.sampling.top_k, (int) chunks.size()); i++) {
LOG("filename: %s\n", chunks[similarities[i].first].filename.c_str());
LOG("filepos: %lld\n", (long long int) chunks[similarities[i].first].filepos);
LOG("similarity: %f\n", similarities[i].second);

8
examples/save-load-state/save-load-state.cpp
View File

@ -9,7 +9,7 @@ int main(int argc, char ** argv) {
common_params params;
params.prompt = "The quick brown fox";
params.sparams.seed = 1234;
params.sampling.seed = 1234;
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
@ -42,7 +42,7 @@ int main(int argc, char ** argv) {
llama_sampler * smpl = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sparams.seed));
llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sampling.seed));
// tokenize prompt
auto tokens = common_tokenize(ctx, params.prompt, true);
@ -106,7 +106,7 @@ int main(int argc, char ** argv) {
llama_sampler * smpl2 = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl2, llama_sampler_init_dist(params.sparams.seed));
llama_sampler_chain_add(smpl2, llama_sampler_init_dist(params.sampling.seed));
printf("\nsecond run: %s", params.prompt.c_str());
@ -169,7 +169,7 @@ int main(int argc, char ** argv) {
llama_sampler * smpl3 = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl3, llama_sampler_init_dist(params.sparams.seed));
llama_sampler_chain_add(smpl3, llama_sampler_init_dist(params.sampling.seed));
printf("\nsingle seq run: %s", params.prompt.c_str());

6
examples/server/server.cpp
View File

@ -175,7 +175,7 @@ struct server_slot {
// sampling
json json_schema;
struct common_sampler_params sparams;
struct common_params_sampling sparams;
struct common_sampler * smpl = nullptr;
llama_token sampled;
@ -687,7 +687,7 @@ struct server_context {
SLT_INF(slot, "new slot n_ctx_slot = %d\n", slot.n_ctx);
slot.sparams = params.sparams;
slot.sparams = params.sampling;
slot.callback_on_release = [this](int) {
queue_tasks.pop_deferred_task();
@ -788,7 +788,7 @@ struct server_context {
bool launch_slot_with_task(server_slot & slot, const server_task & task) {
slot_params default_params;
// Sampling parameter defaults are loaded from the global server context (but individual requests can still override them)
auto default_sparams = params.sparams;
auto default_sparams = params.sampling;
const auto & data = task.data;
if (data.count("__oaicompat") != 0) {

9
examples/speculative-simple/README.md
View File

@ -1,3 +1,12 @@
# llama.cpp/examples/speculative-simple
Demonstration of basic greedy speculative decoding
```bash
./bin/llama-speculative-simple \
-m ../models/qwen2.5-32b-coder-instruct/ggml-model-q8_0.gguf \
-md ../models/qwen2.5-1.5b-coder-instruct/ggml-model-q4_0.gguf \
-f test.txt -c 0 -ngl 99 --color \
--sampling-seq k --top-k 1 -fa --temp 0.0 \
-ngld 99 --draft-max 16 --draft-min 5 --draft-p-min 0.9
```

33
examples/speculative-simple/speculative-simple.cpp
View File

@ -24,7 +24,7 @@ int main(int argc, char ** argv) {
common_init();
if (params.model_draft.empty()) {
if (params.speculative.model.empty()) {
LOG_ERR("%s: --model-draft is required\n", __func__);
return 1;
}
@ -46,13 +46,13 @@ int main(int argc, char ** argv) {
ctx_tgt = llama_init_tgt.context;
// load the draft model
params.model = params.model_draft;
params.n_gpu_layers = params.n_gpu_layers_draft;
if (params.draft_cpuparams.n_threads > 0) {
params.cpuparams.n_threads = params.draft_cpuparams.n_threads;
params.model = params.speculative.model;
params.n_gpu_layers = params.speculative.n_gpu_layers;
if (params.speculative.cpuparams.n_threads > 0) {
params.cpuparams.n_threads = params.speculative.cpuparams.n_threads;
}
params.cpuparams_batch.n_threads = params.draft_cpuparams_batch.n_threads;
params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
common_init_result llama_init_dft = common_init_from_params(params);
model_dft = llama_init_dft.model;
@ -66,11 +66,9 @@ int main(int argc, char ** argv) {
std::vector<llama_token> inp;
inp = common_tokenize(ctx_tgt, params.prompt, true, true);
const int max_context_size = llama_n_ctx(ctx_tgt);
const int max_tokens_list_size = max_context_size - 4;
if ((int) inp.size() > llama_n_ctx(ctx_tgt)) {
LOG_ERR("%s: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), llama_n_ctx(ctx_tgt));
if ((int) inp.size() > max_tokens_list_size) {
LOG_ERR("%s: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
return 1;
}
@ -81,7 +79,10 @@ int main(int argc, char ** argv) {
}
// how many tokens to draft each time
int n_draft = params.n_draft;
int n_draft = params.speculative.n_max;
int n_draft_min = params.speculative.n_min;
float p_min = params.speculative.p_min;
int n_predict = 0;
int n_drafted = 0;
@ -97,7 +98,7 @@ int main(int argc, char ** argv) {
const auto t_enc_start = ggml_time_us();
// target model sampling context
struct common_sampler * smpl = common_sampler_init(model_tgt, params.sparams);
struct common_sampler * smpl = common_sampler_init(model_tgt, params.sampling);
// eval the prompt
llama_decode(ctx_tgt, llama_batch_get_one(inp.data(), inp.size() - 1));
@ -112,9 +113,9 @@ int main(int argc, char ** argv) {
// init the speculator
struct common_speculative_params params_spec;
params_spec.n_draft = n_draft;
params_spec.n_reuse = 256;
params_spec.p_min = 0.9f;
params_spec.n_draft = n_draft;
params_spec.n_reuse = 256;
params_spec.p_min = p_min;
struct common_speculative * spec = common_speculative_init(ctx_dft);
@ -143,7 +144,7 @@ int main(int argc, char ** argv) {
// evaluate the target model on [id_last, draft0, draft1, ..., draftN-1]
{
// do not waste time on small drafts
if (draft.size() < params.n_draft_min) {
if (draft.size() < n_draft_min) {
draft.clear();
}

28
examples/speculative/speculative.cpp
View File

@ -33,7 +33,7 @@ int main(int argc, char ** argv) {
common_params params;
// needed to get candidate probs even for temp <= 0.0
params.sparams.n_probs = 128;
params.sampling.n_probs = 128;
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) {
return 1;
@ -46,7 +46,7 @@ int main(int argc, char ** argv) {
common_init();
if (params.model_draft.empty()) {
if (params.speculative.model.empty()) {
LOG_ERR("%s: --model-draft is required\n", __func__);
return 1;
}
@ -55,9 +55,9 @@ int main(int argc, char ** argv) {
const int n_seq_dft = params.n_parallel;
// probability threshold for splitting a draft branch (only for n_seq_dft > 1)
const float p_split = params.p_split;
const float p_draft_split = params.speculative.p_split;
std::default_random_engine rng(params.sparams.seed == LLAMA_DEFAULT_SEED ? std::random_device()() : params.sparams.seed);
std::default_random_engine rng(params.sampling.seed == LLAMA_DEFAULT_SEED ? std::random_device()() : params.sampling.seed);
std::uniform_real_distribution<> u_dist;
// init llama.cpp
@ -76,13 +76,13 @@ int main(int argc, char ** argv) {
ctx_tgt = llama_init_tgt.context;
// load the draft model
params.model = params.model_draft;
params.n_gpu_layers = params.n_gpu_layers_draft;
if (params.draft_cpuparams.n_threads > 0) {
params.cpuparams.n_threads = params.draft_cpuparams.n_threads;
params.model = params.speculative.model;
params.n_gpu_layers = params.speculative.n_gpu_layers;
if (params.speculative.cpuparams.n_threads > 0) {
params.cpuparams.n_threads = params.speculative.cpuparams.n_threads;
}
params.cpuparams_batch.n_threads = params.draft_cpuparams_batch.n_threads;
params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
common_init_result llama_init_dft = common_init_from_params(params);
model_dft = llama_init_dft.model;
ctx_dft = llama_init_dft.context;
@ -170,7 +170,7 @@ int main(int argc, char ** argv) {
//GGML_ASSERT(n_vocab == llama_n_vocab(model_dft));
// how many tokens to draft each time
int n_draft = params.n_draft;
int n_draft = params.speculative.n_max;
int n_predict = 0;
int n_drafted = 0;
@ -183,14 +183,14 @@ int main(int argc, char ** argv) {
bool has_eos = false;
// target model sampling context (reuse the llama_context's sampling instance)
struct common_sampler * smpl = common_sampler_init(model_tgt, params.sparams);
struct common_sampler * smpl = common_sampler_init(model_tgt, params.sampling);
// draft sequence data
std::vector<seq_draft> drafts(n_seq_dft);
for (int s = 0; s < n_seq_dft; ++s) {
// allocate llama_sampler for each draft sequence
drafts[s].smpl = common_sampler_init(model_dft, params.sparams);
drafts[s].smpl = common_sampler_init(model_dft, params.sampling);
}
llama_batch batch_dft = llama_batch_init(llama_n_batch(ctx_dft), 0, 1);
@ -230,7 +230,7 @@ int main(int argc, char ** argv) {
// for stochastic sampling, attempt to match the token with the drafted tokens
{
bool accept = false;
if (params.sparams.temp > 0) {
if (params.sampling.temp > 0) {
// stochastic verification
common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft], true);
@ -494,7 +494,7 @@ int main(int argc, char ** argv) {
// attempt to split the branch if the probability is high enough
for (int f = 1; f < 8; ++f) {
if (n_seq_cur < n_seq_dft && cur_p->data[f].p > p_split) {
if (n_seq_cur < n_seq_dft && cur_p->data[f].p > p_draft_split) {
LOG_DBG("splitting seq %3d into %3d\n", s, n_seq_cur);
llama_kv_cache_seq_rm(ctx_dft, n_seq_cur, -1, -1);

2
tests/test-arg-parser.cpp
View File

@ -96,7 +96,7 @@ int main(void) {
// --draft cannot be used outside llama-speculative
argv = {"binary_name", "--draft", "123"};
assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SPECULATIVE));
assert(params.n_draft == 123);
assert(params.speculative.n_max == 123);
// skip this part on windows, because setenv is not supported
#ifdef _WIN32