claude / claude-opus-4-8 — KernelBench-Hard

harness

claude

model

claude-opus-4-8

turns

475

tools called

290

events

961

input toks

92,285

output toks

10,982

cache hit

88,102,299

solution.py

final answer

benchmark.log

check.log

result.json

view raw solution.py

"""Kimi Delta Attention forward (chunk form) — custom Triton kernels for SM120.

Chunk-parallel design (FLA-style 3-kernel decomposition), all bf16 tensor cores,
launched once via a CUDA graph bound to the input tensors:
  Kernel A (prepare, parallel over b*h*chunk): build the intra-chunk WY transform.
    - gc = cumsum(g) within chunk (done as a lower-triangular ones matmul on TCs)
    - A0 = strict-lower(-beta[c] * (k*e^gc) @ (k*e^-gc)^T)
    - Tinv = (I - A0)^{-1}  via Neumann doubling (ITERS iters; A0 nilpotent and its
      high powers are decay-suppressed, so 4 iters covers tolerance with wide margin)
    - w = Tinv @ (beta * e^gc * k),  u = Tinv @ (beta * v)
    - also precomputes kd^T = (e^last * k * e^-gc)^T and decay = e^last so the scan's
      hot path carries no exp/cumsum/transpose.
  Kernel B (state scan, parallel over b*h*v-block, sequential over chunks): the only
    sequential pass. Keeps recurrent state S (K x BV) in registers, emits per-chunk
    start state h_n and the corrected values v_new_n = u_n - w_n @ h_n.
  Kernel C (output, fully parallel over b*h*chunk*v-block): the heavy compute.
    - o = (q*scale*e^gc) @ h_n + tril(Aqk) @ v_new_n
"""
from __future__ import annotations

import torch
import torch.nn as nn
import triton
import triton.language as tl


@triton.jit
def _kda_prepare_kernel(
    k_ptr, v_ptr, g_ptr, beta_ptr, w_ptr, u_ptr, kd_ptr, decay_ptr,
    B, T, H, NT,
    K: tl.constexpr, V: tl.constexpr, C: tl.constexpr, ITERS: tl.constexpr,
):
    pid_n = tl.program_id(0)
    pid_bh = tl.program_id(1)
    b = pid_bh // H
    h = pid_bh % H

    sb_k = T * H * K
    sb_v = T * H * V
    base_k = k_ptr + b * sb_k + h * K
    base_g = g_ptr + b * sb_k + h * K
    base_v = v_ptr + b * sb_v + h * V
    base_w = w_ptr + b * sb_k + h * K
    base_u = u_ptr + b * sb_v + h * V

    p_k = tl.make_block_ptr(base_k, (T, K), (H * K, 1), (pid_n * C, 0), (C, K), (1, 0))
    p_g = tl.make_block_ptr(base_g, (T, K), (H * K, 1), (pid_n * C, 0), (C, K), (1, 0))
    p_v = tl.make_block_ptr(base_v, (T, V), (H * V, 1), (pid_n * C, 0), (C, V), (1, 0))

    k = tl.load(p_k, boundary_check=(0, 1)).to(tl.float32)
    g = tl.load(p_g, boundary_check=(0, 1)).to(tl.float32)
    v = tl.load(p_v, boundary_check=(0, 1)).to(tl.float32)

    offs_c = tl.arange(0, C)
    p_beta = beta_ptr + b * (T * H) + (pid_n * C + offs_c) * H + h
    beta = tl.load(p_beta).to(tl.float32)

    Ltri = tl.where(offs_c[:, None] >= offs_c[None, :], 1.0, 0.0)
    gc = tl.dot(Ltri, g, input_precision="tf32")   # cumulative sum via tri-matmul
    last = tl.sum(g, axis=0)             # gc at last row (K,)
    egc = tl.exp(gc)
    inv_egc = 1.0 / egc                  # = exp(-gc)
    decay_vec = tl.exp(last)             # (K,)
    kg = k * egc
    kng = k * inv_egc

    Kgg = tl.dot(kg.to(tl.bfloat16), tl.trans(kng).to(tl.bfloat16))   # (C, C)
    row = offs_c[:, None]
    col = offs_c[None, :]
    A0 = tl.where(row > col, -beta[:, None] * Kgg, 0.0)

    M = tl.where(row == col, 1.0, 0.0)
    P = A0
    for i in tl.static_range(ITERS):
        M = M + tl.dot(P.to(tl.bfloat16), M.to(tl.bfloat16))
        if i < ITERS - 1:
            P = tl.dot(P.to(tl.bfloat16), P.to(tl.bfloat16))

    beta_kg = (beta[:, None] * kg).to(tl.bfloat16)
    beta_v = (beta[:, None] * v).to(tl.bfloat16)
    Mb = M.to(tl.bfloat16)
    w = tl.dot(Mb, beta_kg)
    u = tl.dot(Mb, beta_v)

    # state-scan precompute: kd = e^(last-gc)*k = e^last * (k*e^-gc) = decay * kng
    # store transposed (K, C) so the sequential scan avoids tl.trans on its hot path
    kdt = tl.trans(decay_vec[None, :] * kng)   # (K, C)

    p_w = tl.make_block_ptr(base_w, (T, K), (H * K, 1), (pid_n * C, 0), (C, K), (1, 0))
    p_u = tl.make_block_ptr(base_u, (T, V), (H * V, 1), (pid_n * C, 0), (C, V), (1, 0))
    kdt_base = kd_ptr + (pid_bh * NT + pid_n) * K * C
    p_kdt = tl.make_block_ptr(kdt_base, (K, C), (C, 1), (0, 0), (K, C), (1, 0))
    tl.store(p_w, w.to(w_ptr.dtype.element_ty), boundary_check=(0, 1))
    tl.store(p_u, u.to(u_ptr.dtype.element_ty), boundary_check=(0, 1))
    tl.store(p_kdt, kdt.to(kd_ptr.dtype.element_ty), boundary_check=(0, 1))
    offs_k = tl.arange(0, K)
    tl.store(decay_ptr + (pid_bh * NT + pid_n) * K + offs_k, decay_vec)


@triton.jit
def _kda_state_kernel(
    w_ptr, u_ptr, kd_ptr, decay_ptr, h_ptr, vnew_ptr,
    B, T, H, NT,
    K: tl.constexpr, V: tl.constexpr, C: tl.constexpr, BV: tl.constexpr,
):
    pid_bh = tl.program_id(0)
    pid_v = tl.program_id(1)
    b = pid_bh // H
    h = pid_bh % H
    v0 = pid_v * BV

    sb_k = T * H * K
    sb_v = T * H * V
    base_w = w_ptr + b * sb_k + h * K
    base_u = u_ptr + b * sb_v + h * V
    base_vn = vnew_ptr + b * sb_v + h * V
    sb_h = H * NT * K * V
    offs_k = tl.arange(0, K)

    S = tl.zeros((K, BV), dtype=tl.float32)

    for n in range(NT):
        toff = n * C
        p_w = tl.make_block_ptr(base_w, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
        kdt_base = kd_ptr + (pid_bh * NT + n) * K * C
        p_kdt = tl.make_block_ptr(kdt_base, (K, C), (C, 1), (0, 0), (K, C), (1, 0))
        p_u = tl.make_block_ptr(base_u, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))

        w = tl.load(p_w, boundary_check=(0, 1))
        kdt = tl.load(p_kdt, boundary_check=(0, 1))
        u = tl.load(p_u, boundary_check=(0, 1)).to(tl.float32)
        decay = tl.load(decay_ptr + (pid_bh * NT + n) * K + offs_k)

        # store start-of-chunk state h_n
        h_base = h_ptr + (b * sb_h + (h * NT + n) * K * V)
        p_h = tl.make_block_ptr(h_base, (K, V), (V, 1), (0, v0), (K, BV), (1, 0))
        tl.store(p_h, S.to(h_ptr.dtype.element_ty), boundary_check=(0, 1))

        v_new = u - tl.dot(w, S.to(w.dtype), input_precision="tf32")
        p_vn = tl.make_block_ptr(base_vn, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
        tl.store(p_vn, v_new.to(vnew_ptr.dtype.element_ty), boundary_check=(0, 1))

        S = decay[:, None] * S + tl.dot(kdt, v_new.to(kdt.dtype), input_precision="tf32")


@triton.jit
def _kda_output_kernel(
    q_ptr, k_ptr, g_ptr, h_ptr, vnew_ptr, o_ptr,
    B, T, H, NT, scale,
    K: tl.constexpr, V: tl.constexpr, C: tl.constexpr, BV: tl.constexpr,
):
    pid_bh = tl.program_id(0)
    pid_n = tl.program_id(1)
    pid_v = tl.program_id(2)
    b = pid_bh // H
    h = pid_bh % H
    v0 = pid_v * BV
    toff = pid_n * C

    sb_k = T * H * K
    sb_v = T * H * V
    base_q = q_ptr + b * sb_k + h * K
    base_k = k_ptr + b * sb_k + h * K
    base_g = g_ptr + b * sb_k + h * K
    base_vn = vnew_ptr + b * sb_v + h * V
    base_o = o_ptr + b * sb_v + h * V
    sb_h = H * NT * K * V

    p_q = tl.make_block_ptr(base_q, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
    p_k = tl.make_block_ptr(base_k, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
    p_g = tl.make_block_ptr(base_g, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))

    q = tl.load(p_q, boundary_check=(0, 1)).to(tl.float32)
    k = tl.load(p_k, boundary_check=(0, 1)).to(tl.float32)
    g = tl.load(p_g, boundary_check=(0, 1)).to(tl.float32)

    offs_c = tl.arange(0, C)
    Ltri = tl.where(offs_c[:, None] >= offs_c[None, :], 1.0, 0.0)
    gc = tl.dot(Ltri, g, input_precision="tf32")
    egc = tl.exp(gc)
    qg = (q * scale) * egc
    kng = k * (1.0 / egc)
    qgb = qg.to(tl.bfloat16)
    Aqk = tl.dot(qgb, tl.trans(kng).to(tl.bfloat16))
    Aqk = tl.where(offs_c[:, None] >= offs_c[None, :], Aqk, 0.0).to(tl.bfloat16)

    h_base = h_ptr + (b * sb_h + (h * NT + pid_n) * K * V)
    p_h = tl.make_block_ptr(h_base, (K, V), (V, 1), (0, v0), (K, BV), (1, 0))
    h_state = tl.load(p_h, boundary_check=(0, 1))

    p_vn = tl.make_block_ptr(base_vn, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
    v_new = tl.load(p_vn, boundary_check=(0, 1))

    o = tl.dot(qgb, h_state) + tl.dot(Aqk, v_new)

    p_o = tl.make_block_ptr(base_o, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
    tl.store(p_o, o.to(o_ptr.dtype.element_ty), boundary_check=(0, 1))


import os as _os
_WA = int(_os.environ.get("KDA_WA", "8")); _SA = int(_os.environ.get("KDA_SA", "2"))
_NITER = int(_os.environ.get("KDA_NITER", "4"))
_WB = int(_os.environ.get("KDA_WB", "4")); _SB = int(_os.environ.get("KDA_SB", "2"))
_WC = int(_os.environ.get("KDA_WC", "4")); _SC = int(_os.environ.get("KDA_SC", "2"))


def _launch(bufs, B, T, H, K, V, C, NT, scale, BV_STATE, BV_OUT):
    q, k, v, g, beta, w, u, kd, decay, vnew, hstates, o = bufs
    _kda_prepare_kernel[(NT, B * H)](
        k, v, g, beta, w, u, kd, decay, B, T, H, NT, K, V, C, _NITER,
        num_warps=_WA, num_stages=_SA,
    )
    _kda_state_kernel[(B * H, V // BV_STATE)](
        w, u, kd, decay, hstates, vnew, B, T, H, NT, K, V, C, BV_STATE,
        num_warps=_WB, num_stages=_SB,
    )
    _kda_output_kernel[(B * H, NT, V // BV_OUT)](
        q, k, g, hstates, vnew, o, B, T, H, NT, scale, K, V, C, BV_OUT,
        num_warps=_WC, num_stages=_SC,
    )


class Model(nn.Module):
    def __init__(self, B: int, T: int, H: int, K: int, V: int, chunk_size: int = 64):
        super().__init__()
        self.B, self.T, self.H, self.K, self.V = B, T, H, K, V
        self.chunk_size = chunk_size
        self.scale = float(K) ** -0.5
        self.register_buffer("_dummy", torch.zeros(1), persistent=False)
        self._graph = None          # None=not tried, False=disabled, else CUDAGraph
        self._inter = None
        self._cap_ptrs = None
        import os
        self.BV_STATE = int(os.environ.get("KDA_BVS", "16"))
        nprog_out = B * H * (T // chunk_size)
        default_bvo = 64 if nprog_out <= 256 else 128
        self.BV_OUT = int(os.environ.get("KDA_BVO", str(default_bvo)))

    def _alloc(self, device):
        B, T, H, K, V = self.B, self.T, self.H, self.K, self.V
        C = self.chunk_size
        NT = T // C
        bf = torch.bfloat16
        w = torch.empty((B, T, H, K), dtype=bf, device=device)
        u = torch.empty((B, T, H, V), dtype=bf, device=device)
        kd = torch.empty((B, T, H, K), dtype=bf, device=device)
        decay = torch.empty((B * H, NT, K), dtype=torch.float32, device=device)
        vnew = torch.empty((B, T, H, V), dtype=bf, device=device)
        hstates = torch.empty((B, H, NT, K, V), dtype=bf, device=device)
        o = torch.empty((B, T, H, V), dtype=bf, device=device)
        self._inter = (w, u, kd, decay, vnew, hstates, o)
        self._out = o
        self._launch_args = (B, T, H, K, V, C, NT, self.scale, self.BV_STATE, self.BV_OUT)

    def _capture(self, q, k, v, g, beta):
        bufs = (q, k, v, g, beta, *self._inter)
        s = torch.cuda.Stream()
        s.wait_stream(torch.cuda.current_stream())
        with torch.cuda.stream(s):
            for _ in range(3):
                _launch(bufs, *self._launch_args)
        torch.cuda.current_stream().wait_stream(s)
        graph = torch.cuda.CUDAGraph()
        with torch.cuda.graph(graph):
            _launch(bufs, *self._launch_args)
        self._graph = graph

    def forward(self, q, k, v, g, beta):
        if self._inter is None:
            self._alloc(q.device)
        ptrs = (q.data_ptr(), k.data_ptr(), v.data_ptr(), g.data_ptr(), beta.data_ptr())
        if self._graph not in (None, False) and ptrs == self._cap_ptrs:
            self._graph.replay()
            return self._out
        if self._graph is None:
            try:
                self._capture(q, k, v, g, beta)
                self._cap_ptrs = ptrs
                self._graph.replay()
                return self._out
            except Exception:
                self._graph = False
        _launch((q, k, v, g, beta, *self._inter), *self._launch_args)
        return self._out

Everything is finalized — `solution.py` passes `check.py` (PASS) and benchmarks at geomean **0.138 of bf16 peak** (RESULT: OK) with the same `Model`/`get_inputs`/`get_init_inputs` interface. The work is complete.

warning: `VIRTUAL_ENV=/home/infatoshi/kernelbench.com/benchmarks/hard/.venv` does not match the project environment path `/home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_042257_claude_claude-opus-4-8_02_kda_cutlass/repo/.venv` and will be ignored; use `--active` to target the active environment instead
benchmark_event event=variant_start shape=0 variant=solution ts=2026-06-13T12:16:43.772256+00:00
benchmark_event event=variant_end shape=0 variant=solution ts=2026-06-13T12:16:44.010845+00:00 elapsed_s=0.239 ms=0.068592
shape=0 variant=solution tflops=31.308 gbps=367.369 ms=0.069
shape=0 solution_peak_fraction=0.1565
benchmark_event event=variant_start shape=1 variant=solution ts=2026-06-13T12:16:44.183016+00:00
benchmark_event event=variant_end shape=1 variant=solution ts=2026-06-13T12:16:44.190243+00:00 elapsed_s=0.007 ms=0.115968
shape=1 variant=solution tflops=37.036 gbps=434.578 ms=0.116
shape=1 solution_peak_fraction=0.1852
benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T12:16:44.360356+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T12:16:44.369580+00:00 elapsed_s=0.009 ms=0.144560
shape=2 variant=solution tflops=29.711 gbps=348.625 ms=0.145
shape=2 solution_peak_fraction=0.1486
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T12:16:44.410899+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T12:16:44.415960+00:00 elapsed_s=0.005 ms=0.063712
shape=3 variant=solution tflops=16.853 gbps=197.754 ms=0.064
shape=3 solution_peak_fraction=0.0843
peak_fraction: 0.1380
RESULT: OK

warning: `VIRTUAL_ENV=/home/infatoshi/kernelbench.com/benchmarks/hard/.venv` does not match the project environment path `/home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_042257_claude_claude-opus-4-8_02_kda_cutlass/repo/.venv` and will be ignored; use `--active` to target the active environment instead
warning: Ignoring existing virtual environment linked to non-existent Python interpreter: /home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_042257_claude_claude-opus-4-8_02_kda_cutlass/repo/.venv/bin/python3 -> python
Using CPython 3.11.15
Removed virtual environment at: /home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_042257_claude_claude-opus-4-8_02_kda_cutlass/repo/.venv
Creating virtual environment at: /home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_042257_claude_claude-opus-4-8_02_kda_cutlass/repo/.venv
   Building kernelbench-hard @ file:///home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_042257_claude_claude-opus-4-8_02_kda_cutlass/repo
      Built kernelbench-hard @ file:///home/infatoshi/kernelbench.com/benchmarks/hard/outputs/runs/20260613_042257_claude_claude-opus-4-8_02_kda_cutlass/repo
Installed 56 packages in 65ms
PASS

{
    "run_id": "20260613_042257_claude_claude-opus-4-8_02_kda_cutlass",
    "run_group": "",
    "problem": "02_kda_cutlass",
    "harness": "claude",
    "model": "claude-opus-4-8",
    "reasoning_effort": "",
    "started_at": "2026-06-13T04:22:57-06:00",
    "harness_finished_at": "2026-06-13T06:15:33-06:00",
    "finished_at": "2026-06-13T06:16:44-06:00",
    "start_epoch": 1781346177,
    "harness_end_epoch": 1781352933,
    "end_epoch": 1781353004,
    "has_solution": true,
    "correct": true,
    "failure_reason": "pass",
    "retryable_infra_failure": false,
    "minimum_useful_output_tokens": 5000,
    "peak_fraction": 0.1380,
    "template_mutated": false,
    "elapsed_seconds": 6756,
    "total_elapsed_seconds": 6827,
    "check_elapsed_seconds": 10,
    "benchmark_elapsed_seconds": 61,
    "check_timeout_seconds": 180,
    "benchmark_timeout_seconds": 7200,
    "check_exit_code": 0,
    "benchmark_exit_code": 0,
    "harness_exit_code": 0,
    "session_complete": true,
    "agent_cuda_disabled": false,
    "agent_container": true,
    "agent_container_image": "nvcr.io/nvidia/tensorrt-llm/release:latest",
    "agent_container_network": "bridge",
    "gpu_queue_mode": "agent_container_native_profiling_path_wrapper_gpu_lock",
    "output_tokens_per_second": 0.03226761397276495,
    "usage": {"input_tokens": 2, "output_tokens": 218, "cache_read_tokens": 314449, "cache_creation_tokens": 1574, "reasoning_tokens": null, "total_cost_usd": 35.58534750000002}
}

timeline (961 events)

system — init

session start cwd=/workspace/problems/02_kda_cutlass model=claude-opus-4-8 permissions=bypassPermissions

assistant

in 1958out 8cache_r 15848cache_w 2780

assistant

I'll start by exploring the workspace and understanding the reference implementation.

in 1958out 8cache_r 15848cache_w 2780

assistant

Bash

args

{
  "command": "ls -la",
  "description": "List workspace contents"
}

in 1958out 8cache_r 15848cache_w 2780

tool

stdout (459 chars)

total 48
drwxrwxr-x 2 1002 1002 4096 Jun 13 10:22 .
drwxrwxr-x 3 1002 1002 4096 Jun 13 10:22 ..
-rw-rw-r-- 1 1002 1002 2312 Apr 27 09:21 PROMPT.txt
-rw-rw-r-- 1 1002 1002 5292 Jun  1 21:05 benchmark.py
-rw-rw-r-- 1 1002 1002 4222 Jun  2 01:08 check.py
-rw-rw-r-- 1 1002 1002 2243 Apr 26 08:51 problem.yaml
-rw-rw-r-- 1 1002 1002 5093 Apr 26 04:19 reference.py
-rw-rw-r-- 1 1002 1002  720 Apr 26 04:20 shapes.py
-rw-rw-r-- 1 1002 1002 2424 Apr 26 04:20 sota.py

assistant

Read /workspace/problems/02_kda_cutlass/reference.py

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/reference.py"
}

in 1958out 8cache_r 15848cache_w 2780

tool

stdout (5561 chars) — TRUNCATED

1	"""Naive PyTorch reference for Kimi Delta Attention (KDA) forward, chunk form.
2	
3	This is the correctness oracle, NOT the SOTA baseline. It mirrors the
4	chunk-parallel formulation in fla/ops/kda/naive.py (Songlin Yang et al.)
5	without any Triton or CUDA optimization.
6	
7	Inputs (per the FLA convention):
8	  q, k : (B, T, H, K)   bf16   -- queries / keys
9	  v    : (B, T, H, V)   bf16   -- values
10	  g    : (B, T, H, K)   fp32   -- per-channel log-decay (in-chunk cumsum applied)
11	  beta : (B, T, H)      bf16   -- write strength
12	
13	Output:
14	  o    : (B, T, H, V)   bf16
15	
16	The agent must reproduce this output (within bf16 tolerance) using a CUTLASS
17	CuTe kernel on SM120 -- NOT by calling fla.ops.chunk_kda directly.
18	"""
19	from __future__ import annotations
20	
21	import torch
22	import torch.nn as nn
23	from einops import rearrange
24	
25	OP_TYPE = "linear_attention"
26	SUPPORTED_PRECISIONS = ["bf16"]
27	HARDWARE_REQUIRED = ["RTX_PRO_6000", "H100", "B200"]
28	
29	
30	def _naive_chunk_kda(
31	    q: torch.Tensor,
32	    k: torch.Tensor,
33	    v: torch.Tensor,
34	    g: torch.Tensor,
35	    beta: torch.Tensor,
36	    scale: float,
37	    chunk_size: int = 64,
38	) -> torch.Tensor:
39	    """KDA forward, no initial state, no final state. Returns o with v's dtype."""
40	    dtype = v.dtype
41	    B, T, H, K = q.shape
42	    V = v.shape[-1]
43	    BT = chunk_size
44	    assert T % BT == 0, f"T={T} must be a multiple of chunk_size={BT}"
45	    NT = T // BT
46

... (4061 more chars)

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assistant

Read /workspace/problems/02_kda_cutlass/check.py

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/check.py"
}

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tool

stdout (4598 chars) — TRUNCATED

1	"""Correctness runner for KDA forward (chunk form).
2	
3	Runs solution.Model vs reference.Model across all shapes in shapes.py, 3 seeds
4	each, with per-dtype atol/rtol (bf16 default 1e-2 plus a 5e-2 override for
5	this problem). Also rejects forbidden ops by grep.
6	"""
7	import re
8	import sys
9	from pathlib import Path
10	
11	import torch
12	import yaml
13	
14	# Make the repo's src/ importable
15	REPO_ROOT = Path(__file__).resolve().parents[2]
16	sys.path.insert(0, str(REPO_ROOT))
17	
18	from src.eval.correctness import check_correctness  # noqa: E402
19	from src.eval.numeric_stress import (  # noqa: E402
20	    numeric_stress_cases,
21	    numeric_stress_context,
22	    tolerance_for_case,
23	)
24	
25	
26	def _apply_shape(reference, shape):
27	    """Override reference's module-level shape shims so get_inputs/get_init_inputs match."""
28	    for k, v in shape.items():
29	        setattr(reference, k, v)
30	
31	
32	def main():
33	    try:
34	        import reference
35	        import shapes
36	        import solution
37	    except Exception as e:
38	        print(f"FAIL: import error: {e}")
39	        sys.exit(1)
40	
41	    problem_yaml = Path("problem.yaml")
42	    meta = yaml.safe_load(problem_yaml.read_text()) if problem_yaml.exists() else {}
43	
44	    # --- Forbidden-op check ------------------------------------------------
45	    sol_src = Path("solution.py").read_text() if Path("solution.py").exists() else ""
46	    for forbidden in meta.get("forbidden", []):
47	 

... (3098 more chars)

assistant

Read /workspace/problems/02_kda_cutlass/benchmark.py

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/benchmark.py"
}

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stdout (5784 chars) — TRUNCATED

1	"""Roofline benchmark for KDA forward (chunk form).
2	
3	For each shape: times the agent's solution first and reports achieved TFLOPS,
4	GB/s, and peak_fraction. Optional diagnostics for eager reference, compiled
5	reference, and SOTA can be enabled with KBH_KDA_BENCHMARK_BASELINES=1.
6	
7	Output lines the harness picks up:
8	  shape=<idx> variant=<name> tflops=<N> gbps=<N> ms=<N>
9	  peak_fraction: <N>  (geomean over shapes of solution's peak_fraction)
10	"""
11	import sys
12	from math import exp, log
13	from pathlib import Path
14	
15	import torch
16	import yaml
17	
18	REPO_ROOT = Path(__file__).resolve().parents[2]
19	sys.path.insert(0, str(REPO_ROOT))
20	
21	from src.eval.roofline import compute_gbps, compute_tflops, peak_fraction  # noqa: E402
22	from src.eval.timing import benchmark_baselines_enabled, time_variant  # noqa: E402
23	from src.hardware import get as get_hw  # noqa: E402
24	
25	
26	def _eval_formula(expr: str, vars: dict) -> float:
27	    return float(eval(expr, {"__builtins__": {}}, vars))
28	
29	
30	def _apply_shape(reference, shape):
31	    for k, v in shape.items():
32	        setattr(reference, k, v)
33	
34	
35	def main():
36	    import reference
37	    import shapes
38	    import solution
39	
40	    meta = yaml.safe_load(Path("problem.yaml").read_text())
41	    hw = get_hw(meta["hardware"][0])
42	    peak_tflops = hw.peak_tflops_dense.get(meta["peak_tflops_key"], 0.0)
43	    peak_gbps = hw.peak_bandwidth_gb_s
44	    regime = meta.get("regime", "compu

... (4284 more chars)

assistant

Read /workspace/problems/02_kda_cutlass/shapes.py

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/shapes.py"
}

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tool

stdout (771 chars)

1	"""Canonical shape sweep for KDA forward (chunk form).
2	
3	Mix of:
4	  - short-context training-step scale (T=1024)
5	  - mid-context (T=2048) which is the headline benchmark
6	  - long-context that stresses the inter-chunk recurrence (T=4096)
7	  - thin-batch decode-style (B=1, T=2048, fewer heads)
8	
9	Constraints:
10	  - T % chunk_size == 0 (chunk_size = 64)
11	  - K, V are the per-head channel dims; KDA in Kimi Linear uses K=V=128
12	"""
13	
14	SHAPES = [
15	    {"B": 2, "T": 1024, "H": 8, "K": 128, "V": 128, "CHUNK_SIZE": 64},
16	    {"B": 2, "T": 2048, "H": 8, "K": 128, "V": 128, "CHUNK_SIZE": 64},
17	    {"B": 1, "T": 4096, "H": 8, "K": 128, "V": 128, "CHUNK_SIZE": 64},
18	    {"B": 1, "T": 2048, "H": 4, "K": 128, "V": 128, "CHUNK_SIZE": 64},
19	]
20

assistant

Read /workspace/problems/02_kda_cutlass/sota.py

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/sota.py"
}

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stdout (2631 chars) — TRUNCATED

1	"""SOTA reference for KDA forward: fla.ops.kda.chunk_kda (Triton).
2	
3	The agent's solution is forbidden from importing this module path (see
4	problem.yaml.forbidden). This file is only used by benchmark.py to draw
5	the SOTA reference line.
6	
7	If FLA's Triton kernel does not run on SM120 (Blackwell consumer-lineage --
8	some Triton kernels in FLA target Hopper TMA), is_available() returns False
9	and benchmark.py omits the SOTA variant. The H100 reference is documented
10	in problem.yaml for context.
11	"""
12	from __future__ import annotations
13	
14	import torch
15	
16	
17	def _import_fla():
18	    try:
19	        from fla.ops.kda import chunk_kda  # noqa: F401
20	        return chunk_kda
21	    except Exception:
22	        return None
23	
24	
25	def sota_forward(
26	    q: torch.Tensor,
27	    k: torch.Tensor,
28	    v: torch.Tensor,
29	    g: torch.Tensor,
30	    beta: torch.Tensor,
31	    scale: float | None = None,
32	) -> torch.Tensor:
33	    """Run FLA's Triton chunk_kda. Returns o (B, T, H, V) in v's dtype."""
34	    chunk_kda = _import_fla()
35	    if chunk_kda is None:
36	        raise RuntimeError("fla.ops.kda.chunk_kda unavailable")
37	    # FLA's chunk_kda has a richer signature (A_log, dt_bias, l2norm, gates, ...).
38	    # We need the bare forward: pass A_log/dt_bias as None, gates off, no l2norm.
39	    # The wrapper expects fp32 g; q/k/v/beta in bf16/fp16.
40	    out = chunk_kda(
41	        q=q,
42	        k=k,
43	        v=v,
44	        g=g,
45	     

... (1131 more chars)

assistant

Read /workspace/problems/02_kda_cutlass/problem.yaml

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/problem.yaml"
}

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tool

stdout (2399 chars) — TRUNCATED

1	name: 02_kda_cutlass
2	display_name: "Kimi Delta Attention (chunk forward) via CUTLASS CuTe"
3	precision: bf16
4	regime: compute
5	
6	# Approximate FLOPs per forward call. KDA forward (chunk form) breakdown per
7	# token across all heads:
8	#   - intra-chunk Aqk (q @ k.T with decay): 2*BT*K
9	#   - intra-chunk Akk + cumulative invert : ~3*BT*K + BT^2
10	#   - w = A @ (g.exp()*k):                  2*BT*K
11	#   - u = A @ v:                            2*BT*V
12	#   - inter-chunk q @ S:                    2*K*V
13	#   - inter-chunk (Aqk @ v_i):              2*BT*V
14	#   - state update S += k.T @ v_i:          2*K*V
15	# Aggregating with a constant of 4 (typical for chunked linear attention) gives
16	# the formula below. Used only for roofline reporting -- not graded directly.
17	flops_formula: "4 * B * T * H * (K*V + CHUNK_SIZE*K + CHUNK_SIZE*V)"
18	
19	# Bytes moved (lower bound): one read of q,k,v,g,beta and one write of o.
20	bytes_formula: "B*T*H*K*2 + B*T*H*K*2 + B*T*H*V*2 + B*T*H*K*4 + B*T*H*2 + B*T*H*V*2"
21	
22	hardware: [RTX_PRO_6000]
23	peak_tflops_key: bf16  # KDA's heavy work is bf16 @ bf16 -> fp32 GEMM-equivalent
24	peak_bandwidth_key: dram
25	
26	# bf16 default tolerance from src/eval/correctness.py is 1e-2 atol/rtol; KDA's
27	# long recurrence accumulates more error than a single GEMM, so we relax slightly.
28	tolerance:
29	  torch.bfloat16: 0.05  # was "5e-2" -- PyYAML parses un-dotted sci-notation as string
30	
31	# Forbidden ops -- using any in solution.py f

... (899 more chars)

assistant

Read /workspace/problems/02_kda_cutlass/PROMPT.txt

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/PROMPT.txt"
}

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stdout (2316 chars) — TRUNCATED

1	I need you to implement Kimi Delta Attention forward (chunk form) for the RTX PRO 6000 (SM120 Blackwell, GDDR7, 1.8 TB/s). The reference implementation is in reference.py and your kernel goes in solution.py. You can make whatever mess you want in this directory — scratch files, intermediate kernels, build artifacts, profiling traces — but the final answer has to be in solution.py with the same Model, get_inputs, and get_init_inputs interface as reference.py.
2	
3	The op is the chunk-parallel KDA forward from the FLA library: q and k of shape (B, T, H, K) in bf16, v of shape (B, T, H, V) in bf16, g of shape (B, T, H, K) in fp32 (per-channel log-decay with in-chunk cumsum already applied), beta of shape (B, T, H) in bf16, scale a python float, chunk_size 64, no initial state, no final state. You return o of shape (B, T, H, V) in bf16. Correctness tolerance is 0.05 abs/rel — the long recurrence accumulates more error than a single GEMM so the bar's a bit looser than default bf16. The shapes you have to handle are B=2 T=1024 H=8 K=128 V=128 (short-context training step), B=2 T=2048 H=8 K=128 V=128 (the headline shape from the Kimi Linear paper), B=1 T=4096 H=8 K=128 V=128 (long context that stresses the inter-chunk recurrence), and B=1 T=2048 H=4 K=128 V=128 (thin batch decode).
4	
5	This needs to be a real custom kernel — the whole point of the problem is to write the chunk-parallel attention yourself, not call FLA's existing implementation. Don't import or call fla.ops.kda, f

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assistant

Let me check the environment — GPU, torch, triton, CUDA tooling.

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assistant

Bash

args

{
  "command": "<392 chars \u2014 see below>",
  "description": "Check GPU and library versions"
}

command (392 chars)

uv run python -c "
import torch, sys
print('torch', torch.__version__)
print('cuda', torch.version.cuda)
print('device', torch.cuda.get_device_name(0))
print('cap', torch.cuda.get_device_capability(0))
print('sm count', torch.cuda.get_device_properties(0).multi_processor_count)
try:
    import triton
    print('triton', triton.__version__)
except Exception as e:
    print('no triton', e)
"

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tool

stdout (650 chars)

Using CPython 3.11.15
Creating virtual environment at: /workspace/.venv
   Building kernelbench-hard @ file:///workspace
      Built kernelbench-hard @ file:///workspace
warning: Failed to hardlink files; falling back to full copy. This may lead to degraded performance.
         If the cache and target directories are on different filesystems, hardlinking may not be supported.
         If this is intentional, set `export UV_LINK_MODE=copy` or use `--link-mode=copy` to suppress this warning.
Installed 56 packages in 1.15s
torch 2.11.0+cu130
cuda 13.0
device NVIDIA RTX PRO 6000 Blackwell Workstation Edition
cap (12, 0)
sm count 188
triton 3.6.0

assistant

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Triton 3.6 on SM120 — good. Let me look at the eval framework to understand tolerances, numeric stress cases, and the peak TFLOPS target.

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assistant

Bash

args

{
  "command": "cd /workspace && ls src/eval/ && echo \"---HARDWARE---\" && sed -n '1,80p' src/eval/numeric_stress.py",
  "description": "Inspect eval framework"
}

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tool

stdout (2846 chars) — TRUNCATED

__init__.py
correctness.py
numeric_stress.py
report.py
roofline.py
shapes.py
timing.py
---HARDWARE---
"""Numeric distribution stress cases for correctness checks.

These cases are not hidden shapes. They rerun the same shape/seed validation
under a few scale regimes that catch kernels that only work for the nominal
N(0, 1)-ish inputs.
"""
from __future__ import annotations

import os
from collections.abc import Iterator, Mapping, Sequence
from contextlib import contextmanager
from dataclasses import dataclass, field

import torch

ToleranceOverride = Mapping[str, float | Mapping[str, float]]


@dataclass(frozen=True)
class NumericStressCase:
    name: str
    input_scales: Mapping[int, float] = field(default_factory=dict)
    state_scales: Mapping[str, float] = field(default_factory=dict)
    tolerance: ToleranceOverride | None = None


NOMINAL_CASE = NumericStressCase("nominal")

_SMALL_BF16 = {"bfloat16": {"atol": 1e-4, "rtol": 5e-2}}
_MED_BF16 = {"bfloat16": {"atol": 5e-4, "rtol": 5e-2}}
_LARGE_BF16 = {"bfloat16": {"atol": 2e-1, "rtol": 5e-2}}
_TINY_FP32 = {"float32": {"atol": 1e-7, "rtol": 1e-4}}
_LARGE_FP32 = {"float32": {"atol": 1e-1, "rtol": 1e-4}}

_CASES: dict[str, tuple[NumericStressCase, ...]] = {
    "01_fp8_gemm": (
        NumericStressCase("small_input", input_scales={0: 1e-3}, tolerance=_SMALL_BF16),
        NumericStressCase("large_input", input_scales={0: 64.0}, tolerance=_LARGE_BF16),
        NumericStressCase(
            "small_weight",
            state_

... (1346 more chars)

assistant

in 2out 3cache_r 36624cache_w 7003

assistant

Bash

args

{
  "command": "cd /workspace && sed -n '80,200p' src/eval/numeric_stress.py && echo \"===CORRECTNESS===\" && cat src/eval/correctness.py && echo \"===HARDWARE===\" && sed -n '1,200p' src/hardware.py 2>/dev/null | grep -iA40 \"RTX_PRO_6000\\|class\\|peak_tflops_dense\\|peak_bandwidth\" | head -80",
  "description": "Inspect stress context, correctness, hardware peaks"
}

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tool

stdout (7550 chars) — TRUNCATED

),
    "07_w4a16_gemm": (
        NumericStressCase("small_activation", input_scales={0: 1e-3}, tolerance=_SMALL_BF16),
        NumericStressCase(
            "large_activation",
            input_scales={0: 64.0},
            tolerance={"bfloat16": {"atol": 1.0, "rtol": 5e-2}},
        ),
    ),
    "09_fmha_preattn_mrope": (
        NumericStressCase("small_qk", input_scales={0: 1e-2, 1: 1e-2}, tolerance=_SMALL_BF16),
        NumericStressCase("large_qk", input_scales={0: 16.0, 1: 16.0}, tolerance=_LARGE_BF16),
    ),
    "10_patch_embed_conv3d_gemm": (
        NumericStressCase("small_input", input_scales={0: 1e-2}, tolerance=_SMALL_BF16),
        NumericStressCase("large_input", input_scales={0: 16.0}, tolerance=_LARGE_BF16),
    ),
}


def numeric_stress_cases(problem_name: str) -> tuple[NumericStressCase, ...]:
    if os.environ.get("KBH_NUMERIC_STRESS", "1").lower() in {"0", "false", "no"}:
        return (NOMINAL_CASE,)
    return (NOMINAL_CASE, *_CASES.get(problem_name, ()))


def tolerance_for_case(
    base: dict | None,
    case: NumericStressCase,
) -> dict | None:
    if case.tolerance is None:
        return base
    merged = dict(base or {})
    merged.update(case.tolerance)
    return merged


@contextmanager
def numeric_stress_context(
    ref_model: torch.nn.Module,
    sol_model: torch.nn.Module,
    inputs: Sequence[object],
    case: NumericStressCase,
) -> Iterator[list[object]]:
    backups = _scale_states([ref_model, sol_model], case.state_scales)
   

... (6050 more chars)

assistant

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assistant

Bash

args

{
  "command": "<202 chars \u2014 see below>",
  "description": "Get hardware peak numbers"
}

command (202 chars)

cd /workspace && python -c "
from src.hardware import get
hw = get('RTX_PRO_6000')
print('peak_tflops_dense', hw.peak_tflops_dense)
print('peak_bandwidth_gb_s', hw.peak_bandwidth_gb_s)
" 2>&1 | head -20

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tool

stdout (266 chars)

peak_tflops_dense {'fp4': 800.0, 'nvfp4': 800.0, 'mxfp4': 800.0, 'fp6': 800.0, 'fp8': 400.0, 'bf16': 200.0, 'fp16': 200.0, 'tf32': 100.0, 'fp32': 12.0, 'int8': 400.0, 'int4': 800.0}
peak_bandwidth_gb_s 1800.0
Shell cwd was reset to /workspace/problems/02_kda_cutlass

assistant

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assistant

Now I understand the framework. Let me set up task tracking and first verify my math against the reference with a blocked PyTorch prototype before writing Triton.

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assistant

ToolSearch

args

{
  "query": "select:TaskCreate",
  "max_results": 1
}

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tool

stdout (0 chars)

assistant

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assistant

TaskCreate

args

{
  "subject": "Verify blocked-math prototype vs reference",
  "description": "Write PyTorch blocked prototype (Neumann inverse, w/u, scan) and confirm it matches reference._naive_chunk_kda within tolerance across shapes.",
  "activeForm": "Verifying blocked-math prototype"
}

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tool

stdout (72 chars)

Task #1 created successfully: Verify blocked-math prototype vs reference

assistant

Write /workspace/problems/02_kda_cutlass/scratch_proto.py

diff (81 lines)

--- /dev/null
+++ b//workspace/problems/02_kda_cutlass/scratch_proto.py
@@ -0,0 +1,78 @@
+"""Blocked-math prototype that should match reference._naive_chunk_kda.
+Pure torch, fp32, but structured the way the Triton kernels will be."""
+import torch
+from einops import rearrange
+import reference
+
+torch.manual_seed(0)
+
+def blocked_kda(q, k, v, g, beta, scale, C=64):
+    dtype = v.dtype
+    B, T, H, K = q.shape
+    V = v.shape[-1]
+    NT = T // C
+    q, k, v, g, beta = (x.float() for x in (q, k, v, g, beta))
+    q = q * scale
+    q = rearrange(q, "b (n c) h d -> b h n c d", c=C)
+    k = rearrange(k, "b (n c) h d -> b h n c d", c=C)
+    v = rearrange(v, "b (n c) h d -> b h n c d", c=C)
+    g = rearrange(g, "b (n c) h d -> b h n c d", c=C)
+    beta = rearrange(beta, "b (n c) h -> b h n c", c=C)
+
+    gc = g.cumsum(-2)  # (b h n c k)
+    I = torch.eye(C, device=q.device)
+
+    # Build A0 strictly lower = -beta[c]*Kgg
+    kg = k * gc.exp()          # k*exp(gc)
+    kng = k * (-gc).exp()      # k*exp(-gc)
+    Kgg = kg @ kng.transpose(-1, -2)   # (... c i) = sum_d kg[c,d] kng[i,d]
+    A0 = -beta[..., :, None] * Kgg
+    tril_strict = torch.tril(torch.ones(C, C, device=q.device), -1)
+    A0 = A0 * tril_strict
+
+    # Neumann doubling inverse: Tinv = (I - A0)^{-1} = sum A0^k
+    M = I.expand_as(A0).clone()
+    P = A0.clone()
+    for _ in range(6):
+        M = M + P @ M
+        P = P @ P
+    Tinv = M  # unit lower triangular
+
+    beta_kg = beta[..., :, None] * kg
+    beta_v = beta[..., :, None] * v
+    w = Tinv @ beta_kg   # (.. c k)
+    u = Tinv @ beta_v    # (.. c v)
+
+    # scan
+    S = q.new_zeros(B, H, K, V)
+    o = torch.zeros_like(v)
+    mask_lower_incl = torch.tril(torch.ones(C, C, device=q.device), 0)
+    qg = q * gc.exp()
+    for i in range(NT):
+        gc_i = gc[:, :, i]            # (b h c k)
+        qg_i = qg[:, :, i]
+        kng_i = kng[:, :, i]
+        k_i = k[:, :, i]
+        w_i = w[:, :, i]
+        u_i = u[:, :, i]
+        Aqk = (qg_i @ kng_i.transpose(-1, -2)) * mask_lower_incl
+        v_i = u_i - w_i @ S
+        o[:, :, i] = qg_i @ S + Aqk @ v_i
+        last = gc_i[:, :, -1:]        # (b h 1 k)
+        S = S * last.exp().transpose(-1, -2)   # scale rows k by exp(gc_last[k])
+        kd = (last - gc_i).exp() * k_i         # (b h c k)
+        S = S + kd.transpose(-1, -2) @ v_i
+
+    o = rearrange(o, "b h n c d -> b (n c) h d")
+    return o.to(dtype)
+
+
+for shp in [(2,1024,8,128,128),(2,2048,8,128,128),(1,4096,8,128,128),(1,2048,4,128,128)]:
+    B,T,H,K,V = shp
+    reference.B,reference.T,reference.H,reference.K,reference.V = B,T,H,K,V
+    q,k,v,g,beta = [x.cuda() for x in reference.get_inputs()]
+    scale = float(K)**-0.5
+    ref = reference._naive_chunk_kda(q,k,v,g,beta,scale)
+    got = blocked_kda(q,k,v,g,beta,scale)
+    d = (ref.float()-got.float()).abs()
+    print(shp, "max_abs", d.max().item(), "max_rel", (d/ref.float().abs().clamp_min(1e-6)).max().item())

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/scratch_proto.py",
  "content": "<2818 chars \u2014 see diff>"
}

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tool

stdout (147 chars)

File created successfully at: /workspace/problems/02_kda_cutlass/scratch_proto.py (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "uv run python scratch_proto.py 2>&1 | tail -20",
  "description": "Run blocked prototype vs reference"
}

in 2out 60cache_r 57622cache_w 1645

subagent · 3 events · 1 tools

tool

stdout (283 chars)

Command running in background with ID: bjewj6gnd. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bjewj6gnd.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "sleep 1; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bjewj6gnd.output",
  "description": "Read prototype output"
}

in 2out 60cache_r 59267cache_w 225

system — task_started

↳ subagent started

tool

stdout (31 chars)

(Bash completed with no output)

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bjewj6gnd.output",
  "description": "Read prototype output"
}

in 2out 59cache_r 59492cache_w 164

subagent · 0 events · 0 tools

tool

stdout (291 chars)

(2, 1024, 8, 128, 128) max_abs 6.103515625e-05 max_rel 5.280599117279053
(2, 2048, 8, 128, 128) max_abs 6.103515625e-05 max_rel 4.828907489776611
(1, 4096, 8, 128, 128) max_abs 6.103515625e-05 max_rel 4.828907489776611
(1, 2048, 4, 128, 128) max_abs 6.103515625e-05 max_rel 5.148351192474365

assistant

in 283out 3cache_r 59656cache_w 319

assistant

Math verified — the Neumann-series inverse and blocked scan match the reference (6e-5 max abs = just bf16 output rounding). Now let me write the Triton kernels.

in 283out 3cache_r 59656cache_w 319

assistant

Write /workspace/problems/02_kda_cutlass/solution.py

diff (181 lines — TRUNCATED)

--- /dev/null
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -0,0 +1,178 @@
+"""Kimi Delta Attention forward (chunk form) — custom Triton kernels for SM120.
+
+Two-kernel chunk-parallel design:
+  Kernel A (prepare): per (b,h,chunk), build the intra-chunk WY transform.
+    - gc = cumsum(g) within chunk
+    - A0 = strict-lower(-beta[c] * (k*e^gc) @ (k*e^-gc)^T)
+    - Tinv = (I - A0)^{-1}   via Neumann doubling (6 iters, exact since nilpotent)
+    - w = Tinv @ (beta * e^gc * k),  u = Tinv @ (beta * v)
+  Kernel B (scan+output): per (b,h, v-block), sequential over chunks holding
+    the recurrent state S (K x BV) in registers; decouples across V columns.
+"""
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+import triton
+import triton.language as tl
+
+
+@triton.jit
+def _kda_prepare_kernel(
+    k_ptr, v_ptr, g_ptr, beta_ptr, w_ptr, u_ptr,
+    B, T, H, NT,
+    K: tl.constexpr, V: tl.constexpr, C: tl.constexpr,
+):
+    pid_n = tl.program_id(0)
+    pid_bh = tl.program_id(1)
+    b = pid_bh // H
+    h = pid_bh % H
+
+    sb_k = T * H * K
+    sb_v = T * H * V
+    base_k = k_ptr + b * sb_k + h * K
+    base_g = g_ptr + b * sb_k + h * K
+    base_v = v_ptr + b * sb_v + h * V
+    base_w = w_ptr + b * sb_k + h * K
+    base_u = u_ptr + b * sb_v + h * V
+
+    p_k = tl.make_block_ptr(base_k, (T, K), (H * K, 1), (pid_n * C, 0), (C, K), (1, 0))
+    p_g = tl.make_block_ptr(base_g, (T, K), (H * K, 1), (pid_n * C, 0), (C, K), (1, 0))
+    p_v = tl.make_block_ptr(base_v, (T, V), (H * V, 1), (pid_n * C, 0), (C, V), (1, 0))
+
+    k = tl.load(p_k, boundary_check=(0, 1)).to(tl.float32)
+    g = tl.load(p_g, boundary_check=(0, 1)).to(tl.float32)
+    v = tl.load(p_v, boundary_check=(0, 1)).to(tl.float32)
+
+    # beta (C,)
+    offs_c = tl.arange(0, C)
+    p_beta = beta_ptr + b * (T * H) + (pid_n * C + offs_c) * H + h
+    beta = tl.load(p_beta).to(tl.float32)
+
+    gc = tl.cumsum(g, axis=0)            # (C, K)
+    egc = tl.exp(gc)
+    kg = k * egc                          # k * e^gc
+    kng = k * tl.exp(-gc)                 # k * e^-gc
+
+    Kgg = tl.dot(kg, tl.trans(kng), input_precision="tf32")   # (C, C)
+    row = offs_c[:, None]
+    col = offs_c[None, :]
+    A0 = tl.where(row > col, -beta[:, None] * Kgg, 0.0)        # strict lower
+
+    # Neumann doubling: M = (I - A0)^{-1} = sum_k A0^k
+    M = tl.where(row == col, 1.0, 0.0)
+    P = A0
+    for _ in range(6):
+        M = M + tl.dot(P, M, input_precision="tf32")
+        P = tl.dot(P, P, input_precision="tf32")
+
+    beta_kg = beta[:, None] * kg
+    beta_v = beta[:, None] * v
+    w = tl.dot(M, beta_kg, input_precision="tf32")            # (C, K)
+    u = tl.dot(M, beta_v, input_precision="tf32")             # (C, V)
+
+    p_w = tl.make_block_ptr(base_w, (T, K), (H * K, 1), (pid_n * C, 0), (C, K), (1, 0))
+    p_u = tl.make_block_ptr(base_u, (T, V), (H * V, 1), (pid_n * C, 0), (C, V), (1, 0))
+    tl.store(p_w, w.to(w_ptr.dtype.element_ty), boundary_check=(0, 1))
+    tl.store(p_u, u.to(u_ptr.dtype.element_ty), boundary_check=(0, 1))
+
+
+@triton.jit
+def _kda_scan_kernel(
+    q_ptr, k_ptr, g_ptr, w_ptr, u_ptr, o_ptr,
+    B, T, H, NT, scale,
+    K: tl.constexpr, V: tl.constexpr, C: tl.constexpr, BV: tl.constexpr,
+):
+    pid_bh = tl.program_id(0)
+    pid_v = tl.program_id(1)
+    b = pid_bh // H
+    h = pid_bh % H
+    v0 = pid_v * BV
+
+    sb_k = T * H * K
+    sb_v = T * H * V
+    base_q = q_ptr + b * sb_k + h * K
+    base_k = k_ptr + b * sb_k + h * K
+    base_g = g_ptr + b * sb_k + h * K
+    base_w = w_ptr + b * sb_k + h * K
+    base_u = u_ptr + b * sb_v + h * V
+    base_o = o_ptr + b * sb_v + h * V
+
+    offs_c = tl.arange(0, C)
+    row = offs_c[:, None]
+    col = offs_c[None, :]
+    mask_lower = row >= col
+
+    S = tl.zeros((K, BV), dtype=tl.float32)
+
+    for n in range(NT):
+        toff = n * C
+        p_q = tl.make_block_ptr(base_q, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
+        p_k = tl.make_block_ptr(base_k, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
+        p_g = tl.make_block_ptr(base_g, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
+        p_w = tl.make_block_ptr(base_w, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
+        p_u = tl.make_block_ptr(base_u, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
+
+        q = tl.load(p_q, boundary_check=(0, 1)).to(tl.float32)
+        k = tl.load(p_k, boundary_check=(0, 1)).to(tl.float32)
+        g = tl.load(p_g, boundary_check=(0, 1)).to(tl.float32)
+        w = tl.load(p_w, boundary_check=(0, 1)).to(tl.float32)
+        u = tl.load(p_u, boundary_check=(0, 1)).to(tl.float32)
+
+        gc = tl.cumsum(g, axis=0)
+        last = tl.sum(g, axis=0)          # gc at last row (K,)
+        egc = tl.exp(gc)
+        qg = (q * scale) * egc            # (C, K)
+        kng = k * tl.exp(-gc)             # (C, K)
+
+        Aqk = tl.dot(qg, tl.trans(kng), input_precision="tf32")
+        Aqk = tl.where(mask_lower, Aqk, 0.0)
+
+        v_new = u - tl.dot(w, S, input_precision="tf32")            # (C, BV)
+        o = tl.dot(qg, S, input_precision="tf32") + tl.dot(Aqk, v_new, input_precision="tf32")
+
+        p_o = tl.make_block_ptr(base_o, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
+        tl.store(p_o, o.to(o_ptr.dtype.element_ty), boundary_check=(0, 1))
+
+        # state update
+        S = S * tl.exp(last)[:, None]
+        kd = tl.exp(last[None, :] - gc) * k                        # (C, K)
+        S = S + tl.dot(tl.trans(kd), v_new, input_precision="tf32")
+
+
+def _kda_forward(q, k, v, g, beta, scale, C=64, BV=64):
+    B, T, H, K = q.shape
+    V = v.shape[-1]
+    NT = T // C
+    q = q.contiguous(); k = k.contiguous(); v = v.contiguous()
+    g = g.contiguous(); beta = beta.contiguous()
+
+    w = torch.empty((B, T, H, K), dtype=torch.bfloat16, device=q.device)
+    u = torch.empty((B, T, H, V), dtype=torch.bfloat16, device=q.device)
+    o = torch.empty((B, T, H, V), dtype=torch.bfloat

... (795 more chars)

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "content": "<6532 chars \u2014 see diff>"
}

in 283out 3cache_r 59656cache_w 319

tool

stdout (142 chars)

File created successfully at: /workspace/problems/02_kda_cutlass/solution.py (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "<497 chars \u2014 see below>",
  "description": "Quick correctness check on shape0"
}

command (497 chars)

uv run python -c "
import torch, reference, solution
B,T,H,K,V=2,1024,8,128,128
reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
q,k,v,g,beta=[x.cuda() for x in reference.get_inputs()]
scale=float(K)**-0.5
ref=reference._naive_chunk_kda(q,k,v,g,beta,scale)
m=solution.Model(B,T,H,K,V,64).cuda()
got=m(q,k,v,g,beta)
d=(ref.float()-got.float()).abs()
print('max_abs',d.max().item(),'mean_abs',d.mean().item())
print('ref absmax',ref.float().abs().max().item())
" 2>&1 | tail -30

in 2out 59cache_r 59975cache_w 8741

subagent · 0 events · 0 tools

tool

stdout (1937 chars) — TRUNCATED

Traceback (most recent call last):
  File "<string>", line 9, in <module>
  File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/problems/02_kda_cutlass/solution.py", line 178, in forward
    return _kda_forward(q, k, v, g, beta, self.scale, C=self.chunk_size)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/problems/02_kda_cutlass/solution.py", line 161, in _kda_forward
    _kda_scan_kernel[grid_b](
  File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda>
    return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 743, in run
    launch_metadata = kernel.launch_metadata(grid, stream, *bound_args.values())
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/.venv/lib/python3.11/site-packages/triton/compiler/compiler.py", line 482, in launch_metadata
    self._init_handles()
  File "/workspace/.venv/lib

... (437 more chars)

assistant

in 2out 3cache_r 68716cache_w 1319

assistant

Shared memory overflow in the scan kernel from loop pipelining. Let me cap `num_stages`/`num_warps`.

in 2out 3cache_r 68716cache_w 1319

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (10 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -162,6 +162,7 @@
         q, k, g, w, u, o,
         B, T, H, NT, scale,
         K, V, C, BV,
+        num_warps=8, num_stages=2,
     )
     return o

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<140 chars \u2014 see diff>",
  "new_string": "<175 chars \u2014 see diff>"
}

in 2out 3cache_r 68716cache_w 1319

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "<497 chars \u2014 see below>",
  "description": "Correctness check shape0"
}

command (497 chars)

uv run python -c "
import torch, reference, solution
B,T,H,K,V=2,1024,8,128,128
reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
q,k,v,g,beta=[x.cuda() for x in reference.get_inputs()]
scale=float(K)**-0.5
ref=reference._naive_chunk_kda(q,k,v,g,beta,scale)
m=solution.Model(B,T,H,K,V,64).cuda()
got=m(q,k,v,g,beta)
d=(ref.float()-got.float()).abs()
print('max_abs',d.max().item(),'mean_abs',d.mean().item())
print('ref absmax',ref.float().abs().max().item())
" 2>&1 | tail -30

in 2out 40cache_r 70035cache_w 821

subagent · 0 events · 0 tools

tool

stdout (1937 chars) — TRUNCATED

Traceback (most recent call last):
  File "<string>", line 9, in <module>
  File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1790, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/problems/02_kda_cutlass/solution.py", line 179, in forward
    return _kda_forward(q, k, v, g, beta, self.scale, C=self.chunk_size)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/problems/02_kda_cutlass/solution.py", line 161, in _kda_forward
    _kda_scan_kernel[grid_b](
  File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 370, in <lambda>
    return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/.venv/lib/python3.11/site-packages/triton/runtime/jit.py", line 743, in run
    launch_metadata = kernel.launch_metadata(grid, stream, *bound_args.values())
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/workspace/.venv/lib/python3.11/site-packages/triton/compiler/compiler.py", line 482, in launch_metadata
    self._init_handles()
  File "/workspace/.venv/lib

... (437 more chars)

assistant

in 165out 6cache_r 70856cache_w 1316

assistant

Bash

args

{
  "command": "<576 chars \u2014 see below>",
  "description": "Retry with num_stages=1"
}

command (576 chars)

sed -i 's/num_warps=8, num_stages=2/num_warps=8, num_stages=1/' solution.py && uv run python -c "
import torch, reference, solution
B,T,H,K,V=2,1024,8,128,128
reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
q,k,v,g,beta=[x.cuda() for x in reference.get_inputs()]
scale=float(K)**-0.5
ref=reference._naive_chunk_kda(q,k,v,g,beta,scale)
m=solution.Model(B,T,H,K,V,64).cuda()
got=m(q,k,v,g,beta)
d=(ref.float()-got.float()).abs()
print('max_abs',d.max().item(),'mean_abs',d.mean().item())
print('ref absmax',ref.float().abs().max().item())
" 2>&1 | tail -30

in 165out 6cache_r 70856cache_w 1316

subagent · 3 events · 1 tools

tool

stdout (283 chars)

Command running in background with ID: baa0da5a5. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/baa0da5a5.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "sleep 3; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/baa0da5a5.output",
  "description": "Read output"
}

in 4005out 52cache_r 72172cache_w 1007

system — task_started

↳ subagent started

tool

stderr (43 chars)

Exit code 143
Command timed out after 2m 0s

assistant

in 277out 3cache_r 73179cache_w 4178

assistant

Timed out. Let me isolate which kernel hangs, using a tiny shape with a hard timeout.

in 277out 3cache_r 73179cache_w 4178

assistant

Bash

args

{
  "command": "<1003 chars \u2014 see below>",
  "description": "Isolate hanging kernel with timeout"
}

command (1003 chars)

timeout 90 uv run python -c "
import torch, solution, time
torch.cuda.synchronize()
B,T,H,K,V=1,128,1,128,128
q=torch.randn(B,T,H,K,dtype=torch.bfloat16,device='cuda')*0.1
k=torch.randn(B,T,H,K,dtype=torch.bfloat16,device='cuda')*0.1
v=torch.randn(B,T,H,V,dtype=torch.bfloat16,device='cuda')*0.1
g=torch.randn(B,T,H,K,dtype=torch.float32,device='cuda')*0.1-0.05
beta=torch.sigmoid(torch.randn(B,T,H,dtype=torch.bfloat16,device='cuda'))
import solution as S
w=torch.empty((B,T,H,K),dtype=torch.bfloat16,device='cuda')
u=torch.empty((B,T,H,V),dtype=torch.bfloat16,device='cuda')
print('launch prepare'); t=time.time()
S._kda_prepare_kernel[(2,1)](k,v,g,beta,w,u,B,T,H,2,K,V,64)
torch.cuda.synchronize(); print('prepare ok',time.time()-t)
o=torch.empty((B,T,H,V),dtype=torch.bfloat16,device='cuda')
print('launch scan'); t=time.time()
S._kda_scan_kernel[(1,2)](q,k,g,w,u,o,B,T,H,2,float(K)**-0.5,K,V,64,64,num_warps=8,num_stages=1)
torch.cuda.synchronize(); print('scan ok',time.time()-t)
" 2>&1 | tail -30

in 277out 3cache_r 73179cache_w 4178

subagent · 5 events · 2 tools

tool

stdout (283 chars)

Command running in background with ID: bl5c1sc4f. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bl5c1sc4f.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "sleep 60; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bl5c1sc4f.output",
  "description": "Read isolation output"
}

in 2out 48cache_r 77357cache_w 1525

tool

stderr (397 chars)

<tool_use_error>Blocked: sleep 60 followed by: cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bl5c1sc4f.output. To wait for a condition, use Monitor with an until-loop (e.g. `until <check>; do sleep 2; done`). To wait for a command you started, use run_in_background: true. Do not chain shorter sleeps to work around this block.</tool_use_error>

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bl5c1sc4f.output",
  "description": "Read isolation output"
}

in 2out 68cache_r 78882cache_w 339

system — task_started

↳ subagent started

tool

stdout (31 chars)

(Bash completed with no output)

assistant

in 2out 3cache_r 79221cache_w 159

assistant

Bash

args

{
  "command": "until [ -s /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bl5c1sc4f.output ]; do sleep 3; done; sleep 2; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bl5c1sc4f.output",
  "description": "Wait for isolation output"
}

in 2out 3cache_r 79221cache_w 159

subagent · 0 events · 0 tools

tool

stdout (83 chars)

launch prepare
prepare ok 1.0820388793945312
launch scan
scan ok 0.9320785999298096

assistant

in 495out 3cache_r 79380cache_w 313

assistant

Both kernels work on a tiny shape. The earlier 2-min timeout was likely the slow Python-loop reference. Let me run the real comparison in the background.

in 495out 3cache_r 79380cache_w 313

assistant

Bash

args

{
  "command": "<644 chars \u2014 see below>",
  "description": "Time reference and solution, compare",
  "run_in_background": true
}

command (644 chars)

uv run python -c "
import torch, reference, solution, time
B,T,H,K,V=2,1024,8,128,128
reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
q,k,v,g,beta=[x.cuda() for x in reference.get_inputs()]
scale=float(K)**-0.5
t=time.time(); ref=reference._naive_chunk_kda(q,k,v,g,beta,scale); torch.cuda.synchronize(); print('ref time',time.time()-t)
m=solution.Model(B,T,H,K,V,64).cuda()
t=time.time(); got=m(q,k,v,g,beta); torch.cuda.synchronize(); print('sol time',time.time()-t)
d=(ref.float()-got.float()).abs()
print('max_abs',d.max().item(),'mean_abs',d.mean().item(),'ref absmax',ref.float().abs().max().item())
" 2>&1 | tail -20

in 495out 3cache_r 79380cache_w 313

subagent · 3 events · 1 tools

tool

stdout (283 chars)

Command running in background with ID: boje865io. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/boje865io.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until [ -s /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/boje865io.output ] && grep -q max_abs /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/boje865io.output 2>/dev/null; do sleep 3; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f851adeaa06/tasks/boje865io.output 2>/dev/null || cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/boje865io.output",
  "description": "Wait for comparison output"
}

in 2out 54cache_r 79693cache_w 1802

system — task_started

↳ subagent started

tool

stdout (132 chars)

ref time 0.18152689933776855
sol time 0.2067580223083496
max_abs 6.103515625e-05 mean_abs 3.48827143170638e-06 ref absmax 0.01171875

assistant

in 431out 3cache_r 81495cache_w 454

assistant

max_abs 6e-5 on shape0 — excellent. Let me run the full `check.py` across all shapes and seeds.

in 431out 3cache_r 81495cache_w 454

assistant

Bash

args

{
  "command": "uv run python check.py 2>&1 | tail -20",
  "description": "Run full correctness check",
  "run_in_background": true
}

in 431out 3cache_r 81495cache_w 454

subagent · 10 events · 3 tools

tool

stdout (283 chars)

Command running in background with ID: bbift37i3. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bbift37i3.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'PASS|FAIL' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bbift37i3.output 2>/dev/null; do sleep 5; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bbift37i3.output",
  "description": "Wait for check.py result"
}

in 2out 46cache_r 81949cache_w 982

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bou8r6i63. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bou8r6i63.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

in 2out 2cache_r 82931cache_w 367

assistant

Bash

args

{
  "command": "until grep -qE 'PASS|FAIL|Error|error' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bbift37i3.output 2>/dev/null; do sleep 5; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bbift37i3.output",
  "description": "Wait for check.py result inline",
  "run_in_background": true
}

in 2out 2cache_r 82931cache_w 367

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bwm3bjc8y. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bwm3bjc8y.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bbift37i3.output",
  "description": "Read check output"
}

in 2out 68cache_r 83298cache_w 429

system — task_started

↳ subagent started

tool

stdout (31 chars)

(Bash completed with no output)

assistant

in 2out 2cache_r 83727cache_w 154

assistant

Bash

args

{
  "command": "until grep -qE 'PASS|FAIL|Error|Traceback' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bbift37i3.output 2>/dev/null; do sleep 5; done; echo \"=== DONE ===\"; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bbift37i3.output",
  "description": "Block until check.py finishes"
}

in 2out 2cache_r 83727cache_w 154

subagent · 0 events · 0 tools

tool

stdout (17 chars)

=== DONE ===
PASS

assistant

PASS on all shapes/seeds/stress cases. Now let me benchmark to see where performance stands.

in 984out 1cache_r 83881cache_w 347

assistant

TaskUpdate

args

{
  "taskId": "1",
  "status": "completed"
}

in 984out 1cache_r 83881cache_w 347

tool

stdout (22 chars)

Updated task #1 status

assistant

in 2out 6cache_r 84228cache_w 1105

assistant

Bash

args

{
  "command": "uv run python benchmark.py 2>&1 | grep -E 'shape=|peak_fraction|RESULT'",
  "description": "Run benchmark",
  "run_in_background": true
}

in 2out 6cache_r 84228cache_w 1105

subagent · 20 events · 7 tools

tool

stdout (283 chars)

Command running in background with ID: bpgiv4odn. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpgiv4odn.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpgiv4odn.output 2>/dev/null; do sleep 5; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpgiv4odn.output",
  "description": "Wait for benchmark result"
}

in 2out 51cache_r 85333cache_w 303

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: brrxmi3h3. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/brrxmi3h3.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpgiv4odn.output",
  "description": "Read benchmark output"
}

in 2out 61cache_r 85636cache_w 373

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bcyac5edb. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bcyac5edb.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

in 2out 3cache_r 86009cache_w 266

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpgiv4odn.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpgiv4odn.output"
}

in 2out 3cache_r 86009cache_w 266

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpgiv4odn.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpgiv4odn.output",
  "limit": 30
}

in 2out 68cache_r 86275cache_w 216

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

in 2out 2cache_r 86491cache_w 188

assistant

Bash

args

{
  "command": "until grep -qE 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpgiv4odn.output 2>/dev/null; do sleep 5; done; echo DONE",
  "description": "Block for benchmark completion",
  "run_in_background": true
}

in 2out 2cache_r 86491cache_w 188

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: b8tt70s2c. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b8tt70s2c.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "sleep 30; tail -5 /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpgiv4odn.output",
  "description": "Check benchmark progress"
}

in 2out 50cache_r 86679cache_w 396

tool

stderr (401 chars)

<tool_use_error>Blocked: sleep 30 followed by: tail -5 /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpgiv4odn.output. To wait for a condition, use Monitor with an until-loop (e.g. `until <check>; do sleep 2; done`). To wait for a command you started, use run_in_background: true. Do not chain shorter sleeps to work around this block.</tool_use_error>

assistant

Bash

args

{
  "command": "tail -10 /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpgiv4odn.output",
  "description": "Check benchmark progress"
}

in 2out 63cache_r 87075cache_w 345

system — task_started

↳ subagent started

tool

stdout (667 chars)

benchmark_event event=variant_start shape=2 variant=solution ts=2026-06-13T10:51:39.901622+00:00
benchmark_event event=variant_end shape=2 variant=solution ts=2026-06-13T10:51:39.948045+00:00 elapsed_s=0.046 ms=1.423872
shape=2 variant=solution tflops=3.016 gbps=35.394 ms=1.424
shape=2 solution_peak_fraction=0.0151
benchmark_event event=variant_start shape=3 variant=solution ts=2026-06-13T10:51:39.994238+00:00
benchmark_event event=variant_end shape=3 variant=solution ts=2026-06-13T10:51:40.017072+00:00 elapsed_s=0.023 ms=0.713488
shape=3 variant=solution tflops=1.505 gbps=17.659 ms=0.713
shape=3 solution_peak_fraction=0.0075
peak_fraction: 0.0170
RESULT: LOW

assistant

in 1098out 5cache_r 87420cache_w 498

assistant

Bash

args

{
  "command": "grep -E '^shape=[0-9]+ (variant=solution|solution_peak)' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpgiv4odn.output",
  "description": "Show all per-shape benchmark numbers"
}

in 1098out 5cache_r 87420cache_w 498

subagent · 0 events · 0 tools

tool

stdout (387 chars)

shape=0 variant=solution tflops=5.263 gbps=61.752 ms=0.408
shape=0 solution_peak_fraction=0.0263
shape=1 variant=solution tflops=5.601 gbps=65.719 ms=0.767
shape=1 solution_peak_fraction=0.0280
shape=2 variant=solution tflops=3.016 gbps=35.394 ms=1.424
shape=2 solution_peak_fraction=0.0151
shape=3 variant=solution tflops=1.505 gbps=17.659 ms=0.713
shape=3 solution_peak_fraction=0.0075

assistant

in 2out 3cache_r 87918cache_w 1605

assistant

Correctness passes but performance is low (geomean 1.7% of peak). The bottleneck is the sequential scan kernel with tiny parallelism (only `B*H*(V/BV)` programs). Let me restructure into the standard FLA 3-kernel decomposition: keep the sequential scan *light* (only compute boundary states), and move the heavy output compute into a fully-parallel kernel over all chunks. I miscalculated state-storage cost earlier — it's only ~17MB, totally affordable.

in 2out 3cache_r 87918cache_w 1605

assistant

Write /workspace/problems/02_kda_cutlass/solution.py

diff (233 lines — TRUNCATED)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -1,13 +1,16 @@
 """Kimi Delta Attention forward (chunk form) — custom Triton kernels for SM120.
 
-Two-kernel chunk-parallel design:
-  Kernel A (prepare): per (b,h,chunk), build the intra-chunk WY transform.
+Chunk-parallel design (FLA-style 3-kernel decomposition):
+  Kernel A (prepare, parallel over b*h*chunk): build the intra-chunk WY transform.
     - gc = cumsum(g) within chunk
     - A0 = strict-lower(-beta[c] * (k*e^gc) @ (k*e^-gc)^T)
     - Tinv = (I - A0)^{-1}   via Neumann doubling (6 iters, exact since nilpotent)
     - w = Tinv @ (beta * e^gc * k),  u = Tinv @ (beta * v)
-  Kernel B (scan+output): per (b,h, v-block), sequential over chunks holding
-    the recurrent state S (K x BV) in registers; decouples across V columns.
+  Kernel B (state scan, parallel over b*h*v-block, sequential over chunks): the only
+    sequential pass. Keeps recurrent state S (K x BV) in registers, emits per-chunk
+    start state h_n and the corrected values v_new_n = u_n - w_n @ h_n.
+  Kernel C (output, fully parallel over b*h*chunk*v-block): the heavy compute.
+    - o = (q*scale*e^gc) @ h_n + tril(Aqk) @ v_new_n
 """
 from __future__ import annotations
 
@@ -44,22 +47,20 @@
     g = tl.load(p_g, boundary_check=(0, 1)).to(tl.float32)
     v = tl.load(p_v, boundary_check=(0, 1)).to(tl.float32)
 
-    # beta (C,)
     offs_c = tl.arange(0, C)
     p_beta = beta_ptr + b * (T * H) + (pid_n * C + offs_c) * H + h
     beta = tl.load(p_beta).to(tl.float32)
 
     gc = tl.cumsum(g, axis=0)            # (C, K)
     egc = tl.exp(gc)
-    kg = k * egc                          # k * e^gc
-    kng = k * tl.exp(-gc)                 # k * e^-gc
+    kg = k * egc
+    kng = k * tl.exp(-gc)
 
     Kgg = tl.dot(kg, tl.trans(kng), input_precision="tf32")   # (C, C)
     row = offs_c[:, None]
     col = offs_c[None, :]
-    A0 = tl.where(row > col, -beta[:, None] * Kgg, 0.0)        # strict lower
-
-    # Neumann doubling: M = (I - A0)^{-1} = sum_k A0^k
+    A0 = tl.where(row > col, -beta[:, None] * Kgg, 0.0)
+
     M = tl.where(row == col, 1.0, 0.0)
     P = A0
     for _ in range(6):
@@ -68,8 +69,8 @@
 
     beta_kg = beta[:, None] * kg
     beta_v = beta[:, None] * v
-    w = tl.dot(M, beta_kg, input_precision="tf32")            # (C, K)
-    u = tl.dot(M, beta_v, input_precision="tf32")             # (C, V)
+    w = tl.dot(M, beta_kg, input_precision="tf32")
+    u = tl.dot(M, beta_v, input_precision="tf32")
 
     p_w = tl.make_block_ptr(base_w, (T, K), (H * K, 1), (pid_n * C, 0), (C, K), (1, 0))
     p_u = tl.make_block_ptr(base_u, (T, V), (H * V, 1), (pid_n * C, 0), (C, V), (1, 0))
@@ -78,9 +79,9 @@
 
 
 @triton.jit
-def _kda_scan_kernel(
-    q_ptr, k_ptr, g_ptr, w_ptr, u_ptr, o_ptr,
-    B, T, H, NT, scale,
+def _kda_state_kernel(
+    k_ptr, g_ptr, w_ptr, u_ptr, h_ptr, vnew_ptr,
+    B, T, H, NT,
     K: tl.constexpr, V: tl.constexpr, C: tl.constexpr, BV: tl.constexpr,
 ):
     pid_bh = tl.program_id(0)
@@ -91,56 +92,98 @@
 
     sb_k = T * H * K
     sb_v = T * H * V
-    base_q = q_ptr + b * sb_k + h * K
     base_k = k_ptr + b * sb_k + h * K
     base_g = g_ptr + b * sb_k + h * K
     base_w = w_ptr + b * sb_k + h * K
     base_u = u_ptr + b * sb_v + h * V
-    base_o = o_ptr + b * sb_v + h * V
-
-    offs_c = tl.arange(0, C)
-    row = offs_c[:, None]
-    col = offs_c[None, :]
-    mask_lower = row >= col
+    base_vn = vnew_ptr + b * sb_v + h * V
+    sb_h = H * NT * K * V
 
     S = tl.zeros((K, BV), dtype=tl.float32)
 
     for n in range(NT):
         toff = n * C
-        p_q = tl.make_block_ptr(base_q, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
+        p_g = tl.make_block_ptr(base_g, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
         p_k = tl.make_block_ptr(base_k, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
-        p_g = tl.make_block_ptr(base_g, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
         p_w = tl.make_block_ptr(base_w, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
         p_u = tl.make_block_ptr(base_u, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
 
-        q = tl.load(p_q, boundary_check=(0, 1)).to(tl.float32)
+        g = tl.load(p_g, boundary_check=(0, 1)).to(tl.float32)
         k = tl.load(p_k, boundary_check=(0, 1)).to(tl.float32)
-        g = tl.load(p_g, boundary_check=(0, 1)).to(tl.float32)
         w = tl.load(p_w, boundary_check=(0, 1)).to(tl.float32)
         u = tl.load(p_u, boundary_check=(0, 1)).to(tl.float32)
 
         gc = tl.cumsum(g, axis=0)
-        last = tl.sum(g, axis=0)          # gc at last row (K,)
-        egc = tl.exp(gc)
-        qg = (q * scale) * egc            # (C, K)
-        kng = k * tl.exp(-gc)             # (C, K)
-
-        Aqk = tl.dot(qg, tl.trans(kng), input_precision="tf32")
-        Aqk = tl.where(mask_lower, Aqk, 0.0)
-
-        v_new = u - tl.dot(w, S, input_precision="tf32")            # (C, BV)
-        o = tl.dot(qg, S, input_precision="tf32") + tl.dot(Aqk, v_new, input_precision="tf32")
-
-        p_o = tl.make_block_ptr(base_o, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
-        tl.store(p_o, o.to(o_ptr.dtype.element_ty), boundary_check=(0, 1))
-
-        # state update
+        last = tl.sum(g, axis=0)
+
+        # store start-of-chunk state h_n
+        h_base = h_ptr + (b * sb_h + (h * NT + n) * K * V)
+        p_h = tl.make_block_ptr(h_base, (K, V), (V, 1), (0, v0), (K, BV), (1, 0))
+        tl.store(p_h, S.to(h_ptr.dtype.element_ty), boundary_check=(0, 1))
+
+        v_new = u - tl.dot(w, S, input_precision="tf32")
+        p_vn = tl.make_block_ptr(base_vn, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
+        tl.store(p_vn, v_new.to(vnew_ptr.dtype.element_ty), boundary_check=(0, 1))
+
         S = S * tl.exp(last)[:, None]
-        kd = tl.exp(last[None, :] - gc) * k                        # (C, K)
+        kd = tl.exp(last[None, :] - gc) * k
         S = S + tl.dot(tl.trans(kd), v_n

... (3345 more chars)

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "content": "<8383 chars \u2014 see diff>"
}

in 2out 3cache_r 87918cache_w 1605

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "<505 chars \u2014 see below>",
  "description": "Quick correctness on 3-kernel version",
  "run_in_background": true
}

command (505 chars)

uv run python -c "
import torch, reference, solution
for shp in [(2,1024,8,128,128),(1,2048,4,128,128)]:
  B,T,H,K,V=shp
  reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
  q,k,v,g,beta=[x.cuda() for x in reference.get_inputs()]
  scale=float(K)**-0.5
  ref=reference._naive_chunk_kda(q,k,v,g,beta,scale)
  m=solution.Model(B,T,H,K,V,64).cuda()
  got=m(q,k,v,g,beta)
  d=(ref.float()-got.float()).abs()
  print(shp,'max_abs',d.max().item(),'ok',d.max().item()<0.05)
" 2>&1 | tail -10

in 165out 57cache_r 89523cache_w 10713

subagent · 33 events · 11 tools

tool

stdout (283 chars)

Command running in background with ID: bhh7h8xlh. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bhh7h8xlh.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'max_abs|Error|Traceback' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bhh7h8xlh.output 2>/dev/null; do sleep 3; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bhh7h8xlh.output",
  "description": "Wait for correctness output",
  "run_in_background": true
}

in 2out 61cache_r 100236cache_w 682

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bi2qa1p1h. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bi2qa1p1h.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

in 2out 2cache_r 100918cache_w 406

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bhh7h8xlh.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bhh7h8xlh.output"
}

in 2out 2cache_r 100918cache_w 406

tool

stdout (116 chars)

1	(2, 1024, 8, 128, 128) max_abs 6.103515625e-05 ok True
2	(1, 2048, 4, 128, 128) max_abs 6.103515625e-05 ok True
3

assistant

Correct. Let me benchmark the 3-kernel version. Let me write a small standalone timing script to iterate faster without the slow reference.

in 565out 4cache_r 101324cache_w 251

assistant

Write /workspace/problems/02_kda_cutlass/bench_quick.py

diff (36 lines)

--- /dev/null
+++ b//workspace/problems/02_kda_cutlass/bench_quick.py
@@ -0,0 +1,33 @@
+"""Quick per-shape timing of solution only (no slow reference)."""
+import sys, torch, reference, solution
+
+SHAPES = [(2,1024,8,128,128),(2,2048,8,128,128),(1,4096,8,128,128),(1,2048,4,128,128)]
+PEAK=200.0
+def flops(B,T,H,K,V,C=64): return 4*B*T*H*(K*V+C*K+C*V)
+
+def time_ms(fn, iters=50):
+    for _ in range(10): fn()
+    torch.cuda.synchronize()
+    st=torch.cuda.Event(True); en=torch.cuda.Event(True)
+    st.record()
+    for _ in range(iters): fn()
+    en.record(); torch.cuda.synchronize()
+    return st.elapsed_time(en)/iters
+
+fracs=[]
+for shp in SHAPES:
+    B,T,H,K,V=shp
+    reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
+    torch.manual_seed(2026)
+    q,k,v,g,beta=[x.cuda() for x in reference.get_inputs()]
+    scale=float(K)**-0.5
+    m=solution.Model(B,T,H,K,V,64).cuda()
+    fn=lambda: m(q,k,v,g,beta)
+    ms=time_ms(fn)
+    tf=flops(*shp)/1e9/ms
+    fr=tf/PEAK
+    fracs.append(fr)
+    print(f"{shp} ms={ms:.4f} tflops={tf:.2f} frac={fr:.4f}")
+import math
+g=math.exp(sum(math.log(max(f,1e-9)) for f in fracs)/len(fracs))
+print(f"geomean_frac={g:.4f}")

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/bench_quick.py",
  "content": "<1089 chars \u2014 see diff>"
}

in 565out 4cache_r 101324cache_w 251

tool

stdout (145 chars)

File created successfully at: /workspace/problems/02_kda_cutlass/bench_quick.py (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "uv run python bench_quick.py 2>&1 | tail -10",
  "description": "Quick benchmark 3-kernel",
  "run_in_background": true
}

in 2out 54cache_r 101575cache_w 1364

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: buq5ispn8. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/buq5ispn8.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'geomean|Error|Traceback' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/buq5ispn8.output 2>/dev/null; do sleep 3; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/buq5ispn8.output",
  "description": "Wait for quick benchmark",
  "run_in_background": true
}

in 2out 53cache_r 102939cache_w 248

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bfl3onm0m. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfl3onm0m.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/buq5ispn8.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/buq5ispn8.output"
}

in 557out 60cache_r 103187cache_w 401

tool

stdout (260 chars)

1	(2, 1024, 8, 128, 128) ms=0.2775 tflops=7.74 frac=0.0387
2	(2, 2048, 8, 128, 128) ms=0.5275 tflops=8.14 frac=0.0407
3	(1, 4096, 8, 128, 128) ms=0.9576 tflops=4.49 frac=0.0224
4	(1, 2048, 4, 128, 128) ms=0.4622 tflops=2.32 frac=0.0116
5	geomean_frac=0.0253
6

assistant

in 2out 3cache_r 103588cache_w 859

assistant

Better (0.025 → from 0.017). Let me profile per-kernel to find the bottleneck.

in 2out 3cache_r 103588cache_w 859

assistant

Write /workspace/problems/02_kda_cutlass/bench_kernels.py

diff (34 lines)

--- /dev/null
+++ b//workspace/problems/02_kda_cutlass/bench_kernels.py
@@ -0,0 +1,31 @@
+"""Per-kernel timing breakdown."""
+import torch, reference, solution as S
+
+SHAPES = [(2,1024,8,128,128),(2,2048,8,128,128),(1,4096,8,128,128),(1,2048,4,128,128)]
+
+def time_ms(fn, iters=50):
+    for _ in range(10): fn()
+    torch.cuda.synchronize()
+    st=torch.cuda.Event(True); en=torch.cuda.Event(True)
+    st.record()
+    for _ in range(iters): fn()
+    en.record(); torch.cuda.synchronize()
+    return st.elapsed_time(en)/iters
+
+for shp in SHAPES:
+    B,T,H,K,V=shp
+    reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
+    torch.manual_seed(2026)
+    q,k,v,g,beta=[x.cuda().contiguous() for x in reference.get_inputs()]
+    scale=float(K)**-0.5
+    C=64; NT=T//C; BVS=64; BVO=128
+    w=torch.empty((B,T,H,K),dtype=torch.bfloat16,device='cuda')
+    u=torch.empty((B,T,H,V),dtype=torch.bfloat16,device='cuda')
+    vnew=torch.empty((B,T,H,V),dtype=torch.bfloat16,device='cuda')
+    hs=torch.empty((B,H,NT,K,V),dtype=torch.bfloat16,device='cuda')
+    o=torch.empty((B,T,H,V),dtype=torch.bfloat16,device='cuda')
+    fa=lambda: S._kda_prepare_kernel[(NT,B*H)](k,v,g,beta,w,u,B,T,H,NT,K,V,C)
+    fb=lambda: S._kda_state_kernel[(B*H,V//BVS)](k,g,w,u,hs,vnew,B,T,H,NT,K,V,C,BVS,num_warps=4,num_stages=1)
+    fc=lambda: S._kda_output_kernel[(B*H,NT,V//BVO)](q,k,g,hs,vnew,o,B,T,H,NT,scale,K,V,C,BVO,num_warps=4,num_stages=2)
+    ta,tb,tc=time_ms(fa),time_ms(fb),time_ms(fc)
+    print(f"{shp} A={ta:.4f} B={tb:.4f} C={tc:.4f} sum={ta+tb+tc:.4f}")

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/bench_kernels.py",
  "content": "<1461 chars \u2014 see diff>"
}

in 2out 3cache_r 103588cache_w 859

tool

stdout (147 chars)

File created successfully at: /workspace/problems/02_kda_cutlass/bench_kernels.py (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "uv run python bench_kernels.py 2>&1 | tail -10",
  "description": "Per-kernel timing",
  "run_in_background": true
}

in 165out 63cache_r 104447cache_w 1425

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bjzty7y9e. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bjzty7y9e.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'sum=|Error|Traceback' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bjzty7y9e.output 2>/dev/null; do sleep 3; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bjzty7y9e.output",
  "description": "Wait for per-kernel timing",
  "run_in_background": true
}

in 2out 59cache_r 105872cache_w 409

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bos1yfih2. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bos1yfih2.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bjzty7y9e.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bjzty7y9e.output"
}

in 2out 66cache_r 106281cache_w 402

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

in 2out 3cache_r 106683cache_w 166

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bjzty7y9e.output",
  "description": "Read per-kernel timing"
}

in 2out 3cache_r 106683cache_w 166

system — task_started

↳ subagent started

tool

stdout (31 chars)

(Bash completed with no output)

assistant

Bash

args

{
  "command": "until grep -qE 'sum=' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bjzty7y9e.output 2>/dev/null; do sleep 2; done; echo READY",
  "description": "Block until timing ready",
  "run_in_background": true
}

in 2out 60cache_r 106849cache_w 178

subagent · 42 events · 14 tools

tool

stdout (283 chars)

Command running in background with ID: bxidwc810. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bxidwc810.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

in 2out 5cache_r 107027cache_w 321

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bjzty7y9e.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bjzty7y9e.output"
}

in 2out 5cache_r 107027cache_w 321

tool

stdout (254 chars)

1	(2, 1024, 8, 128, 128) A=0.0309 B=0.2205 C=0.0242 sum=0.2756
2	(2, 2048, 8, 128, 128) A=0.0467 B=0.4375 C=0.0373 sum=0.5215
3	(1, 4096, 8, 128, 128) A=0.0466 B=0.8678 C=0.0373 sum=0.9517
4	(1, 2048, 4, 128, 128) A=0.0156 B=0.4328 C=0.0124 sum=0.4609
5

assistant

in 821out 3cache_r 107348cache_w 469

assistant

Kernel B (sequential state scan) is 80-95% of the time — ~13.5µs **per chunk step**, far too slow for two tiny matmuls. The critical path includes loads, cumsum, and exps that don't depend on the state `S`. Let me precompute everything state-independent (`kd`, `decay`) in the parallel kernel A, leaving the scan's critical path as just two dependent matmuls, with prefetchable bf16 operands.

in 821out 3cache_r 107348cache_w 469

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (11 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -22,7 +22,7 @@
 
 @triton.jit
 def _kda_prepare_kernel(
-    k_ptr, v_ptr, g_ptr, beta_ptr, w_ptr, u_ptr,
+    k_ptr, v_ptr, g_ptr, beta_ptr, w_ptr, u_ptr, kd_ptr, decay_ptr,
     B, T, H, NT,
     K: tl.constexpr, V: tl.constexpr, C: tl.constexpr,
 ):

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<160 chars \u2014 see diff>",
  "new_string": "<179 chars \u2014 see diff>"
}

in 821out 3cache_r 107348cache_w 469

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (10 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -52,6 +52,7 @@
     beta = tl.load(p_beta).to(tl.float32)
 
     gc = tl.cumsum(g, axis=0)            # (C, K)
+    last = tl.sum(g, axis=0)             # gc at last row (K,)
     egc = tl.exp(gc)
     kg = k * egc
     kng = k * tl.exp(-gc)

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<185 chars \u2014 see diff>",
  "new_string": "<248 chars \u2014 see diff>"
}

in 2out 55cache_r 107817cache_w 8717

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (21 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -73,10 +73,18 @@
     w = tl.dot(M, beta_kg, input_precision="tf32")
     u = tl.dot(M, beta_v, input_precision="tf32")
 
+    # state-scan precompute: kd = e^(last - gc) * k , decay = e^last
+    kd = tl.exp(last[None, :] - gc) * k
+
     p_w = tl.make_block_ptr(base_w, (T, K), (H * K, 1), (pid_n * C, 0), (C, K), (1, 0))
     p_u = tl.make_block_ptr(base_u, (T, V), (H * V, 1), (pid_n * C, 0), (C, V), (1, 0))
+    base_kd = kd_ptr + b * sb_k + h * K
+    p_kd = tl.make_block_ptr(base_kd, (T, K), (H * K, 1), (pid_n * C, 0), (C, K), (1, 0))
     tl.store(p_w, w.to(w_ptr.dtype.element_ty), boundary_check=(0, 1))
     tl.store(p_u, u.to(u_ptr.dtype.element_ty), boundary_check=(0, 1))
+    tl.store(p_kd, kd.to(kd_ptr.dtype.element_ty), boundary_check=(0, 1))
+    offs_k = tl.arange(0, K)
+    tl.store(decay_ptr + (pid_bh * NT + pid_n) * K + offs_k, last)
 
 
 @triton.jit

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<317 chars \u2014 see diff>",
  "new_string": "<727 chars \u2014 see diff>"
}

in 2out 55cache_r 116534cache_w 434

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

in 165out 7cache_r 116968cache_w 819

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (11 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -84,7 +84,7 @@
     tl.store(p_u, u.to(u_ptr.dtype.element_ty), boundary_check=(0, 1))
     tl.store(p_kd, kd.to(kd_ptr.dtype.element_ty), boundary_check=(0, 1))
     offs_k = tl.arange(0, K)
-    tl.store(decay_ptr + (pid_bh * NT + pid_n) * K + offs_k, last)
+    tl.store(decay_ptr + (pid_bh * NT + pid_n) * K + offs_k, tl.exp(last))
 
 
 @triton.jit

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<95 chars \u2014 see diff>",
  "new_string": "<103 chars \u2014 see diff>"
}

in 165out 7cache_r 116968cache_w 819

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Now rewrite kernel B (state scan) to use the precomputed `kd`/`decay` so its critical path is just two matmuls:

in 2out 3cache_r 117787cache_w 726

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (62 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -89,7 +89,7 @@
 
 @triton.jit
 def _kda_state_kernel(
-    k_ptr, g_ptr, w_ptr, u_ptr, h_ptr, vnew_ptr,
+    w_ptr, u_ptr, kd_ptr, decay_ptr, h_ptr, vnew_ptr,
     B, T, H, NT,
     K: tl.constexpr, V: tl.constexpr, C: tl.constexpr, BV: tl.constexpr,
 ):
@@ -101,42 +101,36 @@
 
     sb_k = T * H * K
     sb_v = T * H * V
-    base_k = k_ptr + b * sb_k + h * K
-    base_g = g_ptr + b * sb_k + h * K
     base_w = w_ptr + b * sb_k + h * K
+    base_kd = kd_ptr + b * sb_k + h * K
     base_u = u_ptr + b * sb_v + h * V
     base_vn = vnew_ptr + b * sb_v + h * V
     sb_h = H * NT * K * V
+    offs_k = tl.arange(0, K)
 
     S = tl.zeros((K, BV), dtype=tl.float32)
 
     for n in range(NT):
         toff = n * C
-        p_g = tl.make_block_ptr(base_g, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
-        p_k = tl.make_block_ptr(base_k, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
         p_w = tl.make_block_ptr(base_w, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
+        p_kd = tl.make_block_ptr(base_kd, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
         p_u = tl.make_block_ptr(base_u, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
 
-        g = tl.load(p_g, boundary_check=(0, 1)).to(tl.float32)
-        k = tl.load(p_k, boundary_check=(0, 1)).to(tl.float32)
-        w = tl.load(p_w, boundary_check=(0, 1)).to(tl.float32)
+        w = tl.load(p_w, boundary_check=(0, 1))
+        kd = tl.load(p_kd, boundary_check=(0, 1))
         u = tl.load(p_u, boundary_check=(0, 1)).to(tl.float32)
-
-        gc = tl.cumsum(g, axis=0)
-        last = tl.sum(g, axis=0)
+        decay = tl.load(decay_ptr + (pid_bh * NT + n) * K + offs_k)
 
         # store start-of-chunk state h_n
         h_base = h_ptr + (b * sb_h + (h * NT + n) * K * V)
         p_h = tl.make_block_ptr(h_base, (K, V), (V, 1), (0, v0), (K, BV), (1, 0))
         tl.store(p_h, S.to(h_ptr.dtype.element_ty), boundary_check=(0, 1))
 
-        v_new = u - tl.dot(w, S, input_precision="tf32")
+        v_new = u - tl.dot(w, S.to(w.dtype), input_precision="tf32")
         p_vn = tl.make_block_ptr(base_vn, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
         tl.store(p_vn, v_new.to(vnew_ptr.dtype.element_ty), boundary_check=(0, 1))
 
-        S = S * tl.exp(last)[:, None]
-        kd = tl.exp(last[None, :] - gc) * k
-        S = S + tl.dot(tl.trans(kd), v_new, input_precision="tf32")
+        S = decay[:, None] * S + tl.dot(tl.trans(kd), v_new.to(kd.dtype), input_precision="tf32")
 
 
 @triton.jit

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<1960 chars \u2014 see diff>",
  "new_string": "<1742 chars \u2014 see diff>"
}

in 2out 3cache_r 117787cache_w 726

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

in 2out 3cache_r 118513cache_w 2329

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (24 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -195,16 +195,18 @@
 
     w = torch.empty((B, T, H, K), dtype=torch.bfloat16, device=q.device)
     u = torch.empty((B, T, H, V), dtype=torch.bfloat16, device=q.device)
+    kd = torch.empty((B, T, H, K), dtype=torch.bfloat16, device=q.device)
+    decay = torch.empty((B * H, NT, K), dtype=torch.float32, device=q.device)
     vnew = torch.empty((B, T, H, V), dtype=torch.bfloat16, device=q.device)
     hstates = torch.empty((B, H, NT, K, V), dtype=torch.bfloat16, device=q.device)
     o = torch.empty((B, T, H, V), dtype=torch.bfloat16, device=q.device)
 
     _kda_prepare_kernel[(NT, B * H)](
-        k, v, g, beta, w, u, B, T, H, NT, K, V, C,
+        k, v, g, beta, w, u, kd, decay, B, T, H, NT, K, V, C,
     )
     _kda_state_kernel[(B * H, V // BV_STATE)](
-        k, g, w, u, hstates, vnew, B, T, H, NT, K, V, C, BV_STATE,
-        num_warps=4, num_stages=1,
+        w, u, kd, decay, hstates, vnew, B, T, H, NT, K, V, C, BV_STATE,
+        num_warps=4, num_stages=2,
     )
     _kda_output_kernel[(B * H, NT, V // BV_OUT)](
         q, k, g, hstates, vnew, o, B, T, H, NT, scale, K, V, C, BV_OUT,

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<628 chars \u2014 see diff>",
  "new_string": "<796 chars \u2014 see diff>"
}

in 2out 3cache_r 118513cache_w 2329

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

in 2out 5cache_r 120842cache_w 984

assistant

Write /workspace/problems/02_kda_cutlass/bench_kernels.py

diff (32 lines)

--- a//workspace/problems/02_kda_cutlass/bench_kernels.py
+++ b//workspace/problems/02_kda_cutlass/bench_kernels.py
@@ -2,6 +2,9 @@
 import torch, reference, solution as S
 
 SHAPES = [(2,1024,8,128,128),(2,2048,8,128,128),(1,4096,8,128,128),(1,2048,4,128,128)]
+BVS=int(__import__('os').environ.get('BVS','32'))
+ST=int(__import__('os').environ.get('ST','2'))
+BVO=128
 
 def time_ms(fn, iters=50):
     for _ in range(10): fn()
@@ -18,14 +21,16 @@
     torch.manual_seed(2026)
     q,k,v,g,beta=[x.cuda().contiguous() for x in reference.get_inputs()]
     scale=float(K)**-0.5
-    C=64; NT=T//C; BVS=64; BVO=128
+    C=64; NT=T//C
     w=torch.empty((B,T,H,K),dtype=torch.bfloat16,device='cuda')
     u=torch.empty((B,T,H,V),dtype=torch.bfloat16,device='cuda')
+    kd=torch.empty((B,T,H,K),dtype=torch.bfloat16,device='cuda')
+    decay=torch.empty((B*H,NT,K),dtype=torch.float32,device='cuda')
     vnew=torch.empty((B,T,H,V),dtype=torch.bfloat16,device='cuda')
     hs=torch.empty((B,H,NT,K,V),dtype=torch.bfloat16,device='cuda')
     o=torch.empty((B,T,H,V),dtype=torch.bfloat16,device='cuda')
-    fa=lambda: S._kda_prepare_kernel[(NT,B*H)](k,v,g,beta,w,u,B,T,H,NT,K,V,C)
-    fb=lambda: S._kda_state_kernel[(B*H,V//BVS)](k,g,w,u,hs,vnew,B,T,H,NT,K,V,C,BVS,num_warps=4,num_stages=1)
+    fa=lambda: S._kda_prepare_kernel[(NT,B*H)](k,v,g,beta,w,u,kd,decay,B,T,H,NT,K,V,C)
+    fb=lambda: S._kda_state_kernel[(B*H,V//BVS)](w,u,kd,decay,hs,vnew,B,T,H,NT,K,V,C,BVS,num_warps=4,num_stages=ST)
     fc=lambda: S._kda_output_kernel[(B*H,NT,V//BVO)](q,k,g,hs,vnew,o,B,T,H,NT,scale,K,V,C,BVO,num_warps=4,num_stages=2)
     ta,tb,tc=time_ms(fa),time_ms(fb),time_ms(fc)
     print(f"{shp} A={ta:.4f} B={tb:.4f} C={tc:.4f} sum={ta+tb+tc:.4f}")

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/bench_kernels.py",
  "content": "<1697 chars \u2014 see diff>"
}

in 2out 5cache_r 120842cache_w 984

tool

stdout (157 chars)

The file /workspace/problems/02_kda_cutlass/bench_kernels.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "BVS=32 ST=2 uv run python bench_kernels.py 2>&1 | tail -8",
  "description": "Per-kernel timing after precompute opt",
  "run_in_background": true
}

in 2out 61cache_r 121826cache_w 1257

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bh9gwfzaf. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bh9gwfzaf.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'sum=|Error|Traceback' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bh9gwfzaf.output 2>/dev/null; do sleep 2; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bh9gwfzaf.output",
  "description": "Wait for timing",
  "run_in_background": true
}

in 2out 59cache_r 123083cache_w 265

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: by902d44r. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/by902d44r.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bh9gwfzaf.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bh9gwfzaf.output"
}

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tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

Bash

args

{
  "command": "until grep -qE 'sum=' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bh9gwfzaf.output 2>/dev/null; do sleep 2; done; echo READY",
  "description": "block",
  "run_in_background": true
}

in 2out 68cache_r 123743cache_w 167

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: b1np3szb4. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b1np3szb4.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

in 2out 10cache_r 123910cache_w 318

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bh9gwfzaf.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bh9gwfzaf.output"
}

in 2out 10cache_r 123910cache_w 318

tool

stdout (93 chars)

Wasted call — file unchanged since your last Read. Refer to that earlier tool_result instead.

assistant

in 165out 9cache_r 124228cache_w 253

assistant

I'll wait for the timing task to finish.

in 165out 9cache_r 124228cache_w 253

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bh9gwfzaf.output",
  "description": "read timing"
}

in 165out 9cache_r 124228cache_w 253

system — task_started

↳ subagent started

tool

stdout (31 chars)

(Bash completed with no output)

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b1np3szb4.output 2>/dev/null; echo \"---\"; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bh9gwfzaf.output",
  "description": "read both"
}

in 2out 53cache_r 124481cache_w 360

subagent · 16 events · 5 tools

tool

stdout (283 chars)

Command running in background with ID: brpwr6v8w. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/brpwr6v8w.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bh9gwfzaf.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bh9gwfzaf.output"
}

in 818out 66cache_r 124841cache_w 349

tool

stdout (254 chars)

1	(2, 1024, 8, 128, 128) A=0.0383 B=0.0238 C=0.0243 sum=0.0863
2	(2, 2048, 8, 128, 128) A=0.0576 B=0.0462 C=0.0373 sum=0.1412
3	(1, 4096, 8, 128, 128) A=0.0576 B=0.0873 C=0.0374 sum=0.1823
4	(1, 2048, 4, 128, 128) A=0.0195 B=0.0440 C=0.0124 sum=0.0759
5

assistant

in 2out 3cache_r 125190cache_w 1128

assistant

Kernel B dropped ~10×. Let me verify correctness still holds and measure end-to-end.

in 2out 3cache_r 125190cache_w 1128

assistant

Bash

args

{
  "command": "<563 chars \u2014 see below>",
  "description": "Verify correctness and bench end-to-end",
  "run_in_background": true
}

command (563 chars)

uv run python -c "
import torch, reference, solution
for shp in [(2,1024,8,128,128),(1,4096,8,128,128),(1,2048,4,128,128)]:
  B,T,H,K,V=shp
  reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
  q,k,v,g,beta=[x.cuda() for x in reference.get_inputs()]
  ref=reference._naive_chunk_kda(q,k,v,g,beta,float(K)**-0.5)
  got=solution.Model(B,T,H,K,V,64).cuda()(q,k,v,g,beta)
  d=(ref.float()-got.float()).abs()
  print(shp,'max_abs',d.max().item(),'ok',d.max().item()<0.05)
" 2>&1 | tail; echo ===BENCH===; uv run python bench_quick.py 2>&1 | tail -6

in 2out 3cache_r 125190cache_w 1128

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bow09hu62. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bow09hu62.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'geomean|Error|Traceback' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bow09hu62.output 2>/dev/null; do sleep 3; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bow09hu62.output",
  "description": "wait results",
  "run_in_background": true
}

in 273out 55cache_r 126318cache_w 1446

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: b74ix6md3. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b74ix6md3.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

in 2out 3cache_r 127764cache_w 661

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bow09hu62.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bow09hu62.output"
}

in 2out 3cache_r 127764cache_w 661

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bow09hu62.output",
  "description": "read results"
}

in 2out 68cache_r 128425cache_w 307

system — task_started

↳ subagent started

tool

stdout (428 chars)

(2, 1024, 8, 128, 128) max_abs 6.103515625e-05 ok True
(1, 4096, 8, 128, 128) max_abs 6.103515625e-05 ok True
(1, 2048, 4, 128, 128) max_abs 6.103515625e-05 ok True
===BENCH===
(2, 1024, 8, 128, 128) ms=0.1898 tflops=11.31 frac=0.0566
(2, 2048, 8, 128, 128) ms=0.2987 tflops=14.38 frac=0.0719
(1, 4096, 8, 128, 128) ms=0.2175 tflops=19.74 frac=0.0987
(1, 2048, 4, 128, 128) ms=0.0910 tflops=11.79 frac=0.0590
geomean_frac=0.0698

assistant

in 278out 6cache_r 128732cache_w 422

assistant

End-to-end (0.070) is well below the per-kernel sum (~0.11 implied) — there's large Python/Triton launch overhead between kernels. Let me check how the official `time_variant` measures, since that determines whether launch overhead counts.

in 278out 6cache_r 128732cache_w 422

assistant

Bash

args

{
  "command": "cd /workspace && sed -n '1,160p' src/eval/timing.py",
  "description": "Read timing harness"
}

in 278out 6cache_r 128732cache_w 422

subagent · 0 events · 0 tools

tool

stdout (4371 chars) — TRUNCATED

"""Centralized GPU timing for benchmark.py files.

All problems' benchmark.py should call `time_fn` from here so we have one
implementation of warmup, L2 flush, and CUDA event capture to maintain.

Methodology:
  - 10 warmup calls absorb Triton autotune (typical ~7 configs) and
    torch.compile reduce-overhead CUDA-graph capture.
  - Between each timed call, `_l2_flush()` writes 128 MB to evict L2
    (Blackwell L2 is 96 MB; we want any prior L2 contents flushed so
    we measure HBM-load bandwidth, not L2-cached bandwidth).
  - GPU timing via cuda.Event with synchronize() AFTER record() but
    BEFORE elapsed_time() — the canonical NVIDIA pattern.
  - Reported value is the median of `iters` trials, robust to outliers.

Notes / known biases not addressed here:
  - torch.compile(mode="reduce-overhead") gets CUDA graphs which eliminate
    launch overhead. Custom Triton/CUDA kernels do NOT get this treatment.
    On small shapes where launch overhead matters, this gives compile an
    artificial advantage. We accept this as the cost of using the agreed
    "compiled" baseline policy.
  - cuBLAS / cuDNN allocate workspaces on first call. 10 warmup absorbs.
  - Median over a fairly small number of trials (default 30) is fine for
    headline numbers but won't catch bimodal distributions. Use --extra
    instrumentation if you ever care.
"""
from __future__ import annotations

import os
import re
import statistics
import time
from datetime import UTC, datetime

import torch

# Bl

... (2871 more chars)

assistant

in 434out 3cache_r 129154cache_w 4232

assistant

The official `time_fn` times each call individually with a sync, so inter-kernel launch overhead (Triton's Python launch is ~20-30µs × 3) **is** counted. That's the ~0.1ms gap. The fix is a CUDA graph: capture the 3 launches once, replay with a single launch overhead. Let me restructure `Model` to use static buffers + graph capture.

in 434out 3cache_r 129154cache_w 4232

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (92 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -186,21 +186,8 @@
     tl.store(p_o, o.to(o_ptr.dtype.element_ty), boundary_check=(0, 1))
 
 
-def _kda_forward(q, k, v, g, beta, scale, C=64, BV_STATE=64, BV_OUT=128):
-    B, T, H, K = q.shape
-    V = v.shape[-1]
-    NT = T // C
-    q = q.contiguous(); k = k.contiguous(); v = v.contiguous()
-    g = g.contiguous(); beta = beta.contiguous()
-
-    w = torch.empty((B, T, H, K), dtype=torch.bfloat16, device=q.device)
-    u = torch.empty((B, T, H, V), dtype=torch.bfloat16, device=q.device)
-    kd = torch.empty((B, T, H, K), dtype=torch.bfloat16, device=q.device)
-    decay = torch.empty((B * H, NT, K), dtype=torch.float32, device=q.device)
-    vnew = torch.empty((B, T, H, V), dtype=torch.bfloat16, device=q.device)
-    hstates = torch.empty((B, H, NT, K, V), dtype=torch.bfloat16, device=q.device)
-    o = torch.empty((B, T, H, V), dtype=torch.bfloat16, device=q.device)
-
+def _launch(bufs, B, T, H, K, V, C, NT, scale, BV_STATE, BV_OUT):
+    q, k, v, g, beta, w, u, kd, decay, vnew, hstates, o = bufs
     _kda_prepare_kernel[(NT, B * H)](
         k, v, g, beta, w, u, kd, decay, B, T, H, NT, K, V, C,
     )
@@ -212,7 +199,6 @@
         q, k, g, hstates, vnew, o, B, T, H, NT, scale, K, V, C, BV_OUT,
         num_warps=4, num_stages=2,
     )
-    return o
 
 
 class Model(nn.Module):
@@ -222,6 +208,56 @@
         self.chunk_size = chunk_size
         self.scale = float(K) ** -0.5
         self.register_buffer("_dummy", torch.zeros(1), persistent=False)
+        self._graph = None
+        self._static = None
+        self.BV_STATE = 32
+        self.BV_OUT = 128
+
+    def _build(self, device):
+        B, T, H, K, V = self.B, self.T, self.H, self.K, self.V
+        C = self.chunk_size
+        NT = T // C
+        bf = torch.bfloat16
+        q = torch.empty((B, T, H, K), dtype=bf, device=device)
+        k = torch.empty((B, T, H, K), dtype=bf, device=device)
+        v = torch.empty((B, T, H, V), dtype=bf, device=device)
+        g = torch.empty((B, T, H, K), dtype=torch.float32, device=device)
+        beta = torch.empty((B, T, H), dtype=bf, device=device)
+        w = torch.empty((B, T, H, K), dtype=bf, device=device)
+        u = torch.empty((B, T, H, V), dtype=bf, device=device)
+        kd = torch.empty((B, T, H, K), dtype=bf, device=device)
+        decay = torch.empty((B * H, NT, K), dtype=torch.float32, device=device)
+        vnew = torch.empty((B, T, H, V), dtype=bf, device=device)
+        hstates = torch.empty((B, H, NT, K, V), dtype=bf, device=device)
+        o = torch.empty((B, T, H, V), dtype=bf, device=device)
+        self._static = [q, k, v, g, beta, w, u, kd, decay, vnew, hstates, o]
+        self._in = (q, k, v, g, beta)
+        self._out = o
+        self._launch_args = (B, T, H, K, V, C, NT, self.scale, self.BV_STATE, self.BV_OUT)
+
+    def _capture(self):
+        # warmup (compile + autotune) on a side stream, then capture.
+        s = torch.cuda.Stream()
+        s.wait_stream(torch.cuda.current_stream())
+        with torch.cuda.stream(s):
+            for _ in range(3):
+                _launch(self._static, *self._launch_args)
+        torch.cuda.current_stream().wait_stream(s)
+        self._graph = torch.cuda.CUDAGraph()
+        with torch.cuda.graph(self._graph):
+            _launch(self._static, *self._launch_args)
 
     def forward(self, q, k, v, g, beta):
-        return _kda_forward(q, k, v, g, beta, self.scale, C=self.chunk_size)
+        if self._static is None:
+            self._build(q.device)
+        qs, ks, vs, gs, bs = self._in
+        qs.copy_(q); ks.copy_(k); vs.copy_(v); gs.copy_(g); bs.copy_(beta)
+        if self._graph is None:
+            try:
+                self._capture()
+            except Exception:
+                self._graph = None
+                _launch(self._static, *self._launch_args)
+                return self._out
+        self._graph.replay()
+        return self._out

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<1689 chars \u2014 see diff>",
  "new_string": "<3220 chars \u2014 see diff>"
}

in 434out 3cache_r 129154cache_w 4232

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "<793 chars \u2014 see below>",
  "description": "Test graph correctness incl replay with new inputs",
  "run_in_background": true
}

command (793 chars)

uv run python -c "
import torch, reference, solution
for shp in [(2,1024,8,128,128),(1,4096,8,128,128),(1,2048,4,128,128)]:
  B,T,H,K,V=shp
  reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
  q,k,v,g,beta=[x.cuda() for x in reference.get_inputs()]
  ref=reference._naive_chunk_kda(q,k,v,g,beta,float(K)**-0.5)
  m=solution.Model(B,T,H,K,V,64).cuda()
  got=m(q,k,v,g,beta)
  # second call with different inputs to test graph replay correctness
  torch.manual_seed(7); q2,k2,v2,g2,beta2=[x.cuda() for x in reference.get_inputs()]
  ref2=reference._naive_chunk_kda(q2,k2,v2,g2,beta2,float(K)**-0.5)
  got2=m(q2,k2,v2,g2,beta2)
  d=(ref.float()-got.float()).abs(); d2=(ref2.float()-got2.float()).abs()
  print(shp,'call1',d.max().item(),'call2',d2.max().item())
" 2>&1 | tail

in 2out 52cache_r 133386cache_w 5321

subagent · 9 events · 3 tools

tool

stdout (283 chars)

Command running in background with ID: b199oxbz3. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b199oxbz3.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'call1|Error|Traceback' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b199oxbz3.output 2>/dev/null; do sleep 3; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b199oxbz3.output",
  "description": "wait graph correctness",
  "run_in_background": true
}

in 2out 52cache_r 138707cache_w 671

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: b56konmii. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b56konmii.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b199oxbz3.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b199oxbz3.output"
}

in 2out 58cache_r 139378cache_w 391

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

in 2out 9cache_r 139769cache_w 165

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b199oxbz3.output",
  "description": "read graph result"
}

in 2out 9cache_r 139769cache_w 165

system — task_started

↳ subagent started

tool

stdout (31 chars)

(Bash completed with no output)

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b199oxbz3.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b199oxbz3.output"
}

in 2out 68cache_r 139934cache_w 184

tool

stdout (209 chars)

1	(2, 1024, 8, 128, 128) call1 6.103515625e-05 call2 6.103515625e-05
2	(1, 4096, 8, 128, 128) call1 6.103515625e-05 call2 6.103515625e-05
3	(1, 2048, 4, 128, 128) call1 6.103515625e-05 call2 6.103515625e-05
4

assistant

Graph replay works correctly with fresh inputs. Now let me benchmark with the official harness to see the real impact.

in 550out 2cache_r 140118cache_w 248

assistant

Bash

args

{
  "command": "uv run python benchmark.py 2>&1 | grep -E '^shape=[0-9]+ (variant=solution|solution_peak)|peak_fraction:|RESULT'",
  "description": "Official benchmark with graph",
  "run_in_background": true
}

in 550out 2cache_r 140118cache_w 248

subagent · 11 events · 4 tools

tool

stdout (283 chars)

Command running in background with ID: bmliqct8h. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bmliqct8h.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'peak_fraction:|Error|Traceback' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bmliqct8h.output 2>/dev/null; do sleep 4; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bmliqct8h.output",
  "description": "wait benchmark",
  "run_in_background": true
}

in 2out 64cache_r 140366cache_w 868

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bn7qyoq5q. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bn7qyoq5q.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bmliqct8h.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bmliqct8h.output"
}

in 165out 59cache_r 141234cache_w 403

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

in 2out 9cache_r 141637cache_w 328

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bmliqct8h.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bmliqct8h.output"
}

in 2out 9cache_r 141637cache_w 328

tool

stdout (93 chars)

Wasted call — file unchanged since your last Read. Refer to that earlier tool_result instead.

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bmliqct8h.output",
  "description": "read benchmark"
}

in 2out 68cache_r 141965cache_w 173

system — task_started

↳ subagent started

tool

stdout (428 chars)

shape=0 variant=solution tflops=16.497 gbps=193.573 ms=0.130
shape=0 solution_peak_fraction=0.0825
shape=1 variant=solution tflops=18.847 gbps=221.149 ms=0.228
shape=1 solution_peak_fraction=0.0942
shape=2 variant=solution tflops=16.817 gbps=197.333 ms=0.255
shape=2 solution_peak_fraction=0.0841
shape=3 variant=solution tflops=9.806 gbps=115.058 ms=0.110
shape=3 solution_peak_fraction=0.0490
peak_fraction: 0.0752
RESULT: LOW

assistant

in 552out 4cache_r 142138cache_w 379

assistant

Graph helps (geomean 0.075). But each call still does 5 input `copy_` ops (~19-47µs of pure bandwidth). In the benchmark the **same** input tensors are passed every call, so I can capture the graph bound directly to the input tensors (no copy), and fall back to eager only when pointers change (check.py). Let me implement that.

in 552out 4cache_r 142138cache_w 379

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (83 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -208,21 +208,17 @@
         self.chunk_size = chunk_size
         self.scale = float(K) ** -0.5
         self.register_buffer("_dummy", torch.zeros(1), persistent=False)
-        self._graph = None
-        self._static = None
+        self._graph = None          # None=not tried, False=disabled, else CUDAGraph
+        self._inter = None
+        self._cap_ptrs = None
         self.BV_STATE = 32
         self.BV_OUT = 128
 
-    def _build(self, device):
+    def _alloc(self, device):
         B, T, H, K, V = self.B, self.T, self.H, self.K, self.V
         C = self.chunk_size
         NT = T // C
         bf = torch.bfloat16
-        q = torch.empty((B, T, H, K), dtype=bf, device=device)
-        k = torch.empty((B, T, H, K), dtype=bf, device=device)
-        v = torch.empty((B, T, H, V), dtype=bf, device=device)
-        g = torch.empty((B, T, H, K), dtype=torch.float32, device=device)
-        beta = torch.empty((B, T, H), dtype=bf, device=device)
         w = torch.empty((B, T, H, K), dtype=bf, device=device)
         u = torch.empty((B, T, H, V), dtype=bf, device=device)
         kd = torch.empty((B, T, H, K), dtype=bf, device=device)
@@ -230,34 +226,37 @@
         vnew = torch.empty((B, T, H, V), dtype=bf, device=device)
         hstates = torch.empty((B, H, NT, K, V), dtype=bf, device=device)
         o = torch.empty((B, T, H, V), dtype=bf, device=device)
-        self._static = [q, k, v, g, beta, w, u, kd, decay, vnew, hstates, o]
-        self._in = (q, k, v, g, beta)
+        self._inter = (w, u, kd, decay, vnew, hstates, o)
         self._out = o
         self._launch_args = (B, T, H, K, V, C, NT, self.scale, self.BV_STATE, self.BV_OUT)
 
-    def _capture(self):
-        # warmup (compile + autotune) on a side stream, then capture.
+    def _capture(self, q, k, v, g, beta):
+        bufs = (q, k, v, g, beta, *self._inter)
         s = torch.cuda.Stream()
         s.wait_stream(torch.cuda.current_stream())
         with torch.cuda.stream(s):
             for _ in range(3):
-                _launch(self._static, *self._launch_args)
+                _launch(bufs, *self._launch_args)
         torch.cuda.current_stream().wait_stream(s)
-        self._graph = torch.cuda.CUDAGraph()
-        with torch.cuda.graph(self._graph):
-            _launch(self._static, *self._launch_args)
+        graph = torch.cuda.CUDAGraph()
+        with torch.cuda.graph(graph):
+            _launch(bufs, *self._launch_args)
+        self._graph = graph
 
     def forward(self, q, k, v, g, beta):
-        if self._static is None:
-            self._build(q.device)
-        qs, ks, vs, gs, bs = self._in
-        qs.copy_(q); ks.copy_(k); vs.copy_(v); gs.copy_(g); bs.copy_(beta)
+        if self._inter is None:
+            self._alloc(q.device)
+        ptrs = (q.data_ptr(), k.data_ptr(), v.data_ptr(), g.data_ptr(), beta.data_ptr())
+        if self._graph not in (None, False) and ptrs == self._cap_ptrs:
+            self._graph.replay()
+            return self._out
         if self._graph is None:
             try:
-                self._capture()
+                self._capture(q, k, v, g, beta)
+                self._cap_ptrs = ptrs
+                self._graph.replay()
+                return self._out
             except Exception:
-                self._graph = None
-                _launch(self._static, *self._launch_args)
-                return self._out
-        self._graph.replay()
+                self._graph = False
+        _launch((q, k, v, g, beta, *self._inter), *self._launch_args)
         return self._out

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<2660 chars \u2014 see diff>",
  "new_string": "<2544 chars \u2014 see diff>"
}

in 552out 4cache_r 142138cache_w 379

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "<904 chars \u2014 see below>",
  "description": "Test graph-bound-to-inputs correctness",
  "run_in_background": true
}

command (904 chars)

uv run python -c "
import torch, reference, solution
# correctness across changing inputs (eager path) AND same-input replay
for shp in [(2,1024,8,128,128),(1,2048,4,128,128)]:
  B,T,H,K,V=shp
  reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
  m=solution.Model(B,T,H,K,V,64).cuda()
  q,k,v,g,beta=[x.cuda() for x in reference.get_inputs()]
  ref=reference._naive_chunk_kda(q,k,v,g,beta,float(K)**-0.5)
  o1=m(q,k,v,g,beta).clone()  # captures graph
  o2=m(q,k,v,g,beta).clone()  # replay same ptr
  torch.manual_seed(9); q2,k2,v2,g2,beta2=[x.cuda() for x in reference.get_inputs()]
  ref2=reference._naive_chunk_kda(q2,k2,v2,g2,beta2,float(K)**-0.5)
  o3=m(q2,k2,v2,g2,beta2).clone()  # eager path new ptr
  print(shp,'graph',(ref.float()-o1.float()).abs().max().item(),(ref.float()-o2.float()).abs().max().item(),'eager',(ref2.float()-o3.float()).abs().max().item())
" 2>&1 | tail

in 2out 57cache_r 142517cache_w 10133

subagent · 9 events · 3 tools

tool

stdout (283 chars)

Command running in background with ID: bnb6srkir. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnb6srkir.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'graph|Error|Traceback' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnb6srkir.output 2>/dev/null; do sleep 3; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnb6srkir.output",
  "description": "wait",
  "run_in_background": true
}

in 2out 67cache_r 152650cache_w 723

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bzkdkd1gp. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bzkdkd1gp.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnb6srkir.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnb6srkir.output"
}

in 2out 65cache_r 153373cache_w 394

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

in 2out 5cache_r 153767cache_w 165

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnb6srkir.output",
  "description": "read"
}

in 2out 5cache_r 153767cache_w 165

system — task_started

↳ subagent started

tool

stdout (31 chars)

(Bash completed with no output)

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnb6srkir.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnb6srkir.output"
}

in 2out 65cache_r 153932cache_w 198

tool

stdout (93 chars)

Wasted call — file unchanged since your last Read. Refer to that earlier tool_result instead.

assistant

in 165out 3cache_r 154130cache_w 152

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnb6srkir.output",
  "description": "read result again"
}

in 165out 3cache_r 154130cache_w 152

tool

stdout (165 chars)

(2, 1024, 8, 128, 128) graph 6.103515625e-05 6.103515625e-05 eager 6.103515625e-05
(1, 2048, 4, 128, 128) graph 6.103515625e-05 6.103515625e-05 eager 6.103515625e-05

assistant

All paths correct (graph capture, replay, and eager fallback). Let me run the official benchmark.

in 555out 1cache_r 154282cache_w 440

assistant

Bash

args

{
  "command": "uv run python benchmark.py 2>&1 | grep -E '^shape=[0-9]+ (variant=solution|solution_peak)|peak_fraction:|RESULT'",
  "description": "Official benchmark, graph bound to inputs",
  "run_in_background": true
}

in 555out 1cache_r 154282cache_w 440

subagent · 23 events · 8 tools

tool

stdout (283 chars)

Command running in background with ID: b8njwid0w. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b8njwid0w.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'peak_fraction:|Error|Traceback' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b8njwid0w.output 2>/dev/null; do sleep 4; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b8njwid0w.output",
  "description": "wait benchmark",
  "run_in_background": true
}

in 2out 64cache_r 154722cache_w 874

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: brgir1jba. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/brgir1jba.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b8njwid0w.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b8njwid0w.output"
}

in 550out 60cache_r 155596cache_w 397

tool

stdout (454 chars)

1	shape=0 variant=solution tflops=18.111 gbps=212.510 ms=0.119
2	shape=0 solution_peak_fraction=0.0906
3	shape=1 variant=solution tflops=23.508 gbps=275.841 ms=0.183
4	shape=1 solution_peak_fraction=0.1175
5	shape=2 variant=solution tflops=19.057 gbps=223.614 ms=0.225
6	shape=2 solution_peak_fraction=0.0953
7	shape=3 variant=solution tflops=10.707 gbps=125.631 ms=0.100
8	shape=3 solution_peak_fraction=0.0535
9	peak_fraction: 0.0858
10	RESULT: LOW
11

assistant

in 2out 3cache_r 155993cache_w 924

assistant

Geomean 0.086 now. shape3 (thin batch) is the laggard. Let me get realistic per-kernel timing with L2 flush and sweep `BV_STATE` to find the bottleneck.

in 2out 3cache_r 155993cache_w 924

assistant

Write /workspace/problems/02_kda_cutlass/bench_kernels.py

diff (42 lines)

--- a//workspace/problems/02_kda_cutlass/bench_kernels.py
+++ b//workspace/problems/02_kda_cutlass/bench_kernels.py
@@ -1,19 +1,22 @@
-"""Per-kernel timing breakdown."""
-import torch, reference, solution as S
+"""Per-kernel timing with L2 flush (realistic)."""
+import os, torch, reference, solution as S
 
 SHAPES = [(2,1024,8,128,128),(2,2048,8,128,128),(1,4096,8,128,128),(1,2048,4,128,128)]
-BVS=int(__import__('os').environ.get('BVS','32'))
-ST=int(__import__('os').environ.get('ST','2'))
-BVO=128
+BVS=int(os.environ.get('BVS','32'))
+ST=int(os.environ.get('ST','2'))
+BVO=int(os.environ.get('BVO','128'))
+_scratch=torch.empty(128*1024*1024//4,dtype=torch.float32,device='cuda')
 
-def time_ms(fn, iters=50):
+def time_ms(fn, iters=60):
     for _ in range(10): fn()
     torch.cuda.synchronize()
-    st=torch.cuda.Event(True); en=torch.cuda.Event(True)
-    st.record()
-    for _ in range(iters): fn()
-    en.record(); torch.cuda.synchronize()
-    return st.elapsed_time(en)/iters
+    ts=[]
+    for _ in range(iters):
+        _scratch.zero_(); torch.cuda.synchronize()
+        s=torch.cuda.Event(True); e=torch.cuda.Event(True)
+        s.record(); fn(); e.record(); torch.cuda.synchronize()
+        ts.append(s.elapsed_time(e))
+    ts.sort(); return ts[len(ts)//2]
 
 for shp in SHAPES:
     B,T,H,K,V=shp
@@ -33,4 +36,4 @@
     fb=lambda: S._kda_state_kernel[(B*H,V//BVS)](w,u,kd,decay,hs,vnew,B,T,H,NT,K,V,C,BVS,num_warps=4,num_stages=ST)
     fc=lambda: S._kda_output_kernel[(B*H,NT,V//BVO)](q,k,g,hs,vnew,o,B,T,H,NT,scale,K,V,C,BVO,num_warps=4,num_stages=2)
     ta,tb,tc=time_ms(fa),time_ms(fb),time_ms(fc)
-    print(f"{shp} A={ta:.4f} B={tb:.4f} C={tc:.4f} sum={ta+tb+tc:.4f}")
+    print(f"{shp} BVS={BVS} A={ta:.4f} B={tb:.4f} C={tc:.4f} sum={ta+tb+tc:.4f}")

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/bench_kernels.py",
  "content": "<1901 chars \u2014 see diff>"
}

in 2out 3cache_r 155993cache_w 924

tool

stdout (157 chars)

The file /workspace/problems/02_kda_cutlass/bench_kernels.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "for bvs in 16 32 64; do BVS=$bvs uv run python bench_kernels.py 2>&1 | tail -4; echo; done",
  "description": "Sweep BVS with L2 flush",
  "run_in_background": true
}

in 2out 53cache_r 156917cache_w 2250

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bk0qjoenu. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bk0qjoenu.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until [ $(grep -c 'sum=' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bk0qjoenu.output 2>/dev/null) -ge 12 ] || grep -qE 'Error|Traceback' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bk0qjoenu.output 2>/dev/null; do sleep 5; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bk0qjoenu.output",
  "description": "wait BVS sweep",
  "run_in_background": true
}

in 2out 61cache_r 159167cache_w 277

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bgsokfjcb. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bgsokfjcb.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bk0qjoenu.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bk0qjoenu.output"
}

in 2out 66cache_r 159444cache_w 498

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

in 165out 8cache_r 159942cache_w 166

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bk0qjoenu.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bk0qjoenu.output"
}

in 165out 8cache_r 159942cache_w 166

tool

stdout (93 chars)

Wasted call — file unchanged since your last Read. Refer to that earlier tool_result instead.

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bk0qjoenu.output",
  "description": "read sweep"
}

in 2out 58cache_r 160108cache_w 2038

system — task_started

↳ subagent started

tool

stdout (271 chars)

(2, 1024, 8, 128, 128) BVS=16 A=0.0625 B=0.0489 C=0.0614 sum=0.1728
(2, 2048, 8, 128, 128) BVS=16 A=0.0857 B=0.0594 C=0.0718 sum=0.2170
(1, 4096, 8, 128, 128) BVS=16 A=0.0859 B=0.1046 C=0.0812 sum=0.2717
(1, 2048, 4, 128, 128) BVS=16 A=0.0380 B=0.0625 C=0.0354 sum=0.1359

assistant

in 2out 2cache_r 162146cache_w 349

assistant

Per-kernel-with-flush over-counts (each kernel re-reads inputs cold, unlike the graph where intermediates stay in L2). The whole-graph official number is what matters. Let me make the tuning knobs env-configurable and sweep via the official benchmark directly.

in 2out 2cache_r 162146cache_w 349

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (14 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -211,8 +211,9 @@
         self._graph = None          # None=not tried, False=disabled, else CUDAGraph
         self._inter = None
         self._cap_ptrs = None
-        self.BV_STATE = 32
-        self.BV_OUT = 128
+        import os
+        self.BV_STATE = int(os.environ.get("KDA_BVS", "32"))
+        self.BV_OUT = int(os.environ.get("KDA_BVO", "128"))
 
     def _alloc(self, device):
         B, T, H, K, V = self.B, self.T, self.H, self.K, self.V

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<194 chars \u2014 see diff>",
  "new_string": "<280 chars \u2014 see diff>"
}

in 2out 2cache_r 162146cache_w 349

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "<220 chars \u2014 see below>",
  "description": "Sweep BVS/BVO via official benchmark",
  "run_in_background": true
}

command (220 chars)

cd /workspace/problems/02_kda_cutlass
for cfg in "32 128" "16 128" "32 64" "64 128"; do
  set -- $cfg
  echo "=== BVS=$1 BVO=$2 ==="
  KDA_BVS=$1 KDA_BVO=$2 uv run python benchmark.py 2>&1 | grep -E 'peak_fraction:'
done

in 2out 56cache_r 162495cache_w 1086

subagent · 8 events · 2 tools

tool

stdout (283 chars)

Command running in background with ID: b3eav8584. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b3eav8584.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

in 2out 2cache_r 163581cache_w 365

assistant

While the sweep runs, let me reason about kernel A (the Neumann inverse is a ~7-deep dependent matmul chain — latency-bound at low occupancy). Let me check the sweep results first.

in 2out 2cache_r 163581cache_w 365

assistant

Bash

args

{
  "command": "until [ $(grep -c 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b3eav8584.output 2>/dev/null) -ge 4 ]; do sleep 5; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b3eav8584.output",
  "description": "wait sweep",
  "run_in_background": true
}

in 2out 2cache_r 163581cache_w 365

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bndmfuizg. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bndmfuizg.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b3eav8584.output",
  "description": "read sweep so far"
}

in 2out 58cache_r 163946cache_w 2309

system — task_started

↳ subagent started

tool

stdout (22 chars)

=== BVS=32 BVO=128 ===

assistant

in 2out 3cache_r 166255cache_w 162

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b3eav8584.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b3eav8584.output"
}

in 2out 3cache_r 166255cache_w 162

tool

stdout (76 chars)

1	=== BVS=32 BVO=128 ===
2	peak_fraction: 0.0858
3	=== BVS=16 BVO=128 ===
4

assistant

Bash

args

{
  "command": "until [ $(grep -c 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b3eav8584.output 2>/dev/null) -ge 4 ]; do sleep 10; done; echo DONE",
  "description": "wait full sweep",
  "run_in_background": true
}

in 282out 53cache_r 166417cache_w 384

subagent · 3 events · 1 tools

tool

stdout (283 chars)

Command running in background with ID: b4vnxqzh4. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b4vnxqzh4.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b3eav8584.output",
  "description": "read full sweep"
}

in 438out 61cache_r 166801cache_w 621

system — task_started

↳ subagent started

tool

stdout (111 chars)

=== BVS=32 BVO=128 ===
peak_fraction: 0.0858
=== BVS=16 BVO=128 ===
peak_fraction: 0.0895
=== BVS=32 BVO=64 ===

assistant

in 2out 2cache_r 167422cache_w 657

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b3eav8584.output",
  "description": "read sweep"
}

in 2out 2cache_r 167422cache_w 657

subagent · 0 events · 0 tools

tool

stdout (178 chars)

=== BVS=32 BVO=128 ===
peak_fraction: 0.0858
=== BVS=16 BVO=128 ===
peak_fraction: 0.0895
=== BVS=32 BVO=64 ===
peak_fraction: 0.0888
=== BVS=64 BVO=128 ===
peak_fraction: 0.0776

assistant

in 551out 3cache_r 168079cache_w 304

assistant

BVS=16 is best (0.0895). Let me set that default and use `nsys` to get true per-kernel durations *inside* the graph for the laggard shapes (shape2, shape3).

in 551out 3cache_r 168079cache_w 304

assistant

Bash

args

{
  "command": "<605 chars \u2014 see below>",
  "description": "Set BVS=16, prep profiling, check tools"
}

command (605 chars)

sed -i 's/"KDA_BVS", "32"/"KDA_BVS", "16"/' solution.py && grep KDA_BVS solution.py
cat > prof_run.py <<'EOF'
import torch, reference, solution
B,T,H,K,V=(1,4096,8,128,128)
import sys
if len(sys.argv)>1 and sys.argv[1]=='3': B,T,H,K,V=(1,2048,4,128,128)
reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
q,k,v,g,beta=[x.cuda() for x in reference.get_inputs()]
m=solution.Model(B,T,H,K,V,64).cuda()
for _ in range(5): m(q,k,v,g,beta)
torch.cuda.synchronize()
for _ in range(50): m(q,k,v,g,beta)
torch.cuda.synchronize()
EOF
which ncu nsys 2>&1; echo "---"; nsys --version 2>&1 | head -1

in 551out 3cache_r 168079cache_w 304

subagent · 3 events · 1 tools

tool

stdout (283 chars)

Command running in background with ID: bpgy7jec8. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpgy7jec8.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpgy7jec8.output",
  "description": "read tool check"
}

in 279out 68cache_r 168383cache_w 1524

system — task_started

↳ subagent started

tool

stdout (83 chars)

self.BV_STATE = int(os.environ.get("KDA_BVS", "16"))
/kbh/bin/ncu
/kbh/bin/nsys
---

assistant

in 4810out 5cache_r 169907cache_w 480

assistant

nsys is available. Let me capture true per-kernel durations inside the graph for the two laggard shapes.

in 4810out 5cache_r 169907cache_w 480

assistant

Bash

args

{
  "command": "<270 chars \u2014 see below>",
  "description": "nsys profile shape2 kernel summary",
  "run_in_background": true
}

command (270 chars)

cd /workspace/problems/02_kda_cutlass
rm -f prof2.nsys-rep prof3.nsys-rep
nsys profile -o prof2 --force-overwrite true -t cuda --cuda-graph-trace=node uv run python prof_run.py 2 >/dev/null 2>&1
nsys stats --report cuda_gpu_kern_sum prof2.nsys-rep 2>/dev/null | head -25

in 4810out 5cache_r 169907cache_w 480

subagent · 12 events · 4 tools

tool

stdout (283 chars)

Command running in background with ID: bwj942lfh. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bwj942lfh.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'Time|Error|error|No kernels' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bwj942lfh.output 2>/dev/null; do sleep 5; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bwj942lfh.output",
  "description": "wait nsys",
  "run_in_background": true
}

in 556out 47cache_r 170387cache_w 5333

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bynblbkw8. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bynblbkw8.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bwj942lfh.output",
  "description": "read nsys output"
}

in 2out 61cache_r 175720cache_w 949

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bv4tsfrsv. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bv4tsfrsv.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

in 2out 3cache_r 176669cache_w 267

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bwj942lfh.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bwj942lfh.output"
}

in 2out 3cache_r 176669cache_w 267

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bwj942lfh.output",
  "description": "read nsys"
}

in 165out 61cache_r 176936cache_w 218

system — task_started

↳ subagent started

tool

stdout (31 chars)

(Bash completed with no output)

assistant

Bash

args

{
  "command": "until grep -qiE 'Time|Error|graph|kernel|Generat' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bwj942lfh.output 2>/dev/null; do sleep 3; done; echo \"READY\"; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bwj942lfh.output",
  "description": "wait and read nsys",
  "run_in_background": true
}

in 278out 43cache_r 177154cache_w 318

subagent · 19 events · 6 tools

tool

stdout (283 chars)

Command running in background with ID: b4yunapmn. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b4yunapmn.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bwj942lfh.output",
  "description": "read nsys final"
}

in 279out 68cache_r 177472cache_w 685

tool

stdout (31 chars)

(Bash completed with no output)

assistant

in 2out 3cache_r 178157cache_w 433

assistant

nsys is broken on this CUDA 13 setup. Let me build cumulative graphs (A, A+B, A+B+C) and time each with the official method to get realistic in-graph per-kernel costs.

in 2out 3cache_r 178157cache_w 433

assistant

Write /workspace/problems/02_kda_cutlass/prof_cumulative.py

diff (51 lines)

--- /dev/null
+++ b//workspace/problems/02_kda_cutlass/prof_cumulative.py
@@ -0,0 +1,48 @@
+"""In-graph per-kernel timing via cumulative graphs (A, A+B, A+B+C)."""
+import torch, reference, solution as S
+
+SHAPES=[(2,1024,8,128,128),(2,2048,8,128,128),(1,4096,8,128,128),(1,2048,4,128,128)]
+BVS=16; BVO=128
+_scr=torch.empty(128*1024*1024//4,dtype=torch.float32,device='cuda')
+
+def make_graph(fns):
+    st=torch.cuda.Stream(); st.wait_stream(torch.cuda.current_stream())
+    with torch.cuda.stream(st):
+        for _ in range(3):
+            for f in fns: f()
+    torch.cuda.current_stream().wait_stream(st)
+    g=torch.cuda.CUDAGraph()
+    with torch.cuda.graph(g):
+        for f in fns: f()
+    return g
+
+def time_graph(g,iters=60):
+    for _ in range(10): g.replay()
+    torch.cuda.synchronize()
+    ts=[]
+    for _ in range(iters):
+        _scr.zero_(); torch.cuda.synchronize()
+        s=torch.cuda.Event(True);e=torch.cuda.Event(True)
+        s.record(); g.replay(); e.record(); torch.cuda.synchronize()
+        ts.append(s.elapsed_time(e))
+    ts.sort(); return ts[len(ts)//2]
+
+for shp in SHAPES:
+    B,T,H,K,V=shp
+    reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
+    torch.manual_seed(2026)
+    q,k,v,g,beta=[x.cuda().contiguous() for x in reference.get_inputs()]
+    scale=float(K)**-0.5; C=64; NT=T//C
+    w=torch.empty((B,T,H,K),dtype=torch.bfloat16,device='cuda')
+    u=torch.empty((B,T,H,V),dtype=torch.bfloat16,device='cuda')
+    kd=torch.empty((B,T,H,K),dtype=torch.bfloat16,device='cuda')
+    decay=torch.empty((B*H,NT,K),dtype=torch.float32,device='cuda')
+    vnew=torch.empty((B,T,H,V),dtype=torch.bfloat16,device='cuda')
+    hs=torch.empty((B,H,NT,K,V),dtype=torch.bfloat16,device='cuda')
+    o=torch.empty((B,T,H,V),dtype=torch.bfloat16,device='cuda')
+    fa=lambda: S._kda_prepare_kernel[(NT,B*H)](k,v,g,beta,w,u,kd,decay,B,T,H,NT,K,V,C)
+    fb=lambda: S._kda_state_kernel[(B*H,V//BVS)](w,u,kd,decay,hs,vnew,B,T,H,NT,K,V,C,BVS,num_warps=4,num_stages=2)
+    fc=lambda: S._kda_output_kernel[(B*H,NT,V//BVO)](q,k,g,hs,vnew,o,B,T,H,NT,scale,K,V,C,BVO,num_warps=4,num_stages=2)
+    gA=make_graph([fa]); gAB=make_graph([fa,fb]); gABC=make_graph([fa,fb,fc])
+    tA=time_graph(gA); tAB=time_graph(gAB); tABC=time_graph(gABC)
+    print(f"{shp} A={tA:.4f} B={tAB-tA:.4f} C={tABC-tAB:.4f} total={tABC:.4f}")

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/prof_cumulative.py",
  "content": "<2248 chars \u2014 see diff>"
}

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tool

stdout (149 chars)

File created successfully at: /workspace/problems/02_kda_cutlass/prof_cumulative.py (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "uv run python prof_cumulative.py 2>&1 | tail -6",
  "description": "cumulative graph timing",
  "run_in_background": true
}

in 2out 67cache_r 178590cache_w 2202

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bizrofoig. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bizrofoig.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'total=|Error|Traceback' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bizrofoig.output 2>/dev/null; do sleep 5; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bizrofoig.output",
  "description": "wait cumulative",
  "run_in_background": true
}

in 2out 58cache_r 180792cache_w 243

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: b0e6gfrvm. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b0e6gfrvm.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bizrofoig.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bizrofoig.output"
}

in 273out 53cache_r 181035cache_w 396

tool

stdout (262 chars)

1	(2, 1024, 8, 128, 128) A=0.0564 B=0.0221 C=0.0364 total=0.1150
2	(2, 2048, 8, 128, 128) A=0.0866 B=0.0420 C=0.0504 total=0.1789
3	(1, 4096, 8, 128, 128) A=0.0833 B=0.0760 C=0.0530 total=0.2124
4	(1, 2048, 4, 128, 128) A=0.0297 B=0.0386 C=0.0224 total=0.0907
5

assistant

in 279out 9cache_r 181431cache_w 576

assistant

Clear picture now (totals match official within ~5%): - shape0: A=0.056 B=0.022 C=0.036 - shape1: A=0.087 B=0.042 C=0.050 - shape2: A=0.083 B=0.076 C=0.053 - shape3: A=0.030 B=0.039 C=0.022 Kernel A (the Neumann inverse, tf32) dominates. tf32 peak is only 100 TFLOPS (half of bf16). Given my nominal error is 6e-5 vs 0.05 tolerance, there's huge margin to try bf16 tensor cores (2× throughput). Let me convert the dots to bf16 and verify correctness holds.

in 279out 9cache_r 181431cache_w 576

assistant

Bash

args

{
  "command": "<277 chars \u2014 see below>",
  "description": "backup tf32 version"
}

command (277 chars)

cp solution.py solution_tf32.bak && python - <<'EOF'
import re
s=open('solution.py').read()
# Replace all tf32 dots with bf16-cast dots via a helper pattern.
# We'll instead just switch input_precision to ieee? No—cast operands to bf16.
print("manual edits next")
EOF
echo done

in 279out 9cache_r 181431cache_w 576

system — task_started

↳ subagent started

tool

stdout (22 chars)

manual edits next
done

assistant

in 165out 7cache_r 182007cache_w 4379

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (35 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -57,21 +57,23 @@
     kg = k * egc
     kng = k * tl.exp(-gc)
 
-    Kgg = tl.dot(kg, tl.trans(kng), input_precision="tf32")   # (C, C)
+    Kgg = tl.dot(kg.to(tl.bfloat16), tl.trans(kng).to(tl.bfloat16))   # (C, C)
     row = offs_c[:, None]
     col = offs_c[None, :]
     A0 = tl.where(row > col, -beta[:, None] * Kgg, 0.0)
 
     M = tl.where(row == col, 1.0, 0.0)
     P = A0
-    for _ in range(6):
-        M = M + tl.dot(P, M, input_precision="tf32")
-        P = tl.dot(P, P, input_precision="tf32")
-
-    beta_kg = beta[:, None] * kg
-    beta_v = beta[:, None] * v
-    w = tl.dot(M, beta_kg, input_precision="tf32")
-    u = tl.dot(M, beta_v, input_precision="tf32")
+    for i in tl.static_range(6):
+        M = M + tl.dot(P.to(tl.bfloat16), M.to(tl.bfloat16))
+        if i < 5:
+            P = tl.dot(P.to(tl.bfloat16), P.to(tl.bfloat16))
+
+    beta_kg = (beta[:, None] * kg).to(tl.bfloat16)
+    beta_v = (beta[:, None] * v).to(tl.bfloat16)
+    Mb = M.to(tl.bfloat16)
+    w = tl.dot(Mb, beta_kg)
+    u = tl.dot(Mb, beta_v)
 
     # state-scan precompute: kd = e^(last - gc) * k , decay = e^last
     kd = tl.exp(last[None, :] - gc) * k

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<520 chars \u2014 see diff>",
  "new_string": "<593 chars \u2014 see diff>"
}

in 165out 7cache_r 182007cache_w 4379

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

in 2out 3cache_r 186386cache_w 1311

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (27 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -172,17 +172,18 @@
     kng = k * tl.exp(-gc)
 
     offs_c = tl.arange(0, C)
-    Aqk = tl.dot(qg, tl.trans(kng), input_precision="tf32")
-    Aqk = tl.where(offs_c[:, None] >= offs_c[None, :], Aqk, 0.0)
+    qgb = qg.to(tl.bfloat16)
+    Aqk = tl.dot(qgb, tl.trans(kng).to(tl.bfloat16))
+    Aqk = tl.where(offs_c[:, None] >= offs_c[None, :], Aqk, 0.0).to(tl.bfloat16)
 
     h_base = h_ptr + (b * sb_h + (h * NT + pid_n) * K * V)
     p_h = tl.make_block_ptr(h_base, (K, V), (V, 1), (0, v0), (K, BV), (1, 0))
-    h_state = tl.load(p_h, boundary_check=(0, 1)).to(tl.float32)
+    h_state = tl.load(p_h, boundary_check=(0, 1))
 
     p_vn = tl.make_block_ptr(base_vn, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
-    v_new = tl.load(p_vn, boundary_check=(0, 1)).to(tl.float32)
-
-    o = tl.dot(qg, h_state, input_precision="tf32") + tl.dot(Aqk, v_new, input_precision="tf32")
+    v_new = tl.load(p_vn, boundary_check=(0, 1))
+
+    o = tl.dot(qgb, h_state) + tl.dot(Aqk, v_new)
 
     p_o = tl.make_block_ptr(base_o, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
     tl.store(p_o, o.to(o_ptr.dtype.element_ty), boundary_check=(0, 1))

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<606 chars \u2014 see diff>",
  "new_string": "<567 chars \u2014 see diff>"
}

in 2out 3cache_r 186386cache_w 1311

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

in 2out 6cache_r 187697cache_w 1256

assistant

bf16 across all kernels now. Let me verify correctness with the full check, then benchmark.

in 2out 6cache_r 187697cache_w 1256

assistant

Bash

args

{
  "command": "uv run python check.py 2>&1 | tail -5",
  "description": "check.py with bf16",
  "run_in_background": true
}

in 2out 6cache_r 187697cache_w 1256

subagent · 9 events · 3 tools

tool

stdout (283 chars)

Command running in background with ID: behht5jdv. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/behht5jdv.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'PASS|FAIL|Traceback' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/behht5jdv.output 2>/dev/null; do sleep 5; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/behht5jdv.output",
  "description": "wait check",
  "run_in_background": true
}

in 2out 63cache_r 188953cache_w 375

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: b07g23879. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b07g23879.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/behht5jdv.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/behht5jdv.output"
}

in 2out 58cache_r 189328cache_w 388

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

in 2out 8cache_r 189716cache_w 164

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/behht5jdv.output",
  "description": "read check"
}

in 2out 8cache_r 189716cache_w 164

system — task_started

↳ subagent started

tool

stdout (31 chars)

(Bash completed with no output)

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/behht5jdv.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/behht5jdv.output"
}

in 2out 67cache_r 189880cache_w 201

tool

stdout (93 chars)

Wasted call — file unchanged since your last Read. Refer to that earlier tool_result instead.

assistant

I'll wait for the check to complete.

in 2out 1cache_r 190081cache_w 151

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/behht5jdv.output",
  "description": "read check result"
}

in 2out 1cache_r 190081cache_w 151

tool

stdout (31 chars)

(Bash completed with no output)

assistant

Bash

args

{
  "command": "until grep -qE 'PASS|FAIL' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/behht5jdv.output 2>/dev/null; do sleep 5; done; echo \"RESULT:\"; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/behht5jdv.output",
  "description": "block for check PASS/FAIL",
  "run_in_background": true
}

in 165out 58cache_r 190232cache_w 166

subagent · 42 events · 13 tools

tool

stdout (283 chars)

Command running in background with ID: b6knjked2. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b6knjked2.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/behht5jdv.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/behht5jdv.output"
}

in 810out 51cache_r 190398cache_w 566

tool

stdout (9 chars)

1	PASS
2

assistant

in 2out 2cache_r 190964cache_w 935

assistant

bf16 PASSES. Let me benchmark the speedup.

in 2out 2cache_r 190964cache_w 935

assistant

Bash

args

{
  "command": "uv run python benchmark.py 2>&1 | grep -E 'solution_peak|peak_fraction:'; echo \"=== per-kernel ===\"; uv run python prof_cumulative.py 2>&1 | tail -4",
  "description": "benchmark + per-kernel with bf16",
  "run_in_background": true
}

in 2out 2cache_r 190964cache_w 935

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bylly9t4x. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bylly9t4x.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'total=.*total=.*total=.*total=|per-kernel' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bylly9t4x.output 2>/dev/null && [ $(grep -c total= /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bylly9t4x.output) -ge 4 ]; do sleep 5; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bylly9t4x.output",
  "description": "wait bf16 bench",
  "run_in_background": true
}

in 2out 46cache_r 191899cache_w 351

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bzf759xpy. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bzf759xpy.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bylly9t4x.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bylly9t4x.output"
}

in 279out 73cache_r 192250cache_w 494

tool

stdout (469 chars)

1	shape=0 solution_peak_fraction=0.1252
2	shape=1 solution_peak_fraction=0.1433
3	shape=2 solution_peak_fraction=0.1167
4	shape=3 solution_peak_fraction=0.0666
5	peak_fraction: 0.1087
6	=== per-kernel ===
7	(2, 1024, 8, 128, 128) A=0.0456 B=0.0224 C=0.0144 total=0.0824
8	(2, 2048, 8, 128, 128) A=0.0730 B=0.0418 C=0.0201 total=0.1349
9	(1, 4096, 8, 128, 128) A=0.0751 B=0.0772 C=0.0293 total=0.1816
10	(1, 2048, 4, 128, 128) A=0.0281 B=0.0374 C=0.0137 total=0.0792
11

assistant

in 2out 2cache_r 192744cache_w 698

assistant

geomean **0.1087** — above the 0.1 OK threshold. Kernel A and B are now the targets. Let me add env-tunable `num_warps`/`num_stages` knobs and sweep occupancy (smaller warps → more concurrent chains hides latency in these tiny matmuls).

in 2out 2cache_r 192744cache_w 698

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (30 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -189,18 +189,25 @@
     tl.store(p_o, o.to(o_ptr.dtype.element_ty), boundary_check=(0, 1))
 
 
+import os as _os
+_WA = int(_os.environ.get("KDA_WA", "4")); _SA = int(_os.environ.get("KDA_SA", "2"))
+_WB = int(_os.environ.get("KDA_WB", "4")); _SB = int(_os.environ.get("KDA_SB", "2"))
+_WC = int(_os.environ.get("KDA_WC", "4")); _SC = int(_os.environ.get("KDA_SC", "2"))
+
+
 def _launch(bufs, B, T, H, K, V, C, NT, scale, BV_STATE, BV_OUT):
     q, k, v, g, beta, w, u, kd, decay, vnew, hstates, o = bufs
     _kda_prepare_kernel[(NT, B * H)](
         k, v, g, beta, w, u, kd, decay, B, T, H, NT, K, V, C,
+        num_warps=_WA, num_stages=_SA,
     )
     _kda_state_kernel[(B * H, V // BV_STATE)](
         w, u, kd, decay, hstates, vnew, B, T, H, NT, K, V, C, BV_STATE,
-        num_warps=4, num_stages=2,
+        num_warps=_WB, num_stages=_SB,
     )
     _kda_output_kernel[(B * H, NT, V // BV_OUT)](
         q, k, g, hstates, vnew, o, B, T, H, NT, scale, K, V, C, BV_OUT,
-        num_warps=4, num_stages=2,
+        num_warps=_WC, num_stages=_SC,
     )

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<557 chars \u2014 see diff>",
  "new_string": "<878 chars \u2014 see diff>"
}

in 2out 2cache_r 192744cache_w 698

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

in 276out 8cache_r 193442cache_w 2014

assistant

Bash

args

{
  "command": "<156 chars \u2014 see below>",
  "description": "sweep kernel A num_warps",
  "run_in_background": true
}

command (156 chars)

cd /workspace/problems/02_kda_cutlass
for wa in 1 2 8; do echo "=== WA=$wa ==="; KDA_WA=$wa uv run python benchmark.py 2>&1 | grep -E 'peak_fraction:'; done

in 276out 8cache_r 193442cache_w 2014

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bd20t8afw. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bd20t8afw.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until [ $(grep -c 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bd20t8afw.output 2>/dev/null) -ge 3 ]; do sleep 8; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bd20t8afw.output",
  "description": "wait WA sweep",
  "run_in_background": true
}

in 2out 61cache_r 195456cache_w 884

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bv8obsduj. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bv8obsduj.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bd20t8afw.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bd20t8afw.output"
}

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tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

in 2out 8cache_r 196744cache_w 328

assistant

Let me write a single-process tuning harness that times each kernel independently across config grids (warps/stages/block sizes) via one-kernel CUDA graphs — far faster than re-running benchmark.py per config.

in 2out 8cache_r 196744cache_w 328

assistant

Write /workspace/problems/02_kda_cutlass/tune.py

diff (75 lines)

--- /dev/null
+++ b//workspace/problems/02_kda_cutlass/tune.py
@@ -0,0 +1,72 @@
+"""Per-kernel config tuning via single-kernel CUDA graphs (L2-flushed)."""
+import torch, reference, solution as S
+import itertools
+
+SHAPES=[(2,1024,8,128,128),(2,2048,8,128,128),(1,4096,8,128,128),(1,2048,4,128,128)]
+_scr=torch.empty(128*1024*1024//4,dtype=torch.float32,device='cuda')
+
+def graph_of(fn):
+    st=torch.cuda.Stream(); st.wait_stream(torch.cuda.current_stream())
+    with torch.cuda.stream(st):
+        for _ in range(3): fn()
+    torch.cuda.current_stream().wait_stream(st)
+    g=torch.cuda.CUDAGraph()
+    with torch.cuda.graph(g): fn()
+    return g
+
+def t_graph(g,iters=40):
+    for _ in range(8): g.replay()
+    torch.cuda.synchronize(); ts=[]
+    for _ in range(iters):
+        _scr.zero_(); torch.cuda.synchronize()
+        s=torch.cuda.Event(True);e=torch.cuda.Event(True)
+        s.record(); g.replay(); e.record(); torch.cuda.synchronize()
+        ts.append(s.elapsed_time(e))
+    ts.sort(); return ts[len(ts)//2]
+
+def best(cands):
+    res=[]
+    for name,fn in cands:
+        try:
+            g=graph_of(fn); res.append((t_graph(g),name))
+        except Exception as ex:
+            res.append((9e9,name+f"[FAIL]"))
+    res.sort(); return res
+
+for shp in SHAPES:
+    B,T,H,K,V=shp
+    reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
+    torch.manual_seed(2026)
+    q,k,v,g,beta=[x.cuda().contiguous() for x in reference.get_inputs()]
+    scale=float(K)**-0.5; C=64; NT=T//C
+    w=torch.empty((B,T,H,K),dtype=torch.bfloat16,device='cuda')
+    u=torch.empty((B,T,H,V),dtype=torch.bfloat16,device='cuda')
+    kd=torch.empty((B,T,H,K),dtype=torch.bfloat16,device='cuda')
+    decay=torch.empty((B*H,NT,K),dtype=torch.float32,device='cuda')
+    vnew=torch.empty((B,T,H,V),dtype=torch.bfloat16,device='cuda')
+    hs=torch.empty((B,H,NT,K,V),dtype=torch.bfloat16,device='cuda')
+    o=torch.empty((B,T,H,V),dtype=torch.bfloat16,device='cuda')
+    # populate
+    S._kda_prepare_kernel[(NT,B*H)](k,v,g,beta,w,u,kd,decay,B,T,H,NT,K,V,C)
+    S._kda_state_kernel[(B*H,V//16)](w,u,kd,decay,hs,vnew,B,T,H,NT,K,V,C,16,num_warps=4,num_stages=2)
+    torch.cuda.synchronize()
+
+    # Kernel A candidates
+    candA=[]
+    for wa,sa in itertools.product([1,2,4],[1,2,3]):
+        candA.append((f"A w{wa}s{sa}", (lambda wa=wa,sa=sa: S._kda_prepare_kernel[(NT,B*H)](k,v,g,beta,w,u,kd,decay,B,T,H,NT,K,V,C,num_warps=wa,num_stages=sa))))
+    rA=best(candA)
+    # Kernel B candidates
+    candB=[]
+    for bvs,wb,sb in itertools.product([8,16,32],[1,2,4],[1,2,3]):
+        candB.append((f"B bv{bvs}w{wb}s{sb}", (lambda bvs=bvs,wb=wb,sb=sb: S._kda_state_kernel[(B*H,V//bvs)](w,u,kd,decay,hs,vnew,B,T,H,NT,K,V,C,bvs,num_warps=wb,num_stages=sb))))
+    rB=best(candB)
+    # Kernel C candidates
+    candC=[]
+    for bvo,wc,sc in itertools.product([64,128],[2,4],[2,3]):
+        candC.append((f"C bv{bvo}w{wc}s{sc}", (lambda bvo=bvo,wc=wc,sc=sc: S._kda_output_kernel[(B*H,NT,V//bvo)](q,k,g,hs,vnew,o,B,T,H,NT,scale,K,V,C,bvo,num_warps=wc,num_stages=sc))))
+    rC=best(candC)
+    print(f"\n{shp}")
+    print("  A:", rA[:3])
+    print("  B:", rB[:3])
+    print("  C:", rC[:3])

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/tune.py",
  "content": "<3086 chars \u2014 see diff>"
}

in 2out 8cache_r 196744cache_w 328

tool

stdout (138 chars)

File created successfully at: /workspace/problems/02_kda_cutlass/tune.py (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bd20t8afw.output; echo \"===TUNE===\"; uv run python tune.py 2>&1 | tail -20",
  "description": "read WA sweep + run tune",
  "run_in_background": true
}

in 2out 42cache_r 197072cache_w 2711

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: by3z75jxh. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/by3z75jxh.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE '2048, 4' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/by3z75jxh.output 2>/dev/null && [ $(grep -c '  C:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/by3z75jxh.output) -ge 4 ]; do sleep 8; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/by3z75jxh.output",
  "description": "wait tune",
  "run_in_background": true
}

in 2out 56cache_r 199783cache_w 324

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: babwk7suo. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/babwk7suo.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/by3z75jxh.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/by3z75jxh.output"
}

in 2out 57cache_r 200107cache_w 475

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

in 2out 8cache_r 200582cache_w 165

assistant

The tuning sweep will take a few minutes. Let me wait for it.

in 2out 8cache_r 200582cache_w 165

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/by3z75jxh.output",
  "description": "read tune progress"
}

in 2out 8cache_r 200582cache_w 165

system — task_started

↳ subagent started

tool

stdout (23 chars)

=== WA=1 ===
===TUNE===

assistant

in 2out 3cache_r 200747cache_w 260

assistant

Two GPU jobs may be running concurrently and skewing results. Let me check and kill the older WA sweep.

in 2out 3cache_r 200747cache_w 260

assistant

Bash

args

{
  "command": "nvidia-smi --query-compute-apps=pid,used_memory --format=csv 2>/dev/null | head; echo \"---procs---\"; ps aux | grep -E 'benchmark.py|tune.py' | grep -v grep | awk '{print $2, $11, $12, $13}'",
  "description": "check running GPU procs"
}

in 2out 3cache_r 200747cache_w 260

subagent · 17 events · 5 tools

tool

stdout (283 chars)

Command running in background with ID: brn4i4kzg. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/brn4i4kzg.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

in 701out 2cache_r 201007cache_w 548

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/brn4i4kzg.output; echo \"===WA file===\"; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bd20t8afw.output",
  "description": "check procs and WA results"
}

in 701out 2cache_r 201007cache_w 548

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bueezy1z2. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bueezy1z2.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

in 2out 2cache_r 201555cache_w 1264

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bd20t8afw.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bd20t8afw.output"
}

in 2out 2cache_r 201555cache_w 1264

tool

stdout (56 chars)

1	=== WA=1 ===
2	peak_fraction: 0.0304
3	=== WA=2 ===
4

assistant

in 2out 2cache_r 202819cache_w 225

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/by3z75jxh.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/by3z75jxh.output"
}

in 2out 2cache_r 202819cache_w 225

tool

stdout (1540 chars) — TRUNCATED

1	=== WA=1 ===
2	===TUNE===
3	
4	(2, 1024, 8, 128, 128)
5	  A: [(0.04870399832725525, 'A w4s3'), (0.04873599857091904, 'A w4s1'), (0.0488319993019104, 'A w4s2')]
6	  B: [(0.04201599955558777, 'B bv16w2s2'), (0.043296001851558685, 'B bv8w4s2'), (0.04339199885725975, 'B bv8w2s2')]
7	  C: [(0.02521600015461445, 'C bv64w4s2'), (0.025248000398278236, 'C bv64w4s3'), (0.02848000079393387, 'C bv128w4s2')]
8	
9	(2, 2048, 8, 128, 128)
10	  A: [(0.077504001557827, 'A w4s3'), (0.07756800204515457, 'A w4s1'), (0.0777600035071373, 'A w4s2')]
11	  B: [(0.06995200365781784, 'B bv16w2s2'), (0.07119999825954437, 'B bv16w4s2'), (0.07222399860620499, 'B bv16w4s3')]
12	  C: [(0.049536000937223434, 'C bv128w4s2'), (0.050175998359918594, 'C bv128w4s3'), (0.05215999856591225, 'C bv64w4s2')]
13	
14	(1, 4096, 8, 128, 128)
15	  A: [(0.07711999863386154, 'A w4s1'), (0.07718399912118912, 'A w4s2'), (0.077504001557827, 'A w4s3')]
16	  B: [(0.10716799646615982, 'B bv16w2s2'), (0.10742399841547012, 'B bv8w2s2'), (0.11078400164842606, 'B bv8w4s2')]
17	  C: [(0.04819199815392494, 'C bv128w4s3'), (0.048287998884916306, 'C bv128w4s2'), (0.051392000168561935, 'C bv64w4s2')]
18	
19	(1, 2048, 4, 128, 128)
20	  A: [(0.030559999868273735, 'A w4s1'), (0.03062400035560131, 'A w4s2'), (0.03062400035560131, 'A w4s3')]
21	  B: [(0.05580800026655197, 'B bv8w2s2'), (0.055904000997543335, 'B bv16w2s2'), (0.05772799998521805, 'B bv16w4s2')]
22	  C: [(0.01583999954164028, 'C bv64w4s2'), (0.01651199907064438, 'C bv128w4s2'), (

... (40 more chars)

assistant

in 2out 2cache_r 203044cache_w 1169

assistant

The tune ran under contention (a stray sweep overlapped), so absolute numbers are inflated but relative rankings hold: **A→w4**, **B→bv16/w2/s2** (vs current w4), **C→bv64 or bv128 w4**. Let me confirm no stray GPU jobs, then re-run tune cleanly.

in 2out 2cache_r 203044cache_w 1169

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/brn4i4kzg.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/brn4i4kzg.output"
}

in 2out 2cache_r 203044cache_w 1169

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

Bash

args

{
  "command": "sleep 2; nvidia-smi --query-compute-apps=pid,used_memory --format=csv,noheader 2>/dev/null; echo \"exit\"",
  "description": "check GPU procs idle"
}

in 2out 62cache_r 204213cache_w 632

system — task_started

↳ subagent started

tool

stderr (43 chars)

Exit code 143
Command timed out after 2m 0s

assistant

in 1092out 2cache_r 204845cache_w 157

assistant

All jobs done. nvidia-smi is slow on this box — I'll avoid it. Let me re-run the tune cleanly (nothing else running now).

in 1092out 2cache_r 204845cache_w 157

assistant

Bash

args

{
  "command": "uv run python tune.py 2>&1 | tail -24",
  "description": "clean tune run",
  "run_in_background": true
}

in 1092out 2cache_r 204845cache_w 157

subagent · 11 events · 4 tools

tool

stdout (283 chars)

Command running in background with ID: byi5woozt. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/byi5woozt.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until [ $(grep -c '  C:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/byi5woozt.output 2>/dev/null) -ge 4 ]; do sleep 8; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/byi5woozt.output",
  "description": "wait clean tune",
  "run_in_background": true
}

in 2out 57cache_r 205002cache_w 1524

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: ba7o0lia8. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/ba7o0lia8.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/byi5woozt.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/byi5woozt.output"
}

in 2out 60cache_r 206526cache_w 398

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

in 165out 9cache_r 206924cache_w 164

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/byi5woozt.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/byi5woozt.output"
}

in 165out 9cache_r 206924cache_w 164

tool

stdout (93 chars)

Wasted call — file unchanged since your last Read. Refer to that earlier tool_result instead.

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/byi5woozt.output",
  "description": "read tune"
}

in 2out 61cache_r 207088cache_w 1563

system — task_started

↳ subagent started

tool

stdout (1451 chars)

(2, 1024, 8, 128, 128)
  A: [(0.04864000156521797, 'A w4s1'), (0.04870399832725525, 'A w4s2'), (0.04886399954557419, 'A w4s3')]
  B: [(0.0427200011909008, 'B bv16w2s2'), (0.043296001851558685, 'B bv8w4s2'), (0.04339199885725975, 'B bv8w2s2')]
  C: [(0.025119999423623085, 'C bv64w4s3'), (0.02537599951028824, 'C bv64w4s2'), (0.02879999950528145, 'C bv128w4s2')]

(2, 2048, 8, 128, 128)
  A: [(0.07760000228881836, 'A w4s2'), (0.07769600301980972, 'A w4s1'), (0.07772800326347351, 'A w4s3')]
  B: [(0.06985600292682648, 'B bv16w2s2'), (0.07097599655389786, 'B bv16w4s2'), (0.0721919983625412, 'B bv16w4s3')]
  C: [(0.04899200052022934, 'C bv128w4s3'), (0.049056001007556915, 'C bv128w4s2'), (0.05289600044488907, 'C bv64w4s2')]

(1, 4096, 8, 128, 128)
  A: [(0.07785599678754807, 'A w4s2'), (0.078015998005867, 'A w4s1'), (0.078015998005867, 'A w4s3')]
  B: [(0.10688000172376633, 'B bv16w2s2'), (0.10761599987745285, 'B bv8w2s2'), (0.11091200262308121, 'B bv8w4s2')]
  C: [(0.04819199815392494, 'C bv128w4s3'), (0.04835199937224388, 'C bv128w4s2'), (0.05040000006556511, 'C bv64w4s2')]

(1, 2048, 4, 128, 128)
  A: [(0.0307839997112751, 'A w4s1'), (0.030848000198602676, 'A w4s2'), (0.03094400092959404, 'A w4s3')]
  B: [(0.05516799911856651, 'B bv8w2s2'), (0.055456001311540604, 'B bv16w2s2'), (0.057472001761198044, 'B bv16w4s2')]
  C: [(0.015584000386297703, 'C bv64w4s2'), (0.0161920003592968, 'C bv64w4s3'), (0.016383999958634377, 'C bv128w4s2')]

assistant

in 269out 3cache_r 208651cache_w 1024

assistant

Clear winners: **B → WB=2, BVS=16**; **A → WA=4** (config-insensitive); **C → BVO=64 for small shapes, 128 for large**. Let me apply WB=2 and adaptive BVO.

in 269out 3cache_r 208651cache_w 1024

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (11 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -191,7 +191,7 @@
 
 import os as _os
 _WA = int(_os.environ.get("KDA_WA", "4")); _SA = int(_os.environ.get("KDA_SA", "2"))
-_WB = int(_os.environ.get("KDA_WB", "4")); _SB = int(_os.environ.get("KDA_SB", "2"))
+_WB = int(_os.environ.get("KDA_WB", "2")); _SB = int(_os.environ.get("KDA_SB", "2"))
 _WC = int(_os.environ.get("KDA_WC", "4")); _SC = int(_os.environ.get("KDA_SC", "2"))

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "_WB = int(_os.environ.get(\"KDA_WB\", \"4\")); _SB = int(_os.environ.get(\"KDA_SB\", \"2\"))",
  "new_string": "_WB = int(_os.environ.get(\"KDA_WB\", \"2\")); _SB = int(_os.environ.get(\"KDA_SB\", \"2\"))"
}

in 269out 3cache_r 208651cache_w 1024

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (7 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -1,3 +1,5 @@
         import os
         self.BV_STATE = int(os.environ.get("KDA_BVS", "16"))
-        self.BV_OUT = int(os.environ.get("KDA_BVO", "128"))+        nprog_out = B * H * (T // chunk_size)
+        default_bvo = 64 if nprog_out <= 256 else 128
+        self.BV_OUT = int(os.environ.get("KDA_BVO", str(default_bvo)))

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<138 chars \u2014 see diff>",
  "new_string": "<249 chars \u2014 see diff>"
}

in 276out 55cache_r 209675cache_w 1613

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "uv run python benchmark.py 2>&1 | grep -E 'solution_peak|peak_fraction:'",
  "description": "benchmark with WB=2, adaptive BVO",
  "run_in_background": true
}

in 2out 63cache_r 211288cache_w 651

subagent · 8 events · 3 tools

tool

stdout (283 chars)

Command running in background with ID: bb08t1ga5. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bb08t1ga5.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bb08t1ga5.output 2>/dev/null; do sleep 5; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bb08t1ga5.output",
  "description": "wait bench",
  "run_in_background": true
}

in 2out 57cache_r 211939cache_w 269

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: byr322ojj. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/byr322ojj.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bb08t1ga5.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bb08t1ga5.output"
}

in 2out 65cache_r 212208cache_w 389

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bb08t1ga5.output",
  "description": "read bench"
}

in 2out 68cache_r 212597cache_w 166

system — task_started

↳ subagent started

tool

stdout (31 chars)

(Bash completed with no output)

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bb08t1ga5.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bb08t1ga5.output"
}

in 2out 65cache_r 212763cache_w 155

tool

stdout (93 chars)

Wasted call — file unchanged since your last Read. Refer to that earlier tool_result instead.

assistant

I'll wait for the benchmark to finish.

in 165out 4cache_r 212918cache_w 153

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bb08t1ga5.output",
  "description": "read bench result"
}

in 165out 4cache_r 212918cache_w 153

subagent · 0 events · 0 tools

tool

stdout (173 chars)

shape=0 solution_peak_fraction=0.1294
shape=1 solution_peak_fraction=0.1429
shape=2 solution_peak_fraction=0.1136
shape=3 solution_peak_fraction=0.0620
peak_fraction: 0.1068

assistant

in 554out 3cache_r 213071cache_w 417

assistant

geomean 0.1068 — basically flat/noise vs WB=4 (0.1087). The isolated-kernel tune didn't translate to the full graph. Kernel A (the Neumann inverse) is the real bottleneck. Let me test reducing Neumann iterations — high-order terms are decay-suppressed, so fewer may suffice within the 0.05 tolerance.

in 554out 3cache_r 213071cache_w 417

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (11 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -24,7 +24,7 @@
 def _kda_prepare_kernel(
     k_ptr, v_ptr, g_ptr, beta_ptr, w_ptr, u_ptr, kd_ptr, decay_ptr,
     B, T, H, NT,
-    K: tl.constexpr, V: tl.constexpr, C: tl.constexpr,
+    K: tl.constexpr, V: tl.constexpr, C: tl.constexpr, ITERS: tl.constexpr,
 ):
     pid_n = tl.program_id(0)
     pid_bh = tl.program_id(1)

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<103 chars \u2014 see diff>",
  "new_string": "<124 chars \u2014 see diff>"
}

in 554out 3cache_r 213071cache_w 417

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (14 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -64,9 +64,9 @@
 
     M = tl.where(row == col, 1.0, 0.0)
     P = A0
-    for i in tl.static_range(6):
+    for i in tl.static_range(ITERS):
         M = M + tl.dot(P.to(tl.bfloat16), M.to(tl.bfloat16))
-        if i < 5:
+        if i < ITERS - 1:
             P = tl.dot(P.to(tl.bfloat16), P.to(tl.bfloat16))
 
     beta_kg = (beta[:, None] * kg).to(tl.bfloat16)

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<222 chars \u2014 see diff>",
  "new_string": "<234 chars \u2014 see diff>"
}

in 2out 54cache_r 213488cache_w 3773

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (10 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -191,6 +191,7 @@
 
 import os as _os
 _WA = int(_os.environ.get("KDA_WA", "4")); _SA = int(_os.environ.get("KDA_SA", "2"))
+_NITER = int(_os.environ.get("KDA_NITER", "6"))
 _WB = int(_os.environ.get("KDA_WB", "2")); _SB = int(_os.environ.get("KDA_SB", "2"))
 _WC = int(_os.environ.get("KDA_WC", "4")); _SC = int(_os.environ.get("KDA_SC", "2"))

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "_WA = int(_os.environ.get(\"KDA_WA\", \"4\")); _SA = int(_os.environ.get(\"KDA_SA\", \"2\"))",
  "new_string": "<132 chars \u2014 see diff>"
}

in 2out 54cache_r 217261cache_w 448

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (11 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -199,7 +199,7 @@
 def _launch(bufs, B, T, H, K, V, C, NT, scale, BV_STATE, BV_OUT):
     q, k, v, g, beta, w, u, kd, decay, vnew, hstates, o = bufs
     _kda_prepare_kernel[(NT, B * H)](
-        k, v, g, beta, w, u, kd, decay, B, T, H, NT, K, V, C,
+        k, v, g, beta, w, u, kd, decay, B, T, H, NT, K, V, C, _NITER,
         num_warps=_WA, num_stages=_SA,
     )
     _kda_state_kernel[(B * H, V // BV_STATE)](

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<144 chars \u2014 see diff>",
  "new_string": "<152 chars \u2014 see diff>"
}

in 2out 55cache_r 217709cache_w 320

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

in 2out 8cache_r 218029cache_w 359

assistant

Now let me test correctness across ITERS values, including the large_qkv stress (k,v×2) which is the worst case for truncating the Neumann series.

in 2out 8cache_r 218029cache_w 359

assistant

Bash

args

{
  "command": "<995 chars \u2014 see below>",
  "description": "Test Neumann iter reduction correctness",
  "run_in_background": true
}

command (995 chars)

cat > test_iters.py <<'EOF'
import os, torch, reference
SH=[(2,1024,8,128,128),(1,4096,8,128,128),(1,2048,4,128,128)]
for niter in [3,4,5,6]:
    os.environ['KDA_NITER']=str(niter)
    import importlib, solution; importlib.reload(solution)
    worst=0.0
    for shp in SH:
        B,T,H,K,V=shp
        reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
        for scale_kv in [1.0,2.0]:
            torch.manual_seed(42)
            q,k,v,g,beta=[x.cuda() for x in reference.get_inputs()]
            k=(k.float()*scale_kv).to(k.dtype); v=(v.float()*scale_kv).to(v.dtype); q=(q.float()*scale_kv).to(q.dtype)
            ref=reference._naive_chunk_kda(q,k,v,g,beta,float(K)**-0.5)
            m=solution.Model(B,T,H,K,V,64).cuda()
            got=m(q,k,v,g,beta)
            d=(ref.float()-got.float()).abs().max().item()
            worst=max(worst,d)
    print(f"NITER={niter} worst_abs={worst:.5f}  pass(<0.05)={worst<0.05}")
EOF
uv run python test_iters.py 2>&1 | tail -8

in 2out 8cache_r 218029cache_w 359

subagent · 8 events · 3 tools

tool

stdout (283 chars)

Command running in background with ID: b543nnxj0. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b543nnxj0.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until [ $(grep -c 'NITER=' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b543nnxj0.output 2>/dev/null) -ge 4 ] || grep -qE 'Error|Traceback' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b543nnxj0.output 2>/dev/null; do sleep 5; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b543nnxj0.output",
  "description": "wait iter test",
  "run_in_background": true
}

in 2out 63cache_r 218388cache_w 1035

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bs1m3ssv7. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bs1m3ssv7.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b543nnxj0.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b543nnxj0.output"
}

in 165out 66cache_r 219423cache_w 495

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b543nnxj0.output",
  "description": "read iter test"
}

in 2out 67cache_r 219918cache_w 328

system — task_started

↳ subagent started

tool

stdout (31 chars)

(Bash completed with no output)

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b543nnxj0.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b543nnxj0.output"
}

in 2out 60cache_r 220246cache_w 155

tool

stdout (93 chars)

Wasted call — file unchanged since your last Read. Refer to that earlier tool_result instead.

assistant

I'll wait for the iter test to complete.

in 2out 1cache_r 220401cache_w 152

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b543nnxj0.output",
  "description": "read iter test result"
}

in 2out 1cache_r 220401cache_w 152

tool

stdout (31 chars)

(Bash completed with no output)

assistant

Bash

args

{
  "command": "until [ $(grep -c 'NITER=' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b543nnxj0.output 2>/dev/null) -ge 4 ]; do sleep 5; done; echo GO",
  "description": "block for iter test",
  "run_in_background": true
}

in 2out 56cache_r 220553cache_w 171

subagent · 20 events · 7 tools

tool

stdout (283 chars)

Command running in background with ID: bm6h3ix5g. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bm6h3ix5g.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b543nnxj0.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b543nnxj0.output"
}

in 282out 73cache_r 220724cache_w 337

tool

stdout (186 chars)

1	NITER=3 worst_abs=0.04810  pass(<0.05)=True
2	NITER=4 worst_abs=0.00146  pass(<0.05)=True
3	NITER=5 worst_abs=0.00134  pass(<0.05)=True
4	NITER=6 worst_abs=0.00134  pass(<0.05)=True
5

assistant

in 550out 6cache_r 221061cache_w 522

assistant

NITER=4 gives 0.0015 worst error (33× margin), NITER=3 is too close (0.048). Let me set NITER=4 default, then run full `check.py` and benchmark.

in 550out 6cache_r 221061cache_w 522

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (11 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -191,7 +191,7 @@
 
 import os as _os
 _WA = int(_os.environ.get("KDA_WA", "4")); _SA = int(_os.environ.get("KDA_SA", "2"))
-_NITER = int(_os.environ.get("KDA_NITER", "6"))
+_NITER = int(_os.environ.get("KDA_NITER", "4"))
 _WB = int(_os.environ.get("KDA_WB", "2")); _SB = int(_os.environ.get("KDA_SB", "2"))
 _WC = int(_os.environ.get("KDA_WC", "4")); _SC = int(_os.environ.get("KDA_SC", "2"))

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "_NITER = int(_os.environ.get(\"KDA_NITER\", \"6\"))",
  "new_string": "_NITER = int(_os.environ.get(\"KDA_NITER\", \"4\"))"
}

in 550out 6cache_r 221061cache_w 522

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "uv run python check.py 2>&1 | tail -3; echo \"=== BENCH ===\"; uv run python benchmark.py 2>&1 | grep -E 'solution_peak|peak_fraction:'",
  "description": "full check + benchmark with NITER=4",
  "run_in_background": true
}

in 2out 68cache_r 221583cache_w 1302

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: balmy83ve. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/balmy83ve.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/balmy83ve.output 2>/dev/null; do sleep 6; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/balmy83ve.output",
  "description": "wait check+bench",
  "run_in_background": true
}

in 165out 64cache_r 222885cache_w 293

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: b017ju1q2. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b017ju1q2.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/balmy83ve.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/balmy83ve.output"
}

in 2out 65cache_r 223178cache_w 552

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

in 2out 8cache_r 223730cache_w 164

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/balmy83ve.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/balmy83ve.output"
}

in 2out 8cache_r 223730cache_w 164

tool

stdout (93 chars)

Wasted call — file unchanged since your last Read. Refer to that earlier tool_result instead.

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/balmy83ve.output",
  "description": "read check+bench"
}

in 2out 61cache_r 223894cache_w 171

system — task_started

↳ subagent started

tool

stdout (31 chars)

(Bash completed with no output)

assistant

I'll wait for the combined check+benchmark to finish (the slow reference dominates).

in 2out 4cache_r 224065cache_w 155

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/balmy83ve.output",
  "description": "read"
}

in 2out 4cache_r 224065cache_w 155

subagent · 0 events · 0 tools

tool

stdout (18 chars)

PASS
=== BENCH ===

assistant

Bash

args

{
  "command": "until grep -qE 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/balmy83ve.output 2>/dev/null; do sleep 6; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/balmy83ve.output",
  "description": "wait bench part",
  "run_in_background": true
}

in 2out 56cache_r 224220cache_w 182

subagent · 15 events · 5 tools

tool

stdout (283 chars)

Command running in background with ID: bwn7lgtgy. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bwn7lgtgy.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/balmy83ve.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/balmy83ve.output"
}

in 549out 60cache_r 224402cache_w 392

tool

stdout (209 chars)

1	PASS
2	=== BENCH ===
3	shape=0 solution_peak_fraction=0.1352
4	shape=1 solution_peak_fraction=0.1485
5	shape=2 solution_peak_fraction=0.1166
6	shape=3 solution_peak_fraction=0.0635
7	peak_fraction: 0.1104
8

assistant

in 2out 2cache_r 224794cache_w 788

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PASS, geomean **0.1104** (up from 0.1068). Let me re-profile per-kernel with the current config to see the updated breakdown and decide the next target.

in 2out 2cache_r 224794cache_w 788

assistant

Bash

args

{
  "command": "<555 chars \u2014 see below>",
  "description": "re-profile per-kernel current config",
  "run_in_background": true
}

command (555 chars)

cd /workspace/problems/02_kda_cutlass
# update prof_cumulative to match current kernel signatures (NITER, WB=2)
python - <<'EOF'
s=open('prof_cumulative.py').read()
s=s.replace("S._kda_prepare_kernel[(NT,B*H)](k,v,g,beta,w,u,kd,decay,B,T,H,NT,K,V,C)",
            "S._kda_prepare_kernel[(NT,B*H)](k,v,g,beta,w,u,kd,decay,B,T,H,NT,K,V,C,4,num_warps=4,num_stages=2)")
s=s.replace("C,BVS,num_warps=4,num_stages=2)","C,BVS,num_warps=2,num_stages=2)")
open('prof_cumulative.py','w').write(s)
print("patched")
EOF
uv run python prof_cumulative.py 2>&1 | tail -4

in 2out 2cache_r 224794cache_w 788

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bnytog0i5. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnytog0i5.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until [ $(grep -c 'total=' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnytog0i5.output 2>/dev/null) -ge 4 ]; do sleep 5; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnytog0i5.output",
  "description": "wait reprofile",
  "run_in_background": true
}

in 439out 53cache_r 225582cache_w 5183

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bmgpofqux. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bmgpofqux.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnytog0i5.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnytog0i5.output"
}

in 2out 70cache_r 230765cache_w 835

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnytog0i5.output",
  "description": "read reprofile"
}

in 2out 60cache_r 231600cache_w 165

system — task_started

↳ subagent started

tool

stdout (7 chars)

patched

assistant

in 832out 2cache_r 231765cache_w 148

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnytog0i5.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnytog0i5.output"
}

in 832out 2cache_r 231765cache_w 148

tool

stdout (272 chars)

1	patched
2	(2, 1024, 8, 128, 128) A=0.0425 B=0.0236 C=0.0132 total=0.0793
3	(2, 2048, 8, 128, 128) A=0.0713 B=0.0437 C=0.0279 total=0.1429
4	(1, 4096, 8, 128, 128) A=0.0722 B=0.0838 C=0.0258 total=0.1818
5	(1, 2048, 4, 128, 128) A=0.0273 B=0.0413 C=0.0028 total=0.0714
6

assistant

in 548out 3cache_r 231913cache_w 1259

assistant

Kernel A barely moved with fewer Neumann iters — so it's bound by the **transcendentals** (3 big `exp` over 64×128 tiles), not matmuls. I can collapse those: `exp(-gc) = 1/exp(gc)`, and `kd = exp(last-gc)*k = exp(last)·(k·exp(-gc)) = decay·kng`. That cuts 3 big exps → 1 exp + 1 reciprocal. Same trick in kernel C.

in 548out 3cache_r 231913cache_w 1259

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (14 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -54,8 +54,10 @@
     gc = tl.cumsum(g, axis=0)            # (C, K)
     last = tl.sum(g, axis=0)             # gc at last row (K,)
     egc = tl.exp(gc)
+    inv_egc = 1.0 / egc                  # = exp(-gc)
+    decay_vec = tl.exp(last)             # (K,)
     kg = k * egc
-    kng = k * tl.exp(-gc)
+    kng = k * inv_egc
 
     Kgg = tl.dot(kg.to(tl.bfloat16), tl.trans(kng).to(tl.bfloat16))   # (C, C)
     row = offs_c[:, None]

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<176 chars \u2014 see diff>",
  "new_string": "<274 chars \u2014 see diff>"
}

in 548out 3cache_r 231913cache_w 1259

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (13 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -77,8 +77,8 @@
     w = tl.dot(Mb, beta_kg)
     u = tl.dot(Mb, beta_v)
 
-    # state-scan precompute: kd = e^(last - gc) * k , decay = e^last
-    kd = tl.exp(last[None, :] - gc) * k
+    # state-scan precompute: kd = e^(last-gc)*k = e^last * (k*e^-gc) = decay * kng
+    kd = decay_vec[None, :] * kng
 
     p_w = tl.make_block_ptr(base_w, (T, K), (H * K, 1), (pid_n * C, 0), (C, K), (1, 0))
     p_u = tl.make_block_ptr(base_u, (T, V), (H * V, 1), (pid_n * C, 0), (C, V), (1, 0))

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<108 chars \u2014 see diff>",
  "new_string": "<116 chars \u2014 see diff>"
}

in 2out 54cache_r 233172cache_w 2843

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (11 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -88,7 +88,7 @@
     tl.store(p_u, u.to(u_ptr.dtype.element_ty), boundary_check=(0, 1))
     tl.store(p_kd, kd.to(kd_ptr.dtype.element_ty), boundary_check=(0, 1))
     offs_k = tl.arange(0, K)
-    tl.store(decay_ptr + (pid_bh * NT + pid_n) * K + offs_k, tl.exp(last))
+    tl.store(decay_ptr + (pid_bh * NT + pid_n) * K + offs_k, decay_vec)
 
 
 @triton.jit

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<103 chars \u2014 see diff>",
  "new_string": "<100 chars \u2014 see diff>"
}

in 2out 55cache_r 236015cache_w 306

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (11 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -171,7 +171,7 @@
     gc = tl.cumsum(g, axis=0)
     egc = tl.exp(gc)
     qg = (q * scale) * egc
-    kng = k * tl.exp(-gc)
+    kng = k * (1.0 / egc)
 
     offs_c = tl.arange(0, C)
     qgb = qg.to(tl.bfloat16)

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<103 chars \u2014 see diff>",
  "new_string": "<103 chars \u2014 see diff>"
}

in 2out 55cache_r 236321cache_w 312

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "<602 chars \u2014 see below>",
  "description": "verify + benchmark exp-reduction",
  "run_in_background": true
}

command (602 chars)

uv run python -c "
import torch, reference, solution
worst=0
for shp in [(2,1024,8,128,128),(1,4096,8,128,128),(1,2048,4,128,128)]:
  B,T,H,K,V=shp
  reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
  q,k,v,g,beta=[x.cuda() for x in reference.get_inputs()]
  ref=reference._naive_chunk_kda(q,k,v,g,beta,float(K)**-0.5)
  got=solution.Model(B,T,H,K,V,64).cuda()(q,k,v,g,beta)
  d=(ref.float()-got.float()).abs().max().item(); worst=max(worst,d)
print('worst_abs',worst)
" 2>&1 | tail -3; echo "=== bench ==="; uv run python benchmark.py 2>&1 | grep -E 'solution_peak|peak_fraction:'

in 2out 52cache_r 236633cache_w 319

subagent · 34 events · 12 tools

tool

stdout (283 chars)

Command running in background with ID: bfgxvw2sp. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfgxvw2sp.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfgxvw2sp.output 2>/dev/null; do sleep 6; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfgxvw2sp.output",
  "description": "wait verify+bench",
  "run_in_background": true
}

in 165out 56cache_r 236952cache_w 564

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bgviy0sg1. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bgviy0sg1.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfgxvw2sp.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfgxvw2sp.output"
}

in 2out 60cache_r 237516cache_w 561

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfgxvw2sp.output",
  "description": "read result"
}

in 2out 68cache_r 238077cache_w 167

tool

stdout (31 chars)

(Bash completed with no output)

assistant

I'll wait for the verify+benchmark result.

in 2out 4cache_r 238244cache_w 155

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfgxvw2sp.output",
  "description": "read result"
}

in 2out 4cache_r 238244cache_w 155

tool

stdout (40 chars)

worst_abs 9.1552734375e-05
=== bench ===

assistant

Bash

args

{
  "command": "until grep -qE 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfgxvw2sp.output 2>/dev/null; do sleep 6; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfgxvw2sp.output",
  "description": "wait bench part",
  "run_in_background": true
}

in 2out 68cache_r 238399cache_w 184

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bkux46j92. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bkux46j92.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfgxvw2sp.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfgxvw2sp.output"
}

in 556out 59cache_r 238583cache_w 392

tool

stdout (231 chars)

1	worst_abs 9.1552734375e-05
2	=== bench ===
3	shape=0 solution_peak_fraction=0.1418
4	shape=1 solution_peak_fraction=0.1619
5	shape=2 solution_peak_fraction=0.1247
6	shape=3 solution_peak_fraction=0.0705
7	peak_fraction: 0.1192
8

assistant

in 280out 3cache_r 238975cache_w 808

assistant

Big jump: geomean **0.1192**, worst error 9e-5. Kernel A is now throughput-bound (scales with program count). Let me try offloading the `cumsum` to tensor cores (matmul with a lower-triangular ones matrix) and combining the `w`/`u` matmuls, checking precision holds.

in 280out 3cache_r 238975cache_w 808

assistant

Read /workspace/problems/02_kda_cutlass/solution.py

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "offset": 50,
  "limit": 40
}

in 280out 3cache_r 238975cache_w 808

tool

stdout (1781 chars) — TRUNCATED

50	    offs_c = tl.arange(0, C)
51	    p_beta = beta_ptr + b * (T * H) + (pid_n * C + offs_c) * H + h
52	    beta = tl.load(p_beta).to(tl.float32)
53	
54	    gc = tl.cumsum(g, axis=0)            # (C, K)
55	    last = tl.sum(g, axis=0)             # gc at last row (K,)
56	    egc = tl.exp(gc)
57	    inv_egc = 1.0 / egc                  # = exp(-gc)
58	    decay_vec = tl.exp(last)             # (K,)
59	    kg = k * egc
60	    kng = k * inv_egc
61	
62	    Kgg = tl.dot(kg.to(tl.bfloat16), tl.trans(kng).to(tl.bfloat16))   # (C, C)
63	    row = offs_c[:, None]
64	    col = offs_c[None, :]
65	    A0 = tl.where(row > col, -beta[:, None] * Kgg, 0.0)
66	
67	    M = tl.where(row == col, 1.0, 0.0)
68	    P = A0
69	    for i in tl.static_range(ITERS):
70	        M = M + tl.dot(P.to(tl.bfloat16), M.to(tl.bfloat16))
71	        if i < ITERS - 1:
72	            P = tl.dot(P.to(tl.bfloat16), P.to(tl.bfloat16))
73	
74	    beta_kg = (beta[:, None] * kg).to(tl.bfloat16)
75	    beta_v = (beta[:, None] * v).to(tl.bfloat16)
76	    Mb = M.to(tl.bfloat16)
77	    w = tl.dot(Mb, beta_kg)
78	    u = tl.dot(Mb, beta_v)
79	
80	    # state-scan precompute: kd = e^(last-gc)*k = e^last * (k*e^-gc) = decay * kng
81	    kd = decay_vec[None, :] * kng
82	
83	    p_w = tl.make_block_ptr(base_w, (T, K), (H * K, 1), (pid_n * C, 0), (C, K), (1, 0))
84	    p_u = tl.make_block_ptr(base_u, (T, V), (H * V, 1), (pid_n * C, 0), (C, V), (1, 0))
85	    base_kd = kd_ptr + b * sb_k + h * K
86	    p_kd = tl.make_block_ptr(base

... (281 more chars)

assistant

in 2out 5cache_r 239783cache_w 6107

assistant

Let me test cumsum-via-matmul (tf32) in kernel A as a quick experiment, guarded so I can revert if precision or speed regresses.

in 2out 5cache_r 239783cache_w 6107

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (12 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -51,7 +51,8 @@
     p_beta = beta_ptr + b * (T * H) + (pid_n * C + offs_c) * H + h
     beta = tl.load(p_beta).to(tl.float32)
 
-    gc = tl.cumsum(g, axis=0)            # (C, K)
+    Ltri = tl.where(offs_c[:, None] >= offs_c[None, :], 1.0, 0.0)
+    gc = tl.dot(Ltri, g, input_precision="tf32")   # cumulative sum via tri-matmul
     last = tl.sum(g, axis=0)             # gc at last row (K,)
     egc = tl.exp(gc)
     inv_egc = 1.0 / egc                  # = exp(-gc)

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<133 chars \u2014 see diff>",
  "new_string": "<232 chars \u2014 see diff>"
}

in 2out 5cache_r 239783cache_w 6107

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "<583 chars \u2014 see below>",
  "description": "test cumsum-matmul correctness+speed",
  "run_in_background": true
}

command (583 chars)

uv run python -c "
import torch, reference, solution
worst=0
for shp in [(2,1024,8,128,128),(1,4096,8,128,128),(1,2048,4,128,128)]:
  B,T,H,K,V=shp
  reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
  q,k,v,g,beta=[x.cuda() for x in reference.get_inputs()]
  ref=reference._naive_chunk_kda(q,k,v,g,beta,float(K)**-0.5)
  got=solution.Model(B,T,H,K,V,64).cuda()(q,k,v,g,beta)
  worst=max(worst,(ref.float()-got.float()).abs().max().item())
print('worst_abs',worst)
" 2>&1 | tail -2; echo "=== bench ==="; uv run python benchmark.py 2>&1 | grep -E 'peak_fraction:'

in 165out 52cache_r 245890cache_w 1010

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bqsqxg4a4. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bqsqxg4a4.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bqsqxg4a4.output 2>/dev/null; do sleep 6; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bqsqxg4a4.output",
  "description": "wait cumsum test",
  "run_in_background": true
}

in 2out 60cache_r 246900cache_w 718

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: b1a3umm7q. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b1a3umm7q.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bqsqxg4a4.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bqsqxg4a4.output"
}

in 2out 59cache_r 247618cache_w 398

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bqsqxg4a4.output",
  "description": "read cumsum test"
}

in 2out 68cache_r 248016cache_w 167

system — task_started

↳ subagent started

tool

stdout (31 chars)

(Bash completed with no output)

assistant

I'll wait for the cumsum experiment result.

in 2out 1cache_r 248183cache_w 159

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bqsqxg4a4.output",
  "description": "read result"
}

in 2out 1cache_r 248183cache_w 159

subagent · 32 events · 10 tools

tool

stdout (283 chars)

Command running in background with ID: bk43uno0g. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bk43uno0g.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

in 2out 2cache_r 248342cache_w 279

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bqsqxg4a4.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bqsqxg4a4.output"
}

in 2out 2cache_r 248342cache_w 279

tool

stdout (47 chars)

1	worst_abs 9.1552734375e-05
2	=== bench ===
3

assistant

Bash

args

{
  "command": "until grep -qE 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bqsqxg4a4.output 2>/dev/null; do sleep 6; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bqsqxg4a4.output",
  "description": "wait bench part cumsum",
  "run_in_background": true
}

in 2out 64cache_r 248621cache_w 190

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bw58surcx. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bw58surcx.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

in 2out 2cache_r 248811cache_w 396

assistant

worst_abs unchanged (9e-5) — tf32 cumsum-matmul is accurate. Waiting for the benchmark number.

in 2out 2cache_r 248811cache_w 396

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bqsqxg4a4.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bqsqxg4a4.output"
}

in 2out 2cache_r 248811cache_w 396

tool

stdout (71 chars)

1	worst_abs 9.1552734375e-05
2	=== bench ===
3	peak_fraction: 0.1261
4

assistant

in 1262out 2cache_r 249207cache_w 342

assistant

cumsum-via-matmul helped: 0.1192 → **0.1261**. Let me apply the same to kernel C's cumsum.

in 1262out 2cache_r 249207cache_w 342

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (18 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -169,12 +169,12 @@
     k = tl.load(p_k, boundary_check=(0, 1)).to(tl.float32)
     g = tl.load(p_g, boundary_check=(0, 1)).to(tl.float32)
 
-    gc = tl.cumsum(g, axis=0)
+    offs_c = tl.arange(0, C)
+    Ltri = tl.where(offs_c[:, None] >= offs_c[None, :], 1.0, 0.0)
+    gc = tl.dot(Ltri, g, input_precision="tf32")
     egc = tl.exp(gc)
     qg = (q * scale) * egc
     kng = k * (1.0 / egc)
-
-    offs_c = tl.arange(0, C)
     qgb = qg.to(tl.bfloat16)
     Aqk = tl.dot(qgb, tl.trans(kng).to(tl.bfloat16))
     Aqk = tl.where(offs_c[:, None] >= offs_c[None, :], Aqk, 0.0).to(tl.bfloat16)

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<133 chars \u2014 see diff>",
  "new_string": "<217 chars \u2014 see diff>"
}

in 1262out 2cache_r 249207cache_w 342

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

in 2out 4cache_r 249549cache_w 1856

assistant

Read /workspace/problems/02_kda_cutlass/solution.py

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "offset": 168,
  "limit": 22
}

in 2out 4cache_r 249549cache_w 1856

tool

stdout (1022 chars)

168	    q = tl.load(p_q, boundary_check=(0, 1)).to(tl.float32)
169	    k = tl.load(p_k, boundary_check=(0, 1)).to(tl.float32)
170	    g = tl.load(p_g, boundary_check=(0, 1)).to(tl.float32)
171	
172	    offs_c = tl.arange(0, C)
173	    Ltri = tl.where(offs_c[:, None] >= offs_c[None, :], 1.0, 0.0)
174	    gc = tl.dot(Ltri, g, input_precision="tf32")
175	    egc = tl.exp(gc)
176	    qg = (q * scale) * egc
177	    kng = k * (1.0 / egc)
178	    qgb = qg.to(tl.bfloat16)
179	    Aqk = tl.dot(qgb, tl.trans(kng).to(tl.bfloat16))
180	    Aqk = tl.where(offs_c[:, None] >= offs_c[None, :], Aqk, 0.0).to(tl.bfloat16)
181	
182	    h_base = h_ptr + (b * sb_h + (h * NT + pid_n) * K * V)
183	    p_h = tl.make_block_ptr(h_base, (K, V), (V, 1), (0, v0), (K, BV), (1, 0))
184	    h_state = tl.load(p_h, boundary_check=(0, 1))
185	
186	    p_vn = tl.make_block_ptr(base_vn, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
187	    v_new = tl.load(p_vn, boundary_check=(0, 1))
188	
189	    o = tl.dot(qgb, h_state) + tl.dot(Aqk, v_new)

assistant

Good, no duplicate. Let me verify and benchmark.

in 2out 9cache_r 251405cache_w 857

assistant

Bash

args

{
  "command": "<597 chars \u2014 see below>",
  "description": "verify+bench cumsum-C",
  "run_in_background": true
}

command (597 chars)

uv run python -c "
import torch, reference, solution
worst=0
for shp in [(2,1024,8,128,128),(1,4096,8,128,128),(1,2048,4,128,128)]:
  B,T,H,K,V=shp
  reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
  q,k,v,g,beta=[x.cuda() for x in reference.get_inputs()]
  ref=reference._naive_chunk_kda(q,k,v,g,beta,float(K)**-0.5)
  got=solution.Model(B,T,H,K,V,64).cuda()(q,k,v,g,beta)
  worst=max(worst,(ref.float()-got.float()).abs().max().item())
print('worst_abs',worst)
" 2>&1 | tail -2; echo "=== bench ==="; uv run python benchmark.py 2>&1 | grep -E 'solution_peak|peak_fraction:'

in 2out 9cache_r 251405cache_w 857

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bfos4xnqz. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfos4xnqz.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfos4xnqz.output 2>/dev/null; do sleep 6; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfos4xnqz.output",
  "description": "wait",
  "run_in_background": true
}

in 2out 64cache_r 252262cache_w 576

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: be0nhbwez. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/be0nhbwez.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfos4xnqz.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfos4xnqz.output"
}

in 2out 65cache_r 252838cache_w 389

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

in 2out 8cache_r 253227cache_w 166

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfos4xnqz.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfos4xnqz.output"
}

in 2out 8cache_r 253227cache_w 166

tool

stdout (93 chars)

Wasted call — file unchanged since your last Read. Refer to that earlier tool_result instead.

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfos4xnqz.output",
  "description": "read"
}

in 2out 68cache_r 253393cache_w 174

system — task_started

↳ subagent started

tool

stdout (31 chars)

(Bash completed with no output)

assistant

I'll wait for the result to appear.

in 2out 1cache_r 253567cache_w 153

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfos4xnqz.output",
  "description": "read result"
}

in 2out 1cache_r 253567cache_w 153

tool

stdout (31 chars)

(Bash completed with no output)

assistant

Bash

args

{
  "command": "until grep -qE 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfos4xnqz.output 2>/dev/null; do sleep 5; done; echo \"---\"; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfos4xnqz.output",
  "description": "block read",
  "run_in_background": true
}

in 165out 55cache_r 253720cache_w 167

subagent · 41 events · 12 tools

tool

stdout (283 chars)

Command running in background with ID: b5q4eirrl. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b5q4eirrl.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfos4xnqz.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bfos4xnqz.output"
}

in 548out 59cache_r 253887cache_w 560

tool

stdout (231 chars)

1	worst_abs 9.1552734375e-05
2	=== bench ===
3	shape=0 solution_peak_fraction=0.1453
4	shape=1 solution_peak_fraction=0.1677
5	shape=2 solution_peak_fraction=0.1292
6	shape=3 solution_peak_fraction=0.0733
7	peak_fraction: 0.1232
8

assistant

in 270out 3cache_r 254447cache_w 799

assistant

Let me confirm stability — run the benchmark a couple more times since these deltas (0.123 vs 0.126) are near run-to-run noise.

in 270out 3cache_r 254447cache_w 799

assistant

Bash

args

{
  "command": "<119 chars \u2014 see below>",
  "description": "repeat benchmark for noise",
  "run_in_background": true
}

command (119 chars)

cd /workspace/problems/02_kda_cutlass
for i in 1 2; do uv run python benchmark.py 2>&1 | grep -E 'peak_fraction:'; done

in 270out 3cache_r 254447cache_w 799

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bxr9efruw. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bxr9efruw.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until [ $(grep -c 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bxr9efruw.output 2>/dev/null) -ge 2 ]; do sleep 6; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bxr9efruw.output",
  "description": "wait repeat bench",
  "run_in_background": true
}

in 2out 60cache_r 255246cache_w 982

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bva89bvlf. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bva89bvlf.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bxr9efruw.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bxr9efruw.output"
}

in 2out 55cache_r 256228cache_w 404

tool

stdout (50 chars)

1	peak_fraction: 0.1231
2	peak_fraction: 0.1227
3

assistant

in 278out 3cache_r 256632cache_w 152

assistant

Stable at ~0.123. The parallel-scan alternative for B isn't worth it (K=128 makes the K×K compositions more expensive than the current tiny per-step matmuls). Let me do a focused in-graph sweep of kernel B's `num_stages`/`num_warps` since isolated tuning may not reflect the full graph.

in 278out 3cache_r 256632cache_w 152

assistant

Bash

args

{
  "command": "<191 chars \u2014 see below>",
  "description": "sweep B stages/warps in-graph",
  "run_in_background": true
}

command (191 chars)

cd /workspace/problems/02_kda_cutlass
for cfg in "KDA_SB=3" "KDA_SB=4" "KDA_WB=1" "KDA_WB=4"; do echo "=== $cfg ==="; env $cfg uv run python benchmark.py 2>&1 | grep -E 'peak_fraction:'; done

in 278out 3cache_r 256632cache_w 152

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bnf2iafxn. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnf2iafxn.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until [ $(grep -c 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnf2iafxn.output 2>/dev/null) -ge 4 ]; do sleep 8; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnf2iafxn.output",
  "description": "wait B sweep",
  "run_in_background": true
}

in 274out 68cache_r 256784cache_w 4031

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bmep0byjn. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bmep0byjn.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnf2iafxn.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnf2iafxn.output"
}

in 551out 66cache_r 260815cache_w 677

tool

stdout (174 chars)

1	=== KDA_SB=3 ===
2	peak_fraction: 0.0666
3	=== KDA_SB=4 ===
4	peak_fraction: 0.0086
5	=== KDA_WB=1 ===
6	peak_fraction: 0.0504
7	=== KDA_WB=4 ===
8	peak_fraction: 0.1277
9

assistant

in 165out 3cache_r 261492cache_w 796

assistant

WB=4 actually wins in-graph (0.1277 vs WB=2's 0.123) — my isolated tune misled me. SB=3/4 broke (smem overflow → eager fallback). Let me revert to WB=4.

in 165out 3cache_r 261492cache_w 796

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (11 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -195,7 +195,7 @@
 import os as _os
 _WA = int(_os.environ.get("KDA_WA", "4")); _SA = int(_os.environ.get("KDA_SA", "2"))
 _NITER = int(_os.environ.get("KDA_NITER", "4"))
-_WB = int(_os.environ.get("KDA_WB", "2")); _SB = int(_os.environ.get("KDA_SB", "2"))
+_WB = int(_os.environ.get("KDA_WB", "4")); _SB = int(_os.environ.get("KDA_SB", "2"))
 _WC = int(_os.environ.get("KDA_WC", "4")); _SC = int(_os.environ.get("KDA_SC", "2"))

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "_WB = int(_os.environ.get(\"KDA_WB\", \"2\")); _SB = int(_os.environ.get(\"KDA_SB\", \"2\"))",
  "new_string": "_WB = int(_os.environ.get(\"KDA_WB\", \"4\")); _SB = int(_os.environ.get(\"KDA_SB\", \"2\"))"
}

in 165out 3cache_r 261492cache_w 796

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

in 2out 3cache_r 262288cache_w 931

assistant

WB=4 set. My isolated tuning misled me, so let me re-sweep the other knobs in-graph: WA, WC, BVS.

in 2out 3cache_r 262288cache_w 931

assistant

Bash

args

{
  "command": "<216 chars \u2014 see below>",
  "description": "in-graph sweep WA/WC/BVS",
  "run_in_background": true
}

command (216 chars)

cd /workspace/problems/02_kda_cutlass
for cfg in "KDA_WA=2" "KDA_WA=8" "KDA_WC=2" "KDA_WC=8" "KDA_BVS=8" "KDA_BVS=32"; do echo "=== $cfg ==="; env $cfg uv run python benchmark.py 2>&1 | grep -E 'peak_fraction:'; done

in 2out 3cache_r 262288cache_w 931

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bduiss3jg. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bduiss3jg.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until [ $(grep -c 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bduiss3jg.output 2>/dev/null) -ge 6 ]; do sleep 10; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bduiss3jg.output",
  "description": "wait WA/WC/BVS sweep",
  "run_in_background": true
}

in 2out 62cache_r 263219cache_w 647

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bi2952m5b. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bi2952m5b.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

in 2out 2cache_r 263866cache_w 411

assistant

While the sweep runs, let me record key project learnings to memory.

in 2out 2cache_r 263866cache_w 411

assistant

Write /home/agent/.claude/projects/-workspace-problems-02-kda-cutlass/memory/kda-cutlass-approach.md

diff (29 lines)

--- /dev/null
+++ b//home/agent/.claude/projects/-workspace-problems-02-kda-cutlass/memory/kda-cutlass-approach.md
@@ -0,0 +1,26 @@
+---
+name: kda-cutlass-approach
+description: Winning design + perf levers for the 02_kda_cutlass KDA-forward Triton kernel
+metadata:
+  type: project
+---
+
+Problem: Kimi Delta Attention chunk-forward on RTX PRO 6000 (SM120). solution.py must keep Model/get_inputs/get_init_inputs. Grade = geomean over 4 shapes of tflops/200 (bf16 peak). check.py must print PASS (tol 0.05, plus stress cases small_qkv atol 5e-4 and large_qkv k/v×2 atol 5e-2). Forbidden: importing fla.ops.kda etc.
+
+Winning design (Triton, 3 kernels + CUDA graph):
+- Kernel A (parallel b*h*chunk): cumsum(g), Kgg=(k·e^gc)(k·e^-gc)^T, A0=strict-lower(-beta·Kgg), Neumann doubling inverse M=(I-A0)^-1, w=M@(beta·e^gc·k), u=M@(beta·v); also precompute kd=e^last·(k·e^-gc) and decay=e^last so the scan's critical path has no exp/cumsum.
+- Kernel B (sequential over chunks, parallel b*h*v-block, state S in regs): v_new=u-w@S; store h_n=S and v_new; S=decay·S+kd^T@v_new. THE bottleneck (sequential, low parallelism on thin shapes).
+- Kernel C (fully parallel b*h*chunk*v-block): o=(q·scale·e^gc)@h_n + tril(qg@kng^T)@v_new.
+
+Perf levers that worked (0.017 -> ~0.127 geomean):
+1. 3-kernel split so the heavy output compute is fully parallel; scan kept light. (state storage is only ~17MB, cheap — I miscalculated it as 268MB at first.)
+2. Precompute kd/decay in A -> scan critical path = 2 matmuls (was ~13.5us/step -> ~1.3us/step, 10x).
+3. CUDA graph bound directly to the INPUT tensors (not static copies): benchmark passes same tensors every call so data_ptr matches -> replay with no input copy; falls back to eager when ptrs change (check.py). Triton launch overhead (~25us×3) otherwise dominates small shapes.
+4. bf16 tensor-core dots everywhere (tf32 is half rate); nominal err 9e-5 << 0.05 tol.
+5. Neumann iters 6->4 (A0 high powers decay-suppressed; worst err 0.0015). 3 iters too close (0.048).
+6. Collapse exps: e^-gc=1/e^gc, kd=decay·kng -> 3 big exps -> 1 exp + 1 recip. Kernel A was transcendental-bound, not matmul-bound.
+7. cumsum via tri-ones matmul (tf32) instead of tl.cumsum -> offloads scan units to tensor cores.
+
+Config: BV_STATE=16, WB=4 (NOT 2 — isolated kernel tuning lied; in-graph WB=4 wins), WA=4, WC=4, num_stages=2 everywhere (SB=3/4 overflow smem -> eager fallback -> catastrophic), BV_OUT=64 if b*h*NT<=256 else 128, NITER=4.
+
+Gotchas: nsys broken (CUDA 13.2 mismatch); use cumulative-graph timing (prof_cumulative.py) for in-graph per-kernel cost. nvidia-smi is very slow/hangs. Always tune via full benchmark.py (in-graph), isolated per-kernel timing is misleading.

args

{
  "file_path": "/home/agent/.claude/projects/-workspace-problems-02-kda-cutlass/memory/kda-cutlass-approach.md",
  "content": "<2560 chars \u2014 see diff>"
}

in 2out 2cache_r 263866cache_w 411

tool

stdout (190 chars)

File created successfully at: /home/agent/.claude/projects/-workspace-problems-02-kda-cutlass/memory/kda-cutlass-approach.md (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "echo \"- [KDA CUTLASS approach](kda-cutlass-approach.md) \u2014 winning Triton 3-kernel + CUDA-graph design and perf levers for 02_kda_cutlass\" >> /home/agent/.claude/projects/-workspace-problems-02-kda-cutlass/memory/MEMORY.md; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bduiss3jg.output",
  "description": "append memory index, check sweep progress"
}

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system — task_started

↳ subagent started

tool

stdout (55 chars)

=== KDA_WA=2 ===
peak_fraction: 0.0814
=== KDA_WA=8 ===

assistant

in 2out 2cache_r 265994cache_w 304

assistant

Bash

args

{
  "command": "until [ $(grep -c 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bduiss3jg.output 2>/dev/null) -ge 6 ]; do sleep 8; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bduiss3jg.output",
  "description": "wait full sweep done",
  "run_in_background": true
}

in 2out 2cache_r 265994cache_w 304

subagent · 24 events · 8 tools

tool

stdout (283 chars)

Command running in background with ID: b0tq120ke. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b0tq120ke.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bduiss3jg.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bduiss3jg.output"
}

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tool

stdout (107 chars)

1	=== KDA_WA=2 ===
2	peak_fraction: 0.0814
3	=== KDA_WA=8 ===
4	peak_fraction: 0.1353
5	=== KDA_WC=2 ===
6

assistant

in 165out 2cache_r 266757cache_w 200

assistant

WA=8 → 0.1353 (better than WA=4's 0.1277)! Let me wait for WC and BVS.

in 165out 2cache_r 266757cache_w 200

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bduiss3jg.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bduiss3jg.output"
}

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tool

stdout (242 chars)

1	=== KDA_WA=2 ===
2	peak_fraction: 0.0814
3	=== KDA_WA=8 ===
4	peak_fraction: 0.1353
5	=== KDA_WC=2 ===
6	peak_fraction: 0.1075
7	=== KDA_WC=8 ===
8	peak_fraction: 0.1235
9	=== KDA_BVS=8 ===
10	peak_fraction: 0.1198
11	=== KDA_BVS=32 ===
12

assistant

in 2out 2cache_r 266957cache_w 607

assistant

WA=8 wins (0.1353); WC=4 and BVS=16 remain best. Let me set WA=8 default and test WA=16 / SA variations on top.

in 2out 2cache_r 266957cache_w 607

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (11 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -193,7 +193,7 @@
 
 
 import os as _os
-_WA = int(_os.environ.get("KDA_WA", "4")); _SA = int(_os.environ.get("KDA_SA", "2"))
+_WA = int(_os.environ.get("KDA_WA", "8")); _SA = int(_os.environ.get("KDA_SA", "2"))
 _NITER = int(_os.environ.get("KDA_NITER", "4"))
 _WB = int(_os.environ.get("KDA_WB", "4")); _SB = int(_os.environ.get("KDA_SB", "2"))
 _WC = int(_os.environ.get("KDA_WC", "4")); _SC = int(_os.environ.get("KDA_SC", "2"))

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "_WA = int(_os.environ.get(\"KDA_WA\", \"4\")); _SA = int(_os.environ.get(\"KDA_SA\", \"2\"))",
  "new_string": "_WA = int(_os.environ.get(\"KDA_WA\", \"8\")); _SA = int(_os.environ.get(\"KDA_SA\", \"2\"))"
}

in 2out 2cache_r 266957cache_w 607

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "<324 chars \u2014 see below>",
  "description": "confirm WA=8, test WA=16 and SA=3",
  "run_in_background": true
}

command (324 chars)

cd /workspace/problems/02_kda_cutlass
echo "=== default WA=8 ==="; uv run python benchmark.py 2>&1 | grep -E 'solution_peak|peak_fraction:'
echo "=== WA=16 ==="; KDA_WA=16 uv run python benchmark.py 2>&1 | grep -E 'peak_fraction:'
echo "=== WA=8 SA=3 ==="; KDA_SA=3 uv run python benchmark.py 2>&1 | grep -E 'peak_fraction:'

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system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bpqju3qp2. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until [ $(grep -c 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output 2>/dev/null) -ge 3 ]; do sleep 8; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output",
  "description": "wait",
  "run_in_background": true
}

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system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: b7wa74bsg. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b7wa74bsg.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output"
}

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tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output"
}

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tool

stdout (93 chars)

Wasted call — file unchanged since your last Read. Refer to that earlier tool_result instead.

assistant

I'll wait for the three benchmark runs.

in 2out 1cache_r 270039cache_w 153

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output",
  "description": "read"
}

in 2out 1cache_r 270039cache_w 153

system — task_started

↳ subagent started

tool

stdout (20 chars)

=== default WA=8 ===

assistant

Bash

args

{
  "command": "until [ $(grep -c 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output 2>/dev/null) -ge 3 ]; do sleep 8; done; echo DONE",
  "description": "block until 3 done",
  "run_in_background": true
}

in 165out 61cache_r 270192cache_w 169

subagent · 12 events · 4 tools

tool

stdout (283 chars)

Command running in background with ID: br8m5fajs. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/br8m5fajs.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output"
}

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tool

stdout (225 chars)

1	=== default WA=8 ===
2	shape=0 solution_peak_fraction=0.1545
3	shape=1 solution_peak_fraction=0.1844
4	shape=2 solution_peak_fraction=0.1445
5	shape=3 solution_peak_fraction=0.0818
6	peak_fraction: 0.1355
7	=== WA=16 ===
8

assistant

in 2out 3cache_r 270863cache_w 251

assistant

WA=8 default confirmed: geomean **0.1355**, all shapes up. Waiting on WA=16 and SA=3.

in 2out 3cache_r 270863cache_w 251

assistant

Bash

args

{
  "command": "until [ $(grep -c 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output 2>/dev/null) -ge 3 ]; do sleep 8; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output",
  "description": "wait WA16 SA3",
  "run_in_background": true
}

in 2out 3cache_r 270863cache_w 251

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: b4np5u7ue. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b4np5u7ue.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output"
}

in 2out 68cache_r 271114cache_w 528

tool

stdout (93 chars)

Wasted call — file unchanged since your last Read. Refer to that earlier tool_result instead.

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output",
  "description": "read"
}

in 2out 68cache_r 271642cache_w 153

system — task_started

↳ subagent started

tool

stdout (248 chars)

=== default WA=8 ===
shape=0 solution_peak_fraction=0.1545
shape=1 solution_peak_fraction=0.1844
shape=2 solution_peak_fraction=0.1445
shape=3 solution_peak_fraction=0.0818
peak_fraction: 0.1355
=== WA=16 ===
peak_fraction: 0.1260
=== WA=8 SA=3 ===

assistant

in 2out 2cache_r 271795cache_w 284

assistant

Bash

args

{
  "command": "until [ $(grep -c 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output 2>/dev/null) -ge 3 ]; do sleep 6; done; tail -1 /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output",
  "description": "read SA=3",
  "run_in_background": true
}

in 2out 2cache_r 271795cache_w 284

subagent · 15 events · 4 tools

tool

stdout (283 chars)

Command running in background with ID: b8wpm8shr. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b8wpm8shr.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bpqju3qp2.output"
}

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tool

stdout (295 chars)

1	=== default WA=8 ===
2	shape=0 solution_peak_fraction=0.1545
3	shape=1 solution_peak_fraction=0.1844
4	shape=2 solution_peak_fraction=0.1445
5	shape=3 solution_peak_fraction=0.0818
6	peak_fraction: 0.1355
7	=== WA=16 ===
8	peak_fraction: 0.1260
9	=== WA=8 SA=3 ===
10	peak_fraction: 0.1362
11

assistant

in 820out 2cache_r 272538cache_w 586

assistant

WA=8/SA=2 is the sweet spot (WA=16 worse, SA=3 within noise). Config locked: **WA=8, WB=4, WC=4, BVS=16, NITER=4, stages=2, adaptive BVO**, geomean ~0.1355. Let me run the full `check.py` to gate correctness with all the changes (bf16, NITER=4, tf32 cumsum-matmul).

in 820out 2cache_r 272538cache_w 586

assistant

Bash

args

{
  "command": "uv run python check.py 2>&1 | tail -4",
  "description": "full correctness gate",
  "run_in_background": true
}

in 820out 2cache_r 272538cache_w 586

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: b64upgbnf. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b64upgbnf.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'PASS|FAIL' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b64upgbnf.output 2>/dev/null; do sleep 8; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b64upgbnf.output",
  "description": "wait check.py gate",
  "run_in_background": true
}

in 432out 68cache_r 273124cache_w 2067

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: b2sf72pxf. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b2sf72pxf.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

in 2out 3cache_r 275191cache_w 825

assistant

While check.py runs, let me re-profile per-kernel with the new WA=8 to see the updated breakdown and decide if there's more to squeeze.

in 2out 3cache_r 275191cache_w 825

assistant

Bash

args

{
  "command": "<235 chars \u2014 see below>",
  "description": "update prof to WA=8 WB=4"
}

command (235 chars)

cd /workspace/problems/02_kda_cutlass
sed -i 's/C,4,num_warps=4,num_stages=2)/C,4,num_warps=8,num_stages=2)/; s/C,BVS,num_warps=2,num_stages=2)/C,BVS,num_warps=4,num_stages=2)/' prof_cumulative.py
grep -E 'num_warps' prof_cumulative.py

in 2out 3cache_r 275191cache_w 825

system — task_started

↳ subagent started

tool

stdout (344 chars)

fa=lambda: S._kda_prepare_kernel[(NT,B*H)](k,v,g,beta,w,u,kd,decay,B,T,H,NT,K,V,C,4,num_warps=8,num_stages=2)
    fb=lambda: S._kda_state_kernel[(B*H,V//BVS)](w,u,kd,decay,hs,vnew,B,T,H,NT,K,V,C,BVS,num_warps=4,num_stages=2)
    fc=lambda: S._kda_output_kernel[(B*H,NT,V//BVO)](q,k,g,hs,vnew,o,B,T,H,NT,scale,K,V,C,BVO,num_warps=4,num_stages=2)

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b64upgbnf.output 2>/dev/null",
  "description": "peek check progress"
}

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subagent · 0 events · 0 tools

tool

stdout (4 chars)

PASS

assistant

check.py **PASS** with the full optimized config. Let me re-profile per-kernel to see where time remains.

in 550out 1cache_r 278006cache_w 157

assistant

Bash

args

{
  "command": "uv run python prof_cumulative.py 2>&1 | tail -4",
  "description": "reprofile WA=8",
  "run_in_background": true
}

in 550out 1cache_r 278006cache_w 157

subagent · 44 events · 15 tools

tool

stdout (283 chars)

Command running in background with ID: bzyshrckg. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bzyshrckg.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until [ $(grep -c 'total=' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bzyshrckg.output 2>/dev/null) -ge 4 ]; do sleep 6; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bzyshrckg.output",
  "description": "wait reprofile",
  "run_in_background": true
}

in 2out 68cache_r 278163cache_w 829

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: buichfa6q. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/buichfa6q.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bzyshrckg.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bzyshrckg.output"
}

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tool

stdout (262 chars)

1	(2, 1024, 8, 128, 128) A=0.0290 B=0.0224 C=0.0143 total=0.0657
2	(2, 2048, 8, 128, 128) A=0.0486 B=0.0427 C=0.0228 total=0.1141
3	(1, 4096, 8, 128, 128) A=0.0495 B=0.0757 C=0.0224 total=0.1476
4	(1, 2048, 4, 128, 128) A=0.0164 B=0.0368 C=0.0088 total=0.0620
5

assistant

in 271out 2cache_r 279388cache_w 580

assistant

Kernel A dropped nicely with WA=8; now **kernel B (the sequential scan) is the bottleneck** — shape2 B=0.076, shape3 B=0.037. The T/b reformulation (1 matmul/step) just shifts equal work into A, so it's net-neutral. Since shape3 has the most geomean leverage and only 32 scan-chains, let me test whether a smaller BVS (more chains) helps the thin shape specifically.

in 271out 2cache_r 279388cache_w 580

assistant

Bash

args

{
  "command": "<142 chars \u2014 see below>",
  "description": "test BVS=8 per-shape",
  "run_in_background": true
}

command (142 chars)

cd /workspace/problems/02_kda_cutlass
echo "=== BVS=8 ==="; KDA_BVS=8 uv run python benchmark.py 2>&1 | grep -E 'solution_peak|peak_fraction:'

in 271out 2cache_r 279388cache_w 580

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bcmk6yvyv. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bcmk6yvyv.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bcmk6yvyv.output 2>/dev/null; do sleep 6; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bcmk6yvyv.output",
  "description": "wait BVS=8",
  "run_in_background": true
}

in 165out 64cache_r 279968cache_w 5406

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bqix6r51r. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bqix6r51r.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bcmk6yvyv.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bcmk6yvyv.output"
}

in 554out 65cache_r 285374cache_w 559

tool

stdout (202 chars)

1	=== BVS=8 ===
2	shape=0 solution_peak_fraction=0.1363
3	shape=1 solution_peak_fraction=0.1624
4	shape=2 solution_peak_fraction=0.1417
5	shape=3 solution_peak_fraction=0.0813
6	peak_fraction: 0.1264
7

assistant

in 2out 3cache_r 285933cache_w 792

assistant

BVS=8 doesn't help shape3 (latency-bound, not occupancy-bound), and hurts the big shapes. Keeping BVS=16. Let me try one targeted B optimization: store `kd` pre-transposed in kernel A so the sequential scan avoids `tl.trans` on its critical path each step.

in 2out 3cache_r 285933cache_w 792

assistant

Read /workspace/problems/02_kda_cutlass/solution.py

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "offset": 116,
  "limit": 40
}

in 2out 3cache_r 285933cache_w 792

tool

stdout (1722 chars) — TRUNCATED

116	    S = tl.zeros((K, BV), dtype=tl.float32)
117	
118	    for n in range(NT):
119	        toff = n * C
120	        p_w = tl.make_block_ptr(base_w, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
121	        p_kd = tl.make_block_ptr(base_kd, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
122	        p_u = tl.make_block_ptr(base_u, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
123	
124	        w = tl.load(p_w, boundary_check=(0, 1))
125	        kd = tl.load(p_kd, boundary_check=(0, 1))
126	        u = tl.load(p_u, boundary_check=(0, 1)).to(tl.float32)
127	        decay = tl.load(decay_ptr + (pid_bh * NT + n) * K + offs_k)
128	
129	        # store start-of-chunk state h_n
130	        h_base = h_ptr + (b * sb_h + (h * NT + n) * K * V)
131	        p_h = tl.make_block_ptr(h_base, (K, V), (V, 1), (0, v0), (K, BV), (1, 0))
132	        tl.store(p_h, S.to(h_ptr.dtype.element_ty), boundary_check=(0, 1))
133	
134	        v_new = u - tl.dot(w, S.to(w.dtype), input_precision="tf32")
135	        p_vn = tl.make_block_ptr(base_vn, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
136	        tl.store(p_vn, v_new.to(vnew_ptr.dtype.element_ty), boundary_check=(0, 1))
137	
138	        S = decay[:, None] * S + tl.dot(tl.trans(kd), v_new.to(kd.dtype), input_precision="tf32")
139	
140	
141	@triton.jit
142	def _kda_output_kernel(
143	    q_ptr, k_ptr, g_ptr, h_ptr, vnew_ptr, o_ptr,
144	    B, T, H, NT, scale,
145	    K: tl.constexpr, V: tl.constexpr, C: tl.constexpr, BV: tl.constexpr,
146	):
147

... (222 more chars)

assistant

in 2out 5cache_r 286725cache_w 4152

assistant

Let me implement the pre-transposed `kd` (kdt) — moves the transpose from B's per-step critical path into the parallel kernel A. Same buffer size, transposed layout.

in 2out 5cache_r 286725cache_w 4152

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (23 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -79,15 +79,16 @@
     u = tl.dot(Mb, beta_v)
 
     # state-scan precompute: kd = e^(last-gc)*k = e^last * (k*e^-gc) = decay * kng
-    kd = decay_vec[None, :] * kng
+    # store transposed (K, C) so the sequential scan avoids tl.trans on its hot path
+    kdt = tl.trans(decay_vec[None, :] * kng)   # (K, C)
 
     p_w = tl.make_block_ptr(base_w, (T, K), (H * K, 1), (pid_n * C, 0), (C, K), (1, 0))
     p_u = tl.make_block_ptr(base_u, (T, V), (H * V, 1), (pid_n * C, 0), (C, V), (1, 0))
-    base_kd = kd_ptr + b * sb_k + h * K
-    p_kd = tl.make_block_ptr(base_kd, (T, K), (H * K, 1), (pid_n * C, 0), (C, K), (1, 0))
+    kdt_base = kd_ptr + (pid_bh * NT + pid_n) * K * C
+    p_kdt = tl.make_block_ptr(kdt_base, (K, C), (C, 1), (0, 0), (K, C), (1, 0))
     tl.store(p_w, w.to(w_ptr.dtype.element_ty), boundary_check=(0, 1))
     tl.store(p_u, u.to(u_ptr.dtype.element_ty), boundary_check=(0, 1))
-    tl.store(p_kd, kd.to(kd_ptr.dtype.element_ty), boundary_check=(0, 1))
+    tl.store(p_kdt, kdt.to(kd_ptr.dtype.element_ty), boundary_check=(0, 1))
     offs_k = tl.arange(0, K)
     tl.store(decay_ptr + (pid_bh * NT + pid_n) * K + offs_k, decay_vec)

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<740 chars \u2014 see diff>",
  "new_string": "<853 chars \u2014 see diff>"
}

in 2out 5cache_r 286725cache_w 4152

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (17 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -119,11 +119,12 @@
     for n in range(NT):
         toff = n * C
         p_w = tl.make_block_ptr(base_w, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
-        p_kd = tl.make_block_ptr(base_kd, (T, K), (H * K, 1), (toff, 0), (C, K), (1, 0))
+        kdt_base = kd_ptr + (pid_bh * NT + n) * K * C
+        p_kdt = tl.make_block_ptr(kdt_base, (K, C), (C, 1), (0, 0), (K, C), (1, 0))
         p_u = tl.make_block_ptr(base_u, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
 
         w = tl.load(p_w, boundary_check=(0, 1))
-        kd = tl.load(p_kd, boundary_check=(0, 1))
+        kdt = tl.load(p_kdt, boundary_check=(0, 1))
         u = tl.load(p_u, boundary_check=(0, 1)).to(tl.float32)
         decay = tl.load(decay_ptr + (pid_bh * NT + n) * K + offs_k)

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<426 chars \u2014 see diff>",
  "new_string": "<477 chars \u2014 see diff>"
}

in 2out 55cache_r 290877cache_w 2594

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (11 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -137,7 +137,7 @@
         p_vn = tl.make_block_ptr(base_vn, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
         tl.store(p_vn, v_new.to(vnew_ptr.dtype.element_ty), boundary_check=(0, 1))
 
-        S = decay[:, None] * S + tl.dot(tl.trans(kd), v_new.to(kd.dtype), input_precision="tf32")
+        S = decay[:, None] * S + tl.dot(kdt, v_new.to(kdt.dtype), input_precision="tf32")
 
 
 @triton.jit

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "        S = decay[:, None] * S + tl.dot(tl.trans(kd), v_new.to(kd.dtype), input_precision=\"tf32\")",
  "new_string": "        S = decay[:, None] * S + tl.dot(kdt, v_new.to(kdt.dtype), input_precision=\"tf32\")"
}

in 2out 55cache_r 293471cache_w 712

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

in 2out 9cache_r 294183cache_w 288

assistant

Let me remove the now-unused `base_kd` in kernel B and verify correctness + benchmark.

in 2out 9cache_r 294183cache_w 288

assistant

Read /workspace/problems/02_kda_cutlass/solution.py

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "offset": 100,
  "limit": 16
}

in 2out 9cache_r 294183cache_w 288

tool

stdout (513 chars)

100	    K: tl.constexpr, V: tl.constexpr, C: tl.constexpr, BV: tl.constexpr,
101	):
102	    pid_bh = tl.program_id(0)
103	    pid_v = tl.program_id(1)
104	    b = pid_bh // H
105	    h = pid_bh % H
106	    v0 = pid_v * BV
107	
108	    sb_k = T * H * K
109	    sb_v = T * H * V
110	    base_w = w_ptr + b * sb_k + h * K
111	    base_kd = kd_ptr + b * sb_k + h * K
112	    base_u = u_ptr + b * sb_v + h * V
113	    base_vn = vnew_ptr + b * sb_v + h * V
114	    sb_h = H * NT * K * V
115	    offs_k = tl.arange(0, K)

assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (10 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -108,7 +108,6 @@
     sb_k = T * H * K
     sb_v = T * H * V
     base_w = w_ptr + b * sb_k + h * K
-    base_kd = kd_ptr + b * sb_k + h * K
     base_u = u_ptr + b * sb_v + h * V
     base_vn = vnew_ptr + b * sb_v + h * V
     sb_h = H * NT * K * V

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<157 chars \u2014 see diff>",
  "new_string": "<117 chars \u2014 see diff>"
}

in 165out 54cache_r 294471cache_w 1004

tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Bash

args

{
  "command": "<597 chars \u2014 see below>",
  "description": "verify+bench kd-transpose",
  "run_in_background": true
}

command (597 chars)

uv run python -c "
import torch, reference, solution
worst=0
for shp in [(2,1024,8,128,128),(1,4096,8,128,128),(1,2048,4,128,128)]:
  B,T,H,K,V=shp
  reference.B,reference.T,reference.H,reference.K,reference.V=B,T,H,K,V
  q,k,v,g,beta=[x.cuda() for x in reference.get_inputs()]
  ref=reference._naive_chunk_kda(q,k,v,g,beta,float(K)**-0.5)
  got=solution.Model(B,T,H,K,V,64).cuda()(q,k,v,g,beta)
  worst=max(worst,(ref.float()-got.float()).abs().max().item())
print('worst_abs',worst)
" 2>&1 | tail -3; echo "=== bench ==="; uv run python benchmark.py 2>&1 | grep -E 'solution_peak|peak_fraction:'

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system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: b153p2qod. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b153p2qod.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'peak_fraction:|Error|Traceback' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b153p2qod.output 2>/dev/null; do sleep 6; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b153p2qod.output",
  "description": "wait kd-transpose result",
  "run_in_background": true
}

in 2out 51cache_r 295990cache_w 551

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: b7smetelc. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b7smetelc.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b153p2qod.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b153p2qod.output"
}

in 2out 73cache_r 296541cache_w 398

tool

stdout (126 chars)

<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b153p2qod.output",
  "description": "read"
}

in 2out 68cache_r 296939cache_w 164

system — task_started

↳ subagent started

tool

stdout (31 chars)

(Bash completed with no output)

assistant

I'll wait for the kd-transpose result.

in 2out 1cache_r 297103cache_w 151

assistant

Bash

args

{
  "command": "cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b153p2qod.output",
  "description": "read result"
}

in 2out 1cache_r 297103cache_w 151

tool

stdout (31 chars)

(Bash completed with no output)

assistant

Bash

args

{
  "command": "until grep -qE 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b153p2qod.output 2>/dev/null; do sleep 6; done; echo \"===\"; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b153p2qod.output",
  "description": "block read kd-transpose",
  "run_in_background": true
}

in 2out 56cache_r 297254cache_w 168

subagent · 46 events · 14 tools

tool

stdout (283 chars)

Command running in background with ID: bjo0ia17k. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bjo0ia17k.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b153p2qod.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b153p2qod.output"
}

in 2out 68cache_r 297422cache_w 399

tool

stdout (47 chars)

1	worst_abs 9.1552734375e-05
2	=== bench ===
3

assistant

Bash

args

{
  "command": "until grep -qE 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b153p2qod.output 2>/dev/null; do sleep 5; done; tail -6 /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b153p2qod.output",
  "description": "wait bench part kd-T",
  "run_in_background": true
}

in 2out 68cache_r 297821cache_w 148

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bnwogcdya. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bnwogcdya.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b153p2qod.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b153p2qod.output"
}

in 715out 66cache_r 297969cache_w 395

tool

stdout (231 chars)

1	worst_abs 9.1552734375e-05
2	=== bench ===
3	shape=0 solution_peak_fraction=0.1566
4	shape=1 solution_peak_fraction=0.1845
5	shape=2 solution_peak_fraction=0.1479
6	shape=3 solution_peak_fraction=0.0847
7	peak_fraction: 0.1379
8

assistant

in 549out 2cache_r 298364cache_w 964

assistant

kd-transpose helped: **0.1379**, all shapes up, still correct (9e-5). Let me run a final correctness gate plus two benchmark runs to confirm stability of the final config.

in 549out 2cache_r 298364cache_w 964

assistant

Bash

args

{
  "command": "<249 chars \u2014 see below>",
  "description": "final check + 2 benchmarks",
  "run_in_background": true
}

command (249 chars)

cd /workspace/problems/02_kda_cutlass
uv run python check.py 2>&1 | tail -2
echo "=== bench run 1 ==="; uv run python benchmark.py 2>&1 | grep -E 'peak_fraction:'
echo "=== bench run 2 ==="; uv run python benchmark.py 2>&1 | grep -E 'peak_fraction:'

in 549out 2cache_r 298364cache_w 964

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: b5dq9nhuf. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b5dq9nhuf.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until [ $(grep -c 'peak_fraction:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b5dq9nhuf.output 2>/dev/null) -ge 2 ] && grep -qE 'PASS|FAIL' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b5dq9nhuf.output; do sleep 8; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b5dq9nhuf.output",
  "description": "wait final validation",
  "run_in_background": true
}

in 2out 62cache_r 299328cache_w 2772

system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bzmordj79. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bzmordj79.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

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While the final validation runs, let me clean up the redundant `import os` in `__init__` (module already has `import os as _os`).

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Read /workspace/problems/02_kda_cutlass/solution.py

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "offset": 209,
  "limit": 20
}

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tool

stdout (902 chars)

209	    _kda_state_kernel[(B * H, V // BV_STATE)](
210	        w, u, kd, decay, hstates, vnew, B, T, H, NT, K, V, C, BV_STATE,
211	        num_warps=_WB, num_stages=_SB,
212	    )
213	    _kda_output_kernel[(B * H, NT, V // BV_OUT)](
214	        q, k, g, hstates, vnew, o, B, T, H, NT, scale, K, V, C, BV_OUT,
215	        num_warps=_WC, num_stages=_SC,
216	    )
217	
218	
219	class Model(nn.Module):
220	    def __init__(self, B: int, T: int, H: int, K: int, V: int, chunk_size: int = 64):
221	        super().__init__()
222	        self.B, self.T, self.H, self.K, self.V = B, T, H, K, V
223	        self.chunk_size = chunk_size
224	        self.scale = float(K) ** -0.5
225	        self.register_buffer("_dummy", torch.zeros(1), persistent=False)
226	        self._graph = None          # None=not tried, False=disabled, else CUDAGraph
227	        self._inter = None
228	        self._cap_ptrs = None

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b5dq9nhuf.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b5dq9nhuf.output"
}

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stdout (101 chars)

1	PASS
2	=== bench run 1 ===
3	peak_fraction: 0.1382
4	=== bench run 2 ===
5	peak_fraction: 0.1377
6

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PASS, stable at **0.138** (8× over the naive baseline). Let me do a final review of the complete solution.py for correctness and robustness.

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assistant

Read /workspace/problems/02_kda_cutlass/solution.py

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "offset": 1,
  "limit": 100
}

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tool

stdout (4373 chars) — TRUNCATED

1	"""Kimi Delta Attention forward (chunk form) — custom Triton kernels for SM120.
2	
3	Chunk-parallel design (FLA-style 3-kernel decomposition):
4	  Kernel A (prepare, parallel over b*h*chunk): build the intra-chunk WY transform.
5	    - gc = cumsum(g) within chunk
6	    - A0 = strict-lower(-beta[c] * (k*e^gc) @ (k*e^-gc)^T)
7	    - Tinv = (I - A0)^{-1}   via Neumann doubling (6 iters, exact since nilpotent)
8	    - w = Tinv @ (beta * e^gc * k),  u = Tinv @ (beta * v)
9	  Kernel B (state scan, parallel over b*h*v-block, sequential over chunks): the only
10	    sequential pass. Keeps recurrent state S (K x BV) in registers, emits per-chunk
11	    start state h_n and the corrected values v_new_n = u_n - w_n @ h_n.
12	  Kernel C (output, fully parallel over b*h*chunk*v-block): the heavy compute.
13	    - o = (q*scale*e^gc) @ h_n + tril(Aqk) @ v_new_n
14	"""
15	from __future__ import annotations
16	
17	import torch
18	import torch.nn as nn
19	import triton
20	import triton.language as tl
21	
22	
23	@triton.jit
24	def _kda_prepare_kernel(
25	    k_ptr, v_ptr, g_ptr, beta_ptr, w_ptr, u_ptr, kd_ptr, decay_ptr,
26	    B, T, H, NT,
27	    K: tl.constexpr, V: tl.constexpr, C: tl.constexpr, ITERS: tl.constexpr,
28	):
29	    pid_n = tl.program_id(0)
30	    pid_bh = tl.program_id(1)
31	    b = pid_bh // H
32	    h = pid_bh % H
33	
34	    sb_k = T * H * K
35	    sb_v = T * H * V
36	    base_k = k_ptr + b * sb_k + h * K
37	    base_g = g_ptr + b * sb_k + h * K
38	    base_v = v_ptr + b * sb

... (2873 more chars)

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The docstring is stale (says "6 iters"). Let me fix it and review the remaining code.

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assistant

Edit /workspace/problems/02_kda_cutlass/solution.py

diff (21 lines)

--- a//workspace/problems/02_kda_cutlass/solution.py
+++ b//workspace/problems/02_kda_cutlass/solution.py
@@ -1,11 +1,15 @@
 """Kimi Delta Attention forward (chunk form) — custom Triton kernels for SM120.
 
-Chunk-parallel design (FLA-style 3-kernel decomposition):
+Chunk-parallel design (FLA-style 3-kernel decomposition), all bf16 tensor cores,
+launched once via a CUDA graph bound to the input tensors:
   Kernel A (prepare, parallel over b*h*chunk): build the intra-chunk WY transform.
-    - gc = cumsum(g) within chunk
+    - gc = cumsum(g) within chunk (done as a lower-triangular ones matmul on TCs)
     - A0 = strict-lower(-beta[c] * (k*e^gc) @ (k*e^-gc)^T)
-    - Tinv = (I - A0)^{-1}   via Neumann doubling (6 iters, exact since nilpotent)
+    - Tinv = (I - A0)^{-1}  via Neumann doubling (ITERS iters; A0 nilpotent and its
+      high powers are decay-suppressed, so 4 iters covers tolerance with wide margin)
     - w = Tinv @ (beta * e^gc * k),  u = Tinv @ (beta * v)
+    - also precomputes kd^T = (e^last * k * e^-gc)^T and decay = e^last so the scan's
+      hot path carries no exp/cumsum/transpose.
   Kernel B (state scan, parallel over b*h*v-block, sequential over chunks): the only
     sequential pass. Keeps recurrent state S (K x BV) in registers, emits per-chunk
     start state h_n and the corrected values v_new_n = u_n - w_n @ h_n.

args

{
  "replace_all": false,
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "old_string": "<752 chars \u2014 see diff>",
  "new_string": "<1103 chars \u2014 see diff>"
}

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tool

stdout (152 chars)

The file /workspace/problems/02_kda_cutlass/solution.py has been updated successfully. (file state is current in your context — no need to Read it back)

assistant

Read /workspace/problems/02_kda_cutlass/solution.py

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "offset": 191,
  "limit": 90
}

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stdout (4223 chars) — TRUNCATED

191	    p_vn = tl.make_block_ptr(base_vn, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
192	    v_new = tl.load(p_vn, boundary_check=(0, 1))
193	
194	    o = tl.dot(qgb, h_state) + tl.dot(Aqk, v_new)
195	
196	    p_o = tl.make_block_ptr(base_o, (T, V), (H * V, 1), (toff, v0), (C, BV), (1, 0))
197	    tl.store(p_o, o.to(o_ptr.dtype.element_ty), boundary_check=(0, 1))
198	
199	
200	import os as _os
201	_WA = int(_os.environ.get("KDA_WA", "8")); _SA = int(_os.environ.get("KDA_SA", "2"))
202	_NITER = int(_os.environ.get("KDA_NITER", "4"))
203	_WB = int(_os.environ.get("KDA_WB", "4")); _SB = int(_os.environ.get("KDA_SB", "2"))
204	_WC = int(_os.environ.get("KDA_WC", "4")); _SC = int(_os.environ.get("KDA_SC", "2"))
205	
206	
207	def _launch(bufs, B, T, H, K, V, C, NT, scale, BV_STATE, BV_OUT):
208	    q, k, v, g, beta, w, u, kd, decay, vnew, hstates, o = bufs
209	    _kda_prepare_kernel[(NT, B * H)](
210	        k, v, g, beta, w, u, kd, decay, B, T, H, NT, K, V, C, _NITER,
211	        num_warps=_WA, num_stages=_SA,
212	    )
213	    _kda_state_kernel[(B * H, V // BV_STATE)](
214	        w, u, kd, decay, hstates, vnew, B, T, H, NT, K, V, C, BV_STATE,
215	        num_warps=_WB, num_stages=_SB,
216	    )
217	    _kda_output_kernel[(B * H, NT, V // BV_OUT)](
218	        q, k, g, hstates, vnew, o, B, T, H, NT, scale, K, V, C, BV_OUT,
219	        num_warps=_WC, num_stages=_SC,
220	    )
221	
222	
223	class Model(nn.Module):
224	    def __init__(self, B: int, T: int, H: int, K: int, V:

... (2723 more chars)

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Read /workspace/problems/02_kda_cutlass/solution.py

args

{
  "file_path": "/workspace/problems/02_kda_cutlass/solution.py",
  "offset": 280,
  "limit": 15
}

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stdout (218 chars)

280	                return self._out
281	            except Exception:
282	                self._graph = False
283	        _launch((q, k, v, g, beta, *self._inter), *self._launch_args)
284	        return self._out
285

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The solution is complete, clean, and robust. Let me do one final end-to-end confirmation that both `check.py` and `benchmark.py` agree, capturing the full per-shape numbers.

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Bash

args

{
  "command": "<223 chars \u2014 see below>",
  "description": "final combined confirmation",
  "run_in_background": true
}

command (223 chars)

cd /workspace/problems/02_kda_cutlass
uv run python check.py 2>&1 | tail -1
uv run python benchmark.py 2>&1 | grep -E '^shape=[0-9]+ variant=solution|peak_fraction:|RESULT'
echo "framework: $(cat framework.txt 2>/dev/null)"

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system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: b43il8rp5. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b43il8rp5.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Bash

args

{
  "command": "until grep -qE 'RESULT:' /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b43il8rp5.output 2>/dev/null; do sleep 8; done; cat /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b43il8rp5.output",
  "description": "wait final confirm",
  "run_in_background": true
}

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system — task_started

↳ subagent started

tool

stdout (283 chars)

Command running in background with ID: bkdc1kmpm. Output is being written to: /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/bkdc1kmpm.output. You will be notified when it completes. To check interim output, use Read on that file path.

assistant

Read /tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b43il8rp5.output

args

{
  "file_path": "/tmp/claude-1002/-workspace-problems-02-kda-cutlass/9e891295-8a92-4e85-9f50-8f801adeaa06/tasks/b43il8rp5.output"
}

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stdout (318 chars)

1	PASS
2	shape=0 variant=solution tflops=31.768 gbps=372.760 ms=0.068
3	shape=1 variant=solution tflops=36.995 gbps=434.099 ms=0.116
4	shape=2 variant=solution tflops=29.635 gbps=347.739 ms=0.145
5	shape=3 variant=solution tflops=16.811 gbps=197.259 ms=0.064
6	peak_fraction: 0.1383
7	RESULT: OK
8	framework: triton
9

assistant

Done. Final state confirmed: ## Summary I implemented the chunk-parallel Kimi Delta Attention forward as **custom Triton kernels** for the RTX PRO 6000 (SM120) in `solution.py`. Final result: ``` check.py: PASS (all 4 shapes × 3 seeds × nominal+small_qkv+large_qkv stress) benchmark: peak_fraction = 0.1383 → RESULT: OK (framework: triton) shape0 (B2 T1024 H8): 31.8 TFLOPS 0.068 ms shape1 (B2 T2048 H8): 37.0 TFLOPS 0.116 ms shape2 (B1 T4096 H8): 29.6 TFLOPS 0.145 ms shape3 (B1 T2048 H4): 16.8 TFLOPS 0.064 ms ``` That's **~8× over the naive baseline** (0.017 → 0.138 geomean of bf16 peak), comfortably above the 0.10 "OK" line. ### Design (3-kernel chunk-parallel decomposition) - **Kernel A** (parallel over b·h·chunk): cumsum, builds `A0 = strict-lower(-β·(k·eᵍᶜ)(k·e⁻ᵍᶜ)ᵀ)`, inverts `(I−A0)⁻¹` by Neumann doubling, produces `w`, `u`, and precomputes `kdᵀ`/`decay`. - **Kernel B** (sequential over chunks, the only serial pass): holds the K×BV recurrent state in registers, emits per-chunk start states and corrected values. The V dimension decouples, so it parallelizes over b·h·v-blocks. - **Kernel C** (fully parallel over b·h·chunk·v-block): the heavy output GEMMs. ### Key optimizations (each verified to keep `check.py` PASS) 1. **3-kernel split** so the heavy output compute is fully parallel; the sequential scan stays light. 2. **Precomputing `kd`/`decay` in kernel A** → scan critical path is just 2 matmuls (cut ~10× off the scan). 3. **CUDA graph bound directly to the input tensors** — eliminates Triton's per-launch Python overhead with no input copy; falls back to eager when pointers change (so check.py stays correct). 4. **bf16 tensor-core dots** everywhere (2× tf32), with **Neumann iters 6→4** (high powers are decay-suppressed), **collapsed exponentials** (`e⁻ᵍᶜ=1/eᵍᶜ`, `kd=decay·kng`), **cumsum-as-matmul**, and **pre-transposed `kd`** to keep the scan's hot path transpose-free. 5. Tuned launch config (WA=8, WB=4, WC=4, BV_STATE=16, adaptive BV_OUT, stages=2) via in-graph sweeps. The remaining bottleneck is the inherently-sequential inter-chunk scan on thin shapes (shape3); I verified a parallel-scan rewrite would be net-neutral here because K=128 makes the K×K transition compositions as expensive as the savings.

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system — init

session start cwd=/workspace/problems/02_kda_cutlass model=claude-opus-4-8 permissions=bypassPermissions

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Everything is finalized — `solution.py` passes `check.py` (PASS) and benchmarks at geomean **0.138 of bf16 peak** (RESULT: OK) with the same `Model`/`get_inputs`/`get_init_inputs` interface. The work is complete.

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system — task_notification

↳ subagent complete

system — task_notification

↳ subagent complete