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Model: qwen3.6-plus

Total steps: 10

Messages: 22

UserStep 0

Task: llm-pretrain-kernel

LLM Pretraining: Custom GPU Kernel Optimization

Research Question

Write a custom GPU kernel (Triton or CUDA via PyTorch) to implement a fused MLP operation for GPT-2 pretraining. Your kernel should fuse multiple operations to reduce memory bandwidth and improve throughput while maintaining or improving model quality.

What You Can Modify

The fused_mlp_forward function (lines 34-48) in custom_pretrain.py:

The MLP activation function (default: GELU via separate PyTorch ops)
Kernel fusion strategy (fuse linear + activation, save intermediate values)
Memory optimization (avoid materializing intermediate tensors)
Custom autograd Functions for efficient backward pass

Note: The function signature fused_mlp_forward(x, w_fc, w_proj) must be preserved.

x: input tensor (B*T, n_embd)
w_fc: first linear weight (4*n_embd, n_embd)
w_proj: second linear weight (n_embd, 4*n_embd)
Returns: output tensor (B*T, n_embd)

The MLP class calls this function and handles dropout separately.

Evaluation

Metric: Validation loss (cross-entropy, lower is better) — kernel optimizations that also change the activation function may improve loss
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
Hardware: H100 GPU with Triton support

nanoGPT/custom_pretrain.py [EDITABLE — lines 33–48, lines 257–259 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: # ── Custom Kernel / Fused Operation ───────────────────────────────────────
    33: def fused_mlp_forward(x, w_fc, w_proj):
    34:     """MLP forward pass: linear -> activation -> linear.
    35: 
    36:     Default implementatio

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 33,
5  "end_line": 48,
6  "content": "import triton\nimport triton.language as tl\n\n@triton.jit\ndef _swiglu_kernel(\n    gate_ptr, value_ptr, out_ptr,\n    n_elements,\n    BLOCK_SIZE: tl.constexpr,\n):\n    pid = tl.program_id(0)\n    offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)\n    mask = offsets < n_elements\n\n    g = tl.load(gate_ptr + offsets, mask=mask)\n    v = tl.load(value_ptr + offsets, mask=mask)\n\n    # Swish(x) = x * sigmoid(x), computed in fp32 for stability\n    g_f = g.to(tl.float32)\n    sig = tl.sigmoid(g_f)\n    swish = g_f * sig\n\n    # SwiGLU output = Swish(gate) * value\n    out = (swish * v.to(tl.float32)).to(g.dtype)\n    tl.store(out_ptr + offsets, out, mask=mask)\n\n\nclass _SwiGLUMLP(torch.autograd.Function):\n    \"\"\"SwiGLU activation with Triton kernel for the element-wise operation.\n\n    Architecture:\n    - Split w_fc into gate (top 2*n_embd) and value (bottom 2*n_embd)\n    - gate = x @ w_gate.T, value = x @ w_val.T\n    - output = Swish(gate) * value  (dimension: 2*n_embd)\n    - final = output @ w_proj_half.T  (dimension: n_embd)\n\n    SwiGLU provides superior expressivity through multiplicative gating,\n    as demonstrated in LLaMA, PaLM, and other modern LLMs.\n    \"\"\"\n    @staticmethod\n    def forward(ctx, x, w_fc, w_proj):\n        D = w_fc.shape[1]  # n_embd\n\n        # Split weights into gate and value\n        w_fc_gate = w_fc[:2 * D, :]\n        w_fc_val = w_fc[2 * D:, :]\n\n        # First matmul (PyTorch optimized)\n        gate = x @ w_fc_gate.t()  # (M, 2D)\n        value = x @ w_fc_val.t()  # (M, 2D)\n\n        # Apply SwiGLU via Triton kernel\n        n = gate.numel()\n        BLOCK = min(triton.next_power_of_2(n), 65536)\n        BLOCK = max(BLOCK, 256)\n        out = torch.empty_like(gate)\n        _swiglu_kernel[(n + BLOCK - 1) // BLOCK,](\n            gate, value, out, n, BLOCK_SIZE=BLOCK\n        )\n\n        # Use only the half of w_proj that matches SwiGLU's output dimension\n        w_proj_half = w_proj[:, :2 * D]  # (D, 2D)\n        final = out @ w_proj_half.t()  # (M, D)\n\n        ctx.save_for_backward(x, w_fc_gate, w_fc_val, w_proj_half, gate, value, out)\n        return final\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        x, w_fc_gate, w_fc_val, w_proj_half, gate, value, out = ctx.saved_tensors\n        dtype = grad_output.dtype\n\n        # Gradient through second linear: d_act = grad_output @ w_proj_half\n        d_act = grad_output @ w_proj_half  # (M, 2D)\n\n        # Split gradient for gate and value branches\n        d_gate = d_act  # flows through Swish\n        d_val = d_act   # flows directly\n\n        # SwiGLU backward: out = Swish(gate) * value\n        # d(Swish)/dx = sigmoid(x) * (1 + x * (1 - sigmoid(x)))\n        gate_f = gate.float()\n        sig = torch.sigmoid(gate_f)\n        swish_grad = sig * (1.0 + gate_f * (1.0 - sig))\n        swish_val = (gate_f * sig).to(dtype)\n\n        d_gate_pre = (d_gate.float() * swish_grad).to(dtype)\n        d_val_pre = d_val * swish_val\n\n        # Gradient through first linear\n        grad_x = d_gate_pre @ w_fc_gate + d_val_pre @ w_fc_val\n\n        # Weight gradients\n        grad_w_fc_gate = d_gate_pre.t() @ x\n        grad_w_fc_val = d_val_pre.t() @ x\n        grad_w_fc = torch.cat([grad_w_fc_gate, grad_w_fc_val], dim=0)\n\n        grad_w_proj = grad_output.t() @ out\n\n        return grad_x, grad_w_fc, grad_w_proj\n\n\ndef fused_mlp_forward(x, w_fc, w_proj):\n    \"\"\"MLP forward pass with SwiGLU activation and Triton kernel.\n\n    SwiGLU (Swish-Gated Linear Unit) splits the MLP into gated and value paths,\n    applying Swish(x) = x * sigmoid(x) to the gate before element-wise multiplication.\n    This provides superior representational capacity compared to GELU/ReLU².\n\n    Args:\n        x: input tensor (B*T, n_embd)\n        w_fc: first linear weight (4*n_embd, n_embd) - split into gate+value\n        w_proj: second linear weight (n_embd, 4*n_embd) - half used for projection\n    Returns:\n        output tensor (B*T, n_embd)\n    \"\"\"\n    return _SwiGLUMLP.apply(x, w_fc, w_proj)"
7}

Tool ResultStep 1

OK: Replaced lines 33..48 with 116 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–148, 357–359.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–148 | total: 549 lines]
    30|         return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
    31| 
    32| # ── Custom Kernel / Fused Operation ───────────────────────────────────────
    33  import triton
    34  import triton.language as tl
    35  
    36  @triton.jit
    37  def _swiglu_kernel(
    38      gate_ptr, value_ptr, out_ptr,
    39      n_elements,
    40      BLOCK_SIZE: tl.constexpr,
    41  ):
    42      pid = tl.program_id(0)
    43      offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    44      mask = offsets < n_elements
    45  
    46      g = tl.load(gate_ptr + offsets, mask=mask)
    47      v = tl.load(value_ptr + offsets, mask=mask)
    48  
    49      # Swish(x) = x * sigmoid(x), computed in fp32 for stability
    50      g_f = g.to(tl.float32)
    51      sig =

AssistantStep 2edit

Tool Input

1{
2  "filename": "nanoGPT/custom_pretrain.py",
3  "op": "replace",
4  "start_line": 99,
5  "end_line": 131,
6  "content": "    @staticmethod\n    def backward(ctx, grad_output):\n        x, w_fc_gate, w_fc_val, w_proj_half, gate, value, out = ctx.saved_tensors\n        dtype = grad_output.dtype\n\n        # Gradient through second linear: d_out = grad_output @ w_proj_half  (M, 2D)\n        d_out = grad_output @ w_proj_half\n\n        # SwiGLU backward: out = Swish(gate) * value\n        # d(L)/d(gate) = d_out * value * d(Swish)/d(gate)\n        # d(L)/d(value) = d_out * Swish(gate)\n        # d(Swish)/d(x) = sigmoid(x) * (1 + x * (1 - sigmoid(x)))\n\n        # Compute in float32 for numerical stability\n        gate_f = gate.float()\n        value_f = value.float()\n        d_out_f = d_out.float()\n\n        sig = torch.sigmoid(gate_f)\n        swish_grad = sig * (1.0 + gate_f * (1.0 - sig))\n        swish_val = gate_f * sig\n\n        d_gate_pre = (d_out_f * value_f * swish_grad).to(dtype)\n        d_val_pre = (d_out_f * swish_val).to(dtype)\n\n        # Gradient through first linear\n        grad_x = d_gate_pre @ w_fc_gate + d_val_pre @ w_fc_val\n\n        # Weight gradients\n        grad_w_fc_gate = d_gate_pre.t() @ x\n        grad_w_fc_val = d_val_pre.t() @ x\n        grad_w_fc = torch.cat([grad_w_fc_gate, grad_w_fc_val], dim=0)\n\n        grad_w_proj = grad_output.t() @ out\n\n        return grad_x, grad_w_fc, grad_w_proj"
7}

Tool ResultStep 2

OK: Replaced lines 99..131 with 36 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–151, 360–362.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–151 | total: 552 lines]
    30|         return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
    31| 
    32| # ── Custom Kernel / Fused Operation ───────────────────────────────────────
    33  import triton
    34  import triton.language as tl
    35  
    36  @triton.jit
    37  def _swiglu_kernel(
    38      gate_ptr, value_ptr, out_ptr,
    39      n_elements,
    40      BLOCK_SIZE: tl.constexpr,
    41  ):
    42      pid = tl.program_id(0)
    43      offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    44      mask = offsets < n_elements
    45  
    46      g = tl.load(gate_ptr + offsets, mask=mask)
    47      v = tl.load(value_ptr + offsets, mask=mask)
    48  
    49      # Swish(x) = x * sigmoid(x), computed in fp32 for stability
    50      g_f = g.to(tl.float32)
    51      sig =

AssistantStep 3edit

Tool Input

1{
2  "filename": "nanoGPT/custom_pretrain.py",
3  "op": "replace",
4  "start_line": 99,
5  "end_line": 134,
6  "content": "    @staticmethod\n    def backward(ctx, grad_output):\n        x, w_fc_gate, w_fc_val, w_proj_half, gate, value, out = ctx.saved_tensors\n        dtype = grad_output.dtype\n        D = w_fc_gate.shape[1]\n\n        # Gradient through second linear: d_out = grad_output @ w_proj_half  (M, 2D)\n        d_out = grad_output @ w_proj_half\n\n        # SwiGLU backward: out = Swish(gate) * value\n        # d(L)/d(gate) = d_out * value * d(Swish)/d(gate)\n        # d(L)/d(value) = d_out * Swish(gate)\n        # d(Swish)/d(x) = sigmoid(x) * (1 + x * (1 - sigmoid(x)))\n\n        # Compute in float32 for numerical stability\n        gate_f = gate.float()\n        value_f = value.float()\n        d_out_f = d_out.float()\n\n        sig = torch.sigmoid(gate_f)\n        swish_grad = sig * (1.0 + gate_f * (1.0 - sig))\n        swish_val = gate_f * sig\n\n        d_gate_pre = (d_out_f * value_f * swish_grad).to(dtype)\n        d_val_pre = (d_out_f * swish_val).to(dtype)\n\n        # Gradient through first linear\n        grad_x = d_gate_pre @ w_fc_gate + d_val_pre @ w_fc_val\n\n        # Weight gradients for fc (concatenate gate and value portions)\n        grad_w_fc_gate = d_gate_pre.t() @ x\n        grad_w_fc_val = d_val_pre.t() @ x\n        grad_w_fc = torch.cat([grad_w_fc_gate, grad_w_fc_val], dim=0)\n\n        # Weight gradient for proj - must match full w_proj shape (D, 4D)\n        # Only first 2D columns receive gradients; rest are zero\n        grad_w_proj = torch.zeros(w_fc_gate.shape[0], 4 * D, device=grad_output.device, dtype=dtype)\n        grad_w_proj[:, :2 * D] = grad_output.t() @ out\n\n        return grad_x, grad_w_fc, grad_w_proj"
7}

Tool ResultStep 3

OK: Replaced lines 99..134 with 40 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–155, 364–366.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–155 | total: 556 lines]
    30|         return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
    31| 
    32| # ── Custom Kernel / Fused Operation ───────────────────────────────────────
    33  import triton
    34  import triton.language as tl
    35  
    36  @triton.jit
    37  def _swiglu_kernel(
    38      gate_ptr, value_ptr, out_ptr,
    39      n_elements,
    40      BLOCK_SIZE: tl.constexpr,
    41  ):
    42      pid = tl.program_id(0)
    43      offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    44      mask = offsets < n_elements
    45  
    46      g = tl.load(gate_ptr + offsets, mask=mask)
    47      v = tl.load(value_ptr + offsets, mask=mask)
    48  
    49      # Swish(x) = x * sigmoid(x), computed in fp32 for stability
    50      g_f = g.to(tl.float32)
    51      sig =

AssistantStep 4edit

Tool Input

1{
2  "filename": "nanoGPT/custom_pretrain.py",
3  "op": "replace",
4  "start_line": 59,
5  "end_line": 155,
6  "content": "class _SwiGLUMLP(torch.autograd.Function):\n    \"\"\"SwiGLU activation with Triton kernel for the element-wise operation.\n\n    Architecture:\n    - h = x @ w_fc.t()  (M, 4D) - single efficient matmul\n    - gate, value = h.chunk(2)  (M, 2D) each\n    - output = Swish(gate) * value  (M, 2D) via Triton kernel\n    - final = output @ w_proj_half.t()  (M, D)\n\n    SwiGLU provides superior expressivity through multiplicative gating,\n    as demonstrated in LLaMA, PaLM, and other modern LLMs.\n    \"\"\"\n    @staticmethod\n    def forward(ctx, x, w_fc, w_proj):\n        D = x.shape[1]  # n_embd\n\n        # Single matmul: h = x @ w_fc.t()  (M, 4D)\n        h = x @ w_fc.t()\n\n        # Split into gate and value halves\n        gate = h[:, :2 * D]  # (M, 2D)\n        value = h[:, 2 * D:]  # (M, 2D)\n\n        # Apply SwiGLU via Triton kernel\n        n = gate.numel()\n        BLOCK = min(triton.next_power_of_2(n), 65536)\n        BLOCK = max(BLOCK, 256)\n        out = torch.empty_like(gate)\n        _swiglu_kernel[(n + BLOCK - 1) // BLOCK,](\n            gate, value, out, n, BLOCK_SIZE=BLOCK\n        )\n\n        # Project back: only first 2D columns of w_proj used\n        w_proj_half = w_proj[:, :2 * D]  # (D, 2D)\n        final = out @ w_proj_half.t()  # (M, D)\n\n        ctx.save_for_backward(x, w_fc, w_proj_half, gate, value, out)\n        return final\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        x, w_fc, w_proj_half, gate, value, out = ctx.saved_tensors\n        D = x.shape[1]\n\n        dtype = grad_output.dtype\n\n        # Gradient through second linear: d_out = grad_output @ w_proj_half  (M, 2D)\n        d_out = grad_output @ w_proj_half\n\n        # SwiGLU backward: out = Swish(gate) * value\n        # d(L)/d(gate) = d_out * value * d(Swish)/d(gate)\n        # d(L)/d(value) = d_out * Swish(gate)\n        # d(Swish)/d(x) = sigmoid(x) * (1 + x * (1 - sigmoid(x)))\n\n        gate_f = gate.float()\n        value_f = value.float()\n        d_out_f = d_out.float()\n\n        sig = torch.sigmoid(gate_f)\n        swish_grad = sig * (1.0 + gate_f * (1.0 - sig))\n        swish_val = gate_f * sig\n\n        d_gate = (d_out_f * value_f * swish_grad).to(dtype)\n        d_val = (d_out_f * swish_val).to(dtype)\n\n        # Concatenate gradients for the full h tensor\n        d_h = torch.cat([d_gate, d_val], dim=-1)  # (M, 4D)\n\n        # Gradient through first linear\n        grad_x = d_h @ w_fc\n        grad_w_fc = d_h.t() @ x\n\n        # Weight gradient for proj - match full w_proj shape (D, 4D)\n        grad_w_proj = torch.zeros(w_proj_half.shape[0], 4 * D,\n                                   device=grad_output.device, dtype=dtype)\n        grad_w_proj[:, :2 * D] = grad_output.t() @ out\n\n        return grad_x, grad_w_fc, grad_w_proj"
7}

Tool ResultStep 4

OK: Replaced lines 59..155 with 78 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–136, 345–347.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–136 | total: 537 lines]
    30|         return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
    31| 
    32| # ── Custom Kernel / Fused Operation ───────────────────────────────────────
    33  import triton
    34  import triton.language as tl
    35  
    36  @triton.jit
    37  def _swiglu_kernel(
    38      gate_ptr, value_ptr, out_ptr,
    39      n_elements,
    40      BLOCK_SIZE: tl.constexpr,
    41  ):
    42      pid = tl.program_id(0)
    43      offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    44      mask = offsets < n_elements
    45  
    46      g = tl.load(gate_ptr + offsets, mask=mask)
    47      v = tl.load(value_ptr + offsets, mask=mask)
    48  
    49      # Swish(x) = x * sigmoid(x), computed in fp32 for stability
    50      g_f = g.to(tl.float32)
    51      sig =

AssistantStep 5test

Tool ResultStep 5

[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-kernel/qwen3.6-plus_20260412_102247/20260412_103220/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-kernel/qwen3.6-plus_20260412_102247/seed_42/ckpt_gpt-345m.pt

AssistantStep 6edit

Tool Input

1{
2  "filename": "nanoGPT/custom_pretrain.py",
3  "op": "replace",
4  "start_line": 33,
5  "end_line": 136,
6  "content": "class _SwiGLUMLP(torch.autograd.Function):\n    \"\"\"SwiGLU MLP: h=x@w_fc.t(), gate,value=h.split(2), out=Swish(gate)*value, final=out@w_proj_half.t()\n    SwiGLU (Swish-Gated Linear Unit) provides superior expressivity via multiplicative gating.\"\"\"\n\n    @staticmethod\n    def forward(ctx, x, w_fc, w_proj):\n        D = x.shape[1]\n        h = torch.matmul(x, w_fc.t())\n        gate = h[:, :2 * D]\n        value = h[:, 2 * D:]\n\n        # Swish(gate) = gate * sigmoid(gate)\n        sig = torch.sigmoid(gate)\n        swish = gate * sig\n        out = swish * value\n\n        w_proj_half = w_proj[:, :2 * D]\n        final = torch.matmul(out, w_proj_half.t())\n\n        ctx.save_for_backward(x, w_fc, w_proj_half, gate, value, out)\n        return final\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        x, w_fc, w_proj_half, gate, value, out = ctx.saved_tensors\n        D = x.shape[1]\n\n        d_out = torch.matmul(grad_output, w_proj_half)\n\n        gate_f = gate.float()\n        value_f = value.float()\n        d_out_f = d_out.float()\n\n        sig = torch.sigmoid(gate_f)\n        swish_grad = sig * (1.0 + gate_f * (1.0 - sig))\n        swish_val = gate_f * sig\n\n        d_gate = (d_out_f * value_f * swish_grad).to(d_out.dtype)\n        d_val = (d_out_f * swish_val).to(d_out.dtype)\n\n        d_h = torch.cat([d_gate, d_val], dim=-1)\n\n        grad_x = torch.matmul(d_h, w_fc)\n        grad_w_fc = torch.matmul(d_h.t(), x)\n\n        grad_w_proj = torch.empty(w_proj_half.shape[0], 4 * D,\n                                   device=grad_output.device, dtype=grad_output.dtype)\n        grad_w_proj.zero_()\n        grad_w_proj[:, :2 * D] = torch.matmul(grad_output.t(), out)\n\n        return grad_x, grad_w_fc, grad_w_proj\n\n\ndef fused_mlp_forward(x, w_fc, w_proj):\n    \"\"\"MLP forward pass with SwiGLU activation.\n\n    SwiGLU (Swish-Gated Linear Unit) splits the MLP into gated and value paths,\n    applying Swish(x) = x * sigmoid(x) to the gate before element-wise multiplication.\n    This provides superior representational capacity compared to GELU/ReLU^2.\n\n    Args:\n        x: input tensor (B*T, n_embd)\n        w_fc: first linear weight (4*n_embd, n_embd) - split into gate+value\n        w_proj: second linear weight (n_embd, 4*n_embd) - first 2*n_embd cols used\n    Returns:\n        output tensor (B*T, n_embd)\n    \"\"\"\n    return _SwiGLUMLP.apply(x, w_fc, w_proj)"
7}

Tool ResultStep 6

OK: Replaced lines 33..136 with 68 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–100, 309–311.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–100 | total: 501 lines]
    30|         return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
    31| 
    32| # ── Custom Kernel / Fused Operation ───────────────────────────────────────
    33  class _SwiGLUMLP(torch.autograd.Function):
    34      """SwiGLU MLP: h=x@w_fc.t(), gate,value=h.split(2), out=Swish(gate)*value, final=out@w_proj_half.t()
    35      SwiGLU (Swish-Gated Linear Unit) provides superior expressivity via multiplicative gating."""
    36  
    37      @staticmethod
    38      def forward(ctx, x, w_fc, w_proj):
    39          D = x.shape[1]
    40          h = torch.matmul(x, w_fc.t())
    41          gate = h[:, :2 * D]
    42          value = h[:, 2 * D:]
    43  
    44          # Swish(gate) = gate * sigmoid(gate)
    45          sig = torch.sigmoid(gate)
    46          swi

AssistantStep 7edit

Tool Input

1{
2  "content": "class _SwiGLUMLP(torch.autograd.Function):\n    \"\"\"SwiGLU MLP with custom autograd for fused activation.\n    h = x @ w_fc.t(), gate,value = split(h), out = Swish(gate) * value,\n    final = out @ w_proj_half.t()\n    \"\"\"\n\n    @staticmethod\n    def forward(ctx, x, w_fc, w_proj):\n        D = x.shape[1]\n        # h: (M, 4D)\n        h = x @ w_fc.t()\n        gate, value = h.split([2 * D, 2 * D], dim=-1)\n        # Swish(x) = x * sigmoid(x), computed in fp32 for stability\n        gate_f = gate.float()\n        swish = gate_f * torch.sigmoid(gate_f)\n        out = (swish * value.float()).to(h.dtype)\n        # Use first 2D cols of w_proj\n        w_proj_half = w_proj[:, :2 * D]\n        final = out @ w_proj_half.t()\n        ctx.save_for_backward(x, w_fc, w_proj_half, gate, value, out)\n        return final\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        x, w_fc, w_proj_half, gate, value, out = ctx.saved_tensors\n        D = x.shape[1]\n        d_out = grad_output @ w_proj_half  # (M, 2D)\n        # d(Swish)/dx = sigmoid(x)*(1 + x*(1-sigmoid(x)))\n        gate_f, value_f, d_out_f = gate.float(), value.float(), d_out.float()\n        sig = torch.sigmoid(gate_f)\n        swish_grad = sig * (1.0 + gate_f * (1.0 - sig))\n        swish_val = gate_f * sig\n        d_gate = (d_out_f * value_f * swish_grad).to(d_out.dtype)\n        d_val  = (d_out_f * swish_val).to(d_out.dtype)\n        d_h = torch.cat([d_gate, d_val], dim=-1)\n        grad_x = d_h @ w_fc\n        grad_w_fc = d_h.t() @ x\n        grad_w_proj = torch.zeros(w_proj_half.shape[0], 4 * D,\n                                   device=grad_output.device, dtype=grad_output.dtype)\n        grad_w_proj[:, :2 * D] = grad_output.t() @ out\n        return grad_x, grad_w_fc, grad_w_proj\n\n\ndef fused_mlp_forward(x, w_fc, w_proj):\n    \"\"\"MLP forward with SwiGLU activation.\n    Swish-gated linear unit splits 4*n_embd into gate+value (2*n_embd each),\n    applies Swish(x)=x*sigmoid(x) to gate then element-wise multiplies with value.\n    Proven superior to GELU/ReLU^2 in LLaMA/PaLM/Modern LLMs.\n    \"\"\"\n    return _SwiGLUMLP.apply(x, w_fc, w_proj)",
3  "end_line": 100,
4  "filename": "nanoGPT/custom_pretrain.py",
5  "start_line": 33,
6  "op": "replace"
7}

Tool ResultStep 7

OK: Replaced lines 33..100 with 50 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–82, 291–293.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–82 | total: 483 lines]
    30|         return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
    31| 
    32| # ── Custom Kernel / Fused Operation ───────────────────────────────────────
    33  class _SwiGLUMLP(torch.autograd.Function):
    34      """SwiGLU MLP with custom autograd for fused activation.
    35      h = x @ w_fc.t(), gate,value = split(h), out = Swish(gate) * value,
    36      final = out @ w_proj_half.t()
    37      """
    38  
    39      @staticmethod
    40      def forward(ctx, x, w_fc, w_proj):
    41          D = x.shape[1]
    42          # h: (M, 4D)
    43          h = x @ w_fc.t()
    44          gate, value = h.split([2 * D, 2 * D], dim=-1)
    45          # Swish(x) = x * sigmoid(x), computed in fp32 for stability
    46          gate_f = gate.float()
    47          sw

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)
Training metrics (last steps):
TRAIN_METRICS: step=9000, train_loss=2.6691, val_loss=2.3907
TRAIN_METRICS: step=10000, train_loss=2.6482, val_loss=2.3635
TRAIN_METRICS: step=11000, train_loss=2.6314, val_loss=2.3553
TRAIN_METRICS: step=12000, train_loss=2.6133, val_loss=2.3395
TRAIN_METRICS: step=13000, train_loss=2.5983, val_loss=2.3286
Final metrics (gpt-345m): val_loss_gpt-345m=2.3225, wikitext2_ppl_gpt-345m=45.5400, lambada_ppl_gpt-345m=70.1700

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=54.3400, hellaswag_lm-eval-345m=32.6400, piqa_lm-eval-345m=64.0400, winogrande_lm-eval-345m=50.4300

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.3225, 'wikitext2_ppl_gpt-345m': 45.54, 'lambada_ppl_gpt-345m': 70.17, 'elapsed_gpt-345m': 17943, 'arc_easy_lm-eval-345m': 54.34, 'hellaswag_lm-eval-345m': 32.64, 'piqa

AssistantStep 9edit

Tool Input

1{
2  "content": "import triton\nimport triton.language as tl\n\n@triton.jit\ndef _silu_activation_kernel(\n    x_ptr, out_ptr, n_elements,\n    BLOCK_SIZE: tl.constexpr,\n):\n    pid = tl.program_id(0)\n    offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)\n    mask = offsets < n_elements\n    xf = tl.load(x_ptr + offsets, mask=mask).to(tl.float32)\n    sig = tl.sigmoid(xf)\n    out = (xf * sig).to(tl.load(x_ptr + offsets, mask=mask).dtype)\n    tl.store(out_ptr + offsets, out, mask=mask)\n\n\n@triton.jit\ndef _silu_backward_kernel(\n    grad_ptr, x_ptr, out_ptr,\n    n_elements,\n    BLOCK_SIZE: tl.constexpr,\n):\n    \"\"\"d(L)/dx = d(L)/d(silu) * sigmoid(x) * (1 + x*(1-sigmoid(x)))\"\"\"\n    pid = tl.program_id(0)\n    offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)\n    mask = offsets < n_elements\n    xf = tl.load(x_ptr + offsets, mask=mask).to(tl.float32)\n    grad_f = tl.load(grad_ptr + offsets, mask=mask).to(tl.float32)\n    sig = tl.sigmoid(xf)\n    silu_grad = sig * (1.0 + xf * (1.0 - sig))\n    dx = (grad_f * silu_grad).to(tl.load(x_ptr + offsets, mask=mask).dtype)\n    tl.store(out_ptr + offsets, dx, mask=mask)\n\n\nclass _SiLUMMLP(torch.autograd.Function):\n    \"\"\"SiLU (Swish) activation with Triton kernel for element-wise operation.\n    SiLU(x) = x * sigmoid(x) - smooth non-linearity superior to GELU in many cases.\n    Uses all parameters (4*n_embd -> 4*n_embd -> n_embd), unlike SwiGLU variants.\n    \"\"\"\n\n    @staticmethod\n    def forward(ctx, x, w_fc, w_proj):\n        h = x @ w_fc.t()  # (M, 4D)\n        act = torch.empty_like(h)\n        n = h.numel()\n        BLOCK = min(triton.next_power_of_2(n), 65536)\n        BLOCK = max(BLOCK, 256)\n        _silu_activation_kernel[(n + BLOCK - 1) // BLOCK,](\n            h, act, n, BLOCK_SIZE=BLOCK\n        )\n        final = act @ w_proj.t()  # (M, D)\n        ctx.save_for_backward(x, w_fc, w_proj, h, act)\n        return final\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        x, w_fc, w_proj, h, act = ctx.saved_tensors\n        d_act = grad_output @ w_proj  # (M, 4D)\n        dtype = grad_output.dtype\n\n        # SiLU gradient via Triton kernel\n        d_h = torch.empty_like(d_act)\n        n = d_act.numel()\n        BLOCK = min(triton.next_power_of_2(n), 65536)\n        BLOCK = max(BLOCK, 256)\n        _silu_backward_kernel[(n + BLOCK - 1) // BLOCK,](\n            d_act, h, d_h, n, BLOCK_SIZE=BLOCK\n        )\n\n        grad_x = d_h @ w_fc\n        grad_w_fc = d_h.t() @ x\n        grad_w_proj = grad_output.t() @ act\n        return grad_x, grad_w_fc, grad_w_proj\n\n\ndef fused_mlp_forward(x, w_fc, w_proj):\n    \"\"\"MLP forward with SiLU (Swish) activation via custom Triton kernel.\n    SiLU(x) = x * sigmoid(x) provides smooth gradients and has been shown\n    to outperform GELU in modern LLM architectures (e.g., LLaMA).\n    Full parameter utilization: (M,4D) -> SiLU -> (M,4D) -> linear -> (M,D).\n    \"\"\"\n    return _SiLUMMLP.apply(x, w_fc, w_proj)",
3  "end_line": 82,
4  "filename": "nanoGPT/custom_pretrain.py",
5  "start_line": 33,
6  "op": "replace"
7}

Tool ResultStep 9

OK: Replaced lines 33..82 with 83 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–115, 324–326.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–115 | total: 516 lines]
    30|         return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
    31| 
    32| # ── Custom Kernel / Fused Operation ───────────────────────────────────────
    33  import triton
    34  import triton.language as tl
    35  
    36  @triton.jit
    37  def _silu_activation_kernel(
    38      x_ptr, out_ptr, n_elements,
    39      BLOCK_SIZE: tl.constexpr,
    40  ):
    41      pid = tl.program_id(0)
    42      offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    43      mask = offsets < n_elements
    44      xf = tl.load(x_ptr + offsets, mask=mask).to(tl.float32)
    45      sig = tl.sigmoid(xf)
    46      out = (xf * sig).to(tl.load(x_ptr + offsets, mask=mask).dtype)
    47      tl.store(out_ptr + offsets, out, mask=mask)
    48  
    49  
    50  @triton.jit