vbatts/llama.cpp
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Use more consistant naming convention for sampling contexts

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KerfuffleV2 2023-10-11 11:48:23 -06:00
parent 4a34e63500
commit fffa4c0099
7 changed files with 40 additions and 40 deletions
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38
common/sampling.cpp
View file

@ -21,36 +21,36 @@ llama_sampling_context llama_sampling_context_init(
// Note: Creates the context if it doesn't exist, so this always return something.
llama_sampler_sequence_context & llama_sampling_get_sequence_context(
llama_sampling_context & sampling_ctx,
llama_sampling_context & ctx_sampling,
const llama_seq_id seq) {
const auto it = sampling_ctx.sequence_contexts.find(seq);
if (it != sampling_ctx.sequence_contexts.end()) {
const auto it = ctx_sampling.sequence_contexts.find(seq);
if (it != ctx_sampling.sequence_contexts.end()) {
return it->second;
}
llama_sampler_sequence_context new_ctx = {
2.0f * sampling_ctx.params.mirostat_tau,
sampling_ctx.grammar != NULL ? llama_grammar_copy(sampling_ctx.grammar) : NULL,
2.0f * ctx_sampling.params.mirostat_tau,
ctx_sampling.grammar != NULL ? llama_grammar_copy(ctx_sampling.grammar) : NULL,
};
return sampling_ctx.sequence_contexts.insert({seq, new_ctx}).first->second;
return ctx_sampling.sequence_contexts.insert({seq, new_ctx}).first->second;
}
bool llama_sampling_context_reset(
llama_sampling_context & sampling_ctx,
llama_sampling_context & ctx_sampling,
const llama_seq_id seq) {
const auto it = sampling_ctx.sequence_contexts.find(seq);
if (it == sampling_ctx.sequence_contexts.end()) return false;
const auto it = ctx_sampling.sequence_contexts.find(seq);
if (it == ctx_sampling.sequence_contexts.end()) return false;
if (it->second.grammar != NULL) {
llama_grammar_free(it->second.grammar);
it->second.grammar = NULL;
}
sampling_ctx.sequence_contexts.erase(it);
ctx_sampling.sequence_contexts.erase(it);
return true;
}
llama_token llama_sampling_sample(
struct llama_context * ctx,
struct llama_context * ctx_guidance,
struct llama_sampling_context & sampling_ctx,
struct llama_sampling_context & ctx_sampling,
const std::vector<llama_token> & last_tokens,
std::vector<llama_token_data> & candidates,
const int idx,
@ -58,7 +58,7 @@ llama_token llama_sampling_sample(
const int n_ctx = llama_n_ctx(ctx);
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const llama_sampling_params & params = sampling_ctx.params;
const llama_sampling_params & params = ctx_sampling.params;
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;
@ -115,10 +115,10 @@ llama_token llama_sampling_sample(
}
}
llama_sampler_sequence_context & seq_ctx = llama_sampling_get_sequence_context(sampling_ctx, seq);
llama_sampler_sequence_context & ctx_seq = llama_sampling_get_sequence_context(ctx_sampling, seq);
if (seq_ctx.grammar != NULL) {
llama_sample_grammar(ctx, &cur_p, seq_ctx.grammar);
if (ctx_seq.grammar != NULL) {
llama_sample_grammar(ctx, &cur_p, ctx_seq.grammar);
}
if (temp <= 0) {
@ -128,10 +128,10 @@ llama_token llama_sampling_sample(
if (mirostat == 1) {
const int mirostat_m = 100;
llama_sample_temp(ctx, &cur_p, temp);
id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &seq_ctx.mirostat_mu);
id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &ctx_seq.mirostat_mu);
} else if (mirostat == 2) {
llama_sample_temp(ctx, &cur_p, temp);
id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &seq_ctx.mirostat_mu);
id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &ctx_seq.mirostat_mu);
} else {
// Temperature sampling
size_t min_keep = std::max(1, params.n_probs);
@ -158,8 +158,8 @@ llama_token llama_sampling_sample(
}
}
if (seq_ctx.grammar != NULL) {
llama_grammar_accept_token(ctx, seq_ctx.grammar, id);
if (ctx_seq.grammar != NULL) {
llama_grammar_accept_token(ctx, ctx_seq.grammar, id);
}
return id;

10
common/sampling.h
View file

@ -67,16 +67,16 @@ llama_sampling_context llama_sampling_context_init(
// Fetches the sampler context for the specified sequence id (defaults to 0).
// If the context for that sequence id doesn't already exist, it will be created with
// default values based on the parameters in the sampling_ctx argument.
// default values based on the parameters in the ctx_sampling argument.
llama_sampler_sequence_context & llama_sampling_get_sequence_context(
llama_sampling_context & sampling_ctx,
llama_sampling_context & ctx_sampling,
const llama_seq_id seq = 0);
// Reset the sampler context for the supplied sequence id (defaults to 0).
// This is necessary to reuse a sequence id or free memory used by sequences
// that are no longer required.
bool llama_sampling_context_reset(
llama_sampling_context & sampling_ctx,
llama_sampling_context & ctx_sampling,
const llama_seq_id seq = 0);
// this is a common sampling function used across the examples for convenience
@ -86,7 +86,7 @@ bool llama_sampling_context_reset(
//
// required:
// - ctx: context to use for sampling
// - sampling_ctx: sampling-specific context
// - ctx_sampling: sampling-specific context
//
// optional:
// - ctx_guidance: context to use for classifier-free guidance, ignore if NULL
@ -101,7 +101,7 @@ bool llama_sampling_context_reset(
llama_token llama_sampling_sample(
struct llama_context * ctx,
struct llama_context * ctx_guidance,
struct llama_sampling_context & sampling_ctx,
struct llama_sampling_context & ctx_sampling,
const std::vector<llama_token> & last_tokens,
std::vector<llama_token_data> & candidates,
const int idx = 0,

4
examples/infill/infill.cpp
View file

@ -423,7 +423,7 @@ int main(int argc, char ** argv) {
const int n_vocab = llama_n_vocab(model);
llama_sampling_context sampling_context = llama_sampling_context_init(params, grammar);
llama_sampling_context ctx_sampling = llama_sampling_context_init(params, grammar);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
@ -542,7 +542,7 @@ int main(int argc, char ** argv) {
if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
const llama_token id = llama_sampling_sample(ctx, ctx_guidance, sampling_context, last_tokens, candidates);
const llama_token id = llama_sampling_sample(ctx, ctx_guidance, ctx_sampling, last_tokens, candidates);
last_tokens.erase(last_tokens.begin());
last_tokens.push_back(id);

4
examples/main/main.cpp
View file

@ -470,7 +470,7 @@ int main(int argc, char ** argv) {
const int n_vocab = llama_n_vocab(model);
llama_sampling_context sampling_context = llama_sampling_context_init(params, grammar);
llama_sampling_context ctx_sampling = llama_sampling_context_init(params, grammar);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
@ -627,7 +627,7 @@ int main(int argc, char ** argv) {
LOG("saved session to %s\n", path_session.c_str());
}
const llama_token id = llama_sampling_sample(ctx, ctx_guidance, sampling_context, last_tokens, candidates);
const llama_token id = llama_sampling_sample(ctx, ctx_guidance, ctx_sampling, last_tokens, candidates);
last_tokens.erase(last_tokens.begin());
last_tokens.push_back(id);

6
examples/parallel/parallel.cpp
View file

@ -125,7 +125,7 @@ int main(int argc, char ** argv) {
params.logits_all = true;
std::tie(model, ctx) = llama_init_from_gpt_params(params);
llama_sampling_context sampling_context = llama_sampling_context_init(params, NULL);
llama_sampling_context ctx_sampling = llama_sampling_context_init(params, NULL);
// load the prompts from an external file if there are any
if (params.prompt.empty()) {
@ -341,7 +341,7 @@ int main(int argc, char ** argv) {
//printf("client %d, seq %d, token %d, pos %d, batch %d\n",
// client.id, client.seq_id, client.sampled, client.n_decoded, client.i_batch);
const llama_token id = llama_sampling_sample(ctx, NULL, sampling_context, client.tokens_prev, candidates, client.i_batch - i, client.seq_id);
const llama_token id = llama_sampling_sample(ctx, NULL, ctx_sampling, client.tokens_prev, candidates, client.i_batch - i, client.seq_id);
if (client.n_decoded == 1) {
// start measuring generation time after the first token to make sure all concurrent clients
@ -386,7 +386,7 @@ int main(int argc, char ** argv) {
n_total_prompt += client.n_prompt;
n_total_gen += client.n_decoded;
llama_sampling_context_reset(sampling_context, client.seq_id);
llama_sampling_context_reset(ctx_sampling, client.seq_id);
client.seq_id = -1;
}

10
examples/server/server.cpp
View file

@ -200,7 +200,7 @@ struct llama_server_context
llama_model *model = nullptr;
llama_context *ctx = nullptr;
gpt_params params;
llama_sampling_context sampling_context;
llama_sampling_context ctx_sampling;
int n_ctx;
grammar_parser::parse_state parsed_grammar;
@ -255,7 +255,7 @@ struct llama_server_context
if (grammar != nullptr) {
llama_grammar_free(grammar);
grammar = nullptr;
sampling_context = llama_sampling_context_init(params, NULL);
ctx_sampling = llama_sampling_context_init(params, NULL);
}
}
@ -341,7 +341,7 @@ struct llama_server_context
grammar = llama_grammar_init(
grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
}
sampling_context = llama_sampling_context_init(params, grammar);
ctx_sampling = llama_sampling_context_init(params, grammar);
return true;
}
@ -542,7 +542,7 @@ struct llama_server_context
std::vector<llama_token_data> candidates;
candidates.reserve(llama_n_vocab(model));
result.tok = llama_sampling_sample(ctx, NULL, sampling_context, last_n_tokens, candidates);
result.tok = llama_sampling_sample(ctx, NULL, ctx_sampling, last_n_tokens, candidates);
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
@ -1210,7 +1210,7 @@ static void parse_options_completion(const json &body, llama_server_context &lla
}
}
llama.sampling_context = llama_sampling_context_init(llama.params, llama.grammar);
llama.ctx_sampling = llama_sampling_context_init(llama.params, llama.grammar);
LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama));
}

8
examples/speculative/speculative.cpp
View file

@ -125,7 +125,7 @@ int main(int argc, char ** argv) {
grammar_tgt = llama_grammar_init(grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
}
llama_sampling_context sampling_context = llama_sampling_context_init(params, grammar_tgt);
llama_sampling_context ctx_sampling = llama_sampling_context_init(params, grammar_tgt);
const auto t_dec_start = ggml_time_us();
@ -136,7 +136,7 @@ int main(int argc, char ** argv) {
while (true) {
// sample from the target model
llama_token id = llama_sampling_sample(ctx_tgt, NULL, sampling_context, last_tokens, candidates, i_dft);
llama_token id = llama_sampling_sample(ctx_tgt, NULL, ctx_sampling, last_tokens, candidates, i_dft);
// remember which tokens were sampled - used for repetition penalties during sampling
last_tokens.erase(last_tokens.begin());
@ -215,8 +215,8 @@ int main(int argc, char ** argv) {
}
// Note: Hardcoded to sequence id 0, if this ever supports parallel generation
// that will need to change.
auto it = sampling_context.sequence_contexts.find(0);
GGML_ASSERT(it != sampling_context.sequence_contexts.end());
auto it = ctx_sampling.sequence_contexts.find(0);
GGML_ASSERT(it != ctx_sampling.sequence_contexts.end());
// This is necessary because each sequence id in sequence_contexts
// uses a copy of the original grammar.
grammar_dft = llama_grammar_copy(it->second.grammar);