params.sparams.min_p = std::stof(value);
}
).set_sparam());
- add_opt(common_arg(
- {"--tfs"}, "N",
- string_format("tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)params.sparams.tfs_z),
- [](common_params & params, const std::string & value) {
- 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),
).set_sparam());
add_opt(common_arg(
{"--mirostat"}, "N",
- string_format("use Mirostat sampling.\nTop K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\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),
[](common_params & params, int value) {
params.sparams.mirostat = value;
const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());
yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector);
- fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);
fprintf(stream, "threads: %d # default: %u\n", params.cpuparams.n_threads, std::thread::hardware_concurrency());
fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
COMMON_SAMPLER_TYPE_TOP_K = 2,
COMMON_SAMPLER_TYPE_TOP_P = 3,
COMMON_SAMPLER_TYPE_MIN_P = 4,
- COMMON_SAMPLER_TYPE_TFS_Z = 5,
+ //COMMON_SAMPLER_TYPE_TFS_Z = 5,
COMMON_SAMPLER_TYPE_TYPICAL_P = 6,
COMMON_SAMPLER_TYPE_TEMPERATURE = 7,
COMMON_SAMPLER_TYPE_XTC = 8,
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
float dynatemp_range = 0.00f; // 0.0 = disabled
std::vector<enum common_sampler_type> samplers = {
COMMON_SAMPLER_TYPE_DRY,
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,
snprintf(result, sizeof(result),
"\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, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n"
+ "\ttop_k = %d, 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,
dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n,
- top_k, tfs_z, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp,
+ top_k, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp,
mirostat, mirostat_eta, mirostat_tau);
return std::string(result);
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_TYPICAL_P:
llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep));
break;
switch (cnstr) {
case COMMON_SAMPLER_TYPE_DRY: return 'd';
case COMMON_SAMPLER_TYPE_TOP_K: return 'k';
- case COMMON_SAMPLER_TYPE_TFS_Z: return 'f';
case COMMON_SAMPLER_TYPE_TYPICAL_P: return 'y';
case COMMON_SAMPLER_TYPE_TOP_P: return 'p';
case COMMON_SAMPLER_TYPE_MIN_P: return 'm';
switch (cnstr) {
case COMMON_SAMPLER_TYPE_DRY: return "dry";
case COMMON_SAMPLER_TYPE_TOP_K: return "top_k";
- case COMMON_SAMPLER_TYPE_TFS_Z: return "tfs_z";
case COMMON_SAMPLER_TYPE_TYPICAL_P: return "typ_p";
case COMMON_SAMPLER_TYPE_TOP_P: return "top_p";
case COMMON_SAMPLER_TYPE_MIN_P: return "min_p";
{ "top_p", COMMON_SAMPLER_TYPE_TOP_P },
{ "typ_p", COMMON_SAMPLER_TYPE_TYPICAL_P },
{ "min_p", COMMON_SAMPLER_TYPE_MIN_P },
- { "tfs_z", COMMON_SAMPLER_TYPE_TFS_Z },
{ "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE },
{ "xtc", COMMON_SAMPLER_TYPE_XTC },
{ "infill", COMMON_SAMPLER_TYPE_INFILL },
{ "typ-p", COMMON_SAMPLER_TYPE_TYPICAL_P },
{ "typ", COMMON_SAMPLER_TYPE_TYPICAL_P },
{ "min-p", COMMON_SAMPLER_TYPE_MIN_P },
- { "tfs-z", COMMON_SAMPLER_TYPE_TFS_Z },
- { "tfs", COMMON_SAMPLER_TYPE_TFS_Z },
{ "temp", COMMON_SAMPLER_TYPE_TEMPERATURE },
};
std::unordered_map<char, common_sampler_type> sampler_name_map = {
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_DRY), COMMON_SAMPLER_TYPE_DRY },
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_K), COMMON_SAMPLER_TYPE_TOP_K },
- { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TFS_Z), COMMON_SAMPLER_TYPE_TFS_Z },
{ 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 },
Example usage: `--min-p 0.05`
-### Tail-Free Sampling (TFS)
-
-- `--tfs N`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled).
-
-Tail-free sampling (TFS) is a text generation technique that aims to reduce the impact of less likely tokens, which may be less relevant, less coherent, or nonsensical, on the output. Similar to Top-P it tries to determine the bulk of the most likely tokens dynamically. But TFS filters out logits based on the second derivative of their probabilities. Adding tokens is stopped after the sum of the second derivatives reaches the parameter z. In short: TFS looks at how quickly the probabilities of the tokens decrease and cuts off the tail of unlikely tokens using the parameter z. Typical values for z are in the range of 0.9 to 0.95. A value of 1.0 would include all tokens and thus disables the effect of TFS.
-
-Example usage: `--tfs 0.95`
-
### Locally Typical Sampling
- `--typical N`: Enable locally typical sampling with parameter p (default: 1.0, 1.0 = disabled).
| Argument | Explanation |
| -------- | ----------- |
-| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'<br/>(default: top_k;tfs_z;typ_p;top_p;min_p;temperature) |
+| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'<br/>(default: top_k;typ_p;top_p;min_p;temperature) |
| `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) |
| `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: kfypmt) |
| `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) |
| `--top-k N` | top-k sampling (default: 40, 0 = disabled) |
| `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) |
| `--min-p N` | min-p sampling (default: 0.1, 0.0 = disabled) |
-| `--tfs N` | tail free sampling, parameter z (default: 1.0, 1.0 = disabled) |
| `--typical N` | locally typical sampling, parameter p (default: 1.0, 1.0 = disabled) |
| `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) |
| `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled) |
| `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers (`['\n', ':', '"', '*']`) in the process; use `"none"` to not use any sequence breakers
| `--dynatemp-range N` | dynamic temperature range (default: 0.0, 0.0 = disabled) |
| `--dynatemp-exp N` | dynamic temperature exponent (default: 1.0) |
-| `--mirostat N` | use Mirostat sampling.<br/>Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.<br/>(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) |
+| `--mirostat N` | use Mirostat sampling.<br/>Top K, Nucleus and Locally Typical samplers are ignored if used.<br/>(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) |
| `--mirostat-lr N` | Mirostat learning rate, parameter eta (default: 0.1) |
| `--mirostat-ent N` | Mirostat target entropy, parameter tau (default: 5.0) |
| `-l, --logit-bias TOKEN_ID(+/-)BIAS` | modifies the likelihood of token appearing in the completion,<br/>i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',<br/>or `--logit-bias 15043-1` to decrease likelihood of token ' Hello' |
`stop`: Specify a JSON array of stopping strings.
These words will not be included in the completion, so make sure to add them to the prompt for the next iteration. Default: `[]`
- `tfs_z`: Enable tail free sampling with parameter z. Default: `1.0`, which is disabled.
-
`typical_p`: Enable locally typical sampling with parameter p. Default: `1.0`, which is disabled.
`repeat_penalty`: Control the repetition of token sequences in the generated text. Default: `1.1`
`cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `false`
- `samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["top_k", "tfs_z", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values.
+ `samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["top_k", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values.
**Response format**
"repeat_penalty": 1.100000023841858,
"samplers": [
"top_k",
- "tfs_z",
"typical_p",
"top_p",
"min_p",
"stream": false,
"task_id": 0,
"temperature": 0.0,
- "tfs_z": 1.0,
"top_k": 40,
"top_p": 0.949999988079071,
"typical_p": 1.0
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
frequency_penalty: 0.0, // 0.0 = disabled
${FloatField({ label: "DRY Base", title: "Set the DRY repetition penalty base value. Default is 1.75", max: 3.0, min: 1.0, name: "dry_base", step: 0.01, value: params.value.dry_base })}
${IntField({ label: "DRY Allowed Length", title: "Tokens that extend repetition beyond this receive exponentially increasing penalty. Default is 2", max: 10, min: 1, step: 1, name: "dry_allowed_length", value: params.value.dry_allowed_length })}
${IntField({ label: "DRY Penalty Last N", title: "How many tokens to scan for repetitions. Default is -1, where 0 is disabled and -1 is context size", max: 2048, min: -1, step: 16, name: "dry_penalty_last_n", value: params.value.dry_penalty_last_n })}
- ${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 })}
${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>
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 },
repeat_penalty: { snapValue: 1.0, snapRangeMultiplier: 4 },
presence_penalty: { snapValue: 0.0, snapRangeMultiplier: 4 },
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
frequency_penalty: 0.0, // 0.0 = disabled
<details>
<summary>More options</summary>
<fieldset class="two">
- ${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })}
${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 })}
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);
slot.sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range);
{"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},
{"repeat_penalty", slot.sparams.penalty_repeat},
top_k: 40, // <= 0 to use vocab size
top_p: 0.95, // 1.0 = disabled
min_p: 0.05, // 0 = disabled
- tfs_z: 1.0, // 1.0 = disabled
typical_p: 1.0, // 1.0 = disabled
presence_penalty: 0.0, // 0.0 = disabled
frequency_penalty: 0.0, // 0.0 = disabled
<details>
<summary>More options</summary>
<fieldset class="two">
- ${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })}
${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 })}
top_k: 40, // <= 0 to use vocab size
top_p: 0.95, // 1.0 = disabled
min_p: 0.05, // 0 = disabled
- tfs_z: 1.0, // 1.0 = disabled
typical_p: 1.0, // 1.0 = disabled
presence_penalty: 0.0, // 0.0 = disabled
frequency_penalty: 0.0, // 0.0 = disabled
<details>
<summary>More options</summary>
<fieldset class="two">
- ${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })}
${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 })}
}
// Copy remaining properties to llama_params
- // This allows user to use llama.cpp-specific params like "mirostat", "tfs_z",... via OAI endpoint.
+ // This allows user to use llama.cpp-specific params like "mirostat", ... via OAI endpoint.
// See "launch_slot_with_task()" for a complete list of params supported by llama.cpp
for (const auto & item : body.items()) {
// Exception: if "n_predict" is present, we overwrite the value specified earlier by "max_tokens"
/// @details Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
LLAMA_API struct llama_sampler * llama_sampler_init_min_p (float p, size_t min_keep);
- /// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
- LLAMA_API struct llama_sampler * llama_sampler_init_tail_free (float z, size_t min_keep);
-
/// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
LLAMA_API struct llama_sampler * llama_sampler_init_typical (float p, size_t min_keep);
"np-penalize-nl", "numa", "ppl-output-type", "ppl-stride", "presence-penalty", "prompt",
"prompt-cache", "prompt-cache-all", "prompt-cache-ro", "repeat-last-n",
"repeat-penalty", "reverse-prompt", "rope-freq-base", "rope-freq-scale", "rope-scale", "seed",
- "simple-io", "tensor-split", "threads", "temp", "tfs", "top-k", "top-p", "typical",
+ "simple-io", "tensor-split", "threads", "temp", "top-k", "top-p", "typical",
"verbose-prompt"
]
}
static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k) {
- // TODO: move bucket sort to separate function so that top_p/tail_free/typical/softmax first is equally fast
+ // TODO: move bucket sort to separate function so that top_p/typical/softmax first is equally fast
// if (k >= (int32_t)cur_p->size) {
// return;
// }
};
}
-// tail-free
-
-struct llama_sampler_tail_free {
- const float z;
- const size_t min_keep;
-};
-
-static const char * llama_sampler_tail_free_name(const struct llama_sampler * /*smpl*/) {
- return "tail-free";
-}
-
-static void llama_sampler_tail_free_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
- const auto * ctx = (llama_sampler_tail_free *) smpl->ctx;
-
- if (ctx->z >= 1.0f || cur_p->size <= 2) {
- return;
- }
-
- llama_sampler_softmax_impl(cur_p);
-
- // Compute the first and second derivatives
- std::vector<float> first_derivatives(cur_p->size - 1);
- std::vector<float> second_derivatives(cur_p->size - 2);
-
- for (size_t i = 0; i < first_derivatives.size(); ++i) {
- first_derivatives[i] = cur_p->data[i].p - cur_p->data[i + 1].p;
- }
- for (size_t i = 0; i < second_derivatives.size(); ++i) {
- second_derivatives[i] = first_derivatives[i] - first_derivatives[i + 1];
- }
-
- // Calculate absolute value of second derivatives
- for (size_t i = 0; i < second_derivatives.size(); ++i) {
- second_derivatives[i] = std::abs(second_derivatives[i]);
- }
-
- // Normalize the second derivatives
- {
- const float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);
-
- if (second_derivatives_sum > 1e-6f) {
- for (float & value : second_derivatives) {
- value /= second_derivatives_sum;
- }
- } else {
- for (float & value : second_derivatives) {
- value = 1.0f / second_derivatives.size();
- }
- }
- }
-
- float cum_sum = 0.0f;
- size_t last_idx = cur_p->size;
- for (size_t i = 0; i < second_derivatives.size(); ++i) {
- cum_sum += second_derivatives[i];
-
- // Check if the running sum is greater than z or if we have kept at least min_keep tokens
- if (cum_sum > ctx->z && i >= ctx->min_keep) {
- last_idx = i;
- break;
- }
- }
-
- // Resize the output vector to keep only the tokens above the tail location
- cur_p->size = last_idx;
-}
-
-static struct llama_sampler * llama_sampler_tail_free_clone(const struct llama_sampler * smpl) {
- const auto * ctx = (const llama_sampler_tail_free *) smpl->ctx;
- return llama_sampler_init_tail_free(ctx->z, ctx->min_keep);
-}
-
-static void llama_sampler_tail_free_free(struct llama_sampler * smpl) {
- delete (llama_sampler_tail_free *) smpl->ctx;
-}
-
-static struct llama_sampler_i llama_sampler_tail_free_i = {
- /* .name = */ llama_sampler_tail_free_name,
- /* .accept = */ nullptr,
- /* .apply = */ llama_sampler_tail_free_apply,
- /* .reset = */ nullptr,
- /* .clone = */ llama_sampler_tail_free_clone,
- /* .free = */ llama_sampler_tail_free_free,
-};
-
-struct llama_sampler * llama_sampler_init_tail_free(float z, size_t min_keep) {
- return new llama_sampler {
- /* .iface = */ &llama_sampler_tail_free_i,
- /* .ctx = */ new llama_sampler_tail_free {
- /* .z = */ z,
- /*. min_keep = */ min_keep,
- },
- };
-}
-
// typical
struct llama_sampler_typical {
tester.check();
}
-static void test_tfs(const std::vector<float> & probs, const std::vector<float> & probs_expected, float z) {
- sampler_tester tester(probs, probs_expected);
-
- DUMP(&tester.cur_p);
- tester.apply(llama_sampler_init_tail_free(z, 1));
- DUMP(&tester.cur_p);
-
- tester.check();
-}
-
static void test_min_p(const std::vector<float> & probs, const std::vector<float> & probs_expected, float p) {
sampler_tester tester(probs, probs_expected);
for (auto s : samplers_sequence) {
switch (s){
case 'k': tester.apply(llama_sampler_init_top_k(top_k)); break;
- case 'f': GGML_ABORT("tail_free test not implemented");
case 'y': GGML_ABORT("typical test not implemented");
case 'p': tester.apply(llama_sampler_init_top_p(top_p, 1)); break;
case 'm': tester.apply(llama_sampler_init_min_p(min_p, 1)); break;
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_xtc (1.0f, 0.1f, 1, 1), 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_typical(0.5f, 1), data, 32);
+ BENCH(llama_sampler_init_xtc (1.0f, 0.1f, 1, 1), data, 32);
}
int main(void) {
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);
-
test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f}, 0.5f);
test_typical({0.4f, 0.2f, 0.2f, 0.2f}, {0.2f, 0.2f, 0.2f}, 0.5f);
overview / pkg / ggml / sources / llama.cpp / commitdiff