vbatts/llama.cpp
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fix memory "leak" in optimizers

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each iteration a new cplan with new memory for work data was allocated.
now cplan creation only happens at the start of optimization, with each iteration reusing the cplan and its work data.
This commit is contained in:
xaedes 2023-08-14 21:12:02 +02:00
parent 3e6468b097
commit 56228461c8
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GPG key ID: 30030EDD817EA2B1
1 changed files with 13 additions and 6 deletions
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19
ggml.c
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@ -17368,7 +17368,10 @@ static enum ggml_opt_result ggml_opt_adam(
ggml_graph_reset (gf); ggml_graph_reset (gf);
ggml_set_f32 (f->grad, 1.0f); ggml_set_f32 (f->grad, 1.0f);
ggml_graph_compute_with_ctx(ctx, gb, params.n_threads); struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads);
struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
ggml_graph_compute(gb, &cplan);
opt->adam.fx_prev = ggml_get_f32_1d(f, 0); opt->adam.fx_prev = ggml_get_f32_1d(f, 0);
opt->adam.fx_best = opt->adam.fx_prev; opt->adam.fx_best = opt->adam.fx_prev;
@ -17455,7 +17458,7 @@ static enum ggml_opt_result ggml_opt_adam(
ggml_graph_reset (gf); ggml_graph_reset (gf);
ggml_set_f32 (f->grad, 1.0f); ggml_set_f32 (f->grad, 1.0f);
ggml_graph_compute_with_ctx(ctx, gb, params.n_threads); ggml_graph_compute(gb, &cplan);
const float fx = ggml_get_f32_1d(f, 0); const float fx = ggml_get_f32_1d(f, 0);
opt->loss_after = fx; opt->loss_after = fx;
@ -17528,7 +17531,6 @@ struct ggml_lbfgs_iteration_data {
}; };
static enum ggml_opt_result linesearch_backtracking( static enum ggml_opt_result linesearch_backtracking(
struct ggml_context * ctx,
const struct ggml_opt_params * params, const struct ggml_opt_params * params,
int nx, int nx,
float * x, float * x,
@ -17540,6 +17542,7 @@ static enum ggml_opt_result linesearch_backtracking(
struct ggml_tensor * f, struct ggml_tensor * f,
struct ggml_cgraph * gf, struct ggml_cgraph * gf,
struct ggml_cgraph * gb, struct ggml_cgraph * gb,
struct ggml_cplan * cplan,
const int np, const int np,
struct ggml_tensor * ps[], struct ggml_tensor * ps[],
ggml_opt_callback callback, ggml_opt_callback callback,
@ -17588,7 +17591,7 @@ static enum ggml_opt_result linesearch_backtracking(
ggml_graph_reset (gf); ggml_graph_reset (gf);
ggml_set_f32 (f->grad, 1.0f); ggml_set_f32 (f->grad, 1.0f);
ggml_graph_compute_with_ctx(ctx, gb, params->n_threads); ggml_graph_compute(gb, cplan);
ggml_opt_get_grad(np, ps, g); ggml_opt_get_grad(np, ps, g);
@ -17682,6 +17685,10 @@ static enum ggml_opt_result ggml_opt_lbfgs(
opt->iter = iter; opt->iter = iter;
} }
struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads);
struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
float * x = opt->lbfgs.x->data; // current parameters float * x = opt->lbfgs.x->data; // current parameters
float * xp = opt->lbfgs.xp->data; // previous parameters float * xp = opt->lbfgs.xp->data; // previous parameters
float * g = opt->lbfgs.g->data; // current gradient float * g = opt->lbfgs.g->data; // current gradient
@ -17716,7 +17723,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
ggml_graph_reset (gf); ggml_graph_reset (gf);
ggml_set_f32 (f->grad, 1.0f); ggml_set_f32 (f->grad, 1.0f);
ggml_graph_compute_with_ctx(ctx, gb, params.n_threads); ggml_graph_compute(gb, &cplan);
ggml_opt_get_grad(np, ps, g); ggml_opt_get_grad(np, ps, g);
@ -17778,7 +17785,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
ggml_vec_cpy_f32(nx, xp, x); ggml_vec_cpy_f32(nx, xp, x);
ggml_vec_cpy_f32(nx, gp, g); ggml_vec_cpy_f32(nx, gp, g);
ls = linesearch_backtracking(ctx, &params, nx, x, &fx, g, d, step, xp, f, gf, gb, np, ps, callback, callback_data); ls = linesearch_backtracking(&params, nx, x, &fx, g, d, step, xp, f, gf, gb, &cplan, np, ps, callback, callback_data);
if (ls < 0) { if (ls < 0) {
// linesearch failed - go back to the previous point and return // linesearch failed - go back to the previous point and return