Agent Conversation: deepseek-reasoner
Model: deepseek-reasoner
Total steps: 7
Messages: 17
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": "# ── Feed-Forward Network ──────────────────────────────────────────────────\nclass MLP(nn.Module):\n def __init__(self, config):\n super().__init__()\n # Hidden dimension similar to SwiGLU for comparable parameter count\n hidden_dim = int(8 / 3 * config.n_embd)\n # Round to nearest multiple of 64 for efficiency\n hidden_dim = ((hidden_dim + 63) // 64) * 64\n self.w1 = 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.w3 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n # Learnable temperature parameter per feature for gating sharpness\n self.temperature = nn.Parameter(torch.ones(hidden_dim))\n self.dropout = nn.Dropout(config.dropout)\n\n def forward(self, x):\n # LT-GLU: Learned Temperature Gated Linear Unit\n x1 = self.w1(x)\n x3 = self.w3(x)\n # Gate with learnable temperature per channel\n gate = torch.sigmoid(self.temperature * x1)\n # Gated activation\n x = gate * x3\n # Project back to embedding dimension\n x = self.c_proj(x)\n x = self.dropout(x)\n return x"
7}Tool ResultStep 1
OK: Replaced lines 72..86 with 27 line(s) in nanoGPT/custom_pretrain.py. Editable range: 72–98, 257–259.
[Current file: nanoGPT/custom_pretrain.py | editable: 72–98 | total: 449 lines]
69| return y
70|
71| # ── Feed-Forward Network ──────────────────────────────────────────────────
72 # ── Feed-Forward Network ──────────────────────────────────────────────────
73 class MLP(nn.Module):
74 def __init__(self, config):
75 super().__init__()
76 # Hidden dimension similar to SwiGLU for comparable parameter count
77 hidden_dim = int(8 / 3 * config.n_embd)
78 # Round to nearest multiple of 64 for efficiency
79 hidden_dim = ((hidden_dim + 63) // 64) * 64
80 self.w1 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)
81 self.c_proj = nn.Linear(hidden_dim, config.n_embd, bias=config.bias)
82 self.w3 = nn.Linear(config.n_embd, hidden_dim, bias=config
AssistantStep 2test
Tool ResultStep 2
[Test #1] (2 tests remaining; call submit(n=1) to submit this result)
### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=8000, train_loss=2.6629, val_loss=2.3787
TRAIN_METRICS: step=9000, train_loss=2.6333, val_loss=2.3571
TRAIN_METRICS: step=10000, train_loss=2.6179, val_loss=2.3348
TRAIN_METRICS: step=11000, train_loss=2.6106, val_loss=2.3252
TRAIN_METRICS: step=12000, train_loss=2.5924, val_loss=2.3092
Final metrics (gpt-345m): val_loss_gpt-345m=2.3103, wikitext2_ppl_gpt-345m=45.0200, lambada_ppl_gpt-345m=68.6200
### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=52.6900, hellaswag_lm-eval-345m=32.7500, piqa_lm-eval-345m=64.1500, winogrande_lm-eval-345m=49.7200
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.3103, 'wikitext2_ppl_gpt-345m': 45.02, 'lambada_ppl_gpt-345m': 68.62, 'elapsed_gpt-345m': 21302, 'arc_easy_lm-eval-345m': 52.69, 'hellaswag_lm-eval-345m': 32.75, 'piqa_lm-eval-345m': 64.15, 'w
AssistantStep 3edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 72,
5 "end_line": 98,
6 "content": "# ── Feed-Forward Network ──────────────────────────────────────────────────\nclass MLP(nn.Module):\n def __init__(self, config):\n super().__init__()\n # Hidden dimension similar to SwiGLU for comparable parameter count\n hidden_dim = int(8 / 3 * config.n_embd)\n # Round to nearest multiple of 64 for efficiency\n hidden_dim = ((hidden_dim + 63) // 64) * 64\n self.w1 = 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.w3 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)\n # Learnable affine transformation for gating: scale and shift per channel\n self.gate_scale = nn.Parameter(torch.ones(hidden_dim))\n self.gate_shift = nn.Parameter(torch.zeros(hidden_dim))\n self.dropout = nn.Dropout(config.dropout)\n\n def forward(self, x):\n # AGLU: Affine-Gated Linear Unit\n x1 = self.w1(x)\n x3 = self.w3(x)\n # Gate with learnable affine transformation per channel\n gate = torch.sigmoid(self.gate_scale * x1 + self.gate_shift)\n # Gated activation\n x = gate * x3\n # Project back to embedding dimension\n x = self.c_proj(x)\n x = self.dropout(x)\n return x"
7}Tool ResultStep 3
OK: Replaced lines 72..98 with 28 line(s) in nanoGPT/custom_pretrain.py. Editable range: 72–99, 258–260.
[Current file: nanoGPT/custom_pretrain.py | editable: 72–99 | total: 450 lines]
69| return y
70|
71| # ── Feed-Forward Network ──────────────────────────────────────────────────
72 # ── Feed-Forward Network ──────────────────────────────────────────────────
73 class MLP(nn.Module):
74 def __init__(self, config):
75 super().__init__()
76 # Hidden dimension similar to SwiGLU for comparable parameter count
77 hidden_dim = int(8 / 3 * config.n_embd)
78 # Round to nearest multiple of 64 for efficiency
79 hidden_dim = ((hidden_dim + 63) // 64) * 64
80 self.w1 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)
81 self.c_proj = nn.Linear(hidden_dim, config.n_embd, bias=config.bias)
82 self.w3 = nn.Linear(config.n_embd, hidden_dim, bias=config
AssistantStep 4test
Tool ResultStep 4
[Test #2] (1 test remaining; call submit(n=2) to submit this result)
### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=8000, train_loss=2.6605, val_loss=2.3759
TRAIN_METRICS: step=9000, train_loss=2.6310, val_loss=2.3542
TRAIN_METRICS: step=10000, train_loss=2.6162, val_loss=2.3323
TRAIN_METRICS: step=11000, train_loss=2.6086, val_loss=2.3224
TRAIN_METRICS: step=12000, train_loss=2.5907, val_loss=2.3064
Final metrics (gpt-345m): val_loss_gpt-345m=2.3077, wikitext2_ppl_gpt-345m=45.0100, lambada_ppl_gpt-345m=67.3600
### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=55.1300, hellaswag_lm-eval-345m=32.9300, piqa_lm-eval-345m=64.2000, winogrande_lm-eval-345m=50.6700
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.3077, 'wikitext2_ppl_gpt-345m': 45.01, 'lambada_ppl_gpt-345m': 67.36, 'elapsed_gpt-345m': 21809, 'arc_easy_lm-eval-345m': 55.13, 'hellaswag_lm-eval-345m': 32.93, 'piqa_lm-eval-345m': 64.2, 'win
AssistantStep 5edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 258,
5 "end_line": 260,
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 5
OK: Replaced lines 258..260 with 3 line(s) in nanoGPT/custom_pretrain.py. Editable range: 72–99, 258–260.
[Current file: nanoGPT/custom_pretrain.py | editable: 72–99 | total: 450 lines]
69| return y
70|
71| # ── Feed-Forward Network ──────────────────────────────────────────────────
72 # ── Feed-Forward Network ──────────────────────────────────────────────────
73 class MLP(nn.Module):
74 def __init__(self, config):
75 super().__init__()
76 # Hidden dimension similar to SwiGLU for comparable parameter count
77 hidden_dim = int(8 / 3 * config.n_embd)
78 # Round to nearest multiple of 64 for efficiency
79 hidden_dim = ((hidden_dim + 63) // 64) * 64
80 self.w1 = nn.Linear(config.n_embd, hidden_dim, bias=config.bias)
81 self.c_proj = nn.Linear(hidden_dim, config.n_embd, bias=config.bias)
82 self.w3 = nn.Linear(config.n_embd, hidden_dim, bias=confi
AssistantStep 6test
AssistantStep 6test
Tool ResultStep 6
[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=8000, train_loss=2.5671, val_loss=2.2877
TRAIN_METRICS: step=9000, train_loss=2.5351, val_loss=2.2643
TRAIN_METRICS: step=10000, train_loss=2.5177, val_loss=2.2408
TRAIN_METRICS: step=11000, train_loss=2.5078, val_loss=2.2295
TRAIN_METRICS: step=12000, train_loss=2.4891, val_loss=2.2136
Final metrics (gpt-345m): val_loss_gpt-345m=2.2143, wikitext2_ppl_gpt-345m=38.9200, lambada_ppl_gpt-345m=61.7400
### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=57.3700, hellaswag_lm-eval-345m=35.2100, piqa_lm-eval-345m=65.4000, winogrande_lm-eval-345m=51.3000
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2143, 'wikitext2_ppl_gpt-345m': 38.92, 'lamb
AssistantStep 7submit
Tool Input
1{
2 "n": 3
3}Tool ResultStep 7
[submit] Submitting result from test #3 as final.
### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=8000, train_loss=2.5671, val_loss=2.2877
TRAIN_METRICS: step=9000, train_loss=2.5351, val_loss=2.2643
TRAIN_METRICS: step=10000, train_loss=2.5177, val_loss=2.2408
TRAIN_METRICS: step=11000, train_loss=2.5078, val_loss=2.2295
TRAIN_METRICS: step=12000, train_loss=2.4891, val_loss=2.2136
Final metrics (gpt-345m): val_loss_gpt-345m=2.2143, wikitext2_ppl_gpt-345m=38.9200, lambada_ppl_gpt-345m=61.7400
### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=57.3700, hellaswag_lm-eval-345m=35.2100, piqa_lm-eval-345m=65.4000, winogrande_lm-eval-345m=51.3000
[submit] Finalized test #3 as final.
[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2143, 'wikitext2_ppl_gpt-345m': 38.92, 'lambada_ppl_gpt-345m': 61.74, 'elapsed_gpt-345m': 21590, 'arc_easy_lm-eval-345m': 57.37, 'hellaswag_lm-eval-345m': 35.21, 'piqa_lm-eva