# MinGPT decision transcript


=== Event 1 | historical_console_prelude |  ===
{"type": "historical_console_prelude", "text": "Reading additional input from stdin...", "source_event_line": 1}

=== Event 2 | thread.started |  ===
{"type": "thread.started", "thread_id": "01a0dd23-8d63-7ac2-a9de-ed195d9b2d89", "source_event_line": 2}

=== Event 3 | turn.started |  ===
{"type": "turn.started", "source_event_line": 3}

=== Event 4 | item.completed | item_0 ===
I’ll read the TileCas skill and follow its workflow in the session directory.

=== Event 5 | item.started | item_1 ===
COMMAND:
/bin/bash -lc 'cat /home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/deployment/skills/tilecas/SKILL.md'
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 6 | item.completed | item_1 ===
COMMAND:
/bin/bash -lc 'cat /home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/deployment/skills/tilecas/SKILL.md'
OUTPUT:
---
name: tilecas
description: Optimize a kernel in one conversation using measured candidates, cross-layer diagnosis, adaptive abstraction scheduling, and a heterogeneous search tree.
---

# TileCas

Produce the fastest fully correct implementation allowed by the supplied task contract.
One agent and conversation own High, Low, diagnosis and scheduling. Start empty; choose the initial level from the dataflow and available controls.
Choose optimization directions and experiment scope yourself; the guidance below supports that judgment.

## Establish the task and hardware

Read `HINTS.md`, `reference.py`, the current workspace and relevant supplied examples before implementing.
Understand input shapes, dtypes, layouts, output semantics, tolerances and allowed libraries at each level.
Use the supplied standard-normal inputs, seeds, correctness suite and timing protocol unchanged.
Use `scripts/bench.sh` for candidate ranking; do not replace or weaken the evaluator.
Do not delegate, import old runs or calculate hashes/checksums.
Confirm hardware facts that affect the intended implementation from the actual device or installed platform configuration.
In particular, check usable core counts and relevant local-memory limits before choosing persistent grids or buffer placement.
Do not substitute a familiar product's nominal specifications for the current device's resources.
Read the relevant installed API/example when copy shapes, tile layouts, matrix-helper capacity or synchronization semantics are uncertain.
Research what resolves the current uncertainty; no separate planning document or exhaustive reference survey is required.

## Establish a correct baseline

Create `opt/<name>` in the run workspace and implement a complete legal `solution/ModelNew.py`.
Choose a baseline whose intermediates and correctness you can reason about; simplicity alone does not guarantee efficient scheduling.
Account for conversions, intermediate tensors and launch/binding work included in the supplied measurement.
Run `bash scripts/bench.sh iter-1` and retain the first candidate that passes the complete suite.
Before first correctness, fix the first concrete defect or simplify the implementation; there is no validated parent to restore yet.
Inspect the first decisive compile/runtime error instead of treating all failures as performance evidence.
For wrong outputs, inspect the earliest incorrect intermediate, indexing, written regions and producer/consumer ordering.
A hang warrants checking launch bounds and synchronization as well as examining the evaluator output.
Distinguish a compiler limitation from an invalid algorithm; a failed implementation has no valid ranking latency.

## Optimize and select candidates

Use the AKO loop: **modify → benchmark → log → commit**.
Run `bash scripts/bench.sh iter-N` for each candidate and keep numbering across levels.
Finish recording and committing the measured candidate, including failures, before editing the next one.
Do not change the source while its evaluation is running; a result belongs to the exact evaluated source.
Choose directions using measured costs, expected end-to-end benefit, evidence, implementation effort and risk.
A short rationale in the iteration log is enough; no ranked-list ceremony is required for every edit.
Know what result would support the intended mechanism and what would make you revise or reject it.
For a local mechanism, preserve unrelated code, bindings and compiler settings so the comparison can explain the result; coupled changes may be tested together when their interaction is the hypothesis.
Prefer editing the measured implementation over replacing it with a generic template solely to expose tuning parameters. If a rewrite is necessary, first measure an equivalent configuration against its source parent; a regression there confounds later layout or scheduling conclusions.
When a comparison is confounded, retain the winner and choose whether repairing the experimental implementation is worth its expected benefit; do not let repeated framework rewrites displace optimization of the measured bottleneck.
Combine validated improvements from this run on a retained candidate, checking compatibility and the complete-suite result. Choose affordable experiments that can distinguish plausible causes; a long rationale or a level change is not performance evidence.
Use the existing task timings and emitted code when they already answer that question.
Consider where the whole runtime goes: conversions, transfers, computation, synchronization and launch work.
A faster inner kernel is useful only if the complete candidate improves under the same contract.
More fusion, a smaller workspace or a new structure is a hypothesis, not an improvement by itself.
When choosing a secondary cost over a dominant one, consider whether it also changes the dominant work or enables a plausible later gain.
Tiling, layout, reuse, cache policy, decomposition, fusion, scheduling, instructions and permitted mixed precision are all available.
For transfer-heavy kernels, consider supported cache allocation/bypass hints separately for streaming inputs, outputs and reused intermediates; verify the target API and measure the complete candidate. High transfer-unit activity does not establish effective bandwidth saturation or rule out cache-policy gains.
Do not force structural novelty, restrict work to tuning constants, or follow a prescribed sequence of techniques.
Precision changes must preserve the unchanged correctness contract; a plausible error projection does not replace validation.
Retain useful alternatives even when they are not currently fastest; keep the exploration candidate separate from the best validated candidate.
Promote only when the full contract passes and measured end-to-end latency supports the improvement.
Use the evaluator's aggregate and available spread; differences within observed variation are inconclusive.
If a result is surprising or ambiguous, inspect its record and use the same evaluator to resolve the uncertainty when worthwhile.
Record whether a candidate was promoted, retained for exploration, or rejected, and why.
Reuse mechanisms that worked earlier in this run after checking that their conditions still apply.
Repeated repair needs a new reason to expect success; otherwise simplify, restore a correct candidate or change direction.

## 1. Cross-layer diagnosis

Only when existing evidence leaves you unsure what to try next, run `python scripts/diagnose.py`.
First correctness, a new iteration or a level change does not automatically require diagnosis.
Invoke it on the unchanged measured candidate, before editing, so timings and source mappings can correspond.
The single no-argument entry point reports all kernels in descending measured cost order.
Use its available timings, High callsites, corresponding Native code, dataflow and hardware metrics together.
The useful chain is: measured cost → High operation → Native implementation → producer/consumer buffers and dependencies.
A kernel-time table alone locates expensive work; it does not explain the cross-layer mechanism.
Follow relevant copies, buffer lifetimes, synchronization and reuse to understand what control could change the cost.
Keep measured facts, source correspondences and inferred causes distinct; missing mappings remain unknown.
Static allocation sizes do not prove cache residency, and utilization ratios do not establish an attainable latency floor.
If the candidate changed since measurement, do not attribute the old timing to its new source.
Reuse evidence while its source and assumptions remain applicable; do not collect counters merely to complete a routine.
Reading or exporting Native code for diagnosis is not a change of active optimization level.
Diagnostic timings guide reasoning but do not replace the complete-suite ranking measurement.
If a diagnostic component fails or is unavailable, note the gap and continue from available measurements and source.
Do not turn an optimization iteration into rebuilding the profiling infrastructure.

## 2. Abstraction scheduler

Use the current candidate, available diagnosis, hardware facts and this run's history to decide where to work.
The choices are **stay**, **lower** and **rollback**; they are decisions by this same agent, not separate agents or fixed stages.
High is usually convenient for exploring structures; Low exposes finer control over memory, instructions and scheduling.
These are affordances, not restrictions on which changes are allowed at either level.
The starting level is also a scheduling decision: prefer High when it expresses the intended implementation conveniently.
For fan-in (parallel branches meeting in concat/sum) or heterogeneous stages, consider whether branch scheduling, reuse or synchronization needs Native control.
If those controls are unavailable or impractical at High, start directly at Low from iteration 1 and briefly record the reason; no High baseline or export is required.
Fan-in alone does not prove a DSL limitation or that one fused kernel will be faster. Judge the actual implementation options.
A direct-Low start is a root node, not an export from a measured High parent; High remains available for later exploration.
**Stay** where the next useful change is economical to express and evaluate, including ordinary tuning at High.
**Lower** when High is hard to improve, does not express the desired control, or Native makes a promising change practical.
You need not exhaust High structures before trying Low, nor descend merely because Native code is available.
A correct, measured High candidate provides the parent for export; it need not be the fastest High candidate if another has a credible advantage.
The exported structure and intermediate precision are editable, subject to the unchanged task contract.
**Rollback** when Low is unproductive or suggests a different High approach; restore its retained High parent and try another direction.
Choose the level from the hypothesis: instruction scheduling, register lifetimes and local memory access usually belong at Low; computation partitioning, fusion boundaries and intermediate representations should normally be prototyped at High.
For a structural hypothesis, retain the best Native candidate, restore a correct High parent (or create a High sibling for a direct-Low root), and test the new decomposition with the complete suite before deciding whether to lower again.
For example, replacing a large fused stage with independently computed blocks and an explicit combination step changes the decomposition; merely resizing its existing tiles does not. Preserve all mathematical dependencies.
Stay Low for structural work only when a concrete required control is unavailable or substantially harder to express at High; briefly record that reason, rather than staying because Native is already open.
A spill or one slow variant alone does not establish a structural problem; repair a concrete local defect at Low when evidence supports it.
A Low branch can be retained even after rollback; returning to High does not discard its results. If Low started directly, create a High sibling from the task contract rather than inventing a High parent.
After a mechanism is tested, let its actual outcome guide further work rather than treating the chosen level as a commitment.
If a familiar bottleneck recurs, consider a strategy that worked earlier; do not repeat failed changes without a changed premise.
There are no fixed time, iteration or improvement thresholds for level changes, and no mandatory catalogue of structures.
Use the remaining budget and the cost of the proposed experiment when choosing among opportunities.

## 3. Heterogeneous search tree

Use Git, `ITERATIONS.md` and the supplied trajectory records as the tree; no additional planning ledger is needed.
Each measured node identifies its exact source parent (or empty root), level, idea, correctness, latency and disposition.
Structural edges connect different structures at the same level; retain local tuning revisions without calling each a new structure.
Representation edges connect the same program across High and Low; subsequent Native edits are separate candidate nodes.
When transferring an existing High candidate, commit and retain its complete correct source before exporting:

```bash
python scripts/export.py --source solution/ModelNew.py --reference reference.py \
    --destination lowered --record lowered/export.json
```

Use a fresh destination or remove only an obsolete export destination so old files cannot enter the transfer.
On successful export, replace the complete `solution/` tree with that export and commit the representation change separately.
Keep required export validation; the transfer alone has no new measured latency and needs no invented performance result.
Evaluate Native changes with the same `scripts/bench.sh` contract used at High.
Rollback restores the exact complete High source tree, removing Low-only files rather than overlaying High onto them.
Record which High parent a Low branch came from so rollback is reproducible.
Ideas can transfer between branches; distinguish the source of an idea from the actual code parent.
Preserve failed and slower measured nodes with their evidence so later choices need not rediscover them.
The final winner may come from any retained branch or level, not necessarily the most recent candidate.

## Reassess and finish

When progress stalls, review the observed costs, attempted mechanisms and remaining opportunities across levels.
Treat exported Native as a starting point, not a structural constraint. When local tuning stops addressing the dominant cost, use existing evidence—or diagnosis if the next step is unclear—to form a concrete alternative decomposition and predict which cost it would reduce; a short iteration-log note is enough.
Validate a worthwhile structural hypothesis at the level chosen above before declaring that branch exhausted. Do not force an unsupported alternative: if none is justified, explain why the remaining evidence does not support another experiment. A failed variant only rejects the implementation actually tested.
Explore the dominant remaining costs sufficiently before stopping. A few unsuccessful local variants do not establish that a mechanism or branch is exhausted.
For a promising mechanism, test a bounded set of materially different, hardware-feasible configurations and relevant combinations, rather than relying on one parameter setting. Couple choices that affect each other: tile geometry and thread/warp ownership, layout and producer/consumer placement, conversion placement and reuse, input/output cache policies and vector access layout, or scheduling and register/shared-memory pressure.
After a layout, decomposition or resource balance changes, revisit earlier rejected mechanisms when their assumptions have changed. Retain useful intermediate candidates and combine compatible improvements within this run; an isolated regression does not rule out a beneficial combination.
Predicted register/shared-memory pressure is a tradeoff, not grounds to reject an otherwise feasible configuration without measurement. Use hardware limits to exclude invalid configurations; use the full evaluator to judge promising feasible ones.
When progress stalls and budget remains, distinguish insufficient exploration from evidence of low remaining value. Prefer another affordable discriminating experiment on a dominant cost over stopping merely because recent variants failed or improvements became small.
Before stopping for diminishing returns, briefly record which meaningful configurations and interactions were actually tested, which credible opportunities remain, and why those opportunities do not justify another experiment within the remaining budget. If a promising, affordable combination is still untested, continue exploring it.
Sufficient exploration is evidence-based coverage of promising mechanisms and their interactions, not exhaustive Cartesian sweeps, a fixed iteration quota, mandatory coverage of every technique, or a requirement to spend the entire budget.
If the next experiment is clear, proceed; if it is unclear, diagnosis is available to inform the choice.
A local plateau, a failed tool or a difficult implementation is not by itself evidence that the whole search is finished.
Use the actual target, deadline and budget in `HINTS.md`; do not invent a shorter run window.
Stop when the target is met, the budget ends, or the remaining opportunities no longer justify another experiment.
Explain the stop with available evidence; do not call unmeasured headroom a proven hardware limit.
Reserve time to restore the fastest fully correct candidate verbatim from Git, including its complete source tree.
Run `bash scripts/bench.sh final`, record the outcome and selected node, and commit the final state.
If final validation fails, report it and retain the last validated fallback; do not present an unvalidated rewrite as the winner.

STATUS: completed; EXIT_CODE: 0

=== Event 7 | item.started | item_2 ===
COMMAND:
/bin/bash -lc 'pwd && ls -la && cat HINTS.md && cat reference.py'
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 8 | item.completed | item_2 ===
COMMAND:
/bin/bash -lc 'pwd && ls -la && cat HINTS.md && cat reference.py'
OUTPUT:
/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1
total 40
drwxrwxr-x 6 xiaoxinyu xiaoxinyu 4096 Sep 26 09:54 .
drwxrwxr-x 3 xiaoxinyu xiaoxinyu 4096 Sep 26 09:54 ..
drwxrwxr-x 7 xiaoxinyu xiaoxinyu 4096 Sep 26 09:54 .git
-rw-rw-r-- 1 xiaoxinyu xiaoxinyu   43 Sep 26 09:54 .gitignore
drwxrwxr-x 3 xiaoxinyu xiaoxinyu 4096 Sep 26 09:54 .harness
-rw-rw-r-- 1 xiaoxinyu xiaoxinyu 3487 Sep 26 09:54 HINTS.md
-rw-rw-r-- 1 xiaoxinyu xiaoxinyu   34 Sep 26 09:54 RUNTIME_WINDOW
-rw-rw-r-- 1 xiaoxinyu xiaoxinyu 3803 Sep 26 09:54 reference.py
drwxrwxr-x 2 xiaoxinyu xiaoxinyu 4096 Sep 26 09:54 scripts
drwxrwxr-x 2 xiaoxinyu xiaoxinyu 4096 Sep 26 09:54 solution
Ours owns High (TileLang) and Low (HIP C++) in this same conversation. Start empty and choose the initial level using the supplied TileCas skill; follow that skill for diagnosis, stay/lower/rollback and final selection. High prohibits torch imports/framework computation; use target="hip", execution_backend="cython". Low edits native source with allocation/bindings only, without TileLang/Triton regeneration or framework/external-operator fallback. To roll back, restore the entire High solution and remove leftover native sources.
Target AMD Radeon AI PRO R9700, gfx1201, ROCm 7.14, FP32 inputs.
Implement solution/ModelNew.py exposing ModelNew with to(), __call__(), forward().
Read reference.py for the complete task. Input distribution N(0,1); seeds 42,43,50000; ten correctness trials per seed; atol=rtol=1e-3; three warmups followed by five HIP-event timed forwards in one timing block at input seed 42; report the median of five samples. Reference and candidate use the same timing inputs and protocol. The three correctness seed bases are separate from timing repetitions.
Use only bash scripts/bench.sh LABEL for evaluation; do not edit reference.py or scripts/. It remeasures the reference and includes all GPU work in forward.
Native compute may use authored HIP and TileLang HIP headers; no rocBLAS/hipBLAS/MIOpen, CK or complete attention implementations.
Preserve every input dependency and reference mathematics. No distribution-specific approximation.
Read-only platform references: /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang; /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/include.
Work in this workspace only; do not read sibling experiments or optimizer histories. Work alone, no delegation. Do not calculate hashes/checksums.
Nsight Compute is unavailable on AMD; scripts/bench.sh owns ranking.

Task: KernelBench Level 3 MinGPTCausalAttention, full forward including QKV and output projections.
Original dimensions: batch 128, sequence 512, embedding 768, 8 heads (head dimension 96); causal mask; dropout probabilities zero.
Use the same get_init_inputs constructor arguments as reference.py. ModelNew.forward receives five tensors:
x [128,512,768], qkv_weight [2304,768], qkv_bias [2304], projection_weight [768,768], projection_bias [768].
Weights use PyTorch Linear's [out_features,in_features] convention. They are fixed parameters from the original seed-42 initialization, passed explicitly and identically to both implementations; do not reinitialize or mutate them. Activations use N(0,1).
The appended reference adapter only supplies these parameter bindings to the original forward. Preserve all projections, bias additions, scaling, causal masking, softmax, head reassembly and output semantics. No precomputed outputs or sibling-run imports.
Use python scripts/diagnose.py with no arguments only when existing evidence leaves the next experiment unclear. Neither first correctness nor a new iteration triggers diagnosis. The tool reuses source/reference/protocol-matched evidence or collects missing High provenance, call-group costs and available ROCm counters, and reports all calls. It never exports authored Low. Costs are diagnostic, not ranking or a hardware floor. Counter summaries aggregate by symbol and include warmup; ambiguous associations remain unknown. Native changes retain historical High provenance without claiming unchanged correspondence. Export via scripts/export.py performs full Native replay validation; do not add a duplicate transfer baseline.
Run window: 2026-09-26 09:54 to 11:54 UTC, cut off at the end whatever state you are in. `date -u` for the time now.
import torch
import torch.nn as nn
import torch.nn.functional as F
import math

# From https://github.com/karpathy/minGPT/blob/master/mingpt/model.py

class Model(nn.Module):
    """
    A vanilla multi-head masked self-attention layer with a projection at the end.
    It is possible to use torch.nn.MultiheadAttention here but I am including an
    explicit implementation here to show that there is nothing too scary here.
    """

    def __init__(self, n_embd, n_head, attn_pdrop, resid_pdrop, max_seqlen):
        super().__init__()
        assert n_embd % n_head == 0
        # key, query, value projections for all heads, but in a batch
        self.c_attn = nn.Linear(n_embd, 3 * n_embd)
        # output projection
        self.c_proj = nn.Linear(n_embd, n_embd)
        # regularization
        self.attn_dropout = nn.Dropout(attn_pdrop)
        self.resid_dropout = nn.Dropout(resid_pdrop)
        # causal mask to ensure that attention is only applied to the left in the input sequence
        self.register_buffer("bias", torch.tril(torch.ones(max_seqlen, max_seqlen))
                                     .view(1, 1, max_seqlen, max_seqlen))
        self.n_head = n_head
        self.n_embd = n_embd

    def forward(self, x):
        B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)

        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
        q, k ,v  = self.c_attn(x).split(self.n_embd, dim=2)
        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)

        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
        att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
        att = F.softmax(att, dim=-1)
        att = self.attn_dropout(att)
        y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side

        # output projection
        y = self.resid_dropout(self.c_proj(y))
        return y

batch_size = 128
max_seqlen = 1024
seq_len = 512
n_embd = 768
n_head = 8
attn_pdrop = 0.0
resid_pdrop = 0.0

def get_inputs():
    return [torch.randn(batch_size, seq_len, n_embd)]

def get_init_inputs():
    return [n_embd, n_head, attn_pdrop, resid_pdrop, max_seqlen]

# ROCm functional binding: unchanged reference math, fixed original model weights.
# Only activation inputs are resampled N(0,1); parameters retain original seed-42 initialization.
_OriginalModel = Model
_original_get_inputs = get_inputs
PARAMETER_NAMES = ('c_attn.weight', 'c_attn.bias', 'c_proj.weight', 'c_proj.bias')

class Model(nn.Module):
    def __init__(self, *args):
        super().__init__()
        self.inner = _OriginalModel(*args).eval()

    def forward(self, x, qkv_weight, qkv_bias, projection_weight, projection_bias):
        state = dict(zip(PARAMETER_NAMES, (qkv_weight, qkv_bias, projection_weight, projection_bias)))
        return torch.func.functional_call(self.inner, state, (x,))

def get_inputs():
    values = _original_get_inputs()
    devices = [torch.cuda.current_device()] if values[0].is_cuda else []
    with torch.random.fork_rng(devices=devices):
        torch.manual_seed(42)
        with torch.device('cpu'):
            original = _OriginalModel(*get_init_inputs()).eval()
        parameters = dict(original.named_parameters())
        values.extend(parameters[name].detach().to(values[0].device) for name in PARAMETER_NAMES)
    return values

STATUS: completed; EXIT_CODE: 0

=== Event 9 | item.started | item_3 ===
COMMAND:
/bin/bash -lc "ls -la solution scripts .harness && cat scripts/bench.sh && rg --files -g AGENTS.md -g '*example*' .harness && date -u"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 10 | item.completed | item_3 ===
COMMAND:
/bin/bash -lc "ls -la solution scripts .harness && cat scripts/bench.sh && rg --files -g AGENTS.md -g '*example*' .harness && date -u"
OUTPUT:
.harness:
total 12
drwxrwxr-x 3 xiaoxinyu xiaoxinyu 4096 Sep 26 09:54 .
drwxrwxr-x 6 xiaoxinyu xiaoxinyu 4096 Sep 26 09:54 ..
drwxrwxr-x 9 xiaoxinyu xiaoxinyu 4096 Sep 26 09:54 codex-home
lrwxrwxrwx 1 xiaoxinyu xiaoxinyu   19 Sep 26 09:54 codex-sessions -> codex-home/sessions

scripts:
total 104
drwxrwxr-x 2 xiaoxinyu xiaoxinyu  4096 Sep 26 09:54 .
drwxrwxr-x 6 xiaoxinyu xiaoxinyu  4096 Sep 26 09:54 ..
-rw-rw-r-- 1 xiaoxinyu xiaoxinyu   865 Sep 26 09:54 bench.sh
-rw-rw-r-- 1 xiaoxinyu xiaoxinyu 22943 Sep 26 09:54 diagnose.py
-rw-rw-r-- 1 xiaoxinyu xiaoxinyu  1967 Sep 26 09:54 export.py
-rw-rw-r-- 1 xiaoxinyu xiaoxinyu  8238 Sep 26 09:54 export_rocm.py
-rw-rw-r-- 1 xiaoxinyu xiaoxinyu   409 Sep 26 09:54 module_loader.py
-rw-rw-r-- 1 xiaoxinyu xiaoxinyu  4099 Sep 26 09:54 native_runtime.py
-rw-rw-r-- 1 xiaoxinyu xiaoxinyu  4396 Sep 26 09:54 reference-baseline.json
-rw-rw-r-- 1 xiaoxinyu xiaoxinyu  6341 Sep 26 09:54 rocm_bench.py
-rw-rw-r-- 1 xiaoxinyu xiaoxinyu 22943 Sep 26 09:54 rocm_diagnose.py

solution:
total 8
drwxrwxr-x 2 xiaoxinyu xiaoxinyu 4096 Sep 26 09:54 .
drwxrwxr-x 6 xiaoxinyu xiaoxinyu 4096 Sep 26 09:54 ..
#!/bin/bash
set -o pipefail
cd "$(dirname "$0")/.."
LABEL="${1:-manual}"
[[ "$LABEL" =~ ^[a-zA-Z0-9_-]+$ ]] || exit 2
TIMESTAMP=$(date +%Y%m%d_%H%M%S)
TRAJ_DIR="trajectory/${TIMESTAMP}_${LABEL}"
mkdir -p "$TRAJ_DIR"
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python -c 'import json,sys;from pathlib import Path;sys.path.insert(0,'"'"'scripts'"'"');from rocm_diagnose import context;Path(sys.argv[1]).write_text(json.dumps(context(Path.cwd()),indent=2))' "$TRAJ_DIR/evaluation-context.json" || exit $?
timeout --signal=TERM --kill-after=30s 1800 /home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/rocm_bench.py --ref reference.py --solution solution/ModelNew.py --backend auto 2>&1 | tee _bench_output.txt
STATUS=${PIPESTATUS[0]}
cp -r solution/* "$TRAJ_DIR/" 2>/dev/null || true
cp _bench_output.txt "$TRAJ_DIR/output.txt"
rm -f _bench_output.txt
exit "$STATUS"

STATUS: failed; EXIT_CODE: 1

=== Event 11 | item.started | item_4 ===
COMMAND:
/bin/bash -lc "cat scripts/native_runtime.py scripts/rocm_bench.py && /home/xiaoxinyu/venvs/dsl-native-opt/bin/python -c 'import torch; print(torch.cuda.get_device_properties(0))' && date -u"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 12 | item.completed | item_4 ===
COMMAND:
/bin/bash -lc "cat scripts/native_runtime.py scripts/rocm_bench.py && /home/xiaoxinyu/venvs/dsl-native-opt/bin/python -c 'import torch; print(torch.cuda.get_device_properties(0))' && date -u"
OUTPUT:
_CudaDeviceProperties(name='AMD Radeon AI PRO R9700', major=12, minor=0, gcnArchName='gfx1201', total_memory=32624MB, multi_processor_count=32, uuid=64383335-3430-3364-6639-663261663566, pci_bus_id=4, pci_device_id=0, pci_domain_id=0, L2_cache_size=8MB)
Sat Sep 26 09:55:24 UTC 2026

STATUS: completed; EXIT_CODE: 0

=== Event 13 | item.started | item_5 ===
COMMAND:
/bin/bash -lc "rg --files /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang | rg '(example|gemm|attention)' | head -60"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 14 | item.completed | item_5 ===
COMMAND:
/bin/bash -lc "rg --files /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang | rg '(example|gemm|attention)' | head -60"
OUTPUT:
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/rocm/op/gemm/gemm_mfma.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/rocm/op/gemm/__pycache__/gemm_wmma.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/rocm/op/gemm/__pycache__/__init__.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/rocm/op/gemm/__pycache__/gemm_mfma.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/rocm/op/gemm/__init__.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/rocm/op/gemm/gemm_wmma.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/tileop/gemm_sp/gemm_sp_base.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/tileop/gemm_sp/__pycache__/gemm_sp_base.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/tileop/gemm_sp/__pycache__/__init__.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/tileop/gemm_sp/__pycache__/registry.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/tileop/gemm_sp/__init__.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/tileop/gemm_sp/registry.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/tileop/gemm/gemm_base.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/tileop/gemm/__pycache__/__init__.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/tileop/gemm/__pycache__/gemm_base.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/tileop/gemm/__pycache__/registry.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/tileop/gemm/__init__.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/tileop/gemm/registry.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/layout/__pycache__/gemm_sp.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/layout/gemm_sp.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/rocm/op/gemm.cc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/gemm_op.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/layout/gemm_layouts.cc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/cuda/op/gemm.cc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/cuda/op/gemm_sp.cc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/cpu/op/gemm.cc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/__pycache__/gemm_op.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm_sp/__pycache__/gemm_sp_mma.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm_sp/__pycache__/__init__.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm_sp/__pycache__/gemm_sp_wgmma.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm_sp/__init__.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm_sp/gemm_sp_mma.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm_sp/gemm_sp_wgmma.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/experimental/__pycache__/gemm_sp_op.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/experimental/gemm_sp_op.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/op/gemm.cc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/op/gemm.h
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/op/gemm_sp.cc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/op/gemm_sp.h
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/metal/op/gemm/gemm_metal.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm/gemm_tcgen05.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/cpu/gemm.h
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm/gemm_mma_sm70.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm/__init__.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm/gemm_mma_sm120.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm/gemm_mma_sm75.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm/gemm_fma.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm/gemm_wgmma.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm/gemm_mma.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/metal/op/gemm/__pycache__/__init__.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/metal/op/gemm/__pycache__/gemm_metal.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/metal/op/gemm/__init__.py
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm/__pycache__/gemm_mma.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm/__pycache__/gemm_wgmma.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm/__pycache__/gemm_fma.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm/__pycache__/gemm_mma_sm120.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm/__pycache__/__init__.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm/__pycache__/gemm_mma_sm70.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm/__pycache__/gemm_mma_sm75.cpython-312.pyc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cuda/op/gemm/__pycache__/gemm_tcgen05.cpython-312.pyc

STATUS: completed; EXIT_CODE: 0

=== Event 15 | item.completed | item_6 ===
The task is the full attention forward, including both projections. The device reports 32 compute units and 32 GB of memory. I’ll start at High with tiled projections and explicit attention stages, then validate the intermediate precision against the unchanged correctness suite.

=== Event 16 | item.started | item_7 ===
COMMAND:
/bin/bash -lc "cat /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/rocm/op/gemm/gemm_wmma.py | head -180; cat /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/__init__.py | head -100; /home/xiaoxinyu/venvs/dsl-native-opt/bin/python -c 'import torch; p=torch.cuda.get_device_properties(0); print(p.shared_memory_per_block, p.warp_size, p.max_threads_per_block)'"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 17 | item.completed | item_7 ===
COMMAND:
/bin/bash -lc "cat /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/rocm/op/gemm/gemm_wmma.py | head -180; cat /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/__init__.py | head -100; /home/xiaoxinyu/venvs/dsl-native-opt/bin/python -c 'import torch; p=torch.cuda.get_device_properties(0); print(p.shared_memory_per_block, p.warp_size, p.max_threads_per_block)'"
OUTPUT:
65536 32 1024

STATUS: completed; EXIT_CODE: 0

=== Event 18 | item.started | item_8 ===
COMMAND:
/bin/bash -lc "mkdir -p opt/attention && cat > solution/ModelNew.py <<'PY'
import tilelang
import tilelang.language as T

@tilelang.jit(out_idx=[3], target='hip', execution_backend='cython')
def linear(N: int, out_dtype: str):
    @T.prim_func
    def main(X:T.Tensor((128,512,768),'float32'), W:T.Tensor((N,768),'float32'), B:T.Tensor((N,),'float32'), Y:T.Tensor((128,512,N),out_dtype)):
        with T.Kernel(1024,T.ceildiv(N,64),threads=128) as (bm,bn):
            a=T.alloc_shared((64,32),'float16')
            b=T.alloc_shared((64,32),'float16')
            c=T.alloc_fragment((64,64),'float32')
            T.clear(c)
            for k in T.Pipelined(24,num_stages=2):
                for i,j in T.Parallel(64,32):
                    a[i,j]=X[bm//8,(bm%8)*64+i,k*32+j]
                    b[i,j]=W[bn*64+i,k*32+j]
                T.gemm(a,b,c,transpose_B=True)
            for i,j in T.Parallel(64,64):
                Y[bm//8,(bm%8)*64+i,bn*64+j]=c[i,j]+B[bn*64+j]
    return main

@tilelang.jit(out_idx=[1],target='hip',execution_backend='cython')
def scores():
    @T.prim_func
    def main(Q:T.Tensor((128,512,2304),'float16'), S:T.Tensor((128,8,512,512),'float32')):
        with T.Kernel(8,8,1024,threads=128) as (mi,ni,bh):
            a=T.alloc_shared((64,32),'float16')
            b=T.alloc_shared((64,32),'float16')
            c=T.alloc_fragment((64,64),'float32')
            T.clear(c)
            for k in T.Pipelined(3,num_stages=2):
                for i,j in T.Parallel(64,32):
                    a[i,j]=Q[bh//8,mi*64+i,(bh%8)*96+k*32+j]
                    b[i,j]=Q[bh//8,ni*64+i,768+(bh%8)*96+k*32+j]
                T.gemm(a,b,c,transpose_B=True)
            for i,j in T.Parallel(64,64):
                S[bh//8,bh%8,mi*64+i,ni*64+j]=T.if_then_else(ni*64+j<=mi*64+i,c[i,j]*0.10206207261596575,-T.infinity('float32'))
    return main

@tilelang.jit(out_idx=[1],target='hip',execution_backend='cython')
def softmax():
    @T.prim_func
    def main(S:T.Tensor((128,8,512,512),'float32'), P:T.Tensor((128,8,512,512),'float16')):
        with T.Kernel(512,1024,threads=128) as (m,bh):
            a=T.alloc_fragment((512,),'float32')
            mx=T.alloc_fragment((1,),'float32')
            sm=T.alloc_fragment((1,),'float32')
            T.copy(S[bh//8,bh%8,m,:],a)
            T.reduce_max(a,mx,dim=0)
            for i in T.Parallel(512):
                a[i]=T.exp(a[i]-mx[0])
            T.reduce_sum(a,sm,dim=0)
            for i in T.Parallel(512):
                P[bh//8,bh%8,m,i]=a[i]/sm[0]
    return main

@tilelang.jit(out_idx=[2],target='hip',execution_backend='cython')
def values():
    @T.prim_func
    def main(P:T.Tensor((128,8,512,512),'float16'), Q:T.Tensor((128,512,2304),'float16'), Y:T.Tensor((128,512,768),'float32')):
        with T.Kernel(8,3,1024,threads=128) as (mi,ni,bh):
            a=T.alloc_shared((64,32),'float16')
            b=T.alloc_shared((32,32),'float16')
            c=T.alloc_fragment((64,32),'float32')
            T.clear(c)
            for k in T.Pipelined(16,num_stages=2):
                T.copy(P[bh//8,bh%8,mi*64:mi*64+64,k*32:k*32+32],a)
                for i,j in T.Parallel(32,32):
                    b[i,j]=Q[bh//8,k*32+i,1536+(bh%8)*96+ni*32+j]
                T.gemm(a,b,c)
            for i,j in T.Parallel(64,32):
                Y[bh//8,mi*64+i,(bh%8)*96+ni*32+j]=c[i,j]
    return main

class ModelNew:
    def __init__(self,*args):
        self.qkv=linear(2304,'float16')
        self.score=scores()
        self.sm=softmax()
        self.val=values()
        self.proj=linear(768,'float32')
    def to(self,*args,**kwargs): return self
    def forward(self,x,qkv_weight,qkv_bias,projection_weight,projection_bias):
        q=self.qkv(x,qkv_weight,qkv_bias)
        s=self.score(q)
        p=self.sm(s)
        y=self.val(p,q)
        return self.proj(y,projection_weight,projection_bias)
    __call__=forward
PY
bash scripts/bench.sh iter-1"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 19 | item.started | item_9 ===
COMMAND:
/bin/bash -lc 'git status --short && cat .gitignore && cat scripts/export.py && cat scripts/reference-baseline.json | head -8'
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 20 | item.completed | item_9 ===
COMMAND:
/bin/bash -lc 'git status --short && cat .gitignore && cat scripts/export.py && cat scripts/reference-baseline.json | head -8'
OUTPUT:
?? .gitignore
?? HINTS.md
?? RUNTIME_WINDOW
?? _bench_output.txt
?? reference.py
?? scripts/
?? solution/
.cache/
.harness/
trajectory/
__pycache__/
"""Materialise High as native HIP and require the unchanged full replay suite."""
import argparse
import json
from pathlib import Path
import subprocess
import sys
import tempfile

def checked_export(source,reference,destination,record):
    # Release all capture tensors by exiting the materialization process before replay.
    with tempfile.TemporaryDirectory(prefix='rocm-export-') as temporary:
        materialized=Path(temporary)/'materialized.json'
        subprocess.run([sys.executable,str(Path(__file__).with_name('export_rocm.py')),
            '--source',str(source),'--reference',str(reference),'--destination',str(destination),
            '--record',str(materialized)],check=True,timeout=1800)
        result=json.loads(materialized.read_text())
    log=record.with_suffix('.validation.log')
    validation=record.with_suffix('.validation.json')
    with log.open('w') as out:
        completed=subprocess.run([sys.executable,str(Path(__file__).with_name('rocm_bench.py')),
            '--ref',str(reference),'--solution',str(destination/'ModelNew.py'),'--backend','hip',
            '--out',str(validation)],stdout=out,stderr=subprocess.STDOUT,timeout=1800)
    if completed.returncode or not validation.exists() or json.loads(validation.read_text()).get('correct') is not True:
        raise RuntimeError(f'Native replay failed; do not replace solution/. See {log}')
    result['validation']={'correct':True,'log':str(log),'record':str(validation),
        'purpose':'required unchanged export replay; not a new optimization node'}
    record.write_text(json.dumps(result,indent=2))
    print('EXPORT VALIDATED: full 30-trial Native replay passed; see',record)
    return result

if __name__=='__main__':
    p=argparse.ArgumentParser(description=__doc__)
    for name in ('source','reference','destination','record'):p.add_argument('--'+name,type=Path,required=True)
    a=p.parse_args();checked_export(a.source,a.reference,a.destination,a.record)
{
  "reference_text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\n\n# From https://github.com/karpathy/minGPT/blob/master/mingpt/model.py\n\nclass Model(nn.Module):\n    \"\"\"\n    A vanilla multi-head masked self-attention layer with a projection at the end.\n    It is possible to use torch.nn.MultiheadAttention here but I am including an\n    explicit implementation here to show that there is nothing too scary here.\n    \"\"\"\n\n    def __init__(self, n_embd, n_head, attn_pdrop, resid_pdrop, max_seqlen):\n        super().__init__()\n        assert n_embd % n_head == 0\n        # key, query, value projections for all heads, but in a batch\n        self.c_attn = nn.Linear(n_embd, 3 * n_embd)\n        # output projection\n        self.c_proj = nn.Linear(n_embd, n_embd)\n        # regularization\n        self.attn_dropout = nn.Dropout(attn_pdrop)\n        self.resid_dropout = nn.Dropout(resid_pdrop)\n        # causal mask to ensure that attention is only applied to the left in the input sequence\n        self.register_buffer(\"bias\", torch.tril(torch.ones(max_seqlen, max_seqlen))\n                                     .view(1, 1, max_seqlen, max_seqlen))\n        self.n_head = n_head\n        self.n_embd = n_embd\n\n    def forward(self, x):\n        B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)\n\n        # calculate query, key, values for all heads in batch and move head forward to be the batch dim\n        q, k ,v  = self.c_attn(x).split(self.n_embd, dim=2)\n        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)\n        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)\n        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)\n\n        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)\n        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))\n        att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))\n        att = F.softmax(att, dim=-1)\n        att = self.attn_dropout(att)\n        y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)\n        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side\n\n        # output projection\n        y = self.resid_dropout(self.c_proj(y))\n        return y\n\nbatch_size = 128\nmax_seqlen = 1024\nseq_len = 512\nn_embd = 768\nn_head = 8\nattn_pdrop = 0.0\nresid_pdrop = 0.0\n\ndef get_inputs():\n    return [torch.randn(batch_size, seq_len, n_embd)]\n\ndef get_init_inputs():\n    return [n_embd, n_head, attn_pdrop, resid_pdrop, max_seqlen]\n\n# ROCm functional binding: unchanged reference math, fixed original model weights.\n# Only activation inputs are resampled N(0,1); parameters retain original seed-42 initialization.\n_OriginalModel = Model\n_original_get_inputs = get_inputs\nPARAMETER_NAMES = ('c_attn.weight', 'c_attn.bias', 'c_proj.weight', 'c_proj.bias')\n\nclass Model(nn.Module):\n    def __init__(self, *args):\n        super().__init__()\n        self.inner = _OriginalModel(*args).eval()\n\n    def forward(self, x, qkv_weight, qkv_bias, projection_weight, projection_bias):\n        state = dict(zip(PARAMETER_NAMES, (qkv_weight, qkv_bias, projection_weight, projection_bias)))\n        return torch.func.functional_call(self.inner, state, (x,))\n\ndef get_inputs():\n    values = _original_get_inputs()\n    devices = [torch.cuda.current_device()] if values[0].is_cuda else []\n    with torch.random.fork_rng(devices=devices):\n        torch.manual_seed(42)\n        with torch.device('cpu'):\n            original = _OriginalModel(*get_init_inputs()).eval()\n        parameters = dict(original.named_parameters())\n        values.extend(parameters[name].detach().to(values[0].device) for name in PARAMETER_NAMES)\n    return values\n",
  "reference_ms": 44.953800201416016,
  "task": "level3/43_MinGPTCausalAttention",
  "parameter_policy": "original seed-42 weights, explicit immutable arguments",
  "measured": {
    "correct": true,
    "reference_ms": 44.953800201416016,

STATUS: completed; EXIT_CODE: 0

=== Event 21 | item.completed | item_8 ===
COMMAND:
/bin/bash -lc "mkdir -p opt/attention && cat > solution/ModelNew.py <<'PY'
import tilelang
import tilelang.language as T

@tilelang.jit(out_idx=[3], target='hip', execution_backend='cython')
def linear(N: int, out_dtype: str):
    @T.prim_func
    def main(X:T.Tensor((128,512,768),'float32'), W:T.Tensor((N,768),'float32'), B:T.Tensor((N,),'float32'), Y:T.Tensor((128,512,N),out_dtype)):
        with T.Kernel(1024,T.ceildiv(N,64),threads=128) as (bm,bn):
            a=T.alloc_shared((64,32),'float16')
            b=T.alloc_shared((64,32),'float16')
            c=T.alloc_fragment((64,64),'float32')
            T.clear(c)
            for k in T.Pipelined(24,num_stages=2):
                for i,j in T.Parallel(64,32):
                    a[i,j]=X[bm//8,(bm%8)*64+i,k*32+j]
                    b[i,j]=W[bn*64+i,k*32+j]
                T.gemm(a,b,c,transpose_B=True)
            for i,j in T.Parallel(64,64):
                Y[bm//8,(bm%8)*64+i,bn*64+j]=c[i,j]+B[bn*64+j]
    return main

@tilelang.jit(out_idx=[1],target='hip',execution_backend='cython')
def scores():
    @T.prim_func
    def main(Q:T.Tensor((128,512,2304),'float16'), S:T.Tensor((128,8,512,512),'float32')):
        with T.Kernel(8,8,1024,threads=128) as (mi,ni,bh):
            a=T.alloc_shared((64,32),'float16')
            b=T.alloc_shared((64,32),'float16')
            c=T.alloc_fragment((64,64),'float32')
            T.clear(c)
            for k in T.Pipelined(3,num_stages=2):
                for i,j in T.Parallel(64,32):
                    a[i,j]=Q[bh//8,mi*64+i,(bh%8)*96+k*32+j]
                    b[i,j]=Q[bh//8,ni*64+i,768+(bh%8)*96+k*32+j]
                T.gemm(a,b,c,transpose_B=True)
            for i,j in T.Parallel(64,64):
                S[bh//8,bh%8,mi*64+i,ni*64+j]=T.if_then_else(ni*64+j<=mi*64+i,c[i,j]*0.10206207261596575,-T.infinity('float32'))
    return main

@tilelang.jit(out_idx=[1],target='hip',execution_backend='cython')
def softmax():
    @T.prim_func
    def main(S:T.Tensor((128,8,512,512),'float32'), P:T.Tensor((128,8,512,512),'float16')):
        with T.Kernel(512,1024,threads=128) as (m,bh):
            a=T.alloc_fragment((512,),'float32')
            mx=T.alloc_fragment((1,),'float32')
            sm=T.alloc_fragment((1,),'float32')
            T.copy(S[bh//8,bh%8,m,:],a)
            T.reduce_max(a,mx,dim=0)
            for i in T.Parallel(512):
                a[i]=T.exp(a[i]-mx[0])
            T.reduce_sum(a,sm,dim=0)
            for i in T.Parallel(512):
                P[bh//8,bh%8,m,i]=a[i]/sm[0]
    return main

@tilelang.jit(out_idx=[2],target='hip',execution_backend='cython')
def values():
    @T.prim_func
    def main(P:T.Tensor((128,8,512,512),'float16'), Q:T.Tensor((128,512,2304),'float16'), Y:T.Tensor((128,512,768),'float32')):
        with T.Kernel(8,3,1024,threads=128) as (mi,ni,bh):
            a=T.alloc_shared((64,32),'float16')
            b=T.alloc_shared((32,32),'float16')
            c=T.alloc_fragment((64,32),'float32')
            T.clear(c)
            for k in T.Pipelined(16,num_stages=2):
                T.copy(P[bh//8,bh%8,mi*64:mi*64+64,k*32:k*32+32],a)
                for i,j in T.Parallel(32,32):
                    b[i,j]=Q[bh//8,k*32+i,1536+(bh%8)*96+ni*32+j]
                T.gemm(a,b,c)
            for i,j in T.Parallel(64,32):
                Y[bh//8,mi*64+i,(bh%8)*96+ni*32+j]=c[i,j]
    return main

class ModelNew:
    def __init__(self,*args):
        self.qkv=linear(2304,'float16')
        self.score=scores()
        self.sm=softmax()
        self.val=values()
        self.proj=linear(768,'float32')
    def to(self,*args,**kwargs): return self
    def forward(self,x,qkv_weight,qkv_bias,projection_weight,projection_bias):
        q=self.qkv(x,qkv_weight,qkv_bias)
        s=self.score(q)
        p=self.sm(s)
        y=self.val(p,q)
        return self.proj(y,projection_weight,projection_bias)
    __call__=forward
PY
bash scripts/bench.sh iter-1"
OUTPUT:
[09:56:40] : Warning: T.vectorized loop over `local_id` with extent 8 is lowered as a serial loop because TileLang could not find a valid vectorization plan. Scalar accumulator updates inside the loop are a common cause; move reductions to T.unroll or T.serial if this is intended.
2026-09-26 09:56:23  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[3]`
2026-09-26 09:56:28  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`
2026-09-26 09:56:29  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 09:56:34  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`
2026-09-26 09:56:35  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 09:56:40  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`
2026-09-26 09:56:40  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[2]`
2026-09-26 09:56:46  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`
2026-09-26 09:56:46  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[3]`
2026-09-26 09:56:51  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-1
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [45.21326665968054, 44.953800201416016, 44.7822119644764, 44.89457729515448, 45.18668830087833], "candidate": [24.224948842923215, 24.28270814496195, 24.24252700805664, 24.3569720617351, 24.189661413106144]}
RUNTIME: 24.24252700805664
REF_RUNTIME: 44.953800201416016
SPEEDUP: 1.8543363976237417

STATUS: completed; EXIT_CODE: 0

=== Event 22 | item.completed | item_10 ===
The baseline passed all 30 correctness trials at 24.24 ms, versus the fixed 44.95 ms reference. I’ll retain it and test fused attention to avoid writing and rereading the large score and probability tensors.

=== Event 23 | item.started | item_11 ===
COMMAND:
/bin/bash -lc "cat > ITERATIONS.md <<'EOF'
# TileCas iterations

Hardware: gfx1201 Radeon AI PRO R9700; actual device reports 32 CUs, 32-lane warps, 65536 bytes shared memory/block, 1024 threads/block, 8 MiB L2. Window 09:54–11:54 UTC.

## iter-1 — High root
Empty parent. Tiled 64x64 projections (FP16 operands, FP32 accumulation), explicit QK, stable softmax, PV and output projection. High conveniently expresses the full dataflow. FP16 intermediate precision is validated, not assumed.
Complete suite PASS, median 24.242527 ms, samples 24.190–24.357 ms, fixed-reference speedup 1.8543x. Promoted as first correct baseline.
Next: fuse attention with online softmax to remove score/probability traffic and exploit causality; keep projections unchanged for attribution.
EOF
 git add .gitignore HINTS.md RUNTIME_WINDOW reference.py scripts solution ITERATIONS.md && git commit -m 'iter-1: correct High attention baseline, 24.24 ms'"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 24 | item.completed | item_11 ===
COMMAND:
/bin/bash -lc "cat > ITERATIONS.md <<'EOF'
# TileCas iterations

Hardware: gfx1201 Radeon AI PRO R9700; actual device reports 32 CUs, 32-lane warps, 65536 bytes shared memory/block, 1024 threads/block, 8 MiB L2. Window 09:54–11:54 UTC.

## iter-1 — High root
Empty parent. Tiled 64x64 projections (FP16 operands, FP32 accumulation), explicit QK, stable softmax, PV and output projection. High conveniently expresses the full dataflow. FP16 intermediate precision is validated, not assumed.
Complete suite PASS, median 24.242527 ms, samples 24.190–24.357 ms, fixed-reference speedup 1.8543x. Promoted as first correct baseline.
Next: fuse attention with online softmax to remove score/probability traffic and exploit causality; keep projections unchanged for attribution.
EOF
 git add .gitignore HINTS.md RUNTIME_WINDOW reference.py scripts solution ITERATIONS.md && git commit -m 'iter-1: correct High attention baseline, 24.24 ms'"
OUTPUT:
[codex/rocm-workspace (root-commit) 078791f] iter-1: correct High attention baseline, 24.24 ms
 15 files changed, 1401 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 HINTS.md
 create mode 100644 ITERATIONS.md
 create mode 100644 RUNTIME_WINDOW
 create mode 100644 reference.py
 create mode 100644 scripts/bench.sh
 create mode 100644 scripts/diagnose.py
 create mode 100644 scripts/export.py
 create mode 100644 scripts/export_rocm.py
 create mode 100644 scripts/module_loader.py
 create mode 100644 scripts/native_runtime.py
 create mode 100644 scripts/reference-baseline.json
 create mode 100644 scripts/rocm_bench.py
 create mode 100644 scripts/rocm_diagnose.py
 create mode 100644 solution/ModelNew.py

STATUS: completed; EXIT_CODE: 0

=== Event 25 | item.started | item_12 ===
COMMAND:
/bin/bash -lc "python - <<'PY'
p='solution/ModelNew.py'
s=open(p).read(); pos=s.index('class ModelNew:')
s=s[:pos]+'''@tilelang.jit(out_idx=[1],target='hip',execution_backend='cython')
def attention():
    @T.prim_func
    def main(Q:T.Tensor((128,512,2304),'float16'), Y:T.Tensor((128,512,768),'float32')):
        with T.Kernel(16,1024,threads=128) as (mi,bh):
            q=T.alloc_shared((32,96),'float16')
            k=T.alloc_shared((64,96),'float16')
            v=T.alloc_shared((64,96),'float16')
            p=T.alloc_shared((32,64),'float16')
            s=T.alloc_fragment((32,64),'float32')
            o=T.alloc_fragment((32,96),'float32')
            m=T.alloc_fragment((32,),'float32')
            old=T.alloc_fragment((32,),'float32')
            alpha=T.alloc_fragment((32,),'float32')
            l=T.alloc_fragment((32,),'float32')
            rows=T.alloc_fragment((32,),'float32')
            T.fill(m,-T.infinity('float32'))
            T.clear(l)
            T.clear(o)
            for i,j in T.Parallel(32,96):
                q[i,j]=Q[bh//8,mi*32+i,(bh%8)*96+j]
            for nb in T.serial(T.ceildiv((mi+1)*32,64)):
                for i,j in T.Parallel(64,96):
                    k[i,j]=Q[bh//8,nb*64+i,768+(bh%8)*96+j]
                    v[i,j]=Q[bh//8,nb*64+i,1536+(bh%8)*96+j]
                T.clear(s)
                T.gemm(q,k,s,transpose_B=True)
                for i,j in T.Parallel(32,64):
                    s[i,j]=T.if_then_else(nb*64+j<=mi*32+i,s[i,j]*0.10206207261596575,-T.infinity('float32'))
                T.copy(m,old)
                T.reduce_max(s,m,dim=1,clear=False)
                for i in T.Parallel(32):
                    alpha[i]=T.exp(old[i]-m[i])
                for i,j in T.Parallel(32,64):
                    s[i,j]=T.exp(s[i,j]-m[i])
                T.reduce_sum(s,rows,dim=1)
                for i in T.Parallel(32):
                    l[i]=l[i]*alpha[i]+rows[i]
                for i,j in T.Parallel(32,96):
                    o[i,j]=o[i,j]*alpha[i]
                T.copy(s,p)
                T.gemm(p,v,o)
            for i,j in T.Parallel(32,96):
                Y[bh//8,mi*32+i,(bh%8)*96+j]=o[i,j]/l[i]
    return main

'''+s[pos:]
s=s.replace('self.score=scores()\\n        self.sm=softmax()\\n        self.val=values()', 'self.attn=attention()')
s=s.replace('s=self.score(q)\\n        p=self.sm(s)\\n        y=self.val(p,q)', 'y=self.attn(q)')
open(p,'w').write(s)
PY
bash scripts/bench.sh iter-2"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 26 | item.completed | item_12 ===
COMMAND:
/bin/bash -lc "python - <<'PY'
p='solution/ModelNew.py'
s=open(p).read(); pos=s.index('class ModelNew:')
s=s[:pos]+'''@tilelang.jit(out_idx=[1],target='hip',execution_backend='cython')
def attention():
    @T.prim_func
    def main(Q:T.Tensor((128,512,2304),'float16'), Y:T.Tensor((128,512,768),'float32')):
        with T.Kernel(16,1024,threads=128) as (mi,bh):
            q=T.alloc_shared((32,96),'float16')
            k=T.alloc_shared((64,96),'float16')
            v=T.alloc_shared((64,96),'float16')
            p=T.alloc_shared((32,64),'float16')
            s=T.alloc_fragment((32,64),'float32')
            o=T.alloc_fragment((32,96),'float32')
            m=T.alloc_fragment((32,),'float32')
            old=T.alloc_fragment((32,),'float32')
            alpha=T.alloc_fragment((32,),'float32')
            l=T.alloc_fragment((32,),'float32')
            rows=T.alloc_fragment((32,),'float32')
            T.fill(m,-T.infinity('float32'))
            T.clear(l)
            T.clear(o)
            for i,j in T.Parallel(32,96):
                q[i,j]=Q[bh//8,mi*32+i,(bh%8)*96+j]
            for nb in T.serial(T.ceildiv((mi+1)*32,64)):
                for i,j in T.Parallel(64,96):
                    k[i,j]=Q[bh//8,nb*64+i,768+(bh%8)*96+j]
                    v[i,j]=Q[bh//8,nb*64+i,1536+(bh%8)*96+j]
                T.clear(s)
                T.gemm(q,k,s,transpose_B=True)
                for i,j in T.Parallel(32,64):
                    s[i,j]=T.if_then_else(nb*64+j<=mi*32+i,s[i,j]*0.10206207261596575,-T.infinity('float32'))
                T.copy(m,old)
                T.reduce_max(s,m,dim=1,clear=False)
                for i in T.Parallel(32):
                    alpha[i]=T.exp(old[i]-m[i])
                for i,j in T.Parallel(32,64):
                    s[i,j]=T.exp(s[i,j]-m[i])
                T.reduce_sum(s,rows,dim=1)
                for i in T.Parallel(32):
                    l[i]=l[i]*alpha[i]+rows[i]
                for i,j in T.Parallel(32,96):
                    o[i,j]=o[i,j]*alpha[i]
                T.copy(s,p)
                T.gemm(p,v,o)
            for i,j in T.Parallel(32,96):
                Y[bh//8,mi*32+i,(bh%8)*96+j]=o[i,j]/l[i]
    return main

'''+s[pos:]
s=s.replace('self.score=scores()\\n        self.sm=softmax()\\n        self.val=values()', 'self.attn=attention()')
s=s.replace('s=self.score(q)\\n        p=self.sm(s)\\n        y=self.val(p,q)', 'y=self.attn(q)')
open(p,'w').write(s)
PY
bash scripts/bench.sh iter-2"
OUTPUT:
[09:57:52] : Warning: T.vectorized loop over `local_id` with extent 8 is lowered as a serial loop because TileLang could not find a valid vectorization plan. Scalar accumulator updates inside the loop are a common cause; move reductions to T.unroll or T.serial if this is intended.
2026-09-26 09:57:51  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 09:57:58  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-2
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [45.23082925404023, 44.953800201416016, 45.07267008309276, 44.94894170788728, 44.922528877773836], "candidate": [21.115227926157075, 21.078507022330985, 21.111492156982422, 21.161026602022304, 21.04534965447337]}
RUNTIME: 21.111492156982422
REF_RUNTIME: 44.953800201416016
SPEEDUP: 2.1293521020278985

STATUS: completed; EXIT_CODE: 0

=== Event 27 | item.started | item_13 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-2 — High structural child of iter-1 (078791f)
Fused 32-query/64-key online stable softmax attention, FP32 running numerator/denominator, causal block bound. Projection kernels unchanged. Complete suite PASS; 21.111492 ms (21.045–21.161), 2.1294x. Promoted. Removes global S/P tensors, but only 3.13 ms total saved. Next cost split is uncertain between strided attention and projection conversion/reloads; diagnose unchanged candidate.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-2: fuse causal attention, 21.11 ms'
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/diagnose.py"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 28 | item.completed | item_14 ===
Fused attention passed and reduced runtime to 21.11 ms. I’m checking the per-kernel costs now to determine whether projections or attention offer the larger next gain.

=== Event 29 | item.completed | item_13 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-2 — High structural child of iter-1 (078791f)
Fused 32-query/64-key online stable softmax attention, FP32 running numerator/denominator, causal block bound. Projection kernels unchanged. Complete suite PASS; 21.111492 ms (21.045–21.161), 2.1294x. Promoted. Removes global S/P tensors, but only 3.13 ms total saved. Next cost split is uncertain between strided attention and projection conversion/reloads; diagnose unchanged candidate.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-2: fuse causal attention, 21.11 ms'
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/diagnose.py"
OUTPUT:
2026-09-26 09:58:18  [TileLang:tilelang.cache.kernel_cache:WARNING] (kernel_cache.py:380): Found kernel 'main' in memory cache. For better performance, consider using `@tilelang.jit` instead of direct kernel caching.
2026-09-26 09:58:18  [TileLang:tilelang.cache.kernel_cache:WARNING] (kernel_cache.py:380): Found kernel 'main' in memory cache. For better performance, consider using `@tilelang.jit` instead of direct kernel caching.
2026-09-26 09:58:18  [TileLang:tilelang.cache.kernel_cache:WARNING] (kernel_cache.py:380): Found kernel 'main' in memory cache. For better performance, consider using `@tilelang.jit` instead of direct kernel caching.
Level: High | ranking measurement: {"path": "trajectory/20260926_095748_iter-2/output.txt", "valid": true, "reference_and_protocol_verified": true, "COMPILED": "True", "CORRECT": "True", "RUNTIME": "21.111492156982422", "REF_RUNTIME": "44.953800201416016", "SPEEDUP": "2.1293521020278985"}
HIP call-group costs are diagnostic, not per-device-kernel ranking or hardware floors.

Call 0: 13.119339 ms | compiler_and_execution_matched
High: {"file": "solution/ModelNew.py", "line": 128, "function": "forward"} | Native: {"file": "kernel_0.cpp", "line": 1, "end_line": 71}
Tensor bindings: [{"value": "input_0", "shape": [128, 512, 768], "dtype": "float32"}, {"value": "input_1", "shape": [2304, 768], "dtype": "float32"}, {"value": "input_2", "shape": [2304], "dtype": "float32"}, {"value": "value_5", "shape": [128, 512, 2304], "dtype": "float16"}]
High source: 126:     def to(self,*args,**kwargs): return self
127:     def forward(self,x,qkv_weight,qkv_bias,projection_weight,projection_bias):
128:         q=self.qkv(x,qkv_weight,qkv_bias)
129:         y=self.attn(q)
130:         return self.proj(y,projection_weight,projection_bias)
Native sites: 10: extern "C" __global__ void __launch_bounds__(128) main_kernel(float* __restrict__ B, float* __restrict__ W, float* __restrict__ X, half_t* __restrict__ Y) {
11:   extern __shared__ __align__(1024) uchar buf_dyn_shmem[];
21:     __syncthreads();
42:       __syncthreads();
55:       *((tl_v8f32*)c + ((i_3 * 2) + j_1)) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(

Call 2: 4.797291 ms | compiler_and_execution_matched
High: {"file": "solution/ModelNew.py", "line": 130, "function": "forward"} | Native: {"file": "kernel_2.cpp", "line": 1, "end_line": 71}
Tensor bindings: [{"value": "value_6", "shape": [128, 512, 768], "dtype": "float32"}, {"value": "input_3", "shape": [768, 768], "dtype": "float32"}, {"value": "input_4", "shape": [768], "dtype": "float32"}, {"value": "value_7", "shape": [128, 512, 768], "dtype": "float32"}]
High source: 128:         q=self.qkv(x,qkv_weight,qkv_bias)
129:         y=self.attn(q)
130:         return self.proj(y,projection_weight,projection_bias)
131:     __call__=forward
Native sites: 10: extern "C" __global__ void __launch_bounds__(128) main_kernel(float* __restrict__ B, float* __restrict__ W, float* __restrict__ X, float* __restrict__ Y) {
11:   extern __shared__ __align__(1024) uchar buf_dyn_shmem[];
21:     __syncthreads();
42:       __syncthreads();
55:       *((tl_v8f32*)c + ((i_3 * 2) + j_1)) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(

Call 1: 3.181594 ms | compiler_and_execution_matched
High: {"file": "solution/ModelNew.py", "line": 129, "function": "forward"} | Native: {"file": "kernel_1.cpp", "line": 1, "end_line": 210}
Tensor bindings: [{"value": "value_5", "shape": [128, 512, 2304], "dtype": "float16"}, {"value": "value_6", "shape": [128, 512, 768], "dtype": "float32"}]
High source: 127:     def forward(self,x,qkv_weight,qkv_bias,projection_weight,projection_bias):
128:         q=self.qkv(x,qkv_weight,qkv_bias)
129:         y=self.attn(q)
130:         return self.proj(y,projection_weight,projection_bias)
131:     __call__=forward
Native sites: 10: extern "C" __global__ void __launch_bounds__(128) main_kernel(half_t* __restrict__ Q, float* __restrict__ Y) {
11:   extern __shared__ __align__(1024) uchar buf_dyn_shmem[];
46:     __syncthreads();
60:       __syncthreads();
70:       *((tl_v8f32*)s + j_1) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(
99:       __syncthreads();
143:       __syncthreads();
175:     __syncthreads();
183:       __syncthreads();
195:       *((tl_v8f32*)o + j_3) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(
Dataflow: {"edges": [{"value": "value_5", "producer": 0, "consumer": 1}, {"value": "value_6", "producer": 1, "consumer": 2}], "bindings": [{"call": 0, "params": [{"value": "input_0", "shape": [128, 512, 768], "dtype": "float32"}, {"value": "input_1", "shape": [2304, 768], "dtype": "float32"}, {"value": "input_2", "shape": [2304], "dtype": "float32"}, {"value": "value_5", "shape": [128, 512, 2304], "dtype": "float16"}]}, {"call": 1, "params": [{"value": "value_5", "shape": [128, 512, 2304], "dtype": "float16"}, {"value": "value_6", "shape": [128, 512, 768], "dtype": "float32"}]}, {"call": 2, "params": [{"value": "value_6", "shape": [128, 512, 768], "dtype": "float32"}, {"value": "input_3", "shape": [768, 768], "dtype": "float32"}, {"value": "input_4", "shape": [768], "dtype": "float32"}, {"value": "value_7", "shape": [128, 512, 768], "dtype": "float32"}]}], "interpretation": "Observed tensor return/argument dependencies where available; allocation/ABI bindings are not proof of memory read/write or synchronization."}
Counter status: available | candidate association: unknown | missing: []
Device kernel: {"symbol": "main_kernel", "kernel_id": "35", "dispatches": 4, "metrics": {"GRBM_GUI_ACTIVE": {"median": 41279124.0, "min": 39067523.0, "max": 42966863.0, "samples": 4}, "SQ_INSTS_SALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_INSTS_VALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_WAVES": {"median": 147456.0, "min": 147456.0, "max": 147968.0, "samples": 4}}, "trace_samples": 4, "trace_median_ms": 13.0697775, "candidate_calls": [0, 1, 2], "association": "ambiguous_symbol"}
Device kernel: {"symbol": "main_kernel", "kernel_id": "37", "dispatches": 4, "metrics": {"GRBM_GUI_ACTIVE": {"median": 15736668.0, "min": 13633749.0, "max": 15760840.0, "samples": 4}, "SQ_INSTS_SALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_INSTS_VALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_WAVES": {"median": 49152.0, "min": 49152.0, "max": 49152.0, "samples": 4}}, "trace_samples": 4, "trace_median_ms": 4.7845505, "candidate_calls": [0, 1, 2], "association": "ambiguous_symbol"}
Device kernel: {"symbol": "main_kernel", "kernel_id": "36", "dispatches": 4, "metrics": {"GRBM_GUI_ACTIVE": {"median": 10426505.5, "min": 10357565.0, "max": 10440848.0, "samples": 4}, "SQ_INSTS_SALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_INSTS_VALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_WAVES": {"median": 65536.0, "min": 65536.0, "max": 65536.0, "samples": 4}}, "trace_samples": 4, "trace_median_ms": 3.1745140000000003, "candidate_calls": [0, 1, 2], "association": "ambiguous_symbol"}
Device kernel: {"symbol": "void at::native::(anonymous namespace)::distribution_elementwise_grid_stride_kernel<float, 4, at::native::templates::cuda::normal_and_transform<float, float, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1})::{lambda(hiprandStatePhilox4_32_10*)#2}, at::native::(anonymous namespace)::distribution_nullary_kernel<float, float, HIP_vector_type<float, 4u>, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_and_transform<float, float, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1})::{lambda(hiprandStatePhilox4_32_10*)#2}, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_and_transform<float, float, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1})::{lambda(hiprandStatePhilox4_32_10*)#2} const&, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1})::{lambda(int, float)#1}>(long, at::PhiloxCudaState, at::native::templates::cuda::normal_and_transform<float, float, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1})::{lambda(hiprandStatePhilox4_32_10*)#2}, at::native::(anonymous namespace)::distribution_nullary_kernel<float, float, HIP_vector_type<float, 4u>, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_and_transform<float, float, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1})::{lambda(hiprandStatePhilox4_32_10*)#2}, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_and_transform<float, float, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1})::{lambda(hiprandStatePhilox4_32_10*)#2} const&, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1})::{lambda(int, float)#1})", "kernel_id": "1", "dispatches": 1, "metrics": {"GRBM_GUI_ACTIVE": {"median": 4844488.0, "min": 4844488.0, "max": 4844488.0, "samples": 1}, "SQ_INSTS_SALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 1}, "SQ_INSTS_VALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 1}, "SQ_WAVES": {"median": 2048.0, "min": 2048.0, "max": 2048.0, "samples": 1}}, "trace_samples": 1, "trace_median_ms": 1.711218, "candidate_calls": [], "association": "unmapped_or_setup"}
Device kernel: {"symbol": "__amd_rocclr_copyBuffer", "kernel_id": "21", "dispatches": 2, "metrics": {"GRBM_GUI_ACTIVE": {"median": 43508.0, "min": 41031.0, "max": 45985.0, "samples": 2}, "SQ_INSTS_SALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 2}, "SQ_INSTS_VALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 2}, "SQ_WAVES": {"median": 24.0, "min": 16.0, "max": 32.0, "samples": 2}}, "trace_samples": 2, "trace_median_ms": 0.00362, "candidate_calls": [], "association": "unmapped_or_setup"}
Full report: /home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/.cache/diagnosis/report.json

STATUS: completed; EXIT_CODE: 0

=== Event 30 | item.started | item_15 ===
COMMAND:
/bin/bash -lc 'rg --files .cache/diagnosis | head -25 && cat .cache/diagnosis/native/kernel_0.cpp'
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 31 | item.completed | item_15 ===
COMMAND:
/bin/bash -lc 'rg --files .cache/diagnosis | head -25 && cat .cache/diagnosis/native/kernel_0.cpp'
OUTPUT:
.cache/diagnosis/counters.json
.cache/diagnosis/report.json
.cache/diagnosis/mapping.json
.cache/diagnosis/lowering-bqo0fjn2/solution/ModelNew.py
.cache/diagnosis/lowering-bqo0fjn2/solution/kernel_2.cpp
.cache/diagnosis/lowering-bqo0fjn2/solution/kernel_2.tir
.cache/diagnosis/lowering-bqo0fjn2/solution/kernel_1.tir
.cache/diagnosis/profile.json
.cache/diagnosis/lowering-bqo0fjn2/solution/program.json
.cache/diagnosis/lowering-bqo0fjn2/solution/kernel_0.tir
.cache/diagnosis/lowering-bqo0fjn2/solution/kernel_1.cpp
.cache/diagnosis/lowering-bqo0fjn2/solution/kernel_0.cpp
.cache/diagnosis/lowering-bqo0fjn2/solution/runtime.py
.cache/diagnosis/counters-3q9nyskr/collector.log
.cache/diagnosis/counters-3q9nyskr/gpu-vm/2519299_kernel_trace.csv
.cache/diagnosis/counters-3q9nyskr/gpu-vm/2519299_agent_info.csv
.cache/diagnosis/counters-3q9nyskr/gpu-vm/2519299_counter_collection.csv
cat: .cache/diagnosis/native/kernel_0.cpp: No such file or directory

STATUS: failed; EXIT_CODE: 1

=== Event 32 | item.started | item_16 ===
COMMAND:
/bin/bash -lc 'cat .cache/diagnosis/lowering-*/solution/kernel_0.cpp'
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 33 | item.completed | item_16 ===
COMMAND:
/bin/bash -lc 'cat .cache/diagnosis/lowering-*/solution/kernel_0.cpp'
OUTPUT:
#include <hip/hip_runtime.h>
#include <tl_templates/hip/gemm.h>
#include <tl_templates/hip/copy.h>
#include <tl_templates/hip/reduce.h>
#include <tl_templates/hip/scan.h>
#include <tl_templates/hip/ldsm.h>
#include <tl_templates/hip/threadblock_swizzle.h>
#include <tl_templates/hip/debug.h>

extern "C" __global__ void __launch_bounds__(128) main_kernel(float* __restrict__ B, float* __restrict__ W, float* __restrict__ X, half_t* __restrict__ Y) {
  extern __shared__ __align__(1024) uchar buf_dyn_shmem[];
  void* a = ((void*)((char*)buf_dyn_shmem + 0));
  void* b = ((void*)((char*)buf_dyn_shmem + 4096));
  float c[32];
  #pragma unroll
  for (int i = 0; i < 8; ++i) {
    float broadcast_var = 0.000000e+00f;
    *(float4*)(c + (i * 4)) = make_float4(broadcast_var, broadcast_var, broadcast_var, broadcast_var);
  }
  for (int k = 0; k < 24; ++k) {
    __syncthreads();
    #pragma unroll
    for (int i_1 = 0; i_1 < 4; ++i_1) {
      uint2 __1;
      float4 v_ = *(float4*)(X + (((((((int)blockIdx.x) * 49152) + (i_1 * 12288)) + ((((int)threadIdx.x) >> 3) * 768)) + (k * 32)) + ((((int)threadIdx.x) & 7) * 4)));
      *((half_t*)(&(((half2*)(&(__1.x)))->x))) = (half_t)(v_.x);
      *((half_t*)(&(((half2*)(&(__1.x)))->y))) = (half_t)(v_.y);
      *((half_t*)(&(((half2*)(&(__1.y)))->x))) = (half_t)(v_.z);
      *((half_t*)(&(((half2*)(&(__1.y)))->y))) = (half_t)(v_.w);
      *(uint2*)(((half_t*)a) + (((((i_1 * 512) + ((((int)threadIdx.x) >> 3) * 32)) + (((((((int)threadIdx.x) & 63) >> 5) + ((((int)threadIdx.x) & 7) >> 2)) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 8)) + ((((int)threadIdx.x) & 1) * 4))) = __1;
      uint2 __2;
      float4 v__1 = *(float4*)(W + (((((((int)blockIdx.y) * 49152) + (i_1 * 12288)) + ((((int)threadIdx.x) >> 3) * 768)) + (k * 32)) + ((((int)threadIdx.x) & 7) * 4)));
      *((half_t*)(&(((half2*)(&(__2.x)))->x))) = (half_t)(v__1.x);
      *((half_t*)(&(((half2*)(&(__2.x)))->y))) = (half_t)(v__1.y);
      *((half_t*)(&(((half2*)(&(__2.y)))->x))) = (half_t)(v__1.z);
      *((half_t*)(&(((half2*)(&(__2.y)))->y))) = (half_t)(v__1.w);
      *(uint2*)(((half_t*)b) + (((((i_1 * 512) + ((((int)threadIdx.x) >> 3) * 32)) + (((((((int)threadIdx.x) & 63) >> 5) + ((((int)threadIdx.x) & 7) >> 2)) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 8)) + ((((int)threadIdx.x) & 1) * 4))) = __2;
    }
    {
      half_t A_local[16];
      half_t B_local[16];
      __syncthreads();
      for (int ki = 0; ki < 2; ++ki) {
        for (int i_2 = 0; i_2 < 2; ++i_2) {
          *(uint4*)(A_local + (i_2 * 8)) = *(uint4*)(((half_t*)a) + (((((((((int)threadIdx.x) & 63) >> 5) * 1024) + (i_2 * 512)) + ((((int)threadIdx.x) & 15) * 32)) + (((((((int)threadIdx.x) & 7) >> 2) + ki) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 8)));
        }
        for (int j = 0; j < 2; ++j) {
          *(uint4*)(B_local + (j * 8)) = *(uint4*)(((half_t*)b) + ((((((((int)threadIdx.x) >> 6) * 1024) + (j * 512)) + ((((int)threadIdx.x) & 15) * 32)) + (((((((int)threadIdx.x) & 7) >> 2) + ki) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 8)));
        }
        for (int i_3 = 0; i_3 < 2; ++i_3) {
          for (int j_1 = 0; j_1 < 2; ++j_1) {
            {
      typedef __attribute__((__vector_size__(8 * sizeof(__fp16)))) __fp16 tl_v8f16;
      typedef __attribute__((__vector_size__(8 * sizeof(float)))) float tl_v8f32;
      *((tl_v8f32*)c + ((i_3 * 2) + j_1)) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(
          *((tl_v8f16*)A_local + i_3),
          *((tl_v8f16*)B_local + j_1),
          *((tl_v8f32*)c + ((i_3 * 2) + j_1)));
    };
          }
        }
      }
    }
  }
  #pragma unroll
  for (int i_4 = 0; i_4 < 32; ++i_4) {
    Y[(((((((((((int)blockIdx.x) * 147456) + (((((int)threadIdx.x) & 63) >> 5) * 73728)) + ((i_4 >> 4) * 36864)) + (((((int)threadIdx.x) & 31) >> 4) * 18432)) + ((i_4 & 7) * 2304)) + (((int)blockIdx.y) * 64)) + ((((int)threadIdx.x) >> 6) * 32)) + (((i_4 & 15) >> 3) * 16)) + (((int)threadIdx.x) & 15))] = ((half_t)(c[i_4] + B[((((((int)blockIdx.y) * 64) + ((((int)threadIdx.x) >> 6) * 32)) + (((i_4 & 15) >> 3) * 16)) + (((int)threadIdx.x) & 15))]));
  }
}


#ifdef _WIN32
#define TL_EXPORT __declspec(dllexport)
#else
#define TL_EXPORT
#endif

#define ERROR_BUF_SIZE 1024
static char error_buf[ERROR_BUF_SIZE];

extern "C" TL_EXPORT const char* get_last_error() {
    return error_buf;
}

extern "C" TL_EXPORT int init() {
    error_buf[0] = '\0';
    
    int device_main_kernel = 0;
    hipError_t dev_res_main_kernel = hipGetDevice(&device_main_kernel);
    if (dev_res_main_kernel != hipSuccess) {
        snprintf(error_buf, ERROR_BUF_SIZE, "Failed to get HIP device for main_kernel: %s", hipGetErrorString(dev_res_main_kernel));
        return -1;
    }
    int max_smem_main_kernel = 0;
    hipError_t attr_res_main_kernel = hipDeviceGetAttribute(&max_smem_main_kernel, hipDeviceAttributeMaxSharedMemoryPerBlock, device_main_kernel);
    if (attr_res_main_kernel != hipSuccess || max_smem_main_kernel <= 0) {
        snprintf(error_buf, ERROR_BUF_SIZE, "Failed to query HIP max shared memory for main_kernel: %s", hipGetErrorString(attr_res_main_kernel));
        return -1;
    }
    if (8192 > max_smem_main_kernel) {
        snprintf(
            error_buf,
            ERROR_BUF_SIZE,
            "Requested dynamic shared memory %d exceeds device limit %d for main_kernel",
            8192,
            max_smem_main_kernel
        );
        return -1;
    }
    return 0;

    return 0;
}

extern "C" TL_EXPORT int call(float* __restrict__ X, float* __restrict__ W, float* __restrict__ B, half_t* __restrict__ Y, hipStream_t stream=hipStreamDefault) {
	main_kernel<<<dim3(1024, 36, 1), dim3(128, 1, 1), 8192, stream>>>(B, W, X, Y);
	TILELANG_CHECK_LAST_ERROR("main_kernel");

	return 0;
}

STATUS: completed; EXIT_CODE: 0

=== Event 34 | item.completed | item_17 ===
The diagnostic puts QKV projection at about 13.1 ms and output projection at 4.8 ms; attention is about 3.2 ms. The generated projection code repeatedly loads FP32 operands and converts them inside each tile. I’ll test converting each operand once per forward and reusing FP16 buffers.

=== Event 35 | item.started | item_18 ===
COMMAND:
/bin/bash -lc "python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();s=s.replace(\"X:T.Tensor((128,512,768),'float32'), W:T.Tensor((N,768),'float32')\", \"X:T.Tensor((128,512,768),'float16'), W:T.Tensor((N,768),'float16')\")
# remove unused baseline kernels
start=s.index('@tilelang.jit(out_idx=[1]'); end=s.index(\"@tilelang.jit(out_idx=[1]\",s.index('def values'))
s=s[:start]+s[end:]
s=s.replace(\"Y:T.Tensor((128,512,768),'float32')\", \"Y:T.Tensor((128,512,768),'float16')\")
pos=s.index('class ModelNew:')
s=s[:pos]+'''@tilelang.jit(out_idx=[1],target='hip',execution_backend='cython')
def convert(shape):
    @T.prim_func
    def main(X:T.Tensor(shape,'float32'), Y:T.Tensor(shape,'float16')):
        with T.Kernel(T.ceildiv(T.product(shape),1024),threads=256) as b:
            for i in T.Parallel(1024):
                Y.view((T.product(shape),))[b*1024+i]=X.view((T.product(shape),))[b*1024+i]
    return main

'''+s[pos:]
s=s.replace(\"self.qkv=linear(2304,'float16')\", \"self.cx=convert((128,512,768))\\n        self.cw=convert((2304,768))\\n        self.cp=convert((768,768))\\n        self.qkv=linear(2304,'float16')\")
s=s.replace('q=self.qkv(x,qkv_weight,qkv_bias)', 'xh=self.cx(x)\\n        wh=self.cw(qkv_weight)\\n        ph=self.cp(projection_weight)\\n        q=self.qkv(xh,wh,qkv_bias)').replace('self.proj(y,projection_weight,projection_bias)', 'self.proj(y,ph,projection_bias)')
# product/view likely unsupported use buffer flattened T.reshape API? use shape 3 or 2 loops explicit
s=s.replace('T.product(shape)', 'size')
s=s.replace('    @T.prim_func\\n    def main(X:T.Tensor(shape', '    size=1\\n    for d in shape: size*=d\\n    @T.prim_func\\n    def main(X:T.Tensor(shape')
s=s.replace(\"Y.view((size,))[b*1024+i]=X.view((size,))[b*1024+i]\", \"T.reshape(Y,(size,))[b*1024+i]=T.reshape(X,(size,))[b*1024+i]\")
open(p,'w').write(s)
PY
bash scripts/bench.sh iter-3"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 36 | item.started | item_19 ===
COMMAND:
/bin/bash -lc "rg -n 'cache|coalesced|def copy|def reshape' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/*.py | head -50 && date -u"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 37 | item.completed | item_19 ===
COMMAND:
/bin/bash -lc "rg -n 'cache|coalesced|def copy|def reshape' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/*.py | head -50 && date -u"
OUTPUT:
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/customize.py:60:def reshape(src: Buffer, shape: ShapeType) -> Buffer:
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:54:def copy(
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:58:    coalesced_width: int | None = None,
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:70:        coalesced_width (Optional[int], keyword-only): Width for coalesced memory access. Defaults to None.
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:79:            coalesced_width, disable_tma, eviction_policy, and prefer_instruction can also
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:117:    if "coalesced_width" not in ann and coalesced_width is not None:
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:118:        ann["coalesced_width"] = coalesced_width
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:143:def copy_cluster(
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:151:    coalesced_width: int | None = None,
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:167:        coalesced_width: Vectorization width (in elements) for the SIMT loop
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:191:    if "coalesced_width" not in ann and coalesced_width is not None:
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:192:        ann["coalesced_width"] = coalesced_width
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:203:    coalesced_width: int | None = None,
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:217:        coalesced_width (Optional[int], keyword-only): Width for coalesced memory access. Defaults to None.
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:226:    if "coalesced_width" not in ann and coalesced_width is not None:
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:227:        ann["coalesced_width"] = coalesced_width
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:54:def copy(
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:58:    coalesced_width: int | None = None,
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:70:        coalesced_width (Optional[int], keyword-only): Width for coalesced memory access. Defaults to None.
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:79:            coalesced_width, disable_tma, eviction_policy, and prefer_instruction can also
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:117:    if "coalesced_width" not in ann and coalesced_width is not None:
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:118:        ann["coalesced_width"] = coalesced_width
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:143:def copy_cluster(
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:151:    coalesced_width: int | None = None,
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:167:        coalesced_width: Vectorization width (in elements) for the SIMT loop
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:191:    if "coalesced_width" not in ann and coalesced_width is not None:
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:192:        ann["coalesced_width"] = coalesced_width
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:203:    coalesced_width: int | None = None,
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:217:        coalesced_width (Optional[int], keyword-only): Width for coalesced memory access. Defaults to None.
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:226:    if "coalesced_width" not in ann and coalesced_width is not None:
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/copy_op.py:227:        ann["coalesced_width"] = coalesced_width
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/loop.py:15:    coalesced_width: int | None = None,
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/loop.py:28:    coalesced_width : Optional[int]
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/loop.py:29:        The coalesced width of the parallel loop.
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/loop.py:79:    if coalesced_width is not None:
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/loop.py:80:        merged_annotations["coalesced_width"] = coalesced_width
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/builtin.py:51:    """Explicitly load via CUDA read-only data cache.
Sat Sep 26 09:59:36 UTC 2026

STATUS: completed; EXIT_CODE: 0

=== Event 38 | item.completed | item_18 ===
COMMAND:
/bin/bash -lc "python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();s=s.replace(\"X:T.Tensor((128,512,768),'float32'), W:T.Tensor((N,768),'float32')\", \"X:T.Tensor((128,512,768),'float16'), W:T.Tensor((N,768),'float16')\")
# remove unused baseline kernels
start=s.index('@tilelang.jit(out_idx=[1]'); end=s.index(\"@tilelang.jit(out_idx=[1]\",s.index('def values'))
s=s[:start]+s[end:]
s=s.replace(\"Y:T.Tensor((128,512,768),'float32')\", \"Y:T.Tensor((128,512,768),'float16')\")
pos=s.index('class ModelNew:')
s=s[:pos]+'''@tilelang.jit(out_idx=[1],target='hip',execution_backend='cython')
def convert(shape):
    @T.prim_func
    def main(X:T.Tensor(shape,'float32'), Y:T.Tensor(shape,'float16')):
        with T.Kernel(T.ceildiv(T.product(shape),1024),threads=256) as b:
            for i in T.Parallel(1024):
                Y.view((T.product(shape),))[b*1024+i]=X.view((T.product(shape),))[b*1024+i]
    return main

'''+s[pos:]
s=s.replace(\"self.qkv=linear(2304,'float16')\", \"self.cx=convert((128,512,768))\\n        self.cw=convert((2304,768))\\n        self.cp=convert((768,768))\\n        self.qkv=linear(2304,'float16')\")
s=s.replace('q=self.qkv(x,qkv_weight,qkv_bias)', 'xh=self.cx(x)\\n        wh=self.cw(qkv_weight)\\n        ph=self.cp(projection_weight)\\n        q=self.qkv(xh,wh,qkv_bias)').replace('self.proj(y,projection_weight,projection_bias)', 'self.proj(y,ph,projection_bias)')
# product/view likely unsupported use buffer flattened T.reshape API? use shape 3 or 2 loops explicit
s=s.replace('T.product(shape)', 'size')
s=s.replace('    @T.prim_func\\n    def main(X:T.Tensor(shape', '    size=1\\n    for d in shape: size*=d\\n    @T.prim_func\\n    def main(X:T.Tensor(shape')
s=s.replace(\"Y.view((size,))[b*1024+i]=X.view((size,))[b*1024+i]\", \"T.reshape(Y,(size,))[b*1024+i]=T.reshape(X,(size,))[b*1024+i]\")
open(p,'w').write(s)
PY
bash scripts/bench.sh iter-3"
OUTPUT:
[09:59:29] : Warning: T.vectorized loop over `local_id` with extent 8 is lowered as a serial loop because TileLang could not find a valid vectorization plan. Scalar accumulator updates inside the loop are a common cause; move reductions to T.unroll or T.serial if this is intended.
2026-09-26 09:59:06  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 09:59:11  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`
2026-09-26 09:59:11  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 09:59:16  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`
2026-09-26 09:59:17  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 09:59:22  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`
2026-09-26 09:59:22  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[3]`
2026-09-26 09:59:27  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`
2026-09-26 09:59:27  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 09:59:35  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`
2026-09-26 09:59:35  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[3]`
2026-09-26 09:59:40  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-3
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.953800201416016, 44.94582556119795, 45.27346619318364, 45.22574855060818, 44.92933890891355], "candidate": [13.427339326476885, 13.49606139148955, 13.50049183143227, 13.495665550231934, 13.44958500265336]}
RUNTIME: 13.495665550231934
REF_RUNTIME: 44.953800201416016
SPEEDUP: 3.330980605150181

STATUS: completed; EXIT_CODE: 0

=== Event 39 | item.started | item_20 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-3 — High structural child of iter-2 (861d582)
Diagnosis: QKV 13.12 ms, output projection 4.80 ms, attention 3.18 ms. Native projection has repeated FP32 loads/conversions per tile, two barriers per 32-wide reduction chunk; counter symbols ambiguous, no utilization inference. Convert activation and both weights once per forward; retain half attention output (same rounding previously performed by projection loads). No cached parameter data. Remove unused explicit attention definitions.
Complete suite PASS; 13.495666 ms (13.427–13.500), 3.3310x. Promoted. Next increase projection reuse and reduction chunk to reduce traffic/barriers.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-3: reuse converted projection operands, 13.50 ms'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();a=s.index('def linear');b=s.index('@tilelang.jit',a);part=s[a:b]
part=part.replace('out_dtype: str):','out_dtype: str, BM: int=128, BN: int=128, BK: int=64):')
part=part.replace('1024,T.ceildiv(N,64),threads=128','65536//BM,T.ceildiv(N,BN),threads=256').replace('(64,32)', '(BM,BK)',1).replace('(64,32)', '(BN,BK)',1).replace('(64,64)', '(BM,BN)')
part=part.replace('24,num_stages=2','768//BK,num_stages=2')
part=part.replace('for i,j in T.Parallel(64,32):\\n                    a[i,j]=X[bm//8,(bm%8)*64+i,k*32+j]\\n                    b[i,j]=W[bn*64+i,k*32+j]', 'for i,j in T.Parallel(BM,BK):\\n                    a[i,j]=X[bm//(512//BM),(bm%(512//BM))*BM+i,k*BK+j]\\n                for i,j in T.Parallel(BN,BK):\\n                    b[i,j]=W[bn*BN+i,k*BK+j]')
part=part.replace('bm//8,(bm%8)*64+i,bn*64+j', 'bm//(512//BM),(bm%(512//BM))*BM+i,bn*BN+j').replace('B[bn*64+j]', 'B[bn*BN+j]')
s=s[:a]+part+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-4"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 40 | item.completed | item_21 ===
Converting once per forward reduced runtime to 13.50 ms, with all trials passing. I’m now testing larger projection tiles to increase reuse and reduce synchronization between reduction chunks.

=== Event 41 | item.completed | item_20 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-3 — High structural child of iter-2 (861d582)
Diagnosis: QKV 13.12 ms, output projection 4.80 ms, attention 3.18 ms. Native projection has repeated FP32 loads/conversions per tile, two barriers per 32-wide reduction chunk; counter symbols ambiguous, no utilization inference. Convert activation and both weights once per forward; retain half attention output (same rounding previously performed by projection loads). No cached parameter data. Remove unused explicit attention definitions.
Complete suite PASS; 13.495666 ms (13.427–13.500), 3.3310x. Promoted. Next increase projection reuse and reduction chunk to reduce traffic/barriers.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-3: reuse converted projection operands, 13.50 ms'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();a=s.index('def linear');b=s.index('@tilelang.jit',a);part=s[a:b]
part=part.replace('out_dtype: str):','out_dtype: str, BM: int=128, BN: int=128, BK: int=64):')
part=part.replace('1024,T.ceildiv(N,64),threads=128','65536//BM,T.ceildiv(N,BN),threads=256').replace('(64,32)', '(BM,BK)',1).replace('(64,32)', '(BN,BK)',1).replace('(64,64)', '(BM,BN)')
part=part.replace('24,num_stages=2','768//BK,num_stages=2')
part=part.replace('for i,j in T.Parallel(64,32):\\n                    a[i,j]=X[bm//8,(bm%8)*64+i,k*32+j]\\n                    b[i,j]=W[bn*64+i,k*32+j]', 'for i,j in T.Parallel(BM,BK):\\n                    a[i,j]=X[bm//(512//BM),(bm%(512//BM))*BM+i,k*BK+j]\\n                for i,j in T.Parallel(BN,BK):\\n                    b[i,j]=W[bn*BN+i,k*BK+j]')
part=part.replace('bm//8,(bm%8)*64+i,bn*64+j', 'bm//(512//BM),(bm%(512//BM))*BM+i,bn*BN+j').replace('B[bn*64+j]', 'B[bn*BN+j]')
s=s[:a]+part+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-4"
OUTPUT:
2026-09-26 10:00:24  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[3]`
2026-09-26 10:00:30  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`
2026-09-26 10:00:30  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[3]`
2026-09-26 10:00:36  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-4
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.953800201416016, 44.85260903048763, 44.79536516132644, 45.16948093604471, 45.0935201959527], "candidate": [9.10731315612793, 9.09303714209168, 9.05431177694845, 9.113633156254387, 9.146810544420413]}
RUNTIME: 9.10731315612793
REF_RUNTIME: 44.953800201416016
SPEEDUP: 4.936011250603421

STATUS: completed; EXIT_CODE: 0

=== Event 42 | item.started | item_22 ===
COMMAND:
/bin/bash -lc "rg -n 'def use_swizzle|def swizzle|panel_size' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/*.py /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/threadblock_swizzle.h && cat /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/threadblock_swizzle.h"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 43 | item.completed | item_22 ===
COMMAND:
/bin/bash -lc "rg -n 'def use_swizzle|def swizzle|panel_size' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/*.py /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/threadblock_swizzle.h && cat /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/threadblock_swizzle.h"
OUTPUT:
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/threadblock_swizzle.h:11:  const unsigned int panel_size = panel_width * gridDim.x;
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/threadblock_swizzle.h:12:  const unsigned int panel_offset = block_idx % panel_size;
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/threadblock_swizzle.h:13:  const unsigned int panel_idx = block_idx / panel_size;
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/threadblock_swizzle.h:14:  const unsigned int total_panel = ceil_div(grid_size, panel_size);
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/threadblock_swizzle.h:18:          : (grid_size - panel_idx * panel_size) / gridDim.x;
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/threadblock_swizzle.h:30:  const unsigned int panel_size = panel_width * gridDim.y;
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/threadblock_swizzle.h:31:  const unsigned int panel_offset = block_idx % panel_size;
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/threadblock_swizzle.h:32:  const unsigned int panel_idx = block_idx / panel_size;
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/threadblock_swizzle.h:33:  const unsigned int total_panel = ceil_div(grid_size, panel_size);
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/threadblock_swizzle.h:37:          : (grid_size - panel_idx * panel_size) / gridDim.y;
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/annotations.py:29:def use_swizzle(panel_size: int, order: str = "row", enable: bool = True):
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/annotations.py:41:    return attr(None, "threadblock_swizzle_pattern", tvm_tuple(device_func, panel_size))
#pragma once

#include "common.h"

namespace tl {

template <int panel_width> TL_DEVICE dim3 rasterization2DRow() {
  auto ceil_div = [](int a, int b) { return (a + b - 1) / b; };
  const unsigned int block_idx = blockIdx.x + blockIdx.y * gridDim.x;
  const unsigned int grid_size = gridDim.x * gridDim.y;
  const unsigned int panel_size = panel_width * gridDim.x;
  const unsigned int panel_offset = block_idx % panel_size;
  const unsigned int panel_idx = block_idx / panel_size;
  const unsigned int total_panel = ceil_div(grid_size, panel_size);
  const unsigned int stride =
      panel_idx + 1 < total_panel
          ? panel_width
          : (grid_size - panel_idx * panel_size) / gridDim.x;
  const unsigned int col_idx = (panel_idx & 1)
                                   ? gridDim.x - 1 - panel_offset / stride
                                   : panel_offset / stride;
  const unsigned int row_idx = panel_offset % stride + panel_idx * panel_width;
  return {col_idx, row_idx, blockIdx.z};
}

template <int panel_width> TL_DEVICE dim3 rasterization2DColumn() {
  auto ceil_div = [](int a, int b) { return (a + b - 1) / b; };
  const unsigned int block_idx = blockIdx.x + blockIdx.y * gridDim.x;
  const unsigned int grid_size = gridDim.x * gridDim.y;
  const unsigned int panel_size = panel_width * gridDim.y;
  const unsigned int panel_offset = block_idx % panel_size;
  const unsigned int panel_idx = block_idx / panel_size;
  const unsigned int total_panel = ceil_div(grid_size, panel_size);
  const unsigned int stride =
      panel_idx + 1 < total_panel
          ? panel_width
          : (grid_size - panel_idx * panel_size) / gridDim.y;
  const unsigned int row_idx = (panel_idx & 1)
                                   ? gridDim.y - 1 - panel_offset / stride
                                   : panel_offset / stride;
  const unsigned int col_idx = panel_offset % stride + panel_idx * panel_width;
  return {col_idx, row_idx, blockIdx.z};
}

} // namespace tl

STATUS: completed; EXIT_CODE: 0

=== Event 44 | item.started | item_23 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-4 — High tuning child of iter-3 (a147944)
Projection tile 128x128x64, 256 threads (32 KiB LDS), versus 64x64x32/128. Complete suite PASS; 9.107313 ms (9.054–9.147), 4.9360x. Promoted. Coupled geometry/thread ownership increases reuse and halves reduction barriers. Next test grouped grid ordering: current x-major M traversal streams the entire activation once per output tile; nearby output tiles could reuse activation from L2.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-4: enlarge projection tiles, 9.11 ms'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();s=s.replace('as (bm,bn):\\n            a=', 'as (bm,bn):\\n            T.use_swizzle(panel_size=8,order=\"col\")\\n            a=',1);open(p,'w').write(s)
PY
bash scripts/bench.sh iter-5"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 45 | item.completed | item_23 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-4 — High tuning child of iter-3 (a147944)
Projection tile 128x128x64, 256 threads (32 KiB LDS), versus 64x64x32/128. Complete suite PASS; 9.107313 ms (9.054–9.147), 4.9360x. Promoted. Coupled geometry/thread ownership increases reuse and halves reduction barriers. Next test grouped grid ordering: current x-major M traversal streams the entire activation once per output tile; nearby output tiles could reuse activation from L2.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-4: enlarge projection tiles, 9.11 ms'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();s=s.replace('as (bm,bn):\\n            a=', 'as (bm,bn):\\n            T.use_swizzle(panel_size=8,order=\"col\")\\n            a=',1);open(p,'w').write(s)
PY
bash scripts/bench.sh iter-5"
OUTPUT:
2026-09-26 10:01:07  [TileLang:tilelang.language.eager.builder:CRITICAL] (builder.py:1669): Failed to build prim_func from main
args={'X': X, 'W': W, 'B': B, 'Y': Y}
source=def make_closure(BK, BM, BN, N):

    def main(__tb):
        __tb_fl = '/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/solution/ModelNew.py'
        __tb_fn = 'main'
        range = __tb.override('range')
        __tb.set_fileline(__tb_fl, 7, __tb_fn)

        def main(X, W, B, Y, **__kwargs):
            __tb.set_fileline(__tb_fl, 7, __tb_fn)
            X = __tb.arg('X', X)
            __tb.set_fileline(__tb_fl, 7, __tb_fn)
            W = __tb.arg('W', W)
            __tb.set_fileline(__tb_fl, 7, __tb_fn)
            B = __tb.arg('B', B)
            __tb.set_fileline(__tb_fl, 7, __tb_fn)
            Y = __tb.arg('Y', Y)
            __tb.set_fileline(__tb_fl, 1, __tb_fn)
            if __tb.skip_kernel_ctx():
                __tb.set_fileline(__tb_fl, 1, __tb_fn)
                return
            __tb.set_fileline(__tb_fl, 8, __tb_fn)
            with __tb.ctx_with(__tb.rval('T', T).Kernel(65536 // __tb.rval('BM', BM), __tb.rval('T', T).ceildiv(__tb.rval('N', N), __tb.rval('BN', BN)), threads=256)) as (bm, bn):
                __tb.set_fileline(__tb_fl, 9, __tb_fn)
                __tb.eval(__tb.rval('T', T).use_swizzle(panel_size=8, order='col'))
                __tb.set_fileline(__tb_fl, 10, __tb_fn)
                a = __tb.bind('a', __tb.rval('T', T).alloc_shared((__tb.rval('BM', BM), __tb.rval('BK', BK)), 'float16'))
                __tb.set_fileline(__tb_fl, 11, __tb_fn)
                b = __tb.bind('b', __tb.rval('T', T).alloc_shared((__tb.rval('BN', BN), __tb.rval('BK', BK)), 'float16'))
                __tb.set_fileline(__tb_fl, 12, __tb_fn)
                c = __tb.bind('c', __tb.rval('T', T).alloc_fragment((__tb.rval('BM', BM), __tb.rval('BN', BN)), 'float32'))
                __tb.set_fileline(__tb_fl, 13, __tb_fn)
                __tb.eval(__tb.rval('T', T).clear(__tb.rval('c', c)))
                __tb.set_fileline(__tb_fl, 14, __tb_fn)
                for __6 in __tb.ctx_for(__tb.rval('T', T).Pipelined(768 // __tb.rval('BK', BK), num_stages=2)):
                    __tb.set_fileline(__tb_fl, 14, __tb_fn)
                    k = __tb.bind('k', __6)
                    __tb.set_fileline(__tb_fl, 15, __tb_fn)
                    for __0 in __tb.ctx_for(__tb.rval('T', T).Parallel(__tb.rval('BM', BM), __tb.rval('BK', BK))):
                        __1, __2 = __tb.unwrap_value(__0)
                        __tb.set_fileline(__tb_fl, 15, __tb_fn)
                        __1, __2 = (__tb.bind('_', __1), __tb.bind('_', __2))
                        __tb.set_fileline(__tb_fl, 15, __tb_fn)
                        i, j = (__tb.bind('i', __1), __tb.bind('j', __2))
                        __tb.set_fileline(__tb_fl, 16, __tb_fn)
                        __tb.assign_slice(__tb.rval('a', a), (__tb.rval('i', i), __tb.rval('j', j)), __tb.rval('X', X)[__tb.rval('bm', bm) // (512 // __tb.rval('BM', BM)), __tb.rval('bm', bm) % (512 // __tb.rval('BM', BM)) * __tb.rval('BM', BM) + __tb.rval('i', i), __tb.rval('k', k) * __tb.rval('BK', BK) + __tb.rval('j', j)])
                    __tb.set_fileline(__tb_fl, 17, __tb_fn)
                    for __3 in __tb.ctx_for(__tb.rval('T', T).Parallel(__tb.rval('BN', BN), __tb.rval('BK', BK))):
                        __4, __5 = __tb.unwrap_value(__3)
                        __tb.set_fileline(__tb_fl, 17, __tb_fn)
                        __4, __5 = (__tb.bind('_', __4), __tb.bind('_', __5))
                        __tb.set_fileline(__tb_fl, 17, __tb_fn)
                        i, j = (__tb.bind('i', __4), __tb.bind('j', __5))
                        __tb.set_fileline(__tb_fl, 18, __tb_fn)
                        __tb.assign_slice(__tb.rval('b', b), (__tb.rval('i', i), __tb.rval('j', j)), __tb.rval('W', W)[__tb.rval('bn', bn) * __tb.rval('BN', BN) + __tb.rval('i', i), __tb.rval('k', k) * __tb.rval('BK', BK) + __tb.rval('j', j)])
                    __tb.set_fileline(__tb_fl, 19, __tb_fn)
                    __tb.eval(__tb.rval('T', T).gemm(__tb.rval('a', a), __tb.rval('b', b), __tb.rval('c', c), transpose_B=True))
                __tb.set_fileline(__tb_fl, 20, __tb_fn)
                for __7 in __tb.ctx_for(__tb.rval('T', T).Parallel(__tb.rval('BM', BM), __tb.rval('BN', BN))):
                    __8, __9 = __tb.unwrap_value(__7)
                    __tb.set_fileline(__tb_fl, 20, __tb_fn)
                    __8, __9 = (__tb.bind('_', __8), __tb.bind('_', __9))
                    __tb.set_fileline(__tb_fl, 20, __tb_fn)
                    i, j = (__tb.bind('i', __8), __tb.bind('j', __9))
                    __tb.set_fileline(__tb_fl, 21, __tb_fn)
                    __tb.assign_slice(__tb.rval('Y', Y), (__tb.rval('bm', bm) // (512 // __tb.rval('BM', BM)), __tb.rval('bm', bm) % (512 // __tb.rval('BM', BM)) * __tb.rval('BM', BM) + __tb.rval('i', i), __tb.rval('bn', bn) * __tb.rval('BN', BN) + __tb.rval('j', j)), __tb.rval('c', c)[__tb.rval('i', i), __tb.rval('j', j)] + __tb.rval('B', B)[__tb.rval('bn', bn) * __tb.rval('BN', BN) + __tb.rval('j', j)])
        return main
    return main
Traceback (most recent call last):
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/scripts/rocm_bench.py", line 126, in <module>
    result=run(a.ref,a.solution,a.backend,a.reference_only)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/scripts/rocm_bench.py", line 77, in run
    candidate=model(load(solution,'benchmark_candidate'),ref)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/scripts/rocm_bench.py", line 53, in model
    instance = cls(*init).to('cuda')
               ^^^^^^^^^^
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/solution/ModelNew.py", line 86, in __init__
    self.qkv=linear(2304,'float16')
             ^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/__init__.py", line 539, in __call__
    kernel = self.compile(*args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/__init__.py", line 453, in compile
    prim_func = self.get_tir(*args, **kwargs)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/__init__.py", line 374, in get_tir
    tir = self.func(*args, **kwargs)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/eager/builder.py", line 1591, in __call__
    return self.get_tir(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/eager/builder.py", line 1583, in get_tir
    self.p1_cache[bound.p1_key] = self._build_tir_template(**bound.compile_kwargs)
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/eager/builder.py", line 1551, in _build_tir_template
    return TirTemplate.from_lazy_style(self.orig_func.__name__, self.orig_func(*args, **kwargs))
                                                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/solution/ModelNew.py", line 6, in linear
    @T.prim_func
     ^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/eager/builder.py", line 1672, in prim_func
    return impl(func) if func is not None else impl
           ^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/eager/builder.py", line 1670, in impl
    raise e
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/eager/builder.py", line 1663, in impl
    ir_gen.gen(builder)(**annot)
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/solution/ModelNew.py", line 9, in main
    T.use_swizzle(panel_size=8,order="col")
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/annotations.py", line 38, in use_swizzle
    raise ValueError(f"Unsupported swizzle order: {order}")
ValueError: Unsupported swizzle order: col

RECORD_ID: CASE-MINGPT-iter-5
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: False
CORRECT: False
TIMING_SAMPLES_STATUS: unavailable for failed evaluation

STATUS: failed; EXIT_CODE: 1

=== Event 46 | item.started | item_24 ===
COMMAND:
/bin/bash -lc "sed -n '29,43p' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/annotations.py
cat >> ITERATIONS.md <<'EOF'

## iter-5 — High tuning child of iter-4 (532f4ad)
Grouped traversal attempt. Compile FAIL: API accepts "'`column`, not `col`; no latency. Rejected implementation; fix concrete argument error next.
EOF
git add solution ITERATIONS.md && git commit -m '"'iter-5: record swizzle API failure'
sed -i 's/order=\"col\"/order=\"column\"/' solution/ModelNew.py
bash scripts/bench.sh iter-6"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 47 | item.completed | item_24 ===
COMMAND:
/bin/bash -lc "sed -n '29,43p' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/annotations.py
cat >> ITERATIONS.md <<'EOF'

## iter-5 — High tuning child of iter-4 (532f4ad)
Grouped traversal attempt. Compile FAIL: API accepts "'`column`, not `col`; no latency. Rejected implementation; fix concrete argument error next.
EOF
git add solution ITERATIONS.md && git commit -m '"'iter-5: record swizzle API failure'
sed -i 's/order=\"col\"/order=\"column\"/' solution/ModelNew.py
bash scripts/bench.sh iter-6"
OUTPUT:
2026-09-26 10:01:25  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[3]`
2026-09-26 10:01:30  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`
2026-09-26 10:01:30  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[3]`
2026-09-26 10:01:36  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-6
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [45.008677888728904, 44.922521862553644, 44.94110544016583, 44.969177317925855, 44.953800201416016], "candidate": [6.452732516544565, 6.473775214437872, 6.411611031858014, 6.419225215911865, 6.41747334463077]}
RUNTIME: 6.419225215911865
REF_RUNTIME: 44.953800201416016
SPEEDUP: 7.002994705651596

STATUS: completed; EXIT_CODE: 0

=== Event 48 | item.completed | item_25 ===
Larger projection tiles brought runtime to 9.11 ms, and grouped tile traversal brought it to 6.42 ms. Both passed. I’ll next test larger attention query tiles so each loaded key/value block serves more queries.

=== Event 49 | item.started | item_26 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-6 — High repair child of iter-5 (a0ad233)
Correct "'`column` swizzle, group eight M tiles. Complete suite PASS; 6.419225 ms (6.412–6.474), 7.0030x. Promoted. Strong improvement supports activation reuse hypothesis, despite reference timing drift. Next increase attention query tile 32→64 to reuse K/V and reduce block count.
EOF
git add solution ITERATIONS.md && git commit -m '"'iter-6: group projection tiles for cache reuse, 6.42 ms'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();a=s.index('def attention');b=s.index('@tilelang.jit',a);part=s[a:b];part=part.replace('def attention():','def attention(BM: int=64):').replace('T.Kernel(16,','T.Kernel(512//BM,').replace('(32,','(BM,').replace('mi*32','mi*BM').replace('(mi+1)*32','(mi+1)*BM').replace('T.Parallel(32)', 'T.Parallel(BM)');s=s[:a]+part+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-7"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 50 | item.completed | item_26 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-6 — High repair child of iter-5 (a0ad233)
Correct "'`column` swizzle, group eight M tiles. Complete suite PASS; 6.419225 ms (6.412–6.474), 7.0030x. Promoted. Strong improvement supports activation reuse hypothesis, despite reference timing drift. Next increase attention query tile 32→64 to reuse K/V and reduce block count.
EOF
git add solution ITERATIONS.md && git commit -m '"'iter-6: group projection tiles for cache reuse, 6.42 ms'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();a=s.index('def attention');b=s.index('@tilelang.jit',a);part=s[a:b];part=part.replace('def attention():','def attention(BM: int=64):').replace('T.Kernel(16,','T.Kernel(512//BM,').replace('(32,','(BM,').replace('mi*32','mi*BM').replace('(mi+1)*32','(mi+1)*BM').replace('T.Parallel(32)', 'T.Parallel(BM)');s=s[:a]+part+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-7"
OUTPUT:
[10:02:07] : Warning: T.vectorized loop over `local_id` with extent 8 is lowered as a serial loop because TileLang could not find a valid vectorization plan. Scalar accumulator updates inside the loop are a common cause; move reductions to T.unroll or T.serial if this is intended.
2026-09-26 10:02:05  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 10:02:13  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-7
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.87274599400934, 45.062385852003175, 44.953800201416016, 44.92739912838326, 45.05436027707739], "candidate": [7.428918705687471, 7.3774871826171875, 7.316127554586661, 7.464501416954754, 7.371943720068192]}
RUNTIME: 7.3774871826171875
REF_RUNTIME: 44.953800201416016
SPEEDUP: 6.093375574726314

STATUS: completed; EXIT_CODE: 0

=== Event 51 | item.started | item_27 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-7 — High tuning child of iter-6 (8a2c7db)
Attention BM64/BN64/128 threads, 44 KiB nominal LDS versus 32 KiB BM32. Complete suite PASS; 7.377487 ms (7.316–7.465). Retained for exploration, not promoted. Larger tile alone regresses; test coupled 256-thread ownership before rejecting reuse mechanism.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-7: retain slower 64-query attention tile'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read().replace('T.Kernel(512//BM,1024,threads=128)', 'T.Kernel(512//BM,1024,threads=256)');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-8"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 52 | item.completed | item_27 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-7 — High tuning child of iter-6 (8a2c7db)
Attention BM64/BN64/128 threads, 44 KiB nominal LDS versus 32 KiB BM32. Complete suite PASS; 7.377487 ms (7.316–7.465). Retained for exploration, not promoted. Larger tile alone regresses; test coupled 256-thread ownership before rejecting reuse mechanism.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-7: retain slower 64-query attention tile'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read().replace('T.Kernel(512//BM,1024,threads=128)', 'T.Kernel(512//BM,1024,threads=256)');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-8"
OUTPUT:
[10:02:37] : Warning: T.vectorized loop over `local_id` with extent 8 is lowered as a serial loop because TileLang could not find a valid vectorization plan. Scalar accumulator updates inside the loop are a common cause; move reductions to T.unroll or T.serial if this is intended.
2026-09-26 10:02:36  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 10:02:43  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-8
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.792458993066376, 44.953800201416016, 45.018910944624515, 44.67158039680449, 44.989465733846984], "candidate": [5.681180953979492, 5.593118974339323, 5.817388541697282, 5.787434873048869, 5.608494003454358]}
RUNTIME: 5.681180953979492
REF_RUNTIME: 44.953800201416016
SPEEDUP: 7.912756267678336

STATUS: completed; EXIT_CODE: 0

=== Event 53 | item.completed | item_28 ===
The larger attention tile was slower with 128 threads, but improved runtime to 5.68 ms with 256 threads. That points to thread ownership being important alongside reuse. I’ll test another feasible tile shape before selecting the next direction.

=== Event 54 | item.started | item_29 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-8 — High tuning child of iter-7 (26e520e)
BM64/BN64 with 256 attention threads. Complete suite PASS; 5.681181 ms (5.593–5.817), 7.9128x. Promoted over iter-6. Thread ownership materially changes outcome, supporting coupled geometry exploration. Correction: iter-7 nominal array sum is 44 KiB; BM32 sum is 34 KiB (not 32). Actual allocation can differ due to lifetimes/layouts; no occupancy claim from nominal totals.
Next BM128/BN64/256 uses nominal 64 KiB, within queried limit; measure feasibility and speed.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-8: balance larger attention tile across 256 threads, 5.68 ms'
sed -i 's/def attention(BM: int=64):/def attention(BM: int=128):/' solution/ModelNew.py
bash scripts/bench.sh iter-9"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 55 | item.completed | item_29 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-8 — High tuning child of iter-7 (26e520e)
BM64/BN64 with 256 attention threads. Complete suite PASS; 5.681181 ms (5.593–5.817), 7.9128x. Promoted over iter-6. Thread ownership materially changes outcome, supporting coupled geometry exploration. Correction: iter-7 nominal array sum is 44 KiB; BM32 sum is 34 KiB (not 32). Actual allocation can differ due to lifetimes/layouts; no occupancy claim from nominal totals.
Next BM128/BN64/256 uses nominal 64 KiB, within queried limit; measure feasibility and speed.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-8: balance larger attention tile across 256 threads, 5.68 ms'
sed -i 's/def attention(BM: int=64):/def attention(BM: int=128):/' solution/ModelNew.py
bash scripts/bench.sh iter-9"
OUTPUT:
[10:03:12] : Warning: T.vectorized loop over `local_id` with extent 8 is lowered as a serial loop because TileLang could not find a valid vectorization plan. Scalar accumulator updates inside the loop are a common cause; move reductions to T.unroll or T.serial if this is intended.
2026-09-26 10:03:10  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
Traceback (most recent call last):
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/scripts/rocm_bench.py", line 126, in <module>
    result=run(a.ref,a.solution,a.backend,a.reference_only)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/scripts/rocm_bench.py", line 77, in run
    candidate=model(load(solution,'benchmark_candidate'),ref)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/scripts/rocm_bench.py", line 53, in model
    instance = cls(*init).to('cuda')
               ^^^^^^^^^^
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/solution/ModelNew.py", line 87, in __init__
    self.attn=attention()
              ^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/__init__.py", line 539, in __call__
    kernel = self.compile(*args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/__init__.py", line 454, in compile
    kernel_result = compile(
                    ^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/__init__.py", line 162, in compile
    return cached(
           ^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cache/__init__.py", line 82, in cached
    return cache.cached(
           ^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cache/kernel_cache.py", line 428, in cached
    kernel = JITKernel(
             ^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/kernel.py", line 140, in __init__
    adapter = self._compile_and_create_adapter(func, out_idx)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/kernel.py", line 254, in _compile_and_create_adapter
    adapter = self._create_adapter_from_artifact(
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/kernel.py", line 340, in _create_adapter_from_artifact
    adapter = create_adapter(
              ^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/kernel.py", line 315, in create_adapter
    return adapter_cls(**kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/adapter/cython/adapter.py", line 140, in __init__
    raise RuntimeError(f"Initialization failed: {error_msg}")
RuntimeError: Initialization failed: Requested dynamic shared memory 67584 exceeds device limit 65536 for main_kernel
#include <hip/hip_runtime.h>
#include <tl_templates/hip/gemm.h>
#include <tl_templates/hip/copy.h>
#include <tl_templates/hip/reduce.h>
#include <tl_templates/hip/scan.h>
#include <tl_templates/hip/ldsm.h>
#include <tl_templates/hip/threadblock_swizzle.h>
#include <tl_templates/hip/debug.h>

extern "C" __global__ void __launch_bounds__(256) main_kernel(half_t* __restrict__ Q, half_t* __restrict__ Y) {
  extern __shared__ __align__(1024) uchar buf_dyn_shmem[];
  void* q = ((void*)((char*)buf_dyn_shmem + 0));
  void* p = ((void*)((char*)buf_dyn_shmem + 24576));
  void* k = ((void*)((char*)buf_dyn_shmem + 40960));
  void* v = ((void*)((char*)buf_dyn_shmem + 53248));
  void* workspace = ((void*)((char*)buf_dyn_shmem + 65536));
  void* workspace_1 = ((void*)((char*)buf_dyn_shmem + 66560));
  float m[16];
  float l[16];
  float o[48];
  float s[32];
  float old[16];
  float m_clear[16];
  float alpha[16];
  float rows[16];
  #pragma unroll
  for (int i = 0; i < 4; ++i) {
    float broadcast_var = -HUGE_VALF;
    *(float4*)(m + (i * 4)) = make_float4(broadcast_var, broadcast_var, broadcast_var, broadcast_var);
  }
  #pragma unroll
  for (int i_1 = 0; i_1 < 4; ++i_1) {
    float broadcast_var_1 = 0.000000e+00f;
    *(float4*)(l + (i_1 * 4)) = make_float4(broadcast_var_1, broadcast_var_1, broadcast_var_1, broadcast_var_1);
  }
  #pragma unroll
  for (int i_2 = 0; i_2 < 12; ++i_2) {
    float broadcast_var_2 = 0.000000e+00f;
    *(float4*)(o + (i_2 * 4)) = make_float4(broadcast_var_2, broadcast_var_2, broadcast_var_2, broadcast_var_2);
  }
  #pragma unroll
  for (int i_3 = 0; i_3 < 6; ++i_3) {
    *(uint4*)(((half_t*)q) + (((((((i_3 * 64) + (((int)threadIdx.x) >> 2)) % 3) * 4096) + ((((i_3 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 32)) + (((((((i_3 * 16) + (((int)threadIdx.x) >> 4)) % 6) / 3) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 16)) + (((((((i_3 * 32) + (((int)threadIdx.x) >> 3)) % 6) / 3) + (((int)threadIdx.x) & 1)) & 1) * 8))) = *(uint4*)(Q + ((((((((int)blockIdx.y) >> 3) * 1179648) + (((int)blockIdx.x) * 294912)) + ((((i_3 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 2304)) + ((((int)blockIdx.y) & 7) * 96)) + ((((i_3 * 256) + ((int)threadIdx.x)) % 12) * 8)));
  }
  for (int nb = 0; nb < ((((int)blockIdx.x) * 2) + 2); ++nb) {
    __syncthreads();
    #pragma unroll
    for (int i_4 = 0; i_4 < 3; ++i_4) {
      *(uint4*)(((half_t*)k) + (((((((i_4 * 64) + (((int)threadIdx.x) >> 2)) % 3) * 2048) + ((((i_4 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 32)) + (((((((i_4 * 16) + (((int)threadIdx.x) >> 4)) % 6) / 3) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 16)) + (((((((i_4 * 32) + (((int)threadIdx.x) >> 3)) % 6) / 3) + (((int)threadIdx.x) & 1)) & 1) * 8))) = *(uint4*)(Q + (((((((((int)blockIdx.y) >> 3) * 1179648) + (nb * 147456)) + ((((i_4 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 2304)) + ((((int)blockIdx.y) & 7) * 96)) + ((((i_4 * 256) + ((int)threadIdx.x)) % 12) * 8)) + 768));
      *(uint4*)(((half_t*)v) + (((((((i_4 * 64) + (((int)threadIdx.x) >> 2)) % 3) * 2048) + ((((i_4 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 32)) + (((((((i_4 * 16) + (((int)threadIdx.x) >> 4)) % 6) / 3) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 16)) + (((((((i_4 * 32) + (((int)threadIdx.x) >> 3)) % 6) / 3) + (((int)threadIdx.x) & 1)) & 1) * 8))) = *(uint4*)(Q + (((((((((int)blockIdx.y) >> 3) * 1179648) + (nb * 147456)) + ((((i_4 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 2304)) + ((((int)blockIdx.y) & 7) * 96)) + ((((i_4 * 256) + ((int)threadIdx.x)) % 12) * 8)) + 1536));
    }
    #pragma unroll
    for (int i_5 = 0; i_5 < 8; ++i_5) {
      float broadcast_var_3 = 0.000000e+00f;
      *(float4*)(s + (i_5 * 4)) = make_float4(broadcast_var_3, broadcast_var_3, broadcast_var_3, broadcast_var_3);
    }
    {
      half_t A_local[16];
      half_t B_local[16];
      __syncthreads();
      for (int ki = 0; ki < 6; ++ki) {
        for (int i_6 = 0; i_6 < 2; ++i_6) {
          *(uint4*)(A_local + (i_6 * 8)) = *(uint4*)(((half_t*)q) + (((((((ki >> 1) * 4096) + (((((int)threadIdx.x) & 127) >> 5) * 1024)) + (i_6 * 512)) + ((((int)threadIdx.x) & 15) * 32)) + (((((((int)threadIdx.x) & 7) >> 2) + (ki & 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 8)));
        }
        for (int j = 0; j < 2; ++j) {
          *(uint4*)(B_local + (j * 8)) = *(uint4*)(((half_t*)k) + (((((((ki >> 1) * 2048) + ((((int)threadIdx.x) >> 7) * 1024)) + (j * 512)) + ((((int)threadIdx.x) & 15) * 32)) + (((((((int)threadIdx.x) & 7) >> 2) + (ki & 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 8)));
        }
        for (int i_7 = 0; i_7 < 2; ++i_7) {
          for (int j_1 = 0; j_1 < 2; ++j_1) {
            {
      typedef __attribute__((__vector_size__(8 * sizeof(__fp16)))) __fp16 tl_v8f16;
      typedef __attribute__((__vector_size__(8 * sizeof(float)))) float tl_v8f32;
      *((tl_v8f32*)s + ((i_7 * 2) + j_1)) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(
          *((tl_v8f16*)A_local + i_7),
          *((tl_v8f16*)B_local + j_1),
          *((tl_v8f32*)s + ((i_7 * 2) + j_1)));
    };
          }
        }
      }
    }
    #pragma unroll
    for (int i_8 = 0; i_8 < 32; ++i_8) {
      float condval;
      if ((((((nb * 64) + ((((int)threadIdx.x) >> 7) * 32)) + (((i_8 & 15) >> 3) * 16)) + (((int)threadIdx.x) & 15)) <= (((((((int)blockIdx.x) * 128) + (((((int)threadIdx.x) & 127) >> 5) * 32)) + ((i_8 >> 4) * 16)) + (((((int)threadIdx.x) & 31) >> 4) * 8)) + (i_8 & 7)))) {
        condval = (s[i_8] * 1.020621e-01f);
      } else {
        condval = -HUGE_VALF;
      }
      s[i_8] = condval;
    }
    #pragma unroll
    for (int i_9 = 0; i_9 < 4; ++i_9) {
      *(float4*)(old + (i_9 * 4)) = *(float4*)(m + (i_9 * 4));
    }
    #pragma unroll
    for (int i_10 = 0; i_10 < 16; ++i_10) {
      m_clear[i_10] = -HUGE_VALF;
      #pragma unroll
      for (int rv = 0; rv < 2; ++rv) {
        m_clear[i_10] = max(m_clear[i_10], s[((((i_10 >> 3) * 16) + (rv * 8)) + (i_10 & 7))]);
      }
      __syncthreads();
      m_clear[i_10] = tl::AllReduce<tl::MaxOp, 256, 128, 0>::run(m_clear[i_10], (&(((float*)workspace_1)[0])));
      m_clear[i_10] = tl::AllReduce<tl::MaxOp, 16, 1, 0>::run(m_clear[i_10]);
      m[i_10] = max(m[i_10], m_clear[i_10]);
    }
    #pragma unroll
    for (int i_11 = 0; i_11 < 4; ++i_11) {
      float4 __1;
      float4 __2;
        float4 v_ = *(float4*)(old + (i_11 * 4));
        float4 v__1 = *(float4*)(m + (i_11 * 4));
        __2.x = (v_.x-v__1.x);
        __2.y = (v_.y-v__1.y);
        __2.z = (v_.z-v__1.z);
        __2.w = (v_.w-v__1.w);
      __1.x = __expf(__2.x);
      __1.y = __expf(__2.y);
      __1.z = __expf(__2.z);
      __1.w = __expf(__2.w);
      *(float4*)(alpha + (i_11 * 4)) = __1;
    }
    #pragma unroll
    for (int i_12 = 0; i_12 < 8; ++i_12) {
      float4 __3;
      float4 __4;
        float4 v__2 = *(float4*)(s + (i_12 * 4));
        float4 v__3 = *(float4*)(m + (((i_12 >> 2) * 8) + ((i_12 & 1) * 4)));
        __4.x = (v__2.x-v__3.x);
        __4.y = (v__2.y-v__3.y);
        __4.z = (v__2.z-v__3.z);
        __4.w = (v__2.w-v__3.w);
      __3.x = __expf(__4.x);
      __3.y = __expf(__4.y);
      __3.z = __expf(__4.z);
      __3.w = __expf(__4.w);
      *(float4*)(s + (i_12 * 4)) = __3;
    }
    #pragma unroll
    for (int i_13 = 0; i_13 < 16; ++i_13) {
      rows[i_13] = 0.000000e+00f;
      #pragma unroll
      for (int rv_1 = 0; rv_1 < 2; ++rv_1) {
        rows[i_13] = (rows[i_13] + s[((((i_13 >> 3) * 16) + (rv_1 * 8)) + (i_13 & 7))]);
      }
      __syncthreads();
      rows[i_13] = tl::AllReduce<tl::SumOp, 256, 128, 0>::run(rows[i_13], (&(((float*)workspace)[0])));
      rows[i_13] = tl::AllReduce<tl::SumOp, 16, 1, 0>::run(rows[i_13]);
    }
    #pragma unroll
    for (int i_14 = 0; i_14 < 4; ++i_14) {
      float4 __5;
        float4 __6;
          float4 v__4 = *(float4*)(l + (i_14 * 4));
          float4 v__5 = *(float4*)(alpha + (i_14 * 4));
          __6.x = (v__4.x*v__5.x);
          __6.y = (v__4.y*v__5.y);
          __6.z = (v__4.z*v__5.z);
          __6.w = (v__4.w*v__5.w);
        float4 v__6 = *(float4*)(rows + (i_14 * 4));
        __5.x = (__6.x+v__6.x);
        __5.y = (__6.y+v__6.y);
        __5.z = (__6.z+v__6.z);
        __5.w = (__6.w+v__6.w);
      *(float4*)(l + (i_14 * 4)) = __5;
    }
    #pragma unroll
    for (int i_15 = 0; i_15 < 12; ++i_15) {
      float4 __7;
        float4 v__7 = *(float4*)(o + (i_15 * 4));
        float4 v__8 = *(float4*)(alpha + (((i_15 / 6) * 8) + ((i_15 & 1) * 4)));
        __7.x = (v__7.x*v__8.x);
        __7.y = (v__7.y*v__8.y);
        __7.z = (v__7.z*v__8.z);
        __7.w = (v__7.w*v__8.w);
      *(float4*)(o + (i_15 * 4)) = __7;
    }
    __syncthreads();
    #pragma unroll
    for (int i_16 = 0; i_16 < 32; ++i_16) {
      ((half_t*)p)[((((((((((((int)threadIdx.x) & 127) >> 5) * 2048) + ((i_16 >> 4) * 1024)) + (((((int)threadIdx.x) & 31) >> 4) * 512)) + ((i_16 & 7) * 64)) + ((((((int)threadIdx.x) >> 7) + ((i_16 & 7) >> 2)) & 1) * 32)) + (((((i_16 & 15) >> 3) + ((i_16 & 3) >> 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 15) >> 3) + (i_16 & 1)) & 1) * 8)) + (((int)threadIdx.x) & 7))] = ((half_t)s[i_16]);
    }
    {
      half_t A_local_1[16];
      half_t B_local_1[24];
      __syncthreads();
      for (int ki_1 = 0; ki_1 < 4; ++ki_1) {
        for (int i_17 = 0; i_17 < 2; ++i_17) {
          *(uint4*)(A_local_1 + (i_17 * 8)) = *(uint4*)(((half_t*)p) + ((((((((((int)threadIdx.x) & 127) >> 5) * 2048) + (i_17 * 1024)) + ((((int)threadIdx.x) & 15) * 64)) + (((((((int)threadIdx.x) & 7) >> 2) + (ki_1 >> 1)) & 1) * 32)) + (((((((int)threadIdx.x) & 3) >> 1) + (ki_1 & 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + (((int)threadIdx.x) & 1)) & 1) * 8)));
        }
        for (int j_2 = 0; j_2 < 3; ++j_2) {
          for (int local_id = 0; local_id < 8; ++local_id) {
            B_local_1[((j_2 * 8) + local_id)] = ((half_t*)v)[(((((((((((((int)threadIdx.x) >> 7) * 3) + j_2) >> 1) * 2048) + (ki_1 * 512)) + (((((int)threadIdx.x) & 31) >> 4) * 256)) + (local_id * 32)) + ((((local_id >> 2) + ((((((int)threadIdx.x) >> 7) * 3) + j_2) & 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 15) >> 3) + ((local_id & 3) >> 1)) & 1) * 8)) + (((int)threadIdx.x) & 7))];
          }
        }
        for (int i_18 = 0; i_18 < 2; ++i_18) {
          for (int j_3 = 0; j_3 < 3; ++j_3) {
            {
      typedef __attribute__((__vector_size__(8 * sizeof(__fp16)))) __fp16 tl_v8f16;
      typedef __attribute__((__vector_size__(8 * sizeof(float)))) float tl_v8f32;
      *((tl_v8f32*)o + ((i_18 * 3) + j_3)) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(
          *((tl_v8f16*)A_local_1 + i_18),
          *((tl_v8f16*)B_local_1 + j_3),
          *((tl_v8f32*)o + ((i_18 * 3) + j_3)));
    };
          }
        }
      }
    }
  }
  #pragma unroll
  for (int i_19 = 0; i_19 < 48; ++i_19) {
    Y[(((((((((((((int)blockIdx.y) >> 3) * 393216) + (((int)blockIdx.x) * 98304)) + (((((int)threadIdx.x) & 127) >> 5) * 24576)) + ((i_19 / 24) * 12288)) + (((((int)threadIdx.x) & 31) >> 4) * 6144)) + ((i_19 & 7) * 768)) + ((((int)blockIdx.y) & 7) * 96)) + ((((int)threadIdx.x) >> 7) * 48)) + (((i_19 % 24) >> 3) * 16)) + (((int)threadIdx.x) & 15))] = ((half_t)(o[i_19] / l[(((i_19 / 24) * 8) + (i_19 & 7))]));
  }
}


#ifdef _WIN32
#define TL_EXPORT __declspec(dllexport)
#else
#define TL_EXPORT
#endif

#define ERROR_BUF_SIZE 1024
static char error_buf[ERROR_BUF_SIZE];

extern "C" TL_EXPORT const char* get_last_error() {
    return error_buf;
}

extern "C" TL_EXPORT int init() {
    error_buf[0] = '\0';
    
    int device_main_kernel = 0;
    hipError_t dev_res_main_kernel = hipGetDevice(&device_main_kernel);
    if (dev_res_main_kernel != hipSuccess) {
        snprintf(error_buf, ERROR_BUF_SIZE, "Failed to get HIP device for main_kernel: %s", hipGetErrorString(dev_res_main_kernel));
        return -1;
    }
    int max_smem_main_kernel = 0;
    hipError_t attr_res_main_kernel = hipDeviceGetAttribute(&max_smem_main_kernel, hipDeviceAttributeMaxSharedMemoryPerBlock, device_main_kernel);
    if (attr_res_main_kernel != hipSuccess || max_smem_main_kernel <= 0) {
        snprintf(error_buf, ERROR_BUF_SIZE, "Failed to query HIP max shared memory for main_kernel: %s", hipGetErrorString(attr_res_main_kernel));
        return -1;
    }
    if (67584 > max_smem_main_kernel) {
        snprintf(
            error_buf,
            ERROR_BUF_SIZE,
            "Requested dynamic shared memory %d exceeds device limit %d for main_kernel",
            67584,
            max_smem_main_kernel
        );
        return -1;
    }
    return 0;

    return 0;
}

extern "C" TL_EXPORT int call(half_t* __restrict__ Q, half_t* __restrict__ Y, hipStream_t stream=hipStreamDefault) {
	main_kernel<<<dim3(4, 1024, 1), dim3(256, 1, 1), 67584, stream>>>(Q, Y);
	TILELANG_CHECK_LAST_ERROR("main_kernel");

	return 0;
}

RECORD_ID: CASE-MINGPT-iter-9
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: False
CORRECT: False
TIMING_SAMPLES_STATUS: unavailable for failed evaluation

STATUS: failed; EXIT_CODE: 1

=== Event 56 | item.started | item_30 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-9 — High tuning child of iter-8 (571ca47)
BM128/BN64/256. Compile/init FAIL: actual allocation 67584 bytes includes 2048-byte reduction workspace, exceeding 65536. No latency. Next move V load after score/softmax so K and V lifetimes can share LDS; this is a concrete local buffer lifetime repair, not rejection of larger tile.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-9: record attention LDS limit failure'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();s=s.replace('                    v[i,j]=Q[bh//8,nb*64+i,1536+(bh%8)*96+j]\\n','');s=s.replace('                T.gemm(p,v,o)', '                for i,j in T.Parallel(64,96):\\n                    v[i,j]=Q[bh//8,nb*64+i,1536+(bh%8)*96+j]\\n                T.gemm(p,v,o)');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-10"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 57 | item.completed | item_30 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-9 — High tuning child of iter-8 (571ca47)
BM128/BN64/256. Compile/init FAIL: actual allocation 67584 bytes includes 2048-byte reduction workspace, exceeding 65536. No latency. Next move V load after score/softmax so K and V lifetimes can share LDS; this is a concrete local buffer lifetime repair, not rejection of larger tile.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-9: record attention LDS limit failure'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();s=s.replace('                    v[i,j]=Q[bh//8,nb*64+i,1536+(bh%8)*96+j]\\n','');s=s.replace('                T.gemm(p,v,o)', '                for i,j in T.Parallel(64,96):\\n                    v[i,j]=Q[bh//8,nb*64+i,1536+(bh%8)*96+j]\\n                T.gemm(p,v,o)');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-10"
OUTPUT:
[10:03:42] : Warning: T.vectorized loop over `local_id` with extent 8 is lowered as a serial loop because TileLang could not find a valid vectorization plan. Scalar accumulator updates inside the loop are a common cause; move reductions to T.unroll or T.serial if this is intended.
2026-09-26 10:03:41  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
Traceback (most recent call last):
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/scripts/rocm_bench.py", line 126, in <module>
    result=run(a.ref,a.solution,a.backend,a.reference_only)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/scripts/rocm_bench.py", line 77, in run
    candidate=model(load(solution,'benchmark_candidate'),ref)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/scripts/rocm_bench.py", line 53, in model
    instance = cls(*init).to('cuda')
               ^^^^^^^^^^
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/solution/ModelNew.py", line 88, in __init__
    self.attn=attention()
              ^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/__init__.py", line 539, in __call__
    kernel = self.compile(*args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/__init__.py", line 454, in compile
    kernel_result = compile(
                    ^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/__init__.py", line 162, in compile
    return cached(
           ^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cache/__init__.py", line 82, in cached
    return cache.cached(
           ^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cache/kernel_cache.py", line 428, in cached
    kernel = JITKernel(
             ^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/kernel.py", line 140, in __init__
    adapter = self._compile_and_create_adapter(func, out_idx)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/kernel.py", line 254, in _compile_and_create_adapter
    adapter = self._create_adapter_from_artifact(
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/kernel.py", line 340, in _create_adapter_from_artifact
    adapter = create_adapter(
              ^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/kernel.py", line 315, in create_adapter
    return adapter_cls(**kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/adapter/cython/adapter.py", line 140, in __init__
    raise RuntimeError(f"Initialization failed: {error_msg}")
RuntimeError: Initialization failed: Requested dynamic shared memory 67584 exceeds device limit 65536 for main_kernel
#include <hip/hip_runtime.h>
#include <tl_templates/hip/gemm.h>
#include <tl_templates/hip/copy.h>
#include <tl_templates/hip/reduce.h>
#include <tl_templates/hip/scan.h>
#include <tl_templates/hip/ldsm.h>
#include <tl_templates/hip/threadblock_swizzle.h>
#include <tl_templates/hip/debug.h>

extern "C" __global__ void __launch_bounds__(256) main_kernel(half_t* __restrict__ Q, half_t* __restrict__ Y) {
  extern __shared__ __align__(1024) uchar buf_dyn_shmem[];
  void* q = ((void*)((char*)buf_dyn_shmem + 0));
  void* p = ((void*)((char*)buf_dyn_shmem + 24576));
  void* k = ((void*)((char*)buf_dyn_shmem + 40960));
  void* v = ((void*)((char*)buf_dyn_shmem + 53248));
  void* workspace = ((void*)((char*)buf_dyn_shmem + 65536));
  void* workspace_1 = ((void*)((char*)buf_dyn_shmem + 66560));
  float m[16];
  float l[16];
  float o[48];
  float s[32];
  float old[16];
  float m_clear[16];
  float alpha[16];
  float rows[16];
  #pragma unroll
  for (int i = 0; i < 4; ++i) {
    float broadcast_var = -HUGE_VALF;
    *(float4*)(m + (i * 4)) = make_float4(broadcast_var, broadcast_var, broadcast_var, broadcast_var);
  }
  #pragma unroll
  for (int i_1 = 0; i_1 < 4; ++i_1) {
    float broadcast_var_1 = 0.000000e+00f;
    *(float4*)(l + (i_1 * 4)) = make_float4(broadcast_var_1, broadcast_var_1, broadcast_var_1, broadcast_var_1);
  }
  #pragma unroll
  for (int i_2 = 0; i_2 < 12; ++i_2) {
    float broadcast_var_2 = 0.000000e+00f;
    *(float4*)(o + (i_2 * 4)) = make_float4(broadcast_var_2, broadcast_var_2, broadcast_var_2, broadcast_var_2);
  }
  #pragma unroll
  for (int i_3 = 0; i_3 < 6; ++i_3) {
    *(uint4*)(((half_t*)q) + (((((((i_3 * 64) + (((int)threadIdx.x) >> 2)) % 3) * 4096) + ((((i_3 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 32)) + (((((((i_3 * 16) + (((int)threadIdx.x) >> 4)) % 6) / 3) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 16)) + (((((((i_3 * 32) + (((int)threadIdx.x) >> 3)) % 6) / 3) + (((int)threadIdx.x) & 1)) & 1) * 8))) = *(uint4*)(Q + ((((((((int)blockIdx.y) >> 3) * 1179648) + (((int)blockIdx.x) * 294912)) + ((((i_3 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 2304)) + ((((int)blockIdx.y) & 7) * 96)) + ((((i_3 * 256) + ((int)threadIdx.x)) % 12) * 8)));
  }
  for (int nb = 0; nb < ((((int)blockIdx.x) * 2) + 2); ++nb) {
    #pragma unroll
    for (int i_4 = 0; i_4 < 3; ++i_4) {
      *(uint4*)(((half_t*)k) + (((((((i_4 * 64) + (((int)threadIdx.x) >> 2)) % 3) * 2048) + ((((i_4 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 32)) + (((((((i_4 * 16) + (((int)threadIdx.x) >> 4)) % 6) / 3) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 16)) + (((((((i_4 * 32) + (((int)threadIdx.x) >> 3)) % 6) / 3) + (((int)threadIdx.x) & 1)) & 1) * 8))) = *(uint4*)(Q + (((((((((int)blockIdx.y) >> 3) * 1179648) + (nb * 147456)) + ((((i_4 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 2304)) + ((((int)blockIdx.y) & 7) * 96)) + ((((i_4 * 256) + ((int)threadIdx.x)) % 12) * 8)) + 768));
    }
    #pragma unroll
    for (int i_5 = 0; i_5 < 8; ++i_5) {
      float broadcast_var_3 = 0.000000e+00f;
      *(float4*)(s + (i_5 * 4)) = make_float4(broadcast_var_3, broadcast_var_3, broadcast_var_3, broadcast_var_3);
    }
    {
      half_t A_local[16];
      half_t B_local[16];
      __syncthreads();
      for (int ki = 0; ki < 6; ++ki) {
        for (int i_6 = 0; i_6 < 2; ++i_6) {
          *(uint4*)(A_local + (i_6 * 8)) = *(uint4*)(((half_t*)q) + (((((((ki >> 1) * 4096) + (((((int)threadIdx.x) & 127) >> 5) * 1024)) + (i_6 * 512)) + ((((int)threadIdx.x) & 15) * 32)) + (((((((int)threadIdx.x) & 7) >> 2) + (ki & 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 8)));
        }
        for (int j = 0; j < 2; ++j) {
          *(uint4*)(B_local + (j * 8)) = *(uint4*)(((half_t*)k) + (((((((ki >> 1) * 2048) + ((((int)threadIdx.x) >> 7) * 1024)) + (j * 512)) + ((((int)threadIdx.x) & 15) * 32)) + (((((((int)threadIdx.x) & 7) >> 2) + (ki & 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 8)));
        }
        for (int i_7 = 0; i_7 < 2; ++i_7) {
          for (int j_1 = 0; j_1 < 2; ++j_1) {
            {
      typedef __attribute__((__vector_size__(8 * sizeof(__fp16)))) __fp16 tl_v8f16;
      typedef __attribute__((__vector_size__(8 * sizeof(float)))) float tl_v8f32;
      *((tl_v8f32*)s + ((i_7 * 2) + j_1)) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(
          *((tl_v8f16*)A_local + i_7),
          *((tl_v8f16*)B_local + j_1),
          *((tl_v8f32*)s + ((i_7 * 2) + j_1)));
    };
          }
        }
      }
    }
    #pragma unroll
    for (int i_8 = 0; i_8 < 32; ++i_8) {
      float condval;
      if ((((((nb * 64) + ((((int)threadIdx.x) >> 7) * 32)) + (((i_8 & 15) >> 3) * 16)) + (((int)threadIdx.x) & 15)) <= (((((((int)blockIdx.x) * 128) + (((((int)threadIdx.x) & 127) >> 5) * 32)) + ((i_8 >> 4) * 16)) + (((((int)threadIdx.x) & 31) >> 4) * 8)) + (i_8 & 7)))) {
        condval = (s[i_8] * 1.020621e-01f);
      } else {
        condval = -HUGE_VALF;
      }
      s[i_8] = condval;
    }
    #pragma unroll
    for (int i_9 = 0; i_9 < 4; ++i_9) {
      *(float4*)(old + (i_9 * 4)) = *(float4*)(m + (i_9 * 4));
    }
    #pragma unroll
    for (int i_10 = 0; i_10 < 16; ++i_10) {
      m_clear[i_10] = -HUGE_VALF;
      #pragma unroll
      for (int rv = 0; rv < 2; ++rv) {
        m_clear[i_10] = max(m_clear[i_10], s[((((i_10 >> 3) * 16) + (rv * 8)) + (i_10 & 7))]);
      }
      __syncthreads();
      m_clear[i_10] = tl::AllReduce<tl::MaxOp, 256, 128, 0>::run(m_clear[i_10], (&(((float*)workspace_1)[0])));
      m_clear[i_10] = tl::AllReduce<tl::MaxOp, 16, 1, 0>::run(m_clear[i_10]);
      m[i_10] = max(m[i_10], m_clear[i_10]);
    }
    #pragma unroll
    for (int i_11 = 0; i_11 < 4; ++i_11) {
      float4 __1;
      float4 __2;
        float4 v_ = *(float4*)(old + (i_11 * 4));
        float4 v__1 = *(float4*)(m + (i_11 * 4));
        __2.x = (v_.x-v__1.x);
        __2.y = (v_.y-v__1.y);
        __2.z = (v_.z-v__1.z);
        __2.w = (v_.w-v__1.w);
      __1.x = __expf(__2.x);
      __1.y = __expf(__2.y);
      __1.z = __expf(__2.z);
      __1.w = __expf(__2.w);
      *(float4*)(alpha + (i_11 * 4)) = __1;
    }
    #pragma unroll
    for (int i_12 = 0; i_12 < 8; ++i_12) {
      float4 __3;
      float4 __4;
        float4 v__2 = *(float4*)(s + (i_12 * 4));
        float4 v__3 = *(float4*)(m + (((i_12 >> 2) * 8) + ((i_12 & 1) * 4)));
        __4.x = (v__2.x-v__3.x);
        __4.y = (v__2.y-v__3.y);
        __4.z = (v__2.z-v__3.z);
        __4.w = (v__2.w-v__3.w);
      __3.x = __expf(__4.x);
      __3.y = __expf(__4.y);
      __3.z = __expf(__4.z);
      __3.w = __expf(__4.w);
      *(float4*)(s + (i_12 * 4)) = __3;
    }
    #pragma unroll
    for (int i_13 = 0; i_13 < 16; ++i_13) {
      rows[i_13] = 0.000000e+00f;
      #pragma unroll
      for (int rv_1 = 0; rv_1 < 2; ++rv_1) {
        rows[i_13] = (rows[i_13] + s[((((i_13 >> 3) * 16) + (rv_1 * 8)) + (i_13 & 7))]);
      }
      __syncthreads();
      rows[i_13] = tl::AllReduce<tl::SumOp, 256, 128, 0>::run(rows[i_13], (&(((float*)workspace)[0])));
      rows[i_13] = tl::AllReduce<tl::SumOp, 16, 1, 0>::run(rows[i_13]);
    }
    #pragma unroll
    for (int i_14 = 0; i_14 < 4; ++i_14) {
      float4 __5;
        float4 __6;
          float4 v__4 = *(float4*)(l + (i_14 * 4));
          float4 v__5 = *(float4*)(alpha + (i_14 * 4));
          __6.x = (v__4.x*v__5.x);
          __6.y = (v__4.y*v__5.y);
          __6.z = (v__4.z*v__5.z);
          __6.w = (v__4.w*v__5.w);
        float4 v__6 = *(float4*)(rows + (i_14 * 4));
        __5.x = (__6.x+v__6.x);
        __5.y = (__6.y+v__6.y);
        __5.z = (__6.z+v__6.z);
        __5.w = (__6.w+v__6.w);
      *(float4*)(l + (i_14 * 4)) = __5;
    }
    #pragma unroll
    for (int i_15 = 0; i_15 < 12; ++i_15) {
      float4 __7;
        float4 v__7 = *(float4*)(o + (i_15 * 4));
        float4 v__8 = *(float4*)(alpha + (((i_15 / 6) * 8) + ((i_15 & 1) * 4)));
        __7.x = (v__7.x*v__8.x);
        __7.y = (v__7.y*v__8.y);
        __7.z = (v__7.z*v__8.z);
        __7.w = (v__7.w*v__8.w);
      *(float4*)(o + (i_15 * 4)) = __7;
    }
    __syncthreads();
    #pragma unroll
    for (int i_16 = 0; i_16 < 32; ++i_16) {
      ((half_t*)p)[((((((((((((int)threadIdx.x) & 127) >> 5) * 2048) + ((i_16 >> 4) * 1024)) + (((((int)threadIdx.x) & 31) >> 4) * 512)) + ((i_16 & 7) * 64)) + ((((((int)threadIdx.x) >> 7) + ((i_16 & 7) >> 2)) & 1) * 32)) + (((((i_16 & 15) >> 3) + ((i_16 & 3) >> 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 15) >> 3) + (i_16 & 1)) & 1) * 8)) + (((int)threadIdx.x) & 7))] = ((half_t)s[i_16]);
    }
    #pragma unroll
    for (int i_17 = 0; i_17 < 3; ++i_17) {
      *(uint4*)(((half_t*)v) + (((((((i_17 * 64) + (((int)threadIdx.x) >> 2)) % 3) * 2048) + ((((i_17 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 32)) + (((((((i_17 * 16) + (((int)threadIdx.x) >> 4)) % 6) / 3) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 16)) + (((((((i_17 * 32) + (((int)threadIdx.x) >> 3)) % 6) / 3) + (((int)threadIdx.x) & 1)) & 1) * 8))) = *(uint4*)(Q + (((((((((int)blockIdx.y) >> 3) * 1179648) + (nb * 147456)) + ((((i_17 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 2304)) + ((((int)blockIdx.y) & 7) * 96)) + ((((i_17 * 256) + ((int)threadIdx.x)) % 12) * 8)) + 1536));
    }
    {
      half_t A_local_1[16];
      half_t B_local_1[24];
      __syncthreads();
      for (int ki_1 = 0; ki_1 < 4; ++ki_1) {
        for (int i_18 = 0; i_18 < 2; ++i_18) {
          *(uint4*)(A_local_1 + (i_18 * 8)) = *(uint4*)(((half_t*)p) + ((((((((((int)threadIdx.x) & 127) >> 5) * 2048) + (i_18 * 1024)) + ((((int)threadIdx.x) & 15) * 64)) + (((((((int)threadIdx.x) & 7) >> 2) + (ki_1 >> 1)) & 1) * 32)) + (((((((int)threadIdx.x) & 3) >> 1) + (ki_1 & 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + (((int)threadIdx.x) & 1)) & 1) * 8)));
        }
        for (int j_2 = 0; j_2 < 3; ++j_2) {
          for (int local_id = 0; local_id < 8; ++local_id) {
            B_local_1[((j_2 * 8) + local_id)] = ((half_t*)v)[(((((((((((((int)threadIdx.x) >> 7) * 3) + j_2) >> 1) * 2048) + (ki_1 * 512)) + (((((int)threadIdx.x) & 31) >> 4) * 256)) + (local_id * 32)) + ((((local_id >> 2) + ((((((int)threadIdx.x) >> 7) * 3) + j_2) & 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 15) >> 3) + ((local_id & 3) >> 1)) & 1) * 8)) + (((int)threadIdx.x) & 7))];
          }
        }
        for (int i_19 = 0; i_19 < 2; ++i_19) {
          for (int j_3 = 0; j_3 < 3; ++j_3) {
            {
      typedef __attribute__((__vector_size__(8 * sizeof(__fp16)))) __fp16 tl_v8f16;
      typedef __attribute__((__vector_size__(8 * sizeof(float)))) float tl_v8f32;
      *((tl_v8f32*)o + ((i_19 * 3) + j_3)) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(
          *((tl_v8f16*)A_local_1 + i_19),
          *((tl_v8f16*)B_local_1 + j_3),
          *((tl_v8f32*)o + ((i_19 * 3) + j_3)));
    };
          }
        }
      }
    }
  }
  #pragma unroll
  for (int i_20 = 0; i_20 < 48; ++i_20) {
    Y[(((((((((((((int)blockIdx.y) >> 3) * 393216) + (((int)blockIdx.x) * 98304)) + (((((int)threadIdx.x) & 127) >> 5) * 24576)) + ((i_20 / 24) * 12288)) + (((((int)threadIdx.x) & 31) >> 4) * 6144)) + ((i_20 & 7) * 768)) + ((((int)blockIdx.y) & 7) * 96)) + ((((int)threadIdx.x) >> 7) * 48)) + (((i_20 % 24) >> 3) * 16)) + (((int)threadIdx.x) & 15))] = ((half_t)(o[i_20] / l[(((i_20 / 24) * 8) + (i_20 & 7))]));
  }
}


#ifdef _WIN32
#define TL_EXPORT __declspec(dllexport)
#else
#define TL_EXPORT
#endif

#define ERROR_BUF_SIZE 1024
static char error_buf[ERROR_BUF_SIZE];

extern "C" TL_EXPORT const char* get_last_error() {
    return error_buf;
}

extern "C" TL_EXPORT int init() {
    error_buf[0] = '\0';
    
    int device_main_kernel = 0;
    hipError_t dev_res_main_kernel = hipGetDevice(&device_main_kernel);
    if (dev_res_main_kernel != hipSuccess) {
        snprintf(error_buf, ERROR_BUF_SIZE, "Failed to get HIP device for main_kernel: %s", hipGetErrorString(dev_res_main_kernel));
        return -1;
    }
    int max_smem_main_kernel = 0;
    hipError_t attr_res_main_kernel = hipDeviceGetAttribute(&max_smem_main_kernel, hipDeviceAttributeMaxSharedMemoryPerBlock, device_main_kernel);
    if (attr_res_main_kernel != hipSuccess || max_smem_main_kernel <= 0) {
        snprintf(error_buf, ERROR_BUF_SIZE, "Failed to query HIP max shared memory for main_kernel: %s", hipGetErrorString(attr_res_main_kernel));
        return -1;
    }
    if (67584 > max_smem_main_kernel) {
        snprintf(
            error_buf,
            ERROR_BUF_SIZE,
            "Requested dynamic shared memory %d exceeds device limit %d for main_kernel",
            67584,
            max_smem_main_kernel
        );
        return -1;
    }
    return 0;

    return 0;
}

extern "C" TL_EXPORT int call(half_t* __restrict__ Q, half_t* __restrict__ Y, hipStream_t stream=hipStreamDefault) {
	main_kernel<<<dim3(4, 1024, 1), dim3(256, 1, 1), 67584, stream>>>(Q, Y);
	TILELANG_CHECK_LAST_ERROR("main_kernel");

	return 0;
}

RECORD_ID: CASE-MINGPT-iter-10
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: False
CORRECT: False
TIMING_SAMPLES_STATUS: unavailable for failed evaluation

STATUS: failed; EXIT_CODE: 1

=== Event 58 | item.started | item_31 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-10 — High repair child of iter-9 (d9e6ed7)
Delayed V load after score consumption, but compiler still allocates separate K/V buffers, same 67584-byte init failure. No latency. A specific remaining repair is explicit K/V buffer reuse (identical 64x96 shape), letting synchronization enforce overwrite after QK consumption.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-10: record unshared K/V lifetime allocation'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read().replace(\"            v=T.alloc_shared((64,96),'float16')\\n\",'').replace('                    v[i,j]=','                    k[i,j]=').replace('T.gemm(p,v,o)','T.gemm(p,k,o)');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-11"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 59 | item.completed | item_32 ===
The 128-query variant exceeded the device’s shared-memory limit once reduction workspace was included. Delaying the value load did not make the compiler reuse memory, so I’m testing explicit K/V buffer reuse after the score calculation finishes.

=== Event 60 | item.completed | item_31 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-10 — High repair child of iter-9 (d9e6ed7)
Delayed V load after score consumption, but compiler still allocates separate K/V buffers, same 67584-byte init failure. No latency. A specific remaining repair is explicit K/V buffer reuse (identical 64x96 shape), letting synchronization enforce overwrite after QK consumption.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-10: record unshared K/V lifetime allocation'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read().replace(\"            v=T.alloc_shared((64,96),'float16')\\n\",'').replace('                    v[i,j]=','                    k[i,j]=').replace('T.gemm(p,v,o)','T.gemm(p,k,o)');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-11"
OUTPUT:
[10:04:09] : Warning: T.vectorized loop over `local_id` with extent 8 is lowered as a serial loop because TileLang could not find a valid vectorization plan. Scalar accumulator updates inside the loop are a common cause; move reductions to T.unroll or T.serial if this is intended.
2026-09-26 10:04:07  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 10:04:15  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-11
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.93411914671137, 44.953800201416016, 44.82128314842114, 45.1242552518566, 45.23585778778672], "candidate": [5.735105656307908, 5.788878917694092, 5.843804949722372, 5.773159118558516, 5.800948967624759]}
RUNTIME: 5.788878917694092
REF_RUNTIME: 44.953800201416016
SPEEDUP: 7.765545080587494

STATUS: completed; EXIT_CODE: 0

=== Event 61 | item.started | item_33 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-11 — High repair child of iter-10 (18c82a9)
Explicit shared K/V buffer reuse makes BM128/BN64/256 feasible. Complete suite PASS; 5.788879 ms (5.735–5.844). Retain alternative, not promoted over iter-8; spread overlaps and median is worse. Next reduce key tile to 32 and query tile to 64, retaining shared buffer, to test lower LDS with 128 threads.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-11: validate 128-query attention with shared K/V storage'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();a=s.index('def attention');b=s.index('@tilelang.jit',a);part=s[a:b];part=part.replace('BM: int=128','BM: int=64, BN: int=32').replace('threads=256','threads=128');part=part.replace('(64,96)','(BN,96)').replace('(BM,64)','(BM,BN)').replace('BM,64)', 'BM,BN)').replace('nb*64','nb*BN');s=s[:a]+part+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-12"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 62 | item.completed | item_33 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-11 — High repair child of iter-10 (18c82a9)
Explicit shared K/V buffer reuse makes BM128/BN64/256 feasible. Complete suite PASS; 5.788879 ms (5.735–5.844). Retain alternative, not promoted over iter-8; spread overlaps and median is worse. Next reduce key tile to 32 and query tile to 64, retaining shared buffer, to test lower LDS with 128 threads.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-11: validate 128-query attention with shared K/V storage'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();a=s.index('def attention');b=s.index('@tilelang.jit',a);part=s[a:b];part=part.replace('BM: int=128','BM: int=64, BN: int=32').replace('threads=256','threads=128');part=part.replace('(64,96)','(BN,96)').replace('(BM,64)','(BM,BN)').replace('BM,64)', 'BM,BN)').replace('nb*64','nb*BN');s=s[:a]+part+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-12"
OUTPUT:
[10:04:45] : Warning: T.vectorized loop over `local_id` with extent 8 is lowered as a serial loop because TileLang could not find a valid vectorization plan. Scalar accumulator updates inside the loop are a common cause; move reductions to T.unroll or T.serial if this is intended.
2026-09-26 10:04:43  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 10:04:51  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-12
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.953800201416016, 45.05648190641923, 44.841814241594975, 44.99644768797523, 44.86242291639523], "candidate": [6.397365741396174, 6.390875816345215, 6.38489354691639, 6.409846810442244, 6.367250309338393]}
RUNTIME: 6.390875816345215
REF_RUNTIME: 44.953800201416016
SPEEDUP: 7.034059414273519

STATUS: completed; EXIT_CODE: 0

=== Event 63 | item.started | item_34 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-12 — High tuning child of iter-11 (46e217c)
BM64/BN32/128 attention with shared K/V. Complete suite PASS; 6.390876 ms (6.367–6.410). Rejected for speed. More loop/reduction overhead outweighs this tested resource balance. Restore iter-8 and diagnose: projections and attention have both changed materially since old costs, so next dominant opportunity is uncertain.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-12: record narrower attention key tile regression'
git restore --source=571ca47 -- solution
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/diagnose.py"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 64 | item.completed | item_35 ===
The 128-query version passed after buffer reuse, but did not beat the 64-query/256-thread candidate. The narrower key tile was also slower. I’ve restored the 5.68 ms candidate and am refreshing the cost breakdown before further tuning.

=== Event 65 | item.completed | item_34 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-12 — High tuning child of iter-11 (46e217c)
BM64/BN32/128 attention with shared K/V. Complete suite PASS; 6.390876 ms (6.367–6.410). Rejected for speed. More loop/reduction overhead outweighs this tested resource balance. Restore iter-8 and diagnose: projections and attention have both changed materially since old costs, so next dominant opportunity is uncertain.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-12: record narrower attention key tile regression'
git restore --source=571ca47 -- solution
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/diagnose.py"
OUTPUT:
2026-09-26 10:05:13  [TileLang:tilelang.cache.kernel_cache:WARNING] (kernel_cache.py:380): Found kernel 'main' in memory cache. For better performance, consider using `@tilelang.jit` instead of direct kernel caching.
2026-09-26 10:05:14  [TileLang:tilelang.cache.kernel_cache:WARNING] (kernel_cache.py:380): Found kernel 'main' in memory cache. For better performance, consider using `@tilelang.jit` instead of direct kernel caching.
2026-09-26 10:05:14  [TileLang:tilelang.cache.kernel_cache:WARNING] (kernel_cache.py:380): Found kernel 'main' in memory cache. For better performance, consider using `@tilelang.jit` instead of direct kernel caching.
2026-09-26 10:05:14  [TileLang:tilelang.cache.kernel_cache:WARNING] (kernel_cache.py:380): Found kernel 'main' in memory cache. For better performance, consider using `@tilelang.jit` instead of direct kernel caching.
2026-09-26 10:05:14  [TileLang:tilelang.cache.kernel_cache:WARNING] (kernel_cache.py:380): Found kernel 'main' in memory cache. For better performance, consider using `@tilelang.jit` instead of direct kernel caching.
2026-09-26 10:05:14  [TileLang:tilelang.cache.kernel_cache:WARNING] (kernel_cache.py:380): Found kernel 'main' in memory cache. For better performance, consider using `@tilelang.jit` instead of direct kernel caching.
Level: High | ranking measurement: {"path": "trajectory/20260926_100232_iter-8/output.txt", "valid": true, "reference_and_protocol_verified": true, "COMPILED": "True", "CORRECT": "True", "RUNTIME": "5.681180953979492", "REF_RUNTIME": "44.953800201416016", "SPEEDUP": "7.912756267678336"}
HIP call-group costs are diagnostic, not per-device-kernel ranking or hardware floors.

Call 4: 2.786430 ms | compiler_and_execution_matched
High: {"file": "solution/ModelNew.py", "line": 95, "function": "forward"} | Native: {"file": "kernel_4.cpp", "line": 1, "end_line": 210}
Tensor bindings: [{"value": "value_8", "shape": [128, 512, 2304], "dtype": "float16"}, {"value": "value_9", "shape": [128, 512, 768], "dtype": "float16"}]
High source: 93:         ph=self.cp(projection_weight)
94:         q=self.qkv(xh,wh,qkv_bias)
95:         y=self.attn(q)
96:         return self.proj(y,ph,projection_bias)
97:     __call__=forward
Native sites: 10: extern "C" __global__ void __launch_bounds__(256) main_kernel(half_t* __restrict__ Q, half_t* __restrict__ Y) {
11:   extern __shared__ __align__(1024) uchar buf_dyn_shmem[];
46:     __syncthreads();
60:       __syncthreads();
70:       *((tl_v8f32*)s + j_1) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(
99:       __syncthreads();
143:       __syncthreads();
175:     __syncthreads();
183:       __syncthreads();
195:       *((tl_v8f32*)o + j_3) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(

Call 3: 1.758769 ms | compiler_and_execution_matched
High: {"file": "solution/ModelNew.py", "line": 94, "function": "forward"} | Native: {"file": "kernel_3.cpp", "line": 1, "end_line": 63}
Tensor bindings: [{"value": "value_5", "shape": [128, 512, 768], "dtype": "float16"}, {"value": "value_6", "shape": [2304, 768], "dtype": "float16"}, {"value": "input_2", "shape": [2304], "dtype": "float32"}, {"value": "value_8", "shape": [128, 512, 2304], "dtype": "float16"}]
High source: 92:         wh=self.cw(qkv_weight)
93:         ph=self.cp(projection_weight)
94:         q=self.qkv(xh,wh,qkv_bias)
95:         y=self.attn(q)
96:         return self.proj(y,ph,projection_bias)
Native sites: 10: extern "C" __global__ void __launch_bounds__(256) main_kernel(float* __restrict__ B, half_t* __restrict__ W, half_t* __restrict__ X, half_t* __restrict__ Y) {
11:   extern __shared__ __align__(1024) uchar buf_dyn_shmem[];
22:     __syncthreads();
34:       __syncthreads();
47:       *((tl_v8f32*)c + ((i_4 * 4) + j_1)) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(

Call 5: 0.624367 ms | compiler_and_execution_matched
High: {"file": "solution/ModelNew.py", "line": 96, "function": "forward"} | Native: {"file": "kernel_5.cpp", "line": 1, "end_line": 63}
Tensor bindings: [{"value": "value_9", "shape": [128, 512, 768], "dtype": "float16"}, {"value": "value_7", "shape": [768, 768], "dtype": "float16"}, {"value": "input_4", "shape": [768], "dtype": "float32"}, {"value": "value_10", "shape": [128, 512, 768], "dtype": "float32"}]
High source: 94:         q=self.qkv(xh,wh,qkv_bias)
95:         y=self.attn(q)
96:         return self.proj(y,ph,projection_bias)
97:     __call__=forward
Native sites: 10: extern "C" __global__ void __launch_bounds__(256) main_kernel(float* __restrict__ B, half_t* __restrict__ W, half_t* __restrict__ X, float* __restrict__ Y) {
11:   extern __shared__ __align__(1024) uchar buf_dyn_shmem[];
22:     __syncthreads();
34:       __syncthreads();
47:       *((tl_v8f32*)c + ((i_4 * 4) + j_1)) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(

Call 0: 0.543246 ms | compiler_and_execution_matched
High: {"file": "solution/ModelNew.py", "line": 91, "function": "forward"} | Native: {"file": "kernel_0.cpp", "line": 1, "end_line": 26}
Tensor bindings: [{"value": "input_0", "shape": [128, 512, 768], "dtype": "float32"}, {"value": "value_5", "shape": [128, 512, 768], "dtype": "float16"}]
High source: 89:     def to(self,*args,**kwargs): return self
90:     def forward(self,x,qkv_weight,qkv_bias,projection_weight,projection_bias):
91:         xh=self.cx(x)
92:         wh=self.cw(qkv_weight)
93:         ph=self.cp(projection_weight)
Native sites: 10: extern "C" __global__ void __launch_bounds__(256) main_kernel(float* __restrict__ X, half_t* __restrict__ Y) {

Call 2: 0.039100 ms | compiler_and_execution_matched
High: {"file": "solution/ModelNew.py", "line": 93, "function": "forward"} | Native: {"file": "kernel_2.cpp", "line": 1, "end_line": 26}
Tensor bindings: [{"value": "input_3", "shape": [768, 768], "dtype": "float32"}, {"value": "value_7", "shape": [768, 768], "dtype": "float16"}]
High source: 91:         xh=self.cx(x)
92:         wh=self.cw(qkv_weight)
93:         ph=self.cp(projection_weight)
94:         q=self.qkv(xh,wh,qkv_bias)
95:         y=self.attn(q)
Native sites: 10: extern "C" __global__ void __launch_bounds__(256) main_kernel(float* __restrict__ X, half_t* __restrict__ Y) {

Call 1: 0.022840 ms | compiler_and_execution_matched
High: {"file": "solution/ModelNew.py", "line": 92, "function": "forward"} | Native: {"file": "kernel_1.cpp", "line": 1, "end_line": 26}
Tensor bindings: [{"value": "input_1", "shape": [2304, 768], "dtype": "float32"}, {"value": "value_6", "shape": [2304, 768], "dtype": "float16"}]
High source: 90:     def forward(self,x,qkv_weight,qkv_bias,projection_weight,projection_bias):
91:         xh=self.cx(x)
92:         wh=self.cw(qkv_weight)
93:         ph=self.cp(projection_weight)
94:         q=self.qkv(xh,wh,qkv_bias)
Native sites: 10: extern "C" __global__ void __launch_bounds__(256) main_kernel(float* __restrict__ X, half_t* __restrict__ Y) {
Dataflow: {"edges": [{"value": "value_5", "producer": 0, "consumer": 3}, {"value": "value_6", "producer": 1, "consumer": 3}, {"value": "value_8", "producer": 3, "consumer": 4}, {"value": "value_9", "producer": 4, "consumer": 5}, {"value": "value_7", "producer": 2, "consumer": 5}], "bindings": [{"call": 0, "params": [{"value": "input_0", "shape": [128, 512, 768], "dtype": "float32"}, {"value": "value_5", "shape": [128, 512, 768], "dtype": "float16"}]}, {"call": 1, "params": [{"value": "input_1", "shape": [2304, 768], "dtype": "float32"}, {"value": "value_6", "shape": [2304, 768], "dtype": "float16"}]}, {"call": 2, "params": [{"value": "input_3", "shape": [768, 768], "dtype": "float32"}, {"value": "value_7", "shape": [768, 768], "dtype": "float16"}]}, {"call": 3, "params": [{"value": "value_5", "shape": [128, 512, 768], "dtype": "float16"}, {"value": "value_6", "shape": [2304, 768], "dtype": "float16"}, {"value": "input_2", "shape": [2304], "dtype": "float32"}, {"value": "value_8", "shape": [128, 512, 2304], "dtype": "float16"}]}, {"call": 4, "params": [{"value": "value_8", "shape": [128, 512, 2304], "dtype": "float16"}, {"value": "value_9", "shape": [128, 512, 768], "dtype": "float16"}]}, {"call": 5, "params": [{"value": "value_9", "shape": [128, 512, 768], "dtype": "float16"}, {"value": "value_7", "shape": [768, 768], "dtype": "float16"}, {"value": "input_4", "shape": [768], "dtype": "float32"}, {"value": "value_10", "shape": [128, 512, 768], "dtype": "float32"}]}], "interpretation": "Observed tensor return/argument dependencies where available; allocation/ABI bindings are not proof of memory read/write or synchronization."}
Counter status: available | candidate association: unknown | missing: []
Device kernel: {"symbol": "main_kernel", "kernel_id": "39", "dispatches": 4, "metrics": {"GRBM_GUI_ACTIVE": {"median": 8091358.0, "min": 8034585.0, "max": 8121671.0, "samples": 4}, "SQ_INSTS_SALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_INSTS_VALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_WAVES": {"median": 65536.0, "min": 65536.0, "max": 65536.0, "samples": 4}}, "trace_samples": 4, "trace_median_ms": 2.81965, "candidate_calls": [0, 1, 2, 3, 4, 5], "association": "ambiguous_symbol"}
Device kernel: {"symbol": "main_kernel", "kernel_id": "38", "dispatches": 4, "metrics": {"GRBM_GUI_ACTIVE": {"median": 4649661.5, "min": 4644120.0, "max": 4665699.0, "samples": 4}, "SQ_INSTS_SALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_INSTS_VALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_WAVES": {"median": 73728.0, "min": 73728.0, "max": 74240.0, "samples": 4}}, "trace_samples": 4, "trace_median_ms": 1.804399, "candidate_calls": [0, 1, 2, 3, 4, 5], "association": "ambiguous_symbol"}
Device kernel: {"symbol": "void at::native::(anonymous namespace)::distribution_elementwise_grid_stride_kernel<float, 4, at::native::templates::cuda::normal_and_transform<float, float, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1})::{lambda(hiprandStatePhilox4_32_10*)#2}, at::native::(anonymous namespace)::distribution_nullary_kernel<float, float, HIP_vector_type<float, 4u>, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_and_transform<float, float, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1})::{lambda(hiprandStatePhilox4_32_10*)#2}, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_and_transform<float, float, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1})::{lambda(hiprandStatePhilox4_32_10*)#2} const&, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1})::{lambda(int, float)#1}>(long, at::PhiloxCudaState, at::native::templates::cuda::normal_and_transform<float, float, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1})::{lambda(hiprandStatePhilox4_32_10*)#2}, at::native::(anonymous namespace)::distribution_nullary_kernel<float, float, HIP_vector_type<float, 4u>, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_and_transform<float, float, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1})::{lambda(hiprandStatePhilox4_32_10*)#2}, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_and_transform<float, float, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}>(at::TensorIteratorBase&, at::CUDAGeneratorImpl*, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1})::{lambda(hiprandStatePhilox4_32_10*)#2} const&, at::native::templates::cuda::normal_kernel<at::CUDAGeneratorImpl*>(at::TensorBase const&, double, double, at::CUDAGeneratorImpl*)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1})::{lambda(int, float)#1})", "kernel_id": "4", "dispatches": 1, "metrics": {"GRBM_GUI_ACTIVE": {"median": 4334802.0, "min": 4334802.0, "max": 4334802.0, "samples": 1}, "SQ_INSTS_SALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 1}, "SQ_INSTS_VALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 1}, "SQ_WAVES": {"median": 2048.0, "min": 2048.0, "max": 2048.0, "samples": 1}}, "trace_samples": 1, "trace_median_ms": 1.530456, "candidate_calls": [], "association": "unmapped_or_setup"}
Device kernel: {"symbol": "main_kernel", "kernel_id": "40", "dispatches": 4, "metrics": {"GRBM_GUI_ACTIVE": {"median": 1617338.5, "min": 1601250.0, "max": 1623234.0, "samples": 4}, "SQ_INSTS_SALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_INSTS_VALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_WAVES": {"median": 24576.0, "min": 24576.0, "max": 24576.0, "samples": 4}}, "trace_samples": 4, "trace_median_ms": 0.6009260000000001, "candidate_calls": [0, 1, 2, 3, 4, 5], "association": "ambiguous_symbol"}
Device kernel: {"symbol": "main_kernel", "kernel_id": "35", "dispatches": 4, "metrics": {"GRBM_GUI_ACTIVE": {"median": 1540571.0, "min": 1532063.0, "max": 2387041.0, "samples": 4}, "SQ_INSTS_SALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_INSTS_VALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_WAVES": {"median": 393216.0, "min": 393216.0, "max": 393472.0, "samples": 4}}, "trace_samples": 4, "trace_median_ms": 0.5301855, "candidate_calls": [0, 1, 2, 3, 4, 5], "association": "ambiguous_symbol"}
Device kernel: {"symbol": "main_kernel", "kernel_id": "36", "dispatches": 4, "metrics": {"GRBM_GUI_ACTIVE": {"median": 61713.0, "min": 61339.0, "max": 84065.0, "samples": 4}, "SQ_INSTS_SALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_INSTS_VALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_WAVES": {"median": 13824.0, "min": 13824.0, "max": 13824.0, "samples": 4}}, "trace_samples": 4, "trace_median_ms": 0.01512, "candidate_calls": [0, 1, 2, 3, 4, 5], "association": "ambiguous_symbol"}
Device kernel: {"symbol": "main_kernel", "kernel_id": "37", "dispatches": 4, "metrics": {"GRBM_GUI_ACTIVE": {"median": 35171.0, "min": 34982.0, "max": 36972.0, "samples": 4}, "SQ_INSTS_SALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_INSTS_VALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 4}, "SQ_WAVES": {"median": 4608.0, "min": 4608.0, "max": 4608.0, "samples": 4}}, "trace_samples": 4, "trace_median_ms": 0.006, "candidate_calls": [0, 1, 2, 3, 4, 5], "association": "ambiguous_symbol"}
Device kernel: {"symbol": "__amd_rocclr_copyBuffer", "kernel_id": "21", "dispatches": 2, "metrics": {"GRBM_GUI_ACTIVE": {"median": 43856.5, "min": 41070.0, "max": 46643.0, "samples": 2}, "SQ_INSTS_SALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 2}, "SQ_INSTS_VALU": {"median": 0.0, "min": 0.0, "max": 0.0, "samples": 2}, "SQ_WAVES": {"median": 24.0, "min": 16.0, "max": 32.0, "samples": 2}}, "trace_samples": 2, "trace_median_ms": 0.00364, "candidate_calls": [], "association": "unmapped_or_setup"}
Full report: /home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/.cache/diagnosis/report.json

STATUS: completed; EXIT_CODE: 0

=== Event 66 | item.started | item_36 ===
COMMAND:
/bin/bash -lc "rg --files .cache/diagnosis | rg 'kernel_4.cpp"'$'"'"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 67 | item.completed | item_36 ===
COMMAND:
/bin/bash -lc "rg --files .cache/diagnosis | rg 'kernel_4.cpp"'$'"'"
OUTPUT:
.cache/diagnosis/lowering-hywifdln/solution/kernel_4.cpp

STATUS: completed; EXIT_CODE: 0

=== Event 68 | item.started | item_37 ===
COMMAND:
/bin/bash -lc "sed -n '10,80p' .cache/diagnosis/lowering-hywifdln/solution/kernel_4.cpp && sed -n '170,208p' .cache/diagnosis/lowering-hywifdln/solution/kernel_4.cpp"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 69 | item.completed | item_37 ===
COMMAND:
/bin/bash -lc "sed -n '10,80p' .cache/diagnosis/lowering-hywifdln/solution/kernel_4.cpp && sed -n '170,208p' .cache/diagnosis/lowering-hywifdln/solution/kernel_4.cpp"
OUTPUT:
extern "C" __global__ void __launch_bounds__(256) main_kernel(half_t* __restrict__ Q, half_t* __restrict__ Y) {
  extern __shared__ __align__(1024) uchar buf_dyn_shmem[];
  void* q = ((void*)((char*)buf_dyn_shmem + 0));
  void* k = ((void*)((char*)buf_dyn_shmem + 12288));
  void* v = ((void*)((char*)buf_dyn_shmem + 24576));
  void* p = ((void*)((char*)buf_dyn_shmem + 36864));
  void* workspace = ((void*)((char*)buf_dyn_shmem + 45056));
  void* workspace_1 = ((void*)((char*)buf_dyn_shmem + 46080));
  float m[8];
  float l[8];
  float o[24];
  float s[16];
  float old[8];
  float m_clear[8];
  float alpha[8];
  float rows[8];
  #pragma unroll
  for (int i = 0; i < 2; ++i) {
    float broadcast_var = -HUGE_VALF;
    *(float4*)(m + (i * 4)) = make_float4(broadcast_var, broadcast_var, broadcast_var, broadcast_var);
  }
  #pragma unroll
  for (int i_1 = 0; i_1 < 2; ++i_1) {
    float broadcast_var_1 = 0.000000e+00f;
    *(float4*)(l + (i_1 * 4)) = make_float4(broadcast_var_1, broadcast_var_1, broadcast_var_1, broadcast_var_1);
  }
  #pragma unroll
  for (int i_2 = 0; i_2 < 6; ++i_2) {
    float broadcast_var_2 = 0.000000e+00f;
    *(float4*)(o + (i_2 * 4)) = make_float4(broadcast_var_2, broadcast_var_2, broadcast_var_2, broadcast_var_2);
  }
  #pragma unroll
  for (int i_3 = 0; i_3 < 3; ++i_3) {
    *(uint4*)(((half_t*)q) + (((((((i_3 * 64) + (((int)threadIdx.x) >> 2)) % 3) * 2048) + ((((i_3 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 32)) + (((((((i_3 * 16) + (((int)threadIdx.x) >> 4)) % 6) / 3) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 16)) + (((((((i_3 * 32) + (((int)threadIdx.x) >> 3)) % 6) / 3) + (((int)threadIdx.x) & 1)) & 1) * 8))) = *(uint4*)(Q + ((((((((int)blockIdx.y) >> 3) * 1179648) + (((int)blockIdx.x) * 147456)) + ((((i_3 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 2304)) + ((((int)blockIdx.y) & 7) * 96)) + ((((i_3 * 256) + ((int)threadIdx.x)) % 12) * 8)));
  }
  for (int nb = 0; nb < (((int)blockIdx.x) + 1); ++nb) {
    __syncthreads();
    #pragma unroll
    for (int i_4 = 0; i_4 < 3; ++i_4) {
      *(uint4*)(((half_t*)k) + (((((((i_4 * 64) + (((int)threadIdx.x) >> 2)) % 3) * 2048) + ((((i_4 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 32)) + (((((((i_4 * 16) + (((int)threadIdx.x) >> 4)) % 6) / 3) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 16)) + (((((((i_4 * 32) + (((int)threadIdx.x) >> 3)) % 6) / 3) + (((int)threadIdx.x) & 1)) & 1) * 8))) = *(uint4*)(Q + (((((((((int)blockIdx.y) >> 3) * 1179648) + (nb * 147456)) + ((((i_4 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 2304)) + ((((int)blockIdx.y) & 7) * 96)) + ((((i_4 * 256) + ((int)threadIdx.x)) % 12) * 8)) + 768));
      *(uint4*)(((half_t*)v) + (((((((i_4 * 64) + (((int)threadIdx.x) >> 2)) % 3) * 2048) + ((((i_4 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 32)) + (((((((i_4 * 16) + (((int)threadIdx.x) >> 4)) % 6) / 3) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 16)) + (((((((i_4 * 32) + (((int)threadIdx.x) >> 3)) % 6) / 3) + (((int)threadIdx.x) & 1)) & 1) * 8))) = *(uint4*)(Q + (((((((((int)blockIdx.y) >> 3) * 1179648) + (nb * 147456)) + ((((i_4 * 64) + (((int)threadIdx.x) >> 2)) / 3) * 2304)) + ((((int)blockIdx.y) & 7) * 96)) + ((((i_4 * 256) + ((int)threadIdx.x)) % 12) * 8)) + 1536));
    }
    #pragma unroll
    for (int i_5 = 0; i_5 < 4; ++i_5) {
      float broadcast_var_3 = 0.000000e+00f;
      *(float4*)(s + (i_5 * 4)) = make_float4(broadcast_var_3, broadcast_var_3, broadcast_var_3, broadcast_var_3);
    }
    {
      half_t A_local[8];
      half_t B_local[16];
      __syncthreads();
      for (int ki = 0; ki < 6; ++ki) {
        *(uint4*)(A_local + 0) = *(uint4*)(((half_t*)q) + ((((((ki >> 1) * 2048) + (((((int)threadIdx.x) & 127) >> 5) * 512)) + ((((int)threadIdx.x) & 15) * 32)) + (((((((int)threadIdx.x) & 7) >> 2) + (ki & 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 8)));
        for (int j = 0; j < 2; ++j) {
          *(uint4*)(B_local + (j * 8)) = *(uint4*)(((half_t*)k) + (((((((ki >> 1) * 2048) + ((((int)threadIdx.x) >> 7) * 1024)) + (j * 512)) + ((((int)threadIdx.x) & 15) * 32)) + (((((((int)threadIdx.x) & 7) >> 2) + (ki & 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 8)));
        }
        for (int j_1 = 0; j_1 < 2; ++j_1) {
          {
      typedef __attribute__((__vector_size__(8 * sizeof(__fp16)))) __fp16 tl_v8f16;
      typedef __attribute__((__vector_size__(8 * sizeof(float)))) float tl_v8f32;
      *((tl_v8f32*)s + j_1) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(
          *((tl_v8f16*)A_local + 0),
          *((tl_v8f16*)B_local + j_1),
          *((tl_v8f32*)s + j_1));
    };
        }
      }
    }
    #pragma unroll
    for (int i_6 = 0; i_6 < 16; ++i_6) {
      float condval;
        __7.y = (v__7.y*v__8.y);
        __7.z = (v__7.z*v__8.z);
        __7.w = (v__7.w*v__8.w);
      *(float4*)(o + (i_13 * 4)) = __7;
    }
    __syncthreads();
    #pragma unroll
    for (int i_14 = 0; i_14 < 16; ++i_14) {
      ((half_t*)p)[((((((((((int)threadIdx.x) & 127) >> 4) * 512) + ((i_14 & 7) * 64)) + ((((((int)threadIdx.x) >> 7) + ((i_14 & 7) >> 2)) & 1) * 32)) + ((((i_14 >> 3) + ((i_14 & 3) >> 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 15) >> 3) + (i_14 & 1)) & 1) * 8)) + (((int)threadIdx.x) & 7))] = ((half_t)s[i_14]);
    }
    {
      half_t A_local_1[8];
      half_t B_local_1[24];
      __syncthreads();
      for (int ki_1 = 0; ki_1 < 4; ++ki_1) {
        *(uint4*)(A_local_1 + 0) = *(uint4*)(((half_t*)p) + (((((((((int)threadIdx.x) & 127) >> 5) * 1024) + ((((int)threadIdx.x) & 15) * 64)) + (((((((int)threadIdx.x) & 7) >> 2) + (ki_1 >> 1)) & 1) * 32)) + (((((((int)threadIdx.x) & 3) >> 1) + (ki_1 & 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + (((int)threadIdx.x) & 1)) & 1) * 8)));
        for (int j_2 = 0; j_2 < 3; ++j_2) {
          for (int local_id = 0; local_id < 8; ++local_id) {
            B_local_1[((j_2 * 8) + local_id)] = ((half_t*)v)[(((((((((((((int)threadIdx.x) >> 7) * 3) + j_2) >> 1) * 2048) + (ki_1 * 512)) + (((((int)threadIdx.x) & 31) >> 4) * 256)) + (local_id * 32)) + ((((local_id >> 2) + ((((((int)threadIdx.x) >> 7) * 3) + j_2) & 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 15) >> 3) + ((local_id & 3) >> 1)) & 1) * 8)) + (((int)threadIdx.x) & 7))];
          }
        }
        for (int j_3 = 0; j_3 < 3; ++j_3) {
          {
      typedef __attribute__((__vector_size__(8 * sizeof(__fp16)))) __fp16 tl_v8f16;
      typedef __attribute__((__vector_size__(8 * sizeof(float)))) float tl_v8f32;
      *((tl_v8f32*)o + j_3) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(
          *((tl_v8f16*)A_local_1 + 0),
          *((tl_v8f16*)B_local_1 + j_3),
          *((tl_v8f32*)o + j_3));
    };
        }
      }
    }
  }
  #pragma unroll
  for (int i_15 = 0; i_15 < 24; ++i_15) {
    Y[(((((((((((int)blockIdx.y) >> 3) * 393216) + (((int)blockIdx.x) * 49152)) + (((((int)threadIdx.x) & 127) >> 4) * 6144)) + ((i_15 & 7) * 768)) + ((((int)blockIdx.y) & 7) * 96)) + ((((int)threadIdx.x) >> 7) * 48)) + ((i_15 >> 3) * 16)) + (((int)threadIdx.x) & 15))] = ((half_t)(o[i_15] / l[(i_15 & 7)]));
  }
}

STATUS: completed; EXIT_CODE: 0

=== Event 70 | item.started | item_38 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

### Restore/diagnose iter-8
Restored exact High iter-8 source. Refreshed matched costs: attention 2.786 ms, QKV 1.759 ms, projection 0.624 ms, x conversion 0.543 ms. Native PV uses scalar strided LDS loads (8 scalar half elements per operand) while QK uses uint4 vector LDS loads. Hypothesis: store V transposed in shared memory to permit vector PV operand reads. Stay High because shared layout change is directly expressible; no Native control required yet.
EOF
git add solution ITERATIONS.md && git commit -m 'restore iter-8 for attention layout exploration'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read().replace(\"v=T.alloc_shared((64,96),'float16')\", \"v=T.alloc_shared((96,64),'float16')\").replace('v[i,j]=Q','v[j,i]=Q').replace('T.gemm(p,v,o)', 'T.gemm(p,v,o,transpose_B=True)');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-13"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 71 | item.completed | item_39 ===
Attention is now the largest stage at about 2.8 ms. Its generated value-multiply code uses scalar shared-memory loads, while the score multiply uses vector loads. I’m testing transposed value storage in shared memory to enable vector loads in both.

=== Event 72 | item.completed | item_38 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

### Restore/diagnose iter-8
Restored exact High iter-8 source. Refreshed matched costs: attention 2.786 ms, QKV 1.759 ms, projection 0.624 ms, x conversion 0.543 ms. Native PV uses scalar strided LDS loads (8 scalar half elements per operand) while QK uses uint4 vector LDS loads. Hypothesis: store V transposed in shared memory to permit vector PV operand reads. Stay High because shared layout change is directly expressible; no Native control required yet.
EOF
git add solution ITERATIONS.md && git commit -m 'restore iter-8 for attention layout exploration'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read().replace(\"v=T.alloc_shared((64,96),'float16')\", \"v=T.alloc_shared((96,64),'float16')\").replace('v[i,j]=Q','v[j,i]=Q').replace('T.gemm(p,v,o)', 'T.gemm(p,v,o,transpose_B=True)');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-13"
OUTPUT:
2026-09-26 10:05:51  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 10:05:59  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-13
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.953800201416016, 44.963240975136706, 45.08290090648288, 44.947073358402335, 44.81353261482293], "candidate": [5.83906888961792, 5.874931077263874, 5.819622923173252, 5.792386557483759, 5.901417528740954]}
RUNTIME: 5.83906888961792
REF_RUNTIME: 44.953800201416016
SPEEDUP: 7.69879599833897

STATUS: completed; EXIT_CODE: 0

=== Event 73 | item.started | item_40 ===
COMMAND:
/bin/bash -lc "sed -n '1,150p' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/gemm_op.py && rg -n 'class GemmWarpPolicy|FullRow|compute_warp_partition' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/tileop/gemm/gemm_base.py /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/ir.py"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 74 | item.completed | item_40 ===
COMMAND:
/bin/bash -lc "sed -n '1,150p' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/gemm_op.py && rg -n 'class GemmWarpPolicy|FullRow|compute_warp_partition' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/tileop/gemm/gemm_base.py /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/ir.py"
OUTPUT:
"""GEMM (General Matrix Multiplication) operators exposed on the TileLang language surface."""

from __future__ import annotations

from tilelang._typing import BufferLikeType, BarrierType
from tilelang.tileop.base import GemmWarpPolicy
import tilelang.language as T
from tilelang.layout import Layout
from tvm import tirx
from tilelang.utils.language import (
    to_buffer_region,
    retrieve_shape,
    retrieve_stride,
    retrieve_offset,
    prim_expr_equal,
)
from tilelang.language.utils import (
    _normalize_annotations,
    buffer_region_to_tile_region,
)


def _gemm_impl(
    op_key: str,
    A: BufferLikeType,
    B: BufferLikeType,
    C: BufferLikeType,
    transpose_A: bool = False,
    transpose_B: bool = False,
    policy: GemmWarpPolicy = GemmWarpPolicy.Square,
    clear_accum: bool = False,
    k_pack: int = 1,
    wg_wait: int = 0,
    mbar: BarrierType | None = None,
    annotations: dict | None = None,
) -> tirx.PrimExpr:
    """Shared GEMM implementation.

    Returns a call_intrin handle for the given op key.
    """
    if not (isinstance(k_pack, int) and not isinstance(k_pack, bool) and k_pack in (1, 2)):
        raise ValueError(f"T.gemm k_pack must be an int equal to 1 or 2, got {k_pack!r}")

    def legalize_arguments(arg: BufferLikeType | tirx.Var) -> BufferLikeType:
        """Convert let-bound variables to their corresponding buffers.

        Args:
            arg (Union[tirx.Buffer, tirx.Var]): Input argument to legalize

        Returns:
            Union[tirx.Buffer, tirx.Var]: The legalized argument
        """
        if isinstance(arg, tirx.Var) and T.has_let_value(arg):
            return T.get_let_value(arg).buffer
        return arg

    annotations = _normalize_annotations(annotations)

    A = legalize_arguments(A)
    B = legalize_arguments(B)
    C = legalize_arguments(C)
    mbar = legalize_arguments(mbar) if mbar is not None else None

    # Normalize A/B/C to BufferRegion for shape/stride/offset analysis
    A_region = to_buffer_region(A)
    B_region = to_buffer_region(B)
    C_region = to_buffer_region(C)

    A_shape = retrieve_shape(A_region)
    B_shape = retrieve_shape(B_region)
    C_shape = retrieve_shape(C_region)

    assert len(C_shape) >= 2, "current only support C as a 2D or higher-order tensor"
    assert len(A_shape) >= 2, "current only support A as a 2D or higher-order tensor"
    assert len(B_shape) >= 2, "current only support B as a 2D or higher-order tensor"
    for shape, name in ((A_shape, "A"), (B_shape, "B"), (C_shape, "C")):
        for i in range(len(shape) - 2):
            assert shape[i] == 1, (
                f"current only support {name} as a 2D or higher-order tensor with the last two dimensions being the matrix dimensions"
            )

    M, N = C_shape[-2], C_shape[-1]
    M_A = A_shape[-1] if transpose_A else A_shape[-2]
    K = A_shape[-2] if transpose_A else A_shape[-1]
    N_B = B_shape[-2] if transpose_B else B_shape[-1]
    K_B = B_shape[-1] if transpose_B else B_shape[-2]
    assert prim_expr_equal(M_A, M), f"T.gemm M shape check failed: M_A = {M_A}, M_C = {M}"
    assert prim_expr_equal(K, K_B), f"T.gemm K shape check failed: K_A = {K}, K_B = {K_B}"
    use_2cta = annotations.get("use_2cta", 0)
    if use_2cta:
        # In 2CTA mode each CTA holds half of B along N, so N_B should be N // 2
        assert prim_expr_equal(N_B * 2, N), f"T.gemm N shape check failed for 2CTA: N_B = {N_B}, expected N_C / 2 = {N} / 2"
    else:
        assert prim_expr_equal(N_B, N), f"T.gemm N shape check failed: N_B = {N_B}, N_C = {N}"

    for name, dim in (("M", M), ("N", N), ("K", K)):
        if not isinstance(dim, tirx.IntImm):
            raise ValueError(f"T.gemm requires static tile dimensions, but {name} is symbolic: {dim}")

    # Deprecated: every lowering consumes the complete operand BufferRegions,
    # so the serialized per-axis strides and final-axis offsets below are no
    # longer read in-tree and are NOT validated (the historic
    # ``A_offset[-2] == 0`` assertions are gone).  They are kept in the call
    # protocol only for out-of-tree consumers of the GemmNode fields.
    A_stride = retrieve_stride(A_region)
    B_stride = retrieve_stride(B_region)
    stride_a = A_stride[-2]
    stride_b = B_stride[-2]
    A_offset = retrieve_offset(A_region)
    B_offset = retrieve_offset(B_region)
    offset_a = A_offset[-1]
    offset_b = B_offset[-1]

    if mbar is not None:
        assert isinstance(mbar, (tirx.Buffer, tirx.BufferLoad)), (
            f"mbar for tcgen5mma must be a tirx.Buffer or tirx.BufferLoad, but got {type(mbar)}"
        )
        mbar = to_buffer_region(mbar, access_type="rw")
    C_coords = [r.min for r in C_region.region[-2:]]
    # Convert BufferRegion to tl.region calls for arguments
    A_arg = buffer_region_to_tile_region(A_region, "r", [r for r in A_shape])
    B_arg = buffer_region_to_tile_region(B_region, "r", [r for r in B_shape])
    C_arg = buffer_region_to_tile_region(C_region, "rw", [r for r in C_shape])
    # When mbar is None, pass a placeholder constant (0).
    # The C++ side checks if arg 16 is a BufferLoadNode before using it,
    # so a non-BufferLoad value will be correctly ignored.
    mbar_arg = mbar if mbar is not None else tirx.const(0, dtype="int32")
    return tirx.call_intrin(
        "handle",
        tirx.op.Op.get(op_key),
        A_arg,
        B_arg,
        C_arg,
        transpose_A,
        transpose_B,
        M,
        N,
        K,
        policy,
        clear_accum,
        stride_a,
        stride_b,
        offset_a,
        offset_b,
        k_pack,
        wg_wait,
        mbar_arg,
        C_coords[0],
        C_coords[1],
        annotations=annotations,
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/ir.py:26:class GemmWarpPolicy(Node, Scriptable):
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/ir.py:31:    def compute_warp_partition(self, M: int, N: int, block_size: int, target: Target, gemm_inst: str):
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/ir.py:42:    def compute_warp_partition(self, M: int, N: int, block_size: int, target: Target, gemm_inst: str):

STATUS: completed; EXIT_CODE: 0

=== Event 75 | item.started | item_41 ===
COMMAND:
/bin/bash -lc 'cat /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/tileop/base.py | head -180 && cat /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/ir.py | head -90'
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 76 | item.completed | item_41 ===
COMMAND:
/bin/bash -lc 'cat /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/tileop/base.py | head -180 && cat /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/ir.py | head -90'
OUTPUT:
from __future__ import annotations
from enum import IntEnum


class GemmWarpPolicy(IntEnum):
    """
    Enumeration for GEMM Warp Partitioning Policies.
    """

    Square = 0  # Balance warps evenly in a "square" aspect ratio.
    FullRow = 1  # Assign all warps to rows.
    FullCol = 2  # Assign all warps to columns.

    def is_square(self) -> bool:
        """
        Check if the policy is a square partitioning.

        Returns:
            bool: True if the policy is square, False otherwise.
        """
        return self == GemmWarpPolicy.Square

    def is_full_row(self) -> bool:
        """
        Check if the policy is a full row partitioning.

        Returns:
            bool: True if the policy is full row, False otherwise.
        """
        return self == GemmWarpPolicy.FullRow

    def is_full_col(self) -> bool:
        """
        Check if the policy is a full column partitioning.

        Returns:
            bool: True if the policy is full column, False otherwise.
        """
        return self == GemmWarpPolicy.FullCol

    @staticmethod
    def to_prime_factors(num):
        """
        Compute the prime factorization of a given number.

        Args:
            num (int): The number to factorize.

        Returns:
            list: A list of prime factors of the number.
        """
        factors = []
        i = 2
        # Find all prime factors up to the square root of the number.
        while i * i <= num:
            while num % i == 0:  # Check divisibility by `i`.
                factors.append(i)
                num //= i
            i += 1
        # If the remaining number is greater than 1, it's a prime factor.
        if num > 1:
            factors.append(num)
        return factors

    def compute_warp_partition(self, M, N, num_warps):
        """
        Compute the warp partition (m_warp, n_warp) based on the given policy.

        Args:
            M (int): The number of rows in the GEMM workload.
            N (int): The number of columns in the GEMM workload.
            num_warps (int): The total number of warps available.

        Returns:
            tuple: A tuple (m_warp, n_warp) representing the partitioning of warps.

        Raises:
            ValueError: If the policy is invalid or the partitioning fails.
            AssertionError: If M or N is not divisible by the required factor for FullRow or FullCol policies.
        """
        m_warp = 1  # Initial warp count for rows.
        n_warp = 1  # Initial warp count for columns.

        if self.is_full_row():
            # FullRow policy: Allocate all warps to rows.
            m_warp = num_warps
            n_warp = 1

            # If M cannot be evenly divided by m_warp*16, try to split remaining warps to N
            if M % (m_warp * 16) != 0:
                # Calculate how many warps we can use for M
                max_m_warps = M // 16
                m_warp = max_m_warps
                # Use remaining warps for N
                n_warp = num_warps // m_warp
                if n_warp == 0:
                    n_warp = 1

        elif self.is_full_col():
            # FullCol policy: Allocate all warps to columns.
            m_warp = 1
            n_warp = num_warps

            # If N cannot be evenly divided by n_warp*8, try to split remaining warps to M
            if N % (n_warp * 8) != 0:
                # Calculate how many warps we can use for N
                max_n_warps = N // 8
                n_warp = max_n_warps
                # Use remaining warps for M
                m_warp = num_warps // n_warp
                if m_warp == 0:
                    m_warp = 1

        elif self.is_square():
            # First calculate the maximum possible warps for each dimension
            max_m_warps = M // 16  # Each warp needs at least 16 elements in M
            max_n_warps = N // 8  # Each warp needs at least 8 elements in N

            # Calculate the ideal ratio of M/N warps based on the matrix dimensions
            ideal_ratio = 1.0
            if N > 0:
                ideal_ratio = float(M) / N

            # Start with a balanced initial guess
            m_warp = 1
            n_warp = 1

            # Try to find the best balanced partition
            best_m = 1
            best_n = 1
            best_balance = float("inf")

            # Try all possible combinations that satisfy the constraints
            for m in range(1, min(max_m_warps, num_warps) + 1):
                n = num_warps // m
                if n > max_n_warps:
                    continue
                if m * n != num_warps:
                    continue

                # Calculate how balanced this partition is
                m_per_warp = float(M) / (m * 16)
                n_per_warp = float(N) / (n * 8)
                balance = abs(m_per_warp / n_per_warp - ideal_ratio)

                if balance < best_balance:
                    best_balance = balance
                    best_m = m
                    best_n = n

            m_warp = best_m
            n_warp = best_n

        else:
            # Raise an error for unknown policies.
            raise ValueError(f"Unknown GemmWarpPolicy: {self}")

        return m_warp, n_warp

    @classmethod
    def from_warp_partition(cls, m_warp: int, n_warp: int) -> GemmWarpPolicy:
        """
        Determine the warp policy based on the given warp partitioning.

        Args:
            m_warp (int): Number of warps in the row dimension
            n_warp (int): Number of warps in the column dimension

        Returns:
            GemmWarpPolicy: The corresponding warp policy

        Examples:
            >>> GemmWarpPolicy.from_block_row_cols(4, 1)  # All warps in rows
            GemmWarpPolicy.FullRow
            >>> GemmWarpPolicy.from_block_row_cols(1, 4)  # All warps in columns
            GemmWarpPolicy.FullCol
            >>> GemmWarpPolicy.from_block_row_cols(2, 2)  # Balanced distribution
            GemmWarpPolicy.Square
        """
        if n_warp == 1 and m_warp > 1:
from tilelang import tvm as tvm
from tvm.ir.base import Node, SourceName, Span
from tvm.runtime import Scriptable
import tvm_ffi
from tvm.target import Target
from tilelang import _ffi_api


@tvm_ffi.register_object("tl.Fill")
class Fill(Node, Scriptable): ...


@tvm_ffi.register_object("tl.AtomicAdd")
class AtomicAdd(Node, Scriptable): ...


@tvm_ffi.register_object("tl.Copy")
class Copy(Node, Scriptable): ...


@tvm_ffi.register_object("tl.Im2Col")
class Im2ColOp(Node, Scriptable): ...


@tvm_ffi.register_object("tl.GemmWarpPolicy")
class GemmWarpPolicy(Node, Scriptable):
    policy_type: int
    m_warp: int
    n_warp: int

    def compute_warp_partition(self, M: int, N: int, block_size: int, target: Target, gemm_inst: str):
        _ffi_api.GemmWarpPolicyComputeWarpPartition(self, int(M), int(N), int(block_size), target, gemm_inst)
        return self.m_warp, self.n_warp


@tvm_ffi.register_object("tl.GemmSPWarpPolicy")
class GemmSPWarpPolicy(Node, Scriptable):
    policy_type: int
    m_warp: int
    n_warp: int

    def compute_warp_partition(self, M: int, N: int, block_size: int, target: Target, gemm_inst: str):
        _ffi_api.GemmSPWarpPolicyComputeWarpPartition(self, int(M), int(N), int(block_size), target, gemm_inst)
        return self.m_warp, self.n_warp


@tvm_ffi.register_object("tl.FinalizeReducerOp")
class FinalizeReducerOp(Node, Scriptable): ...


@tvm_ffi.register_object("tl.ParallelOp")
class ParallelOp(Node, Scriptable): ...


@tvm_ffi.register_object("tl.ReduceOp")
class ReduceOp(Node, Scriptable): ...


@tvm_ffi.register_object("tl.CumSumOp")
class CumSumOp(Node, Scriptable): ...


@tvm_ffi.register_object("tl.CumMaxOp")
class CumMaxOp(Node, Scriptable): ...


@tvm_ffi.register_object("tl.ReduceType")
class ReduceType(Node, Scriptable): ...


# ---------------------------------------------------------------------------
# Source span helpers
#
# tirx Stmt/Buffer/PrimFunc nodes carry a mutable `span` field that is
# reflected read-only to Python and never participates in structural
# equality/hashing. The functions below write spans during script parsing
# (see tilelang/language/eager/builder.py) and read them back from
# diagnostics, the LSP analyzer, and visualization tools.
# ---------------------------------------------------------------------------


_span_ffi_cache: dict = {}


def _span_ffi(name: str):
    # `tl.ir.*` names contain a dot and are therefore skipped by
    # `init_ffi_api`; fetch them from the global registry directly. Cache the
    # lookup: the stamping hot path calls this per IR node.
    f = _span_ffi_cache.get(name)
    if f is None:

STATUS: completed; EXIT_CODE: 0

=== Event 77 | item.started | item_42 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-13 — High layout child of restored iter-8 (ceba0f4)
Transpose V in LDS; PV now transposed-B WMMA. Complete suite PASS; 5.839069 ms (5.792–5.901). Not promoted; no demonstrated gain and possible global-to-LDS transpose cost. Retain layout for thread ownership experiment: assign full rows to each warp to remove cross-warp softmax reductions, using 128 threads and FullRow on both attention GEMMs.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-13: retain transposed V layout experiment'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();a=s.index('def attention');b=s.index('@tilelang.jit',a);part=s[a:b].replace('threads=256','threads=128').replace('transpose_B=True)', 'transpose_B=True,policy=T.GemmWarpPolicy.FullRow)');s=s[:a]+part+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-14"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 78 | item.completed | item_42 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-13 — High layout child of restored iter-8 (ceba0f4)
Transpose V in LDS; PV now transposed-B WMMA. Complete suite PASS; 5.839069 ms (5.792–5.901). Not promoted; no demonstrated gain and possible global-to-LDS transpose cost. Retain layout for thread ownership experiment: assign full rows to each warp to remove cross-warp softmax reductions, using 128 threads and FullRow on both attention GEMMs.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-13: retain transposed V layout experiment'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();a=s.index('def attention');b=s.index('@tilelang.jit',a);part=s[a:b].replace('threads=256','threads=128').replace('transpose_B=True)', 'transpose_B=True,policy=T.GemmWarpPolicy.FullRow)');s=s[:a]+part+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-14"
OUTPUT:
2026-09-26 10:06:47  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 10:06:55  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-14
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [45.21904817329199, 45.05480612331786, 44.77168262455912, 44.953800201416016, 44.82966768392758], "candidate": [8.428092837146432, 8.471289892576689, 8.463777800694626, 8.444855690002441, 8.413371625946107]}
RUNTIME: 8.444855690002441
REF_RUNTIME: 44.953800201416016
SPEEDUP: 5.323217098266722

STATUS: completed; EXIT_CODE: 0

=== Event 79 | item.started | item_43 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-14 — High tuning child of iter-13 (de28860)
Transposed V plus 128 threads/FullRow GEMMs. Complete suite PASS; 8.444856 ms (8.413–8.471). Rejected; row ownership does not compensate for this register/layout balance.
Next structural High sibling of iter-8: keep full 64x512 score tile in registers, normalize once, then accumulate PV in 32-key chunks. This avoids online max/sum/rescaling at every key block, at the cost of more score registers and computing masked scores. On-chip score storage avoids iter-1's global S/P traffic. Actual code parent will be restored iter-8.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-14: record FullRow attention regression'
git restore --source=571ca47 -- solution
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();a=s.index('@tilelang.jit(out_idx=[1]');b=s.index('@tilelang.jit(out_idx=[1]',a+1)
s=s[:a]+'''@tilelang.jit(out_idx=[1],target='hip',execution_backend='cython')
def attention():
    @T.prim_func
    def main(Q:T.Tensor((128,512,2304),'float16'), Y:T.Tensor((128,512,768),'float16')):
        with T.Kernel(8,1024,threads=256) as (mi,bh):
            q=T.alloc_shared((64,32),'float16')
            k=T.alloc_shared((512,32),'float16')
            v=T.alloc_shared((32,96),'float16')
            p=T.alloc_shared((64,32),'float16')
            s=T.alloc_fragment((64,512),'float32')
            o=T.alloc_fragment((64,96),'float32')
            m=T.alloc_fragment((64,),'float32')
            l=T.alloc_fragment((64,),'float32')
            T.clear(s)
            T.clear(o)
            for d in T.serial(3):
                for i,j in T.Parallel(64,32):
                    q[i,j]=Q[bh//8,mi*64+i,(bh%8)*96+d*32+j]
                for i,j in T.Parallel(512,32):
                    k[i,j]=Q[bh//8,i,768+(bh%8)*96+d*32+j]
                T.gemm(q,k,s,transpose_B=True)
            for i,j in T.Parallel(64,512):
                s[i,j]=T.if_then_else(j<=mi*64+i,s[i,j]*0.10206207261596575,-T.infinity('float32'))
            T.reduce_max(s,m,dim=1)
            for i,j in T.Parallel(64,512):
                s[i,j]=T.exp(s[i,j]-m[i])
            T.reduce_sum(s,l,dim=1)
            for nb in T.serial((mi+1)*2):
                for i,j in T.Parallel(64,32):
                    p[i,j]=s[i,nb*32+j]/l[i]
                for i,j in T.Parallel(32,96):
                    v[i,j]=Q[bh//8,nb*32+i,1536+(bh%8)*96+j]
                T.gemm(p,v,o)
            for i,j in T.Parallel(64,96):
                Y[bh//8,mi*64+i,(bh%8)*96+j]=o[i,j]
    return main

'''+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-15"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 80 | item.completed | item_43 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-14 — High tuning child of iter-13 (de28860)
Transposed V plus 128 threads/FullRow GEMMs. Complete suite PASS; 8.444856 ms (8.413–8.471). Rejected; row ownership does not compensate for this register/layout balance.
Next structural High sibling of iter-8: keep full 64x512 score tile in registers, normalize once, then accumulate PV in 32-key chunks. This avoids online max/sum/rescaling at every key block, at the cost of more score registers and computing masked scores. On-chip score storage avoids iter-1's global S/P traffic. Actual code parent will be restored iter-8.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-14: record FullRow attention regression'
git restore --source=571ca47 -- solution
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();a=s.index('@tilelang.jit(out_idx=[1]');b=s.index('@tilelang.jit(out_idx=[1]',a+1)
s=s[:a]+'''@tilelang.jit(out_idx=[1],target='hip',execution_backend='cython')
def attention():
    @T.prim_func
    def main(Q:T.Tensor((128,512,2304),'float16'), Y:T.Tensor((128,512,768),'float16')):
        with T.Kernel(8,1024,threads=256) as (mi,bh):
            q=T.alloc_shared((64,32),'float16')
            k=T.alloc_shared((512,32),'float16')
            v=T.alloc_shared((32,96),'float16')
            p=T.alloc_shared((64,32),'float16')
            s=T.alloc_fragment((64,512),'float32')
            o=T.alloc_fragment((64,96),'float32')
            m=T.alloc_fragment((64,),'float32')
            l=T.alloc_fragment((64,),'float32')
            T.clear(s)
            T.clear(o)
            for d in T.serial(3):
                for i,j in T.Parallel(64,32):
                    q[i,j]=Q[bh//8,mi*64+i,(bh%8)*96+d*32+j]
                for i,j in T.Parallel(512,32):
                    k[i,j]=Q[bh//8,i,768+(bh%8)*96+d*32+j]
                T.gemm(q,k,s,transpose_B=True)
            for i,j in T.Parallel(64,512):
                s[i,j]=T.if_then_else(j<=mi*64+i,s[i,j]*0.10206207261596575,-T.infinity('float32'))
            T.reduce_max(s,m,dim=1)
            for i,j in T.Parallel(64,512):
                s[i,j]=T.exp(s[i,j]-m[i])
            T.reduce_sum(s,l,dim=1)
            for nb in T.serial((mi+1)*2):
                for i,j in T.Parallel(64,32):
                    p[i,j]=s[i,nb*32+j]/l[i]
                for i,j in T.Parallel(32,96):
                    v[i,j]=Q[bh//8,nb*32+i,1536+(bh%8)*96+j]
                T.gemm(p,v,o)
            for i,j in T.Parallel(64,96):
                Y[bh//8,mi*64+i,(bh%8)*96+j]=o[i,j]
    return main

'''+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-15"
OUTPUT:
2026-09-26 10:08:06  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
Traceback (most recent call last):
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/scripts/rocm_bench.py", line 126, in <module>
    result=run(a.ref,a.solution,a.backend,a.reference_only)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/scripts/rocm_bench.py", line 77, in run
    candidate=model(load(solution,'benchmark_candidate'),ref)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/scripts/rocm_bench.py", line 53, in model
    instance = cls(*init).to('cuda')
               ^^^^^^^^^^
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/solution/ModelNew.py", line 78, in __init__
    self.attn=attention()
              ^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/__init__.py", line 539, in __call__
    kernel = self.compile(*args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/__init__.py", line 454, in compile
    kernel_result = compile(
                    ^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/__init__.py", line 162, in compile
    return cached(
           ^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cache/__init__.py", line 82, in cached
    return cache.cached(
           ^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/cache/kernel_cache.py", line 428, in cached
    kernel = JITKernel(
             ^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/kernel.py", line 140, in __init__
    adapter = self._compile_and_create_adapter(func, out_idx)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/kernel.py", line 253, in _compile_and_create_adapter
    self.artifact = self._compile_artifact(tilelang_func, pass_configs, compile_metadata)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/kernel.py", line 294, in _compile_artifact
    artifact = lower_with_context(
               ^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/engine/lower.py", line 165, in lower_with_context
    return _lower_with_context_impl(
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/engine/lower.py", line 184, in _lower_with_context_impl
    host_mod, device_mod, params, target, target_host = lower_to_host_device_ir(
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/engine/lower.py", line 136, in lower_to_host_device_ir
    mod = context.lower(mod)
          ^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/backend/module.py", line 214, in lower
    return self.module.lower(mod, self.target)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/backend/module.py", line 115, in lower
    return self.get_pipeline(target).lower(mod, target)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/backend/pass_pipeline/pipeline.py", line 31, in lower
    return self._lower(mod, target)
           ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/rocm/pipeline.py", line 44, in ROCMPassPipelineBody
    mod = tilelang.transform.LayoutInference()(mod)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/3rdparty/tvm/python/tvm/ir/transform.py", line 171, in __call__
    return _ffi_transform_api.RunPass(self, mod)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "python/tvm_ffi/cython/function.pxi", line 968, in tvm_ffi.core.Function.__call__
tvm.error.InternalError: Layout infer conflict between l and s in T.Parallel loop:
    loop Fragment((64, 32) -> (16,), replicate: 1, thread: nb // 8 * 128 + 112 + 15 + 1, forward_thread: nb // 8 * 128 + _i // 8 * 16 + _j % 16, forward_index: (_i % 8 * 2 + _j // 16,), thread_range: I.Range(0, 256))
    fragment Fragment((64,) -> (8,), replicate: 32, thread: 256, forward_thread: _rep % 2 * 128 + _i // 8 * 16 + _rep // 2, forward_index: (_i % 8,), thread_range: I.Range(0, 256))

RECORD_ID: CASE-MINGPT-iter-15
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: False
CORRECT: False
TIMING_SAMPLES_STATUS: unavailable for failed evaluation

STATUS: failed; EXIT_CODE: 1

=== Event 81 | item.completed | item_44 ===
The layout variants have not beaten the retained candidate. I’m testing a different attention structure: keep a full score tile on-chip, normalize it once, then accumulate values. This trades more registers and masked computation for fewer repeated softmax reductions.

=== Event 82 | item.started | item_45 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-15 — High structural child of iter-8 (571ca47)
Full-score on-chip attention attempt. Compile FAIL: layout inference conflicts for dynamically indexed score fragment and denominator in PV chunk loop. No latency. Repair by normalizing whole score fragment once into reused K shared buffer, then slicing shared storage; BM32/128 threads keeps full-score shared storage within 64 KiB.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-15: record full-score fragment indexing conflict'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();a=s.index('def attention');b=s.index('@tilelang.jit',a);part=s[a:b].replace('T.Kernel(8,1024,threads=256)', 'T.Kernel(16,1024,threads=128)').replace('(64,','(32,').replace('mi*64','mi*32').replace('T.serial((mi+1)*2)','T.serial(mi+1)');part=part.replace('            for nb in T.serial(mi+1):', '            for i,j in T.Parallel(32,512):\\n                k[j,i]=s[i,j]/l[i]\\n            for nb in T.serial(mi+1):').replace('p[i,j]=s[i,nb*32+j]/l[i]', 'p[i,j]=k[nb*32+j,i]');s=s[:a]+part+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-16"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 83 | item.completed | item_45 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-15 — High structural child of iter-8 (571ca47)
Full-score on-chip attention attempt. Compile FAIL: layout inference conflicts for dynamically indexed score fragment and denominator in PV chunk loop. No latency. Repair by normalizing whole score fragment once into reused K shared buffer, then slicing shared storage; BM32/128 threads keeps full-score shared storage within 64 KiB.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-15: record full-score fragment indexing conflict'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();a=s.index('def attention');b=s.index('@tilelang.jit',a);part=s[a:b].replace('T.Kernel(8,1024,threads=256)', 'T.Kernel(16,1024,threads=128)').replace('(64,','(32,').replace('mi*64','mi*32').replace('T.serial((mi+1)*2)','T.serial(mi+1)');part=part.replace('            for nb in T.serial(mi+1):', '            for i,j in T.Parallel(32,512):\\n                k[j,i]=s[i,j]/l[i]\\n            for nb in T.serial(mi+1):').replace('p[i,j]=s[i,nb*32+j]/l[i]', 'p[i,j]=k[nb*32+j,i]');s=s[:a]+part+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-16"
OUTPUT:
[10:08:39] : Warning: T.vectorized loop over `local_id` with extent 8 is lowered as a serial loop because TileLang could not find a valid vectorization plan. Scalar accumulator updates inside the loop are a common cause; move reductions to T.unroll or T.serial if this is intended.
2026-09-26 10:08:39  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 10:08:46  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-16
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.75212951623303, 44.9994820962326, 45.024195343167634, 44.80704557055184, 44.953800201416016], "candidate": [6.022663958613733, 5.993096445201578, 6.007559776306152, 6.015739620912108, 6.00025455024905]}
RUNTIME: 6.007559776306152
REF_RUNTIME: 44.953800201416016
SPEEDUP: 7.482871893961678

STATUS: completed; EXIT_CODE: 0

=== Event 84 | item.started | item_46 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-16 — High repair child of iter-15 (29825cd)
Full-score BM32/512, normalized probabilities reuse K LDS, PV chunks32. Complete suite PASS; 6.007560 ms (5.993–6.023). Retain structural alternative, not promoted. This is close enough to justify an intermediate decomposition: online softmax at 128-key granularity, QK reduced in 32-dimension slices and PV in 32-key slices. Larger normalization blocks reduce repeated reductions without full-sequence masked QK or BM32's low K/V reuse.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-16: retain correct full-score on-chip attention alternative'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();a=s.index('@tilelang.jit(out_idx=[1]');b=s.index('@tilelang.jit(out_idx=[1]',a+1)
s=s[:a]+'''@tilelang.jit(out_idx=[1],target='hip',execution_backend='cython')
def attention(BM: int=64, BN: int=128):
    @T.prim_func
    def main(Q:T.Tensor((128,512,2304),'float16'), Y:T.Tensor((128,512,768),'float16')):
        with T.Kernel(512//BM,1024,threads=256) as (mi,bh):
            q=T.alloc_shared((BM,32),'float16')
            k=T.alloc_shared((BN,32),'float16')
            v=T.alloc_shared((32,96),'float16')
            p=T.alloc_shared((BM,BN),'float16')
            s=T.alloc_fragment((BM,BN),'float32')
            o=T.alloc_fragment((BM,96),'float32')
            m=T.alloc_fragment((BM,),'float32')
            old=T.alloc_fragment((BM,),'float32')
            alpha=T.alloc_fragment((BM,),'float32')
            l=T.alloc_fragment((BM,),'float32')
            rows=T.alloc_fragment((BM,),'float32')
            T.fill(m,-T.infinity('float32'))
            T.clear(l)
            T.clear(o)
            for nb in T.serial(T.ceildiv((mi+1)*BM,BN)):
                T.clear(s)
                for d in T.serial(3):
                    for i,j in T.Parallel(BM,32):
                        q[i,j]=Q[bh//8,mi*BM+i,(bh%8)*96+d*32+j]
                    for i,j in T.Parallel(BN,32):
                        k[i,j]=Q[bh//8,nb*BN+i,768+(bh%8)*96+d*32+j]
                    T.gemm(q,k,s,transpose_B=True)
                for i,j in T.Parallel(BM,BN):
                    s[i,j]=T.if_then_else(nb*BN+j<=mi*BM+i,s[i,j]*0.10206207261596575,-T.infinity('float32'))
                T.copy(m,old)
                T.reduce_max(s,m,dim=1,clear=False)
                for i in T.Parallel(BM):
                    alpha[i]=T.exp(old[i]-m[i])
                for i,j in T.Parallel(BM,BN):
                    s[i,j]=T.exp(s[i,j]-m[i])
                T.reduce_sum(s,rows,dim=1)
                for i in T.Parallel(BM):
                    l[i]=l[i]*alpha[i]+rows[i]
                for i,j in T.Parallel(BM,96):
                    o[i,j]=o[i,j]*alpha[i]
                T.copy(s,p)
                for vb in T.serial(BN//32):
                    for i,j in T.Parallel(BM,32):
                        q[i,j]=p[i,vb*32+j]
                    for i,j in T.Parallel(32,96):
                        v[i,j]=Q[bh//8,nb*BN+vb*32+i,1536+(bh%8)*96+j]
                    T.gemm(q,v,o)
            for i,j in T.Parallel(BM,96):
                Y[bh//8,mi*BM+i,(bh%8)*96+j]=o[i,j]/l[i]
    return main

'''+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-17"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 85 | item.completed | item_47 ===
The full-score structure passed at 6.01 ms, close to the best but slower. I’m testing an intermediate version with 128-key normalization blocks to see whether it can reduce softmax overhead without the full-row version’s extra work.

=== Event 86 | item.completed | item_46 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-16 — High repair child of iter-15 (29825cd)
Full-score BM32/512, normalized probabilities reuse K LDS, PV chunks32. Complete suite PASS; 6.007560 ms (5.993–6.023). Retain structural alternative, not promoted. This is close enough to justify an intermediate decomposition: online softmax at 128-key granularity, QK reduced in 32-dimension slices and PV in 32-key slices. Larger normalization blocks reduce repeated reductions without full-sequence masked QK or BM32's low K/V reuse.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-16: retain correct full-score on-chip attention alternative'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();a=s.index('@tilelang.jit(out_idx=[1]');b=s.index('@tilelang.jit(out_idx=[1]',a+1)
s=s[:a]+'''@tilelang.jit(out_idx=[1],target='hip',execution_backend='cython')
def attention(BM: int=64, BN: int=128):
    @T.prim_func
    def main(Q:T.Tensor((128,512,2304),'float16'), Y:T.Tensor((128,512,768),'float16')):
        with T.Kernel(512//BM,1024,threads=256) as (mi,bh):
            q=T.alloc_shared((BM,32),'float16')
            k=T.alloc_shared((BN,32),'float16')
            v=T.alloc_shared((32,96),'float16')
            p=T.alloc_shared((BM,BN),'float16')
            s=T.alloc_fragment((BM,BN),'float32')
            o=T.alloc_fragment((BM,96),'float32')
            m=T.alloc_fragment((BM,),'float32')
            old=T.alloc_fragment((BM,),'float32')
            alpha=T.alloc_fragment((BM,),'float32')
            l=T.alloc_fragment((BM,),'float32')
            rows=T.alloc_fragment((BM,),'float32')
            T.fill(m,-T.infinity('float32'))
            T.clear(l)
            T.clear(o)
            for nb in T.serial(T.ceildiv((mi+1)*BM,BN)):
                T.clear(s)
                for d in T.serial(3):
                    for i,j in T.Parallel(BM,32):
                        q[i,j]=Q[bh//8,mi*BM+i,(bh%8)*96+d*32+j]
                    for i,j in T.Parallel(BN,32):
                        k[i,j]=Q[bh//8,nb*BN+i,768+(bh%8)*96+d*32+j]
                    T.gemm(q,k,s,transpose_B=True)
                for i,j in T.Parallel(BM,BN):
                    s[i,j]=T.if_then_else(nb*BN+j<=mi*BM+i,s[i,j]*0.10206207261596575,-T.infinity('float32'))
                T.copy(m,old)
                T.reduce_max(s,m,dim=1,clear=False)
                for i in T.Parallel(BM):
                    alpha[i]=T.exp(old[i]-m[i])
                for i,j in T.Parallel(BM,BN):
                    s[i,j]=T.exp(s[i,j]-m[i])
                T.reduce_sum(s,rows,dim=1)
                for i in T.Parallel(BM):
                    l[i]=l[i]*alpha[i]+rows[i]
                for i,j in T.Parallel(BM,96):
                    o[i,j]=o[i,j]*alpha[i]
                T.copy(s,p)
                for vb in T.serial(BN//32):
                    for i,j in T.Parallel(BM,32):
                        q[i,j]=p[i,vb*32+j]
                    for i,j in T.Parallel(32,96):
                        v[i,j]=Q[bh//8,nb*BN+vb*32+i,1536+(bh%8)*96+j]
                    T.gemm(q,v,o)
            for i,j in T.Parallel(BM,96):
                Y[bh//8,mi*BM+i,(bh%8)*96+j]=o[i,j]/l[i]
    return main

'''+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-17"
OUTPUT:
[10:09:50] : Warning: T.vectorized loop over `local_id` with extent 8 is lowered as a serial loop because TileLang could not find a valid vectorization plan. Scalar accumulator updates inside the loop are a common cause; move reductions to T.unroll or T.serial if this is intended.
2026-09-26 10:09:49  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 10:09:56  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-17
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [45.06114731286877, 44.99649442838497, 44.89766795998545, 44.88573404787717, 44.953800201416016], "candidate": [5.1585926281851595, 5.144232493588772, 5.146622380190643, 5.149131774902344, 5.206461903004946]}
RUNTIME: 5.149131774902344
REF_RUNTIME: 44.953800201416016
SPEEDUP: 8.730365072521103

STATUS: completed; EXIT_CODE: 0

=== Event 87 | item.started | item_48 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-17 — High structural child of iter-16 (14ec20c)
BM64/BN128/256 attention; QK reduction chunks32, one normalization per128 keys, PV chunks32; Q shared reused for probability subtiles. Complete suite PASS; 5.149132 ms (5.144–5.206), 8.7304x. Promoted. This supports larger normalization blocks rather than full-row or simple online tile enlargement. Next BN256 under same decomposition.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-17: chunk attention around 128-key normalization, 5.15 ms'
sed -i 's/BN: int=128):/BN: int=256):/' solution/ModelNew.py
bash scripts/bench.sh iter-18"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 88 | item.completed | item_48 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-17 — High structural child of iter-16 (14ec20c)
BM64/BN128/256 attention; QK reduction chunks32, one normalization per128 keys, PV chunks32; Q shared reused for probability subtiles. Complete suite PASS; 5.149132 ms (5.144–5.206), 8.7304x. Promoted. This supports larger normalization blocks rather than full-row or simple online tile enlargement. Next BN256 under same decomposition.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-17: chunk attention around 128-key normalization, 5.15 ms'
sed -i 's/BN: int=128):/BN: int=256):/' solution/ModelNew.py
bash scripts/bench.sh iter-18"
OUTPUT:
[10:10:20] : Warning: T.vectorized loop over `local_id` with extent 8 is lowered as a serial loop because TileLang could not find a valid vectorization plan. Scalar accumulator updates inside the loop are a common cause; move reductions to T.unroll or T.serial if this is intended.
2026-09-26 10:10:19  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 10:10:26  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-18
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.99072373091862, 44.80455675268593, 44.78596271394156, 44.953800201416016, 44.96245051644217], "candidate": [5.478033846579647, 5.501222058073038, 5.508458137512207, 5.508781661584878, 5.529592887842825]}
RUNTIME: 5.508458137512207
REF_RUNTIME: 44.953800201416016
SPEEDUP: 8.160868083081878

STATUS: completed; EXIT_CODE: 0

=== Event 89 | item.started | item_49 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-18 — High tuning child of iter-17 (4abe8a2)
BN256 instead of128 with chunked QK/PV. Complete suite PASS; 5.508458 ms (5.478–5.530). Rejected for speed; increased score/LDS footprint and masked work are tradeoffs, not isolated causes.
Next restore iter-17 and revisit transposed V: now V is only32 keys per copy and lifetime/resource balance differs from iter-13.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-18: record 256-key normalization regression'
git restore --source=4abe8a2 -- solution
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read().replace(\"v=T.alloc_shared((32,96),'float16')\", \"v=T.alloc_shared((96,32),'float16')\").replace('v[i,j]=Q','v[j,i]=Q').replace('T.gemm(q,v,o)', 'T.gemm(q,v,o,transpose_B=True)');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-19"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 90 | item.completed | item_50 ===
The 128-key structure improved runtime to 5.15 ms; 256 keys was slower. I’m revisiting transposed value storage with the new 32-key value chunks, since that changes the costs that made the earlier transpose unhelpful.

=== Event 91 | item.completed | item_49 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-18 — High tuning child of iter-17 (4abe8a2)
BN256 instead of128 with chunked QK/PV. Complete suite PASS; 5.508458 ms (5.478–5.530). Rejected for speed; increased score/LDS footprint and masked work are tradeoffs, not isolated causes.
Next restore iter-17 and revisit transposed V: now V is only32 keys per copy and lifetime/resource balance differs from iter-13.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-18: record 256-key normalization regression'
git restore --source=4abe8a2 -- solution
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read().replace(\"v=T.alloc_shared((32,96),'float16')\", \"v=T.alloc_shared((96,32),'float16')\").replace('v[i,j]=Q','v[j,i]=Q').replace('T.gemm(q,v,o)', 'T.gemm(q,v,o,transpose_B=True)');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-19"
OUTPUT:
2026-09-26 10:10:50  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 10:10:57  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-19
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [45.075047451430464, 44.92833334908403, 45.26845799043719, 44.918968043277815, 44.953800201416016], "candidate": [5.409598089243276, 5.4764224929532945, 5.510344785867469, 5.432324613308352, 5.467778205871582]}
RUNTIME: 5.467778205871582
REF_RUNTIME: 44.953800201416016
SPEEDUP: 8.221584436827065

STATUS: completed; EXIT_CODE: 0

=== Event 92 | item.started | item_51 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-19 — High layout child of iter-17 (4abe8a2)
Revisit transposed V for32-key PV chunks. Complete suite PASS; 5.467778 ms (5.410–5.510). Rejected for speed; no validated transpose gain in either tested decomposition.
Scheduler: lower retained iter-17. High has validated multiple structures; Native now exposes memory cache hints, instruction unrolling and load scheduling directly. Export exact iter-17, retaining its full High source in Git. Representation validation is required and is not a new optimization measurement.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-19: record transposed chunked V regression'
git restore --source=4abe8a2 -- solution
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/export.py --source solution/ModelNew.py --reference reference.py --destination lowered17 --record opt/attention/export17.json"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 93 | item.started | item_52 ===
COMMAND:
/bin/bash -lc "ls lowered17 && sed -n '1,230p' lowered17/kernel_4.cpp"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 94 | item.completed | item_52 ===
COMMAND:
/bin/bash -lc "ls lowered17 && sed -n '1,230p' lowered17/kernel_4.cpp"
OUTPUT:
ModelNew.py
__pycache__
kernel_0.cpp
kernel_0.tir
kernel_1.cpp
kernel_1.tir
kernel_2.cpp
kernel_2.tir
kernel_3.cpp
kernel_3.tir
kernel_4.cpp
kernel_4.tir
kernel_5.cpp
kernel_5.tir
program.json
runtime.py
#include <hip/hip_runtime.h>
#include <tl_templates/hip/gemm.h>
#include <tl_templates/hip/copy.h>
#include <tl_templates/hip/reduce.h>
#include <tl_templates/hip/scan.h>
#include <tl_templates/hip/ldsm.h>
#include <tl_templates/hip/threadblock_swizzle.h>
#include <tl_templates/hip/debug.h>

extern "C" __global__ void __launch_bounds__(256) main_kernel(half_t* __restrict__ Q, half_t* __restrict__ Y) {
  extern __shared__ __align__(1024) uchar buf_dyn_shmem[];
  void* p = ((void*)((char*)buf_dyn_shmem + 0));
  void* k = ((void*)((char*)buf_dyn_shmem + 16384));
  void* v = ((void*)((char*)buf_dyn_shmem + 24576));
  void* q = ((void*)((char*)buf_dyn_shmem + 30720));
  void* workspace = ((void*)((char*)buf_dyn_shmem + 34816));
  void* workspace_1 = ((void*)((char*)buf_dyn_shmem + 35840));
  float m[8];
  float l[8];
  float o[24];
  float s[32];
  float old[8];
  float m_clear[8];
  float alpha[8];
  float rows[8];
  #pragma unroll
  for (int i = 0; i < 2; ++i) {
    float broadcast_var = -HUGE_VALF;
    *(float4*)(m + (i * 4)) = make_float4(broadcast_var, broadcast_var, broadcast_var, broadcast_var);
  }
  #pragma unroll
  for (int i_1 = 0; i_1 < 2; ++i_1) {
    float broadcast_var_1 = 0.000000e+00f;
    *(float4*)(l + (i_1 * 4)) = make_float4(broadcast_var_1, broadcast_var_1, broadcast_var_1, broadcast_var_1);
  }
  #pragma unroll
  for (int i_2 = 0; i_2 < 6; ++i_2) {
    float broadcast_var_2 = 0.000000e+00f;
    *(float4*)(o + (i_2 * 4)) = make_float4(broadcast_var_2, broadcast_var_2, broadcast_var_2, broadcast_var_2);
  }
  for (int nb = 0; nb < ((((int)blockIdx.x) >> 1) + 1); ++nb) {
    #pragma unroll
    for (int i_3 = 0; i_3 < 8; ++i_3) {
      float broadcast_var_3 = 0.000000e+00f;
      *(float4*)(s + (i_3 * 4)) = make_float4(broadcast_var_3, broadcast_var_3, broadcast_var_3, broadcast_var_3);
    }
    for (int d = 0; d < 3; ++d) {
      __syncthreads();
      *(uint4*)(((half_t*)q) + ((((((int)threadIdx.x) >> 2) * 32) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 15) >> 3) + (((int)threadIdx.x) & 1)) & 1) * 8))) = *(uint4*)(Q + (((((((((int)blockIdx.y) >> 3) * 1179648) + (((int)blockIdx.x) * 147456)) + ((((int)threadIdx.x) >> 2) * 2304)) + ((((int)blockIdx.y) & 7) * 96)) + (d * 32)) + ((((int)threadIdx.x) & 3) * 8)));
      #pragma unroll
      for (int i_4 = 0; i_4 < 2; ++i_4) {
        *(uint4*)(((half_t*)k) + ((((i_4 * 2048) + ((((int)threadIdx.x) >> 2) * 32)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 15) >> 3) + (((int)threadIdx.x) & 1)) & 1) * 8))) = *(uint4*)(Q + (((((((((((int)blockIdx.y) >> 3) * 1179648) + (nb * 294912)) + (i_4 * 147456)) + ((((int)threadIdx.x) >> 2) * 2304)) + ((((int)blockIdx.y) & 7) * 96)) + (d * 32)) + ((((int)threadIdx.x) & 3) * 8)) + 768));
      }
      {
        half_t A_local[8];
        half_t B_local[32];
        __syncthreads();
        for (int ki = 0; ki < 2; ++ki) {
          *(uint4*)(A_local + 0) = *(uint4*)(((half_t*)q) + ((((((((int)threadIdx.x) & 127) >> 5) * 512) + ((((int)threadIdx.x) & 15) * 32)) + (((((((int)threadIdx.x) & 7) >> 2) + ki) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 8)));
          for (int j = 0; j < 4; ++j) {
            *(uint4*)(B_local + (j * 8)) = *(uint4*)(((half_t*)k) + ((((((((int)threadIdx.x) >> 7) * 2048) + (j * 512)) + ((((int)threadIdx.x) & 15) * 32)) + (((((((int)threadIdx.x) & 7) >> 2) + ki) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 8)));
          }
          for (int j_1 = 0; j_1 < 4; ++j_1) {
            {
      typedef __attribute__((__vector_size__(8 * sizeof(__fp16)))) __fp16 tl_v8f16;
      typedef __attribute__((__vector_size__(8 * sizeof(float)))) float tl_v8f32;
      *((tl_v8f32*)s + j_1) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(
          *((tl_v8f16*)A_local + 0),
          *((tl_v8f16*)B_local + j_1),
          *((tl_v8f32*)s + j_1));
    };
          }
        }
      }
    }
    #pragma unroll
    for (int i_5 = 0; i_5 < 32; ++i_5) {
      float condval;
      if ((((((nb * 128) + ((((int)threadIdx.x) >> 7) * 64)) + ((i_5 >> 3) * 16)) + (((int)threadIdx.x) & 15)) <= (((((int)blockIdx.x) * 64) + (((((int)threadIdx.x) & 127) >> 4) * 8)) + (i_5 & 7)))) {
        condval = (s[i_5] * 1.020621e-01f);
      } else {
        condval = -HUGE_VALF;
      }
      s[i_5] = condval;
    }
    #pragma unroll
    for (int i_6 = 0; i_6 < 2; ++i_6) {
      *(float4*)(old + (i_6 * 4)) = *(float4*)(m + (i_6 * 4));
    }
    #pragma unroll
    for (int i_7 = 0; i_7 < 8; ++i_7) {
      m_clear[i_7] = -HUGE_VALF;
      #pragma unroll
      for (int rv = 0; rv < 4; ++rv) {
        m_clear[i_7] = max(m_clear[i_7], s[((rv * 8) + i_7)]);
      }
      __syncthreads();
      m_clear[i_7] = tl::AllReduce<tl::MaxOp, 256, 128, 0>::run(m_clear[i_7], (&(((float*)workspace_1)[0])));
      m_clear[i_7] = tl::AllReduce<tl::MaxOp, 16, 1, 0>::run(m_clear[i_7]);
      m[i_7] = max(m[i_7], m_clear[i_7]);
    }
    #pragma unroll
    for (int i_8 = 0; i_8 < 2; ++i_8) {
      float4 __1;
      float4 __2;
        float4 v_ = *(float4*)(old + (i_8 * 4));
        float4 v__1 = *(float4*)(m + (i_8 * 4));
        __2.x = (v_.x-v__1.x);
        __2.y = (v_.y-v__1.y);
        __2.z = (v_.z-v__1.z);
        __2.w = (v_.w-v__1.w);
      __1.x = __expf(__2.x);
      __1.y = __expf(__2.y);
      __1.z = __expf(__2.z);
      __1.w = __expf(__2.w);
      *(float4*)(alpha + (i_8 * 4)) = __1;
    }
    #pragma unroll
    for (int i_9 = 0; i_9 < 8; ++i_9) {
      float4 __3;
      float4 __4;
        float4 v__2 = *(float4*)(s + (i_9 * 4));
        float4 v__3 = *(float4*)(m + ((i_9 & 1) * 4));
        __4.x = (v__2.x-v__3.x);
        __4.y = (v__2.y-v__3.y);
        __4.z = (v__2.z-v__3.z);
        __4.w = (v__2.w-v__3.w);
      __3.x = __expf(__4.x);
      __3.y = __expf(__4.y);
      __3.z = __expf(__4.z);
      __3.w = __expf(__4.w);
      *(float4*)(s + (i_9 * 4)) = __3;
    }
    #pragma unroll
    for (int i_10 = 0; i_10 < 8; ++i_10) {
      rows[i_10] = 0.000000e+00f;
      #pragma unroll
      for (int rv_1 = 0; rv_1 < 4; ++rv_1) {
        rows[i_10] = (rows[i_10] + s[((rv_1 * 8) + i_10)]);
      }
      __syncthreads();
      rows[i_10] = tl::AllReduce<tl::SumOp, 256, 128, 0>::run(rows[i_10], (&(((float*)workspace)[0])));
      rows[i_10] = tl::AllReduce<tl::SumOp, 16, 1, 0>::run(rows[i_10]);
    }
    #pragma unroll
    for (int i_11 = 0; i_11 < 2; ++i_11) {
      float4 __5;
        float4 __6;
          float4 v__4 = *(float4*)(l + (i_11 * 4));
          float4 v__5 = *(float4*)(alpha + (i_11 * 4));
          __6.x = (v__4.x*v__5.x);
          __6.y = (v__4.y*v__5.y);
          __6.z = (v__4.z*v__5.z);
          __6.w = (v__4.w*v__5.w);
        float4 v__6 = *(float4*)(rows + (i_11 * 4));
        __5.x = (__6.x+v__6.x);
        __5.y = (__6.y+v__6.y);
        __5.z = (__6.z+v__6.z);
        __5.w = (__6.w+v__6.w);
      *(float4*)(l + (i_11 * 4)) = __5;
    }
    #pragma unroll
    for (int i_12 = 0; i_12 < 6; ++i_12) {
      float4 __7;
        float4 v__7 = *(float4*)(o + (i_12 * 4));
        float4 v__8 = *(float4*)(alpha + ((i_12 & 1) * 4));
        __7.x = (v__7.x*v__8.x);
        __7.y = (v__7.y*v__8.y);
        __7.z = (v__7.z*v__8.z);
        __7.w = (v__7.w*v__8.w);
      *(float4*)(o + (i_12 * 4)) = __7;
    }
    __syncthreads();
    #pragma unroll
    for (int i_13 = 0; i_13 < 32; ++i_13) {
      ((half_t*)p)[(((((((((int)threadIdx.x) & 127) >> 4) * 1024) + ((i_13 & 7) * 128)) + ((((int)threadIdx.x) >> 7) * 64)) + ((i_13 >> 3) * 16)) + (((int)threadIdx.x) & 15))] = ((half_t)s[i_13]);
    }
    for (int vb = 0; vb < 4; ++vb) {
      __syncthreads();
      *(uint4*)(((half_t*)q) + ((((((int)threadIdx.x) >> 2) * 32) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 15) >> 3) + (((int)threadIdx.x) & 1)) & 1) * 8))) = *(uint4*)(((half_t*)p) + ((((((int)threadIdx.x) >> 2) * 128) + (vb * 32)) + ((((int)threadIdx.x) & 3) * 8)));
      #pragma unroll
      for (int i_14 = 0; i_14 < 3; ++i_14) {
        *(uint2*)(((half_t*)v) + ((((((((i_14 * 32) + (((int)threadIdx.x) >> 3)) % 3) * 1024) + ((((i_14 * 32) + (((int)threadIdx.x) >> 3)) / 3) * 32)) + (((((((i_14 * 8) + (((int)threadIdx.x) >> 5)) % 6) / 3) + ((((int)threadIdx.x) & 7) >> 2)) & 1) * 16)) + (((((((i_14 * 16) + (((int)threadIdx.x) >> 4)) % 6) / 3) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 8)) + ((((int)threadIdx.x) & 1) * 4))) = *(uint2*)(Q + ((((((((((int)blockIdx.y) >> 3) * 1179648) + (nb * 294912)) + (vb * 73728)) + ((((i_14 * 32) + (((int)threadIdx.x) >> 3)) / 3) * 2304)) + ((((int)blockIdx.y) & 7) * 96)) + ((((i_14 * 256) + ((int)threadIdx.x)) % 24) * 4)) + 1536));
      }
      {
        half_t A_local_1[8];
        half_t B_local_1[24];
        __syncthreads();
        for (int ki_1 = 0; ki_1 < 2; ++ki_1) {
          *(uint4*)(A_local_1 + 0) = *(uint4*)(((half_t*)q) + ((((((((int)threadIdx.x) & 127) >> 5) * 512) + ((((int)threadIdx.x) & 15) * 32)) + (((((((int)threadIdx.x) & 7) >> 2) + ki_1) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 8)));
          for (int j_2 = 0; j_2 < 3; ++j_2) {
            for (int local_id = 0; local_id < 8; ++local_id) {
              B_local_1[((j_2 * 8) + local_id)] = ((half_t*)v)[(((((((((((((int)threadIdx.x) >> 7) * 3) + j_2) >> 1) * 1024) + (ki_1 * 512)) + (((((int)threadIdx.x) & 31) >> 4) * 256)) + (local_id * 32)) + ((((local_id >> 2) + ((((((int)threadIdx.x) >> 7) * 3) + j_2) & 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 15) >> 3) + ((local_id & 3) >> 1)) & 1) * 8)) + (((int)threadIdx.x) & 7))];
            }
          }
          for (int j_3 = 0; j_3 < 3; ++j_3) {
            {
      typedef __attribute__((__vector_size__(8 * sizeof(__fp16)))) __fp16 tl_v8f16;
      typedef __attribute__((__vector_size__(8 * sizeof(float)))) float tl_v8f32;
      *((tl_v8f32*)o + j_3) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(
          *((tl_v8f16*)A_local_1 + 0),
          *((tl_v8f16*)B_local_1 + j_3),
          *((tl_v8f32*)o + j_3));
    };
          }
        }
      }
    }
  }
  #pragma unroll
  for (int i_15 = 0; i_15 < 24; ++i_15) {
    Y[(((((((((((int)blockIdx.y) >> 3) * 393216) + (((int)blockIdx.x) * 49152)) + (((((int)threadIdx.x) & 127) >> 4) * 6144)) + ((i_15 & 7) * 768)) + ((((int)blockIdx.y) & 7) * 96)) + ((((int)threadIdx.x) >> 7) * 48)) + ((i_15 >> 3) * 16)) + (((int)threadIdx.x) & 15))] = ((half_t)(o[i_15] / l[(i_15 & 7)]));
  }
}


#ifdef _WIN32
#define TL_EXPORT __declspec(dllexport)
#else
#define TL_EXPORT
#endif

#define ERROR_BUF_SIZE 1024
static char error_buf[ERROR_BUF_SIZE];

extern "C" TL_EXPORT const char* get_last_error() {
    return error_buf;
}

extern "C" TL_EXPORT int init() {

STATUS: completed; EXIT_CODE: 0

=== Event 95 | item.completed | item_53 ===
I’m exporting the 5.15 ms candidate to Native HIP for finer control. The generated softmax code performs a separate block-wide synchronization for each row fragment. The next experiment will batch those row reductions so they share barriers.

=== Event 96 | item.started | item_54 ===
COMMAND:
/bin/bash -lc 'tail -12 opt/attention/export17.validation.log && date -u'
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 97 | item.completed | item_54 ===
COMMAND:
/bin/bash -lc 'tail -12 opt/attention/export17.validation.log && date -u'
OUTPUT:
STAGE: Low
COMPILED: True
Sat Sep 26 10:11:58 UTC 2026

STATUS: completed; EXIT_CODE: 0

=== Event 98 | item.completed | item_51 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-19 — High layout child of iter-17 (4abe8a2)
Revisit transposed V for32-key PV chunks. Complete suite PASS; 5.467778 ms (5.410–5.510). Rejected for speed; no validated transpose gain in either tested decomposition.
Scheduler: lower retained iter-17. High has validated multiple structures; Native now exposes memory cache hints, instruction unrolling and load scheduling directly. Export exact iter-17, retaining its full High source in Git. Representation validation is required and is not a new optimization measurement.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-19: record transposed chunked V regression'
git restore --source=4abe8a2 -- solution
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/export.py --source solution/ModelNew.py --reference reference.py --destination lowered17 --record opt/attention/export17.json"
OUTPUT:
EXPORT VALIDATED: full 30-trial Native replay passed; see opt/attention/export17.json

STATUS: completed; EXIT_CODE: 0

=== Event 99 | historical_console_prelude |  ===
{"type": "historical_console_prelude", "text": "2026-09-26T10:12:35.576127Z ERROR codex_core::tools::router: error=exec_command failed: CreateProcess { message: \"Rejected(\\\"`/bin/bash -lc \\\\\\\"rm -rf solution && cp -r lowered17 solution\\\\ncat >> ITERATIONS.md <<'EOF'\\\\n\\\\n## Representation edge — iter-17 High to Native\\\\nExport \\\\\\\"'`opt/attention/export17.json`, exact High parent 4abe8a2. Required full 30-trial replay PASS. Replaced entire solution tree with validated export. No new ranking latency attributed to transfer. Native attention source shows eight…1608 chars truncated…adIdx.x & 15) == 0) ((float*)workspace)[(threadIdx.x >> 4) * 8 + r] = z;\\\\n    }\\\\n    __syncthreads();\\\\n    #pragma unroll\\\\n    for (int r = 0; r < 8; ++r) {\\\\n      int row = ((threadIdx.x & 127) >> 4) * 8 + r;\\\\n      rows[r] = ((float*)workspace)[row] + ((float*)workspace)[64 + row];\\\\n    }\\\\n'''+s[b:];open(p,'w').write(s)\\\\nPY\\\\nbash scripts/bench.sh iter-20\\\\\\\"` rejected: rm -f style commands are not permitted. Use a safer approach\\\")\" }", "source_event_line": 99}

=== Event 100 | item.started | item_55 ===
COMMAND:
/bin/bash -lc "mv solution opt/attention/high17 && cp -r lowered17 solution
cat >> ITERATIONS.md <<'EOF'

## Representation edge — iter-17 High to Native
Export opt/attention/export17.json, exact High parent 4abe8a2. Required full 30-trial replay PASS. Replaced entire solution tree with validated export. No new ranking latency attributed to transfer. Native attention source shows eight serial cross-warp reductions, each with block barriers, for both max and sum. Next batch all eight row reductions into one shared-memory exchange per reduction, preserving all data dependencies.
EOF
git add solution opt/attention ITERATIONS.md && git commit -m 'export iter-17 to validated Native HIP'"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 101 | item.completed | item_55 ===
COMMAND:
/bin/bash -lc "mv solution opt/attention/high17 && cp -r lowered17 solution
cat >> ITERATIONS.md <<'EOF'

## Representation edge — iter-17 High to Native
Export opt/attention/export17.json, exact High parent 4abe8a2. Required full 30-trial replay PASS. Replaced entire solution tree with validated export. No new ranking latency attributed to transfer. Native attention source shows eight serial cross-warp reductions, each with block barriers, for both max and sum. Next batch all eight row reductions into one shared-memory exchange per reduction, preserving all data dependencies.
EOF
git add solution opt/attention ITERATIONS.md && git commit -m 'export iter-17 to validated Native HIP'"
OUTPUT:
[codex/rocm-workspace 4727242] export iter-17 to validated Native HIP
 20 files changed, 1702 insertions(+), 98 deletions(-)
 create mode 100644 opt/attention/export17.json
 create mode 100644 opt/attention/export17.validation.json
 create mode 100644 opt/attention/export17.validation.log
 create mode 100644 opt/attention/high17/ModelNew.py
 create mode 100644 solution/kernel_0.cpp
 create mode 100644 solution/kernel_0.tir
 create mode 100644 solution/kernel_1.cpp
 create mode 100644 solution/kernel_1.tir
 create mode 100644 solution/kernel_2.cpp
 create mode 100644 solution/kernel_2.tir
 create mode 100644 solution/kernel_3.cpp
 create mode 100644 solution/kernel_3.tir
 create mode 100644 solution/kernel_4.cpp
 create mode 100644 solution/kernel_4.tir
 create mode 100644 solution/kernel_5.cpp
 create mode 100644 solution/kernel_5.tir
 create mode 100644 solution/program.json
 create mode 100644 solution/runtime.py

STATUS: completed; EXIT_CODE: 0

=== Event 102 | item.started | item_56 ===
COMMAND:
/bin/bash -lc "python - <<'PY'
p='solution/kernel_4.cpp';s=open(p).read();a=s.index('    #pragma unroll\\n    for (int i_7');b=s.index('    #pragma unroll\\n    for (int i_8',a)
s=s[:a]+'''    __syncthreads();
    #pragma unroll
    for (int r = 0; r < 8; ++r) {
      float z = -HUGE_VALF;
      #pragma unroll
      for (int c = 0; c < 4; ++c) z = fmaxf(z, s[c * 8 + r]);
      #pragma unroll
      for (int offset = 1; offset < 16; offset *= 2) z = fmaxf(z, __shfl_xor(z, offset, 16));
      if ((threadIdx.x & 15) == 0) ((float*)workspace_1)[(threadIdx.x >> 4) * 8 + r] = z;
    }
    __syncthreads();
    #pragma unroll
    for (int r = 0; r < 8; ++r) {
      int row = ((threadIdx.x & 127) >> 4) * 8 + r;
      m[r] = fmaxf(m[r], fmaxf(((float*)workspace_1)[row], ((float*)workspace_1)[64 + row]));
    }
'''+s[b:]
a=s.index('    #pragma unroll\\n    for (int i_10');b=s.index('    #pragma unroll\\n    for (int i_11',a)
s=s[:a]+'''    __syncthreads();
    #pragma unroll
    for (int r = 0; r < 8; ++r) {
      float z = 0.0f;
      #pragma unroll
      for (int c = 0; c < 4; ++c) z += s[c * 8 + r];
      #pragma unroll
      for (int offset = 1; offset < 16; offset *= 2) z += __shfl_xor(z, offset, 16);
      if ((threadIdx.x & 15) == 0) ((float*)workspace)[(threadIdx.x >> 4) * 8 + r] = z;
    }
    __syncthreads();
    #pragma unroll
    for (int r = 0; r < 8; ++r) {
      int row = ((threadIdx.x & 127) >> 4) * 8 + r;
      rows[r] = ((float*)workspace)[row] + ((float*)workspace)[64 + row];
    }
'''+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-20"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 103 | item.started | item_57 ===
COMMAND:
/bin/bash -lc 'cat solution/program.json | head -70 && cat solution/ModelNew.py'
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 104 | item.completed | item_57 ===
COMMAND:
/bin/bash -lc 'cat solution/program.json | head -70 && cat solution/ModelNew.py'
OUTPUT:
{
  "inputs": [
    "input_0",
    "input_1",
    "input_2",
    "input_3",
    "input_4"
  ],
  "output": "value_10",
  "kernels": [
    {
      "source": "kernel_0.cpp",
      "params": [
        {
          "value": "input_0",
          "shape": [
            128,
            512,
            768
          ],
          "dtype": "float32"
        },
        {
          "value": "value_5",
          "shape": [
            128,
            512,
            768
          ],
          "dtype": "float16"
        }
      ],
      "compiler": [
        "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc",
        "-std=c++17",
        "-O3",
        "-fPIC",
        "--shared",
        "--offload-arch=gfx1201",
        "-I/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/3rdparty/../src"
      ]
    },
    {
      "source": "kernel_1.cpp",
      "params": [
        {
          "value": "input_1",
          "shape": [
            2304,
            768
          ],
          "dtype": "float32"
        },
        {
          "value": "value_6",
          "shape": [
            2304,
            768
          ],
          "dtype": "float16"
        }
      ],
      "compiler": [
        "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc",
        "-std=c++17",
        "-O3",
        "-fPIC",
        "--shared",
        "--offload-arch=gfx1201",
        "-I/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/3rdparty/../src"
from pathlib import Path
import importlib.util

class ModelNew:
    def __init__(self, *args):
        root = Path(__file__).resolve().parent
        spec = importlib.util.spec_from_file_location("candidate_runtime", root / "runtime.py")
        module = importlib.util.module_from_spec(spec)
        spec.loader.exec_module(module)
        self.runtime = module.Runtime(root)

    def to(self, *args, **kwargs):
        return self

    def __call__(self, *args):
        return self.forward(*args)

    def forward(self, *args):
        return self.runtime.forward(*args)

STATUS: completed; EXIT_CODE: 0

=== Event 105 | item.started | item_58 ===
COMMAND:
/bin/bash -lc 'ls /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/llvm/bin/llvm-objdump /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/llvm-objdump 2>/dev/null && date -u'
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 106 | item.completed | item_58 ===
COMMAND:
/bin/bash -lc 'ls /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/llvm/bin/llvm-objdump /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/llvm-objdump 2>/dev/null && date -u'
OUTPUT:
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/llvm/bin/llvm-objdump

STATUS: failed; EXIT_CODE: 2

=== Event 107 | item.completed | item_56 ===
COMMAND:
/bin/bash -lc "python - <<'PY'
p='solution/kernel_4.cpp';s=open(p).read();a=s.index('    #pragma unroll\\n    for (int i_7');b=s.index('    #pragma unroll\\n    for (int i_8',a)
s=s[:a]+'''    __syncthreads();
    #pragma unroll
    for (int r = 0; r < 8; ++r) {
      float z = -HUGE_VALF;
      #pragma unroll
      for (int c = 0; c < 4; ++c) z = fmaxf(z, s[c * 8 + r]);
      #pragma unroll
      for (int offset = 1; offset < 16; offset *= 2) z = fmaxf(z, __shfl_xor(z, offset, 16));
      if ((threadIdx.x & 15) == 0) ((float*)workspace_1)[(threadIdx.x >> 4) * 8 + r] = z;
    }
    __syncthreads();
    #pragma unroll
    for (int r = 0; r < 8; ++r) {
      int row = ((threadIdx.x & 127) >> 4) * 8 + r;
      m[r] = fmaxf(m[r], fmaxf(((float*)workspace_1)[row], ((float*)workspace_1)[64 + row]));
    }
'''+s[b:]
a=s.index('    #pragma unroll\\n    for (int i_10');b=s.index('    #pragma unroll\\n    for (int i_11',a)
s=s[:a]+'''    __syncthreads();
    #pragma unroll
    for (int r = 0; r < 8; ++r) {
      float z = 0.0f;
      #pragma unroll
      for (int c = 0; c < 4; ++c) z += s[c * 8 + r];
      #pragma unroll
      for (int offset = 1; offset < 16; offset *= 2) z += __shfl_xor(z, offset, 16);
      if ((threadIdx.x & 15) == 0) ((float*)workspace)[(threadIdx.x >> 4) * 8 + r] = z;
    }
    __syncthreads();
    #pragma unroll
    for (int r = 0; r < 8; ++r) {
      int row = ((threadIdx.x & 127) >> 4) * 8 + r;
      rows[r] = ((float*)workspace)[row] + ((float*)workspace)[64 + row];
    }
'''+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-20"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-20
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.953800201416016, 45.2415032696695, 44.82642940508098, 44.94564423171305, 44.97181211919941], "candidate": [5.365402027582029, 5.467944990090361, 5.3117173486304825, 5.365053176879883, 5.35083629818234]}
RUNTIME: 5.365053176879883
REF_RUNTIME: 44.953800201416016
SPEEDUP: 8.379003659300071

STATUS: completed; EXIT_CODE: 0

=== Event 108 | item.started | item_59 ===
COMMAND:
/bin/bash -lc "cat opt/attention/export17.validation.json && sed -n '1,100p' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/reduce.h"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 109 | item.completed | item_59 ===
COMMAND:
/bin/bash -lc "cat opt/attention/export17.validation.json && sed -n '1,100p' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/reduce.h"
OUTPUT:
{
  "record_kind": "historical_export_validation_summary",
  "correct": true
}#pragma once

#include "common.h"

namespace tl {

struct SumOp {
  template <typename T> TL_DEVICE T operator()(T const &x, T const &y) {
    return x + y;
  }
};

struct MaxOp {
  template <typename T> TL_DEVICE T operator()(T const &x, T const &y) {
    return tl::max(x, y);
  }
};

struct MinOp {
  template <typename T> TL_DEVICE T operator()(T const &x, T const &y) {
    return tl::min(x, y);
  }
};

struct BitAndOp {
  template <typename T> TL_DEVICE T operator()(T const &x, T const &y) {
    return x & y;
  }
};

struct BitOrOp {
  template <typename T> TL_DEVICE T operator()(T const &x, T const &y) {
    return x | y;
  }
};

struct BitXorOp {
  template <typename T> TL_DEVICE T operator()(T const &x, T const &y) {
    return x ^ y;
  }
};

template <class Reducer, int Threads, bool UseAbs, bool NeedAccumulate>
struct SharedReduceWarp {
  template <typename T>
  static TL_DEVICE void run(const T *__restrict__ src, T *__restrict__ dst,
                            int total_dest, int reduce_extent, int tail,
                            T init_value) {
    if (total_dest <= 0 || reduce_extent <= 0)
      return;
    const int kWarpSize = __builtin_amdgcn_wavefrontsize();
    if (Threads % kWarpSize != 0)
      return;
    const int tid = threadIdx.x;
    const int warp_id = tid / kWarpSize;
    const int lane = tid % kWarpSize;
    const int num_warps = Threads / kWarpSize;

    for (int dest_idx = warp_id; dest_idx < total_dest; dest_idx += num_warps) {
      const int prefix = tail == 1 ? dest_idx : dest_idx / tail;
      const int suffix = tail == 1 ? 0 : dest_idx % tail;
      const int src_base = (prefix * reduce_extent) * tail + suffix;
      const int dst_index = prefix * tail + suffix;

      T partial = init_value;
      for (int rv = lane; rv < reduce_extent; rv += kWarpSize) {
        T val = src[src_base + rv * tail];
        if (UseAbs) {
          val = val < T(0) ? -val : val;
        }
        partial = Reducer()(partial, val);
      }

      for (int offset = kWarpSize / 2; offset > 0; offset >>= 1) {
        T other = tl::shfl_down(partial, offset, kWarpSize);
        partial = Reducer()(partial, other);
      }

      if (lane == 0) {
        if (NeedAccumulate) {
          partial = Reducer()(dst[dst_index], partial);
        }
        dst[dst_index] = partial;
      }
    }
  }
};

template <class Reducer, int threads, int scale, int thread_offset = 0,
          int batch_size = 1, int workspace_stride = 0>
struct AllReduce {
  static_assert(threads == 1024 || threads == 512 || threads == 256 ||
                threads == 128 || threads == 64 || threads == 32 ||
                threads == 16 || threads == 8 || threads == 4 || threads == 2);
  static_assert(threads % scale == 0);

  // Scalar interface (backward-compatible).
  template <typename T> static __device__ T run(T x, T *red_buf = nullptr) {
    constexpr int offset = threads / 2;
    const int warpSize = __builtin_amdgcn_wavefrontsize();

STATUS: completed; EXIT_CODE: 0

=== Event 110 | item.started | item_60 ===
COMMAND:
/bin/bash -lc "sed -n '95,260p' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/reduce.h"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 111 | item.completed | item_60 ===
COMMAND:
/bin/bash -lc "sed -n '95,260p' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src/tl_templates/hip/reduce.h"
OUTPUT:
  static_assert(threads % scale == 0);

  // Scalar interface (backward-compatible).
  template <typename T> static __device__ T run(T x, T *red_buf = nullptr) {
    constexpr int offset = threads / 2;
    const int warpSize = __builtin_amdgcn_wavefrontsize();

    if (offset >= warpSize) {
      __syncthreads();
      red_buf[threadIdx.x] = x;
      __syncthreads();
      x = Reducer()(x, red_buf[threadIdx.x ^ offset]);
    } else {
      x = Reducer()(x, tl::shfl_xor(x, offset));
    }
    if constexpr (offset == scale) {
      return x;
    } else {
      return AllReduce<Reducer, offset, scale, thread_offset, batch_size,
                       workspace_stride>::run(x, red_buf);
    }
  }

  // Batch interface (named run_batch to avoid overload-resolution ambiguity).
  template <typename T>
  static __device__ void run_batch(T *x, T *red_buf = nullptr) {
    constexpr int offset = threads / 2;
    const int warpSize = __builtin_amdgcn_wavefrontsize();

    if (offset >= warpSize) {
      __syncthreads();
#pragma unroll
      for (int i = 0; i < batch_size; i++)
        red_buf[(threadIdx.x - thread_offset) + i * workspace_stride] = x[i];
      __syncthreads();
#pragma unroll
      for (int i = 0; i < batch_size; i++)
        x[i] =
            Reducer()(x[i], red_buf[((threadIdx.x - thread_offset) ^ offset) +
                                    i * workspace_stride]);
    } else {
#pragma unroll
      for (int i = 0; i < batch_size; i++)
        x[i] = Reducer()(x[i], tl::shfl_xor(x[i], offset));
    }
    if constexpr (offset == scale) {
      return;
    } else {
      AllReduce<Reducer, offset, scale, thread_offset, batch_size,
                workspace_stride>::run_batch(x, red_buf);
    }
  }
};

template <typename T, typename ReduceOp>
TL_DEVICE T warp_reduce(T value, ReduceOp op) {
  // 5-step butterfly reduction with width=32, matching CUDA's 32-lane warp
  // semantics on CDNA (wave64) and RDNA (wave32) targets.
  //
  // On CDNA (wave64, 64-lane wavefronts) with threads=32 per block:
  //   Only lanes 0-31 are active; lanes 32-63 hold uninitialised VGPRs.
  //   The old step `__shfl_xor(value, 32)` without a width argument read
  //   from those uninitialised lanes, producing NaN or garbage.
  //   width=32 confines every shuffle to the [0,31] group, preventing this.
  //
  // On CDNA with threads=64 (one full wave64):
  //   width=32 splits the wavefront into two independent 32-lane groups.
  //   Lane 0 of each group holds the group partial sum, which is exactly what
  //   kernels that assume logical warp_size=32 expect for their inter-warp
  //   shared-memory reductions.
  //
  // On RDNA (wave32, 32-lane wavefronts):
  //   width=32 equals the wavefront size, so behaviour is identical to
  //   omitting the width argument.
  //
  // Note: this intentionally preserves 32-lane logical-warp semantics for
  // backward compatibility.  Full wave64 utilisation (6-step, width=64) would
  // require restructuring inter-warp shared-memory communication in all
  // kernels and is deferred to a separate optimisation pass.
  value = op(value, __shfl_xor(value, 16, 32));
  value = op(value, __shfl_xor(value, 8, 32));
  value = op(value, __shfl_xor(value, 4, 32));
  value = op(value, __shfl_xor(value, 2, 32));
  value = op(value, __shfl_xor(value, 1, 32));
  return value;
}

template <typename T> TL_DEVICE T warp_reduce_sum(T value) {
  return warp_reduce<T>(value, SumOp());
}

template <typename T> TL_DEVICE T warp_reduce_max(T value) {
  return warp_reduce<T>(value, MaxOp());
}

template <typename T> TL_DEVICE T warp_reduce_min(T value) {
  return warp_reduce<T>(value, MinOp());
}

template <typename T> TL_DEVICE T warp_reduce_bitand(T value) {
  return warp_reduce<T>(value, BitAndOp());
}

template <typename T> TL_DEVICE T warp_reduce_bitor(T value) {
  return warp_reduce<T>(value, BitOrOp());
}

} // namespace tl

STATUS: completed; EXIT_CODE: 0

=== Event 112 | item.started | item_61 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-20 — Low child of representation 4727242 (High parent iter-17)
Batch eight softmax rows: subgroup shuffle first, lane-leader shared exchange second. Complete suite PASS; 5.365053 ms (5.312–5.468). Not promoted over High iter-17. Retain exploration; result does not prove barriers unimportant. Next test installed run_batch helper, preserving original cross-warp-first reduction order with contiguous per-lane shared exchange; larger scratch (8 KiB reused) remains feasible.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-20: retain batched row reduction alternative'
python - <<'PY'
p='solution/kernel_4.cpp';s=open(p).read();s=s.replace('buf_dyn_shmem + 35840','buf_dyn_shmem + 34816').replace('36864','43008')
a=s.index('    __syncthreads();\\n    #pragma unroll\\n    for (int r = 0; r < 8; ++r)');b=s.index('    #pragma unroll\\n    for (int i_8',a)
s=s[:a]+'''    #pragma unroll
    for (int r=0;r<8;++r) {
      m_clear[r]=-HUGE_VALF;
      #pragma unroll
      for (int c=0;c<4;++c) m_clear[r]=max(m_clear[r],s[c*8+r]);
    }
    tl::AllReduce<tl::MaxOp,256,128,0,8,256>::run_batch(m_clear,(float*)workspace);
    tl::AllReduce<tl::MaxOp,16,1,0,8,256>::run_batch(m_clear);
    #pragma unroll
    for (int r=0;r<8;++r) m[r]=max(m[r],m_clear[r]);
'''+s[b:]
a=s.index('    __syncthreads();\\n    #pragma unroll\\n    for (int r = 0; r < 8; ++r)');b=s.index('    #pragma unroll\\n    for (int i_11',a)
s=s[:a]+'''    #pragma unroll
    for (int r=0;r<8;++r) {
      rows[r]=0.0f;
      #pragma unroll
      for (int c=0;c<4;++c) rows[r]+=s[c*8+r];
    }
    tl::AllReduce<tl::SumOp,256,128,0,8,256>::run_batch(rows,(float*)workspace);
    tl::AllReduce<tl::SumOp,16,1,0,8,256>::run_batch(rows);
'''+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-21"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 113 | item.completed | item_62 ===
The first batched reduction passed but was slower at 5.37 ms. I’m testing a second version that preserves the original reduction order and uses contiguous shared-memory exchanges through the installed HIP helper.

=== Event 114 | item.started | item_63 ===
COMMAND:
/bin/bash -lc "rg -n 'nontemporal|__builtin_nontemporal' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/include/hip | head -25 && sed -n '1,75p' solution/kernel_3.cpp"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 115 | item.completed | item_63 ===
COMMAND:
/bin/bash -lc "rg -n 'nontemporal|__builtin_nontemporal' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/include/hip | head -25 && sed -n '1,75p' solution/kernel_3.cpp"
OUTPUT:
#include <hip/hip_runtime.h>
#include <tl_templates/hip/gemm.h>
#include <tl_templates/hip/copy.h>
#include <tl_templates/hip/reduce.h>
#include <tl_templates/hip/scan.h>
#include <tl_templates/hip/ldsm.h>
#include <tl_templates/hip/threadblock_swizzle.h>
#include <tl_templates/hip/debug.h>

extern "C" __global__ void __launch_bounds__(256) main_kernel(float* __restrict__ B, half_t* __restrict__ W, half_t* __restrict__ X, half_t* __restrict__ Y) {
  extern __shared__ __align__(1024) uchar buf_dyn_shmem[];
  void* a = ((void*)((char*)buf_dyn_shmem + 0));
  void* b = ((void*)((char*)buf_dyn_shmem + 16384));
  float c[64];
  const dim3 blockIdx = tl::rasterization2DColumn<8>();
  #pragma unroll
  for (int i = 0; i < 16; ++i) {
    float broadcast_var = 0.000000e+00f;
    *(float4*)(c + (i * 4)) = make_float4(broadcast_var, broadcast_var, broadcast_var, broadcast_var);
  }
  for (int k = 0; k < 12; ++k) {
    __syncthreads();
    #pragma unroll
    for (int i_1 = 0; i_1 < 4; ++i_1) {
      *(uint4*)(((half_t*)a) + (((((i_1 * 2048) + ((((int)threadIdx.x) >> 3) * 64)) + (((((((int)threadIdx.x) & 63) >> 5) + ((((int)threadIdx.x) & 7) >> 2)) & 1) * 32)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 15) >> 3) + (((int)threadIdx.x) & 1)) & 1) * 8))) = *(uint4*)(X + (((((((int)blockIdx.x) * 98304) + (i_1 * 24576)) + ((((int)threadIdx.x) >> 3) * 768)) + (k * 64)) + ((((int)threadIdx.x) & 7) * 8)));
    }
    #pragma unroll
    for (int i_2 = 0; i_2 < 4; ++i_2) {
      *(uint4*)(((half_t*)b) + (((((i_2 * 2048) + ((((int)threadIdx.x) >> 3) * 64)) + (((((((int)threadIdx.x) & 63) >> 5) + ((((int)threadIdx.x) & 7) >> 2)) & 1) * 32)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 15) >> 3) + (((int)threadIdx.x) & 1)) & 1) * 8))) = *(uint4*)(W + (((((((int)blockIdx.y) * 98304) + (i_2 * 24576)) + ((((int)threadIdx.x) >> 3) * 768)) + (k * 64)) + ((((int)threadIdx.x) & 7) * 8)));
    }
    {
      half_t A_local[16];
      half_t B_local[32];
      __syncthreads();
      for (int ki = 0; ki < 4; ++ki) {
        for (int i_3 = 0; i_3 < 2; ++i_3) {
          *(uint4*)(A_local + (i_3 * 8)) = *(uint4*)(((half_t*)a) + ((((((((((int)threadIdx.x) & 127) >> 5) * 2048) + (i_3 * 1024)) + ((((int)threadIdx.x) & 15) * 64)) + (((((((int)threadIdx.x) & 7) >> 2) + (ki >> 1)) & 1) * 32)) + (((((((int)threadIdx.x) & 3) >> 1) + (ki & 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + (((int)threadIdx.x) & 1)) & 1) * 8)));
        }
        for (int j = 0; j < 4; ++j) {
          *(uint4*)(B_local + (j * 8)) = *(uint4*)(((half_t*)b) + (((((((((int)threadIdx.x) >> 7) * 4096) + (j * 1024)) + ((((int)threadIdx.x) & 15) * 64)) + (((((((int)threadIdx.x) & 7) >> 2) + (ki >> 1)) & 1) * 32)) + (((((((int)threadIdx.x) & 3) >> 1) + (ki & 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + (((int)threadIdx.x) & 1)) & 1) * 8)));
        }
        for (int i_4 = 0; i_4 < 2; ++i_4) {
          for (int j_1 = 0; j_1 < 4; ++j_1) {
            {
      typedef __attribute__((__vector_size__(8 * sizeof(__fp16)))) __fp16 tl_v8f16;
      typedef __attribute__((__vector_size__(8 * sizeof(float)))) float tl_v8f32;
      *((tl_v8f32*)c + ((i_4 * 4) + j_1)) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(
          *((tl_v8f16*)A_local + i_4),
          *((tl_v8f16*)B_local + j_1),
          *((tl_v8f32*)c + ((i_4 * 4) + j_1)));
    };
          }
        }
      }
    }
  }
  #pragma unroll
  for (int i_5 = 0; i_5 < 64; ++i_5) {
    Y[(((((((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) & 127) >> 5) * 73728)) + ((i_5 >> 5) * 36864)) + (((((int)threadIdx.x) & 31) >> 4) * 18432)) + ((i_5 & 7) * 2304)) + (((int)blockIdx.y) * 128)) + ((((int)threadIdx.x) >> 7) * 64)) + (((i_5 & 31) >> 3) * 16)) + (((int)threadIdx.x) & 15))] = ((half_t)(c[i_5] + B[((((((int)blockIdx.y) * 128) + ((((int)threadIdx.x) >> 7) * 64)) + (((i_5 & 31) >> 3) * 16)) + (((int)threadIdx.x) & 15))]));
  }
}


#ifdef _WIN32
#define TL_EXPORT __declspec(dllexport)
#else
#define TL_EXPORT
#endif

#define ERROR_BUF_SIZE 1024
static char error_buf[ERROR_BUF_SIZE];

extern "C" TL_EXPORT const char* get_last_error() {
    return error_buf;
}

STATUS: completed; EXIT_CODE: 0

=== Event 116 | item.completed | item_61 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-20 — Low child of representation 4727242 (High parent iter-17)
Batch eight softmax rows: subgroup shuffle first, lane-leader shared exchange second. Complete suite PASS; 5.365053 ms (5.312–5.468). Not promoted over High iter-17. Retain exploration; result does not prove barriers unimportant. Next test installed run_batch helper, preserving original cross-warp-first reduction order with contiguous per-lane shared exchange; larger scratch (8 KiB reused) remains feasible.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-20: retain batched row reduction alternative'
python - <<'PY'
p='solution/kernel_4.cpp';s=open(p).read();s=s.replace('buf_dyn_shmem + 35840','buf_dyn_shmem + 34816').replace('36864','43008')
a=s.index('    __syncthreads();\\n    #pragma unroll\\n    for (int r = 0; r < 8; ++r)');b=s.index('    #pragma unroll\\n    for (int i_8',a)
s=s[:a]+'''    #pragma unroll
    for (int r=0;r<8;++r) {
      m_clear[r]=-HUGE_VALF;
      #pragma unroll
      for (int c=0;c<4;++c) m_clear[r]=max(m_clear[r],s[c*8+r]);
    }
    tl::AllReduce<tl::MaxOp,256,128,0,8,256>::run_batch(m_clear,(float*)workspace);
    tl::AllReduce<tl::MaxOp,16,1,0,8,256>::run_batch(m_clear);
    #pragma unroll
    for (int r=0;r<8;++r) m[r]=max(m[r],m_clear[r]);
'''+s[b:]
a=s.index('    __syncthreads();\\n    #pragma unroll\\n    for (int r = 0; r < 8; ++r)');b=s.index('    #pragma unroll\\n    for (int i_11',a)
s=s[:a]+'''    #pragma unroll
    for (int r=0;r<8;++r) {
      rows[r]=0.0f;
      #pragma unroll
      for (int c=0;c<4;++c) rows[r]+=s[c*8+r];
    }
    tl::AllReduce<tl::SumOp,256,128,0,8,256>::run_batch(rows,(float*)workspace);
    tl::AllReduce<tl::SumOp,16,1,0,8,256>::run_batch(rows);
'''+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-21"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-21
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.953800201416016, 45.17679672491889, 45.01406084426412, 44.939720759145345, 44.87510228085149], "candidate": [4.848738281151911, 4.951461763961745, 4.932665910039605, 4.900244365568365, 4.913488864898682]}
RUNTIME: 4.913488864898682
REF_RUNTIME: 44.953800201416016
SPEEDUP: 9.14905913851969

STATUS: completed; EXIT_CODE: 0

=== Event 117 | item.started | item_64 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-21 — Low child of iter-20 (acc5cc5)
Contiguous batched cross-warp exchange with installed run_batch helper, original reduction order; scratch reused between max/sum, allocation43008 bytes. Complete suite PASS; 4.913489 ms (4.849–4.951), 9.1491x. Promoted. Barrier batching works with suitable exchange layout.
Next software-prefetch projection operands into registers one K chunk ahead while current WMMA consumes LDS; retain tile geometry, precision, output stores, grid and compiler flags. Native enables explicit prefetch lifetime/scheduling.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-21: batch contiguous softmax reductions, 4.91 ms'
cat > opt/attention/prefetch_projection.py <<'PY'
from pathlib import Path
for name in ['kernel_3.cpp','kernel_5.cpp']:
    p=Path('solution')/name
    s=p.read_text()
    start=s.index('  for (int k = 0; k < 12; ++k) {')
    end=s.index('    {\\n      half_t A_local',start)
    old=s[start:end]
    lines=[line for line in old.splitlines() if ' = *(uint4*)(' in line]
    store_a,load_a=lines[0].strip().split(' = ',1)
    store_b,load_b=lines[1].strip().split(' = ',1)
    s=s[:start]+'''  uint4 pre_a[4], pre_b[4];
  #pragma unroll
  for (int i_1=0;i_1<4;++i_1) pre_a[i_1] = '''+load_a.replace('(k * 64)','0')+'''
  #pragma unroll
  for (int i_2=0;i_2<4;++i_2) pre_b[i_2] = '''+load_b.replace('(k * 64)','0')+'''
  for (int k = 0; k < 12; ++k) {
    __syncthreads();
    #pragma unroll
    for (int i_1=0;i_1<4;++i_1) '''+store_a+''' = pre_a[i_1];
    #pragma unroll
    for (int i_2=0;i_2<4;++i_2) '''+store_b+''' = pre_b[i_2];
'''+s[end:]
    anchor='      __syncthreads();\\n      for (int ki = 0; ki < 4; ++ki) {'
    repl='''      __syncthreads();
      if (k+1<12) {
        #pragma unroll
        for (int i_1=0;i_1<4;++i_1) pre_a[i_1] = '''+load_a.replace('(k * 64)','((k+1) * 64)')+'''
        #pragma unroll
        for (int i_2=0;i_2<4;++i_2) pre_b[i_2] = '''+load_b.replace('(k * 64)','((k+1) * 64)')+'''
      }
      for (int ki = 0; ki < 4; ++ki) {'''
    assert anchor in s
    s=s.replace(anchor,repl)
    p.write_text(s)
PY
python opt/attention/prefetch_projection.py
bash scripts/bench.sh iter-22"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 118 | item.completed | item_65 ===
The contiguous batched reduction improved runtime to 4.91 ms, with all trials passing. I’m now testing projection prefetching: load the next operand tile into registers while the current tile’s matrix instructions run.

=== Event 119 | item.started | item_66 ===
COMMAND:
/bin/bash -lc "rg -n 'prefetch|pre_a|pre_b' solution/kernel_3.cpp && date -u"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 120 | item.completed | item_66 ===
COMMAND:
/bin/bash -lc "rg -n 'prefetch|pre_a|pre_b' solution/kernel_3.cpp && date -u"
OUTPUT:
21:  uint4 pre_a[4], pre_b[4];
23:  for (int i_1=0;i_1<4;++i_1) pre_a[i_1] = *(uint4*)(X + (((((((int)blockIdx.x) * 98304) + (i_1 * 24576)) + ((((int)threadIdx.x) >> 3) * 768)) + 0) + ((((int)threadIdx.x) & 7) * 8)));
25:  for (int i_2=0;i_2<4;++i_2) pre_b[i_2] = *(uint4*)(W + (((((((int)blockIdx.y) * 98304) + (i_2 * 24576)) + ((((int)threadIdx.x) >> 3) * 768)) + 0) + ((((int)threadIdx.x) & 7) * 8)));
29:    for (int i_1=0;i_1<4;++i_1) *(uint4*)(((half_t*)a) + (((((i_1 * 2048) + ((((int)threadIdx.x) >> 3) * 64)) + (((((((int)threadIdx.x) & 63) >> 5) + ((((int)threadIdx.x) & 7) >> 2)) & 1) * 32)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 15) >> 3) + (((int)threadIdx.x) & 1)) & 1) * 8))) = pre_a[i_1];
31:    for (int i_2=0;i_2<4;++i_2) *(uint4*)(((half_t*)b) + (((((i_2 * 2048) + ((((int)threadIdx.x) >> 3) * 64)) + (((((((int)threadIdx.x) & 63) >> 5) + ((((int)threadIdx.x) & 7) >> 2)) & 1) * 32)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 15) >> 3) + (((int)threadIdx.x) & 1)) & 1) * 8))) = pre_b[i_2];
38:        for (int i_1=0;i_1<4;++i_1) pre_a[i_1] = *(uint4*)(X + (((((((int)blockIdx.x) * 98304) + (i_1 * 24576)) + ((((int)threadIdx.x) >> 3) * 768)) + ((k+1) * 64)) + ((((int)threadIdx.x) & 7) * 8)));
40:        for (int i_2=0;i_2<4;++i_2) pre_b[i_2] = *(uint4*)(W + (((((((int)blockIdx.y) * 98304) + (i_2 * 24576)) + ((((int)threadIdx.x) >> 3) * 768)) + ((k+1) * 64)) + ((((int)threadIdx.x) & 7) * 8)));
Sat Sep 26 10:16:12 UTC 2026

STATUS: completed; EXIT_CODE: 0

=== Event 121 | item.completed | item_64 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-21 — Low child of iter-20 (acc5cc5)
Contiguous batched cross-warp exchange with installed run_batch helper, original reduction order; scratch reused between max/sum, allocation43008 bytes. Complete suite PASS; 4.913489 ms (4.849–4.951), 9.1491x. Promoted. Barrier batching works with suitable exchange layout.
Next software-prefetch projection operands into registers one K chunk ahead while current WMMA consumes LDS; retain tile geometry, precision, output stores, grid and compiler flags. Native enables explicit prefetch lifetime/scheduling.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-21: batch contiguous softmax reductions, 4.91 ms'
cat > opt/attention/prefetch_projection.py <<'PY'
from pathlib import Path
for name in ['kernel_3.cpp','kernel_5.cpp']:
    p=Path('solution')/name
    s=p.read_text()
    start=s.index('  for (int k = 0; k < 12; ++k) {')
    end=s.index('    {\\n      half_t A_local',start)
    old=s[start:end]
    lines=[line for line in old.splitlines() if ' = *(uint4*)(' in line]
    store_a,load_a=lines[0].strip().split(' = ',1)
    store_b,load_b=lines[1].strip().split(' = ',1)
    s=s[:start]+'''  uint4 pre_a[4], pre_b[4];
  #pragma unroll
  for (int i_1=0;i_1<4;++i_1) pre_a[i_1] = '''+load_a.replace('(k * 64)','0')+'''
  #pragma unroll
  for (int i_2=0;i_2<4;++i_2) pre_b[i_2] = '''+load_b.replace('(k * 64)','0')+'''
  for (int k = 0; k < 12; ++k) {
    __syncthreads();
    #pragma unroll
    for (int i_1=0;i_1<4;++i_1) '''+store_a+''' = pre_a[i_1];
    #pragma unroll
    for (int i_2=0;i_2<4;++i_2) '''+store_b+''' = pre_b[i_2];
'''+s[end:]
    anchor='      __syncthreads();\\n      for (int ki = 0; ki < 4; ++ki) {'
    repl='''      __syncthreads();
      if (k+1<12) {
        #pragma unroll
        for (int i_1=0;i_1<4;++i_1) pre_a[i_1] = '''+load_a.replace('(k * 64)','((k+1) * 64)')+'''
        #pragma unroll
        for (int i_2=0;i_2<4;++i_2) pre_b[i_2] = '''+load_b.replace('(k * 64)','((k+1) * 64)')+'''
      }
      for (int ki = 0; ki < 4; ++ki) {'''
    assert anchor in s
    s=s.replace(anchor,repl)
    p.write_text(s)
PY
python opt/attention/prefetch_projection.py
bash scripts/bench.sh iter-22"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-22
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [45.183371840800035, 44.95256964291334, 44.85235294287705, 44.953800201416016, 45.06990732637602], "candidate": [5.6792235926127095, 5.705149946019686, 5.441505380810577, 5.737074441942231, 5.695986747741699]}
RUNTIME: 5.695986747741699
REF_RUNTIME: 44.953800201416016
SPEEDUP: 7.892188341069956

STATUS: completed; EXIT_CODE: 0

=== Event 122 | item.started | item_67 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-22 — Low child of iter-21 (2acf219)
One-chunk projection register prefetch for both operands, same128x128x64 tile. Complete suite PASS; 5.695987 ms (5.442–5.737). Rejected; added register lifetime/scheduling does not pay here. Restore iter-21.
Next attention LDS alias: K, reduction scratch and P have disjoint phases. Reuse the same16 KiB region for them, retain separate Q/P-subtile and V buffers. All transitions already have full block barriers. Allocation falls from43008 to26624 bytes, below half the actual65536 limit. This changes feasible residency without changing decomposition or arithmetic.
EOF
git add solution opt/attention/prefetch_projection.py ITERATIONS.md && git commit -m 'iter-22: record projection prefetch regression'
git restore --source=2acf219 -- solution
python - <<'PY'
p='solution/kernel_4.cpp';s=open(p).read();s=s.replace('buf_dyn_shmem + 16384','buf_dyn_shmem + 0').replace('buf_dyn_shmem + 24576','buf_dyn_shmem + 16384').replace('buf_dyn_shmem + 30720','buf_dyn_shmem + 22528').replace('buf_dyn_shmem + 34816','buf_dyn_shmem + 0').replace('43008','26624');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-23"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 123 | item.completed | item_68 ===
Projection prefetching was slower, so I restored the winner. The next test reuses attention shared memory across three non-overlapping phases—keys, reduction scratch, and probabilities—reducing allocation from 43 KB to 26 KB. That may allow more blocks to run concurrently.

=== Event 124 | item.started | item_69 ===
COMMAND:
/bin/bash -lc "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/llvm/bin/llvm-objdump --help | rg -n 'offload|amdhsa|resource' && date -u"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 125 | item.completed | item_69 ===
COMMAND:
/bin/bash -lc "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/llvm/bin/llvm-objdump --help | rg -n 'offload|amdhsa|resource' && date -u"
OUTPUT:
57:  --offload-fatbin        Display the content of the offload FatBin section
58:  --offloading            Display the content of the offloading section
Sat Sep 26 10:17:11 UTC 2026

STATUS: completed; EXIT_CODE: 0

=== Event 126 | item.started | item_70 ===
COMMAND:
/bin/bash -lc "rg -n '__builtin_nontemporal_load|__builtin_nontemporal_store' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/llvm/lib/clang/*/include | head -20"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 127 | item.completed | item_70 ===
COMMAND:
/bin/bash -lc "rg -n '__builtin_nontemporal_load|__builtin_nontemporal_store' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/llvm/lib/clang/*/include | head -20"
OUTPUT:

STATUS: completed; EXIT_CODE: 0

=== Event 128 | item.started | item_71 ===
COMMAND:
/bin/bash -lc "rg --files /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel | rg '(Builtins|IntrinsicsAMDGPU|AMDGPUUsage|LanguageExtensions)' | head -20"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 129 | item.completed | item_71 ===
COMMAND:
/bin/bash -lc "rg --files /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel | rg '(Builtins|IntrinsicsAMDGPU|AMDGPUUsage|LanguageExtensions)' | head -20"
OUTPUT:
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/llvm/IR/IntrinsicsAMDGPU.td
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/llvm/IR/IntrinsicsAMDGPU.h
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/BuiltinsVE.def
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/BuiltinsPPC.def
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/BuiltinsLoongArchLSX.def
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/BuiltinsVEVL.gen.def
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/BuiltinsARM.def
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/BuiltinsAArch64NeonSVEBridge.def
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/Builtins.inc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/BuiltinsHexagon.inc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/BuiltinsAArch64.inc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/BuiltinsX86.inc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/BuiltinsLoongArchLASX.def
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/BuiltinsDirectX.inc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/BuiltinsSPIRVCommon.inc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/BuiltinsRISCV.inc
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/BuiltinsWebAssembly.def
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/BuiltinsHexagonMapCustomDep.def
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/TargetBuiltins.h
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/BuiltinsLoongArchBase.def

STATUS: completed; EXIT_CODE: 0

=== Event 130 | item.completed | item_67 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-22 — Low child of iter-21 (2acf219)
One-chunk projection register prefetch for both operands, same128x128x64 tile. Complete suite PASS; 5.695987 ms (5.442–5.737). Rejected; added register lifetime/scheduling does not pay here. Restore iter-21.
Next attention LDS alias: K, reduction scratch and P have disjoint phases. Reuse the same16 KiB region for them, retain separate Q/P-subtile and V buffers. All transitions already have full block barriers. Allocation falls from43008 to26624 bytes, below half the actual65536 limit. This changes feasible residency without changing decomposition or arithmetic.
EOF
git add solution opt/attention/prefetch_projection.py ITERATIONS.md && git commit -m 'iter-22: record projection prefetch regression'
git restore --source=2acf219 -- solution
python - <<'PY'
p='solution/kernel_4.cpp';s=open(p).read();s=s.replace('buf_dyn_shmem + 16384','buf_dyn_shmem + 0').replace('buf_dyn_shmem + 24576','buf_dyn_shmem + 16384').replace('buf_dyn_shmem + 30720','buf_dyn_shmem + 22528').replace('buf_dyn_shmem + 34816','buf_dyn_shmem + 0').replace('43008','26624');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-23"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-23
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.82527560100757, 44.99796091015612, 45.00973294130467, 44.953800201416016, 44.820955181565594], "candidate": [4.906705940345259, 4.702438335357093, 4.602705801776768, 4.932678656141531, 4.704755783081055]}
RUNTIME: 4.704755783081055
REF_RUNTIME: 44.953800201416016
SPEEDUP: 9.554969965301074

STATUS: completed; EXIT_CODE: 0

=== Event 131 | item.started | item_72 ===
COMMAND:
/bin/bash -lc "rg -n -C 2 'nontemporal' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/Builtins.inc | head -30
cat > opt/attention/cache_probe.cpp <<'EOF'
#include <hip/hip_runtime.h>
extern \"C\" __global__ void probe(const float4* x, float4* y) {
  float4 a=__builtin_nontemporal_load(x+threadIdx.x);
  __builtin_nontemporal_store(a,y+threadIdx.x);
}
EOF
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc -O3 --offload-arch=gfx1201 --cuda-device-only -S opt/attention/cache_probe.cpp -o opt/attention/cache_probe.s
rg -n 'global_load|global_store|th:|scope:' opt/attention/cache_probe.s"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 132 | item.completed | item_72 ===
COMMAND:
/bin/bash -lc "rg -n -C 2 'nontemporal' /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/include/clang/Basic/Builtins.inc | head -30
cat > opt/attention/cache_probe.cpp <<'EOF'
#include <hip/hip_runtime.h>
extern \"C\" __global__ void probe(const float4* x, float4* y) {
  float4 a=__builtin_nontemporal_load(x+threadIdx.x);
  __builtin_nontemporal_store(a,y+threadIdx.x);
}
EOF
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc -O3 --offload-arch=gfx1201 --cuda-device-only -S opt/attention/cache_probe.cpp -o opt/attention/cache_probe.s
rg -n 'global_load|global_store|th:|scope:' opt/attention/cache_probe.s"
OUTPUT:
opt/attention/cache_probe.cpp:3:12: error: address argument to nontemporal builtin must be a pointer to integer, float, pointer, or a vector of such types ('const float4 *' (aka 'const HIP_vector_type<float, 4> *') invalid)
    3 |   float4 a=__builtin_nontemporal_load(x+threadIdx.x);
      |            ^                          ~~~~~~~~~~~~~
opt/attention/cache_probe.cpp:4:3: error: address argument to nontemporal builtin must be a pointer to integer, float, pointer, or a vector of such types ('float4 *' (aka 'HIP_vector_type<float, 4> *') invalid)
    4 |   __builtin_nontemporal_store(a,y+threadIdx.x);
      |   ^                             ~~~~~~~~~~~~~
2 errors generated when compiling for gfx1201.
failed to execute:/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/lib/llvm/bin/clang++  --offload-arch=gfx1201 --driver-mode=g++ -Llib --hip-link  -O3 --cuda-device-only -S -x hip opt/attention/cache_probe.cpp -o "opt/attention/cache_probe.s"
rg: opt/attention/cache_probe.s: IO error for operation on opt/attention/cache_probe.s: No such file or directory (os error 2)

STATUS: failed; EXIT_CODE: 2

=== Event 133 | item.started | item_73 ===
COMMAND:
/bin/bash -lc "cat > opt/attention/cache_probe.cpp <<'EOF'
#include <hip/hip_runtime.h>
typedef float v4f __attribute__((ext_vector_type(4)));
extern \"C\" __global__ void probe(const v4f* x, v4f* y) {
  v4f a=__builtin_nontemporal_load(x+threadIdx.x);
  __builtin_nontemporal_store(a,y+threadIdx.x);
}
EOF
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc -O3 --offload-arch=gfx1201 --cuda-device-only -S opt/attention/cache_probe.cpp -o opt/attention/cache_probe.s
rg -n 'global_load|global_store|th:|scope:' opt/attention/cache_probe.s
cat >> ITERATIONS.md <<'EOF'

## iter-23 — Low child of iter-21 (2acf219)
Alias non-overlapping K/reduction/P phases;26624-byte attention LDS. Complete suite PASS; 4.704756 ms (4.603–4.933), 9.5550x. Promoted provisionally, spread overlaps lower end of iter-21 but median and most samples improve.
Next test cache policy for final streaming output only. Installed compiler lists nontemporal builtins; compile-only local probe checks gfx1201 lowering (HIP float4 struct is not accepted, Clang vector is). Keep reused intermediates/default input caching unchanged for attribution.
EOF
git add solution opt/attention/cache_probe.cpp opt/attention/cache_probe.s ITERATIONS.md && git commit -m 'iter-23: reuse attention LDS across phases, 4.70 ms'
python - <<'PY'
p='solution/kernel_5.cpp';s=open(p).read();lines=s.splitlines();out=[]
for line in lines:
    if line.strip().startswith('Y['):
        lhs,rhs=line.strip().split(' = ',1);idx=lhs[2:-1];line='    __builtin_nontemporal_store('+rhs[:-1]+', Y + ('+idx+'));'
    out.append(line)
open(p,'w').write('\\n'.join(out)+'\\n')
PY
bash scripts/bench.sh iter-24"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 134 | item.completed | item_74 ===
Shared-memory reuse passed at 4.70 ms. I’m testing non-temporal writes for the final output, which is streamed once; a compile-only probe confirmed the intended gfx1201 cache hint. After that, I’ll check whether the Native reduction improvements also help the earlier online-attention structure.

=== Event 135 | item.completed | item_73 ===
COMMAND:
/bin/bash -lc "cat > opt/attention/cache_probe.cpp <<'EOF'
#include <hip/hip_runtime.h>
typedef float v4f __attribute__((ext_vector_type(4)));
extern \"C\" __global__ void probe(const v4f* x, v4f* y) {
  v4f a=__builtin_nontemporal_load(x+threadIdx.x);
  __builtin_nontemporal_store(a,y+threadIdx.x);
}
EOF
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc -O3 --offload-arch=gfx1201 --cuda-device-only -S opt/attention/cache_probe.cpp -o opt/attention/cache_probe.s
rg -n 'global_load|global_store|th:|scope:' opt/attention/cache_probe.s
cat >> ITERATIONS.md <<'EOF'

## iter-23 — Low child of iter-21 (2acf219)
Alias non-overlapping K/reduction/P phases;26624-byte attention LDS. Complete suite PASS; 4.704756 ms (4.603–4.933), 9.5550x. Promoted provisionally, spread overlaps lower end of iter-21 but median and most samples improve.
Next test cache policy for final streaming output only. Installed compiler lists nontemporal builtins; compile-only local probe checks gfx1201 lowering (HIP float4 struct is not accepted, Clang vector is). Keep reused intermediates/default input caching unchanged for attribution.
EOF
git add solution opt/attention/cache_probe.cpp opt/attention/cache_probe.s ITERATIONS.md && git commit -m 'iter-23: reuse attention LDS across phases, 4.70 ms'
python - <<'PY'
p='solution/kernel_5.cpp';s=open(p).read();lines=s.splitlines();out=[]
for line in lines:
    if line.strip().startswith('Y['):
        lhs,rhs=line.strip().split(' = ',1);idx=lhs[2:-1];line='    __builtin_nontemporal_store('+rhs[:-1]+', Y + ('+idx+'));'
    out.append(line)
open(p,'w').write('\\n'.join(out)+'\\n')
PY
bash scripts/bench.sh iter-24"
OUTPUT:
13:	global_load_b128 v[0:3], v4, s[0:1] th:TH_LOAD_NT
15:	global_store_b128 v4, v[0:3], s[2:3] th:TH_STORE_NT
[codex/rocm-workspace 754ba43] iter-23: reuse attention LDS across phases, 4.70 ms
 6 files changed, 183 insertions(+), 34 deletions(-)
 create mode 100644 opt/attention/cache_probe.cpp
 create mode 100644 opt/attention/cache_probe.s

RECORD_ID: CASE-MINGPT-iter-24
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.953800201416016, 45.11775780475084, 45.08623411403849, 44.95156863482456, 44.72468979934878], "candidate": [4.576971507250552, 4.546342231297217, 4.826098765107522, 4.5977678298950195, 4.668798746863647]}
RUNTIME: 4.5977678298950195
REF_RUNTIME: 44.953800201416016
SPEEDUP: 9.777309743463588

STATUS: completed; EXIT_CODE: 0

=== Event 136 | item.started | item_75 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-24 — Low child of iter-23 (754ba43)
Final output only uses non-temporal stores; installed probe emits TH_STORE_NT on gfx1201. Complete suite PASS; 4.597768 ms (4.546–4.826), 9.7773x. Retain provisionally as best median, but0.107-ms change overlaps sample spread; not a conclusive cache-policy win yet.
Scheduler rollback for combination: preserve Low branch, restore exact High iter-8 (571ca47) and export it. That validated64-key online structure has more reduction calls; batched reductions and phase aliasing may give a larger gain there. This revisits an already validated structural alternative with new local mechanisms, not an unmeasured rewrite.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-24: test streaming output cache hint, 4.60 ms'
mv solution opt/attention/low24
mkdir solution
git restore --source=571ca47 -- solution
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/export.py --source solution/ModelNew.py --reference reference.py --destination lowered8 --record opt/attention/export8.json"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 137 | item.completed | item_75 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-24 — Low child of iter-23 (754ba43)
Final output only uses non-temporal stores; installed probe emits TH_STORE_NT on gfx1201. Complete suite PASS; 4.597768 ms (4.546–4.826), 9.7773x. Retain provisionally as best median, but0.107-ms change overlaps sample spread; not a conclusive cache-policy win yet.
Scheduler rollback for combination: preserve Low branch, restore exact High iter-8 (571ca47) and export it. That validated64-key online structure has more reduction calls; batched reductions and phase aliasing may give a larger gain there. This revisits an already validated structural alternative with new local mechanisms, not an unmeasured rewrite.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-24: test streaming output cache hint, 4.60 ms'
mv solution opt/attention/low24
mkdir solution
git restore --source=571ca47 -- solution
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/export.py --source solution/ModelNew.py --reference reference.py --destination lowered8 --record opt/attention/export8.json"
OUTPUT:
EXPORT VALIDATED: full 30-trial Native replay passed; see opt/attention/export8.json

STATUS: completed; EXIT_CODE: 0

=== Event 138 | item.started | item_76 ===
COMMAND:
/bin/bash -lc "/home/xiaoxinyu/venvs/dsl-native-opt/bin/python -c 'import torch;p=torch.cuda.get_device_properties(0);print({a:getattr(p,a) for a in dir(p) if \"shared\" in a or \"register\" in a or \"threads\" in a})' && date -u"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 139 | item.completed | item_76 ===
COMMAND:
/bin/bash -lc "/home/xiaoxinyu/venvs/dsl-native-opt/bin/python -c 'import torch;p=torch.cuda.get_device_properties(0);print({a:getattr(p,a) for a in dir(p) if \"shared\" in a or \"register\" in a or \"threads\" in a})' && date -u"
OUTPUT:
{'max_threads_per_block': 1024, 'max_threads_per_multi_processor': 2048, 'shared_memory_per_block': 65536, 'shared_memory_per_multiprocessor': 65536}
Sat Sep 26 10:20:02 UTC 2026

STATUS: completed; EXIT_CODE: 0

=== Event 140 | item.started | item_77 ===
COMMAND:
/bin/bash -lc "mv solution opt/attention/high8 && cp -r lowered8 solution
cat >> ITERATIONS.md <<'EOF'

## Representation edge — iter-8 High to Native
Export opt/attention/export8.json, exact High source571ca47. Required full replay PASS. Entire solution replaced by export. Confirmed actual shared_memory_per_multiprocessor=65536 and max_threads_per_multi_processor=2048 from device, in addition to prior per-block limits. Native combination will batch reductions and alias K/P/scratch (disjoint phases); keep original Q/V buffers and projection cache policy initially.
EOF
git add solution opt/attention/export8* opt/attention/high8 ITERATIONS.md && git commit -m 'export retained iter-8 for Native mechanism combination'
cat > opt/attention/batch_online.py <<'PY'
from pathlib import Path
p=Path('solution/kernel_4.cpp');s=p.read_text()
a=s.index('    #pragma unroll\\n    for (int i_7') if 'for (int i_7 = 0; i_7 < 8;' in s else -1
# Identify max reduction by m_clear initialization, finding enclosing loop.
start=s.index('      m_clear[')
a=s.rfind('    #pragma unroll',0,start)
b=s.index('    #pragma unroll',s.index('      m[',start))
# b is next top-level pragma following closing max loop.
s=s[:a]+'''    #pragma unroll
    for (int r=0;r<8;++r) {
      m_clear[r]=-HUGE_VALF;
      #pragma unroll
      for (int c=0;c<2;++c) m_clear[r]=max(m_clear[r],s[c*8+r]);
    }
    tl::AllReduce<tl::MaxOp,256,128,0,8,256>::run_batch(m_clear,(float*)workspace);
    tl::AllReduce<tl::MaxOp,16,1,0,8,256>::run_batch(m_clear);
    #pragma unroll
    for (int r=0;r<8;++r) m[r]=max(m[r],m_clear[r]);
'''+s[b:]
start=s.index('      rows[')
a=s.rfind('    #pragma unroll',0,start)
# end at next loop after final AllReduce row
end=s.index('      rows[',s.index('tl::AllReduce<tl::SumOp',start))
b=s.index('    #pragma unroll',end)
s=s[:a]+'''    #pragma unroll
    for (int r=0;r<8;++r) {
      rows[r]=0.0f;
      #pragma unroll
      for (int c=0;c<2;++c) rows[r]+=s[c*8+r];
    }
    tl::AllReduce<tl::SumOp,256,128,0,8,256>::run_batch(rows,(float*)workspace);
    tl::AllReduce<tl::SumOp,16,1,0,8,256>::run_batch(rows);
'''+s[b:]
s=s.replace('buf_dyn_shmem + 36864','buf_dyn_shmem + 12288').replace('buf_dyn_shmem + 45056','buf_dyn_shmem + 12288').replace('buf_dyn_shmem + 46080','buf_dyn_shmem + 12288').replace('47104','36864')
p.write_text(s)
PY
python opt/attention/batch_online.py
bash scripts/bench.sh iter-25"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 141 | item.started | item_78 ===
COMMAND:
/bin/bash -lc "sed -n '75,160p' solution/kernel_4.cpp"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 142 | item.completed | item_78 ===
COMMAND:
/bin/bash -lc "sed -n '75,160p' solution/kernel_4.cpp"
OUTPUT:
        }
      }
    }
    #pragma unroll
    for (int i_6 = 0; i_6 < 16; ++i_6) {
      float condval;
      if ((((((nb * 64) + ((((int)threadIdx.x) >> 7) * 32)) + ((i_6 >> 3) * 16)) + (((int)threadIdx.x) & 15)) <= (((((int)blockIdx.x) * 64) + (((((int)threadIdx.x) & 127) >> 4) * 8)) + (i_6 & 7)))) {
        condval = (s[i_6] * 1.020621e-01f);
      } else {
        condval = -HUGE_VALF;
      }
      s[i_6] = condval;
    }
    #pragma unroll
    for (int i_7 = 0; i_7 < 2; ++i_7) {
      *(float4*)(old + (i_7 * 4)) = *(float4*)(m + (i_7 * 4));
    }
    #pragma unroll
    for (int r=0;r<8;++r) {
      m_clear[r]=-HUGE_VALF;
      #pragma unroll
      for (int c=0;c<2;++c) m_clear[r]=max(m_clear[r],s[c*8+r]);
    }
    tl::AllReduce<tl::MaxOp,256,128,0,8,256>::run_batch(m_clear,(float*)workspace);
    tl::AllReduce<tl::MaxOp,16,1,0,8,256>::run_batch(m_clear);
    #pragma unroll
    for (int r=0;r<8;++r) m[r]=max(m[r],m_clear[r]);
    #pragma unroll
    for (int i_9 = 0; i_9 < 2; ++i_9) {
      float4 __1;
      float4 __2;
        float4 v_ = *(float4*)(old + (i_9 * 4));
        float4 v__1 = *(float4*)(m + (i_9 * 4));
        __2.x = (v_.x-v__1.x);
        __2.y = (v_.y-v__1.y);
        __2.z = (v_.z-v__1.z);
        __2.w = (v_.w-v__1.w);
      __1.x = __expf(__2.x);
      __1.y = __expf(__2.y);
      __1.z = __expf(__2.z);
      __1.w = __expf(__2.w);
      *(float4*)(alpha + (i_9 * 4)) = __1;
    }
    #pragma unroll
    for (int i_10 = 0; i_10 < 4; ++i_10) {
      float4 __3;
      float4 __4;
        float4 v__2 = *(float4*)(s + (i_10 * 4));
        float4 v__3 = *(float4*)(m + ((i_10 & 1) * 4));
        __4.x = (v__2.x-v__3.x);
        __4.y = (v__2.y-v__3.y);
        __4.z = (v__2.z-v__3.z);
        __4.w = (v__2.w-v__3.w);
      __3.x = __expf(__4.x);
      __3.y = __expf(__4.y);
      __3.z = __expf(__4.z);
      __3.w = __expf(__4.w);
      *(float4*)(s + (i_10 * 4)) = __3;
    }
    #pragma unroll
    for (int r=0;r<8;++r) {
      rows[r]=0.0f;
      #pragma unroll
      for (int c=0;c<2;++c) rows[r]+=s[c*8+r];
    }
    tl::AllReduce<tl::SumOp,256,128,0,8,256>::run_batch(rows,(float*)workspace);
    tl::AllReduce<tl::SumOp,16,1,0,8,256>::run_batch(rows);
    #pragma unroll
    for (int i_12 = 0; i_12 < 2; ++i_12) {
      float4 __5;
        float4 __6;
          float4 v__4 = *(float4*)(l + (i_12 * 4));
          float4 v__5 = *(float4*)(alpha + (i_12 * 4));
          __6.x = (v__4.x*v__5.x);
          __6.y = (v__4.y*v__5.y);
          __6.z = (v__4.z*v__5.z);
          __6.w = (v__4.w*v__5.w);
        float4 v__6 = *(float4*)(rows + (i_12 * 4));
        __5.x = (__6.x+v__6.x);
        __5.y = (__6.y+v__6.y);
        __5.z = (__6.z+v__6.z);
        __5.w = (__6.w+v__6.w);
      *(float4*)(l + (i_12 * 4)) = __5;
    }
    #pragma unroll
    for (int i_13 = 0; i_13 < 6; ++i_13) {

STATUS: completed; EXIT_CODE: 0

=== Event 143 | item.completed | item_79 ===
The output cache hint measured 4.60 ms, but its gain overlaps timing variation, so I’m treating it as provisional. The earlier online-attention candidate has passed export validation, and I’m testing it with batched reductions and phase-based memory reuse to compare the two structures under similar improvements.

=== Event 144 | item.completed | item_77 ===
COMMAND:
/bin/bash -lc "mv solution opt/attention/high8 && cp -r lowered8 solution
cat >> ITERATIONS.md <<'EOF'

## Representation edge — iter-8 High to Native
Export opt/attention/export8.json, exact High source571ca47. Required full replay PASS. Entire solution replaced by export. Confirmed actual shared_memory_per_multiprocessor=65536 and max_threads_per_multi_processor=2048 from device, in addition to prior per-block limits. Native combination will batch reductions and alias K/P/scratch (disjoint phases); keep original Q/V buffers and projection cache policy initially.
EOF
git add solution opt/attention/export8* opt/attention/high8 ITERATIONS.md && git commit -m 'export retained iter-8 for Native mechanism combination'
cat > opt/attention/batch_online.py <<'PY'
from pathlib import Path
p=Path('solution/kernel_4.cpp');s=p.read_text()
a=s.index('    #pragma unroll\\n    for (int i_7') if 'for (int i_7 = 0; i_7 < 8;' in s else -1
# Identify max reduction by m_clear initialization, finding enclosing loop.
start=s.index('      m_clear[')
a=s.rfind('    #pragma unroll',0,start)
b=s.index('    #pragma unroll',s.index('      m[',start))
# b is next top-level pragma following closing max loop.
s=s[:a]+'''    #pragma unroll
    for (int r=0;r<8;++r) {
      m_clear[r]=-HUGE_VALF;
      #pragma unroll
      for (int c=0;c<2;++c) m_clear[r]=max(m_clear[r],s[c*8+r]);
    }
    tl::AllReduce<tl::MaxOp,256,128,0,8,256>::run_batch(m_clear,(float*)workspace);
    tl::AllReduce<tl::MaxOp,16,1,0,8,256>::run_batch(m_clear);
    #pragma unroll
    for (int r=0;r<8;++r) m[r]=max(m[r],m_clear[r]);
'''+s[b:]
start=s.index('      rows[')
a=s.rfind('    #pragma unroll',0,start)
# end at next loop after final AllReduce row
end=s.index('      rows[',s.index('tl::AllReduce<tl::SumOp',start))
b=s.index('    #pragma unroll',end)
s=s[:a]+'''    #pragma unroll
    for (int r=0;r<8;++r) {
      rows[r]=0.0f;
      #pragma unroll
      for (int c=0;c<2;++c) rows[r]+=s[c*8+r];
    }
    tl::AllReduce<tl::SumOp,256,128,0,8,256>::run_batch(rows,(float*)workspace);
    tl::AllReduce<tl::SumOp,16,1,0,8,256>::run_batch(rows);
'''+s[b:]
s=s.replace('buf_dyn_shmem + 36864','buf_dyn_shmem + 12288').replace('buf_dyn_shmem + 45056','buf_dyn_shmem + 12288').replace('buf_dyn_shmem + 46080','buf_dyn_shmem + 12288').replace('47104','36864')
p.write_text(s)
PY
python opt/attention/batch_online.py
bash scripts/bench.sh iter-25"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-25
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.953800201416016, 45.14979635307855, 44.950430609477, 44.866613088189176, 45.083736898865546], "candidate": [4.847190856933594, 4.816422658204941, 4.790654593529988, 4.925999863027422, 4.927141658804362]}
RUNTIME: 4.847190856933594
REF_RUNTIME: 44.953800201416016
SPEEDUP: 9.274196442484312

STATUS: completed; EXIT_CODE: 0

=== Event 145 | item.started | item_80 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-25 — Low child of representation057b661 (High parent iter-8)
Combine contiguous batched reductions with K/P/scratch aliasing; online64-key structure,36 KiB LDS. Complete suite PASS; 4.847191 ms (4.791–4.927). Retain exploration, not promoted over iter-23/24. Improvement over original iter-8 is real but does not yet beat chunked structure.
Next keep Q WMMA operands in registers for reuse across key blocks, then reuse Q's12 KiB LDS region for V. K/P/scratch remain aliased; total24 KiB. Explicit unroll preserves register indexing; block barriers protect initial Q loads before V overwrite. This couples register lifetime with shared-memory placement rather than changing attention decomposition.
EOF
git add solution opt/attention/batch_online.py ITERATIONS.md && git commit -m 'iter-25: combine Native reductions with online attention'
python - <<'PY'
p='solution/kernel_4.cpp';s=open(p).read();line=next(l for l in s.splitlines() if '*(uint4*)(A_local + 0) = ' in l);rhs=line.strip().split(' = ',1)[1]
anchor='  for (int nb = 0;'
pos=s.index(anchor)
s=s[:pos]+'''  uint4 q_regs[6];
  __syncthreads();
  #pragma unroll
  for (int ki=0;ki<6;++ki) q_regs[ki] = '''+rhs+'\\n'+s[pos:]
s=s.replace(line,'        *(uint4*)(A_local + 0) = q_regs[ki];').replace('      for (int ki = 0; ki < 6;', '      #pragma unroll\\n      for (int ki = 0; ki < 6;')
s=s.replace('buf_dyn_shmem + 24576','buf_dyn_shmem + 0').replace('36864','24576');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-26"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 146 | item.started | item_81 ===
COMMAND:
/bin/bash -lc "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc -O3 --offload-arch=gfx1201 --cuda-device-only -S -I/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src solution/kernel_4.cpp -o opt/attention/iter26.s
rg -n '(vgpr_count|sgpr_count|private_segment_fixed_size|group_segment_fixed_size|vgpr_spill|sgpr_spill)' opt/attention/iter26.s"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 147 | item.completed | item_81 ===
COMMAND:
/bin/bash -lc "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc -O3 --offload-arch=gfx1201 --cuda-device-only -S -I/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src solution/kernel_4.cpp -o opt/attention/iter26.s
rg -n '(vgpr_count|sgpr_count|private_segment_fixed_size|group_segment_fixed_size|vgpr_spill|sgpr_spill)' opt/attention/iter26.s"
OUTPUT:
1514:		.amdhsa_group_segment_fixed_size 0
1515:		.amdhsa_private_segment_fixed_size 0
1517:		.amdhsa_user_sgpr_count 2
1622:    .group_segment_fixed_size: 0
1631:    .private_segment_fixed_size: 0
1632:    .sgpr_count:     24
1633:    .sgpr_spill_count: 0
1637:    .vgpr_count:     185
1638:    .vgpr_spill_count: 0

STATUS: completed; EXIT_CODE: 0

=== Event 148 | item.completed | item_80 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-25 — Low child of representation057b661 (High parent iter-8)
Combine contiguous batched reductions with K/P/scratch aliasing; online64-key structure,36 KiB LDS. Complete suite PASS; 4.847191 ms (4.791–4.927). Retain exploration, not promoted over iter-23/24. Improvement over original iter-8 is real but does not yet beat chunked structure.
Next keep Q WMMA operands in registers for reuse across key blocks, then reuse Q's12 KiB LDS region for V. K/P/scratch remain aliased; total24 KiB. Explicit unroll preserves register indexing; block barriers protect initial Q loads before V overwrite. This couples register lifetime with shared-memory placement rather than changing attention decomposition.
EOF
git add solution opt/attention/batch_online.py ITERATIONS.md && git commit -m 'iter-25: combine Native reductions with online attention'
python - <<'PY'
p='solution/kernel_4.cpp';s=open(p).read();line=next(l for l in s.splitlines() if '*(uint4*)(A_local + 0) = ' in l);rhs=line.strip().split(' = ',1)[1]
anchor='  for (int nb = 0;'
pos=s.index(anchor)
s=s[:pos]+'''  uint4 q_regs[6];
  __syncthreads();
  #pragma unroll
  for (int ki=0;ki<6;++ki) q_regs[ki] = '''+rhs+'\\n'+s[pos:]
s=s.replace(line,'        *(uint4*)(A_local + 0) = q_regs[ki];').replace('      for (int ki = 0; ki < 6;', '      #pragma unroll\\n      for (int ki = 0; ki < 6;')
s=s.replace('buf_dyn_shmem + 24576','buf_dyn_shmem + 0').replace('36864','24576');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-26"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-26
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.953800201416016, 44.90952063175164, 45.04359967808996, 44.79860228572431, 44.978124812058056], "candidate": [4.8381722514675065, 4.81228940627146, 4.878804884136889, 4.842864990234375, 4.875579640683484]}
RUNTIME: 4.842864990234375
REF_RUNTIME: 44.953800201416016
SPEEDUP: 9.282480575457965

STATUS: completed; EXIT_CODE: 0

=== Event 149 | item.completed | item_82 ===
The online structure with batched reductions measured 4.85 ms. I’m testing persistent query operands in registers so their shared-memory space can be reused for values. The compiler reports no spills, though register use is high; the benchmark will decide whether that tradeoff pays.

=== Event 150 | item.started | item_83 ===
COMMAND:
/bin/bash -lc "cat > opt/attention/occupancy.cpp <<'EOF'
#include \"../../solution/kernel_4.cpp\"
int main(){hipFuncAttributes a;hipFuncGetAttributes(&a,(const void*)main_kernel);int n=0;hipOccupancyMaxActiveBlocksPerMultiprocessor(&n,main_kernel,256,24576);printf(\"regs=%d local=%zu active_blocks=%d\\n\",a.numRegs,a.localSizeBytes,n);}
EOF
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc -O3 --offload-arch=gfx1201 -I/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src opt/attention/occupancy.cpp -o opt/attention/occupancy
opt/attention/occupancy"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 151 | item.completed | item_83 ===
COMMAND:
/bin/bash -lc "cat > opt/attention/occupancy.cpp <<'EOF'
#include \"../../solution/kernel_4.cpp\"
int main(){hipFuncAttributes a;hipFuncGetAttributes(&a,(const void*)main_kernel);int n=0;hipOccupancyMaxActiveBlocksPerMultiprocessor(&n,main_kernel,256,24576);printf(\"regs=%d local=%zu active_blocks=%d\\n\",a.numRegs,a.localSizeBytes,n);}
EOF
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc -O3 --offload-arch=gfx1201 -I/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src opt/attention/occupancy.cpp -o opt/attention/occupancy
opt/attention/occupancy"
OUTPUT:
opt/attention/occupancy.cpp:2:32: warning: ignoring return value of type 'hipError_t' declared with 'nodiscard' attribute [-Wunused-value]
    2 | int main(){hipFuncAttributes a;hipFuncGetAttributes(&a,(const void*)main_kernel);int n=0;hipOccupancyMaxActiveBlocksPerMultiprocessor(&n,main_kernel,256,24576);printf("regs=%d local=%zu active_blocks=%d\n",a.numRegs,a.localSizeBytes,n);}
      |                                ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
opt/attention/occupancy.cpp:2:90: warning: ignoring return value of type 'hipError_t' declared with 'nodiscard' attribute [-Wunused-value]
    2 | int main(){hipFuncAttributes a;hipFuncGetAttributes(&a,(const void*)main_kernel);int n=0;hipOccupancyMaxActiveBlocksPerMultiprocessor(&n,main_kernel,256,24576);printf("regs=%d local=%zu active_blocks=%d\n",a.numRegs,a.localSizeBytes,n);}
      |                                                                                          ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 warnings generated when compiling for gfx1201.
opt/attention/occupancy.cpp:2:32: warning: ignoring return value of type 'hipError_t' declared with 'nodiscard' attribute [-Wunused-value]
    2 | int main(){hipFuncAttributes a;hipFuncGetAttributes(&a,(const void*)main_kernel);int n=0;hipOccupancyMaxActiveBlocksPerMultiprocessor(&n,main_kernel,256,24576);printf("regs=%d local=%zu active_blocks=%d\n",a.numRegs,a.localSizeBytes,n);}
      |                                ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
opt/attention/occupancy.cpp:2:90: warning: ignoring return value of type 'hipError_t' declared with 'nodiscard' attribute [-Wunused-value]
    2 | int main(){hipFuncAttributes a;hipFuncGetAttributes(&a,(const void*)main_kernel);int n=0;hipOccupancyMaxActiveBlocksPerMultiprocessor(&n,main_kernel,256,24576);printf("regs=%d local=%zu active_blocks=%d\n",a.numRegs,a.localSizeBytes,n);}
      |                                                                                          ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 warnings generated when compiling for host.
regs=185 local=0 active_blocks=2

STATUS: completed; EXIT_CODE: 0

=== Event 152 | item.started | item_84 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-26 — Low child of iter-25 (f4f2db9)
Cache Q's six WMMA operand vectors in registers, alias Q/V LDS;24 KiB LDS. Complete suite PASS; 4.842865 ms (4.812–4.879). Not promoted; indistinguishable from iter-25. Compile-only emitted metadata:185 VGPR, no spills; actual HIP occupancy query reports2 resident256-thread blocks with24576-byte LDS. Extra residency alone does not ensure a gain. Reference timing drifted to46.49 ms; comparisons remain based on unchanged ranking, not normalized ad hoc.
Next explicit V shared-memory transpose while preserving uint4 global loads. Earlier High transposes gave no gain; Native can scatter each loaded vector into transposed/swizzled LDS and replace many scalar WMMA operand reads with uint4 reads. This is a physical layout/copy control change at Low, same mathematical tiles/dependencies.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-26: retain register-resident query alternative'
python - <<'PY'
p='solution/kernel_4.cpp';s=open(p).read()
# Remove old V stores interleaved with K stores, then add explicit vector-load transpose.
lines=s.splitlines();s='\\n'.join(l for l in lines if not ('*(uint4*)(((half_t*)v)' in l and ' = ' in l))+'\\n'
anchor='    #pragma unroll\\n    for (int i_5 = 0; i_5 < 4; ++i_5)'
pos=s.index(anchor)
s=s[:pos]+'''    #pragma unroll
    for (int t=0;t<3;++t) {
      int chunk=t*256+threadIdx.x;
      int ri=chunk/12, cj=(chunk%12)*8;
      uint4 packed=*(uint4*)(Q+(blockIdx.y/8)*1179648+nb*147456+ri*2304+(blockIdx.y%8)*96+1536+cj);
      #pragma unroll
      for(int e=0;e<8;++e) ((half_t*)v)[(cj+e)*64+(ri"'^(((cj+e)&7)*8))]=((half_t*)&packed)[e];
    }
'"'''+s[pos:]
a=s.index('        for (int j_2 = 0; j_2 < 3; ++j_2) {');b=s.index('        for (int j_3',a)
s=s[:a]+'''        #pragma unroll
        for (int j_2=0;j_2<3;++j_2) {
          int row=(threadIdx.x>>7)*48+j_2*16+(threadIdx.x&15);
          int col=ki_1*16+((threadIdx.x&31)>>4)*8;
          *(uint4*)(B_local_1+j_2*8)=*(uint4*)(((half_t*)v)+row*64+(col"'^((row&7)*8)));
        }
'"'''+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-27"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 153 | item.started | item_85 ===
COMMAND:
/bin/bash -lc 'git status --short | head -30 && date -u'
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 154 | item.completed | item_85 ===
COMMAND:
/bin/bash -lc 'git status --short | head -30 && date -u'
OUTPUT:
 M solution/kernel_4.cpp
?? .rocprofv3/
?? _bench_output.txt
?? lowered17/
?? lowered8/
?? opt/attention/iter26.s
?? opt/attention/low24/
?? opt/attention/occupancy
?? opt/attention/occupancy.cpp
Sat Sep 26 10:23:55 UTC 2026

STATUS: completed; EXIT_CODE: 0

=== Event 155 | item.completed | item_86 ===
Keeping queries in registers passed, but did not improve timing on its own. I’m now testing a value-copy layout that keeps global loads vectorized while arranging shared memory for vectorized matrix-operand reads—control the earlier High-level transpose did not provide.

=== Event 156 | item.completed | item_84 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-26 — Low child of iter-25 (f4f2db9)
Cache Q's six WMMA operand vectors in registers, alias Q/V LDS;24 KiB LDS. Complete suite PASS; 4.842865 ms (4.812–4.879). Not promoted; indistinguishable from iter-25. Compile-only emitted metadata:185 VGPR, no spills; actual HIP occupancy query reports2 resident256-thread blocks with24576-byte LDS. Extra residency alone does not ensure a gain. Reference timing drifted to46.49 ms; comparisons remain based on unchanged ranking, not normalized ad hoc.
Next explicit V shared-memory transpose while preserving uint4 global loads. Earlier High transposes gave no gain; Native can scatter each loaded vector into transposed/swizzled LDS and replace many scalar WMMA operand reads with uint4 reads. This is a physical layout/copy control change at Low, same mathematical tiles/dependencies.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-26: retain register-resident query alternative'
python - <<'PY'
p='solution/kernel_4.cpp';s=open(p).read()
# Remove old V stores interleaved with K stores, then add explicit vector-load transpose.
lines=s.splitlines();s='\\n'.join(l for l in lines if not ('*(uint4*)(((half_t*)v)' in l and ' = ' in l))+'\\n'
anchor='    #pragma unroll\\n    for (int i_5 = 0; i_5 < 4; ++i_5)'
pos=s.index(anchor)
s=s[:pos]+'''    #pragma unroll
    for (int t=0;t<3;++t) {
      int chunk=t*256+threadIdx.x;
      int ri=chunk/12, cj=(chunk%12)*8;
      uint4 packed=*(uint4*)(Q+(blockIdx.y/8)*1179648+nb*147456+ri*2304+(blockIdx.y%8)*96+1536+cj);
      #pragma unroll
      for(int e=0;e<8;++e) ((half_t*)v)[(cj+e)*64+(ri"'^(((cj+e)&7)*8))]=((half_t*)&packed)[e];
    }
'"'''+s[pos:]
a=s.index('        for (int j_2 = 0; j_2 < 3; ++j_2) {');b=s.index('        for (int j_3',a)
s=s[:a]+'''        #pragma unroll
        for (int j_2=0;j_2<3;++j_2) {
          int row=(threadIdx.x>>7)*48+j_2*16+(threadIdx.x&15);
          int col=ki_1*16+((threadIdx.x&31)>>4)*8;
          *(uint4*)(B_local_1+j_2*8)=*(uint4*)(((half_t*)v)+row*64+(col"'^((row&7)*8)));
        }
'"'''+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-27"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-27
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.94597715809971, 44.900138068543896, 44.953800201416016, 45.17115772908962, 45.014685502873526], "candidate": [5.218771683122902, 5.27774077512504, 5.2709808349609375, 5.29985355536277, 5.220496339346036]}
RUNTIME: 5.2709808349609375
REF_RUNTIME: 44.953800201416016
SPEEDUP: 8.528545560866029

STATUS: completed; EXIT_CODE: 0

=== Event 157 | item.started | item_87 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-27 — Low layout child of iter-26 (3e169ff)
Vector global loads, scatter into transposed V LDS, vector PV operand reads. Complete suite PASS; see trajectory for aggregate (about5.28 ms), slower than retained winners. Retain only for a concrete layout repair: current swizzle depends on row low3 bits; adjacent global vectors advance V rows by8, so scatter writes concentrate on the same banks. XOR row high3 bits into the swizzle as well; vector reads remain8-half contiguous and adjacent-row read distribution is preserved. This tests a different producer/consumer layout balance rather than repeating the same transpose.
EOF"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 158 | item.completed | item_87 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-27 — Low layout child of iter-26 (3e169ff)
Vector global loads, scatter into transposed V LDS, vector PV operand reads. Complete suite PASS; see trajectory for aggregate (about5.28 ms), slower than retained winners. Retain only for a concrete layout repair: current swizzle depends on row low3 bits; adjacent global vectors advance V rows by8, so scatter writes concentrate on the same banks. XOR row high3 bits into the swizzle as well; vector reads remain8-half contiguous and adjacent-row read distribution is preserved. This tests a different producer/consumer layout balance rather than repeating the same transpose.
EOF"
OUTPUT:

STATUS: completed; EXIT_CODE: 0

=== Event 159 | item.started | item_88 ===
COMMAND:
/bin/bash -lc "python - <<'PY'
p='ITERATIONS.md';s=open(p).read().replace('see trajectory for aggregate (about5.28 ms)','5.270981 ms (5.219–5.300)');open(p,'w').write(s)
PY
git add solution ITERATIONS.md && git commit -m 'iter-27: record vector transpose layout regression'
python - <<'PY'
p='solution/kernel_4.cpp';s=open(p).read().replace('((cj+e)&7)*8','(((cj+e)&7)"'^(((cj+e)>>3)&7))*8'"').replace('(row&7)*8','((row&7)"'^((row>>3)&7))*8'"');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-28"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 160 | item.completed | item_89 ===
The explicit value transpose passed but was slower. Its write addresses appear to concentrate accesses in the same shared-memory banks. I’m testing a revised address mapping that spreads those writes while preserving contiguous reads for the matrix instructions.

=== Event 161 | item.started | item_90 ===
COMMAND:
/bin/bash -lc "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc -O3 --offload-arch=gfx1201 --cuda-device-only -S -I/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src solution/kernel_3.cpp -o opt/attention/projection.s
rg -n 'vgpr_count|vgpr_spill|private_segment|v_wmma|s_cbranch' opt/attention/projection.s | head -35"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 162 | item.completed | item_90 ===
COMMAND:
/bin/bash -lc "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc -O3 --offload-arch=gfx1201 --cuda-device-only -S -I/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src solution/kernel_3.cpp -o opt/attention/projection.s
rg -n 'vgpr_count|vgpr_spill|private_segment|v_wmma|s_cbranch' opt/attention/projection.s | head -35"
OUTPUT:
84:	s_cbranch_scc1 .LBB0_2
295:	v_wmma_f32_16x16x16_f16 v[41:48], v[86:89], v[90:93], v[41:48]
297:	v_wmma_f32_16x16x16_f16 v[57:64], v[86:89], v[94:97], v[57:64]
299:	v_wmma_f32_16x16x16_f16 v[49:56], v[86:89], v[98:101], v[49:56]
301:	v_wmma_f32_16x16x16_f16 v[33:40], v[86:89], v[102:105], v[33:40]
305:	v_wmma_f32_16x16x16_f16 v[25:32], v[106:109], v[90:93], v[25:32]
306:	v_wmma_f32_16x16x16_f16 v[17:24], v[106:109], v[94:97], v[17:24]
309:	v_wmma_f32_16x16x16_f16 v[9:16], v[106:109], v[98:101], v[9:16]
311:	v_wmma_f32_16x16x16_f16 v[1:8], v[106:109], v[102:105], v[1:8]
314:	v_wmma_f32_16x16x16_f16 v[41:48], v[110:113], v[86:89], v[41:48]
316:	v_wmma_f32_16x16x16_f16 v[57:64], v[110:113], v[114:117], v[57:64]
318:	v_wmma_f32_16x16x16_f16 v[49:56], v[110:113], v[90:93], v[49:56]
320:	v_wmma_f32_16x16x16_f16 v[33:40], v[110:113], v[94:97], v[33:40]
324:	v_wmma_f32_16x16x16_f16 v[25:32], v[98:101], v[86:89], v[25:32]
325:	v_wmma_f32_16x16x16_f16 v[17:24], v[98:101], v[114:117], v[17:24]
328:	v_wmma_f32_16x16x16_f16 v[9:16], v[98:101], v[90:93], v[9:16]
330:	v_wmma_f32_16x16x16_f16 v[1:8], v[98:101], v[94:97], v[1:8]
333:	v_wmma_f32_16x16x16_f16 v[41:48], v[102:105], v[106:109], v[41:48]
335:	v_wmma_f32_16x16x16_f16 v[57:64], v[102:105], v[110:113], v[57:64]
337:	v_wmma_f32_16x16x16_f16 v[49:56], v[102:105], v[86:89], v[49:56]
339:	v_wmma_f32_16x16x16_f16 v[33:40], v[102:105], v[114:117], v[33:40]
343:	v_wmma_f32_16x16x16_f16 v[25:32], v[90:93], v[106:109], v[25:32]
344:	v_wmma_f32_16x16x16_f16 v[17:24], v[90:93], v[110:113], v[17:24]
347:	v_wmma_f32_16x16x16_f16 v[9:16], v[90:93], v[86:89], v[9:16]
349:	v_wmma_f32_16x16x16_f16 v[1:8], v[90:93], v[114:117], v[1:8]
351:	v_wmma_f32_16x16x16_f16 v[41:48], v[94:97], v[98:101], v[41:48]
353:	v_wmma_f32_16x16x16_f16 v[57:64], v[94:97], v[102:105], v[57:64]
355:	v_wmma_f32_16x16x16_f16 v[49:56], v[94:97], v[106:109], v[49:56]
357:	v_wmma_f32_16x16x16_f16 v[33:40], v[94:97], v[110:113], v[33:40]
359:	v_wmma_f32_16x16x16_f16 v[25:32], v[86:89], v[98:101], v[25:32]
360:	v_wmma_f32_16x16x16_f16 v[17:24], v[86:89], v[102:105], v[17:24]
361:	v_wmma_f32_16x16x16_f16 v[9:16], v[86:89], v[106:109], v[9:16]
362:	v_wmma_f32_16x16x16_f16 v[1:8], v[86:89], v[110:113], v[1:8]
363:	s_cbranch_scc0 .LBB0_3
583:		.amdhsa_private_segment_fixed_size 0

STATUS: completed; EXIT_CODE: 0

=== Event 163 | item.completed | item_88 ===
COMMAND:
/bin/bash -lc "python - <<'PY'
p='ITERATIONS.md';s=open(p).read().replace('see trajectory for aggregate (about5.28 ms)','5.270981 ms (5.219–5.300)');open(p,'w').write(s)
PY
git add solution ITERATIONS.md && git commit -m 'iter-27: record vector transpose layout regression'
python - <<'PY'
p='solution/kernel_4.cpp';s=open(p).read().replace('((cj+e)&7)*8','(((cj+e)&7)"'^(((cj+e)>>3)&7))*8'"').replace('(row&7)*8','((row&7)"'^((row>>3)&7))*8'"');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-28"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-28
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.987344978316905, 44.953800201416016, 44.88991561062005, 44.95946338269587, 44.91761894412378], "candidate": [4.507715225219727, 4.674621531042635, 4.27225483125035, 4.494205139364729, 4.633087242402475]}
RUNTIME: 4.507715225219727
REF_RUNTIME: 44.953800201416016
SPEEDUP: 9.972635349701969

STATUS: completed; EXIT_CODE: 0

=== Event 164 | item.started | item_91 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-28 — Low layout child of iter-27 (8efd7cd)
XOR both low/high row bits in transposed V LDS swizzle. Complete suite PASS; 4.507715 ms (4.272–4.675), 9.9726x. Clear win over iter-27/26 supports producer bank mapping as the missing interaction. Best aggregate provisionally; cross-branch gain over iter-24 is small relative to spread. Next combine iter-24's final-output cache hint without changing attention.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-28: balance V producer and consumer bank mapping, 4.51 ms'
git restore --source=fc00ae6 -- solution/kernel_5.cpp
bash scripts/bench.sh iter-29"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 165 | item.completed | item_92 ===
The revised bank mapping improved the online branch to 4.51 ms. I’m testing it with the output cache hint next. I also see a reason to revisit input conversion: grouped projection tiles now reuse inputs in cache, which may make conversion inside those tiles more competitive than it was earlier.

=== Event 166 | item.completed | item_91 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-28 — Low layout child of iter-27 (8efd7cd)
XOR both low/high row bits in transposed V LDS swizzle. Complete suite PASS; 4.507715 ms (4.272–4.675), 9.9726x. Clear win over iter-27/26 supports producer bank mapping as the missing interaction. Best aggregate provisionally; cross-branch gain over iter-24 is small relative to spread. Next combine iter-24's final-output cache hint without changing attention.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-28: balance V producer and consumer bank mapping, 4.51 ms'
git restore --source=fc00ae6 -- solution/kernel_5.cpp
bash scripts/bench.sh iter-29"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-29
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [45.19859065466189, 44.953800201416016, 44.83916356738286, 45.04684180341494, 44.940803109235745], "candidate": [4.588338424241796, 4.606031894683838, 4.621554590715856, 4.649220739427078, 4.597695798330268]}
RUNTIME: 4.606031894683838
REF_RUNTIME: 44.953800201416016
SPEEDUP: 9.759767459122575

STATUS: completed; EXIT_CODE: 0

=== Event 167 | item.started | item_93 ===
COMMAND:
/bin/bash -lc "sed -n '10,32p' solution/kernel_0.cpp && date -u"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 168 | item.completed | item_93 ===
COMMAND:
/bin/bash -lc "sed -n '10,32p' solution/kernel_0.cpp && date -u"
OUTPUT:
extern "C" __global__ void __launch_bounds__(256) main_kernel(float* __restrict__ X, half_t* __restrict__ Y) {
  {
    float _local_cast_1[4];
    half_t _local_cast[4];
    *(float4*)(_local_cast_1 + 0) = *(float4*)(X + ((((int)blockIdx.x) * 1024) + (((int)threadIdx.x) * 4)));
    uint2 __1;
    float4 v_ = *(float4*)(_local_cast_1 + 0);
    *((half_t*)(&(((half2*)(&(__1.x)))->x))) = (half_t)(v_.x);
    *((half_t*)(&(((half2*)(&(__1.x)))->y))) = (half_t)(v_.y);
    *((half_t*)(&(((half2*)(&(__1.y)))->x))) = (half_t)(v_.z);
    *((half_t*)(&(((half2*)(&(__1.y)))->y))) = (half_t)(v_.w);
    *(uint2*)(_local_cast + 0) = __1;
    *(uint2*)(Y + ((((int)blockIdx.x) * 1024) + (((int)threadIdx.x) * 4))) = *(uint2*)(_local_cast + 0);
  }
}


#ifdef _WIN32
#define TL_EXPORT __declspec(dllexport)
#else
#define TL_EXPORT
#endif

Sat Sep 26 10:27:12 UTC 2026

STATUS: completed; EXIT_CODE: 0

=== Event 169 | item.started | item_94 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-29 — Low combination child of iter-28 (a3d31c5)
Final-output non-temporal store combined with online bank mapping. Complete suite PASS; 4.606032 ms (4.588–4.649). Not promoted; cache hint is inconclusive across branches and slower here. Restore default stores. Next independently test streaming FP32 activation conversion input loads with TH_LOAD_NT; converted output and reused GEMM operands retain default caching.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-29: reject output cache hint on online winner'
git restore --source=a3d31c5 -- solution
python - <<'PY'
p='solution/kernel_0.cpp';s=open(p).read();s=s.replace('extern \"C\" __global__', 'typedef float f4v __attribute__((ext_vector_type(4)));\\nextern \"C\" __global__',1)
s=s.replace('*(float4*)(_local_cast_1 + 0) = *(float4*)(X + ((((int)blockIdx.x) * 1024) + (((int)threadIdx.x) * 4)));', '*(f4v*)(_local_cast_1 + 0) = __builtin_nontemporal_load((const f4v*)(X + ((((int)blockIdx.x) * 1024) + (((int)threadIdx.x) * 4))));');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-30"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 170 | item.started | item_95 ===
COMMAND:
/bin/bash -lc 'date -u && tail -6 opt/attention/export8.validation.log'
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 171 | item.completed | item_95 ===
COMMAND:
/bin/bash -lc 'date -u && tail -6 opt/attention/export8.validation.log'
OUTPUT:
Sat Sep 26 10:28:09 UTC 2026
CORRECT: True

AUXILIARY_VALIDATION: Historical export replay; correctness retained. Its historical timing fields are archived, not represented as paper-protocol measurements.

STATUS: completed; EXIT_CODE: 0

=== Event 172 | item.completed | item_94 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-29 — Low combination child of iter-28 (a3d31c5)
Final-output non-temporal store combined with online bank mapping. Complete suite PASS; 4.606032 ms (4.588–4.649). Not promoted; cache hint is inconclusive across branches and slower here. Restore default stores. Next independently test streaming FP32 activation conversion input loads with TH_LOAD_NT; converted output and reused GEMM operands retain default caching.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-29: reject output cache hint on online winner'
git restore --source=a3d31c5 -- solution
python - <<'PY'
p='solution/kernel_0.cpp';s=open(p).read();s=s.replace('extern \"C\" __global__', 'typedef float f4v __attribute__((ext_vector_type(4)));\\nextern \"C\" __global__',1)
s=s.replace('*(float4*)(_local_cast_1 + 0) = *(float4*)(X + ((((int)blockIdx.x) * 1024) + (((int)threadIdx.x) * 4)));', '*(f4v*)(_local_cast_1 + 0) = __builtin_nontemporal_load((const f4v*)(X + ((((int)blockIdx.x) * 1024) + (((int)threadIdx.x) * 4))));');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-30"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-30
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.83869666833434, 44.953800201416016, 45.06134193508006, 44.60921778249237, 45.21120836093408], "candidate": [4.424071280024592, 4.467650890350342, 4.441586904105167, 4.470366319580597, 4.644323187081486]}
RUNTIME: 4.467650890350342
REF_RUNTIME: 44.953800201416016
SPEEDUP: 10.062066465065907

STATUS: completed; EXIT_CODE: 0

=== Event 173 | item.started | item_96 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-30 — Low cache-policy child of iter-28 (a3d31c5)
Only streaming activation-conversion input uses non-temporal load. Complete suite PASS; 4.467651 ms (4.424–4.644), 10.0621x. Retain provisional best median;0.040 ms is within spread, no proven cache-policy gain.
Scheduler rollback to High iter-8 for conversion-placement sibling. Grouped128x128 projection now reuses FP32 input tiles in cache, unlike iter-2; inline activation conversion may avoid the0.54-ms full-tensor conversion without the old streaming-reload cost. Weights remain converted once. Native winner retained in Git.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-30: test streaming activation cache hint, 4.47 ms'
mv solution opt/attention/low30
mkdir solution
git restore --source=571ca47 -- solution
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();s=s.replace('def linear(N: int, out_dtype: str,','def linear(N: int, out_dtype: str, in_dtype: str=\"float16\",').replace(\"X:T.Tensor((128,512,768),'float16')\", \"X:T.Tensor((128,512,768),in_dtype)\",1).replace('        self.cx=convert((128,512,768))\\n','').replace(\"self.qkv=linear(2304,'float16')\", \"self.qkv=linear(2304,'float16','float32')\").replace('        xh=self.cx(x)\\n','').replace('q=self.qkv(xh,wh,qkv_bias)', 'q=self.qkv(x,wh,qkv_bias)');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-31"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 174 | item.completed | item_97 ===
The activation cache hint passed at 4.47 ms, but the gain is within observed variation. I’ve retained that Native candidate and returned to High to test conversion inside grouped QKV tiles, which would remove the separate activation-conversion buffer and launch.

=== Event 175 | item.completed | item_96 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-30 — Low cache-policy child of iter-28 (a3d31c5)
Only streaming activation-conversion input uses non-temporal load. Complete suite PASS; 4.467651 ms (4.424–4.644), 10.0621x. Retain provisional best median;0.040 ms is within spread, no proven cache-policy gain.
Scheduler rollback to High iter-8 for conversion-placement sibling. Grouped128x128 projection now reuses FP32 input tiles in cache, unlike iter-2; inline activation conversion may avoid the0.54-ms full-tensor conversion without the old streaming-reload cost. Weights remain converted once. Native winner retained in Git.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-30: test streaming activation cache hint, 4.47 ms'
mv solution opt/attention/low30
mkdir solution
git restore --source=571ca47 -- solution
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();s=s.replace('def linear(N: int, out_dtype: str,','def linear(N: int, out_dtype: str, in_dtype: str=\"float16\",').replace(\"X:T.Tensor((128,512,768),'float16')\", \"X:T.Tensor((128,512,768),in_dtype)\",1).replace('        self.cx=convert((128,512,768))\\n','').replace(\"self.qkv=linear(2304,'float16')\", \"self.qkv=linear(2304,'float16','float32')\").replace('        xh=self.cx(x)\\n','').replace('q=self.qkv(xh,wh,qkv_bias)', 'q=self.qkv(x,wh,qkv_bias)');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-31"
OUTPUT:
2026-09-26 10:28:38  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[3]`
2026-09-26 10:28:43  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-31
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.88315480233355, 44.89928737175076, 45.0337893955764, 44.953800201416016, 44.98685130199704], "candidate": [5.277814302104471, 5.335856987431155, 5.441549864988235, 5.417612303263819, 5.394363880157471]}
RUNTIME: 5.394363880157471
REF_RUNTIME: 44.953800201416016
SPEEDUP: 8.333475679453745

STATUS: completed; EXIT_CODE: 0

=== Event 176 | item.started | item_98 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-31 — High structural sibling from iter-8 (571ca47)
Inline FP32 activation conversion in grouped QKV projection; remove x-half materialization, retain once-per-forward weight conversion. Complete suite PASS; 5.394364 ms (5.278–5.442), versus original High iter-8 5.681 ms. Retain for combination; promising after cache/grid changes. Export validated structure, then combine Native attention improvements from iter-28/30. This reverses iter-3's activation conversion placement under materially changed reuse conditions; not a repeat of its original environment.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-31: validate inline input conversion with grouped projection'
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/export.py --source solution/ModelNew.py --reference reference.py --destination lowered31 --record opt/attention/export31.json"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 177 | item.completed | item_98 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-31 — High structural sibling from iter-8 (571ca47)
Inline FP32 activation conversion in grouped QKV projection; remove x-half materialization, retain once-per-forward weight conversion. Complete suite PASS; 5.394364 ms (5.278–5.442), versus original High iter-8 5.681 ms. Retain for combination; promising after cache/grid changes. Export validated structure, then combine Native attention improvements from iter-28/30. This reverses iter-3's activation conversion placement under materially changed reuse conditions; not a repeat of its original environment.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-31: validate inline input conversion with grouped projection'
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/export.py --source solution/ModelNew.py --reference reference.py --destination lowered31 --record opt/attention/export31.json"
OUTPUT:
EXPORT VALIDATED: full 30-trial Native replay passed; see opt/attention/export31.json

STATUS: completed; EXIT_CODE: 0

=== Event 178 | item.started | item_99 ===
COMMAND:
/bin/bash -lc 'date -u && tail -4 opt/attention/export31.validation.log'
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 179 | item.completed | item_99 ===
COMMAND:
/bin/bash -lc 'date -u && tail -4 opt/attention/export31.validation.log'
OUTPUT:
Sat Sep 26 10:30:11 UTC 2026
CORRECT: True

AUXILIARY_VALIDATION: Historical export replay; correctness retained. Its historical timing fields are archived, not represented as paper-protocol measurements.

STATUS: completed; EXIT_CODE: 0

=== Event 180 | item.completed | item_100 ===
Inline conversion passed at 5.39 ms on the High branch, improving on its 5.68 ms parent. Export validation also passed. I’m combining it with the retained Native attention improvements now to measure the complete result.

=== Event 181 | item.started | item_101 ===
COMMAND:
/bin/bash -lc "mv solution opt/attention/high31 && cp -r lowered31 solution
cat >> ITERATIONS.md <<'EOF'

## Representation edge — iter-31 High to Native
Export opt/attention/export31.json from ecfad78; required full replay PASS. Replaced entire solution tree. Five calls instead of six; no new ranking latency assigned to transfer. Next use exact Native attention from iter-30 (same QKV/output ABI and math), renamed from kernel_4.cpp to kernel_3.cpp to match this call graph. No activation cache hint remains because its standalone conversion kernel is removed.
EOF
git add solution opt/attention/export31* opt/attention/high31 ITERATIONS.md && git commit -m 'export inline-conversion High candidate to Native'
git show f0e291b:solution/kernel_4.cpp > solution/kernel_3.cpp
bash scripts/bench.sh iter-32"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 182 | item.started | item_102 ===
COMMAND:
/bin/bash -lc "cat > opt/attention/half_store_probe.cpp <<'EOF'
#include <hip/hip_runtime.h>
#include <hip/hip_fp16.h>
extern \"C\" __global__ void probe(const float* x, __half* y) {
  __half a=__float2half(x[threadIdx.x]);
  __builtin_nontemporal_store(__builtin_bit_cast(unsigned short,a),(unsigned short*)(y+threadIdx.x));
}
EOF
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc -O3 --offload-arch=gfx1201 --cuda-device-only -S opt/attention/half_store_probe.cpp -o opt/attention/half_store_probe.s
rg -n 'global_store|TH_STORE' opt/attention/half_store_probe.s"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 183 | item.completed | item_102 ===
COMMAND:
/bin/bash -lc "cat > opt/attention/half_store_probe.cpp <<'EOF'
#include <hip/hip_runtime.h>
#include <hip/hip_fp16.h>
extern \"C\" __global__ void probe(const float* x, __half* y) {
  __half a=__float2half(x[threadIdx.x]);
  __builtin_nontemporal_store(__builtin_bit_cast(unsigned short,a),(unsigned short*)(y+threadIdx.x));
}
EOF
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc -O3 --offload-arch=gfx1201 --cuda-device-only -S opt/attention/half_store_probe.cpp -o opt/attention/half_store_probe.s
rg -n 'global_store|TH_STORE' opt/attention/half_store_probe.s"
OUTPUT:
17:	global_store_b16 v0, v1, s[2:3] th:TH_STORE_NT

STATUS: completed; EXIT_CODE: 0

=== Event 184 | item.completed | item_101 ===
COMMAND:
/bin/bash -lc "mv solution opt/attention/high31 && cp -r lowered31 solution
cat >> ITERATIONS.md <<'EOF'

## Representation edge — iter-31 High to Native
Export opt/attention/export31.json from ecfad78; required full replay PASS. Replaced entire solution tree. Five calls instead of six; no new ranking latency assigned to transfer. Next use exact Native attention from iter-30 (same QKV/output ABI and math), renamed from kernel_4.cpp to kernel_3.cpp to match this call graph. No activation cache hint remains because its standalone conversion kernel is removed.
EOF
git add solution opt/attention/export31* opt/attention/high31 ITERATIONS.md && git commit -m 'export inline-conversion High candidate to Native'
git show f0e291b:solution/kernel_4.cpp > solution/kernel_3.cpp
bash scripts/bench.sh iter-32"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-32
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.69899895570212, 44.802058140846725, 45.03421644588217, 44.953800201416016, 45.027124099384054], "candidate": [4.371443220754443, 4.375436871974207, 4.322025370002771, 4.330150127410889, 4.225256845120836]}
RUNTIME: 4.330150127410889
REF_RUNTIME: 44.953800201416016
SPEEDUP: 10.381580055815544

STATUS: completed; EXIT_CODE: 0

=== Event 185 | item.started | item_103 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-32 — Low combination child of representation053a68a
Inline-conversion projection from High iter-31 plus exact attention kernel from iter-30 (ABI-compatible). Complete suite PASS; 4.330150 ms (4.225–4.375), 10.3816x. Promoted; compatible improvements combine successfully.
Next QKV-output streaming policy:300-MB intermediate is consumed only after projection finishes, much larger than8-MiB L2. Non-temporal stores may reduce interference with reused weights/activation tiles. This is distinct from final-output policy; compiler probe confirms bit-preserving half stores emit TH_STORE_NT.
EOF
git add solution opt/attention/half_store_probe.cpp opt/attention/half_store_probe.s ITERATIONS.md && git commit -m 'iter-32: combine inline conversion and Native attention, 4.33 ms'
python - <<'PY'
p='solution/kernel_2.cpp';s=open(p).read();out=[]
for line in s.splitlines():
    if line.strip().startswith('Y['):
        lhs,rhs=line.strip().split(' = ',1);idx=lhs[2:-1];line='    __builtin_nontemporal_store(__builtin_bit_cast(unsigned short, '+rhs[:-1]+'), (unsigned short*)(Y + ('+idx+')));'
    out.append(line)
open(p,'w').write('\\n'.join(out)+'\\n')
PY
bash scripts/bench.sh iter-33"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 186 | item.completed | item_104 ===
The combination passed at 4.33 ms. I’m testing a separate cache policy for the large QKV intermediate: streaming its writes may preserve cache space for weights and input tiles reused during the projection.

=== Event 187 | item.started | item_105 ===
COMMAND:
/bin/bash -lc "sed -n '17,58p' solution/kernel_2.cpp && date -u"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 188 | item.completed | item_105 ===
COMMAND:
/bin/bash -lc "sed -n '17,58p' solution/kernel_2.cpp && date -u"
OUTPUT:
  for (int i = 0; i < 16; ++i) {
    float broadcast_var = 0.000000e+00f;
    *(float4*)(c + (i * 4)) = make_float4(broadcast_var, broadcast_var, broadcast_var, broadcast_var);
  }
  for (int k = 0; k < 12; ++k) {
    __syncthreads();
    #pragma unroll
    for (int i_1 = 0; i_1 < 8; ++i_1) {
      float X_local_cast_1[4];
      half_t a_local_cast[4];
      *(float4*)(X_local_cast_1 + 0) = *(float4*)(X + (((((((int)blockIdx.x) * 98304) + (i_1 * 12288)) + ((((int)threadIdx.x) >> 4) * 768)) + (k * 64)) + ((((int)threadIdx.x) & 15) * 4)));
      uint2 __1;
      float4 v_ = *(float4*)(X_local_cast_1 + 0);
      *((half_t*)(&(((half2*)(&(__1.x)))->x))) = (half_t)(v_.x);
      *((half_t*)(&(((half2*)(&(__1.x)))->y))) = (half_t)(v_.y);
      *((half_t*)(&(((half2*)(&(__1.y)))->x))) = (half_t)(v_.z);
      *((half_t*)(&(((half2*)(&(__1.y)))->y))) = (half_t)(v_.w);
      *(uint2*)(a_local_cast + 0) = __1;
      *(uint2*)(((half_t*)a) + ((((((i_1 * 1024) + ((((int)threadIdx.x) >> 4) * 64)) + (((((((int)threadIdx.x) & 127) >> 6) + ((((int)threadIdx.x) & 15) >> 3)) & 1) * 32)) + (((((((int)threadIdx.x) & 63) >> 5) + ((((int)threadIdx.x) & 7) >> 2)) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 8)) + ((((int)threadIdx.x) & 1) * 4))) = *(uint2*)(a_local_cast + 0);
    }
    #pragma unroll
    for (int i_2 = 0; i_2 < 4; ++i_2) {
      *(uint4*)(((half_t*)b) + (((((i_2 * 2048) + ((((int)threadIdx.x) >> 3) * 64)) + (((((((int)threadIdx.x) & 63) >> 5) + ((((int)threadIdx.x) & 7) >> 2)) & 1) * 32)) + (((((((int)threadIdx.x) & 31) >> 4) + ((((int)threadIdx.x) & 3) >> 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 15) >> 3) + (((int)threadIdx.x) & 1)) & 1) * 8))) = *(uint4*)(W + (((((((int)blockIdx.y) * 98304) + (i_2 * 24576)) + ((((int)threadIdx.x) >> 3) * 768)) + (k * 64)) + ((((int)threadIdx.x) & 7) * 8)));
    }
    {
      half_t A_local[16];
      half_t B_local[32];
      __syncthreads();
      for (int ki = 0; ki < 4; ++ki) {
        for (int i_3 = 0; i_3 < 2; ++i_3) {
          *(uint4*)(A_local + (i_3 * 8)) = *(uint4*)(((half_t*)a) + ((((((((((int)threadIdx.x) & 127) >> 5) * 2048) + (i_3 * 1024)) + ((((int)threadIdx.x) & 15) * 64)) + (((((((int)threadIdx.x) & 7) >> 2) + (ki >> 1)) & 1) * 32)) + (((((((int)threadIdx.x) & 3) >> 1) + (ki & 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + (((int)threadIdx.x) & 1)) & 1) * 8)));
        }
        for (int j = 0; j < 4; ++j) {
          *(uint4*)(B_local + (j * 8)) = *(uint4*)(((half_t*)b) + (((((((((int)threadIdx.x) >> 7) * 4096) + (j * 1024)) + ((((int)threadIdx.x) & 15) * 64)) + (((((((int)threadIdx.x) & 7) >> 2) + (ki >> 1)) & 1) * 32)) + (((((((int)threadIdx.x) & 3) >> 1) + (ki & 1)) & 1) * 16)) + (((((((int)threadIdx.x) & 31) >> 4) + (((int)threadIdx.x) & 1)) & 1) * 8)));
        }
        for (int i_4 = 0; i_4 < 2; ++i_4) {
          for (int j_1 = 0; j_1 < 4; ++j_1) {
            {
      typedef __attribute__((__vector_size__(8 * sizeof(__fp16)))) __fp16 tl_v8f16;
      typedef __attribute__((__vector_size__(8 * sizeof(float)))) float tl_v8f32;
      *((tl_v8f32*)c + ((i_4 * 4) + j_1)) = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(
          *((tl_v8f16*)A_local + i_4),
Sat Sep 26 10:31:42 UTC 2026

STATUS: completed; EXIT_CODE: 0

=== Event 189 | item.completed | item_103 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-32 — Low combination child of representation053a68a
Inline-conversion projection from High iter-31 plus exact attention kernel from iter-30 (ABI-compatible). Complete suite PASS; 4.330150 ms (4.225–4.375), 10.3816x. Promoted; compatible improvements combine successfully.
Next QKV-output streaming policy:300-MB intermediate is consumed only after projection finishes, much larger than8-MiB L2. Non-temporal stores may reduce interference with reused weights/activation tiles. This is distinct from final-output policy; compiler probe confirms bit-preserving half stores emit TH_STORE_NT.
EOF
git add solution opt/attention/half_store_probe.cpp opt/attention/half_store_probe.s ITERATIONS.md && git commit -m 'iter-32: combine inline conversion and Native attention, 4.33 ms'
python - <<'PY'
p='solution/kernel_2.cpp';s=open(p).read();out=[]
for line in s.splitlines():
    if line.strip().startswith('Y['):
        lhs,rhs=line.strip().split(' = ',1);idx=lhs[2:-1];line='    __builtin_nontemporal_store(__builtin_bit_cast(unsigned short, '+rhs[:-1]+'), (unsigned short*)(Y + ('+idx+')));'
    out.append(line)
open(p,'w').write('\\n'.join(out)+'\\n')
PY
bash scripts/bench.sh iter-33"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-33
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.98188162558217, 44.953800201416016, 45.2705228895947, 44.92125874682619, 44.91837744441455], "candidate": [4.3875898757156655, 4.342196941375732, 4.342401843480239, 4.321938427276119, 4.263231899848958]}
RUNTIME: 4.342196941375732
REF_RUNTIME: 44.953800201416016
SPEEDUP: 10.352777823838954

STATUS: completed; EXIT_CODE: 0

=== Event 190 | item.started | item_106 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-33 — Low cache-policy child of iter-32 (3749de7)
QKV half-output non-temporal stores. Complete suite PASS; 4.342197 ms (4.263–4.388). No measurable gain versus4.330-ms parent; reject and restore default stores. Static working-set projections alone did not predict a cache-policy benefit.
Next QKV group size4 versus8, inline FP32 input: smaller group bounds live activation/output footprint; weights remain reused across groups. Keep output projection grouping unchanged.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-33: reject QKV streaming-store hint'
git restore --source=3749de7 -- solution
sed -i 's/rasterization2DColumn<8>/rasterization2DColumn<4>/' solution/kernel_2.cpp
bash scripts/bench.sh iter-34"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 191 | item.completed | item_106 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-33 — Low cache-policy child of iter-32 (3749de7)
QKV half-output non-temporal stores. Complete suite PASS; 4.342197 ms (4.263–4.388). No measurable gain versus4.330-ms parent; reject and restore default stores. Static working-set projections alone did not predict a cache-policy benefit.
Next QKV group size4 versus8, inline FP32 input: smaller group bounds live activation/output footprint; weights remain reused across groups. Keep output projection grouping unchanged.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-33: reject QKV streaming-store hint'
git restore --source=3749de7 -- solution
sed -i 's/rasterization2DColumn<8>/rasterization2DColumn<4>/' solution/kernel_2.cpp
bash scripts/bench.sh iter-34"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-34
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.85357837923508, 44.79506090344806, 45.01582743353516, 44.953800201416016, 45.17291126128644], "candidate": [4.321193218231201, 4.37120572106737, 4.274733727207058, 4.31781452916463, 4.338792861340559]}
RUNTIME: 4.321193218231201
REF_RUNTIME: 44.953800201416016
SPEEDUP: 10.403098850510787

STATUS: completed; EXIT_CODE: 0

=== Event 192 | item.completed | item_107 ===
The QKV store hint and smaller tile group produced no clear gain. I’ll check a larger group, then test wider projection tiles, which could reduce repeated input loads and conversions more directly.

=== Event 193 | item.started | item_108 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-34 — Low tuning child of iter-32 (3749de7)
QKV M-group4 versus8. Complete suite PASS; 4.321193 ms (4.275–4.371). Inconclusive9-us median difference; retain baseline8 as equivalent, not a demonstrated promotion. Test group16 as opposite reuse/footprint balance before moving to wider projection tiles.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-34: record inconclusive smaller projection grouping'
sed -i 's/rasterization2DColumn<4>/rasterization2DColumn<16>/' solution/kernel_2.cpp
bash scripts/bench.sh iter-35"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 194 | item.completed | item_108 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-34 — Low tuning child of iter-32 (3749de7)
QKV M-group4 versus8. Complete suite PASS; 4.321193 ms (4.275–4.371). Inconclusive9-us median difference; retain baseline8 as equivalent, not a demonstrated promotion. Test group16 as opposite reuse/footprint balance before moving to wider projection tiles.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-34: record inconclusive smaller projection grouping'
sed -i 's/rasterization2DColumn<4>/rasterization2DColumn<16>/' solution/kernel_2.cpp
bash scripts/bench.sh iter-35"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-35
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [45.031136614466924, 44.91700602355676, 44.953800201416016, 45.20048982212336, 44.86056525153817], "candidate": [4.1856369972229, 4.342218198036217, 4.11074545577401, 4.226194572717458, 4.185391145922005]}
RUNTIME: 4.1856369972229
REF_RUNTIME: 44.953800201416016
SPEEDUP: 10.740014060283322

STATUS: completed; EXIT_CODE: 0

=== Event 195 | item.started | item_109 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-35 — Low tuning child of iter-34 (6df91fb)
QKV M-group16. Complete suite PASS; 4.185637 ms (4.111–4.342), 10.7400x. Retain provisional best; wider variation calls for later repeat, but plausible benefit from more weight reuse. Preserve Native branch.
Scheduler: restore High iter-31 (ecfad78) to economically test wider QKV tile geometry. Keep group8 in these High comparisons to isolate geometry from grouping; group16 remains a compatible mechanism to revisit after geometry changes. Test128x256x32 and64x256x32, both256 threads,24/20 KiB operand LDS; trade fewer activation conversions for different register/weight reuse.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-35: retain larger projection group, 4.19 ms'
mv solution opt/attention/low35
mkdir solution
git restore --source=ecfad78 -- solution
sed -i \"s/linear(2304,'float16','float32')/linear(2304,'float16','float32',128,256,32)/\" solution/ModelNew.py
bash scripts/bench.sh iter-36"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 196 | item.completed | item_109 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-35 — Low tuning child of iter-34 (6df91fb)
QKV M-group16. Complete suite PASS; 4.185637 ms (4.111–4.342), 10.7400x. Retain provisional best; wider variation calls for later repeat, but plausible benefit from more weight reuse. Preserve Native branch.
Scheduler: restore High iter-31 (ecfad78) to economically test wider QKV tile geometry. Keep group8 in these High comparisons to isolate geometry from grouping; group16 remains a compatible mechanism to revisit after geometry changes. Test128x256x32 and64x256x32, both256 threads,24/20 KiB operand LDS; trade fewer activation conversions for different register/weight reuse.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-35: retain larger projection group, 4.19 ms'
mv solution opt/attention/low35
mkdir solution
git restore --source=ecfad78 -- solution
sed -i \"s/linear(2304,'float16','float32')/linear(2304,'float16','float32',128,256,32)/\" solution/ModelNew.py
bash scripts/bench.sh iter-36"
OUTPUT:
2026-09-26 10:34:20  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[3]`
2026-09-26 10:34:26  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-36
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.953800201416016, 44.93876414462174, 45.22846613621284, 44.899718833622735, 45.126547239518835], "candidate": [5.426574594362847, 5.311499199799352, 5.246605210823481, 5.315911769866943, 5.349324874119437]}
RUNTIME: 5.315911769866943
REF_RUNTIME: 44.953800201416016
SPEEDUP: 8.4564609322967

STATUS: completed; EXIT_CODE: 0

=== Event 197 | item.completed | item_110 ===
The larger projection group measured 4.19 ms, with more variation, so I’ve retained it for later comparison. The first wider-tile High candidate passed but showed only a small gain. I’m testing a smaller row tile at the same width to reduce register pressure.

=== Event 198 | item.started | item_111 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-36 — High tuning sibling from iter-31 (ecfad78)
QKV128x256x32/256 threads/group8, projection unchanged. Complete suite PASS; 5.315912 ms (5.247–5.427). Small gain over5.394-ms High parent overlaps spread; retain exploration. Next64x256x32 reduces accumulator footprint while changing weight reuse and block count.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-36: retain wider QKV tile'
sed -i \"s/'float32',128,256,32)/'float32',64,256,32)/\" solution/ModelNew.py
bash scripts/bench.sh iter-37"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 199 | item.completed | item_111 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-36 — High tuning sibling from iter-31 (ecfad78)
QKV128x256x32/256 threads/group8, projection unchanged. Complete suite PASS; 5.315912 ms (5.247–5.427). Small gain over5.394-ms High parent overlaps spread; retain exploration. Next64x256x32 reduces accumulator footprint while changing weight reuse and block count.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-36: retain wider QKV tile'
sed -i \"s/'float32',128,256,32)/'float32',64,256,32)/\" solution/ModelNew.py
bash scripts/bench.sh iter-37"
OUTPUT:
2026-09-26 10:35:02  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[3]`
2026-09-26 10:35:08  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-37
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.80536370986794, 44.953800201416016, 44.81136378504473, 45.27684173469436, 44.96132760136726], "candidate": [5.800072193145752, 5.832606681272611, 5.817023742682797, 5.78138712904251, 5.793213815078918]}
RUNTIME: 5.800072193145752
REF_RUNTIME: 44.953800201416016
SPEEDUP: 7.750558735206825

STATUS: completed; EXIT_CODE: 0

=== Event 200 | item.started | item_112 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-37 — High tuning child of iter-36 (d7ab0e5)
QKV64x256x32/256. Complete suite PASS; 5.800072 ms (5.781–5.833). Rejected; smaller row tile is materially slower. Next restore High iter-31 and double reduction chunk to128 (128x128 tile,256 threads,64 KiB LDS) to test fewer barriers versus residency.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-37: reject smaller-row wide QKV tile'
git restore --source=ecfad78 -- solution
sed -i \"s/linear(2304,'float16','float32')/linear(2304,'float16','float32',128,128,128)/\" solution/ModelNew.py
bash scripts/bench.sh iter-38"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 201 | item.completed | item_112 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-37 — High tuning child of iter-36 (d7ab0e5)
QKV64x256x32/256. Complete suite PASS; 5.800072 ms (5.781–5.833). Rejected; smaller row tile is materially slower. Next restore High iter-31 and double reduction chunk to128 (128x128 tile,256 threads,64 KiB LDS) to test fewer barriers versus residency.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-37: reject smaller-row wide QKV tile'
git restore --source=ecfad78 -- solution
sed -i \"s/linear(2304,'float16','float32')/linear(2304,'float16','float32',128,128,128)/\" solution/ModelNew.py
bash scripts/bench.sh iter-38"
OUTPUT:
2026-09-26 10:35:59  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[3]`
2026-09-26 10:36:05  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-38
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [45.079427538720616, 44.953800201416016, 44.880978549848635, 45.158267844995095, 44.950078143144125], "candidate": [6.014954562117787, 5.8924802700626016, 5.650824782475372, 5.7922868728637695, 5.639590573977535]}
RUNTIME: 5.7922868728637695
REF_RUNTIME: 44.953800201416016
SPEEDUP: 7.760976137425039

STATUS: completed; EXIT_CODE: 0

=== Event 202 | item.started | item_113 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-38 — High tuning sibling from iter-31 (ecfad78)
QKV128x128x128/256,64 KiB LDS. Complete suite PASS; 5.792287 ms (5.640–6.015). Rejected; fewer reduction barriers do not compensate for this resource balance. Next test128x256x32 with512 threads, retaining geometry of iter-36 but reducing per-thread accumulator pressure.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-38: reject larger projection reduction chunk'
git restore --source=d7ab0e5 -- solution
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read().replace('BK: int=64):','BK: int=64, NT: int=256):',1).replace('T.ceildiv(N,BN),threads=256)', 'T.ceildiv(N,BN),threads=NT)',1).replace(\"'float32',128,256,32)\",\"'float32',128,256,32,512)\");open(p,'w').write(s)
PY
bash scripts/bench.sh iter-39"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 203 | item.completed | item_114 ===
The smaller row tile and larger reduction chunk were slower. I’m testing thread ownership on the retained geometries next; register pressure can change whether a wider tile helps. The best Native candidate remains safely retained.

=== Event 204 | item.completed | item_113 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-38 — High tuning sibling from iter-31 (ecfad78)
QKV128x128x128/256,64 KiB LDS. Complete suite PASS; 5.792287 ms (5.640–6.015). Rejected; fewer reduction barriers do not compensate for this resource balance. Next test128x256x32 with512 threads, retaining geometry of iter-36 but reducing per-thread accumulator pressure.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-38: reject larger projection reduction chunk'
git restore --source=d7ab0e5 -- solution
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read().replace('BK: int=64):','BK: int=64, NT: int=256):',1).replace('T.ceildiv(N,BN),threads=256)', 'T.ceildiv(N,BN),threads=NT)',1).replace(\"'float32',128,256,32)\",\"'float32',128,256,32,512)\");open(p,'w').write(s)
PY
bash scripts/bench.sh iter-39"
OUTPUT:
2026-09-26 10:37:53  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[3]`
2026-09-26 10:37:58  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-39
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.945025214861644, 45.08667308638832, 45.04543202225962, 44.953800201416016, 44.86584539360541], "candidate": [5.336948871612549, 5.31899968885887, 5.2536312830456975, 5.342495256002818, 5.3700409727587175]}
RUNTIME: 5.336948871612549
REF_RUNTIME: 44.953800201416016
SPEEDUP: 8.423127386609815

STATUS: completed; EXIT_CODE: 0

=== Event 205 | item.started | item_115 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-39 — High tuning child of iter-36 (d7ab0e5)
QKV128x256x32/512 threads. Complete suite PASS; 5.336949 ms (5.254–5.370). Inconclusive/slightly slower than256 threads; not promoted. Next128x128x64/128 threads tests the opposite ownership balance on the retained baseline geometry; output projection remains256 threads.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-39: record wider tile with 512 threads'
sed -i \"s/'float32',128,256,32,512)/'float32',128,128,64,128)/\" solution/ModelNew.py
bash scripts/bench.sh iter-40"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 206 | item.completed | item_115 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-39 — High tuning child of iter-36 (d7ab0e5)
QKV128x256x32/512 threads. Complete suite PASS; 5.336949 ms (5.254–5.370). Inconclusive/slightly slower than256 threads; not promoted. Next128x128x64/128 threads tests the opposite ownership balance on the retained baseline geometry; output projection remains256 threads.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-39: record wider tile with 512 threads'
sed -i \"s/'float32',128,256,32,512)/'float32',128,128,64,128)/\" solution/ModelNew.py
bash scripts/bench.sh iter-40"
OUTPUT:
2026-09-26 10:38:20  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[3]`
2026-09-26 10:38:26  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-40
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.898665806447354, 44.995756629443335, 44.93514817084407, 44.953800201416016, 45.1274634405609], "candidate": [5.4755116189823, 5.6002068519592285, 5.503944118414484, 5.726289981710727, 5.779305535655453]}
RUNTIME: 5.6002068519592285
REF_RUNTIME: 44.953800201416016
SPEEDUP: 8.027167815361848

STATUS: completed; EXIT_CODE: 0

=== Event 207 | item.started | item_116 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-40 — High tuning child of iter-39 (72de921)
QKV128x128x64/128 threads. Complete suite PASS; 5.600207 ms (5.476–5.779). Rejected versus retained256-thread configurations. Next restore iter-36 and apply its128x256x32 geometry to output projection too, checking compatibility at full-forward scope.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-40: reject 128-thread inline QKV tile'
git restore --source=d7ab0e5 -- solution
sed -i \"s/linear(768,'float32')/linear(768,'float32','float16',128,256,32)/\" solution/ModelNew.py
bash scripts/bench.sh iter-41"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 208 | item.completed | item_116 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-40 — High tuning child of iter-39 (72de921)
QKV128x128x64/128 threads. Complete suite PASS; 5.600207 ms (5.476–5.779). Rejected versus retained256-thread configurations. Next restore iter-36 and apply its128x256x32 geometry to output projection too, checking compatibility at full-forward scope.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-40: reject 128-thread inline QKV tile'
git restore --source=d7ab0e5 -- solution
sed -i \"s/linear(768,'float32')/linear(768,'float32','float16',128,256,32)/\" solution/ModelNew.py
bash scripts/bench.sh iter-41"
OUTPUT:
2026-09-26 10:39:05  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[3]`
2026-09-26 10:39:11  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-41
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [45.008195784185716, 44.750880138358866, 44.85071422513081, 44.953800201416016, 45.12104441243995], "candidate": [5.357182752788047, 5.399652010252216, 5.266722620606825, 5.377309799194336, 5.384759857355174]}
RUNTIME: 5.377309799194336
REF_RUNTIME: 44.953800201416016
SPEEDUP: 8.359905209134741

STATUS: completed; EXIT_CODE: 0

=== Event 209 | item.started | item_117 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-41 — High combination child of iter-36 (d7ab0e5)
Apply128x256x32 to output projection as well. Complete suite PASS; 5.377310 ms (5.267–5.400). No gain; retain original output projection geometry.
Next structural sibling from iter-36: QKV head-major intermediate, with producer and attention consumer updated together. This keeps each head's sequence contiguous, potentially improving spatial locality for repeated K/V reads; no extra reorder kernel. Cost is more complex projection output addressing. Native attention improvements remain retained and can be adapted if this representation is worthwhile.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-41: reject wider output projection combination'
git restore --source=d7ab0e5 -- solution
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read()
old='''            for i,j in T.Parallel(BM,BN):
                Y[bm//(512//BM),(bm%(512//BM))*BM+i,bn*BN+j]=c[i,j]+B[bn*BN+j]'''
new='''            if N == 2304:
                for i,j in T.Parallel(BM,BN):
                    T.reshape(Y,(3,128,8,512,96))[(bn*BN+j)//768,bm//(512//BM),((bn*BN+j)%768)//96,(bm%(512//BM))*BM+i,(bn*BN+j)%96]=c[i,j]+B[bn*BN+j]
            else:
                for i,j in T.Parallel(BM,BN):
                    Y[bm//(512//BM),(bm%(512//BM))*BM+i,bn*BN+j]=c[i,j]+B[bn*BN+j]'''
assert old in s;s=s.replace(old,new)
s=s.replace('Q[bh//8,mi*BM+i,(bh%8)*96+j]', 'T.reshape(Q,(3,128,8,512,96))[0,bh//8,bh%8,mi*BM+i,j]')
s=s.replace('Q[bh//8,nb*64+i,768+(bh%8)*96+j]', 'T.reshape(Q,(3,128,8,512,96))[1,bh//8,bh%8,nb*64+i,j]')
s=s.replace('Q[bh//8,nb*64+i,1536+(bh%8)*96+j]', 'T.reshape(Q,(3,128,8,512,96))[2,bh//8,bh%8,nb*64+i,j]')
open(p,'w').write(s)
PY
bash scripts/bench.sh iter-42"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 210 | item.completed | item_117 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-41 — High combination child of iter-36 (d7ab0e5)
Apply128x256x32 to output projection as well. Complete suite PASS; 5.377310 ms (5.267–5.400). No gain; retain original output projection geometry.
Next structural sibling from iter-36: QKV head-major intermediate, with producer and attention consumer updated together. This keeps each head's sequence contiguous, potentially improving spatial locality for repeated K/V reads; no extra reorder kernel. Cost is more complex projection output addressing. Native attention improvements remain retained and can be adapted if this representation is worthwhile.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-41: reject wider output projection combination'
git restore --source=d7ab0e5 -- solution
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read()
old='''            for i,j in T.Parallel(BM,BN):
                Y[bm//(512//BM),(bm%(512//BM))*BM+i,bn*BN+j]=c[i,j]+B[bn*BN+j]'''
new='''            if N == 2304:
                for i,j in T.Parallel(BM,BN):
                    T.reshape(Y,(3,128,8,512,96))[(bn*BN+j)//768,bm//(512//BM),((bn*BN+j)%768)//96,(bm%(512//BM))*BM+i,(bn*BN+j)%96]=c[i,j]+B[bn*BN+j]
            else:
                for i,j in T.Parallel(BM,BN):
                    Y[bm//(512//BM),(bm%(512//BM))*BM+i,bn*BN+j]=c[i,j]+B[bn*BN+j]'''
assert old in s;s=s.replace(old,new)
s=s.replace('Q[bh//8,mi*BM+i,(bh%8)*96+j]', 'T.reshape(Q,(3,128,8,512,96))[0,bh//8,bh%8,mi*BM+i,j]')
s=s.replace('Q[bh//8,nb*64+i,768+(bh%8)*96+j]', 'T.reshape(Q,(3,128,8,512,96))[1,bh//8,bh%8,nb*64+i,j]')
s=s.replace('Q[bh//8,nb*64+i,1536+(bh%8)*96+j]', 'T.reshape(Q,(3,128,8,512,96))[2,bh//8,bh%8,nb*64+i,j]')
open(p,'w').write(s)
PY
bash scripts/bench.sh iter-42"
OUTPUT:
2026-09-26 10:40:35  [TileLang:tilelang.language.eager.builder:CRITICAL] (builder.py:1669): Failed to build prim_func from main
args={'X': X, 'W': W, 'B': B, 'Y': Y}
source=def make_closure(BK, BM, BN, N):

    def main(__tb):
        __tb_fl = '/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/solution/ModelNew.py'
        __tb_fn = 'main'
        range = __tb.override('range')
        __tb.set_fileline(__tb_fl, 7, __tb_fn)

        def main(X, W, B, Y, **__kwargs):
            __tb.set_fileline(__tb_fl, 7, __tb_fn)
            X = __tb.arg('X', X)
            __tb.set_fileline(__tb_fl, 7, __tb_fn)
            W = __tb.arg('W', W)
            __tb.set_fileline(__tb_fl, 7, __tb_fn)
            B = __tb.arg('B', B)
            __tb.set_fileline(__tb_fl, 7, __tb_fn)
            Y = __tb.arg('Y', Y)
            __tb.set_fileline(__tb_fl, 1, __tb_fn)
            if __tb.skip_kernel_ctx():
                __tb.set_fileline(__tb_fl, 1, __tb_fn)
                return
            __tb.set_fileline(__tb_fl, 8, __tb_fn)
            with __tb.ctx_with(__tb.rval('T', T).Kernel(65536 // __tb.rval('BM', BM), __tb.rval('T', T).ceildiv(__tb.rval('N', N), __tb.rval('BN', BN)), threads=256)) as (bm, bn):
                __tb.set_fileline(__tb_fl, 9, __tb_fn)
                __tb.eval(__tb.rval('T', T).use_swizzle(panel_size=8, order='column'))
                __tb.set_fileline(__tb_fl, 10, __tb_fn)
                a = __tb.bind('a', __tb.rval('T', T).alloc_shared((__tb.rval('BM', BM), __tb.rval('BK', BK)), 'float16'))
                __tb.set_fileline(__tb_fl, 11, __tb_fn)
                b = __tb.bind('b', __tb.rval('T', T).alloc_shared((__tb.rval('BN', BN), __tb.rval('BK', BK)), 'float16'))
                __tb.set_fileline(__tb_fl, 12, __tb_fn)
                c = __tb.bind('c', __tb.rval('T', T).alloc_fragment((__tb.rval('BM', BM), __tb.rval('BN', BN)), 'float32'))
                __tb.set_fileline(__tb_fl, 13, __tb_fn)
                __tb.eval(__tb.rval('T', T).clear(__tb.rval('c', c)))
                __tb.set_fileline(__tb_fl, 14, __tb_fn)
                for __6 in __tb.ctx_for(__tb.rval('T', T).Pipelined(768 // __tb.rval('BK', BK), num_stages=2)):
                    __tb.set_fileline(__tb_fl, 14, __tb_fn)
                    k = __tb.bind('k', __6)
                    __tb.set_fileline(__tb_fl, 15, __tb_fn)
                    for __0 in __tb.ctx_for(__tb.rval('T', T).Parallel(__tb.rval('BM', BM), __tb.rval('BK', BK))):
                        __1, __2 = __tb.unwrap_value(__0)
                        __tb.set_fileline(__tb_fl, 15, __tb_fn)
                        __1, __2 = (__tb.bind('_', __1), __tb.bind('_', __2))
                        __tb.set_fileline(__tb_fl, 15, __tb_fn)
                        i, j = (__tb.bind('i', __1), __tb.bind('j', __2))
                        __tb.set_fileline(__tb_fl, 16, __tb_fn)
                        __tb.assign_slice(__tb.rval('a', a), (__tb.rval('i', i), __tb.rval('j', j)), __tb.rval('X', X)[__tb.rval('bm', bm) // (512 // __tb.rval('BM', BM)), __tb.rval('bm', bm) % (512 // __tb.rval('BM', BM)) * __tb.rval('BM', BM) + __tb.rval('i', i), __tb.rval('k', k) * __tb.rval('BK', BK) + __tb.rval('j', j)])
                    __tb.set_fileline(__tb_fl, 17, __tb_fn)
                    for __3 in __tb.ctx_for(__tb.rval('T', T).Parallel(__tb.rval('BN', BN), __tb.rval('BK', BK))):
                        __4, __5 = __tb.unwrap_value(__3)
                        __tb.set_fileline(__tb_fl, 17, __tb_fn)
                        __4, __5 = (__tb.bind('_', __4), __tb.bind('_', __5))
                        __tb.set_fileline(__tb_fl, 17, __tb_fn)
                        i, j = (__tb.bind('i', __4), __tb.bind('j', __5))
                        __tb.set_fileline(__tb_fl, 18, __tb_fn)
                        __tb.assign_slice(__tb.rval('b', b), (__tb.rval('i', i), __tb.rval('j', j)), __tb.rval('W', W)[__tb.rval('bn', bn) * __tb.rval('BN', BN) + __tb.rval('i', i), __tb.rval('k', k) * __tb.rval('BK', BK) + __tb.rval('j', j)])
                    __tb.set_fileline(__tb_fl, 19, __tb_fn)
                    __tb.eval(__tb.rval('T', T).gemm(__tb.rval('a', a), __tb.rval('b', b), __tb.rval('c', c), transpose_B=True))
                __tb.set_fileline(__tb_fl, 20, __tb_fn)
                for __13 in __tb.ctx_if(__tb.rval('N', N) == 2304):
                    __tb.set_fileline(__tb_fl, 20, __tb_fn)
                    for _ in __tb.ctx_then(__13):
                        __tb.set_fileline(__tb_fl, 21, __tb_fn)
                        for __7 in __tb.ctx_for(__tb.rval('T', T).Parallel(__tb.rval('BM', BM), __tb.rval('BN', BN))):
                            __8, __9 = __tb.unwrap_value(__7)
                            __tb.set_fileline(__tb_fl, 21, __tb_fn)
                            __8, __9 = (__tb.bind('_', __8), __tb.bind('_', __9))
                            __tb.set_fileline(__tb_fl, 21, __tb_fn)
                            i, j = (__tb.bind('i', __8), __tb.bind('j', __9))
                            __tb.set_fileline(__tb_fl, 22, __tb_fn)
                            __tb.assign_slice(__tb.rval('T', T).reshape(__tb.rval('Y', Y), (3, 128, 8, 512, 96)), ((__tb.rval('bn', bn) * __tb.rval('BN', BN) + __tb.rval('j', j)) // 768, __tb.rval('bm', bm) // (512 // __tb.rval('BM', BM)), (__tb.rval('bn', bn) * __tb.rval('BN', BN) + __tb.rval('j', j)) % 768 // 96, __tb.rval('bm', bm) % (512 // __tb.rval('BM', BM)) * __tb.rval('BM', BM) + __tb.rval('i', i), (__tb.rval('bn', bn) * __tb.rval('BN', BN) + __tb.rval('j', j)) % 96), __tb.rval('c', c)[__tb.rval('i', i), __tb.rval('j', j)] + __tb.rval('B', B)[__tb.rval('bn', bn) * __tb.rval('BN', BN) + __tb.rval('j', j)])
                    __tb.set_fileline(__tb_fl, 20, __tb_fn)
                    for _ in __tb.ctx_else(__13):
                        __tb.set_fileline(__tb_fl, 24, __tb_fn)
                        for __10 in __tb.ctx_for(__tb.rval('T', T).Parallel(__tb.rval('BM', BM), __tb.rval('BN', BN))):
                            __11, __12 = __tb.unwrap_value(__10)
                            __tb.set_fileline(__tb_fl, 24, __tb_fn)
                            __11, __12 = (__tb.bind('_', __11), __tb.bind('_', __12))
                            __tb.set_fileline(__tb_fl, 24, __tb_fn)
                            i, j = (__tb.bind('i', __11), __tb.bind('j', __12))
                            __tb.set_fileline(__tb_fl, 25, __tb_fn)
                            __tb.assign_slice(__tb.rval('Y', Y), (__tb.rval('bm', bm) // (512 // __tb.rval('BM', BM)), __tb.rval('bm', bm) % (512 // __tb.rval('BM', BM)) * __tb.rval('BM', BM) + __tb.rval('i', i), __tb.rval('bn', bn) * __tb.rval('BN', BN) + __tb.rval('j', j)), __tb.rval('c', c)[__tb.rval('i', i), __tb.rval('j', j)] + __tb.rval('B', B)[__tb.rval('bn', bn) * __tb.rval('BN', BN) + __tb.rval('j', j)])
        return main
    return main
Traceback (most recent call last):
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/scripts/rocm_bench.py", line 126, in <module>
    result=run(a.ref,a.solution,a.backend,a.reference_only)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/scripts/rocm_bench.py", line 77, in run
    candidate=model(load(solution,'benchmark_candidate'),ref)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/scripts/rocm_bench.py", line 53, in model
    instance = cls(*init).to('cuda')
               ^^^^^^^^^^
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/solution/ModelNew.py", line 89, in __init__
    self.qkv=linear(2304,'float16','float32',128,256,32)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/__init__.py", line 539, in __call__
    kernel = self.compile(*args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/__init__.py", line 453, in compile
    prim_func = self.get_tir(*args, **kwargs)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/jit/__init__.py", line 374, in get_tir
    tir = self.func(*args, **kwargs)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/eager/builder.py", line 1591, in __call__
    return self.get_tir(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/eager/builder.py", line 1583, in get_tir
    self.p1_cache[bound.p1_key] = self._build_tir_template(**bound.compile_kwargs)
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/eager/builder.py", line 1551, in _build_tir_template
    return TirTemplate.from_lazy_style(self.orig_func.__name__, self.orig_func(*args, **kwargs))
                                                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/solution/ModelNew.py", line 6, in linear
    @T.prim_func
     ^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/eager/builder.py", line 1672, in prim_func
    return impl(func) if func is not None else impl
           ^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/eager/builder.py", line 1670, in impl
    raise e
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/eager/builder.py", line 1663, in impl
    ir_gen.gen(builder)(**annot)
  File "/home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/solution/ModelNew.py", line 22, in main
    T.reshape(Y,(3,128,8,512,96))[(bn*BN+j)//768,bm//(512//BM),((bn*BN+j)%768)//96,(bm%(512//BM))*BM+i,(bn*BN+j)%96]=c[i,j]+B[bn*BN+j]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/language/customize.py", line 71, in reshape
    assert prim_expr_equal(bits, src_bits) or arith.Analyzer().can_prove_equal(bits, src_bits), (
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/utils/language.py", line 476, in prim_expr_equal
    lhs = tirx.IntImm("int32", lhs)
          ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/3rdparty/tvm/python/tvm/tirx/expr.py", line 623, in __init__
    self.__init_handle_by_constructor__(
  File "python/tvm_ffi/cython/object.pxi", line 355, in tvm_ffi.core.Object.__init_handle_by_constructor__
  File "python/tvm_ffi/cython/object.pxi", line 183, in tvm_ffi.core.CObject.__init_handle_by_constructor__
  File "python/tvm_ffi/cython/function.pxi", line 126, in tvm_ffi.core.ConstructorCall
  File "python/tvm_ffi/cython/error.pxi", line 193, in tvm_ffi.core.CHECK_CALL
  File "/project/3rdparty/tvm/src/ir/expr.cc", line 73, in tvm::IntImm::IntImm(tvm::DataType, int64_t, tvm::Span)
ValueError: Check failed: value < 1LL << (dtype.bits() - 1) (2415919104 vs. 2147483648) : Literal value 2415919104 exceeds maximum of int32

RECORD_ID: CASE-MINGPT-iter-42
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: False
CORRECT: False
TIMING_SAMPLES_STATUS: unavailable for failed evaluation

STATUS: failed; EXIT_CODE: 1

=== Event 211 | item.completed | item_118 ===
The projection variants have shown only small or inconsistent gains. I’m now testing a different QKV layout that stores each head’s sequence contiguously. This may improve repeated attention reads without adding a reorder kernel, though it makes projection output addressing more complex.

=== Event 212 | item.started | item_119 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-42 — High structural child of iter-36 (d7ab0e5)
Head-major QKV intermediate attempt. Compile FAIL: installed T.reshape computes bit count as int32; this tensor has2415919104 bits, exceeding int32 maximum. No latency. This is an API arithmetic limit, not invalid dataflow. Fix by declaring the intermediate's5D shape directly in producer/consumer signatures, avoiding reshape entirely.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-42: record reshape bit-count overflow'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();s=s.replace('    @T.prim_func\\n    def main(X:', '    out_shape=(3,128,8,512,96) if N==2304 else (128,512,N)\\n    @T.prim_func\\n    def main(X:',1).replace('Y:T.Tensor((128,512,N),out_dtype)', 'Y:T.Tensor(out_shape,out_dtype)',1).replace('T.reshape(Y,(3,128,8,512,96))','Y').replace(\"Q:T.Tensor((128,512,2304),'float16')\", \"Q:T.Tensor((3,128,8,512,96),'float16')\").replace('T.reshape(Q,(3,128,8,512,96))','Q');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-43"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 213 | item.completed | item_119 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-42 — High structural child of iter-36 (d7ab0e5)
Head-major QKV intermediate attempt. Compile FAIL: installed T.reshape computes bit count as int32; this tensor has2415919104 bits, exceeding int32 maximum. No latency. This is an API arithmetic limit, not invalid dataflow. Fix by declaring the intermediate's5D shape directly in producer/consumer signatures, avoiding reshape entirely.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-42: record reshape bit-count overflow'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();s=s.replace('    @T.prim_func\\n    def main(X:', '    out_shape=(3,128,8,512,96) if N==2304 else (128,512,N)\\n    @T.prim_func\\n    def main(X:',1).replace('Y:T.Tensor((128,512,N),out_dtype)', 'Y:T.Tensor(out_shape,out_dtype)',1).replace('T.reshape(Y,(3,128,8,512,96))','Y').replace(\"Q:T.Tensor((128,512,2304),'float16')\", \"Q:T.Tensor((3,128,8,512,96),'float16')\").replace('T.reshape(Q,(3,128,8,512,96))','Q');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-43"
OUTPUT:
[10:41:12] : Warning: T.vectorized loop over `local_id` with extent 8 is lowered as a serial loop because TileLang could not find a valid vectorization plan. Scalar accumulator updates inside the loop are a common cause; move reductions to T.unroll or T.serial if this is intended.
2026-09-26 10:41:05  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[3]`
2026-09-26 10:41:11  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`
2026-09-26 10:41:11  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:137): TileLang begins to compile kernel `main` with `out_idx=[1]`
2026-09-26 10:41:18  [TileLang:tilelang.jit.kernel:INFO] (kernel.py:145): TileLang completes to compile kernel `main`

RECORD_ID: CASE-MINGPT-iter-43
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: High
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [45.08674365454674, 44.953800201416016, 44.88901259959294, 45.196233077374416, 44.89917105312114], "candidate": [5.5219206174365, 5.504693220104384, 5.555777439428435, 5.529796123504639, 5.550942983385801]}
RUNTIME: 5.529796123504639
REF_RUNTIME: 44.953800201416016
SPEEDUP: 8.129377502786754

STATUS: completed; EXIT_CODE: 0

=== Event 214 | item.completed | item_120 ===
The head-major layout passed after avoiding a reshape API overflow, but measured slower at 5.53 ms. I’m checking which stage caused the regression before deciding whether it’s worth combining with the Native attention code.

=== Event 215 | item.started | item_121 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-43 — High repair child of iter-42 (8358789)
Declare5D QKV buffer directly, avoiding reshape bit-count overflow. Complete suite PASS; 5.529796 ms (5.505–5.556). Not promoted versus iter-36. Need cost split to distinguish producer addressing regression from attention locality effect before deciding whether Native combination is justified; diagnose unchanged measured source.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-43: validate head-major QKV representation'
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/diagnose.py > opt/attention/diagnose43.txt 2>&1"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 216 | item.completed | item_121 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-43 — High repair child of iter-42 (8358789)
Declare5D QKV buffer directly, avoiding reshape bit-count overflow. Complete suite PASS; 5.529796 ms (5.505–5.556). Not promoted versus iter-36. Need cost split to distinguish producer addressing regression from attention locality effect before deciding whether Native combination is justified; diagnose unchanged measured source.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-43: validate head-major QKV representation'
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/diagnose.py > opt/attention/diagnose43.txt 2>&1"
OUTPUT:

STATUS: completed; EXIT_CODE: 0

=== Event 217 | item.started | item_122 ===
COMMAND:
/bin/bash -lc "rg -n '"'^Call |''^Native sites:|''^Full report'"' opt/attention/diagnose43.txt"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 218 | item.completed | item_122 ===
COMMAND:
/bin/bash -lc "rg -n '"'^Call |''^Native sites:|''^Full report'"' opt/attention/diagnose43.txt"
OUTPUT:
9:Call 3: 2.893782 ms | compiler_and_execution_matched
17:Native sites: 10: extern "C" __global__ void __launch_bounds__(256) main_kernel(half_t* __restrict__ Q, half_t* __restrict__ Y) {
28:Call 2: 1.845769 ms | compiler_and_execution_matched
36:Native sites: 10: extern "C" __global__ void __launch_bounds__(256) main_kernel(float* __restrict__ B, half_t* __restrict__ W, float* __restrict__ X, half_t* __restrict__ Y) {
42:Call 4: 0.626533 ms | compiler_and_execution_matched
49:Native sites: 10: extern "C" __global__ void __launch_bounds__(256) main_kernel(float* __restrict__ B, half_t* __restrict__ W, half_t* __restrict__ X, float* __restrict__ Y) {
55:Call 1: 0.067081 ms | compiler_and_execution_matched
63:Native sites: 10: extern "C" __global__ void __launch_bounds__(256) main_kernel(float* __restrict__ X, half_t* __restrict__ Y) {
65:Call 0: 0.027400 ms | compiler_and_execution_matched
73:Native sites: 10: extern "C" __global__ void __launch_bounds__(256) main_kernel(float* __restrict__ X, half_t* __restrict__ Y) {
83:Full report: /home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/.cache/diagnosis/report.json

STATUS: completed; EXIT_CODE: 0

=== Event 219 | item.started | item_123 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

### Diagnosis / scheduler after iter-43
Matched head-major costs: attention2.894 ms, QKV1.846 ms, output projection0.627 ms, weight conversions0.094 ms. Native source still uses scalar PV LDS reads at this High point; locality change did not produce a full-forward gain and no measured evidence supports further head-major work now. Restore retained High iter-36 for export and combine its wider projection with the proven Native attention. This also lets the small geometry effect be judged under the faster full candidate.
EOF
git add opt/attention/diagnose43.txt ITERATIONS.md && git commit -m 'retain head-major diagnosis and select wider projection branch'
git restore --source=d7ab0e5 -- solution
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/export.py --source solution/ModelNew.py --reference reference.py --destination lowered36 --record opt/attention/export36.json"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 220 | item.completed | item_124 ===
The head-major diagnostic did not show a useful gain, so I’m retaining the original layout. I’m exporting the wider QKV tile to combine it with the faster Native attention code, then checking its interaction with projection grouping.

=== Event 221 | item.started | item_125 ===
COMMAND:
/bin/bash -lc 'tail -5 opt/attention/export36.validation.log && date -u'
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 222 | item.completed | item_125 ===
COMMAND:
/bin/bash -lc 'tail -5 opt/attention/export36.validation.log && date -u'
OUTPUT:
STAGE: Low
COMPILED: True
Sat Sep 26 10:43:23 UTC 2026

STATUS: completed; EXIT_CODE: 0

=== Event 223 | item.completed | item_123 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

### Diagnosis / scheduler after iter-43
Matched head-major costs: attention2.894 ms, QKV1.846 ms, output projection0.627 ms, weight conversions0.094 ms. Native source still uses scalar PV LDS reads at this High point; locality change did not produce a full-forward gain and no measured evidence supports further head-major work now. Restore retained High iter-36 for export and combine its wider projection with the proven Native attention. This also lets the small geometry effect be judged under the faster full candidate.
EOF
git add opt/attention/diagnose43.txt ITERATIONS.md && git commit -m 'retain head-major diagnosis and select wider projection branch'
git restore --source=d7ab0e5 -- solution
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/export.py --source solution/ModelNew.py --reference reference.py --destination lowered36 --record opt/attention/export36.json"
OUTPUT:
EXPORT VALIDATED: full 30-trial Native replay passed; see opt/attention/export36.json

STATUS: completed; EXIT_CODE: 0

=== Event 224 | item.started | item_126 ===
COMMAND:
/bin/bash -lc "mv solution opt/attention/high36 && cp -r lowered36 solution
cat >> ITERATIONS.md <<'EOF'

## Representation edge — iter-36 High to Native
Export opt/attention/export36.json, exact High parent d7ab0e5; required full replay PASS. Entire solution replaced. Combine exact online Native attention from iter-35 with wider128x256x32 QKV/group8; output projection stays128x128x64/group8. No extra transfer ranking invented.
EOF
git add solution opt/attention/export36* opt/attention/high36 ITERATIONS.md && git commit -m 'export wider QKV tile for Native combination'
git show 84aa7d4:solution/kernel_3.cpp > solution/kernel_3.cpp
bash scripts/bench.sh iter-44"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 225 | item.completed | item_126 ===
COMMAND:
/bin/bash -lc "mv solution opt/attention/high36 && cp -r lowered36 solution
cat >> ITERATIONS.md <<'EOF'

## Representation edge — iter-36 High to Native
Export opt/attention/export36.json, exact High parent d7ab0e5; required full replay PASS. Entire solution replaced. Combine exact online Native attention from iter-35 with wider128x256x32 QKV/group8; output projection stays128x128x64/group8. No extra transfer ranking invented.
EOF
git add solution opt/attention/export36* opt/attention/high36 ITERATIONS.md && git commit -m 'export wider QKV tile for Native combination'
git show 84aa7d4:solution/kernel_3.cpp > solution/kernel_3.cpp
bash scripts/bench.sh iter-44"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-44
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.953800201416016, 44.655991548464634, 44.941310967235246, 44.99903268147425, 44.96089684320456], "candidate": [4.120983185063122, 4.178524460293987, 4.2982985246421785, 4.286132715483107, 4.179405212402344]}
RUNTIME: 4.179405212402344
REF_RUNTIME: 44.953800201416016
SPEEDUP: 10.756028170711003

STATUS: completed; EXIT_CODE: 0

=== Event 226 | item.started | item_127 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-44 — Low combination child of representation6e35fbd
Wider QKV128x256x32/group8 plus exact optimized online attention from iter-35. Complete suite PASS; 4.179405 ms (4.121–4.298), 10.7560x. Equivalent to best iter-35 within variation; retain. Next group16 on this new geometry checks the known grouping interaction.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-44: combine wider QKV with Native attention, 4.18 ms'
sed -i 's/rasterization2DColumn<8>/rasterization2DColumn<16>/' solution/kernel_2.cpp
bash scripts/bench.sh iter-45"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 227 | item.completed | item_128 ===
The wider QKV tile plus optimized Native attention passed at 4.18 ms, essentially tied with the retained best. I’m testing the larger projection group on this geometry, then revisiting the chunked-attention alternative with the bank-layout improvement.

=== Event 228 | item.completed | item_127 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-44 — Low combination child of representation6e35fbd
Wider QKV128x256x32/group8 plus exact optimized online attention from iter-35. Complete suite PASS; 4.179405 ms (4.121–4.298), 10.7560x. Equivalent to best iter-35 within variation; retain. Next group16 on this new geometry checks the known grouping interaction.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-44: combine wider QKV with Native attention, 4.18 ms'
sed -i 's/rasterization2DColumn<8>/rasterization2DColumn<16>/' solution/kernel_2.cpp
bash scripts/bench.sh iter-45"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-45
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.90110811123845, 45.075860095272404, 44.953800201416016, 45.05786241134638, 44.94092696373529], "candidate": [4.231358146711908, 4.24525722789734, 4.145616827234785, 4.243116855621338, 4.295168660731783]}
RUNTIME: 4.243116855621338
REF_RUNTIME: 44.953800201416016
SPEEDUP: 10.594523255201096

STATUS: completed; EXIT_CODE: 0

=== Event 229 | item.started | item_129 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-45 — Low tuning child of iter-44 (4a51d37)
Group16 on wider QKV. Complete suite PASS; 4.243117 ms (4.146–4.295). No gain; restore group8 on this geometry. Grouping benefit from iter-35 does not transfer unchanged.
Next combine retained chunked128-key attention from iter-23 with the producer/consumer V bank-layout mechanism from iter-28 and current projection. Both structures were validated at High and Low; this is a bounded cross-branch mechanism combination. K/P/reduction phase alias and batched reductions remain. Test total result rather than attributing this coupled combination to one isolated change.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-45: reject larger group on wider QKV'
git restore --source=4a51d37 -- solution
git show 754ba43:solution/kernel_4.cpp > solution/kernel_3.cpp
python - <<'PY'
p='solution/kernel_3.cpp';s=open(p).read();a=s.index('      #pragma unroll\\n      for (int i_14');b=s.index('      {\\n        half_t A_local_1',a)
s=s[:a]+'''      #pragma unroll
      for(int t=0;t<3;++t) {
        int chunk=t*256+threadIdx.x;
        int ri=chunk/24, cj=(chunk%24)*4;
        uint2 packed=*(uint2*)(Q+(blockIdx.y/8)*1179648+nb*294912+vb*73728+ri*2304+(blockIdx.y%8)*96+1536+cj);
        #pragma unroll
        for(int e=0;e<4;++e) {
          int row=cj+e;
          ((half_t*)v)[row*32+(ri"'^(((row&3)''^((row>>2)&3))*8))]=((half_t*)&packed)[e];
        }
      }
'"'''+s[b:]
a=s.index('          for (int j_2 = 0; j_2 < 3; ++j_2)');b=s.index('          for (int j_3',a)
s=s[:a]+'''          #pragma unroll
          for(int j_2=0;j_2<3;++j_2) {
            int row=(threadIdx.x>>7)*48+j_2*16+(threadIdx.x&15);
            int col=ki_1*16+((threadIdx.x&31)>>4)*8;
            *(uint4*)(B_local_1+j_2*8)=*(uint4*)(((half_t*)v)+row*32+(col"'^(((row&3)''^((row>>2)&3))*8)));
          }
'"'''+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-46"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 230 | item.completed | item_129 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-45 — Low tuning child of iter-44 (4a51d37)
Group16 on wider QKV. Complete suite PASS; 4.243117 ms (4.146–4.295). No gain; restore group8 on this geometry. Grouping benefit from iter-35 does not transfer unchanged.
Next combine retained chunked128-key attention from iter-23 with the producer/consumer V bank-layout mechanism from iter-28 and current projection. Both structures were validated at High and Low; this is a bounded cross-branch mechanism combination. K/P/reduction phase alias and batched reductions remain. Test total result rather than attributing this coupled combination to one isolated change.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-45: reject larger group on wider QKV'
git restore --source=4a51d37 -- solution
git show 754ba43:solution/kernel_4.cpp > solution/kernel_3.cpp
python - <<'PY'
p='solution/kernel_3.cpp';s=open(p).read();a=s.index('      #pragma unroll\\n      for (int i_14');b=s.index('      {\\n        half_t A_local_1',a)
s=s[:a]+'''      #pragma unroll
      for(int t=0;t<3;++t) {
        int chunk=t*256+threadIdx.x;
        int ri=chunk/24, cj=(chunk%24)*4;
        uint2 packed=*(uint2*)(Q+(blockIdx.y/8)*1179648+nb*294912+vb*73728+ri*2304+(blockIdx.y%8)*96+1536+cj);
        #pragma unroll
        for(int e=0;e<4;++e) {
          int row=cj+e;
          ((half_t*)v)[row*32+(ri"'^(((row&3)''^((row>>2)&3))*8))]=((half_t*)&packed)[e];
        }
      }
'"'''+s[b:]
a=s.index('          for (int j_2 = 0; j_2 < 3; ++j_2)');b=s.index('          for (int j_3',a)
s=s[:a]+'''          #pragma unroll
          for(int j_2=0;j_2<3;++j_2) {
            int row=(threadIdx.x>>7)*48+j_2*16+(threadIdx.x&15);
            int col=ki_1*16+((threadIdx.x&31)>>4)*8;
            *(uint4*)(B_local_1+j_2*8)=*(uint4*)(((half_t*)v)+row*32+(col"'^(((row&3)''^((row>>2)&3))*8)));
          }
'"'''+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-46"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-46
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.84911928841747, 44.76415235286295, 45.04139637349858, 45.08983338062096, 44.953800201416016], "candidate": [4.36840532058556, 4.362001322887329, 4.406213031837388, 4.397590670723518, 4.37760591506958]}
RUNTIME: 4.37760591506958
REF_RUNTIME: 44.953800201416016
SPEEDUP: 10.26903770544213

STATUS: completed; EXIT_CODE: 0

=== Event 231 | item.completed | item_130 ===
The larger group did not help the wider projection tile. I’ve kept the group-8 version and am testing whether the earlier 128-key attention structure benefits from the improved value-bank mapping. This checks whether that improvement transfers to the other validated structure.

=== Event 232 | item.started | item_131 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-46 — Low cross-branch combination from iter-44 (4a51d37)
Chunked128-key attention from iter-23 with vector V global loads, mixed-row-bit transposed LDS and vector PV reads, plus current wider inline projection. Complete suite PASS; 4.377606 ms (4.362–4.406). Rejected versus online iter-44; tested interaction does not make chunked structure faster.
Restore iter-44. Next direct Q global→WMMA-register loads avoid initial LDS staging, its address divisions and barrier, while retaining register reuse across key blocks. Each N warp duplicates Q operand reads, so cache reuse versus saved staging is measured rather than assumed.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-46: reject chunked attention bank-layout combination'
git restore --source=4a51d37 -- solution
python - <<'PY'
p='solution/kernel_3.cpp';s=open(p).read();a=s.index('  #pragma unroll\\n  for (int i_3 = 0; i_3 < 3; ++i_3)');b=s.index('  for (int nb = 0;',a)
s=s[:a]+'''  uint4 q_regs[6];
  #pragma unroll
  for(int ki=0;ki<6;++ki) {
    int row=((threadIdx.x&127)>>5)*16+(threadIdx.x&15);
    int col=ki*16+((threadIdx.x&31)>>4)*8;
    q_regs[ki]=*(uint4*)(Q+(blockIdx.y/8)*1179648+(blockIdx.x*64+row)*2304+(blockIdx.y%8)*96+col);
  }
'''+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-47"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 233 | item.started | item_132 ===
COMMAND:
/bin/bash -lc "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc -O3 --offload-arch=gfx1201 --cuda-device-only -S -I/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src solution/kernel_3.cpp -o opt/attention/iter47.s
rg -n 'vgpr_count|vgpr_spill|private_segment_fixed_size' opt/attention/iter47.s"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 234 | item.completed | item_132 ===
COMMAND:
/bin/bash -lc "/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc -O3 --offload-arch=gfx1201 --cuda-device-only -S -I/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src solution/kernel_3.cpp -o opt/attention/iter47.s
rg -n 'vgpr_count|vgpr_spill|private_segment_fixed_size' opt/attention/iter47.s"
OUTPUT:
1496:		.amdhsa_private_segment_fixed_size 0
1612:    .private_segment_fixed_size: 0
1618:    .vgpr_count:     202
1619:    .vgpr_spill_count: 0

STATUS: completed; EXIT_CODE: 0

=== Event 235 | item.completed | item_131 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-46 — Low cross-branch combination from iter-44 (4a51d37)
Chunked128-key attention from iter-23 with vector V global loads, mixed-row-bit transposed LDS and vector PV reads, plus current wider inline projection. Complete suite PASS; 4.377606 ms (4.362–4.406). Rejected versus online iter-44; tested interaction does not make chunked structure faster.
Restore iter-44. Next direct Q global→WMMA-register loads avoid initial LDS staging, its address divisions and barrier, while retaining register reuse across key blocks. Each N warp duplicates Q operand reads, so cache reuse versus saved staging is measured rather than assumed.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-46: reject chunked attention bank-layout combination'
git restore --source=4a51d37 -- solution
python - <<'PY'
p='solution/kernel_3.cpp';s=open(p).read();a=s.index('  #pragma unroll\\n  for (int i_3 = 0; i_3 < 3; ++i_3)');b=s.index('  for (int nb = 0;',a)
s=s[:a]+'''  uint4 q_regs[6];
  #pragma unroll
  for(int ki=0;ki<6;++ki) {
    int row=((threadIdx.x&127)>>5)*16+(threadIdx.x&15);
    int col=ki*16+((threadIdx.x&31)>>4)*8;
    q_regs[ki]=*(uint4*)(Q+(blockIdx.y/8)*1179648+(blockIdx.x*64+row)*2304+(blockIdx.y%8)*96+col);
  }
'''+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-47"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-47
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.97295434819693, 44.885938940301045, 44.94052632493648, 44.953800201416016, 45.1679765769046], "candidate": [4.099544881888316, 4.06795384336518, 4.105734198032016, 3.9394565955785423, 4.086939811706543]}
RUNTIME: 4.086939811706543
REF_RUNTIME: 44.953800201416016
SPEEDUP: 10.999379064172002

STATUS: completed; EXIT_CODE: 0

=== Event 236 | item.completed | item_133 ===
The chunked-attention combination was slower, so I kept online attention. Loading query operands directly into registers passed at 4.09 ms, though the gain is small. I’m testing direct key-operand loads next; they could remove more shared-memory staging, at the cost of repeated cache reads.

=== Event 237 | item.started | item_134 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-47 — Low child of iter-44 (4a51d37)
Direct global→register Q operands, retain per-block Q reuse and all other online attention mechanisms. Complete suite PASS; 4.086940 ms (3.939–4.106), 10.9994x. Provisional best; small gain overlaps variation. Compile-only metadata202 VGPR, zero spills. Next direct K operands remove K LDS staging and one barrier; K/P/scratch region can shrink from12 to8 KiB, total20 KiB. Repeated K loads across M warps trade cache reads for staging; measure full candidate.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-47: load query operands directly into registers, 4.09 ms'
python - <<'PY'
p='solution/kernel_3.cpp';s=open(p).read();a=s.index('    #pragma unroll\\n    for (int i_4 = 0; i_4 < 3; ++i_4)');b=s.index('    #pragma unroll\\n    for (int t=',a);s=s[:a]+s[b:]
# Replace the K LDS operand reads in QK, preserving the same logical WMMA lanes.
a=s.index('        for (int j = 0; j < 2; ++j)');b=s.index('        for (int j_1',a)
s=s[:a]+'''        #pragma unroll
        for(int j=0;j<2;++j) {
          int row=(threadIdx.x>>7)*32+j*16+(threadIdx.x&15);
          int col=ki*16+((threadIdx.x&31)>>4)*8;
          *(uint4*)(B_local+j*8)=*(uint4*)(Q+(blockIdx.y/8)*1179648+(nb*64+row)*2304+(blockIdx.y%8)*96+768+col);
        }
'''+s[b:]
s=s.replace('      __syncthreads();\\n      #pragma unroll\\n      for (int ki = 0; ki < 6;', '      #pragma unroll\\n      for (int ki = 0; ki < 6;')
s=s.replace('if (24576 >','if (20480 >').replace('\\n            24576,','\\n            20480,').replace(' 24576, stream',' 20480, stream')
open(p,'w').write(s)
PY
bash scripts/bench.sh iter-48"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 238 | item.completed | item_134 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-47 — Low child of iter-44 (4a51d37)
Direct global→register Q operands, retain per-block Q reuse and all other online attention mechanisms. Complete suite PASS; 4.086940 ms (3.939–4.106), 10.9994x. Provisional best; small gain overlaps variation. Compile-only metadata202 VGPR, zero spills. Next direct K operands remove K LDS staging and one barrier; K/P/scratch region can shrink from12 to8 KiB, total20 KiB. Repeated K loads across M warps trade cache reads for staging; measure full candidate.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-47: load query operands directly into registers, 4.09 ms'
python - <<'PY'
p='solution/kernel_3.cpp';s=open(p).read();a=s.index('    #pragma unroll\\n    for (int i_4 = 0; i_4 < 3; ++i_4)');b=s.index('    #pragma unroll\\n    for (int t=',a);s=s[:a]+s[b:]
# Replace the K LDS operand reads in QK, preserving the same logical WMMA lanes.
a=s.index('        for (int j = 0; j < 2; ++j)');b=s.index('        for (int j_1',a)
s=s[:a]+'''        #pragma unroll
        for(int j=0;j<2;++j) {
          int row=(threadIdx.x>>7)*32+j*16+(threadIdx.x&15);
          int col=ki*16+((threadIdx.x&31)>>4)*8;
          *(uint4*)(B_local+j*8)=*(uint4*)(Q+(blockIdx.y/8)*1179648+(nb*64+row)*2304+(blockIdx.y%8)*96+768+col);
        }
'''+s[b:]
s=s.replace('      __syncthreads();\\n      #pragma unroll\\n      for (int ki = 0; ki < 6;', '      #pragma unroll\\n      for (int ki = 0; ki < 6;')
s=s.replace('if (24576 >','if (20480 >').replace('\\n            24576,','\\n            20480,').replace(' 24576, stream',' 20480, stream')
open(p,'w').write(s)
PY
bash scripts/bench.sh iter-48"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-48
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.95978619484718, 44.870678497325805, 45.11756278296641, 44.953800201416016, 44.89962229140815], "candidate": [4.111861729436422, 3.938998042295192, 3.9635999983218957, 4.0622056960556, 4.023087024688721]}
RUNTIME: 4.023087024688721
REF_RUNTIME: 44.953800201416016
SPEEDUP: 11.17395669682145

STATUS: completed; EXIT_CODE: 0

=== Event 239 | item.completed | item_135 ===
Direct key loads passed at 4.02 ms, giving a modest further gain. I’m now shortening the lifetime of softmax rescaling values to reduce register pressure while preserving the same attention calculation and checking the full tolerance suite.

=== Event 240 | item.started | item_136 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-48 — Low child of iter-47 (5188cfb)
Direct global K WMMA operands; remove K staging/barrier,20 KiB LDS. Complete suite PASS; 4.023087 ms (3.939–4.112), 11.1740x. Provisional best; modest gain overlaps some spread. Next shorten softmax state lifetimes: rescale running numerator/denominator immediately when new max is known, eliminating separate old/alpha arrays across exponentiation/reduction. Same real arithmetic; full suite will validate changed FP32 scheduling.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-48: direct key operands and smaller LDS, 4.02 ms'
python - <<'PY'
p='solution/kernel_3.cpp';s=open(p).read()
a=s.index('    #pragma unroll\\n    for (int i_7 = 0; i_7 < 2; ++i_7)');b=s.index('    #pragma unroll\\n    for (int r=0;r<8;++r)',a);s=s[:a]+s[b:]
a=s.index('    #pragma unroll\\n    for (int r=0;r<8;++r) m[r]=max');b=s.index('    #pragma unroll\\n    for (int i_10',a)
s=s[:a]+'''    #pragma unroll
    for(int r=0;r<8;++r) {
      float next_m=max(m[r],m_clear[r]);
      float a=__expf(m[r]-next_m);
      m[r]=next_m;
      l[r]*=a;
      #pragma unroll
      for(int j=0;j<3;++j) o[j*8+r]*=a;
    }
'''+s[b:]
a=s.index('    #pragma unroll\\n    for (int i_12');b=s.index('    __syncthreads();\\n    #pragma unroll\\n    for (int i_14',a)
s=s[:a]+'''    #pragma unroll
    for(int r=0;r<8;++r) l[r]+=rows[r];
'''+s[b:]
s=s.replace('  float old[8];\\n','').replace('  float alpha[8];\\n','');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-49"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 241 | item.completed | item_136 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-48 — Low child of iter-47 (5188cfb)
Direct global K WMMA operands; remove K staging/barrier,20 KiB LDS. Complete suite PASS; 4.023087 ms (3.939–4.112), 11.1740x. Provisional best; modest gain overlaps some spread. Next shorten softmax state lifetimes: rescale running numerator/denominator immediately when new max is known, eliminating separate old/alpha arrays across exponentiation/reduction. Same real arithmetic; full suite will validate changed FP32 scheduling.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-48: direct key operands and smaller LDS, 4.02 ms'
python - <<'PY'
p='solution/kernel_3.cpp';s=open(p).read()
a=s.index('    #pragma unroll\\n    for (int i_7 = 0; i_7 < 2; ++i_7)');b=s.index('    #pragma unroll\\n    for (int r=0;r<8;++r)',a);s=s[:a]+s[b:]
a=s.index('    #pragma unroll\\n    for (int r=0;r<8;++r) m[r]=max');b=s.index('    #pragma unroll\\n    for (int i_10',a)
s=s[:a]+'''    #pragma unroll
    for(int r=0;r<8;++r) {
      float next_m=max(m[r],m_clear[r]);
      float a=__expf(m[r]-next_m);
      m[r]=next_m;
      l[r]*=a;
      #pragma unroll
      for(int j=0;j<3;++j) o[j*8+r]*=a;
    }
'''+s[b:]
a=s.index('    #pragma unroll\\n    for (int i_12');b=s.index('    __syncthreads();\\n    #pragma unroll\\n    for (int i_14',a)
s=s[:a]+'''    #pragma unroll
    for(int r=0;r<8;++r) l[r]+=rows[r];
'''+s[b:]
s=s.replace('  float old[8];\\n','').replace('  float alpha[8];\\n','');open(p,'w').write(s)
PY
bash scripts/bench.sh iter-49"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-49
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.953800201416016, 45.0756268502008, 44.83319858160891, 45.097262665672254, 44.82243563178575], "candidate": [4.062650399860894, 3.987041565291932, 3.9382270265948436, 4.092739488215443, 4.036014080047607]}
RUNTIME: 4.036014080047607
REF_RUNTIME: 44.953800201416016
SPEEDUP: 11.138167337831922

STATUS: completed; EXIT_CODE: 0

=== Event 242 | item.started | item_137 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-49 — Low child of iter-48 (48cf6db)
Early numerator/denominator rescale shortens old/alpha lifetimes. Complete suite PASS; 4.036014 ms (3.938–4.093). Inconclusive versus4.023-ms parent, retained but not promoted. Recent attention gains are small; refresh Native call costs to choose between projection prefetch and remaining LDS layout work. Diagnose unchanged measured iter-49; High provenance is historical, not unchanged generated-code correspondence.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-49: retain shorter softmax state lifetimes'
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/diagnose.py > opt/attention/diagnose49.txt 2>&1"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 243 | item.completed | item_137 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-49 — Low child of iter-48 (48cf6db)
Early numerator/denominator rescale shortens old/alpha lifetimes. Complete suite PASS; 4.036014 ms (3.938–4.093). Inconclusive versus4.023-ms parent, retained but not promoted. Recent attention gains are small; refresh Native call costs to choose between projection prefetch and remaining LDS layout work. Diagnose unchanged measured iter-49; High provenance is historical, not unchanged generated-code correspondence.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-49: retain shorter softmax state lifetimes'
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/diagnose.py > opt/attention/diagnose49.txt 2>&1"
OUTPUT:

STATUS: completed; EXIT_CODE: 0

=== Event 244 | item.completed | item_138 ===
The rescaling change passed but did not improve runtime beyond the 4.02 ms candidate. I’m refreshing Native per-kernel timings now to choose between further attention work and another projection scheduling test.

=== Event 245 | item.started | item_139 ===
COMMAND:
/bin/bash -lc "date -u && rg -n '"'^Call |''^Native sites:|''^Full report'"' opt/attention/diagnose49.txt"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 246 | item.completed | item_139 ===
COMMAND:
/bin/bash -lc "date -u && rg -n '"'^Call |''^Native sites:|''^Full report'"' opt/attention/diagnose49.txt"
OUTPUT:
Sat Sep 26 10:53:54 UTC 2026
4:Call 2: 1.781450 ms | runtime_native
7:Native sites: 10: extern "C" __global__ void __launch_bounds__(256) main_kernel(float* __restrict__ B, half_t* __restrict__ W, float* __restrict__ X, half_t* __restrict__ Y) {
13:Call 3: 1.301007 ms | runtime_native
16:Native sites: 10: extern "C" __global__ void __launch_bounds__(256) main_kernel(half_t* __restrict__ Q, half_t* __restrict__ Y) {
24:Call 4: 0.633884 ms | runtime_native
27:Native sites: 10: extern "C" __global__ void __launch_bounds__(256) main_kernel(float* __restrict__ B, half_t* __restrict__ W, half_t* __restrict__ X, float* __restrict__ Y) {
33:Call 0: 0.026760 ms | runtime_native
36:Native sites: 10: extern "C" __global__ void __launch_bounds__(256) main_kernel(float* __restrict__ X, half_t* __restrict__ Y) {
38:Call 1: 0.013760 ms | runtime_native
41:Native sites: 10: extern "C" __global__ void __launch_bounds__(256) main_kernel(float* __restrict__ X, half_t* __restrict__ Y) {
51:Full report: /home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/sessions/1/.cache/diagnosis/report.json

STATUS: completed; EXIT_CODE: 0

=== Event 247 | item.started | item_140 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

### Native diagnosis after iter-49
Costs: QKV1.781 ms, attention1.301 ms, output projection0.634 ms, weight conversions0.041 ms. Runtime-native mappings only; old High source is provenance. QKV is largest remaining cost. Revisit prefetch with changed premise: inline FP32 conversion and BK32 need only four uint2 activation registers, versus prior both-operand prefetch's much larger live state. Prefetch activation only, keeping weight loads unchanged. Restore best iter-48 before editing; diagnosis remains applicable to identical projection source.
EOF
git add opt/attention/diagnose49.txt ITERATIONS.md && git commit -m 'record Native cost split and select activation-only prefetch'
git restore --source=48cf6db -- solution
cat > opt/attention/prefetch_activation.py <<'PY'
from pathlib import Path
import re
p=Path('solution/kernel_2.cpp');s=p.read_text()
ks=s.index('  for (int k = 0; k < ')
nk=int(re.search(r'k < (\\d+)',s[ks:]).group(1))
a=s.index('    #pragma unroll\\n    for (int i_1',ks)
brace=s.index('{',a);depth=1;b=brace+1
while depth:
    if s[b]=='{':depth+=1
    elif s[b]=='}':depth-=1
    b+=1
old=s[a:b]
na=int(re.search(r'i_1 < (\\d+)',old).group(1))
bk=int(re.search(r'\\(k \\* (\\d+)\\)',old).group(1))
store=next(l for l in old.splitlines() if '*(uint2*)(((half_t*)a)' in l)
lhs,rhs=store.strip().split(' = ',1)
pref=old.replace(store,'      pre_a[i_1] = '+rhs)
init=pref.replace(f'(k * {bk})','0')
next_pref=pref.replace(f'(k * {bk})',f'((k+1) * {bk})')
stage=f'    #pragma unroll\\n    for(int i_1=0;i_1<{na};++i_1) {lhs} = pre_a[i_1];'
s=s[:a]+stage+s[b:]
s=s[:ks]+f'  uint2 pre_a[{na}];\\n'+init+'\\n'+s[ks:]
anchor=s.index('      __syncthreads();',s.index('half_t B_local['))
anchor+=len('      __syncthreads();')
s=s[:anchor]+f'\\n      if(k+1<{nk}) {{\\n'+next_pref+'\\n      }'+s[anchor:]
p.write_text(s)
PY
python opt/attention/prefetch_activation.py
bash scripts/bench.sh iter-50"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 248 | item.completed | item_141 ===
QKV projection is now the largest stage at about 1.78 ms; attention is about 1.30 ms. I’m revisiting prefetching with a smaller scope: only the next activation tile, converted to FP16. The current tile shape needs far fewer prefetch registers than the earlier unsuccessful version.

=== Event 249 | item.completed | item_140 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

### Native diagnosis after iter-49
Costs: QKV1.781 ms, attention1.301 ms, output projection0.634 ms, weight conversions0.041 ms. Runtime-native mappings only; old High source is provenance. QKV is largest remaining cost. Revisit prefetch with changed premise: inline FP32 conversion and BK32 need only four uint2 activation registers, versus prior both-operand prefetch's much larger live state. Prefetch activation only, keeping weight loads unchanged. Restore best iter-48 before editing; diagnosis remains applicable to identical projection source.
EOF
git add opt/attention/diagnose49.txt ITERATIONS.md && git commit -m 'record Native cost split and select activation-only prefetch'
git restore --source=48cf6db -- solution
cat > opt/attention/prefetch_activation.py <<'PY'
from pathlib import Path
import re
p=Path('solution/kernel_2.cpp');s=p.read_text()
ks=s.index('  for (int k = 0; k < ')
nk=int(re.search(r'k < (\\d+)',s[ks:]).group(1))
a=s.index('    #pragma unroll\\n    for (int i_1',ks)
brace=s.index('{',a);depth=1;b=brace+1
while depth:
    if s[b]=='{':depth+=1
    elif s[b]=='}':depth-=1
    b+=1
old=s[a:b]
na=int(re.search(r'i_1 < (\\d+)',old).group(1))
bk=int(re.search(r'\\(k \\* (\\d+)\\)',old).group(1))
store=next(l for l in old.splitlines() if '*(uint2*)(((half_t*)a)' in l)
lhs,rhs=store.strip().split(' = ',1)
pref=old.replace(store,'      pre_a[i_1] = '+rhs)
init=pref.replace(f'(k * {bk})','0')
next_pref=pref.replace(f'(k * {bk})',f'((k+1) * {bk})')
stage=f'    #pragma unroll\\n    for(int i_1=0;i_1<{na};++i_1) {lhs} = pre_a[i_1];'
s=s[:a]+stage+s[b:]
s=s[:ks]+f'  uint2 pre_a[{na}];\\n'+init+'\\n'+s[ks:]
anchor=s.index('      __syncthreads();',s.index('half_t B_local['))
anchor+=len('      __syncthreads();')
s=s[:anchor]+f'\\n      if(k+1<{nk}) {{\\n'+next_pref+'\\n      }'+s[anchor:]
p.write_text(s)
PY
python opt/attention/prefetch_activation.py
bash scripts/bench.sh iter-50"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-50
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [45.021227651326875, 44.953800201416016, 44.84583259742725, 44.98820557114195, 44.92582203377742], "candidate": [3.9843265335769815, 4.1229087834101295, 4.120078086853027, 4.012667665269417, 4.1405281271126]}
RUNTIME: 4.120078086853027
REF_RUNTIME: 44.953800201416016
SPEEDUP: 10.910909757963434

STATUS: completed; EXIT_CODE: 0

=== Event 250 | item.started | item_142 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-50 — Low child of iter-48 (48cf6db)
Activation-only prefetch for inline-conversion QKV, BK32, four uint2 live vectors; weights unchanged. Complete suite PASS; 4.120078 ms (3.984–4.141). Rejected versus4.023-ms parent. Both broad and reduced-state prefetch configurations have now been measured under their relevant input representations.
Restore iter-48. Next probability LDS bank layout: producer warp writes two rows separated by8, which share low-row-bit swizzle. Add row bit3 into column bit4 while preserving contiguous8-half consumer reads. This is a local producer/consumer physical mapping test, no changed arithmetic.
EOF
git add solution opt/attention/prefetch_activation.py ITERATIONS.md && git commit -m 'iter-50: reject reduced-state activation prefetch'
git restore --source=48cf6db -- solution
python - <<'PY'
p='solution/kernel_3.cpp';s=open(p).read();a=s.index('    #pragma unroll\\n    for (int i_14 = 0; i_14 < 16; ++i_14)');b=s.index('    {\\n      half_t A_local_1',a)
s=s[:a]+'''    #pragma unroll
    for(int e=0;e<16;++e) {
      int row=((threadIdx.x&127)>>4)*8+(e&7);
      int col=(threadIdx.x>>7)*32+(e>>3)*16+(threadIdx.x&15);
      ((half_t*)p)[row*64+(col"'^((row&7)*8)''^((row&8)*2))]=(half_t)s[e];
    }
'"'''+s[b:]
line=next(l for l in s.splitlines() if '*(uint4*)(A_local_1 + 0) = ' in l)
s=s.replace(line,'''        int prow=((threadIdx.x&127)>>5)*16+(threadIdx.x&15);
        int pcol=ki_1*16+((threadIdx.x&31)>>4)*8;
        *(uint4*)(A_local_1 + 0)=*(uint4*)(((half_t*)p)+prow*64+(pcol"'^((prow&7)*8)''^((prow&8)*2)));'"''')
open(p,'w').write(s)
PY
bash scripts/bench.sh iter-51"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 251 | item.completed | item_142 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-50 — Low child of iter-48 (48cf6db)
Activation-only prefetch for inline-conversion QKV, BK32, four uint2 live vectors; weights unchanged. Complete suite PASS; 4.120078 ms (3.984–4.141). Rejected versus4.023-ms parent. Both broad and reduced-state prefetch configurations have now been measured under their relevant input representations.
Restore iter-48. Next probability LDS bank layout: producer warp writes two rows separated by8, which share low-row-bit swizzle. Add row bit3 into column bit4 while preserving contiguous8-half consumer reads. This is a local producer/consumer physical mapping test, no changed arithmetic.
EOF
git add solution opt/attention/prefetch_activation.py ITERATIONS.md && git commit -m 'iter-50: reject reduced-state activation prefetch'
git restore --source=48cf6db -- solution
python - <<'PY'
p='solution/kernel_3.cpp';s=open(p).read();a=s.index('    #pragma unroll\\n    for (int i_14 = 0; i_14 < 16; ++i_14)');b=s.index('    {\\n      half_t A_local_1',a)
s=s[:a]+'''    #pragma unroll
    for(int e=0;e<16;++e) {
      int row=((threadIdx.x&127)>>4)*8+(e&7);
      int col=(threadIdx.x>>7)*32+(e>>3)*16+(threadIdx.x&15);
      ((half_t*)p)[row*64+(col"'^((row&7)*8)''^((row&8)*2))]=(half_t)s[e];
    }
'"'''+s[b:]
line=next(l for l in s.splitlines() if '*(uint4*)(A_local_1 + 0) = ' in l)
s=s.replace(line,'''        int prow=((threadIdx.x&127)>>5)*16+(threadIdx.x&15);
        int pcol=ki_1*16+((threadIdx.x&31)>>4)*8;
        *(uint4*)(A_local_1 + 0)=*(uint4*)(((half_t*)p)+prow*64+(pcol"'^((prow&7)*8)''^((prow&8)*2)));'"''')
open(p,'w').write(s)
PY
bash scripts/bench.sh iter-51"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-51
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [45.02178895619648, 44.8081100349213, 44.652364426478194, 45.057099299123614, 44.953800201416016], "candidate": [3.964001228204117, 3.887555856027065, 3.908475925771291, 3.9550158977508545, 3.9864154050026936]}
RUNTIME: 3.9550158977508545
REF_RUNTIME: 44.953800201416016
SPEEDUP: 11.366275474892635

STATUS: completed; EXIT_CODE: 0

=== Event 252 | item.completed | item_143 ===
Activation prefetching was slower again. The probability-bank layout change passed at 3.96 ms with a tighter sample range, so I’m retaining it. Next I’ll check binding overhead and compare the two close projection configurations with this improved attention kernel.

=== Event 253 | item.started | item_144 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-51 — Low layout child of iter-48 (48cf6db)
Probability producer/consumer swizzle adds row bit3 to column bit4. Complete suite PASS; 3.955016 ms (3.888–3.986), 11.3663x. Provisional promotion; tighter spread supports keeping it for repeat validation. Both P and V bank mappings now measured with producer/consumer interactions.
Next specialize Python allocation/binding path for the fixed five-call graph (no tensor computation). This is an inexpensive end-to-end overhead check; diagnostic kernel sums do not by themselves prove the residual is binding cost. Native kernels and compiler flags remain verbatim.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-51: improve probability LDS bank mapping, 3.96 ms'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();s='import torch\\n'+s;s=s.replace('        self.runtime = module.Runtime(root)','        self.runtime = module.Runtime(root)\\n        self.calls = tuple(lib.call for lib in self.runtime.libraries)')
a=s.index('    def forward(self, *args):')
s=s[:a]+'''    def forward(self, x, qw, qb, pw, pb):
        with torch.cuda.device(x.device):
            stream = torch.cuda.current_stream().cuda_stream
            wh = torch.empty((2304,768),dtype=torch.float16,device=x.device)
            ph = torch.empty((768,768),dtype=torch.float16,device=x.device)
            q = torch.empty((128,512,2304),dtype=torch.float16,device=x.device)
            y = torch.empty((128,512,768),dtype=torch.float16,device=x.device)
            out = torch.empty((128,512,768),dtype=torch.float32,device=x.device)
            if self.calls[0](qw.data_ptr(),wh.data_ptr(),stream): raise RuntimeError(\"weight conversion failed\")
            if self.calls[1](pw.data_ptr(),ph.data_ptr(),stream): raise RuntimeError(\"weight conversion failed\")
            if self.calls[2](x.data_ptr(),wh.data_ptr(),qb.data_ptr(),q.data_ptr(),stream): raise RuntimeError(\"QKV launch failed\")
            if self.calls[3](q.data_ptr(),y.data_ptr(),stream): raise RuntimeError(\"attention launch failed\")
            if self.calls[4](y.data_ptr(),ph.data_ptr(),pb.data_ptr(),out.data_ptr(),stream): raise RuntimeError(\"projection launch failed\")
            return out
''';open(p,'w').write(s)
PY
bash scripts/bench.sh iter-52"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 254 | item.completed | item_144 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-51 — Low layout child of iter-48 (48cf6db)
Probability producer/consumer swizzle adds row bit3 to column bit4. Complete suite PASS; 3.955016 ms (3.888–3.986), 11.3663x. Provisional promotion; tighter spread supports keeping it for repeat validation. Both P and V bank mappings now measured with producer/consumer interactions.
Next specialize Python allocation/binding path for the fixed five-call graph (no tensor computation). This is an inexpensive end-to-end overhead check; diagnostic kernel sums do not by themselves prove the residual is binding cost. Native kernels and compiler flags remain verbatim.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-51: improve probability LDS bank mapping, 3.96 ms'
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read();s='import torch\\n'+s;s=s.replace('        self.runtime = module.Runtime(root)','        self.runtime = module.Runtime(root)\\n        self.calls = tuple(lib.call for lib in self.runtime.libraries)')
a=s.index('    def forward(self, *args):')
s=s[:a]+'''    def forward(self, x, qw, qb, pw, pb):
        with torch.cuda.device(x.device):
            stream = torch.cuda.current_stream().cuda_stream
            wh = torch.empty((2304,768),dtype=torch.float16,device=x.device)
            ph = torch.empty((768,768),dtype=torch.float16,device=x.device)
            q = torch.empty((128,512,2304),dtype=torch.float16,device=x.device)
            y = torch.empty((128,512,768),dtype=torch.float16,device=x.device)
            out = torch.empty((128,512,768),dtype=torch.float32,device=x.device)
            if self.calls[0](qw.data_ptr(),wh.data_ptr(),stream): raise RuntimeError(\"weight conversion failed\")
            if self.calls[1](pw.data_ptr(),ph.data_ptr(),stream): raise RuntimeError(\"weight conversion failed\")
            if self.calls[2](x.data_ptr(),wh.data_ptr(),qb.data_ptr(),q.data_ptr(),stream): raise RuntimeError(\"QKV launch failed\")
            if self.calls[3](q.data_ptr(),y.data_ptr(),stream): raise RuntimeError(\"attention launch failed\")
            if self.calls[4](y.data_ptr(),ph.data_ptr(),pb.data_ptr(),out.data_ptr(),stream): raise RuntimeError(\"projection launch failed\")
            return out
''';open(p,'w').write(s)
PY
bash scripts/bench.sh iter-52"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-52
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.953800201416016, 44.81166399081232, 44.904537293522615, 45.06767229106704, 45.19012880230447], "candidate": [3.7815020887341144, 4.020604164806275, 3.9124410152435303, 4.142340330897839, 3.867678370732514]}
RUNTIME: 3.9124410152435303
REF_RUNTIME: 44.953800201416016
SPEEDUP: 11.489962411258963

STATUS: completed; EXIT_CODE: 0

=== Event 255 | item.started | item_145 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-52 — Low binding child of iter-51 (b2ce42f)
Static allocations and five explicit library calls for the fixed contract; native kernels unchanged, all input-dependent GPU work retained. Complete suite PASS; 3.912441 ms (3.782–4.142), 11.4900x. Retain provisionally; small gain and wider spread do not establish binding overhead as the cause. Next combine the earlier128x128x64/group16 projection with current attention/bindings to resolve the close projection branch comparison.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-52: specialize fixed tensor bindings, 3.91 ms'
cat > opt/attention/occupancy51.cpp <<'EOF'
#include \"../../solution/kernel_3.cpp\"
int main(){
 hipFuncAttributes a;
 if(hipFuncGetAttributes(&a,(const void*)main_kernel)"'!=hipSuccess)return 1;
 int n=0;
 if(hipOccupancyMaxActiveBlocksPerMultiprocessor(&n,main_kernel,256,20480)!=hipSuccess)return 2;
 printf("regs=%d local=%zu active_blocks=%d'"\\n\",a.numRegs,a.localSizeBytes,n);
}
EOF
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc -O3 --offload-arch=gfx1201 -I/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src opt/attention/occupancy51.cpp -o opt/attention/occupancy51
opt/attention/occupancy51
date -u"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 256 | item.completed | item_145 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-52 — Low binding child of iter-51 (b2ce42f)
Static allocations and five explicit library calls for the fixed contract; native kernels unchanged, all input-dependent GPU work retained. Complete suite PASS; 3.912441 ms (3.782–4.142), 11.4900x. Retain provisionally; small gain and wider spread do not establish binding overhead as the cause. Next combine the earlier128x128x64/group16 projection with current attention/bindings to resolve the close projection branch comparison.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-52: specialize fixed tensor bindings, 3.91 ms'
cat > opt/attention/occupancy51.cpp <<'EOF'
#include \"../../solution/kernel_3.cpp\"
int main(){
 hipFuncAttributes a;
 if(hipFuncGetAttributes(&a,(const void*)main_kernel)"'!=hipSuccess)return 1;
 int n=0;
 if(hipOccupancyMaxActiveBlocksPerMultiprocessor(&n,main_kernel,256,20480)!=hipSuccess)return 2;
 printf("regs=%d local=%zu active_blocks=%d'"\\n\",a.numRegs,a.localSizeBytes,n);
}
EOF
/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/bin/hipcc -O3 --offload-arch=gfx1201 -I/home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang/src opt/attention/occupancy51.cpp -o opt/attention/occupancy51
opt/attention/occupancy51
date -u"
OUTPUT:
regs=205 local=0 active_blocks=3
Sat Sep 26 11:01:37 UTC 2026

STATUS: completed; EXIT_CODE: 0

=== Event 257 | item.started | item_146 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

Actual occupancy check of iter-51's unchanged attention kernel:205 registers,0 local bytes,3 active256-thread blocks per reported multiprocessor with20480-byte LDS. Shorter softmax state is therefore not needed to unlock a fourth block (LDS alone permits only3); no occupancy-based reason to combine iter-49 now. Hardware count wording: device reports32 multiprocessors; this is the queried scheduling-unit count, not an assumption about nominal physical CU specifications.
EOF
git show 84aa7d4:solution/kernel_2.cpp > solution/kernel_2.cpp
bash scripts/bench.sh iter-53"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 258 | item.started | item_147 ===
COMMAND:
/bin/bash -lc "sed -n '68,90p' solution/kernel_2.cpp && rg -n 'float c\\[|buf_dyn_shmem|main_kernel<<<' solution/kernel_2.cpp && date -u"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 259 | item.completed | item_147 ===
COMMAND:
/bin/bash -lc "sed -n '68,90p' solution/kernel_2.cpp && rg -n 'float c\\[|buf_dyn_shmem|main_kernel<<<' solution/kernel_2.cpp && date -u"
OUTPUT:
  for (int i_5 = 0; i_5 < 64; ++i_5) {
    Y[(((((((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) & 127) >> 5) * 73728)) + ((i_5 >> 5) * 36864)) + (((((int)threadIdx.x) & 31) >> 4) * 18432)) + ((i_5 & 7) * 2304)) + (((int)blockIdx.y) * 128)) + ((((int)threadIdx.x) >> 7) * 64)) + (((i_5 & 31) >> 3) * 16)) + (((int)threadIdx.x) & 15))] = ((half_t)(c[i_5] + B[((((((int)blockIdx.y) * 128) + ((((int)threadIdx.x) >> 7) * 64)) + (((i_5 & 31) >> 3) * 16)) + (((int)threadIdx.x) & 15))]));
  }
}


#ifdef _WIN32
#define TL_EXPORT __declspec(dllexport)
#else
#define TL_EXPORT
#endif

#define ERROR_BUF_SIZE 1024
static char error_buf[ERROR_BUF_SIZE];

extern "C" TL_EXPORT const char* get_last_error() {
    return error_buf;
}

extern "C" TL_EXPORT int init() {
    error_buf[0] = '\0';
    
    int device_main_kernel = 0;
11:  extern __shared__ __align__(1024) uchar buf_dyn_shmem[];
12:  void* a = ((void*)((char*)buf_dyn_shmem + 0));
13:  void* b = ((void*)((char*)buf_dyn_shmem + 16384));
14:  float c[64];
118:	main_kernel<<<dim3(512, 18, 1), dim3(256, 1, 1), 32768, stream>>>(B, W, X, Y);
Sat Sep 26 11:05:03 UTC 2026

STATUS: completed; EXIT_CODE: 0

=== Event 260 | item.completed | item_146 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

Actual occupancy check of iter-51's unchanged attention kernel:205 registers,0 local bytes,3 active256-thread blocks per reported multiprocessor with20480-byte LDS. Shorter softmax state is therefore not needed to unlock a fourth block (LDS alone permits only3); no occupancy-based reason to combine iter-49 now. Hardware count wording: device reports32 multiprocessors; this is the queried scheduling-unit count, not an assumption about nominal physical CU specifications.
EOF
git show 84aa7d4:solution/kernel_2.cpp > solution/kernel_2.cpp
bash scripts/bench.sh iter-53"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-53
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [45.05482814642801, 45.084308747800996, 44.893481157805056, 44.91475339437401, 44.953800201416016], "candidate": [3.9654553025137242, 3.703101826388217, 3.8748643117755077, 3.831209897994995, 3.3723669743785942]}
RUNTIME: 3.831209897994995
REF_RUNTIME: 44.953800201416016
SPEEDUP: 11.73357800754845

STATUS: completed; EXIT_CODE: 0

=== Event 261 | item.completed | item_148 ===
The specialized bindings passed at 3.91 ms, but that small gain needs a repeat check. I’m also testing one remaining projection idea: reuse shared memory after the matrix multiply to pack scalar output writes into aligned vector writes.

=== Event 262 | item.started | item_149 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-53 — Low combination child of iter-52 (9c004bd)
Import128x128x64/group16 projection from iter-35 with latest attention and static bindings. Complete suite PASS; 3.831210 ms (3.372–3.965), 11.7336x. Retain provisional best; large first-sample variation requires repeat comparison.
Next local epilogue layout on the wider iter-52 projection: reuse dead operand LDS for64x256 half-output tiles, then write aligned uint4 vectors. Increase dynamic LDS24→32 KiB (still at most two blocks by shared-memory capacity), two output phases. This reduces scalar global store instruction count but adds LDS traffic/barriers; measure. Narrow tile can stage its entire output in its existing32 KiB, a useful second resource configuration.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-53: combine narrow grouped projection with latest attention, 3.83 ms'
git restore --source=9c004bd -- solution
cat > opt/attention/stage_epilogue.py <<'PY'
from pathlib import Path
import sys
mode=sys.argv[1]
if mode=='wide':
    file='kernel_2.cpp';bn=256;rows=64;dtype='half_t';vec='uint4';vw=8;outn=2304;oldsm=24576
elif mode=='narrow':
    file='kernel_2.cpp';bn=128;rows=128;dtype='half_t';vec='uint4';vw=8;outn=2304;oldsm=32768
elif mode=='projection':
    file='kernel_4.cpp';bn=128;rows=64;dtype='float';vec='float4';vw=4;outn=768;oldsm=32768
else: raise ValueError(mode)
p=Path('solution')/file;s=p.read_text();nc=bn//2
assert f'float c[{nc}]' in s
start=s.index('  #pragma unroll\\n  for (int i_5 = 0;')
end=s.index('\\n}\\n\\n\\n#ifdef',start)
condition='true' if rows==128 else '((threadIdx.x&127)>>6)==stage'
code=f'''  // Operand buffers are dead; reuse LDS for coalesced output writes.
  {dtype}* epilogue=({dtype}*)buf_dyn_shmem;
  #pragma unroll
  for(int stage=0;stage<{128//rows};++stage) {{
    __syncthreads();
    if({condition}) {{
      #pragma unroll
      for(int e=0;e<{nc};++e) {{
        int row=((threadIdx.x&127)>>5)*32+(e/{nc//2})*16+((threadIdx.x&31)>>4)*8+(e&7)-stage*{rows};
        int col=(threadIdx.x>>7)*{nc}+((e%{nc//2})>>3)*16+(threadIdx.x&15);
        epilogue[row*{bn}+(col"'^((row&7)*{vw})''^((row&8)*2))]=({dtype})(c[e]+B[blockIdx.y*{bn}+col]);
      }}
    }}
    __syncthreads();
    #pragma unroll
    for(int t=0;t<{rows*bn//256//vw};++t) {{
      int idx=t*256+threadIdx.x;
      int row=idx/{bn//vw}, col=(idx%{bn//vw})*{vw};
      *({vec}*)(Y+(blockIdx.x*128+stage*{rows}+row)*{outn}+blockIdx.y*{bn}+col)=*({vec}*)(epilogue+row*{bn}+(col''^((row&7)*{vw})''^((row&8)*2)));
    }}
  }}
'"'''
s=s[:start]+code+s[end:]
if oldsm"'!=32768:
    s=s.replace(f'"'if ({oldsm} >','if (32768 >').replace(f'\\n            {oldsm},','\\n            32768,').replace(f' {oldsm}, stream',' 32768, stream')
p.write_text(s)
PY
python opt/attention/stage_epilogue.py wide
bash scripts/bench.sh iter-54"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 263 | item.started | item_150 ===
COMMAND:
/bin/bash -lc "git show 9c004bd:solution/kernel_2.cpp | sed -n '65,76p'"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 264 | item.completed | item_150 ===
COMMAND:
/bin/bash -lc "git show 9c004bd:solution/kernel_2.cpp | sed -n '65,76p'"
OUTPUT:
    }
  }
  #pragma unroll
  for (int i_5 = 0; i_5 < 128; ++i_5) {
    Y[(((((((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) & 127) >> 5) * 73728)) + ((i_5 >> 6) * 36864)) + (((((int)threadIdx.x) & 31) >> 4) * 18432)) + ((i_5 & 7) * 2304)) + (((int)blockIdx.y) * 256)) + ((((int)threadIdx.x) >> 7) * 128)) + (((i_5 & 63) >> 3) * 16)) + (((int)threadIdx.x) & 15))] = ((half_t)(c[i_5] + B[((((((int)blockIdx.y) * 256) + ((((int)threadIdx.x) >> 7) * 128)) + (((i_5 & 63) >> 3) * 16)) + (((int)threadIdx.x) & 15))]));
  }
}


#ifdef _WIN32
#define TL_EXPORT __declspec(dllexport)
#else

STATUS: completed; EXIT_CODE: 0

=== Event 265 | item.completed | item_149 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-53 — Low combination child of iter-52 (9c004bd)
Import128x128x64/group16 projection from iter-35 with latest attention and static bindings. Complete suite PASS; 3.831210 ms (3.372–3.965), 11.7336x. Retain provisional best; large first-sample variation requires repeat comparison.
Next local epilogue layout on the wider iter-52 projection: reuse dead operand LDS for64x256 half-output tiles, then write aligned uint4 vectors. Increase dynamic LDS24→32 KiB (still at most two blocks by shared-memory capacity), two output phases. This reduces scalar global store instruction count but adds LDS traffic/barriers; measure. Narrow tile can stage its entire output in its existing32 KiB, a useful second resource configuration.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-53: combine narrow grouped projection with latest attention, 3.83 ms'
git restore --source=9c004bd -- solution
cat > opt/attention/stage_epilogue.py <<'PY'
from pathlib import Path
import sys
mode=sys.argv[1]
if mode=='wide':
    file='kernel_2.cpp';bn=256;rows=64;dtype='half_t';vec='uint4';vw=8;outn=2304;oldsm=24576
elif mode=='narrow':
    file='kernel_2.cpp';bn=128;rows=128;dtype='half_t';vec='uint4';vw=8;outn=2304;oldsm=32768
elif mode=='projection':
    file='kernel_4.cpp';bn=128;rows=64;dtype='float';vec='float4';vw=4;outn=768;oldsm=32768
else: raise ValueError(mode)
p=Path('solution')/file;s=p.read_text();nc=bn//2
assert f'float c[{nc}]' in s
start=s.index('  #pragma unroll\\n  for (int i_5 = 0;')
end=s.index('\\n}\\n\\n\\n#ifdef',start)
condition='true' if rows==128 else '((threadIdx.x&127)>>6)==stage'
code=f'''  // Operand buffers are dead; reuse LDS for coalesced output writes.
  {dtype}* epilogue=({dtype}*)buf_dyn_shmem;
  #pragma unroll
  for(int stage=0;stage<{128//rows};++stage) {{
    __syncthreads();
    if({condition}) {{
      #pragma unroll
      for(int e=0;e<{nc};++e) {{
        int row=((threadIdx.x&127)>>5)*32+(e/{nc//2})*16+((threadIdx.x&31)>>4)*8+(e&7)-stage*{rows};
        int col=(threadIdx.x>>7)*{nc}+((e%{nc//2})>>3)*16+(threadIdx.x&15);
        epilogue[row*{bn}+(col"'^((row&7)*{vw})''^((row&8)*2))]=({dtype})(c[e]+B[blockIdx.y*{bn}+col]);
      }}
    }}
    __syncthreads();
    #pragma unroll
    for(int t=0;t<{rows*bn//256//vw};++t) {{
      int idx=t*256+threadIdx.x;
      int row=idx/{bn//vw}, col=(idx%{bn//vw})*{vw};
      *({vec}*)(Y+(blockIdx.x*128+stage*{rows}+row)*{outn}+blockIdx.y*{bn}+col)=*({vec}*)(epilogue+row*{bn}+(col''^((row&7)*{vw})''^((row&8)*2)));
    }}
  }}
'"'''
s=s[:start]+code+s[end:]
if oldsm"'!=32768:
    s=s.replace(f'"'if ({oldsm} >','if (32768 >').replace(f'\\n            {oldsm},','\\n            32768,').replace(f' {oldsm}, stream',' 32768, stream')
p.write_text(s)
PY
python opt/attention/stage_epilogue.py wide
bash scripts/bench.sh iter-54"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-54
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.953800201416016, 44.97205915879348, 44.90443787140234, 44.81126900464928, 44.97013876478933], "candidate": [4.230112341996476, 4.386680653487881, 4.23185920715332, 4.097540702968674, 4.291436011446326]}
RUNTIME: 4.23185920715332
REF_RUNTIME: 44.953800201416016
SPEEDUP: 10.622706947676424

STATUS: completed; EXIT_CODE: 0

=== Event 266 | item.started | item_151 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-54 — Low epilogue child of iter-52 (9c004bd)
Wider QKV stages output in two64x256 half tiles,32 KiB LDS, aligned uint4 global stores. Complete suite PASS; 4.231859 ms (4.098–4.387). Rejected; extra shared traffic/phases outweigh tested coalescing gain. Original generated C-fragment mapping was checked against the retained source. Next narrow iter-53 can stage its entire128x128 output at once in existing32 KiB, avoiding partial-wave phases and additional allocation.
EOF
git add solution opt/attention/stage_epilogue.py ITERATIONS.md && git commit -m 'iter-54: reject phased wide projection epilogue'
git restore --source=6428f5e -- solution
python opt/attention/stage_epilogue.py narrow
bash scripts/bench.sh iter-55"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 267 | item.completed | item_151 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-54 — Low epilogue child of iter-52 (9c004bd)
Wider QKV stages output in two64x256 half tiles,32 KiB LDS, aligned uint4 global stores. Complete suite PASS; 4.231859 ms (4.098–4.387). Rejected; extra shared traffic/phases outweigh tested coalescing gain. Original generated C-fragment mapping was checked against the retained source. Next narrow iter-53 can stage its entire128x128 output at once in existing32 KiB, avoiding partial-wave phases and additional allocation.
EOF
git add solution opt/attention/stage_epilogue.py ITERATIONS.md && git commit -m 'iter-54: reject phased wide projection epilogue'
git restore --source=6428f5e -- solution
python opt/attention/stage_epilogue.py narrow
bash scripts/bench.sh iter-55"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-55
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [45.181289340131535, 44.88538678106629, 44.813316179066426, 45.079262060120804, 44.953800201416016], "candidate": [4.102438926696777, 4.010779502848259, 4.07019050707266, 4.199608530220568, 4.111344329256444]}
RUNTIME: 4.102438926696777
REF_RUNTIME: 44.953800201416016
SPEEDUP: 10.957823140005193

STATUS: completed; EXIT_CODE: 0

=== Event 268 | item.completed | item_152 ===
Both staged-output variants were slower, so I restored direct writes. I’m checking reusable intermediate workspaces next. Every forward will still recompute all five stages and return a fresh output; the change only reuses intermediate allocations.

=== Event 269 | item.started | item_153 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-55 — Low epilogue child of iter-53 (6428f5e)
Narrow QKV stages full128x128 output in existing32 KiB, one producer/copy phase. Complete suite PASS; 4.102439 ms (4.011–4.200). Rejected. Both full-tile and phased vector-store configurations lose; do not extrapolate coalescing into a gain. Original final-output projection already writes full32-bit elements; extra staging there has lower expected value after these results.
Restore iter-53. Next binding-only workspace reuse: four intermediate allocations cached per device/stream, fresh output each forward, all input-dependent kernels still execute. A CPU lock protects enqueue ordering for concurrent calls; different streams get separate workspaces. This tests allocation overhead without precomputed tensors or skipped computation.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-55: reject full-tile projection output staging'
git restore --source=6428f5e -- solution
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read().replace('import torch\\n','import torch\\nimport threading\\n',1)
s=s.replace('        self.calls = tuple(lib.call for lib in self.runtime.libraries)', '        self.calls = tuple(lib.call for lib in self.runtime.libraries)\\n        self.workspaces = {}\\n        self.workspace_lock = threading.Lock()')
s=s.replace('        with torch.cuda.device(x.device):','        with self.workspace_lock, torch.cuda.device(x.device):')
a=s.index('            wh = torch.empty');b=s.index('            out = torch.empty',a)
s=s[:a]+'''            key = (x.device.index, stream)
            workspace = self.workspaces.get(key)
            if workspace is None:
                workspace = (
                    torch.empty((2304,768),dtype=torch.float16,device=x.device),
                    torch.empty((768,768),dtype=torch.float16,device=x.device),
                    torch.empty((128,512,2304),dtype=torch.float16,device=x.device),
                    torch.empty((128,512,768),dtype=torch.float16,device=x.device),
                )
                self.workspaces[key] = workspace
            wh, ph, q, y = workspace
'''+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-56"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 270 | item.completed | item_153 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-55 — Low epilogue child of iter-53 (6428f5e)
Narrow QKV stages full128x128 output in existing32 KiB, one producer/copy phase. Complete suite PASS; 4.102439 ms (4.011–4.200). Rejected. Both full-tile and phased vector-store configurations lose; do not extrapolate coalescing into a gain. Original final-output projection already writes full32-bit elements; extra staging there has lower expected value after these results.
Restore iter-53. Next binding-only workspace reuse: four intermediate allocations cached per device/stream, fresh output each forward, all input-dependent kernels still execute. A CPU lock protects enqueue ordering for concurrent calls; different streams get separate workspaces. This tests allocation overhead without precomputed tensors or skipped computation.
EOF
git add solution ITERATIONS.md && git commit -m 'iter-55: reject full-tile projection output staging'
git restore --source=6428f5e -- solution
python - <<'PY'
p='solution/ModelNew.py';s=open(p).read().replace('import torch\\n','import torch\\nimport threading\\n',1)
s=s.replace('        self.calls = tuple(lib.call for lib in self.runtime.libraries)', '        self.calls = tuple(lib.call for lib in self.runtime.libraries)\\n        self.workspaces = {}\\n        self.workspace_lock = threading.Lock()')
s=s.replace('        with torch.cuda.device(x.device):','        with self.workspace_lock, torch.cuda.device(x.device):')
a=s.index('            wh = torch.empty');b=s.index('            out = torch.empty',a)
s=s[:a]+'''            key = (x.device.index, stream)
            workspace = self.workspaces.get(key)
            if workspace is None:
                workspace = (
                    torch.empty((2304,768),dtype=torch.float16,device=x.device),
                    torch.empty((768,768),dtype=torch.float16,device=x.device),
                    torch.empty((128,512,2304),dtype=torch.float16,device=x.device),
                    torch.empty((128,512,768),dtype=torch.float16,device=x.device),
                )
                self.workspaces[key] = workspace
            wh, ph, q, y = workspace
'''+s[b:];open(p,'w').write(s)
PY
bash scripts/bench.sh iter-56"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-56
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [45.09563170635308, 45.037473728407356, 44.953800201416016, 44.872138129953925, 44.95325034366555], "candidate": [3.9865419141735776, 4.165383603241873, 4.018008702082388, 4.065489822634016, 4.064620018005371]}
RUNTIME: 4.064620018005371
REF_RUNTIME: 44.953800201416016
SPEEDUP: 11.059779266519524

STATUS: completed; EXIT_CODE: 0

=== Event 271 | item.started | item_154 ===
COMMAND:
/bin/bash -lc 'date -u; git status --short; tail -45 ITERATIONS.md'
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 272 | item.completed | item_154 ===
COMMAND:
/bin/bash -lc 'date -u; git status --short; tail -45 ITERATIONS.md'
OUTPUT:
Sat Sep 26 11:17:09 UTC 2026
 M solution/ModelNew.py
 M solution/kernel_2.cpp
?? .rocprofv3/
?? lowered17/
?? lowered31/
?? lowered36/
?? lowered8/
?? opt/attention/iter26.s
?? opt/attention/iter47.s
?? opt/attention/low24/
?? opt/attention/low30/
?? opt/attention/low35/
?? opt/attention/occupancy
?? opt/attention/occupancy.cpp
?? opt/attention/occupancy51
?? opt/attention/occupancy51.cpp
?? opt/attention/projection.s
Wider QKV128x256x32/group8 plus exact optimized online attention from iter-35. Complete suite PASS; 4.179405 ms (4.121–4.298), 10.7560x. Equivalent to best iter-35 within variation; retain. Next group16 on this new geometry checks the known grouping interaction.

## iter-45 — Low tuning child of iter-44 (4a51d37)
Group16 on wider QKV. Complete suite PASS; 4.243117 ms (4.146–4.295). No gain; restore group8 on this geometry. Grouping benefit from iter-35 does not transfer unchanged.
Next combine retained chunked128-key attention from iter-23 with the producer/consumer V bank-layout mechanism from iter-28 and current projection. Both structures were validated at High and Low; this is a bounded cross-branch mechanism combination. K/P/reduction phase alias and batched reductions remain. Test total result rather than attributing this coupled combination to one isolated change.

## iter-46 — Low cross-branch combination from iter-44 (4a51d37)
Chunked128-key attention from iter-23 with vector V global loads, mixed-row-bit transposed LDS and vector PV reads, plus current wider inline projection. Complete suite PASS; 4.377606 ms (4.362–4.406). Rejected versus online iter-44; tested interaction does not make chunked structure faster.
Restore iter-44. Next direct Q global→WMMA-register loads avoid initial LDS staging, its address divisions and barrier, while retaining register reuse across key blocks. Each N warp duplicates Q operand reads, so cache reuse versus saved staging is measured rather than assumed.

## iter-47 — Low child of iter-44 (4a51d37)
Direct global→register Q operands, retain per-block Q reuse and all other online attention mechanisms. Complete suite PASS; 4.086940 ms (3.939–4.106), 10.9994x. Provisional best; small gain overlaps variation. Compile-only metadata202 VGPR, zero spills. Next direct K operands remove K LDS staging and one barrier; K/P/scratch region can shrink from12 to8 KiB, total20 KiB. Repeated K loads across M warps trade cache reads for staging; measure full candidate.

## iter-48 — Low child of iter-47 (5188cfb)
Direct global K WMMA operands; remove K staging/barrier,20 KiB LDS. Complete suite PASS; 4.023087 ms (3.939–4.112), 11.1740x. Provisional best; modest gain overlaps some spread. Next shorten softmax state lifetimes: rescale running numerator/denominator immediately when new max is known, eliminating separate old/alpha arrays across exponentiation/reduction. Same real arithmetic; full suite will validate changed FP32 scheduling.

## iter-49 — Low child of iter-48 (48cf6db)
Early numerator/denominator rescale shortens old/alpha lifetimes. Complete suite PASS; 4.036014 ms (3.938–4.093). Inconclusive versus4.023-ms parent, retained but not promoted. Recent attention gains are small; refresh Native call costs to choose between projection prefetch and remaining LDS layout work. Diagnose unchanged measured iter-49; High provenance is historical, not unchanged generated-code correspondence.

### Native diagnosis after iter-49
Costs: QKV1.781 ms, attention1.301 ms, output projection0.634 ms, weight conversions0.041 ms. Runtime-native mappings only; old High source is provenance. QKV is largest remaining cost. Revisit prefetch with changed premise: inline FP32 conversion and BK32 need only four uint2 activation registers, versus prior both-operand prefetch's much larger live state. Prefetch activation only, keeping weight loads unchanged. Restore best iter-48 before editing; diagnosis remains applicable to identical projection source.

## iter-50 — Low child of iter-48 (48cf6db)
Activation-only prefetch for inline-conversion QKV, BK32, four uint2 live vectors; weights unchanged. Complete suite PASS; 4.120078 ms (3.984–4.141). Rejected versus4.023-ms parent. Both broad and reduced-state prefetch configurations have now been measured under their relevant input representations.
Restore iter-48. Next probability LDS bank layout: producer warp writes two rows separated by8, which share low-row-bit swizzle. Add row bit3 into column bit4 while preserving contiguous8-half consumer reads. This is a local producer/consumer physical mapping test, no changed arithmetic.

## iter-51 — Low layout child of iter-48 (48cf6db)
Probability producer/consumer swizzle adds row bit3 to column bit4. Complete suite PASS; 3.955016 ms (3.888–3.986), 11.3663x. Provisional promotion; tighter spread supports keeping it for repeat validation. Both P and V bank mappings now measured with producer/consumer interactions.
Next specialize Python allocation/binding path for the fixed five-call graph (no tensor computation). This is an inexpensive end-to-end overhead check; diagnostic kernel sums do not by themselves prove the residual is binding cost. Native kernels and compiler flags remain verbatim.

## iter-52 — Low binding child of iter-51 (b2ce42f)
Static allocations and five explicit library calls for the fixed contract; native kernels unchanged, all input-dependent GPU work retained. Complete suite PASS; 3.912441 ms (3.782–4.142), 11.4900x. Retain provisionally; small gain and wider spread do not establish binding overhead as the cause. Next combine the earlier128x128x64/group16 projection with current attention/bindings to resolve the close projection branch comparison.

Actual occupancy check of iter-51's unchanged attention kernel:205 registers,0 local bytes,3 active256-thread blocks per reported multiprocessor with20480-byte LDS. Shorter softmax state is therefore not needed to unlock a fourth block (LDS alone permits only3); no occupancy-based reason to combine iter-49 now. Hardware count wording: device reports32 multiprocessors; this is the queried scheduling-unit count, not an assumption about nominal physical CU specifications.

## iter-53 — Low combination child of iter-52 (9c004bd)
Import128x128x64/group16 projection from iter-35 with latest attention and static bindings. Complete suite PASS; 3.831210 ms (3.372–3.965), 11.7336x. Retain provisional best; large first-sample variation requires repeat comparison.
Next local epilogue layout on the wider iter-52 projection: reuse dead operand LDS for64x256 half-output tiles, then write aligned uint4 vectors. Increase dynamic LDS24→32 KiB (still at most two blocks by shared-memory capacity), two output phases. This reduces scalar global store instruction count but adds LDS traffic/barriers; measure. Narrow tile can stage its entire output in its existing32 KiB, a useful second resource configuration.

## iter-54 — Low epilogue child of iter-52 (9c004bd)
Wider QKV stages output in two64x256 half tiles,32 KiB LDS, aligned uint4 global stores. Complete suite PASS; 4.231859 ms (4.098–4.387). Rejected; extra shared traffic/phases outweigh tested coalescing gain. Original generated C-fragment mapping was checked against the retained source. Next narrow iter-53 can stage its entire128x128 output at once in existing32 KiB, avoiding partial-wave phases and additional allocation.

## iter-55 — Low epilogue child of iter-53 (6428f5e)
Narrow QKV stages full128x128 output in existing32 KiB, one producer/copy phase. Complete suite PASS; 4.102439 ms (4.011–4.200). Rejected. Both full-tile and phased vector-store configurations lose; do not extrapolate coalescing into a gain. Original final-output projection already writes full32-bit elements; extra staging there has lower expected value after these results.
Restore iter-53. Next binding-only workspace reuse: four intermediate allocations cached per device/stream, fresh output each forward, all input-dependent kernels still execute. A CPU lock protects enqueue ordering for concurrent calls; different streams get separate workspaces. This tests allocation overhead without precomputed tensors or skipped computation.

STATUS: completed; EXIT_CODE: 0

=== Event 273 | item.started | item_155 ===
COMMAND:
/bin/bash -lc 'cat /home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/deployment/skills/tilecas/SKILL.md'
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 274 | item.completed | item_155 ===
COMMAND:
/bin/bash -lc 'cat /home/xiaoxinyu/dsl-native-opt/runs/kb-ours-rocm-mingpt-combinations-astra-low-20260926-082611/deployment/skills/tilecas/SKILL.md'
OUTPUT:
---
name: tilecas
description: Optimize a kernel in one conversation using measured candidates, cross-layer diagnosis, adaptive abstraction scheduling, and a heterogeneous search tree.
---

# TileCas

Produce the fastest fully correct implementation allowed by the supplied task contract.
One agent and conversation own High, Low, diagnosis and scheduling. Start empty; choose the initial level from the dataflow and available controls.
Choose optimization directions and experiment scope yourself; the guidance below supports that judgment.

## Establish the task and hardware

Read `HINTS.md`, `reference.py`, the current workspace and relevant supplied examples before implementing.
Understand input shapes, dtypes, layouts, output semantics, tolerances and allowed libraries at each level.
Use the supplied standard-normal inputs, seeds, correctness suite and timing protocol unchanged.
Use `scripts/bench.sh` for candidate ranking; do not replace or weaken the evaluator.
Do not delegate, import old runs or calculate hashes/checksums.
Confirm hardware facts that affect the intended implementation from the actual device or installed platform configuration.
In particular, check usable core counts and relevant local-memory limits before choosing persistent grids or buffer placement.
Do not substitute a familiar product's nominal specifications for the current device's resources.
Read the relevant installed API/example when copy shapes, tile layouts, matrix-helper capacity or synchronization semantics are uncertain.
Research what resolves the current uncertainty; no separate planning document or exhaustive reference survey is required.

## Establish a correct baseline

Create `opt/<name>` in the run workspace and implement a complete legal `solution/ModelNew.py`.
Choose a baseline whose intermediates and correctness you can reason about; simplicity alone does not guarantee efficient scheduling.
Account for conversions, intermediate tensors and launch/binding work included in the supplied measurement.
Run `bash scripts/bench.sh iter-1` and retain the first candidate that passes the complete suite.
Before first correctness, fix the first concrete defect or simplify the implementation; there is no validated parent to restore yet.
Inspect the first decisive compile/runtime error instead of treating all failures as performance evidence.
For wrong outputs, inspect the earliest incorrect intermediate, indexing, written regions and producer/consumer ordering.
A hang warrants checking launch bounds and synchronization as well as examining the evaluator output.
Distinguish a compiler limitation from an invalid algorithm; a failed implementation has no valid ranking latency.

## Optimize and select candidates

Use the AKO loop: **modify → benchmark → log → commit**.
Run `bash scripts/bench.sh iter-N` for each candidate and keep numbering across levels.
Finish recording and committing the measured candidate, including failures, before editing the next one.
Do not change the source while its evaluation is running; a result belongs to the exact evaluated source.
Choose directions using measured costs, expected end-to-end benefit, evidence, implementation effort and risk.
A short rationale in the iteration log is enough; no ranked-list ceremony is required for every edit.
Know what result would support the intended mechanism and what would make you revise or reject it.
For a local mechanism, preserve unrelated code, bindings and compiler settings so the comparison can explain the result; coupled changes may be tested together when their interaction is the hypothesis.
Prefer editing the measured implementation over replacing it with a generic template solely to expose tuning parameters. If a rewrite is necessary, first measure an equivalent configuration against its source parent; a regression there confounds later layout or scheduling conclusions.
When a comparison is confounded, retain the winner and choose whether repairing the experimental implementation is worth its expected benefit; do not let repeated framework rewrites displace optimization of the measured bottleneck.
Combine validated improvements from this run on a retained candidate, checking compatibility and the complete-suite result. Choose affordable experiments that can distinguish plausible causes; a long rationale or a level change is not performance evidence.
Use the existing task timings and emitted code when they already answer that question.
Consider where the whole runtime goes: conversions, transfers, computation, synchronization and launch work.
A faster inner kernel is useful only if the complete candidate improves under the same contract.
More fusion, a smaller workspace or a new structure is a hypothesis, not an improvement by itself.
When choosing a secondary cost over a dominant one, consider whether it also changes the dominant work or enables a plausible later gain.
Tiling, layout, reuse, cache policy, decomposition, fusion, scheduling, instructions and permitted mixed precision are all available.
For transfer-heavy kernels, consider supported cache allocation/bypass hints separately for streaming inputs, outputs and reused intermediates; verify the target API and measure the complete candidate. High transfer-unit activity does not establish effective bandwidth saturation or rule out cache-policy gains.
Do not force structural novelty, restrict work to tuning constants, or follow a prescribed sequence of techniques.
Precision changes must preserve the unchanged correctness contract; a plausible error projection does not replace validation.
Retain useful alternatives even when they are not currently fastest; keep the exploration candidate separate from the best validated candidate.
Promote only when the full contract passes and measured end-to-end latency supports the improvement.
Use the evaluator's aggregate and available spread; differences within observed variation are inconclusive.
If a result is surprising or ambiguous, inspect its record and use the same evaluator to resolve the uncertainty when worthwhile.
Record whether a candidate was promoted, retained for exploration, or rejected, and why.
Reuse mechanisms that worked earlier in this run after checking that their conditions still apply.
Repeated repair needs a new reason to expect success; otherwise simplify, restore a correct candidate or change direction.

## 1. Cross-layer diagnosis

Only when existing evidence leaves you unsure what to try next, run `python scripts/diagnose.py`.
First correctness, a new iteration or a level change does not automatically require diagnosis.
Invoke it on the unchanged measured candidate, before editing, so timings and source mappings can correspond.
The single no-argument entry point reports all kernels in descending measured cost order.
Use its available timings, High callsites, corresponding Native code, dataflow and hardware metrics together.
The useful chain is: measured cost → High operation → Native implementation → producer/consumer buffers and dependencies.
A kernel-time table alone locates expensive work; it does not explain the cross-layer mechanism.
Follow relevant copies, buffer lifetimes, synchronization and reuse to understand what control could change the cost.
Keep measured facts, source correspondences and inferred causes distinct; missing mappings remain unknown.
Static allocation sizes do not prove cache residency, and utilization ratios do not establish an attainable latency floor.
If the candidate changed since measurement, do not attribute the old timing to its new source.
Reuse evidence while its source and assumptions remain applicable; do not collect counters merely to complete a routine.
Reading or exporting Native code for diagnosis is not a change of active optimization level.
Diagnostic timings guide reasoning but do not replace the complete-suite ranking measurement.
If a diagnostic component fails or is unavailable, note the gap and continue from available measurements and source.
Do not turn an optimization iteration into rebuilding the profiling infrastructure.

## 2. Abstraction scheduler

Use the current candidate, available diagnosis, hardware facts and this run's history to decide where to work.
The choices are **stay**, **lower** and **rollback**; they are decisions by this same agent, not separate agents or fixed stages.
High is usually convenient for exploring structures; Low exposes finer control over memory, instructions and scheduling.
These are affordances, not restrictions on which changes are allowed at either level.
The starting level is also a scheduling decision: prefer High when it expresses the intended implementation conveniently.
For fan-in (parallel branches meeting in concat/sum) or heterogeneous stages, consider whether branch scheduling, reuse or synchronization needs Native control.
If those controls are unavailable or impractical at High, start directly at Low from iteration 1 and briefly record the reason; no High baseline or export is required.
Fan-in alone does not prove a DSL limitation or that one fused kernel will be faster. Judge the actual implementation options.
A direct-Low start is a root node, not an export from a measured High parent; High remains available for later exploration.
**Stay** where the next useful change is economical to express and evaluate, including ordinary tuning at High.
**Lower** when High is hard to improve, does not express the desired control, or Native makes a promising change practical.
You need not exhaust High structures before trying Low, nor descend merely because Native code is available.
A correct, measured High candidate provides the parent for export; it need not be the fastest High candidate if another has a credible advantage.
The exported structure and intermediate precision are editable, subject to the unchanged task contract.
**Rollback** when Low is unproductive or suggests a different High approach; restore its retained High parent and try another direction.
Choose the level from the hypothesis: instruction scheduling, register lifetimes and local memory access usually belong at Low; computation partitioning, fusion boundaries and intermediate representations should normally be prototyped at High.
For a structural hypothesis, retain the best Native candidate, restore a correct High parent (or create a High sibling for a direct-Low root), and test the new decomposition with the complete suite before deciding whether to lower again.
For example, replacing a large fused stage with independently computed blocks and an explicit combination step changes the decomposition; merely resizing its existing tiles does not. Preserve all mathematical dependencies.
Stay Low for structural work only when a concrete required control is unavailable or substantially harder to express at High; briefly record that reason, rather than staying because Native is already open.
A spill or one slow variant alone does not establish a structural problem; repair a concrete local defect at Low when evidence supports it.
A Low branch can be retained even after rollback; returning to High does not discard its results. If Low started directly, create a High sibling from the task contract rather than inventing a High parent.
After a mechanism is tested, let its actual outcome guide further work rather than treating the chosen level as a commitment.
If a familiar bottleneck recurs, consider a strategy that worked earlier; do not repeat failed changes without a changed premise.
There are no fixed time, iteration or improvement thresholds for level changes, and no mandatory catalogue of structures.
Use the remaining budget and the cost of the proposed experiment when choosing among opportunities.

## 3. Heterogeneous search tree

Use Git, `ITERATIONS.md` and the supplied trajectory records as the tree; no additional planning ledger is needed.
Each measured node identifies its exact source parent (or empty root), level, idea, correctness, latency and disposition.
Structural edges connect different structures at the same level; retain local tuning revisions without calling each a new structure.
Representation edges connect the same program across High and Low; subsequent Native edits are separate candidate nodes.
When transferring an existing High candidate, commit and retain its complete correct source before exporting:

```bash
python scripts/export.py --source solution/ModelNew.py --reference reference.py \
    --destination lowered --record lowered/export.json
```

Use a fresh destination or remove only an obsolete export destination so old files cannot enter the transfer.
On successful export, replace the complete `solution/` tree with that export and commit the representation change separately.
Keep required export validation; the transfer alone has no new measured latency and needs no invented performance result.
Evaluate Native changes with the same `scripts/bench.sh` contract used at High.
Rollback restores the exact complete High source tree, removing Low-only files rather than overlaying High onto them.
Record which High parent a Low branch came from so rollback is reproducible.
Ideas can transfer between branches; distinguish the source of an idea from the actual code parent.
Preserve failed and slower measured nodes with their evidence so later choices need not rediscover them.
The final winner may come from any retained branch or level, not necessarily the most recent candidate.

## Reassess and finish

When progress stalls, review the observed costs, attempted mechanisms and remaining opportunities across levels.
Treat exported Native as a starting point, not a structural constraint. When local tuning stops addressing the dominant cost, use existing evidence—or diagnosis if the next step is unclear—to form a concrete alternative decomposition and predict which cost it would reduce; a short iteration-log note is enough.
Validate a worthwhile structural hypothesis at the level chosen above before declaring that branch exhausted. Do not force an unsupported alternative: if none is justified, explain why the remaining evidence does not support another experiment. A failed variant only rejects the implementation actually tested.
Explore the dominant remaining costs sufficiently before stopping. A few unsuccessful local variants do not establish that a mechanism or branch is exhausted.
For a promising mechanism, test a bounded set of materially different, hardware-feasible configurations and relevant combinations, rather than relying on one parameter setting. Couple choices that affect each other: tile geometry and thread/warp ownership, layout and producer/consumer placement, conversion placement and reuse, input/output cache policies and vector access layout, or scheduling and register/shared-memory pressure.
After a layout, decomposition or resource balance changes, revisit earlier rejected mechanisms when their assumptions have changed. Retain useful intermediate candidates and combine compatible improvements within this run; an isolated regression does not rule out a beneficial combination.
Predicted register/shared-memory pressure is a tradeoff, not grounds to reject an otherwise feasible configuration without measurement. Use hardware limits to exclude invalid configurations; use the full evaluator to judge promising feasible ones.
When progress stalls and budget remains, distinguish insufficient exploration from evidence of low remaining value. Prefer another affordable discriminating experiment on a dominant cost over stopping merely because recent variants failed or improvements became small.
Before stopping for diminishing returns, briefly record which meaningful configurations and interactions were actually tested, which credible opportunities remain, and why those opportunities do not justify another experiment within the remaining budget. If a promising, affordable combination is still untested, continue exploring it.
Sufficient exploration is evidence-based coverage of promising mechanisms and their interactions, not exhaustive Cartesian sweeps, a fixed iteration quota, mandatory coverage of every technique, or a requirement to spend the entire budget.
If the next experiment is clear, proceed; if it is unclear, diagnosis is available to inform the choice.
A local plateau, a failed tool or a difficult implementation is not by itself evidence that the whole search is finished.
Use the actual target, deadline and budget in `HINTS.md`; do not invent a shorter run window.
Stop when the target is met, the budget ends, or the remaining opportunities no longer justify another experiment.
Explain the stop with available evidence; do not call unmeasured headroom a proven hardware limit.
Reserve time to restore the fastest fully correct candidate verbatim from Git, including its complete source tree.
Run `bash scripts/bench.sh final`, record the outcome and selected node, and commit the final state.
If final validation fails, report it and retain the last validated fallback; do not present an unvalidated rewrite as the winner.

STATUS: completed; EXIT_CODE: 0

=== Event 275 | item.completed | item_156 ===
Workspace reuse was slower. I’m restoring the best candidate and testing warp shuffles to pack projection outputs into vector stores without the shared-memory traffic added by the previous variants.

=== Event 276 | item.started | item_157 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-56 — Low binding child of iter-53 (6428f5e)
Per-device/stream reusable intermediate allocations, fresh output, lock-protected enqueue. Complete suite PASS; 4.064620 ms (4.002–4.298). Rejected versus static fresh allocations. Restore iter-53. Next warp-local 8x8 half transpose packs QKV stores without LDS or barriers; this directly distinguishes vector-store benefit from the extra shared traffic of iter-54/55. Preserve all GEMM arithmetic and resource allocation.
EOF
git add solution ITERATIONS.md
git commit -m 'Record iter56 reusable workspace regression'
git restore --source=6428f5e -- solution
sed -n '/for (int i_5/,"'$p'"' solution/kernel_2.cpp | head -35"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 277 | item.completed | item_157 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-56 — Low binding child of iter-53 (6428f5e)
Per-device/stream reusable intermediate allocations, fresh output, lock-protected enqueue. Complete suite PASS; 4.064620 ms (4.002–4.298). Rejected versus static fresh allocations. Restore iter-53. Next warp-local 8x8 half transpose packs QKV stores without LDS or barriers; this directly distinguishes vector-store benefit from the extra shared traffic of iter-54/55. Preserve all GEMM arithmetic and resource allocation.
EOF
git add solution ITERATIONS.md
git commit -m 'Record iter56 reusable workspace regression'
git restore --source=6428f5e -- solution
sed -n '/for (int i_5/,"'$p'"' solution/kernel_2.cpp | head -35"
OUTPUT:
[codex/rocm-workspace 0f0d34c] Record iter56 reusable workspace regression
 3 files changed, 20 insertions(+), 25 deletions(-)
  for (int i_5 = 0; i_5 < 64; ++i_5) {
    Y[(((((((((((int)blockIdx.x) * 294912) + (((((int)threadIdx.x) & 127) >> 5) * 73728)) + ((i_5 >> 5) * 36864)) + (((((int)threadIdx.x) & 31) >> 4) * 18432)) + ((i_5 & 7) * 2304)) + (((int)blockIdx.y) * 128)) + ((((int)threadIdx.x) >> 7) * 64)) + (((i_5 & 31) >> 3) * 16)) + (((int)threadIdx.x) & 15))] = ((half_t)(c[i_5] + B[((((((int)blockIdx.y) * 128) + ((((int)threadIdx.x) >> 7) * 64)) + (((i_5 & 31) >> 3) * 16)) + (((int)threadIdx.x) & 15))]));
  }
}


#ifdef _WIN32
#define TL_EXPORT __declspec(dllexport)
#else
#define TL_EXPORT
#endif

#define ERROR_BUF_SIZE 1024
static char error_buf[ERROR_BUF_SIZE];

extern "C" TL_EXPORT const char* get_last_error() {
    return error_buf;
}

extern "C" TL_EXPORT int init() {
    error_buf[0] = '\0';
    
    int device_main_kernel = 0;
    hipError_t dev_res_main_kernel = hipGetDevice(&device_main_kernel);
    if (dev_res_main_kernel != hipSuccess) {
        snprintf(error_buf, ERROR_BUF_SIZE, "Failed to get HIP device for main_kernel: %s", hipGetErrorString(dev_res_main_kernel));
        return -1;
    }
    int max_smem_main_kernel = 0;
    hipError_t attr_res_main_kernel = hipDeviceGetAttribute(&max_smem_main_kernel, hipDeviceAttributeMaxSharedMemoryPerBlock, device_main_kernel);
    if (attr_res_main_kernel != hipSuccess || max_smem_main_kernel <= 0) {
        snprintf(error_buf, ERROR_BUF_SIZE, "Failed to query HIP max shared memory for main_kernel: %s", hipGetErrorString(attr_res_main_kernel));
        return -1;
    }
    if (32768 > max_smem_main_kernel) {

STATUS: completed; EXIT_CODE: 0

=== Event 278 | item.started | item_158 ===
COMMAND:
/bin/bash -lc "cat > opt/attention/warp_epilogue.py <<'PY'
from pathlib import Path
p=Path('solution/kernel_2.cpp')
s=p.read_text(); a=s.index('  #pragma unroll\\n  for (int i_5 = 0;'); b=s.index('\\n}\\n\\n\\n#ifdef',a)
s=s[:a]+'''  const int tid = threadIdx.x;
  #pragma unroll
  for (int g = 0; g < 8; ++g) {
    unsigned w[4];
    const int bc = blockIdx.y * 128 + (tid >> 7) * 64 + (g % 4) * 16 + (tid & 15);
    #pragma unroll
    for (int j = 0; j < 4; ++j) {
      half_t lo = (half_t)(c[g*8+j*2] + B[bc]);
      half_t hi = (half_t)(c[g*8+j*2+1] + B[bc]);
      w[j] = (unsigned)__builtin_bit_cast(unsigned short, lo) | ((unsigned)__builtin_bit_cast(unsigned short, hi) << 16);
    }
    #pragma unroll
    for (int j = 0; j < 4; ++j) {
      unsigned other = __shfl_xor(w[j], 1, 8);
      w[j] = (tid & 1) ? ((other >> 16) | (w[j] & 0xffff0000u)) : ((w[j] & 0xffffu) | (other << 16));
    }
    #pragma unroll
    for (int j = 0; j < 4; j += 2) {
      unsigned other = __shfl_xor((tid & 2) ? w[j] : w[j+1], 2, 8);
      w[j] = (tid & 2) ? other : w[j];
      w[j+1] = (tid & 2) ? w[j+1] : other;
    }
    #pragma unroll
    for (int j = 0; j < 2; ++j) {
      unsigned other = __shfl_xor((tid & 4) ? w[j] : w[j+2], 4, 8);
      w[j] = (tid & 4) ? other : w[j];
      w[j+2] = (tid & 4) ? w[j+2] : other;
    }
    int row = ((tid & 127) >> 5)*32 + (g/4)*16 + ((tid & 31)>>4)*8 + (tid & 7);
    int col = (tid>>7)*64 + (g%4)*16 + (tid & 8);
    *(uint4*)(Y + (blockIdx.x*128+row)*2304 + blockIdx.y*128+col) = make_uint4(w[0],w[1],w[2],w[3]);
  }'''+s[b:];p.write_text(s)
PY
python opt/attention/warp_epilogue.py
bash scripts/bench.sh iter-57"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 279 | item.started | item_159 ===
COMMAND:
/bin/bash -lc 'cat HINTS.md; cat scripts/bench.sh | tail -45'
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 280 | item.completed | item_159 ===
COMMAND:
/bin/bash -lc 'cat HINTS.md; cat scripts/bench.sh | tail -45'
OUTPUT:
Ours owns High (TileLang) and Low (HIP C++) in this same conversation. Start empty and choose the initial level using the supplied TileCas skill; follow that skill for diagnosis, stay/lower/rollback and final selection. High prohibits torch imports/framework computation; use target="hip", execution_backend="cython". Low edits native source with allocation/bindings only, without TileLang/Triton regeneration or framework/external-operator fallback. To roll back, restore the entire High solution and remove leftover native sources.
Target AMD Radeon AI PRO R9700, gfx1201, ROCm 7.14, FP32 inputs.
Implement solution/ModelNew.py exposing ModelNew with to(), __call__(), forward().
Read reference.py for the complete task. Input distribution N(0,1); seeds 42,43,50000; ten correctness trials per seed; atol=rtol=1e-3; three warmups followed by five HIP-event timed forwards in one timing block at input seed 42; report the median of five samples. Reference and candidate use the same timing inputs and protocol. The three correctness seed bases are separate from timing repetitions.
Use only bash scripts/bench.sh LABEL for evaluation; do not edit reference.py or scripts/. It remeasures the reference and includes all GPU work in forward.
Native compute may use authored HIP and TileLang HIP headers; no rocBLAS/hipBLAS/MIOpen, CK or complete attention implementations.
Preserve every input dependency and reference mathematics. No distribution-specific approximation.
Read-only platform references: /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/tilelang; /home/xiaoxinyu/venvs/dsl-native-opt/lib/python3.12/site-packages/_rocm_sdk_devel/include.
Work in this workspace only; do not read sibling experiments or optimizer histories. Work alone, no delegation. Do not calculate hashes/checksums.
Nsight Compute is unavailable on AMD; scripts/bench.sh owns ranking.

Task: KernelBench Level 3 MinGPTCausalAttention, full forward including QKV and output projections.
Original dimensions: batch 128, sequence 512, embedding 768, 8 heads (head dimension 96); causal mask; dropout probabilities zero.
Use the same get_init_inputs constructor arguments as reference.py. ModelNew.forward receives five tensors:
x [128,512,768], qkv_weight [2304,768], qkv_bias [2304], projection_weight [768,768], projection_bias [768].
Weights use PyTorch Linear's [out_features,in_features] convention. They are fixed parameters from the original seed-42 initialization, passed explicitly and identically to both implementations; do not reinitialize or mutate them. Activations use N(0,1).
The appended reference adapter only supplies these parameter bindings to the original forward. Preserve all projections, bias additions, scaling, causal masking, softmax, head reassembly and output semantics. No precomputed outputs or sibling-run imports.
Use python scripts/diagnose.py with no arguments only when existing evidence leaves the next experiment unclear. Neither first correctness nor a new iteration triggers diagnosis. The tool reuses source/reference/protocol-matched evidence or collects missing High provenance, call-group costs and available ROCm counters, and reports all calls. It never exports authored Low. Costs are diagnostic, not ranking or a hardware floor. Counter summaries aggregate by symbol and include warmup; ambiguous associations remain unknown. Native changes retain historical High provenance without claiming unchanged correspondence. Export via scripts/export.py performs full Native replay validation; do not add a duplicate transfer baseline.
Run window: 2026-09-26 09:54 to 11:54 UTC, cut off at the end whatever state you are in. `date -u` for the time now.
#!/bin/bash
set -o pipefail
cd "$(dirname "$0")/.."
LABEL="${1:-manual}"
[[ "$LABEL" =~ ^[a-zA-Z0-9_-]+$ ]] || exit 2
TIMESTAMP=$(date +%Y%m%d_%H%M%S)
TRAJ_DIR="trajectory/${TIMESTAMP}_${LABEL}"
mkdir -p "$TRAJ_DIR"
/home/xiaoxinyu/venvs/dsl-native-opt/bin/python -c 'import json,sys;from pathlib import Path;sys.path.insert(0,'"'"'scripts'"'"');from rocm_diagnose import context;Path(sys.argv[1]).write_text(json.dumps(context(Path.cwd()),indent=2))' "$TRAJ_DIR/evaluation-context.json" || exit $?
timeout --signal=TERM --kill-after=30s 1800 /home/xiaoxinyu/venvs/dsl-native-opt/bin/python scripts/rocm_bench.py --ref reference.py --solution solution/ModelNew.py --backend auto 2>&1 | tee _bench_output.txt
STATUS=${PIPESTATUS[0]}
cp -r solution/* "$TRAJ_DIR/" 2>/dev/null || true
cp _bench_output.txt "$TRAJ_DIR/output.txt"
rm -f _bench_output.txt
exit "$STATUS"

STATUS: completed; EXIT_CODE: 0

=== Event 281 | item.completed | item_158 ===
COMMAND:
/bin/bash -lc "cat > opt/attention/warp_epilogue.py <<'PY'
from pathlib import Path
p=Path('solution/kernel_2.cpp')
s=p.read_text(); a=s.index('  #pragma unroll\\n  for (int i_5 = 0;'); b=s.index('\\n}\\n\\n\\n#ifdef',a)
s=s[:a]+'''  const int tid = threadIdx.x;
  #pragma unroll
  for (int g = 0; g < 8; ++g) {
    unsigned w[4];
    const int bc = blockIdx.y * 128 + (tid >> 7) * 64 + (g % 4) * 16 + (tid & 15);
    #pragma unroll
    for (int j = 0; j < 4; ++j) {
      half_t lo = (half_t)(c[g*8+j*2] + B[bc]);
      half_t hi = (half_t)(c[g*8+j*2+1] + B[bc]);
      w[j] = (unsigned)__builtin_bit_cast(unsigned short, lo) | ((unsigned)__builtin_bit_cast(unsigned short, hi) << 16);
    }
    #pragma unroll
    for (int j = 0; j < 4; ++j) {
      unsigned other = __shfl_xor(w[j], 1, 8);
      w[j] = (tid & 1) ? ((other >> 16) | (w[j] & 0xffff0000u)) : ((w[j] & 0xffffu) | (other << 16));
    }
    #pragma unroll
    for (int j = 0; j < 4; j += 2) {
      unsigned other = __shfl_xor((tid & 2) ? w[j] : w[j+1], 2, 8);
      w[j] = (tid & 2) ? other : w[j];
      w[j+1] = (tid & 2) ? w[j+1] : other;
    }
    #pragma unroll
    for (int j = 0; j < 2; ++j) {
      unsigned other = __shfl_xor((tid & 4) ? w[j] : w[j+2], 4, 8);
      w[j] = (tid & 4) ? other : w[j];
      w[j+2] = (tid & 4) ? w[j+2] : other;
    }
    int row = ((tid & 127) >> 5)*32 + (g/4)*16 + ((tid & 31)>>4)*8 + (tid & 7);
    int col = (tid>>7)*64 + (g%4)*16 + (tid & 8);
    *(uint4*)(Y + (blockIdx.x*128+row)*2304 + blockIdx.y*128+col) = make_uint4(w[0],w[1],w[2],w[3]);
  }'''+s[b:];p.write_text(s)
PY
python opt/attention/warp_epilogue.py
bash scripts/bench.sh iter-57"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-57
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.953800201416016, 44.73502595352229, 44.96814047360988, 45.11180007135796, 44.76441194384484], "candidate": [4.086981717920288, 4.315301879723842, 4.13945198059082, 4.308528595071975, 3.873541930958872]}
RUNTIME: 4.13945198059082
REF_RUNTIME: 44.953800201416016
SPEEDUP: 10.859843383181316

STATUS: completed; EXIT_CODE: 0

=== Event 282 | item.completed | item_160 ===
The warp-shuffle stores passed all correctness checks but were slower too. I’ll repeat the two strongest projection configurations to resolve their overlapping timing ranges before selecting the final source.

=== Event 283 | item.started | item_161 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-57 — Low epilogue child of iter-53 (6428f5e)
Warp-local packed half 8x8 transpose, uint4 global stores, no extra LDS/barriers. Complete suite PASS; 4.139452 ms (3.874–4.315). Rejected. Together with staged full-tile and phased stores, this tests materially different producer/consumer placements; no measured benefit supports further output-packing work now.
Next repeat the close retained narrow and wide direct-store candidates under unchanged evaluator; best single aggregates overlap substantial timing spread.
EOF
git add solution ITERATIONS.md opt/attention/warp_epilogue.py
git commit -m 'Record iter57 warp packed epilogue regression'
git restore --source=6428f5e -- solution
bash scripts/bench.sh iter-58"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 284 | item.started | item_162 ===
COMMAND:
/bin/bash -lc 'cat solution/ModelNew.py; head -25 ITERATIONS.md; git status --short'
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 285 | item.completed | item_162 ===
COMMAND:
/bin/bash -lc 'cat solution/ModelNew.py; head -25 ITERATIONS.md; git status --short'
OUTPUT:
import torch
from pathlib import Path
import importlib.util

class ModelNew:
    def __init__(self, *args):
        root = Path(__file__).resolve().parent
        spec = importlib.util.spec_from_file_location("candidate_runtime", root / "runtime.py")
        module = importlib.util.module_from_spec(spec)
        spec.loader.exec_module(module)
        self.runtime = module.Runtime(root)
        self.calls = tuple(lib.call for lib in self.runtime.libraries)

    def to(self, *args, **kwargs):
        return self

    def __call__(self, *args):
        return self.forward(*args)

    def forward(self, x, qw, qb, pw, pb):
        with torch.cuda.device(x.device):
            stream = torch.cuda.current_stream().cuda_stream
            wh = torch.empty((2304,768),dtype=torch.float16,device=x.device)
            ph = torch.empty((768,768),dtype=torch.float16,device=x.device)
            q = torch.empty((128,512,2304),dtype=torch.float16,device=x.device)
            y = torch.empty((128,512,768),dtype=torch.float16,device=x.device)
            out = torch.empty((128,512,768),dtype=torch.float32,device=x.device)
            if self.calls[0](qw.data_ptr(),wh.data_ptr(),stream): raise RuntimeError("weight conversion failed")
            if self.calls[1](pw.data_ptr(),ph.data_ptr(),stream): raise RuntimeError("weight conversion failed")
            if self.calls[2](x.data_ptr(),wh.data_ptr(),qb.data_ptr(),q.data_ptr(),stream): raise RuntimeError("QKV launch failed")
            if self.calls[3](q.data_ptr(),y.data_ptr(),stream): raise RuntimeError("attention launch failed")
            if self.calls[4](y.data_ptr(),ph.data_ptr(),pb.data_ptr(),out.data_ptr(),stream): raise RuntimeError("projection launch failed")
            return out
# TileCas iterations

Hardware: gfx1201 Radeon AI PRO R9700; actual device reports 32 CUs, 32-lane warps, 65536 bytes shared memory/block, 1024 threads/block, 8 MiB L2. Window 09:54–11:54 UTC.

## iter-1 — High root
Empty parent. Tiled 64x64 projections (FP16 operands, FP32 accumulation), explicit QK, stable softmax, PV and output projection. High conveniently expresses the full dataflow. FP16 intermediate precision is validated, not assumed.
Complete suite PASS, median 24.242527 ms, samples 24.190–24.357 ms, fixed-reference speedup 1.8543x. Promoted as first correct baseline.
Next: fuse attention with online softmax to remove score/probability traffic and exploit causality; keep projections unchanged for attribution.

## iter-2 — High structural child of iter-1 (078791f)
Fused 32-query/64-key online stable softmax attention, FP32 running numerator/denominator, causal block bound. Projection kernels unchanged. Complete suite PASS; 21.111492 ms (21.045–21.161), 2.1294x. Promoted. Removes global S/P tensors, but only 3.13 ms total saved. Next cost split is uncertain between strided attention and projection conversion/reloads; diagnose unchanged candidate.

## iter-3 — High structural child of iter-2 (861d582)
Diagnosis: QKV 13.12 ms, output projection 4.80 ms, attention 3.18 ms. Native projection has repeated FP32 loads/conversions per tile, two barriers per 32-wide reduction chunk; counter symbols ambiguous, no utilization inference. Convert activation and both weights once per forward; retain half attention output (same rounding previously performed by projection loads). No cached parameter data. Remove unused explicit attention definitions.
Complete suite PASS; 13.495666 ms (13.427–13.500), 3.3310x. Promoted. Next increase projection reuse and reduction chunk to reduce traffic/barriers.

## iter-4 — High tuning child of iter-3 (a147944)
Projection tile 128x128x64, 256 threads (32 KiB LDS), versus 64x64x32/128. Complete suite PASS; 9.107313 ms (9.054–9.147), 4.9360x. Promoted. Coupled geometry/thread ownership increases reuse and halves reduction barriers. Next test grouped grid ordering: current x-major M traversal streams the entire activation once per output tile; nearby output tiles could reuse activation from L2.

## iter-5 — High tuning child of iter-4 (532f4ad)
Grouped traversal attempt. Compile FAIL: API accepts `column`, not `col`; no latency. Rejected implementation; fix concrete argument error next.

## iter-6 — High repair child of iter-5 (a0ad233)
Correct `column` swizzle, group eight M tiles. Complete suite PASS; 6.419225 ms (6.412–6.474), 7.0030x. Promoted. Strong improvement supports activation reuse hypothesis, despite reference timing drift. Next increase attention query tile 32→64 to reuse K/V and reduce block count.

 M solution/kernel_2.cpp
?? .rocprofv3/
?? _bench_output.txt
?? lowered17/
?? lowered31/
?? lowered36/
?? lowered8/
?? opt/attention/iter26.s
?? opt/attention/iter47.s
?? opt/attention/low24/
?? opt/attention/low30/
?? opt/attention/low35/
?? opt/attention/occupancy
?? opt/attention/occupancy.cpp
?? opt/attention/occupancy51
?? opt/attention/occupancy51.cpp
?? opt/attention/projection.s

STATUS: completed; EXIT_CODE: 0

=== Event 286 | item.completed | item_161 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-57 — Low epilogue child of iter-53 (6428f5e)
Warp-local packed half 8x8 transpose, uint4 global stores, no extra LDS/barriers. Complete suite PASS; 4.139452 ms (3.874–4.315). Rejected. Together with staged full-tile and phased stores, this tests materially different producer/consumer placements; no measured benefit supports further output-packing work now.
Next repeat the close retained narrow and wide direct-store candidates under unchanged evaluator; best single aggregates overlap substantial timing spread.
EOF
git add solution ITERATIONS.md opt/attention/warp_epilogue.py
git commit -m 'Record iter57 warp packed epilogue regression'
git restore --source=6428f5e -- solution
bash scripts/bench.sh iter-58"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-58
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.92213770449576, 44.94943541675951, 45.11360138260987, 44.953800201416016, 45.0012127019877], "candidate": [3.8016796910047366, 3.991462053211514, 3.82854751428226, 4.044786573451128, 3.8803210258483887]}
RUNTIME: 3.8803210258483887
REF_RUNTIME: 44.953800201416016
SPEEDUP: 11.585072446831218

STATUS: completed; EXIT_CODE: 0

=== Event 287 | item.started | item_163 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-58 — Low unchanged repeat of iter-53 (6428f5e)
Restored entire solution verbatim. Complete suite PASS; 3.880321 ms (3.802–4.045), 11.5851x fixed reference. Confirms narrow candidate near its original3.831-ms aggregate, with repeatable high first-sample variation. Retain for final selection; next unchanged wide iter-52 comparison.
EOF
git add solution ITERATIONS.md
git commit -m 'Record iter58 narrow candidate repeat'
git restore --source=9c004bd -- solution
bash scripts/bench.sh iter-59"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 288 | item.completed | item_163 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-58 — Low unchanged repeat of iter-53 (6428f5e)
Restored entire solution verbatim. Complete suite PASS; 3.880321 ms (3.802–4.045), 11.5851x fixed reference. Confirms narrow candidate near its original3.831-ms aggregate, with repeatable high first-sample variation. Retain for final selection; next unchanged wide iter-52 comparison.
EOF
git add solution ITERATIONS.md
git commit -m 'Record iter58 narrow candidate repeat'
git restore --source=9c004bd -- solution
bash scripts/bench.sh iter-59"
OUTPUT:
RECORD_ID: CASE-MINGPT-iter-59
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.90321401300745, 45.03490718378754, 44.953800201416016, 45.013295562584766, 44.86786638917757], "candidate": [4.027993854935246, 3.8913719294091926, 3.8964130878448486, 3.85862153419005, 3.9487190758147337]}
RUNTIME: 3.8964130878448486
REF_RUNTIME: 44.953800201416016
SPEEDUP: 11.537226466478298

STATUS: completed; EXIT_CODE: 0

=== Event 289 | item.completed | item_164 ===
The repeats were close: 3.880 ms for the narrow projection and 3.896 ms for the wide projection, both passing all 30 checks. I’ll retain the narrow candidate, which also had the lowest earlier aggregate, and run final validation. The explored layouts, tilings, prefetching, cache policies, and output-store variants don’t currently support another promising change.

=== Event 290 | item.started | item_165 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-59 — Low unchanged repeat of iter-52 (9c004bd)
Restored entire wide solution verbatim. Complete suite PASS; 3.896413 ms (3.859–4.028), 11.5372x fixed reference. Equivalent to narrow repeat within spread. Retain as alternative; select narrow iter-53 for final, since both its original and repeat aggregates are lower, without claiming a statistically established geometry advantage.

## Reassessment and stop decision
No latency target was supplied; current time is around11:23 UTC, before the11:54 cutoff. Stop for low expected value from remaining supported opportunities, not a shortened time budget or proven hardware floor.
Measured structural coverage includes explicit scores/probabilities, online attention with multiple query/key geometries and warp ownerships, full-row on-chip attention,128/256-key normalization chunks, preconverted versus inline activations, and head-major intermediate storage. Chunked and online branches were lowered, optimized, and combined with compatible later mechanisms. Dominant QKV coverage includes64/128-row and128/256-column tiles,32/64/128 reduction chunks,128/256/512 threads where feasible, grouped traversal, both-operand and activation-only prefetch under changed representations, and direct/scalar, LDS-staged/full or phased, and warp-packed epilogues. Output projection geometry and streaming cache policies were also measured. Attention coverage includes batched reductions, lifetime/phase aliasing, register Q reuse, direct Q/K operands, V/P producer-consumer bank mappings and combination with alternate normalization structure. Bindings and intermediate reuse were measured end to end.
The most recent diagnosis places QKV around1.78 ms, attention1.30 ms and output projection0.63 ms; these guide direction, not a floor or current exact decomposition. Remaining full-forward fusion would require cross-block producer/consumer coordination or duplicated QKV work; current evidence does not support that expense. Splitting attention adds merge traffic to a grid already containing8192 blocks on32 reported multiprocessors, without an observed parallelism shortage. Larger double-buffered projection designs introduce resource/inline-conversion costs; measured prefetch variants and reduction/resource alternatives give no concrete favorable configuration to pursue now. Weight conversion totals only about0.04 ms, below observed total timing variation, so fusion there has limited expected value. These are opportunity assessments, not proofs that all variants are exhausted. Restore complete iter-53 solution from Git, then run mandatory final suite without source changes.
EOF
git add solution ITERATIONS.md
git commit -m 'Record iter59 wide repeat and final selection rationale'
git restore --source=6428f5e -- solution
bash scripts/bench.sh final"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 291 | item.completed | item_165 ===
COMMAND:
/bin/bash -lc "cat >> ITERATIONS.md <<'EOF'

## iter-59 — Low unchanged repeat of iter-52 (9c004bd)
Restored entire wide solution verbatim. Complete suite PASS; 3.896413 ms (3.859–4.028), 11.5372x fixed reference. Equivalent to narrow repeat within spread. Retain as alternative; select narrow iter-53 for final, since both its original and repeat aggregates are lower, without claiming a statistically established geometry advantage.

## Reassessment and stop decision
No latency target was supplied; current time is around11:23 UTC, before the11:54 cutoff. Stop for low expected value from remaining supported opportunities, not a shortened time budget or proven hardware floor.
Measured structural coverage includes explicit scores/probabilities, online attention with multiple query/key geometries and warp ownerships, full-row on-chip attention,128/256-key normalization chunks, preconverted versus inline activations, and head-major intermediate storage. Chunked and online branches were lowered, optimized, and combined with compatible later mechanisms. Dominant QKV coverage includes64/128-row and128/256-column tiles,32/64/128 reduction chunks,128/256/512 threads where feasible, grouped traversal, both-operand and activation-only prefetch under changed representations, and direct/scalar, LDS-staged/full or phased, and warp-packed epilogues. Output projection geometry and streaming cache policies were also measured. Attention coverage includes batched reductions, lifetime/phase aliasing, register Q reuse, direct Q/K operands, V/P producer-consumer bank mappings and combination with alternate normalization structure. Bindings and intermediate reuse were measured end to end.
The most recent diagnosis places QKV around1.78 ms, attention1.30 ms and output projection0.63 ms; these guide direction, not a floor or current exact decomposition. Remaining full-forward fusion would require cross-block producer/consumer coordination or duplicated QKV work; current evidence does not support that expense. Splitting attention adds merge traffic to a grid already containing8192 blocks on32 reported multiprocessors, without an observed parallelism shortage. Larger double-buffered projection designs introduce resource/inline-conversion costs; measured prefetch variants and reduction/resource alternatives give no concrete favorable configuration to pursue now. Weight conversion totals only about0.04 ms, below observed total timing variation, so fusion there has limited expected value. These are opportunity assessments, not proofs that all variants are exhausted. Restore complete iter-53 solution from Git, then run mandatory final suite without source changes.
EOF
git add solution ITERATIONS.md
git commit -m 'Record iter59 wide repeat and final selection rationale'
git restore --source=6428f5e -- solution
bash scripts/bench.sh final"
OUTPUT:
RECORD_ID: CASE-MINGPT-final
TIMING_METRIC: forward_elapsed_ms
WARMUP_EXECUTIONS: 3
TIMED_EXECUTIONS: 5
STAGE: Low
COMPILED: True
CORRECT: True
TIMING_SAMPLES_PER_ROLE: 5
SAMPLES_MS: {"reference": [44.93503685529109, 45.04440072920029, 44.869621078419684, 45.146832774488246, 44.953800201416016], "candidate": [4.137706756591797, 4.123047105085603, 3.9984438754845533, 4.162789653307052, 4.146060367553606]}
RUNTIME: 4.137706756591797
REF_RUNTIME: 44.953800201416016
SPEEDUP: 10.864423905778228

STATUS: completed; EXIT_CODE: 0

=== Event 292 | item.completed | item_166 ===
Final validation passed all 30 checks at 4.138 ms. That is slower than the selection repeats; the source is unchanged, so I’ll report the final measurement and preserve the timing variation in the log.

=== Event 293 | item.started | item_167 ===
COMMAND:
/bin/bash -lc "python - <<'PY'
from pathlib import Path
import shutil
root=Path('.cache/exploration-artifacts');root.mkdir(parents=True,exist_ok=True)
paths=['.rocprofv3','lowered17','lowered31','lowered36','lowered8','opt/attention/iter26.s','opt/attention/iter47.s','opt/attention/low24','opt/attention/low30','opt/attention/low35','opt/attention/occupancy','opt/attention/occupancy.cpp','opt/attention/occupancy51','opt/attention/occupancy51.cpp','opt/attention/projection.s']
for name in paths:
 p=Path(name)
 if p.exists(): shutil.move(str(p),str(root/p.name))
p=Path('ITERATIONS.md');s=p.read_text().replace('actual device reports 32 CUs,','actual device reports 32 multiprocessors,');p.write_text(s)
PY
cat >> ITERATIONS.md <<'EOF'

## final — exact restored iter-53 (6428f5e), Low
Mandatory "'`bash scripts/bench.sh final`: compiled, complete30-trial correctness PASS. Median4.137706757 ms, samples3.998–4.163 ms. Fixed-reference44.953800201-ms speedup10.8644x; reference median44.953800201 ms. Final timing is slower than selection repeats; no source change or unvalidated substitution was made. Report final result, retaining original/repeat evidence and alternative wide branch. Final solution exactly matches selected node. Generated exploration exports, assembly and occupancy probes moved into ignored `.cache/exploration-artifacts`; committed source/records and evaluator trajectories remain available. All benchmark scripts and reference remain unchanged.
EOF
git diff --exit-code 6428f5e -- solution
git add solution ITERATIONS.md
git commit -m '"'Finalize validated iter53 attention implementation'
git status --short"
OUTPUT:

STATUS: in_progress; EXIT_CODE: None

=== Event 294 | item.completed | item_167 ===
COMMAND:
/bin/bash -lc "python - <<'PY'
from pathlib import Path
import shutil
root=Path('.cache/exploration-artifacts');root.mkdir(parents=True,exist_ok=True)
paths=['.rocprofv3','lowered17','lowered31','lowered36','lowered8','opt/attention/iter26.s','opt/attention/iter47.s','opt/attention/low24','opt/attention/low30','opt/attention/low35','opt/attention/occupancy','opt/attention/occupancy.cpp','opt/attention/occupancy51','opt/attention/occupancy51.cpp','opt/attention/projection.s']
for name in paths:
 p=Path(name)
 if p.exists(): shutil.move(str(p),str(root/p.name))
p=Path('ITERATIONS.md');s=p.read_text().replace('actual device reports 32 CUs,','actual device reports 32 multiprocessors,');p.write_text(s)
PY
cat >> ITERATIONS.md <<'EOF'

## final — exact restored iter-53 (6428f5e), Low
Mandatory "'`bash scripts/bench.sh final`: compiled, complete30-trial correctness PASS. Median4.137706757 ms, samples3.998–4.163 ms. Fixed-reference44.953800201-ms speedup10.8644x; reference median44.953800201 ms. Final timing is slower than selection repeats; no source change or unvalidated substitution was made. Report final result, retaining original/repeat evidence and alternative wide branch. Final solution exactly matches selected node. Generated exploration exports, assembly and occupancy probes moved into ignored `.cache/exploration-artifacts`; committed source/records and evaluator trajectories remain available. All benchmark scripts and reference remain unchanged.
EOF
git diff --exit-code 6428f5e -- solution
git add solution ITERATIONS.md
git commit -m '"'Finalize validated iter53 attention implementation'
git status --short"
OUTPUT:
[codex/rocm-workspace 94f1164] Finalize validated iter53 attention implementation
 2 files changed, 27 insertions(+), 24 deletions(-)

STATUS: completed; EXIT_CODE: 0

=== Event 295 | item.completed | item_168 ===
Implemented and committed the optimized HIP solution.

- Final validation: **30/30 correctness checks passed**
- Median runtime: **4.138 ms**
- Speedup: **10.86×** against the benchmark’s fixed reference
- Commit: `94f1164`

Details: [ITERATIONS.md](ITERATIONS.md).

=== Event 296 | turn.completed |  ===
{"type": "turn.completed", "usage": {"input_tokens": 23954923, "cached_input_tokens": 23337216, "cache_write_input_tokens": 0, "output_tokens": 126451, "reasoning_output_tokens": 83343}, "source_event_line": 296}
