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Model: deepseek-reasoner
Total steps: 8
Messages: 21
UserStep 0

Task: llm-pretrain-embedding

LLM Pretraining: Embedding Strategy Optimization

Research Question

Design an improved embedding strategy for GPT-2 language model pretraining. Your modifications should reduce validation loss compared to standard token + position embeddings with weight tying.

What You Can Modify

The TokenEmbedding class (lines 116-140) in custom_pretrain.py:

  • Token embedding representation (default: learned token + position embeddings)
  • Weight tying strategy (default: input embedding = output lm_head weight)
  • Additional embedding sources (e.g., n-gram embeddings, hash-based embeddings)
  • Value embeddings that inject into transformer layers via get_value_embed(layer_idx)

Interface: Your TokenEmbedding class must implement:

  • forward(idx) -> x: Takes token indices (B, T), returns embeddings (B, T, n_embd)
  • get_lm_head_weight() -> weight: Returns the weight tensor for the output projection
  • get_num_pos_params() -> int: Returns count of position parameters (excluded from reported param count)
  • get_value_embed(layer_idx) -> Optional[Tensor]: (Optional) Returns per-layer value embedding residual (B, T, n_embd) or None

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 115–140, lines 265–267 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 con
AssistantStep 1edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 115,
5  "end_line": 140,
6  "content": "# ── Embedding Strategy ────────────────────────────────────────────────────\nclass TokenEmbedding(nn.Module):\n    \"\"\"Token embedding with per-layer scaled position embeddings.\"\"\"\n    def __init__(self, config):\n        super().__init__()\n        self.wte = nn.Embedding(config.vocab_size, config.n_embd)\n        self.wpe = nn.Embedding(config.block_size, config.n_embd)\n        self.drop = nn.Dropout(config.dropout)\n        self.block_size = config.block_size\n        self.n_embd = config.n_embd\n        self.vocab_size = config.vocab_size\n        self.n_layer = config.n_layer\n        # Per-layer scaling factors for position embeddings\n        self.layer_scales = nn.Parameter(torch.ones(config.n_layer))\n        # Cache position indices and embeddings for get_value_embed\n        self._cached_pos = None\n        self._cached_pos_emb = None\n        \n    def forward(self, idx):\n        b, t = idx.size()\n        tok_emb = self.wte(idx)\n        # Cache position indices and embeddings for later use\n        pos = torch.arange(0, t, dtype=torch.long, device=idx.device)\n        pos_emb = self.wpe(pos)\n        self._cached_pos = pos\n        self._cached_pos_emb = pos_emb\n        # Return only token embeddings; position will be injected per layer\n        return self.drop(tok_emb)\n    \n    def get_lm_head_weight(self):\n        \"\"\"Return weight for the language model head (tied by default).\"\"\"\n        return self.wte.weight\n    \n    def get_num_pos_params(self):\n        \"\"\"Return number of position embedding parameters (excluded from param count).\"\"\"\n        return self.wpe.weight.numel()\n    \n    def get_value_embed(self, layer_idx):\n        \"\"\"Return scaled position embedding for this layer.\"\"\"\n        if self._cached_pos_emb is None:\n            return None\n        # Scale position embedding by layer-specific factor\n        scale = self.layer_scales[layer_idx]\n        return scale * self._cached_pos_emb"
7}
Tool ResultStep 1
OK: Replaced lines 115..140 with 44 line(s) in nanoGPT/custom_pretrain.py. Editable range: 115–158, 283–285.

[Current file: nanoGPT/custom_pretrain.py | editable: 115–158 | total: 491 lines]
   112|     bias: bool = False
   113| 
   114| # ── Embedding Strategy ────────────────────────────────────────────────────
   115  # ── Embedding Strategy ────────────────────────────────────────────────────
   116  class TokenEmbedding(nn.Module):
   117      """Token embedding with per-layer scaled position embeddings."""
   118      def __init__(self, config):
   119          super().__init__()
   120          self.wte = nn.Embedding(config.vocab_size, config.n_embd)
   121          self.wpe = nn.Embedding(config.block_size, config.n_embd)
   122          self.drop = nn.Dropout(config.dropout)
   123          self.block_size = config.block_size
   124          self.n_embd = config.n_embd
   125          self.vocab_size = config.vocab_size
   126          self.n_layer = config.n_layer
   127
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=2.6645, val_loss=2.3880
TRAIN_METRICS: step=9000, train_loss=2.6288, val_loss=2.3561
TRAIN_METRICS: step=10000, train_loss=2.6179, val_loss=2.3437
TRAIN_METRICS: step=11000, train_loss=2.5954, val_loss=2.3170
TRAIN_METRICS: step=12000, train_loss=2.5914, val_loss=2.3144
Final metrics (gpt-345m): val_loss_gpt-345m=2.3081, wikitext2_ppl_gpt-345m=45.2500, lambada_ppl_gpt-345m=70.2900

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=55.3500, hellaswag_lm-eval-345m=33.3500, piqa_lm-eval-345m=63.8700, winogrande_lm-eval-345m=52.4900

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.3081, 'wikitext2_ppl_gpt-345m': 45.25, 'lambada_ppl_gpt-345m': 70.29, 'elapsed_gpt-345m': 20629, 'arc_easy_lm-eval-345m': 55.35, 'hellaswag_lm-eval-345m': 33.35, 'piqa_lm-eval-345m': 63.87, 'w
AssistantStep 3edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 115,
5  "end_line": 158,
6  "content": "# ── Embedding Strategy ────────────────────────────────────────────────────\nclass TokenEmbedding(nn.Module):\n    \"\"\"Token embedding with content-aware position modulation and per-layer scaling.\"\"\"\n    def __init__(self, config):\n        super().__init__()\n        self.wte = nn.Embedding(config.vocab_size, config.n_embd)\n        self.wpe = nn.Embedding(config.block_size, config.n_embd)\n        self.drop = nn.Dropout(config.dropout)\n        self.block_size = config.block_size\n        self.n_embd = config.n_embd\n        self.vocab_size = config.vocab_size\n        self.n_layer = config.n_layer\n        \n        # Gating network for content-aware position modulation\n        # Takes concatenated token and position embeddings, outputs per-dimension gate\n        self.gate_net = nn.Sequential(\n            nn.Linear(2 * config.n_embd, config.n_embd),\n            nn.Tanh(),\n            nn.Linear(config.n_embd, config.n_embd),\n            nn.Sigmoid()\n        )\n        # Initialize gate net weights small so initial modulation is near 0.5\n        nn.init.normal_(self.gate_net[0].weight, mean=0.0, std=0.02)\n        nn.init.zeros_(self.gate_net[0].bias)\n        nn.init.normal_(self.gate_net[2].weight, mean=0.0, std=0.02)\n        nn.init.zeros_(self.gate_net[2].bias)\n        \n        # Per-layer scaling factors for residual position injection\n        self.layer_scales = nn.Parameter(torch.ones(config.n_layer))\n        # Cache position embeddings for get_value_embed\n        self._cached_pos_emb = None\n        \n    def forward(self, idx):\n        b, t = idx.size()\n        tok_emb = self.wte(idx)  # (B, T, n_embd)\n        pos = torch.arange(0, t, dtype=torch.long, device=idx.device)\n        pos_emb = self.wpe(pos)  # (T, n_embd) -> broadcast to (B, T, n_embd)\n        pos_emb = pos_emb.unsqueeze(0).expand(b, -1, -1)\n        \n        # Compute content-aware gate\n        combined = torch.cat([tok_emb, pos_emb], dim=-1)  # (B, T, 2*n_embd)\n        # Reshape for linear layers\n        combined_flat = combined.view(-1, 2 * self.n_embd)\n        gate_flat = self.gate_net(combined_flat)\n        gate = gate_flat.view(b, t, self.n_embd)\n        \n        # Modulate position embedding with gate: pos_emb * gate\n        modulated_pos_emb = pos_emb * gate\n        # Combine: token embedding + modulated position embedding\n        x = tok_emb + modulated_pos_emb\n        \n        # Cache position embedding for per-layer injection\n        self._cached_pos_emb = pos_emb.detach()  # detach to avoid gradient issues\n        \n        return self.drop(x)\n    \n    def get_lm_head_weight(self):\n        \"\"\"Return weight for the language model head (tied by default).\"\"\"\n        return self.wte.weight\n    \n    def get_num_pos_params(self):\n        \"\"\"Return number of position embedding parameters (excluded from param count).\"\"\"\n        return self.wpe.weight.numel()\n    \n    def get_value_embed(self, layer_idx):\n        \"\"\"Return scaled position embedding for this layer (residual injection).\"\"\"\n        if self._cached_pos_emb is None:\n            return None\n        scale = self.layer_scales[layer_idx]\n        return scale * self._cached_pos_emb"
7}
Tool ResultStep 3
OK: Replaced lines 115..158 with 70 line(s) in nanoGPT/custom_pretrain.py. Editable range: 115–184, 309–311.

[Current file: nanoGPT/custom_pretrain.py | editable: 115–184 | total: 517 lines]
   112|     bias: bool = False
   113| 
   114| # ── Embedding Strategy ────────────────────────────────────────────────────
   115  # ── Embedding Strategy ────────────────────────────────────────────────────
   116  class TokenEmbedding(nn.Module):
   117      """Token embedding with content-aware position modulation and per-layer scaling."""
   118      def __init__(self, config):
   119          super().__init__()
   120          self.wte = nn.Embedding(config.vocab_size, config.n_embd)
   121          self.wpe = nn.Embedding(config.block_size, config.n_embd)
   122          self.drop = nn.Dropout(config.dropout)
   123          self.block_size = config.block_size
   124          self.n_embd = config.n_embd
   125          self.vocab_size = config.vocab_size
   126          self.n_layer = conf
AssistantStep 4test
AssistantStep 4edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 309,
5  "end_line": 311,
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': 5e-4}"
7}
Tool ResultStep 4
ERROR: Lines 309..311 exceed the editable range. You may only edit lines 115–184.

[Current file: nanoGPT/custom_pretrain.py | editable: 115–184 | total: 517 lines]
   112|     bias: bool = False
   113| 
   114| # ── Embedding Strategy ────────────────────────────────────────────────────
   115  # ── Embedding Strategy ────────────────────────────────────────────────────
   116  class TokenEmbedding(nn.Module):
   117      """Token embedding with content-aware position modulation and per-layer scaling."""
   118      def __init__(self, config):
   119          super().__init__()
   120          self.wte = nn.Embedding(config.vocab_size, config.n_embd)
   121          self.wpe = nn.Embedding(config.block_size, config.n_embd)
   122          self.drop = nn.Dropout(config.dropout)
   123          self.block_size = config.block_size
   124          self.n_embd = config.n_embd
   125          self.vocab_size = config.vocab_size
   126          self.n_layer = config.n_layer
   127
AssistantStep 5test
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=8000, train_loss=2.6731, val_loss=2.3960
TRAIN_METRICS: step=9000, train_loss=2.6442, val_loss=2.3720
TRAIN_METRICS: step=10000, train_loss=2.6212, val_loss=2.3521
TRAIN_METRICS: step=11000, train_loss=2.6114, val_loss=2.3325
TRAIN_METRICS: step=12000, train_loss=2.6003, val_loss=2.3291
Final metrics (gpt-345m): val_loss_gpt-345m=2.3280, wikitext2_ppl_gpt-345m=46.6200, lambada_ppl_gpt-345m=70.2500

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=51.9800, hellaswag_lm-eval-345m=32.8800, piqa_lm-eval-345m=64.0900, winogrande_lm-eval-345m=53.2000

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.328, 'wikitext2_ppl_gpt-345m': 46.62, 'lambada_ppl_gpt-345m': 70.25, 'elapsed_gpt-345m': 20568, 'arc_easy_lm-eval-345m': 51.98, 'hellaswag_lm-eval-345m': 32.88, 'piqa_l
AssistantStep 6edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 115,
5  "end_line": 184,
6  "content": "# ── Embedding Strategy ────────────────────────────────────────────────────\nclass TokenEmbedding(nn.Module):\n    \"\"\"Token embedding with content-dependent position scaling.\"\"\"\n    def __init__(self, config):\n        super().__init__()\n        self.wte = nn.Embedding(config.vocab_size, config.n_embd)\n        self.wpe = nn.Embedding(config.block_size, config.n_embd)\n        self.drop = nn.Dropout(config.dropout)\n        self.block_size = config.block_size\n        self.n_embd = config.n_embd\n        self.vocab_size = config.vocab_size\n        \n        # Content-dependent scaling gate: linear transformation from token embedding\n        # to per-dimension scaling factors (sigmoid output)\n        self.gate_layer = nn.Linear(config.n_embd, config.n_embd, bias=True)\n        # Initialize gate layer weights small so initial scaling ~0.5\n        nn.init.normal_(self.gate_layer.weight, mean=0.0, std=0.02)\n        nn.init.zeros_(self.gate_layer.bias)\n        \n    def forward(self, idx):\n        b, t = idx.size()\n        tok_emb = self.wte(idx)  # (B, T, n_embd)\n        pos = torch.arange(0, t, dtype=torch.long, device=idx.device)\n        pos_emb = self.wpe(pos)  # (T, n_embd)\n        pos_emb = pos_emb.unsqueeze(0).expand(b, -1, -1)  # (B, T, n_embd)\n        \n        # Compute per-dimension scaling gate based on token content\n        gate = torch.sigmoid(self.gate_layer(tok_emb))  # (B, T, n_embd)\n        # Modulate position embedding\n        modulated_pos_emb = pos_emb * gate\n        # Combine\n        x = tok_emb + modulated_pos_emb\n        return self.drop(x)\n    \n    def get_lm_head_weight(self):\n        \"\"\"Return weight for the language model head (tied by default).\"\"\"\n        return self.wte.weight\n    \n    def get_num_pos_params(self):\n        \"\"\"Return number of position embedding parameters (excluded from param count).\"\"\"\n        return self.wpe.weight.numel()\n    \n    # No value embedding injection\n    def get_value_embed(self, layer_idx):\n        return None"
7}
Tool ResultStep 6
OK: Replaced lines 115..184 with 45 line(s) in nanoGPT/custom_pretrain.py. Editable range: 115–159.

[Current file: nanoGPT/custom_pretrain.py | editable: 115–159 | total: 492 lines]
   112|     bias: bool = False
   113| 
   114| # ── Embedding Strategy ────────────────────────────────────────────────────
   115  # ── Embedding Strategy ────────────────────────────────────────────────────
   116  class TokenEmbedding(nn.Module):
   117      """Token embedding with content-dependent position scaling."""
   118      def __init__(self, config):
   119          super().__init__()
   120          self.wte = nn.Embedding(config.vocab_size, config.n_embd)
   121          self.wpe = nn.Embedding(config.block_size, config.n_embd)
   122          self.drop = nn.Dropout(config.dropout)
   123          self.block_size = config.block_size
   124          self.n_embd = config.n_embd
   125          self.vocab_size = config.vocab_size
   126          
   127          # Content-dependent scaling gate
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=0, train_loss=11.0263, val_loss=11.0173
TRAIN_METRICS: step=1000, train_loss=3.5334, val_loss=3.3032
TRAIN_METRICS: step=2000, train_loss=3.0799, val_loss=2.7969

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=51.9800, hellaswag_lm-eval-345m=32.8800, piqa_lm-eval-345m=64.0900, winogrande_lm-eval-345m=53.2000

[Leaderboard] Results saved: {'arc_easy_lm-eval-345m': 51.98, 'hellaswag_lm-eval-345m': 32.88, 'piqa_lm-eval-345m': 64.09, 'winogrande_lm-eval-345m': 53.2, 'elapsed_lm-eval-345m': 479}
AssistantStep 8submit
Tool Input
1{
2  "n": 1
3}