FlashOptim techniques can reduce the memory footprint of training LLMs for mRNA sequence design.

Applying FlashOptim memory-optimization techniques (specifically: gradient checkpointing with selective layer recomputation, mixed-precision training via bfloat16/float16, fused attention kernels analogous to FlashAttention-2/3, and optimizer state sharding) to large language models fine-tuned or pre-trained on mRNA nucleotide sequences will reduce peak GPU memory consumption by ≥30% relative to a standard baseline training configuration, without degrading model perplexity on held-out mRNA sequence validation sets by more than 2% (absolute), and without increasing wall-clock training time per token by more than 25%. The hypothesis is falsifiable: if memory reduction is <30% OR perplexity degrades >2% OR throughput penalty exceeds 25%, the claim is not supported at the stated threshold.

MINIMUM VIABLE TEST (MVT) — 3-condition, 3-model-size factorial design:

Conditions: C0 (Baseline): fp32 attention, Adam full-state, no gradient checkpointing C1 (Partial FlashOptim): bf16 + FlashAttention-2 kernel only C2 (Full FlashOptim): bf16 + FlashAttention-2 + gradient checkpointing (every 2 layers) + ZeRO Stage 2

Model sizes: M1=125M, M2=350M, M3=1.3B parameters (GPT-2/GPT-NeoX architecture adapted for nucleotide vocab)

Sequence corpus: 50,000 human mRNA sequences from GENCODE v47 (train/val/test: 40k/5k/5k), tokenized at nucleotide level (vocab size 7), max length 2,048 tokens (padded/truncated)

Per-cell measurements (3 conditions × 3 sizes × 5 seeds = 45 runs):

• Peak GPU memory (nvidia-smi dmon, 100ms polling; torch.cuda.max_memory_allocated())
• Validation perplexity at 1,000 training steps
• Tokens/second throughput (averaged over steps 100–500, excluding warmup)
• Training loss curve (logged every 50 steps)
• Wall-clock time to 1,000 steps

Statistical analysis: Two-way ANOVA (condition × model size) on memory reduction %; paired t-tests (C2 vs C0) per model size; Bonferroni correction for 3 model sizes (α=0.0167 per test).

# FlashOptim mRNA LLM Memory Benchmark
# Architecture: GPT-style transformer, nucleotide tokenizer, 3 model sizes

# ── 0. DEPENDENCIES ──────────────────────────────────────────────────────────
import torch
import torch.nn as nn
from torch.utils.checkpoint import checkpoint
from flash_attn import flash_attn_func          # FlashAttention-2
from flash_attn.modules.mha import FlashSelfAttention
import deepspeed                                 # ZeRO Stage 2
from transformers import GPT2Config, GPT2LMHeadModel
from datasets import load_dataset, Dataset
import wandb, numpy as np, pandas as pd

# ── 1. TOKENIZER (nucleotide-level, vocab=7) ──────────────────────────────────
VOCAB = {'A': 0, 'U': 1, 'G': 2, 'C': 3, 'N': 4, 'PAD': 5, 'MASK': 6}
T2U  = str.maketrans('T', 'U')   # DNA→RNA conversion

def tokenize_mrna(seq: str, max_len: int = 2048) -> list[int]:
    seq = seq.upper().translate(T2U)
    tokens = [VOCAB.get(c, VOCAB['N']) for c in seq[:max_len]]
    tokens += [VOCAB['PAD']] * (max_len - len(tokens))
    return tokens

# ── 2. MODEL FACTORY ──────────────────────────────────────────────────────────
MODEL_CONFIGS = {
    '125M': dict(n_layer=12, n_head=12, n_embd=768),
    '350M': dict(n_layer=24, n_head=16, n_embd=1024),
    '1.3B': dict(n_layer=24, n_head=16, n_embd=2048),
}

def build_model(size: str, use_flash_attn: bool = False,
                use_grad_ckpt: bool = False) -> nn.Module:
    cfg = GPT2Config(
        vocab_size=7,
        n_positions=2048,
        **MODEL_CONFIGS[size],
        attn_pdrop=0.0,
        resid_pdrop=0.0,
    )
    model = GPT2LMHeadModel(cfg)
    if use_flash_attn:
        _replace_attention_with_flash(model)   # monkey-patch MHA → FlashSelfAttention
    if use_grad_ckpt:
        model.gradient_checkpointing_enable()  # HF built-in; checkpoints every layer
    return model

def _replace_attention_with_flash(model: nn.Module):
    """Replace all GPT2Attention modules with FlashSelfAttention."""
    for name, module in model.named_modules():
        if 'attn' in name and hasattr(module, 'c_attn'):
            # Swap in FlashSelfAttention (simplified; real impl needs careful weight mapping)
            parent = _get_parent(model, name)
            setattr(parent, name.split('.')[-1],
                    FlashSelfAttention(causal=True, softmax_scale=None))

# ── 3. TRAINING CONDITIONS ────────────────────────────────────────────────────
CONDITIONS = {
    'C0_baseline':      dict(dtype=torch.float32, flash=False, ckpt=False, zero=False),
    'C1_partial':       dict(dtype=torch.bfloat16, flash=True,  ckpt=False, zero=False),
    'C2_full_flashopt': dict(dtype=torch.bfloat16, flash=True,  ckpt=True,  zero=True),
    # Ablations (M2 only):
    'C2a_bf16_only':    dict(dtype=torch.bfloat16, flash=False, ckpt=False, zero=False),
    'C2b_flash_only':   dict(dtype=torch.float32,  flash=True,  ckpt=False, zero=False),
    'C2c_ckpt_only':    dict(dtype=torch.float32,  flash=False, ckpt=True,  zero=False),
    'C2d_zero_only':    dict(dtype=torch.float32,  flash=False, ckpt=False, zero=True),
}

# ── 4. MEMORY PROFILER ────────────────────────────────────────────────────────
class MemoryProfiler:
    def __init__(self):
        torch.cuda.reset_peak_memory_stats()
        self.snapshots = []

    def record(self, step: int):
        self.snapshots.append({
            'step': step,
            'allocated_gb': torch.cuda.memory_allocated() / 1e9,
            'peak_gb':      torch.cuda.max_memory_allocated() / 1e9,
        })

    def peak(self) -> float:
        return max(s['peak_gb'] for s in self.snapshots)

# ── 5. TRAINING LOOP ──────────────────────────────────────────────────────────
def train_and_profile(model_size: str, condition: str, seed: int,
                      dataloader, n_steps: int = 1000) -> dict:
    torch.manual_seed(seed)
    cfg = CONDITIONS[condition]

    model = build_model(model_size, use_flash_attn=cfg['flash'],
                        use_grad_ckpt=cfg['ckpt'])

    # ZeRO Stage 2 via DeepSpeed
    if cfg['zero']:
        ds_config = {
            "zero_optimization": {"stage": 2, "allgather_partitions": True,
                                  "reduce_scatter": True},
            "bf16": {"enabled": cfg['dtype'] == torch.bfloat16},
            "train_micro_batch_size_per_gpu": 8,
        }
        model, optimizer, _, _ = deepspeed.initialize(
            model=model, config=ds_config,
            model_parameters=model.parameters())
    else:
        optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4,
                                      betas=(0.9, 0.95), weight_decay=0.1)

    profiler = MemoryProfiler()
    loss_fn  = nn.CrossEntropyLoss(ignore_index=VOCAB['PAD'])
    throughput_log = []
    t0 = torch.cuda.Event(enable_timing=True)
    t1 = torch.cuda.Event(enable_timing=True)

    model.train()
    for step, batch in enumerate(dataloader):
        if step >= n_steps:
            break

        input_ids = batch['input_ids'].cuda()   # (B, 2048)
        labels    = input_ids.clone()
        labels[:, :-1] = input_ids[:, 1:]
        labels[:, -1]  = VOCAB['PAD']

        t0.record()
        with torch.autocast(device_type='cuda', dtype=cfg['dtype'],
                             enabled=(cfg['dtype'] != torch.float32)):
            logits = model(input_ids).logits          # (B, 2048, 7)
            loss   = loss_fn(logits.view(-1, 7), labels.view(-1))

        if cfg['zero']:
            model.backward(loss)
            model.step()
        else:
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

        t1.record()
        torch.cuda.synchronize()

        if step >= 100:
            elapsed_ms = t0.elapsed_time(t1)
            tokens_per_sec = (input_ids.numel()) / (elapsed_ms / 1000)
            throughput_log.append(tokens_per_sec)

        if step % 50 == 0:
            profiler.record(step)
            wandb.log({'loss': loss.item(), 'step': step,
                       'peak_mem_gb': profiler.peak()})

    # Validation perplexity
    val_ppl = evaluate_perplexity(model, val_dataloader, cfg['dtype'])

    return {
        'peak_