haraldwolff/llama.cpp
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llama.cpp/examples/finetune/finetune.cpp
Daniel Bevenius 879b690a9e
finetune : fix output formatting in print_params (#4653) 
This commit fixes the output formatting in the print_params function
which currently looks like this:
```console
print_params: n_vocab:   32000
print_params: n_ctx:     128
print_params: n_embd:    4096
print_params: n_ff:      11008
print_params: n_head:    32
print_params: n_head_kv: 32
print_params: n_layer:   32
print_params: norm_rms_eps          : 0.000010
print_params: rope_freq_base        : 10000.000000
print_params: rope_freq_scale       : 1.000000
```
With this comit the output will look like this:
```console
print_params: n_vocab               : 32000
print_params: n_ctx                 : 128
print_params: n_embd                : 4096
print_params: n_ff                  : 11008
print_params: n_head                : 32
print_params: n_head_kv             : 32
print_params: n_layer               : 32
print_params: norm_rms_eps          : 0.000010
print_params: rope_freq_base        : 10000.000000
print_params: rope_freq_scale       : 1.000000
```

Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
2023-12-27 16:16:55 +02:00

1938 lines
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#include "ggml.h"
#include "ggml-alloc.h"
#include "llama.h"
#include "common.h"
#include "train.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
static const size_t tensor_alignment = 32;
struct my_llama_hparams {
uint32_t n_vocab = 32000;
uint32_t n_ctx = 512;
uint32_t n_embd = 4096;
uint32_t n_ff = 11008;
uint32_t n_head = 32;
uint32_t n_head_kv = 32;
uint32_t n_layer = 32;
// 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;
uint32_t n_gqa() const {
return n_head/n_head_kv;
}
uint32_t n_embd_head() const {
return n_embd/n_head;
}
uint32_t n_embd_gqa() const {
return n_embd/n_gqa();
}
bool operator!=(const my_llama_hparams& other) const {
return memcmp(this, &other, sizeof(other));
}
};
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_model {
struct my_llama_hparams hparams;
struct ggml_tensor * tok_embeddings;
struct ggml_tensor * norm;
struct ggml_tensor * output;
std::vector<my_llama_layer> layers;
};
struct my_llama_lora_hparams {
uint32_t lora_r = 1;
uint32_t lora_alpha = 1;
uint32_t n_rank_attention_norm = 1;
uint32_t n_rank_wq = 4;
uint32_t n_rank_wk = 4;
uint32_t n_rank_wv = 4;
uint32_t n_rank_wo = 4;
uint32_t n_rank_ffn_norm = 1;
uint32_t n_rank_w1 = 4;
uint32_t n_rank_w2 = 4;
uint32_t n_rank_w3 = 4;
uint32_t n_rank_tok_embeddings = 4;
uint32_t n_rank_norm = 1;
uint32_t n_rank_output = 4;
bool operator!=(const my_llama_lora_hparams& other) const {
return memcmp(this, &other, sizeof(other));
}
};
struct my_llama_lora_layer {
// normalization
struct ggml_tensor * attention_norm_a;
struct ggml_tensor * attention_norm_b;
// attention
struct ggml_tensor * wq_a;
struct ggml_tensor * wq_b;
struct ggml_tensor * wk_a;
struct ggml_tensor * wk_b;
struct ggml_tensor * wv_a;
struct ggml_tensor * wv_b;
struct ggml_tensor * wo_a;
struct ggml_tensor * wo_b;
// normalization
struct ggml_tensor * ffn_norm_a;
struct ggml_tensor * ffn_norm_b;
// ff
struct ggml_tensor * w1_a;
struct ggml_tensor * w1_b;
struct ggml_tensor * w2_a;
struct ggml_tensor * w2_b;
struct ggml_tensor * w3_a;
struct ggml_tensor * w3_b;
};
struct my_llama_lora {
struct ggml_context * ctx = NULL;
std::vector<uint8_t> data;
my_llama_lora_hparams hparams;
struct ggml_tensor * tok_embeddings_a;
struct ggml_tensor * tok_embeddings_b;
struct ggml_tensor * norm_a;
struct ggml_tensor * norm_b;
struct ggml_tensor * output_a;
struct ggml_tensor * output_b;
std::vector<my_llama_lora_layer> layers;
};
// gguf constants
static const char * LLM_KV_TRAINING_TYPE_FINETUNE_LORA = "finetune_lora";
static const char * LLM_KV_TRAINING_TYPE = "training.type";
static const char * LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD = "training.lora.rank.token_embd";
static const char * LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM = "training.lora.rank.output_norm";
static const char * LLM_KV_TRAINING_LORA_RANK_OUTPUT = "training.lora.rank.output";
static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_NORM = "training.lora.rank.attn_norm";
static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_Q = "training.lora.rank.attn_q";
static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_K = "training.lora.rank.attn_k";
static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_V = "training.lora.rank.attn_v";
static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_OUT = "training.lora.rank.attn_output";
static const char * LLM_KV_TRAINING_LORA_RANK_FFN_NORM = "training.lora.rank.ffn_norm";
static const char * LLM_KV_TRAINING_LORA_RANK_FFN_GATE = "training.lora.rank.ffn_gate";
static const char * LLM_KV_TRAINING_LORA_RANK_FFN_DOWN = "training.lora.rank.ffn_down";
static const char * LLM_KV_TRAINING_LORA_RANK_FFN_UP = "training.lora.rank.ffn_up";
// gguf constants (sync with gguf.py)
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_HEAD_COUNT_KV = "%s.attention.head_count_kv";
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_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_ff : %u\n", __func__, params->n_ff);
printf("%s: n_head : %u\n", __func__, params->n_head);
printf("%s: n_head_kv : %u\n", __func__, params->n_head_kv);
printf("%s: n_layer : %u\n", __func__, params->n_layer);
printf("%s: norm_rms_eps : %f\n", __func__, params->f_norm_rms_eps);
printf("%s: rope_freq_base : %f\n", __func__, params->rope_freq_base);
printf("%s: rope_freq_scale : %f\n", __func__, params->rope_freq_scale);
}
static void print_lora_params(struct my_llama_lora_hparams * params) {
printf("%s: n_rank_attention_norm : %u\n", __func__, params->n_rank_attention_norm);
printf("%s: n_rank_wq : %u\n", __func__, params->n_rank_wq);
printf("%s: n_rank_wk : %u\n", __func__, params->n_rank_wk);
printf("%s: n_rank_wv : %u\n", __func__, params->n_rank_wv);
printf("%s: n_rank_wo : %u\n", __func__, params->n_rank_wo);
printf("%s: n_rank_ffn_norm : %u\n", __func__, params->n_rank_ffn_norm);
printf("%s: n_rank_w1 : %u\n", __func__, params->n_rank_w1);
printf("%s: n_rank_w2 : %u\n", __func__, params->n_rank_w2);
printf("%s: n_rank_w3 : %u\n", __func__, params->n_rank_w3);
printf("%s: n_rank_tok_embeddings : %u\n", __func__, params->n_rank_tok_embeddings);
printf("%s: n_rank_norm : %u\n", __func__, params->n_rank_norm);
printf("%s: n_rank_output : %u\n", __func__, params->n_rank_output);
}
#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \
{ \
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()); \
} \
}
static void load_model_hparams_gguf(struct gguf_context * ctx, struct my_llama_hparams * hparams, const char * expected_arch) {
std::string arch;
GGUF_GET_KEY(ctx, arch, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_GENERAL_ARCHITECTURE);
if (expected_arch != NULL) {
if (arch != expected_arch) {
printf("%s: arch=%s expected_arch=%s\n", __func__, arch.c_str(), expected_arch);
}
GGML_ASSERT(arch == expected_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();
};
GGUF_GET_KEY(ctx, hparams->n_embd, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_EMBEDDING_LENGTH));
GGUF_GET_KEY(ctx, hparams->n_ctx, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_CONTEXT_LENGTH));
GGUF_GET_KEY(ctx, hparams->n_ff, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_FEED_FORWARD_LENGTH));
GGUF_GET_KEY(ctx, hparams->n_head, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_ATTENTION_HEAD_COUNT));
GGUF_GET_KEY(ctx, hparams->n_layer, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_BLOCK_COUNT));
// n_head_kv is optional, default to n_head
hparams->n_head_kv = hparams->n_head;
GGUF_GET_KEY(ctx, hparams->n_head_kv, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV));
float rope_freq_scale = 1.0f;
GGUF_GET_KEY(ctx, hparams->f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
GGUF_GET_KEY(ctx, hparams->rope_freq_base, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE));
GGUF_GET_KEY(ctx, rope_freq_scale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
if (rope_freq_scale != 1.0f) {
hparams->rope_freq_scale = 1.0f / rope_freq_scale;
}
}
static void init_model(struct llama_model * input, struct my_llama_model * model, const char * fn_model, uint32_t n_ctx) {
auto & hparams = model->hparams;
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();
};
// get parameters directly from gguf file
{
struct gguf_init_params params = {
/*.no_alloc = */ false,
/*.ctx = */ NULL,
};
struct gguf_context * mctx = gguf_init_from_file(fn_model, params);
load_model_hparams_gguf(mctx, &hparams, "llama");
gguf_free(mctx);
}
hparams.n_vocab = llama_n_vocab(input);
hparams.n_ctx = n_ctx;
// get tensors from llama_model (possibly mmapped)
model->tok_embeddings = llama_get_model_tensor(input, tn(LLM_TENSOR_TOKEN_EMBD));
model->norm = llama_get_model_tensor(input, tn(LLM_TENSOR_OUTPUT_NORM));
model->output = llama_get_model_tensor(input, tn(LLM_TENSOR_OUTPUT));
assert_shape_2d(model->tok_embeddings, hparams.n_embd, hparams.n_vocab);
assert_shape_1d(model->norm, hparams.n_embd);
assert_shape_2d(model->output, hparams.n_embd, hparams.n_vocab);
model->layers.resize(hparams.n_layer);
for (uint32_t i = 0; i < hparams.n_layer; ++i) {
auto & layer = model->layers[i];
layer.attention_norm = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_NORM, i));
layer.wq = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_Q, i));
layer.wk = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_K, i));
layer.wv = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_V, i));
layer.wo = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_OUT, i));
layer.ffn_norm = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_NORM, i));
layer.w1 = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_GATE, i));
layer.w2 = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_DOWN, i));
layer.w3 = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_UP, i));
assert_shape_1d(layer.attention_norm, hparams.n_embd);
assert_shape_2d(layer.wq, hparams.n_embd, hparams.n_embd);
assert_shape_2d(layer.wk, hparams.n_embd, hparams.n_embd_gqa());
assert_shape_2d(layer.wv, hparams.n_embd, hparams.n_embd_gqa());
assert_shape_2d(layer.wo, hparams.n_embd, hparams.n_embd);
assert_shape_1d(layer.ffn_norm, hparams.n_embd);
assert_shape_2d(layer.w1, hparams.n_embd, hparams.n_ff);
assert_shape_2d(layer.w2, hparams.n_ff, hparams.n_embd);
assert_shape_2d(layer.w3, hparams.n_embd, hparams.n_ff);
}
}
static void set_param_lora(struct my_llama_lora * lora) {
const uint32_t n_layer = lora->layers.size();
struct ggml_context* ctx = lora->ctx;
ggml_set_param(ctx, lora->tok_embeddings_a);
ggml_set_param(ctx, lora->tok_embeddings_b);
ggml_set_param(ctx, lora->norm_a);
ggml_set_param(ctx, lora->norm_b);
ggml_set_param(ctx, lora->output_a);
ggml_set_param(ctx, lora->output_b);
for (uint32_t i = 0; i < n_layer; ++i) {
auto & layer = lora->layers[i];
ggml_set_param(ctx, layer.attention_norm_a);
ggml_set_param(ctx, layer.attention_norm_b);
ggml_set_param(ctx, layer.wq_a);
ggml_set_param(ctx, layer.wq_b);
ggml_set_param(ctx, layer.wk_a);
ggml_set_param(ctx, layer.wk_b);
ggml_set_param(ctx, layer.wv_a);
ggml_set_param(ctx, layer.wv_b);
ggml_set_param(ctx, layer.wo_a);
ggml_set_param(ctx, layer.wo_b);
ggml_set_param(ctx, layer.ffn_norm_a);
ggml_set_param(ctx, layer.ffn_norm_b);
ggml_set_param(ctx, layer.w1_a);
ggml_set_param(ctx, layer.w1_b);
ggml_set_param(ctx, layer.w2_a);
ggml_set_param(ctx, layer.w2_b);
ggml_set_param(ctx, layer.w3_a);
ggml_set_param(ctx, layer.w3_b);
}
}
static void alloc_lora(struct ggml_allocr * alloc, struct my_llama_lora * lora) {
ggml_allocr_alloc(alloc, lora->tok_embeddings_a);
ggml_allocr_alloc(alloc, lora->tok_embeddings_b);
ggml_allocr_alloc(alloc, lora->norm_a);
ggml_allocr_alloc(alloc, lora->norm_b);
ggml_allocr_alloc(alloc, lora->output_a);
ggml_allocr_alloc(alloc, lora->output_b);
for (uint32_t i = 0; i < lora->layers.size(); ++i) {
auto & layer = lora->layers[i];
ggml_allocr_alloc(alloc, layer.attention_norm_a);
ggml_allocr_alloc(alloc, layer.attention_norm_b);
ggml_allocr_alloc(alloc, layer.wq_a);
ggml_allocr_alloc(alloc, layer.wq_b);
ggml_allocr_alloc(alloc, layer.wk_a);
ggml_allocr_alloc(alloc, layer.wk_b);
ggml_allocr_alloc(alloc, layer.wv_a);
ggml_allocr_alloc(alloc, layer.wv_b);
ggml_allocr_alloc(alloc, layer.wo_a);
ggml_allocr_alloc(alloc, layer.wo_b);
ggml_allocr_alloc(alloc, layer.ffn_norm_a);
ggml_allocr_alloc(alloc, layer.ffn_norm_b);
ggml_allocr_alloc(alloc, layer.w1_a);
ggml_allocr_alloc(alloc, layer.w1_b);
ggml_allocr_alloc(alloc, layer.w2_a);
ggml_allocr_alloc(alloc, layer.w2_b);
ggml_allocr_alloc(alloc, layer.w3_a);
ggml_allocr_alloc(alloc, layer.w3_b);
}
ggml_allocr_alloc(alloc, lora->tok_embeddings_a->grad);
ggml_allocr_alloc(alloc, lora->tok_embeddings_b->grad);
ggml_allocr_alloc(alloc, lora->norm_a->grad);
ggml_allocr_alloc(alloc, lora->norm_b->grad);
ggml_allocr_alloc(alloc, lora->output_a->grad);
ggml_allocr_alloc(alloc, lora->output_b->grad);
for (uint32_t i = 0; i < lora->layers.size(); ++i) {
auto & layer = lora->layers[i];
ggml_allocr_alloc(alloc, layer.attention_norm_a->grad);
ggml_allocr_alloc(alloc, layer.attention_norm_b->grad);
ggml_allocr_alloc(alloc, layer.wq_a->grad);
ggml_allocr_alloc(alloc, layer.wq_b->grad);
ggml_allocr_alloc(alloc, layer.wk_a->grad);
ggml_allocr_alloc(alloc, layer.wk_b->grad);
ggml_allocr_alloc(alloc, layer.wv_a->grad);
ggml_allocr_alloc(alloc, layer.wv_b->grad);
ggml_allocr_alloc(alloc, layer.wo_a->grad);
ggml_allocr_alloc(alloc, layer.wo_b->grad);
ggml_allocr_alloc(alloc, layer.ffn_norm_a->grad);
ggml_allocr_alloc(alloc, layer.ffn_norm_b->grad);
ggml_allocr_alloc(alloc, layer.w1_a->grad);
ggml_allocr_alloc(alloc, layer.w1_b->grad);
ggml_allocr_alloc(alloc, layer.w2_a->grad);
ggml_allocr_alloc(alloc, layer.w2_b->grad);
ggml_allocr_alloc(alloc, layer.w3_a->grad);
ggml_allocr_alloc(alloc, layer.w3_b->grad);
}
}
static void init_lora(const struct my_llama_model * model, struct my_llama_lora * lora) {
const auto & lparams = lora->hparams;
const uint32_t n_embd = model->hparams.n_embd;
const uint32_t n_embd_gqa = model->hparams.n_embd_gqa();
const uint32_t n_layer = model->hparams.n_layer;
const uint32_t n_vocab = model->hparams.n_vocab;
const uint32_t n_ff = model->hparams.n_ff;
std::vector<char> tn_buf;
tn_buf.resize(GGML_MAX_NAME);
auto tn = [&tn_buf](const char * key, const char * suffix) -> const char * {
snprintf(tn_buf.data(), tn_buf.size(), "%s%s", key, suffix);
return tn_buf.data();
};
auto tni = [&tn_buf](const char * key, const char * suffix, 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%s", s.c_str(), suffix);
return tn_buf.data();
};
// context for lora tensors without their data
struct ggml_init_params ctx_lora_params;
ctx_lora_params.mem_size = ggml_tensor_overhead()*2*(6 + n_layer*18);
ctx_lora_params.mem_buffer = NULL;
ctx_lora_params.no_alloc = true;
struct ggml_context * ctx = ggml_init(ctx_lora_params);
lora->ctx = ctx;
lora->tok_embeddings_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_tok_embeddings, n_embd);
lora->tok_embeddings_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_tok_embeddings, n_vocab);
lora->norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_norm, n_embd);
lora->norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_norm, 1);
lora->output_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_output, n_embd);
lora->output_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_output, n_vocab);
ggml_set_name(lora->tok_embeddings_a, tn(LLM_TENSOR_TOKEN_EMBD, ".weight.lora_a"));
ggml_set_name(lora->tok_embeddings_b, tn(LLM_TENSOR_TOKEN_EMBD, ".weight.lora_b"));
ggml_set_name(lora->norm_a, tn(LLM_TENSOR_OUTPUT_NORM, ".weight.lora_a"));
ggml_set_name(lora->norm_b, tn(LLM_TENSOR_OUTPUT_NORM, ".weight.lora_b"));
ggml_set_name(lora->output_a, tn(LLM_TENSOR_OUTPUT, ".weight.lora_a"));
ggml_set_name(lora->output_b, tn(LLM_TENSOR_OUTPUT, ".weight.lora_b"));
lora->layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
auto & layer = lora->layers[i];
layer.attention_norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_attention_norm, n_embd);
layer.attention_norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_attention_norm, 1);
layer.wq_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wq, n_embd);
layer.wq_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wq, n_embd);
layer.wk_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wk, n_embd);
layer.wk_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wk, n_embd_gqa);
layer.wv_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wv, n_embd);
layer.wv_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wv, n_embd_gqa);
layer.wo_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wo, n_embd);
layer.wo_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wo, n_embd);
layer.ffn_norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, n_embd);
layer.ffn_norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, 1);
layer.w1_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w1, n_embd);
layer.w1_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w1, n_ff);
layer.w2_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w2, n_ff);
layer.w2_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w2, n_embd);
layer.w3_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w3, n_embd);
layer.w3_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w3, n_ff);
ggml_set_name(layer.attention_norm_a, tni(LLM_TENSOR_ATTN_NORM, ".weight.lora_a", i));
ggml_set_name(layer.attention_norm_b, tni(LLM_TENSOR_ATTN_NORM, ".weight.lora_b", i));
ggml_set_name(layer.wq_a, tni(LLM_TENSOR_ATTN_Q, ".weight.lora_a", i));
ggml_set_name(layer.wq_b, tni(LLM_TENSOR_ATTN_Q, ".weight.lora_b", i));
ggml_set_name(layer.wk_a, tni(LLM_TENSOR_ATTN_K, ".weight.lora_a", i));
ggml_set_name(layer.wk_b, tni(LLM_TENSOR_ATTN_K, ".weight.lora_b", i));
ggml_set_name(layer.wv_a, tni(LLM_TENSOR_ATTN_V, ".weight.lora_a", i));
ggml_set_name(layer.wv_b, tni(LLM_TENSOR_ATTN_V, ".weight.lora_b", i));
ggml_set_name(layer.wo_a, tni(LLM_TENSOR_ATTN_OUT, ".weight.lora_a", i));
ggml_set_name(layer.wo_b, tni(LLM_TENSOR_ATTN_OUT, ".weight.lora_b", i));
ggml_set_name(layer.ffn_norm_a, tni(LLM_TENSOR_FFN_NORM, ".weight.lora_a", i));
ggml_set_name(layer.ffn_norm_b, tni(LLM_TENSOR_FFN_NORM, ".weight.lora_b", i));
ggml_set_name(layer.w1_a, tni(LLM_TENSOR_FFN_GATE, ".weight.lora_a", i));
ggml_set_name(layer.w1_b, tni(LLM_TENSOR_FFN_GATE, ".weight.lora_b", i));
ggml_set_name(layer.w2_a, tni(LLM_TENSOR_FFN_DOWN, ".weight.lora_a", i));
ggml_set_name(layer.w2_b, tni(LLM_TENSOR_FFN_DOWN, ".weight.lora_b", i));
ggml_set_name(layer.w3_a, tni(LLM_TENSOR_FFN_UP, ".weight.lora_a", i));
ggml_set_name(layer.w3_b, tni(LLM_TENSOR_FFN_UP, ".weight.lora_b", i));
}
set_param_lora(lora);
// measure data size
size_t size = 0;
for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
size += GGML_PAD(ggml_nbytes(t), tensor_alignment);
}
// allocate data
struct ggml_allocr * alloc = NULL;
lora->data.resize(size + tensor_alignment);
alloc = ggml_allocr_new(lora->data.data(), lora->data.size(), tensor_alignment);
alloc_lora(alloc, lora);
ggml_allocr_free(alloc);
}
static void randomize_lora(struct my_llama_lora * lora, int seed, float mean, float std, float min, float max) {
const uint32_t n_layer = lora->layers.size();
struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max);
randomize_tensor_normal(lora->tok_embeddings_a, rnd);
ggml_set_zero(lora->tok_embeddings_b);
randomize_tensor_normal(lora->norm_a, rnd);
ggml_set_zero(lora->norm_b);
randomize_tensor_normal(lora->output_a, rnd);
ggml_set_zero(lora->output_b);
for (uint32_t i = 0; i < n_layer; ++i) {
auto & layer = lora->layers[i];
randomize_tensor_normal(layer.attention_norm_a, rnd);
ggml_set_zero(layer.attention_norm_b);
randomize_tensor_normal(layer.wq_a, rnd);
ggml_set_zero(layer.wq_b);
randomize_tensor_normal(layer.wk_a, rnd);
ggml_set_zero(layer.wk_b);
randomize_tensor_normal(layer.wv_a, rnd);
ggml_set_zero(layer.wv_b);
randomize_tensor_normal(layer.wo_a, rnd);
ggml_set_zero(layer.wo_b);
randomize_tensor_normal(layer.ffn_norm_a, rnd);
ggml_set_zero(layer.ffn_norm_b);
randomize_tensor_normal(layer.w1_a, rnd);
ggml_set_zero(layer.w1_b);
randomize_tensor_normal(layer.w2_a, rnd);
ggml_set_zero(layer.w2_b);
randomize_tensor_normal(layer.w3_a, rnd);
ggml_set_zero(layer.w3_b);
}
free_random_normal_distribution(rnd);
}
static struct ggml_tensor * llama_build_lora_finetune_graphs(
struct my_llama_model * model,
struct my_llama_lora * lora,
struct ggml_allocr * 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) {
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_head_kv = hparams.n_head_kv;
const int n_ff = hparams.n_ff;
const int n_rot = hparams.n_embd_head();
const int n_embd_head = hparams.n_embd_head();
const int n_embd_gqa = hparams.n_embd_gqa();
const float rms_norm_eps = hparams.f_norm_rms_eps;
const float rope_freq_base = hparams.rope_freq_base;
const float rope_freq_scale = hparams.rope_freq_scale;
GGML_ASSERT((size_t) n_layer == lora->layers.size());
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_allocr_alloc(alloc, KQ_pos);
if (!ggml_allocr_is_measure(alloc)) {
int * data = (int *) KQ_pos->data;
for (int i = 0; i < N; ++i) {
data[i] = n_past + i;
}
}
// 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);
auto add_to_f32 = [] (struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
if (ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16) {
return ggml_add_cast(ctx, a, b, GGML_TYPE_F32);
} else if (a->type == GGML_TYPE_F32) {
return ggml_add(ctx, a, b);
} else {
die_fmt("%s: Finetuning on tensors with type '%s' is not yet supported.\n",
__func__, ggml_type_name(a->type));
}
};
struct ggml_tensor * tok_embeddings = add_to_f32(ctx, model->tok_embeddings, ggml_mul_mat(ctx, lora->tok_embeddings_a, lora->tok_embeddings_b));
struct ggml_tensor * norm = add_to_f32(ctx, model->norm, ggml_mul_mat(ctx, lora->norm_a, lora->norm_b));
struct ggml_tensor * output = add_to_f32(ctx, model->output, ggml_mul_mat(ctx, lora->output_a, lora->output_b));
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, 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;
if (enable_checkpointing) {
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 my_llama_lora_layer & llayer = lora->layers[il];
struct ggml_tensor * attention_norm = add_to_f32(ctx, layer.attention_norm, ggml_mul_mat(ctx, llayer.attention_norm_a, llayer.attention_norm_b));
struct ggml_tensor * ffn_norm = add_to_f32(ctx, layer.ffn_norm, ggml_mul_mat(ctx, llayer.ffn_norm_a, llayer.ffn_norm_b));
struct ggml_tensor * wq = add_to_f32(ctx, layer.wq, ggml_mul_mat(ctx, llayer.wq_a, llayer.wq_b));
struct ggml_tensor * wk = add_to_f32(ctx, layer.wk, ggml_mul_mat(ctx, llayer.wk_a, llayer.wk_b));
struct ggml_tensor * wv = add_to_f32(ctx, layer.wv, ggml_mul_mat(ctx, llayer.wv_a, llayer.wv_b));
struct ggml_tensor * wo = add_to_f32(ctx, layer.wo, ggml_mul_mat(ctx, llayer.wo_a, llayer.wo_b));
struct ggml_tensor * w1 = add_to_f32(ctx, layer.w1, ggml_mul_mat(ctx, llayer.w1_a, llayer.w1_b));
struct ggml_tensor * w2 = add_to_f32(ctx, layer.w2, ggml_mul_mat(ctx, llayer.w2_a, llayer.w2_b));
struct ggml_tensor * w3 = add_to_f32(ctx, layer.w3, ggml_mul_mat(ctx, llayer.w3_a, llayer.w3_b));
struct ggml_tensor * t02 = ggml_rms_norm (ctx, cur, rms_norm_eps); set_name(t02, "t02"); assert_shape_2d(t02, n_embd, N*n_batch);
struct ggml_tensor * t03 = ggml_repeat (ctx, 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, 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_head, n_head, N, n_batch); set_name(t06, "t06"); assert_shape_4d(t06, n_embd_head, n_head, N, n_batch);
struct ggml_tensor * t07 = rope (t06); set_name(t07, "t07"); assert_shape_4d(t07, n_embd_head, n_head, N, n_batch);
struct ggml_tensor * t08 = ggml_mul_mat (ctx, wk, t04); set_name(t08, "t08"); assert_shape_2d(t08, n_embd_gqa, N*n_batch);
struct ggml_tensor * t09 = ggml_reshape_4d (ctx, t08, n_embd_head, n_head_kv, N, n_batch); set_name(t09, "t09"); assert_shape_4d(t09, n_embd_head, n_head_kv, N, n_batch);
struct ggml_tensor * t10 = rope (t09); set_name(t10, "t10"); assert_shape_4d(t10, n_embd_head, n_head_kv, N, n_batch);
struct ggml_tensor * t11;
if (ggml_is_quantized(wv->type)) {
struct ggml_tensor * t11_1 = ggml_mul_mat (ctx, wv, t04); set_name(t11_1, "t11_1"); assert_shape_2d(t11_1, n_embd_gqa, N*n_batch);
struct ggml_tensor * t11_2 = ggml_transpose(ctx, t11_1); set_name(t11_2, "t11_2"); assert_shape_2d(t11_2, N*n_batch, n_embd_gqa);
t11 = ggml_cont (ctx, t11_2); set_name(t11, "t11"); assert_shape_2d(t11, N*n_batch, n_embd_gqa);
} else {
t11 = ggml_mul_mat (ctx, t04, wv); set_name(t11, "t11"); assert_shape_2d(t11, N*n_batch, n_embd_gqa);
}
struct ggml_tensor * t12 = ggml_reshape_4d (ctx, t11, N, n_batch, n_embd_head, n_head_kv); set_name(t12, "t12"); assert_shape_4d(t12, N, n_batch, n_embd_head, n_head_kv);
struct ggml_tensor * t13 = ggml_permute (ctx, t07, 0, 2, 1, 3); set_name(t13, "t13"); assert_shape_4d(t13, n_embd_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_head, N, n_head_kv, 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_head, n_head_kv, 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_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_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_head, n_head, N, n_batch);
struct ggml_tensor * t18 = ggml_cont (ctx, t17); set_name(t18, "t18"); assert_shape_4d(t18, n_embd_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, 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, rms_norm_eps); set_name(t22, "t22"); assert_shape_2d(t22, n_embd, N*n_batch);
struct ggml_tensor * t23 = ggml_repeat (ctx, 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, w3, t24); set_name(t25, "t25"); assert_shape_2d(t25, n_ff, N*n_batch);
struct ggml_tensor * t26 = ggml_mul_mat (ctx, w1, 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, w2, 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;
if (enable_checkpointing) {
checkpoints.push_back(cur);
}
}
struct ggml_tensor * t31 = ggml_rms_norm (ctx, cur, rms_norm_eps); set_name(t31, "t31"); assert_shape_2d(t31, n_embd, N*n_batch);
struct ggml_tensor * t32 = ggml_repeat (ctx, 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, 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);
if (enable_checkpointing) {
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);
}
GGML_ASSERT(alloc != NULL);
// 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));
GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL);
ggml_allocr_alloc(alloc, t36->grad);
// KQ_pos
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f));
// make sure base model tensors data cannot be used in viewable operations
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->tok_embeddings, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->norm, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->output, 1.0f));
for (int il = 0; il < n_layer; ++il) {
struct my_llama_layer & layer = model->layers[il];
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.attention_norm, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_norm, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wq, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wk, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wv, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wo, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w1, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w2, 1.0f));
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w3, 1.0f));
}
// allocating checkpoints in one block to reduce memory fragmentation
// note: they will be freed in reverse order
for (unsigned int i = 0; i < checkpoints.size(); ++i) {
if (checkpoints[i]->data == NULL && checkpoints[i]->view_src == NULL) {
ggml_allocr_alloc(alloc, checkpoints[i]);
}
}
ggml_allocr_alloc_graph(alloc, gb);
// 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;
}
static void load_llama_lora_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct my_llama_lora * lora) {
// 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);
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);
struct my_llama_hparams hparams;
load_model_hparams_gguf(fctx, &hparams, arch.c_str());
// parameters that define tensor shapes must match
GGML_ASSERT(hparams.n_embd == model->hparams.n_embd);
GGML_ASSERT(hparams.n_ff == model->hparams.n_ff);
GGML_ASSERT(hparams.n_head == model->hparams.n_head);
GGML_ASSERT(hparams.n_head_kv == model->hparams.n_head_kv);
GGML_ASSERT(hparams.n_layer == model->hparams.n_layer);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_tok_embeddings, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_norm, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_output, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_OUTPUT);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_attention_norm, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_NORM);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_wq, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_Q);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_wk, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_K);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_wv, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_V);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_wo, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_norm, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_NORM);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_w1, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_GATE);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_w2, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN);
GGUF_GET_KEY(fctx, lora->hparams.n_rank_w3, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_UP);
init_lora(model, lora);
copy_tensor_by_name(lora->tok_embeddings_a, f_ggml_ctx, ggml_get_name(lora->tok_embeddings_a));
copy_tensor_by_name(lora->tok_embeddings_b, f_ggml_ctx, ggml_get_name(lora->tok_embeddings_b));
copy_tensor_by_name(lora->norm_a, f_ggml_ctx, ggml_get_name(lora->norm_a));
copy_tensor_by_name(lora->norm_b, f_ggml_ctx, ggml_get_name(lora->norm_b));
copy_tensor_by_name(lora->output_a, f_ggml_ctx, ggml_get_name(lora->output_a));
copy_tensor_by_name(lora->output_b, f_ggml_ctx, ggml_get_name(lora->output_b));
for (uint32_t i = 0; i < lora->layers.size(); ++i) {
auto & layer = lora->layers[i];
copy_tensor_by_name(layer.attention_norm_a, f_ggml_ctx, ggml_get_name(layer.attention_norm_a));
copy_tensor_by_name(layer.attention_norm_b, f_ggml_ctx, ggml_get_name(layer.attention_norm_b));
copy_tensor_by_name(layer.wq_a, f_ggml_ctx, ggml_get_name(layer.wq_a));
copy_tensor_by_name(layer.wq_b, f_ggml_ctx, ggml_get_name(layer.wq_b));
copy_tensor_by_name(layer.wk_a, f_ggml_ctx, ggml_get_name(layer.wk_a));
copy_tensor_by_name(layer.wk_b, f_ggml_ctx, ggml_get_name(layer.wk_b));
copy_tensor_by_name(layer.wv_a, f_ggml_ctx, ggml_get_name(layer.wv_a));
copy_tensor_by_name(layer.wv_b, f_ggml_ctx, ggml_get_name(layer.wv_b));
copy_tensor_by_name(layer.wo_a, f_ggml_ctx, ggml_get_name(layer.wo_a));
copy_tensor_by_name(layer.wo_b, f_ggml_ctx, ggml_get_name(layer.wo_b));
copy_tensor_by_name(layer.ffn_norm_a, f_ggml_ctx, ggml_get_name(layer.ffn_norm_a));
copy_tensor_by_name(layer.ffn_norm_b, f_ggml_ctx, ggml_get_name(layer.ffn_norm_b));
copy_tensor_by_name(layer.w1_a, f_ggml_ctx, ggml_get_name(layer.w1_a));
copy_tensor_by_name(layer.w1_b, f_ggml_ctx, ggml_get_name(layer.w1_b));
copy_tensor_by_name(layer.w2_a, f_ggml_ctx, ggml_get_name(layer.w2_a));
copy_tensor_by_name(layer.w2_b, f_ggml_ctx, ggml_get_name(layer.w2_b));
copy_tensor_by_name(layer.w3_a, f_ggml_ctx, ggml_get_name(layer.w3_a));
copy_tensor_by_name(layer.w3_b, f_ggml_ctx, ggml_get_name(layer.w3_b));
}
}
static void save_llama_lora_gguf(struct gguf_context * fctx, struct my_llama_model * model, struct my_llama_lora * lora) {
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();
};
gguf_set_val_str(fctx, LLM_KV_GENERAL_ARCHITECTURE, arch);
gguf_set_val_u32(fctx, LLM_KV_GENERAL_FILE_TYPE, ftype);
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_ATTENTION_HEAD_COUNT_KV), model->hparams.n_head_kv);
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_embd_head());
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);
gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_SCALE_LINEAR), model->hparams.rope_freq_scale);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD, lora->hparams.n_rank_tok_embeddings);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM, lora->hparams.n_rank_norm);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_OUTPUT, lora->hparams.n_rank_output);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_NORM, lora->hparams.n_rank_attention_norm);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_Q, lora->hparams.n_rank_wq);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_K, lora->hparams.n_rank_wk);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_V, lora->hparams.n_rank_wv);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT, lora->hparams.n_rank_wo);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_NORM, lora->hparams.n_rank_ffn_norm);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_GATE, lora->hparams.n_rank_w1);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN, lora->hparams.n_rank_w2);
gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_UP, lora->hparams.n_rank_w3);
gguf_add_tensor(fctx, lora->tok_embeddings_a);
gguf_add_tensor(fctx, lora->tok_embeddings_b);
gguf_add_tensor(fctx, lora->norm_a);
gguf_add_tensor(fctx, lora->norm_b);
gguf_add_tensor(fctx, lora->output_a);
gguf_add_tensor(fctx, lora->output_b);
for (uint32_t i = 0; i < lora->layers.size(); ++i) {
auto & layer = lora->layers[i];
gguf_add_tensor(fctx, layer.attention_norm_a);
gguf_add_tensor(fctx, layer.attention_norm_b);
gguf_add_tensor(fctx, layer.wq_a);
gguf_add_tensor(fctx, layer.wq_b);
gguf_add_tensor(fctx, layer.wk_a);
gguf_add_tensor(fctx, layer.wk_b);
gguf_add_tensor(fctx, layer.wv_a);
gguf_add_tensor(fctx, layer.wv_b);
gguf_add_tensor(fctx, layer.wo_a);
gguf_add_tensor(fctx, layer.wo_b);
gguf_add_tensor(fctx, layer.ffn_norm_a);
gguf_add_tensor(fctx, layer.ffn_norm_b);
gguf_add_tensor(fctx, layer.w1_a);
gguf_add_tensor(fctx, layer.w1_b);
gguf_add_tensor(fctx, layer.w2_a);
gguf_add_tensor(fctx, layer.w2_b);
gguf_add_tensor(fctx, layer.w3_a);
gguf_add_tensor(fctx, layer.w3_b);
}
}
static void load_checkpoint_lora_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {
std::string train_type = LLM_KV_TRAINING_TYPE_FINETUNE_LORA;
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_FINETUNE_LORA);
load_train_state_gguf(fctx, f_ggml_ctx, train);
load_llama_lora_gguf(fctx, f_ggml_ctx, model, lora);
}
static void save_checkpoint_lora_gguf(struct gguf_context * fctx, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {
gguf_set_val_str(fctx, LLM_KV_TRAINING_TYPE, LLM_KV_TRAINING_TYPE_FINETUNE_LORA);
save_llama_lora_gguf(fctx, model, lora);
save_train_state_gguf(fctx, train);
}
static bool load_checkpoint_lora_file(const char * filename, struct my_llama_model * model, struct my_llama_lora * lora, 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_lora_gguf(fctx, f_ggml_ctx, model, lora, train);
gguf_free(fctx);
return true;
}
static void save_checkpoint_lora_file(const char * filename, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {
printf("%s: saving to %s\n", __func__, filename);
struct gguf_context * fctx = gguf_init_empty();
save_checkpoint_lora_gguf(fctx, model, lora, train);
// write file
const bool only_meta = false;
gguf_write_to_file(fctx, filename, only_meta);
gguf_free(fctx);
}
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)) {
die_fmt("read error: %s", strerror(errno));
}
if (ret != 1) {
die("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) {
die_fmt("write error: %s", strerror(errno));
}
}
void write_u32(std::uint32_t val) {
write_raw(&val, sizeof(val));
}
~llama_file() {
if (fp) {
std::fclose(fp);
}
}
};
static void write_tensor(struct llama_file * file, struct ggml_tensor * tensor, const char * name) {
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;
}
if (name == NULL) {
name = ggml_get_name(tensor);
}
uint32_t name_len = strlen(name);
uint32_t nd = ggml_n_dims(tensor);
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));
}
static void save_as_llama_lora(const char * filename, struct my_llama_lora * lora) {
printf("%s: saving to %s\n", __func__, filename);
struct llama_file file(filename, "wb");
if (file.fp == NULL) {
return;
}
std::vector<char> tn_buf;
tn_buf.resize(GGML_MAX_NAME);
auto tn = [&tn_buf](const char * key, const char * suffix) -> const char * {
snprintf(tn_buf.data(), tn_buf.size(), "%s%s", key, suffix);
return tn_buf.data();
};
auto tni = [&tn_buf](const char * key, int bid, const char * suffix) -> 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%s", s.c_str(), suffix);
return tn_buf.data();
};
uint32_t LLAMA_FILE_MAGIC_LORA = 0x67676C61; // 'ggla'
// write_magic
file.write_u32(LLAMA_FILE_MAGIC_LORA); // magic
file.write_u32(1); // version
// write_hparams
file.write_u32(lora->hparams.lora_r);
file.write_u32(lora->hparams.lora_alpha);
// write tensors
write_tensor(&file, lora->tok_embeddings_a, tn(LLM_TENSOR_TOKEN_EMBD, ".weight.loraA"));
write_tensor(&file, lora->tok_embeddings_b, tn(LLM_TENSOR_TOKEN_EMBD, ".weight.loraB"));
write_tensor(&file, lora->norm_a, tn(LLM_TENSOR_OUTPUT_NORM, ".weight.loraA"));
write_tensor(&file, lora->norm_b, tn(LLM_TENSOR_OUTPUT_NORM, ".weight.loraB"));
write_tensor(&file, lora->output_a, tn(LLM_TENSOR_OUTPUT, ".weight.loraA"));
write_tensor(&file, lora->output_b, tn(LLM_TENSOR_OUTPUT, ".weight.loraB"));
for (uint32_t i = 0; i < lora->layers.size(); ++i) {
auto & layer = lora->layers[i];
write_tensor(&file, layer.attention_norm_a, tni(LLM_TENSOR_ATTN_NORM, i, ".weight.loraA"));
write_tensor(&file, layer.attention_norm_b, tni(LLM_TENSOR_ATTN_NORM, i, ".weight.loraB"));
write_tensor(&file, layer.wq_a, tni(LLM_TENSOR_ATTN_Q, i, ".weight.loraA"));
write_tensor(&file, layer.wq_b, tni(LLM_TENSOR_ATTN_Q, i, ".weight.loraB"));
write_tensor(&file, layer.wk_a, tni(LLM_TENSOR_ATTN_K, i, ".weight.loraA"));
write_tensor(&file, layer.wk_b, tni(LLM_TENSOR_ATTN_K, i, ".weight.loraB"));
write_tensor(&file, layer.wv_a, tni(LLM_TENSOR_ATTN_V, i, ".weight.loraA"));
write_tensor(&file, layer.wv_b, tni(LLM_TENSOR_ATTN_V, i, ".weight.loraB"));
write_tensor(&file, layer.wo_a, tni(LLM_TENSOR_ATTN_OUT, i, ".weight.loraA"));
write_tensor(&file, layer.wo_b, tni(LLM_TENSOR_ATTN_OUT, i, ".weight.loraB"));
write_tensor(&file, layer.ffn_norm_a, tni(LLM_TENSOR_FFN_NORM, i, ".weight.loraA"));
write_tensor(&file, layer.ffn_norm_b, tni(LLM_TENSOR_FFN_NORM, i, ".weight.loraB"));
write_tensor(&file, layer.w1_a, tni(LLM_TENSOR_FFN_GATE, i, ".weight.loraA"));
write_tensor(&file, layer.w1_b, tni(LLM_TENSOR_FFN_GATE, i, ".weight.loraB"));
write_tensor(&file, layer.w2_a, tni(LLM_TENSOR_FFN_DOWN, i, ".weight.loraA"));
write_tensor(&file, layer.w2_b, tni(LLM_TENSOR_FFN_DOWN, i, ".weight.loraB"));
write_tensor(&file, layer.w3_a, tni(LLM_TENSOR_FFN_UP, i, ".weight.loraA"));
write_tensor(&file, layer.w3_b, tni(LLM_TENSOR_FFN_UP, i, ".weight.loraB"));
}
}
struct train_params {
struct train_params_common common;
const char * fn_model_base;
const char * fn_lora_out;
bool only_write_lora;
float f_norm_rms_eps;
float rope_freq_base;
float rope_freq_scale;
bool custom_f_norm_rms_eps;
bool custom_rope_freq_base;
bool custom_rope_freq_scale;
int32_t lora_r;
int32_t lora_alpha;
bool custom_lora_alpha;
uint32_t n_rank_attention_norm;
uint32_t n_rank_wq;
uint32_t n_rank_wk;
uint32_t n_rank_wv;
uint32_t n_rank_wo;
uint32_t n_rank_ffn_norm;
uint32_t n_rank_w1;
uint32_t n_rank_w2;
uint32_t n_rank_w3;
uint32_t n_rank_tok_embeddings;
uint32_t n_rank_norm;
uint32_t n_rank_output;
bool custom_n_rank_attention_norm;
bool custom_n_rank_wq;
bool custom_n_rank_wk;
bool custom_n_rank_wv;
bool custom_n_rank_wo;
bool custom_n_rank_ffn_norm;
bool custom_n_rank_w1;
bool custom_n_rank_w2;
bool custom_n_rank_w3;
bool custom_n_rank_tok_embeddings;
bool custom_n_rank_norm;
bool custom_n_rank_output;
};
static struct train_params get_default_train_params() {
struct train_params params;
params.common = get_default_train_params_common();
params.fn_model_base = "";
params.fn_lora_out = "ggml-lora-ITERATION-f32.gguf";
params.only_write_lora = false;
params.f_norm_rms_eps = 1e-5f;
params.rope_freq_base = 10000.0f;
params.rope_freq_scale = 1.0f;
params.custom_f_norm_rms_eps = false;
params.custom_rope_freq_base = false;
params.custom_rope_freq_scale = false;
params.lora_r = 4;
params.lora_alpha = 4;
params.custom_lora_alpha = false;
params.n_rank_attention_norm = 1;
params.n_rank_wq = 4;
params.n_rank_wk = 4;
params.n_rank_wv = 4;
params.n_rank_wo = 4;
params.n_rank_ffn_norm = 1;
params.n_rank_w1 = 4;
params.n_rank_w2 = 4;
params.n_rank_w3 = 4;
params.n_rank_tok_embeddings = 4;
params.n_rank_norm = 1;
params.n_rank_output = 4;
params.custom_n_rank_attention_norm = false;
params.custom_n_rank_wq = false;
params.custom_n_rank_wk = false;
params.custom_n_rank_wv = false;
params.custom_n_rank_wo = false;
params.custom_n_rank_ffn_norm = false;
params.custom_n_rank_w1 = false;
params.custom_n_rank_w2 = false;
params.custom_n_rank_w3 = false;
params.custom_n_rank_tok_embeddings = false;
params.custom_n_rank_norm = false;
params.custom_n_rank_output = false;
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, " --model-base FNAME model path from which to load base model (default '%s')\n", params->fn_model_base);
fprintf(stderr, " --lora-out FNAME path to save llama lora (default '%s')\n", params->fn_lora_out);
fprintf(stderr, " --only-write-lora only save llama lora, don't do any training. use this if you only want to convert a checkpoint to a lora adapter.\n");
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);
fprintf(stderr, " --lora-alpha N LORA alpha : resulting LORA scaling is alpha/r. (default %d)\n", params->lora_alpha);
fprintf(stderr, " --lora-r N LORA r: default rank. Also specifies resulting scaling together with lora-alpha. (default %d)\n", params->lora_r);
fprintf(stderr, " --rank-att-norm N LORA rank for attention norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n");
fprintf(stderr, " --rank-ffn-norm N LORA rank for feed-forward norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n");
fprintf(stderr, " --rank-out-norm N LORA rank for output norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n");
fprintf(stderr, " --rank-tok-embd N LORA rank for token embeddings tensor, overrides default rank.\n");
fprintf(stderr, " --rank-out N LORA rank for output tensor, overrides default rank.\n");
fprintf(stderr, " --rank-wq N LORA rank for wq tensor, overrides default rank.\n");
fprintf(stderr, " --rank-wk N LORA rank for wk tensor, overrides default rank.\n");
fprintf(stderr, " --rank-wv N LORA rank for wv tensor, overrides default rank.\n");
fprintf(stderr, " --rank-wo N LORA rank for wo tensor, overrides default rank.\n");
fprintf(stderr, " --rank-w1 N LORA rank for w1 tensor, overrides default rank.\n");
fprintf(stderr, " --rank-w2 N LORA rank for w2 tensor, overrides default rank.\n");
fprintf(stderr, " --rank-w3 N LORA rank for w3 tensor, overrides default rank.\n");
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 == "--model-base") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->fn_model_base = argv[i];
} else if (arg == "--lora-out") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->fn_lora_out = argv[i];
} else if (arg == "--only-write-lora") {
params->only_write_lora = true;
} else if (arg == "--norm-rms-eps") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->f_norm_rms_eps = std::stof(argv[i]);
params->custom_f_norm_rms_eps = true;
} else if (arg == "--rope-freq-base") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->rope_freq_base = std::stof(argv[i]);
params->custom_rope_freq_base = true;
} else if (arg == "--rope-freq-scale") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->rope_freq_scale = std::stof(argv[i]);
params->custom_rope_freq_scale = true;
} else if (arg == "--lora-alpha") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->lora_alpha = std::stoi(argv[i]);
params->custom_lora_alpha = true;
} else if (arg == "--lora-r") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->lora_r = std::stoi(argv[i]);
} else if (arg == "--rank-att-norm") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_rank_attention_norm = std::stoi(argv[i]);
params->custom_n_rank_attention_norm = true;
} else if (arg == "--rank-ffn-norm") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_rank_ffn_norm = std::stoi(argv[i]);
params->custom_n_rank_ffn_norm = true;
} else if (arg == "--rank-out-norm") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_rank_norm = std::stoi(argv[i]);
params->custom_n_rank_norm = true;
} else if (arg == "--rank-tok-embd") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_rank_tok_embeddings = std::stoi(argv[i]);
params->custom_n_rank_tok_embeddings = true;
} else if (arg == "--rank-out") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_rank_output = std::stoi(argv[i]);
params->custom_n_rank_output = true;
} else if (arg == "--rank-wq") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_rank_wq = std::stoi(argv[i]);
params->custom_n_rank_wq = true;
} else if (arg == "--rank-wk") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_rank_wk = std::stoi(argv[i]);
params->custom_n_rank_wk = true;
} else if (arg == "--rank-wv") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_rank_wv = std::stoi(argv[i]);
params->custom_n_rank_wv = true;
} else if (arg == "--rank-wo") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_rank_wo = std::stoi(argv[i]);
params->custom_n_rank_wo = true;
} else if (arg == "--rank-w1") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_rank_w1 = std::stoi(argv[i]);
params->custom_n_rank_w1 = true;
} else if (arg == "--rank-w2") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_rank_w2 = std::stoi(argv[i]);
params->custom_n_rank_w2 = true;
} else if (arg == "--rank-w3") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->n_rank_w3 = std::stoi(argv[i]);
params->custom_n_rank_w3 = true;
} 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_lora_out;
const char * pattern_fn_it;
const char * fn_latest;
struct my_llama_model * model;
struct my_llama_lora * lora;
};
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_lora_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->model, data->lora, train);
save_checkpoint_lora_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, -1 ).c_str(), data->model, data->lora, train);
}
if (strlen(data->fn_lora_out) > 0) {
save_as_llama_lora(get_train_filename(data->fn_lora_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->lora);
save_as_llama_lora(get_train_filename(data->fn_lora_out, data->pattern_fn_it, data->fn_latest, -1 ).c_str(), data->lora);
}
}
static int64_t get_parameter_count(struct my_llama_lora* lora) {
int64_t nx = 0;
nx += ggml_nelements(lora->tok_embeddings_a);
nx += ggml_nelements(lora->tok_embeddings_b);
nx += ggml_nelements(lora->norm_a);
nx += ggml_nelements(lora->norm_b);
nx += ggml_nelements(lora->output_a);
nx += ggml_nelements(lora->output_b);
for (uint32_t i = 0; i < lora->layers.size(); ++i) {
auto & layer = lora->layers[i];
nx += ggml_nelements(layer.attention_norm_a);
nx += ggml_nelements(layer.attention_norm_b);
nx += ggml_nelements(layer.wq_a);
nx += ggml_nelements(layer.wq_b);
nx += ggml_nelements(layer.wk_a);
nx += ggml_nelements(layer.wk_b);
nx += ggml_nelements(layer.wv_a);
nx += ggml_nelements(layer.wv_b);
nx += ggml_nelements(layer.wo_a);
nx += ggml_nelements(layer.wo_b);
nx += ggml_nelements(layer.ffn_norm_a);
nx += ggml_nelements(layer.ffn_norm_b);
nx += ggml_nelements(layer.w1_a);
nx += ggml_nelements(layer.w1_b);
nx += ggml_nelements(layer.w2_a);
nx += ggml_nelements(layer.w2_b);
nx += ggml_nelements(layer.w3_a);
nx += ggml_nelements(layer.w3_b);
}
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 llama_mparams = llama_model_default_params();
llama_mparams.n_gpu_layers = params.common.n_gpu_layers;
llama_mparams.vocab_only = false;
printf("%s: model base = '%s'\n", __func__, params.fn_model_base);
struct llama_model * lmodel = llama_load_model_from_file(params.fn_model_base, llama_mparams);
struct llama_context_params llama_cparams = llama_context_default_params();
struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_cparams);
struct my_llama_model model;
init_model(lmodel, &model, params.fn_model_base, params.common.n_ctx);
struct my_llama_lora lora;
struct train_state * train = init_train_state();
struct ggml_opt_context * opt = train->opt;
// set params from command line
if (params.custom_f_norm_rms_eps) {
model.hparams.f_norm_rms_eps = params.f_norm_rms_eps;
}
if (params.custom_rope_freq_base) {
model.hparams.rope_freq_base = params.rope_freq_base;
}
if (params.custom_rope_freq_scale) {
model.hparams.rope_freq_scale = params.rope_freq_scale;
}
lora.hparams.lora_r = params.lora_r;
lora.hparams.lora_alpha = params.custom_lora_alpha ? params.lora_alpha : params.lora_r;
uint32_t n_rank_attention_norm = params.custom_n_rank_attention_norm ? params.n_rank_attention_norm : 1;
uint32_t n_rank_wq = params.custom_n_rank_wq ? params.n_rank_wq : params.lora_r;
uint32_t n_rank_wk = params.custom_n_rank_wk ? params.n_rank_wk : params.lora_r;
uint32_t n_rank_wv = params.custom_n_rank_wv ? params.n_rank_wv : params.lora_r;
uint32_t n_rank_wo = params.custom_n_rank_wo ? params.n_rank_wo : params.lora_r;
uint32_t n_rank_ffn_norm = params.custom_n_rank_ffn_norm ? params.n_rank_ffn_norm : 1;
uint32_t n_rank_w1 = params.custom_n_rank_w1 ? params.n_rank_w1 : params.lora_r;
uint32_t n_rank_w2 = params.custom_n_rank_w2 ? params.n_rank_w2 : params.lora_r;
uint32_t n_rank_w3 = params.custom_n_rank_w3 ? params.n_rank_w3 : params.lora_r;
uint32_t n_rank_tok_embeddings = params.custom_n_rank_tok_embeddings ? params.n_rank_tok_embeddings : params.lora_r;
uint32_t n_rank_norm = params.custom_n_rank_norm ? params.n_rank_norm : 1;
uint32_t n_rank_output = params.custom_n_rank_output ? params.n_rank_output : params.lora_r;
lora.hparams.n_rank_attention_norm = n_rank_attention_norm;
lora.hparams.n_rank_wq = n_rank_wq;
lora.hparams.n_rank_wk = n_rank_wk;
lora.hparams.n_rank_wv = n_rank_wv;
lora.hparams.n_rank_wo = n_rank_wo;
lora.hparams.n_rank_ffn_norm = n_rank_ffn_norm;
lora.hparams.n_rank_w1 = n_rank_w1;
lora.hparams.n_rank_w2 = n_rank_w2;
lora.hparams.n_rank_w3 = n_rank_w3;
lora.hparams.n_rank_tok_embeddings = n_rank_tok_embeddings;
lora.hparams.n_rank_norm = n_rank_norm;
lora.hparams.n_rank_output = n_rank_output;
// set opt params from command line
opt->params = ggml_opt_default_params(GGML_OPT_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_lora_file(params.common.fn_checkpoint_in, &model, &lora, 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_param_count_changed = (
(lora.hparams.n_rank_attention_norm != n_rank_attention_norm)
|| (lora.hparams.n_rank_wq != n_rank_wq)
|| (lora.hparams.n_rank_wk != n_rank_wk)
|| (lora.hparams.n_rank_wv != n_rank_wv)
|| (lora.hparams.n_rank_wo != n_rank_wo)
|| (lora.hparams.n_rank_ffn_norm != n_rank_ffn_norm)
|| (lora.hparams.n_rank_w1 != n_rank_w1)
|| (lora.hparams.n_rank_w2 != n_rank_w2)
|| (lora.hparams.n_rank_w3 != n_rank_w3)
|| (lora.hparams.n_rank_tok_embeddings != n_rank_tok_embeddings)
|| (lora.hparams.n_rank_norm != n_rank_norm)
|| (lora.hparams.n_rank_output != n_rank_output)
);
const bool opt_past_changed = opt->params.past != params.common.opt_past;
if (opt_param_count_changed) {
print_lora_params(&lora.hparams);
die("Provided rank differs from checkpoint file. To use different rank start finetune from scratch with empty input checkpoint, e.g --checkpoint-in ''. Aborting.");
// need to discard previous optimizer gradient statistics and opt_init with new shapes
// TODO
}
if (opt_past_changed) {
die("Optimizer parameter '--opt-past N' differs from checkpoint file. To use different value finetune 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 { // existed == false
init_lora(&model, &lora);
randomize_lora(&lora, params.common.seed, 0.0f, 1.0f, -1.0f, +1.0f);
if (!params.only_write_lora) {
ggml_opt_init(opt->ctx, opt, opt->params, get_parameter_count(&lora));
}
}
opt->iter = train->train_its;
print_params(&model.hparams);
print_lora_params(&lora.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: lora_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(lora.ctx) + lora.data.size()), (float) (ggml_used_mem(lora.ctx) + lora.data.size()) / (1024.0f*1024.0f));
if (params.only_write_lora) {
save_train_files_data save_data;
save_data.fn_checkpoint_out = "";
save_data.fn_lora_out = params.fn_lora_out;
save_data.pattern_fn_it = params.common.pattern_fn_it;
save_data.fn_latest = params.common.fn_latest;
save_data.model = &model;
save_data.lora = &lora;
save_train_files(&save_data, train);
free_train_state(train);
ggml_free(lora.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;
std::vector<uint8_t> mem_input_data;
std::vector<uint8_t> mem_compute_data;
// 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
size_t max_input_size = GGML_PAD(ggml_nbytes(tokens_input), tensor_alignment) +
GGML_PAD(ggml_nbytes(target_probs), tensor_alignment) +
tensor_alignment;
printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f));
// allocate input tensors
mem_input_data.resize(max_input_size);
ggml_allocr_t alloc_inps = ggml_allocr_new(mem_input_data.data(), mem_input_data.size(), tensor_alignment);
ggml_allocr_alloc(alloc_inps, tokens_input);
ggml_allocr_alloc(alloc_inps, target_probs);
// 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_allocr_t alloc = ggml_allocr_new_measure(tensor_alignment);
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_lora_finetune_graphs(
&model, &lora, alloc, ctx_compute,
gf, gb, gb_tmp,
&logits, tokens_input, target_probs,
n_tokens, n_batch,
params.common.use_flash,
params.common.use_checkpointing
);
size_t max_compute_size = ggml_allocr_max_size(alloc) + tensor_alignment;
if (max_compute_size < best_compute_size) {
best_compute_size = max_compute_size;
best_order = gf->order;
}
ggml_allocr_free(alloc);
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
mem_compute_data.resize(max_compute_size);
ctx_compute = ggml_init(ctx_compute_params);
ggml_allocr_t alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment);
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_lora_finetune_graphs(
&model, &lora, alloc, ctx_compute,
gf, gb, gb_tmp,
&logits, tokens_input, target_probs,
n_tokens, n_batch,
params.common.use_flash,
params.common.use_checkpointing
);
ggml_allocr_free(alloc);
ggml_allocr_free(alloc_inps);
// tokenize data
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());
std::vector<size_t> token_noccurs;
token_noccurs.resize(model.hparams.n_vocab, 0);
for (unsigned int i = 0; i < train_tokens.size(); ++i) {
++token_noccurs[train_tokens[i]];
}
int n_unique_tokens = 0;
for (unsigned int i = 0; i < token_noccurs.size(); ++i) {
if (token_noccurs[i] == 0) continue;
++n_unique_tokens;
}
printf("%s: number of unique tokens: %d\n", __func__, n_unique_tokens);
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_lora_out = params.fn_lora_out;
save_data.pattern_fn_it = params.common.pattern_fn_it;
save_data.fn_latest = params.common.fn_latest;
save_data.model = &model;
save_data.lora = &lora;
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(lora.ctx);
llama_free(lctx);
llama_free_model(lmodel);
return 0;
}
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