Agent Conversation: qwen3.6-plus
Model: qwen3.6-plus
Total steps: 6
Messages: 14
UserStep 0
Task: llm-pretrain-mlp
LLM Pretraining: Feed-Forward Network Optimization
Research Question
Design an improved feed-forward network (MLP) for GPT-2 language model pretraining. Your modifications should reduce validation loss compared to the standard GELU MLP.
What You Can Modify
The MLP class (lines 73-86 in custom_pretrain.py), including:
- Activation function (default: GELU)
- Network architecture (default: two linear layers with 4x expansion)
- Gating mechanisms
- Hidden dimension sizing
Constraint: The MLP must accept input of shape (B, T, n_embd) and return output of the same shape.
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 72–86, 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_embd = config.n_embd
43: self.dropout = config.dropout
44: self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
45: AssistantStep 1edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 72,
5 "end_line": 86,
6 "content": "class MLP(nn.Module):\n \"\"\"Gated Squared Linear (GSL): Combines Squared ReLU expressivity \n with adaptive gating. Squared ReLU has been shown competitive with \n GELU in transformers (Chen et al., 2023). The learnable gate_scale \n parameter helps training stability by allowing the model to find \n the optimal gate magnitude.\"\"\"\n def __init__(self, config):\n super().__init__()\n # Parameter-matched to standard 4x expansion MLP with gating\n hidden_dim = int(8 / 3 * config.n_embd)\n hidden_dim = ((hidden_dim + 63) // 64) * 64\n self.w1 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n self.w_gate = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n self.c_proj = nn.Linear(hidden_dim, config.n_embd, bias=config.bias)\n self.dropout = nn.Dropout(config.dropout)\n # Learnable gate scaling for training stability\n self.gate_scale = nn.Parameter(torch.tensor(1.0))\n\n def forward(self, x):\n x_hidden = F.relu(self.w1(x)).square()\n g = self.gate_scale * self.w_gate(x)\n return self.dropout(self.c_proj(x_hidden * g))"
7}Tool ResultStep 1
OK: Replaced lines 72..86 with 22 line(s) in nanoGPT/custom_pretrain.py. Editable range: 72–93, 252–254.
[Current file: nanoGPT/custom_pretrain.py | editable: 72–93 | total: 444 lines]
69| return y
70|
71| # ── Feed-Forward Network ──────────────────────────────────────────────────
72 class MLP(nn.Module):
73 """Gated Squared Linear (GSL): Combines Squared ReLU expressivity
74 with adaptive gating. Squared ReLU has been shown competitive with
75 GELU in transformers (Chen et al., 2023). The learnable gate_scale
76 parameter helps training stability by allowing the model to find
77 the optimal gate magnitude."""
78 def __init__(self, config):
79 super().__init__()
80 # Parameter-matched to standard 4x expansion MLP with gating
81 hidden_dim = int(8 / 3 * config.n_embd)
82 hidden_dim = ((hidden_dim + 63) // 64) * 64
83 self.w1 = nn.Linear(c
AssistantStep 2edit
Tool Input
1{
2 "filename": "nanoGPT/custom_pretrain.py",
3 "op": "replace",
4 "content": "class MLP(nn.Module):\n \"\"\"Gated Squared ReLU (GSR): Combines Squared ReLU nonlinearity\n with adaptive gating for improved expressivity. Key ideas:\n 1) Squared ReLU provides higher-order feature interaction\n 2) Sigmoid-gated modulation learns input-dependent scaling\n 3) gate_scale learnable parameter enables controlled exploration\n of gating strength while maintaining training stability\"\"\"\n def __init__(self, config):\n super().__init__()\n # Hidden dim chosen to match params of standard 4x expansion MLP\n hidden_dim = int(8 / 3 * config.n_embd)\n hidden_dim = ((hidden_dim + 63) // 64) * 64\n self.w1 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n self.w_gate = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n self.c_proj = nn.Linear(hidden_dim, config.n_embd, bias=config.bias)\n self.dropout = nn.Dropout(config.dropout)\n # Learnable gate scale for initial stability (starts at 0.5 for\n # ~equal blend of gated/ungated signal)\n self.gate_scale = nn.Parameter(torch.tensor(0.5))\n\n def forward(self, x):\n x_hidden = F.relu(self.w1(x)).square()\n g = torch.sigmoid(self.w_gate(x))\n return self.dropout(self.c_proj(x_hidden * self.gate_scale * g))",
5 "start_line": 72,
6 "end_line": 93
7}Tool ResultStep 2
OK: Replaced lines 72..93 with 24 line(s) in nanoGPT/custom_pretrain.py. Editable range: 72–95, 254–256.
[Current file: nanoGPT/custom_pretrain.py | editable: 72–95 | total: 446 lines]
69| return y
70|
71| # ── Feed-Forward Network ──────────────────────────────────────────────────
72 class MLP(nn.Module):
73 """Gated Squared ReLU (GSR): Combines Squared ReLU nonlinearity
74 with adaptive gating for improved expressivity. Key ideas:
75 1) Squared ReLU provides higher-order feature interaction
76 2) Sigmoid-gated modulation learns input-dependent scaling
77 3) gate_scale learnable parameter enables controlled exploration
78 of gating strength while maintaining training stability"""
79 def __init__(self, config):
80 super().__init__()
81 # Hidden dim chosen to match params of standard 4x expansion MLP
82 hidden_dim = int(8 / 3 * config.n_embd)
83
AssistantStep 3test
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)
Training metrics (last steps):
TRAIN_METRICS: step=8000, train_loss=2.6902, val_loss=2.4035
TRAIN_METRICS: step=9000, train_loss=2.6617, val_loss=2.3835
TRAIN_METRICS: step=10000, train_loss=2.6472, val_loss=2.3619
TRAIN_METRICS: step=11000, train_loss=2.6405, val_loss=2.3521
TRAIN_METRICS: step=12000, train_loss=2.6222, val_loss=2.3357
Final metrics (gpt-345m): val_loss_gpt-345m=2.3370, wikitext2_ppl_gpt-345m=46.2900, lambada_ppl_gpt-345m=70.7300
### lm-eval-345m (scripts/lm_eval_345m.sh)
ERROR: Checkpoint not found: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-mlp/qwen3.6-plus_20260409_070906/seed_42/ckpt_gpt-345m.pt
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.337, 'wikitext2_ppl_gpt-345m': 46.29, 'lambada_ppl_gpt-345m': 70.73, 'elapsed_gpt-345m': 20787, 'elapsed_lm-eval-345m': 1}AssistantStep 4edit
Tool Input
1{
2 "filename": "nanoGPT/custom_pretrain.py",
3 "op": "replace",
4 "content": "class MLP(nn.Module):\n \"\"\"Gated Squared GELU (GSG): Key novelty - combines smooth GELU \n nonlinearity with squaring operation and learned sigmoid gating.\n The squared GELU provides higher-order feature interactions while\n maintaining smooth gradients (unlike squared ReLU which has gradient\n discontinuities). Sigmoid gating enables input-dependent modulation.\"\"\"\n def __init__(self, config):\n super().__init__()\n # Hidden dim ~2.67x to match parameter budget with 3 linear layers\n hidden_dim = int(8 / 3 * config.n_embd)\n hidden_dim = ((hidden_dim + 63) // 64) * 64\n self.w1 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n self.w_gate = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n self.c_proj = nn.Linear(hidden_dim, config.n_embd, bias=config.bias)\n self.dropout = nn.Dropout(config.dropout)\n self.gate_scale = nn.Parameter(torch.tensor(0.5))\n\n def forward(self, x):\n x_hidden = F.gelu(self.w1(x)).square()\n gate = torch.sigmoid(self.w_gate(x))\n return self.dropout(self.c_proj(x_hidden * self.gate_scale * gate))",
5 "start_line": 72,
6 "end_line": 95
7}Tool ResultStep 4
OK: Replaced lines 72..95 with 21 line(s) in nanoGPT/custom_pretrain.py. Editable range: 72–92.
[Current file: nanoGPT/custom_pretrain.py | editable: 72–92 | total: 443 lines]
69| return y
70|
71| # ── Feed-Forward Network ──────────────────────────────────────────────────
72 class MLP(nn.Module):
73 """Gated Squared GELU (GSG): Key novelty - combines smooth GELU
74 nonlinearity with squaring operation and learned sigmoid gating.
75 The squared GELU provides higher-order feature interactions while
76 maintaining smooth gradients (unlike squared ReLU which has gradient
77 discontinuities). Sigmoid gating enables input-dependent modulation."""
78 def __init__(self, config):
79 super().__init__()
80 # Hidden dim ~2.67x to match parameter budget with 3 linear layers
81 hidden_dim = int(8 / 3 * config.n_embd)
82 hidden_dim = ((hidden_dim + 63) // 64) * 64
AssistantStep 5edit
Tool Input
1{
2 "content": "class MLP(nn.Module):\n \"\"\"Normalized Gated Squared GELU (NGSG): Novel combination of:\n 1) Squared GELU for higher-order feature interactions with smooth gradients\n 2) LayerNorm after squaring to stabilize activation magnitudes\n 3) Sigmoid gating for input-dependent feature selection\n 4) Parameter-matched to standard SwiGLU/GeGLU baselines\"\"\"\n def __init__(self, config):\n super().__init__()\n hidden_dim = int(8 / 3 * config.n_embd)\n hidden_dim = ((hidden_dim + 63) // 64) * 64\n self.w1 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n self.w_gate = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n self.c_proj = nn.Linear(hidden_dim, config.n_embd, bias=config.bias)\n self.ln = LayerNorm(hidden_dim, bias=config.bias)\n self.dropout = nn.Dropout(config.dropout)\n self.gate_scale = nn.Parameter(torch.tensor(1.0))\n\n def forward(self, x):\n x_hidden = F.gelu(self.w1(x)).square()\n x_hidden = self.ln(x_hidden)\n gate = torch.sigmoid(self.w_gate(x))\n return self.dropout(self.c_proj(x_hidden * gate * self.gate_scale))",
3 "end_line": 92,
4 "filename": "nanoGPT/custom_pretrain.py",
5 "start_line": 72,
6 "op": "replace"
7}Tool ResultStep 5
OK: Replaced lines 72..92 with 22 line(s) in nanoGPT/custom_pretrain.py. Editable range: 72–93.
[Current file: nanoGPT/custom_pretrain.py | editable: 72–93 | total: 444 lines]
69| return y
70|
71| # ── Feed-Forward Network ──────────────────────────────────────────────────
72 class MLP(nn.Module):
73 """Normalized Gated Squared GELU (NGSG): Novel combination of:
74 1) Squared GELU for higher-order feature interactions with smooth gradients
75 2) LayerNorm after squaring to stabilize activation magnitudes
76 3) Sigmoid gating for input-dependent feature selection
77 4) Parameter-matched to standard SwiGLU/GeGLU baselines"""
78 def __init__(self, config):
79 super().__init__()
80 hidden_dim = int(8 / 3 * config.n_embd)
81 hidden_dim = ((hidden_dim + 63) // 64) * 64
82 self.w1 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)
83 self.w_ga
AssistantStep 6test