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- package llama
- /*
- #cgo CPPFLAGS: -O3 -DNDEBUG=1
- #cgo CXXFLAGS: -std=c++11
- #cgo darwin CPPFLAGS: -DGGML_USE_METAL=1 -DGGML_METAL_NDEBUG=1
- #cgo darwin LDFLAGS: -framework Accelerate -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
- #include <stdlib.h>
- #include "llama.h"
- struct llama_sample_options
- {
- float repeat_penalty;
- float frequency_penalty;
- float presence_penalty;
- float temperature;
- int32_t top_k;
- float top_p;
- float tfs_z;
- float typical_p;
- int mirostat;
- float mirostat_tau;
- float mirostat_eta;
- };
- llama_token llama_sample(
- struct llama_context *ctx,
- struct llama_token_data *candidates,
- size_t n_candidates,
- const llama_token *last_tokens,
- size_t n_last_tokens,
- struct llama_sample_options *opts)
- {
- llama_token_data_array candidates_p = {
- candidates,
- n_candidates,
- false,
- };
- llama_sample_repetition_penalty(
- ctx, &candidates_p,
- last_tokens, n_last_tokens,
- opts->repeat_penalty);
- llama_sample_frequency_and_presence_penalties(
- ctx, &candidates_p,
- last_tokens, n_last_tokens,
- opts->frequency_penalty, opts->presence_penalty);
- if (opts->temperature <= 0) {
- return llama_sample_token_greedy(ctx, &candidates_p);
- }
- if (opts->mirostat == 1) {
- int mirostat_m = 100;
- float mirostat_mu = 2.0f * opts->mirostat_tau;
- llama_sample_temperature(ctx, &candidates_p, opts->temperature);
- return llama_sample_token_mirostat(
- ctx, &candidates_p,
- opts->mirostat_tau, opts->mirostat_eta,
- mirostat_m, &mirostat_mu);
- } else if (opts->mirostat == 2) {
- float mirostat_mu = 2.0f * opts->mirostat_tau;
- llama_sample_temperature(ctx, &candidates_p, opts->temperature);
- return llama_sample_token_mirostat_v2(
- ctx, &candidates_p,
- opts->mirostat_tau, opts->mirostat_eta,
- &mirostat_mu);
- } else {
- llama_sample_top_k(ctx, &candidates_p, opts->top_k, 1);
- llama_sample_tail_free(ctx, &candidates_p, opts->tfs_z, 1);
- llama_sample_typical(ctx, &candidates_p, opts->typical_p, 1);
- llama_sample_top_p(ctx, &candidates_p, opts->top_p, 1);
- llama_sample_temperature(ctx, &candidates_p, opts->temperature);
- return llama_sample_token(ctx, &candidates_p);
- }
- }
- */
- import "C"
- import (
- "errors"
- "io"
- "os"
- "strings"
- "unsafe"
- "github.com/jmorganca/ollama/api"
- )
- type llama struct {
- params *C.struct_llama_context_params
- model *C.struct_llama_model
- ctx *C.struct_llama_context
- api.Options
- }
- func New(model string, opts api.Options) (*llama, error) {
- if _, err := os.Stat(model); err != nil {
- return nil, err
- }
- llm := llama{Options: opts}
- C.llama_backend_init(C.bool(llm.UseNUMA))
- params := C.llama_context_default_params()
- params.seed = C.uint(llm.Seed)
- params.n_ctx = C.int(llm.NumCtx)
- params.n_batch = C.int(llm.NumBatch)
- params.n_gpu_layers = C.int(llm.NumGPU)
- params.main_gpu = C.int(llm.MainGPU)
- params.low_vram = C.bool(llm.LowVRAM)
- params.f16_kv = C.bool(llm.F16KV)
- params.logits_all = C.bool(llm.LogitsAll)
- params.vocab_only = C.bool(llm.VocabOnly)
- params.use_mmap = C.bool(llm.UseMMap)
- params.use_mlock = C.bool(llm.UseMLock)
- params.embedding = C.bool(llm.EmbeddingOnly)
- llm.params = ¶ms
- cModel := C.CString(model)
- defer C.free(unsafe.Pointer(cModel))
- llm.model = C.llama_load_model_from_file(cModel, params)
- llm.ctx = C.llama_new_context_with_model(llm.model, params)
- // warm up the model
- bos := []C.llama_token{C.llama_token_bos()}
- C.llama_eval(llm.ctx, unsafe.SliceData(bos), C.int(len(bos)), 0, C.int(opts.NumThread))
- C.llama_reset_timings(llm.ctx)
- return &llm, nil
- }
- func (llm *llama) Close() {
- defer C.llama_free_model(llm.model)
- defer C.llama_free(llm.ctx)
- C.llama_print_timings(llm.ctx)
- }
- func (llm *llama) Predict(prompt string, fn func(string)) error {
- if tokens := llm.tokenize(prompt); tokens != nil {
- return llm.generate(tokens, fn)
- }
- return errors.New("llama: tokenize")
- }
- func (llm *llama) tokenize(prompt string) []C.llama_token {
- cPrompt := C.CString(prompt)
- defer C.free(unsafe.Pointer(cPrompt))
- tokens := make([]C.llama_token, llm.NumCtx)
- if n := C.llama_tokenize(llm.ctx, cPrompt, unsafe.SliceData(tokens), C.int(len(tokens)), true); n > 0 {
- return tokens[:n]
- }
- return nil
- }
- func (llm *llama) detokenize(tokens ...C.llama_token) string {
- var sb strings.Builder
- for _, token := range tokens {
- sb.WriteString(C.GoString(C.llama_token_to_str(llm.ctx, token)))
- }
- return sb.String()
- }
- func (llm *llama) generate(tokens []C.llama_token, fn func(string)) error {
- var opts C.struct_llama_sample_options
- opts.repeat_penalty = C.float(llm.RepeatPenalty)
- opts.frequency_penalty = C.float(llm.FrequencyPenalty)
- opts.presence_penalty = C.float(llm.PresencePenalty)
- opts.temperature = C.float(llm.Temperature)
- opts.top_k = C.int(llm.TopK)
- opts.top_p = C.float(llm.TopP)
- opts.tfs_z = C.float(llm.TFSZ)
- opts.typical_p = C.float(llm.TypicalP)
- opts.mirostat = C.int(llm.Mirostat)
- opts.mirostat_tau = C.float(llm.MirostatTau)
- opts.mirostat_eta = C.float(llm.MirostatEta)
- pastTokens := deque[C.llama_token]{capacity: llm.RepeatLastN}
- for C.llama_get_kv_cache_token_count(llm.ctx) < C.int(llm.NumCtx) {
- if retval := C.llama_eval(llm.ctx, unsafe.SliceData(tokens), C.int(len(tokens)), C.llama_get_kv_cache_token_count(llm.ctx), C.int(llm.NumThread)); retval != 0 {
- return errors.New("llama: eval")
- }
- token, err := llm.sample(pastTokens, &opts)
- switch {
- case err != nil:
- return err
- case errors.Is(err, io.EOF):
- return nil
- }
- fn(llm.detokenize(token))
- tokens = []C.llama_token{token}
- pastTokens.PushLeft(token)
- }
- return nil
- }
- func (llm *llama) sample(pastTokens deque[C.llama_token], opts *C.struct_llama_sample_options) (C.llama_token, error) {
- numVocab := int(C.llama_n_vocab(llm.ctx))
- logits := unsafe.Slice(C.llama_get_logits(llm.ctx), numVocab)
- candidates := make([]C.struct_llama_token_data, 0, numVocab)
- for i := 0; i < numVocab; i++ {
- candidates = append(candidates, C.llama_token_data{
- id: C.int(i),
- logit: logits[i],
- p: 0,
- })
- }
- token := C.llama_sample(
- llm.ctx,
- unsafe.SliceData(candidates), C.ulong(len(candidates)),
- unsafe.SliceData(pastTokens.Data()), C.ulong(pastTokens.Len()),
- opts)
- if token != C.llama_token_eos() {
- return token, nil
- }
- return 0, io.EOF
- }
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