Agent Conversation: deepseek-reasoner

Model: deepseek-reasoner

Total steps: 9

Messages: 20

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": "def fused_mlp_forward(x, w_fc, w_proj):\n    \"\"\"MLP forward pass with SwiGLU activation (split weight).\n    \n    Splits w_fc into two halves: w_fc1 (2*n_embd, n_embd) and w_fc2 (2*n_embd, n_embd).\n    Computes h1 = x @ w_fc1.T, h2 = x @ w_fc2.T, gate = sigmoid(h2), activated = h1 * gate.\n    Then projects with w_proj.\n    \n    Args:\n        x: input tensor (B*T, n_embd)\n        w_fc: first linear weight (4*n_embd, n_embd)\n        w_proj: second linear weight (n_embd, 4*n_embd)\n    Returns:\n        output tensor (B*T, n_embd)\n    \"\"\"\n    # Split w_fc into two equal parts along the first dimension\n    d = w_fc.size(0) // 2  # should be 2*n_embd\n    w_fc1 = w_fc[:d, :]\n    w_fc2 = w_fc[d:, :]\n    h1 = x @ w_fc1.t()\n    h2 = x @ w_fc2.t()\n    gate = torch.sigmoid(h2)  # Swish activation (beta=1)\n    activated = h1 * gate\n    return activated @ w_proj.t()"
7}

Tool ResultStep 1

OK: Replaced lines 33..48 with 23 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–55, 264–266.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–55 | total: 456 lines]
    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 with SwiGLU activation (split weight).
    35      
    36      Splits w_fc into two halves: w_fc1 (2*n_embd, n_embd) and w_fc2 (2*n_embd, n_embd).
    37      Computes h1 = x @ w_fc1.T, h2 = x @ w_fc2.T, gate = sigmoid(h2), activated = h1 * gate.
    38      Then projects with w_proj.
    39      
    40      Args:
    41          x: input tensor (B*T, n_embd)
    42          w_fc: first linear weight (4*n_embd, n_embd)
    43          w_proj: second linear weight (n_embd, 4*n_embd)
    44      Returns:
    45          output tensor (B*T, n_

AssistantStep 2undo

Tool Input

1{
2  "n": 1
3}

Tool ResultStep 2

Undo complete:
Restored: nanoGPT/custom_pretrain.py

AssistantStep 3edit

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\nfrom triton.language.extra.cuda import libdevice\n\n@triton.jit\ndef _mish_kernel(\n    x_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    x = tl.load(x_ptr + offsets, mask=mask)\n    # Compute Mish: x * tanh(softplus(x))\n    # softplus(x) = log(1 + exp(x))\n    # compute in float32 for precision\n    xf = x.to(tl.float32)\n    exp_x = libdevice.exp(xf)\n    softplus = libdevice.log(1.0 + exp_x)\n    tanh_softplus = libdevice.tanh(softplus)\n    mish = xf * tanh_softplus\n    tl.store(out_ptr + offsets, mish.to(x.dtype), mask=mask)\n\nclass _TritonMishMLP(torch.autograd.Function):\n    @staticmethod\n    def forward(ctx, x, w_fc, w_proj):\n        h = x @ w_fc.t()\n        act = torch.empty_like(h)\n        n = h.numel()\n        BLOCK = 1024\n        grid = ((n + BLOCK - 1) // BLOCK,)\n        _mish_kernel[grid](h, act, n, BLOCK_SIZE=BLOCK)\n        out = act @ w_proj.t()\n        ctx.save_for_backward(x, w_fc, w_proj, h, act)\n        return out\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        x, w_fc, w_proj, h, act = ctx.saved_tensors\n        dtype = grad_output.dtype\n        # gradient through second linear\n        d_act = grad_output @ w_proj.to(dtype)\n        grad_w_proj = grad_output.reshape(-1, grad_output.shape[-1]).t() @ act.to(dtype).reshape(-1, act.shape[-1])\n        # gradient through Mish activation\n        # derivative of Mish: mish'(x) = tanh(softplus(x)) + x * sech^2(softplus(x)) * sigmoid(x)\n        # where sigmoid(x) = 1/(1+exp(-x))\n        h_f = h.float()\n        exp_h = torch.exp(h_f)\n        sigmoid = 1.0 / (1.0 + torch.exp(-h_f))\n        softplus = torch.log(1.0 + exp_h)\n        tanh_sp = torch.tanh(softplus)\n        sech2 = 1.0 - tanh_sp * tanh_sp\n        mish_grad = tanh_sp + h_f * sech2 * sigmoid\n        d_h = (d_act.float() * mish_grad).to(dtype)\n        grad_x = d_h @ w_fc.to(dtype)\n        grad_w_fc = d_h.reshape(-1, d_h.shape[-1]).t() @ x.to(dtype).reshape(-1, x.shape[-1])\n        return grad_x, grad_w_fc, grad_w_proj\n\ndef fused_mlp_forward(x, w_fc, w_proj):\n    \"\"\"MLP forward with Triton fused Mish activation kernel.\"\"\"\n    return _TritonMishMLP.apply(x, w_fc, w_proj)"
7}

Tool ResultStep 3

OK: Replaced lines 33..48 with 62 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–94, 303–305.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–94 | total: 495 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  from triton.language.extra.cuda import libdevice
    36  
    37  @triton.jit
    38  def _mish_kernel(
    39      x_ptr, out_ptr,
    40      n_elements,
    41      BLOCK_SIZE: tl.constexpr,
    42  ):
    43      pid = tl.program_id(0)
    44      offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    45      mask = offsets < n_elements
    46      x = tl.load(x_ptr + offsets, mask=mask)
    47      # Compute Mish: x * tanh(softplus(x))
    48      # softplus(x) = log(1 + exp(x))
    49      # compute in float32 for precision
    50      xf = x.to(

AssistantStep 4test

Tool ResultStep 4

[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=1000, train_loss=3.7041, val_loss=3.4815
TRAIN_METRICS: step=2000, train_loss=3.2006, val_loss=2.9319
TRAIN_METRICS: step=3000, train_loss=3.0080, val_loss=2.7249
TRAIN_METRICS: step=4000, train_loss=2.8969, val_loss=2.6112
TRAIN_METRICS: step=5000, train_loss=2.8219, val_loss=2.5285

### 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/deepseek-reasoner_20260412_102246/seed_42/ckpt_gpt-345m.pt

AssistantStep 5edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 33,
5  "end_line": 94,
6  "content": "import torch\nimport torch.nn.functional as F\n\nclass GatedConcatMLP(torch.autograd.Function):\n    @staticmethod\n    def forward(ctx, x, w_fc, w_proj):\n        # split weights\n        D2 = w_fc.size(0) // 2\n        w_fc1 = w_fc[:D2, :]\n        w_fc2 = w_fc[D2:, :]\n        h1 = x @ w_fc1.t()\n        h2 = x @ w_fc2.t()\n        gate = torch.sigmoid(h2)\n        gated = h1 * gate\n        concat = torch.cat([h1, gated], dim=-1)\n        out = concat @ w_proj.t()\n        ctx.save_for_backward(x, w_fc1, w_fc2, w_proj, h1, h2, gate, gated)\n        return out\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        x, w_fc1, w_fc2, w_proj, h1, h2, gate, gated = ctx.saved_tensors\n        dtype = grad_output.dtype\n        # gradient w.r.t. w_proj and concat\n        grad_concat = grad_output @ w_proj.to(dtype)\n        grad_w_proj = grad_output.reshape(-1, grad_output.shape[-1]).t() @ \\\n                      torch.cat([h1, gated], dim=-1).to(dtype).reshape(-1, grad_output.shape[-1])\n        # split grad_concat into h1 and gated parts\n        D = grad_concat.size(-1) // 2\n        grad_h1 = grad_concat[:, :D]\n        grad_gated = grad_concat[:, D:]\n        # gradient through gated = h1 * gate\n        grad_h1_from_gated = grad_gated * gate\n        grad_gate = grad_gated * h1\n        # gradient through gate = sigmoid(h2)\n        grad_h2 = grad_gate * gate * (1 - gate)\n        # total gradient for h1\n        grad_h1_total = grad_h1 + grad_h1_from_gated\n        # gradient through h1 = x @ w_fc1.T\n        grad_x1 = grad_h1_total @ w_fc1.to(dtype)\n        grad_w_fc1 = grad_h1_total.reshape(-1, grad_h1_total.shape[-1]).t() @ x.to(dtype).reshape(-1, x.shape[-1])\n        # gradient through h2 = x @ w_fc2.T\n        grad_x2 = grad_h2 @ w_fc2.to(dtype)\n        grad_w_fc2 = grad_h2.reshape(-1, grad_h2.shape[-1]).t() @ x.to(dtype).reshape(-1, x.shape[-1])\n        # combine gradients for w_fc\n        grad_w_fc = torch.cat([grad_w_fc1, grad_w_fc2], dim=0)\n        grad_x = grad_x1 + grad_x2\n        return grad_x, grad_w_fc, grad_w_proj\n\ndef fused_mlp_forward(x, w_fc, w_proj):\n    \"\"\"MLP forward with gated concatenation (SwiGLU variant).\"\"\"\n    return GatedConcatMLP.apply(x, w_fc, w_proj)"
7}

Tool ResultStep 5

OK: Replaced lines 33..94 with 52 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–84, 293–295.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–84 | total: 485 lines]
    30|         return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
    31| 
    32| # ── Custom Kernel / Fused Operation ───────────────────────────────────────
    33  import torch
    34  import torch.nn.functional as F
    35  
    36  class GatedConcatMLP(torch.autograd.Function):
    37      @staticmethod
    38      def forward(ctx, x, w_fc, w_proj):
    39          # split weights
    40          D2 = w_fc.size(0) // 2
    41          w_fc1 = w_fc[:D2, :]
    42          w_fc2 = w_fc[D2:, :]
    43          h1 = x @ w_fc1.t()
    44          h2 = x @ w_fc2.t()
    45          gate = torch.sigmoid(h2)
    46          gated = h1 * gate
    47          concat = torch.cat([h1, gated], dim=-1)
    48          out = concat @ w_proj.t()
    49          ctx.save_for_backward(x, w

AssistantStep 6test

Tool ResultStep 6

[Test #2] (1 test remaining; call submit(n=N) to choose which test result to submit as final)

### gpt-345m (scripts/gpt_345m.sh)
[COMMAND FAILED exit=1]
W0412 20:37:51.661000 430030 site-packages/torch/distributed/run.py:792] 
W0412 20:37:51.661000 430030 site-packages/torch/distributed/run.py:792] *****************************************
W0412 20:37:51.661000 430030 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. 
W0412 20:37:51.661000 430030 site-packages/torch/distributed/run.py:792] *****************************************
[W412 20:37:51.819544497 socket.cpp:204] [c10d] The hostname of the client socket cannot be retrieved. err=-3
[W412 20:37:51.819863175 socket.cpp:204] [c10d] The hostname of the client socket cannot be retrieved. err=-3
[W412 20:37:51.832432636 socket.cpp:204

AssistantStep 7edit

Tool Input

1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "start_line": 52,
5  "end_line": 80,
6  "content": "    @staticmethod\n    def backward(ctx, grad_output):\n        x, w_fc1, w_fc2, w_proj, h1, h2, gate, gated = ctx.saved_tensors\n        dtype = grad_output.dtype\n        # gradient w.r.t. w_proj: grad_w_proj = grad_output^T @ concat\n        concat = torch.cat([h1, gated], dim=-1)\n        grad_w_proj = grad_output.t() @ concat.to(dtype)\n        # gradient w.r.t. concat: grad_concat = grad_output @ w_proj\n        grad_concat = grad_output @ w_proj.to(dtype)\n        # split grad_concat into h1 and gated parts\n        D = grad_concat.size(-1) // 2\n        grad_h1 = grad_concat[:, :D]\n        grad_gated = grad_concat[:, D:]\n        # gradient through gated = h1 * gate\n        grad_h1_from_gated = grad_gated * gate\n        grad_gate = grad_gated * h1\n        # gradient through gate = sigmoid(h2)\n        grad_h2 = grad_gate * gate * (1 - gate)\n        # total gradient for h1\n        grad_h1_total = grad_h1 + grad_h1_from_gated\n        # gradient through h1 = x @ w_fc1.T\n        grad_x1 = grad_h1_total @ w_fc1.to(dtype)\n        grad_w_fc1 = grad_h1_total.t() @ x.to(dtype)\n        # gradient through h2 = x @ w_fc2.T\n        grad_x2 = grad_h2 @ w_fc2.to(dtype)\n        grad_w_fc2 = grad_h2.t() @ x.to(dtype)\n        # combine gradients for w_fc\n        grad_w_fc = torch.cat([grad_w_fc1, grad_w_fc2], dim=0)\n        grad_x = grad_x1 + grad_x2\n        return grad_x, grad_w_fc, grad_w_proj"
7}

Tool ResultStep 7

OK: Replaced lines 52..80 with 30 line(s) in nanoGPT/custom_pretrain.py. Editable range: 33–85, 294–296.

[Current file: nanoGPT/custom_pretrain.py | editable: 33–85 | total: 486 lines]
    30|         return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
    31| 
    32| # ── Custom Kernel / Fused Operation ───────────────────────────────────────
    33  import torch
    34  import torch.nn.functional as F
    35  
    36  class GatedConcatMLP(torch.autograd.Function):
    37      @staticmethod
    38      def forward(ctx, x, w_fc, w_proj):
    39          # split weights
    40          D2 = w_fc.size(0) // 2
    41          w_fc1 = w_fc[:D2, :]
    42          w_fc2 = w_fc[D2:, :]
    43          h1 = x @ w_fc1.t()
    44          h2 = x @ w_fc2.t()
    45          gate = torch.sigmoid(h2)
    46          gated = h1 * gate
    47          concat = torch.cat([h1, gated], dim=-1)
    48          out = concat @ w_proj.t()
    49          ctx.save_for_backward(x, w

AssistantStep 8test

Tool ResultStep 8

[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=2.7319, val_loss=2.4415
TRAIN_METRICS: step=10000, train_loss=2.7058, val_loss=2.4087
TRAIN_METRICS: step=11000, train_loss=2.6858, val_loss=2.3963
TRAIN_METRICS: step=12000, train_loss=2.6646, val_loss=2.3763
TRAIN_METRICS: step=13000, train_loss=2.6476, val_loss=2.3643
Final metrics (gpt-345m): val_loss_gpt-345m=2.3575, wikitext2_ppl_gpt-345m=47.3700, lambada_ppl_gpt-345m=74.3500

### lm-eval-345m (scripts/lm_eval_345m.sh)
[COMMAND FAILED exit=-1]
Evaluating checkpoint: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-kernel/deepseek-reasoner_20260412_102246/seed_42/ckpt_gpt-345m.pt
Model source: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-kernel/deep

AssistantStep 9submit

Tool Input

1{
2  "n": 3
3}

Tool ResultStep 9

[submit] Submitting result from test #3 as final.

### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=9000, train_loss=2.7319, val_loss=2.4415
TRAIN_METRICS: step=10000, train_loss=2.7058, val_loss=2.4087
TRAIN_METRICS: step=11000, train_loss=2.6858, val_loss=2.3963
TRAIN_METRICS: step=12000, train_loss=2.6646, val_loss=2.3763
TRAIN_METRICS: step=13000, train_loss=2.6476, val_loss=2.3643
Final metrics (gpt-345m): val_loss_gpt-345m=2.3575, wikitext2_ppl_gpt-345m=47.3700, lambada_ppl_gpt-345m=74.3500

### lm-eval-345m (scripts/lm_eval_345m.sh)
[COMMAND FAILED exit=-1]
Evaluating checkpoint: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-kernel/deepseek-reasoner_20260412_102246/seed_42/ckpt_gpt-345m.pt
Model source: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-kernel/deepseek-reasoner_20260412_102246/seed_42/model_source_gpt-345m.py
Using the latest cached version of the dataset since Rowan/hellaswag couldn't be found on the Hugging Fac