params.sparams.tfs_z = 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),
+ [](common_params & params, const std::string & value) {
+ params.sparams.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),
+ [](common_params & params, const std::string & value) {
+ params.sparams.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),
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, "min_p: %f # default: 0.0\n", sparams.min_p);
+ fprintf(stream, "xtc_probability: %f # default: 0.0\n", sparams.xtc_probability);
+ fprintf(stream, "xtc_threshold: %f # default: 0.1\n", sparams.xtc_threshold);
fprintf(stream, "typ_p: %f # default: 1.0\n", sparams.typ_p);
fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false");
COMMON_SAMPLER_TYPE_TFS_Z = 4,
COMMON_SAMPLER_TYPE_TYPICAL_P = 5,
COMMON_SAMPLER_TYPE_TEMPERATURE = 6,
+ COMMON_SAMPLER_TYPE_XTC = 7,
+
};
// dimensionality reduction methods, used by cvector-generator
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 tfs_z = 1.00f; // 1.0 = disabled
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
bool ignore_eos = false;
bool no_perf = false; // disable performance metrics
+
std::vector<enum common_sampler_type> samplers = {
COMMON_SAMPLER_TYPE_TOP_K,
COMMON_SAMPLER_TYPE_TFS_Z,
COMMON_SAMPLER_TYPE_TYPICAL_P,
COMMON_SAMPLER_TYPE_TOP_P,
COMMON_SAMPLER_TYPE_MIN_P,
+ COMMON_SAMPLER_TYPE_XTC,
COMMON_SAMPLER_TYPE_TEMPERATURE
};
snprintf(result, sizeof(result),
"\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
- "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, typical_p = %.3f, temp = %.3f\n"
+ "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n"
"\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f",
penalty_last_n, penalty_repeat, penalty_freq, penalty_present,
- top_k, tfs_z, top_p, min_p, typ_p, temp,
+ top_k, tfs_z, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp,
mirostat, mirostat_eta, mirostat_tau);
return std::string(result);
case COMMON_SAMPLER_TYPE_MIN_P:
llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep));
break;
+ case COMMON_SAMPLER_TYPE_XTC:
+ llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
+ break;
case COMMON_SAMPLER_TYPE_TFS_Z:
llama_sampler_chain_add(result->chain, llama_sampler_init_tail_free(params.tfs_z, params.min_keep));
break;
case COMMON_SAMPLER_TYPE_TOP_P: return 'p';
case COMMON_SAMPLER_TYPE_MIN_P: return 'm';
case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't';
+ case COMMON_SAMPLER_TYPE_XTC: return 'x';
default : return '?';
}
}
case COMMON_SAMPLER_TYPE_TOP_P: return "top_p";
case COMMON_SAMPLER_TYPE_MIN_P: return "min_p";
case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature";
+ case COMMON_SAMPLER_TYPE_XTC: return "xtc";
default : return "";
}
}
{ "min_p", COMMON_SAMPLER_TYPE_MIN_P },
{ "tfs_z", COMMON_SAMPLER_TYPE_TFS_Z },
{ "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE },
+ { "xtc", COMMON_SAMPLER_TYPE_XTC },
};
// since samplers names are written multiple ways
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P), COMMON_SAMPLER_TYPE_TYPICAL_P },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P), COMMON_SAMPLER_TYPE_TOP_P },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P), COMMON_SAMPLER_TYPE_MIN_P },
- { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE }
+ { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE },
+ { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC), COMMON_SAMPLER_TYPE_XTC }
};
std::vector<common_sampler_type> samplers;
Example usage: `--mirostat 2 --mirostat-lr 0.05 --mirostat-ent 3.0`
+### XTC Sampling
+
+- `--xtc-probability N`: Sets the chance for token removal (checked once on sampler start) (default: 0.0).
+- `--xtc-threshold N`: Sets a minimum probability threshold for tokens to be removed (default: 0.1).
+
+Exclude Top Choices (XTC) is a unique sampler that is designed to remove top tokens from consideration and avoid more obvious and repetitive outputs. With a chance of `xtc-probability` it searches for tokens with probabilities of `xtc-threshold` and above, then removes all such tokens except the least probable one.
+
+By removing top tokens XTC can improve the variety of answers, break writing clichés and inhibit repition, since clichés and repeated phrases are usually more likely to appear. By keeping the last token above the threshold, XTC ensures that the answer is still coherent. XTC is meant to be used for creative tasks, but feel free to experiment with different settings for different models.
+
+Being experimental and unique, XTC is disabled by default. The recommended combination of samplers is Min-P followed by XTC on its default settings: `--sampling-seq mx --min-p 0.02 --xtc-probability 0.5`.
+
+Example usage: `--xtc-probability 0.5 --xtc-threshold 0.1`
+
### Logit Bias
- `-l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS`: Modify the likelihood of a token appearing in the generated text completion.
top_k: 0, // <= 0 to use vocab size
top_p: 1.0, // 1.0 = disabled
min_p: 0.05, // 0 = disabled; recommended for non-english: ~ 0.4
+ xtc_probability: 0.0, // 0 = disabled;
+ xtc_threshold: 0.1, // > 0.5 disables XTC;
tfs_z: 1.0, // 1.0 = disabled
typical_p: 1.0, // 1.0 = disabled
presence_penalty: 0.0, // 0.0 = disabled
${FloatField({ label: "TFS-Z", title: "Activates tail-free sampling, a method used to limit the prediction of tokens that are too frequent. The parameter z controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })}
${FloatField({ label: "Frequency Penalty", title: "A penalty that is applied based on the frequency with which certain tokens occur in the training data set. A higher value results in rare tokens being favoured.", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}
${FloatField({ label: "Typical-P", title: "Activates local typical sampling, a method used to limit the prediction of tokens that are atypical in the current context. The parameter p controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })}
+ ${FloatField({ label: "XTC probability", title: "Sets the chance for token removal (checked once on sampler start)", max: 1.0, min: 0.0, name: "xtc_probability", step: 0.01, value: params.value.xtc_probability })}
+ ${FloatField({ label: "XTC threshold", title: "Sets a minimum probability threshold for tokens to be removed", max: 0.5, min: 0.0, name: "xtc_threshold", step: 0.01, value: params.value.xtc_threshold })}
${IntField({ label: "Min Keep", title: "If greater than 0, samplers are forced to return N possible tokens at minimum. Default is 0", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })}
</fieldset>
const snapSettings = {
temperature: { snapValue: 1.0, snapRangeMultiplier: 6 },
min_p: { snapValue: 0.05, snapRangeMultiplier: 2 },
+ xtc_probability: { snapValue: 0.0, snapRangeMultiplier: 4 },
+ xtc_threshold: { snapValue: 0.5, snapRangeMultiplier: 4 },
top_p: { snapValue: 1.0, snapRangeMultiplier: 4 },
tfs_z: { snapValue: 1.0, snapRangeMultiplier: 4 },
typical_p: { snapValue: 1.0, snapRangeMultiplier: 4 },
top_k: 40, // <= 0 to use vocab size
top_p: 0.95, // 1.0 = disabled
min_p: 0.05, // 0 = disabled
+ xtc_probability: 0.0, // 0 = disabled;
+ xtc_threshold: 0.1, // > 0.5 disables XTC;
tfs_z: 1.0, // 1.0 = disabled
typical_p: 1.0, // 1.0 = disabled
presence_penalty: 0.0, // 0.0 = disabled
${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })}
${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })}
${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}
+ ${FloatField({ label: "XTC probability", max: 1.0, min: 0.0, name: "xtc_probability", step: 0.01, value: params.value.xtc_probability })}
+ ${FloatField({ label: "XTC threshold", max: 0.5, min: 0.0, name: "xtc_threshold", step: 0.01, value: params.value.xtc_threshold })}
</fieldset>
<hr />
<fieldset class="three">
slot.sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
slot.sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
slot.sparams.min_p = json_value(data, "min_p", default_sparams.min_p);
+ slot.sparams.xtc_probability = json_value(data, "xtc_probability", default_sparams.xtc_probability);
+ slot.sparams.xtc_threshold = json_value(data, "xtc_threshold", default_sparams.xtc_threshold);
slot.sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z);
slot.sparams.typ_p = json_value(data, "typical_p", default_sparams.typ_p);
slot.sparams.temp = json_value(data, "temperature", default_sparams.temp);
{"top_k", slot.sparams.top_k},
{"top_p", slot.sparams.top_p},
{"min_p", slot.sparams.min_p},
+ {"xtc_probability", slot.sparams.xtc_probability},
+ {"xtc_threshold", slot.sparams.xtc_threshold},
{"tfs_z", slot.sparams.tfs_z},
{"typical_p", slot.sparams.typ_p},
{"repeat_last_n", slot.sparams.penalty_last_n},
/// @details Dynamic temperature implementation (a.k.a. entropy) described in the paper https://arxiv.org/abs/2309.02772.
LLAMA_API struct llama_sampler * llama_sampler_init_temp_ext (float t, float delta, float exponent);
+ /// @details XTC sampler as described in https://github.com/oobabooga/text-generation-webui/pull/6335
+ LLAMA_API struct llama_sampler * llama_sampler_init_xtc (float p, float t, size_t min_keep, uint32_t seed);
+
/// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
/// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
};
}
+// xtc
+
+struct llama_sampler_xtc {
+ const float probability;
+ const float threshold;
+ const size_t min_keep;
+
+ const uint32_t seed;
+ uint32_t seed_cur;
+
+ std::mt19937 rng;
+};
+
+static const char * llama_sampler_xtc_name(const struct llama_sampler * /*smpl*/) {
+ return "xtc";
+}
+
+static void llama_sample_xtc_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
+ auto * ctx = (llama_sampler_xtc *) smpl->ctx;
+
+ if (ctx->probability <= 0.0f
+ || ctx->threshold > 0.5f
+ || cur_p->size < 2) {
+ return;
+ }
+
+ std::uniform_real_distribution<float> distribution(0.0f, 1.0f);
+ float chance = distribution(ctx->rng);
+ if (chance > ctx->probability) return;
+
+ // in case it's not sorted/recalculated yet
+ llama_sampler_softmax_impl(cur_p);
+
+ int pos_last = 0;
+
+ for (size_t i = 0; i < cur_p->size; ++i) {
+ if (cur_p->data[i].p >= ctx->threshold) {
+ pos_last = i;
+ } else break;
+ }
+
+ if (cur_p->size - pos_last >= ctx->min_keep && pos_last > 0) {
+ cur_p->data += pos_last;
+ cur_p->size -= pos_last;
+ }
+}
+
+static struct llama_sampler * llama_sampler_xtc_clone(const struct llama_sampler * smpl) {
+ const auto * ctx = (const llama_sampler_xtc *) smpl->ctx;
+ auto * result = llama_sampler_init_xtc(ctx->probability, ctx->threshold, ctx->min_keep, ctx->seed);
+
+ // copy the state
+ {
+ auto * result_ctx = (llama_sampler_xtc *) result->ctx;
+
+ result_ctx->rng = ctx->rng;
+ }
+
+ return result;
+}
+
+static void llama_sampler_xtc_free(struct llama_sampler * smpl) {
+ delete (llama_sampler_xtc *) smpl->ctx;
+}
+
+static void llama_sampler_xtc_reset(struct llama_sampler * smpl) {
+ auto * ctx = (llama_sampler_xtc *) smpl->ctx;
+ ctx->seed_cur = get_rng_seed(ctx->seed);
+ ctx->rng.seed(ctx->seed_cur);
+}
+
+static struct llama_sampler_i llama_sampler_xtc_i = {
+ /* .name = */ llama_sampler_xtc_name,
+ /* .accept = */ nullptr,
+ /* .apply = */ llama_sample_xtc_apply,
+ /* .reset = */ llama_sampler_xtc_reset,
+ /* .clone = */ llama_sampler_xtc_clone,
+ /* .free = */ llama_sampler_xtc_free,
+};
+
+struct llama_sampler * llama_sampler_init_xtc(float p, float t, size_t min_keep, uint32_t seed) {
+ auto seed_cur = get_rng_seed(seed);
+ return new llama_sampler {
+ /* .iface = */ &llama_sampler_xtc_i,
+ /* .ctx = */ new llama_sampler_xtc {
+ /* .probability = */ p,
+ /* .threshold = */ t,
+ /* .min_keep = */ min_keep,
+ /* .seed = */ seed,
+ /* .seed_cur = */ seed_cur,
+ /* .rng = */ std::mt19937(seed_cur),
+ },
+ };
+}
+
// mirostat
struct llama_sampler_mirostat {
}
}
+static void test_xtc(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p, float t) {
+ const size_t n_vocab = probs.size();
+
+ std::vector<llama_token_data> cur;
+ cur.reserve(n_vocab);
+ for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
+ const float logit = logf(probs[token_id]);
+ cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
+ }
+
+ llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
+ APPLY(llama_sampler_init_softmax(), &cur_p);
+ DUMP(&cur_p);
+ APPLY(llama_sampler_init_xtc(p, t, 0, 0), &cur_p);
+ DUMP(&cur_p);
+
+ GGML_ASSERT(cur_p.size == expected_probs.size());
+ for (size_t i = 0; i < cur_p.size; i++) {
+ GGML_ASSERT(fabs(cur_p.data[i].p - expected_probs[i]) < 1e-5);
+ }
+}
+
static void test_typical(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p) {
const size_t n_vocab = probs.size();
}
const int64_t t_end = ggml_time_us();
llama_sampler_free(cnstr);
- printf("%-42s: %8.3f us/iter\n", cnstr_name, (t_end - t_start) / (float)n_iter);
+ printf("%-43s: %8.3f us/iter\n", cnstr_name, (t_end - t_start) / (float)n_iter);
}
#define BENCH(__cnstr, __data, __n_iter) bench((__cnstr), #__cnstr, (__data), (__n_iter))
data.emplace_back(llama_token_data{i, logit, 0.0f});
}
- BENCH(llama_sampler_init_top_k (40), data, 32);
- BENCH(llama_sampler_init_top_p (0.8f, 1), data, 32);
- BENCH(llama_sampler_init_min_p (0.2f, 1), data, 32);
- BENCH(llama_sampler_init_tail_free(0.5f, 1), data, 32);
- BENCH(llama_sampler_init_typical (0.5f, 1), data, 32);
- BENCH(llama_sampler_init_softmax (), data, 32);
+ BENCH(llama_sampler_init_top_k (40), data, 32);
+ BENCH(llama_sampler_init_top_p (0.8f, 1), data, 32);
+ BENCH(llama_sampler_init_min_p (0.2f, 1), data, 32);
+ BENCH(llama_sampler_init_tail_free(0.5f, 1), data, 32);
+ BENCH(llama_sampler_init_typical (0.5f, 1), data, 32);
+ BENCH(llama_sampler_init_xtc (1.0f, 0.1f, 1, 1), data, 32);
+ BENCH(llama_sampler_init_softmax (), data, 32);
}
int main(void) {
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f}, 0.76f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f}, 1.00f);
+ printf("XTC should:\n");
+ test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.1f}, 0.99f, 0.09f);
+ test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.2f, 0.1f}, 0.99f, 0.19f);
+ test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.3f, 0.2f, 0.1f}, 0.99f, 0.29f);
+
+ printf("XTC should not:\n");
+ test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0.99f, 0.39f);
+
test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f}, 0.25f);
test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.75f);
test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.99f);
overview / pkg / ggml / sources / llama.cpp / commitdiff