Agent Conversation: gpt-5.4
Model: gpt-5.4
Total steps: 8
Messages: 18
UserStep 0
Task: llm-pretrain-attention
LLM Pretraining: Attention Mechanism Optimization
Research Question
Design an improved self-attention mechanism for GPT-2 language model pretraining. Your modifications should reduce validation loss compared to the standard multi-head attention with learned absolute position embeddings.
What You Can Modify
The CausalSelfAttention class (lines 34-70 in custom_pretrain.py), including:
- Position encoding scheme (the default uses learned absolute position embeddings via
wpe) - Query/Key/Value computation and projection
- Attention score computation and masking
- Any attention-related hyperparameters
Note: If your attention mechanism implements its own position encoding (replacing the learned wpe), set self.use_pos_emb = False in __init__ — the model will then skip adding position embeddings in the forward pass.
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: 13535 iterations, BSZ=64, GA=8, 2-GPU DDP
- Hardware: H200 GPU
nanoGPT/custom_pretrain.py [EDITABLE — lines 33–70, 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, bAssistantStep 1edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 33,
5 "end_line": 70,
6 "content": "class CausalSelfAttention(nn.Module):\n def __init__(self, config):\n super().__init__()\n assert config.n_embd % config.n_head == 0\n self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)\n self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)\n self.attn_dropout = nn.Dropout(config.dropout)\n self.resid_dropout = nn.Dropout(config.dropout)\n self.n_head = config.n_head\n self.n_embd = config.n_embd\n self.head_dim = config.n_embd // config.n_head\n self.dropout = config.dropout\n self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')\n if not self.flash:\n self.register_buffer(\"bias\", torch.tril(torch.ones(config.block_size, config.block_size))\n .view(1, 1, config.block_size, config.block_size))\n # Use rotary position encoding instead of learned absolute embeddings.\n self.use_pos_emb = False\n inv_freq = 1.0 / (10000 ** (torch.arange(0, self.head_dim, 2).float() / self.head_dim))\n self.register_buffer(\"inv_freq\", inv_freq)\n # Novel contribution: content-adaptive local recency bias.\n # A learned per-head gate mixes standard causal attention with a smooth ALiBi-style\n # distance penalty whose strength depends on current sequence content.\n self.recency_gate = nn.Linear(config.n_embd, config.n_head, bias=True)\n self.recency_strength = nn.Parameter(torch.full((config.n_head,), 0.05))\n\n def _apply_rope(self, x, seq_len):\n t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype)\n freqs = torch.outer(t, self.inv_freq)\n cos = freqs.cos().unsqueeze(0).unsqueeze(0)\n sin = freqs.sin().unsqueeze(0).unsqueeze(0)\n x1 = x[..., ::2]\n x2 = x[..., 1::2]\n y = torch.stack((x1 * cos - x2 * sin, x1 * sin + x2 * cos), dim=-1)\n return y.flatten(-2).type_as(x)\n\n def forward(self, x):\n B, T, C = x.size()\n q, k, v = self.c_attn(x).split(self.n_embd, dim=2)\n k = k.view(B, T, self.n_head, self.head_dim).transpose(1, 2)\n q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2)\n v = v.view(B, T, self.n_head, self.head_dim).transpose(1, 2)\n q = self._apply_rope(F.rms_norm(q, (q.size(-1),)), T)\n k = self._apply_rope(F.rms_norm(k, (k.size(-1),)), T)\n gate = torch.sigmoid(self.recency_gate(x).mean(dim=1)).transpose(0, 1).unsqueeze(0).unsqueeze(-1)\n strength = F.softplus(self.recency_strength).view(1, self.n_head, 1, 1)\n pos = torch.arange(T, device=x.device, dtype=q.dtype)\n dist = (pos.view(T, 1) - pos.view(1, T)).clamp_min(0)\n recency_bias = -(gate * strength) * dist.view(1, 1, T, T)\n if self.flash:\n y = torch.nn.functional.scaled_dot_product_attention(\n q, k, v, attn_mask=recency_bias,\n dropout_p=self.dropout if self.training else 0, is_causal=True)\n else:\n att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) + recency_bias\n att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf'))\n att = F.softmax(att, dim=-1)\n att = self.attn_dropout(att)\n y = att @ v\n y = y.transpose(1, 2).contiguous().view(B, T, C)\n y = self.resid_dropout(self.c_proj(y))\n return y"
7}Tool ResultStep 1
OK: Replaced lines 33..70 with 62 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–94, 269–271.
[Current file: nanoGPT/custom_pretrain.py | editable: 33–94, 269–271 | total: 461 lines]
33 class CausalSelfAttention(nn.Module):
34 def __init__(self, config):
35 super().__init__()
... (56 more lines) ...
92 y = y.transpose(1, 2).contiguous().view(B, T, C)
93 y = self.resid_dropout(self.c_proj(y))
94 return y
...
269 # CONFIG_OVERRIDES: override training hyperparameters for your method.
270 # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
271 CONFIG_OVERRIDES = {}AssistantStep 2edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 269,
5 "end_line": 271,
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 2
OK: Replaced lines 269..271 with 3 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–94, 269–271.
[Current file: nanoGPT/custom_pretrain.py | editable: 33–94, 269–271 | total: 461 lines]
33 class CausalSelfAttention(nn.Module):
34 def __init__(self, config):
35 super().__init__()
... (56 more lines) ...
92 y = y.transpose(1, 2).contiguous().view(B, T, C)
93 y = self.resid_dropout(self.c_proj(y))
94 return y
...
269 # CONFIG_OVERRIDES: override training hyperparameters for your method.
270 # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
271 CONFIG_OVERRIDES = {}AssistantStep 3test
Tool ResultStep 3
[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 23:51:53.773000 2078364 site-packages/torch/distributed/run.py:792] W0414 23:51:53.773000 2078364 site-packages/torch/distributed/run.py:792] ***************************************** W0414 23:51:53.773000 2078364 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 23:51:53.773000 2078364 site-packages/torch/distributed/run.py:792] ***************************************** [W414 23:51:53.799069992 socket.cpp:204] [c10d] The hostname of the client socket cannot be retrieved. err=-3 [W414 23:51:53.799377743 socket.cpp:204] [c10d] The hostname of the client socket cannot be retrieved. err=-3 [W414 23:51:53.941237013 socket.cp
AssistantStep 4edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 73,
5 "end_line": 81,
6 "content": " q = self._apply_rope(F.rms_norm(q, (q.size(-1),)), T)\n k = self._apply_rope(F.rms_norm(k, (k.size(-1),)), T)\n gate = torch.sigmoid(self.recency_gate(x).mean(dim=1)).transpose(0, 1).unsqueeze(0).unsqueeze(-1)\n strength = F.softplus(self.recency_strength).view(1, self.n_head, 1, 1)\n pos = torch.arange(T, device=x.device, dtype=q.dtype)\n dist = (pos.view(T, 1) - pos.view(1, T)).clamp_min(0).view(1, 1, T, T)\n recency_bias = -(gate * strength).expand(B, -1, T, T) * dist\n if self.flash:\n y = torch.nn.functional.scaled_dot_product_attention("
7}Tool ResultStep 4
OK: Replaced lines 73..81 with 9 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–94, 269–271.
[Current file: nanoGPT/custom_pretrain.py | editable: 33–94, 269–271 | total: 461 lines]
33 class CausalSelfAttention(nn.Module):
34 def __init__(self, config):
35 super().__init__()
... (56 more lines) ...
92 y = y.transpose(1, 2).contiguous().view(B, T, C)
93 y = self.resid_dropout(self.c_proj(y))
94 return y
...
269 # CONFIG_OVERRIDES: override training hyperparameters for your method.
270 # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
271 CONFIG_OVERRIDES = {}AssistantStep 5test
Tool ResultStep 5
[Test #2] (1 test remaining; call submit(n=N) to choose which test result to submit as final) ### gpt-345m (scripts/gpt_345m.sh) [COMMAND FAILED exit=1] W0415 03:48:31.463000 564546 site-packages/torch/distributed/run.py:792] W0415 03:48:31.463000 564546 site-packages/torch/distributed/run.py:792] ***************************************** W0415 03:48:31.463000 564546 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. W0415 03:48:31.463000 564546 site-packages/torch/distributed/run.py:792] ***************************************** [W415 03:48:31.496213468 socket.cpp:204] [c10d] The hostname of the client socket cannot be retrieved. err=-3 [W415 03:48:31.496515721 socket.cpp:204] [c10d] The hostname of the client socket cannot be retrieved. err=-3 [W415 03:48:31.670403215 socket.cpp:204
AssistantStep 6edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 33,
5 "end_line": 94,
6 "content": "class CausalSelfAttention(nn.Module):\n def __init__(self, config):\n super().__init__()\n assert config.n_embd % config.n_head == 0\n self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)\n self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)\n self.attn_dropout = nn.Dropout(config.dropout)\n self.resid_dropout = nn.Dropout(config.dropout)\n self.n_head = config.n_head\n self.n_embd = config.n_embd\n self.head_dim = config.n_embd // config.n_head\n self.dropout = config.dropout\n self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')\n if not self.flash:\n self.register_buffer(\"bias\", torch.tril(torch.ones(config.block_size, config.block_size))\n .view(1, 1, config.block_size, config.block_size))\n self.use_pos_emb = False\n inv_freq = 1.0 / (10000 ** (torch.arange(0, self.head_dim, 2).float() / self.head_dim))\n self.register_buffer(\"inv_freq\", inv_freq)\n # Novel contribution: adaptive residualized recency attention.\n # A learned per-head scalar mixes relative-distance bias into attention logits,\n # while preserving RoPE+QK norm stability.\n self.recency_strength = nn.Parameter(torch.full((config.n_head,), -2.0))\n\n def _apply_rope(self, x, seq_len):\n t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype)\n freqs = torch.outer(t, self.inv_freq)\n cos = freqs.cos().unsqueeze(0).unsqueeze(0)\n sin = freqs.sin().unsqueeze(0).unsqueeze(0)\n x1 = x[..., ::2]\n x2 = x[..., 1::2]\n y = torch.stack((x1 * cos - x2 * sin, x1 * sin + x2 * cos), dim=-1)\n return y.flatten(-2).type_as(x)\n\n def forward(self, x):\n B, T, C = x.size()\n q, k, v = self.c_attn(x).split(self.n_embd, dim=2)\n k = k.view(B, T, self.n_head, self.head_dim).transpose(1, 2)\n q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2)\n v = v.view(B, T, self.n_head, self.head_dim).transpose(1, 2)\n\n q = self._apply_rope(F.rms_norm(q, (q.size(-1),)), T)\n k = self._apply_rope(F.rms_norm(k, (k.size(-1),)), T)\n\n pos = torch.arange(T, device=x.device, dtype=q.dtype)\n dist = (pos.view(T, 1) - pos.view(1, T)).clamp_min(0)\n dist = dist / max(T, 1)\n recency_bias = -F.softplus(self.recency_strength).to(q.dtype).view(1, self.n_head, 1, 1) * dist.view(1, 1, T, T)\n\n if self.flash:\n y = torch.nn.functional.scaled_dot_product_attention(\n q, k, v, attn_mask=recency_bias,\n dropout_p=self.dropout if self.training else 0, is_causal=True)\n else:\n att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))\n att = att + recency_bias\n att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf'))\n att = F.softmax(att, dim=-1)\n att = self.attn_dropout(att)\n y = att @ v\n y = y.transpose(1, 2).contiguous().view(B, T, C)\n y = self.resid_dropout(self.c_proj(y))\n return y"
7}Tool ResultStep 6
OK: Replaced lines 33..94 with 63 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–95, 270–272.
[Current file: nanoGPT/custom_pretrain.py | editable: 33–95, 270–272 | total: 462 lines]
33 class CausalSelfAttention(nn.Module):
34 def __init__(self, config):
35 super().__init__()
... (57 more lines) ...
93 y = y.transpose(1, 2).contiguous().view(B, T, C)
94 y = self.resid_dropout(self.c_proj(y))
95 return y
...
270 # CONFIG_OVERRIDES: override training hyperparameters for your method.
271 # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
272 CONFIG_OVERRIDES = {}AssistantStep 7test
Tool ResultStep 7
[Test #3] (0 tests remaining; call submit(n=N) to choose which test result to submit as final)
[NOTE] This was your last test. You MUST now call submit(n=X) to choose which test result to submit as your final answer.
### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
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
TRAIN_METRICS: step=13000, 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': 2
AssistantStep 8submit
Tool Input
1{
2 "n": 3
3}Tool ResultStep 8
[submit] Submitting result from test #3 as final.
### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
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
TRAIN_METRICS: step=13000, 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
[submit] Finalized test #3 as final.
[Leaderboard] Results saved: {'val_loss_gpt-345m': nan, 'wikitext2_ppl_gpt-345m': nan, 'lambada_ppl_gpt-345m': nan, 'elapsed_gpt-345m': 27141, 'arc_easy_lm-eval-345m': 25.08, 'hellaswag_lm-eval-345m': 25.04, 'piqa_lm-eval-345m': 49.51, 'winogrande_lm-eval-345m': 49.5