Training

Training is GPU-only. uform_mesh_bootstrap joins this process to a run once, through files in a directory every host sees, and after it processes talk only over NVLink and InfiniBand. The mesh is the process-wide half of the transport, which every trainer borrows; uform_mesh_describe reports the topology every process agreed on, its multimem tier, and the gpu_class every GPU of the run dispatches as, the executor’s pin or the devices’ own. Every process bootstraps with the same count, multicast extent, directory, job and GPU class, or the call reports uform_status_config_mismatch_k.

uform_trainer_init_vram makes a trainer on a VRAM executor, owning one replica of the tower per device, its gradients, the Adafactor moments, the reconstruction heads and the learnable temperatures. One uform_trainer_step is one optimizer step over this process’s share of the global batch: forward, the losses, backward, the world gradient reduction, and the update. Items arrive tagged with a uform_modality_t, grouped into bags that travel together, with attract, repel and ignore relations indexing the step’s own items. Beside the masked and pooled contrastive losses, a trainer relaxes two optional terms, each read when its weight is positive:

• late_interaction trains the token head with hard MaxSim scores between the items each step’s sides names a query, a document or both.
• choices trains the byte decoder on the legal bytes each step’s choices tape gives, the tape uform_text_choose reads, and their target probabilities in the choice_targets tape, shaped as the probabilities it lands, within the batch’s max_choices_per_item.

uform_pair_loss_form_t picks how a contrastive term turns a row of scores into forces: balanced, ranked or SigLIP’s sigmoid, where derived keeps sigmoid for pooled embeddings and ranked for token vectors. The step’s stats report every loss as its world mean, with late_interaction_loss and choices_loss beside the contrastive and masked terms, the items truncated at a row’s width, and the segments dropped whole for want of room. A training call that fails after a GPU recorded a fault, such as a peer missing a multimem barrier or an InfiniBand round past its deadline, carries that fault as its outcome message. uform_trainer_save writes the safetensors checkpoint the model verbs load. Every path, the mesh’s directory, a resume checkpoint and a save’s path, arrives as the operating system’s own bytes, not necessarily UTF-8, and one holding a NUL byte is refused with uform_status_invalid_argument_k; the mesh’s job is a UTF-8 name.

uform_mesh_t mesh = NULL;
uform_mesh_bootstrap(&(uform_mesh_bootstrap_t){.mesh = &mesh, .executor = executor, .outcome = &outcome,
    .options = {.host_domain_index = node, .host_domain_count = nodes, .directory = {exchange, exchange_length},
                .job = {job, job_length}}});
uform_trainer_t trainer = NULL;
uform_trainer_init_vram(&(uform_trainer_init_vram_t){
    .trainer = &trainer, .executor = executor, .outcome = &outcome, .mesh = mesh, .depth = 12, .hidden = 512,
    .batch = {.max_rows_per_device = 64, .max_segments_per_device = 256, .max_columns_per_row = 1024,
              .max_bags_per_device = 128, .image = {.patch_side = 16}},
    .objective = uform_objective_joint_k, .schedule = {.steps = 100000}, .seed = 42});