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Messages: 32
UserStep 0

Task: llm-pretrain-lr-schedule

LLM Pretraining: Learning Rate Schedule Optimization

Research Question

Design an improved learning rate schedule for GPT-2 language model pretraining. Your modifications should reduce validation loss compared to the standard cosine annealing schedule with linear warmup.

What You Can Modify

The get_lr function (lines 192-201) in custom_pretrain.py:

  • Schedule shape (default: cosine decay with linear warmup)
  • Warmup strategy and duration
  • Decay behavior (shape, rate, final LR)
  • Multi-phase scheduling (e.g., warmup-stable-decay)

Note: The function signature get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr) must be preserved. The training loop calls this function at every iteration to set the learning rate.

Evaluation

  • Metric: Validation loss (cross-entropy, lower is better)
  • Model sizes: GPT-2 124M (12L/12H/768D) and GPT-2 1.5B (48L/25H/1600D, 4-GPU DDP)
  • Dataset: FineWeb 10B (GPT-2 tokenizer)
  • Training: 5000 iterations, batch_size=12, block_size=1024, grad_accum=5

nanoGPT/custom_pretrain.py [EDITABLE — lines 191–201, 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.n_embd % config.n_head == 0
    37:         self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
    38:         self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
    39:         self.attn_dropout = nn.Dropout(config.dropout)
    40:         self.resid_dropout = nn.Dropout(config.dropout)
    41:         self.n_head = config.n_head
    42:         self.n
AssistantStep 1edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 191,
5  "end_line": 201,
6  "content": "def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):\n    \"\"\"Exponential warmup with cosine hold decay schedule.\"\"\"\n    if it > lr_decay_iters:\n        return min_lr\n    # Exponential warmup\n    if it < warmup_iters:\n        # Exponential growth: lr = learning_rate * (1 - exp(-5 * t)) where t = it / warmup_iters\n        t = it / warmup_iters\n        return learning_rate * (1.0 - math.exp(-5.0 * t))\n    # Hold fraction: keep learning_rate for 30% of training after warmup\n    hold_frac = 0.3\n    hold_end = warmup_iters + int(hold_frac * (lr_decay_iters - warmup_iters))\n    if it < hold_end:\n        return learning_rate\n    # Cosine decay from hold_end to lr_decay_iters\n    decay_ratio = (it - hold_end) / (lr_decay_iters - hold_end)\n    assert 0 <= decay_ratio <= 1\n    coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))\n    return min_lr + coeff * (learning_rate - min_lr)"
7}
Tool ResultStep 1
OK: Replaced lines 191..201 with 19 line(s) in nanoGPT/custom_pretrain.py. Editable range: 191–209, 253–255.

[Current file: nanoGPT/custom_pretrain.py | editable: 191–209 | total: 445 lines]
   188|         return optimizer
   189| 
   190| # ── Learning Rate Schedule ─────────────────────────────────────────────────
   191  def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):
   192      """Exponential warmup with cosine hold decay schedule."""
   193      if it > lr_decay_iters:
   194          return min_lr
   195      # Exponential warmup
   196      if it < warmup_iters:
   197          # Exponential growth: lr = learning_rate * (1 - exp(-5 * t)) where t = it / warmup_iters
   198          t = it / warmup_iters
   199          return learning_rate * (1.0 - math.exp(-5.0 * t))
   200      # Hold fraction: keep learning_rate for 30% of training after warmup
   201      hold_frac = 0.3
   202      hold_end = warmup_iters + int(hold_frac * (lr_decay_iters - warmup_ite
AssistantStep 2edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 245,
5  "end_line": 247,
6  "content": "    CONFIG_OVERRIDES = {'warmup_iters': int(max_iters * 0.08)}"
7}
Tool ResultStep 2
ERROR: Lines 245..247 exceed the editable range. You may only edit lines 191–209, 253–255.

[Current file: nanoGPT/custom_pretrain.py | editable: 191–209 | total: 445 lines]
   188|         return optimizer
   189| 
   190| # ── Learning Rate Schedule ─────────────────────────────────────────────────
   191  def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):
   192      """Exponential warmup with cosine hold decay schedule."""
   193      if it > lr_decay_iters:
   194          return min_lr
   195      # Exponential warmup
   196      if it < warmup_iters:
   197          # Exponential growth: lr = learning_rate * (1 - exp(-5 * t)) where t = it / warmup_iters
   198          t = it / warmup_iters
   199          return learning_rate * (1.0 - math.exp(-5.0 * t))
   200      # Hold fraction: keep learning_rate for 30% of training after warmup
   201      hold_frac = 0.3
   202      hold_end = warmup_iters + int(hold_frac * (lr_decay_iters - warmup_iters))
   203      i
AssistantStep 3undo
Tool Input
1{
2  "n": 1
3}
Tool ResultStep 3
Undo complete:
Restored: nanoGPT/custom_pretrain.py
AssistantStep 4edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 191,
5  "end_line": 201,
6  "content": "def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):\n    \"\"\"Exponential warmup with cosine hold decay schedule.\"\"\"\n    if it > lr_decay_iters:\n        return min_lr\n    # Exponential warmup\n    if it < warmup_iters:\n        # Exponential growth: lr = learning_rate * (1 - exp(-5 * t)) where t = it / warmup_iters\n        t = it / warmup_iters\n        return learning_rate * (1.0 - math.exp(-5.0 * t))\n    # Hold fraction: keep learning_rate for 30% of training after warmup\n    hold_frac = 0.3\n    hold_end = warmup_iters + int(hold_frac * (lr_decay_iters - warmup_iters))\n    if it < hold_end:\n        return learning_rate\n    # Cosine decay from hold_end to lr_decay_iters\n    decay_ratio = (it - hold_end) / (lr_decay_iters - hold_end)\n    assert 0 <= decay_ratio <= 1\n    coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))\n    return min_lr + coeff * (learning_rate - min_lr)"
7}
Tool ResultStep 4
OK: Replaced lines 191..201 with 19 line(s) in nanoGPT/custom_pretrain.py. Editable range: 191–209, 253–255.

[Current file: nanoGPT/custom_pretrain.py | editable: 191–209 | total: 445 lines]
   188|         return optimizer
   189| 
   190| # ── Learning Rate Schedule ─────────────────────────────────────────────────
   191  def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):
   192      """Exponential warmup with cosine hold decay schedule."""
   193      if it > lr_decay_iters:
   194          return min_lr
   195      # Exponential warmup
   196      if it < warmup_iters:
   197          # Exponential growth: lr = learning_rate * (1 - exp(-5 * t)) where t = it / warmup_iters
   198          t = it / warmup_iters
   199          return learning_rate * (1.0 - math.exp(-5.0 * t))
   200      # Hold fraction: keep learning_rate for 30% of training after warmup
   201      hold_frac = 0.3
   202      hold_end = warmup_iters + int(hold_frac * (lr_decay_iters - warmup_ite
AssistantStep 5edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 253,
5  "end_line": 255,
6  "content": "    CONFIG_OVERRIDES = {'warmup_iters': int(max_iters * 0.08)}"
7}
Tool ResultStep 5
OK: Replaced lines 253..255 with 1 line(s) in nanoGPT/custom_pretrain.py. Editable range: 191–209, 253–253.

[Current file: nanoGPT/custom_pretrain.py | editable: 191–209 | total: 443 lines]
   188|         return optimizer
   189| 
   190| # ── Learning Rate Schedule ─────────────────────────────────────────────────
   191  def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):
   192      """Exponential warmup with cosine hold decay schedule."""
   193      if it > lr_decay_iters:
   194          return min_lr
   195      # Exponential warmup
   196      if it < warmup_iters:
   197          # Exponential growth: lr = learning_rate * (1 - exp(-5 * t)) where t = it / warmup_iters
   198          t = it / warmup_iters
   199          return learning_rate * (1.0 - math.exp(-5.0 * t))
   200      # Hold fraction: keep learning_rate for 30% of training after warmup
   201      hold_frac = 0.3
   202      hold_end = warmup_iters + int(hold_frac * (lr_decay_iters - warmup_iter
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=8000, train_loss=2.6602, val_loss=2.3828
TRAIN_METRICS: step=9000, train_loss=2.6159, val_loss=2.3435
TRAIN_METRICS: step=10000, train_loss=2.5962, val_loss=2.3218
TRAIN_METRICS: step=11000, train_loss=2.5671, val_loss=2.2891
TRAIN_METRICS: step=12000, train_loss=2.5616, val_loss=2.2857
Final metrics (gpt-345m): val_loss_gpt-345m=2.2784, wikitext2_ppl_gpt-345m=43.4500, lambada_ppl_gpt-345m=67.3600

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=55.7700, hellaswag_lm-eval-345m=33.2900, piqa_lm-eval-345m=64.0400, winogrande_lm-eval-345m=51.7000

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2784, 'wikitext2_ppl_gpt-345m': 43.45, 'lambada_ppl_gpt-345m': 67.36, 'elapsed_gpt-345m': 19736, 'arc_easy_lm-eval-345m': 55.77, 'hellaswag_lm-eval-345m': 33.29, 'piqa
AssistantStep 7edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 191,
5  "end_line": 209,
6  "content": "def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):\n    \"\"\"Cosine annealing with warm restart at 70% of training.\"\"\"\n    total_iters = lr_decay_iters\n    restart_iter = int(0.7 * total_iters)\n    # Linear warmup\n    if it < warmup_iters:\n        return learning_rate * (it + 1) / (warmup_iters + 1)\n    # First phase: cosine decay from warmup_iters to restart_iter\n    if it < restart_iter:\n        decay_ratio = (it - warmup_iters) / (restart_iter - warmup_iters)\n        coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))\n        return min_lr + coeff * (learning_rate - min_lr)\n    # Second phase: restart with scaled max LR (0.8 * learning_rate)\n    # Cosine decay from restart_iter to total_iters\n    decay_ratio2 = (it - restart_iter) / (total_iters - restart_iter)\n    coeff2 = 0.5 * (1.0 + math.cos(math.pi * decay_ratio2))\n    max_lr2 = learning_rate * 0.8\n    return min_lr + coeff2 * (max_lr2 - min_lr)"
7}
Tool ResultStep 7
OK: Replaced lines 191..209 with 18 line(s) in nanoGPT/custom_pretrain.py. Editable range: 191–208, 252–252.

[Current file: nanoGPT/custom_pretrain.py | editable: 191–208 | total: 442 lines]
   188|         return optimizer
   189| 
   190| # ── Learning Rate Schedule ─────────────────────────────────────────────────
   191  def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):
   192      """Cosine annealing with warm restart at 70% of training."""
   193      total_iters = lr_decay_iters
   194      restart_iter = int(0.7 * total_iters)
   195      # Linear warmup
   196      if it < warmup_iters:
   197          return learning_rate * (it + 1) / (warmup_iters + 1)
   198      # First phase: cosine decay from warmup_iters to restart_iter
   199      if it < restart_iter:
   200          decay_ratio = (it - warmup_iters) / (restart_iter - warmup_iters)
   201          coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
   202          return min_lr + coeff * (learnin
AssistantStep 8edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 252,
5  "end_line": 252,
6  "content": "    CONFIG_OVERRIDES = {'warmup_iters': int(max_iters * 0.05), 'min_lr': learning_rate / 20.0}"
7}
Tool ResultStep 8
OK: Replaced lines 252..252 with 1 line(s) in nanoGPT/custom_pretrain.py. Editable range: 191–208, 252–252.

[Current file: nanoGPT/custom_pretrain.py | editable: 191–208 | total: 442 lines]
   188|         return optimizer
   189| 
   190| # ── Learning Rate Schedule ─────────────────────────────────────────────────
   191  def get_lr(it, warmup_iters, lr_decay_iters, learning_rate, min_lr):
   192      """Cosine annealing with warm restart at 70% of training."""
   193      total_iters = lr_decay_iters
   194      restart_iter = int(0.7 * total_iters)
   195      # Linear warmup
   196      if it < warmup_iters:
   197          return learning_rate * (it + 1) / (warmup_iters + 1)
   198      # First phase: cosine decay from warmup_iters to restart_iter
   199      if it < restart_iter:
   200          decay_ratio = (it - warmup_iters) / (restart_iter - warmup_iters)
   201          coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
   202          return min_lr + coeff * (learning
AssistantStep 9test
Tool ResultStep 9
[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=8000, train_loss=2.6635, val_loss=2.3857
TRAIN_METRICS: step=9000, train_loss=2.7334, val_loss=2.4538
TRAIN_METRICS: step=10000, train_loss=2.6892, val_loss=2.4088
TRAIN_METRICS: step=11000, train_loss=2.6238, val_loss=2.3411
TRAIN_METRICS: step=12000, train_loss=2.6020, val_loss=2.3213
Final metrics (gpt-345m): val_loss_gpt-345m=2.3146, wikitext2_ppl_gpt-345m=46.6900, lambada_ppl_gpt-345m=71.6700

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=55.3000, hellaswag_lm-eval-345m=32.8700, piqa_lm-eval-345m=63.3800, winogrande_lm-eval-345m=51.1400

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.3146, 'wikitext2_ppl_gpt-345m': 46.69, 'lambada_ppl_gpt-345m': 71.67, 'elapsed_gpt-345m': 19692, 'arc_easy_lm-eval-345m': 55.3, 'hellaswag_lm-eval-345m': 32.87, 'piqa_l