413 lines
10 KiB
Go
413 lines
10 KiB
Go
package llama
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/*
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#cgo CPPFLAGS: -O3 -Wall -Wextra -Werror -Wno-unused-function -Wno-unused-variable -DNDEBUG -DGGML_USE_K_QUANTS
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#cgo CXXFLAGS: -std=gnu++11
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#cgo darwin CPPFLAGS: -DGGML_USE_ACCELERATE -DGGML_USE_METAL -DGGML_METAL_NDEBUG
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#cgo darwin LDFLAGS: -framework Accelerate -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
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#include <stdlib.h>
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#include "llama.h"
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struct llama_sample_options
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{
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float repeat_penalty;
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float frequency_penalty;
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float presence_penalty;
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float temperature;
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int32_t top_k;
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float top_p;
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float tfs_z;
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float typical_p;
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int mirostat;
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float mirostat_tau;
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float mirostat_eta;
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bool penalize_newline;
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};
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llama_token llama_sample(
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struct llama_context *ctx,
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struct llama_token_data *candidates,
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size_t n_candidates,
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const llama_token *last_tokens,
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size_t n_last_tokens,
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struct llama_sample_options *opts)
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{
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llama_token_data_array candidates_p = {
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candidates,
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n_candidates,
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false,
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};
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struct llama_token_data newline = candidates_p.data[llama_token_nl()];
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llama_sample_repetition_penalty(
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ctx, &candidates_p,
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last_tokens, n_last_tokens,
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opts->repeat_penalty);
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llama_sample_frequency_and_presence_penalties(
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ctx, &candidates_p,
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last_tokens, n_last_tokens,
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opts->frequency_penalty, opts->presence_penalty);
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if (!opts->penalize_newline) {
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candidates_p.data[llama_token_nl()] = newline;
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}
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if (opts->temperature <= 0) {
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return llama_sample_token_greedy(ctx, &candidates_p);
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}
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if (opts->mirostat == 1) {
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int mirostat_m = 100;
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float mirostat_mu = 2.0f * opts->mirostat_tau;
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llama_sample_temperature(ctx, &candidates_p, opts->temperature);
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return llama_sample_token_mirostat(
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ctx, &candidates_p,
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opts->mirostat_tau, opts->mirostat_eta,
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mirostat_m, &mirostat_mu);
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} else if (opts->mirostat == 2) {
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float mirostat_mu = 2.0f * opts->mirostat_tau;
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llama_sample_temperature(ctx, &candidates_p, opts->temperature);
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return llama_sample_token_mirostat_v2(
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ctx, &candidates_p,
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opts->mirostat_tau, opts->mirostat_eta,
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&mirostat_mu);
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} else {
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llama_sample_top_k(ctx, &candidates_p, opts->top_k, 1);
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llama_sample_tail_free(ctx, &candidates_p, opts->tfs_z, 1);
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llama_sample_typical(ctx, &candidates_p, opts->typical_p, 1);
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llama_sample_top_p(ctx, &candidates_p, opts->top_p, 1);
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llama_sample_temperature(ctx, &candidates_p, opts->temperature);
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return llama_sample_token(ctx, &candidates_p);
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}
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}
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*/
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import "C"
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import (
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"bytes"
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"embed"
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"errors"
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"fmt"
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"io"
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"log"
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"os"
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"strings"
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"sync"
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"unicode/utf8"
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"unsafe"
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"github.com/jmorganca/ollama/api"
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)
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//go:embed ggml-metal.metal
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var fs embed.FS
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type LLM struct {
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params *C.struct_llama_context_params
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model *C.struct_llama_model
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ctx *C.struct_llama_context
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last []C.llama_token
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embd []C.llama_token
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cursor int
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mu sync.Mutex
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gc bool
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api.Options
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}
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func New(model string, opts api.Options) (*LLM, error) {
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if _, err := os.Stat(model); err != nil {
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return nil, err
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}
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llm := LLM{Options: opts}
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C.llama_backend_init(C.bool(llm.UseNUMA))
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params := C.llama_context_default_params()
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params.seed = C.uint(llm.Seed)
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params.n_ctx = C.int(llm.NumCtx)
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params.n_batch = C.int(llm.NumBatch)
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params.n_gqa = C.int(llm.NumGQA)
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params.n_gpu_layers = C.int(llm.NumGPU)
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params.main_gpu = C.int(llm.MainGPU)
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params.low_vram = C.bool(llm.LowVRAM)
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params.f16_kv = C.bool(llm.F16KV)
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params.logits_all = C.bool(llm.LogitsAll)
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params.vocab_only = C.bool(llm.VocabOnly)
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params.use_mmap = C.bool(llm.UseMMap)
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params.use_mlock = C.bool(llm.UseMLock)
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params.embedding = C.bool(llm.EmbeddingOnly)
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llm.params = ¶ms
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cModel := C.CString(model)
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defer C.free(unsafe.Pointer(cModel))
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llm.model = C.llama_load_model_from_file(cModel, params)
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if llm.model == nil {
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return nil, errors.New("failed to load model")
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}
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llm.ctx = C.llama_new_context_with_model(llm.model, params)
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if llm.ctx == nil {
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return nil, errors.New("failed to create context")
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}
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// warm up the model
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bos := []C.llama_token{C.llama_token_bos()}
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C.llama_eval(llm.ctx, unsafe.SliceData(bos), C.int(len(bos)), 0, C.int(opts.NumThread))
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C.llama_reset_timings(llm.ctx)
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return &llm, nil
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}
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func (llm *LLM) Close() {
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llm.gc = true
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llm.mu.Lock()
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defer llm.mu.Unlock()
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defer C.llama_free_model(llm.model)
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defer C.llama_free(llm.ctx)
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C.llama_print_timings(llm.ctx)
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}
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var errNeedMoreData = errors.New("need more data")
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func (llm *LLM) Predict(ctx []int, prompt string, fn func(api.GenerateResponse)) error {
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C.llama_reset_timings(llm.ctx)
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tokens := make([]C.llama_token, len(ctx))
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for i := range tokens {
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tokens[i] = C.llama_token(ctx[i])
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}
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if len(tokens) == 0 {
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tokens = llm.tokenize(" ")
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}
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llm.marshalPrompt(tokens, prompt)
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C.llama_set_rng_seed(llm.ctx, C.uint(llm.Seed))
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var b bytes.Buffer
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for {
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token, err := llm.next()
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if llm.gc {
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return nil
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} else if errors.Is(err, io.EOF) {
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break
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} else if err != nil {
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return err
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}
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b.WriteString(llm.detokenize(token))
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if err := llm.checkStopConditions(b); err != nil {
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if errors.Is(err, io.EOF) {
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break
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} else if errors.Is(err, errNeedMoreData) {
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continue
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}
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return err
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}
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if utf8.Valid(b.Bytes()) || b.Len() >= utf8.UTFMax {
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fn(api.GenerateResponse{Response: b.String()})
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b.Reset()
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}
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}
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last := make([]int, 0, len(llm.last))
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for _, i := range llm.last {
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if i != 0 {
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last = append(last, int(i))
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}
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}
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timings := C.llama_get_timings(llm.ctx)
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fn(api.GenerateResponse{
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Done: true,
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Context: last,
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SampleCount: int(timings.n_sample),
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SampleDuration: parseDurationMs(float64(timings.t_sample_ms)),
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PromptEvalCount: int(timings.n_p_eval),
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PromptEvalDuration: parseDurationMs(float64(timings.t_p_eval_ms)),
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EvalCount: int(timings.n_eval),
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EvalDuration: parseDurationMs(float64(timings.t_eval_ms)),
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})
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return nil
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}
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func (llm *LLM) checkStopConditions(b bytes.Buffer) error {
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for _, stopCondition := range llm.Stop {
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if stopCondition == b.String() {
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return io.EOF
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} else if strings.HasPrefix(stopCondition, b.String()) {
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return errNeedMoreData
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}
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}
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return nil
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}
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func (llm *LLM) marshalPrompt(ctx []C.llama_token, prompt string) []C.llama_token {
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tokens := append(ctx, llm.tokenize(prompt)...)
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if llm.NumKeep < 0 {
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llm.NumKeep = len(tokens)
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}
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// min(llm.NumCtx - 4, llm.NumKeep)
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if llm.NumCtx-4 < llm.NumKeep {
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llm.NumKeep = llm.NumCtx - 4
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}
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if len(tokens) >= llm.NumCtx {
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// truncate input
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numLeft := (llm.NumCtx - llm.NumKeep) / 2
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truncated := tokens[:llm.NumKeep]
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erasedBlocks := (len(tokens) - llm.NumKeep - numLeft - 1) / numLeft
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truncated = append(truncated, tokens[llm.NumKeep+erasedBlocks*numLeft:]...)
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copy(llm.last, tokens[len(tokens)-llm.NumCtx:])
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tokens = truncated
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log.Printf("input truncated: num_ctx=%d num_keep=%d num_left=%d num_tokens=%d", llm.NumCtx, llm.NumKeep, numLeft, len(truncated))
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} else {
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llm.last = make([]C.llama_token, llm.NumCtx-len(tokens))
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llm.last = append(llm.last, tokens...)
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}
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var i int
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for i = 0; i < len(llm.embd) && i < len(tokens) && llm.embd[i] == tokens[i]; i++ {
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// noop
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}
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llm.embd = tokens
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if i == len(tokens) {
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// evaluate at least one token to generate logits
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i--
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}
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llm.cursor = i
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log.Printf("prompt: num_past=%d cached=%v eval=%v", i, len(llm.embd[:i]), len(llm.embd[i:]))
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return tokens
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}
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func (llm *LLM) tokenize(prompt string) []C.llama_token {
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cPrompt := C.CString(prompt)
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defer C.free(unsafe.Pointer(cPrompt))
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tokens := make([]C.llama_token, len(prompt)+1)
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if n := C.llama_tokenize(llm.ctx, cPrompt, unsafe.SliceData(tokens), C.int(len(tokens)), true); n > 0 {
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return tokens[:n]
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}
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return nil
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}
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func (llm *LLM) detokenize(tokens ...C.llama_token) string {
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var sb strings.Builder
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for _, token := range tokens {
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sb.WriteString(C.GoString(C.llama_token_to_str(llm.ctx, token)))
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}
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return sb.String()
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}
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func (llm *LLM) next() (C.llama_token, error) {
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llm.mu.Lock()
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defer llm.mu.Unlock()
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if len(llm.embd) >= llm.NumCtx {
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numLeft := (llm.NumCtx - llm.NumKeep) / 2
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truncated := llm.embd[:llm.NumKeep]
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truncated = append(truncated, llm.embd[len(llm.embd)-numLeft:]...)
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llm.embd = truncated
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llm.cursor = llm.NumKeep
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log.Printf("input truncated: num_ctx=%d num_keep=%d num_left=%d num_tokens=%d cursor=%d", llm.NumCtx, llm.NumKeep, numLeft, len(truncated), llm.cursor)
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}
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for {
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if llm.gc {
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return 0, io.EOF
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}
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if llm.cursor >= len(llm.embd) {
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break
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}
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numEval := len(llm.embd) - llm.cursor
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if numEval > llm.NumBatch {
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numEval = llm.NumBatch
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}
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if retval := C.llama_eval(llm.ctx, unsafe.SliceData(llm.embd[llm.cursor:]), C.int(numEval), C.int(llm.cursor), C.int(llm.NumThread)); retval != 0 {
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return 0, fmt.Errorf("llama_eval: %d", retval)
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}
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llm.cursor += numEval
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}
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var sampleOpts C.struct_llama_sample_options
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sampleOpts.repeat_penalty = C.float(llm.RepeatPenalty)
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sampleOpts.frequency_penalty = C.float(llm.FrequencyPenalty)
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sampleOpts.presence_penalty = C.float(llm.PresencePenalty)
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sampleOpts.temperature = C.float(llm.Temperature)
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sampleOpts.top_k = C.int(llm.TopK)
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sampleOpts.top_p = C.float(llm.TopP)
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sampleOpts.tfs_z = C.float(llm.TFSZ)
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sampleOpts.typical_p = C.float(llm.TypicalP)
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sampleOpts.mirostat = C.int(llm.Mirostat)
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sampleOpts.mirostat_tau = C.float(llm.MirostatTau)
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sampleOpts.mirostat_eta = C.float(llm.MirostatEta)
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sampleOpts.penalize_newline = C.bool(llm.PenalizeNewline)
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numVocab := C.llama_n_vocab(llm.ctx)
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logits := unsafe.Slice(C.llama_get_logits(llm.ctx), numVocab)
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// TODO: logit bias
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candidates := make([]C.llama_token_data, numVocab)
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for i := range logits {
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candidates[i] = C.llama_token_data{
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id: C.int(i),
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logit: logits[i],
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p: 0,
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}
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}
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repeatLastN := llm.RepeatLastN
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if len(llm.last) < repeatLastN {
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repeatLastN = len(llm.last)
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}
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if llm.NumCtx < repeatLastN {
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repeatLastN = llm.NumCtx
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}
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lastN := llm.last[len(llm.last)-repeatLastN:]
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token := C.llama_sample(
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llm.ctx,
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unsafe.SliceData(candidates), C.size_t(len(candidates)),
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unsafe.SliceData(lastN), C.size_t(len(lastN)),
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&sampleOpts,
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)
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llm.last = append(llm.last, token)
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llm.embd = append(llm.embd, token)
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if token == C.llama_token_eos() {
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return 0, io.EOF
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}
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return token, nil
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}
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