Agent Conversation: gemini-3.1-pro-preview

Model: gemini-3.1-pro-preview
Total steps: 8
Messages: 19
UserStep 0

Task: llm-pretrain-linear-attention

LLM Pretraining: Linear/Subquadratic Attention Mechanism

Research Question

Design a novel linear or subquadratic attention mechanism for GPT-2 language model pretraining that achieves competitive validation loss while replacing standard softmax attention. The mechanism should scale better than O(n^2) in sequence length.

What You Can Modify

Two editable regions in custom_pretrain.py:

  1. CausalSelfAttention class (lines 33-70): The attention mechanism itself, including:

    • The attention computation (replace softmax attention with linear/subquadratic alternatives)
    • Feature maps, gating mechanisms, decay factors
    • Query/Key/Value projections and transformations
    • Internal state management (recurrent states, convolutions, etc.)

  2. Block class (lines 88-100): The transformer block structure, including:

    • How attention and MLP sublayers are composed
    • Normalization placement (pre-norm, post-norm)
    • Residual connection patterns

Note: The flash-linear-attention (FLA) library is pre-installed and provides 27+ optimized linear attention implementations with Triton kernels. You can import from fla.layers (e.g., GatedLinearAttention, DeltaNet, MultiScaleRetention, LinearAttention, HGRN2, Mamba2, etc.) or implement your own mechanism from scratch.

Note: If your attention mechanism does not use learned absolute position embeddings, set self.use_pos_emb = False in __init__ — the model will then skip adding position embeddings in the forward pass.

Note: torch.compile is disabled for this task since FLA's Triton kernels are not compatible with it.

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=32, GA=16, 2-GPU DDP
  • Hardware: H200 GPU

nanoGPT/custom_pretrain.py [EDITABLE — lines 33–70, lines 88–100, lines 246–248 only]

     1: """Custom GPT-2 Pretraining Script
     2: Based on Andrej Karpathy's nanoGPT, evaluated on FineWeb dataset.
     3: # flash-linear-attention is available: from fla.layers import GatedLinearAttention, DeltaNet, MultiScaleRetention, etc.
     4: """
     5: 
     6: import math
     7: import inspect
     8: import os
     9: import time
    10: from contextlib import nullcontext
    11: from dataclasses import dataclass
    12: 
    13: import numpy as np
    14: import torch
    15: import torch.nn as nn
    16: from torch.nn import functional as F
    17: 
    18: # ============================================================================
    19: # Model Components
    20: # ============================================================================
    21: 
    22: # ── Normalization ─
AssistantStep 1edit
Tool Input
1{
2  "content": "class CausalSelfAttention(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        self.n_embd = config.n_embd\n        self.d1 = config.n_embd // 2\n        self.d2 = config.n_embd - self.d1\n        self.h1 = config.n_head // 2\n        self.h2 = config.n_head - self.h1\n        \n        from fla.layers import GatedLinearAttention, DeltaNet\n        self.attn1 = GatedLinearAttention(\n            mode='chunk',\n            hidden_size=self.d1,\n            num_heads=self.h1,\n            expand_k=0.5,\n            expand_v=1.0,\n            use_output_gate=True,\n            gate_fn='swish',\n        )\n        self.attn2 = DeltaNet(\n            hidden_size=self.d2,\n            num_heads=self.h2,\n            use_beta=True,\n            use_short_conv=True,\n            conv_size=4,\n            qk_activation='silu',\n            qk_norm='l2',\n        )\n        \n        self.gate_conv = nn.Conv1d(config.n_embd, config.n_embd, kernel_size=3, padding=2, groups=config.n_embd)\n        self.gate = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)\n        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)\n        self.resid_dropout = nn.Dropout(config.dropout)\n        self.use_pos_emb = False\n\n    def forward(self, x):\n        x1, x2 = torch.split(x, [self.d1, self.d2], dim=-1)\n        o1, _, _ = self.attn1(x1)\n        o2, _, _ = self.attn2(x2)\n        o = torch.cat([o1, o2], dim=-1)\n        \n        gx = x.transpose(1, 2)\n        gx = self.gate_conv(gx)[..., :-2].transpose(1, 2)\n        gate_val = F.silu(self.gate(gx))\n        \n        o = o * gate_val\n        return self.resid_dropout(self.c_proj(o))",
3  "start_line": 33,
4  "op": "replace",
5  "filename": "nanoGPT/custom_pretrain.py",
6  "end_line": 70
7}
Tool ResultStep 1
OK: Replaced lines 33..70 with 47 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–79, 97–109, 255–257.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–79, 97–109, 255–257 | total: 447 lines]
    33  class CausalSelfAttention(nn.Module):
    34      def __init__(self, config):
    35          super().__init__()
       ... (41 more lines) ...
    77          
    78          o = o * gate_val
    79          return self.resid_dropout(self.c_proj(o))
...
    97  # ── Transformer Block ─────────────────────────────────────────────────────
    98  class Block(nn.Module):
    99      def __init__(self, config):
       ... (7 more lines) ...
   107          x = x + self.attn(self.ln_1(x))
   108          x = x + self.mlp(self.ln_2(x))
   109          return x
...
   255      # CONFIG_OVERRIDES: override training hyperparameters for your method.
   256      # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
   257      CONFIG_OVERRIDES = {}
AssistantStep 2edit
Tool Input
1{
2  "start_line": 255,
3  "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': 1e-3, 'weight_decay': 0.05, 'warmup_iters': 500}",
4  "filename": "nanoGPT/custom_pretrain.py",
5  "end_line": 257,
6  "op": "replace"
7}
Tool ResultStep 2
OK: Replaced lines 255..257 with 3 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–79, 97–109, 255–257.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–79, 97–109, 255–257 | total: 447 lines]
    33  class CausalSelfAttention(nn.Module):
    34      def __init__(self, config):
    35          super().__init__()
       ... (41 more lines) ...
    77          
    78          o = o * gate_val
    79          return self.resid_dropout(self.c_proj(o))
...
    97  # ── Transformer Block ─────────────────────────────────────────────────────
    98  class Block(nn.Module):
    99      def __init__(self, config):
       ... (7 more lines) ...
   107          x = x + self.attn(self.ln_1(x))
   108          x = x + self.mlp(self.ln_2(x))
   109          return x
...
   255      # CONFIG_OVERRIDES: override training hyperparameters for your method.
   256      # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
   257      CONFIG_OVERRIDES = {'learnin
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=9000, train_loss=2.5288, val_loss=2.2711
TRAIN_METRICS: step=10000, train_loss=2.5171, val_loss=2.2586
TRAIN_METRICS: step=11000, train_loss=2.4971, val_loss=2.2489
TRAIN_METRICS: step=12000, train_loss=2.4697, val_loss=2.2067
TRAIN_METRICS: step=13000, train_loss=2.4695, val_loss=2.2218
Final metrics (gpt-345m): val_loss_gpt-345m=2.2138, wikitext2_ppl_gpt-345m=40.0700, lambada_ppl_gpt-345m=60.2700

### lm-eval-345m (scripts/lm_eval_345m.sh)
[COMMAND FAILED exit=1]
Evaluating checkpoint: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-linear-attention/gemini-3.1-pro-preview_20260413_120858/seed_42/ckpt_gpt-345m.pt
Model source: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-linear-attention/gemini-3.1-pro-preview_20260413_120858/seed_42/model_source_gpt-345m.py
Using the latest cached version
AssistantStep 4edit
Tool Input
1{
2  "filename": "nanoGPT/custom_pretrain.py",
3  "end_line": 79,
4  "op": "replace",
5  "content": "class CausalSelfAttention(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        import os\n        if \"TRITON_CACHE_DIR\" not in os.environ:\n            os.environ[\"TRITON_CACHE_DIR\"] = os.path.join(os.environ.get('OUTPUT_DIR', '.'), \"triton_cache\")\n\n        self.n_embd = config.n_embd\n        self.d1 = config.n_embd // 2\n        self.d2 = config.n_embd - self.d1\n        self.h1 = config.n_head // 2\n        self.h2 = config.n_head - self.h1\n        \n        self.in_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)\n        \n        from fla.layers import GatedLinearAttention, DeltaNet\n        self.attn1 = GatedLinearAttention(\n            mode='chunk',\n            hidden_size=self.d1,\n            num_heads=self.h1,\n            expand_k=0.5,\n            expand_v=1.0,\n            use_output_gate=True,\n            gate_fn='swish',\n        )\n        self.attn2 = DeltaNet(\n            hidden_size=self.d2,\n            num_heads=self.h2,\n            use_beta=True,\n            use_short_conv=True,\n            conv_size=4,\n            qk_activation='silu',\n            qk_norm='l2',\n        )\n        \n        self.gate_conv = nn.Conv1d(config.n_embd, config.n_embd, kernel_size=3, padding=2, groups=config.n_embd)\n        self.gate = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)\n        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)\n        self.resid_dropout = nn.Dropout(config.dropout)\n        self.use_pos_emb = False\n\n    def forward(self, x):\n        hx = self.in_proj(x)\n        x1, x2 = torch.split(hx, [self.d1, self.d2], dim=-1)\n        \n        o1, _, _ = self.attn1(x1)\n        o2, _, _ = self.attn2(x2)\n        o = torch.cat([o1, o2], dim=-1)\n        \n        gx = x.transpose(1, 2)\n        gx = self.gate_conv(gx)[..., :-2].transpose(1, 2)\n        gate_val = F.silu(self.gate(gx))\n        \n        o = o * gate_val\n        return self.resid_dropout(self.c_proj(o))",
6  "start_line": 33
7}
Tool ResultStep 4
OK: Replaced lines 33..79 with 55 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–87, 105–117, 263–265.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–87, 105–117, 263–265 | total: 455 lines]
    33  class CausalSelfAttention(nn.Module):
    34      def __init__(self, config):
    35          super().__init__()
       ... (49 more lines) ...
    85          
    86          o = o * gate_val
    87          return self.resid_dropout(self.c_proj(o))
...
   105  # ── Transformer Block ─────────────────────────────────────────────────────
   106  class Block(nn.Module):
   107      def __init__(self, config):
       ... (7 more lines) ...
   115          x = x + self.attn(self.ln_1(x))
   116          x = x + self.mlp(self.ln_2(x))
   117          return x
...
   263      # CONFIG_OVERRIDES: override training hyperparameters for your method.
   264      # Allowed keys: learning_rate, weight_decay, warmup_iters, min_lr, grad_clip.
   265      CONFIG_OVERRIDES = {'learni
AssistantStep 5test
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)
Training metrics (last steps):
TRAIN_METRICS: step=9000, train_loss=2.5395, val_loss=2.2684
TRAIN_METRICS: step=10000, train_loss=2.5184, val_loss=2.2544
TRAIN_METRICS: step=11000, train_loss=2.4845, val_loss=2.2538
TRAIN_METRICS: step=12000, train_loss=2.4780, val_loss=2.2089
TRAIN_METRICS: step=13000, train_loss=2.4657, val_loss=2.2149
Final metrics (gpt-345m): val_loss_gpt-345m=2.2014, wikitext2_ppl_gpt-345m=40.4400, lambada_ppl_gpt-345m=60.0900

### lm-eval-345m (scripts/lm_eval_345m.sh)
[COMMAND FAILED exit=1]
Evaluating checkpoint: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-linear-attention/gemini-3.1-pro-preview_20260413_120858/seed_42/ckpt_gpt-345m.pt
Model source: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-linear-attention/gemini-3.1-pro-preview_20260413_120858/seed_42/model_source_gpt-345m.py
Using the latest cached version
AssistantStep 6edit
Tool Input
1{
2  "filename": "nanoGPT/custom_pretrain.py",
3  "start_line": 33,
4  "end_line": 87,
5  "op": "replace",
6  "content": "class CausalSelfAttention(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n        import os\n        import tempfile\n        triton_cache = os.path.join(tempfile.gettempdir(), f\"triton_cache_{os.getpid()}\")\n        os.makedirs(triton_cache, exist_ok=True)\n        os.environ[\"TRITON_CACHE_DIR\"] = triton_cache\n\n        self.n_embd = config.n_embd\n        self.d1 = config.n_embd // 2\n        self.d2 = config.n_embd - self.d1\n        self.h1 = config.n_head // 2\n        self.h2 = config.n_head - self.h1\n        \n        self.in_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)\n        \n        from fla.layers import GatedLinearAttention, DeltaNet\n        self.attn1 = GatedLinearAttention(\n            mode='chunk',\n            hidden_size=self.d1,\n            num_heads=self.h1,\n            expand_k=0.5,\n            expand_v=1.0,\n            use_output_gate=True,\n            gate_fn='swish',\n        )\n        self.attn2 = DeltaNet(\n            hidden_size=self.d2,\n            num_heads=self.h2,\n            use_beta=True,\n            use_short_conv=True,\n            conv_size=4,\n            qk_activation='silu',\n            qk_norm='l2',\n        )\n        \n        self.gate_conv = nn.Conv1d(config.n_embd, config.n_embd, kernel_size=3, padding=2, groups=config.n_embd)\n        self.gate = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)\n        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)\n        self.resid_dropout = nn.Dropout(config.dropout)\n        self.use_pos_emb = False\n\n    def forward(self, x):\n        hx = self.in_proj(x)\n        x1, x2 = torch.split(hx, [self.d1, self.d2], dim=-1)\n        \n        o1, _, _ = self.attn1(x1)\n        o2, _, _ = self.attn2(x2)\n        o = torch.cat([o1, o2], dim=-1)\n        \n        gx = x.transpose(1, 2)\n        gx = self.gate_conv(gx)[..., :-2].transpose(1, 2)\n        gate_val = F.silu(self.gate(gx))\n        \n        o = o * gate_val\n        return self.resid_dropout(self.c_proj(o))"
7}
Tool ResultStep 6
OK: Replaced lines 33..87 with 57 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–89.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–89 | total: 457 lines]
    33  class CausalSelfAttention(nn.Module):
    34      def __init__(self, config):
    35          super().__init__()
       ... (51 more lines) ...
    87          
    88          o = o * gate_val
    89          return self.resid_dropout(self.c_proj(o))
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=2.5395, val_loss=2.2684
TRAIN_METRICS: step=10000, train_loss=2.5184, val_loss=2.2544
TRAIN_METRICS: step=11000, train_loss=2.4845, val_loss=2.2538
TRAIN_METRICS: step=12000, train_loss=2.4780, val_loss=2.2089
TRAIN_METRICS: step=13000, train_loss=2.4657, val_loss=2.2149
Final metrics (gpt-345m): val_loss_gpt-345m=2.2014, wikitext2_ppl_gpt-345m=40.4400, lambada_ppl_gpt-345m=60.1000

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=59.6000, hellaswag_lm-eval-345m=35.9500, piqa_lm-eval-345m=65.7200, winogrande_lm-eval-345m=50.9100

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2014, 'wikitext2_ppl_gpt-345m': 40.44, 'lam
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=2.5395, val_loss=2.2684
TRAIN_METRICS: step=10000, train_loss=2.5184, val_loss=2.2544
TRAIN_METRICS: step=11000, train_loss=2.4845, val_loss=2.2538
TRAIN_METRICS: step=12000, train_loss=2.4780, val_loss=2.2089
TRAIN_METRICS: step=13000, train_loss=2.4657, val_loss=2.2149
Final metrics (gpt-345m): val_loss_gpt-345m=2.2014, wikitext2_ppl_gpt-345m=40.4400, lambada_ppl_gpt-345m=60.1000

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=59.6000, hellaswag_lm-eval-345m=35.9500, piqa_lm-eval-345m=65.7200, winogrande_lm-eval-345m=50.9100

[submit] Finalized test #3 as final.

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2014, 'wikitext2_ppl_gpt-345m': 40.44, 'lambada_ppl_gpt-345m': 60.1, 'elapsed_gpt-345m': 35051, 'arc_easy_lm-eval-345m': 59.6, 'hellaswag_lm-eval-345m': 35.95, 'piqa_lm-eval