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speculative : PoC for speeding-up inference via speculative sampling (g…
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…gerganov#2926)

* speculative : initial example

* speculative : print encoding speed

* speculative : add --draft CLI arg
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@ggerganov
ggerganov authored Sep 3, 2023
1 parent 8f429fa commit 47068e5
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Showing 6 changed files with 440 additions and 115 deletions.
140 changes: 140 additions & 0 deletions common/common.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -305,6 +305,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break;
}
params.n_keep = std::stoi(argv[i]);
} else if (arg == "--draft") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.n_draft = std::stoi(argv[i]);
} else if (arg == "--chunks") {
if (++i >= argc) {
invalid_param = true;
Expand All @@ -317,6 +323,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break;
}
params.model = argv[i];
} else if (arg == "-md" || arg == "--model-draft") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.model_draft = argv[i];
} else if (arg == "-a" || arg == "--alias") {
if (++i >= argc) {
invalid_param = true;
Expand Down Expand Up @@ -638,6 +650,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stdout, " --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
fprintf(stdout, " --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
fprintf(stdout, " --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
fprintf(stdout, " --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft);
fprintf(stdout, " --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
if (llama_mlock_supported()) {
fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
Expand Down Expand Up @@ -669,6 +682,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
fprintf(stdout, " -m FNAME, --model FNAME\n");
fprintf(stdout, " model path (default: %s)\n", params.model.c_str());
fprintf(stdout, " -md FNAME, --model-draft FNAME\n");
fprintf(stdout, " draft model for speculative decoding (default: %s)\n", params.model.c_str());
fprintf(stdout, " -ld LOGDIR, --logdir LOGDIR\n");
fprintf(stdout, " path under which to save YAML logs (no logging if unset)\n");
fprintf(stdout, "\n");
Expand Down Expand Up @@ -832,6 +847,130 @@ std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_to
return result;
}

//
// Sampling utils
//

llama_token llama_sample_token(
struct llama_context * ctx,
struct llama_context * ctx_guidance,
struct llama_grammar * grammar,
const struct gpt_params & params,
const std::vector<llama_token> & last_tokens,
std::vector<llama_token_data> & candidates,
int idx) {
const int n_ctx = llama_n_ctx(ctx);
const int n_vocab = llama_n_vocab(ctx);

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 tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
const float repeat_penalty = params.repeat_penalty;
const float alpha_presence = params.presence_penalty;
const float alpha_frequency = params.frequency_penalty;
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;

llama_token id = 0;

float * logits = llama_get_logits(ctx) + idx * n_vocab;

// Apply params.logit_bias map
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
logits[it->first] += it->second;
}

candidates.clear();
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}

llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };

if (ctx_guidance) {
llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale);
}

// apply penalties
if (!last_tokens.empty()) {
const float nl_logit = logits[llama_token_nl(ctx)];
const int last_n_repeat = std::min(std::min((int)last_tokens.size(), repeat_last_n), n_ctx);

llama_sample_repetition_penalty(ctx, &cur_p,
last_tokens.data() + last_tokens.size() - last_n_repeat,
last_n_repeat, repeat_penalty);
llama_sample_frequency_and_presence_penalties(ctx, &cur_p,
last_tokens.data() + last_tokens.size() - last_n_repeat,
last_n_repeat, alpha_frequency, alpha_presence);

if (!penalize_nl) {
for (size_t idx = 0; idx < cur_p.size; idx++) {
if (cur_p.data[idx].id == llama_token_nl(ctx)) {
cur_p.data[idx].logit = nl_logit;
break;
}
}
}
}

if (grammar != NULL) {
llama_sample_grammar(ctx, &cur_p, grammar);
}

if (temp <= 0) {
// Greedy sampling
id = llama_sample_token_greedy(ctx, &cur_p);
} else {
if (mirostat == 1) {
static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100;
llama_sample_temperature(ctx, &cur_p, temp);
id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
} else if (mirostat == 2) {
static float mirostat_mu = 2.0f * mirostat_tau;
llama_sample_temperature(ctx, &cur_p, temp);
id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu);
} else {
// Temperature sampling
llama_sample_top_k (ctx, &cur_p, top_k, 1);
llama_sample_tail_free (ctx, &cur_p, tfs_z, 1);
llama_sample_typical (ctx, &cur_p, typical_p, 1);
llama_sample_top_p (ctx, &cur_p, top_p, 1);
llama_sample_temperature(ctx, &cur_p, temp);

{
const int n_top = 10;
LOG("top %d candidates:\n", n_top);

for (int i = 0; i < n_top; i++) {
const llama_token id = cur_p.data[i].id;
LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p);
}
}

id = llama_sample_token(ctx, &cur_p);

LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str());
}
}
// printf("`%d`", candidates_p.size);

if (grammar != NULL) {
llama_grammar_accept_token(ctx, grammar, id);
}

return id;
}

//
// YAML utils
//

// returns true if successful, false otherwise
bool create_directory_with_parents(const std::string & path) {
#ifdef _WIN32
Expand Down Expand Up @@ -1070,6 +1209,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
fprintf(stream, "mirostat_lr: %f # default: 0.1\n", params.mirostat_eta);
fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str());
fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());
fprintf(stream, "mtest: %s # default: false\n", params.mem_test ? "true" : "false");
fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
fprintf(stream, "n_gpu_layers: %d # default: 0\n", params.n_gpu_layers);
Expand Down
36 changes: 36 additions & 0 deletions common/common.h
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Original file line number Diff line number Diff line change
Expand Up @@ -32,6 +32,7 @@ struct gpt_params {
int32_t n_ctx = 512; // context size
int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_draft = 16; // number of tokens to draft during speculative decoding
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
int32_t n_gpu_layers = 0; // number of layers to store in VRAM
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
Expand Down Expand Up @@ -63,6 +64,7 @@ struct gpt_params {
float cfg_scale = 1.f; // How strong is guidance

std::string model = "models/7B/ggml-model-f16.gguf"; // model path
std::string model_draft = ""; // draft model for speculative decoding
std::string model_alias = "unknown"; // model alias
std::string prompt = "";
std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state
Expand Down Expand Up @@ -156,6 +158,40 @@ std::string llama_detokenize_bpe(
llama_context * ctx,
const std::vector<llama_token> & tokens);

//
// Sampling utils
//

// 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
//
// required:
// - ctx: context to use for sampling
// - params: sampling parameters
//
// optional:
// - ctx_guidance: context to use for classifier-free guidance, ignore if NULL
// - grammar: grammar to use for sampling, ignore if NULL
// - last_tokens: needed for repetition penalty, ignore if empty
// - idx: sample from llama_get_logits(ctx) + idx * n_vocab
//
// returns:
// - token: sampled token
// - candidates: vector of candidate tokens
//
llama_token llama_sample_token(
struct llama_context * ctx,
struct llama_context * ctx_guidance,
struct llama_grammar * grammar,
const struct gpt_params & params,
const std::vector<llama_token> & last_tokens,
std::vector<llama_token_data> & candidates,
int idx = 0);

//
// YAML utils
//

bool create_directory_with_parents(const std::string & path);
void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector<float> & data);
void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector<int> & data);
Expand Down
1 change: 1 addition & 0 deletions examples/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@ else()
add_subdirectory(train-text-from-scratch)
add_subdirectory(convert-llama2c-to-ggml)
add_subdirectory(simple)
add_subdirectory(speculative)
add_subdirectory(embd-input)
add_subdirectory(llama-bench)
add_subdirectory(beam-search)
Expand Down
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