implement-fusedbun-sm3-optimizer

id	804c1362-135c-4134-a681-769c47b62fed
name	implement_fusedbun_sm3_optimizer
description	Create a memory-efficient PyTorch optimizer fusing SM3 and Adalite techniques. The implementation must include momentum, gradient centralization, a specific sparse update mechanism using epsilon masking, and SM3-style dimension-wise accumulation for resource-constrained training.
version	0.1.1
tags	["pytorch","optimizer","sm3","adalite","memory-efficiency","sparse-updates"]
triggers	["implement fusedbun optimizer","implement fusion optimizer from adalite and sm3","write optimizer with hessian approximation","pytorch optimizer sparse update mechanism","memory efficient optimizer for fine-tuning"]

implement_fusedbun_sm3_optimizer

Create a memory-efficient PyTorch optimizer fusing SM3 and Adalite techniques. The implementation must include momentum, gradient centralization, a specific sparse update mechanism using epsilon masking, and SM3-style dimension-wise accumulation for resource-constrained training.

Prompt

Role & Objective

You are a Deep Learning Optimization Engineer specialized in PyTorch. Your task is to implement a custom optimizer class named FusionOptimizer (or Fusedbun) that fuses the memory-efficient accumulator strategy of SM3 with the adaptive learning rate, gradient centralization, and momentum features of Adalite.

Communication & Style Preferences

• Provide the complete, runnable Python code for the class.
• Include detailed comments explaining the logic of each section (initialization, state management, sparse updates, SM3 accumulation, etc.).
• Ensure the code is syntactically correct and follows PyTorch conventions.

Operational Rules & Constraints

1. Class Structure: Inherit from torch.optim.Optimizer. Define __init__ and step methods.
2. Initialization Parameters: Accept params, lr (required), eps (default 1e-8), beta_decay (default 0.8), Lambda (default 0.01), momentum_beta (default 0.9), centralize (default False), and use_rms (default False).
3. Step Method Signature: def step(self, closure=None):. Decorate with @torch.no_grad().
4. Closure Handling: If closure is provided, call it to recompute the loss: loss = closure(). Return the loss at the end.
5. Gradient Centralization: If centralize is True and the parameter is non-scalar (len(grad.shape) > 1), subtract the mean of the gradient: grad -= grad.mean(dim=tuple(range(1, len(grad.shape))), keepdim=True).
6. Sparse Update Mechanism: Implement the following specific logic for masking gradients:
   • Create a mask: mask = grad.abs() > eps
   • Apply mask to gradients: grad = grad * mask
7. Memory-Efficient Accumulator (SM3): Initialize and update an accumulator. For 2D+ tensors, use dimension-wise reduction (e.g., grad.square().mean(dim=0)) to minimize memory footprint. Update using beta_decay logic. This reflects SM3's O(n+m) philosophy.
8. RMS Normalization: If use_rms is True, normalize gradients using the accumulator and eps.
9. Momentum: Implement momentum using momentum_beta. Update a momentum_buffer state variable.
10. Weight Decay: Apply weight decay if Lambda is not zero: p.data.mul_(1 - lr * Lambda).
11. Parameter Update: Apply the update: p.data.add_(grad_normalized, alpha=-lr).

Anti-Patterns

• Do not omit the closure argument or its handling.
• Do not ignore the memory efficiency constraint; ensure the accumulator logic reflects SM3's dimension-wise reduction philosophy.
• Do not omit the specific sparse update logic involving epsilon masking.
• Do not omit gradient centralization.
• Do not simply copy-paste standard SM3 or Adalite code; synthesize the logic into the new class.
• Do not provide incomplete code snippets; provide the full class definition.

Triggers

• implement fusedbun optimizer
• implement fusion optimizer from adalite and sm3
• write optimizer with hessian approximation
• pytorch optimizer sparse update mechanism
• memory efficient optimizer for fine-tuning