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Persistent state for gpt-j models too.

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pull/520/head
Adam Treat 1 year ago
parent 5b71d39024
commit 659442394f
2 changed files with 239 additions and 32 deletions
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268
llmodel/gptj.cpp
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@ -13,8 +13,11 @@
#include <vector>
#include <iostream>
#include <unistd.h>
#include <sstream>
// default hparams (GPT-J 6B)
static const size_t MB = 1024*1024;
struct gptj_hparams {
int32_t n_vocab = 50400;
int32_t n_ctx = 2048;
@ -45,6 +48,40 @@ struct gptj_layer {
struct ggml_tensor * c_mlp_proj_b;
};
struct gptj_buffer {
uint8_t * addr = NULL;
size_t size = 0;
void resize(size_t size) {
delete[] addr;
addr = new uint8_t[size];
this->size = size;
}
~gptj_buffer() {
std::cout << "yes we are cleaning up" << std::endl;
fflush(stdout);
delete[] addr;
}
};
struct gptj_kv_cache {
struct ggml_tensor * k;
struct ggml_tensor * v;
struct ggml_context * ctx = NULL;
gptj_buffer buf;
int n; // number of tokens currently in the cache
~gptj_kv_cache() {
if (ctx) {
ggml_free(ctx);
}
}
};
struct gptj_model {
gptj_hparams hparams;
@ -60,14 +97,52 @@ struct gptj_model {
std::vector<gptj_layer> layers;
// key + value memory
struct ggml_tensor * memory_k;
struct ggml_tensor * memory_v;
struct gptj_kv_cache kv_self;
//
struct ggml_context * ctx;
std::map<std::string, struct ggml_tensor *> tensors;
gptj_buffer buf;
~gptj_model() {
if (ctx) {
ggml_free(ctx);
}
}
};
static bool kv_cache_init(
const struct gptj_hparams & hparams,
struct gptj_kv_cache & cache,
ggml_type wtype,
int n_ctx) {
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int64_t n_mem = (int64_t)n_layer*n_ctx;
const int64_t n_elements = n_embd*n_mem;
cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB);
struct ggml_init_params params;
params.mem_size = cache.buf.size;
params.mem_buffer = cache.buf.addr;
params.no_alloc = false;
cache.ctx = ggml_init(params);
if (!cache.ctx) {
fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
return false;
}
cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
return true;
}
// load the model's weights from a stream
bool gptj_model_load(const std::string &fname, std::istream &fin, gptj_model & model, gpt_vocab & vocab) {
printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
@ -277,12 +352,14 @@ bool gptj_model_load(const std::string &fname, std::istream &fin, gptj_model & m
const int n_mem = n_layer*n_ctx;
const int n_elements = n_embd*n_mem;
model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
if (!kv_cache_init(hparams, model.kv_self, GGML_TYPE_F32, model.hparams.n_ctx)) {
fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
ggml_free(ctx);
return false;
}
printf("%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
const size_t memory_size = ggml_nbytes(model.kv_self.k) + ggml_nbytes(model.kv_self.v);
printf("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
}
// load weights
@ -400,7 +477,7 @@ bool gptj_model_load(const std::string & fname, gptj_model & model, gpt_vocab &
// The GPT-J model requires about 16MB of memory per input token.
//
bool gptj_eval(
const gptj_model & model,
gptj_model & model,
const int n_threads,
const int n_past,
const std::vector<gpt_vocab::id> & embd_inp,
@ -419,25 +496,25 @@ bool gptj_eval(
const int d_key = n_embd/n_head;
static size_t buf_size = 1024u*1024*1024;
static void * buf = malloc(buf_size);
static size_t buf_size = 1024u*MB;
if (!model.buf.addr || model.buf.size < buf_size)
model.buf.resize(buf_size);
if (mem_per_token > 0 && mem_per_token*N > buf_size) {
if (mem_per_token > 0 && mem_per_token*N > model.buf.size) {
const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, model.buf.size, buf_size_new);
// reallocate
buf_size = buf_size_new;
buf = realloc(buf, buf_size);
if (buf == nullptr) {
fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
model.buf.resize(buf_size_new);
if (model.buf.addr == nullptr) {
fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, model.buf.size);
return false;
}
}
struct ggml_init_params params = {
.mem_size = buf_size,
.mem_buffer = buf,
.mem_size = model.buf.size,
.mem_buffer = model.buf.addr,
};
struct ggml_context * ctx0 = ggml_init(params);
@ -474,8 +551,8 @@ bool gptj_eval(
// store key and value to memory
if (N >= 1) {
struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));
struct ggml_tensor * k = ggml_view_1d(ctx0, model.kv_self.k, N*n_embd, (ggml_element_size(model.kv_self.k)*n_embd)*(il*n_ctx + n_past));
struct ggml_tensor * v = ggml_view_1d(ctx0, model.kv_self.v, N*n_embd, (ggml_element_size(model.kv_self.v)*n_embd)*(il*n_ctx + n_past));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
@ -496,7 +573,7 @@ bool gptj_eval(
ggml_permute(ctx0,
ggml_rope(ctx0,
ggml_reshape_3d(ctx0,
ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
ggml_view_1d(ctx0, model.kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.kv_self.k)*n_embd),
n_embd/n_head, n_head, n_past + N),
n_past, n_rot, 1),
0, 2, 1, 3);
@ -522,10 +599,10 @@ bool gptj_eval(
ggml_cpy(ctx0,
ggml_permute(ctx0,
ggml_reshape_3d(ctx0,
ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),
ggml_view_1d(ctx0, model.kv_self.v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.kv_self.v)*n_embd),
n_embd/n_head, n_head, n_past + N),
1, 2, 0, 3),
ggml_new_tensor_3d(ctx0, model.memory_v->type, n_past + N, n_embd/n_head, n_head));
ggml_new_tensor_3d(ctx0, model.kv_self.v->type, n_past + N, n_embd/n_head, n_head));
// KQV = transpose(V) * KQ_soft_max
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
@ -629,11 +706,122 @@ bool gptj_eval(
return true;
}
#define GPTJ_MAX_RNG_STATE 64*1024
size_t gptj_get_state_size(const gptj_model &model)
{
// we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
// for reference, std::mt19937(1337) serializes to 6701 bytes.
const size_t s_rng_size = sizeof(size_t);
const size_t s_rng = GPTJ_MAX_RNG_STATE;
const size_t s_kv_size = sizeof(size_t);
const size_t s_kv_ntok = sizeof(int);
const size_t s_kv = model.kv_self.buf.size;
const size_t s_total = (
+ s_rng_size
+ s_rng
+ s_kv_size
+ s_kv_ntok
+ s_kv
);
fflush(stdout);
return s_total;
}
size_t gptj_copy_state_data(const gptj_model &model, const std::mt19937 &rng, uint8_t *dest)
{
uint8_t * out = dest;
fflush(stdout);
// copy rng
{
std::stringstream rng_ss;
rng_ss << rng;
const size_t rng_size = rng_ss.str().size();
char rng_buf[GPTJ_MAX_RNG_STATE];
memset(&rng_buf[0], 0, GPTJ_MAX_RNG_STATE);
memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size());
memcpy(out, &rng_size, sizeof(rng_size)); out += sizeof(rng_size);
memcpy(out, &rng_buf[0], GPTJ_MAX_RNG_STATE); out += GPTJ_MAX_RNG_STATE;
}
// copy kv cache
{
const size_t kv_size = model.kv_self.buf.size;
const int kv_ntok = model.kv_self.n;
memcpy(out, &kv_size, sizeof(kv_size)); out += sizeof(kv_size);
memcpy(out, &kv_ntok, sizeof(kv_ntok)); out += sizeof(kv_ntok);
if (kv_size) {
memcpy(out, model.kv_self.buf.addr, kv_size); out += kv_size;
}
}
const size_t written = out - dest;
const size_t expected = gptj_get_state_size(model);
assert(written == expected);
fflush(stdout);
return written;
}
size_t gptj_set_state_data(gptj_model *model, std::mt19937 *rng, const uint8_t *src)
{
const uint8_t * in = src;
// set rng
{
size_t rng_size;
char rng_buf[GPTJ_MAX_RNG_STATE];
memcpy(&rng_size, in, sizeof(rng_size)); in += sizeof(rng_size);
memcpy(&rng_buf[0], in, GPTJ_MAX_RNG_STATE); in += GPTJ_MAX_RNG_STATE;
std::stringstream rng_ss;
rng_ss.str(std::string(&rng_buf[0], rng_size));
rng_ss >> *rng;
assert(rng_ss.fail() == false);
}
// set kv cache
{
size_t kv_size;
int kv_ntok;
memcpy(&kv_size, in, sizeof(kv_size)); in += sizeof(kv_size);
memcpy(&kv_ntok, in, sizeof(kv_ntok)); in += sizeof(kv_ntok);
if (kv_size) {
assert(model->kv_self.buf.size == kv_size);
void * k_data = model->kv_self.k->data; // remember data pointers
void * v_data = model->kv_self.v->data; // because their value is stored in buf and overwritten by memcpy
memcpy(model->kv_self.buf.addr, in, kv_size); in += kv_size;
model->kv_self.k->data = k_data; // restore correct data pointers
model->kv_self.v->data = v_data;
}
model->kv_self.n = kv_ntok;
}
const size_t nread = in - src;
const size_t expected = gptj_get_state_size(*model);
assert(nread == expected);
fflush(stdout);
return nread;
}
struct GPTJPrivate {
const std::string modelPath;
bool modelLoaded;
gpt_vocab vocab;
gptj_model model;
gptj_model *model = nullptr;
int64_t n_threads = 0;
size_t mem_per_token = 0;
std::mt19937 rng;
@ -642,6 +830,7 @@ struct GPTJPrivate {
GPTJ::GPTJ()
: d_ptr(new GPTJPrivate) {
d_ptr->model = new gptj_model;
d_ptr->modelLoaded = false;
}
@ -652,7 +841,7 @@ bool GPTJ::loadModel(const std::string &modelPath) {
auto fin = std::ifstream(modelPath, std::ios::binary);
// load the model
if (!gptj_model_load(modelPath, fin, d_ptr->model, d_ptr->vocab)) {
if (!gptj_model_load(modelPath, fin, *d_ptr->model, d_ptr->vocab)) {
std::cerr << "GPT-J ERROR: failed to load model from " << modelPath;
return false;
}
@ -673,7 +862,7 @@ int32_t GPTJ::threadCount() {
GPTJ::~GPTJ()
{
ggml_free(d_ptr->model.ctx);
delete d_ptr->model;
}
bool GPTJ::isModelLoaded() const
@ -681,6 +870,21 @@ bool GPTJ::isModelLoaded() const
return d_ptr->modelLoaded;
}
size_t GPTJ::stateSize() const
{
return gptj_get_state_size(*d_ptr->model);
}
size_t GPTJ::saveState(uint8_t *dest) const
{
return gptj_copy_state_data(*d_ptr->model, d_ptr->rng, dest);
}
size_t GPTJ::restoreState(const uint8_t *src)
{
return gptj_set_state_data(d_ptr->model, &d_ptr->rng, src);
}
void GPTJ::prompt(const std::string &prompt,
std::function<bool(int32_t)> promptCallback,
std::function<bool(int32_t, const std::string&)> responseCallback,
@ -702,7 +906,7 @@ void GPTJ::prompt(const std::string &prompt,
std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(d_ptr->vocab, prompt);
// save the context size
promptCtx.n_ctx = d_ptr->model.hparams.n_ctx;
promptCtx.n_ctx = d_ptr->model->hparams.n_ctx;
if ((int) embd_inp.size() > promptCtx.n_ctx - 4) {
responseCallback(-1, "ERROR: The prompt size exceeds the context window size and cannot be processed.");
@ -719,7 +923,7 @@ void GPTJ::prompt(const std::string &prompt,
static std::vector<gpt_vocab::id> p_instruct;
static std::vector<gpt_vocab::id> r_instruct;
if (!initialized) {
gptj_eval(d_ptr->model, d_ptr->n_threads, 0, { 0, 1, 2, 3 }, promptCtx.logits,
gptj_eval(*d_ptr->model, d_ptr->n_threads, 0, { 0, 1, 2, 3 }, promptCtx.logits,
d_ptr->mem_per_token);
initialized = true;
}
@ -742,7 +946,7 @@ void GPTJ::prompt(const std::string &prompt,
assert(promptCtx.n_past + batch.size() <= promptCtx.n_ctx);
}
if (!gptj_eval(d_ptr->model, d_ptr->n_threads, promptCtx.n_past, batch, promptCtx.logits,
if (!gptj_eval(*d_ptr->model, d_ptr->n_threads, promptCtx.n_past, batch, promptCtx.logits,
d_ptr->mem_per_token)) {
std::cerr << "GPT-J ERROR: Failed to process prompt\n";
return;
@ -769,7 +973,7 @@ void GPTJ::prompt(const std::string &prompt,
for (int i = 0; i < promptCtx.n_predict; i++) {
// sample next token
const int n_vocab = d_ptr->model.hparams.n_vocab;
const int n_vocab = d_ptr->model->hparams.n_vocab;
gpt_vocab::id id = 0;
{
const int64_t t_start_sample_us = ggml_time_us();
@ -796,7 +1000,7 @@ void GPTJ::prompt(const std::string &prompt,
}
const int64_t t_start_predict_us = ggml_time_us();
if (!gptj_eval(d_ptr->model, d_ptr->n_threads, promptCtx.n_past, { id }, promptCtx.logits,
if (!gptj_eval(*d_ptr->model, d_ptr->n_threads, promptCtx.n_past, { id }, promptCtx.logits,
d_ptr->mem_per_token)) {
std::cerr << "GPT-J ERROR: Failed to predict next token\n";
return;
@ -846,7 +1050,7 @@ void GPTJ::recalculateContext(PromptContext &promptCtx, std::function<bool(bool)
assert(promptCtx.n_past + batch.size() <= promptCtx.n_ctx);
if (!gptj_eval(d_ptr->model, d_ptr->n_threads, promptCtx.n_past, batch, promptCtx.logits,
if (!gptj_eval(*d_ptr->model, d_ptr->n_threads, promptCtx.n_past, batch, promptCtx.logits,
d_ptr->mem_per_token)) {
std::cerr << "GPTJ ERROR: Failed to process prompt\n";
goto stop_generating;

3
llmodel/gptj.h
Unescape Escape View File

@ -14,6 +14,9 @@ public:
bool loadModel(const std::string &modelPath) override;
bool isModelLoaded() const override;
size_t stateSize() const override;
size_t saveState(uint8_t *dest) const override;
size_t restoreState(const uint8_t *src) override;
void prompt(const std::string &prompt,
std::function<bool(int32_t)> promptCallback,
std::function<bool(int32_t, const std::string&)> responseCallback,

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