haraldwolff/llama.cpp
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llama.cpp/examples/train-text-from-scratch/train-text-from-scratch.cpp
Georgi Gerganov 04606a1599
train : fix compile warning
2023-07-01 18:45:44 +03:00

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#include "ggml.h"
#include "llama.h"
#include <unordered_map>
#include <vector>
#include <cassert>
#include <climits>
#include <cstring>
#include <cstdarg>
#include <ctime>
#include <random>
#include <stdexcept>
#include <algorithm>
#include <string>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
struct random_normal_distribution {
std::mt19937 gen;
std::normal_distribution<float> rd;
float min;
float max;
};
struct random_uniform_distribution {
std::mt19937 gen;
std::uniform_real_distribution<float> rd;
};
void init_random_normal_distribution(struct random_normal_distribution * rnd, int seed, float mean, float std, float min, float max) {
rnd->gen = std::mt19937(seed);
rnd->rd = std::normal_distribution<float>{mean, std};
rnd->min = min;
rnd->max = max;
}
void init_random_uniform_distribution(struct random_uniform_distribution * rnd, int seed, float min, float max) {
rnd->gen = std::mt19937(seed);
rnd->rd = std::uniform_real_distribution<float>{min, max};
}
int clamp(const int v, const int min, const int max) {
return ((v < min) ? (min) : (v > max) ? (max) : v);
}
float fclamp(const float v, const float min, const float max) {
return ((v < min) ? (min) : (v > max) ? (max) : v);
}
float frand() {
return (float)rand()/(float)RAND_MAX;
}
float frand_normal(struct random_normal_distribution * rnd) {
return fclamp(rnd->rd(rnd->gen), rnd->min, rnd->max);
}
float frand_uniform(struct random_uniform_distribution * rnd) {
return rnd->rd(rnd->gen);
}
struct ggml_tensor * randomize_tensor_normal(struct ggml_tensor * tensor, struct random_normal_distribution * rnd) {
float scale = 1.0f; // xavier
switch (tensor->n_dims) {
case 1:
scale /= sqrtf(tensor->ne[0]);
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0]);
*dst = scale * frand_normal(rnd);
}
break;
case 2:
scale /= sqrtf(tensor->ne[0]+tensor->ne[1]);
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);
*dst = scale * frand_normal(rnd);
}
}
break;
case 3:
scale /= sqrtf(tensor->ne[0]+tensor->ne[1]);
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2]);
*dst = scale * frand_normal(rnd);
}
}
}
break;
case 4:
scale /= sqrtf(tensor->ne[0]+tensor->ne[1]);
for (int i3 = 0; i3 < tensor->ne[3]; i3++) {
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]);
*dst = scale * frand_normal(rnd);
}
}
}
}
break;
default:
assert(false);
};
return tensor;
}
struct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struct random_uniform_distribution * rnd) {
switch (tensor->n_dims) {
case 1:
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0]);
*dst = frand_uniform(rnd);
}
break;
case 2:
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);
*dst = frand_uniform(rnd);
}
}
break;
case 3:
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2]);
*dst = frand_uniform(rnd);
}
}
}
break;
case 4:
for (int i3 = 0; i3 < tensor->ne[3]; i3++) {
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]);
*dst = frand_uniform(rnd);
}
}
}
}
break;
default:
assert(false);
};
return tensor;
}
struct llama_vocab {
using id = int32_t;
using token = std::string;
struct token_score {
token tok;
float score;
};
std::unordered_map<token, id> token_to_id;
std::vector<token_score> id_to_token;
};
struct my_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 my_llama_hparams& other) const {
return memcmp(this, &other, sizeof(my_llama_hparams));
}
};
struct my_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 my_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 my_llama_model {
struct ggml_context * ctx = NULL;
my_llama_hparams hparams;
struct ggml_tensor * tok_embeddings;
struct ggml_tensor * norm;
struct ggml_tensor * output;
std::vector<my_llama_layer> layers;
uint32_t train_its = 0;
uint32_t train_samples = 0;
uint32_t train_tokens = 0;
};
uint32_t get_n_ff(const struct my_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;
}
void print_params(struct my_llama_hparams * params) {
printf("%s: n_vocab: %d\n", __func__, params->n_vocab);
printf("%s: n_ctx: %d\n", __func__, params->n_ctx);
printf("%s: n_embd: %d\n", __func__, params->n_embd);
printf("%s: n_mult: %d\n", __func__, params->n_mult);
printf("%s: n_head: %d\n", __func__, params->n_head);
printf("%s: n_ff: %d\n", __func__, get_n_ff(params));
printf("%s: n_layer: %d\n", __func__, params->n_layer);
printf("%s: n_rot: %d\n", __func__, params->n_rot);
}
void init_model(struct my_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->train_its = 0;
model->train_samples = 0;
model->train_tokens = 0;
model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
ggml_set_name(model->tok_embeddings, "tok_embeddings.weight");
ggml_set_name(model->norm, "norm.weight");
ggml_set_name(model->output, "output.weight");
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);
layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd);
layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
ggml_set_name(layer.attention_norm, (layers_i + ".attention_norm.weight").c_str());
ggml_set_name(layer.wq, (layers_i + ".attention.wq.weight").c_str());
ggml_set_name(layer.wk, (layers_i + ".attention.wk.weight").c_str());
ggml_set_name(layer.wv, (layers_i + ".attention.wv.weight").c_str());
ggml_set_name(layer.wo, (layers_i + ".attention.wo.weight").c_str());
ggml_set_name(layer.ffn_norm, (layers_i + ".ffn_norm.weight").c_str());
ggml_format_name(layer.w1, "%s.feed_forward.w1.weight", layers_i.c_str());
ggml_format_name(layer.w2, "%s.feed_forward.w2.weight", layers_i.c_str());
ggml_format_name(layer.w3, "%s.feed_forward.w3.weight", layers_i.c_str());
}
}
void set_param_model(struct my_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);
}
}
void randomize_model(struct my_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(&rnd, 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);
}
}
bool init_kv_cache(struct my_llama_kv_cache* cache, struct my_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__);
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;
}
struct ggml_tensor * forward(
struct my_llama_model * model,
struct my_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 my_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;
// 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);
// 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_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 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_inplace(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_inplace(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,
ggml_new_f32(ctx0, 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);
// 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);
// 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;
}
void assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0) {
GGML_ASSERT(tensor->n_dims == 1);
GGML_ASSERT(tensor->ne[0] == ne0);
}
void assert_shape_2d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1) {
GGML_ASSERT(tensor->n_dims == 2);
GGML_ASSERT(tensor->ne[0] == ne0);
GGML_ASSERT(tensor->ne[1] == ne1);
}
void assert_shape_3d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2) {
GGML_ASSERT(tensor->n_dims == 3);
GGML_ASSERT(tensor->ne[0] == ne0);
GGML_ASSERT(tensor->ne[1] == ne1);
GGML_ASSERT(tensor->ne[2] == ne2);
}
void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) {
GGML_ASSERT(tensor->n_dims == 4);
GGML_ASSERT(tensor->ne[0] == ne0);
GGML_ASSERT(tensor->ne[1] == ne1);
GGML_ASSERT(tensor->ne[2] == ne2);
GGML_ASSERT(tensor->ne[3] == ne3);
}
struct ggml_tensor * forward_batch(
struct my_llama_model * model,
struct my_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 my_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;
// 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);
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_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 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_inplace(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_inplace(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_inplace(ctx0,
KQ,
ggml_new_f32(ctx0, 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_inplace(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_inplace(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_inplace(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);
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_inplace(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);
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;
}
struct ggml_tensor * forward_batch_wo_cache(
struct my_llama_model * model,
struct ggml_context * ctx0,
struct ggml_cgraph * gf,
struct ggml_tensor * tokens_input,
const int n_tokens,
const int n_batch) {
const int n_past = 0;
const int N = n_tokens;
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);
// 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);
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_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 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);
// Vcur shape [N, n_batch, n_embd/n_head, n_head]
struct ggml_tensor * Vcur = ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, cur, model->layers[il].wv), N, n_batch, n_embd/n_head, n_head);
assert_shape_4d(Vcur, N, n_batch, n_embd/n_head, n_head);
// 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, n_head, n_batch]
struct ggml_tensor * K =
ggml_permute(ctx0,
Kcur,
0, 2, 1, 3);
assert_shape_4d(K, n_embd/n_head, N, n_head, n_batch);
// K * Q
// KQ shape [N, N, n_head, n_batch]
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
assert_shape_4d(KQ, N, N, n_head, n_batch);
// KQ_scaled = KQ / sqrt(n_embd/n_head)
// KQ_scaled shape [N, N, n_head, n_batch]
struct ggml_tensor * KQ_scaled =
ggml_scale_inplace(ctx0,
KQ,
ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));
assert_shape_4d(KQ_scaled, N, N, n_head, n_batch);
// KQ_masked = mask_past(KQ_scaled)
// KQ_masked shape [N, N, n_head, n_batch]
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
assert_shape_4d(KQ_masked, N, N, n_head, n_batch);
// KQ = soft_max(KQ_masked)
// KQ_soft_max shape [N, N, n_head, n_batch]
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
assert_shape_4d(KQ_soft_max, N, N, n_head, n_batch);
// Vcur shape [N, n_batch, n_embd/n_head, n_head]
// V shape [N, n_embd/n_head, n_head, n_batch]
struct ggml_tensor * V =
ggml_permute(ctx0,
Vcur,
0, 3, 1, 2);
assert_shape_4d(V, 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 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);
// 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_inplace(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);
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_inplace(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);
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;
}
struct ggml_tensor * forward_batch_wo_cache_flash_attn(
struct my_llama_model * model,
struct ggml_context * ctx0,
struct ggml_cgraph * gf,
struct ggml_tensor * tokens_input,
const int n_tokens,
const int n_batch) {
const int n_past = 0;
const int N = n_tokens;
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 * 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;
// norm
{
cur = ggml_rms_norm(ctx0, inpL);
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]
struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 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);
struct ggml_tensor * Vcur = ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, cur, model->layers[il].wv), N, n_batch, n_embd/n_head, n_head);
assert_shape_4d(Vcur, N, n_batch, n_embd/n_head, n_head);
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);
struct ggml_tensor * K =
ggml_permute(ctx0,
Kcur,
0, 2, 1, 3);
assert_shape_4d(K, n_embd/n_head, N, n_head, n_batch);
struct ggml_tensor * V =
ggml_permute(ctx0,
Vcur,
0, 3, 1, 2);
assert_shape_4d(V, N, n_embd/n_head, n_head, n_batch);
bool masked = true;
struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, masked);
assert_shape_4d(KQV, n_embd/n_head, N, n_head, 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);
cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N*n_batch);
assert_shape_2d(cur, n_embd, N*n_batch);
// projection (no bias)
cur = ggml_mul_mat(ctx0,
model->layers[il].wo,
cur);
assert_shape_2d(cur, n_embd, N*n_batch);
}
struct ggml_tensor * inpFF = ggml_add_inplace(ctx0, cur, inpSA);
assert_shape_2d(inpFF, n_embd, N*n_batch);
// feed-forward network
{
// norm
{
cur = ggml_rms_norm(ctx0, inpFF);
assert_shape_2d(cur, n_embd, N*n_batch);
// cur = ffn_norm*cur
cur = ggml_mul(ctx0,
ggml_repeat(ctx0, model->layers[il].ffn_norm, cur),
cur);
assert_shape_2d(cur, n_embd, N*n_batch);
}
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
model->layers[il].w3,
cur);
assert_shape_2d(tmp, n_ff, N*n_batch);
cur = ggml_mul_mat(ctx0,
model->layers[il].w1,
cur);
assert_shape_2d(cur, n_ff, N*n_batch);
// SILU activation
cur = ggml_silu(ctx0, cur);
assert_shape_2d(cur, n_ff, N*n_batch);
cur = ggml_mul(ctx0, cur, tmp);
assert_shape_2d(cur, n_ff, N*n_batch);
cur = ggml_mul_mat(ctx0,
model->layers[il].w2,
cur);
assert_shape_2d(cur, n_embd, N*n_batch);
}
cur = ggml_add_inplace(ctx0, cur, inpFF);
assert_shape_2d(cur, n_embd, N*n_batch);
// input for next layer
inpL = cur;
assert_shape_2d(inpL, n_embd, N*n_batch);
}
// norm
{
inpL = ggml_rms_norm(ctx0, inpL);
assert_shape_2d(inpL, n_embd, N*n_batch);
// inpL = norm*inpL
inpL = ggml_mul(ctx0,
ggml_repeat(ctx0, model->norm, inpL),
inpL);
assert_shape_2d(inpL, n_embd, N*n_batch);
}
// lm_head
inpL = ggml_mul_mat(ctx0, model->output, inpL);
assert_shape_2d(inpL, n_vocab, N*n_batch);
{
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;
}
// expand the graph nodes without creating leafs.
struct ggml_tensor * expand(struct ggml_cgraph * g, struct ggml_tensor * t) {
// check if already visited
for (int i = 0; i < g->n_nodes; i++) {
if (g->nodes[i] == t) {
return t;
}
}
for (int i = 0; i < g->n_leafs; i++) {
if (g->leafs[i] == t) {
return t;
}
}
if (t->src0) {
expand(g, t->src0);
}
if (t->src1) {
expand(g, t->src1);
}
for (int i = 0; i < GGML_MAX_OPT; ++i) {
if (t->opt[i]) {
expand(g, t->opt[i]);
}
}
GGML_ASSERT(g->n_nodes < GGML_MAX_NODES);
if (strlen(t->name) == 0) {
snprintf(t->name, sizeof(t->name), "node_%d", g->n_nodes);
}
g->nodes[g->n_nodes] = t;
g->grads[g->n_nodes] = t->grad;
g->n_nodes++;
return t;
}
void graph_set_leafs_grads(struct ggml_cgraph * g) {
// moves leaf nodes to g->leafs.
// i.e. g->n_nodes might change.
int n_nodes = 0;
for (int i = 0; i < g->n_nodes; ++i) {
struct ggml_tensor * node = g->nodes[i];
const bool is_leaf = node->op == GGML_OP_NONE && node->grad == NULL;
if (is_leaf) {
GGML_ASSERT(g->n_leafs < GGML_MAX_NODES);
if (strlen(node->name) == 0) {
snprintf(node->name, sizeof(node->name), "leaf_%d", g->n_leafs);
}
g->leafs[g->n_leafs] = node;
g->n_leafs++;
} else {
GGML_ASSERT(n_nodes < GGML_MAX_NODES);
if (strlen(node->name) == 0) {
snprintf(node->name, sizeof(node->name), "node_%d", n_nodes);
}
g->nodes[n_nodes] = node;
g->grads[n_nodes] = node->grad;
n_nodes++;
}
}
for (int i=n_nodes; i < g->n_nodes; ++i) {
g->nodes[n_nodes] = NULL;
g->grads[n_nodes] = NULL;
}
g->n_nodes = n_nodes;
}
struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
struct my_llama_model * model,
struct ggml_context * ctx0,
struct ggml_cgraph * gf,
struct ggml_cgraph * gb,
struct ggml_tensor * * logits,
struct ggml_tensor * tokens_input,
struct ggml_tensor * targets,
void * compute_buf_0,
void * compute_buf_1,
size_t size_buf_0,
size_t size_buf_1,
const int n_tokens,
const int n_batch) {
ggml_set_scratch(ctx0, { 0, 0, nullptr, });
const int n_past = 0;
const int N = n_tokens;
gf->n_nodes = 0;
gf->n_leafs = 0;
gf->work_size = 0;
gf->perf_runs = 0;
gf->perf_cycles = 0;
gf->perf_time_us = 0;
gf->work = NULL;
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);
const int rope_mode = 0;
int last_buf = -1;
size_t buf_offs[2] = { 0, 0 };
size_t buf_size[2] = { size_buf_0,
size_buf_1 };
void * buf_data[2] = { compute_buf_0,
compute_buf_1 };
auto use_buf = [ctx0, &last_buf, &buf_offs, &buf_size, &buf_data] (int buf) {
size_t last_offs = 0;
last_offs = ggml_set_scratch(ctx0, { 0, 0, nullptr, });
if (last_buf >= 0) {
buf_offs[last_buf] = last_offs;
}
if (buf >= 0) {
size_t offs = buf_offs[buf];
size_t size = buf_size[buf];
void * data = buf_data[buf];
ggml_set_scratch(ctx0, { offs, size, data, });
}
last_buf = buf;
};
bool track_max_mem = false;
size_t buf_maxs[2] = { 0, 0 };
auto clr_buf = [ctx0, &last_buf, &buf_offs, &buf_size, &buf_data, &buf_maxs, track_max_mem] (int buf) {
if (buf < 0) return;
if (track_max_mem) {
size_t last_offs = 0;
last_offs = ggml_set_scratch(ctx0, { 0, 0, nullptr, });
if (last_buf >= 0) {
buf_offs[last_buf] = last_offs;
buf_maxs[last_buf] = std::max(buf_maxs[last_buf], buf_offs[last_buf]);
}
}
buf_offs[buf] = 0;
if (track_max_mem && last_buf >= 0) {
size_t offs = buf_offs[last_buf];
size_t size = buf_size[last_buf];
void * data = buf_data[last_buf];
ggml_set_scratch(ctx0, { offs, size, data, });
}
};
auto view__q = [ctx0, n_embd, n_head, N, n_batch] (struct ggml_tensor * t) -> struct ggml_tensor * {
int64_t ne0 = n_embd/n_head;
int64_t ne1 = N;
int64_t ne2 = n_head;
int64_t ne3 = n_batch;
size_t nb0 = ggml_element_size(t);
size_t nb1 = nb0*ne0;
size_t nb2 = nb1*ne1;
size_t nb3 = nb2*ne2;
size_t offset = 0;
return ggml_view_4d(ctx0, t, ne0, ne1, ne2, ne3, nb1, nb2, nb3, offset);
};
auto view__k = [ctx0, n_embd, n_head, N, n_batch] (struct ggml_tensor * t) -> struct ggml_tensor * {
int64_t ne0 = n_embd/n_head;
int64_t ne1 = N;
int64_t ne2 = n_head;
int64_t ne3 = n_batch;
size_t nb0 = ggml_element_size(t);
size_t nb1 = nb0*ne0;
size_t nb2 = nb1*ne1;
size_t nb3 = nb2*ne2;
size_t offset = nb3*ne3;
return ggml_view_4d(ctx0, t, ne0, ne1, ne2, ne3, nb1, nb2, nb3, offset);
};
auto view__v = [ctx0, n_embd, n_head, N, n_batch] (struct ggml_tensor * t) -> struct ggml_tensor * {
int64_t ne0 = N;
int64_t ne1 = n_embd/n_head;
int64_t ne2 = n_head;
int64_t ne3 = n_batch;
size_t nb0 = ggml_element_size(t);
size_t nb1 = nb0*ne0;
size_t nb2 = nb1*ne1;
size_t nb3 = nb2*ne2;
size_t offset = 2*nb3*ne3;
return ggml_view_4d(ctx0, t, ne0, ne1, ne2, ne3, nb1, nb2, nb3, offset);
};
auto add_or_set = [ctx0] (struct ggml_tensor * a, struct ggml_tensor * b) -> struct ggml_tensor * {
if (a == NULL) {
return b;
} else {
return ggml_add_inplace(ctx0, a, b);
}
};
use_buf(-1);
model->tok_embeddings->grad = NULL;
model->norm->grad = NULL;
model->output->grad = NULL;
for (int il = 0; il < n_layer; ++il) {
struct my_llama_layer & layer = model->layers[il];
layer.attention_norm->grad = NULL;
layer.wq->grad = NULL;
layer.wk->grad = NULL;
layer.wv->grad = NULL;
layer.wo->grad = NULL;
layer.ffn_norm->grad = NULL;
layer.w1->grad = NULL;
layer.w2->grad = NULL;
layer.w3->grad = NULL;
}
clr_buf(0);
clr_buf(1);
use_buf(-1);
struct ggml_tensor * t00 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N*n_batch); assert_shape_1d(t00, N*n_batch);
memcpy(t00->data, tokens_input->data, ggml_element_size(t00)*N*n_batch);
use_buf(-1);
struct ggml_tensor * t01 = expand(gf, ggml_get_rows(ctx0, model->tok_embeddings, t00)); assert_shape_2d(t01, n_embd, N*n_batch);
// need to remember these for the backward pass
std::vector<struct ggml_tensor *> t02L; t02L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t03L; t03L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t04L; t04L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t05L; t05L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t06L; t06L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t07L; t07L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t08L; t08L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t09L; t09L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t10L; t10L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t11L; t11L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t12L; t12L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t13L; t13L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t14L; t14L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t15L; t15L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t16L; t16L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t17L; t17L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t18L; t18L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t19L; t19L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t20L; t20L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t21L; t21L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t22L; t22L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t23L; t23L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t24L; t24L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t25L; t25L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t26L; t26L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t27L; t27L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t28L; t28L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t29L; t29L.resize(n_layer, NULL);
std::vector<struct ggml_tensor *> t30L; t30L.resize(n_layer, NULL);
struct ggml_tensor * cur = t01;
for (int il = 0; il < n_layer; ++il) {
clr_buf(0);
struct my_llama_layer & layer = model->layers[il];
// tensors with values necessary for backward pass are in persistent buf(-1)
// other tensors with buf(0) and buf(1) are only temporary needed, and their memory reused after layer is completed.
use_buf(-1); struct ggml_tensor * t02 = expand(gf, ggml_rms_norm (ctx0, cur)); assert_shape_2d(t02, n_embd, N*n_batch);
use_buf( 0); struct ggml_tensor * t03 = expand(gf, ggml_repeat (ctx0, layer.attention_norm, t02)); assert_shape_2d(t03, n_embd, N*n_batch);
use_buf(-1); struct ggml_tensor * t04 = expand(gf, ggml_mul (ctx0, t02, t03)); assert_shape_2d(t04, n_embd, N*n_batch);
use_buf(-1); struct ggml_tensor * t05 = expand(gf, ggml_mul_mat (ctx0, layer.wq, t04)); assert_shape_2d(t05, n_embd, N*n_batch);
use_buf(-1); struct ggml_tensor * t06 = expand(gf, ggml_reshape_4d (ctx0, t05, n_embd/n_head, n_head, N, n_batch)); assert_shape_4d(t06, n_embd/n_head, n_head, N, n_batch);
use_buf(-1); struct ggml_tensor * t07 = expand(gf, ggml_rope_inplace (ctx0, t06, n_past, n_rot, rope_mode, 0)); assert_shape_4d(t07, n_embd/n_head, n_head, N, n_batch);
use_buf(-1); struct ggml_tensor * t08 = expand(gf, ggml_mul_mat (ctx0, layer.wk, t04)); assert_shape_2d(t08, n_embd, N*n_batch);
use_buf(-1); struct ggml_tensor * t09 = expand(gf, ggml_reshape_4d (ctx0, t08, n_embd/n_head, n_head, N, n_batch)); assert_shape_4d(t09, n_embd/n_head, n_head, N, n_batch);
use_buf(-1); struct ggml_tensor * t10 = expand(gf, ggml_rope_inplace (ctx0, t09, n_past, n_rot, rope_mode, 0)); assert_shape_4d(t10, n_embd/n_head, n_head, N, n_batch);
use_buf(-1); struct ggml_tensor * t11 = expand(gf, ggml_mul_mat (ctx0, t04, layer.wv)); assert_shape_2d(t11, N*n_batch, n_embd);
use_buf(-1); struct ggml_tensor * t12 = expand(gf, ggml_reshape_4d (ctx0, t11, N, n_batch, n_embd/n_head, n_head)); assert_shape_4d(t12, N, n_batch, n_embd/n_head, n_head);
use_buf(-1); struct ggml_tensor * t13 = expand(gf, ggml_permute (ctx0, t07, 0, 2, 1, 3)); assert_shape_4d(t13, n_embd/n_head, N, n_head, n_batch);
use_buf(-1); struct ggml_tensor * t14 = expand(gf, ggml_permute (ctx0, t10, 0, 2, 1, 3)); assert_shape_4d(t14, n_embd/n_head, N, n_head, n_batch);
use_buf(-1); struct ggml_tensor * t15 = expand(gf, ggml_permute (ctx0, t12, 0, 3, 1, 2)); assert_shape_4d(t15, N, n_embd/n_head, n_head, n_batch);
use_buf(-1); struct ggml_tensor * t16 = expand(gf, ggml_flash_attn (ctx0, t13, t14, t15, true)); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch);
use_buf( 0); struct ggml_tensor * t17 = expand(gf, ggml_permute (ctx0, t16, 0, 2, 1, 3)); assert_shape_4d(t17, n_embd/n_head, n_head, N, n_batch);
use_buf(-1); struct ggml_tensor * t18 = expand(gf, ggml_cont (ctx0, t17)); assert_shape_4d(t18, n_embd/n_head, n_head, N, n_batch);
use_buf(-1); struct ggml_tensor * t19 = expand(gf, ggml_reshape_2d (ctx0, t18, n_embd, N*n_batch)); assert_shape_2d(t19, n_embd, N*n_batch);
use_buf( 0); struct ggml_tensor * t20 = expand(gf, ggml_mul_mat (ctx0, layer.wo, t19)); assert_shape_2d(t20, n_embd, N*n_batch);
use_buf(-1); struct ggml_tensor * t21 = expand(gf, ggml_add (ctx0, t20, cur)); assert_shape_2d(t21, n_embd, N*n_batch);
use_buf(-1); struct ggml_tensor * t22 = expand(gf, ggml_rms_norm (ctx0, t21)); assert_shape_2d(t22, n_embd, N*n_batch);
use_buf( 0); struct ggml_tensor * t23 = expand(gf, ggml_repeat (ctx0, layer.ffn_norm, t22)); assert_shape_2d(t23, n_embd, N*n_batch);
use_buf(-1); struct ggml_tensor * t24 = expand(gf, ggml_mul (ctx0, t23, t22)); assert_shape_2d(t24, n_embd, N*n_batch);
use_buf(-1); struct ggml_tensor * t25 = expand(gf, ggml_mul_mat (ctx0, layer.w3, t24)); assert_shape_2d(t25, n_ff, N*n_batch);
use_buf(-1); struct ggml_tensor * t26 = expand(gf, ggml_mul_mat (ctx0, layer.w1, t24)); assert_shape_2d(t26, n_ff, N*n_batch);
use_buf(-1); struct ggml_tensor * t27 = expand(gf, ggml_silu (ctx0, t26)); assert_shape_2d(t27, n_ff, N*n_batch);
use_buf(-1); struct ggml_tensor * t28 = expand(gf, ggml_mul (ctx0, t27, t25)); assert_shape_2d(t28, n_ff, N*n_batch);
use_buf( 0); struct ggml_tensor * t29 = expand(gf, ggml_mul_mat (ctx0, layer.w2, t28)); assert_shape_2d(t29, n_embd, N*n_batch);
use_buf(-1); struct ggml_tensor * t30 = expand(gf, ggml_add (ctx0, t21, t29)); assert_shape_2d(t30, n_embd, N*n_batch);
t02L[il] = t02;
t03L[il] = t03;
t04L[il] = t04;
t05L[il] = t05;
t06L[il] = t06;
t07L[il] = t07;
t08L[il] = t08;
t09L[il] = t09;
t10L[il] = t10;
t11L[il] = t11;
t12L[il] = t12;
t13L[il] = t13;
t14L[il] = t14;
t15L[il] = t15;
t16L[il] = t16;
t17L[il] = t17;
t18L[il] = t18;
t19L[il] = t19;
t20L[il] = t20;
t21L[il] = t21;
t22L[il] = t22;
t23L[il] = t23;
t24L[il] = t24;
t25L[il] = t25;
t26L[il] = t26;
t27L[il] = t27;
t28L[il] = t28;
t29L[il] = t29;
t30L[il] = t30;
cur = t30;
}
clr_buf(0);
use_buf(0);
struct ggml_tensor * t31 = expand(gf, ggml_rms_norm (ctx0, cur)); assert_shape_2d(t31, n_embd, N*n_batch);
struct ggml_tensor * t32 = expand(gf, ggml_repeat (ctx0, model->norm, t31)); assert_shape_2d(t32, n_embd, N*n_batch);
struct ggml_tensor * t33 = expand(gf, ggml_mul (ctx0, t32, t31)); assert_shape_2d(t33, n_embd, N*n_batch);
use_buf(-1);
struct ggml_tensor * t34 = expand(gf, ggml_mul_mat (ctx0, model->output, t33)); assert_shape_2d(t34, n_vocab, N*n_batch);
struct ggml_tensor * t35 = expand(gf, ggml_reshape_3d(ctx0, t34, n_vocab, N, n_batch)); assert_shape_3d(t35, n_vocab, N, n_batch);
struct ggml_tensor * t36 = expand(gf, ggml_cross_entropy_loss(ctx0, t35, targets)); assert_shape_1d(t36, 1);
{
/*
tok_embeddings | grad_tok_embeddings = ggml_get_rows_back(grad_t01, t00)
L0_att_norm | grad_L0_att_norm = ggml_repeat_back(grad_t03L0, L0_att_norm.shape)
L0_wq | grad_L0_wq = ggml_out_prod(t04L0, grad_t05L0)
L0_wk | grad_L0_wk = ggml_out_prod(t04L0, grad_t08L0)
L0_wv | grad_L0_wv = ggml_out_prod(t04L0, ggml_transpose(grad_t11L0))
L0_wo | grad_L0_wo = ggml_out_prod(t19L0, grad_t20L0)
L0_ffn_norm | grad_L0_ffn_norm = ggml_repeat_back(grad_t23L0, L0_ffn_norm.shape)
L0_w1 | grad_L0_w1 = ggml_out_prod(t24L0, grad_t26L0)
L0_w2 | grad_L0_w2 = ggml_out_prod(t28L0, grad_t29L0)
L0_w3 | grad_L0_w3 = ggml_out_prod(t24L0, grad_t25L0)
L1_att_norm | grad_L1_att_norm = ggml_repeat_back(grad_t03L1, L1_att_norm.shape)
L1_wq | grad_L1_wq = ggml_out_prod(t04L1, grad_t05L1)
L1_wk | grad_L1_wk = ggml_out_prod(t04L1, grad_t08L1)
L1_wv | grad_L1_wv = ggml_out_prod(t04L1, ggml_transpose(grad_t11L1))
L1_wo | grad_L1_wo = ggml_out_prod(t19L1, grad_t20L1)
L1_ffn_norm | grad_L1_ffn_norm = ggml_repeat_back(grad_t23L1, L1_ffn_norm.shape)
L1_w1 | grad_L1_w1 = ggml_out_prod(t24L1, grad_t26L1)
L1_w2 | grad_L1_w2 = ggml_out_prod(t28L1, grad_t29L1)
L1_w3 | grad_L1_w3 = ggml_out_prod(t24L1, grad_t25L1)
norm | grad_norm = ggml_repeat_back(grad_t32, norm.shape)
output | grad_output = ggml_out_prod(t33, grad_t34)
|
t01 = ggml_get_rows(tok_embeddings, t00) | grad_t01 = grad_t21L0 + ggml_rms_norm_back(t01, grad_t02L0)
for layer: |
t02L0*= ggml_rms_norm (t01) | grad_t02L0 = ggml_mul(grad_t04L0, t03L0)
t03L0 = ggml_repeat (L0_att_norm, t02L0_shape) | grad_t03L0 = ggml_mul(grad_t04L0, t02L0)
t04L0*= ggml_mul (t02L0, t03L0) | grad_t04L0 = ggml_out_prod(L0_wv, grad_t11L0) + ggml_out_prod(L0_wk, ggml_transpose(grad_t08L0)) + ggml_out_prod(L0_wq, ggml_transpose(grad_t05L0))
t05L0 = ggml_mul_mat (L0_wq, t04L0) | grad_t05L0 = ggml_reshape(grad_t06L0, t05L0_shape)
t06L0 = ggml_reshape_4d (t05L0, n_embd/n_head, n_head, N, n_batch) | grad_t06L0 = ggml_rope_back(grad_t07L0)
t07L0 = ggml_rope_inplace (t06L0) | grad_t07L0 = ggml_permute_back(grad_t13L0, 0, 2, 1, 3) = ggml_permute(grad_t13L0, 0, 2, 1, 3)
t08L0 = ggml_mul_mat (L0_wk, t04L0) | grad_t08L0 = ggml_reshape(grad_t09L0, t08L0_shape)
t09L0 = ggml_reshape_4d (t08L0, n_embd/n_head, n_head, N, n_batch) | grad_t09L0 = ggml_rope_back(grad_t10L0)
t10L0 = ggml_rope_inplace (t09L0) | grad_t10L0 = ggml_permute_back(grad_t14L0, 0, 2, 1, 3) = ggml_permute(grad_t14L0, 0, 2, 1, 3)
t11L0 = ggml_mul_mat (t04L0, L0_wv) | grad_t11L0 = ggml_reshape(grad_t12L0, t11L0_shape)
t12L0 = ggml_reshape_4d (t11L0, N, n_batch, n_embd/n_head, n_head) | grad_t12L0 = ggml_permute_back(grad_t15L0, 0, 3, 1, 2) = ggml_permute(grad_t15L0, 0, 2, 3, 1)
t13L0*= ggml_permute (t07L0, 0, 2, 1, 3) | grad_t13L0 = view__q(ggml_flash_attn_back(t13L0, t14L0, t15L0, grad_t16L0))
t14L0*= ggml_permute (t10L0, 0, 2, 1, 3) | grad_t14L0 = view__k(ggml_flash_attn_back(t13L0, t14L0, t15L0, grad_t16L0))
t15L0*= ggml_permute (t12L0, 0, 3, 1, 2) | grad_t15L0 = view__v(ggml_flash_attn_back(t13L0, t14L0, t15L0, grad_t16L0))
t16L0 = ggml_flash_attn (t13L0, t14L0, t15L0) | grad_t16L0 = ggml_permute_back(grad_t17L0, 0, 2, 1, 3) = ggml_permute(grad_t17L0, 0, 2, 1, 3)
t17L0 = ggml_permute (t16L0, 0, 2, 1, 3) | grad_t17L0 = grad_t18L0
t18L0 = ggml_cont (t17L0) | grad_t18L0 = ggml_reshape(grad_t19L0, t18L0_shape)
t19L0*= ggml_reshape_2d (t18L0, n_embd, N*n_batch) | grad_t19L0 = ggml_out_prod(L0_wo, ggml_transpose(grad_t20L0))
t20L0 = ggml_mul_mat (L0_wo, t19L0) | grad_t20L0 = grad_t21L0
t21L0*= ggml_add (t20L0, t01) | grad_t21L0 = grad_t30L0 + ggml_rms_norm_back(t21L0, grad_t22L0)
t22L0*= ggml_rms_norm (t21L0) | grad_t22L0 = ggml_mul(grad_t24L0, t23L0)
t23L0 = ggml_repeat (L0_ffn_norm, t22L0_shape) | grad_t23L0 = ggml_mul(grad_t24L0, t22L0)
t24L0*= ggml_mul (t23L0, t22L0) | grad_t24L0 = ggml_out_prod(L0_w1, ggml_transpose(grad_t26L0)) + ggml_out_prod(L0_w3, ggml_transpose(grad_t25L0))
t25L0*= ggml_mul_mat (L0_w3, t24L0) | grad_t25L0 = ggml_mul(grad_t28L0, t27L0)
t26L0*= ggml_mul_mat (L0_w1, t24L0) | grad_t26L0 = ggml_silu_back(t26L0, grad_t27L0)
t27L0*= ggml_silu (t26L0) | grad_t27L0 = ggml_mul(grad_t28L0, t25L0)
t28L0*= ggml_mul (t27L0, t25L0) | grad_t28L0 = ggml_out_prod(L0_w2, ggml_transpose(grad_t29L0))
t29L0 = ggml_mul_mat (L0_w2, t28L0) | grad_t29L0 = grad_t30L0
t30L0*= ggml_add (t21L0, t29L0) | grad_t30L0 = ggml_rms_norm_back(t30L0, grad_t02L1) + grad_t21L1
^
t02L1*= ggml_rms_norm (t30L0) | grad_t02L1 = ggml_mul(grad_t04L1, t03L1)
t03L1 = ggml_repeat (L1_att_norm, t02L1_shape) | grad_t03L1 = ggml_mul(grad_t04L1, t02L1)
t04L1*= ggml_mul (t02L1, t03L1) | grad_t04L1 = ggml_out_prod(L1_wv, grad_t11L1) + ggml_out_prod(L1_wk, ggml_transpose(grad_t08L1)) + ggml_out_prod(L1_wq, ggml_transpose(grad_t05L1))
t05L1 = ggml_mul_mat (L1_wq, t04L1) | grad_t05L1 = ggml_reshape(grad_t06L1, t05L1_shape)
t06L1 = ggml_reshape_4d (t05L1, n_embd/n_head, n_head, N, n_batch) | grad_t06L1 = ggml_rope_back(grad_t07L1)
t07L1 = ggml_rope_inplace (t06L1) | grad_t07L1 = ggml_permute_back(grad_t13L1, 0, 2, 1, 3) = ggml_permute(grad_t13L1, 0, 2, 1, 3)
t08L1 = ggml_mul_mat (L1_wk, t04L1) | grad_t08L1 = ggml_reshape(grad_t09L1, t08L1_shape)
t09L1 = ggml_reshape_4d (t08L1, n_embd/n_head, n_head, N, n_batch) | grad_t09L1 = ggml_rope_back(grad_t10L1)
t10L1 = ggml_rope_inplace (t09L1) | grad_t10L1 = ggml_permute_back(grad_t14L1, 0, 2, 1, 3) = ggml_permute(grad_t14L1, 0, 2, 1, 3)
t11L1 = ggml_mul_mat (t04L1, L1_wv) | grad_t11L1 = ggml_reshape(grad_t12L1, t11L1_shape)
t12L1 = ggml_reshape_4d (t11L1, N, n_batch, n_embd/n_head, n_head) | grad_t12L1 = ggml_permute_back(grad_t15L1, 0, 3, 1, 2) = ggml_permute(grad_t15L1, 0, 2, 3, 1)
t13L1*= ggml_permute (t07L1, 0, 2, 1, 3) | grad_t13L1 = view__q(ggml_flash_attn_back(t13L1, t14L1, t15L1, grad_t16L1))
t14L1*= ggml_permute (t10L1, 0, 2, 1, 3) | grad_t14L1 = view__k(ggml_flash_attn_back(t13L1, t14L1, t15L1, grad_t16L1))
t15L1*= ggml_permute (t12L1, 0, 3, 1, 2) | grad_t15L1 = view__v(ggml_flash_attn_back(t13L1, t14L1, t15L1, grad_t16L1))
t16L1 = ggml_flash_attn (t13L1, t14L1, t15L1) | grad_t16L1 = ggml_permute_back(grad_t17L1, 0, 2, 1, 3) = ggml_permute(grad_t17L1, 0, 2, 1, 3)
t17L1 = ggml_permute (t16L1, 0, 2, 1, 3) | grad_t17L1 = grad_t18L1
t18L1 = ggml_cont (t17L1) | grad_t18L1 = ggml_reshape(grad_t19L1, t18L1_shape)
t19L1*= ggml_reshape_2d (t18L1, n_embd, N*n_batch) | grad_t19L1 = ggml_out_prod(L1_wo, ggml_transpose(grad_t20L1))
t20L1 = ggml_mul_mat (L1_wo, t19L1) | grad_t20L1 = grad_t21L1
t21L1*= ggml_add (t20L1, t30L0) | grad_t21L1 = grad_t30L1 + ggml_rms_norm_back(t21L1, grad_t22L1)
t22L1*= ggml_rms_norm (t21L1) | grad_t22L1 = ggml_mul(grad_t24L1, t23L1)
t23L1 = ggml_repeat (L1_ffn_norm, t22L1_shape) | grad_t23L1 = ggml_mul(grad_t24L1, t22L1)
t24L1*= ggml_mul (t23L1, t22L1) | grad_t24L1 = ggml_out_prod(L1_w1, ggml_transpose(grad_t26L1)) + ggml_out_prod(L1_w3, ggml_transpose(grad_t25L1))
t25L1*= ggml_mul_mat (L1_w3, t24L1) | grad_t25L1 = ggml_mul(grad_t28L1, t27L1)
t26L1*= ggml_mul_mat (L1_w1, t24L1) | grad_t26L1 = ggml_silu_back(t26L1, grad_t27L1)
t27L1*= ggml_silu (t26L1) | grad_t27L1 = ggml_mul(grad_t28L1, t25L1)
t28L1*= ggml_mul (t27L1, t25L1) | grad_t28L1 = ggml_out_prod(L1_w2, ggml_transpose(grad_t29L1))
t29L1 = ggml_mul_mat (L1_w2, t28L1) | grad_t29L1 = grad_t30L1
t30L1*= ggml_add (t21L1, t29L1) | grad_t30L1 = ggml_rms_norm_back(t30L1, grad_t31)
^
t31 = ggml_rms_norm (t30L1) | grad_t31 = ggml_mul(grad_t33, t32)
t32 = ggml_repeat (norm, t31.shape) | grad_t32 = ggml_mul(grad_t33, t31)
t33 = ggml_mul (t32, t31) | grad_t33 = ggml_out_prod(output, ggml_transpose(grad_t34))
t34 = ggml_mul_mat (output, t33) | grad_t34 = ggml_reshape(grad_t35, t34.shape)
t35 = ggml_reshape_3d (t34, n_vocab, N, n_batch) | grad_t35 = ggml_cross_entropy_loss_back(t35, targets, grad_t36)
t36 = ggml_cross_entropy_loss(t35, targets) | grad_t36 = 1 (optimizer)
tensors marked with * need to be stored until grad computation
tensors during grad computation are all temporary
*/
}
*gb = *gf;
// t36->grad gets set to one by optimizer, so we need the tensor.
// initialize it with 1.0f to make sure.
use_buf(-1);
t36->grad = expand(gb, ggml_new_f32(ctx0, 1.0f));
use_buf(0);
t35->grad = expand(gb, ggml_cross_entropy_loss_back(ctx0, t35, targets, t36->grad)); assert_shape_3d(t35->grad, n_vocab, N, n_batch);
t34->grad = expand(gb, ggml_reshape_2d (ctx0, t35->grad, n_vocab, N*n_batch)); assert_shape_2d(t34->grad, n_vocab, N*n_batch);
t33->grad = expand(gb, ggml_out_prod (ctx0, model->output, ggml_transpose(ctx0, t34->grad))); assert_shape_2d(t33->grad, n_embd, N*n_batch);
t32->grad = expand(gb, ggml_mul (ctx0, t33->grad, t31)); assert_shape_2d(t32->grad, n_embd, N*n_batch);
use_buf(-1);
model->norm->grad = expand(gb, add_or_set(model->norm->grad, ggml_repeat_back(ctx0, t32->grad, model->norm))); assert_shape_1d(model->norm->grad, n_embd);
model->output->grad = expand(gb, add_or_set(model->output->grad, ggml_out_prod(ctx0, t33, t34->grad))); assert_shape_2d(model->output->grad, n_embd, n_vocab);
clr_buf(1);
use_buf(1);
t31->grad = expand(gb, ggml_mul(ctx0, t33->grad, t32)); assert_shape_2d(t31->grad, n_embd, N*n_batch);
struct ggml_tensor * back_layer_inp = t31;
struct ggml_tensor * grad_layer_inp = NULL;
for (int k = 0; k < n_layer; ++k) {
int il = n_layer-1-k;
struct my_llama_layer & layer = model->layers[il];
struct ggml_tensor * t02 = t02L[il];
struct ggml_tensor * t03 = t03L[il];
struct ggml_tensor * t04 = t04L[il];
struct ggml_tensor * t05 = t05L[il];
struct ggml_tensor * t06 = t06L[il];
struct ggml_tensor * t07 = t07L[il];
struct ggml_tensor * t08 = t08L[il];
struct ggml_tensor * t09 = t09L[il];
struct ggml_tensor * t10 = t10L[il];
struct ggml_tensor * t11 = t11L[il];
struct ggml_tensor * t12 = t12L[il];
struct ggml_tensor * t13 = t13L[il];
struct ggml_tensor * t14 = t14L[il];
struct ggml_tensor * t15 = t15L[il];
struct ggml_tensor * t16 = t16L[il];
struct ggml_tensor * t17 = t17L[il];
struct ggml_tensor * t18 = t18L[il];
struct ggml_tensor * t19 = t19L[il];
struct ggml_tensor * t20 = t20L[il];
struct ggml_tensor * t21 = t21L[il];
struct ggml_tensor * t22 = t22L[il];
struct ggml_tensor * t23 = t23L[il];
struct ggml_tensor * t24 = t24L[il];
struct ggml_tensor * t25 = t25L[il];
struct ggml_tensor * t26 = t26L[il];
struct ggml_tensor * t27 = t27L[il];
struct ggml_tensor * t28 = t28L[il];
struct ggml_tensor * t29 = t29L[il];
struct ggml_tensor * t30 = t30L[il];
clr_buf(0);
use_buf(0);
t30->grad = expand(gb, ggml_rms_norm_back(ctx0, t30, back_layer_inp->grad)); assert_shape_2d(t30->grad, n_embd, N*n_batch);
if (grad_layer_inp) {
t30->grad = expand(gb, ggml_add(ctx0, t30->grad, grad_layer_inp->grad)); assert_shape_2d(t30->grad, n_embd, N*n_batch);
}
clr_buf(1);
t29->grad = t30->grad; assert_shape_2d(t29->grad, n_embd, N*n_batch);
t28->grad = expand(gb, ggml_out_prod(ctx0, layer.w2, ggml_transpose(ctx0, t29->grad))); assert_shape_2d(t28->grad, n_ff, N*n_batch);
t27->grad = expand(gb, ggml_mul(ctx0, t28->grad, t25)); assert_shape_2d(t27->grad, n_ff, N*n_batch);
t26->grad = expand(gb, ggml_silu_back(ctx0, t26, t27->grad)); assert_shape_2d(t26->grad, n_ff, N*n_batch);
t25->grad = expand(gb, ggml_mul(ctx0, t28->grad, t27)); assert_shape_2d(t25->grad, n_ff, N*n_batch);
t24->grad = expand(gb, ggml_add_inplace(ctx0,
ggml_out_prod(ctx0, layer.w1, ggml_transpose(ctx0, t26->grad)),
ggml_out_prod(ctx0, layer.w3, ggml_transpose(ctx0, t25->grad)))); assert_shape_2d(t24->grad, n_embd, N*n_batch);
t23->grad = expand(gb, ggml_mul(ctx0, t24->grad, t22)); assert_shape_2d(t23->grad, n_embd, N*n_batch);
t22->grad = expand(gb, ggml_mul(ctx0, t24->grad, ggml_repeat(ctx0, layer.ffn_norm, t24->grad))); assert_shape_2d(t22->grad, n_embd, N*n_batch);
use_buf(1);
t21->grad = expand(gb, ggml_add(ctx0, t30->grad, ggml_rms_norm_back(ctx0, t21, t22->grad))); assert_shape_2d(t21->grad, n_embd, N*n_batch);
grad_layer_inp = t21;
use_buf(0);
t20->grad = t21->grad; assert_shape_2d(t20->grad, n_embd, N*n_batch);
t19->grad = expand(gb, ggml_out_prod(ctx0, layer.wo, ggml_transpose(ctx0, t20->grad))); assert_shape_2d(t19->grad, n_embd, N*n_batch);
t18->grad = expand(gb, ggml_reshape_4d(ctx0, t19->grad, n_embd/n_head, n_head, N, n_batch)); assert_shape_4d(t18->grad, n_embd/n_head, n_head, N, n_batch);
t17->grad = t18->grad; assert_shape_4d(t17->grad, n_embd/n_head, n_head, N, n_batch);
t16->grad = expand(gb, ggml_permute(ctx0, t17->grad, 0, 2, 1, 3)); assert_shape_4d(t16->grad, n_embd/n_head, N, n_head, n_batch);
struct ggml_tensor * flash_attn = expand(gb, ggml_flash_attn_back(ctx0, t13, t14, t15, t16->grad, true)); assert_shape_4d(flash_attn, n_embd/n_head, N*3, n_head, n_batch);
t15->grad = expand(gb, view__v(flash_attn)); assert_shape_4d(t15->grad, N, n_embd/n_head, n_head, n_batch);
t14->grad = expand(gb, view__k(flash_attn)); assert_shape_4d(t14->grad, n_embd/n_head, N, n_head, n_batch);
t13->grad = expand(gb, view__q(flash_attn)); assert_shape_4d(t13->grad, n_embd/n_head, N, n_head, n_batch);
t12->grad = expand(gb, ggml_permute(ctx0, t15->grad, 0, 2, 3, 1)); assert_shape_4d(t12->grad, N, n_batch, n_embd/n_head, n_head);
t11->grad = expand(gb, ggml_reshape_2d(ctx0, ggml_cont(ctx0, t12->grad), N*n_batch, n_embd)); assert_shape_2d(t11->grad, N*n_batch, n_embd);
t10->grad = expand(gb, ggml_permute(ctx0, t14->grad, 0, 2, 1, 3)); assert_shape_4d(t10->grad, n_embd/n_head, n_head, N, n_batch);
t09->grad = expand(gb, ggml_rope_back(ctx0, t10->grad, n_past, n_rot, rope_mode)); assert_shape_4d(t09->grad, n_embd/n_head, n_head, N, n_batch);
t08->grad = expand(gb, ggml_reshape_2d(ctx0, t09->grad, n_embd, N*n_batch)); assert_shape_2d(t08->grad, n_embd, N*n_batch);
t07->grad = expand(gb, ggml_permute(ctx0, t13->grad, 0, 2, 1, 3)); assert_shape_4d(t07->grad, n_embd/n_head, n_head, N, n_batch);
t06->grad = expand(gb, ggml_rope_back(ctx0, t07->grad, n_past, n_rot, rope_mode)); assert_shape_4d(t06->grad, n_embd/n_head, n_head, N, n_batch);
t05->grad = expand(gb, ggml_reshape_2d(ctx0, t06->grad, n_embd, N*n_batch)); assert_shape_2d(t05->grad, n_embd, N*n_batch);
t04->grad = expand(gb, ggml_add_inplace(ctx0,
ggml_add_inplace(ctx0,
ggml_out_prod(ctx0, layer.wv, t11->grad),
ggml_out_prod(ctx0, layer.wk, ggml_transpose(ctx0, t08->grad))),
ggml_out_prod(ctx0, layer.wq, ggml_transpose(ctx0, t05->grad)))); assert_shape_2d(t04->grad, n_embd, N*n_batch);
t03->grad = expand(gb, ggml_mul(ctx0, t04->grad, t02)); assert_shape_2d(t04->grad, n_embd, N*n_batch);
use_buf(1);
t02->grad = expand(gb, ggml_mul(ctx0, t04->grad, ggml_repeat(ctx0, layer.attention_norm, t02))); assert_shape_2d(t02->grad, n_embd, N*n_batch);
back_layer_inp = t02;
// use_buf(0);
use_buf(-1);
layer.attention_norm->grad = expand(gb, add_or_set(layer.attention_norm->grad, ggml_repeat_back(ctx0, t03->grad, layer.attention_norm))); assert_shape_1d(layer.attention_norm->grad, n_embd);
layer.wq->grad = expand(gb, add_or_set(layer.wq->grad, ggml_out_prod(ctx0, t04, t05->grad))); assert_shape_2d(layer.wq->grad, n_embd, n_embd);
layer.wk->grad = expand(gb, add_or_set(layer.wk->grad, ggml_out_prod(ctx0, t04, t08->grad))); assert_shape_2d(layer.wk->grad, n_embd, n_embd);
layer.wv->grad = expand(gb, add_or_set(layer.wv->grad, ggml_out_prod(ctx0, t04, ggml_transpose(ctx0, t11->grad)))); assert_shape_2d(layer.wv->grad, n_embd, n_embd);
layer.wo->grad = expand(gb, add_or_set(layer.wo->grad, ggml_out_prod(ctx0, t19, t20->grad))); assert_shape_2d(layer.wo->grad, n_embd, n_embd);
layer.ffn_norm->grad = expand(gb, add_or_set(layer.ffn_norm->grad, ggml_repeat_back(ctx0, t23->grad, layer.ffn_norm))); assert_shape_1d(layer.ffn_norm->grad, n_embd);
layer.w1->grad = expand(gb, add_or_set(layer.w1->grad, ggml_out_prod(ctx0, t24, t26->grad))); assert_shape_2d(layer.w1->grad, n_embd, n_ff);
layer.w2->grad = expand(gb, add_or_set(layer.w2->grad, ggml_out_prod(ctx0, t28, t29->grad))); assert_shape_2d(layer.w2->grad, n_ff, n_embd);
layer.w3->grad = expand(gb, add_or_set(layer.w3->grad, ggml_out_prod(ctx0, t24, t25->grad))); assert_shape_2d(layer.w3->grad, n_embd, n_ff);
// use_buf(0);
}
clr_buf(0);
use_buf(0);
t01->grad = expand(gb, ggml_add_inplace(ctx0, grad_layer_inp->grad, ggml_rms_norm_back(ctx0, t01, back_layer_inp->grad))); assert_shape_2d(t01->grad, n_embd, N*n_batch);
use_buf(-1);
model->tok_embeddings->grad = expand(gb, ggml_get_rows_back(ctx0, t01->grad, t00, model->tok_embeddings)); assert_shape_2d(model->tok_embeddings->grad, n_embd, n_vocab);
// clr_buf(1);
// clr_buf(0);
*logits = t35;
if (track_max_mem) {
printf("%s: max size compute buf0: %zu\n", __func__, buf_maxs[0]);
printf("%s: max size compute buf1: %zu\n", __func__, buf_maxs[1]);
}
// now that all grads are created, set the graph leafs and grads
graph_set_leafs_grads(gf);
graph_set_leafs_grads(gb);
return t36;
}
void set_f32_3d(struct ggml_tensor * tensor, int64_t i0, int64_t i1, int64_t i2, float value) {
float * ptr = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2]);
*ptr = value;
}
void set_f32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1, float value) {
float * ptr = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);
*ptr = value;
}
void set_i32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1, int32_t value) {
int32_t * ptr = (int32_t *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);
*ptr = value;
}
float get_f32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {
float * ptr = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);
return *ptr;
}
int32_t get_i32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {
int32_t * ptr = (int32_t *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);
return *ptr;
}
void print_row(struct ggml_tensor * probs, int i) {
for (int k = 0; k < probs->ne[0]; ++k) {
float p = get_f32_2d(probs, k, i);
printf(" %.2f", p);
}
printf("\n");
}
void print_matrix(struct ggml_tensor * probs) {
assert(probs->n_dims == 2);
for (int i = 0; i < probs->ne[1]; ++i) {
for (int k = 0; k < probs->ne[0]; ++k) {
float p = get_f32_2d(probs, k, i);
printf(" %.2f", p);
}
printf("\n");
}
}
void print_token(struct llama_context * ctx, llama_token token) {
printf("%s", llama_token_to_str(ctx, token));
}
void print_tokens(struct llama_context* ctx, struct ggml_tensor * tokens) {
for (int i=0; i<tokens->ne[0]; ++i) {
int token = ggml_get_i32_1d(tokens, i);
print_token(ctx, token);
}
}
void print_tokens_batch(struct llama_context* ctx, struct ggml_tensor * tokens) {
for (int i1=0; i1<tokens->ne[1]; ++i1) {
//int num_newline = 0;
for (int i0=0; i0<tokens->ne[0]; ++i0) {
int token = get_i32_2d(tokens, i0, i1);
print_token(ctx, token);
// bool isnl = (token == llama_token_nl());
// if (isnl) {
// ++num_newline;
// }
// if (isnl) {
// if (num_newline < 2) {
// print_token(ctx, token);
// } else {
// printf("\\n");
// }
// } else {
// print_token(ctx, token);
// }
}
printf("\n--\n");
}
}
void get_example_targets(const int * train_samples, size_t n_train_samples, const llama_token * train_data, size_t n_train_data, int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * target_logits, struct ggml_tensor * target_probs) {
int n_tokens = tokens_input->ne[0];
int n_vocab = target_logits->ne[0];
size_t sample = train_samples[example_id % n_train_samples];
GGML_ASSERT(sample+n_tokens-1 < n_train_data);
ggml_set_f32(target_logits, -1.0f/n_vocab);
ggml_set_f32(target_probs, 0.0f);
ggml_set_i32_1d(tokens_input, 0, llama_token_bos());
for (int i=1; i<n_tokens+1; ++i) {
int token = clamp(train_data[sample+i-1], 0, n_vocab-1);
set_f32_2d(target_logits, token, i-1, +1.0f);
set_f32_2d(target_probs, token, i-1, +1.0f);
if (i<n_tokens) {
ggml_set_i32_1d(tokens_input, i, token);
}
}
}
void get_example_targets_batch(struct llama_context * /*lctx*/, const int * train_samples, size_t n_train_samples, const llama_token * train_data, size_t n_train_data, int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * target_logits, struct ggml_tensor * target_probs) {
GGML_ASSERT(tokens_input->n_dims == 2);
GGML_ASSERT(target_logits->n_dims == 3);
GGML_ASSERT(target_probs->n_dims == 3);
int n_vocab = target_logits->ne[0];
int n_tokens = tokens_input->ne[0];
int n_batch = tokens_input->ne[1];
GGML_ASSERT(n_tokens == target_logits->ne[1]);
GGML_ASSERT(n_batch == target_logits->ne[2]);
GGML_ASSERT(n_vocab == target_probs->ne[0]);
GGML_ASSERT(n_tokens == target_probs->ne[1]);
GGML_ASSERT(n_batch == target_probs->ne[2]);
ggml_set_f32(target_logits, -1.0f/n_vocab);
ggml_set_f32(target_probs, 0.0f);
for (int k=0; k<n_batch; ++k) {
// printf("%s: batch %d\n", __func__, k);
size_t sample = train_samples[(example_id*n_batch + k) % n_train_samples];
GGML_ASSERT(sample+n_tokens-1 < n_train_data);
set_i32_2d(tokens_input, 0, k, llama_token_bos());
for (int i=1; i<n_tokens+1; ++i) {
int token = clamp(train_data[sample+i-1], 0, n_vocab-1);
// print_token(lctx, token);
set_f32_3d(target_logits, token, i-1, k, +1.0f);
set_f32_3d(target_probs, token, i-1, k, +1.0f);
if (i<n_tokens) {
set_i32_2d(tokens_input, i, k, token);
}
}
// printf("\n=\n");
// for (int i=0; i<n_tokens; ++i) {
// int token = get_i32_2d(tokens_input, i, k);
// print_token(lctx, token);
// }
// printf("\n-\n");
}
}
void lshift_examples(struct ggml_tensor * tokens_input, struct ggml_tensor * target_logits, struct ggml_tensor * target_probs, int n_shift) {
int n_tokens = tokens_input->ne[0];
int n_vocab = target_logits->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(target_logits, i*n_vocab + k, ggml_get_f32_1d(target_logits, (i + n_shift)*n_vocab + k));
ggml_set_f32_1d(target_probs, i*n_vocab + k, ggml_get_f32_1d(target_probs, (i + n_shift)*n_vocab + k));
}
}
}
struct ggml_tensor * square_error_loss(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * target) {
return ggml_sum(ctx, ggml_sqr(ctx, ggml_sub(ctx, target, a)));
}
struct ggml_tensor * cross_entropy_loss(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * probs) {
return ggml_cross_entropy_loss(ctx, a, probs);
}
#ifdef __GNUC__
#ifdef __MINGW32__
__attribute__((format(gnu_printf, 1, 2)))
#else
__attribute__((format(printf, 1, 2)))
#endif
#endif
static std::string format(const char * fmt, ...) {
va_list ap, ap2;
va_start(ap, fmt);
va_copy(ap2, ap);
int size = vsnprintf(NULL, 0, fmt, ap);
GGML_ASSERT(size >= 0 && size < INT_MAX);
std::vector<char> buf(size + 1);
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
GGML_ASSERT(size2 == size);
va_end(ap2);
va_end(ap);
return std::string(buf.data(), size);
}
struct llama_file {
// use FILE * so we don't have to re-open the file to mmap
FILE * fp;
size_t size;
llama_file(const char * fname, const char * mode) {
fp = std::fopen(fname, mode);
if (fp == NULL) {
size = 0;
} else {
seek(0, SEEK_END);
size = tell();
seek(0, SEEK_SET);
}
}
size_t tell() const {
#ifdef _WIN32
__int64 ret = _ftelli64(fp);
#else
long ret = std::ftell(fp);
#endif
GGML_ASSERT(ret != -1); // this really shouldn't fail
return (size_t) ret;
}
void seek(size_t offset, int whence) {
#ifdef _WIN32
int ret = _fseeki64(fp, (__int64) offset, whence);
#else
int ret = std::fseek(fp, (long) offset, whence);
#endif
GGML_ASSERT(ret == 0); // same
}
void read_raw(void * ptr, size_t size) {
if (size == 0) {
return;
}
errno = 0;
std::size_t ret = std::fread(ptr, size, 1, fp);
if (ferror(fp)) {
throw std::runtime_error(format("read error: %s", strerror(errno)));
}
if (ret != 1) {
throw std::runtime_error(std::string("unexpectedly reached end of file"));
}
}
std::uint32_t read_u32() {
std::uint32_t ret;
read_raw(&ret, sizeof(ret));
return ret;
}
std::string read_string(std::uint32_t len) {
std::vector<char> chars(len);
read_raw(chars.data(), len);
return std::string(chars.data(), len);
}
void write_raw(const void * ptr, size_t size) {
if (size == 0) {
return;
}
errno = 0;
size_t ret = std::fwrite(ptr, size, 1, fp);
if (ret != 1) {
throw std::runtime_error(format("write error: %s", strerror(errno)));
}
}
void write_u32(std::uint32_t val) {
write_raw(&val, sizeof(val));
}
~llama_file() {
if (fp) {
std::fclose(fp);
}
}
};
int tokenize_file(struct llama_context * lctx, const char * filename, std::vector<llama_token>& out) {
struct llama_file f(filename, "rb");
std::vector<char> buf;
buf.resize(f.size+1);
f.read_raw(buf.data(), f.size);
buf[f.size] = '\0';
out.resize(buf.size());
int n_tokens = llama_tokenize(lctx, buf.data(), out.data(), buf.size(), false);
if (n_tokens >= 0) {
out.resize(n_tokens);
}
bool verify = false;
if (verify) {
const char * in = buf.data();
const char * end = buf.data() + buf.size();
for (int i = 0; i < (int) out.size(); ++i) {
const char * s = llama_token_to_str(lctx, out[i]);
int len = strlen(s);
if (in >= end) {
printf("%s: unexpected end of original text.\n", __func__);
break;
}
const bool matches = (strncmp(in, s, len) == 0);
if (matches) {
in += len;
} else {
printf("%s: mismatch: expected '%s', but got '%s'\n", __func__, std::string(in, len).c_str(), s);
}
}
}
return n_tokens;
}
void shuffle_ints(int * begin, int * end) {
if (end <= begin) return;
int max=begin[0];
for (int i=1; i<end-begin; ++i) {
if (begin[i] > max) {
max = begin[i];
}
}
std::vector<float> vals;
vals.resize(max+1);
for (int i=0; i<max+1; ++i) {
vals[i] = frand();
}
std::sort(begin, end, [&vals](int a, int b){
return vals.at(a) < vals.at(b);
});
}
struct my_llama_sampler_params {
float temp = 0.0f; // <= 0.0 disabled
int top_k = 20; // <= 0 to use vocab size
float top_p = 0.95f; // 1.0 = disabled
float tfs_z = 1.00f; // 1.0 = disabled
float typical_p = 1.00f; // 1.0 = disabled
int repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float repeat_penalty = 1.0f; // 1.0 = disabled
float alpha_presence = 0.0f; // 0.0 = disabled
float alpha_frequency = 0.0f; // 0.0 = disabled
int mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool penalize_nl = true; // consider newlines as a repeatable token
};
struct my_llama_sampler {
struct llama_context * ctx = NULL;
my_llama_sampler_params params;
int n_vocab = 0;
int n_ctx = 0;
float mirostat_mu;
std::vector<llama_token_data> candidates;
llama_token_data_array candidates_p;
};
void init_sampler(struct my_llama_sampler * sampler, struct llama_context * ctx) {
sampler->ctx = ctx;
sampler->n_vocab = llama_n_vocab(sampler->ctx);
sampler->n_ctx = llama_n_ctx(sampler->ctx);
sampler->mirostat_mu = 2.0f * sampler->params.mirostat_tau;
}
llama_token sample(struct my_llama_sampler * sampler, float * logits, const llama_token * last_tokens, int n_last_tokens) {
GGML_ASSERT(sampler->ctx != NULL);
struct llama_context * ctx = sampler->ctx;
sampler->candidates.resize(sampler->n_vocab);
for (llama_token token_id = 0; token_id < sampler->n_vocab; ++token_id) {
sampler->candidates[token_id].id = token_id;
sampler->candidates[token_id].logit = logits[token_id];
sampler->candidates[token_id].p = 0.0;
}
llama_token_data_array * candidates_p = & sampler->candidates_p;
candidates_p->data = sampler->candidates.data();
candidates_p->size = sampler->candidates.size();
candidates_p->sorted = false;
const auto params = sampler->params;
// Apply penalties
const float nl_logit = logits[llama_token_nl()];
const int n_last = std::min(std::min(n_last_tokens, params.repeat_last_n), sampler->n_ctx);
llama_sample_repetition_penalty(
ctx,
candidates_p,
last_tokens + n_last_tokens - n_last,
n_last,
params.repeat_penalty);
llama_sample_frequency_and_presence_penalties(
ctx,
candidates_p,
last_tokens + n_last_tokens - n_last,
n_last,
params.alpha_frequency,
params.alpha_presence);
if (!params.penalize_nl) {
logits[llama_token_nl()] = nl_logit;
}
llama_token token = 0;
if (params.temp <= 0) {
// Greedy sampling
token = llama_sample_token_greedy(ctx, candidates_p);
} else {
if (params.mirostat == 1) {
int mirostat_m = 100;
llama_sample_temperature(ctx, candidates_p, params.temp);
token = llama_sample_token_mirostat(ctx, candidates_p, params.mirostat_tau, params.mirostat_eta, mirostat_m, &sampler->mirostat_mu);
} else if (params.mirostat == 2) {
llama_sample_temperature(ctx, candidates_p, params.temp);
token = llama_sample_token_mirostat_v2(ctx, candidates_p, params.mirostat_tau, params.mirostat_eta, &sampler->mirostat_mu);
} else {
// Temperature sampling
llama_sample_top_k (ctx, candidates_p, params.top_k, 1);
llama_sample_tail_free (ctx, candidates_p, params.tfs_z, 1);
llama_sample_typical (ctx, candidates_p, params.typical_p, 1);
llama_sample_top_p (ctx, candidates_p, params.top_p, 1);
llama_sample_temperature (ctx, candidates_p, params.temp);
token = llama_sample_token(ctx, candidates_p);
}
}
return token;
}
void set_logits_masked(struct ggml_tensor * logits, std::vector<bool>& mask, float value) {
GGML_ASSERT(logits->ne[0] == (int64_t) mask.size());
for (int i2 = 0; i2 < logits->ne[2]; ++i2) {
for (int i1 = 0; i1 < logits->ne[1]; ++i1) {
for (int i0 = 0; i0 < logits->ne[0]; ++i0) {
if (!mask[i0]) continue;
float * ptr = (float *) ((char *) logits->data + i2*logits->nb[2] + i1*logits->nb[1] + i0*logits->nb[0]);
*ptr = value;
}
}
}
}
void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
if (tensor == NULL) {
file->write_u32(0);
file->write_u32(0);
file->write_u32(GGML_TYPE_F32);
file->seek((0-file->tell()) & 31, SEEK_CUR);
return;
}
const char * name = ggml_get_name(tensor);
uint32_t name_len = strlen(name);
uint32_t nd = tensor->n_dims;
uint32_t ne[4] = { (uint32_t)tensor->ne[0],
(uint32_t)tensor->ne[1],
(uint32_t)tensor->ne[2],
(uint32_t)tensor->ne[3] };
file->write_u32(nd);
file->write_u32(name_len);
file->write_u32(tensor->type);
file->write_raw(ne, sizeof(ne[0]) * nd);
file->write_raw(name, name_len);
file->seek((0-file->tell()) & 31, SEEK_CUR);
file->write_raw(tensor->data, ggml_nbytes(tensor));
}
void read_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
int32_t nd = file->read_u32();
GGML_ASSERT(nd == tensor->n_dims);
uint32_t name_len = file->read_u32();
enum ggml_type type = (enum ggml_type) file->read_u32();
GGML_ASSERT(type == tensor->type);
uint32_t ne[4];
file->read_raw(ne, sizeof(ne[0]) * nd);
for (int i=0; i<nd; ++i) {
GGML_ASSERT(ne[i] == tensor->ne[i]);
}
std::string name = file->read_string(name_len);
GGML_ASSERT(strncmp(ggml_get_name(tensor), name.c_str(), sizeof(tensor->name)-1) == 0);
file->seek((0-file->tell()) & 31, SEEK_CUR);
file->read_raw(tensor->data, ggml_nbytes(tensor));
}
void write_opt_context(struct llama_file * file, struct ggml_opt_context * opt) {
const uint32_t version = 0;
GGML_ASSERT(opt->nx >= 0);
GGML_ASSERT(opt->iter >= 0);
file->write_u32(version);
file->write_raw(&opt->params, sizeof(opt->params));
file->write_raw(&opt->nx, sizeof(opt->nx));
file->write_raw(&opt->iter, sizeof(opt->iter));
file->write_u32((uint32_t) opt->just_initialized);
switch (opt->params.type) {
case GGML_OPT_ADAM:
{
GGML_ASSERT(opt->adam.x != NULL);
write_tensor(file, opt->adam.x);
write_tensor(file, opt->adam.g1);
write_tensor(file, opt->adam.g2);
write_tensor(file, opt->adam.m);
write_tensor(file, opt->adam.v);
write_tensor(file, opt->adam.mh);
write_tensor(file, opt->adam.vh);
write_tensor(file, opt->adam.pf);
file->write_raw(&opt->adam.fx_best, sizeof(opt->adam.fx_best));
file->write_raw(&opt->adam.fx_prev, sizeof(opt->adam.fx_prev));
file->write_raw(&opt->adam.n_no_improvement, sizeof(opt->adam.n_no_improvement));
} break;
case GGML_OPT_LBFGS:
{
GGML_ASSERT(opt->adam.x != NULL);
write_tensor(file, opt->lbfgs.x);
write_tensor(file, opt->lbfgs.xp);
write_tensor(file, opt->lbfgs.g);
write_tensor(file, opt->lbfgs.gp);
write_tensor(file, opt->lbfgs.d);
write_tensor(file, opt->lbfgs.pf);
write_tensor(file, opt->lbfgs.lmal);
write_tensor(file, opt->lbfgs.lmys);
write_tensor(file, opt->lbfgs.lms);
write_tensor(file, opt->lbfgs.lmy);
file->write_raw(&opt->lbfgs.fx_best, sizeof(opt->lbfgs.fx_best));
file->write_raw(&opt->lbfgs.step, sizeof(opt->lbfgs.step));
file->write_raw(&opt->lbfgs.j, sizeof(opt->lbfgs.j));
file->write_raw(&opt->lbfgs.k, sizeof(opt->lbfgs.k));
file->write_raw(&opt->lbfgs.end, sizeof(opt->lbfgs.end));
file->write_raw(&opt->lbfgs.n_no_improvement, sizeof(opt->lbfgs.n_no_improvement));
} break;
}
}
void read_opt_context(struct llama_file * file, struct ggml_context * ctx, struct ggml_opt_context * opt) {
uint32_t version = file->read_u32();
GGML_ASSERT(version == 0);
file->read_raw(&opt->params, sizeof(opt->params));
file->read_raw(&opt->nx, sizeof(opt->nx));
ggml_opt_init(ctx, opt, opt->params, opt->nx);
file->read_raw(&opt->iter, sizeof(opt->iter));
opt->just_initialized = (bool) file->read_u32();
switch (opt->params.type) {
case GGML_OPT_ADAM:
{
read_tensor(file, opt->adam.x);
read_tensor(file, opt->adam.g1);
read_tensor(file, opt->adam.g2);
read_tensor(file, opt->adam.m);
read_tensor(file, opt->adam.v);
read_tensor(file, opt->adam.mh);
read_tensor(file, opt->adam.vh);
if (opt->adam.pf) { read_tensor(file, opt->adam.pf); }
file->read_raw(&opt->adam.fx_best, sizeof(opt->adam.fx_best));
file->read_raw(&opt->adam.fx_prev, sizeof(opt->adam.fx_prev));
file->read_raw(&opt->adam.n_no_improvement, sizeof(opt->adam.n_no_improvement));
} break;
case GGML_OPT_LBFGS:
{
GGML_ASSERT(opt->adam.x != NULL);
read_tensor(file, opt->lbfgs.x);
read_tensor(file, opt->lbfgs.xp);
read_tensor(file, opt->lbfgs.g);
read_tensor(file, opt->lbfgs.gp);
read_tensor(file, opt->lbfgs.d);
if (opt->lbfgs.pf) { read_tensor(file, opt->lbfgs.pf); }
read_tensor(file, opt->lbfgs.lmal);
read_tensor(file, opt->lbfgs.lmys);
read_tensor(file, opt->lbfgs.lms);
read_tensor(file, opt->lbfgs.lmy);
file->read_raw(&opt->lbfgs.fx_best, sizeof(opt->lbfgs.fx_best));
file->read_raw(&opt->lbfgs.step, sizeof(opt->lbfgs.step));
file->read_raw(&opt->lbfgs.j, sizeof(opt->lbfgs.j));
file->read_raw(&opt->lbfgs.k, sizeof(opt->lbfgs.k));
file->read_raw(&opt->lbfgs.end, sizeof(opt->lbfgs.end));
file->read_raw(&opt->lbfgs.n_no_improvement, sizeof(opt->lbfgs.n_no_improvement));
} break;
}
}
void save_checkpoint(struct my_llama_model * model, struct ggml_opt_context * opt, const char * filename) {
struct llama_file file(filename, "wb");
if (file.fp == NULL) {
return;
}
const uint32_t magic = 'ggcp';
const uint32_t version = 0;
file.write_u32(magic);
file.write_u32(version);
file.write_u32(model->train_its);
file.write_u32(model->train_samples);
file.write_u32(model->train_tokens);
file.write_u32(model->hparams.n_vocab);
file.write_u32(model->hparams.n_embd);
file.write_u32(model->hparams.n_mult);
file.write_u32(model->hparams.n_head);
file.write_u32(model->hparams.n_layer);
file.write_u32(model->hparams.n_rot);
write_tensor(&file, model->tok_embeddings);
write_tensor(&file, model->norm);
write_tensor(&file, model->output);
for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
auto & layer = model->layers[i];
write_tensor(&file, layer.attention_norm);
write_tensor(&file, layer.wq);
write_tensor(&file, layer.wk);
write_tensor(&file, layer.wv);
write_tensor(&file, layer.wo);
write_tensor(&file, layer.ffn_norm);
write_tensor(&file, layer.w1);
write_tensor(&file, layer.w2);
write_tensor(&file, layer.w3);
}
write_opt_context(&file, opt);
}
bool load_checkpoint(struct my_llama_model * model, struct ggml_opt_context * opt, const char * filename, bool init) {
struct llama_file file(filename, "rb");
uint32_t magic;
uint32_t version;
uint32_t train_its = 0;
uint32_t train_samples = 0;
uint32_t train_tokens = 0;
if (file.fp) {
printf("%s: Loading model from '%s'.\n", __func__, filename);
magic = file.read_u32();
GGML_ASSERT(magic == 'ggcp');
version = file.read_u32();
GGML_ASSERT(version == 0);
train_its = file.read_u32();
train_samples = file.read_u32();
train_tokens = file.read_u32();
model->hparams.n_vocab = file.read_u32();
model->hparams.n_embd = file.read_u32();
model->hparams.n_mult = file.read_u32();
model->hparams.n_head = file.read_u32();
model->hparams.n_layer = file.read_u32();
model->hparams.n_rot = file.read_u32();
print_params(&model->hparams);
}
if (init) {
init_model(model);
}
if (file.fp) {
model->train_its = train_its;
model->train_samples = train_samples;
model->train_tokens = train_tokens;
}
printf("%s: Training iterations: %u.\n", __func__, model->train_its);
printf("%s: Training samples: %u.\n", __func__, model->train_samples);
printf("%s: Training tokens: %u.\n", __func__, model->train_tokens);
if (file.fp) {
read_tensor(&file, model->tok_embeddings);
read_tensor(&file, model->norm);
read_tensor(&file, model->output);
for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
auto & layer = model->layers[i];
read_tensor(&file, layer.attention_norm);
read_tensor(&file, layer.wq);
read_tensor(&file, layer.wk);
read_tensor(&file, layer.wv);
read_tensor(&file, layer.wo);
read_tensor(&file, layer.ffn_norm);
read_tensor(&file, layer.w1);
read_tensor(&file, layer.w2);
read_tensor(&file, layer.w3);
}
read_opt_context(&file, model->ctx, opt);
}
return (file.fp != NULL);
}
void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * model, const char * filename) {
struct llama_file file(filename, "wb");
if (file.fp == NULL) {
return;
}
// write_magic
file.write_u32(LLAMA_FILE_MAGIC); // magic
file.write_u32(LLAMA_FILE_VERSION); // version
// write_hparams
file.write_u32(model->hparams.n_vocab);
file.write_u32(model->hparams.n_embd);
file.write_u32(model->hparams.n_mult);
file.write_u32(model->hparams.n_head);
file.write_u32(model->hparams.n_layer);
file.write_u32(model->hparams.n_rot);
file.write_u32(LLAMA_FTYPE_ALL_F32);
// write_vocab
uint32_t n_vocab = model->hparams.n_vocab;
for (uint32_t i = 0; i < n_vocab; i++) {
const auto & token_score = vocab->id_to_token.at(i);
file.write_u32((uint32_t) token_score.tok.size());
file.write_raw(token_score.tok.data(), token_score.tok.size());
file.write_raw(&token_score.score, sizeof(token_score.score));
}
// write tensors
write_tensor(&file, model->tok_embeddings);
write_tensor(&file, model->norm);
write_tensor(&file, model->output);
for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
auto & layer = model->layers[i];
write_tensor(&file, layer.attention_norm);
write_tensor(&file, layer.wq);
write_tensor(&file, layer.wk);
write_tensor(&file, layer.wv);
write_tensor(&file, layer.wo);
write_tensor(&file, layer.ffn_norm);
write_tensor(&file, layer.w1);
write_tensor(&file, layer.w2);
write_tensor(&file, layer.w3);
}
}
float cosine_decay(const int decay_steps, const float alpha, int step) {
if (step > decay_steps) {
step = decay_steps;
}
const float cosine_decay = 0.50f*(1.0f + cosf(3.14159265359f*step/decay_steps));
const float decay = (1 - alpha)*cosine_decay + alpha;
return decay;
}
float cosine_decay_restart(int decay_steps, const float alpha, int step, float restart_step_mult) {
while (step > decay_steps) {
step -= decay_steps;
decay_steps = (int) restart_step_mult * decay_steps;
}
return cosine_decay(decay_steps, alpha, step);
}
struct train_params {
const char * fn_vocab_model;
const char * fn_train_data;
const char * fn_checkpoint_in;
const char * fn_checkpoint_out;
const char * fn_model_out;
uint32_t seed;
int n_ctx;
int n_embd;
int n_mult;
int n_head;
int n_layer;
int n_rotmax;
int n_threads;
int n_batch;
int n_examples;
int n_predict;
int print_info_interval;
int print_details_interval;
bool samples_start_after_nl;
bool use_adam;
bool use_flash;
bool use_scratch;
// only adam
int warmup;
int cos_decay_steps;
float cos_decay_restart;
float cos_decay_alpha;
int lbfgs_n_iter;
int adam_n_iter;
float adam_alpha;
float adam_decay;
int mem_model_gb;
int mem_compute_gb;
int mem_compute0_gb;
int mem_compute1_gb;
};
struct train_params get_default_train_params() {
struct train_params params;
params.fn_vocab_model = "ggml-vic7b-uncensored-q4_0.bin";
params.fn_train_data = "shakespeare.txt";
params.fn_checkpoint_in = "checkpoint.bin";
params.fn_checkpoint_out = "checkpoint.bin";
params.fn_model_out = "ggml-checkpoint-f32.bin";
params.seed = -1;
params.n_ctx = 128;
params.n_embd = 256;
params.n_mult = 256;
params.n_head = 8;
params.n_layer = 16;
params.n_rotmax = 64;
params.n_threads = 6;
params.n_batch = 8;
params.n_examples = 8;
params.n_predict = 1024;
params.print_info_interval = 1;
params.print_details_interval = 2;
params.samples_start_after_nl = false;
params.use_adam = true;
params.use_flash = true;
params.use_scratch = true;
// only adam
params.warmup = 100;
params.cos_decay_steps = 1000;
params.cos_decay_restart = 1.1f;
params.cos_decay_alpha = 0.0f;
params.lbfgs_n_iter = 16;
params.adam_n_iter = 16;
params.adam_alpha = 1e-3f;
params.adam_decay = 1e-3f;
params.mem_model_gb = 2;
params.mem_compute_gb = 24;
params.mem_compute0_gb = 8;
params.mem_compute1_gb = 2;
return params;
}
void train_print_usage(int /*argc*/, char ** argv, const struct train_params * params) {
fprintf(stderr, "usage: %s [options]\n", argv[0]);
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " --vocab-model FNAME model path from which to load vocab (default '%s')\n", params->fn_vocab_model);
fprintf(stderr, " --train-data FNAME path from which to load training data (default '%s')\n", params->fn_train_data);
fprintf(stderr, " --checkpoint-in FNAME path from which to load training checkpoint (default '%s')\n", params->fn_checkpoint_in);
fprintf(stderr, " --checkpoint-out FNAME path to save training checkpoint (default '%s')\n", params->fn_checkpoint_out);
fprintf(stderr, " --model-out FNAME path to save ggml model (default '%s')\n", params->fn_model_out);
fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for -1)\n");
fprintf(stderr, " -c N, --ctx N Context size used during training (default %d)\n", params->n_ctx);
fprintf(stderr, " --embd N Embedding size used for new models (default %d)\n", params->n_embd);
fprintf(stderr, " --mult N Mult size used for new models, influences feedforward size. (default %d)\n", params->n_mult);
fprintf(stderr, " --head N Number of heads for new models (default %d)\n", params->n_head);
fprintf(stderr, " --layer N Number of layers for new models (default %d)\n", params->n_layer);
fprintf(stderr, " --rotmax N Maximal number Rope dimensions for new models (default %d)\n", params->n_rotmax);
fprintf(stderr, " -t N, --threads N Number of threads (default %d)\n", params->n_threads);
fprintf(stderr, " -b N, --batch N Parallel batch size (default %d)\n", params->n_batch);
fprintf(stderr, " -n N, --examples N Number of examples to train (default %d)\n", params->n_examples);
fprintf(stderr, " --predict N Number of tokens to generate after training (default %d)\n", params->n_predict);
fprintf(stderr, " --print-info-interval N Print infos during training each N examples (default %d)\n", params->print_info_interval);
fprintf(stderr, " --print-details-interval N Print details during training each N examples (default %d)\n", params->print_details_interval);
fprintf(stderr, " --samples-after-nl Training samples start after newlines. (default %s)\n", params->samples_start_after_nl ? "on" : "off");
fprintf(stderr, " --use-lbfgs Use LBFGS optimizer instead of default Adam\n");
fprintf(stderr, " --use-adam Use Adam optimizer (default)\n");
fprintf(stderr, " --no-flash Don't use flash attention.\n");
fprintf(stderr, " --use-flash Use flash attention (default)\n");
fprintf(stderr, " --no-scratch Don't use scratch buffers\n");
fprintf(stderr, " --use-scratch Use scratch buffers (default)\n");
fprintf(stderr, " --warmup N Number of warmup steps (default %d)\n", params->warmup);
fprintf(stderr, " --cos-decay-steps N Number of cosine decay steps (default %d)\n", params->cos_decay_steps);
fprintf(stderr, " --cos-decay-restart N Increase of cosine decay steps after restart (default %f)\n", params->cos_decay_restart);
fprintf(stderr, " --cos-decay-alpha N Cosine decay alpha (default %f)\n", params->cos_decay_alpha);
fprintf(stderr, " --lbfgs-iter N Maximum number of LBFGS optimization iterations for each batch (default %d)\n", params->lbfgs_n_iter);
fprintf(stderr, " --adam-iter N Maximum number of Adam optimization iterations for each batch (default %d)\n", params->adam_n_iter);
fprintf(stderr, " --adam-alpha N Adam learning rate alpha (default %f)\n", params->adam_alpha);
fprintf(stderr, " --adam-decay N AdamW weight decay. Values greater zero enable AdamW instead of regular Adam. (default %f)\n", params->adam_decay);
fprintf(stderr, " --mem-model N Memory to allocate for model and cache in gigabytes. (default %d)\n", params->mem_model_gb);
fprintf(stderr, " --mem-compute N Memory to allocate for compute in gigabytes. (default %d)\n", params->mem_compute_gb);
fprintf(stderr, " --mem-compute0 N Memory to allocate for compute in gigabytes. (default %d)\n", params->mem_compute0_gb);
fprintf(stderr, " --mem-compute1 N Memory to allocate for compute in gigabytes. (default %d)\n", params->mem_compute1_gb);
fprintf(stderr, "\n");
}
bool train_params_parse(int argc, char ** argv, struct train_params * params) {
bool invalid_param = false;
std::string arg;
struct train_params default_params = get_default_train_params();
const std::string arg_prefix = "--";
for (int i = 1; i < argc; i++) {
arg = argv[i];
if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
std::replace(arg.begin(), arg.end(), '_', '-');
}
if (arg == "--vocab-model") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->fn_vocab_model = argv[i];
} else if (arg == "--train-data") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->fn_train_data = argv[i];
} else if (arg == "--checkpoint-in") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->fn_checkpoint_in = argv[i];
} else if (arg == "--checkpoint-out") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->fn_checkpoint_out = argv[i];
} else if (arg == "--model-out") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->fn_model_out = argv[i];
} else if (arg == "-s" || arg == "--seed") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->seed = std::stoi(argv[i]);
} else if (arg == "-c" || arg == "--ctx") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_ctx = std::stoi(argv[i]);
} else if (arg == "--embd") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_embd = std::stoi(argv[i]);
} else if (arg == "--mult") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_mult = std::stoi(argv[i]);
} else if (arg == "--head") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_head = std::stoi(argv[i]);
} else if (arg == "--layer") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_layer = std::stoi(argv[i]);
} else if (arg == "--rotmax") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_rotmax = std::stoi(argv[i]);
} else if (arg == "-t" || arg == "--threads") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_threads = std::stoi(argv[i]);
} else if (arg == "-b" || arg == "--batch") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_batch = std::stoi(argv[i]);
} else if (arg == "-n" || arg == "--examples") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_examples = std::stoi(argv[i]);
} else if (arg == "--predict") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_predict = std::stoi(argv[i]);
} else if (arg == "--print-info-interval") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->print_info_interval = std::stoi(argv[i]);
} else if (arg == "--print-details-interval") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->print_details_interval = std::stoi(argv[i]);
} else if (arg == "--samples-after-nl") {
params->samples_start_after_nl = true;
} else if (arg == "--use-lbfgs") {
params->use_adam = false;
} else if (arg == "--use-adam") {
params->use_adam = true;
} else if (arg == "--no-flash") {
params->use_flash = false;
} else if (arg == "--use-flash") {
params->use_flash = true;
} else if (arg == "--no-scratch") {
params->use_scratch = false;
} else if (arg == "--use-scratch") {
params->use_scratch = true;
} else if (arg == "--warmup") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->warmup = std::stoi(argv[i]);
} else if (arg == "--cos-decay-steps") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->cos_decay_steps = std::stof(argv[i]);
} else if (arg == "--cos-decay-restart") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->cos_decay_restart = std::stof(argv[i]);
} else if (arg == "--cos-decay-alpha") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->cos_decay_alpha = std::stof(argv[i]);
} else if (arg == "--lbfgs-iter") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->lbfgs_n_iter = std::stoi(argv[i]);
} else if (arg == "--adam-iter") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->adam_n_iter = std::stoi(argv[i]);
} else if (arg == "--adam-alpha") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->adam_alpha = std::stof(argv[i]);
} else if (arg == "--adam-decay") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->adam_decay = std::stof(argv[i]);
} else if (arg == "--mem-model") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->mem_model_gb = std::stoi(argv[i]);
} else if (arg == "--mem-compute") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->mem_compute_gb = std::stoi(argv[i]);
} else if (arg == "--mem-compute0") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->mem_compute0_gb = std::stoi(argv[i]);
} else if (arg == "--mem-compute1") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->mem_compute1_gb = std::stoi(argv[i]);
} else if (arg == "-h" || arg == "--help") {
train_print_usage(argc, argv, &default_params);
exit(0);
} else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
train_print_usage(argc, argv, &default_params);
exit(1);
}
}
if (invalid_param) {
fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
train_print_usage(argc, argv, &default_params);
exit(1);
}
return true;
}
int main(int argc, char ** argv) {
struct train_params params = get_default_train_params();
if (!train_params_parse(argc, argv, &params)) {
return 1;
}
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
printf("%s: seed: %u\n", __func__, params.seed);
srand(params.seed);
struct llama_context_params llama_params = llama_context_default_params();
llama_params.vocab_only = true;
struct llama_model * lmodel = llama_load_model_from_file(params.fn_vocab_model, llama_params);
struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_params);
struct llama_vocab vocab;
{
std::vector<const char *> strings;
std::vector<float> scores;
int n_vocab = llama_n_vocab(lctx);
strings.resize(n_vocab, NULL);
scores.resize(n_vocab, 0);
n_vocab = llama_get_vocab(lctx, strings.data(), scores.data(), n_vocab);
GGML_ASSERT(n_vocab == llama_n_vocab(lctx));
vocab.id_to_token.resize(n_vocab);
for (int i=0; i<n_vocab; ++i) {
std::string tok = std::string(strings[i]);
float score = scores[i];
vocab.id_to_token[i].tok = tok;
vocab.id_to_token[i].score = score;
vocab.token_to_id.emplace(tok, i);
}
}
printf("%s: tokenize training data\n", __func__);
std::vector<llama_token> train_tokens;
if (tokenize_file(lctx, params.fn_train_data, train_tokens) < 0) {
fprintf(stderr, "%s: failed to tokenize file '%s'\n", __func__, params.fn_train_data);
}
printf("%s: number of training tokens: %d\n", __func__, (int) train_tokens.size());
struct my_llama_model model;
model.hparams.n_vocab = llama_n_vocab(lctx);
model.hparams.n_ctx = params.n_ctx;
model.hparams.n_embd = params.n_embd;
model.hparams.n_mult = params.n_mult;
model.hparams.n_head = params.n_head;
model.hparams.n_layer = params.n_layer;
model.hparams.n_rot = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head);
print_params(&model.hparams);
std::vector<size_t> token_noccurs;
std::vector<bool> token_notavail;
token_noccurs.resize(model.hparams.n_vocab, 0);
token_notavail.resize(model.hparams.n_vocab, true);
for (int i = 0; i < (int) train_tokens.size(); ++i) {
++token_noccurs[train_tokens[i]];
token_notavail[train_tokens[i]] = false;
}
std::vector<float> token_freq;
token_freq.resize(model.hparams.n_vocab, 0);
int n_unique_tokens = 0;
for (int i = 0; i < (int) token_noccurs.size(); ++i) {
token_freq[i] = (float) token_noccurs[i] / (float) train_tokens.size();
n_unique_tokens += (token_noccurs[i] > 0) ? 1 : 0;
}
printf("%s: number of unique tokens: %d\n", __func__, n_unique_tokens);
struct my_llama_kv_cache kv_self;
struct ggml_init_params lcparams;
lcparams.mem_size = 1024ll*1024ll*1024ll*((size_t) params.mem_model_gb);
lcparams.mem_buffer = NULL;
lcparams.no_alloc = false;
model.ctx = ggml_init(lcparams);
kv_self.ctx = model.ctx;
my_llama_sampler sampler;
int n_tokens = model.hparams.n_ctx;
int n_vocab = model.hparams.n_vocab;
int n_batch = params.n_batch;
struct ggml_opt_context * opt = (struct ggml_opt_context *) alloca(sizeof(struct ggml_opt_context));
memset(opt, 0, sizeof(struct ggml_opt_context));
struct ggml_opt_params opt_params_adam = ggml_opt_default_params(GGML_OPT_ADAM);
struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_LBFGS);
opt_params_adam.print_forward_graph = false;
opt_params_adam.print_backward_graph = false;
opt_params_adam.n_threads = params.n_threads;
opt_params_adam.adam.n_iter = params.adam_n_iter;
opt_params_adam.adam.sched = 1.0f;
opt_params_adam.adam.alpha = params.adam_alpha;
opt_params_adam.adam.decay = params.adam_decay;
opt_params_lbfgs.print_forward_graph = false;
opt_params_lbfgs.print_backward_graph = false;
opt_params_lbfgs.n_threads = params.n_threads;
opt_params_lbfgs.lbfgs.n_iter = params.lbfgs_n_iter;
opt->ctx = model.ctx;
opt->params = params.use_adam ? opt_params_adam : opt_params_lbfgs;
printf("%s: init model\n", __func__);
bool existed = load_checkpoint(&model, opt, params.fn_checkpoint_in, true);
set_param_model(&model);
opt->params = params.use_adam ? opt_params_adam : opt_params_lbfgs;
opt->iter = model.train_its;
printf("%s: opt iter %d\n", __func__, opt->iter);
bool from_scratch = !existed;
if (from_scratch) {
randomize_model(&model, params.seed, 0.0f, 1.0f, -1.0f, +1.0f);
}
init_kv_cache(&kv_self, &model, 1);
// init_kv_cache(&kv_self, &model, n_batch);
init_sampler(&sampler, lctx);
printf("used_mem model+cache: %zu bytes\n", ggml_used_mem(model.ctx));
// ggml_print_tensor_objects(model.ctx);
size_t compute_size = 1024ll*1024ll*1024ll*((size_t) params.mem_compute_gb);
uint8_t * compute_addr = new uint8_t[compute_size];
size_t size_buf_0 = 1024ll*1024ll*1024ll*((size_t) params.mem_compute0_gb);
size_t size_buf_1 = 1024ll*1024ll*1024ll*((size_t) params.mem_compute1_gb);
uint8_t * compute_buf_0 = new uint8_t[size_buf_0];
uint8_t * compute_buf_1 = new uint8_t[size_buf_1];
GGML_ASSERT(n_tokens < (int) train_tokens.size());
std::vector<int> train_samples;
train_samples.push_back(0);
for (int i = 1; i < (int) train_tokens.size() - n_tokens; ++i) {
if (!params.samples_start_after_nl || (train_tokens[i-1] == llama_token_nl())) {
train_samples.push_back(i);
}
}
shuffle_ints(train_samples.data(), train_samples.data() + train_samples.size());
for (int i = 0; i < (int) train_samples.size(); ++i) {
GGML_ASSERT(train_samples[i]+n_tokens-1 < (int) train_tokens.size());
}
printf("%s: begin training\n", __func__);
for (int ex = 0; ex < params.n_examples; ++ex) {
if (ex*n_batch >= (int) train_samples.size()) {
shuffle_ints(train_samples.data(), train_samples.data() + train_samples.size());
for (int i = 0; i < (int) train_samples.size(); ++i) {
GGML_ASSERT(train_samples[i]+n_tokens-1 < (int) train_tokens.size());
}
}
struct ggml_init_params cparams = {
/*.mem_size =*/ compute_size,
/*.mem_buffer =*/ compute_addr,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx0 = ggml_init(cparams);
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 * target_logits = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_vocab, n_tokens, n_batch);
struct ggml_tensor * target_probs = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_vocab, n_tokens, n_batch);
int n_past = 0;
struct ggml_tensor * gfbuf = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32) + (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0));
struct ggml_tensor * gbbuf = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32) + (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0));
memset(gfbuf->data, 0, ggml_nbytes(gfbuf));
memset(gbbuf->data, 0, ggml_nbytes(gbbuf));
struct ggml_cgraph * gf = (struct ggml_cgraph *) gfbuf->data;
struct ggml_cgraph * gb = (struct ggml_cgraph *) gbbuf->data;
// ggml_cgraph gf = {};
gf->n_threads = params.n_threads;
gb->n_threads = params.n_threads;
get_example_targets_batch(lctx, train_samples.data(), train_samples.size(), train_tokens.data(), train_tokens.size(), ex, tokens_input, target_logits, target_probs);
GGML_ASSERT(n_past == 0);
struct ggml_tensor * loss = NULL;
struct ggml_tensor * logits = NULL;
if (params.use_scratch) {
loss = forward_batch_wo_cache_flash_attn_train(
&model, ctx0,
gf, gb,
&logits, tokens_input, target_probs,
compute_buf_0, compute_buf_1,
size_buf_0, size_buf_1,
n_tokens, n_batch);
} else if (params.use_flash) {
logits = forward_batch_wo_cache_flash_attn(&model, ctx0, gf, tokens_input, n_tokens, n_batch);
loss = cross_entropy_loss(ctx0, logits, target_probs);
ggml_build_forward_expand(gf, loss);
*gb = ggml_build_backward(ctx0, gf, true);
} else {
logits = forward_batch_wo_cache(&model, ctx0, gf, tokens_input, n_tokens, n_batch);
loss = cross_entropy_loss(ctx0, logits, target_probs);
ggml_build_forward_expand(gf, loss);
*gb = ggml_build_backward(ctx0, gf, true);
}
ggml_graph_compute(ctx0, gf);
size_t used_mem_before_opt = ggml_used_mem(ctx0);
float error_before_opt = ggml_get_f32_1d(loss, 0);
opt->params.adam.sched = (opt->iter < params.warmup)
? (float) opt->iter / (float) params.warmup
: cosine_decay_restart(
params.cos_decay_steps,
params.cos_decay_alpha,
opt->iter - params.warmup,
params.cos_decay_restart);
printf("%s: opt->params.adam.sched %.5f\n", __func__, opt->params.adam.sched);
ggml_opt_resume_g(ctx0, opt, loss, gf, gb);
size_t used_mem_after_opt = ggml_used_mem(ctx0);
model.train_its = opt->iter;
model.train_samples += n_batch;
model.train_tokens += n_batch * n_tokens;
ggml_graph_compute(ctx0, gf);
float error_after_opt = ggml_get_f32_1d(loss, 0);
if (params.print_info_interval > 0 && ex % params.print_info_interval == 0) {
printf("Example %d, opt iter %d\n", ex, opt->iter);
printf("error_before_opt: %.6f\n", error_before_opt);
printf("error_after_opt: %.6f\n", error_after_opt);
printf("used_mem_before_opt: %zu bytes\n", used_mem_before_opt);
printf("used_mem_after_opt: %zu bytes\n", used_mem_after_opt);
}
if (params.print_details_interval > 0 && ex % params.print_details_interval == 0) {
// set_logits_masked(logits, token_notavail, -1e9);
for (int i=0; i<n_batch; ++i) {
init_sampler(&sampler, lctx);
for (int k=0; k<n_tokens; ++k) {
int32_t token = sample(&sampler,
(float *) ((char *) logits->data + i*logits->nb[2] + k*logits->nb[1]),
(llama_token *) ((char *) tokens_input->data + i*tokens_input->nb[1]),
k);
* ((int32_t *) ((char *) after_opt_best_samples->data + i*after_opt_best_samples->nb[1] + k*after_opt_best_samples->nb[0])) = token;
}
}
// printf("probabilities after optimization:\n");
// print_matrix(after_opt_probs);
printf("Example:\n---\n");
print_tokens_batch(lctx, tokens_input);
printf("\n---\n");
// printf("best samples after optimization:\n---\n");
printf("samples after optimization:\n---\n");
print_tokens_batch(lctx, after_opt_best_samples);
printf("\n---\n");
}
ggml_free(ctx0);
}
if (params.n_examples > 0) {
save_checkpoint(&model, opt, params.fn_checkpoint_out);
}
if (strlen(params.fn_model_out) > 0) {
save_as_llama_model(&vocab, &model, params.fn_model_out);
}
{
int n_gen = params.n_predict;
int sample_ctx = n_tokens - n_tokens/8;
sampler.params.temp = 0.2f;
sampler.params.repeat_penalty = 1.1f;
sampler.params.mirostat = 2;
init_sampler(&sampler, lctx);
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 * target_logits = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, n_vocab, n_tokens);
struct ggml_tensor * target_probs = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, n_vocab, n_tokens);
get_example_targets(train_samples.data(), train_samples.size(), train_tokens.data(), train_tokens.size(), rand()%train_samples.size(), tokens_input, target_logits, target_probs);
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(lctx, ggml_get_i32_1d(tokens_input, i));
}
printf("---\n");
for (int i=0; i<n_gen; ++i) {
struct ggml_init_params cparams = {
/*.mem_size =*/ compute_size,
/*.mem_buffer =*/ compute_addr,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx0 = ggml_init(cparams);
ggml_cgraph gf = {};
gf.n_threads = params.n_threads;
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(ctx0, &gf);
//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);
// set_logits_masked(logits, token_notavail, -1e9);
int token = sample(&sampler,
(float *) ((char *) logits->data + (sample_ctx-1)*logits->nb[1]),
(llama_token *) tokens_input->data,
sample_ctx-1);
//int token = ggml_get_i32_1d(best_samples, sample_ctx-1);
// print_row(probs, sample_at);
print_token(lctx, token);
lshift_examples(tokens_input, target_logits, target_probs, 1);
ggml_set_i32_1d(tokens_input, 0, 0);
ggml_set_i32_1d(tokens_input, sample_ctx-1, token);
ggml_free(ctx0);
}
}
delete[] compute_addr;
delete[] compute_buf_0;
delete[] compute_buf_1;
llama_free(lctx);
llama_free_model(lmodel);
ggml_free(model.ctx);
return 0;
}
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