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-rw-r--r--examples/train-text-from-scratch/train-text-from-scratch.cpp112
1 files changed, 32 insertions, 80 deletions
diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp
index eee9d4de..2e2a8ce0 100644
--- a/examples/train-text-from-scratch/train-text-from-scratch.cpp
+++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp
@@ -1,5 +1,6 @@
#include "ggml.h"
#include "ggml-alloc.h"
+#include "ggml-backend.h"
#include "common.h"
#include "train.h"
#include "llama.h"
@@ -19,8 +20,6 @@
#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;
@@ -58,7 +57,7 @@ struct my_llama_layer {
struct my_llama_model {
struct ggml_context * ctx = NULL;
- std::vector<uint8_t> data;
+ ggml_backend_buffer_t data = NULL;
my_llama_hparams hparams;
@@ -147,39 +146,6 @@ static void set_param_model(struct my_llama_model * model) {
}
}
-static void alloc_model(struct ggml_allocr * alloc, struct my_llama_model * model) {
- ggml_allocr_alloc(alloc, model->tok_embeddings);
- ggml_allocr_alloc(alloc, model->norm);
- ggml_allocr_alloc(alloc, model->output);
- for (uint32_t i = 0; i < model->layers.size(); ++i) {
- auto & layer = model->layers[i];
- ggml_allocr_alloc(alloc, layer.attention_norm);
- ggml_allocr_alloc(alloc, layer.wq);
- ggml_allocr_alloc(alloc, layer.wk);
- ggml_allocr_alloc(alloc, layer.wv);
- ggml_allocr_alloc(alloc, layer.wo);
- ggml_allocr_alloc(alloc, layer.ffn_norm);
- ggml_allocr_alloc(alloc, layer.w1);
- ggml_allocr_alloc(alloc, layer.w2);
- ggml_allocr_alloc(alloc, layer.w3);
- }
- ggml_allocr_alloc(alloc, model->tok_embeddings->grad);
- ggml_allocr_alloc(alloc, model->norm->grad);
- ggml_allocr_alloc(alloc, model->output->grad);
- for (uint32_t i = 0; i < model->layers.size(); ++i) {
- auto & layer = model->layers[i];
- ggml_allocr_alloc(alloc, layer.attention_norm->grad);
- ggml_allocr_alloc(alloc, layer.wq->grad);
- ggml_allocr_alloc(alloc, layer.wk->grad);
- ggml_allocr_alloc(alloc, layer.wv->grad);
- ggml_allocr_alloc(alloc, layer.wo->grad);
- ggml_allocr_alloc(alloc, layer.ffn_norm->grad);
- ggml_allocr_alloc(alloc, layer.w1->grad);
- ggml_allocr_alloc(alloc, layer.w2->grad);
- ggml_allocr_alloc(alloc, layer.w3->grad);
- }
-}
-
static void init_model(struct my_llama_model * model) {
const auto & hparams = model->hparams;
@@ -252,17 +218,8 @@ static void init_model(struct my_llama_model * model) {
set_param_model(model);
- // 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;
- model->data.resize(size + tensor_alignment);
- alloc = ggml_allocr_new(model->data.data(), model->data.size(), tensor_alignment);
- alloc_model(alloc, model);
+ 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) {
@@ -297,7 +254,7 @@ static void randomize_model(struct my_llama_model * model, int seed, float mean,
static struct ggml_tensor * llama_build_train_graphs(
struct my_llama_model * model,
- struct ggml_allocr * alloc,
+ ggml_gallocr_t alloc,
struct ggml_context * ctx,
struct ggml_cgraph * gf,
struct ggml_cgraph * gb,
@@ -308,7 +265,8 @@ static struct ggml_tensor * llama_build_train_graphs(
const int n_tokens,
const int n_batch,
const bool enable_flash_attn,
- const bool enable_checkpointing) {
+ const bool enable_checkpointing,
+ const bool measure_only) {
ggml_set_scratch(ctx, { 0, 0, nullptr, });
const int n_past = 0;
@@ -334,13 +292,7 @@ static struct ggml_tensor * llama_build_train_graphs(
// 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;
- }
- }
+ 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]
@@ -448,21 +400,31 @@ static struct ggml_tensor * llama_build_train_graphs(
// 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_allocr_alloc(alloc, t36->grad);
+ 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_allocr_alloc(alloc, checkpoints[i]);
+ 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);
- ggml_allocr_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) {
@@ -1046,7 +1008,7 @@ int main(int argc, char ** argv) {
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) + model.data.size()), (float) (ggml_used_mem(model.ctx) + model.data.size()) / (1024.0f*1024.0f));
+ 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;
@@ -1073,11 +1035,6 @@ int main(int argc, char ** argv) {
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;
-
- ggml_allocr * alloc = NULL;
-
// context for input tensors without their data
struct ggml_init_params ctx_input_params = {
ggml_tensor_overhead() * 2, // mem_size
@@ -1091,16 +1048,10 @@ int main(int argc, char ** argv) {
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);
- alloc = ggml_allocr_new(mem_input_data.data(), mem_input_data.size(), tensor_alignment);
- ggml_allocr_alloc(alloc, tokens_input);
- ggml_allocr_alloc(alloc, target_probs);
+ 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 = (
@@ -1127,7 +1078,7 @@ int main(int argc, char ** argv) {
// find best evaluation order
for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) {
ctx_compute = ggml_init(ctx_compute_params);
- alloc = ggml_allocr_new_measure(tensor_alignment);
+ 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);
@@ -1140,9 +1091,10 @@ int main(int argc, char ** argv) {
&logits, tokens_input, target_probs,
n_tokens, n_batch,
params.common.use_flash,
- params.common.use_checkpointing
+ params.common.use_checkpointing,
+ true
);
- size_t max_compute_size = ggml_allocr_max_size(alloc) + tensor_alignment;
+ 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;
@@ -1157,9 +1109,8 @@ int main(int argc, char ** argv) {
"invalid");
// allocate compute tensors
- mem_compute_data.resize(max_compute_size);
ctx_compute = ggml_init(ctx_compute_params);
- alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment);
+ 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);
@@ -1172,7 +1123,8 @@ int main(int argc, char ** argv) {
&logits, tokens_input, target_probs,
n_tokens, n_batch,
params.common.use_flash,
- params.common.use_checkpointing
+ params.common.use_checkpointing,
+ false
);
std::vector<llama_token> train_tokens;
generated by cgit v1.2.3 (git 2.25.1) at 2025-08-17 19:12:47 +0000