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sync : ggml (backend v2) #3912

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merged 19 commits into from
Nov 13, 2023
Merged

sync : ggml (backend v2) #3912

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aa7a2c4
sync : ggml (backend v2) (wip)
ggerganov Nov 2, 2023
e819070
sync : migrate examples and llama.cpp to dynamic graphs (wip)
ggerganov Nov 2, 2023
4fe646f
sync : update tests + fix max op params to 64
ggerganov Nov 2, 2023
83c96d5
sync : ggml-cuda
ggerganov Nov 2, 2023
8401e3e
llama : fix save/load state context size
ggerganov Nov 2, 2023
815f44e
sync : try to fix build on tvOS
ggerganov Nov 2, 2023
16e819d
sync : pass custom graph sizes in training examples
ggerganov Nov 2, 2023
e2349ec
sync : update graph copies to new ggml API
ggerganov Nov 2, 2023
f3fb45b
Merge branch 'master' into sync
ggerganov Nov 2, 2023
7f8e2a5
sync : update sync-ggml.sh with new files
ggerganov Nov 2, 2023
075ee61
Merge branch 'master' into sync
ggerganov Nov 3, 2023
dc22db7
scripts : fix header in sync script
ggerganov Nov 3, 2023
b1592ea
train : fix context size calculations
ggerganov Nov 3, 2023
e50ab5a
llama : increase inference graph size up to 4096 nodes
ggerganov Nov 3, 2023
081a86d
Merge branch 'master' into sync
ggerganov Nov 6, 2023
aa1f36c
Merge branch 'master' into sync
ggerganov Nov 7, 2023
a4de804
train : allocate grads for backward graphs
ggerganov Nov 7, 2023
548ec46
train : allocate grads for gb_tmp
ggerganov Nov 7, 2023
9efc6b9
Merge branch 'master' into sync
ggerganov Nov 12, 2023
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1 change: 1 addition & 0 deletions common/train.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -32,6 +32,7 @@ struct train_state * init_train_state() {
state->opt = new struct ggml_opt_context;
state->opt->ctx = NULL;
state->opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
state->opt->params.graph_size = LLAMA_TRAIN_MAX_NODES;
state->opt->loss_after = 0.0f;

return state;
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2 changes: 2 additions & 0 deletions common/train.h
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Expand Up @@ -9,6 +9,8 @@
#include "ggml.h"
#include "llama.h"

#define LLAMA_TRAIN_MAX_NODES 16384

typedef std::string mt19937_state;

struct train_state {
Expand Down
21 changes: 12 additions & 9 deletions examples/benchmark/benchmark-matmult.cpp
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Expand Up @@ -171,7 +171,8 @@ int main(int argc, char ** argv) {
struct ggml_tensor * m11xm2 = ggml_mul_mat(ctx, m11, m2);

// printf("Creating compute graph\n");
struct ggml_cgraph gf = ggml_build_forward(m11xm2);
struct ggml_cgraph * gf = ggml_new_graph(ctx);
ggml_build_forward_expand(gf, m11xm2);

printf("n_threads=%i\n", benchmark_params.n_threads);

Expand All @@ -180,9 +181,9 @@ int main(int argc, char ** argv) {

std::vector<uint8_t> work_buffer;

ggml_graph_compute_helper(work_buffer, &gf, benchmark_params.n_threads);
ggml_graph_compute_helper(work_buffer, gf, benchmark_params.n_threads);

TENSOR_DUMP(gf.nodes[0]);
TENSOR_DUMP(gf->nodes[0]);

printf("\n------ Test 2 - Matrix Mult via %s code\n", ggml_type_name(qtype));

Expand All @@ -200,7 +201,8 @@ int main(int argc, char ** argv) {
struct ggml_tensor * q31 = ggml_mul_mat(ctx, q11, m2);

// printf("Creating compute graph\n");
struct ggml_cgraph gf31 = ggml_build_forward(q31);
struct ggml_cgraph * gf31 = ggml_new_graph(ctx);
ggml_build_forward_expand(gf31, q31);

// Set up a second graph computation to make sure we override the CPU cache lines
// printf("Creating new tensor q12 & Running quantize\n");
Expand All @@ -211,7 +213,8 @@ int main(int argc, char ** argv) {
struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2);

//printf("Creating compute graph\n");
struct ggml_cgraph gf32 = ggml_build_forward(q32);
struct ggml_cgraph * gf32 = ggml_new_graph(ctx);
ggml_build_forward_expand(gf32, q32);
printf("n_threads=%i\n", benchmark_params.n_threads);

const int dimx = sizex;
Expand All @@ -223,7 +226,7 @@ int main(int argc, char ** argv) {


// Let's use the F32 result from above as a reference for the quantized multiplication
float sum_of_F32_reference = tensor_sum_elements(gf.nodes[0]);
float sum_of_F32_reference = tensor_sum_elements(gf->nodes[0]);

printf("Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_u_Seconds; gigaFLOPS\n");
printf("=====================================================================================\n");
Expand All @@ -233,7 +236,7 @@ int main(int argc, char ** argv) {

long long int start = ggml_time_us();
//printf("Running ggml_graph_compute\n");
ggml_graph_compute_helper(work_buffer, &gf31, benchmark_params.n_threads);
ggml_graph_compute_helper(work_buffer, gf31, benchmark_params.n_threads);

long long int stop = ggml_time_us();
long long int usec = stop-start;
Expand All @@ -251,7 +254,7 @@ int main(int argc, char ** argv) {

// Check that the matrix multiplication result is in the right ballpark
// We cannot use the exact value from the F32 multiplication because the quantizuation will be slightly different
float sum_of_Q4_result = tensor_sum_elements(gf31.nodes[0]);
float sum_of_Q4_result = tensor_sum_elements(gf31->nodes[0]);
float delta = std::abs(sum_of_Q4_result - sum_of_F32_reference);
float allowed_delta = (sum_of_F32_reference) / 1000 / 1000; // Let's accept an epsilon of 10^-6

Expand All @@ -266,7 +269,7 @@ int main(int argc, char ** argv) {
}

// Running a different graph computation to make sure we override the CPU cache lines
ggml_graph_compute_helper(work_buffer, &gf32, benchmark_params.n_threads);
ggml_graph_compute_helper(work_buffer, gf32, benchmark_params.n_threads);
}
printf("\n");
printf("Average%78.2f\n",gflops_sum/((double)benchmark_params.n_iterations));
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4 changes: 2 additions & 2 deletions examples/export-lora/export-lora.cpp
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Expand Up @@ -240,7 +240,7 @@ static struct lora_data * load_lora(struct lora_info * info) {
}

struct ggml_init_params params_ggml;
params_ggml.mem_size = ggml_tensor_overhead() * GGML_MAX_NODES;
params_ggml.mem_size = ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE;
params_ggml.mem_buffer = NULL;
params_ggml.no_alloc = true;
result->ctx = ggml_init(params_ggml);
Expand Down Expand Up @@ -334,7 +334,7 @@ static bool apply_lora(struct ggml_tensor * tensor, struct lora_data * lora, int
float scaling = lora->info.scale * (float)lora->lora_alpha / (float)lora->lora_r;

struct ggml_init_params params;
params.mem_size = GGML_OBJECT_SIZE + GGML_GRAPH_SIZE + ggml_tensor_overhead()*4 + GGML_MEM_ALIGN*5;
params.mem_size = GGML_OBJECT_SIZE + ggml_graph_overhead() + ggml_tensor_overhead()*4 + GGML_MEM_ALIGN*5;
params.mem_buffer = NULL;
params.no_alloc = true;
struct ggml_context * ctx = NULL;
Expand Down
17 changes: 9 additions & 8 deletions examples/finetune/finetune.cpp
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Expand Up @@ -1615,6 +1615,7 @@ int main(int argc, char ** argv) {
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;
Expand Down Expand Up @@ -1742,8 +1743,8 @@ int main(int argc, char ** argv) {

// context for compute tensors without their data
size_t estimated_compute_size_wo_data = (
ggml_tensor_overhead()*GGML_MAX_NODES*2
+ (GGML_OBJECT_SIZE+GGML_GRAPH_SIZE)*(
ggml_tensor_overhead()*LLAMA_TRAIN_MAX_NODES*2
+ (GGML_OBJECT_SIZE+ggml_graph_overhead())*(
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params.common.use_checkpointing ? 3 : 2
)
);
Expand All @@ -1768,11 +1769,11 @@ int main(int argc, char ** argv) {
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);
gf = ggml_new_graph(ctx_compute);
gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gf->order = (enum ggml_cgraph_eval_order) order;
gb = ggml_new_graph(ctx_compute);
gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, false);
gb_tmp = params.common.use_checkpointing
? ggml_new_graph(ctx_compute)
? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, false)
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@slaren Does this look OK?

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I think so, I don't know if gb here needs grads or not.

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@xaedes xaedes Nov 6, 2023 •
edited
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gb needs grads, because gb also contains the gf nodes, which have grads.

Changing the bool grads argument from false to true resolves a triggered assert in ggml.c ggml_graph_cpy.

GGML_ASSERT(dst->grads != NULL);

With this change finetune runs, I will report back if the results are good as well.

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@slaren slaren Nov 6, 2023 •
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Should ggml_graph_cpy be changed to allow skipping the grads if the src has them but not the dst?

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There was an additional - unrelated to this PR - regression in finetune and train-text-from-scratch due to new yarn rope implementation.

Changing bool grads argument to true and applying #3974 to fix the backward process of rope, the output of finetune is correct.

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@CoruNethron CoruNethron Nov 7, 2023 •
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Just to note, lines 1805 and 1807 below needs that change as well, I missed them at first attempt to copy this fix.
Also, mentioned regression and #3974 fix seems to be critical, because otherwise finetune produces LORA's without any progress from one checkpoint to another.

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I think gb_tmp also needs the grads=true argument.

: NULL;
loss = llama_build_lora_finetune_graphs(
&model, &lora, alloc, ctx_compute,
Expand Down Expand Up @@ -1801,11 +1802,11 @@ int main(int argc, char ** argv) {
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);
gf = ggml_new_graph(ctx_compute);
gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gf->order = best_order;
gb = ggml_new_graph(ctx_compute);
gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, false);
gb_tmp = params.common.use_checkpointing
? ggml_new_graph(ctx_compute)
? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, false)
: NULL;
loss = llama_build_lora_finetune_graphs(
&model, &lora, alloc, ctx_compute,
Expand Down
2 changes: 1 addition & 1 deletion examples/llava/clip.cpp
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Expand Up @@ -664,7 +664,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
// measure mem requirement and allocate
{
static const size_t tensor_alignment = 32;
new_clip->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead());
new_clip->buf_compute.resize(ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead());
new_clip->alloc = ggml_allocr_new_measure(tensor_alignment);
clip_image_f32_batch batch;
batch.size = 1;
Expand Down
10 changes: 5 additions & 5 deletions examples/metal/metal.cpp
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Expand Up @@ -34,7 +34,7 @@ int main(int argc, char ** argv) {
struct ggml_context * ctx_data = NULL;
struct ggml_context * ctx_eval = NULL;

struct ggml_cgraph gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval);
struct ggml_cgraph * gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval);

// this allocates all Metal resources and memory buffers
auto * ctx_metal = ggml_metal_init(1);
Expand All @@ -46,21 +46,21 @@ int main(int argc, char ** argv) {

// main
{
struct ggml_tensor * input = ggml_graph_get_tensor(&gf, "embd");
struct ggml_tensor * input = ggml_graph_get_tensor(gf, "embd");
*(int32_t *) input->data = 1; // BOS

ggml_metal_set_tensor(ctx_metal, input);

// warmup
ggml_metal_graph_compute(ctx_metal, &gf);
ggml_metal_graph_compute(ctx_metal, gf);

const int n_iter = 16;

const int64_t t0 = ggml_time_us();

// the actual inference happens here
for (int i = 0; i < n_iter; ++i) {
ggml_metal_graph_compute(ctx_metal, &gf);
ggml_metal_graph_compute(ctx_metal, gf);
}

const int64_t t1 = ggml_time_us();
Expand All @@ -70,7 +70,7 @@ int main(int argc, char ** argv) {

// debug output
{
struct ggml_tensor * logits = gf.nodes[gf.n_nodes - 1];
struct ggml_tensor * logits = gf->nodes[gf->n_nodes - 1];
ggml_metal_get_tensor(ctx_metal, logits);

float * ptr = (float *) ggml_get_data(logits);
Expand Down
17 changes: 9 additions & 8 deletions examples/train-text-from-scratch/train-text-from-scratch.cpp
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Expand Up @@ -1006,6 +1006,7 @@ int main(int argc, char ** argv) {
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;
Expand Down Expand Up @@ -1109,8 +1110,8 @@ int main(int argc, char ** argv) {

// context for compute tensors without their data
size_t estimated_compute_size_wo_data = (
ggml_tensor_overhead()*GGML_MAX_NODES*2
+ (GGML_OBJECT_SIZE+GGML_GRAPH_SIZE)*(
ggml_tensor_overhead()*LLAMA_TRAIN_MAX_NODES*2
+ (GGML_OBJECT_SIZE+ggml_graph_overhead())*(
params.common.use_checkpointing ? 3 : 2
)
);
Expand All @@ -1135,11 +1136,11 @@ int main(int argc, char ** argv) {
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);
gf = ggml_new_graph(ctx_compute);
gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gf->order = (enum ggml_cgraph_eval_order) order;
gb = ggml_new_graph(ctx_compute);
gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, false);
gb_tmp = params.common.use_checkpointing
? ggml_new_graph(ctx_compute)
? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, false)
: NULL;
loss = llama_build_train_graphs(
&model, alloc, ctx_compute,
Expand Down Expand Up @@ -1168,11 +1169,11 @@ int main(int argc, char ** argv) {
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);
gf = ggml_new_graph(ctx_compute);
gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gf->order = best_order;
gb = ggml_new_graph(ctx_compute);
gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, false);
gb_tmp = params.common.use_checkpointing
? ggml_new_graph(ctx_compute)
? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, false)
: NULL;
loss = llama_build_train_graphs(
&model, alloc, ctx_compute,
Expand Down
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