Agent Conversation: qwen3.6-plus

Model: qwen3.6-plus

Total steps: 6

Messages: 13

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)
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 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, bias=config.bias)
    39:         self.attn_dropout = nn.Dropout(config.dropout)
    40:         self.resid_dropout = n

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 33,
5  "end_line": 69,
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.dropout = config.dropout\n        self.flash = False  # Manual attention for ALiBi bias\n        \n        # Precompute ALiBi slopes following the original paper\n        # Geometric sequence of slopes for each attention head\n        n = config.n_head\n        if n % 2 == 0:\n            slopes = [1.0 / (2 ** ((i + 1) * (8 / n))) for i in range(n)]\n        else:\n            slopes = [1.0 / (2 ** ((i + 1) * (4 / n))) for i in range(n)]\n            if n % 2 == 1:\n                second = 2 ** (-((n - 1) / 2))\n                for i in range(1, 1 + (n - len(slopes))):\n                    slopes.append(1.0 / (second ** (2 * i)))\n        self.register_buffer(\"alibi_slopes\", torch.tensor(slopes))\n        \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        # ALiBi handles position encoding through attention biases\n        self.use_pos_emb = False\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, C // self.n_head).transpose(1, 2)  # (B, H, T, D)\n        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)\n        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)\n        \n        # QK-Normalization for training stability\n        q = F.rms_norm(q, (q.size(-1),))\n        k = F.rms_norm(k, (k.size(-1),))\n        \n        # ALiBi: compute relative position bias\n        # relative_pos[i,j] = j - i (negative for causal, i.e., keys are behind queries)\n        relative_pos = torch.arange(T, device=x.device).unsqueeze(0) - torch.arange(T, device=x.device).unsqueeze(1)  # (T, T)\n        alibi_bias = relative_pos.to(self.alibi_slopes.dtype) * self.alibi_slopes.view(1, -1, 1, 1)  # (1, H, T, T)\n        \n        # Attention scores: Q @ K^T / sqrt(d) + ALiBi bias\n        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))\n        att = att + alibi_bias  # Add position-dependent 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..69 with 58 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–91, 266–268.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–91 | total: 458 lines]
    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

AssistantStep 2edit

Tool Input

1{
2  "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.dropout = config.dropout\n        self.flash = False  # Manual attention for ALiBi bias\n        \n        # Precompute ALiBi slopes: m_i = 2^(-8/n_head) ^ i  for i=1..n_head\n        m = 2 ** (-8.0 / config.n_head)\n        slopes = [m ** (i + 1) for i in range(config.n_head)]\n        self.register_buffer(\"alibi_slopes\", torch.tensor(slopes))\n        \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        # ALiBi handles position encoding through attention biases\n        self.use_pos_emb = False\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, C // self.n_head).transpose(1, 2)  # (B, H, T, D)\n        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)\n        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)\n        \n        # QK-Normalization for training stability\n        q = F.rms_norm(q, (q.size(-1),))\n        k = F.rms_norm(k, (k.size(-1),))\n        \n        # ALiBi: compute relative position bias\n        # relative_pos[i,j] = j - i (negative for causal, i.e., keys are behind queries)\n        relative_pos = torch.arange(T, device=x.device).unsqueeze(0) - torch.arange(T, device=x.device).unsqueeze(1)  # (T, T)\n        alibi_bias = relative_pos.to(self.alibi_slopes.dtype) * self.alibi_slopes.view(1, -1, 1, 1)  # (1, H, T, T)\n        \n        # Attention scores: Q @ K^T / sqrt(d) + ALiBi bias\n        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))\n        att = att + alibi_bias  # Add position-dependent 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",
3  "end_line": 90,
4  "filename": "nanoGPT/custom_pretrain.py",
5  "start_line": 33,
6  "op": "replace"
7}

Tool ResultStep 2

OK: Replaced lines 33..90 with 50 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–83, 258–260.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–83 | total: 450 lines]
    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

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=0, train_loss=10.9934, val_loss=10.9932

### lm-eval-345m (scripts/lm_eval_345m.sh)
[COMMAND FAILED exit=1]
ERROR: Checkpoint not found: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-attention/qwen3.6-plus_20260411_022218/seed_42/ckpt_gpt-345m.pt

AssistantStep 4edit

Tool Input

1{
2  "end_line": 82,
3  "filename": "nanoGPT/custom_pretrain.py",
4  "op": "replace",
5  "start_line": 33,
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  # RoPE replaces learned position embeddings\n        # Precompute RoPE frequencies\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\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        d = x.shape[-1] // 2\n        x1, x2 = x[..., :d], x[..., d:]\n        y1 = x1 * cos - x2 * sin\n        y2 = x1 * sin + x2 * cos\n        return torch.cat([y1, y2], dim=-1).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, C // self.n_head).transpose(1, 2)\n        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)\n        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)\n        # QK-Norm then RoPE (normalizing before rotation stabilizes training)\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        if self.flash:\n            y = torch.nn.functional.scaled_dot_product_attention(\n                q, k, v, attn_mask=None,\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.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 4

OK: Replaced lines 33..82 with 54 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–87, 262–264.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–87 | total: 454 lines]
    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.head_dim = config.n_embd // conf

AssistantStep 5edit

Tool Input

1{
2  "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  # RoPE replaces learned position embeddings\n        # Precompute RoPE frequencies\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        # Learned per-head temperature scaling for attention\n        # Initialized to 1.0 so training starts from standard scaling\n        self.log_temperatures = nn.Parameter(torch.zeros(self.n_head))\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        d = x.shape[-1] // 2\n        x1, x2 = x[..., :d], x[..., d:]\n        y1 = x1 * cos - x2 * sin\n        y2 = x1 * sin + x2 * cos\n        return torch.cat([y1, y2], dim=-1).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, C // self.n_head).transpose(1, 2)\n        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)\n        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)\n        # Apply RoPE for position encoding\n        q = self._apply_rope(q, T)\n        k = self._apply_rope(k, T)\n        # QK-Normalization for training stability\n        q = F.rms_norm(q, (q.size(-1),))\n        k = F.rms_norm(k, (k.size(-1),))\n        \n        if self.flash:\n            # For flash attention, we can't use custom scaling directly\n            # so we scale q before passing to flash attention\n            temp = torch.exp(self.log_temperatures).view(1, -1, 1, 1)\n            q_scaled = q * temp\n            y = torch.nn.functional.scaled_dot_product_attention(\n                q_scaled, k, v, attn_mask=None,\n                dropout_p=self.dropout if self.training else 0, is_causal=True)\n        else:\n            # Manual attention with learned temperature scaling\n            att = (q @ k.transpose(-2, -1))  # (B, H, T, T)\n            # Apply per-head temperature: att * exp(log_temp) / sqrt(d)\n            temp = torch.exp(self.log_temperatures).view(1, -1, 1, 1)\n            att = att * temp / math.sqrt(k.size(-1))\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",
3  "end_line": 86,
4  "filename": "nanoGPT/custom_pretrain.py",
5  "start_line": 33,
6  "op": "replace"
7}

Tool ResultStep 5

OK: Replaced lines 33..86 with 69 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–102, 277–279.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–102 | total: 469 lines]
    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.head_dim = config.n_embd // co

AssistantStep 6test