root/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-11-11 21:39:52 +00:00
Code Issues Actions 12 Packages Projects Releases Wiki Activity
efe6a83e30
llama.cpp/examples/baby-llama/baby-llama.cpp
Faisal Zaghloul 42c76d1358
Threadpool: take 2 (#8672) 
* Introduce ggml_compute_threadpool

- OpenMP functional: check
- Vanilla ggml functional: Check
- ggml w/threadpool functional: Check
- OpenMP no regression: No glaring problems
- Vanilla ggml no regression: No glaring problems
- ggml w/threadpool no regression: No glaring problems

* Minor fixes

* fixed use after release bug

* fixed a harmless race condition

* Fix Android bulid issue

* fix more race conditions

* fix deadlock for cases where cgraph.n_nodes == 1

and fix --poll case

* threadpool: use cpu_get_num_math to set the default number of threadpool threads

This way we avoid using E-Cores and Hyperthreaded siblings.

* bench: create fresh threadpool for each test

For benchmarking it's better to start a fresh pool for each test with the exact number of threads
needed for that test. Having larger pools is suboptimal (causes more load, etc).

* atomics: always use stdatomics with clang and use relaxed memory order when polling in ggml_barrier

This also removes sched_yield() calls from ggml_barrier() to match OpenMP behavior.

* threadpool: make polling the default to match openmp behavior

All command line args now allow for setting poll to 0 (false).

* threadpool: do not wakeup threads in already paused threadpool

* fix potential race condition in check_for_work

* threadpool: do not create two threadpools if their params are identical

* threadpool: reduce pause/resume/wakeup overhead in common cases

We now start threadpool in paused state only if we have two.
The resume is now implicit (ie new work) which allows for reduced locking and context-switch overhead.

* threadpool: add support for hybrid polling

poll params (--poll, ...) now specify "polling level", i.e. how aggresively we poll before waiting on cond.var.
poll=0 means no polling, 1 means poll for 128K rounds then wait, 2 for 256K rounds, ...

The default value of 50 (ie 50x128K rounds) seems like a decent default across modern platforms.
We can tune this further as things evolve.

* threadpool: reduce the number of barrier required

New work is now indicated with an atomic counter that is incremented for
each new graph that needs to be computed.
This removes the need for extra barrier for clearing the "new_work" and
removes the special case for trivial graphs.

* threadpool: remove special-casing for disposable threadpools

With the efficient hybrid polling there is no need to make disposable pools any different.
This simplifies the overall logic and reduces branching.

Include n_threads in debug print for disposable threadpool.

Declare pause and stop flags as atomic_bool
This doesn't actually generate any memory barriers and simply informs
the thread sanitizer that these flags can be written & read by different
threads without locking.

* threadpool: do not clear barrier counters between graphs computes (fixes race with small graphs)

This fixes the race condition with very small graphs where the main thread happens to
start a new graph while the workers are just about to exit from barriers.

* threadpool: use relaxed order for chunk sync

Full memory barrier is an overkill for this since each thread works on different chunk

* threadpool: remove abort_callback from threadpool state

* threadpool: better naming for thread/cpumask releated functions

* threadpool: consistent use of int type for n_threads params

* threadpool: add support for ggml_threadpool_params_default/init

Also removes the need for explicit mask_specified param.
all-zero cpumask means use default (usually inherited) cpu affinity mask.

* threadpool: move typedef into ggml.h

* threadpool: fix apply_priority() function name

* threadpool: fix swift wrapper errors due to n_threads int type cleanup

* threadpool: enable --cpu-mask and other threadpool related options only if threadpool is enabled

* threadpool: replace checks for compute_thread ret code with proper status check

* threadpool: simplify threadpool init logic and fix main thread affinity application

Most of the init code is now exactly the same between threadpool and openmp.

* threadpool: update threadpool resume/pause function names

* threadpool: enable openmp by default for now

* threadpool: don't forget to free workers state when omp is enabled

* threadpool: avoid updating process priority on the platforms that do not require it

On Windows we need to change overall process priority class in order to set thread priorities,
but on Linux, Mac, etc we do not need to touch the overall process settings.

* threadpool: update calling thread prio and affinity only at start/resume

This avoids extra syscalls for each graph_compute()

* llama-bench: turn threadpool params into vectors, add output headers, etc

* llama-bench: add support for cool off between tests --delay

This helps for long running tests on platforms that are thermally limited (phones, laptops, etc).
--delay (disabled by default) introduces the sleep for N seconds before starting each test.

* threadpool: move process priority setting into the apps (bench and cli)

This avoids changing the overall process priority on Windows for the apps
that use ggml/llama.cpp directy.

* threadpool: move all pause/resume logic into ggml

* threadpool: futher api cleanup and prep for future refactoring

All threadpool related functions and structs use ggml_threadpool prefix.

* threadpool: minor indent fixes

* threadpool: improve setprioty error message

* Update examples/llama-bench/llama-bench.cpp

Co-authored-by: slaren <slarengh@gmail.com>

* threadpool: fix indent in set_threadpool call

* use int32_t for n_thread type in public llama.cpp API

* threadpool: use _new and _free instead of _create and _release

* fix two more public APIs to use int32_t for n_threads

* build: set _GNU_SOURCE for Adroid

---------

Co-authored-by: Max Krasnyansky <quic_maxk@quicinc.com>
Co-authored-by: fmz <quic_fzaghlou@quic.com>
Co-authored-by: Max Krasnyansky <max.krasnyansky@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
2024-08-30 01:20:53 +02:00

1640 lines
61 KiB
C++
Raw Blame History

#include "ggml.h"
#include "train.h"
#include <cassert>
#include <cstdlib>
#include <cstring>
#include <random>
#include <vector>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
#ifdef LLAMA_DEFAULT_RMS_EPS
constexpr float rms_norm_eps = LLAMA_DEFAULT_RMS_EPS;
#else
constexpr float rms_norm_eps = 5e-6f;
#endif
static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
struct ggml_cplan plan = ggml_graph_plan(graph, n_threads, nullptr);
if (plan.work_size > 0) {
buf.resize(plan.work_size);
plan.work_data = buf.data();
}
ggml_graph_compute(graph, &plan);
}
static struct ggml_tensor * randomize_tensor(
struct ggml_tensor * tensor, int ndims, const int64_t ne[], float fmin, float fmax
) {
switch (ndims) {
case 1:
for (int i0 = 0; i0 < ne[0]; i0++) {
((float *)tensor->data)[i0] = frand()*(fmax - fmin) + fmin;
}
break;
case 2:
for (int i1 = 0; i1 < ne[1]; i1++) {
for (int i0 = 0; i0 < ne[0]; i0++) {
((float *)tensor->data)[i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
}
}
break;
case 3:
for (int i2 = 0; i2 < ne[2]; i2++) {
for (int i1 = 0; i1 < ne[1]; i1++) {
for (int i0 = 0; i0 < ne[0]; i0++) {
((float *)tensor->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
}
}
}
break;
case 4:
for (int i3 = 0; i3 < ne[3]; i3++) {
for (int i2 = 0; i2 < ne[2]; i2++) {
for (int i1 = 0; i1 < ne[1]; i1++) {
for (int i0 = 0; i0 < ne[0]; i0++) {
((float *)tensor->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
}
}
}
}
break;
default:
assert(false);
}
return tensor;
}
struct llama_hparams {
uint32_t n_vocab = 32000;
uint32_t n_ctx = 512; // this is provided as user input?
uint32_t n_embd = 4096;
uint32_t n_mult = 4;
uint32_t n_head = 32;
uint32_t n_layer = 32;
uint32_t n_rot = 64;
bool operator!=(const llama_hparams & other) const {
return memcmp(this, &other, sizeof(llama_hparams));
}
};
static uint32_t get_n_ff(const struct llama_hparams* hparams) {
const uint32_t n_ff = ((2*(4*hparams->n_embd)/3 + hparams->n_mult - 1)/hparams->n_mult)*hparams->n_mult;
return n_ff;
}
struct llama_hparams_lora {
uint32_t n_vocab = 32000;
uint32_t n_ctx = 512; // this is provided as user input?
uint32_t n_embd = 4096;
uint32_t n_mult = 4;
uint32_t n_head = 32;
uint32_t n_layer = 32;
uint32_t n_rot = 64;
uint32_t n_lora = 64;
bool operator!=(const llama_hparams_lora & other) const {
return memcmp(this, &other, sizeof(llama_hparams_lora)) != 0;
}
};
struct llama_layer {
// normalization
struct ggml_tensor * attention_norm;
// attention
struct ggml_tensor * wq;
struct ggml_tensor * wk;
struct ggml_tensor * wv;
struct ggml_tensor * wo;
// normalization
struct ggml_tensor * ffn_norm;
// ff
struct ggml_tensor * w1;
struct ggml_tensor * w2;
struct ggml_tensor * w3;
};
struct llama_layer_lora {
// normalization
struct ggml_tensor * attention_norm;
// attention
struct ggml_tensor * wqa;
struct ggml_tensor * wqb;
struct ggml_tensor * wka;
struct ggml_tensor * wkb;
struct ggml_tensor * wva;
struct ggml_tensor * wvb;
struct ggml_tensor * woa;
struct ggml_tensor * wob;
// normalization
struct ggml_tensor * ffn_norm;
// ff
struct ggml_tensor * w1;
struct ggml_tensor * w2;
struct ggml_tensor * w3;
};
struct llama_kv_cache {
struct ggml_context * ctx = NULL;
struct ggml_tensor * k;
struct ggml_tensor * v;
// llama_ctx_buffer buf;
int n; // number of tokens currently in the cache
};
struct llama_model {
struct ggml_context * ctx = NULL;
llama_hparams hparams;
struct ggml_tensor * tok_embeddings;
struct ggml_tensor * norm;
struct ggml_tensor * output;
std::vector<llama_layer> layers;
};
struct llama_model_lora {
struct ggml_context * ctx = NULL;
llama_hparams_lora hparams;
struct ggml_tensor * tok_embeddings;
struct ggml_tensor * norm;
struct ggml_tensor * outputa;
struct ggml_tensor * outputb;
std::vector<llama_layer_lora> layers;
};
static void init_model(struct llama_model * model) {
const auto & hparams = model->hparams;
const uint32_t n_embd = hparams.n_embd;
const uint32_t n_layer = hparams.n_layer;
const uint32_t n_vocab = hparams.n_vocab;
const uint32_t n_ff = get_n_ff(&hparams);
struct ggml_context * ctx = model->ctx;
model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); // ("tok_embeddings.weight", {n_embd, n_vocab});
model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // ("norm.weight", {n_embd});
model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); // ("output.weight", {n_embd, n_vocab});
model->layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
auto & layer = model->layers[i];
// std::string layers_i = "layers." + std::to_string(i);
layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".attention_norm.weight", {n_embd});
layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + ".attention.wq.weight", {n_embd, n_embd});
layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + ".attention.wk.weight", {n_embd, n_embd});
layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + ".attention.wv.weight", {n_embd, n_embd});
layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + ".attention.wo.weight", {n_embd, n_embd});
layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".ffn_norm.weight", {n_embd});
layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w1.weight", {n_embd, n_ff});
layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd); // (layers_i + ".feed_forward.w2.weight", { n_ff, n_embd});
layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w3.weight", {n_embd, n_ff});
}
}
static void init_model_lora(struct llama_model_lora * model) {
const auto & hparams = model->hparams;
const uint32_t n_embd = hparams.n_embd;
const uint32_t n_mult = hparams.n_mult;
const uint32_t n_layer = hparams.n_layer;
const uint32_t n_vocab = hparams.n_vocab;
const uint32_t n_lora = hparams.n_lora;
const uint32_t n_ff = ((2*(4*n_embd)/3 + n_mult - 1)/n_mult)*n_mult;
struct ggml_context * ctx = model->ctx;
model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); // ("tok_embeddings.weight", {n_embd, n_vocab});
model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // ("norm.weight", {n_embd});
model->outputa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_vocab); // ("output.weight", {n_embd, n_vocab});
model->outputb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // ("output.weight", {n_embd, n_vocab});
model->layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
auto & layer = model->layers[i];
// std::string layers_i = "layers." + std::to_string(i);
layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".attention_norm.weight", {n_embd});
layer.wqa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wq.weight", {n_embd, n_embd});
layer.wqb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wq.weight", {n_embd, n_embd});
layer.wka = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wk.weight", {n_embd, n_embd});
layer.wkb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wk.weight", {n_embd, n_embd});
layer.wva = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wv.weight", {n_embd, n_embd});
layer.wvb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wv.weight", {n_embd, n_embd});
layer.woa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wo.weight", {n_embd, n_embd});
layer.wob = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wo.weight", {n_embd, n_embd});
layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".ffn_norm.weight", {n_embd});
layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w1.weight", {n_embd, n_ff});
layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd); // (layers_i + ".feed_forward.w2.weight", { n_ff, n_embd});
layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w3.weight", {n_embd, n_ff});
}
}
static void set_param_model(struct llama_model * model) {
const auto& hparams = model->hparams;
const uint32_t n_layer = hparams.n_layer;
struct ggml_context* ctx = model->ctx;
ggml_set_param(ctx, model->tok_embeddings);
ggml_set_param(ctx, model->norm);
ggml_set_param(ctx, model->output);
for (uint32_t i = 0; i < n_layer; ++i) {
auto & layer = model->layers[i];
ggml_set_param(ctx, layer.attention_norm);
ggml_set_param(ctx, layer.wq);
ggml_set_param(ctx, layer.wk);
ggml_set_param(ctx, layer.wv);
ggml_set_param(ctx, layer.wo);
ggml_set_param(ctx, layer.ffn_norm);
ggml_set_param(ctx, layer.w1);
ggml_set_param(ctx, layer.w2);
ggml_set_param(ctx, layer.w3);
}
}
static void set_param_model_lora(struct llama_model_lora * model) {
const auto& hparams = model->hparams;
const uint32_t n_layer = hparams.n_layer;
struct ggml_context* ctx = model->ctx;
ggml_set_param(ctx, model->tok_embeddings);
ggml_set_param(ctx, model->norm);
ggml_set_param(ctx, model->outputa);
ggml_set_param(ctx, model->outputb);
for (uint32_t i = 0; i < n_layer; ++i) {
auto & layer = model->layers[i];
ggml_set_param(ctx, layer.attention_norm);
ggml_set_param(ctx, layer.wqa);
ggml_set_param(ctx, layer.wqb);
ggml_set_param(ctx, layer.wka);
ggml_set_param(ctx, layer.wkb);
ggml_set_param(ctx, layer.wva);
ggml_set_param(ctx, layer.wvb);
ggml_set_param(ctx, layer.woa);
ggml_set_param(ctx, layer.wob);
ggml_set_param(ctx, layer.ffn_norm);
ggml_set_param(ctx, layer.w1);
ggml_set_param(ctx, layer.w2);
ggml_set_param(ctx, layer.w3);
}
}
static void randomize_model(struct llama_model * model, int seed, float mean, float std, float min, float max) {
const auto & hparams = model->hparams;
const uint32_t n_layer = hparams.n_layer;
struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max);
randomize_tensor_normal(model->tok_embeddings , rnd);
randomize_tensor_normal(model->norm , rnd);
randomize_tensor_normal(model->output , rnd);
for (uint32_t i = 0; i < n_layer; ++i) {
auto & layer = model->layers[i];
randomize_tensor_normal(layer.attention_norm, rnd);
randomize_tensor_normal(layer.wq, rnd);
randomize_tensor_normal(layer.wk, rnd);
randomize_tensor_normal(layer.wv, rnd);
randomize_tensor_normal(layer.wo, rnd);
randomize_tensor_normal(layer.ffn_norm, rnd);
randomize_tensor_normal(layer.w1, rnd);
randomize_tensor_normal(layer.w2, rnd);
randomize_tensor_normal(layer.w3, rnd);
}
free_random_normal_distribution(rnd);
}
static void randomize_model_lora(
struct llama_model_lora * model, int seed, float mean, float std, float min, float max
) {
const auto & hparams = model->hparams;
const uint32_t n_layer = hparams.n_layer;
struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max);
randomize_tensor_normal(model->tok_embeddings, rnd);
randomize_tensor_normal(model->norm , rnd);
randomize_tensor_normal(model->outputa , rnd);
randomize_tensor_normal(model->outputb , rnd);
for (uint32_t i = 0; i < n_layer; ++i) {
auto & layer = model->layers[i];
randomize_tensor_normal(layer.attention_norm, rnd);
randomize_tensor_normal(layer.wqa, rnd);
randomize_tensor_normal(layer.wqb, rnd);
randomize_tensor_normal(layer.wka, rnd);
randomize_tensor_normal(layer.wkb, rnd);
randomize_tensor_normal(layer.wva, rnd);
randomize_tensor_normal(layer.wvb, rnd);
randomize_tensor_normal(layer.woa, rnd);
randomize_tensor_normal(layer.wob, rnd);
randomize_tensor_normal(layer.ffn_norm, rnd);
randomize_tensor_normal(layer.w1, rnd);
randomize_tensor_normal(layer.w2, rnd);
randomize_tensor_normal(layer.w3, rnd);
}
free_random_normal_distribution(rnd);
}
static void init_kv_cache(struct llama_kv_cache* cache, struct llama_model * model, int n_batch) {
const auto & hparams = model->hparams;
const uint32_t n_ctx = hparams.n_ctx;
const uint32_t n_embd = hparams.n_embd;
const uint32_t n_layer = hparams.n_layer;
const int64_t n_mem = n_layer*n_ctx*n_batch;
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;
if (!cache->ctx) {
struct ggml_init_params params;
params.mem_size = 2u*n_elements*ggml_type_size(GGML_TYPE_F32) + 2u*1024*1024;
params.mem_buffer = NULL;
params.no_alloc = false;
cache->ctx = ggml_init(params);
if (!cache->ctx) {
fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
exit(1);
}
}
cache->k = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements);
cache->v = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements);
}
static bool init_kv_cache_lora(struct llama_kv_cache* cache, struct llama_model_lora * model, int n_batch) {
const auto & hparams = model->hparams;
const uint32_t n_ctx = hparams.n_ctx;
const uint32_t n_embd = hparams.n_embd;
const uint32_t n_layer = hparams.n_layer;
const int64_t n_mem = n_layer*n_ctx*n_batch;
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;
if (!cache->ctx) {
struct ggml_init_params params;
params.mem_size = 2u*n_elements*ggml_type_size(GGML_TYPE_F32) + 2u*1024*1024;
params.mem_buffer = NULL;
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, GGML_TYPE_F32, n_elements);
cache->v = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements);
return true;
}
static struct ggml_tensor * forward(
struct llama_model * model,
struct llama_kv_cache * cache,
struct ggml_context * ctx0,
struct ggml_cgraph * gf,
struct ggml_tensor * tokens_input,
const int n_tokens,
const int n_past
) {
const int N = n_tokens;
struct llama_kv_cache& kv_self = *cache;
const auto & hparams = model->hparams;
const int n_ctx = hparams.n_ctx;
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int n_head = hparams.n_head;
const int n_rot = hparams.n_rot;
struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
memcpy(tokens->data, tokens_input->data, N*ggml_element_size(tokens));
struct ggml_tensor * kc = kv_self.k;
struct ggml_tensor * vc = kv_self.v;
struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
{
int * data = (int *) KQ_pos->data;
for (int i = 0; i < N; ++i) {
data[i] = n_past + i;
}
}
// inpL shape [n_embd,N,1,1]
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens);
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
struct ggml_tensor * cur;
// lctx.use_buf(ctx0, 0);
// norm
{
// cur shape [n_embd,N,1,1]
cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
// cur = attention_norm*cur
cur = ggml_mul(ctx0,
ggml_repeat(ctx0, model->layers[il].attention_norm, cur),
cur);
}
// self-attention
{
// compute Q and K and RoPE them
// wq shape [n_embd, n_embd, 1, 1]
// wk shape [n_embd, n_embd, 1, 1]
// Qcur shape [n_embd/n_head, n_head, N, 1]
// Kcur shape [n_embd/n_head, n_head, N, 1]
struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0);
struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0);
// store key and value to memory
{
// compute the transposed [N, n_embd] V matrix
// wv shape [n_embd, n_embd, 1, 1]
// Vcur shape [n_embd, N, 1, 1]
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wv, cur), n_embd, N)));
// kv_self.k shape [n_embd * n_ctx * n_layer, 1]
// kv_self.v shape [n_embd * n_ctx * n_layer, 1]
// k shape [n_embd * N, 1] == kv_self.k[:,n_past:n_past+N,il,0]
// v shape [N, n_embd, 1, 1] == kv_self.v[:,n_past:n_past+N,il,0]
/* {
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,
( n_ctx)*ggml_element_size(kv_self.v),
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
// important: storing RoPE-ed version of K in the KV cache!
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
} //*/
kc = ggml_set_1d(ctx0, kc, ggml_reshape_1d(ctx0, Kcur, n_embd*N), (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
vc = ggml_set_2d(ctx0, vc, Vcur, ( n_ctx)*ggml_element_size(kv_self.v),
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
}
// Qcur shape [n_embd/n_head, n_head, N, 1]
// Q shape [n_embd/n_head, N, n_head, 1]
struct ggml_tensor * Q =
ggml_permute(ctx0,
Qcur,
0, 2, 1, 3);
// kv_self.k shape [n_embd * n_ctx * n_layer, 1]
// K shape [n_embd/n_head, n_past + N, n_head, 1]
struct ggml_tensor * K =
ggml_permute(ctx0,
ggml_reshape_3d(ctx0,
ggml_view_1d(ctx0, kc, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kc)*n_embd),
n_embd/n_head, n_head, n_past + N),
0, 2, 1, 3);
// K * Q
// KQ shape [n_past + N, N, n_head, 1]
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
// KQ_scaled = KQ / sqrt(n_embd/n_head)
// KQ_scaled shape [n_past + N, N, n_head, 1]
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, 1.0f/sqrtf(float(n_embd)/n_head));
// KQ_masked = mask_past(KQ_scaled)
// KQ_masked shape [n_past + N, N, n_head, 1]
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
// KQ = soft_max(KQ_masked)
// KQ_soft_max shape [n_past + N, N, n_head, 1]
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
// split cached V into n_head heads
//// V shape [n_past + N, n_embd/n_head, n_head, 1]
// V shape [n_past + N, n_embd/n_head, n_head, 1] == kv_self.v[:,:(n_past+N),il,1]
struct ggml_tensor * V =
ggml_view_3d(ctx0, vc,
n_past + N, n_embd/n_head, n_head,
n_ctx*ggml_element_size(vc),
n_ctx*ggml_element_size(vc)*n_embd/n_head,
il*n_ctx*ggml_element_size(vc)*n_embd);
// KQV shape [n_embd/n_head, N, n_head, 1]
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
// KQV_merged = KQV.permute(0, 2, 1, 3)
// KQV_merged shape [n_embd/n_head, n_head, N, 1]
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
// KQV_merged shape
// cur = KQV_merged.contiguous().view(n_embd, N)
// cur shape [n_embd,N,1,1]
cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N);
// cur = ggml_cpy(ctx0,
// KQV_merged,
// ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
// projection (no bias)
// cur shape [n_embd,N,1,1]
cur = ggml_mul_mat(ctx0,
model->layers[il].wo,
cur);
}
// lctx.use_buf(ctx0, 1);
// inpFF shape [n_embd,N,1,1]
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
// feed-forward network
{
// norm
{
// cur shape [n_embd,N,1,1]
cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps);
// cur = ffn_norm*cur
// cur shape [n_embd,N,1,1]
cur = ggml_mul(ctx0,
ggml_repeat(ctx0, model->layers[il].ffn_norm, cur),
cur);
}
// tmp shape [n_ff,N,1,1]
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
model->layers[il].w3,
cur);
// cur shape [n_ff,N,1,1]
cur = ggml_mul_mat(ctx0,
model->layers[il].w1,
cur);
// SILU activation
// cur shape [n_ff,N,1,1]
cur = ggml_silu(ctx0, cur);
// cur shape [n_ff,N,1,1]
cur = ggml_mul(ctx0, cur, tmp);
// cur shape [n_embd,N,1,1]
cur = ggml_mul_mat(ctx0,
model->layers[il].w2,
cur);
}
// cur shape [n_embd,N,1,1]
cur = ggml_add(ctx0, cur, inpFF);
// input for next layer
// inpL shape [n_embd,N,1,1]
inpL = cur;
}
// norm
{
// inpL shape [n_embd,N,1,1]
inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
// inpL = norm*inpL
// inpL shape [n_embd,N,1,1]
inpL = ggml_mul(ctx0,
ggml_repeat(ctx0, model->norm, inpL),
inpL);
//embeddings = inpL;
}
// lm_head
// inpL shape [n_vocab,N,1,1]
inpL = ggml_mul_mat(ctx0, model->output, inpL);
// run the computation
ggml_build_forward_expand(gf, inpL);
return inpL;
}
static struct ggml_tensor * forward_batch(
struct llama_model * model,
struct llama_kv_cache * cache,
struct ggml_context * ctx0,
struct ggml_cgraph * gf,
struct ggml_tensor * tokens_input,
const int n_tokens,
const int n_past,
const int n_batch
) {
const int N = n_tokens;
struct llama_kv_cache& kv_self = *cache;
const auto & hparams = model->hparams;
const int n_ctx = hparams.n_ctx;
const int n_vocab = hparams.n_vocab;
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int n_head = hparams.n_head;
const int n_rot = hparams.n_rot;
const int n_ff = get_n_ff(&hparams);
struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N*n_batch);
memcpy(tokens->data, tokens_input->data, ggml_element_size(tokens)*N*n_batch);
struct ggml_tensor * kc = kv_self.k;
struct ggml_tensor * vc = kv_self.v;
struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
{
int * data = (int *) KQ_pos->data;
for (int i = 0; i < N; ++i) {
data[i] = n_past + i;
}
}
// inpL shape [n_embd,N*n_batch,1]
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens);
assert_shape_2d(inpL, n_embd, N*n_batch);
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
struct ggml_tensor * cur;
// lctx.use_buf(ctx0, 0);
// norm
{
// cur shape [n_embd,N*n_batch,1,1]
cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
assert_shape_2d(cur, n_embd, N*n_batch);
// cur = attention_norm*cur
cur = ggml_mul(ctx0,
ggml_repeat(ctx0, model->layers[il].attention_norm, cur),
cur);
assert_shape_2d(cur, n_embd, N*n_batch);
}
// self-attention
{
// compute Q and K and RoPE them
// wq shape [n_embd, n_embd, 1, 1]
// wk shape [n_embd, n_embd, 1, 1]
// Qcur shape [n_embd/n_head, n_head, N, n_batch]
// Kcur shape [n_embd/n_head, n_head, N, n_batch]
struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0);
struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0);
assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch);
assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch);
// store key and value to memory
{
// compute the transposed [N, n_embd] V matrix
// wv shape [n_embd, n_embd, 1, 1]
// Vcur shape [N, n_embd, n_batch, 1]
struct ggml_tensor * Vcur = ggml_cont(ctx0,
ggml_permute(ctx0,
ggml_reshape_3d(ctx0,
ggml_mul_mat(ctx0,
model->layers[il].wv,
cur),
n_embd, N, n_batch),
1, 0, 2, 3));
assert_shape_3d(Vcur, N, n_embd, n_batch);
// kv_self.k shape [n_embd * n_ctx * n_batch * n_layer]
// kv_self.v shape [n_ctx * n_embd * n_batch * n_layer]
// k shape [n_embd * N, n_batch] == kv_self.k[:,n_past:n_past+N,:,il]
// v shape [N, n_embd, n_batch, 1] == kv_self.v[:,n_past:n_past+N,:,il]
/* {
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,
( n_ctx)*ggml_element_size(kv_self.v),
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
// important: storing RoPE-ed version of K in the KV cache!
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
} //*/
kc = ggml_set_2d(ctx0, kc,
ggml_reshape_2d(ctx0, Kcur, n_embd*N, n_batch),
ggml_element_size(kc)*n_embd*n_ctx,
(ggml_element_size(kc)*n_embd)*(il*n_batch*n_ctx + n_past));
vc = ggml_set_2d(ctx0, vc,
ggml_reshape_2d(ctx0, Vcur, N*n_embd, n_batch),
ggml_element_size(vc)*n_ctx*n_embd,
ggml_element_size(vc)*(n_past + il*n_embd*n_batch*n_ctx));
assert_shape_1d(kc, n_embd * n_ctx * n_batch * n_layer);
assert_shape_1d(vc, n_embd * n_ctx * n_batch * n_layer);
}
// Qcur shape [n_embd/n_head, n_head, N, n_batch]
// Q shape [n_embd/n_head, N, n_head, n_batch]
struct ggml_tensor * Q =
ggml_permute(ctx0,
Qcur,
0, 2, 1, 3);
assert_shape_4d(Q, n_embd/n_head, N, n_head, n_batch);
// kv_self.k shape [n_embd * n_ctx * n_batch * n_layer]
// K shape [n_embd/n_head, n_past + N, n_head, n_batch]
struct ggml_tensor * K =
ggml_permute(ctx0,
ggml_reshape_4d(ctx0,
ggml_view_3d(ctx0,
kc,
n_embd,
(n_past + N),
n_batch,
n_embd*ggml_element_size(kc),
n_ctx*n_embd*ggml_element_size(kc),
il*n_batch*n_ctx*n_embd*ggml_element_size(kc)),
n_embd/n_head, n_head, n_past + N, n_batch),
0, 2, 1, 3);
assert_shape_4d(K, n_embd/n_head, n_past + N, n_head, n_batch);
// K * Q
// KQ shape [n_past + N, N, n_head, n_batch]
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
assert_shape_4d(KQ, n_past + N, N, n_head, n_batch);
// KQ_scaled = KQ / sqrt(n_embd/n_head)
// KQ_scaled shape [n_past + N, N, n_head, n_batch]
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, 1.0f/sqrtf(float(n_embd)/n_head));
assert_shape_4d(KQ_scaled, n_past + N, N, n_head, n_batch);
// KQ_masked = mask_past(KQ_scaled)
// KQ_masked shape [n_past + N, N, n_head, n_batch]
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
assert_shape_4d(KQ_masked, n_past + N, N, n_head, n_batch);
// KQ = soft_max(KQ_masked)
// KQ_soft_max shape [n_past + N, N, n_head, n_batch]
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
assert_shape_4d(KQ_soft_max, n_past + N, N, n_head, n_batch);
// split cached V into n_head heads
// kv_self.v shape [n_ctx * n_embd * n_batch * n_layer]
// V shape [n_past + N, n_embd/n_head, n_head, n_batch] == kv_self.v[:(n_past+N),:,:,il]
struct ggml_tensor * V =
ggml_view_4d(ctx0, vc,
n_past + N, n_embd/n_head, n_head, n_batch,
ggml_element_size(vc)*n_ctx,
ggml_element_size(vc)*n_ctx*n_embd/n_head,
ggml_element_size(vc)*n_ctx*n_embd,
il*n_batch*n_ctx*n_embd*ggml_element_size(vc));
assert_shape_4d(V, n_past + N, n_embd/n_head, n_head, n_batch);
// KQV shape [n_embd/n_head, N, n_head, n_batch]
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
assert_shape_4d(KQV, n_embd/n_head, N, n_head, n_batch);
// KQV_merged = KQV.permute(0, 2, 1, 3)
// KQV_merged shape [n_embd/n_head, n_head, N, n_batch]
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
assert_shape_4d(KQV_merged, n_embd/n_head, n_head, N, n_batch);
// KQV_merged shape
// cur = KQV_merged.contiguous().view(n_embd, N)
// cur shape [n_embd,N*n_batch,1,1]
cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N*n_batch);
assert_shape_2d(cur, n_embd, N*n_batch);
// cur = ggml_cpy(ctx0,
// KQV_merged,
// ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
// projection (no bias)
// cur shape [n_embd,N*n_batch,1,1]
cur = ggml_mul_mat(ctx0,
model->layers[il].wo,
cur);
assert_shape_2d(cur, n_embd, N*n_batch);
}
// lctx.use_buf(ctx0, 1);
// inpFF shape [n_embd,N*n_batch,1,1]
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
assert_shape_2d(inpFF, n_embd, N*n_batch);
// feed-forward network
{
// norm
{
// cur shape [n_embd,N*n_batch,1,1]
cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps);
assert_shape_2d(cur, n_embd, N*n_batch);
// cur = ffn_norm*cur
// cur shape [n_embd,N*n_batch,1,1]
cur = ggml_mul(ctx0,
ggml_repeat(ctx0, model->layers[il].ffn_norm, cur),
cur);
assert_shape_2d(cur, n_embd, N*n_batch);
}
// tmp shape [n_ff,N*n_batch,1,1]
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
model->layers[il].w3,
cur);
assert_shape_2d(tmp, n_ff, N*n_batch);
// cur shape [n_ff,N*n_batch,1,1]
cur = ggml_mul_mat(ctx0,
model->layers[il].w1,
cur);
assert_shape_2d(cur, n_ff, N*n_batch);
// SILU activation
// cur shape [n_ff,N*n_batch,1,1]
cur = ggml_silu(ctx0, cur);
assert_shape_2d(cur, n_ff, N*n_batch);
// cur shape [n_ff,N*n_batch,1,1]
cur = ggml_mul(ctx0, cur, tmp);
assert_shape_2d(cur, n_ff, N*n_batch);
// cur shape [n_embd,N*n_batch,1,1]
cur = ggml_mul_mat(ctx0,
model->layers[il].w2,
cur);
assert_shape_2d(cur, n_embd, N*n_batch);
}
// cur shape [n_embd,N*n_batch,1,1]
cur = ggml_add(ctx0, cur, inpFF);
assert_shape_2d(cur, n_embd, N*n_batch);
// input for next layer
// inpL shape [n_embd,N*n_batch,1,1]
inpL = cur;
assert_shape_2d(inpL, n_embd, N*n_batch);
}
// norm
{
// inpL shape [n_embd,N*n_batch,1,1]
inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
assert_shape_2d(inpL, n_embd, N*n_batch);
// inpL = norm*inpL
// inpL shape [n_embd,N*n_batch,1,1]
inpL = ggml_mul(ctx0,
ggml_repeat(ctx0, model->norm, inpL),
inpL);
assert_shape_2d(inpL, n_embd, N*n_batch);
//embeddings = inpL;
}
// lm_head
// inpL shape [n_vocab,N*n_batch,1,1]
inpL = ggml_mul_mat(ctx0, model->output, inpL);
assert_shape_2d(inpL, n_vocab, N*n_batch);
{
// inpL shape [n_vocab,N,n_batch,1]
inpL = ggml_reshape_3d(ctx0,
inpL,
n_vocab, N, n_batch);
assert_shape_3d(inpL, n_vocab, N, n_batch);
}
// run the computation
ggml_build_forward_expand(gf, inpL);
return inpL;
}
static struct ggml_tensor * forward_lora(
struct llama_model_lora * model,
struct llama_kv_cache * cache,
struct ggml_context * ctx0,
struct ggml_cgraph * gf,
struct ggml_tensor * tokens_input,
const int n_tokens,
const int n_past
) {
const int N = n_tokens;
struct llama_kv_cache& kv_self = *cache;
const auto & hparams = model->hparams;
const int n_ctx = hparams.n_ctx;
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int n_head = hparams.n_head;
const int n_rot = hparams.n_rot;
struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
memcpy(tokens->data, tokens_input->data, N*ggml_element_size(tokens));
struct ggml_tensor * kc = kv_self.k;
struct ggml_tensor * vc = kv_self.v;
struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
{
int * data = (int *) KQ_pos->data;
for (int i = 0; i < N; ++i) {
data[i] = n_past + i;
}
}
// inpL shape [n_embd,N,1,1]
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens);
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
struct ggml_tensor * cur;
// norm
{
// cur shape [n_embd,N,1,1]
cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
// cur = attention_norm*cur
cur = ggml_mul(ctx0,
ggml_repeat(ctx0, model->layers[il].attention_norm, cur),
cur);
}
// self-attention
{
// compute Q and K and RoPE them
// wq shape [n_embd, n_embd, 1, 1]
// wk shape [n_embd, n_embd, 1, 1]
// Qcur shape [n_embd/n_head, n_head, N, 1]
// Kcur shape [n_embd/n_head, n_head, N, 1]
struct ggml_tensor * Qcur = ggml_rope(ctx0,
ggml_reshape_3d(ctx0,
ggml_mul_mat(ctx0,
model->layers[il].wqa,
ggml_mul_mat(ctx0,
model->layers[il].wqb,
cur)),
n_embd/n_head, n_head, N),
KQ_pos, n_rot, 0);
struct ggml_tensor * Kcur = ggml_rope(ctx0,
ggml_reshape_3d(ctx0,
ggml_mul_mat(ctx0,
model->layers[il].wka,
ggml_mul_mat(ctx0,
model->layers[il].wkb,
cur)),
n_embd/n_head, n_head, N),
KQ_pos, n_rot, 0);
// store key and value to memory
{
// compute the transposed [N, n_embd] V matrix
// wv shape [n_embd, n_embd, 1, 1]
// Vcur shape [n_embd, N, 1, 1]
struct ggml_tensor * Vcur = ggml_cont(ctx0,
ggml_transpose(ctx0,
ggml_reshape_2d(ctx0,
ggml_mul_mat(ctx0,
model->layers[il].wva,
ggml_mul_mat(ctx0,
model->layers[il].wvb,
cur)),
n_embd, N)));
// kv_self.k shape [n_embd * n_ctx * n_layer, 1]
// kv_self.v shape [n_embd * n_ctx * n_layer, 1]
// k shape [n_embd * N, 1] == kv_self.k[:,n_past:n_past+N,il,0]
// v shape [N, n_embd, 1, 1] == kv_self.v[:,n_past:n_past+N,il,0]
/* {
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,
( n_ctx)*ggml_element_size(kv_self.v),
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
// important: storing RoPE-ed version of K in the KV cache!
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
} //*/
kc = ggml_set_1d(ctx0, kc, ggml_reshape_1d(ctx0, Kcur, n_embd*N), (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
vc = ggml_set_2d(ctx0, vc, Vcur, ( n_ctx)*ggml_element_size(kv_self.v),
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
}
// Qcur shape [n_embd/n_head, n_head, N, 1]
// Q shape [n_embd/n_head, N, n_head, 1]
struct ggml_tensor * Q =
ggml_permute(ctx0,
Qcur,
0, 2, 1, 3);
// kv_self.k shape [n_embd * n_ctx * n_layer, 1]
// K shape [n_embd/n_head, n_past + N, n_head, 1]
struct ggml_tensor * K =
ggml_permute(ctx0,
ggml_reshape_3d(ctx0,
ggml_view_1d(ctx0, kc, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kc)*n_embd),
n_embd/n_head, n_head, n_past + N),
0, 2, 1, 3);
// K * Q
// KQ shape [n_past + N, N, n_head, 1]
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
// KQ_scaled = KQ / sqrt(n_embd/n_head)
// KQ_scaled shape [n_past + N, N, n_head, 1]
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, 1.0f/sqrtf(float(n_embd)/n_head));
// KQ_masked = mask_past(KQ_scaled)
// KQ_masked shape [n_past + N, N, n_head, 1]
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
// KQ = soft_max(KQ_masked)
// KQ_soft_max shape [n_past + N, N, n_head, 1]
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
// split cached V into n_head heads
//// V shape [n_past + N, n_embd/n_head, n_head, 1]
// V shape [n_past + N, n_embd/n_head, n_head, 1] == kv_self.v[:,:(n_past+N),il,1]
struct ggml_tensor * V =
ggml_view_3d(ctx0, vc,
n_past + N, n_embd/n_head, n_head,
n_ctx*ggml_element_size(vc),
n_ctx*ggml_element_size(vc)*n_embd/n_head,
il*n_ctx*ggml_element_size(vc)*n_embd);
// KQV shape [n_embd/n_head, N, n_head, 1]
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
// KQV_merged = KQV.permute(0, 2, 1, 3)
// KQV_merged shape [n_embd/n_head, n_head, N, 1]
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
// KQV_merged shape
// cur = KQV_merged.contiguous().view(n_embd, N)
// cur shape [n_embd,N,1,1]
cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N);
// cur = ggml_cpy(ctx0,
// KQV_merged,
// ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
// projection (no bias)
// cur shape [n_embd,N,1,1]
cur = ggml_mul_mat(ctx0,
model->layers[il].woa,
ggml_mul_mat(ctx0,
model->layers[il].wob,
cur));
}
// inpFF shape [n_embd,N,1,1]
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
// feed-forward network
{
// norm
{
// cur shape [n_embd,N,1,1]
cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps);
// cur = ffn_norm*cur
// cur shape [n_embd,N,1,1]
cur = ggml_mul(ctx0,
ggml_repeat(ctx0, model->layers[il].ffn_norm, cur),
cur);
}
// tmp shape [n_ff,N,1,1]
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
model->layers[il].w3,
cur);
// cur shape [n_ff,N,1,1]
cur = ggml_mul_mat(ctx0,
model->layers[il].w1,
cur);
// SILU activation
// cur shape [n_ff,N,1,1]
cur = ggml_silu(ctx0, cur);
// cur shape [n_ff,N,1,1]
cur = ggml_mul(ctx0, cur, tmp);
// cur shape [n_embd,N,1,1]
cur = ggml_mul_mat(ctx0,
model->layers[il].w2,
cur);
}
// cur shape [n_embd,N,1,1]
cur = ggml_add(ctx0, cur, inpFF);
// input for next layer
// inpL shape [n_embd,N,1,1]
inpL = cur;
}
// norm
{
// inpL shape [n_embd,N,1,1]
inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
// inpL = norm*inpL
// inpL shape [n_embd,N,1,1]
inpL = ggml_mul(ctx0,
ggml_repeat(ctx0, model->norm, inpL),
inpL);
//embeddings = inpL;
}
// lm_head
// inpL shape [n_vocab,N,1,1]
inpL = ggml_mul_mat(ctx0,
model->outputa,
ggml_mul_mat(ctx0,
model->outputb,
inpL));
// ggml_set_scratch(ctx0, { 0, 0, nullptr, });
// run the computation
ggml_build_forward_expand(gf, inpL);
return inpL;
}
static void sample_softmax(struct ggml_tensor * logits, struct ggml_tensor * probs, struct ggml_tensor * best_samples) {
assert(ggml_is_matrix(logits));
assert(ggml_is_matrix(probs));
assert(ggml_is_vector(best_samples));
assert(logits->ne[1] == best_samples->ne[0]);
assert(logits->ne[0] == probs->ne[0]);
assert(logits->ne[1] == probs->ne[1]);
for (int i = 0; i < logits->ne[1]; ++i) {
float max_logit = ggml_get_f32_1d(logits, i * logits->ne[0]);
ggml_set_i32_1d(best_samples, i, 0);
for (int k = 0; k < logits->ne[0]; ++k) {
float logit = ggml_get_f32_1d(logits, i * logits->ne[0] + k);
if (logit > max_logit) {
max_logit = logit;
ggml_set_i32_1d(best_samples, i, k);
}
}
float psum = 0;
for (int k = 0; k < logits->ne[0]; ++k) {
float logit = ggml_get_f32_1d(logits, i * logits->ne[0] + k);
float p = (logit == -INFINITY) ? 0 : expf(logit - max_logit);
psum += p;
ggml_set_f32_1d(probs, i * probs->ne[0] + k, p);
}
for (int k = 0; k < logits->ne[0]; ++k) {
float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k);
ggml_set_f32_1d(probs, i * probs->ne[0] + k, p / psum);
}
}
}
static void sample_softmax_batch(
struct ggml_context * ctx, struct ggml_tensor * logits, struct ggml_tensor * probs,
struct ggml_tensor * best_samples
) {
GGML_ASSERT(ggml_is_matrix(best_samples));
GGML_ASSERT(ggml_is_3d(logits));
GGML_ASSERT(ggml_is_3d(probs));
int n_tokens = best_samples->ne[0];
int n_batch = best_samples->ne[1];
int n_vocab = logits->ne[0];
GGML_ASSERT(n_tokens == logits->ne[1]);
GGML_ASSERT(n_batch == logits->ne[2]);
GGML_ASSERT(n_vocab == probs->ne[0]);
GGML_ASSERT(n_tokens == probs->ne[1]);
GGML_ASSERT(n_batch == probs->ne[2]);
for (int k = 0; k < n_batch; ++k) {
struct ggml_tensor * best_samples_k = ggml_view_1d(ctx,
best_samples,
best_samples->ne[0],
k*best_samples->nb[1]);
struct ggml_tensor * logits_k = ggml_view_2d(ctx,
logits,
logits->ne[0],
logits->ne[1],
logits->nb[1],
k*logits->nb[2]);
struct ggml_tensor * probs_k = ggml_view_2d(ctx,
probs,
probs->ne[0],
probs->ne[1],
probs->nb[1],
k*probs->nb[2]);
sample_softmax(logits_k, probs_k, best_samples_k);
}
}
static void print_row(struct ggml_tensor * probs, int i) {
for (int k = 0; k < probs->ne[0]; ++k) {
float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k);
printf(" %.2f", p);
}
printf("\n");
}
static void print_matrix(struct ggml_tensor * probs) {
assert(ggml_is_matrix(probs));
for (int i = 0; i < probs->ne[1]; ++i) {
for (int k = 0; k < probs->ne[0]; ++k) {
float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k);
printf(" %.2f", p);
}
printf("\n");
}
}
static void print_token(int token, int n_vocab) {
for (int k = 0; k < token; ++k) {
printf(" ");
}
printf("X");
for (int k = token+1; k < n_vocab; ++k) {
printf(" ");
}
printf("\n");
}
static void print_tokens(struct ggml_tensor * tokens, int n_vocab) {
for (int i=0; i<tokens->ne[0]; ++i) {
int token = ggml_get_i32_1d(tokens, i);
print_token(token, n_vocab);
}
}
static void get_example_targets(int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * targets) {
int n_tokens = tokens_input->ne[0];
int n_vocab = targets->ne[0];
float randomness = 0.0f;
// ggml_set_zero(targets);
ggml_set_f32(targets, -1.0f);
ggml_set_i32_1d(tokens_input, 0, 0);
for (int i=1; i<n_tokens+1; ++i) {
float x = example_id + i * 3.14159f * 2.0f * 1.0f * 0.5f / n_tokens;
float y = sinf(x);//*cosf(x*1.1f+1.0f);
float z = (y+1.0f)*0.5f; // scale to [0..1]
z += (frand()-0.5f)*(randomness/n_vocab);
z = (z < 0.0f) ? 0.0f : (z > 1.0f) ? 1.0f : z; // clamp to [0..1]
int token = std::max(1,std::min(1+(int)(z*(float)(n_vocab-1)), n_vocab-1));
ggml_set_f32_1d(targets, (i-1)*n_vocab + token, +1.0f);
if (i<n_tokens) {
ggml_set_i32_1d(tokens_input, i, token);
}
}
}
static void get_example_targets_batch(
struct ggml_context * ctx, int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * targets
) {
GGML_ASSERT(ggml_is_matrix(tokens_input));
GGML_ASSERT(ggml_is_3d(targets));
int n_tokens = tokens_input->ne[0];
int n_batch = tokens_input->ne[1];
GGML_ASSERT(n_tokens == targets->ne[1]);
GGML_ASSERT(n_batch == targets->ne[2]);
for (int k=0; k<n_batch; ++k) {
struct ggml_tensor * tokens_input_k = ggml_view_1d(ctx,
tokens_input,
tokens_input->ne[0],
k*tokens_input->nb[1]);
struct ggml_tensor * targets_k = ggml_view_2d(ctx,
targets,
targets->ne[0],
targets->ne[1],
targets->nb[1],
k*targets->nb[2]);
get_example_targets(example_id*n_batch + k, tokens_input_k, targets_k);
}
}
static void lshift_examples(struct ggml_tensor * tokens_input, struct ggml_tensor * targets, int n_shift) {
int n_tokens = tokens_input->ne[0];
int n_vocab = targets->ne[0];
for (int i=0; i<n_tokens-n_shift; ++i) {
ggml_set_i32_1d(tokens_input, i, ggml_get_i32_1d(tokens_input, i + n_shift));
for (int k=0; k<n_vocab; ++k) {
ggml_set_f32_1d(targets, i*n_vocab + k, ggml_get_f32_1d(targets, (i + n_shift)*n_vocab + k));
}
}
}
static struct ggml_tensor * square_error_loss(
struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b
) {
// todo: instead of a-b: a[1:]-b[:-1]
return ggml_sum(ctx, ggml_sqr(ctx, ggml_sub(ctx, a, b)));
}
static struct ggml_tensor * cross_entropy_loss(
struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b
) {
const float eps = 1e-3f;
return
ggml_sum(ctx,
ggml_neg(ctx,
ggml_sum_rows(ctx,
ggml_mul(ctx,
ggml_soft_max(ctx, a),
ggml_log(ctx,
ggml_add1(ctx,
ggml_soft_max(ctx, b),
ggml_new_f32(ctx, eps)))))));
}
int main(int argc, char ** argv) {
if (argc < 1) {
fprintf(stderr, "usage: %s\n", argv[0]);
return 1;
}
struct ggml_init_params lcparams;
lcparams.mem_size = 1024ll*1024ll*1024ll;
lcparams.mem_buffer = NULL;
lcparams.no_alloc = false;
struct llama_model model;
model.hparams.n_vocab = 8;
model.hparams.n_ctx = 8;
model.hparams.n_embd = 32;
model.hparams.n_mult = 2;
model.hparams.n_head = 8;
model.hparams.n_layer = 1;
model.hparams.n_rot = std::min(16u, model.hparams.n_embd / model.hparams.n_head);
// model.hparams.n_embd = 32;
// model.hparams.n_mult = 2;
// model.hparams.n_head = 4;
// model.hparams.n_layer = 8;
// model.hparams.n_rot = 8;
model.ctx = ggml_init(lcparams);
printf("init model\n");
init_model(&model);
set_param_model(&model);
randomize_model(&model, 1337, 0.0f, 1.0f, -1.0f, +1.0f);
/*
struct llama_model_lora model_lora;
// model.hparams.n_vocab = 6;
// model.hparams.n_ctx = 64;
// model.hparams.n_embd = 128;
// model.hparams.n_mult = 2;
// model.hparams.n_head = 8;
// model.hparams.n_layer = 6;
// model.hparams.n_rot = model.hparams.n_embd / model.hparams.n_head;
model_lora.hparams.n_vocab = 16;
model_lora.hparams.n_ctx = 32;
model_lora.hparams.n_embd = 256;
model_lora.hparams.n_mult = 2;
model_lora.hparams.n_head = 16;
model_lora.hparams.n_layer = 1;
model_lora.hparams.n_lora = 64;
model_lora.hparams.n_rot = MIN(16, model_lora.hparams.n_embd / model_lora.hparams.n_head);
// model.hparams.n_rot = (model.hparams.n_embd / model.hparams.n_head) / 2;
// model.hparams.n_embd = 32;
// model.hparams.n_mult = 2;
// model.hparams.n_head = 4;
// model.hparams.n_layer = 8;
// model.hparams.n_rot = 8;
model_lora.ctx = ggml_init(lcparams);
printf("init model_lora\n");
init_model_lora(&model_lora);
set_param_model_lora(&model_lora);
randomize_model_lora(&model_lora, 1337, 0.0f, 1.0f, -1.0f, +1.0f);
*/
int n_batch = 8;
// key + value cache for the self attention
struct llama_kv_cache kv_self;
printf("init_kv_cache\n");
kv_self.ctx = model.ctx;
init_kv_cache(&kv_self, &model, n_batch);
//init_kv_cache_lora(&kv_self, &model_lora);
size_t compute_size = 1024ll*1024ll*1024ll;
uint8_t * compute_addr = new uint8_t[compute_size];
int n_examples = 256;
int n_tokens = model.hparams.n_ctx;
int n_vocab = model.hparams.n_vocab;
std::vector<uint8_t> work_buffer;
for (int ex=0; ex<n_examples; ++ex) {
struct ggml_init_params params = {
/*.mem_size =*/ compute_size,
/*.mem_buffer =*/ compute_addr,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_tensor * after_opt_best_samples = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_tokens, n_batch);
struct ggml_tensor * after_opt_probs = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_vocab, n_tokens, n_batch);
struct ggml_tensor * tokens_input = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_tokens, n_batch);
struct ggml_tensor * targets = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_vocab, n_tokens, n_batch);
int n_past = 0;
struct ggml_cgraph * gf = NULL;
gf = ggml_new_graph_custom(ctx0, LLAMA_TRAIN_MAX_NODES, true);
get_example_targets_batch(ctx0, 64*ex+0, tokens_input, targets);
struct ggml_tensor * logits = forward_batch(&model, &kv_self, ctx0, gf, tokens_input, n_tokens, n_past, n_batch);
// struct ggml_tensor * e = cross_entropy_loss(ctx0, targets, logits);
struct ggml_tensor * e = square_error_loss(ctx0, targets, logits);
ggml_build_forward_expand(gf, e);
ggml_graph_compute_helper(work_buffer, gf, /*n_threads*/ 1);
float error_before_opt = ggml_get_f32_1d(e, 0);
struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_TYPE_LBFGS);
opt_params_lbfgs.print_forward_graph = false;
opt_params_lbfgs.print_backward_graph = false;
opt_params_lbfgs.lbfgs.n_iter = 16;
ggml_opt(ctx0, opt_params_lbfgs, e);
//
ggml_build_forward_expand(gf, e);
ggml_graph_compute_helper(work_buffer, gf, /*n_threads*/ 1);
float error_after_opt = ggml_get_f32_1d(e, 0);
if (ex % 8 == 0) {
printf("Example %d\n", (ex+1));
printf("error_before_opt: %.2f\n", error_before_opt);
printf("error_after_opt: %.2f\n", error_after_opt);
}
if (ex % 64 == 0) {
sample_softmax_batch(ctx0, logits, after_opt_probs, after_opt_best_samples);
// printf("probabilities after optimization:\n");
// print_matrix(after_opt_probs);
printf("best samples after optimization:\n");
print_tokens(after_opt_best_samples, n_vocab);
}
ggml_free(ctx0);
}
{
int n_gen = 128;
int sample_ctx = n_tokens-n_tokens/8;
printf("Generating %d tokens.\n", n_gen);
struct ggml_tensor * tokens_input = ggml_new_tensor_1d(model.ctx, GGML_TYPE_I32, n_tokens);
struct ggml_tensor * targets = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, n_vocab, n_tokens);
get_example_targets(137, tokens_input, targets);
for (int i=sample_ctx; i<n_tokens; ++i) {
ggml_set_i32_1d(tokens_input, i, n_vocab/2);
}
for (int i=0; i<sample_ctx-1; ++i) {
print_token(ggml_get_i32_1d(tokens_input, i), n_vocab);
}
printf("---\n");
for (int i=0; i<n_gen; ++i) {
struct ggml_init_params params = {
/*.mem_size =*/ compute_size,
/*.mem_buffer =*/ compute_addr,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_cgraph * gf = NULL;
gf = ggml_new_graph_custom(ctx0, LLAMA_TRAIN_MAX_NODES, true);
int n_past = 0;
struct ggml_tensor * logits = forward(&model, &kv_self, ctx0, gf, tokens_input, sample_ctx, n_past);
ggml_build_forward_expand(gf, logits);
ggml_graph_compute_helper(work_buffer, gf, /*n_threads*/ 1);
struct ggml_tensor * best_samples = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, sample_ctx);
struct ggml_tensor * probs = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_vocab, sample_ctx);
sample_softmax(logits, probs, best_samples);
// int sample_at = n_tokens-1;
int token = ggml_get_i32_1d(best_samples, sample_ctx-1);
// print_row(probs, sample_at);
print_token(token, n_vocab);
lshift_examples(tokens_input, targets, 1);
ggml_set_i32_1d(tokens_input, 0, 0);
ggml_set_i32_1d(tokens_input, sample_ctx-1, token);
ggml_free(ctx0);
}
}
print_matrix(model.tok_embeddings);
printf("done\n");
// ggml_free(kv_self.ctx);
// ggml_free(model_lora.ctx);
ggml_free(model.ctx);
return 0;
}
Reference in New Issue View Git Blame Copy Permalink