Agent Conversation: gemini-3.1-pro-preview

Model: gemini-3.1-pro-preview

Total steps: 6

Messages: 14

UserStep 0

Task: llm-pretrain-bitlinear

LLM Pretraining: Native Low-Bit Linear (BitLinear)

Research Question

Design a low-bit linear layer for GPT-2 pretraining that uses native low-precision weights (binary/ternary) during both training and inference, instead of standard float weights. The goal is to minimize validation loss while constraining weights to a small discrete set.

Background

Standard neural networks store and compute with full-precision (FP32/BF16) weights. Post-training quantization (PTQ) and quantization-aware training (QAT) attempt to compress these weights after or during training, but the model fundamentally trains with float weights. Native low-bit training takes a different approach: weights are inherently discrete (e.g., {-1, +1} or {-1, 0, +1}) during every forward pass, with float latent weights maintained only for gradient accumulation.

This paradigm was introduced by BitNet (Wang et al., 2023), which binarized weights to {-1, +1} using the sign function, and extended by BitNet b1.58 (Ma et al., 2024), which used ternary {-1, 0, +1} weights via absmean quantization. The key insight is that these models can match or approach full-precision performance at a fraction of the effective parameter cost.

Key differences from QAT (the llm-pretrain-quantization task):

QAT: float weights -> fake quantize during training -> real quantize at deployment (weights are float during training)
BitLinear: float latent weights -> discrete quantize in every forward pass (weights are always discrete during computation)

Key differences from mixed-precision (the llm-pretrain-precision task):

Mixed precision: changes the float format (FP32 -> BF16/FP8) but values are still continuous
BitLinear: weights are restricted to a small discrete set (1-2 bits)

What You Can Modify

The BitLinear module (lines 38-115) in custom_pretrain.py:

weight_quant(weight) -- quantizes float latent weights to discrete values, returns (quantized_weight, scale)
activation_quant(x) -- optional activation quantization, returns (quantized_x, scale)
BitLinear class -- linear layer that uses the above functions

Notes on the interface:

BitLinear.__init__(self, in_features, out_features, bias=True) must keep self.weight as a Parameter
BitLinear.forward(self, x) -> output where x has shape (..., in_features) and output has shape (..., out_features)
The quantization is applied in every forward pass (both training and eval) -- there is no separate train/eval path
weight_quant should return (quantized_weight, scale) where quantized_weight * scale approximates the original weight
activation_quant should return (quantized_x, scale) similarly
All model linear layers (attention, MLP, lm_head) use BitLinear
You may add helper classes (autograd Functions, learned parameters) alongside the existing functions
Must be compatible with torch.compile (no @torch.compiler.disable)

Evaluation

**Primary me

AssistantStep 1edit

Tool Input

1{
2  "filename": "nanoGPT/custom_pretrain.py",
3  "content": "def weight_quant(weight):\n    \"\"\"L2-Optimal Ternary Quantization with Weight Centralization.\n    \n    Computes the analytically optimal scale that minimizes L2 quantization error \n    for the given ternary assignments, rather than using a static mean absolute value.\n    \"\"\"\n    # 1. Centralize weights per output channel\n    mean = weight.mean(dim=1, keepdim=True)\n    w_c = weight - mean\n    \n    # 2. Initial scale estimation to determine assignments\n    scale_est = w_c.detach().abs().mean(dim=1, keepdim=True).clamp(min=1e-12)\n    w_normed = w_c / scale_est\n    \n    # 3. Get ternary assignments {-1, 0, 1}\n    w_q = w_normed.clamp(-1, 1).round()\n    \n    # 4. Compute L2-optimal scale per channel dynamically\n    num = (w_c.detach() * w_q).sum(dim=1, keepdim=True)\n    den = (w_q * w_q).sum(dim=1, keepdim=True).clamp(min=1.0)\n    opt_scale = num / den\n    \n    # 5. Straight-Through Estimator (STE)\n    w_q_ste = (w_q - w_normed).detach() + w_normed\n    \n    return w_q_ste, opt_scale\n\n\ndef activation_quant(x):\n    \"\"\"Token-wise L2-Optimal 8-bit Quantization.\"\"\"\n    Qb = 127.0\n    # 1. Per-token initial scale estimation\n    scale_est = x.detach().abs().max(dim=-1, keepdim=True)[0].clamp(min=1e-12) / Qb\n    x_normed = x / scale_est\n    \n    # 2. 8-bit assignments\n    x_q = x_normed.round().clamp(-Qb, Qb)\n    \n    # 3. Compute L2-optimal scale per token\n    num = (x.detach() * x_q).sum(dim=-1, keepdim=True)\n    den = (x_q * x_q).sum(dim=-1, keepdim=True).clamp(min=1.0)\n    opt_scale = num / den\n    \n    # 4. STE\n    x_q_ste = (x_q - x_normed).detach() + x_normed\n    \n    return x_q_ste, opt_scale\n\n\nclass BitLinear(nn.Module):\n    \"\"\"Linear layer with L2-Optimal Native Low-Bit Weights.\"\"\"\n    def __init__(self, in_features, out_features, bias=True):\n        super().__init__()\n        self.in_features = in_features\n        self.out_features = out_features\n        self.weight = nn.Parameter(torch.empty(out_features, in_features))\n        if bias:\n            self.bias = nn.Parameter(torch.zeros(out_features))\n        else:\n            self.bias = None\n        nn.init.normal_(self.weight, mean=0.0, std=0.02)\n\n    def forward(self, x):\n        w_q, w_scale = weight_quant(self.weight)\n        x_q, x_scale = activation_quant(x)\n        \n        # Core low-bit matmul\n        out = F.linear(x_q, w_q, None)\n        \n        # Rescale output: w_scale is [O, 1], x_scale is [..., 1]\n        out = out * (w_scale.view(-1) * x_scale)\n        \n        if self.bias is not None:\n            out = out + self.bias\n        return out\n",
4  "end_line": 115,
5  "op": "replace",
6  "start_line": 38
7}

Tool ResultStep 1

OK: Replaced lines 38..115 with 75 line(s) in nanoGPT/custom_pretrain.py. Editable range: 38–112, 325–325.

[Current file: nanoGPT/custom_pretrain.py | editable: 38–112 | total: 515 lines]
    35|         return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
    36| 
    37| # -- Native Low-Bit Linear (BitLinear) Module ---------------------------------
    38  def weight_quant(weight):
    39      """L2-Optimal Ternary Quantization with Weight Centralization.
    40      
    41      Computes the analytically optimal scale that minimizes L2 quantization error 
    42      for the given ternary assignments, rather than using a static mean absolute value.
    43      """
    44      # 1. Centralize weights per output channel
    45      mean = weight.mean(dim=1, keepdim=True)
    46      w_c = weight - mean
    47      
    48      # 2. Initial scale estimation to determine assignments
    49      scale_est = w_c.detach().abs().mean(dim=1, keepdim=True).clamp(min=1

AssistantStep 2edit

Tool Input

1{
2  "end_line": 325,
3  "content": "    CONFIG_OVERRIDES = {'weight_decay': 0.0}",
4  "start_line": 325,
5  "op": "replace",
6  "filename": "nanoGPT/custom_pretrain.py"
7}

Tool ResultStep 2

OK: Replaced lines 325..325 with 1 line(s) in nanoGPT/custom_pretrain.py. Editable range: 38–112, 325–325.

[Current file: nanoGPT/custom_pretrain.py | editable: 38–112 | total: 515 lines]
    35|         return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
    36| 
    37| # -- Native Low-Bit Linear (BitLinear) Module ---------------------------------
    38  def weight_quant(weight):
    39      """L2-Optimal Ternary Quantization with Weight Centralization.
    40      
    41      Computes the analytically optimal scale that minimizes L2 quantization error 
    42      for the given ternary assignments, rather than using a static mean absolute value.
    43      """
    44      # 1. Centralize weights per output channel
    45      mean = weight.mean(dim=1, keepdim=True)
    46      w_c = weight - mean
    47      
    48      # 2. Initial scale estimation to determine assignments
    49      scale_est = w_c.detach().abs().mean(dim=1, keepdim=True).clamp(min=1

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)
[COMMAND FAILED exit=-1]
[output file not found: /scratch/gpfs/CHIJ/bohan/MLS-Bench/logs/llm-pretrain-bitlinear/gemini-3.1-pro-preview_20260412_102253/20260412_102712/group_1/gpt-345m_s42.out]

### 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-bitlinear/gemini-3.1-pro-preview_20260412_102253/seed_42/ckpt_gpt-345m.pt

AssistantStep 4edit

Tool Input

1{
2  "op": "replace",
3  "content": "def weight_quant(weight):\n    \"\"\"L2-Optimal Ternary Quantization with Weight Centralization.\"\"\"\n    # 1. Centralize weights per output channel\n    mean = weight.mean(dim=1, keepdim=True)\n    w_c = weight - mean\n    \n    # 2. Initial scale estimation (detached)\n    scale_est = w_c.detach().abs().mean(dim=1, keepdim=True).clamp(min=1e-12)\n    w_normed = w_c / scale_est\n    \n    # 3. Discrete assignments (detached)\n    w_q_det = w_normed.detach().clamp(-1, 1).round()\n    \n    # 4. Compute L2-optimal scale per channel dynamically (detached)\n    num = (w_c.detach() * w_q_det).sum(dim=1, keepdim=True)\n    den = (w_q_det * w_q_det).sum(dim=1, keepdim=True).clamp(min=1.0)\n    opt_scale = num / den\n    \n    # 5. Straight-Through Estimator (STE)\n    w_q_ste = (w_q_det - w_normed).detach() + w_normed\n    \n    return w_q_ste, opt_scale\n\n\ndef activation_quant(x):\n    \"\"\"Token-wise L2-Optimal 8-bit Quantization.\"\"\"\n    Qb = 127.0\n    # 1. Per-token initial scale estimation (detached)\n    scale_est = x.detach().abs().amax(dim=-1, keepdim=True).clamp(min=1e-12) / Qb\n    x_normed = x / scale_est\n    \n    # 2. Discrete 8-bit assignments (detached)\n    x_q_det = x_normed.detach().round().clamp(-Qb, Qb)\n    \n    # 3. Compute L2-optimal scale per token (detached)\n    num = (x.detach() * x_q_det).sum(dim=-1, keepdim=True)\n    den = (x_q_det * x_q_det).sum(dim=-1, keepdim=True).clamp(min=1.0)\n    opt_scale = num / den\n    \n    # 4. STE\n    x_q_ste = (x_q_det - x_normed).detach() + x_normed\n    \n    return x_q_ste, opt_scale\n\n\nclass BitLinear(nn.Module):\n    \"\"\"Linear layer with L2-Optimal Native Low-Bit Weights.\"\"\"\n    def __init__(self, in_features, out_features, bias=True):\n        super().__init__()\n        self.in_features = in_features\n        self.out_features = out_features\n        self.weight = nn.Parameter(torch.empty(out_features, in_features))\n        if bias:\n            self.bias = nn.Parameter(torch.zeros(out_features))\n        else:\n            self.bias = None\n        nn.init.normal_(self.weight, mean=0.0, std=0.02)\n\n    def forward(self, x):\n        w_q, w_scale = weight_quant(self.weight)\n        x_q, x_scale = activation_quant(x)\n        \n        # Core low-bit matmul\n        out = F.linear(x_q, w_q, None)\n        \n        # Rescale output sequentially to avoid large intermediate broadcast tensors\n        out = out * w_scale.view(-1)\n        out = out * x_scale\n        \n        if self.bias is not None:\n            out = out + self.bias\n        return out\n",
4  "start_line": 38,
5  "filename": "nanoGPT/custom_pretrain.py",
6  "end_line": 112
7}

Tool ResultStep 4

OK: Replaced lines 38..112 with 72 line(s) in nanoGPT/custom_pretrain.py. Editable range: 38–109, 322–322.

[Current file: nanoGPT/custom_pretrain.py | editable: 38–109 | total: 512 lines]
    35|         return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
    36| 
    37| # -- Native Low-Bit Linear (BitLinear) Module ---------------------------------
    38  def weight_quant(weight):
    39      """L2-Optimal Ternary Quantization with Weight Centralization."""
    40      # 1. Centralize weights per output channel
    41      mean = weight.mean(dim=1, keepdim=True)
    42      w_c = weight - mean
    43      
    44      # 2. Initial scale estimation (detached)
    45      scale_est = w_c.detach().abs().mean(dim=1, keepdim=True).clamp(min=1e-12)
    46      w_normed = w_c / scale_est
    47      
    48      # 3. Discrete assignments (detached)
    49      w_q_det = w_normed.detach().clamp(-1, 1).round()
    50      
    51      # 4. Compute L2-optimal scale pe

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=2.8681, val_loss=2.5806
TRAIN_METRICS: step=10000, train_loss=2.8492, val_loss=2.5511
TRAIN_METRICS: step=11000, train_loss=2.8291, val_loss=2.5380
TRAIN_METRICS: step=12000, train_loss=2.8099, val_loss=2.5220
TRAIN_METRICS: step=13000, train_loss=2.7946, val_loss=2.5093
Final metrics (gpt-345m): val_loss_gpt-345m=2.5040, wikitext2_ppl_gpt-345m=57.3300, lambada_ppl_gpt-345m=86.7200

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=50.2500, hellaswag_lm-eval-345m=30.8600, piqa_lm-eval-345m=61.6400, winogrande_lm-eval-345m=50.5100

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.504, 'wikitext2_ppl_gpt-345m': 57.33, 'lambada_ppl_gpt-345m': 86.72, 'elapsed_gpt-345m': 24767, 'arc_easy_lm-eval-345m': 50.25, 'hellaswag_lm-eval-345m': 30.86, 'piqa_

AssistantStep 6submit

Tool Input

1{
2  "n": 2
3}

Tool ResultStep 6

[submit] Submitting result from test #2 as final.

### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=9000, train_loss=2.8681, val_loss=2.5806
TRAIN_METRICS: step=10000, train_loss=2.8492, val_loss=2.5511
TRAIN_METRICS: step=11000, train_loss=2.8291, val_loss=2.5380
TRAIN_METRICS: step=12000, train_loss=2.8099, val_loss=2.5220
TRAIN_METRICS: step=13000, train_loss=2.7946, val_loss=2.5093
Final metrics (gpt-345m): val_loss_gpt-345m=2.5040, wikitext2_ppl_gpt-345m=57.3300, lambada_ppl_gpt-345m=86.7200

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=50.2500, hellaswag_lm-eval-345m=30.8600, piqa_lm-eval-345m=61.6400, winogrande_lm-eval-345m=50.5100

[submit] Finalized test #2 as final.

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.504, 'wikitext2_ppl_gpt-345m': 57.33, 'lambada_ppl_gpt-345m': 86.72, 'elapsed_gpt-345m': 24767, 'arc_easy_lm-eval-345m': 50.25, 'hellaswag_lm-eval-345m': 30.86, 'piqa_lm-eva