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Merged
merged 13 commits into from
Dec 5, 2023
Merged

Samplers order parameters #4285

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d4dc3d2
Samplers sequence order w parameter
MaggotHATE Dec 1, 2023
7601a49
Cleaned commented code
MaggotHATE Dec 1, 2023
d363df3
Fixed formatting
MaggotHATE Dec 1, 2023
bd08c8f
Rewrote with unordered_map
MaggotHATE Dec 2, 2023
8d2b460
Revert and rewrite, too many problems and safeguards would be needed
MaggotHATE Dec 2, 2023
e115b5a
Merge branch 'ggerganov:master' into master
MaggotHATE Dec 2, 2023
ff8adc1
Fixed code style
MaggotHATE Dec 2, 2023
e6dc166
Code style fixes according to review
MaggotHATE Dec 2, 2023
3fa6726
More readable samplers input string, fixed help
MaggotHATE Dec 3, 2023
ddda6dd
Style fix in sampler_queue
MaggotHATE Dec 3, 2023
c879b6d
Merge branch 'ggerganov:master' into master
MaggotHATE Dec 5, 2023
a6c3278
Formatting fixes
MaggotHATE Dec 5, 2023
0b87ef4
Fixing whitespaces
MaggotHATE Dec 5, 2023
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6 changes: 6 additions & 0 deletions common/common.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -280,6 +280,12 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
params.yarn_beta_slow = std::stof(argv[i]);
} else if (arg == "--memory-f32") {
params.memory_f16 = false;
} else if (arg == "--sampling-seq") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.samplers_sequence = argv[i];
} else if (arg == "--top-p") {
if (++i >= argc) {
invalid_param = true;
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78 changes: 58 additions & 20 deletions common/sampling.cpp
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Expand Up @@ -99,6 +99,54 @@ std::string llama_sampling_print(const llama_sampling_params & params) {
return std::string(result);
}

std::string llama_sampling_order_print(const llama_sampling_params & params) {
std::string result = "CFG -> Penalties ";
if (params.mirostat == 0){
for (auto s : params.samplers_sequence){
switch (s){
case 'k': result += "-> top_k "; break;
case 'f': result += "-> tfs_z "; break;
case 'y': result += "-> typical_p "; break;
case 'p': result += "-> top_p "; break;
case 'm': result += "-> min_p "; break;
case 't': result += "-> temp "; break;
default : break;
}
}
} else result += "-> mirostat ";

return result;
}

// no reasons to expose this function in header
void sampler_queue(
struct llama_context * ctx_main,
const llama_sampling_params & params,
llama_token_data_array & cur_p,
size_t & min_keep) {
const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));

const float temp = params.temp;
const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k;
const float top_p = params.top_p;
const float min_p = params.min_p;
const float tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
const std::string samplers_sequence = params.samplers_sequence;

for (auto s : samplers_sequence){
switch (s){
case 'k': llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep); break;
case 'f': llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep); break;
case 'y': llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep); break;
case 'p': llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep); break;
case 'm': llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep); break;
case 't': llama_sample_temp (ctx_main, &cur_p, temp); break;
default : break;
}
}
}

llama_token llama_sampling_sample(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
Expand All @@ -108,20 +156,15 @@ llama_token llama_sampling_sample(

const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));

const float temp = params.temp;
const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k;
const float top_p = params.top_p;
const float min_p = params.min_p;
const float tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
const float penalty_repeat = params.penalty_repeat;
const float penalty_freq = params.penalty_freq;
const float penalty_present = params.penalty_present;
const int mirostat = params.mirostat;
const float mirostat_tau = params.mirostat_tau;
const float mirostat_eta = params.mirostat_eta;
const bool penalize_nl = params.penalize_nl;
const float temp = params.temp;
const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
const float penalty_repeat = params.penalty_repeat;
const float penalty_freq = params.penalty_freq;
const float penalty_present = params.penalty_present;
const int mirostat = params.mirostat;
const float mirostat_tau = params.mirostat_tau;
const float mirostat_eta = params.mirostat_eta;
const bool penalize_nl = params.penalize_nl;

auto & prev = ctx_sampling->prev;
auto & cur = ctx_sampling->cur;
Expand Down Expand Up @@ -188,12 +231,7 @@ llama_token llama_sampling_sample(
// temperature sampling
size_t min_keep = std::max(1, params.n_probs);

llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep);
llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep);
llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep);
llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep);
llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep);
llama_sample_temp (ctx_main, &cur_p, temp);
sampler_queue(ctx_main, params, cur_p, min_keep);

id = llama_sample_token(ctx_main, &cur_p);

Expand Down
36 changes: 20 additions & 16 deletions common/sampling.h
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Expand Up @@ -10,22 +10,23 @@

// sampling parameters
typedef struct llama_sampling_params {
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 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 tfs_z = 1.00f; // 1.0 = disabled
float typical_p = 1.00f; // 1.0 = disabled
float temp = 0.80f; // 1.0 = disabled
int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat = 1.10f; // 1.0 = disabled
float penalty_freq = 0.00f; // 0.0 = disabled
float penalty_present = 0.00f; // 0.0 = disabled
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool penalize_nl = true; // consider newlines as a repeatable token
int32_t n_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 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 tfs_z = 1.00f; // 1.0 = disabled
float typical_p = 1.00f; // 1.0 = disabled
float temp = 0.80f; // 1.0 = disabled
int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat = 1.10f; // 1.0 = disabled
float penalty_freq = 0.00f; // 0.0 = disabled
float penalty_present = 0.00f; // 0.0 = disabled
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool penalize_nl = true; // consider newlines as a repeatable token
std::string samplers_sequence = "kfypmt"; // top_k, tail_free, typical_p, top_p, min_p, temp

std::string grammar; // optional BNF-like grammar to constrain sampling

Expand Down Expand Up @@ -80,6 +81,9 @@ std::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama
// Print sampling parameters into a string
std::string llama_sampling_print(const llama_sampling_params & params);

// Print sampling order into a string
std::string llama_sampling_order_print(const llama_sampling_params & params);

// this is a common sampling function used across the examples for convenience
// it can serve as a starting point for implementing your own sampling function
// Note: When using multiple sequences, it is the caller's responsibility to call
Expand Down
1 change: 1 addition & 0 deletions examples/main/main.cpp
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Expand Up @@ -437,6 +437,7 @@ int main(int argc, char ** argv) {
}
}
LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str());
LOG_TEE("sampling order: \n%s\n", llama_sampling_order_print(sparams).c_str());
LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
LOG_TEE("\n\n");

Expand Down