Agent Conversation: gpt-5.4

Model: gpt-5.4
Total steps: 11
Messages: 24
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), 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 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:         se
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    \"\"\"Centered RMSNorm with a learned residual gate on the centered component.\"\"\"\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.center_scale = nn.Parameter(torch.ones(ndim) * 0.5)\n        self.eps = 1e-5\n\n    def forward(self, input):\n        x = input.float()\n        mu = x.mean(dim=-1, keepdim=True)\n        xc = x - mu\n        mix = x + self.center_scale.float() * xc\n        rms = mix.pow(2).mean(dim=-1, keepdim=True).add(self.eps).rsqrt()\n        y = (mix * rms).type_as(input) * self.weight\n        return y if self.bias is None else y + self.bias"
7}
Tool ResultStep 1
OK: Replaced lines 22..31 with 17 line(s) in nanoGPT/custom_pretrain.py. Editable range: 22–38, 95–107, 252–254.

[Current file: nanoGPT/custom_pretrain.py | editable: 22–38, 95–107, 252–254 | total: 444 lines]
    22  class LayerNorm(nn.Module):
    23      """Centered RMSNorm with a learned residual gate on the centered component."""
    24      def __init__(self, ndim, bias):
       ... (11 more lines) ...
    36          rms = mix.pow(2).mean(dim=-1, keepdim=True).add(self.eps).rsqrt()
    37          y = (mix * rms).type_as(input) * self.weight
    38          return y if self.bias is None else y + self.bias
...
    95  class Block(nn.Module):
    96      def __init__(self, config):
    97          super().__init__()
       ... (7 more lines) ...
   105          x = x + self.mlp(self.ln_2(x))
   106          return x
   107  
...
   252      # CONFIG_OVERRIDES: override training hyperparameters for your method.
   253      # Allowed keys: learning_rate, weight_decay, warmup_iters,
AssistantStep 2edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 95,
5  "end_line": 107,
6  "content": "class Block(nn.Module):\n    \"\"\"Dual-path gated parallel block with sequential refinement.\"\"\"\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\n    def forward(self, x):\n        h1 = self.ln_1(x)\n        a = self.attn(h1)\n        m_parallel = self.mlp(h1)\n        x = x + a + 0.5 * m_parallel\n        x = x + 0.5 * self.mlp(self.ln_2(x))\n        return x"
7}
Tool ResultStep 2
OK: Replaced lines 95..107 with 16 line(s) in nanoGPT/custom_pretrain.py. Editable range: 22–38, 95–110, 255–257.

[Current file: nanoGPT/custom_pretrain.py | editable: 22–38, 95–110, 255–257 | total: 447 lines]
    22  class LayerNorm(nn.Module):
    23      """Centered RMSNorm with a learned residual gate on the centered component."""
    24      def __init__(self, ndim, bias):
       ... (11 more lines) ...
    36          rms = mix.pow(2).mean(dim=-1, keepdim=True).add(self.eps).rsqrt()
    37          y = (mix * rms).type_as(input) * self.weight
    38          return y if self.bias is None else y + self.bias
...
    95  class Block(nn.Module):
    96      """Dual-path gated parallel block with sequential refinement."""
    97      def __init__(self, config):
       ... (10 more lines) ...
   108          x = x + a + 0.5 * m_parallel
   109          x = x + 0.5 * self.mlp(self.ln_2(x))
   110          return x
...
   255      # CONFIG_OVERRIDES: override training hyperparameters f
AssistantStep 3edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 255,
5  "end_line": 257,
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 = {}"
7}
Tool ResultStep 3
OK: Replaced lines 255..257 with 3 line(s) in nanoGPT/custom_pretrain.py. Editable range: 22–38, 95–110, 255–257.

[Current file: nanoGPT/custom_pretrain.py | editable: 22–38, 95–110, 255–257 | total: 447 lines]
    22  class LayerNorm(nn.Module):
    23      """Centered RMSNorm with a learned residual gate on the centered component."""
    24      def __init__(self, ndim, bias):
       ... (11 more lines) ...
    36          rms = mix.pow(2).mean(dim=-1, keepdim=True).add(self.eps).rsqrt()
    37          y = (mix * rms).type_as(input) * self.weight
    38          return y if self.bias is None else y + self.bias
...
    95  class Block(nn.Module):
    96      """Dual-path gated parallel block with sequential refinement."""
    97      def __init__(self, config):
       ... (10 more lines) ...
   108          x = x + a + 0.5 * m_parallel
   109          x = x + 0.5 * self.mlp(self.ln_2(x))
   110          return x
...
   255      # CONFIG_OVERRIDES: override training hyperparameters f
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)
[COMMAND FAILED exit=1]
W0414 22:14:20.371000 459610 site-packages/torch/distributed/run.py:792] 
W0414 22:14:20.371000 459610 site-packages/torch/distributed/run.py:792] *****************************************
W0414 22:14:20.371000 459610 site-packages/torch/distributed/run.py:792] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W0414 22:14:20.371000 459610 site-packages/torch/distributed/run.py:792] *****************************************
[W414 22:14:20.403281791 socket.cpp:204] [c10d] The hostname of the client socket cannot be retrieved. err=-3
[W414 22:14:20.403567117 socket.cpp:204] [c10d] The hostname of the client socket cannot be retrieved. err=-3
[W414 22:14:20.608066793 socket.cpp:20
AssistantStep 5edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 22,
5  "end_line": 38,
6  "content": "class LayerNorm(nn.Module):\n    \"\"\"Mean-preserving RMSNorm: normalize centered activations, then restore mean path.\"\"\"\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        x = input.float()\n        mu = x.mean(dim=-1, keepdim=True)\n        xc = x - mu\n        rms = xc.pow(2).mean(dim=-1, keepdim=True).add(self.eps).rsqrt()\n        y = (xc * rms + mu).type_as(input) * self.weight\n        return y if self.bias is None else y + self.bias"
7}
Tool ResultStep 5
OK: Replaced lines 22..38 with 15 line(s) in nanoGPT/custom_pretrain.py. Editable range: 22–36, 93–108, 253–255.

[Current file: nanoGPT/custom_pretrain.py | editable: 22–36, 93–108, 253–255 | total: 445 lines]
    22  class LayerNorm(nn.Module):
    23      """Mean-preserving RMSNorm: normalize centered activations, then restore mean path."""
    24      def __init__(self, ndim, bias):
       ... (9 more lines) ...
    34          rms = xc.pow(2).mean(dim=-1, keepdim=True).add(self.eps).rsqrt()
    35          y = (xc * rms + mu).type_as(input) * self.weight
    36          return y if self.bias is None else y + self.bias
...
    93  class Block(nn.Module):
    94      """Dual-path gated parallel block with sequential refinement."""
    95      def __init__(self, config):
       ... (10 more lines) ...
   106          x = x + a + 0.5 * m_parallel
   107          x = x + 0.5 * self.mlp(self.ln_2(x))
   108          return x
...
   253      # CONFIG_OVERRIDES: override training hyperpar
AssistantStep 6edit
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    \"\"\"Variance-controlled parallel block with shared normalized input.\"\"\"\n    def __init__(self, config):\n        super().__init__()\n        self.ln = LayerNorm(config.n_embd, bias=config.bias)\n        self.attn = CausalSelfAttention(config)\n        self.mlp = MLP(config)\n\n    def forward(self, x):\n        h = self.ln(x)\n        a = self.attn(h)\n        m = self.mlp(h)\n        x = x + (a + m) / math.sqrt(2.0)\n        return x"
7}
Tool ResultStep 6
OK: Replaced lines 93..108 with 14 line(s) in nanoGPT/custom_pretrain.py. Editable range: 22–36, 93–106, 251–253.

[Current file: nanoGPT/custom_pretrain.py | editable: 22–36, 93–106, 251–253 | total: 443 lines]
    22  class LayerNorm(nn.Module):
    23      """Mean-preserving RMSNorm: normalize centered activations, then restore mean path."""
    24      def __init__(self, ndim, bias):
       ... (9 more lines) ...
    34          rms = xc.pow(2).mean(dim=-1, keepdim=True).add(self.eps).rsqrt()
    35          y = (xc * rms + mu).type_as(input) * self.weight
    36          return y if self.bias is None else y + self.bias
...
    93  class Block(nn.Module):
    94      """Variance-controlled parallel block with shared normalized input."""
    95      def __init__(self, config):
       ... (8 more lines) ...
   104          m = self.mlp(h)
   105          x = x + (a + m) / math.sqrt(2.0)
   106          return x
...
   251      # CONFIG_OVERRIDES: override training hyperparameters for
AssistantStep 7test
Tool ResultStep 7
[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=nan, val_loss=nan
TRAIN_METRICS: step=9000, train_loss=nan, val_loss=nan
TRAIN_METRICS: step=10000, train_loss=nan, val_loss=nan
TRAIN_METRICS: step=11000, train_loss=nan, val_loss=nan
TRAIN_METRICS: step=12000, train_loss=nan, val_loss=nan
Final metrics (gpt-345m): val_loss_gpt-345m=nan, wikitext2_ppl_gpt-345m=nan, lambada_ppl_gpt-345m=nan

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=25.0800, hellaswag_lm-eval-345m=25.0400, piqa_lm-eval-345m=49.5100, winogrande_lm-eval-345m=49.5700

[Leaderboard] Results saved: {'val_loss_gpt-345m': nan, 'wikitext2_ppl_gpt-345m': nan, 'lambada_ppl_gpt-345m': nan, 'elapsed_gpt-345m': 17506, 'arc_easy_lm-eval-345m': 25.08, 'hellaswag_lm-eval-345m': 25.04, 'piqa_lm-eval-345m': 49.51, 'winogrande_lm-eval-345m':
AssistantStep 8edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 22,
5  "end_line": 36,
6  "content": "class LayerNorm(nn.Module):\n    \"\"\"Stable pRMSNorm: partial RMS normalization with preserved feature mean.\"\"\"\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        self.prms = max(1, ndim // 4)\n\n    def forward(self, input):\n        x = input.float()\n        mu = x.mean(dim=-1, keepdim=True)\n        xc = x - mu\n        rms = xc[..., :self.prms].pow(2).mean(dim=-1, keepdim=True).add(self.eps).rsqrt()\n        y = (xc * rms + mu).type_as(input) * self.weight\n        return y if self.bias is None else y + self.bias"
7}
Tool ResultStep 8
OK: Replaced lines 22..36 with 16 line(s) in nanoGPT/custom_pretrain.py. Editable range: 22–37, 94–107, 252–254.

[Current file: nanoGPT/custom_pretrain.py | editable: 22–37, 94–107, 252–254 | total: 444 lines]
    22  class LayerNorm(nn.Module):
    23      """Stable pRMSNorm: partial RMS normalization with preserved feature mean."""
    24      def __init__(self, ndim, bias):
       ... (10 more lines) ...
    35          rms = xc[..., :self.prms].pow(2).mean(dim=-1, keepdim=True).add(self.eps).rsqrt()
    36          y = (xc * rms + mu).type_as(input) * self.weight
    37          return y if self.bias is None else y + self.bias
...
    94  class Block(nn.Module):
    95      """Variance-controlled parallel block with shared normalized input."""
    96      def __init__(self, config):
       ... (8 more lines) ...
   105          m = self.mlp(h)
   106          x = x + (a + m) / math.sqrt(2.0)
   107          return x
...
   252      # CONFIG_OVERRIDES: override training hyperparame
AssistantStep 9edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 94,
5  "end_line": 107,
6  "content": "class Block(nn.Module):\n    \"\"\"Serial-parallel hybrid block with variance-preserving residual mixing.\"\"\"\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\n    def forward(self, x):\n        a = self.attn(self.ln_1(x))\n        x = x + a\n        m = self.mlp(self.ln_2(x))\n        x = x + m / math.sqrt(2.0)\n        return x"
7}
Tool ResultStep 9
OK: Replaced lines 94..107 with 15 line(s) in nanoGPT/custom_pretrain.py. Editable range: 22–37, 94–108, 253–255.

[Current file: nanoGPT/custom_pretrain.py | editable: 22–37, 94–108, 253–255 | total: 445 lines]
    22  class LayerNorm(nn.Module):
    23      """Stable pRMSNorm: partial RMS normalization with preserved feature mean."""
    24      def __init__(self, ndim, bias):
       ... (10 more lines) ...
    35          rms = xc[..., :self.prms].pow(2).mean(dim=-1, keepdim=True).add(self.eps).rsqrt()
    36          y = (xc * rms + mu).type_as(input) * self.weight
    37          return y if self.bias is None else y + self.bias
...
    94  class Block(nn.Module):
    95      """Serial-parallel hybrid block with variance-preserving residual mixing."""
    96      def __init__(self, config):
       ... (9 more lines) ...
   106          m = self.mlp(self.ln_2(x))
   107          x = x + m / math.sqrt(2.0)
   108          return x
...
   253      # CONFIG_OVERRIDES: override training