diff --git a/common/arg.cpp b/common/arg.cpp index 62d31393c4..946ffbc182 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -1596,6 +1596,26 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } } ).set_sparam()); + add_opt(common_arg( + {"--adaptive-target"}, "N", + string_format("adaptive-p: select tokens near this probability (valid range 0.0 " + "to 1.0; negative = disabled) (default: %.2f)\n" + "[(more info)](https://github.com/ggml-org/llama.cpp/pull/17927)", + (double)params.sampling.adaptive_target), + [](common_params & params, const std::string & value) { + params.sampling.adaptive_target = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--adaptive-decay"}, "N", + string_format("adaptive-p: decay rate for target adaptation over time. lower values " + "are more reactive, higher values are more stable.\n" + "(valid range 0.0 to 0.99) (default: %.2f)", + (double)params.sampling.adaptive_decay), + [](common_params & params, const std::string & value) { + params.sampling.adaptive_decay = std::stof(value); + } + ).set_sparam()); add_opt(common_arg( {"--dynatemp-range"}, "N", string_format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sampling.dynatemp_range), diff --git a/common/common.h b/common/common.h index f8bc686b6f..17023f0f89 100644 --- a/common/common.h +++ b/common/common.h @@ -117,6 +117,7 @@ enum common_sampler_type { COMMON_SAMPLER_TYPE_INFILL = 9, COMMON_SAMPLER_TYPE_PENALTIES = 10, COMMON_SAMPLER_TYPE_TOP_N_SIGMA = 11, + COMMON_SAMPLER_TYPE_ADAPTIVE_P = 12, }; // dimensionality reduction methods, used by cvector-generator @@ -164,32 +165,34 @@ enum common_params_sampling_config : uint64_t { 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 - 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 xtc_probability = 0.00f; // 0.0 = disabled - float xtc_threshold = 0.10f; // > 0.5 disables XTC - float typ_p = 1.00f; // typical_p, 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 - float dry_multiplier = 0.0f; // 0.0 = disabled; DRY repetition penalty for tokens extending repetition: - float dry_base = 1.75f; // 0.0 = disabled; multiplier * base ^ (length of sequence before token - allowed length) - int32_t dry_allowed_length = 2; // tokens extending repetitions beyond this receive penalty - int32_t dry_penalty_last_n = -1; // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size) - int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 - float top_n_sigma = -1.00f;// -1.0 = disabled - float mirostat_tau = 5.00f; // target entropy - float mirostat_eta = 0.10f; // learning rate + 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 xtc_probability = 0.00f; // 0.0 = disabled + float xtc_threshold = 0.10f; // > 0.5 disables XTC + float typ_p = 1.00f; // typical_p, 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 + float dry_multiplier = 0.0f; // 0.0 = disabled; DRY repetition penalty for tokens extending repetition: + float dry_base = 1.75f; // 0.0 = disabled; multiplier * base ^ (length of sequence before token - allowed length) + int32_t dry_allowed_length = 2; // tokens extending repetitions beyond this receive penalty + int32_t dry_penalty_last_n = -1; // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size) + float adaptive_target = -1.0f; // select tokens near this probability (valid range 0.0 to 1.0; negative = disabled) + float adaptive_decay = 0.90f; // EMA decay for adaptation; history ≈ 1/(1-decay) tokens (0.0 - 0.99) + int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 + float top_n_sigma = -1.00f; // -1.0 = disabled + float mirostat_tau = 5.00f; // target entropy + float mirostat_eta = 0.10f; // learning rate bool ignore_eos = false; - bool no_perf = false; // disable performance metrics + bool no_perf = false; // disable performance metrics bool timing_per_token = false; uint64_t user_sampling_config = 0; // bitfield to track user-specified samplers diff --git a/common/sampling.cpp b/common/sampling.cpp index c66f935c65..140404f12f 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -150,11 +150,11 @@ std::string common_params_sampling::print() const { "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n" "\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n" "\ttop_k = %d, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, top_n_sigma = %.3f, temp = %.3f\n" - "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f", + "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f, adaptive_target = %.3f, adaptive_decay = %.3f", penalty_last_n, penalty_repeat, penalty_freq, penalty_present, dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, top_k, top_p, min_p, xtc_probability, xtc_threshold, typ_p, top_n_sigma, temp, - mirostat, mirostat_eta, mirostat_tau); + mirostat, mirostat_eta, mirostat_tau, adaptive_target, adaptive_decay); return std::string(result); } @@ -236,6 +236,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co } if (params.mirostat == 0) { + + bool use_adaptive_p = false; // see below + for (const auto & cnstr : params.samplers) { switch (cnstr) { case COMMON_SAMPLER_TYPE_DRY: @@ -245,43 +248,54 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co for (const auto & str : params.dry_sequence_breakers) { c_breakers.push_back(str.c_str()); } - - samplers.push_back(llama_sampler_init_dry (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size())); + samplers.push_back(llama_sampler_init_dry(vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size())); } break; case COMMON_SAMPLER_TYPE_TOP_K: - samplers.push_back(llama_sampler_init_top_k (params.top_k)); + samplers.push_back(llama_sampler_init_top_k(params.top_k)); break; case COMMON_SAMPLER_TYPE_TOP_P: - samplers.push_back(llama_sampler_init_top_p (params.top_p, params.min_keep)); + samplers.push_back(llama_sampler_init_top_p(params.top_p, params.min_keep)); break; case COMMON_SAMPLER_TYPE_TOP_N_SIGMA: samplers.push_back(llama_sampler_init_top_n_sigma(params.top_n_sigma)); break; case COMMON_SAMPLER_TYPE_MIN_P: - samplers.push_back(llama_sampler_init_min_p (params.min_p, params.min_keep)); + samplers.push_back(llama_sampler_init_min_p(params.min_p, params.min_keep)); break; case COMMON_SAMPLER_TYPE_XTC: - samplers.push_back(llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); + samplers.push_back(llama_sampler_init_xtc(params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); break; case COMMON_SAMPLER_TYPE_TYPICAL_P: - samplers.push_back(llama_sampler_init_typical (params.typ_p, params.min_keep)); + samplers.push_back(llama_sampler_init_typical(params.typ_p, params.min_keep)); break; case COMMON_SAMPLER_TYPE_TEMPERATURE: - samplers.push_back(llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent)); + samplers.push_back(llama_sampler_init_temp_ext(params.temp, params.dynatemp_range, params.dynatemp_exponent)); break; case COMMON_SAMPLER_TYPE_INFILL: - samplers.push_back(llama_sampler_init_infill (vocab)); + samplers.push_back(llama_sampler_init_infill(vocab)); break; case COMMON_SAMPLER_TYPE_PENALTIES: - samplers.push_back(llama_sampler_init_penalties (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present)); + samplers.push_back(llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present)); + break; + case COMMON_SAMPLER_TYPE_ADAPTIVE_P: + // the `adaptive-p` sampler is like `dist` and `mirostat` in that it selects + // a single token, so we will add `dist` at the end of the chain by default, + // unless the user specifically included `adaptive-p`. we set this flag here + // so we know to add the sampler at the very end. + use_adaptive_p = true; break; default: GGML_ASSERT(false && "unknown sampler type"); } } - - samplers.push_back(llama_sampler_init_dist(params.seed)); + if (use_adaptive_p) { + // only if user explicitly included adaptive-p sampler + samplers.push_back(llama_sampler_init_adaptive_p(params.adaptive_target, params.adaptive_decay, params.seed)); + } else { + // default: sample from distribution + samplers.push_back(llama_sampler_init_dist(params.seed)); + } } else if (params.mirostat == 1) { samplers.push_back(llama_sampler_init_temp(params.temp)); samplers.push_back(llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100)); @@ -567,6 +581,7 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) { case COMMON_SAMPLER_TYPE_XTC: return 'x'; case COMMON_SAMPLER_TYPE_INFILL: return 'i'; case COMMON_SAMPLER_TYPE_PENALTIES: return 'e'; + case COMMON_SAMPLER_TYPE_ADAPTIVE_P: return 'a'; default : return '?'; } } @@ -583,6 +598,7 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) { case COMMON_SAMPLER_TYPE_XTC: return "xtc"; case COMMON_SAMPLER_TYPE_INFILL: return "infill"; case COMMON_SAMPLER_TYPE_PENALTIES: return "penalties"; + case COMMON_SAMPLER_TYPE_ADAPTIVE_P: return "adaptive_p"; default : return ""; } } @@ -599,6 +615,7 @@ std::vector common_sampler_types_from_names(const std::vect { "xtc", COMMON_SAMPLER_TYPE_XTC }, { "infill", COMMON_SAMPLER_TYPE_INFILL }, { "penalties", COMMON_SAMPLER_TYPE_PENALTIES }, + { "adaptive_p", COMMON_SAMPLER_TYPE_ADAPTIVE_P }, }; // since samplers names are written multiple ways @@ -614,6 +631,7 @@ std::vector common_sampler_types_from_names(const std::vect { "typ", COMMON_SAMPLER_TYPE_TYPICAL_P }, { "min-p", COMMON_SAMPLER_TYPE_MIN_P }, { "temp", COMMON_SAMPLER_TYPE_TEMPERATURE }, + { "adaptive-p", COMMON_SAMPLER_TYPE_ADAPTIVE_P }, }; std::vector samplers; diff --git a/include/llama.h b/include/llama.h index 8b3c8a7b10..1de1a5f38d 100644 --- a/include/llama.h +++ b/include/llama.h @@ -1313,6 +1313,33 @@ extern "C" { const char ** seq_breakers, size_t num_breakers); + /// adaptive-p: select tokens near a configurable target probability over time. + /// + /// the adaptive-p sampler transforms the token probability distribution to favor tokens + /// that fall near a user-configurable probability target. + /// + /// internally, the sampler maintains an exponential moving average of the *ORIGINAL* + /// probabilities of selected tokens at each sampling step. it uses this EMA to compute an + /// adapted target probability at each sampling step, thus maintaining the desired target + /// probability over time. + /// + /// adaptive-p selects a token ID rather than just mutating candidates, so it must be last + /// in the sampler chain (like mirostat, dist, greedy). + /// + /// only mild truncation before this sampler is recommended. we suggest applying min-p + /// before adaptive-p as the only other active sampler in the chain. + /// + /// @param target select tokens near this probability (valid range 0.0 to 1.0; negative = disabled) + /// @param decay EMA decay for adaptation; history ≈ 1/(1-decay) tokens (valid range 0.0 - 0.99) + /// @param seed RNG seed + /// + /// ref: https://github.com/ggml-org/llama.cpp/pull/17927 + /// + LLAMA_API struct llama_sampler * llama_sampler_init_adaptive_p( + float target, + float decay, + uint32_t seed); + LLAMA_API struct llama_sampler * llama_sampler_init_logit_bias( int32_t n_vocab, int32_t n_logit_bias, diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp index f3891453e4..553faa3c14 100644 --- a/src/llama-sampling.cpp +++ b/src/llama-sampling.cpp @@ -2340,6 +2340,138 @@ struct llama_sampler * llama_sampler_init_dry_testing(int32_t context_size, floa return result; } +// adaptive-p sampler state +// +// maintains an exponential moving average of the *ORIGINAL* probabilities +// of selected tokens, used to compute an adapted target at each sampling step. +// +// see llama.h for a full description of the sampler +// +// ref: https://github.com/ggml-org/llama.cpp/pull/17927 +// +struct llama_sampler_adaptive_p { + const float target; // target probability (0.0 - 1.0; negative = disabled) + const float decay; // EMA decay; history ~= 1/(1-decay) tokens (0.0 - 0.99) + const uint32_t seed; // RNG seed + std::mt19937 rng; // RNG + float weighted_sum; // sum(p_i * decay^i) + float total_weight; // sum(decay^i), converges to 1/(1-decay) + std::vector original_probs; // pre-transform probs, cached for EMA update +}; + +// adaptive probability transformation constants +static constexpr float DISTRIBUTION_WIDTH = 0.3f; +static constexpr float PEAK_LOGIT_VALUE = 5.0f; +static constexpr float SHARPNESS = 10.0f; +static constexpr float INV_WIDTH = 1.0f / DISTRIBUTION_WIDTH; + +static const char * llama_sampler_adaptive_p_name(const struct llama_sampler * /*smpl*/) { + return "adaptive-p"; +} + +static void llama_sampler_adaptive_p_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) { + auto * ctx = (llama_sampler_adaptive_p *) smpl->ctx; + + if (ctx->target < 0.0f) { + // at negative target values, adaptive-p is no-op + // we simply sample from the existing distribution + llama_sampler_softmax_impl(cur_p, false); + cur_p->selected = llama_sample_dist(cur_p, ctx->rng); + return; + } + + // softmax and store the original probabilities + llama_sampler_softmax_impl(cur_p, false); + ctx->original_probs.resize(cur_p->size); + for (size_t i = 0; i < cur_p->size; ++i) { + ctx->original_probs[i] = cur_p->data[i].p; + } + + // compute the adapted target probability for the current sampling step + auto target = std::clamp(ctx->target, 0.0f, 1.0f); + float adapted_target = std::clamp( + ctx->total_weight == 0.0f ? target : 2.0f * target - (ctx->weighted_sum / ctx->total_weight), + 0.0f, 1.0f + ); + + // adaptive probability transform + // + // quadratic near target for fine differentiation, transitioning to linear decay in the + // tails. unbounded negative logits ensure proper suppression of far-from-target tokens + // after the softmax. + // + for (size_t i = 0; i < cur_p->size; ++i) { + float dist = std::abs((cur_p->data[i].p - adapted_target) * INV_WIDTH); + cur_p->data[i].logit = PEAK_LOGIT_VALUE - SHARPNESS * dist * dist / (1.0f + dist); + } + + // softmax and sample from the transformed distribution + llama_sampler_softmax_impl(cur_p, false); + const int idx = llama_sample_dist(cur_p, ctx->rng); + cur_p->selected = idx; + + // update EMA with the original probability of the selected token + ctx->weighted_sum = ctx->original_probs[idx] + ctx->decay * ctx->weighted_sum; + ctx->total_weight = 1.0f + ctx->decay * ctx->total_weight; +} + +static void llama_sampler_adaptive_p_reset(struct llama_sampler * smpl) { + auto * ctx = (llama_sampler_adaptive_p *) smpl->ctx; + // ctx->target and ctx->decay never change after init, so it's safe to keep them as is. + // original_probs is completely overwritten on every call to _apply. + // so we only need to reset the EMA state. + ctx->weighted_sum = ctx->target / (1.0f - ctx->decay); + ctx->total_weight = 1.0f / (1.0f - ctx->decay); +} + +static struct llama_sampler * llama_sampler_adaptive_p_clone(const struct llama_sampler * smpl) { + const auto * ctx = (const llama_sampler_adaptive_p *) smpl->ctx; + auto * result = llama_sampler_init_adaptive_p(ctx->target, ctx->decay, ctx->seed); + auto * result_ctx = (llama_sampler_adaptive_p *) result->ctx; + + // copy everything (target, decay, and seed are already set) + result_ctx->original_probs = ctx->original_probs; + result_ctx->weighted_sum = ctx->weighted_sum; + result_ctx->total_weight = ctx->total_weight; + result_ctx->rng = ctx->rng; + + return result; +} + +static void llama_sampler_adaptive_p_free(struct llama_sampler * smpl) { + delete (llama_sampler_adaptive_p *) smpl->ctx; +} + +static struct llama_sampler_i llama_sampler_adaptive_p_i = { + /* .name = */ llama_sampler_adaptive_p_name, + /* .accept = */ nullptr, + /* .apply = */ llama_sampler_adaptive_p_apply, + /* .reset = */ llama_sampler_adaptive_p_reset, + /* .clone = */ llama_sampler_adaptive_p_clone, + /* .free = */ llama_sampler_adaptive_p_free, +}; + +struct llama_sampler * llama_sampler_init_adaptive_p( + float target, + float decay, + uint32_t seed +) { + auto seed_cur = get_rng_seed(seed); + float clamped_decay = std::clamp(decay, 0.0f, 0.99f); + return llama_sampler_init( + /* .iface = */ &llama_sampler_adaptive_p_i, + /* .ctx = */ new llama_sampler_adaptive_p { + /* .target = */ target, + /* .decay = */ clamped_decay, + /* .seed = */ seed_cur, + /* .rng = */ std::mt19937(seed_cur), + /* .weighted_sum = */ target / (1.0f - clamped_decay), + /* .total_weight = */ 1.0f / (1.0f - clamped_decay), + /* .original_probs = */ {}, + } + ); +} + // logit-bias struct llama_sampler_logit_bias { diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp index 22f5b2059c..d0b547c235 100644 --- a/tools/server/server-task.cpp +++ b/tools/server/server-task.cpp @@ -201,6 +201,8 @@ task_params server_task::params_from_json_cmpl( params.sampling.mirostat = json_value(data, "mirostat", defaults.sampling.mirostat); params.sampling.mirostat_tau = json_value(data, "mirostat_tau", defaults.sampling.mirostat_tau); params.sampling.mirostat_eta = json_value(data, "mirostat_eta", defaults.sampling.mirostat_eta); + params.sampling.adaptive_target = json_value(data, "adaptive_target", defaults.sampling.adaptive_target); + params.sampling.adaptive_decay = json_value(data, "adaptive_decay", defaults.sampling.adaptive_decay); params.sampling.seed = json_value(data, "seed", defaults.sampling.seed); params.sampling.n_probs = json_value(data, "n_probs", defaults.sampling.n_probs); params.sampling.min_keep = json_value(data, "min_keep", defaults.sampling.min_keep);