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Messages: 38
UserStep 0
Task: llm-pretrain-optimizer
LLM Pretraining: Optimizer & Learning Rate Schedule Optimization
Research Question
Design an improved optimizer and/or learning rate schedule for GPT-2 language model pretraining. Your modifications should reduce validation loss compared to the standard AdamW with cosine annealing schedule.
What You Can Modify
Two regions in custom_pretrain.py:
- configure_optimizers method (lines 172-189): Optimizer creation and parameter grouping
- get_lr function (lines 192-201): Learning rate schedule
You can modify:
- The optimization algorithm (default: AdamW with fused implementation)
- Parameter grouping strategy (default: weight decay for 2D params, no decay for 1D)
- Learning rate schedule shape (default: cosine with linear warmup)
- Any optimizer hyperparameters
Note: The training loop calls get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr) — keep this signature compatible. The optimizer returned by configure_optimizers must support .zero_grad(), .step(), and .param_groups.
Evaluation
- Metric: Validation loss (cross-entropy, lower is better), plus perplexity (WikiText-2, LAMBADA) and downstream accuracy (ARC-Easy, HellaSwag, PIQA, WinoGrande)
- Model: GPT-2 Medium (24L/16H/1024D, ~355M params)
- Dataset: FineWeb 10B (GPT-2 tokenizer), ~7.1B tokens (D=20N Chinchilla-optimal)
- Training: 12030 iterations, BSZ=96, GA=6, 2-GPU DDP
- Hardware: H200 GPU
nanoGPT/custom_pretrain.py [EDITABLE — lines 171–189, lines 245–247 only]
1: """Custom GPT-2 Pretraining Script
2: Based on Andrej Karpathy's nanoGPT, evaluated on FineWeb dataset.
3: """
4:
5: import math
6: import inspect
7: import os
8: import time
9: from contextlib import nullcontext
10: from dataclasses import dataclass
11:
12: import numpy as np
13: import torch
14: import torch.nn as nn
15: from torch.nn import functional as F
16:
17: # ============================================================================
18: # Model Components
19: # ============================================================================
20:
21: # ── Normalization ──────────────────────────────────────────────────────────
22: class LayerNorm(nn.Module):
23: """LayerNorm but with an optional bias."""
24: def __init__(self, ndim, bias):
25: super().__init__()
26: self.weight = nn.Parameter(torch.ones(ndim))
27: self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
28:
29: def forward(self, input):
30: return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
31:
32: # ── Self-Attention ─────────────────────────────────────────────────────────
33: class CausalSelfAttention(nn.Module):
34: def __init__(self, config):
35: super().__init__()
36: assert config.nAssistantStep 1edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 172,
5 "end_line": 189,
6 "content": " param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}\n decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]\n nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]\n optim_groups = [\n {'params': decay_params, 'weight_decay': weight_decay},\n {'params': nodecay_params, 'weight_decay': 0.0},\n ]\n num_decay_params = sum(p.numel() for p in decay_params)\n num_nodecay_params = sum(p.numel() for p in nodecay_params)\n print(f\"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters\")\n print(f\"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters\")\n \n class RAdam(torch.optim.Optimizer):\n \"\"\"Rectified Adam optimizer (Liu et al., 2019) with decoupled weight decay.\"\"\"\n def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):\n defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)\n super().__init__(params, defaults)\n \n @torch.no_grad()\n def step(self):\n for group in self.param_groups:\n for p in group['params']:\n if p.grad is None:\n continue\n grad = p.grad\n if grad.is_sparse:\n raise RuntimeError('RAdam does not support sparse gradients')\n state = self.state[p]\n if len(state) == 0:\n state['step'] = 0\n state['exp_avg'] = torch.zeros_like(p)\n state['exp_avg_sq'] = torch.zeros_like(p)\n exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']\n beta1, beta2 = group['betas']\n state['step'] += 1\n t = state['step']\n # Decoupled weight decay\n if group['weight_decay'] != 0:\n p.mul_(1 - group['lr'] * group['weight_decay'])\n # Update biased first moment estimate\n exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)\n # Update biased second raw moment estimate\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)\n # Compute bias-corrected first moment estimate\n m_hat = exp_avg / (1 - beta1 ** t)\n # Compute maximum length of the approximated SMA\n beta2_t = beta2 ** t\n N_sma_max = 2 / (1 - beta2) - 1\n N_sma = N_sma_max - 2 * t * beta2_t / (1 - beta2_t)\n if N_sma >= 5:\n # Variance rectification term\n r_t = math.sqrt((N_sma - 4) * (N_sma - 2) * N_sma_max / ((N_sma_max - 4) * (N_sma_max - 2) * N_sma))\n # Adaptive learning rate\n denom = exp_avg_sq.sqrt().add_(group['eps'])\n step_size = group['lr'] * r_t\n p.addcdiv_(m_hat, denom, value=-step_size)\n else:\n # Un-adapted learning rate\n step_size = group['lr'] / (1 - beta1 ** t)\n p.add_(exp_avg, alpha=-step_size)\n \n optimizer = RAdam(optim_groups, lr=learning_rate, betas=betas, weight_decay=weight_decay)\n print(\"using RAdam optimizer\")\n return optimizer"
7}Tool ResultStep 1
OK: Replaced lines 172..189 with 64 line(s) in nanoGPT/custom_pretrain.py. Editable range: 171–235, 291–293.
[Current file: nanoGPT/custom_pretrain.py | editable: 171–235, 291–293 | total: 483 lines]
171 def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
172 param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}
173 decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
... (59 more lines) ...
233 optimizer = RAdam(optim_groups, lr=learning_rate, betas=betas, weight_decay=weight_decay)
234 print("using RAdam optimizer")
235 return optimizer
...
291 # CONFIG_OVERRIDES: override training hyperparameters for your method.
292 # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
293 CONFIG_OVERRIDES = {}AssistantStep 2undo
Tool Input
1{
2 "n": 1
3}Tool ResultStep 2
Undo complete: Restored: nanoGPT/custom_pretrain.py
AssistantStep 3edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 192,
5 "end_line": 201,
6 "content": "def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):\n \"\"\"Linear warmup followed by linear decay.\"\"\"\n if it < warmup_iters:\n return learning_rate * (it + 1) / (warmup_iters + 1)\n if it > lr_decay_iters:\n return min_lr\n decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)\n assert 0 <= decay_ratio <= 1\n return min_lr + (learning_rate - min_lr) * (1 - decay_ratio)"
7}Tool ResultStep 3
ERROR: Lines 192..201 exceed the editable range. You may only edit lines 171–189, 245–247.
[Current file: nanoGPT/custom_pretrain.py | editable: 171–189, 245–247 | total: 437 lines]
171 def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
172 param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}
173 decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
... (13 more lines) ...
187 print(f"using fused AdamW: {use_fused}")
188 return optimizer
189
...
245 # CONFIG_OVERRIDES: override training hyperparameters for your method.
246 # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
247 CONFIG_OVERRIDES = {}AssistantStep 4edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 172,
5 "end_line": 189,
6 "content": " param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}\n decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]\n nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]\n optim_groups = [\n {'params': decay_params, 'weight_decay': weight_decay},\n {'params': nodecay_params, 'weight_decay': 0.0},\n ]\n num_decay_params = sum(p.numel() for p in decay_params)\n num_nodecay_params = sum(p.numel() for p in nodecay_params)\n print(f\"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters\")\n print(f\"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters\")\n \n class RAdam(torch.optim.Optimizer):\n \"\"\"Rectified Adam optimizer (Liu et al., 2019) with decoupled weight decay.\"\"\"\n def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):\n defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)\n super().__init__(params, defaults)\n \n @torch.no_grad()\n def step(self):\n for group in self.param_groups:\n for p in group['params']:\n if p.grad is None:\n continue\n grad = p.grad\n if grad.is_sparse:\n raise RuntimeError('RAdam does not support sparse gradients')\n state = self.state[p]\n if len(state) == 0:\n state['step'] = 0\n state['exp_avg'] = torch.zeros_like(p)\n state['exp_avg_sq'] = torch.zeros_like(p)\n exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']\n beta1, beta2 = group['betas']\n state['step'] += 1\n t = state['step']\n # Decoupled weight decay\n if group['weight_decay'] != 0:\n p.mul_(1 - group['lr'] * group['weight_decay'])\n # Update biased first moment estimate\n exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)\n # Update biased second raw moment estimate\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)\n # Compute bias-corrected first moment estimate\n m_hat = exp_avg / (1 - beta1 ** t)\n # Compute maximum length of the approximated SMA\n beta2_t = beta2 ** t\n N_sma_max = 2 / (1 - beta2) - 1\n N_sma = N_sma_max - 2 * t * beta2_t / (1 - beta2_t)\n if N_sma >= 5:\n # Variance rectification term\n r_t = math.sqrt((N_sma - 4) * (N_sma - 2) * N_sma_max / ((N_sma_max - 4) * (N_sma_max - 2) * N_sma))\n # Adaptive learning rate\n denom = exp_avg_sq.sqrt().add_(group['eps'])\n step_size = group['lr'] * r_t\n p.addcdiv_(m_hat, denom, value=-step_size)\n else:\n # Un-adapted learning rate\n step_size = group['lr'] / (1 - beta1 ** t)\n p.add_(exp_avg, alpha=-step_size)\n \n optimizer = RAdam(optim_groups, lr=learning_rate, betas=betas, weight_decay=weight_decay)\n print(\"using RAdam optimizer\")\n return optimizer"
7}Tool ResultStep 4
OK: Replaced lines 172..189 with 64 line(s) in nanoGPT/custom_pretrain.py. Editable range: 171–235, 291–293.
[Current file: nanoGPT/custom_pretrain.py | editable: 171–235, 291–293 | total: 483 lines]
171 def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
172 param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}
173 decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
... (59 more lines) ...
233 optimizer = RAdam(optim_groups, lr=learning_rate, betas=betas, weight_decay=weight_decay)
234 print("using RAdam optimizer")
235 return optimizer
...
291 # CONFIG_OVERRIDES: override training hyperparameters for your method.
292 # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
293 CONFIG_OVERRIDES = {}AssistantStep 5edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 291,
5 "end_line": 293,
6 "content": " # CONFIG_OVERRIDES: override training hyperparameters for your method.\n # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.\n CONFIG_OVERRIDES = {'learning_rate': 8e-4, 'warmup_iters': 400, 'min_lr': 4e-5}"
7}Tool ResultStep 5
OK: Replaced lines 291..293 with 3 line(s) in nanoGPT/custom_pretrain.py. Editable range: 171–235, 291–293.
[Current file: nanoGPT/custom_pretrain.py | editable: 171–235, 291–293 | total: 483 lines]
171 def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
172 param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}
173 decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
... (59 more lines) ...
233 optimizer = RAdam(optim_groups, lr=learning_rate, betas=betas, weight_decay=weight_decay)
234 print("using RAdam optimizer")
235 return optimizer
...
291 # CONFIG_OVERRIDES: override training hyperparameters for your method.
292 # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
293 CONFIG_OVERRIDES = {'learning_rate': 8e-4, 'warmup_iters': 400, 'min_lr': 4e-5}AssistantStep 6test
Tool ResultStep 6
[Test #1] (2 tests remaining; call submit(n=N) to choose which test result to submit as final) ### gpt-345m (scripts/gpt_345m.sh) Training metrics (last steps): TRAIN_METRICS: step=4000, train_loss=2.7416, val_loss=2.4610 TRAIN_METRICS: step=5000, train_loss=2.6887, val_loss=2.4074 TRAIN_METRICS: step=6000, train_loss=2.6442, val_loss=2.3568 TRAIN_METRICS: step=7000, train_loss=2.6045, val_loss=2.3233 TRAIN_METRICS: step=8000, train_loss=2.5684, val_loss=2.2968 ### lm-eval-345m (scripts/lm_eval_345m.sh) [COMMAND FAILED exit=1] ERROR: Checkpoint not found: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-optimizer/deepseek-reasoner_20260413_043229/seed_42/ckpt_gpt-345m.pt
AssistantStep 7edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 171,
5 "end_line": 235,
6 "content": " def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):\n param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}\n decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]\n nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]\n optim_groups = [\n {'params': decay_params, 'weight_decay': weight_decay},\n {'params': nodecay_params, 'weight_decay': 0.0},\n ]\n num_decay_params = sum(p.numel() for p in decay_params)\n num_nodecay_params = sum(p.numel() for p in nodecay_params)\n print(f\"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters\")\n print(f\"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters\")\n \n class RAdam(torch.optim.Optimizer):\n \"\"\"Rectified Adam optimizer (Liu et al., 2019) with decoupled weight decay.\"\"\"\n def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):\n defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)\n super().__init__(params, defaults)\n \n @torch.no_grad()\n def step(self):\n for group in self.param_groups:\n for p in group['params']:\n if p.grad is None:\n continue\n grad = p.grad\n if grad.is_sparse:\n raise RuntimeError('RAdam does not support sparse gradients')\n state = self.state[p]\n if len(state) == 0:\n state['step'] = 0\n state['exp_avg'] = torch.zeros_like(p)\n state['exp_avg_sq'] = torch.zeros_like(p)\n exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']\n beta1, beta2 = group['betas']\n state['step'] += 1\n t = state['step']\n # Decoupled weight decay\n if group['weight_decay'] != 0:\n p.mul_(1 - group['lr'] * group['weight_decay'])\n # Update biased first moment estimate\n exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)\n # Update biased second raw moment estimate\n exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)\n # Compute bias-corrected first moment estimate\n m_hat = exp_avg / (1 - beta1 ** t)\n # Compute maximum length of the approximated SMA\n beta2_t = beta2 ** t\n N_sma_max = 2 / (1 - beta2) - 1\n N_sma = N_sma_max - 2 * t * beta2_t / (1 - beta2_t)\n if N_sma >= 5:\n # Variance rectification term\n r_t = math.sqrt((N_sma - 4) * (N_sma - 2) * N_sma_max / ((N_sma_max - 4) * (N_sma_max - 2) * N_sma))\n # Adaptive learning rate\n denom = exp_avg_sq.sqrt().add_(group['eps'])\n step_size = group['lr'] * r_t\n p.addcdiv_(m_hat, denom, value=-step_size)\n else:\n # Un-adapted learning rate\n step_size = group['lr'] / (1 - beta1 ** t)\n p.add_(exp_avg, alpha=-step_size)\n \n # Create inner RAdam optimizer\n inner_opt = RAdam(optim_groups, lr=learning_rate, betas=betas, weight_decay=weight_decay)\n \n # Lookahead wrapper\n class Lookahead(torch.optim.Optimizer):\n \"\"\"Lookahead optimizer wrapper (Zhang et al., 2019).\"\"\"\n def __init__(self, optimizer, k=5, alpha=0.5):\n self.optimizer = optimizer\n self.k = k\n self.alpha = alpha\n self.param_groups = optimizer.param_groups\n self.state = {}\n self._step = 0\n # Initialize slow weights\n for group in self.param_groups:\n for p in group['params']:\n self.state[p] = {'slow_param': p.data.clone()}\n \n @torch.no_grad()\n def step(self):\n self._step += 1\n # Inner optimizer step\n self.optimizer.step()\n # Lookahead step every k iterations\n if self._step % self.k == 0:\n for group in self.param_groups:\n for p in group['params']:\n slow = self.state[p]['slow_param']\n # Update slow weights: slow = slow + alpha * (fast - slow)\n slow.add_(p.data - slow, alpha=self.alpha)\n # Update fast weights: fast = slow\n p.data.copy_(slow)\n \n def zero_grad(self, set_to_none=True):\n self.optimizer.zero_grad(set_to_none=set_to_none)\n \n def state_dict(self):\n return self.optimizer.state_dict()\n \n def load_state_dict(self, state_dict):\n self.optimizer.load_state_dict(state_dict)\n \n optimizer = Lookahead(inner_opt, k=5, alpha=0.5)\n print(\"using Lookahead-RAdam optimizer\")\n return optimizer"
7}Tool ResultStep 7
OK: Replaced lines 171..235 with 107 line(s) in nanoGPT/custom_pretrain.py. Editable range: 171–277, 333–335.
[Current file: nanoGPT/custom_pretrain.py | editable: 171–277, 333–335 | total: 525 lines]
171 def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
172 param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}
173 decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
... (101 more lines) ...
275 optimizer = Lookahead(inner_opt, k=5, alpha=0.5)
276 print("using Lookahead-RAdam optimizer")
277 return optimizer
...
333 # CONFIG_OVERRIDES: override training hyperparameters for your method.
334 # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
335 CONFIG_OVERRIDES = {'learning_rate': 8e-4, 'warmup_iters': 400, 'min_lr': 4e-5}AssistantStep 8test
AssistantStep 8test
AssistantStep 8test
Tool ResultStep 8
[Test #2] (1 test remaining; call submit(n=N) to choose which test result to submit as final) ### gpt-345m (scripts/gpt_345m.sh) Training metrics (last steps): TRAIN_METRICS: step=0, train_loss=11.0307, val_loss=11.0296 TRAIN_METRICS: step=1000, train_loss=3.6798, val_loss=3.4524 TRAIN_METRICS: step=2000, train_loss=3.1380, val_loss=2.8405 TRAIN_METRICS: step=3000, train_loss=2.9476, val_loss=2.6633 ### lm-eval-345m (scripts/lm_eval_345m.sh) [COMMAND FAILED exit=1] ERROR: Checkpoint not found: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-optimizer/deepseek-reasoner_20260413_043229/seed_42/ckpt_gpt-345m.pt