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Merge branch 'master' into concedo_experimental

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# Conflicts:
#	.flake8
#	.github/workflows/python-lint.yml
#	flake.lock
#	ggml-cuda.cu
#	ggml-quants.c
#	llama.cpp
#	pocs/vdot/q8dot.cpp
#	pocs/vdot/vdot.cpp
#	tests/test-quantize-fns.cpp
#	tests/test-quantize-perf.cpp
This commit is contained in:
Concedo
2024-02-13 00:14:22 +08:00
parent fdc54efcf0 43fe07c1a4
commit 3cec37c2e0
35 changed files with 2814 additions and 1749 deletions
Download Patch File Download Diff File Expand all files Collapse all files
examples/embedding/embedding.cpp
+11 -1
View File
@@ -88,7 +88,17 @@ int main(int argc, char ** argv) {
}
const int n_embd = llama_n_embd(model);
const auto * embeddings = llama_get_embeddings(ctx);
auto * embeddings = llama_get_embeddings(ctx);
// l2-normalize embeddings
float norm = 0;
for (int i = 0; i < n_embd; i++) {
norm += embeddings[i] * embeddings[i];
}
norm = sqrt(norm);
for (int i = 0; i < n_embd; i++) {
embeddings[i] /= norm;
}
for (int i = 0; i < n_embd; i++) {
printf("%f ", embeddings[i]);
examples/export-lora/export-lora.cpp
+5 -14
View File
@@ -337,24 +337,14 @@ static bool apply_lora(struct ggml_tensor * tensor, struct lora_data * lora, int
params.mem_buffer = NULL;
params.no_alloc = true;
struct ggml_context * ctx = NULL;
struct ggml_allocr * alloc = NULL;
struct ggml_cgraph * gf = NULL;
struct ggml_gallocr * alloc = NULL;
struct ggml_cgraph * gf = NULL;
ctx = ggml_init(params);
alloc = ggml_allocr_new_measure(tensor_alignment);
alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
gf = build_graph_lora(ctx, tensor, lora_a, lora_b, scaling);
size_t alloc_size = ggml_allocr_alloc_graph(alloc, gf);
ggml_allocr_free(alloc);
ggml_free(ctx);
static std::vector<uint8_t> data_compute;
data_compute.resize(alloc_size + tensor_alignment);
ctx = ggml_init(params);
alloc = ggml_allocr_new(data_compute.data(), data_compute.size(), tensor_alignment);
gf = build_graph_lora(ctx, tensor, lora_a, lora_b, scaling);
ggml_allocr_alloc_graph(alloc, gf);
ggml_allocr_free(alloc);
ggml_gallocr_alloc_graph(alloc, gf);
struct ggml_cplan cplan = ggml_graph_plan(gf, n_threads);
static std::vector<uint8_t> data_work;
@@ -363,6 +353,7 @@ static bool apply_lora(struct ggml_tensor * tensor, struct lora_data * lora, int
ggml_graph_compute(gf, &cplan);
ggml_gallocr_free(alloc);
ggml_free(ctx);
return true;
}
examples/finetune/finetune.cpp
+38 -109
View File
@@ -1,5 +1,6 @@
#include "ggml.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "llama.h"
#include "common.h"
#include "train.h"
@@ -13,8 +14,6 @@
#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;
@@ -128,7 +127,7 @@ struct my_llama_lora_layer {
struct my_llama_lora {
struct ggml_context * ctx = NULL;
std::vector<uint8_t> data;
ggml_backend_buffer_t data;
my_llama_lora_hparams hparams;
@@ -372,63 +371,6 @@ static void set_param_lora(struct my_llama_lora * lora) {
}
}
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;
@@ -522,18 +464,8 @@ static void init_lora(const struct my_llama_model * model, struct my_llama_lora
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);
// allocate data for lora tensors
lora->data = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_cpu_buffer_type());
}
static void randomize_lora(struct my_llama_lora * lora, int seed, float mean, float std, float min, float max) {
@@ -579,7 +511,7 @@ static void randomize_lora(struct my_llama_lora * lora, int seed, float mean, fl
static struct ggml_tensor * llama_build_lora_finetune_graphs(
struct my_llama_model * model,
struct my_llama_lora * lora,
struct ggml_allocr * alloc,
ggml_gallocr_t alloc,
struct ggml_context * ctx,
struct ggml_cgraph * gf,
struct ggml_cgraph * gb,
@@ -590,7 +522,8 @@ static struct ggml_tensor * llama_build_lora_finetune_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;
@@ -622,13 +555,7 @@ static struct ggml_tensor * llama_build_lora_finetune_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]
@@ -780,7 +707,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
// 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);
ggml_set_input(t36->grad);
// KQ_pos
ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f));
@@ -805,11 +732,23 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
// 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_set_input(checkpoints[i]);
}
}
ggml_allocr_alloc_graph(alloc, gb);
if (measure_only) {
ggml_gallocr_reserve(alloc, gb);
} else {
ggml_gallocr_alloc_graph(alloc, gb);
// set KQ_pos
{
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) {
@@ -1663,7 +1602,7 @@ 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: 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));
printf("%s: lora_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(lora.ctx) + ggml_backend_buffer_get_size(lora.data)), (float) (ggml_used_mem(lora.ctx) + ggml_backend_buffer_get_size(lora.data)) / (1024.0f*1024.0f));
if (params.only_write_lora) {
save_train_files_data save_data;
@@ -1690,10 +1629,6 @@ 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;
// context for input tensors without their data
struct ggml_init_params ctx_input_params = {
ggml_tensor_overhead() * 2, // mem_size
@@ -1706,17 +1641,11 @@ int main(int argc, char ** argv) {
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);
// measure required memory for 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 = (
@@ -1743,7 +1672,7 @@ 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);
ggml_allocr_t 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);
@@ -1756,14 +1685,15 @@ 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;
}
ggml_allocr_free(alloc);
ggml_gallocr_free(alloc);
ggml_free(ctx_compute);
}
size_t max_compute_size = best_compute_size;
@@ -1774,9 +1704,8 @@ int main(int argc, char ** argv) {
"invalid");
// allocate compute tensors
mem_compute_data.resize(max_compute_size);
ctx_compute = ggml_init(ctx_compute_params);
ggml_allocr_t 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);
@@ -1789,11 +1718,9 @@ 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
);
ggml_allocr_free(alloc);
ggml_allocr_free(alloc_inps);
// tokenize data
std::vector<llama_token> train_tokens;
@@ -1908,6 +1835,8 @@ int main(int argc, char ** argv) {
ggml_free(ctx_work);
ggml_free(ctx_compute);
ggml_free(ctx_input);
ggml_gallocr_free(alloc);
int64_t t1 = ggml_time_ms();
printf("%s: total training time: ", __func__);
examples/llava/clip.cpp
+81 -71
View File
@@ -367,7 +367,7 @@ struct clip_ctx {
ggml_backend_buffer_t params_buffer = NULL;
ggml_backend_buffer_t compute_buffer = NULL;
ggml_backend_t backend = NULL;
ggml_allocr * compute_alloc = NULL;
ggml_gallocr_t compute_alloc = NULL;
};
static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) {
@@ -405,31 +405,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size, image_size, 3, batch_size);
ggml_allocr_alloc(ctx->compute_alloc, inp_raw);
if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
float * data = (float *)malloc(ggml_nbytes(inp_raw));
for (size_t i = 0; i < imgs->size; i++) {
const int nx = imgs->data[i].nx;
const int ny = imgs->data[i].ny;
GGML_ASSERT(nx == image_size && ny == image_size);
const int n = nx * ny;
for (int b = 0; b < batch_size; b++) {
for (int k = 0; k < 3; k++) {
for (int y = 0; y < ny; y++) {
for (int x = 0; x < nx; x++) {
data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].buf[3 * (y * nx + x) + k];
}
}
}
}
}
ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw));
free(data);
}
ggml_set_name(inp_raw, "inp_raw");
ggml_set_input(inp_raw);
struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
@@ -438,13 +415,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
// concat class_embeddings and patch_embeddings
struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);
ggml_allocr_alloc(ctx->compute_alloc, embeddings);
if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
void* zero_mem = malloc(ggml_nbytes(embeddings));
memset(zero_mem, 0, ggml_nbytes(embeddings));
ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings));
free(zero_mem);
}
ggml_set_name(embeddings, "embeddings");
ggml_set_input(embeddings);
embeddings = ggml_acc(ctx0, embeddings, model.class_embedding,
embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0);
@@ -453,15 +425,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]);
struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);
ggml_allocr_alloc(ctx->compute_alloc, positions);
if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
int* positions_data = (int*)malloc(ggml_nbytes(positions));
for (int i = 0; i < num_positions; i++) {
positions_data[i] = i;
}
ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
free(positions_data);
}
ggml_set_name(positions, "positions");
ggml_set_input(positions);
embeddings =
ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));
@@ -560,15 +525,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]);
struct ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_patches);
ggml_allocr_alloc(ctx->compute_alloc, patches);
if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
int* patches_data = (int*)malloc(ggml_nbytes(patches));
for (int i = 0; i < num_patches; i++) {
patches_data[i] = i + 1;
}
ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
free(patches_data);
}
ggml_set_name(patches, "patches");
ggml_set_input(patches);
// shape [1, 576, 1024]
// ne is whcn, ne = [1024, 576, 1, 1]
@@ -809,7 +767,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
}
// data
size_t buffer_size = 0;
size_t model_size = 0;
{
for (int i = 0; i < n_tensors; ++i) {
const char * name = gguf_get_tensor_name(ctx, i);
@@ -817,7 +775,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
enum ggml_type type = gguf_get_tensor_type(ctx, i);
struct ggml_tensor * cur = ggml_get_tensor(meta, name);
size_t tensor_size = ggml_nbytes(cur);
buffer_size += tensor_size;
model_size += tensor_size;
if (verbosity >= 3) {
printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
__func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type));
@@ -825,8 +783,6 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
}
}
buffer_size += n_tensors * 128 /* CLIP PADDING */;
clip_ctx * new_clip = new clip_ctx;
// update projector type
@@ -886,12 +842,12 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
printf("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder);
printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
printf("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector);
printf("%s: model size: %.2f MB\n", __func__, buffer_size / 1024.0 / 1024.0);
printf("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
printf("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
}
}
printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, buffer_size / (1024.0 * 1024.0), n_tensors);
printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors);
// load tensors
{
@@ -925,12 +881,10 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
}
// alloc memory and offload data
new_clip->params_buffer = ggml_backend_alloc_buffer(new_clip->backend, buffer_size);
ggml_allocr* alloc = ggml_allocr_new_from_buffer(new_clip->params_buffer);
new_clip->params_buffer = ggml_backend_alloc_ctx_tensors(new_clip->ctx_data, new_clip->backend);
for (int i = 0; i < n_tensors; ++i) {
const char * name = gguf_get_tensor_name(ctx, i);
struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx_data, name);
ggml_allocr_alloc(alloc, cur);
const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i);
fin.seekg(offset, std::ios::beg);
if (!fin) {
@@ -949,7 +903,6 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
ggml_backend_tensor_set(cur, read_buf.data(), 0, num_bytes);
}
}
ggml_allocr_free(alloc);
fin.close();
}
@@ -1077,15 +1030,12 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
// measure mem requirement and allocate
{
new_clip->buf_compute_meta.resize(GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead());
new_clip->compute_alloc = ggml_allocr_new_measure_from_backend(new_clip->backend);
new_clip->compute_alloc = ggml_gallocr_new(ggml_backend_get_default_buffer_type(new_clip->backend));
clip_image_f32_batch batch;
batch.size = 1;
ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch);
size_t compute_memory_buffer_size = ggml_allocr_alloc_graph(new_clip->compute_alloc, gf);
ggml_allocr_free(new_clip->compute_alloc);
new_clip->compute_buffer = ggml_backend_alloc_buffer(new_clip->backend, compute_memory_buffer_size);
new_clip->compute_alloc = ggml_allocr_new_from_buffer(new_clip->compute_buffer);
ggml_gallocr_reserve(new_clip->compute_alloc, gf);
size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0);
printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
}
@@ -1267,12 +1217,72 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
GGML_ASSERT(batch_size == 1); // TODO: support multiple images
}
// reset alloc buffer to clean the memory from previous invocations
ggml_allocr_reset(ctx->compute_alloc);
// build the inference graph
ggml_cgraph * gf = clip_image_build_graph(ctx, imgs);
ggml_allocr_alloc_graph(ctx->compute_alloc, gf);
ggml_gallocr_alloc_graph(ctx->compute_alloc, gf);
// set inputs
const auto & model = ctx->vision_model;
const auto & hparams = model.hparams;
const int image_size = hparams.image_size;
const int patch_size = hparams.patch_size;
const int num_patches = ((image_size / patch_size) * (image_size / patch_size));
const int num_positions = num_patches + 1;
{
struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw");
float * data = (float *)malloc(ggml_nbytes(inp_raw));
for (size_t i = 0; i < imgs->size; i++) {
const int nx = imgs->data[i].nx;
const int ny = imgs->data[i].ny;
GGML_ASSERT(nx == image_size && ny == image_size);
const int n = nx * ny;
for (int b = 0; b < batch_size; b++) {
for (int k = 0; k < 3; k++) {
for (int y = 0; y < ny; y++) {
for (int x = 0; x < nx; x++) {
data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].buf[3 * (y * nx + x) + k];
}
}
}
}
}
ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw));
free(data);
}
{
struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings");
void* zero_mem = malloc(ggml_nbytes(embeddings));
memset(zero_mem, 0, ggml_nbytes(embeddings));
ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings));
free(zero_mem);
}
{
struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
int* positions_data = (int*)malloc(ggml_nbytes(positions));
for (int i = 0; i < num_positions; i++) {
positions_data[i] = i;
}
ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
free(positions_data);
}
{
struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
int* patches_data = (int*)malloc(ggml_nbytes(patches));
for (int i = 0; i < num_patches; i++) {
patches_data[i] = i + 1;
}
ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
free(patches_data);
}
if (ggml_backend_is_cpu(ctx->backend)) {
ggml_backend_cpu_set_n_threads(ctx->backend, n_threads);
examples/llava/convert-image-encoder-to-gguf.py
+1 -1
View File
@@ -71,7 +71,7 @@ def bytes_to_unicode():
return dict(zip(bs, cs))
ap = argparse.ArgumentParser(prog="convert_hf_to_gguf.py")
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
ap.add_argument("--text-only", action="store_true", required=False,
examples/lookup/lookup.cpp
+11 -1
View File
@@ -1,7 +1,9 @@
#include "common.h"
#include "ggml.h"
#include "llama.h"
#include <cmath>
#include <cstdint>
#include <cstdio>
#include <string>
#include <vector>
@@ -73,6 +75,8 @@ int main(int argc, char ** argv){
int n_drafted = 0;
int n_accept = 0;
int64_t t_draft_us = 0;
int n_past = inp.size();
bool has_eos = false;
@@ -160,7 +164,7 @@ int main(int argc, char ** argv){
// generate n_pred tokens through prompt lookup
auto prompt_lookup = [&]() -> void {
int inp_size = inp.size();
const int inp_size = inp.size();
for (int ngram_size = ngram_max ; ngram_size > ngram_min; --ngram_size){
const llama_token * ngram = &inp[inp_size - ngram_size];
@@ -191,8 +195,12 @@ int main(int argc, char ** argv){
return;
};
const int64_t t_start_draft_us = ggml_time_us();
prompt_lookup();
t_draft_us += ggml_time_us() - t_start_draft_us;
llama_decode(ctx, batch_tgt);
++n_past;
@@ -210,6 +218,8 @@ int main(int argc, char ** argv){
LOG_TEE("n_draft = %d\n", n_draft);
LOG_TEE("n_predict = %d\n", n_predict);
LOG_TEE("n_drafted = %d\n", n_drafted);
LOG_TEE("t_draft = %.2f ms, %.2f us per token, %.2f tokens per second\n",
t_draft_us*1e-3, 1.0f*t_draft_us/n_drafted, n_drafted/(1e-6*t_draft_us));
LOG_TEE("n_accept = %d\n", n_accept);
LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
examples/main/main.cpp
+5 -2
View File
@@ -99,7 +99,7 @@ static void write_logfile(
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
static void sigint_handler(int signo) {
if (signo == SIGINT) {
if (!is_interacting) {
if (!is_interacting && g_params->interactive) {
is_interacting = true;
} else {
console::cleanup();
@@ -393,7 +393,8 @@ int main(int argc, char ** argv) {
LOG_TEE("\n");
}
if (params.interactive) {
// ctrl+C handling
{
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
struct sigaction sigint_action;
sigint_action.sa_handler = sigint_handler;
@@ -406,7 +407,9 @@ int main(int argc, char ** argv) {
};
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
#endif
}
if (params.interactive) {
LOG_TEE("%s: interactive mode on.\n", __func__);
if (!params.antiprompt.empty()) {
examples/server/README.md
+1 -1
View File
@@ -185,7 +185,7 @@ node index.js
`ignore_eos`: Ignore end of stream token and continue generating (default: false).
`logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced (default: []).
`logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. (default: []).
`n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0)
examples/server/oai.hpp
+6 -2
View File
@@ -15,9 +15,13 @@
using json = nlohmann::json;
inline static json oaicompat_completion_params_parse(
const json &body /* openai api json semantics */)
const json &body, /* openai api json semantics */
const std::string &chat_template)
{
json llama_params;
std::string formatted_prompt = chat_template == "chatml"
? format_chatml(body["messages"]) // OpenAI 'messages' to chatml (with <|im_start|>,...)
: format_llama2(body["messages"]); // OpenAI 'messages' to llama2 (with [INST],...)
llama_params["__oaicompat"] = true;
@@ -30,7 +34,7 @@ inline static json oaicompat_completion_params_parse(
// https://platform.openai.com/docs/api-reference/chat/create
llama_sampling_params default_sparams;
llama_params["model"] = json_value(body, "model", std::string("unknown"));
llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt'
llama_params["prompt"] = formatted_prompt;
llama_params["cache_prompt"] = json_value(body, "cache_prompt", false);
llama_params["temperature"] = json_value(body, "temperature", 0.0);
llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k);
examples/server/server.cpp
+45 -9
View File
@@ -37,6 +37,7 @@ struct server_params
std::string hostname = "127.0.0.1";
std::vector<std::string> api_keys;
std::string public_path = "examples/server/public";
std::string chat_template = "chatml";
int32_t port = 8080;
int32_t read_timeout = 600;
int32_t write_timeout = 600;
@@ -626,18 +627,36 @@ struct llama_server_context
const int n_vocab = llama_n_vocab(model);
for (const auto &el : *logit_bias)
{
if (el.is_array() && el.size() == 2 && el[0].is_number_integer())
if (el.is_array() && el.size() == 2)
{
llama_token tok = el[0].get<llama_token>();
if (tok >= 0 && tok < n_vocab)
float bias;
if (el[1].is_number())
{
if (el[1].is_number())
bias = el[1].get<float>();
}
else if (el[1].is_boolean() && !el[1].get<bool>())
{
bias = -INFINITY;
}
else
{
continue;
}
if (el[0].is_number_integer())
{
llama_token tok = el[0].get<llama_token>();
if (tok >= 0 && tok < n_vocab)
{
slot->sparams.logit_bias[tok] = el[1].get<float>();
slot->sparams.logit_bias[tok] = bias;
}
else if (el[1].is_boolean() && !el[1].get<bool>())
}
else if (el[0].is_string())
{
auto toks = llama_tokenize(model, el[0].get<std::string>(), false);
for (auto tok : toks)
{
slot->sparams.logit_bias[tok] = -INFINITY;
slot->sparams.logit_bias[tok] = bias;
}
}
}
@@ -1860,6 +1879,8 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`");
printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`");
printf(" --chat-template FORMAT_NAME");
printf(" set chat template, possible valus is: llama2, chatml (default %s)", sparams.chat_template.c_str());
printf("\n");
}
@@ -2291,6 +2312,21 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
log_set_target(stdout);
LOG_INFO("logging to file is disabled.", {});
}
else if (arg == "--chat-template")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
std::string value(argv[i]);
if (value != "chatml" && value != "llama2") {
fprintf(stderr, "error: chat template can be \"llama2\" or \"chatml\", but got: %s\n", value.c_str());
invalid_param = true;
break;
}
sparams.chat_template = value;
}
else if (arg == "--override-kv")
{
if (++i >= argc) {
@@ -2744,13 +2780,13 @@ int main(int argc, char **argv)
// TODO: add mount point without "/v1" prefix -- how?
svr.Post("/v1/chat/completions", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
svr.Post("/v1/chat/completions", [&llama, &validate_api_key, &sparams](const httplib::Request &req, httplib::Response &res)
{
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
if (!validate_api_key(req, res)) {
return;
}
json data = oaicompat_completion_params_parse(json::parse(req.body));
json data = oaicompat_completion_params_parse(json::parse(req.body), sparams.chat_template);
const int task_id = llama.queue_tasks.get_new_id();
llama.queue_results.add_waiting_task_id(task_id);
examples/server/utils.hpp
+30
View File
@@ -167,6 +167,34 @@ static T json_value(const json &body, const std::string &key, const T &default_v
: default_value;
}
inline std::string format_llama2(std::vector<json> messages)
{
std::ostringstream output;
bool is_inside_turn = false;
for (auto it = messages.begin(); it != messages.end(); ++it) {
if (!is_inside_turn) {
output << "[INST] ";
}
std::string role = json_value(*it, "role", std::string("user"));
std::string content = json_value(*it, "content", std::string(""));
if (role == "system") {
output << "<<SYS>>\n" << content << "\n<<SYS>>\n\n";
is_inside_turn = true;
} else if (role == "user") {
output << content << " [/INST]";
is_inside_turn = true;
} else {
output << " " << content << " </s>";
is_inside_turn = false;
}
}
LOG_VERBOSE("format_llama2", {{"text", output.str()}});
return output.str();
}
inline std::string format_chatml(std::vector<json> messages)
{
std::ostringstream chatml_msgs;
@@ -180,6 +208,8 @@ inline std::string format_chatml(std::vector<json> messages)
chatml_msgs << "<|im_start|>assistant" << '\n';
LOG_VERBOSE("format_chatml", {{"text", chatml_msgs.str()}});
return chatml_msgs.str();
}
examples/train-text-from-scratch/train-text-from-scratch.cpp
+32 -80
View File
@@ -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;