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llama.cpp/examples/train-text-from-scratch/train-text-from-scratch.cpp
Georgi Gerganov ab336a9d5e
code : normalize enum names (#5697) 
* coda : normalize enum names

ggml-ci

* code : cont

* code : cont
2024-02-25 12:09:09 +02:00

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#include "ggml.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "common.h"
#include "train.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 my_llama_hparams {
uint32_t n_vocab = 32000;
uint32_t n_ctx = 512;
uint32_t n_embd = 4096;
uint32_t n_head = 32;
uint32_t n_layer = 32;
uint32_t n_rot = 64;
uint32_t n_ff = 11008;
// float f_norm_eps = 1e-5f; // falcon
float f_norm_rms_eps = 1e-5f; // llama
float rope_freq_base = 10000.0f;
float rope_freq_scale = 1.0f;
};
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 * ffn_gate; // w1
struct ggml_tensor * ffn_down; // w2
struct ggml_tensor * ffn_up; // w3
};
struct my_llama_model {
struct ggml_context * ctx = NULL;
ggml_backend_buffer_t data = NULL;
my_llama_hparams hparams;
struct ggml_tensor * tok_embeddings;
struct ggml_tensor * norm;
struct ggml_tensor * output;
std::vector<my_llama_layer> layers;
};
// gguf constants (sync with gguf.py)
static const char * LLM_KV_TRAINING_TYPE_TRAIN_MODEL = "train_model";
static const char * LLM_KV_TRAINING_TYPE = "training.type";
static const char * LLM_KV_GENERAL_ARCHITECTURE = "general.architecture";
static const char * LLM_KV_GENERAL_FILE_TYPE = "general.file_type";
static const char * LLM_KV_CONTEXT_LENGTH = "%s.context_length";
static const char * LLM_KV_EMBEDDING_LENGTH = "%s.embedding_length";
static const char * LLM_KV_BLOCK_COUNT = "%s.block_count";
static const char * LLM_KV_FEED_FORWARD_LENGTH = "%s.feed_forward_length";
static const char * LLM_KV_ATTENTION_HEAD_COUNT = "%s.attention.head_count";
static const char * LLM_KV_ATTENTION_LAYERNORM_RMS_EPS = "%s.attention.layer_norm_rms_epsilon";
static const char * LLM_KV_ROPE_DIMENSION_COUNT = "%s.rope.dimension_count";
static const char * LLM_KV_ROPE_FREQ_BASE = "%s.rope.freq_base"; // TODO load in llama.cpp
static const char * LLM_KV_ROPE_SCALE_LINEAR = "%s.rope.scale_linear";
static const char * LLM_KV_TOKENIZER_MODEL = "tokenizer.ggml.model";
static const char * LLM_KV_TOKENIZER_LIST = "tokenizer.ggml.tokens";
static const char * LLM_KV_TOKENIZER_TOKEN_TYPE = "tokenizer.ggml.token_type";
static const char * LLM_KV_TOKENIZER_SCORES = "tokenizer.ggml.scores";
static const char * LLM_KV_TOKENIZER_MERGES = "tokenizer.ggml.merges";
static const char * LLM_KV_TOKENIZER_BOS_ID = "tokenizer.ggml.bos_token_id";
static const char * LLM_KV_TOKENIZER_EOS_ID = "tokenizer.ggml.eos_token_id";
static const char * LLM_KV_TOKENIZER_UNK_ID = "tokenizer.ggml.unknown_token_id";
static const char * LLM_KV_TOKENIZER_SEP_ID = "tokenizer.ggml.seperator_token_id";
static const char * LLM_KV_TOKENIZER_PAD_ID = "tokenizer.ggml.padding_token_id";
static const char * LLM_TENSOR_TOKEN_EMBD = "token_embd";
static const char * LLM_TENSOR_OUTPUT_NORM = "output_norm";
static const char * LLM_TENSOR_OUTPUT = "output";
static const char * LLM_TENSOR_ATTN_NORM = "blk.%d.attn_norm";
static const char * LLM_TENSOR_ATTN_Q = "blk.%d.attn_q";
static const char * LLM_TENSOR_ATTN_K = "blk.%d.attn_k";
static const char * LLM_TENSOR_ATTN_V = "blk.%d.attn_v";
static const char * LLM_TENSOR_ATTN_OUT = "blk.%d.attn_output";
static const char * LLM_TENSOR_FFN_NORM = "blk.%d.ffn_norm";
static const char * LLM_TENSOR_FFN_GATE = "blk.%d.ffn_gate";
static const char * LLM_TENSOR_FFN_DOWN = "blk.%d.ffn_down";
static const char * LLM_TENSOR_FFN_UP = "blk.%d.ffn_up";
static void print_params(struct my_llama_hparams * params) {
printf("%s: n_vocab: %u\n", __func__, params->n_vocab);
printf("%s: n_ctx: %u\n", __func__, params->n_ctx);
printf("%s: n_embd: %u\n", __func__, params->n_embd);
printf("%s: n_head: %u\n", __func__, params->n_head);
printf("%s: n_ff: %u\n", __func__, params->n_ff);
printf("%s: n_layer: %u\n", __func__, params->n_layer);
printf("%s: n_rot: %u\n", __func__, params->n_rot);
}
static 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.ffn_gate);
ggml_set_param(ctx, layer.ffn_down);
ggml_set_param(ctx, layer.ffn_up);
}
}
static 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 = hparams.n_ff;
std::vector<char> tn_buf;
tn_buf.resize(GGML_MAX_NAME);
auto tn = [&tn_buf](const char * key) -> const char * {
snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", key);
return tn_buf.data();
};
auto tni = [&tn_buf](const char * key, int bid) -> const char * {
snprintf(tn_buf.data(), tn_buf.size(), key, bid);
std::string s = tn_buf.data();
snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", s.c_str());
return tn_buf.data();
};
// context for model tensors without their data
struct ggml_init_params ctx_model_params;
ctx_model_params.mem_size = ggml_tensor_overhead()*2*(6 + n_layer*18);
ctx_model_params.mem_buffer = NULL;
ctx_model_params.no_alloc = true;
struct ggml_context * ctx = ggml_init(ctx_model_params);
model->ctx = ctx;
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, tn(LLM_TENSOR_TOKEN_EMBD));
ggml_set_name(model->norm, tn(LLM_TENSOR_OUTPUT_NORM));
ggml_set_name(model->output, tn(LLM_TENSOR_OUTPUT));
model->layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
auto & layer = model->layers[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.ffn_gate = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
layer.ffn_down = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd);
layer.ffn_up = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
ggml_set_name(layer.attention_norm, tni(LLM_TENSOR_ATTN_NORM, i));
ggml_set_name(layer.wq, tni(LLM_TENSOR_ATTN_Q, i));
ggml_set_name(layer.wk, tni(LLM_TENSOR_ATTN_K, i));
ggml_set_name(layer.wv, tni(LLM_TENSOR_ATTN_V, i));
ggml_set_name(layer.wo, tni(LLM_TENSOR_ATTN_OUT, i));
ggml_set_name(layer.ffn_norm, tni(LLM_TENSOR_FFN_NORM, i));
ggml_set_name(layer.ffn_gate, tni(LLM_TENSOR_FFN_GATE, i));
ggml_set_name(layer.ffn_down, tni(LLM_TENSOR_FFN_DOWN, i));
ggml_set_name(layer.ffn_up, tni(LLM_TENSOR_FFN_UP, i));
}
set_param_model(model);
// allocate data
model->data = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_cpu_buffer_type());
}
static 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(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.ffn_gate, rnd);
randomize_tensor_normal(layer.ffn_down, rnd);
randomize_tensor_normal(layer.ffn_up, rnd);
}
free_random_normal_distribution(rnd);
}
static struct ggml_tensor * llama_build_train_graphs(
struct my_llama_model * model,
ggml_gallocr_t alloc,
struct ggml_context * ctx,
struct ggml_cgraph * gf,
struct ggml_cgraph * gb,
struct ggml_cgraph * gb_tmp,
struct ggml_tensor * * logits,
struct ggml_tensor * tokens_input,
struct ggml_tensor * targets,
const int n_tokens,
const int n_batch,
const bool enable_flash_attn,
const bool enable_checkpointing,
const bool measure_only) {
ggml_set_scratch(ctx, { 0, 0, nullptr, });
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 = hparams.n_ff;
const float f_norm_rms_eps = hparams.f_norm_rms_eps;
const float rope_freq_base = hparams.rope_freq_base;
const float rope_freq_scale = hparams.rope_freq_scale;
auto set_name = [](struct ggml_tensor * t, const char * n) {
ggml_set_name(t, n);
if (t->grad) {
ggml_format_name(t->grad, "%s->grad", n);
}
};
// KQ_pos - contains the positions
struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, N);
ggml_set_input(KQ_pos);
// rope has so much parameters that we make a custom function for it
auto rope = [ctx, KQ_pos, n_rot, n_ctx, rope_freq_base, rope_freq_scale]
(struct ggml_tensor * t) -> struct ggml_tensor * {
// not capturing these, to silcence warnings
const int rope_mode = 0;
return ggml_rope_custom(
ctx, t, KQ_pos, n_rot, rope_mode, n_ctx, 0, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f
);
};
set_name(tokens_input, "tokens_input");
set_name(targets, "targets");
GGML_ASSERT(tokens_input->type == GGML_TYPE_I32);
struct ggml_tensor * t00 = ggml_reshape_1d(ctx, tokens_input, N*n_batch); set_name(t00, "t00"); assert_shape_1d(t00, N*n_batch);
struct ggml_tensor * t01 = ggml_get_rows(ctx, model->tok_embeddings, t00); set_name(t01, "t01"); assert_shape_2d(t01, n_embd, N*n_batch);
struct ggml_tensor * cur = t01;
std::vector<struct ggml_tensor *> checkpoints;
checkpoints.push_back(tokens_input);
checkpoints.push_back(targets);
checkpoints.push_back(t00);
checkpoints.push_back(t01);
const float kv_scale = 1.0f/sqrtf(float(n_embd)/n_head);
for (int il = 0; il < n_layer; ++il) {
struct my_llama_layer & layer = model->layers[il];
struct ggml_tensor * t02 = ggml_rms_norm (ctx, cur, f_norm_rms_eps); set_name(t02, "t02"); assert_shape_2d(t02, n_embd, N*n_batch);
struct ggml_tensor * t03 = ggml_repeat (ctx, layer.attention_norm, t02); set_name(t03, "t03"); assert_shape_2d(t03, n_embd, N*n_batch);
struct ggml_tensor * t04 = ggml_mul (ctx, t03, t02); set_name(t04, "t04"); assert_shape_2d(t04, n_embd, N*n_batch);
struct ggml_tensor * t05 = ggml_mul_mat (ctx, layer.wq, t04); set_name(t05, "t05"); assert_shape_2d(t05, n_embd, N*n_batch);
struct ggml_tensor * t06 = ggml_reshape_4d (ctx, t05, n_embd/n_head, n_head, N, n_batch); set_name(t06, "t06"); assert_shape_4d(t06, n_embd/n_head, n_head, N, n_batch);
struct ggml_tensor * t07 = rope (t06); set_name(t07, "t07"); assert_shape_4d(t07, n_embd/n_head, n_head, N, n_batch);
struct ggml_tensor * t08 = ggml_mul_mat (ctx, layer.wk, t04); set_name(t08, "t08"); assert_shape_2d(t08, n_embd, N*n_batch);
struct ggml_tensor * t09 = ggml_reshape_4d (ctx, t08, n_embd/n_head, n_head, N, n_batch); set_name(t09, "t09"); assert_shape_4d(t09, n_embd/n_head, n_head, N, n_batch);
struct ggml_tensor * t10 = rope (t09); set_name(t10, "t10"); assert_shape_4d(t10, n_embd/n_head, n_head, N, n_batch);
struct ggml_tensor * t11 = ggml_mul_mat (ctx, t04, layer.wv); set_name(t11, "t11"); assert_shape_2d(t11, N*n_batch, n_embd);
struct ggml_tensor * t12 = ggml_reshape_4d (ctx, t11, N, n_batch, n_embd/n_head, n_head); set_name(t12, "t12"); assert_shape_4d(t12, N, n_batch, n_embd/n_head, n_head);
struct ggml_tensor * t13 = ggml_permute (ctx, t07, 0, 2, 1, 3); set_name(t13, "t13"); assert_shape_4d(t13, n_embd/n_head, N, n_head, n_batch);
struct ggml_tensor * t14 = ggml_permute (ctx, t10, 0, 2, 1, 3); set_name(t14, "t14"); assert_shape_4d(t14, n_embd/n_head, N, n_head, n_batch);
struct ggml_tensor * t15 = ggml_permute (ctx, t12, 0, 3, 1, 2); set_name(t15, "t15"); assert_shape_4d(t15, N, n_embd/n_head, n_head, n_batch);
struct ggml_tensor * t16;
if (enable_flash_attn) {
t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch);
} else {
struct ggml_tensor * t16_0 = ggml_mul_mat (ctx, t14, t13); set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch);
struct ggml_tensor * t16_1 = ggml_scale_inplace (ctx, t16_0, kv_scale); set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch);
struct ggml_tensor * t16_2 = ggml_diag_mask_inf_inplace(ctx, t16_1, n_past); set_name(t16_2, "t16_2"); assert_shape_4d(t16_2, N, N, n_head, n_batch);
struct ggml_tensor * t16_3 = ggml_soft_max_inplace (ctx, t16_2); set_name(t16_3, "t16_3"); assert_shape_4d(t16_3, N, N, n_head, n_batch);
t16 = ggml_mul_mat(ctx, t15, t16_3); set_name(t16, "t16"); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch);
}
struct ggml_tensor * t17 = ggml_permute (ctx, t16, 0, 2, 1, 3); set_name(t17, "t17"); assert_shape_4d(t17, n_embd/n_head, n_head, N, n_batch);
struct ggml_tensor * t18 = ggml_cont (ctx, t17); set_name(t18, "t18"); assert_shape_4d(t18, n_embd/n_head, n_head, N, n_batch);
struct ggml_tensor * t19 = ggml_reshape_2d (ctx, t18, n_embd, N*n_batch); set_name(t19, "t19"); assert_shape_2d(t19, n_embd, N*n_batch);
struct ggml_tensor * t20 = ggml_mul_mat (ctx, layer.wo, t19); set_name(t20, "t20"); assert_shape_2d(t20, n_embd, N*n_batch);
struct ggml_tensor * t21 = ggml_add (ctx, t20, cur); set_name(t21, "t21"); assert_shape_2d(t21, n_embd, N*n_batch);
struct ggml_tensor * t22 = ggml_rms_norm (ctx, t21, f_norm_rms_eps); set_name(t22, "t22"); assert_shape_2d(t22, n_embd, N*n_batch);
struct ggml_tensor * t23 = ggml_repeat (ctx, layer.ffn_norm, t22); set_name(t23, "t23"); assert_shape_2d(t23, n_embd, N*n_batch);
struct ggml_tensor * t24 = ggml_mul (ctx, t23, t22); set_name(t24, "t24"); assert_shape_2d(t24, n_embd, N*n_batch);
struct ggml_tensor * t25 = ggml_mul_mat (ctx, layer.ffn_up, t24); set_name(t25, "t25"); assert_shape_2d(t25, n_ff, N*n_batch);
struct ggml_tensor * t26 = ggml_mul_mat (ctx, layer.ffn_gate, t24); set_name(t26, "t26"); assert_shape_2d(t26, n_ff, N*n_batch);
struct ggml_tensor * t27 = ggml_silu (ctx, t26); set_name(t27, "t27"); assert_shape_2d(t27, n_ff, N*n_batch);
struct ggml_tensor * t28 = ggml_mul (ctx, t27, t25); set_name(t28, "t28"); assert_shape_2d(t28, n_ff, N*n_batch);
struct ggml_tensor * t29 = ggml_mul_mat (ctx, layer.ffn_down, t28); set_name(t29, "t29"); assert_shape_2d(t29, n_embd, N*n_batch);
struct ggml_tensor * t30 = ggml_add (ctx, t29, t21); set_name(t30, "t30"); assert_shape_2d(t30, n_embd, N*n_batch);
cur = t30;
checkpoints.push_back(cur);
}
struct ggml_tensor * t31 = ggml_rms_norm (ctx, cur, f_norm_rms_eps); set_name(t31, "t31"); assert_shape_2d(t31, n_embd, N*n_batch);
struct ggml_tensor * t32 = ggml_repeat (ctx, model->norm, t31); set_name(t32, "t32"); assert_shape_2d(t32, n_embd, N*n_batch);
struct ggml_tensor * t33 = ggml_mul (ctx, t32, t31); set_name(t33, "t33"); assert_shape_2d(t33, n_embd, N*n_batch);
struct ggml_tensor * t34 = ggml_mul_mat (ctx, model->output, t33); set_name(t34, "t34"); assert_shape_2d(t34, n_vocab, N*n_batch);
struct ggml_tensor * t35 = ggml_reshape_3d (ctx, t34, n_vocab, N, n_batch); set_name(t35, "t35"); assert_shape_3d(t35, n_vocab, N, n_batch);
struct ggml_tensor * t36 = ggml_cross_entropy_loss(ctx, t35, targets); set_name(t36, "t36"); assert_shape_1d(t36, 1);
checkpoints.push_back(t31);
checkpoints.push_back(t32);
checkpoints.push_back(t33);
checkpoints.push_back(t34);
checkpoints.push_back(t35);
checkpoints.push_back(t36);
ggml_build_forward_expand(gf, t36);
if (enable_checkpointing) {
ggml_build_backward_gradient_checkpointing(ctx, gf, gb, gb_tmp, checkpoints.data(), (int) checkpoints.size());
} else {
ggml_graph_cpy(gf, gb);
ggml_build_backward_expand(ctx, gf, gb, true);
}
if (alloc) {
// make sure some tensors are not reallocated by inserting new temporary nodes depending on them
int n_leafs_before = gb->n_leafs;
int n_nodes_before = gb->n_nodes;
// output tensors
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, 1.0f));
// input gradient
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, 1.0f));
// KQ_pos
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f));
GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL);
ggml_set_input(t36->grad);
// allocating checkpoints in one block to reduce memory fragmentation
// note: they will be freed in reverse order
for (int i = 0; i < (int) checkpoints.size(); ++i) {
if (checkpoints[i]->data == NULL && checkpoints[i]->view_src == NULL) {
ggml_set_input(checkpoints[i]);
}
}
//int n_leafs_after = gb->n_leafs;
//int n_nodes_after = gb->n_nodes;
if (measure_only) {
// FIXME: will still allocate
ggml_gallocr_reserve(alloc, gb);
} else {
ggml_gallocr_alloc_graph(alloc, gb);
if (!measure_only) {
int * data = (int *) KQ_pos->data;
for (int i = 0; i < N; ++i) {
data[i] = n_past + i;
}
}
}
// remove the additional nodes and leafs
for (int i = n_leafs_before; i < gb->n_leafs; ++i) {
gb->leafs[i] = NULL;
}
for (int i = n_nodes_before; i < gb->n_nodes; ++i) {
gb->nodes[i] = NULL;
}
gb->n_leafs = n_leafs_before;
gb->n_nodes = n_nodes_before;
}
*logits = t35;
return t36;
}
#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \
do { \
const std::string skey(key); \
const int kid = gguf_find_key(ctx, skey.c_str()); \
if (kid >= 0) { \
enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \
if (ktype != (type)) { \
die_fmt("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype)); \
} \
(dst) = func(ctx, kid); \
} else if (req) { \
die_fmt("key not found in model: %s", skey.c_str()); \
} \
} while (0)
static void load_llama_model_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model) {
// NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read
std::string arch;
std::vector<char> keybuf;
keybuf.resize(512);
auto kv = [&arch, &keybuf](const char * key) -> const char * {
snprintf(keybuf.data(), keybuf.size(), key, arch.c_str());
return keybuf.data();
};
std::vector<char> tn_buf;
tn_buf.resize(GGML_MAX_NAME);
auto tn = [&tn_buf](const char * key) -> const char * {
snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", key);
return tn_buf.data();
};
auto tni = [&tn_buf](const char * key, int bid) -> const char * {
snprintf(tn_buf.data(), tn_buf.size(), key, bid);
std::string s = tn_buf.data();
snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", s.c_str());
return tn_buf.data();
};
GGUF_GET_KEY(fctx, arch, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_GENERAL_ARCHITECTURE);
GGML_ASSERT(arch == "llama");
uint32_t ftype_u;
GGUF_GET_KEY(fctx, ftype_u, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_GENERAL_FILE_TYPE);
GGML_ASSERT((enum llama_ftype) ftype_u == LLAMA_FTYPE_ALL_F32);
// n_ctx was not saved in earlier checkpoint file versions, so we make it optional here
GGUF_GET_KEY(fctx, model->hparams.n_ctx, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_CONTEXT_LENGTH));
GGUF_GET_KEY(fctx, model->hparams.n_embd, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_EMBEDDING_LENGTH));
GGUF_GET_KEY(fctx, model->hparams.n_ff, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_FEED_FORWARD_LENGTH));
GGUF_GET_KEY(fctx, model->hparams.n_head, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_ATTENTION_HEAD_COUNT));
GGUF_GET_KEY(fctx, model->hparams.n_layer, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_BLOCK_COUNT));
model->hparams.n_rot = model->hparams.n_embd / model->hparams.n_head;
GGUF_GET_KEY(fctx, model->hparams.n_rot, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ROPE_DIMENSION_COUNT));
float rope_freq_scale = 1.0f;
GGUF_GET_KEY(fctx, model->hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
GGUF_GET_KEY(fctx, model->hparams.rope_freq_base, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE));
GGUF_GET_KEY(fctx, rope_freq_scale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
if (rope_freq_scale != 1.0f) {
model->hparams.rope_freq_scale = 1.0f / rope_freq_scale;
}
init_model(model);
copy_tensor_by_name(model->tok_embeddings, f_ggml_ctx, tn(LLM_TENSOR_TOKEN_EMBD));
copy_tensor_by_name(model->norm, f_ggml_ctx, tn(LLM_TENSOR_OUTPUT_NORM));
copy_tensor_by_name(model->output, f_ggml_ctx, tn(LLM_TENSOR_OUTPUT));
for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
auto & layer = model->layers[i];
copy_tensor_by_name(layer.attention_norm, f_ggml_ctx, tni(LLM_TENSOR_ATTN_NORM, i));
copy_tensor_by_name(layer.wq, f_ggml_ctx, tni(LLM_TENSOR_ATTN_Q, i));
copy_tensor_by_name(layer.wk, f_ggml_ctx, tni(LLM_TENSOR_ATTN_K, i));
copy_tensor_by_name(layer.wv, f_ggml_ctx, tni(LLM_TENSOR_ATTN_V, i));
copy_tensor_by_name(layer.wo, f_ggml_ctx, tni(LLM_TENSOR_ATTN_OUT, i));
copy_tensor_by_name(layer.ffn_norm, f_ggml_ctx, tni(LLM_TENSOR_FFN_NORM, i));
copy_tensor_by_name(layer.ffn_gate, f_ggml_ctx, tni(LLM_TENSOR_FFN_GATE, i));
copy_tensor_by_name(layer.ffn_down, f_ggml_ctx, tni(LLM_TENSOR_FFN_DOWN, i));
copy_tensor_by_name(layer.ffn_up, f_ggml_ctx, tni(LLM_TENSOR_FFN_UP, i));
}
}
static void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vocab_model, struct my_llama_model * model) {
const char * arch = "llama";
enum llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
std::vector<char> keybuf;
keybuf.resize(512);
auto kv = [arch, &keybuf](const char * key) -> const char * {
snprintf(keybuf.data(), keybuf.size(), key, arch);
return keybuf.data();
};
// set arch
gguf_set_val_str(fctx, LLM_KV_GENERAL_ARCHITECTURE, arch);
gguf_set_val_u32(fctx, LLM_KV_GENERAL_FILE_TYPE, ftype);
// set hparams
gguf_set_val_u32(fctx, kv(LLM_KV_CONTEXT_LENGTH), model->hparams.n_ctx );
gguf_set_val_u32(fctx, kv(LLM_KV_EMBEDDING_LENGTH), model->hparams.n_embd );
gguf_set_val_u32(fctx, kv(LLM_KV_FEED_FORWARD_LENGTH), model->hparams.n_ff );
gguf_set_val_u32(fctx, kv(LLM_KV_ATTENTION_HEAD_COUNT), model->hparams.n_head );
gguf_set_val_u32(fctx, kv(LLM_KV_BLOCK_COUNT), model->hparams.n_layer );
gguf_set_val_u32(fctx, kv(LLM_KV_ROPE_DIMENSION_COUNT), model->hparams.n_rot );
gguf_set_val_f32(fctx, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS), model->hparams.f_norm_rms_eps );
gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_FREQ_BASE), model->hparams.rope_freq_base ); // TODO load in llama.cpp
gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_SCALE_LINEAR), 1.0f / model->hparams.rope_freq_scale );
// set vocab by copying from vocab_model gguf file
{
struct gguf_init_params params = {
/*.no_alloc = */ false,
/*.ctx = */ NULL,
};
struct gguf_context * vctx = gguf_init_from_file(fn_vocab_model, params);
const int token_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_LIST));
if (token_idx == -1) {
die("cannot find tokenizer vocab in model file");
}
const uint32_t n_vocab = gguf_get_arr_n(vctx, token_idx);
const int score_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_SCORES));
if (score_idx == -1) {
die("cannot find tokenizer scores in model file");
}
const float * scores = (const float * ) gguf_get_arr_data(vctx, score_idx);
const int toktype_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE));
if (toktype_idx == -1) {
die("cannot find token type list in GGUF file");
}
const int * toktypes = (const int * ) gguf_get_arr_data(vctx, toktype_idx);
std::string tokenizer_name;
GGUF_GET_KEY(vctx, tokenizer_name, gguf_get_val_str, GGUF_TYPE_STRING, true, kv(LLM_KV_TOKENIZER_MODEL));
gguf_set_val_str(fctx, kv(LLM_KV_TOKENIZER_MODEL), tokenizer_name.c_str());
gguf_set_arr_data(fctx, kv(LLM_KV_TOKENIZER_SCORES), GGUF_TYPE_FLOAT32, scores, n_vocab);
gguf_set_arr_data(fctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE), GGUF_TYPE_INT32, toktypes, n_vocab);
int32_t special_bos_id = 1;
int32_t special_eos_id = 2;
int32_t special_unk_id = 0;
int32_t special_sep_id = -1;
int32_t special_pad_id = -1;
if (tokenizer_name == "llama") {
// default special tokens
special_bos_id = 1;
special_eos_id = 2;
special_unk_id = 0;
special_sep_id = -1;
special_pad_id = -1;
} else if (tokenizer_name == "gpt2") {
// read and copy bpe merges
const int merges_keyidx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_MERGES));
if (merges_keyidx == -1) {
die("cannot find tokenizer merges in model file");
}
const int n_merges = gguf_get_arr_n(vctx, merges_keyidx);
std::vector<const char*> merges;
merges.resize(n_merges);
for (int i = 0; i < n_merges; i++) {
merges[i] = gguf_get_arr_str(vctx, merges_keyidx, i);
}
gguf_set_arr_str(fctx, kv(LLM_KV_TOKENIZER_MERGES), merges.data(), n_merges);
// default special tokens
special_bos_id = 11;
special_eos_id = 11;
special_unk_id = -1;
special_sep_id = -1;
special_pad_id = -1;
} else {
fprintf(stderr, "%s: unknown tokenizer: '%s'", __func__, tokenizer_name.c_str());
fprintf(stderr, "%s: using default tokenizer: 'llama'", __func__);
}
std::vector<const char*> tokens;
tokens.resize(n_vocab);
for (uint32_t i = 0; i < n_vocab; i++) {
tokens[i] = gguf_get_arr_str(vctx, token_idx, i);
}
gguf_set_arr_str(fctx, kv(LLM_KV_TOKENIZER_LIST), tokens.data(), n_vocab);
GGUF_GET_KEY(vctx, special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_BOS_ID));
GGUF_GET_KEY(vctx, special_eos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_EOS_ID));
GGUF_GET_KEY(vctx, special_unk_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_UNK_ID));
GGUF_GET_KEY(vctx, special_sep_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_SEP_ID));
GGUF_GET_KEY(vctx, special_pad_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_PAD_ID));
gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_BOS_ID), special_bos_id);
gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_EOS_ID), special_eos_id);
gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_UNK_ID), special_unk_id);
gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_SEP_ID), special_sep_id);
gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_PAD_ID), special_pad_id);
gguf_free(vctx);
}
// add tensors
gguf_add_tensor(fctx, model->tok_embeddings);
gguf_add_tensor(fctx, model->norm);
gguf_add_tensor(fctx, model->output);
for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
auto & layer = model->layers[i];
gguf_add_tensor(fctx, layer.attention_norm);
gguf_add_tensor(fctx, layer.wq);
gguf_add_tensor(fctx, layer.wk);
gguf_add_tensor(fctx, layer.wv);
gguf_add_tensor(fctx, layer.wo);
gguf_add_tensor(fctx, layer.ffn_norm);
gguf_add_tensor(fctx, layer.ffn_gate);
gguf_add_tensor(fctx, layer.ffn_down);
gguf_add_tensor(fctx, layer.ffn_up);
}
}
static void save_llama_model_file(const char * filename, const char * fn_vocab_model, struct my_llama_model * model) {
printf("%s: saving to %s\n", __func__, filename);
struct gguf_context * fctx = gguf_init_empty();
save_llama_model_gguf(fctx, fn_vocab_model, model);
// write file
const bool only_meta = false;
gguf_write_to_file(fctx, filename, only_meta);
gguf_free(fctx);
}
static void load_checkpoint_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct train_state * train) {
load_llama_model_gguf(fctx, f_ggml_ctx, model);
if (load_train_state_gguf(fctx, f_ggml_ctx, train)) {
std::string train_type = LLM_KV_TRAINING_TYPE_TRAIN_MODEL;
GGUF_GET_KEY(fctx, train_type, gguf_get_val_str, GGUF_TYPE_STRING, false, LLM_KV_TRAINING_TYPE);
GGML_ASSERT(train_type == LLM_KV_TRAINING_TYPE_TRAIN_MODEL);
} else {
printf("%s: loaded llama model as checkpoint\n", __func__);
}
}
static void save_checkpoint_gguf(struct gguf_context * fctx, const char * fn_vocab_model, struct my_llama_model * model, struct train_state * train) {
gguf_set_val_str(fctx, LLM_KV_TRAINING_TYPE, LLM_KV_TRAINING_TYPE_TRAIN_MODEL);
save_llama_model_gguf(fctx, fn_vocab_model, model);
save_train_state_gguf(fctx, train);
}
static bool load_checkpoint_file(const char * filename, struct my_llama_model * model, struct train_state * train) {
struct ggml_context * f_ggml_ctx;
struct gguf_init_params params;
params.no_alloc = false;
params.ctx = &f_ggml_ctx;
struct gguf_context * fctx = gguf_init_from_file(filename, params);
if (fctx == NULL) {
return false;
}
load_checkpoint_gguf(fctx, f_ggml_ctx, model, train);
return true;
}
static void save_checkpoint_file(const char * filename, const char * fn_vocab_model, struct my_llama_model * model, struct train_state * train) {
printf("%s: saving to %s\n", __func__, filename);
struct gguf_context * fctx = gguf_init_empty();
save_checkpoint_gguf(fctx, fn_vocab_model, model, train);
// write file
const bool only_meta = false;
gguf_write_to_file(fctx, filename, only_meta);
gguf_free(fctx);
}
struct train_params {
struct train_params_common common;
const char * fn_vocab_model;
const char * fn_model_out;
bool only_write_model;
int n_ctx;
int n_embd;
int n_head;
int n_layer;
int n_ff;
float f_norm_rms_eps;
float rope_freq_base;
float rope_freq_scale;
};
static struct train_params get_default_train_params() {
struct train_params params;
params.common = get_default_train_params_common();
params.fn_vocab_model = "ggml-vic7b-uncensored-q4_0.bin";
params.fn_model_out = "ggml-checkpoint-f32.bin";
params.only_write_model = false;
params.n_ctx = 128;
params.n_embd = 256;
params.n_head = 8;
params.n_layer = 16;
params.n_ff = 768;
params.f_norm_rms_eps = 1e-5f;
params.rope_freq_base = 10000.0f;
params.rope_freq_scale = 1.0f;
return params;
}
static 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, " --model-out FNAME path to save ggml model (default '%s')\n", params->fn_model_out);
fprintf(stderr, " --only-write-model only save llama model, don't do any training. use this if you only want to convert a checkpoint to a model.\n");
fprintf(stderr, " --embd N Embedding size used for new models (default %d)\n", params->n_embd);
fprintf(stderr, " --ff N Feedforward size used for new models. (default %d)\n", params->n_ff);
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, " --norm-rms-eps F RMS-Norm epsilon value (default %f)\n", params->f_norm_rms_eps);
fprintf(stderr, " --rope-freq-base F Frequency base for ROPE (default %f)\n", params->rope_freq_base);
fprintf(stderr, " --rope-freq-scale F Frequency scale for ROPE (default %f)\n", params->rope_freq_scale);
print_common_train_usage(argc, argv, &params->common);
}
static 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 (consume_common_train_arg(argc, argv, &i, &params->common, &invalid_param)) {
if (invalid_param) {
break;
} else if (params->common.print_usage) {
train_print_usage(argc, argv, &default_params);
exit(0);
}
} else if (arg == "--vocab-model") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->fn_vocab_model = argv[i];
} else if (arg == "--model-out") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->fn_model_out = argv[i];
} else if (arg == "--only-write-model") {
params->only_write_model = true;
} else if (arg == "--embd") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_embd = std::stoi(argv[i]);
} else if (arg == "--ff") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_ff = 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 == "--norm-rms-eps") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->f_norm_rms_eps = std::stof(argv[i]);
} else if (arg == "--rope-freq-base") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->rope_freq_base = std::stof(argv[i]);
} else if (arg == "--rope-freq-scale") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->rope_freq_scale = std::stof(argv[i]);
} 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);
}
finish_processing_train_args(&params->common);
return true;
}
struct save_train_files_data {
const char * fn_checkpoint_out;
const char * fn_model_out;
const char * fn_vocab_model;
const char * pattern_fn_it;
const char * fn_latest;
struct my_llama_model * model;
};
static void save_train_files(void * vdata, struct train_state * train) {
struct save_train_files_data * data = (struct save_train_files_data *) vdata;
int64_t iter = train->opt->iter;
if (strlen(data->fn_checkpoint_out) > 0) {
save_checkpoint_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->fn_vocab_model, data->model, train);
save_checkpoint_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, -1 ).c_str(), data->fn_vocab_model, data->model, train);
}
if (strlen(data->fn_model_out) > 0) {
save_llama_model_file(get_train_filename(data->fn_model_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->fn_vocab_model, data->model);
save_llama_model_file(get_train_filename(data->fn_model_out, data->pattern_fn_it, data->fn_latest, -1 ).c_str(), data->fn_vocab_model, data->model);
}
}
static int64_t get_parameter_count(struct my_llama_model* model) {
int64_t nx = 0;
nx += ggml_nelements(model->tok_embeddings);
nx += ggml_nelements(model->norm);
nx += ggml_nelements(model->output);
for (uint32_t i = 0; i < model->layers.size(); ++i) {
auto & layer = model->layers[i];
nx += ggml_nelements(layer.attention_norm);
nx += ggml_nelements(layer.wq);
nx += ggml_nelements(layer.wk);
nx += ggml_nelements(layer.wv);
nx += ggml_nelements(layer.wo);
nx += ggml_nelements(layer.ffn_norm);
nx += ggml_nelements(layer.ffn_gate);
nx += ggml_nelements(layer.ffn_down);
nx += ggml_nelements(layer.ffn_up);
}
return nx;
}
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.common.seed == LLAMA_DEFAULT_SEED) {
params.common.seed = time(NULL);
}
printf("%s: seed: %u\n", __func__, params.common.seed);
srand(params.common.seed);
struct llama_model_params mparams = llama_model_default_params();
mparams.vocab_only = true;
struct llama_context_params cparams = llama_context_default_params();
struct llama_model * lmodel = llama_load_model_from_file(params.fn_vocab_model, mparams);
struct llama_context * lctx = llama_new_context_with_model(lmodel, cparams);
struct my_llama_model model;
model.hparams.n_vocab = llama_n_vocab(lmodel);
model.hparams.n_ctx = params.common.n_ctx;
model.hparams.n_embd = params.n_embd;
model.hparams.n_head = params.n_head;
model.hparams.n_layer = params.n_layer;
model.hparams.n_ff = params.n_ff;
// llama.cpp requires n_rot to be exactly n_embd / n_head
model.hparams.n_rot = model.hparams.n_embd / model.hparams.n_head;
model.hparams.f_norm_rms_eps = params.f_norm_rms_eps;
model.hparams.rope_freq_base = params.rope_freq_base;
model.hparams.rope_freq_scale = params.rope_freq_scale;
struct train_state * train = init_train_state();
struct ggml_opt_context * opt = train->opt;
// set opt params from command line
opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
opt->params.print_forward_graph = false;
opt->params.print_backward_graph = false;
opt->params.graph_size = LLAMA_TRAIN_MAX_NODES;
opt->params.n_threads = params.common.n_threads;
opt->params.past = params.common.opt_past;
opt->params.delta = params.common.opt_delta;
opt->params.max_no_improvement = params.common.opt_max_no_improvement;
opt->params.n_gradient_accumulation = params.common.n_gradient_accumulation;
opt->params.adam.n_iter = params.common.adam_n_iter;
opt->params.adam.sched = 1.0f;
opt->params.adam.alpha = params.common.adam_alpha;
opt->params.adam.decay = params.common.adam_decay;
opt->params.adam.decay_min_ndim = params.common.adam_decay_min_ndim;
opt->params.adam.beta1 = params.common.adam_beta1;
opt->params.adam.beta2 = params.common.adam_beta2;
opt->params.adam.gclip = params.common.adam_gclip;
opt->params.adam.eps_f = params.common.adam_eps_f;
printf("%s: init model\n", __func__);
bool existed = load_checkpoint_file(params.common.fn_checkpoint_in, &model, train);
if (existed) {
// overwrite last n_ctx with user provided n_ctx
if (params.common.custom_n_ctx) {
model.hparams.n_ctx = params.common.n_ctx;
}
const bool opt_past_changed = opt->params.past != params.common.opt_past;
if (opt_past_changed) {
die("Optimizer parameter '--opt-past N' differs from checkpoint file. To use different value train from scratch with empty input checkpoint, e.g --checkpoint-in ''. Aborting");
// need to discard previous optimizer past function value statistics and opt_init with new shapes
// TODO
}
} else {
init_model(&model);
randomize_model(&model, params.common.seed, 0.0f, 1.0f, -1.0f, +1.0f);
if (!params.only_write_model) {
ggml_opt_init(opt->ctx, opt, opt->params, get_parameter_count(&model));
}
}
opt->iter = train->train_its;
print_params(&model.hparams);
printf("%s: total train_iterations %llu\n", __func__, (long long unsigned) train->train_its);
printf("%s: seen train_samples %llu\n", __func__, (long long unsigned) train->train_samples);
printf("%s: seen train_tokens %llu\n", __func__, (long long unsigned) train->train_tokens);
printf("%s: completed train_epochs %llu\n", __func__, (long long unsigned) train->train_epochs);
printf("%s: model_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(model.ctx) + ggml_backend_buffer_get_size(model.data)), (float) (ggml_used_mem(model.ctx) + ggml_backend_buffer_get_size(model.data)) / (1024.0f*1024.0f));
if (params.only_write_model) {
save_train_files_data save_data;
save_data.fn_checkpoint_out = "";
save_data.fn_model_out = params.fn_model_out;
save_data.fn_vocab_model = params.fn_vocab_model;
save_data.pattern_fn_it = params.common.pattern_fn_it;
save_data.fn_latest = params.common.fn_latest;
save_data.model = &model;
save_train_files(&save_data, train);
free_train_state(train);
ggml_free(model.ctx);
llama_free(lctx);
llama_free_model(lmodel);
return 0;
}
printf("%s: opt_size = %zu bytes (%.1f MB)\n", __func__, ggml_get_mem_size(opt->ctx), (float) ggml_get_mem_size(opt->ctx) / (1024.0f*1024.0f));
printf("%s: opt iter %d\n", __func__, opt->iter);
int n_tokens = model.hparams.n_ctx;
int n_vocab = model.hparams.n_vocab;
int n_batch = params.common.n_batch;
// context for input tensors without their data
struct ggml_init_params ctx_input_params = {
ggml_tensor_overhead() * 2, // mem_size
NULL, // mem_buffer
true, // no_alloc
};
struct ggml_context * ctx_input = ggml_init(ctx_input_params);
// the input tensors
struct ggml_tensor * tokens_input = ggml_new_tensor_2d(ctx_input, GGML_TYPE_I32, n_tokens, n_batch);
struct ggml_tensor * target_probs = ggml_new_tensor_3d(ctx_input, GGML_TYPE_F32, n_vocab, n_tokens, n_batch);
// measure required memory for input tensors
// allocate input tensors
ggml_backend_buffer_t input_data = ggml_backend_alloc_ctx_tensors_from_buft(ctx_input, ggml_backend_cpu_buffer_type());
size_t max_input_size = ggml_backend_buffer_get_size(input_data);
printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f));
// context for compute tensors without their data
const size_t estimated_compute_size_wo_data = (
2*LLAMA_TRAIN_MAX_NODES*ggml_tensor_overhead() +
(params.common.use_checkpointing ? 3 : 2)*(GGML_OBJECT_SIZE+ggml_graph_overhead_custom(LLAMA_TRAIN_MAX_NODES, true))
);
struct ggml_init_params ctx_compute_params = {
estimated_compute_size_wo_data, // mem_size
NULL, // mem_buffer
true, // no_alloc
};
struct ggml_context * ctx_compute = NULL;
struct ggml_tensor * loss = NULL;
struct ggml_tensor * logits = NULL;
struct ggml_cgraph * gf = NULL;
struct ggml_cgraph * gb = NULL;
struct ggml_cgraph * gb_tmp = NULL;
// measure required memory for compute tensors
size_t best_compute_size = SIZE_MAX;
enum ggml_cgraph_eval_order best_order = GGML_CGRAPH_EVAL_ORDER_COUNT;
// find best evaluation order
for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) {
ctx_compute = ggml_init(ctx_compute_params);
ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gf->order = (enum ggml_cgraph_eval_order) order;
gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gb_tmp = params.common.use_checkpointing
? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true)
: NULL;
loss = llama_build_train_graphs(
&model, alloc, ctx_compute,
gf, gb, gb_tmp,
&logits, tokens_input, target_probs,
n_tokens, n_batch,
params.common.use_flash,
params.common.use_checkpointing,
true
);
size_t max_compute_size = ggml_gallocr_get_buffer_size(alloc, 0); // FIXME: this will still allocate the buffer
if (max_compute_size < best_compute_size) {
best_compute_size = max_compute_size;
best_order = gf->order;
}
ggml_free(ctx_compute);
}
size_t max_compute_size = best_compute_size;
printf("%s: compute_size = %zu bytes (%.1f MB)\n", __func__, max_compute_size, (float) max_compute_size / (1024.0f*1024.0f));
printf("%s: evaluation order = %s\n", __func__,
(best_order == GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT) ? "LEFT_TO_RIGHT" :
(best_order == GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT) ? "RIGHT_TO_LEFT" :
"invalid");
// allocate compute tensors
ctx_compute = ggml_init(ctx_compute_params);
ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gf->order = best_order;
gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gb_tmp = params.common.use_checkpointing
? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true)
: NULL;
loss = llama_build_train_graphs(
&model, alloc, ctx_compute,
gf, gb, gb_tmp,
&logits, tokens_input, target_probs,
n_tokens, n_batch,
params.common.use_flash,
params.common.use_checkpointing,
false
);
std::vector<llama_token> train_tokens;
std::vector<size_t> train_samples_begin;
std::vector<size_t> train_samples_size;
printf("%s: tokenize training data\n", __func__);
tokenize_file(lctx,
params.common.fn_train_data,
params.common.sample_start,
params.common.include_sample_start,
params.common.overlapping_samples,
n_tokens,
train_tokens,
train_samples_begin,
train_samples_size);
GGML_ASSERT(train_samples_begin.size() == train_samples_size.size());
printf("%s: number of training tokens: %zu\n", __func__, train_tokens.size());
size_t shuffle_samples_hash = compute_samples_hash(params.common.fn_train_data, train_samples_begin.data(), train_samples_size.data(), train_samples_size.size());
const bool changed_train_data = (shuffle_samples_hash != train->shuffle_samples_hash) || (train->shuffle_sample_count != train_samples_size.size());
if (changed_train_data) {
printf("%s: train data seems to have changed. restarting shuffled epoch.\n", __func__);
}
if (params.common.force_reshuffle) {
printf("%s: forced reshuffling of data. restarting with newly shuffled epoch.\n", __func__);
}
if ((train->shuffle_rng_state_current == "") || changed_train_data || params.common.force_reshuffle) {
train->shuffle_rng_state_current = mt19937_seed_to_state(params.common.seed);
train->shuffle_sample_count = train_samples_size.size();
train->shuffle_next_sample = 0;
train->shuffle_samples_hash = shuffle_samples_hash;
}
std::vector<size_t> train_shuffled_samples_offs;
std::vector<size_t> train_shuffled_samples_begin;
std::vector<size_t> train_shuffled_samples_size;
train_shuffled_samples_offs.resize(train_samples_begin.size());
train_shuffled_samples_begin.resize(train_samples_begin.size());
train_shuffled_samples_size.resize(train_samples_size.size());
train->shuffle_rng_state_next = shuffle_samples(
train->shuffle_rng_state_current,
train_shuffled_samples_offs.data(),
train_shuffled_samples_begin.data(),
train_shuffled_samples_size.data(),
train_samples_begin.data(),
train_samples_size.data(),
train_samples_size.size());
printf("%s: begin training\n", __func__);
save_train_files_data save_data;
save_data.fn_checkpoint_out = params.common.fn_checkpoint_out;
save_data.fn_model_out = params.fn_model_out;
save_data.fn_vocab_model = params.fn_vocab_model;
save_data.pattern_fn_it = params.common.pattern_fn_it;
save_data.fn_latest = params.common.fn_latest;
save_data.model = &model;
struct train_opt_callback_data opt_cb_data;
opt_cb_data.params = &params.common;
opt_cb_data.train = train;
opt_cb_data.save_cb = &save_train_files;
opt_cb_data.save_data = &save_data;
opt_cb_data.lctx = lctx;
opt_cb_data.last_save_iter = opt->iter;
opt_cb_data.tokens_data = train_tokens.data();
opt_cb_data.tokens_size = train_tokens.size();
opt_cb_data.samples_begin = train_samples_begin.data();
opt_cb_data.samples_size = train_samples_size.data();
opt_cb_data.shuffled_samples_offs = train_shuffled_samples_offs.data();
opt_cb_data.shuffled_samples_begin = train_shuffled_samples_begin.data();
opt_cb_data.shuffled_samples_size = train_shuffled_samples_size.data();
opt_cb_data.samples_count = train_samples_size.size();
opt_cb_data.tokens_input = tokens_input;
opt_cb_data.target_probs = target_probs;
opt_cb_data.first_iter = opt->iter;
opt_cb_data.first_epoch = train->train_epochs;
opt_cb_data.iter_at_last_epoch = -1;
opt_cb_data.last_time = ggml_time_ms();
opt_cb_data.millis_per_iter = 0.0;
// measure required memory for work buffer
size_t max_work_size = ggml_graph_plan(gb, params.common.n_threads).work_size + GGML_OBJECT_SIZE;
printf("%s: work_size = %zu bytes (%.1f MB)\n", __func__, max_work_size, (float) max_work_size / (1024.0f*1024.0f));
// context for work buffer
struct ggml_init_params ctx_work_params = {
max_work_size, // mem_size
NULL, // mem_buffer
false, // no_alloc
};
struct ggml_context * ctx_work = ggml_init(ctx_work_params);
int64_t t0 = ggml_time_ms();
ggml_opt_resume_g(ctx_work, opt, loss, gf, gb, &train_opt_callback, (void *) &opt_cb_data);
ggml_free(ctx_work);
ggml_free(ctx_compute);
ggml_free(ctx_input);
int64_t t1 = ggml_time_ms();
printf("%s: total training time: ", __func__);
print_duration((double) (t1 - t0));
printf("\n");
int new_iters = opt->iter - opt_cb_data.last_save_iter;
if (new_iters > 0) {
train->train_its += new_iters;
train->train_tokens += new_iters * opt->params.n_gradient_accumulation * n_batch * n_tokens;
save_train_files(&save_data, train);
opt_cb_data.last_save_iter = opt->iter;
}
ggml_free(opt->ctx);
free_train_state(train);
ggml_free(model.ctx);
llama_free(lctx);
llama_free_model(lmodel);
return 0;
}
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