FlashOptim's memory-efficient mixed-precision training can be extended to surrogate models used in amortized optimization, enabling larger surrogate networks on memory-constrained accelerators.

Applying FlashOptim's mixed-precision training pipeline (combining FP16/BF16 forward passes with FP32 master weights and gradient accumulation) to surrogate neural networks within amortized optimization frameworks will enable training of surrogate models that are ≥2× larger (by parameter count) on a fixed GPU memory budget (e.g., 24 GB VRAM), while maintaining surrogate prediction accuracy within 5% relative error compared to full-precision baselines, and without degrading the quality of the amortized optimizer's output solutions by more than 10% on standard benchmark tasks.

Minimum Viable Test (MVT): Train a surrogate model for one amortized optimization benchmark (e.g., molecular property optimization using a GNN surrogate on QM9 or GuacaMol) in three conditions: (A) FP32 baseline, (B) FlashOptim mixed-precision on same model size, (C) FlashOptim mixed-precision on 2× larger model. Compare prediction accuracy, solution quality, memory usage, and training speed. Full validation extends to 3 domains and ablates loss scaling strategies.

# ============================================================
# FlashOptim Mixed-Precision Surrogate Training — Core Sketch
# ============================================================

import torch
import torch.nn as nn
from torch.cuda.amp import autocast, GradScaler
from torch_geometric.nn import DimeNetPlusPlus  # example surrogate arch

# --- Configuration ---
CONFIG = {
    "model_size": "2x",          # "1x" (baseline) or "2x" (scale-up test)
    "precision": "mixed_bf16",   # "fp32", "mixed_fp16", "mixed_bf16"
    "loss_scale_init": 65536.0,
    "grad_clip_norm": 1.0,
    "batch_size": 64,
    "lr": 3e-4,
    "epochs": 100,
}

# --- Surrogate Model (GNN for molecular property prediction) ---
class ScalableSurrogate(nn.Module):
    def __init__(self, hidden_dim=256, num_layers=6, scale_factor=1):
        super().__init__()
        # scale_factor=2 doubles hidden_dim → ~4x params (width scaling)
        h = hidden_dim * scale_factor
        self.encoder = DimeNetPlusPlus(
            hidden_channels=h,
            out_channels=1,
            num_blocks=num_layers,
            num_bilinear=8,
            num_spherical=7,
            num_radial=6,
        )
    
    def forward(self, batch):
        return self.encoder(batch.z, batch.pos, batch.batch)

# --- Mixed-Precision Training Loop ---
def train_surrogate(model, train_loader, val_loader, config):
    optimizer = torch.optim.AdamW(model.parameters(), lr=config["lr"])
    scaler = GradScaler(init_scale=config["loss_scale_init"],
                        enabled=(config["precision"] != "fp32"))
    
    dtype_map = {
        "fp32": torch.float32,
        "mixed_fp16": torch.float16,
        "mixed_bf16": torch.bfloat16,
    }
    amp_dtype = dtype_map[config["precision"]]
    
    memory_log = []
    
    for epoch in range(config["epochs"]):
        model.train()
        for batch in train_loader:
            batch = batch.to("cuda")
            optimizer.zero_grad(set_to_none=True)
            
            # --- Mixed-precision forward pass ---
            with autocast(dtype=amp_dtype, 
                         enabled=(config["precision"] != "fp32")):
                pred = model(batch)
                loss = nn.functional.mse_loss(pred.squeeze(), batch.y)
            
            # --- Scaled backward pass ---
            scaler.scale(loss).backward()
            
            # --- Gradient clipping (unscale first) ---
            scaler.unscale_(optimizer)
            torch.nn.utils.clip_grad_norm_(
                model.parameters(), config["grad_clip_norm"]
            )
            
            # --- Optimizer step with loss scale update ---
            scaler.step(optimizer)
            scaler.update()
        
        # --- Memory profiling ---
        mem_allocated = torch.cuda.memory_allocated() / 1e9  # GB
        mem_reserved = torch.cuda.memory_reserved() / 1e9
        memory_log.append({
            "epoch": epoch,
            "allocated_gb": mem_allocated,
            "reserved_gb": mem_reserved,
            "loss_scale": scaler.get_scale(),
        })
        
        # --- Validation ---
        val_mae = evaluate(model, val_loader, amp_dtype)
        
        # --- Abort checkpoint: loss scale collapse ---
        if scaler.get_scale() < 1.0:
            print(f"ABORT: Loss scale collapsed at epoch {epoch}")
            return None, memory_log
        
        print(f"Epoch {epoch}: MAE={val_mae:.4f}, "
              f"Mem={mem_allocated:.2f}GB, Scale={scaler.get_scale():.0f}")
    
    return model, memory_log

# --- Amortized Optimization Integration ---
class AmortizedOptimizer(nn.Module):
    """Learned proposal network using surrogate as reward model."""
    def __init__(self, surrogate, proposal_net):
        super().__init__()
        self.surrogate = surrogate
        self.proposal_net = proposal_net
    
    def optimize(self, n_steps=100, n_samples=64):
        solutions = []
        for step in range(n_steps):
            # Sample candidates from proposal network
            candidates = self.proposal_net.sample(n_samples)
            
            # Evaluate with surrogate (mixed-precision)
            with autocast(dtype=torch.bfloat16):
                with torch.no_grad():
                    scores = self.surrogate(candidates)
            
            # REINFORCE update for proposal network
            loss = -scores.mean()  # maximize surrogate score
            loss.backward()
            
            best_idx = scores.argmax()
            solutions.append((candidates[best_idx], scores[best_idx].item()))
        
        return solutions

# --- Evaluation ---
@torch.no_grad()
def evaluate(model, loader, amp_dtype):
    model.eval()
    total_mae, n = 0.0, 0
    for batch in loader:
        batch = batch.to("cuda")
        with autocast(dtype=amp_dtype):
            pred = model(batch).squeeze()
        total_mae += (pred - batch.y).abs().sum().item()
        n += batch.num_graphs
    return total_mae / n

# --- Memory Comparison Experiment ---
def run_memory_comparison():
    results = {}
    for precision in ["fp32", "mixed_bf16"]:
        for scale in [1, 2]:
            if precision == "fp32" and scale == 2:
                # Expected OOM — confirm and skip
                try:
                    model = ScalableSurrogate(scale_factor=scale).cuda()
                    # ... attempt training ...
                    results[f"{precision}_scale{scale}"] = "OOM"
                except torch.cuda.OutOfMemoryError:
                    results[f"{precision}_scale{scale}"] = "OOM_confirmed"
                continue
            
            model = ScalableSurrogate(scale_factor=scale).cuda()
            trained_model, mem_log = train_surrogate(
                model, train_loader, val_loader,
                {**CONFIG, "precision": precision}
            )
            peak_mem = max(e["allocated_gb"] for e in mem_log)
            results[f"{precision}_scale{scale}"] = {
                "peak_mem_gb": peak_mem,
                "final_mae": evaluate(trained_model, val_loader, 
                                     torch.bfloat16 if "bf16" in precision 
                                     else torch.float32),
            }
    return results