An Efficient Large-scale Training Codebase for MLLMs

Goals

• Support billion-parameter MLLMs with stable throughput and low idle time.
• Modular components for data, model, kernels, logging/observability, and inference.
• Keep the same code path for offline training and online evaluation where possible.

1) Data pipeline

• Iterable datasets + streaming: avoid full materialization; stream shards from object storage; prefetch and pin memory. Use Iterabledataset with worker-local RNG seeds.
• Sharding and ordering: deterministic global index → shard → worker; support resume with checkpointed iterator state.
• Tokenization/cache: async tokenize queue; on-the-fly packing for multi-modal sequences; cache hot prefixes.
• Collation: bucket by length/modality mix; pad to tensor cores friendly sizes (e.g., multiple of 8/16).
• Throughput checks: dataloader step time < model forward; track data_wait_time.

2) Model parallel (4D)

• Data parallel: global batch sharded across nodes; zero/optimizer state sharding (ZeRO-2/3).
• Tensor parallel: split projections and attention; fuse QKV projections; topo-aware groups.
• Pipeline parallel: balance layers per stage; schedule 1F1B (interleaved if needed).
• Sequence/context parallel: split sequence dim for very long contexts.
• Combine as 4D: DP × TP × PP × Seq. Derive microbatch = global_batch / (DP × accum_steps).

3) Fused kernels and Triton tips

• Fuse bias+activation+dropout where safe; prefer fused softmax for attention.
• FlashAttention/SDPA as default; fall back for very long sequences.
• Triton snippets usually follow:

import triton
import triton.language as tl

@triton.jit
def fused_bias_gelu(X_ptr, B_ptr, Out_ptr, n_elements, BLOCK: tl.constexpr):
    pid = tl.program_id(axis=0)
    offsets = pid * BLOCK + tl.arange(0, BLOCK)
    mask = offsets < n_elements
    x = tl.load(X_ptr + offsets, mask=mask, other=0.0)
    b = tl.load(B_ptr + offsets, mask=mask, other=0.0)
    y = x + b
    # fast approximate GELU
    y = 0.5 * y * (1.0 + tl.tanh(0.79788456 * (y + 0.044715 * tl.pow(y, 3))))
    tl.store(Out_ptr + offsets, y, mask=mask)

• Provide unit benchmarks per kernel; guard with capability checks (SM version, BF16/FP16).

4) Optimizer and scheduling

• BF16 or FP16 with dynamic loss scaling; overlap grad reduce with backward (no_sync windows).
• Use fused optimizers (AdamW fused) and parameter bucketing to reduce kernel launches.
• Warmup + cosine or polynomial decay; plan eval intervals that respect pipeline bubbles.

5) Logging and observability

• Structured logs (jsonl) per step: step, lr, loss, grad_norm, data_wait, tokens/sec, replay buffer stats.
• Emit health metrics: NaN counts, step time p50/p90, NCCL overlap %, GPU memory.
• Integrations: lightweight stdout for local runs; adapters for WandB/Neptune/Prometheus.
• Checkpoint metadata: global step, data iterator state, RNG seeds, optimizer/zero states, parallel topologies.

6) Inference/serving (vLLM-style)

• Paged KV cache: page-based allocator to share KV across requests; recycle pages on EOS.
• Continuous batching: greedy + beam search in the same loop; speculative decoding where possible.
• Parallelism: reuse TP/PP mappings; allow smaller DP for serving; pin weights in GPU; enable quantization (FP8/INT4) if quality allows.
• Admission control: max tokens/sec per GPU, preemptible queues, SLA buckets.
• Observability: per-request latency breakdown (queue, decode, postproc), cache hit rate, OOM/eviction counts.

7) Suggested repo layout

• configs/: parallel/optimizer/data configs.
• data/: dataloader, streaming reader, packing.
• model/: modules, parallel wrappers, init.
• kernels/: Triton/fused CUDA kernels + tests.
• engine/: train loop, scheduler, checkpoint.
• logging/: metrics, structured loggers, exporters.
• serving/: vLLM-style engine, paged cache, router.

Next steps

• Replace the placeholder diagram with a real schematic of your stack.
• Add real config examples (YAML/JSON) for DP×TP×PP settings and dataloader options.
• Hook logging to your preferred backend and add a small Triton kernel benchmark script.