Agent Conversation: deepseek-reasoner
Model: deepseek-reasoner
Total steps: 19
Messages: 42
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 = nAssistantStep 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.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 # ALiBi: no position embeddings needed\n self.use_pos_emb = False\n \n # ALiBi slopes: geometric sequence as in original paper\n # For n_head heads, slopes = 2^(-8/n_head * i) for i in 1..n_head\n slopes = torch.tensor([2**(-8 * (i+1) / self.n_head) for i in range(self.n_head)])\n # Reshape to (1, n_head, 1, 1) for broadcasting\n self.register_buffer(\"alibi_slopes\", slopes.view(1, self.n_head, 1, 1))\n \n # Precompute ALiBi bias matrix for max sequence length\n self.max_seq_len = config.block_size\n self._precompute_alibi_bias()\n\n def _precompute_alibi_bias(self):\n \"\"\"Precompute ALiBi bias matrix for causal masking.\"\"\"\n # Create distance matrix: i - j for i, j in [0, block_size-1]\n # Only need lower triangular (causal)\n distances = torch.arange(self.max_seq_len).view(-1, 1) - torch.arange(self.max_seq_len).view(1, -1)\n distances = torch.tril(distances) # causal\n # Convert to float and expand for heads\n alibi_bias = distances.unsqueeze(0).unsqueeze(0) # (1, 1, T, T)\n # Multiply by slopes (will be applied per-head in forward)\n # Actually we'll apply slopes in forward for flexibility\n self.register_buffer(\"distances\", distances)\n \n def _get_alibi_bias(self, T, device):\n \"\"\"Get ALiBi bias matrix for sequence length T.\"\"\"\n # distances is (T, T) lower triangular\n distances = self.distances[:T, :T] # slice to current length\n # Expand to (1, n_head, T, T) and multiply by slopes\n alibi_bias = distances.unsqueeze(0).unsqueeze(0) # (1, 1, T, T)\n alibi_bias = alibi_bias * self.alibi_slopes[:, :, :, :] # (1, n_head, T, T)\n return alibi_bias.to(device)\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 \n if self.flash:\n # For flash attention, we need to disable it or implement ALiBi differently\n # Since flash attention doesn't support custom bias easily, fall back to manual\n # We'll just use manual implementation for now\n att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))\n # Add ALiBi bias\n alibi_bias = self._get_alibi_bias(T, x.device)\n att = att + alibi_bias\n # Apply causal mask (already in distances matrix, but ensure -inf for future)\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 else:\n att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))\n # Add ALiBi bias\n alibi_bias = self._get_alibi_bias(T, x.device)\n att = att + alibi_bias\n # Apply causal mask (already in distances matrix, but ensure -inf for future)\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 \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 83 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–115, 290–292.
[Current file: nanoGPT/custom_pretrain.py | editable: 33–115 | total: 482 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
4
AssistantStep 2edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 33,
5 "end_line": 115,
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 # Disable flash attention for ALiBi compatibility\n self.flash = False\n # Register causal mask buffer\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: no position embeddings needed\n self.use_pos_emb = False\n \n # ALiBi slopes: geometric sequence as in original paper\n # For n_head heads, slopes = 2^(-8/n_head * i) for i in 1..n_head\n slopes = torch.tensor([2**(-8 * (i+1) / self.n_head) for i in range(self.n_head)])\n # Reshape to (1, n_head, 1, 1) for broadcasting\n self.register_buffer(\"alibi_slopes\", slopes.view(1, self.n_head, 1, 1))\n \n # Precompute ALiBi bias matrix for max sequence length\n self.max_seq_len = config.block_size\n self._precompute_alibi_bias()\n\n def _precompute_alibi_bias(self):\n \"\"\"Precompute ALiBi bias matrix for causal masking.\"\"\"\n # Create distance matrix: i - j for i, j in [0, block_size-1]\n # Only need lower triangular (causal)\n distances = torch.arange(self.max_seq_len).view(-1, 1) - torch.arange(self.max_seq_len).view(1, -1)\n distances = torch.tril(distances) # causal\n # Convert to float and expand for heads\n alibi_bias = distances.unsqueeze(0).unsqueeze(0) # (1, 1, T, T)\n # Multiply by slopes (will be applied per-head in forward)\n # Actually we'll apply slopes in forward for flexibility\n self.register_buffer(\"distances\", distances)\n \n def _get_alibi_bias(self, T, device):\n \"\"\"Get ALiBi bias matrix for sequence length T.\"\"\"\n # distances is (T, T) lower triangular\n distances = self.distances[:T, :T] # slice to current length\n # Expand to (1, n_head, T, T) and multiply by -slopes (negative bias for past)\n alibi_bias = distances.unsqueeze(0).unsqueeze(0) # (1, 1, T, T)\n alibi_bias = alibi_bias * (-self.alibi_slopes[:, :, :, :]) # (1, n_head, T, T)\n return alibi_bias.to(device)\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 \n # Manual attention with ALiBi\n att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))\n # Add ALiBi bias (negative for past positions)\n alibi_bias = self._get_alibi_bias(T, x.device)\n att = att + alibi_bias\n # Apply causal mask (set future positions to -inf)\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 \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 2
OK: Replaced lines 33..115 with 71 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–103, 278–280.
[Current file: nanoGPT/custom_pretrain.py | editable: 33–103 | total: 470 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
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=10.9942, val_loss=10.9957 ### lm-eval-345m (scripts/lm_eval_345m.sh) ERROR: Checkpoint not found: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-attention/deepseek-reasoner_20260410_042139/seed_42/ckpt_gpt-345m.pt
AssistantStep 4edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 33,
5 "end_line": 103,
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 # Disable flash attention for ALiBi compatibility\n self.flash = False\n # Register causal mask buffer\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: no position embeddings needed\n self.use_pos_emb = False\n \n # ALiBi slopes: geometric sequence as in original paper\n # For n_head heads, slopes = 2^(-8/n_head * i) for i in 1..n_head\n slopes = torch.tensor([2**(-8 * (i+1) / self.n_head) for i in range(self.n_head)])\n # Reshape to (1, n_head, 1, 1) for broadcasting\n self.register_buffer(\"alibi_slopes\", slopes.view(1, self.n_head, 1, 1))\n \n # Precompute ALiBi bias matrix for max sequence length\n self.max_seq_len = config.block_size\n self._precompute_alibi_bias()\n\n def _precompute_alibi_bias(self):\n \"\"\"Precompute ALiBi bias matrix for causal masking.\"\"\"\n # Create distance matrix: i - j for i, j in [0, block_size-1]\n # Only need lower triangular (causal)\n distances = torch.arange(self.max_seq_len).view(-1, 1) - torch.arange(self.max_seq_len).view(1, -1)\n distances = torch.tril(distances) # causal\n # Convert to float for consistency with slopes\n distances = distances.float()\n self.register_buffer(\"distances\", distances)\n \n def _get_alibi_bias(self, T, device):\n \"\"\"Get ALiBi bias matrix for sequence length T.\"\"\"\n # distances is (T, T) lower triangular\n distances = self.distances[:T, :T] # slice to current length\n # Expand to (1, n_head, T, T) and multiply by -slopes (negative bias for past)\n alibi_bias = distances.unsqueeze(0).unsqueeze(0) # (1, 1, T, T)\n alibi_bias = alibi_bias * (-self.alibi_slopes[:, :, :, :]) # (1, n_head, T, T)\n return alibi_bias.to(device)\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 \n # Manual attention with ALiBi\n att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))\n # Add ALiBi bias (negative for past positions)\n alibi_bias = self._get_alibi_bias(T, x.device)\n att = att + alibi_bias\n # Apply causal mask (set future positions to -inf)\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 \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..103 with 69 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–101, 276–278.
[Current file: nanoGPT/custom_pretrain.py | editable: 33–101 | total: 468 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
AssistantStep 5edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 33,
5 "end_line": 101,
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 # Disable flash attention for ALiBi compatibility\n self.flash = False\n # Register causal mask buffer\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: no position embeddings needed\n self.use_pos_emb = False\n \n # ALiBi slopes: learnable per-head slopes, initialized as geometric sequence\n # For n_head heads, slopes = 2^(-8/n_head * i) for i in 1..n_head\n slopes_init = torch.tensor([2**(-8 * (i+1) / self.n_head) for i in range(self.n_head)])\n self.alibi_slopes = nn.Parameter(slopes_init.view(1, self.n_head, 1, 1))\n \n # Precompute ALiBi distance matrix for max sequence length\n self.max_seq_len = config.block_size\n self._precompute_alibi_distances()\n\n def _precompute_alibi_distances(self):\n \"\"\"Precompute distance matrix for causal masking.\"\"\"\n # Create distance matrix: i - j for i, j in [0, block_size-1]\n # Only need lower triangular (causal)\n distances = torch.arange(self.max_seq_len).view(-1, 1) - torch.arange(self.max_seq_len).view(1, -1)\n distances = torch.tril(distances) # causal\n # Convert to float for consistency with slopes\n distances = distances.float()\n self.register_buffer(\"distances\", distances)\n \n def _get_alibi_bias(self, T, device):\n \"\"\"Get ALiBi bias matrix for sequence length T.\"\"\"\n # distances is (T, T) lower triangular\n distances = self.distances[:T, :T] # slice to current length\n # Expand to (1, n_head, T, T) and multiply by -slopes (negative bias for past)\n alibi_bias = distances.unsqueeze(0).unsqueeze(0) # (1, 1, T, T)\n alibi_bias = alibi_bias * (-self.alibi_slopes[:, :, :, :]) # (1, n_head, T, T)\n return alibi_bias.to(device)\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 \n # Manual attention with ALiBi\n att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))\n # Add ALiBi bias (negative for past positions)\n alibi_bias = self._get_alibi_bias(T, x.device)\n att = att + alibi_bias\n # Apply causal mask (set future positions to -inf)\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 \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 5
OK: Replaced lines 33..101 with 68 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–100, 275–277.
[Current file: nanoGPT/custom_pretrain.py | editable: 33–100 | total: 467 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
AssistantStep 6edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 33,
5 "end_line": 100,
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 = hasattr(torch.nn.functional, 'scaled_dot_product_attention')\n # Register causal mask buffer for fallback\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: no position embeddings needed\n self.use_pos_emb = False\n \n # ALiBi slopes: learnable per-head slopes, initialized as geometric sequence\n # For n_head heads, slopes = 2^(-8/n_head * i) for i in 1..n_head\n slopes_init = torch.tensor([2**(-8 * (i+1) / self.n_head) for i in range(self.n_head)])\n self.alibi_slopes = nn.Parameter(slopes_init.view(1, self.n_head, 1, 1))\n \n # Precompute ALiBi distance matrix for max sequence length\n self.max_seq_len = config.block_size\n self._precompute_alibi_distances()\n\n def _precompute_alibi_distances(self):\n \"\"\"Precompute distance matrix for causal masking.\"\"\"\n # Create distance matrix: i - j for i, j in [0, block_size-1]\n # Only need lower triangular (causal)\n distances = torch.arange(self.max_seq_len).view(-1, 1) - torch.arange(self.max_seq_len).view(1, -1)\n distances = torch.tril(distances) # causal\n # Convert to float for consistency with slopes\n distances = distances.float()\n self.register_buffer(\"distances\", distances)\n \n def _get_alibi_bias(self, T, device):\n \"\"\"Get ALiBi bias matrix for sequence length T.\"\"\"\n # distances is (T, T) lower triangular\n distances = self.distances[:T, :T] # slice to current length\n # Expand to (1, n_head, T, T) and multiply by -slopes (negative bias for past)\n alibi_bias = distances.unsqueeze(0).unsqueeze(0) # (1, 1, T, T)\n alibi_bias = alibi_bias * (-self.alibi_slopes[:, :, :, :]) # (1, n_head, T, T)\n return alibi_bias.to(device)\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 \n if self.flash:\n import inspect\n sig = inspect.signature(torch.nn.functional.scaled_dot_product_attention)\n if 'attn_bias' in sig.parameters:\n # Use flash attention with ALiBi bias\n alibi_bias = self._get_alibi_bias(T, x.device)\n y = torch.nn.functional.scaled_dot_product_attention(\n q, k, v, attn_mask=None, attn_bias=alibi_bias,\n dropout_p=self.dropout if self.training else 0, is_causal=True)\n else:\n # Flash attention doesn't support attn_bias, fallback\n att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))\n alibi_bias = self._get_alibi_bias(T, x.device)\n att = att + alibi_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 else:\n att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))\n alibi_bias = self._get_alibi_bias(T, x.device)\n att = att + alibi_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 \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..100 with 83 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–115, 290–292.
[Current file: nanoGPT/custom_pretrain.py | editable: 33–115 | total: 482 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
AssistantStep 7edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 79,
5 "end_line": 115,
6 "content": " 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 \n # Apply QK normalization via RMSNorm (stabilizes training)\n q = F.rms_norm(q, (q.size(-1),))\n k = F.rms_norm(k, (k.size(-1),))\n \n if self.flash:\n import inspect\n sig = inspect.signature(torch.nn.functional.scaled_dot_product_attention)\n if 'attn_bias' in sig.parameters:\n # Use flash attention with ALiBi bias\n alibi_bias = self._get_alibi_bias(T, x.device)\n y = torch.nn.functional.scaled_dot_product_attention(\n q, k, v, attn_mask=None, attn_bias=alibi_bias,\n dropout_p=self.dropout if self.training else 0, is_causal=True)\n else:\n # Flash attention doesn't support attn_bias, fallback\n att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))\n alibi_bias = self._get_alibi_bias(T, x.device)\n att = att + alibi_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 else:\n att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))\n alibi_bias = self._get_alibi_bias(T, x.device)\n att = att + alibi_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 \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 7
OK: Replaced lines 79..115 with 41 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–119, 294–296.
[Current file: nanoGPT/custom_pretrain.py | editable: 33–119 | total: 486 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
AssistantStep 8test
Tool ResultStep 8
[Test #2] (1 test remaining; call submit(n=N) to choose which test result to submit as final) ### gpt-345m (scripts/gpt_345m.sh) W0410 13:23:51.131000 2894286 site-packages/torch/distributed/run.py:792] W0410 13:23:51.131000 2894286 site-packages/torch/distributed/run.py:792] ***************************************** W0410 13:23:51.131000 2894286 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. W0410 13:23:51.131000 2894286 site-packages/torch/distributed/run.py:792] ***************************************** [W410 13:23:51.208192497 socket.cpp:204] [c10d] The hostname of the client socket cannot be retrieved. err=-3 [W410 13:23:51.208525236 socket.cpp:204] [c10d] The hostname of the client socket cannot be retrieved. err=-3 [W410 13:23:51.392329352 socket.cpp:204] [c10d] The hostnam
AssistantStep 9edit
Tool Input
1{
2 "op": "replace",
3 "filename": "nanoGPT/custom_pretrain.py",
4 "start_line": 33,
5 "end_line": 119,
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 = hasattr(torch.nn.functional, 'scaled_dot_product_attention')\n # Register causal mask buffer for fallback\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: no position embeddings needed\n self.use_pos_emb = False\n \n # ALiBi slopes: learnable per-head slopes, initialized as geometric sequence\n # For n_head heads, slopes = 2^(-8/n_head * i) for i in 1..n_head\n slopes_init = torch.tensor([2**(-8 * (i+1) / self.n_head) for i in range(self.n_head)])\n self.alibi_slopes = nn.Parameter(slopes_init.view(1, self.n_head, 1, 1))\n \n # Precompute ALiBi distance matrix for max sequence length\n self.max_seq_len = config.block_size\n self._precompute_alibi_distances()\n \n # Determine if flash attention supports attn_bias\n self.flash_supports_attn_bias = False\n if self.flash:\n import inspect\n try:\n sig = inspect.signature(torch.nn.functional.scaled_dot_product_attention)\n self.flash_supports_attn_bias = 'attn_bias' in sig.parameters\n except (ValueError, TypeError):\n # Built-in function may not have signature, assume not supported\n pass\n\n def _precompute_alibi_distances(self):\n \"\"\"Precompute distance matrix for causal masking.\"\"\"\n # Create distance matrix: i - j for i, j in [0, block_size-1]\n # Only need lower triangular (causal)\n distances = torch.arange(self.max_seq_len).view(-1, 1) - torch.arange(self.max_seq_len).view(1, -1)\n distances = torch.tril(distances) # causal\n # Convert to float for consistency with slopes\n distances = distances.float()\n self.register_buffer(\"distances\", distances)\n \n def _get_alibi_bias(self, T, device):\n \"\"\"Get ALiBi bias matrix for sequence length T.\"\"\"\n # distances is (T, T) lower triangular\n distances = self.distances[:T, :T] # slice to current length\n # Expand to (1, n_head, T, T) and multiply by -slopes (negative bias for past)\n alibi_bias = distances.unsqueeze(0).unsqueeze(0) # (1, 1, T, T)\n alibi_bias = alibi_bias * (-self.alibi_slopes[:, :, :, :]) # (1, n_head, T, T)\n return alibi_bias.to(device)\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 \n # Apply QK normalization via RMSNorm (stabilizes training)\n q = F.rms_norm(q, (q.size(-1),))\n k = F.rms_norm(k, (k.size(-1),))\n \n if self.flash and self.flash_supports_attn_bias:\n # Use flash attention with ALiBi bias\n alibi_bias = self._get_alibi_bias(T, x.device)\n y = torch.nn.functional.scaled_dot_product_attention(\n q, k, v, attn_mask=None, attn_bias=alibi_bias,\n dropout_p=self.dropout if self.training else 0, is_causal=True)\n else:\n # Manual attention with ALiBi\n att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))\n alibi_bias = self._get_alibi_bias(T, x.device)\n att = att + alibi_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 \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 9
OK: Replaced lines 33..119 with 87 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–119, 294–296.
[Current file: nanoGPT/custom_pretrain.py | editable: 33–119 | total: 486 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