ggml : change ggml_graph_compute() API to not require context (#1999)

* ggml_graph_compute: deprecate using ggml_context, try resolve issue #287

* rewrite: no longer consider backward compitability; plan and make_plan

* minor: rename ctx as plan; const

* remove ggml_graph_compute from tests/test-grad0.c, but current change breaks backward

* add static ggml_graph_compute_sugar()

* minor: update comments

* reusable buffers

* ggml : more consistent naming + metal fixes

* ggml : fix docs

* tests : disable grad / opt + minor naming changes

* ggml : add ggml_graph_compute_with_ctx()

- backwards compatible API
- deduplicates a lot of copy-paste

* ci : enable test-grad0

* examples : factor out plan allocation into a helper function

* llama : factor out plan stuff into a helper function

* ci : fix env

* llama : fix duplicate symbols + refactor example benchmark

* ggml : remove obsolete assert + refactor n_tasks section

* ggml : fix indentation in switch

* llama : avoid unnecessary bool

* ggml : remove comments from source file and match order in header

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

1 files changed, 18 insertions, 6 deletions

diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp
index 212f54d..4965881 100644
--- a/examples/baby-llama/baby-llama.cpp
+++ b/examples/baby-llama/baby-llama.cpp
@@ -31,6 +31,17 @@ float frand_normal(struct random_normal_distribution * rnd) {
     return ((r < rnd->min) ? (rnd->min) : (r > rnd->max) ? (rnd->max) : r);
 }
 
+void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
+    struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
+
+    if (plan.work_size > 0) {
+        buf.resize(plan.work_size);
+        plan.work_data = buf.data();
+    }
+
+    ggml_graph_compute(graph, &plan);
+}
+
 struct ggml_tensor * randomize_tensor(
         struct ggml_tensor * tensor,
         int ndims,
@@ -1569,6 +1580,8 @@ int main(int argc, char ** argv) {
     int n_tokens = model.hparams.n_ctx;
     int n_vocab  = model.hparams.n_vocab;
 
+    std::vector<uint8_t> work_buffer;
+
     for (int ex=0; ex<n_examples; ++ex) {
         struct ggml_init_params params = {
             /*.mem_size   =*/ compute_size,
@@ -1586,7 +1599,6 @@ int main(int argc, char ** argv) {
         int n_past = 0;
 
         ggml_cgraph gf = {};
-        gf.n_threads = 1;
 
         get_example_targets_batch(ctx0, 64*ex+0,  tokens_input, targets);
 
@@ -1595,7 +1607,7 @@ int main(int argc, char ** argv) {
         struct ggml_tensor * e = square_error_loss(ctx0, targets, logits);
 
         ggml_build_forward_expand(&gf, e);
-        ggml_graph_compute(ctx0, &gf);
+        ggml_graph_compute_helper(work_buffer, &gf, /*n_threads*/ 1);
 
         float error_before_opt = ggml_get_f32_1d(e, 0);
 
@@ -1611,7 +1623,7 @@ int main(int argc, char ** argv) {
         ggml_opt(ctx0, opt_params_lbfgs, e);
         //
         ggml_build_forward_expand(&gf, e);
-        ggml_graph_compute(ctx0, &gf);
+        ggml_graph_compute_helper(work_buffer, &gf, /*n_threads*/ 1);
 
         float error_after_opt = ggml_get_f32_1d(e, 0);
 
@@ -1659,13 +1671,12 @@ int main(int argc, char ** argv) {
             struct ggml_context * ctx0 = ggml_init(params);
 
             ggml_cgraph gf = {};
-            gf.n_threads = 1;
 
             int n_past = 0;
             struct ggml_tensor * logits = forward(&model, &kv_self, ctx0, &gf, tokens_input, sample_ctx, n_past);
 
             ggml_build_forward_expand(&gf, logits);
-            ggml_graph_compute(ctx0, &gf);
+            ggml_graph_compute_helper(work_buffer, &gf, /*n_threads*/ 1);
 
             struct ggml_tensor * best_samples = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, sample_ctx);
             struct ggml_tensor * probs        = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_vocab, sample_ctx);
@@ -1687,10 +1698,11 @@ int main(int argc, char ** argv) {
     }
 
     print_matrix(model.tok_embeddings);
-
     printf("done\n");
+
     // ggml_free(kv_self.ctx);
     // ggml_free(model_lora.ctx);
     ggml_free(model.ctx);
+
     return 0;
 }