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Model: deepseek-reasoner
Total steps: 11
Messages: 25
UserStep 0

Task: llm-pretrain-normalization

LLM Pretraining: Normalization & Block Architecture Optimization

Research Question

Design improved normalization and/or transformer block architecture for GPT-2 language model pretraining. Your modifications should reduce validation loss compared to the standard LayerNorm with Pre-LN block structure.

What You Can Modify

Two regions in custom_pretrain.py:

  1. LayerNorm class (lines 23-31): The normalization implementation
  2. Block class (lines 89-100): How attention and MLP are composed with residual connections

You can modify:

  • The normalization algorithm (default: LayerNorm with bias)
  • Where normalization is applied (Pre-LN, Post-LN, or other placements)
  • The residual connection structure
  • How attention and MLP sublayers are combined (sequential vs parallel)

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 22–31, lines 88–100, 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:
AssistantStep 1edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 22,
5  "end_line": 31,
6  "content": "class LayerNorm(nn.Module):\n    \"\"\"RMSNorm with optional bias.\"\"\"\n    def __init__(self, ndim, bias):\n        super().__init__()\n        self.weight = nn.Parameter(torch.ones(ndim))\n        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None\n        self.eps = 1e-5\n\n    def forward(self, input):\n        # RMSNorm: normalize by root mean square, no mean subtraction\n        rms = input.float().pow(2).mean(-1, keepdim=True).add(self.eps).rsqrt()\n        out = (input * rms).type_as(input) * self.weight\n        if self.bias is not None:\n            out = out + self.bias\n        return out"
7}
Tool ResultStep 1
OK: Replaced lines 22..31 with 15 line(s) in nanoGPT/custom_pretrain.py. Editable range: 22–36, 93–105, 250–252.

[Current file: nanoGPT/custom_pretrain.py | editable: 22–36 | total: 442 lines]
    19| # ============================================================================
    20| 
    21| # ── Normalization ──────────────────────────────────────────────────────────
    22  class LayerNorm(nn.Module):
    23      """RMSNorm with 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          self.eps = 1e-5
    29  
    30      def forward(self, input):
    31          # RMSNorm: normalize by root mean square, no mean subtraction
    32          rms = input.float().pow(2).mean(-1, keepdim=True).add(self.eps).rsqrt()
    33          out = (input * rms).type_as(input) * self.weight
    34
AssistantStep 2edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 88,
5  "end_line": 100,
6  "content": "class Block(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)\n        self.attn = CausalSelfAttention(config)\n        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)\n        self.mlp = MLP(config)\n        # LayerScale: per-channel scaling factors for residual branches\n        self.alpha_attn = nn.Parameter(torch.ones(config.n_embd) * 1e-2)\n        self.alpha_mlp = nn.Parameter(torch.ones(config.n_embd) * 1e-2)\n\n    def forward(self, x):\n        # Pre-LN with LayerScale\n        x = x + self.alpha_attn * self.attn(self.ln_1(x))\n        x = x + self.alpha_mlp * self.mlp(self.ln_2(x))\n        return x"
7}
Tool ResultStep 2
ERROR: Lines 88..100 exceed the editable range. You may only edit lines 22–36, 93–105, 250–252.

[Current file: nanoGPT/custom_pretrain.py | editable: 22–36 | total: 442 lines]
    19| # ============================================================================
    20| 
    21| # ── Normalization ──────────────────────────────────────────────────────────
    22  class LayerNorm(nn.Module):
    23      """RMSNorm with 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          self.eps = 1e-5
    29  
    30      def forward(self, input):
    31          # RMSNorm: normalize by root mean square, no mean subtraction
    32          rms = input.float().pow(2).mean(-1, keepdim=True).add(self.eps).rsqrt()
    33          out = (input * rms).type_as(input) * self.weight
    34          if self.bias is
AssistantStep 3edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 93,
5  "end_line": 105,
6  "content": "class Block(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)\n        self.attn = CausalSelfAttention(config)\n        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)\n        self.mlp = MLP(config)\n        # LayerScale: per-channel scaling factors for residual branches\n        self.alpha_attn = nn.Parameter(torch.ones(config.n_embd) * 1e-2)\n        self.alpha_mlp = nn.Parameter(torch.ones(config.n_embd) * 1e-2)\n\n    def forward(self, x):\n        # Pre-LN with LayerScale\n        x = x + self.alpha_attn * self.attn(self.ln_1(x))\n        x = x + self.alpha_mlp * self.mlp(self.ln_2(x))\n        return x"
7}
Tool ResultStep 3
OK: Replaced lines 93..105 with 16 line(s) in nanoGPT/custom_pretrain.py. Editable range: 22–36, 93–108, 253–255.

[Current file: nanoGPT/custom_pretrain.py | editable: 22–36 | total: 445 lines]
    19| # ============================================================================
    20| 
    21| # ── Normalization ──────────────────────────────────────────────────────────
    22  class LayerNorm(nn.Module):
    23      """RMSNorm with 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          self.eps = 1e-5
    29  
    30      def forward(self, input):
    31          # RMSNorm: normalize by root mean square, no mean subtraction
    32          rms = input.float().pow(2).mean(-1, keepdim=True).add(self.eps).rsqrt()
    33          out = (input * rms).type_as(input) * self.weight
    34
AssistantStep 4test
Tool ResultStep 4
[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=2.7058, val_loss=2.4284
TRAIN_METRICS: step=9000, train_loss=2.6704, val_loss=2.3966
TRAIN_METRICS: step=10000, train_loss=2.6596, val_loss=2.3835
TRAIN_METRICS: step=11000, train_loss=2.6371, val_loss=2.3562
TRAIN_METRICS: step=12000, train_loss=2.6334, val_loss=2.3535
Final metrics (gpt-345m): val_loss_gpt-345m=2.3471, wikitext2_ppl_gpt-345m=47.3800, lambada_ppl_gpt-345m=72.9100

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=52.9900, hellaswag_lm-eval-345m=32.0800, piqa_lm-eval-345m=63.2200, winogrande_lm-eval-345m=50.2800

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.3471, 'wikitext2_ppl_gpt-345m': 47.38, 'lambada_ppl_gpt-345m': 72.91, 'elapsed_gpt-345m': 20321, 'arc_easy_lm-eval-345m': 52.99, 'hellaswag_lm-eval-345m': 32.08, 'piqa
AssistantStep 5edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 93,
5  "end_line": 108,
6  "content": "class Block(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        # Single normalization for parallel branches\n        self.ln = LayerNorm(config.n_embd, bias=config.bias)\n        self.attn = CausalSelfAttention(config)\n        self.mlp = MLP(config)\n        # Per-channel scaling factors for parallel residual branches\n        # Initialized small to stabilize early training\n        self.alpha_attn = nn.Parameter(torch.ones(config.n_embd) * 1e-5)\n        self.alpha_mlp = nn.Parameter(torch.ones(config.n_embd) * 1e-5)\n\n    def forward(self, x):\n        h = self.ln(x)\n        attn_out = self.attn(h)\n        mlp_out = self.mlp(h)\n        # Combine with per-channel scaling\n        x = x + self.alpha_attn * attn_out + self.alpha_mlp * mlp_out\n        return x"
7}
Tool ResultStep 5
OK: Replaced lines 93..108 with 19 line(s) in nanoGPT/custom_pretrain.py. Editable range: 22–36, 93–111, 256–258.

[Current file: nanoGPT/custom_pretrain.py | editable: 22–36 | total: 448 lines]
    19| # ============================================================================
    20| 
    21| # ── Normalization ──────────────────────────────────────────────────────────
    22  class LayerNorm(nn.Module):
    23      """RMSNorm with 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          self.eps = 1e-5
    29  
    30      def forward(self, input):
    31          # RMSNorm: normalize by root mean square, no mean subtraction
    32          rms = input.float().pow(2).mean(-1, keepdim=True).add(self.eps).rsqrt()
    33          out = (input * rms).type_as(input) * self.weight
    34
AssistantStep 6test
Tool ResultStep 6
[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=2.7323, val_loss=2.4559
TRAIN_METRICS: step=9000, train_loss=2.6975, val_loss=2.4249
TRAIN_METRICS: step=10000, train_loss=2.6869, val_loss=2.4112
TRAIN_METRICS: step=11000, train_loss=2.6649, val_loss=2.3840
TRAIN_METRICS: step=12000, train_loss=2.6616, val_loss=2.3817
Final metrics (gpt-345m): val_loss_gpt-345m=2.3759, wikitext2_ppl_gpt-345m=49.4800, lambada_ppl_gpt-345m=73.4300

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=53.7900, hellaswag_lm-eval-345m=31.5800, piqa_lm-eval-345m=63.9300, winogrande_lm-eval-345m=51.8500

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.3759, 'wikitext2_ppl_gpt-345m': 49.48, 'lambada_ppl_gpt-345m': 73.43, 'elapsed_gpt-345m': 19375, 'arc_easy_lm-eval-345m': 53.79, 'hellaswag_lm-eval-345m': 31.58, 'piqa_
AssistantStep 7undo
Tool Input
1{
2  "n": 1
3}
Tool ResultStep 7
Undo complete:
Restored: nanoGPT/custom_pretrain.py
AssistantStep 8edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 93,
5  "end_line": 108,
6  "content": "class Block(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)\n        self.attn = CausalSelfAttention(config)\n        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)\n        self.mlp = MLP(config)\n        # LayerScale: per-channel scaling factors for residual branches\n        # Initialized to 1e-3 to stabilize early training\n        self.alpha_attn = nn.Parameter(torch.ones(config.n_embd) * 1e-3)\n        self.alpha_mlp = nn.Parameter(torch.ones(config.n_embd) * 1e-3)\n\n    def forward(self, x):\n        # Pre-LN with LayerScale\n        x = x + self.alpha_attn * self.attn(self.ln_1(x))\n        x = x + self.alpha_mlp * self.mlp(self.ln_2(x))\n        return x"
7}
Tool ResultStep 8
OK: Replaced lines 93..108 with 17 line(s) in nanoGPT/custom_pretrain.py. Editable range: 22–36, 93–109, 254–256.

[Current file: nanoGPT/custom_pretrain.py | editable: 22–36 | total: 446 lines]
    19| # ============================================================================
    20| 
    21| # ── Normalization ──────────────────────────────────────────────────────────
    22  class LayerNorm(nn.Module):
    23      """RMSNorm with 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          self.eps = 1e-5
    29  
    30      def forward(self, input):
    31          # RMSNorm: normalize by root mean square, no mean subtraction
    32          rms = input.float().pow(2).mean(-1, keepdim=True).add(self.eps).rsqrt()
    33          out = (input * rms).type_as(input) * self.weight
    34
AssistantStep 9edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 254,
5  "end_line": 256,
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': 7e-4}"
7}
Tool ResultStep 9
OK: Replaced lines 254..256 with 3 line(s) in nanoGPT/custom_pretrain.py. Editable range: 22–36, 93–109, 254–256.

[Current file: nanoGPT/custom_pretrain.py | editable: 22–36 | total: 446 lines]
    19| # ============================================================================
    20| 
    21| # ── Normalization ──────────────────────────────────────────────────────────
    22  class LayerNorm(nn.Module):
    23      """RMSNorm with 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          self.eps = 1e-5
    29  
    30      def forward(self, input):
    31          # RMSNorm: normalize by root mean square, no mean subtraction
    32          rms = input.float().pow(2).mean(-1, keepdim=True).add(self.eps).rsqrt()
    33          out = (input * rms).type_as(input) * self.weight
    34