ollama/llama/llama.go
2023-07-28 11:02:04 -04:00

413 lines
10 KiB
Go

package llama
/*
#cgo CPPFLAGS: -O3 -Wall -Wextra -Werror -Wno-unused-function -Wno-unused-variable -DNDEBUG -DGGML_USE_K_QUANTS
#cgo CXXFLAGS: -std=gnu++11
#cgo darwin CPPFLAGS: -DGGML_USE_ACCELERATE -DGGML_USE_METAL -DGGML_METAL_NDEBUG
#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;
bool penalize_newline;
};
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,
};
struct llama_token_data newline = candidates_p.data[llama_token_nl()];
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->penalize_newline) {
candidates_p.data[llama_token_nl()] = newline;
}
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 (
"bytes"
"embed"
"errors"
"fmt"
"io"
"log"
"os"
"strings"
"sync"
"unicode/utf8"
"unsafe"
"github.com/jmorganca/ollama/api"
)
//go:embed ggml-metal.metal
var fs embed.FS
type LLM struct {
params *C.struct_llama_context_params
model *C.struct_llama_model
ctx *C.struct_llama_context
last []C.llama_token
embd []C.llama_token
cursor int
mu sync.Mutex
gc bool
api.Options
}
func New(model string, opts api.Options) (*LLM, error) {
if _, err := os.Stat(model); err != nil {
return nil, err
}
llm := LLM{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_gqa = C.int(llm.NumGQA)
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 = &params
cModel := C.CString(model)
defer C.free(unsafe.Pointer(cModel))
llm.model = C.llama_load_model_from_file(cModel, params)
if llm.model == nil {
return nil, errors.New("failed to load model")
}
llm.ctx = C.llama_new_context_with_model(llm.model, params)
if llm.ctx == nil {
return nil, errors.New("failed to create context")
}
// 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 *LLM) Close() {
llm.gc = true
llm.mu.Lock()
defer llm.mu.Unlock()
defer C.llama_free_model(llm.model)
defer C.llama_free(llm.ctx)
C.llama_print_timings(llm.ctx)
}
var errNeedMoreData = errors.New("need more data")
func (llm *LLM) Predict(ctx []int, prompt string, fn func(api.GenerateResponse)) error {
C.llama_reset_timings(llm.ctx)
tokens := make([]C.llama_token, len(ctx))
for i := range tokens {
tokens[i] = C.llama_token(ctx[i])
}
if len(tokens) == 0 {
tokens = llm.tokenize(" ")
}
llm.marshalPrompt(tokens, prompt)
C.llama_set_rng_seed(llm.ctx, C.uint(llm.Seed))
var b bytes.Buffer
for {
token, err := llm.next()
if llm.gc {
return nil
} else if errors.Is(err, io.EOF) {
break
} else if err != nil {
return err
}
b.WriteString(llm.detokenize(token))
if err := llm.checkStopConditions(b); err != nil {
if errors.Is(err, io.EOF) {
break
} else if errors.Is(err, errNeedMoreData) {
continue
}
return err
}
if utf8.Valid(b.Bytes()) || b.Len() >= utf8.UTFMax {
fn(api.GenerateResponse{Response: b.String()})
b.Reset()
}
}
last := make([]int, 0, len(llm.last))
for _, i := range llm.last {
if i != 0 {
last = append(last, int(i))
}
}
timings := C.llama_get_timings(llm.ctx)
fn(api.GenerateResponse{
Done: true,
Context: last,
SampleCount: int(timings.n_sample),
SampleDuration: parseDurationMs(float64(timings.t_sample_ms)),
PromptEvalCount: int(timings.n_p_eval),
PromptEvalDuration: parseDurationMs(float64(timings.t_p_eval_ms)),
EvalCount: int(timings.n_eval),
EvalDuration: parseDurationMs(float64(timings.t_eval_ms)),
})
return nil
}
func (llm *LLM) checkStopConditions(b bytes.Buffer) error {
for _, stopCondition := range llm.Stop {
if stopCondition == b.String() {
return io.EOF
} else if strings.HasPrefix(stopCondition, b.String()) {
return errNeedMoreData
}
}
return nil
}
func (llm *LLM) marshalPrompt(ctx []C.llama_token, prompt string) []C.llama_token {
tokens := append(ctx, llm.tokenize(prompt)...)
if llm.NumKeep < 0 {
llm.NumKeep = len(tokens)
}
// min(llm.NumCtx - 4, llm.NumKeep)
if llm.NumCtx-4 < llm.NumKeep {
llm.NumKeep = llm.NumCtx - 4
}
if len(tokens) >= llm.NumCtx {
// truncate input
numLeft := (llm.NumCtx - llm.NumKeep) / 2
truncated := tokens[:llm.NumKeep]
erasedBlocks := (len(tokens) - llm.NumKeep - numLeft - 1) / numLeft
truncated = append(truncated, tokens[llm.NumKeep+erasedBlocks*numLeft:]...)
copy(llm.last, tokens[len(tokens)-llm.NumCtx:])
tokens = truncated
log.Printf("input truncated: num_ctx=%d num_keep=%d num_left=%d num_tokens=%d", llm.NumCtx, llm.NumKeep, numLeft, len(truncated))
} else {
llm.last = make([]C.llama_token, llm.NumCtx-len(tokens))
llm.last = append(llm.last, tokens...)
}
var i int
for i = 0; i < len(llm.embd) && i < len(tokens) && llm.embd[i] == tokens[i]; i++ {
// noop
}
llm.embd = tokens
if i == len(tokens) {
// evaluate at least one token to generate logits
i--
}
llm.cursor = i
log.Printf("prompt: num_past=%d cached=%v eval=%v", i, len(llm.embd[:i]), len(llm.embd[i:]))
return tokens
}
func (llm *LLM) tokenize(prompt string) []C.llama_token {
cPrompt := C.CString(prompt)
defer C.free(unsafe.Pointer(cPrompt))
tokens := make([]C.llama_token, len(prompt)+1)
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 *LLM) 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 *LLM) next() (C.llama_token, error) {
llm.mu.Lock()
defer llm.mu.Unlock()
if len(llm.embd) >= llm.NumCtx {
numLeft := (llm.NumCtx - llm.NumKeep) / 2
truncated := llm.embd[:llm.NumKeep]
truncated = append(truncated, llm.embd[len(llm.embd)-numLeft:]...)
llm.embd = truncated
llm.cursor = llm.NumKeep
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)
}
for {
if llm.gc {
return 0, io.EOF
}
if llm.cursor >= len(llm.embd) {
break
}
numEval := len(llm.embd) - llm.cursor
if numEval > llm.NumBatch {
numEval = llm.NumBatch
}
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 {
return 0, fmt.Errorf("llama_eval: %d", retval)
}
llm.cursor += numEval
}
var sampleOpts C.struct_llama_sample_options
sampleOpts.repeat_penalty = C.float(llm.RepeatPenalty)
sampleOpts.frequency_penalty = C.float(llm.FrequencyPenalty)
sampleOpts.presence_penalty = C.float(llm.PresencePenalty)
sampleOpts.temperature = C.float(llm.Temperature)
sampleOpts.top_k = C.int(llm.TopK)
sampleOpts.top_p = C.float(llm.TopP)
sampleOpts.tfs_z = C.float(llm.TFSZ)
sampleOpts.typical_p = C.float(llm.TypicalP)
sampleOpts.mirostat = C.int(llm.Mirostat)
sampleOpts.mirostat_tau = C.float(llm.MirostatTau)
sampleOpts.mirostat_eta = C.float(llm.MirostatEta)
sampleOpts.penalize_newline = C.bool(llm.PenalizeNewline)
numVocab := C.llama_n_vocab(llm.ctx)
logits := unsafe.Slice(C.llama_get_logits(llm.ctx), numVocab)
// TODO: logit bias
candidates := make([]C.llama_token_data, numVocab)
for i := range logits {
candidates[i] = C.llama_token_data{
id: C.int(i),
logit: logits[i],
p: 0,
}
}
repeatLastN := llm.RepeatLastN
if len(llm.last) < repeatLastN {
repeatLastN = len(llm.last)
}
if llm.NumCtx < repeatLastN {
repeatLastN = llm.NumCtx
}
lastN := llm.last[len(llm.last)-repeatLastN:]
token := C.llama_sample(
llm.ctx,
unsafe.SliceData(candidates), C.size_t(len(candidates)),
unsafe.SliceData(lastN), C.size_t(len(lastN)),
&sampleOpts,
)
llm.last = append(llm.last, token)
llm.embd = append(llm.embd, token)
if token == C.llama_token_eos() {
return 0, io.EOF
}
return token, nil
}