From d279bc3884e097b527101783aaa551f07ca2bf37 Mon Sep 17 00:00:00 2001 From: Jan Fostier Date: Tue, 27 Oct 2026 18:53:32 +0100 Subject: [PATCH] fix: size vLLM block FP8 weight rotation to L2 Target a weight pool twice the GPU L2 capacity, capped at the six-copy convention used by the other GEMM collectors. Large weights need only one copy, avoiding the extra VRAM pressure from unconditional rotation. Keep graph timing and per-operation latency normalization. Validate L2 boundaries, the six-copy cap, distinct allocation reuse and latency normalization with 22 focused unit tests. Verify real CUDA-graph collection on H100 NVL with vLLM 0.24.0, including a large-matrix case that fails with six copies under a 26 GiB allocator quota and passes with one. Include reproducible before/after evidence: 504 published collector timings, pinned environments, raw historical traces and CPU-only reanalysis. Explicitly report that normal collector latencies mostly decrease and that varying graph length prevents claiming improved production-row accuracy. Request maintainer feedback before merge. Signed-off-by: Jan Fostier --- .../README.md | 105 +++ .../SHA256SUMS | 58 ++ .../analysis/direct.csv | 13 + .../analysis/historical.csv | 9 + .../analysis/summary.json | 673 ++++++++++++++++++ .../analysis/validation.json | 36 + .../analyze.py | 324 +++++++++ .../direct/base-0-environment.json | 285 ++++++++ .../direct/base-0-gpu-after.json | 3 + .../direct/base-0-measurements.jsonl | 84 +++ .../direct/base-0.log | 105 +++ .../direct/base-1-environment.json | 285 ++++++++ .../direct/base-1-gpu-after.json | 3 + .../direct/base-1-measurements.jsonl | 84 +++ .../direct/base-1.log | 105 +++ .../direct/base-2-environment.json | 285 ++++++++ .../direct/base-2-gpu-after.json | 3 + .../direct/base-2-measurements.jsonl | 84 +++ .../direct/base-2.log | 105 +++ .../direct/head-0-environment.json | 285 ++++++++ .../direct/head-0-gpu-after.json | 3 + .../direct/head-0-measurements.jsonl | 84 +++ .../direct/head-0.log | 105 +++ .../direct/head-1-environment.json | 285 ++++++++ .../direct/head-1-gpu-after.json | 3 + .../direct/head-1-measurements.jsonl | 84 +++ .../direct/head-1.log | 105 +++ .../direct/head-2-environment.json | 285 ++++++++ .../direct/head-2-gpu-after.json | 3 + .../direct/head-2-measurements.jsonl | 84 +++ .../direct/head-2.log | 105 +++ .../direct/invocation.json | 11 + .../direct/jobs.json | 116 +++ .../direct/protocol.json | 87 +++ .../direct/runner-source.tar.gz | Bin 0 -> 4252 bytes .../direct/system.json | 6 + .../historical/analysis-contract.json | 5 + .../historical/collection-sources.tar.gz | Bin 0 -> 5196 bytes .../historical/cuda-traces.tar.gz | Bin 0 -> 2276563 bytes .../historical/environment.json | 250 +++++++ .../historical/forward-0.jsonl | 2 + .../historical/forward-1.jsonl | 2 + .../historical/forward-2.jsonl | 2 + .../historical/freeze.json | 9 + .../historical/full-forward-comparison.json | 461 ++++++++++++ .../historical/ops-0.jsonl | 67 ++ .../historical/ops-1.jsonl | 67 ++ .../historical/ops-2.jsonl | 67 ++ .../historical/protocol.json | 76 ++ .../historical/qualification.json | 15 + .../memory-checks/after-one-copy.log | 11 + .../memory-checks/before-six-copy.log | 11 + .../memory-checks/near-l2-two-copy.log | 11 + .../memory-checks/runner-source.tar.gz | Bin 0 -> 1331 bytes .../memory-checks/small-deepgemm.log | 11 + .../memory-checks/small-flashinfer.log | 11 + .../protocol.json | 39 + .../run.py | 299 ++++++++ .../validate.py | 63 ++ .../aisimulate/collector/vllm/collect_gemm.py | 11 +- .../collector/test_vllm_gemm_graph_timing.py | 110 ++- 61 files changed, 5882 insertions(+), 18 deletions(-) create mode 100644 benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/README.md create mode 100644 benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/SHA256SUMS create mode 100644 benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/analysis/direct.csv create mode 100644 benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/analysis/historical.csv create mode 100644 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benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/validate.py diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/README.md b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/README.md new file mode 100644 index 000000000..eaf1760ae --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/README.md @@ -0,0 +1,105 @@ + + +# PR #613: reproducible block-FP8 rotation evidence + +This capsule addresses CodeRabbit's request for actual before/after collector timings. It contains two distinct experiments. The fresh comparison measures the exact latency passed to `log_perf` with the collector's normal settings. The historical experiment compares profiled kernels in fixed-length graphs with the same kernels inside a real model graph. These measurements answer different questions. + +The controlled kernel experiment supports the cache mechanism. The fresh comparison does **not** establish that the production collector's published latencies are more accurate. Most published row latencies decrease with the adaptive policy, so the earlier kernel-duration benefit must not be generalized to the production timing path without qualification. + +## Fresh direct comparison + +- Base: `cd6efc5e3cac59d7a6d63c32e32210077d6c7d58` (single-copy block-FP8). +- PR: `64489fe6d79008fdffa474571404c74dc2597520` (adaptive count). +- Hardware/runtime: NVIDIA H100 NVL, 60 MiB L2, vLLM 0.24.0, PyTorch 2.11.0+cu130. Full installed-package inventory, runtime source hashes, device details, clocks and power-limit observations are in `direct/*-environment.json` and `direct/*-gpu-after.json`. +- Unmodified production timing arguments: three warmups, six timed replays, one repetition per graph, CUDA graphs required, power measurement disabled. Graph operation count follows the actual allocation count; it is not equalized between arms. +- Three fresh processes per arm, seven collector calls per shape/start: **504 published latencies** across 12 shapes. Arm order alternates across starts; paired arms share seeded shape order and initial inputs. The protocol was saved before launch. All six processes exited zero. +- Only the publication sink is replaced with JSONL. The real timing helper is wrapped to observe settings, allocations and results; no allocation count, kernel, warmup count or graph length is overridden. Each worker imports the entire collector package extracted from its exact Git revision. The timing helper and vLLM utility sources match across arms. +- Statistics: median within a start, then median across three starts. All observations are retained. There are zero missing/invalid/duplicate observations and zero samples flagged by the predeclared rule (>15% from the within-start median). These three starts do not establish a population confidence interval. + +| M | N x K | Copies | Base (us) | PR (us) | Change | +| ---: | --- | ---: | ---: | ---: | ---: | +| 1 | 6144 x 2560 | 6 | 11.061 | 10.482 | -5.24% | +| 1 | 2560 x 4096 | 6 | 11.771 | 11.024 | -6.34% | +| 1 | 19456 x 2560 | 3 | 25.867 | 24.466 | -5.42% | +| 1 | 2560 x 9728 | 6 | 19.381 | 18.664 | -3.70% | +| 16 | 6144 x 2560 | 6 | 10.677 | 10.621 | -0.52% | +| 16 | 2560 x 4096 | 6 | 11.643 | 11.078 | -4.85% | +| 16 | 19456 x 2560 | 3 | 26.235 | 25.013 | -4.66% | +| 16 | 2560 x 9728 | 6 | 19.504 | 18.935 | -2.92% | +| 64 | 6144 x 2560 | 6 | 9.771 | 10.346 | +5.89% | +| 64 | 19456 x 2560 | 3 | 25.552 | 24.578 | -3.81% | +| 1 | 8448 x 8192 | 2 | 30.416 | 29.675 | -2.44% | +| 16 | 16384 x 8192 | 1 | 50.128 | 50.197 | +0.14% | + +`analysis/direct.csv` and `analysis/summary.json` contain the full-precision results and per-start medians. `direct/*-measurements.jsonl` contains every observed published and loop latency, allocation count, distinct-storage count, graph status, seed and selected kernel label. Dispatch labels match between arms at every shape. + +These are complete collector row latencies, including activation quantization, GEMM and graph/event/submission effects. Changing the number of allocations also changes graph length. A decrease here can coexist with an increase in the isolated GEMM kernel duration. This experiment cannot attribute the difference uniquely to caching or graph overhead. The one-copy control above twice L2 changes by only about +0.14%; the M64 QKV point increases by about +5.89%. Both are retained alongside the decreases. + +## Historical model-kernel comparison + +`historical/` preserves the prior fixed-72-operation sweep: 201 isolated cases and six model cases across three independent process starts, with **207 raw compressed CUDA traces** in `cuda-traces.tar.gz`. The complete shape/copy grid includes the eight Qwen projection shapes, counts 1/2/3/4/6/8/12/24, and a 96 MiB negative-control matrix at counts 1/2/4. + +Reference: Qwen/Qwen3-4B-FP8 at immutable revision `96b30dc13593a244a5e59e84687309f53c375cfa`, TP=1, real checkpoint weights and prefilled KV. Model cases are batch/context 1/128 and 16/512. Captured logits exactly match the runtime reference at all six observations. + +The CPU analysis reads the raw traces, checks their hashes, verifies complete shape/copy coverage, requires 180 model and 360 isolated kernel observations per shape/start, and matches exact CUDA kernel names, grids and block dimensions. It selects this PR's capped copy counts from the sweep and recomputes the following values: + +| Measurement | Single-copy MAPE | PR-count MAPE | Shapes improved | +| --- | ---: | ---: | ---: | +| GEMM kernel duration | 9.067856% | 1.518637% | 8/8 | +| Quantization plus GEMM active interval union | 8.023906% | 2.275138% | 8/8 | + +MAPE is `mean(abs(isolated / model - 1)) * 100`, with equal weight for each of the eight shape points. The median is taken within each independent start, then across starts. No kernel or timing observation is excluded. Full per-shape values and per-start medians are regenerated into `analysis/historical.csv` and `analysis/summary.json`. + +This reproduces the earlier **controlled kernel** claim; it is not new model validation of the shorter production graphs. No L2 hit-rate counters were collected. The two-L2 target remains a heuristic, and tiny matrices can be limited by the six-copy cap. + +The earlier uncapped-policy full-forward experiment remains negative: decode MAPE goes from 5.823069% to 10.686789%. `historical/full-forward-comparison.json` contains the recorded comparison points, and the analysis checks that arithmetic. These are historical derived points, not a new forward run or a result for the capped PR policy. No improved overall prediction accuracy is claimed. + +## Reproduce + +Run from the repository root. CPU reanalysis needs only the Python standard library and makes no GitHub or GPU calls: + +```bash +capsule=benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027 +python3 "$capsule/analyze.py" --output /tmp/pr613-reanalysis +cd "$capsule" +sha256sum -c SHA256SUMS +``` + +The analyzer fails on missing, duplicated or nonfinite/nonpositive direct measurements, unexpected allocation counts, broken normalization, runtime/source drift, invalid model geometry/parity, incomplete raw trace coverage or kernel-identity mismatches. Outlier flags are diagnostic; observations are never dropped. The integrity checks were also exercised with deliberate missing, duplicate, negative, wrong-kernel and outlier mutations; see `analysis/validation.json`. + +For a fresh GPU comparison, use Python 3.12 with the pinned runtime above and a CUDA development toolkit available to DeepGEMM. Both immutable source revisions must be available in the local Git object database (the original PR head is intentionally pinned even if the PR is amended later). If needed, fetch it with `git fetch https://github.com/jfostier/aisimulate.git 64489fe6d79008fdffa474571404c74dc2597520`. The runner refuses a non-idle GPU or an existing output directory. + +The exact campaign command on this host was: + +```bash +module load CUDA +export PATH=/tmp/aisimulate-forward-ab/venv/bin:$PATH +/tmp/aisimulate-forward-ab/venv/bin/python benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/run.py --output benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct +``` + +To reproduce without overwriting the archived observations, use a new `--output`, such as `/tmp/pr613-fresh`, then analyze it with `analyze.py --direct /tmp/pr613-fresh --output /tmp/pr613-fresh-analysis`. Successful collection checks include graph replay, distinct weight storage, six actual timed runs and the unmodified published-latency normalization. + +`direct/runner-source.tar.gz` preserves the exact executed runner bytes, matching the SHA-256 recorded by all workers. The top-level executable adds the repository-required NVIDIA SPDX copyright line; the analyzer verifies that its Python AST is identical to the executed snapshot. `historical/collection-sources.tar.gz` preserves the prior collection scripts and pre-launch protocol byte-for-byte; their frozen hashes are checked during analysis. The historical scripts retain their original `/tmp/aisimulate-rotation-case` paths and require the pinned model checkpoint there; they are provenance inputs, not part of the fresh direct comparison. No framework source is vendored or modified. + +## Other validation and limits + +`memory-checks/` retains the earlier real-collector smoke logs and original harness: six-copy failure versus one-copy success under a 26 GiB allocator quota for a 1 GiB matrix, a 66 MiB/two-copy case, and small six-copy FlashInfer/DeepGEMM cases. The six-copy failure is a guard against an unconditional-rotation variant, not a failure of the single-copy base collector. These checks are separate from the fresh paired campaign. + +The evidence covers H100 NVL/vLLM 0.24.0. Blackwell, other runtime versions and NVFP4 rotation remain unverified. Unit tests use synthetic timing to verify control flow, whereas this capsule contains GPU measurements. No packaged profiles, model coefficients or production collector code are changed by this evidence update. + +## Local checks + +The existing 22 focused collector tests pass on the unchanged production source in `64489fe6`. The application-wide conftest is excluded because this collector environment does not have the AISimulate application package installed. This is not a full-suite or hosted-CI result. + +```bash +/tmp/aisimulate-forward-ab/venv/bin/python -m pytest -c python/aisimulate/pytest.ini --confcutdir=python/aisimulate/tests/unit/collector python/aisimulate/tests/unit/collector/test_vllm_gemm_graph_timing.py -m unit +/tmp/aisimulate-forward-ab/venv/bin/python -m ruff check --config python/aisimulate/pyproject.toml benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/{run,analyze,validate}.py python/aisimulate/collector/vllm/collect_gemm.py python/aisimulate/tests/unit/collector/test_vllm_gemm_graph_timing.py +/tmp/aisimulate-forward-ab/venv/bin/python -m ruff format --check --config python/aisimulate/pyproject.toml benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/{run,analyze,validate}.py python/aisimulate/collector/vllm/collect_gemm.py python/aisimulate/tests/unit/collector/test_vllm_gemm_graph_timing.py +python3 benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/validate.py +``` + +Results: 22 tests passed; Ruff lint/format passed on five files; six evidence-reader validation scenarios passed. The runner's and analyzer's numerical behavior were not changed to obtain a preferred benchmark direction. SHA256SUMS covers all packaged source, raw measurements, logs, archived traces, provenance and computed reports; it excludes itself and Python bytecode caches. diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/SHA256SUMS b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/SHA256SUMS new file mode 100644 index 000000000..845788551 --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/SHA256SUMS @@ -0,0 +1,58 @@ +0c020e06d817ff60921f6c8e7bc6abe8fb29b874996fdea00b7b1d933a497971 README.md +85f32a15ab9676042602c576017fce9a99482fdaed4389be2166dd3b2cfc033b analysis/direct.csv +5fdcc94e6b9d6fba4b9878d2f3709292e81bf7197ca44468cbc79a5834b0cf83 analysis/historical.csv +f24b69d6343745b26440c290b6443b4fd95c3181db32b9e0f253e3dfe92360e2 analysis/summary.json +ec225f5cdb6b6cd7a9548f927d4407d342aec3a79bc0b200a3e52cb904988f7c analysis/validation.json +39aac89898c50cba669466f727e427fc178453d69288f0d57d71c71530e8f505 analyze.py 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3.053435114503822, + "model_pair_us": 10.432, + "model_start_medians_pair_us": [ + 10.432, + 10.4, + 10.432 + ], + "base_pair_us": 9.248000000000001, + "base_start_medians_pair_us": [ + 9.248000000000001, + 9.216000000000001, + 9.280000000000001 + ], + "base_error_pct_pair_us": 11.349693251533733, + "head_pair_us": 9.92, + "head_start_medians_pair_us": [ + 9.92, + 9.888, + 9.9205 + ], + "head_error_pct_pair_us": 4.907975460122705 + }, + { + "m": 16, + "n": 19456, + "k": 2560, + "copies": 3, + "model_core_us": 19.872, + "model_start_medians_core_us": [ + 19.872, + 19.808, + 19.904 + ], + "base_core_us": 17.6, + "base_start_medians_core_us": [ + 18.4, + 16.896, + 17.6 + ], + "base_error_pct_core_us": 11.433172302737516, + "head_core_us": 20.192500000000003, + "head_start_medians_core_us": [ + 20.192500000000003, + 20.672, + 20.001 + ], + "head_error_pct_core_us": 1.6128220611916433, + "model_pair_us": 21.792, + "model_start_medians_pair_us": [ + 21.792, + 21.7285, + 21.823999999999998 + ], + "base_pair_us": 19.84, + "base_start_medians_pair_us": [ + 20.64, + 19.104, + 19.84 + ], + "base_error_pct_pair_us": 8.957415565345084, + "head_pair_us": 22.465, + "head_start_medians_pair_us": [ + 22.465, + 23.009, + 22.272 + ], + "head_error_pct_pair_us": 3.0882892804698914 + }, + { + "m": 16, + "n": 2560, + "k": 9728, + "copies": 6, + "model_core_us": 15.808, + "model_start_medians_core_us": [ + 15.808, + 15.808, + 15.808 + ], + "base_core_us": 15.168, + "base_start_medians_core_us": [ + 15.2, + 15.073, + 15.168 + ], + "base_error_pct_core_us": 4.048582995951422, + "head_core_us": 15.648, + "head_start_medians_core_us": [ + 15.616, + 15.744, + 15.648 + ], + "head_error_pct_core_us": 1.0121457489878583, + "model_pair_us": 18.048, + "model_start_medians_pair_us": [ + 18.048, + 18.016, + 18.048000000000002 + ], + "base_pair_us": 17.216, + "base_start_medians_pair_us": [ + 17.216, + 17.119999999999997, + 17.216 + ], + "base_error_pct_pair_us": 4.609929078014174, + "head_pair_us": 17.631999999999998, + "head_start_medians_pair_us": [ + 17.6, + 17.728, + 17.631999999999998 + ], + "head_error_pct_pair_us": 2.3049645390070927 + } + ], + "summary": { + "core_us": { + "base_mape_pct": 9.067856311789944, + "head_mape_pct": 1.518636773073452, + "improved_shapes": 8 + }, + "pair_us": { + "base_mape_pct": 8.023905516035203, + "head_mape_pct": 2.275138478815719, + "improved_shapes": 8 + } + }, + "trace_count": 207, + "sweep_case_count": 201, + "kernel_name_grid_block_matches": true, + "model_output_parity": "exact for all six model cases", + "missing_or_invalid_measurements": 0, + "excluded_measurements": 0, + "earlier_full_forward": { + "baseline_mape_pct": 5.823069415276825, + "rotate_2l2_mape_pct": 10.68678877511213, + "input_status": "Recorded comparison points from the earlier uncapped-policy investigation; not a new run." + }, + "aggregation": "Median within start, median across three starts; MAPE weights eight shapes equally.", + "scope": "Earlier fixed-72-operation graph sweep; reanalysis of this PR's copy counts. Not fresh production-row accuracy." + } +} diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/analysis/validation.json b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/analysis/validation.json new file mode 100644 index 000000000..e0d45ace2 --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/analysis/validation.json @@ -0,0 +1,36 @@ +{ + "status": "passed", + "checks": [ + { + "scenario": "valid", + "result": "accepted; all samples retained", + "outliers": 0 + }, + { + "scenario": "missing", + "result": "rejected", + "reason": "Missing rows" + }, + { + "scenario": "duplicate", + "result": "rejected", + "reason": "Duplicate measurement: ('base', 0, (16, 6144, 2560), 0)" + }, + { + "scenario": "negative", + "result": "rejected", + "reason": "Nonpositive or nonfinite timing" + }, + { + "scenario": "wrong_kernel", + "result": "rejected", + "reason": "Kernel dispatch changed between arms: (16, 6144, 2560): ['FlashInferFp8BlockScaledMMKernel', 'intentionally_incorrect_kernel']" + }, + { + "scenario": "outlier", + "result": "accepted; all samples retained", + "outliers": 1 + } + ], + "raw_measurements_modified": false +} diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/analyze.py b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/analyze.py new file mode 100644 index 000000000..50e54446c --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/analyze.py @@ -0,0 +1,324 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 2026 Jan Fostier +# SPDX-License-Identifier: Apache-2.0 + +"""CPU-only integrity checks and statistics for direct and historical evidence.""" + +import argparse +import ast +import csv +import gzip +import hashlib +import json +import math +import re +import statistics +import tarfile +from collections import defaultdict +from pathlib import Path + +HERE = Path(__file__).resolve().parent +KERNEL = re.compile(r"fp8_gemm_kernel_swapAB<(\d+)u, (\d+)u,") +PROJECTIONS = [(6144, 2560), (2560, 4096), (19456, 2560), (2560, 9728)] + + +def require(condition, message): + if not condition: + raise ValueError(message) + + +def read_json(path): + return json.loads(Path(path).read_text()) + + +def read_rows(path): + return [json.loads(line) for line in Path(path).read_text().splitlines() if line.strip()] + + +def positive(values): + require(all(math.isfinite(x) and x > 0 for x in values), "Nonpositive or nonfinite timing") + + +def direct(root): + protocol = read_json(root / "protocol.json") + jobs = read_json(root / "jobs.json") + require(len(jobs) == 2 * protocol["replicates"], "Incomplete independent starts") + require(all(job["exit_code"] == 0 for job in jobs), "Failed collector worker") + groups = defaultdict(list) + environments = [] + keys = set() + for arm in ["base", "head"]: + for replicate in range(protocol["replicates"]): + environment = read_json(root / f"{arm}-{replicate}-environment.json") + environments.append(environment) + rows = read_rows(root / f"{arm}-{replicate}-measurements.jsonl") + require(len(rows) == len(protocol["shapes_m_n_k"]) * protocol["samples_per_shape"], "Missing rows") + for row in rows: + shape = tuple(row[k] for k in ["m", "n", "k"]) + key = (arm, replicate, shape, row["sample"]) + require(key not in keys, f"Duplicate measurement: {key}") + keys.add(key) + require(list(shape) == protocol["shapes_m_n_k"][row["shape_index"]], "Shape mismatch") + require(row["arm"] == arm and row["replicate"] == replicate, "Worker mismatch") + require(row["revision"] == protocol[f"{arm}_revision"], "Revision mismatch") + require(row["used_cuda_graph"] is True and row["num_runs_executed"] == 6, "Timing mode mismatch") + require(row["distinct_weight_storages"] == row["copies"], "Aliased weights") + positive([row["published_latency_ms"], row["loop_latency_ms"]]) + require( + math.isclose(row["published_latency_ms"] * row["copies"], row["loop_latency_ms"], rel_tol=1e-12), + "Normalization mismatch", + ) + wanted = ( + 1 + if arm == "base" + else max(1, min(6, math.ceil(2 * environment["device"]["l2_bytes"] / (row["n"] * row["k"])))) + ) + require(row["copies"] == wanted, "Allocation-count mismatch") + groups[arm, replicate, shape].append(row) + for field in ["device", "torch", "torch_cuda", "vllm", "runtime_source_sha256", "protocol_sha256", "runner_sha256"]: + require(all(e[field] == environments[0][field] for e in environments), f"Runtime changed: {field}") + require( + hashlib.sha256((root / "protocol.json").read_bytes()).hexdigest() == environments[0]["protocol_sha256"], + "Protocol changed after measurement", + ) + with tarfile.open(root / "runner-source.tar.gz") as source: + original = source.extractfile("run.py").read() + require(hashlib.sha256(original).hexdigest() == environments[0]["runner_sha256"], "Runner snapshot mismatch") + # The executable copy adds the repository-required SPDX copyright line; + # the archived copy preserves the exact bytes used for collection. + require( + ast.dump(ast.parse(original)) == ast.dump(ast.parse((HERE / "run.py").read_bytes())), + "Packaged runner behavior differs from executed snapshot", + ) + for dependency in ["helper.py", "vllm/utils.py"]: + require( + len({e["collector_source_sha256"][dependency] for e in environments}) == 1, + f"Collector dependency changed: {dependency}", + ) + outliers = [] + comparisons = [] + for shape_list in protocol["shapes_m_n_k"]: + shape = tuple(shape_list) + comparison = {"m": shape[0], "n": shape[1], "k": shape[2]} + kernels = set() + for arm in ["base", "head"]: + medians = [] + for replicate in range(protocol["replicates"]): + rows = groups[arm, replicate, shape] + require({r["sample"] for r in rows} == set(range(protocol["samples_per_shape"])), "Sample gap") + values = [row["published_latency_ms"] for row in rows] + median = statistics.median(values) + medians.append(median) + for row in rows: + if abs(row["published_latency_ms"] / median - 1) > protocol["outlier_relative_threshold"]: + outliers.append( + { + "arm": arm, + "replicate": replicate, + "shape": shape, + "sample": row["sample"], + "latency_ms": row["published_latency_ms"], + "within_start_median_ms": median, + } + ) + kernels.update(row["kernel_source"] for row in rows) + comparison[f"{arm}_start_medians_ms"] = medians + comparison[f"{arm}_median_ms"] = statistics.median(medians) + comparison[f"{arm}_copies"] = groups[arm, 0, shape][0]["copies"] + require(len(kernels) == 1, f"Kernel dispatch changed between arms: {shape}: {sorted(kernels)}") + comparison["kernel_source"] = next(iter(kernels)) + comparison["change_pct"] = (comparison["head_median_ms"] / comparison["base_median_ms"] - 1) * 100 + comparisons.append(comparison) + return { + "rows": comparisons, + "measurement_count": len(keys), + "missing_or_invalid_measurements": 0, + "kernel_dispatch_matches": True, + "flagged_outliers": outliers, + "excluded_measurements": 0, + "outlier_rule": protocol["anomaly_checks"], + "runner_sha256": environments[0]["runner_sha256"], + "device": environments[0]["device"], + "unit": "ms per published collector operation", + } + + +def pairs(events): + kernels = sorted((e for e in events if e.get("cat") == "kernel"), key=lambda e: e["ts"]) + groups = defaultdict(list) + for index, event in enumerate(kernels): + match = KERNEL.search(event["name"]) + if not match: + continue + require(index > 0, "GEMM without preceding quantization kernel") + quant = kernels[index - 1] + require("scale_1x128_kernel" in quant["name"] and quant["tid"] == event["tid"], "Unmatched quantization") + overlap = max(0, min(quant["ts"] + quant["dur"], event["ts"] + event["dur"]) - max(quant["ts"], event["ts"])) + positive([event["dur"], quant["dur"]]) + groups[tuple(map(int, match.groups()))].append( + { + "core_us": event["dur"], + "pair_us": quant["dur"] + event["dur"] - overlap, + "identity": [event["name"], event["args"]["grid"], event["args"]["block"]], + } + ) + return groups + + +def historical(root): + protocol = read_json(root / "protocol.json") + with tarfile.open(root / "collection-sources.tar.gz") as sources: + for name, expected_hash in read_json(root / "freeze.json")["hashes"].items(): + require( + hashlib.sha256(sources.extractfile(name).read()).hexdigest() == expected_hash, + f"Frozen historical source changed: {name}", + ) + model = {} + operations = {} + for replicate in range(3): + records = read_rows(root / f"forward-{replicate}.jsonl") + require(len(records) == 2, "Missing model cases") + for record in records: + require( + record["finite_logits"] and record["capture_max_abs_logit_difference"] == 0, + "Model graph output differs from runtime reference", + ) + require( + record["actual_requests"] == record["batch"] and record["input_tokens"] == record["batch"], + "Model request geometry mismatch", + ) + require(all(past == record["context"] for past in record["past_kv_lengths"]), "KV geometry mismatch") + records = read_rows(root / f"ops-{replicate}.jsonl") + require(len(records) == 67, "Missing sweep cases") + for record in records: + key = (replicate, record["m"], record["n"], record["k"], record["copies"]) + require(key not in operations, "Duplicate sweep case") + require(record["graph_ops"] == 72 and record["profiled_replays"] == 5, "Unexpected sweep graph") + positive(record["per_op_samples_ms"]) + operations[key] = record + expected = { + (rep, m, n, k, copies) + for rep in range(3) + for m in [1, 16] + for n, k in PROJECTIONS + for copies in protocol["copies"] + } + expected.update((rep, 16, 12288, 8192, copies) for rep in range(3) for copies in [1, 2, 4]) + require(set(operations) == expected, "Historical shape/copy coverage differs from the frozen protocol") + extracted = {} + with tarfile.open(root / "cuda-traces.tar.gz") as archive: + members = {m.name: m for m in archive.getmembers()} + expected_members = {r["trace_file"] for r in operations.values()} + expected_members.update( + f"graph-r{rep}-b{m}-c{128 if m == 1 else 512}.json.gz" for rep in range(3) for m in [1, 16] + ) + require(set(members) == expected_members, "Missing or unexpected raw traces") + for name, member in members.items(): + handle = archive.extractfile(member) + require(handle is not None, f"Trace is not a regular file: {name}") + payload = gzip.decompress(handle.read()) + extracted[name] = pairs(json.loads(payload)["traceEvents"]) + for record in operations.values(): + if record["trace_file"] == name: + require(hashlib.sha256(payload).hexdigest() == record["trace_json_sha256"], "Trace hash mismatch") + for replicate in range(3): + for m in [1, 16]: + name = f"graph-r{replicate}-b{m}-c{128 if m == 1 else 512}.json.gz" + groups = extracted[name] + require(set(groups) == set(PROJECTIONS), "Unexpected model projections") + require(all(len(values) == 180 for values in groups.values()), "Incomplete model kernel observations") + model[replicate, m] = groups + for (replicate, m, n, k, copies), record in operations.items(): + groups = extracted[record["trace_file"]] + require(set(groups) == {(n, k)} and len(groups[n, k]) == 360, "Incomplete isolated kernel observations") + if (n, k) in PROJECTIONS: + identities = {json.dumps(p["identity"]) for p in groups[n, k]} + reference = {json.dumps(p["identity"]) for p in model[replicate, m][n, k]} + require(identities == reference, "Kernel name, grid or block mismatch") + rows = [] + l2 = {record["l2_bytes"] for record in operations.values()} + require(l2 == {60 << 20}, "Unexpected historical L2 capacity") + for m in [1, 16]: + for n, k in PROJECTIONS: + copies = min(6, max(1, math.ceil(2 * (60 << 20) / (n * k)))) + row = {"m": m, "n": n, "k": k, "copies": copies} + for metric in ["core_us", "pair_us"]: + reference = [statistics.median(p[metric] for p in model[rep, m][n, k]) for rep in range(3)] + row[f"model_{metric}"] = statistics.median(reference) + row[f"model_start_medians_{metric}"] = reference + for arm, count in [("base", 1), ("head", copies)]: + medians = [ + statistics.median( + p[metric] for p in extracted[operations[rep, m, n, k, count]["trace_file"]][n, k] + ) + for rep in range(3) + ] + row[f"{arm}_{metric}"] = statistics.median(medians) + row[f"{arm}_start_medians_{metric}"] = medians + row[f"{arm}_error_pct_{metric}"] = abs(row[f"{arm}_{metric}"] / row[f"model_{metric}"] - 1) * 100 + rows.append(row) + summary = {} + for metric in ["core_us", "pair_us"]: + summary[metric] = { + f"{arm}_mape_pct": statistics.mean(r[f"{arm}_error_pct_{metric}"] for r in rows) for arm in ["base", "head"] + } + summary[metric]["improved_shapes"] = sum( + r[f"head_error_pct_{metric}"] < r[f"base_error_pct_{metric}"] for r in rows + ) + negative = read_json(root / "full-forward-comparison.json") + decode = [p for p in negative["points"] if p["phase"] == "decode"] + require(len(decode) == 12, "Incomplete historical full-forward comparison") + full_forward = {} + for arm in ["baseline", "rotate_2l2"]: + positive([p[f"{arm}_ms"] for p in decode] + [p["measured_ms"] for p in decode]) + value = statistics.mean(abs(p[f"{arm}_ms"] / p["measured_ms"] - 1) * 100 for p in decode) + require(math.isclose(value, negative["summary"]["decode"][arm]["mape_pct"]), "Full-forward MAPE mismatch") + full_forward[f"{arm}_mape_pct"] = value + return { + "rows": rows, + "summary": summary, + "trace_count": len(extracted), + "sweep_case_count": len(operations), + "kernel_name_grid_block_matches": True, + "model_output_parity": "exact for all six model cases", + "missing_or_invalid_measurements": 0, + "excluded_measurements": 0, + "earlier_full_forward": { + **full_forward, + "input_status": "Recorded comparison points from the earlier uncapped-policy investigation; not a new run.", + }, + "aggregation": "Median within start, median across three starts; MAPE weights eight shapes equally.", + "scope": "Earlier fixed-72-operation graph sweep; reanalysis of this PR's copy counts." + " Not fresh production-row accuracy.", + } + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--direct", type=Path, default=HERE / "direct") + parser.add_argument("--output", type=Path, default=HERE / "analysis") + args = parser.parse_args() + result = {"schema_version": 1, "direct": direct(args.direct), "historical": historical(HERE / "historical")} + args.output.mkdir(exist_ok=True) + (args.output / "summary.json").write_text(json.dumps(result, indent=2, allow_nan=False) + "\n") + for name in ["direct", "historical"]: + rows = result[name]["rows"] + with (args.output / f"{name}.csv").open("w", newline="") as stream: + writer = csv.DictWriter(stream, fieldnames=list(rows[0]), lineterminator="\n") + writer.writeheader() + writer.writerows(rows) + print( + json.dumps( + { + "direct_measurements": result["direct"]["measurement_count"], + "direct_flagged_outliers": len(result["direct"]["flagged_outliers"]), + "direct_rows": result["direct"]["rows"], + "historical": result["historical"]["summary"], + }, + indent=2, + ) + ) + + +if __name__ == "__main__": + main() diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-0-environment.json b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-0-environment.json new file mode 100644 index 000000000..6a1ee4118 --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-0-environment.json @@ -0,0 +1,285 @@ +{ + "arm": "base", + "replicate": 0, + "revision": "cd6efc5e3cac59d7a6d63c32e32210077d6c7d58", + "protocol_sha256": "7121239a74b76c01da99e5bdd7834e0489876c7105694e0b11f20d616d011c1e", + "runner_sha256": "4ead3a081bca704d0ed121bb2355687dfa3ac9838f4c6fdcc355ef31bec8b836", + "python": "3.12.12 (main, Oct 14 2025, 21:25:31) [Clang 20.1.4 ]", + "torch": "2.11.0+cu130", + "torch_cuda": "13.0", + "vllm": "0.24.0", + "packages": { + "anyio": "4.15.1", + "nvidia-curand": "10.4.0.35", + "nvidia-cufile": "1.15.1.6", + "nvidia-cuda-nvdisasm": "13.4.92", + "email-validator": "2.3.0", + "click": "8.5.0", + "tabulate": "0.10.0", + "nvidia-cudnn-cu13": "9.19.0.56", + "rich-toolkit": "0.20.6", + "rpds-py": "2026.9.1", + "opentelemetry-semantic-conventions": "0.66b1", + "hf-xet": "1.7.0", + "kiwisolver": "1.5.1", + "tokenspeed-mla": "0.1.2", + "mcp-types": "2.3.0", + "networkx": "3.7", + "nvidia-nvvm": "13.2.86", + "fsspec": "2026.9.0", + "aiohttp": "3.14.5", + "pillow": "13.0.0", + "fastapi-cloud-cli": "0.26.0", 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"8.7.1", + "more-itertools": "10.8.0", + "platformdirs": "4.4.0", + "jaraco.text": "4.0.0" + }, + "device": { + "name": "NVIDIA H100 NVL", + "l2_bytes": 62914560, + "total_memory_bytes": 99951706112, + "compute_capability": [ + 9, + 0 + ] + }, + "runtime_source_sha256": { + "model_executor/layers/quantization/fp8.py": "efde0be91112ae27a67b27543c7315b3e69d495fd816cbf997e5b9b47d1bc6de", + "model_executor/layers/quantization/utils/fp8_utils.py": "de4ea2610dcecc07c697bf71c68e9ca414df74942bbf3f2f07aa20ca2b701689", + "model_executor/kernels/linear/scaled_mm/flashinfer.py": "ab2c9485c0ba38d99b47da1b101cbd6de239f35c800252dd4aa563d7fb1853f2", + "model_executor/kernels/linear/scaled_mm/deep_gemm.py": "26268c14b1597a0f733a0cc408cfc39b3f0b3454fa50e30098e15e86f47955b8", + "utils/deep_gemm.py": "c7adac1775842e9b0d5a1be9a323abb2840b5a93cc2a02d4f232cc3cfb0685a4" + }, + "collector_source_sha256": { + "vllm/collect_gemm.py": "9bc09efa5bc5a3d8786f10d502d74e085e0fc1a4ea3a8c9874aea63a479536c1", + "vllm/utils.py": "5775eee3bb4a1d7fac5f36ad73e65ad80670242b318771c8dd0528ed81cec54e", + "helper.py": "4a4e311cad867846d20a7ced2a1f9c4b67f11f9dcf0fb31a51ae144f9fcbacdd" + }, + "environment": { + "COLLECTOR_MEASURE_POWER": "0", + "CUDA_HOME": "/apps/gent/RHEL9/zen4-hopper-ib/software/CUDA/12.9.1", + "CUDA_VISIBLE_DEVICES": "0", + "OMP_NUM_THREADS": "4", + "VLLM_USE_DEEP_GEMM": null, + "VLLM_USE_DEEP_GEMM_E8M0": null, + "VLLM_BATCH_INVARIANT": null + }, + "gpu_before": "name, uuid, memory.total [MiB], memory.used [MiB], temperature.gpu, pstate, clocks.current.sm [MHz], clocks.current.memory [MHz], power.limit [W]\nNVIDIA H100 NVL, GPU-70bff730-c00c-c620-2b33-ac346d3103fe, 95830 MiB, 4 MiB, 35, P0, 345 MHz, 2619 MHz, 400.00 W" +} diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-0-gpu-after.json b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-0-gpu-after.json new file mode 100644 index 000000000..85efe45ff --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-0-gpu-after.json @@ -0,0 +1,3 @@ +{ + "gpu_after": "name, uuid, memory.total [MiB], memory.used [MiB], temperature.gpu, pstate, clocks.current.sm [MHz], clocks.current.memory [MHz], power.limit [W]\nNVIDIA H100 NVL, GPU-70bff730-c00c-c620-2b33-ac346d3103fe, 95830 MiB, 927 MiB, 38, P0, 1785 MHz, 2619 MHz, 400.00 W" +} diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-0-measurements.jsonl b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-0-measurements.jsonl new file mode 100644 index 000000000..3f10bd5b6 --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-0-measurements.jsonl @@ -0,0 +1,84 @@ +{"m": 16, "n": 6144, "k": 2560, "shape_index": 4, "sample": 0, "seed": 28013, "copies": 1, "distinct_weight_storages": 1, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 15728640, "loop_latency_ms": 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IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:23 [parallel_state.py:1588] world_size=1 rank=0 local_rank=0 distributed_init_method=file:///tmp/aic-vllm-rendezvous-foynmtml/store backend=nccl +INFO 10-27 19:23:23 [parallel_state.py:1923] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank 0, EPLB rank N/A +INFO 10-27 19:23:23 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:23 [__init__.py:563] Selected FlashInferFp8DeepGEMMDynamicBlockScaledKernel for Fp8LinearMethod +INFO 10-27 19:23:23 [deep_gemm.py:175] deep_gemm not found in site-packages, trying vendored vllm.third_party.deep_gemm +INFO 10-27 19:23:23 [deep_gemm.py:202] DeepGEMM PDL enabled on vllm.third_party.deep_gemm. +INFO 10-27 19:23:23 [deep_gemm.py:120] DeepGEMM E8M0 enabled on current platform. +INFO 10-27 19:23:28 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:28 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:28 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:28 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:28 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:28 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-0 shape=(16, 6144, 2560) +INFO 10-27 19:23:28 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-0 shape=(16, 16384, 8192) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-0 shape=(1, 6144, 2560) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-0 shape=(16, 2560, 4096) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-0 shape=(1, 19456, 2560) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-0 shape=(1, 8448, 8192) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:34 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-0 shape=(1, 2560, 9728) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-0 shape=(16, 2560, 9728) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-0 shape=(1, 2560, 4096) +INFO 10-27 19:23:35 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-0 shape=(64, 19456, 2560) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-0 shape=(64, 6144, 2560) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:37 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-0 shape=(16, 19456, 2560) diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-1-environment.json b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-1-environment.json new file mode 100644 index 000000000..a00619c6f --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-1-environment.json @@ -0,0 +1,285 @@ +{ + "arm": "base", + "replicate": 1, + "revision": "cd6efc5e3cac59d7a6d63c32e32210077d6c7d58", + "protocol_sha256": "7121239a74b76c01da99e5bdd7834e0489876c7105694e0b11f20d616d011c1e", + "runner_sha256": "4ead3a081bca704d0ed121bb2355687dfa3ac9838f4c6fdcc355ef31bec8b836", + "python": "3.12.12 (main, Oct 14 2025, 21:25:31) [Clang 20.1.4 ]", + "torch": "2.11.0+cu130", + "torch_cuda": "13.0", + "vllm": "0.24.0", + "packages": { + "anyio": "4.15.1", + "nvidia-curand": "10.4.0.35", + "nvidia-cufile": "1.15.1.6", + "nvidia-cuda-nvdisasm": "13.4.92", + "email-validator": "2.3.0", + "click": "8.5.0", + "tabulate": "0.10.0", + "nvidia-cudnn-cu13": "9.19.0.56", + "rich-toolkit": "0.20.6", + "rpds-py": "2026.9.1", + "opentelemetry-semantic-conventions": "0.66b1", + "hf-xet": "1.7.0", + "kiwisolver": "1.5.1", + "tokenspeed-mla": "0.1.2", + "mcp-types": "2.3.0", + "networkx": "3.7", + "nvidia-nvvm": "13.2.86", + "fsspec": "2026.9.0", + "aiohttp": "3.14.5", + "pillow": "13.0.0", + "fastapi-cloud-cli": "0.26.0", + "opentelemetry-sdk": "1.45.1", + "torchcodec": "0.17.0", + "markdown-it-py": "4.2.0", + "jiter": "0.18.0", + "partial-json-parser": "0.2.1.1.post7", + "pycparser": "3.12", + "narwhals": "2.28.0", + "numba": "0.65.0", + "annotated-types": "0.8.0", + "torch": "2.11.0", + "matplotlib": "3.11.2", + "mpmath": "1.3.0", + "nvidia-ml-py": "13.615.71", + "watchfiles": "1.3.0", + "typer": "0.27.3", + "lm-format-enforcer": "0.11.3", + "prometheus_client": "0.26.0", + "filelock": "4.2.4", + "flashinfer-python": "0.6.12", + "nvidia-nvjitlink": "13.0.88", + "fastapi-cli": "0.0.32", + "huggingface_hub": "1.33.0", + "outlines_core": "0.2.14", + "PyJWT": "2.15.1", + "iniconfig": "2.3.1", + "scipy": "1.18.1", + "nccl4py": "0.6.0", + "sentencepiece": "0.2.2", + "nvidia-cuda-cccl": "13.3.4.3.1", + "plotext": "6.1.0", + "cuda-core": "1.2.2", + "opentelemetry-proto": "1.45.1", + "opentelemetry-exporter-otlp-common": "0.66b1", + "torchaudio": "2.11.0", + "charset-normalizer": "3.5.2", + "tornado": "6.6", + "torch_c_dlpack_ext": "0.1.5", + "httpcore": "1.0.9", + "pydantic-extra-types": "2.11.1", + "opentelemetry-exporter-otlp": "1.45.1", + "msgspec": "0.22.0", + "Jinja2": "3.1.6", + "tokenizers": "0.22.2", + "rignore": "0.8.1", + "apache-tvm-ffi": "0.1.9", + "aiohappyeyeballs": "2.7.1", + "tokenspeed-triton": "3.9.10.post20261008", + "nvidia-nvtx": "13.0.85", + "mdurl": "0.1.2", + "safetensors": "0.8.0", + "six": "1.17.0", + "docstring_parser": "0.18.0", + "nvidia-cuda-runtime": "13.0.96", + "nvtx": "0.2.15", + "nvidia-cutlass-dsl-libs-cu13": "4.5.2", + "munch": "4.0.0", + "tqdm": "4.70.1", + "cryptography": "50.0.2", + "nvidia-cudnn-frontend": "1.28.0", + "packaging": "26.0", + "nvidia-cutlass-dsl-libs-core": "4.7.1", + "jsonschema-specifications": "2025.9.1", + "grpcio": "1.85.0", + "detect-installer": "0.2.1", + "pandas": "3.1.0", + "compressed-tensors": "0.17.0", + "nccl-extensions": "0.1.0", + "ruff": "0.17.3", + "oss-harmony": "0.0.11", + "model-hosting-container-standards": "0.1.16", + "supervisor": "4.3.0", + "shellingham": "1.5.4", + "PyYAML": "6.0.3", + "pydantic-settings": "2.16.1", + "cachetools": "7.2.6", + "prettytable": "3.18.0", + "nvidia-nvshmem-cu13": "3.4.5", + "pluggy": "1.7.0", + "sympy": "1.14.0", + "cffi": "2.1.1", + "starlette": "1.10.0", + "jsonschema": "4.26.0", + "opentelemetry-semantic-conventions-ai": "0.5.1", + "pyelftools": "0.33", + "jmespath": "1.1.0", + "cuda-bindings": "13.4.4", + "contourpy": "1.4.0", + "idna": "3.20", + "anthropic": "2.3.0", + "vllm": "0.24.0", + "tiktoken": "0.14.0", + "bokeh": "3.10.1", + "loguru": "0.7.3", + "cuda-pathfinder": "1.8.4", + "fastapi": "0.136.3", + "nvidia-cutlass-dsl-libs-base": "4.5.2", + "certifi": "2026.10.23", + "cbor2": "6.1.5", + "nvidia-cusparselt-cu13": "0.8.0", + "maturin": "1.15.0", + "nvidia-cusolver": "12.0.4.66", + "referencing": "0.37.0", + "typing_extensions": "4.16.0", + "truststore": "0.10.5", + "xgrammar": "0.2.3", + "pyzmq": "27.2.0", + "nvidia-cuda-crt": "13.4.92", + "nvidia-cuda-nvrtc": "13.0.88", + "pydantic_core": "2.50.1", + "requests": "2.34.2", + "depyf": "0.20.0", + "z3-solver": "4.15.4.0", + "pytest-timeout": "2.4.0", + "numpy": "2.3.5", + "interegular": "0.3.3", + "blake3": "1.0.11", + "aiosignal": "1.4.0", + "psutil": "7.2.2", + "python-multipart": "0.0.32", + "dnspython": "2.9.0", + "sse-starlette": "3.6.0", + "propcache": "0.5.4", + "tilelang": "0.1.9", + "ninja": "1.13.2", + "dill": "0.4.1", + "cuda-python": "13.4.4", + "nvidia-cutlass-dsl": "4.5.2", + "flashinfer-cubin": "0.6.12", + "nvidia-nccl-cu13": "2.28.9", + "httpx2": "2.16.0", + "Pygments": "2.21.0", + "openai-harmony": "0.0.8", + "attrs": "26.1.0", + "humming-kernels": "0.1.6", + "nvidia-cuda-cupti": "13.0.85", + "fonttools": "4.67.0", + "opentelemetry-api": "1.45.1", + "websockets": "17.3", + "cuda-toolkit": "13.0.2", + "urllib3": "2.8.0", + "agent-detector": "2.0.0", + "googleapis-common-protos": "1.76.0", + "MarkupSafe": "3.0.4", + "python-dotenv": "1.2.4", + "cloudpickle": "3.1.2", + "httpcore2": "2.16.0", + "yarl": "1.25.1", + "llvmlite": "0.47.0", + "python-json-logger": "4.2.0", + "opentelemetry-exporter-otlp-proto-common": "1.45.1", + "nvidia-cusparse": "12.6.3.3", + "rich": "15.0.0", + "multidict": "7.2.1", + "lark": "1.2.2", + "pyarrow": "26.0.0", + "httpx": "0.28.1", + "mistral_common": "1.12.0", + "protobuf": "7.36.2", + "cycler": "0.12.1", + "uvloop": "0.23.0", + "opentelemetry-exporter-http-transport": "0.66b1", + "sentry-sdk": "2.73.0", + "ml_dtypes": "0.6.0", + "opencv-python-headless": "5.0.0.93", + "opentelemetry-exporter-otlp-proto-http": 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a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-1.log b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-1.log new file mode 100644 index 000000000..cb565f99c --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-1.log @@ -0,0 +1,105 @@ +INFO 10-27 19:24:16 [scheduler.py:252] Chunked prefill is enabled with max_num_batched_tokens=2048. +INFO 10-27 19:24:16 [vllm.py:1006] Asynchronous scheduling is enabled. +INFO 10-27 19:24:16 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:16 [parallel_state.py:1588] world_size=1 rank=0 local_rank=0 distributed_init_method=file:///tmp/aic-vllm-rendezvous-doa5m4_r/store backend=nccl +INFO 10-27 19:24:16 [parallel_state.py:1923] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank 0, EPLB rank N/A +INFO 10-27 19:24:16 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:16 [__init__.py:563] Selected FlashInferFp8DeepGEMMDynamicBlockScaledKernel for Fp8LinearMethod +INFO 10-27 19:24:16 [deep_gemm.py:175] deep_gemm not found in site-packages, trying vendored vllm.third_party.deep_gemm +INFO 10-27 19:24:16 [deep_gemm.py:202] DeepGEMM PDL enabled on vllm.third_party.deep_gemm. +INFO 10-27 19:24:16 [deep_gemm.py:120] DeepGEMM E8M0 enabled on current platform. +INFO 10-27 19:24:18 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:18 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:18 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:18 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:18 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:18 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-1 shape=(16, 6144, 2560) +INFO 10-27 19:24:18 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:18 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:18 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:18 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:18 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:18 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:18 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-1 shape=(64, 6144, 2560) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-1 shape=(1, 2560, 9728) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-1 shape=(16, 19456, 2560) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-1 shape=(1, 6144, 2560) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-1 shape=(64, 19456, 2560) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-1 shape=(16, 2560, 4096) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-1 shape=(16, 2560, 9728) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-1 shape=(1, 8448, 8192) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-1 shape=(1, 2560, 4096) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-1 shape=(16, 16384, 8192) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:19 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-1 shape=(1, 19456, 2560) diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-2-environment.json b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-2-environment.json new file mode 100644 index 000000000..0d775649a --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-2-environment.json @@ -0,0 +1,285 @@ +{ + "arm": "base", + "replicate": 2, + "revision": "cd6efc5e3cac59d7a6d63c32e32210077d6c7d58", + "protocol_sha256": "7121239a74b76c01da99e5bdd7834e0489876c7105694e0b11f20d616d011c1e", + "runner_sha256": "4ead3a081bca704d0ed121bb2355687dfa3ac9838f4c6fdcc355ef31bec8b836", + "python": "3.12.12 (main, Oct 14 2025, 21:25:31) [Clang 20.1.4 ]", + "torch": "2.11.0+cu130", + "torch_cuda": "13.0", + "vllm": "0.24.0", + "packages": { + "anyio": "4.15.1", + "nvidia-curand": "10.4.0.35", + "nvidia-cufile": "1.15.1.6", + "nvidia-cuda-nvdisasm": "13.4.92", + "email-validator": "2.3.0", + "click": "8.5.0", + "tabulate": "0.10.0", + "nvidia-cudnn-cu13": "9.19.0.56", + "rich-toolkit": "0.20.6", + "rpds-py": "2026.9.1", + "opentelemetry-semantic-conventions": "0.66b1", + "hf-xet": "1.7.0", + "kiwisolver": "1.5.1", + "tokenspeed-mla": "0.1.2", + "mcp-types": "2.3.0", + "networkx": "3.7", + "nvidia-nvvm": "13.2.86", + "fsspec": "2026.9.0", + "aiohttp": "3.14.5", + "pillow": "13.0.0", + "fastapi-cloud-cli": "0.26.0", + "opentelemetry-sdk": "1.45.1", + "torchcodec": "0.17.0", + "markdown-it-py": "4.2.0", + "jiter": "0.18.0", + "partial-json-parser": "0.2.1.1.post7", + "pycparser": "3.12", + "narwhals": "2.28.0", + "numba": "0.65.0", + "annotated-types": "0.8.0", + "torch": "2.11.0", + "matplotlib": "3.11.2", + "mpmath": "1.3.0", + "nvidia-ml-py": "13.615.71", + "watchfiles": "1.3.0", + "typer": "0.27.3", + "lm-format-enforcer": "0.11.3", + "prometheus_client": "0.26.0", + "filelock": "4.2.4", + "flashinfer-python": "0.6.12", + "nvidia-nvjitlink": "13.0.88", + "fastapi-cli": "0.0.32", + "huggingface_hub": "1.33.0", + "outlines_core": "0.2.14", + "PyJWT": "2.15.1", + "iniconfig": "2.3.1", + "scipy": "1.18.1", + "nccl4py": "0.6.0", + "sentencepiece": "0.2.2", + "nvidia-cuda-cccl": "13.3.4.3.1", + "plotext": "6.1.0", + "cuda-core": "1.2.2", + "opentelemetry-proto": "1.45.1", + "opentelemetry-exporter-otlp-common": "0.66b1", + "torchaudio": "2.11.0", + "charset-normalizer": "3.5.2", + "tornado": "6.6", + "torch_c_dlpack_ext": "0.1.5", + "httpcore": "1.0.9", + "pydantic-extra-types": "2.11.1", + 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"DeepGemmFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 64, "n": 6144, "k": 2560, "shape_index": 8, "sample": 5, "seed": 48418, "copies": 1, "distinct_weight_storages": 1, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 15728640, "loop_latency_ms": 0.009759999811649323, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "base", "replicate": 2, "revision": "cd6efc5e3cac59d7a6d63c32e32210077d6c7d58", "published_latency_ms": 0.009759999811649323, "kernel_source": "DeepGemmFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 64, "n": 6144, "k": 2560, "shape_index": 8, "sample": 6, "seed": 48419, "copies": 1, "distinct_weight_storages": 1, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 15728640, "loop_latency_ms": 0.009786666681369146, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "base", "replicate": 2, "revision": "cd6efc5e3cac59d7a6d63c32e32210077d6c7d58", "published_latency_ms": 0.009786666681369146, "kernel_source": "DeepGemmFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 1, "n": 2560, "k": 4096, "shape_index": 1, "sample": 0, "seed": 47713, "copies": 1, "distinct_weight_storages": 1, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 10485760, "loop_latency_ms": 0.011519999553759893, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "base", "replicate": 2, "revision": "cd6efc5e3cac59d7a6d63c32e32210077d6c7d58", "published_latency_ms": 0.011519999553759893, "kernel_source": "FlashInferFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 1, "n": 2560, "k": 4096, "shape_index": 1, "sample": 1, "seed": 47714, "copies": 1, "distinct_weight_storages": 1, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 10485760, "loop_latency_ms": 0.011877333124478659, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "base", "replicate": 2, "revision": "cd6efc5e3cac59d7a6d63c32e32210077d6c7d58", "published_latency_ms": 0.011877333124478659, "kernel_source": "FlashInferFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 1, "n": 2560, "k": 4096, "shape_index": 1, "sample": 2, "seed": 47715, "copies": 1, "distinct_weight_storages": 1, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 10485760, "loop_latency_ms": 0.011578666667143503, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "base", "replicate": 2, "revision": "cd6efc5e3cac59d7a6d63c32e32210077d6c7d58", "published_latency_ms": 0.011578666667143503, "kernel_source": "FlashInferFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 1, "n": 2560, "k": 4096, "shape_index": 1, "sample": 3, "seed": 47716, "copies": 1, "distinct_weight_storages": 1, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 10485760, "loop_latency_ms": 0.011674666156371435, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "base", "replicate": 2, "revision": "cd6efc5e3cac59d7a6d63c32e32210077d6c7d58", "published_latency_ms": 0.011674666156371435, "kernel_source": "FlashInferFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 1, "n": 2560, "k": 4096, "shape_index": 1, "sample": 4, "seed": 47717, "copies": 1, "distinct_weight_storages": 1, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 10485760, "loop_latency_ms": 0.011839999506870905, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "base", "replicate": 2, "revision": "cd6efc5e3cac59d7a6d63c32e32210077d6c7d58", "published_latency_ms": 0.011839999506870905, "kernel_source": "FlashInferFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 1, "n": 2560, "k": 4096, "shape_index": 1, "sample": 5, "seed": 47718, "copies": 1, "distinct_weight_storages": 1, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 10485760, "loop_latency_ms": 0.011637333780527115, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "base", "replicate": 2, "revision": "cd6efc5e3cac59d7a6d63c32e32210077d6c7d58", "published_latency_ms": 0.011637333780527115, "kernel_source": "FlashInferFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 1, "n": 2560, "k": 4096, "shape_index": 1, "sample": 6, "seed": 47719, "copies": 1, "distinct_weight_storages": 1, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 10485760, "loop_latency_ms": 0.011727999895811081, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "base", "replicate": 2, "revision": "cd6efc5e3cac59d7a6d63c32e32210077d6c7d58", "published_latency_ms": 0.011727999895811081, "kernel_source": "FlashInferFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-2.log b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-2.log new file mode 100644 index 000000000..a0271c462 --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/base-2.log @@ -0,0 +1,105 @@ +INFO 10-27 19:24:28 [scheduler.py:252] Chunked prefill is enabled with max_num_batched_tokens=2048. +INFO 10-27 19:24:28 [vllm.py:1006] Asynchronous scheduling is enabled. +INFO 10-27 19:24:28 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:28 [parallel_state.py:1588] world_size=1 rank=0 local_rank=0 distributed_init_method=file:///tmp/aic-vllm-rendezvous-j5p2yafo/store backend=nccl +INFO 10-27 19:24:28 [parallel_state.py:1923] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank 0, EPLB rank N/A +INFO 10-27 19:24:28 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:29 [__init__.py:563] Selected FlashInferFp8DeepGEMMDynamicBlockScaledKernel for Fp8LinearMethod +INFO 10-27 19:24:29 [deep_gemm.py:175] deep_gemm not found in site-packages, trying vendored vllm.third_party.deep_gemm +INFO 10-27 19:24:29 [deep_gemm.py:202] DeepGEMM PDL enabled on vllm.third_party.deep_gemm. +INFO 10-27 19:24:29 [deep_gemm.py:120] DeepGEMM E8M0 enabled on current platform. +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-2 shape=(16, 16384, 8192) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-2 shape=(1, 6144, 2560) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-2 shape=(1, 19456, 2560) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-2 shape=(16, 2560, 9728) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-2 shape=(16, 6144, 2560) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-2 shape=(1, 8448, 8192) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-2 shape=(16, 2560, 4096) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-2 shape=(16, 19456, 2560) +INFO 10-27 19:24:31 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-2 shape=(64, 19456, 2560) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-2 shape=(1, 2560, 9728) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-2 shape=(64, 6144, 2560) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:32 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE base-2 shape=(1, 2560, 4096) diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-0-environment.json b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-0-environment.json new file 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a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-0-gpu-after.json b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-0-gpu-after.json new file mode 100644 index 000000000..3be08f526 --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-0-gpu-after.json @@ -0,0 +1,3 @@ +{ + "gpu_after": "name, uuid, memory.total [MiB], memory.used [MiB], temperature.gpu, pstate, clocks.current.sm [MHz], clocks.current.memory [MHz], power.limit [W]\nNVIDIA H100 NVL, GPU-70bff730-c00c-c620-2b33-ac346d3103fe, 95830 MiB, 2465 MiB, 39, P0, 1785 MHz, 2619 MHz, 400.00 W" +} diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-0-measurements.jsonl b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-0-measurements.jsonl new file mode 100644 index 000000000..406a1bb9f --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-0-measurements.jsonl @@ 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"used_cuda_graph": true, "num_runs_executed": 6, "arm": "head", "replicate": 0, "revision": "64489fe6d79008fdffa474571404c74dc2597520", "published_latency_ms": 0.010335110955768162, "kernel_source": "DeepGemmFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 64, "n": 6144, "k": 2560, "shape_index": 8, "sample": 3, "seed": 28416, "copies": 6, "distinct_weight_storages": 6, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 15728640, "loop_latency_ms": 0.0620000014702479, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "head", "replicate": 0, "revision": "64489fe6d79008fdffa474571404c74dc2597520", "published_latency_ms": 0.01033333357837465, "kernel_source": "DeepGemmFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 64, "n": 6144, "k": 2560, "shape_index": 8, "sample": 4, "seed": 28417, "copies": 6, "distinct_weight_storages": 6, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 15728640, "loop_latency_ms": 0.062074666221936546, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "head", "replicate": 0, "revision": "64489fe6d79008fdffa474571404c74dc2597520", "published_latency_ms": 0.01034577770365609, "kernel_source": "DeepGemmFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 64, "n": 6144, "k": 2560, "shape_index": 8, "sample": 5, "seed": 28418, "copies": 6, "distinct_weight_storages": 6, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 15728640, "loop_latency_ms": 0.0618399977684021, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "head", "replicate": 0, "revision": "64489fe6d79008fdffa474571404c74dc2597520", "published_latency_ms": 0.010306666294733683, "kernel_source": "DeepGemmFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 64, "n": 6144, "k": 2560, "shape_index": 8, "sample": 6, "seed": 28419, "copies": 6, "distinct_weight_storages": 6, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 15728640, "loop_latency_ms": 0.06226666768391927, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "head", "replicate": 0, "revision": "64489fe6d79008fdffa474571404c74dc2597520", "published_latency_ms": 0.010377777947319878, "kernel_source": "DeepGemmFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 16, "n": 19456, "k": 2560, "shape_index": 6, "sample": 0, "seed": 28213, "copies": 3, "distinct_weight_storages": 3, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 49807360, "loop_latency_ms": 0.07533333202203114, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "head", "replicate": 0, "revision": "64489fe6d79008fdffa474571404c74dc2597520", "published_latency_ms": 0.02511111067401038, "kernel_source": "FlashInferFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 16, "n": 19456, "k": 2560, "shape_index": 6, "sample": 1, "seed": 28214, "copies": 3, "distinct_weight_storages": 3, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 49807360, "loop_latency_ms": 0.07486933469772339, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "head", "replicate": 0, "revision": "64489fe6d79008fdffa474571404c74dc2597520", "published_latency_ms": 0.02495644489924113, "kernel_source": "FlashInferFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 16, "n": 19456, "k": 2560, "shape_index": 6, "sample": 2, "seed": 28215, "copies": 3, "distinct_weight_storages": 3, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 49807360, "loop_latency_ms": 0.07539199789365132, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "head", "replicate": 0, "revision": "64489fe6d79008fdffa474571404c74dc2597520", "published_latency_ms": 0.02513066596455044, "kernel_source": "FlashInferFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 16, "n": 19456, "k": 2560, "shape_index": 6, "sample": 3, "seed": 28216, "copies": 3, "distinct_weight_storages": 3, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 49807360, "loop_latency_ms": 0.07412266731262207, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "head", "replicate": 0, "revision": "64489fe6d79008fdffa474571404c74dc2597520", "published_latency_ms": 0.024707555770874023, "kernel_source": "FlashInferFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 16, "n": 19456, "k": 2560, "shape_index": 6, "sample": 4, "seed": 28217, "copies": 3, "distinct_weight_storages": 3, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 49807360, "loop_latency_ms": 0.07515733440717061, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "head", "replicate": 0, "revision": "64489fe6d79008fdffa474571404c74dc2597520", "published_latency_ms": 0.025052444802390203, "kernel_source": "FlashInferFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 16, "n": 19456, "k": 2560, "shape_index": 6, "sample": 5, "seed": 28218, "copies": 3, "distinct_weight_storages": 3, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 49807360, "loop_latency_ms": 0.07497600217660268, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "head", "replicate": 0, "revision": "64489fe6d79008fdffa474571404c74dc2597520", "published_latency_ms": 0.024992000725534227, "kernel_source": "FlashInferFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 16, "n": 19456, "k": 2560, "shape_index": 6, "sample": 6, "seed": 28219, "copies": 3, "distinct_weight_storages": 3, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 49807360, "loop_latency_ms": 0.07520000139872234, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "head", "replicate": 0, "revision": "64489fe6d79008fdffa474571404c74dc2597520", "published_latency_ms": 0.025066667132907446, "kernel_source": "FlashInferFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-0.log b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-0.log new file mode 100644 index 000000000..c3a26b6db --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-0.log @@ -0,0 +1,105 @@ +INFO 10-27 19:23:45 [scheduler.py:252] Chunked prefill is enabled with max_num_batched_tokens=2048. +INFO 10-27 19:23:45 [vllm.py:1006] Asynchronous scheduling is enabled. +INFO 10-27 19:23:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:45 [parallel_state.py:1588] world_size=1 rank=0 local_rank=0 distributed_init_method=file:///tmp/aic-vllm-rendezvous-q5zpmxux/store backend=nccl +INFO 10-27 19:23:45 [parallel_state.py:1923] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank 0, EPLB rank N/A +INFO 10-27 19:23:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:46 [__init__.py:563] Selected FlashInferFp8DeepGEMMDynamicBlockScaledKernel for Fp8LinearMethod +INFO 10-27 19:23:46 [deep_gemm.py:175] deep_gemm not found in site-packages, trying vendored vllm.third_party.deep_gemm +INFO 10-27 19:23:46 [deep_gemm.py:202] DeepGEMM PDL enabled on vllm.third_party.deep_gemm. +INFO 10-27 19:23:46 [deep_gemm.py:120] DeepGEMM E8M0 enabled on current platform. +INFO 10-27 19:23:48 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:48 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:48 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:48 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:48 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:48 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-0 shape=(16, 6144, 2560) +INFO 10-27 19:23:48 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:48 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:48 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:48 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:48 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:48 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:48 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-0 shape=(16, 16384, 8192) +INFO 10-27 19:23:48 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:49 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:49 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:49 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:49 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:49 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:49 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-0 shape=(1, 6144, 2560) +INFO 10-27 19:23:49 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:49 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:49 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:49 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:49 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:49 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:49 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-0 shape=(16, 2560, 4096) +INFO 10-27 19:23:49 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:49 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:49 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:49 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-0 shape=(1, 19456, 2560) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-0 shape=(1, 8448, 8192) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-0 shape=(1, 2560, 9728) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:50 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-0 shape=(16, 2560, 9728) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-0 shape=(1, 2560, 4096) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-0 shape=(64, 19456, 2560) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:51 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:52 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:52 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:52 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:52 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:52 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-0 shape=(64, 6144, 2560) +INFO 10-27 19:23:52 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:52 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:52 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:52 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:52 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:52 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:23:52 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-0 shape=(16, 19456, 2560) diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-1-environment.json b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-1-environment.json new file mode 100644 index 000000000..0d01011c0 --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-1-environment.json @@ -0,0 +1,285 @@ +{ + "arm": "head", + "replicate": 1, + "revision": "64489fe6d79008fdffa474571404c74dc2597520", + "protocol_sha256": "7121239a74b76c01da99e5bdd7834e0489876c7105694e0b11f20d616d011c1e", + "runner_sha256": "4ead3a081bca704d0ed121bb2355687dfa3ac9838f4c6fdcc355ef31bec8b836", + "python": "3.12.12 (main, Oct 14 2025, 21:25:31) [Clang 20.1.4 ]", + "torch": "2.11.0+cu130", + "torch_cuda": "13.0", + "vllm": "0.24.0", + "packages": { + "anyio": "4.15.1", + "nvidia-curand": "10.4.0.35", + "nvidia-cufile": "1.15.1.6", + "nvidia-cuda-nvdisasm": "13.4.92", + 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"gpu_before": "name, uuid, memory.total [MiB], memory.used [MiB], temperature.gpu, pstate, clocks.current.sm [MHz], clocks.current.memory [MHz], power.limit [W]\nNVIDIA H100 NVL, GPU-70bff730-c00c-c620-2b33-ac346d3103fe, 95830 MiB, 4 MiB, 37, P0, 345 MHz, 2619 MHz, 400.00 W" +} diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-1-gpu-after.json b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-1-gpu-after.json new file mode 100644 index 000000000..da9e60b5d --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-1-gpu-after.json @@ -0,0 +1,3 @@ +{ + "gpu_after": "name, uuid, memory.total [MiB], memory.used [MiB], temperature.gpu, pstate, clocks.current.sm [MHz], clocks.current.memory [MHz], power.limit [W]\nNVIDIA H100 NVL, GPU-70bff730-c00c-c620-2b33-ac346d3103fe, 95830 MiB, 2527 MiB, 40, P0, 1785 MHz, 2619 MHz, 400.00 W" +} diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-1-measurements.jsonl b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-1-measurements.jsonl new file mode 100644 index 000000000..e57bc056e --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-1-measurements.jsonl @@ -0,0 +1,84 @@ +{"m": 16, "n": 6144, "k": 2560, "shape_index": 4, "sample": 0, "seed": 38013, "copies": 6, "distinct_weight_storages": 6, "weight_dtype": "torch.float8_e4m3fn", "one_weight_storage_bytes": 15728640, "loop_latency_ms": 0.05955733358860016, "used_cuda_graph": true, "num_runs_executed": 6, "arm": "head", "replicate": 1, "revision": "64489fe6d79008fdffa474571404c74dc2597520", "published_latency_ms": 0.009926222264766693, "kernel_source": "FlashInferFp8BlockScaledMMKernel", "framework": "VLLM", "framework_version": "0.24.0", "device_name": "NVIDIA H100 NVL"} +{"m": 16, "n": 6144, "k": 2560, "shape_index": 4, "sample": 1, "seed": 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000000000..27ad2b230 --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-1.log @@ -0,0 +1,105 @@ +INFO 10-27 19:24:00 [scheduler.py:252] Chunked prefill is enabled with max_num_batched_tokens=2048. +INFO 10-27 19:24:00 [vllm.py:1006] Asynchronous scheduling is enabled. +INFO 10-27 19:24:00 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:00 [parallel_state.py:1588] world_size=1 rank=0 local_rank=0 distributed_init_method=file:///tmp/aic-vllm-rendezvous-1zjxnp8u/store backend=nccl +INFO 10-27 19:24:00 [parallel_state.py:1923] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank 0, EPLB rank N/A +INFO 10-27 19:24:01 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:01 [__init__.py:563] Selected FlashInferFp8DeepGEMMDynamicBlockScaledKernel for Fp8LinearMethod +INFO 10-27 19:24:01 [deep_gemm.py:175] deep_gemm not found in site-packages, trying vendored vllm.third_party.deep_gemm +INFO 10-27 19:24:01 [deep_gemm.py:202] DeepGEMM PDL enabled on vllm.third_party.deep_gemm. +INFO 10-27 19:24:01 [deep_gemm.py:120] DeepGEMM E8M0 enabled on current platform. +INFO 10-27 19:24:03 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:03 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:03 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:03 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:03 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:03 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-1 shape=(16, 6144, 2560) +INFO 10-27 19:24:04 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:04 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:04 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:04 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:04 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:04 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:04 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-1 shape=(64, 6144, 2560) +INFO 10-27 19:24:04 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:04 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:04 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:04 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:04 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:04 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:04 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-1 shape=(1, 2560, 9728) +INFO 10-27 19:24:04 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:04 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:04 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-1 shape=(16, 19456, 2560) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-1 shape=(1, 6144, 2560) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-1 shape=(64, 19456, 2560) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:05 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-1 shape=(16, 2560, 4096) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-1 shape=(16, 2560, 9728) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-1 shape=(1, 8448, 8192) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-1 shape=(1, 2560, 4096) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-1 shape=(16, 16384, 8192) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:07 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-1 shape=(1, 19456, 2560) diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-2-environment.json b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-2-environment.json new file mode 100644 index 000000000..7280511eb --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-2-environment.json @@ -0,0 +1,285 @@ +{ + "arm": "head", + "replicate": 2, + "revision": "64489fe6d79008fdffa474571404c74dc2597520", + "protocol_sha256": "7121239a74b76c01da99e5bdd7834e0489876c7105694e0b11f20d616d011c1e", + "runner_sha256": "4ead3a081bca704d0ed121bb2355687dfa3ac9838f4c6fdcc355ef31bec8b836", + "python": "3.12.12 (main, Oct 14 2025, 21:25:31) [Clang 20.1.4 ]", + "torch": "2.11.0+cu130", + "torch_cuda": "13.0", + "vllm": "0.24.0", + "packages": { + "anyio": "4.15.1", + "nvidia-curand": "10.4.0.35", + "nvidia-cufile": "1.15.1.6", + "nvidia-cuda-nvdisasm": "13.4.92", + "email-validator": "2.3.0", + "click": "8.5.0", + "tabulate": "0.10.0", + "nvidia-cudnn-cu13": "9.19.0.56", + "rich-toolkit": "0.20.6", + "rpds-py": "2026.9.1", + "opentelemetry-semantic-conventions": "0.66b1", + "hf-xet": "1.7.0", + "kiwisolver": "1.5.1", + "tokenspeed-mla": "0.1.2", + "mcp-types": "2.3.0", + "networkx": "3.7", + "nvidia-nvvm": "13.2.86", + "fsspec": "2026.9.0", + "aiohttp": "3.14.5", + "pillow": "13.0.0", + "fastapi-cloud-cli": "0.26.0", + "opentelemetry-sdk": "1.45.1", + "torchcodec": "0.17.0", + "markdown-it-py": "4.2.0", + "jiter": "0.18.0", + "partial-json-parser": "0.2.1.1.post7", + "pycparser": "3.12", + "narwhals": "2.28.0", + "numba": "0.65.0", + "annotated-types": "0.8.0", + "torch": "2.11.0", + "matplotlib": "3.11.2", + 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"python-multipart": "0.0.32", + "dnspython": "2.9.0", + "sse-starlette": "3.6.0", + "propcache": "0.5.4", + "tilelang": "0.1.9", + "ninja": "1.13.2", + "dill": "0.4.1", + "cuda-python": "13.4.4", + "nvidia-cutlass-dsl": "4.5.2", + "flashinfer-cubin": "0.6.12", + "nvidia-nccl-cu13": "2.28.9", + "httpx2": "2.16.0", + "Pygments": "2.21.0", + "openai-harmony": "0.0.8", + "attrs": "26.1.0", + "humming-kernels": "0.1.6", + "nvidia-cuda-cupti": "13.0.85", + "fonttools": "4.67.0", + "opentelemetry-api": "1.45.1", + "websockets": "17.3", + "cuda-toolkit": "13.0.2", + "urllib3": "2.8.0", + "agent-detector": "2.0.0", + "googleapis-common-protos": "1.76.0", + "MarkupSafe": "3.0.4", + "python-dotenv": "1.2.4", + "cloudpickle": "3.1.2", + "httpcore2": "2.16.0", + "yarl": "1.25.1", + "llvmlite": "0.47.0", + "python-json-logger": "4.2.0", + "opentelemetry-exporter-otlp-proto-common": "1.45.1", + "nvidia-cusparse": "12.6.3.3", + "rich": "15.0.0", + "multidict": "7.2.1", + "lark": "1.2.2", + "pyarrow": "26.0.0", + "httpx": "0.28.1", + "mistral_common": "1.12.0", + "protobuf": "7.36.2", + "cycler": "0.12.1", + "uvloop": "0.23.0", + "opentelemetry-exporter-http-transport": "0.66b1", + "sentry-sdk": "2.73.0", + "ml_dtypes": "0.6.0", + "opencv-python-headless": "5.0.0.93", + "opentelemetry-exporter-otlp-proto-http": "1.45.1", + "tzdata": "2026.5", + "uvicorn": "0.54.0", + "sniffio": "1.3.1", + "triton": "3.6.0", + "pyparsing": "3.3.3", + "pytest": "9.1.1", + "h11": "0.16.0", + "xyzservices": "2026.9.1", + "llguidance": "1.7.6", + "frozenlist": "1.8.0", + "torchvision": "0.26.0", + "pybase64": "1.5.1", + "ijson": "3.6.0", + "nvidia-cufft": "12.0.0.61", + "httptools": "0.9.0", + "astor": "0.8.1", + "pycountry": "26.2.16", + "plotly": "7.1.0", + "wcwidth": "0.9.4", + "typing-inspection": "0.4.4", + "nvidia-cublas": "13.1.0.3", + "prometheus-fastapi-instrumentator": "8.1.0", + "diskcache": "5.6.3", + "py-cpuinfo": "9.0.0", + "instanttensor": "0.2.2", + "setuptools": "80.10.2", + "cuda-tile": 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b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-2.log new file mode 100644 index 000000000..4eae3589e --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/head-2.log @@ -0,0 +1,105 @@ +INFO 10-27 19:24:40 [scheduler.py:252] Chunked prefill is enabled with max_num_batched_tokens=2048. +INFO 10-27 19:24:40 [vllm.py:1006] Asynchronous scheduling is enabled. +INFO 10-27 19:24:40 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:40 [parallel_state.py:1588] world_size=1 rank=0 local_rank=0 distributed_init_method=file:///tmp/aic-vllm-rendezvous-4pve628q/store backend=nccl +INFO 10-27 19:24:40 [parallel_state.py:1923] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank 0, EPLB rank N/A +INFO 10-27 19:24:40 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:41 [__init__.py:563] Selected FlashInferFp8DeepGEMMDynamicBlockScaledKernel for Fp8LinearMethod +INFO 10-27 19:24:41 [deep_gemm.py:175] deep_gemm not found in site-packages, trying vendored vllm.third_party.deep_gemm +INFO 10-27 19:24:41 [deep_gemm.py:202] DeepGEMM PDL enabled on vllm.third_party.deep_gemm. +INFO 10-27 19:24:41 [deep_gemm.py:120] DeepGEMM E8M0 enabled on current platform. +INFO 10-27 19:24:43 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:43 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:43 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:43 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:43 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:43 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-2 shape=(16, 16384, 8192) +INFO 10-27 19:24:43 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:43 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:43 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:43 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:43 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-2 shape=(1, 6144, 2560) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-2 shape=(1, 19456, 2560) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-2 shape=(16, 2560, 9728) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:44 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-2 shape=(16, 6144, 2560) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-2 shape=(1, 8448, 8192) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-2 shape=(16, 2560, 4096) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:45 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-2 shape=(16, 19456, 2560) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-2 shape=(64, 19456, 2560) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-2 shape=(1, 2560, 9728) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:46 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:47 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:47 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:47 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:47 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-2 shape=(64, 6144, 2560) +INFO 10-27 19:24:47 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:47 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:47 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:47 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:47 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:47 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 19:24:47 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +DONE head-2 shape=(1, 2560, 4096) diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/invocation.json b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/invocation.json new file mode 100644 index 000000000..b192535a9 --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/invocation.json @@ -0,0 +1,11 @@ +{ + "argv": [ + "benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/run.py", + "--output", + "benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct" + ], + "cwd": "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate", + "python": "/tmp/aisimulate-forward-ab/venv/bin/python", + "runner_sha256": "4ead3a081bca704d0ed121bb2355687dfa3ac9838f4c6fdcc355ef31bec8b836", + "gpu_before": "name, uuid, memory.total [MiB], memory.used [MiB], temperature.gpu, pstate, clocks.current.sm [MHz], clocks.current.memory [MHz], power.limit [W]\nNVIDIA H100 NVL, GPU-70bff730-c00c-c620-2b33-ac346d3103fe, 95830 MiB, 0 MiB, 35, P0, 345 MHz, 2619 MHz, 400.00 W" +} diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/jobs.json b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/jobs.json new file mode 100644 index 000000000..dbd447235 --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/jobs.json @@ -0,0 +1,116 @@ +[ + { + "arm": "base", + "replicate": 0, + "command": [ + "/tmp/aisimulate-forward-ab/venv/bin/python", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/run.py", + "--worker", + "base", + "--replicate", + "0", + "--repo", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate", + "--output", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct" + ], + "started_unix": 1793125393.3609657, + "finished_unix": 1793125418.8857136, + "exit_code": 0 + }, + { + "arm": "head", + "replicate": 0, + "command": [ + "/tmp/aisimulate-forward-ab/venv/bin/python", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/run.py", + "--worker", + "head", + "--replicate", + "0", + "--repo", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate", + "--output", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct" + ], + "started_unix": 1793125418.8864434, + "finished_unix": 1793125434.0885172, + "exit_code": 0 + }, + { + "arm": "head", + "replicate": 1, + "command": [ + "/tmp/aisimulate-forward-ab/venv/bin/python", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/run.py", + "--worker", + "head", + "--replicate", + "1", + "--repo", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate", + "--output", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct" + ], + "started_unix": 1793125434.0890117, + "finished_unix": 1793125449.2112272, + "exit_code": 0 + }, + { + "arm": "base", + "replicate": 1, + "command": [ + "/tmp/aisimulate-forward-ab/venv/bin/python", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/run.py", + "--worker", + "base", + "--replicate", + "1", + "--repo", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate", + "--output", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct" + ], + "started_unix": 1793125449.2117524, + "finished_unix": 1793125461.5097861, + "exit_code": 0 + }, + { + "arm": "base", + "replicate": 2, + "command": [ + "/tmp/aisimulate-forward-ab/venv/bin/python", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/run.py", + "--worker", + "base", + "--replicate", + "2", + "--repo", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate", + "--output", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct" + ], + "started_unix": 1793125461.5103, + "finished_unix": 1793125473.8696222, + "exit_code": 0 + }, + { + "arm": "head", + "replicate": 2, + "command": [ + "/tmp/aisimulate-forward-ab/venv/bin/python", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/run.py", + "--worker", + "head", + "--replicate", + "2", + "--repo", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate", + "--output", + "/kyukon/scratch/gent/vo/000/gvo00024/vsc40013/aisimulate/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct" + ], + "started_unix": 1793125473.870236, + "finished_unix": 1793125489.2613149, + "exit_code": 0 + } +] diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/protocol.json b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/protocol.json new file mode 100644 index 000000000..fac9f1c4b --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/protocol.json @@ -0,0 +1,87 @@ +{ + "schema_version": 1, + "purpose": "Compare actual log_perf latencies from the base and PR collectors without changing their timing or allocation policies.", + "base_revision": "cd6efc5e3cac59d7a6d63c32e32210077d6c7d58", + "head_revision": "64489fe6d79008fdffa474571404c74dc2597520", + "framework": "vllm==0.24.0", + "torch": "2.11.0+cu130", + "device_name": "NVIDIA H100 NVL", + "replicates": 3, + "samples_per_shape": 7, + "seed": 27613, + "timing": { + "num_warmups": 3, + "num_runs": 6, + "repeat_n": 1, + "use_cuda_graph": true, + "allow_graph_fail": false, + "measure_power": false + }, + "shapes_m_n_k": [ + [ + 1, + 6144, + 2560 + ], + [ + 1, + 2560, + 4096 + ], + [ + 1, + 19456, + 2560 + ], + [ + 1, + 2560, + 9728 + ], + [ + 16, + 6144, + 2560 + ], + [ + 16, + 2560, + 4096 + ], + [ + 16, + 19456, + 2560 + ], + [ + 16, + 2560, + 9728 + ], + [ + 64, + 6144, + 2560 + ], + [ + 64, + 19456, + 2560 + ], + [ + 1, + 8448, + 8192 + ], + [ + 16, + 16384, + 8192 + ] + ], + "ordering": "Alternating arm order across independent process starts; identical seeded shape order and first-weight inputs within each paired replicate.", + "statistics": "Median of seven published latencies within each start, then median across three starts. Keep every sample.", + "anomaly_checks": "Require complete unique keys, finite positive latencies, CUDA graph replay, six timed runs, consistent kernel_source, distinct weight storage and correct publication normalization. Flag samples more than 15% from their within-start median; do not exclude them.", + "outlier_relative_threshold": 0.15, + "scope": "Isolated collector row latency in milliseconds. This is not a model accuracy experiment or a fixed-operation-count graph benchmark." +} diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/runner-source.tar.gz b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/direct/runner-source.tar.gz new file mode 100644 index 0000000000000000000000000000000000000000..99103990c2924df0c5a64cbdf8c20b431f0a3b00 GIT binary patch literal 4252 zcmV;N5M%EjiwFP!00002|Lq#tZsfRep8G2pErM0gs^w!hvwzk)lI@P+e@_(;-LgFil9x0AyCtfsNEYiRd6~NT=FNY#?LvF^!w>QoTIcus z-5>XUG<~}N$Gz{re?xZP{0~|w3ZKJ}Z~i~qH{{~%G@B`z*C3Bbv@p-a?n_Q3;13;fFITLG= z^DJOOOmF}nXIOBUBMTi;rb!m!TEco>1lDxxuNWX_35kGC9;GQjG}q)#PSbcp;%r58 z#+M`#@F|QqL%O8M2*3fK0!IFFaeDIky`&TS1$1o?IL(k&kR^Eu2re>jGEobJRjhKK z3ufMfEG=NpadcyP*1lLbUeynDxCtx#&@cSzODb}hQuUgdr!OK)&2!=xYxA55^TdH$ zSz_Kqd1JU}dN$P~aC`|;RlTw#Z@oten@DEnFcM5tPnewHz>`n&^EvV4i%lsaQaW{E zgf`60d86inFDsk>$mnuZ~H&!d7-+#iYHkUKvv&9v{M zVoe&vvYe&1^Y? z=$yGg%E^%ybAmVvZeb5fj(hEjq}!utSj_>ygAtuPyJy^u!GIK2lmBM-trYYy@@FE6 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op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 18:28:06 [parallel_state.py:1588] world_size=1 rank=0 local_rank=0 distributed_init_method=file:///tmp/aic-vllm-rendezvous-qb8sl2ee/store backend=nccl +INFO 10-27 18:28:06 [parallel_state.py:1923] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank 0, EPLB rank N/A +INFO 10-27 18:28:06 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 18:28:07 [__init__.py:563] Selected FlashInferFp8DeepGEMMDynamicBlockScaledKernel for Fp8LinearMethod +INFO 10-27 18:28:07 [deep_gemm.py:175] deep_gemm not found in site-packages, trying vendored vllm.third_party.deep_gemm +INFO 10-27 18:28:07 [deep_gemm.py:202] DeepGEMM PDL enabled on vllm.third_party.deep_gemm. +INFO 10-27 18:28:07 [deep_gemm.py:120] DeepGEMM E8M0 enabled on current platform. +{"result": "passed", "limit_bytes": 27917287424, "copies": 6, "latency_ms": 0.062208001812299095, "power_stats": null, "throttled": false, "num_runs_executed": 6, "used_cuda_graph": true, "kernel_source": "DeepGemmFp8BlockScaledMMKernel", "published_latency_ms": 0.01036800030204985, "peak_allocated_bytes": 397210112} diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/memory-checks/small-flashinfer.log b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/memory-checks/small-flashinfer.log new file mode 100644 index 000000000..4e6db8e56 --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/memory-checks/small-flashinfer.log @@ -0,0 +1,11 @@ +INFO 10-27 18:27:56 [scheduler.py:252] Chunked prefill is enabled with max_num_batched_tokens=2048. +INFO 10-27 18:27:56 [vllm.py:1006] Asynchronous scheduling is enabled. +INFO 10-27 18:27:56 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 18:27:56 [parallel_state.py:1588] world_size=1 rank=0 local_rank=0 distributed_init_method=file:///tmp/aic-vllm-rendezvous-0l271stu/store backend=nccl +INFO 10-27 18:27:56 [parallel_state.py:1923] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank 0, EPLB rank N/A +INFO 10-27 18:27:57 [kernel.py:276] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']) +INFO 10-27 18:27:57 [__init__.py:563] Selected FlashInferFp8DeepGEMMDynamicBlockScaledKernel for Fp8LinearMethod +INFO 10-27 18:27:57 [deep_gemm.py:175] deep_gemm not found in site-packages, trying vendored vllm.third_party.deep_gemm +INFO 10-27 18:27:57 [deep_gemm.py:202] DeepGEMM PDL enabled on vllm.third_party.deep_gemm. +INFO 10-27 18:27:57 [deep_gemm.py:120] DeepGEMM E8M0 enabled on current platform. +{"result": "passed", "limit_bytes": 27917287424, "copies": 6, "latency_ms": 0.058890665570894875, "power_stats": null, "throttled": false, "num_runs_executed": 6, "used_cuda_graph": true, "kernel_source": "FlashInferFp8BlockScaledMMKernel", "published_latency_ms": 0.009815110928482479, "peak_allocated_bytes": 396887552} diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/protocol.json b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/protocol.json new file mode 100644 index 000000000..f2c3b70be --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/protocol.json @@ -0,0 +1,39 @@ +{ + "schema_version": 1, + "purpose": "Compare actual log_perf latencies from the base and PR collectors without changing their timing or allocation policies.", + "base_revision": "cd6efc5e3cac59d7a6d63c32e32210077d6c7d58", + "head_revision": "64489fe6d79008fdffa474571404c74dc2597520", + "framework": "vllm==0.24.0", + "torch": "2.11.0+cu130", + "device_name": "NVIDIA H100 NVL", + "replicates": 3, + "samples_per_shape": 7, + "seed": 27613, + "timing": { + "num_warmups": 3, + "num_runs": 6, + "repeat_n": 1, + "use_cuda_graph": true, + "allow_graph_fail": false, + "measure_power": false + }, + "shapes_m_n_k": [ + [1, 6144, 2560], + [1, 2560, 4096], + [1, 19456, 2560], + [1, 2560, 9728], + [16, 6144, 2560], + [16, 2560, 4096], + [16, 19456, 2560], + [16, 2560, 9728], + [64, 6144, 2560], + [64, 19456, 2560], + [1, 8448, 8192], + [16, 16384, 8192] + ], + "ordering": "Alternating arm order across independent process starts; identical seeded shape order and first-weight inputs within each paired replicate.", + "statistics": "Median of seven published latencies within each start, then median across three starts. Keep every sample.", + "anomaly_checks": "Require complete unique keys, finite positive latencies, CUDA graph replay, six timed runs, consistent kernel_source, distinct weight storage and correct publication normalization. Flag samples more than 15% from their within-start median; do not exclude them.", + "outlier_relative_threshold": 0.15, + "scope": "Isolated collector row latency in milliseconds. This is not a model accuracy experiment or a fixed-operation-count graph benchmark." +} diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/run.py b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/run.py new file mode 100644 index 000000000..4f1f1509c --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/run.py @@ -0,0 +1,299 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 2026 Jan Fostier +# SPDX-License-Identifier: Apache-2.0 + +"""Run exact Git revisions of the real collector; observe timing and publication. + +GPU/framework imports stay inside workers. Each arm/start gets a fresh process +and an extracted, unmodified collector package from its pinned Git revision. +Only log_perf is redirected to evidence JSONL; its latency is not recomputed. +""" + +import argparse +import contextlib +import hashlib +import importlib.metadata +import inspect +import io +import json +import math +import os +import random +import subprocess +import sys +import tarfile +import tempfile +import time +from pathlib import Path + +HERE = Path(__file__).resolve().parent +COLLECTOR = "python/aisimulate/collector" + + +def command(*args, cwd=None): + return subprocess.check_output(args, cwd=cwd, text=True).strip() + + +def digest(path): + return hashlib.sha256(Path(path).read_bytes()).hexdigest() + + +def write_json(path, value): + with Path(path).open("x") as stream: + json.dump(value, stream, indent=2, allow_nan=False) + stream.write("\n") + + +def gpu_status(): + fields = "name,uuid,memory.total,memory.used,temperature.gpu,pstate,clocks.current.sm,clocks.current.memory" + fields += ",power.limit" + return command("nvidia-smi", f"--query-gpu={fields}", "--format=csv") + + +def worker(args, protocol): + # Use all collector dependencies from the same revision, not just the + # changed module. No source replacement or allocation-count override. + revision = protocol[f"{args.worker}_revision"] + archive = subprocess.check_output(["git", "archive", revision, COLLECTOR], cwd=args.repo) + with tempfile.TemporaryDirectory(prefix="pr613-collector-") as temporary: + with tarfile.open(fileobj=io.BytesIO(archive)) as source: + source.extractall(temporary, filter="data") + sys.path.insert(0, str(Path(temporary) / "python/aisimulate")) + import torch + import vllm + from collector.vllm import collect_gemm + + properties = torch.cuda.get_device_properties(0) + if properties.name != protocol["device_name"]: + raise RuntimeError(f"Expected {protocol['device_name']}; found {properties.name}") + if vllm.__version__ != protocol["framework"].split("==")[1] or torch.__version__ != protocol["torch"]: + raise RuntimeError(f"Unexpected runtime: vLLM {vllm.__version__}, torch {torch.__version__}") + prefix = f"{args.worker}-{args.replicate}" + runtime_paths = [ + "model_executor/layers/quantization/fp8.py", + "model_executor/layers/quantization/utils/fp8_utils.py", + "model_executor/kernels/linear/scaled_mm/flashinfer.py", + "model_executor/kernels/linear/scaled_mm/deep_gemm.py", + "utils/deep_gemm.py", + ] + root = Path(vllm.__file__).parent + environment = { + "arm": args.worker, + "replicate": args.replicate, + "revision": revision, + "protocol_sha256": digest(args.output / "protocol.json"), + "runner_sha256": digest(__file__), + "python": sys.version, + "torch": torch.__version__, + "torch_cuda": torch.version.cuda, + "vllm": vllm.__version__, + "packages": { + d.metadata["Name"]: d.version for d in importlib.metadata.distributions() if d.metadata["Name"] + }, + "device": { + "name": properties.name, + "l2_bytes": properties.L2_cache_size, + "total_memory_bytes": properties.total_memory, + "compute_capability": [properties.major, properties.minor], + }, + "runtime_source_sha256": {p: digest(root / p) for p in runtime_paths}, + "collector_source_sha256": { + p: digest(Path(temporary) / COLLECTOR / p) + for p in ["vllm/collect_gemm.py", "vllm/utils.py", "helper.py"] + }, + "environment": { + name: os.environ.get(name) + for name in [ + "COLLECTOR_MEASURE_POWER", + "CUDA_HOME", + "CUDA_VISIBLE_DEVICES", + "OMP_NUM_THREADS", + "VLLM_USE_DEEP_GEMM", + "VLLM_USE_DEEP_GEMM_E8M0", + "VLLM_BATCH_INVARIANT", + ] + }, + "gpu_before": gpu_status(), + } + write_json(args.output / f"{prefix}-environment.json", environment) + original_benchmark = collect_gemm.benchmark_with_power + state = {} + + @contextlib.contextmanager + def observe(**kwargs): + expected = protocol["timing"] + for key in ["num_warmups", "num_runs", "repeat_n", "use_cuda_graph", "allow_graph_fail"]: + if kwargs[key] != expected[key]: + raise RuntimeError(f"Collector changed timing setting {key}: {kwargs[key]}") + ops = inspect.getclosurevars(kwargs["kernel_func"]).nonlocals["op_list"] + pointers = [op.weight.untyped_storage().data_ptr() for op in ops] + copies = len(ops) + wanted = ( + 1 + if args.worker == "base" + else max( + 1, min(6, (2 * properties.L2_cache_size + state["n"] * state["k"] - 1) // (state["n"] * state["k"])) + ) + ) + if copies != wanted or len(set(pointers)) != copies: + raise RuntimeError(f"Invalid allocation rotation: {copies=}, {wanted=}") + state.update( + copies=copies, + distinct_weight_storages=len(set(pointers)), + weight_dtype=str(ops[0].weight.dtype), + one_weight_storage_bytes=ops[0].weight.untyped_storage().nbytes(), + ) + # Call the original helper with its original arguments. In + # particular, do not lengthen graphs, warmup or timed repeats. + with original_benchmark(**kwargs) as result: + if result["used_cuda_graph"] is not True or result["num_runs_executed"] != 6: + raise RuntimeError(f"Unexpected timing mode: {result}") + state.update( + loop_latency_ms=result["latency_ms"], + used_cuda_graph=result["used_cuda_graph"], + num_runs_executed=result["num_runs_executed"], + ) + yield result + + raw_path = args.output / f"{prefix}-measurements.jsonl" + with raw_path.open("x") as raw: + + def publish(**kwargs): + if state.get("published"): + raise RuntimeError("More than one performance row from a collector call") + (row,) = kwargs["item_list"] + if [row[key] for key in ["m", "n", "k"]] != [state[key] for key in ["m", "n", "k"]]: + raise RuntimeError("Published shape does not match the requested case") + latency = row["latency"] + if not math.isfinite(latency) or latency <= 0: + raise RuntimeError(f"Invalid published latency: {latency}") + if not math.isclose(latency * state["copies"], state["loop_latency_ms"], rel_tol=1e-12): + raise RuntimeError("Incorrect per-operation latency normalization") + result = { + **state, + "arm": args.worker, + "replicate": args.replicate, + "revision": revision, + "published_latency_ms": latency, + "kernel_source": kwargs["kernel_source"], + "framework": kwargs["framework"], + "framework_version": kwargs["version"], + "device_name": kwargs["device_name"], + } + raw.write(json.dumps(result, allow_nan=False) + "\n") + raw.flush() + state["published"] = True + return True + + collect_gemm.benchmark_with_power = observe + collect_gemm.log_perf = publish + order = list(enumerate(protocol["shapes_m_n_k"])) + random.Random(protocol["seed"] + args.replicate).shuffle(order) + for shape_index, (m, n, k) in order: + for sample in range(protocol["samples_per_shape"]): + seed = protocol["seed"] + args.replicate * 10000 + shape_index * 100 + sample + state.clear() + state.update(m=m, n=n, k=k, shape_index=shape_index, sample=sample, seed=seed) + torch.manual_seed(seed) + with torch.inference_mode(): + collect_gemm.run_gemm("fp8_block", m, n, k, perf_filename="evidence-only") + if not state.get("published"): + raise RuntimeError("Collector returned without a performance row") + print(f"DONE {prefix} shape={m, n, k}", flush=True) + write_json(args.output / f"{prefix}-gpu-after.json", {"gpu_after": gpu_status()}) + + +def campaign(args, protocol): + if args.output.exists(): + raise RuntimeError(f"Refusing to overwrite {args.output}") + args.output.mkdir(parents=True) + for arm in ["base", "head"]: + ref = protocol[f"{arm}_revision"] + if command("git", "rev-parse", ref, cwd=args.repo) != ref: + raise RuntimeError(f"Protocol requires an immutable full Git revision: {ref}") + changed = command( + "git", + "diff", + "--name-only", + protocol["base_revision"], + protocol["head_revision"], + "--", + COLLECTOR, + cwd=args.repo, + ).splitlines() + if changed != [f"{COLLECTOR}/vllm/collect_gemm.py"]: + raise RuntimeError(f"Unexpected collector changes: {changed}") + active = command("nvidia-smi", "--query-compute-apps=pid,process_name,used_memory", "--format=csv,noheader") + if active: + raise RuntimeError(f"Start with an idle GPU; compute processes are present: {active}") + write_json(args.output / "protocol.json", protocol) + environment = os.environ.copy() + environment["COLLECTOR_MEASURE_POWER"] = "0" + environment.setdefault("OMP_NUM_THREADS", "4") + environment.pop("PYTHONPATH", None) + jobs = [] + write_json( + args.output / "invocation.json", + { + "argv": sys.argv, + "cwd": str(Path.cwd()), + "python": sys.executable, + "runner_sha256": digest(__file__), + "gpu_before": gpu_status(), + }, + ) + for replicate in range(protocol["replicates"]): + arms = ["base", "head"] if replicate % 2 == 0 else ["head", "base"] + for arm in arms: + prefix = f"{arm}-{replicate}" + child = [ + sys.executable, + str(Path(__file__).resolve()), + "--worker", + arm, + "--replicate", + str(replicate), + "--repo", + str(args.repo), + "--output", + str(args.output), + ] + started = time.time() + print(f"START {prefix}", flush=True) + with (args.output / f"{prefix}.log").open("x") as log: + result = subprocess.run(child, cwd=args.repo, env=environment, stdout=log, stderr=subprocess.STDOUT) + jobs.append( + { + "arm": arm, + "replicate": replicate, + "command": child, + "started_unix": started, + "finished_unix": time.time(), + "exit_code": result.returncode, + } + ) + (args.output / "jobs.json").write_text(json.dumps(jobs, indent=2) + "\n") + if result.returncode: + raise RuntimeError(f"{prefix} exited {result.returncode}; inspect {prefix}.log") + print(f"FINISHED {prefix} ({time.time() - started:.1f}s)", flush=True) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--repo", type=Path, default=HERE.parents[2]) + parser.add_argument("--worker", choices=["base", "head"]) + parser.add_argument("--replicate", type=int) + args = parser.parse_args() + args.repo = args.repo.resolve() + args.output = args.output.resolve() + protocol_path = args.output / "protocol.json" if args.worker else HERE / "protocol.json" + protocol = json.loads(protocol_path.read_text()) + if args.worker: + worker(args, protocol) + else: + campaign(args, protocol) + + +if __name__ == "__main__": + main() diff --git a/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/validate.py b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/validate.py new file mode 100644 index 000000000..9c12352de --- /dev/null +++ b/benchmarks/evidence/vllm-fp8-block-rotation-pr613-20261027/validate.py @@ -0,0 +1,63 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 2026 Jan Fostier +# SPDX-License-Identifier: Apache-2.0 + +"""Exercise the evidence reader against corrupt records without changing raw data.""" + +import json +import shutil +import tempfile +from pathlib import Path + +import analyze + +HERE = Path(__file__).resolve().parent + + +def main(): + checks = [] + for scenario in ["valid", "missing", "duplicate", "negative", "wrong_kernel", "outlier"]: + with tempfile.TemporaryDirectory(prefix="pr613-reader-check-") as temporary: + direct = Path(temporary) / "direct" + shutil.copytree(HERE / "direct", direct) + path = direct / "base-0-measurements.jsonl" + rows = analyze.read_rows(path) + if scenario == "missing": + rows.pop() + elif scenario == "duplicate": + rows[1] = dict(rows[0]) + elif scenario == "negative": + rows[0]["published_latency_ms"] = -1 + elif scenario == "wrong_kernel": + rows[0]["kernel_source"] = "intentionally_incorrect_kernel" + elif scenario == "outlier": + rows[0]["published_latency_ms"] *= 2 + rows[0]["loop_latency_ms"] *= 2 + path.write_text("".join(json.dumps(row) + "\n" for row in rows)) + try: + result = analyze.direct(direct) + except ValueError as error: + if scenario in ["valid", "outlier"]: + raise + checks.append({"scenario": scenario, "result": "rejected", "reason": str(error)}) + else: + if scenario not in ["valid", "outlier"]: + raise RuntimeError(f"Corrupt records were accepted: {scenario}") + if result["measurement_count"] != 504 or result["excluded_measurements"] != 0: + raise RuntimeError("Reader discarded measurements") + if scenario == "outlier" and len(result["flagged_outliers"]) != 1: + raise RuntimeError("Injected outlier was not flagged exactly once") + checks.append( + { + "scenario": scenario, + "result": "accepted; all samples retained", + "outliers": len(result["flagged_outliers"]), + } + ) + report = {"status": "passed", "checks": checks, "raw_measurements_modified": False} + (HERE / "analysis/validation.json").write_text(json.dumps(report, indent=2) + "\n") + print(json.dumps(report, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/python/aisimulate/collector/vllm/collect_gemm.py b/python/aisimulate/collector/vllm/collect_gemm.py index 0d5a94908..957905205 100644 --- a/python/aisimulate/collector/vllm/collect_gemm.py +++ b/python/aisimulate/collector/vllm/collect_gemm.py @@ -348,7 +348,16 @@ def create_gemm(): ) exit_stack.enter_context(set_current_vllm_config(vllm_config)) - outside_loop_count = 1 if gemm_type in ("fp8_block", "nvfp4") else 6 + outside_loop_count = 1 if gemm_type == "nvfp4" else 6 + if gemm_type == "fp8_block": + # FP8 stores one byte per weight. Target a two-L2 working set to + # reduce cache reuse, including for matrices slightly larger than L2. + weight_bytes = n * k + target_bytes = 2 * torch.cuda.get_device_properties(device).L2_cache_size + # Retain the six-copy cap used by the BF16/per-tensor FP8 paths here + # and collector/sglang/collect_gemm.py to bound setup and graph size. + # Very small matrices may not reach the target; cold reads are not guaranteed. + outside_loop_count = max(1, min(6, (target_bytes + weight_bytes - 1) // weight_bytes)) op_list = [] for i in range(outside_loop_count): op_list.append(create_gemm()) diff --git a/python/aisimulate/tests/unit/collector/test_vllm_gemm_graph_timing.py b/python/aisimulate/tests/unit/collector/test_vllm_gemm_graph_timing.py index 0dade2506..925675cfc 100644 --- a/python/aisimulate/tests/unit/collector/test_vllm_gemm_graph_timing.py +++ b/python/aisimulate/tests/unit/collector/test_vllm_gemm_graph_timing.py @@ -3,10 +3,10 @@ """Run the real GEMM timing/publication block without a GPU or vLLM install. -Only layer construction is omitted: the suffix beginning at ``kernel_func`` -executes unchanged against a recording benchmark context. This checks behavior, -not a source substring, and catches both graph-off requests and publication of -an eager result even when a benchmark helper silently changes its behavior. +Layer construction and kernel selection are stubbed; the real rotation loop and +timing/publication block execute against a recording benchmark context. This +checks distinct weight reuse, per-operation normalization, graph-off requests, +and publication of an eager result even if the helper changes its behavior. """ import ast @@ -20,13 +20,32 @@ SOURCE = Path(__file__).resolve().parents[3] / "collector" / "vllm" / "collect_gemm.py" -def _run_timing_block(gemm_type, result, *, capture_error=None): +def _run_timing_block( + gemm_type, + result, + *, + capture_error=None, + shape=(1, 768, 7168), + l2_cache_bytes=60 << 20, +): tree = ast.parse(SOURCE.read_text(), filename=str(SOURCE)) run = next(n for n in tree.body if isinstance(n, ast.FunctionDef) and n.name == "run_gemm") start = next(i for i, n in enumerate(run.body) if isinstance(n, ast.FunctionDef) and n.name == "kernel_func") - calls = {"benchmark": [], "forward": [], "published": []} + rotation_start = next( + i + for i, n in enumerate(run.body) + if isinstance(n, ast.Assign) + and any(isinstance(target, ast.Name) and target.id == "outside_loop_count" for target in n.targets) + ) + rotation_end = next(i + 1 for i in range(rotation_start, start) if isinstance(run.body[i], ast.For)) + calls = {"benchmark": [], "weights": [], "forward": [], "published": []} x = object() + def create_gemm(): + weight = object() + calls["weights"].append(weight) + return SimpleNamespace(forward=lambda arg: calls["forward"].append((weight, arg))) + @contextmanager def benchmark_with_power(**kwargs): calls["benchmark"].append(kwargs) @@ -36,33 +55,42 @@ def benchmark_with_power(**kwargs): if capture_error is not None: raise capture_error kwargs["kernel_func"]() + kwargs["kernel_func"]() yield result namespace = { - "torch": SimpleNamespace(device=lambda d: d, cuda=SimpleNamespace(get_device_name=lambda d: "test GPU")), + "torch": SimpleNamespace( + device=lambda d: d, + cuda=SimpleNamespace( + get_device_name=lambda d: "test GPU", + get_device_properties=lambda d: SimpleNamespace(L2_cache_size=l2_cache_bytes), + ), + ), "benchmark_with_power": benchmark_with_power, "log_perf": lambda **kwargs: calls["published"].append(kwargs), "device": "cuda:0", "gemm_type": gemm_type, - "m": 1, - "n": 768, - "k": 7168, + "m": shape[0], + "n": shape[1], + "k": shape[2], "x": x, - "op_list": [SimpleNamespace(forward=lambda arg: calls["forward"].append(arg))], - "outside_loop_count": 1, + "create_gemm": create_gemm, "vllm_version": "test-version", "kernel_source": "test-selected-kernel", "perf_filename": "test-perf-file", } - code = compile(ast.Module(body=run.body[start:], type_ignores=[]), str(SOURCE), "exec") + body = run.body[rotation_start:rotation_end] + run.body[start:] + code = compile(ast.Module(body=body, type_ignores=[]), str(SOURCE), "exec") return lambda: exec(code, namespace), calls, x -@pytest.mark.parametrize("gemm_type", ["bfloat16", "fp8", "fp8_block", "nvfp4"]) -def test_gemm_publishes_only_graph_timing(gemm_type): +@pytest.mark.parametrize("gemm_type,copies", [("bfloat16", 6), ("fp8", 6), ("fp8_block", 6), ("nvfp4", 1)]) +def test_gemm_rotates_weights_and_publishes_per_operation_graph_timing(gemm_type, copies): run, calls, x = _run_timing_block(gemm_type, {"used_cuda_graph": True, "latency_ms": 0.012, "power_stats": None}) run() - assert calls["forward"] == [x] + assert len(calls["weights"]) == copies + assert len({id(weight) for weight in calls["weights"]}) == copies + assert calls["forward"] == [(weight, x) for weight in calls["weights"]] * 2 assert len(calls["benchmark"]) == 1 benchmark = calls["benchmark"][0] assert (benchmark["num_warmups"], benchmark["num_runs"], benchmark["repeat_n"]) == (3, 6, 1) @@ -70,7 +98,55 @@ def test_gemm_publishes_only_graph_timing(gemm_type): published = calls["published"][0] assert published["kernel_source"] == "test-selected-kernel" assert published["item_list"][0]["gemm_dtype"] == gemm_type - assert published["item_list"][0]["latency"] == 0.012 + assert published["item_list"][0]["latency"] == pytest.approx(0.012 / copies) + + +@pytest.mark.parametrize( + "shape,l2_cache_bytes,copies", + [ + ((1, 1024, 1024), 1 << 20, 2), + ((1, 11264, 1024), 10 << 20, 2), # A matrix 10% larger than L2 still rotates. + ((1, 20480, 1024), 10 << 20, 1), # Exactly twice L2. + ((1, 20480, 1024), (10 << 20) + 1, 2), + ((1, 20480, 1024), (10 << 20) - 1, 1), + ((1, 1024, 1024), 60 << 20, 6), # Bound graph size for tiny weights. + ], +) +def test_fp8_block_rotation_targets_twice_l2_with_a_six_copy_cap(shape, l2_cache_bytes, copies): + run, calls, x = _run_timing_block( + "fp8_block", + {"used_cuda_graph": True, "latency_ms": 0.012, "power_stats": None}, + shape=shape, + l2_cache_bytes=l2_cache_bytes, + ) + run() + assert len(calls["weights"]) == copies + assert calls["forward"] == [(weight, x) for weight in calls["weights"]] * 2 + assert calls["published"][0]["item_list"][0]["latency"] == pytest.approx(0.012 / copies) + + +def test_fp8_block_large_weights_are_allocated_only_once(): + # Six 1 GiB weights plus vLLM's FP32 preparation buffers can OOM even + # though the original single-copy case fits. The matrix already exceeds twice L2. + run, calls, _ = _run_timing_block( + "fp8_block", + {"used_cuda_graph": True, "latency_ms": 0.012, "power_stats": None}, + shape=(1, 16384, 65536), + ) + run() + assert len(calls["weights"]) == 1 + assert calls["published"][0]["item_list"][0]["latency"] == pytest.approx(0.012) + + +@pytest.mark.parametrize("gemm_type,copies", [("bfloat16", 6), ("fp8", 6), ("nvfp4", 1)]) +def test_l2_limit_preserves_other_precision_rotation(gemm_type, copies): + run, calls, _ = _run_timing_block( + gemm_type, + {"used_cuda_graph": True, "latency_ms": 0.012, "power_stats": None}, + l2_cache_bytes=1, + ) + run() + assert len(calls["weights"]) == copies @pytest.mark.parametrize("graph_flag", [False, None, 0, 1, "false", "true", "missing"])

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