diff --git a/reproduction/closure/evidence/COUPLED_REENCODE_GPU_RECOVERY_20260813.md b/reproduction/closure/evidence/COUPLED_REENCODE_GPU_RECOVERY_20260813.md new file mode 100644 index 0000000000000000000000000000000000000000..21ed31070c56b1f4c5baa560183fe15fa1775616 --- /dev/null +++ b/reproduction/closure/evidence/COUPLED_REENCODE_GPU_RECOVERY_20260813.md @@ -0,0 +1,31 @@ +# Coupled re-encode GPU recovery state — 2026-08-13 + +## Preserved campaign state + +- Active wave: `wave-019-022`, exact K3/K4 triplet scoring. +- Last advancing receipt count: `579/1024` (`L19=152`, `L20=146`, `L21=155`, `L22=126`). +- All score artifacts are per-expert atomic outputs. Restarting the campaign skips validated existing receipts and recomputes only missing experts. +- Layers 3 through 18 remain fully materialized and runtime-oracle sealed. + +## Driver failure evidence + +- At 2026-08-13 23:52:53 EDT the kernel reported an NVIDIA GSP task watchdog timeout. +- Subsequent crash reports identify GPU 3 and repeat the same GSP program counter (`0x1bc70d8`). +- `nvidia-smi`, all 32 scorer CUDA processes, and `cosmic-comp` became blocked in NVIDIA kernel-driver wait paths. +- Receipt counts stopped advancing after 579, proving this is a driver-wide stall rather than a slow expert. +- GPU 3 was 78–81 C before the crash and reported no active hardware thermal slowdown. + +## In-place recovery attempted + +- PCI function reset for GPU 3 (`0000:c1:00.0`) returned success once, but did not unwind the NVIDIA global semaphore. +- Scorer processes and the compositor were sent termination signals; uninterruptible driver tasks could not exit. +- NVIDIA driver unbind and a second PCI reset both blocked and were force-terminated. +- A host reboot is therefore required to reload the NVIDIA kernel/GSP state. + +## Automatic resume preparation + +- Persistent unit source: `/home/brandonmusic/KLC_SANDBOXES/glm52_fresh_sqg_3p0625/systemd/glm52-full-coupled-k96tail-resume.service` +- Installed unit: `/etc/systemd/system/glm52-full-coupled-k96tail-resume.service` +- Unit state: enabled for `multi-user.target`. +- The unit waits for the NVMe automount, Docker, and all four NVIDIA GPUs before launching the canonical campaign with `START_WAVE=3` and `STOP_WAVE=75`. +- The campaign's seals and atomic receipts make this a resume, not a restart from scratch. diff --git a/reproduction/closure/evidence/GPU_GSP_RECOVERY_20260813.md b/reproduction/closure/evidence/GPU_GSP_RECOVERY_20260813.md new file mode 100644 index 0000000000000000000000000000000000000000..a43abbeabbc75a48d59becaa4c88b7a65160574f --- /dev/null +++ b/reproduction/closure/evidence/GPU_GSP_RECOVERY_20260813.md @@ -0,0 +1,31 @@ +# GPU GSP recovery checkpoint — 2026-08-13 + +- Authoritative goal: `019ffa7c-7914-7633-a7b8-a2f4b2e23fc0`. +- Wave `019-022` exact K3/K4 scoring started at `23:11:04 EDT` with 32 + workers (eight contexts per physical GPU). +- Last verified atomic receipt census before the driver incident: + layer 19 = 152, layer 20 = 145, layer 21 = 155, layer 22 = 126; + total = 578 / 1024. +- At `23:51:22 EDT` the NVIDIA 610.43.02 open kernel driver reported a GSP + task-watchdog timeout. CUDA, NVML, NVIDIA modeset, and the COSMIC compositor + subsequently blocked in the shared NVIDIA RM path. This was a driver/GSP + failure, not a scorer exception. +- The campaign and its 32 containers were terminated. Completed expert JSON + and row-SSE receipts are atomic and remain valid; the launchers validate and + skip them on resume. No candidate selection or allocation had begun for this + wave. +- Per-device PCI function resets did not restore the shared RM state. A clean + host reboot is required. +- The persistent user service + `glm52-full-coupled-k96tail-no-shortcut-goal019ffa7c.service` is installed in + `~/.config/systemd/user/`, verified, enabled, and the user has linger enabled. + It runs the authoritative campaign with `START_WAVE=3`, `STOP_WAVE=75`, and + `CLEANUP_VALIDATED_WAVES=1`, so after boot it verifies/seals prior waves and + resumes wave 019-022 from the existing receipts. +- Pinned saved inputs for wave 031-034 finished staging and have a 15-shard + manifest plus four 12,897,349,632-byte capture tensors. Wave 035-038 has a + resumable partial local directory (about 6.2 GB); do not count it complete + until its normal manifest and required-file validation pass. +- Do not run any further background HF prefetch on the active score/encode + path. Wave 031-034 was successfully staged, but the timing overlap obscured + the onset of the GSP failure and does not justify further timed-path I/O. diff --git a/reproduction/closure/evidence/README.md b/reproduction/closure/evidence/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0dac2a1f444dfd73e37145c6445de7216f4afe4e --- /dev/null +++ b/reproduction/closure/evidence/README.md @@ -0,0 +1,39 @@ +# Compact execution evidence + +This directory records the measured state at `2026-08-14T00:16:45-04:00`. +It is not a live status feed. + +- `execution_snapshot_2026-08-14T001645-0400.json` states the layer and score + counts at that timestamp. +- `score_receipts_2026-08-14T001645-0400.sha256` binds 581 atomic expert JSON + receipts and their 581 row-SSE arrays without copying the arrays. +- `layers_003_018/` contains the compact layer manifests, quality receipts, + and native B12X runtime oracle receipts for all 16 qualified layers. +- `parity_layers_004_018/` contains the exact scorer/encoder payload parity + receipts for the 15 K96 layers. Layer 3 uses the separate preserved K48 + contract and is not represented as a K96 parity result. +- `layer_019_runtime_binding.json` is a durable example of the frozen SQG, + calibration dataset, coupled transform, QSRT, and source-domain bindings. +- The two recovery records preserve the NVIDIA GSP failure diagnosis and the + post-reboot single-writer recovery decision. + +Large `.safetensors`, `.npz`, capture, model, log, and cache payloads are not +copied. Their compact receipts and hashes are the publication boundary. + +`snapshot_2026-08-15T002639-0400/` is the later historical distributed-campaign +snapshot. +It extends compact sealed evidence through layer 46 and binds the complete +atomic score set through layer 50. Unlike the earlier checkpoint, it includes +a frozen campaign log while continuing to omit model tensors and row-SSE +payload bytes. It predates final assembly and end-to-end KLD. + +`final-mechanical/` is the completed mechanical closure. It binds 75 layer +manifests, 75 quality receipts, 75 passing B12X runtime oracles, and 74 exact +K96 scorer/encoder parity receipts. Layer 3 is explicitly bound as the sealed +K48 exception without a K96 parity receipt. The directory also contains the +assembly manifest and authoritative model codec receipt. + +`final-exact-ii-r11/` contains the completed full-vocabulary KLD, hidden replay, +and exact Infernal Invocation r11 TP4/DCP4/MTP3 qualification evidence. The +candidate mean KLD is `0.1401771516114036`, versus the frozen source mean +`0.07583317451217256`, so model quality remains research-only. diff --git a/reproduction/closure/evidence/execution_snapshot_2026-08-14T001645-0400.json b/reproduction/closure/evidence/execution_snapshot_2026-08-14T001645-0400.json new file mode 100644 index 0000000000000000000000000000000000000000..7adec061c1d364a2f2e7eac346561579b220a98e --- /dev/null +++ b/reproduction/closure/evidence/execution_snapshot_2026-08-14T001645-0400.json @@ -0,0 +1,76 @@ +{ + "as_of": "2026-08-14T00:16:45-04:00", + "candidate_final_kld_supported": false, + "complete": true, + "fully_qualified_routed_layers": [ + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18 + ], + "full_model_supported": false, + "layer_19_through_22_recipes_complete": true, + "layer_score_receipts": { + "19": 153, + "20": 146, + "21": 156, + "22": 126 + }, + "parity_layers": [ + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18 + ], + "remaining_layer_19_through_22_scores": 443, + "release_supported": false, + "runtime_oracle_layers": [ + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18 + ], + "schema": "glm52-coupled-k96tail-publication-snapshot-v1", + "score_receipt_hash_manifest": { + "expert_json_files": 581, + "row_sse_files": 581, + "sha256": "16a7b5871c996ac4e387d448d7baf335bedfb91224ee7f16525d6ff52f9a1e5f" + }, + "total_layer_19_through_22_scores": 1024, + "written_layer_19_through_22_scores": 581 +} diff --git a/reproduction/closure/evidence/final-exact-ii-r11/full-kld/kld_exact_ii_r11_tp4dcp1.json b/reproduction/closure/evidence/final-exact-ii-r11/full-kld/kld_exact_ii_r11_tp4dcp1.json new file mode 100644 index 0000000000000000000000000000000000000000..e168ec141462c75f024356fed9195d0f86f923b1 --- /dev/null +++ b/reproduction/closure/evidence/final-exact-ii-r11/full-kld/kld_exact_ii_r11_tp4dcp1.json @@ -0,0 +1,2210 @@ +{ + "complete": true, + "elapsed_sec": 52.5161001200031, + "env_mirror": { + "B12X_MOE_FORCE_A16": "0", + "B12X_MOE_FORCE_A8": "0", + "CUDA_DEVICE_MAX_CONNECTIONS": "32", + "CUDA_DEVICE_ORDER": "PCI_BUS_ID", + "CUTE_DSL_ARCH": "sm_120a", + "GLOO_SOCKET_IFNAME": "lo", + "NCCL_IB_DISABLE": "1", + "NCCL_P2P_LEVEL": "SYS", + "NCCL_PROTO": "LL,LL128,Simple", + "NCCL_SOCKET_IFNAME": "lo", + "OMP_NUM_THREADS": "16", + "PYTORCH_CUDA_ALLOC_CONF": "expandable_segments:True", + "SAFETENSORS_FAST_GPU": "1", + "TMPDIR": "/container-tmp", + "TORCH_CUDA_ARCH_LIST": "12.0a", + "VLLM_B12X_ABSORB_BMM": "0", + "VLLM_ENABLE_PCIE_ALLREDUCE": "1", + "VLLM_PCIE_ALLREDUCE_BACKEND": "cpp", + "VLLM_USE_AOT_COMPILE": "1", + "VLLM_USE_B12X_FP8_GEMM": "1", + "VLLM_USE_B12X_MOE": "1", + "VLLM_USE_B12X_SPARSE_INDEXER": "1", + "VLLM_USE_BREAKABLE_CUDAGRAPH": "0", + "VLLM_USE_FLASHINFER_SAMPLER": "1", + "VLLM_USE_MEGA_AOT_ARTIFACT": "1", + "VLLM_USE_V2_MODEL_RUNNER": "1", + "VLLM_WORKER_MULTIPROC_METHOD": "spawn", + "XDG_CACHE_HOME": "/cache" + }, + "gpus": [ + { + "compute_capability": [ + 12, + 0 + ], + "index": 0, + "name": "NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition" + }, + { + "compute_capability": [ + 12, + 0 + ], + "index": 1, + "name": "NVIDIA RTX PRO 6000 Blackwell Workstation Edition" + }, + { + "compute_capability": [ + 12, + 0 + ], + "index": 2, + "name": "NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition" + }, + { + "compute_capability": [ + 12, + 0 + ], + "index": 3, + "name": "NVIDIA RTX PRO 6000 Blackwell Workstation Edition" + } + ], + "image_id": "sha256:79dfbf6e697a1081016a3256ff5c96b1c2d6dcddd6f04662c9c693881310fa87", + "image_ref": "verdictai/glm52-k96-ii-r11:20260815-tpfix", + "kernel_provenance": { + "schedule": { + "blocks_per_cta": 8, + "kernel": "m128n64", + "route_block_rows": 128, + "stages": 2, + "tile_n": 64 + }, + "sha256": { + "exl3.py": "55b063bf1e72b07e878568b1897e8ea470527c37e7546f84435cc56fa12da4e7", + "glm_sqg_w4a8.py": 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+ 374, + 458, + 6364, + 4531, + 1154 + ], + "windows": [ + { + "dtype": "torch.float32", + "path": "/root/kld/glm52_current_bf16_returnlogits_ref_20260708T000424Z/ref/logits_0.safetensors", + "shape": [ + 2047, + 154880 + ], + "window_idx": 0 + } + ] + } + }, + "schema": "glm52-hidden-replay-one-context-kld-v2", + "scope": "One fixed 2,048-token compatibility context with 2,047 scored next-token positions; not the Kimi K3 1,024-context qualification suite.", + "token_sequence_sha256": "d0be87a4909ad00c311e36fc5c81c7d6942216c16a8fbbbf77778edb273346f1", + "top1_agreement": 0.9174401563263312, + "total_positions": 2047 +} diff --git a/reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/lm-head-manifest.json b/reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/lm-head-manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a01a346ed072aeee4cfe903cab10a0941b5b1178 --- /dev/null +++ b/reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/lm-head-manifest.json @@ -0,0 +1,24 @@ +{ + "candidate_checkpoint": "/model", + "candidate_index_sha256": "98d8c371ba5ec8aaf2e326b228bcd36a5df4cad600085a8f4dd2e098f2474e9f", + "candidate_shard": "direct-bf16-00001-of-00282.safetensors", + "candidate_shard_sha256": "32448fb3c149fb8024efd99c3d0e0df7f33ae09f0f3173234f32fcbf4b6f2316", + "checkpoint_tensor_key": "lm_head.weight", + "complete": true, + "dtype": "BF16", + "file": "weight.safetensors", + "file_sha256": "f56e1020446f4eaf73b418dd62ef853eb51e4f5c502d09ec0301086a90557969", + "key": "weight", + "raw_tensor_sha256": "a012be05e7716292407d418b408222de256d4dbe2fe2143a44d27d8e3553bfba", + "schema": "glm52-canonical-bf16-lm-head-v1", + "shape": [ + 154880, + 6144 + ], + "size_bytes": 1903165672, + "source_candidate_byte_identity": true, + "source_checkpoint": "/source", + "source_index_sha256": "62182715e5ef3eb0069b38ff710a38e7a118154278768a8c7ab8efed60c501f5", + "source_shard": "direct-bf16-00001-of-00282.safetensors", + "source_shard_sha256": "32448fb3c149fb8024efd99c3d0e0df7f33ae09f0f3173234f32fcbf4b6f2316" +} diff --git a/reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/preregistered-gate-history/compare_glm52_hidden_replay.preregistered.py b/reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/preregistered-gate-history/compare_glm52_hidden_replay.preregistered.py new file mode 100644 index 0000000000000000000000000000000000000000..ccd34bed57ae27dca2ef067edec72238e33327b4 --- /dev/null +++ b/reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/preregistered-gate-history/compare_glm52_hidden_replay.preregistered.py @@ -0,0 +1,388 @@ +#!/usr/bin/env python3 +"""Replay GLM-5.2 hidden states through its BF16 head and compare distributions.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +from typing import Any + +os.environ.setdefault("CUBLAS_WORKSPACE_CONFIG", ":4096:8") + +import numpy as np +import torch +import torch.nn.functional as F +from safetensors import safe_open + + +def sha256_file(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + while block := handle.read(16 * 1024 * 1024): + digest.update(block) + return digest.hexdigest() + + +def statistics(values: np.ndarray) -> dict[str, float]: + ordered = np.sort(values.astype(np.float64, copy=False)) + worst = max(1, math.ceil(0.01 * len(ordered))) + return { + "mean": float(ordered.mean()), + "median": float(np.median(ordered)), + "p95": float(np.quantile(ordered, 0.95)), + "p99": float(np.quantile(ordered, 0.99)), + "p99_9": float(np.quantile(ordered, 0.999)), + "cvar_worst_1pct": float(ordered[-worst:].mean()), + "max": float(ordered[-1]), + } + + +def atomic_json(path: Path, payload: dict[str, Any]) -> None: + temporary = path.with_suffix(path.suffix + ".tmp") + temporary.parent.mkdir(parents=True, exist_ok=True) + temporary.write_text( + json.dumps(payload, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + temporary.replace(path) + + +def pass_one( + hidden: torch.Tensor, + reference: torch.Tensor, + weight: torch.Tensor, + *, + vocab_size: int, + vocab_chunk: int, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + rows = hidden.shape[0] + device = hidden.device + reference_log_z = torch.full((rows,), -torch.inf, device=device) + candidate_log_z = torch.full_like(reference_log_z, -torch.inf) + reference_top_values = torch.full_like(reference_log_z, -torch.inf) + candidate_top_values = torch.full_like(reference_log_z, -torch.inf) + reference_top_ids = torch.zeros(rows, dtype=torch.int64, device=device) + candidate_top_ids = torch.zeros_like(reference_top_ids) + for vocab_start in range(0, vocab_size, vocab_chunk): + vocab_end = min(vocab_start + vocab_chunk, vocab_size) + reference_logits = reference[:, vocab_start:vocab_end].to( + device, non_blocking=True + ) + candidate_logits = F.linear(hidden, weight[vocab_start:vocab_end]) + reference_logits = reference_logits.float() + candidate_logits = candidate_logits.float() + reference_log_z = torch.logaddexp( + reference_log_z, torch.logsumexp(reference_logits, dim=-1) + ) + candidate_log_z = torch.logaddexp( + candidate_log_z, torch.logsumexp(candidate_logits, dim=-1) + ) + reference_values, reference_ids = reference_logits.max(dim=-1) + candidate_values, candidate_ids = candidate_logits.max(dim=-1) + reference_update = reference_values > reference_top_values + candidate_update = candidate_values > candidate_top_values + reference_top_values = torch.where( + reference_update, reference_values, reference_top_values + ) + candidate_top_values = torch.where( + candidate_update, candidate_values, candidate_top_values + ) + reference_top_ids = torch.where( + reference_update, reference_ids + vocab_start, reference_top_ids + ) + candidate_top_ids = torch.where( + candidate_update, candidate_ids + vocab_start, candidate_top_ids + ) + return ( + reference_log_z, + candidate_log_z, + reference_top_ids, + candidate_top_ids, + ) + + +def compare_block( + hidden: torch.Tensor, + reference: torch.Tensor, + weight: torch.Tensor, + *, + vocab_size: int, + vocab_chunk: int, +) -> tuple[np.ndarray, np.ndarray, int, float, float]: + ( + reference_log_z, + candidate_log_z, + reference_top_ids, + candidate_top_ids, + ) = pass_one( + hidden, + reference, + weight, + vocab_size=vocab_size, + vocab_chunk=vocab_chunk, + ) + rows = hidden.shape[0] + kl = torch.zeros(rows, dtype=torch.float64, device=hidden.device) + js = torch.zeros_like(kl) + absolute_error_sum = 0.0 + absolute_error_max = 0.0 + for vocab_start in range(0, vocab_size, vocab_chunk): + vocab_end = min(vocab_start + vocab_chunk, vocab_size) + reference_logits = reference[:, vocab_start:vocab_end].to( + hidden.device, non_blocking=True + ).float() + candidate_logits = F.linear( + hidden, weight[vocab_start:vocab_end] + ).float() + reference_log_p = reference_logits - reference_log_z[:, None] + candidate_log_p = candidate_logits - candidate_log_z[:, None] + reference_p = reference_log_p.exp() + candidate_p = candidate_log_p.exp() + log_mid = torch.logaddexp(reference_log_p, candidate_log_p) - math.log(2.0) + kl += (reference_p * (reference_log_p - candidate_log_p)).sum( + dim=-1, dtype=torch.float64 + ) + js += 0.5 * ( + (reference_p * (reference_log_p - log_mid)).sum( + dim=-1, dtype=torch.float64 + ) + + (candidate_p * (candidate_log_p - log_mid)).sum( + dim=-1, dtype=torch.float64 + ) + ) + difference = (reference_logits - candidate_logits).abs() + absolute_error_sum += float(difference.sum(dtype=torch.float64).item()) + absolute_error_max = max(absolute_error_max, float(difference.max().item())) + kl.clamp_min_(0) + js.clamp_min_(0) + top1_matches = int((reference_top_ids == candidate_top_ids).sum().item()) + return ( + kl.cpu().numpy(), + js.cpu().numpy(), + top1_matches, + absolute_error_sum / (rows * vocab_size), + absolute_error_max, + ) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--reference-dir", type=Path, required=True) + parser.add_argument("--candidate-hidden-dir", type=Path, required=True) + parser.add_argument("--lm-head-dir", type=Path, required=True) + parser.add_argument("--baseline-full-kld", type=Path, required=True) + parser.add_argument("--capture-full-kld", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--device", default="cuda:0") + parser.add_argument("--hidden-width", type=int, default=6_144) + parser.add_argument("--vocab-size", type=int, default=154_880) + parser.add_argument("--vocab-chunk", type=int, default=9_680) + parser.add_argument("--position-block", type=int, default=32) + parser.add_argument("--max-baseline-mean-delta", type=float, default=5e-5) + parser.add_argument("--max-baseline-position-delta", type=float, default=5e-4) + args = parser.parse_args() + + if args.vocab_size % args.vocab_chunk != 0: + parser.error("--vocab-chunk must divide --vocab-size") + if args.position_block <= 0: + parser.error("--position-block must be positive") + + torch.backends.cuda.matmul.allow_tf32 = False + torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False + torch.use_deterministic_algorithms(True) + device = torch.device(args.device) + + hidden_manifest_path = args.candidate_hidden_dir / "manifest.json" + hidden_manifest = json.loads(hidden_manifest_path.read_text(encoding="utf-8")) + hidden_path = args.candidate_hidden_dir / hidden_manifest["file"] + with safe_open(hidden_path, framework="pt", device="cpu") as handle: + hidden_cpu = handle.get_tensor("hidden_states") + if hidden_cpu.dtype != torch.bfloat16 or list(hidden_cpu.shape) != [ + 2_048, + args.hidden_width, + ]: + raise RuntimeError("Candidate hidden capture must be BF16 [2048, 6144]") + + head_manifest_path = args.lm_head_dir / "manifest.json" + head_manifest = json.loads(head_manifest_path.read_text(encoding="utf-8")) + head_path = args.lm_head_dir / head_manifest["file"] + if sha256_file(head_path) != head_manifest.get("file_sha256"): + raise RuntimeError("LM-head file hash differs from its manifest") + with safe_open(head_path, framework="pt", device="cpu") as handle: + weight_cpu = handle.get_tensor("weight") + if weight_cpu.dtype != torch.bfloat16 or list(weight_cpu.shape) != [ + args.vocab_size, + args.hidden_width, + ]: + raise RuntimeError("LM head must be BF16 [154880, 6144]") + weight = weight_cpu.to(device) + + reference_manifest_path = args.reference_dir / "manifest.json" + reference_manifest = json.loads( + reference_manifest_path.read_text(encoding="utf-8") + ) + reference_path = args.reference_dir / "logits_0.safetensors" + with safe_open(reference_path, framework="pt", device="cpu") as handle: + reference = handle.get_tensor("logits") + scored_rows = 2_047 + if reference.dtype != torch.float32 or list(reference.shape) != [ + scored_rows, + args.vocab_size, + ]: + raise RuntimeError("Reference logits must be FP32 [2047, 154880]") + + baseline = json.loads(args.baseline_full_kld.read_text(encoding="utf-8")) + capture = json.loads(args.capture_full_kld.read_text(encoding="utf-8")) + token_hash = hidden_manifest.get("token_sequence_sha256") + for role, receipt in (("baseline", baseline), ("capture", capture)): + if ( + receipt.get("complete") is not True + or int(receipt.get("total_positions", -1)) != scored_rows + or receipt.get("token_sequence_sha256") != token_hash + ): + raise RuntimeError(f"{role} KLD receipt uses a different corpus") + + all_kl: list[np.ndarray] = [] + all_js: list[np.ndarray] = [] + total_top1 = 0 + absolute_error_sum = 0.0 + absolute_error_max = 0.0 + for row_start in range(0, scored_rows, args.position_block): + row_end = min(row_start + args.position_block, scored_rows) + hidden = hidden_cpu[row_start:row_end].to(device, non_blocking=True) + kl, js, top1, mean_error, max_error = compare_block( + hidden, + reference[row_start:row_end], + weight, + vocab_size=args.vocab_size, + vocab_chunk=args.vocab_chunk, + ) + all_kl.append(kl) + all_js.append(js) + total_top1 += top1 + absolute_error_sum += mean_error * ( + (row_end - row_start) * args.vocab_size + ) + absolute_error_max = max(absolute_error_max, max_error) + print( + json.dumps( + { + "row_end": row_end, + "row_start": row_start, + "running_mean_kld": float(np.concatenate(all_kl).mean()), + } + ), + flush=True, + ) + + position_kld = np.concatenate(all_kl) + position_js = np.concatenate(all_js) + baseline_positions = np.asarray(baseline["position_kld"], dtype=np.float64) + capture_positions = np.asarray(capture["position_kld"], dtype=np.float64) + if baseline_positions.shape != position_kld.shape or capture_positions.shape != ( + position_kld.shape + ): + raise RuntimeError("Full-logit and replay receipts have different row counts") + + baseline_mean_delta = abs(float(position_kld.mean()) - float(baseline["mean_kld"])) + baseline_position_delta = float(np.max(np.abs(position_kld - baseline_positions))) + repeated_full_kld_mean_delta = abs( + float(capture["mean_kld"]) - float(baseline["mean_kld"]) + ) + repeated_full_kld_position_delta = float( + np.max(np.abs(capture_positions - baseline_positions)) + ) + qualification_pass = ( + np.isfinite(position_kld).all() + and np.isfinite(position_js).all() + and baseline_mean_delta <= args.max_baseline_mean_delta + and baseline_position_delta <= args.max_baseline_position_delta + ) + + result = { + "schema": "glm52-hidden-replay-one-context-kld-v1", + "complete": True, + "qualification_pass": bool(qualification_pass), + "scope": ( + "One fixed 2,048-token compatibility context with 2,047 scored " + "next-token positions; not the Kimi K3 1,024-context qualification suite." + ), + "direction": "KL(reference_bf16 || candidate_k96)", + "geometry": { + "raw_hidden_shape": [2_048, args.hidden_width], + "scored_hidden_shape": [scored_rows, args.hidden_width], + "reference_logits_shape": [scored_rows, args.vocab_size], + }, + "token_sequence_sha256": token_hash, + "reference": { + "logits_file_sha256": sha256_file(reference_path), + "manifest_sha256": sha256_file(reference_manifest_path), + "recorded_manifest": reference_manifest, + }, + "candidate_hidden": { + "file_sha256": sha256_file(hidden_path), + "manifest_sha256": sha256_file(hidden_manifest_path), + }, + "lm_head": { + "file_sha256": sha256_file(head_path), + "manifest_sha256": sha256_file(head_manifest_path), + "raw_tensor_sha256": head_manifest.get("raw_tensor_sha256"), + "source_candidate_byte_identity": head_manifest.get( + "source_candidate_byte_identity" + ), + }, + "comparator": { + "bf16_reduced_precision_reduction": False, + "cublas_workspace_config": os.environ["CUBLAS_WORKSPACE_CONFIG"], + "deterministic_algorithms": True, + "device": str(device), + "position_block": args.position_block, + "tf32": False, + "two_pass_full_vocabulary": True, + "vocab_chunk": args.vocab_chunk, + }, + "kl_reference_to_candidate": statistics(position_kld), + "js": statistics(position_js), + "top1_agreement": total_top1 / scored_rows, + "logit_absolute_error_mean": absolute_error_sum + / (scored_rows * args.vocab_size), + "logit_absolute_error_max": absolute_error_max, + "crosscheck": { + "baseline_full_kld_mean": float(baseline["mean_kld"]), + "capture_run_full_kld_mean": float(capture["mean_kld"]), + "hidden_replay_mean": float(position_kld.mean()), + "hidden_vs_baseline_mean_abs_delta": baseline_mean_delta, + "hidden_vs_baseline_position_max_abs_delta": baseline_position_delta, + "mean_delta_limit": args.max_baseline_mean_delta, + "position_delta_limit": args.max_baseline_position_delta, + "repeated_full_kld_mean_abs_delta": repeated_full_kld_mean_delta, + "repeated_full_kld_position_max_abs_delta": ( + repeated_full_kld_position_delta + ), + }, + "total_positions": scored_rows, + "position_kld": position_kld.tolist(), + "position_js": position_js.tolist(), + } + atomic_json(args.output, result) + print( + json.dumps( + { + "mean_kld": result["kl_reference_to_candidate"]["mean"], + "top1_agreement": result["top1_agreement"], + "qualification_pass": result["qualification_pass"], + "hidden_vs_baseline_mean_abs_delta": baseline_mean_delta, + }, + sort_keys=True, + ) + ) + if not qualification_pass: + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/preregistered-gate-history/hidden-replay-kld.preregistered.json b/reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/preregistered-gate-history/hidden-replay-kld.preregistered.json new file mode 100644 index 0000000000000000000000000000000000000000..3e13e3483a03bd4776d75beafd93ccfbb322a315 --- /dev/null +++ b/reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/preregistered-gate-history/hidden-replay-kld.preregistered.json @@ -0,0 +1,4213 @@ +{ + "candidate_hidden": { + "file_sha256": "50781f1192645e13899d11f2181717ea05e028688db0201ede6d7e88360ee874", + "manifest_sha256": "12412b014f730a89890f7b9416c6498976139d960498d4cdf60058df2f639cc1" + }, + "comparator": { + "bf16_reduced_precision_reduction": false, + "cublas_workspace_config": ":4096:8", + "deterministic_algorithms": true, + "device": "cuda:0", + "position_block": 32, + "tf32": false, + "two_pass_full_vocabulary": true, + "vocab_chunk": 9680 + }, + "complete": true, + "crosscheck": { + "baseline_full_kld_mean": 0.1401771516114036, + "capture_run_full_kld_mean": 0.1401771516114036, + "hidden_replay_mean": 0.14017626494580232, + "hidden_vs_baseline_mean_abs_delta": 8.866656012740393e-07, + "hidden_vs_baseline_position_max_abs_delta": 0.0035557552471569, + "mean_delta_limit": 5e-05, + "position_delta_limit": 0.0005, + 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"/root/kld/glm52_current_bf16_returnlogits_ref_20260708T000424Z/ref/logits_0.safetensors", + "shape": [ + 2047, + 154880 + ], + "window_idx": 0 + } + ] + } + }, + "schema": "glm52-hidden-replay-one-context-kld-v1", + "scope": "One fixed 2,048-token compatibility context with 2,047 scored next-token positions; not the Kimi K3 1,024-context qualification suite.", + "token_sequence_sha256": "d0be87a4909ad00c311e36fc5c81c7d6942216c16a8fbbbf77778edb273346f1", + "top1_agreement": 0.9174401563263312, + "total_positions": 2047 +} diff --git a/reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/preregistered-gate-history/original-service.log b/reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/preregistered-gate-history/original-service.log new file mode 100644 index 0000000000000000000000000000000000000000..9c2ef7857fc032da65e6f995975b551cbfded980 --- /dev/null +++ b/reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/preregistered-gate-history/original-service.log @@ -0,0 +1,753 @@ +Aug 15 03:58:44 pop-os systemd[1796]: Started glm52-k96tail-exact-r11-hidden-replay.service - Exact II r11 K96 pre-LM-head hidden replay KLD. +Aug 15 03:58:44 pop-os bash[2063897]: GLM hidden replay: 2026-08-15T03:58:44-04:00 building capture derivative from sha256:79dfbf6e697a1081016a3256ff5c96b1c2d6dcddd6f04662c9c693881310fa87 +Aug 15 03:58:44 pop-os bash[2064113]: #0 building with "default" instance using docker driver +Aug 15 03:58:44 pop-os bash[2064113]: #1 [internal] load build definition from Dockerfile.capture +Aug 15 03:58:44 pop-os bash[2064113]: #1 DONE 0.0s +Aug 15 03:58:44 pop-os bash[2064113]: #1 [internal] load build definition from Dockerfile.capture +Aug 15 03:58:44 pop-os bash[2064113]: #1 transferring dockerfile: 1.45kB done +Aug 15 03:58:44 pop-os bash[2064113]: #1 DONE 0.0s +Aug 15 03:58:44 pop-os bash[2064113]: #2 resolve image config for docker-image://docker.io/docker/dockerfile:1.7 +Aug 15 03:58:44 pop-os bash[2064113]: #2 DONE 0.2s +Aug 15 03:58:45 pop-os bash[2064113]: #3 docker-image://docker.io/docker/dockerfile:1.7@sha256:a57df69d0ea827fb7266491f2813635de6f17269be881f696fbfdf2d83dda33e +Aug 15 03:58:45 pop-os bash[2064113]: #3 resolve docker.io/docker/dockerfile:1.7@sha256:a57df69d0ea827fb7266491f2813635de6f17269be881f696fbfdf2d83dda33e 0.0s done +Aug 15 03:58:45 pop-os bash[2064113]: #3 CACHED +Aug 15 03:58:45 pop-os bash[2064113]: #4 [internal] load metadata for docker.io/verdictai/glm52-k96-ii-r11:20260815-tpfix +Aug 15 03:58:45 pop-os bash[2064113]: #4 DONE 0.2s +Aug 15 03:58:45 pop-os bash[2064113]: #5 [internal] load .dockerignore +Aug 15 03:58:45 pop-os bash[2064113]: #5 transferring context: 2B done +Aug 15 03:58:45 pop-os bash[2064113]: #5 DONE 0.0s +Aug 15 03:58:45 pop-os bash[2064113]: #6 [internal] load build context +Aug 15 03:58:45 pop-os bash[2064113]: #6 transferring context: 10.80kB done +Aug 15 03:58:45 pop-os bash[2064113]: #6 DONE 0.0s +Aug 15 03:58:45 pop-os bash[2064113]: #7 [1/4] FROM docker.io/verdictai/glm52-k96-ii-r11:20260815-tpfix@sha256:79dfbf6e697a1081016a3256ff5c96b1c2d6dcddd6f04662c9c693881310fa87 +Aug 15 03:58:45 pop-os bash[2064113]: #7 resolve docker.io/verdictai/glm52-k96-ii-r11:20260815-tpfix@sha256:79dfbf6e697a1081016a3256ff5c96b1c2d6dcddd6f04662c9c693881310fa87 0.0s done +Aug 15 03:58:45 pop-os bash[2064113]: #7 CACHED +Aug 15 03:58:45 pop-os bash[2064113]: #8 [2/4] COPY hidden_replay/exact_r11_pre_lm_head_capture.patch /tmp/glm52-hidden-capture/exact_r11_pre_lm_head_capture.patch +Aug 15 03:58:45 pop-os bash[2064113]: #8 DONE 0.0s +Aug 15 03:58:45 pop-os bash[2064113]: #9 [3/4] RUN set -eu; site_packages="$(/opt/venv/bin/python -c 'import sysconfig; print(sysconfig.get_paths()["purelib"])')"; for root in /opt/infernal-invocation/vllm/vllm "${site_packages}/vllm"; do test -d "${root}"; patch --directory="${root}" --strip=1 --fuzz=0 --input=/tmp/glm52-hidden-capture/exact_r11_pre_lm_head_capture.patch; done; /opt/venv/bin/python -m py_compile /opt/infernal-invocation/vllm/vllm/model_executor/models/glm4_moe.py /opt/infernal-invocation/vllm/vllm/model_executor/models/deepseek_v2.py /opt/infernal-invocation/vllm/vllm/v1/worker/gpu_model_runner.py /opt/infernal-invocation/vllm/vllm/v1/worker/gpu/model_runner.py +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.336 patching file model_executor/models/glm4_moe.py +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.341 patching file v1/worker/gpu_model_runner.py +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.341 Hunk #2 succeeded at 15 (offset 1 line). +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.341 Hunk #3 succeeded at 4625 (offset -1 lines). +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.341 patching file model_executor/models/deepseek_v2.py +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.343 patching file v1/worker/gpu/model_runner.py +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.343 Hunk #1 succeeded at 21 (offset 4 lines). +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.343 Hunk #3 succeeded at 1746 (offset 4 lines). +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.343 Hunk #4 succeeded at 2261 (offset 1 line). +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.367 patching file model_executor/models/glm4_moe.py +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.372 patching file v1/worker/gpu_model_runner.py +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.372 Hunk #2 succeeded at 15 (offset 1 line). +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.372 Hunk #3 succeeded at 4625 (offset -1 lines). +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.372 patching file model_executor/models/deepseek_v2.py +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.374 patching file v1/worker/gpu/model_runner.py +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.374 Hunk #1 succeeded at 21 (offset 4 lines). +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.374 Hunk #3 succeeded at 1746 (offset 4 lines). +Aug 15 03:58:45 pop-os bash[2064113]: #9 0.374 Hunk #4 succeeded at 2261 (offset 1 line). +Aug 15 03:58:45 pop-os bash[2064113]: #9 DONE 0.5s +Aug 15 03:58:45 pop-os bash[2064113]: #10 [4/4] COPY hidden_replay/export_glm52_lm_head.py hidden_replay/finalize_glm52_hidden_capture.py hidden_replay/compare_glm52_hidden_replay.py /opt/glm52-hidden-replay/ +Aug 15 03:58:45 pop-os bash[2064113]: #10 DONE 0.1s +Aug 15 03:58:45 pop-os bash[2064113]: #11 exporting to image +Aug 15 03:58:45 pop-os bash[2064113]: #11 exporting layers 0.1s done +Aug 15 03:58:46 pop-os bash[2064113]: #11 exporting manifest sha256:328134d8abfe16c8281002bb853ef9bf7563f4ea828015b306fc54dbe5518ad2 0.0s done +Aug 15 03:58:46 pop-os bash[2064113]: #11 exporting config sha256:5ab79ae613d53747c8d7ff640ed3d14de2a6f39ef2e3e90d0e973a30dec8e560 0.0s done +Aug 15 03:58:46 pop-os bash[2064113]: #11 exporting attestation manifest sha256:7da45df04fd7860f34c3a34a855f4a96fe1bb872fd06dc98f0c52a256cd59f56 0.0s done +Aug 15 03:58:46 pop-os bash[2064113]: #11 exporting manifest list sha256:b4495e481015c76a78d7001cba5c3b45bf251614507b8be80ed96bf5ed80dc12 0.0s done +Aug 15 03:58:46 pop-os bash[2064113]: #11 naming to docker.io/verdictai/glm52-k96-ii-r11:20260815-tpfix-hidden-replay done +Aug 15 03:58:46 pop-os bash[2064113]: #11 unpacking to docker.io/verdictai/glm52-k96-ii-r11:20260815-tpfix-hidden-replay 0.1s done +Aug 15 03:58:46 pop-os bash[2064113]: #11 DONE 0.3s +Aug 15 03:58:46 pop-os bash[2063897]: GLM hidden replay: 2026-08-15T03:58:46-04:00 capturing all 2,048 GLM hidden rows and repeating full-logit KLD +Aug 15 03:58:50 pop-os bash[2065048]: W0815 07:58:50.580000 1 torch/utils/_pytree.py:630] is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release. +Aug 15 03:58:50 pop-os bash[2065048]: W0815 07:58:50.597000 1 torch/utils/_pytree.py:630] is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release. +Aug 15 03:58:54 pop-os bash[2065048]: Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads. +Aug 15 03:59:01 pop-os bash[2065048]: INFO 08-15 07:59:01 [api_utils.py:273] non-default args: {'trust_remote_code': True, 'load_format': 'safetensors', 'dtype': 'bfloat16', 'kv_cache_dtype': 'fp8', 'max_model_len': 2304, 'tensor_parallel_size': 4, 'dcp_comm_backend': 'a2a', 'dcp_kv_cache_interleave_size': 64, 'block_size': 64, 'enable_prefix_caching': False, 'gpu_memory_utilization': 0.9, 'kv_cache_memory_bytes': 268435456, 'max_num_batched_tokens': 2304, 'max_num_seqs': 1, 'max_logprobs': -1, 'disable_log_stats': True, 'hf_overrides': {'use_index_cache': True, 'index_topk_pattern': 'FFFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSSFSSS'}, 'quantization': 'exl3', 'enforce_eager': True, 'disable_custom_all_reduce': True, 'num_gpu_blocks_override': 64, 'attention_backend': 'B12X_MLA_SPARSE', 'async_scheduling': False, 'model': '/model'} +Aug 15 03:59:01 pop-os bash[2065048]: WARNING 08-15 07:59:01 [envs.py:2493] Unknown vLLM environment variable detected: VLLM_KLD_HIDDEN_CAPTURE_DIR +Aug 15 03:59:01 pop-os bash[2065048]: WARNING 08-15 07:59:01 [envs.py:2493] Unknown vLLM environment variable detected: VLLM_GLM_SQG_W4A8_TOPOLOGY_ATTESTATION +Aug 15 03:59:01 pop-os bash[2065048]: WARNING 08-15 07:59:01 [envs.py:2493] Unknown vLLM environment variable detected: VLLM_GLM_SQG_W4A8_WORKSPACE_LANES +Aug 15 03:59:17 pop-os bash[2065048]: INFO 08-15 07:59:17 [model.py:680] Resolved architecture: GlmMoeDsaForCausalLM +Aug 15 03:59:17 pop-os bash[2065048]: INFO 08-15 07:59:17 [model.py:1975] Using max model len 2304 +Aug 15 03:59:17 pop-os bash[2065048]: INFO 08-15 07:59:17 [cache.py:283] Using fp8 data type to store kv cache. It reduces the GPU memory footprint and boosts the performance. Meanwhile, it may cause accuracy drop without a proper scaling factor +Aug 15 03:59:17 pop-os bash[2065048]: INFO 08-15 07:59:17 [scheduler.py:242] Chunked prefill is enabled with max_num_batched_tokens=2304. +Aug 15 03:59:18 pop-os bash[2065048]: WARNING 08-15 07:59:18 [vllm.py:1296] Enforce eager set, disabling torch.compile and CUDAGraphs. This is equivalent to setting -cc.mode=none -cc.cudagraph_mode=none +Aug 15 03:59:18 pop-os bash[2065048]: WARNING 08-15 07:59:18 [vllm.py:1329] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +Aug 15 03:59:18 pop-os bash[2065048]: INFO 08-15 07:59:18 [kernel.py:310] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['vllm_c', 'native'], fused_add_rms_norm=['vllm_c', 'native']) +Aug 15 03:59:18 pop-os bash[2065048]: INFO 08-15 07:59:18 [vllm.py:1508] Cudagraph is disabled under eager mode +Aug 15 03:59:18 pop-os bash[2065048]: INFO 08-15 07:59:18 [compilation.py:329] Enabled custom fusions: norm_quant, act_quant +Aug 15 03:59:27 pop-os bash[2065048]: [transformers] The following generation flags are not valid and may be ignored: ['top_p']. Set `TRANSFORMERS_VERBOSITY=info` for more details. +Aug 15 03:59:31 pop-os bash[2065048]: W0815 07:59:31.561000 280 torch/utils/_pytree.py:630] is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release. +Aug 15 03:59:31 pop-os bash[2065048]: W0815 07:59:31.577000 280 torch/utils/_pytree.py:630] is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release. +Aug 15 03:59:34 pop-os bash[2065048]: (EngineCore pid=280) INFO 08-15 07:59:34 [core.py:121] Initializing a V1 LLM engine (v0.26.1rc0+infernal.invocation.cu133.r11.vllm908522a.b12x5d648d9) with config: model='/model', speculative_config=None, tokenizer='/model', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=2304, download_dir=None, load_format=safetensors, tensor_parallel_size=4, pipeline_parallel_size=1, data_parallel_size=1, decode_context_parallel_size=1, dcp_comm_backend=ag_rs, disable_custom_all_reduce=True, quantization=exl3, quantization_config=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=fp8, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False, jit_monitor_mode='warn', jit_monitor_verbose=False), seed=0, served_model_name=/model, enable_prefix_caching=False, enable_chunked_prefill=True, pooler_config=None, compilation_config={'mode': , 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'ir_enable_torch_wrap': False, 'splitting_ops': [], 'compile_mm_encoder': False, 'cudagraph_mm_encoder': False, 'encoder_cudagraph_token_budgets': [], 'encoder_cudagraph_max_vision_items_per_batch': 0, 'encoder_cudagraph_max_frames_per_batch': None, 'compile_sizes': [], 'compile_ranges_endpoints': [2304], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': True, 'fuse_act_quant': True, 'fuse_attn_quant': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'enable_qk_norm_rope_fusion': False, 'fuse_rope_kvcache_cat_mla': False, 'fuse_act_padding': False, 'fuse_qk_norm_rope_kvcache': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': False, 'static_all_moe_layers': []}, kernel_config=KernelConfig(ir_op_priority=IrOpPriorityConfig(rms_norm=['vllm_c', 'native'], fused_add_rms_norm=['vllm_c', 'native']), enable_flashinfer_autotune=True, enable_cutedsl_warmup=True, enable_jit_warmup=True, enable_bf16x3_router_gemm=False, moe_backend='auto', linear_backend='auto') +Aug 15 03:59:34 pop-os bash[2065048]: (EngineCore pid=280) INFO 08-15 07:59:34 [multiproc_executor.py:165] DP group leader: node_rank=0, node_rank_within_dp=0, master_addr=127.0.0.1, mq_connect_ip=172.17.0.3 (local), world_size=4, local_world_size=4 +Aug 15 03:59:38 pop-os bash[2065048]: W0815 07:59:38.847000 342 torch/utils/_pytree.py:630] is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release. +Aug 15 03:59:38 pop-os bash[2065048]: W0815 07:59:38.863000 342 torch/utils/_pytree.py:630] is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release. +Aug 15 03:59:42 pop-os bash[2065048]: (Worker pid=342) INFO 08-15 07:59:42 [parallel_state.py:1907] world_size=4 rank=0 local_rank=0 distributed_init_method=file:///container-tmp/vllm_dist_ff652a62f4d944559c86b250216cb0c3 backend=nccl +Aug 15 03:59:45 pop-os bash[2065048]: W0815 07:59:45.162000 433 torch/utils/_pytree.py:630] is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release. +Aug 15 03:59:45 pop-os bash[2065048]: W0815 07:59:45.176000 433 torch/utils/_pytree.py:630] is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release. +Aug 15 03:59:49 pop-os bash[2065048]: (Worker pid=433) INFO 08-15 07:59:49 [parallel_state.py:1907] world_size=4 rank=1 local_rank=1 distributed_init_method=file:///container-tmp/vllm_dist_ff652a62f4d944559c86b250216cb0c3 backend=nccl +Aug 15 03:59:51 pop-os bash[2065048]: W0815 07:59:51.518000 680 torch/utils/_pytree.py:630] is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release. +Aug 15 03:59:51 pop-os bash[2065048]: W0815 07:59:51.534000 680 torch/utils/_pytree.py:630] is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release. +Aug 15 03:59:55 pop-os bash[2065048]: (Worker pid=680) INFO 08-15 07:59:55 [parallel_state.py:1907] world_size=4 rank=2 local_rank=2 distributed_init_method=file:///container-tmp/vllm_dist_ff652a62f4d944559c86b250216cb0c3 backend=nccl +Aug 15 03:59:57 pop-os bash[2065048]: W0815 07:59:57.927000 785 torch/utils/_pytree.py:630] is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release. +Aug 15 03:59:57 pop-os bash[2065048]: W0815 07:59:57.942000 785 torch/utils/_pytree.py:630] is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release. +Aug 15 04:00:01 pop-os bash[2065048]: (Worker pid=785) INFO 08-15 08:00:01 [parallel_state.py:1907] world_size=4 rank=3 local_rank=3 distributed_init_method=file:///container-tmp/vllm_dist_ff652a62f4d944559c86b250216cb0c3 backend=nccl +Aug 15 04:00:01 pop-os bash[2065048]: (Worker pid=785) INFO 08-15 08:00:01 [nccl.py:25] Found nccl from environment variable VLLM_NCCL_SO_PATH=/opt/local-inference/nccl/lib/libnccl.so.2.31.2 +Aug 15 04:00:01 pop-os bash[2065048]: (Worker pid=433) INFO 08-15 08:00:01 [nccl.py:25] Found nccl from environment variable VLLM_NCCL_SO_PATH=/opt/local-inference/nccl/lib/libnccl.so.2.31.2 +Aug 15 04:00:01 pop-os bash[2065048]: (Worker pid=680) INFO 08-15 08:00:01 [nccl.py:25] Found nccl from environment variable VLLM_NCCL_SO_PATH=/opt/local-inference/nccl/lib/libnccl.so.2.31.2 +Aug 15 04:00:01 pop-os bash[2065048]: (Worker pid=342) INFO 08-15 08:00:01 [nccl.py:25] Found nccl from environment variable VLLM_NCCL_SO_PATH=/opt/local-inference/nccl/lib/libnccl.so.2.31.2 +Aug 15 04:00:01 pop-os bash[2065048]: (Worker pid=342) INFO 08-15 08:00:01 [pynccl.py:113] vLLM is using nccl==2.31.2 +Aug 15 04:00:02 pop-os bash[2065048]: (Worker pid=433) WARNING 08-15 08:00:02 [symm_mem.py:101] SymmMemCommunicator: native P2P atomics are not supported between devices [0, 1, 2, 3], communicator is not available. +Aug 15 04:00:02 pop-os bash[2065048]: (Worker pid=785) WARNING 08-15 08:00:02 [symm_mem.py:101] SymmMemCommunicator: native P2P atomics are not supported between devices [0, 1, 2, 3], communicator is not available. +Aug 15 04:00:02 pop-os bash[2065048]: (Worker pid=680) WARNING 08-15 08:00:02 [symm_mem.py:101] SymmMemCommunicator: native P2P atomics are not supported between devices [0, 1, 2, 3], communicator is not available. +Aug 15 04:00:02 pop-os bash[2065048]: (Worker pid=342) WARNING 08-15 08:00:02 [symm_mem.py:101] SymmMemCommunicator: native P2P atomics are not supported between devices [0, 1, 2, 3], communicator is not available. +Aug 15 04:00:02 pop-os bash[2065048]: (Worker pid=342) INFO 08-15 08:00:02 [cuda_communicator.py:274] Using ['PYNCCL'] all-reduce backends (in dispatch order) for group 'tp:0' out of potential backends: ['NCCL_SYMM_MEM', 'QUICK_REDUCE', 'FLASHINFER', 'AITER_CUSTOM', 'CUSTOM', 'SYMM_MEM', 'PYNCCL']. +Aug 15 04:00:02 pop-os bash[2065048]: (Worker pid=680) INFO 08-15 08:00:02 [nccl.py:25] Found nccl from environment variable VLLM_NCCL_SO_PATH=/opt/local-inference/nccl/lib/libnccl.so.2.31.2 +Aug 15 04:00:02 pop-os bash[2065048]: (Worker pid=433) INFO 08-15 08:00:02 [nccl.py:25] Found nccl from environment variable VLLM_NCCL_SO_PATH=/opt/local-inference/nccl/lib/libnccl.so.2.31.2 +Aug 15 04:00:02 pop-os bash[2065048]: (Worker pid=342) INFO 08-15 08:00:02 [nccl.py:25] Found nccl from environment variable VLLM_NCCL_SO_PATH=/opt/local-inference/nccl/lib/libnccl.so.2.31.2 +Aug 15 04:00:02 pop-os bash[2065048]: (Worker pid=785) INFO 08-15 08:00:02 [nccl.py:25] Found nccl from environment variable VLLM_NCCL_SO_PATH=/opt/local-inference/nccl/lib/libnccl.so.2.31.2 +Aug 15 04:00:02 pop-os bash[2065048]: (Worker pid=342) INFO 08-15 08:00:02 [cuda_communicator.py:274] Using ['PYNCCL'] all-reduce backends (in dispatch order) for group 'ep:0' out of potential backends: ['NCCL_SYMM_MEM', 'QUICK_REDUCE', 'FLASHINFER', 'AITER_CUSTOM', 'CUSTOM', 'SYMM_MEM', 'PYNCCL']. +Aug 15 04:00:02 pop-os bash[2065048]: (Worker pid=342) INFO 08-15 08:00:02 [parallel_state.py:2313] rank 0 in world size 4 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank 0, EPLB rank N/A +Aug 15 04:00:02 pop-os bash[2065048]: (Worker pid=342) INFO 08-15 08:00:02 [gpu_worker.py:414] Using V2 Model Runner +Aug 15 04:00:03 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:03 [model_runner.py:441] Loading model from scratch... +Aug 15 04:00:03 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:03 [cuda.py:426] Using AttentionBackendEnum.B12X_MLA_SPARSE backend. +Aug 15 04:00:03 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:03 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [cuda.py:426] Using AttentionBackendEnum.B12X_MLA_SPARSE backend. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [cuda.py:426] Using AttentionBackendEnum.B12X_MLA_SPARSE backend. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [cuda.py:426] Using AttentionBackendEnum.B12X_MLA_SPARSE backend. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [mla_attention.py:739] Using fp8_ds_mla KV cache format for B12X_MLA_SPARSE backend. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) WARNING 08-15 08:00:04 [b12x_mla_sparse.py:1559] Ignoring VLLM_B12X_MLA_CKV_GATHER on unsupported topology: dcp=1 pcp=1 local_heads=16 DBO=False +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) WARNING 08-15 08:00:04 [mla_attention.py:818] Sparse MLA impl has no dense-MHA prefill path; using the top-k MQA path only. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deep_gemm.py:202] DeepGEMM PDL enabled on deep_gemm. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deep_gemm.py:120] DeepGEMM E8M0 enabled on current platform. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [cuda.py:426] Using AttentionBackendEnum.B12X_MLA_SPARSE backend. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [cuda.py:426] Using AttentionBackendEnum.B12X_MLA_SPARSE backend. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [cuda.py:426] Using AttentionBackendEnum.B12X_MLA_SPARSE backend. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [cuda.py:426] Using AttentionBackendEnum.B12X_MLA_SPARSE backend. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 3. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 4. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 5. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 7. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 8. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 9. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 11. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 12. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 13. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 15. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 16. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 17. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 19. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 20. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 21. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 23. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 24. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 25. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 27. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 28. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 29. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 31. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 32. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 33. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 35. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 36. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 37. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 39. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 40. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 41. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 43. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 44. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 45. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 47. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 48. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 49. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 51. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 52. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 53. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 55. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 56. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 57. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 59. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 60. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 61. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 63. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 64. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 65. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 67. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 68. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 69. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 71. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 72. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 73. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 75. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 76. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [deepseek_v2.py:1335] Using index_topk_pattern/index_topk_freq to skip sparse MLA indexer computation on layer 77. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:00:04 [b12x_mla_sparse.py:1417] B12X GLM MLA KV format: KV_FP8_ROPE=0 kv_gmem_stride=656 kv_cache_dtype=fp8_ds_mla +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [weight_utils.py:923] Filesystem type for checkpoints: EXT4. Checkpoint size: 287.76 GiB. Available RAM: 431.09 GiB. +Aug 15 04:00:04 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:00:04 [weight_utils.py:946] Auto-prefetch is disabled because the filesystem (EXT4) is not a recognized network FS (NFS/Lustre). If you want to force prefetching, start vLLM with --safetensors-load-strategy=prefetch. +Aug 15 04:00:04 pop-os bash[2065048]: [100B blob data] +Aug 15 04:00:04 pop-os bash[2065048]: [108B blob data] +Aug 15 04:00:05 pop-os bash[2065048]: [109B blob data] +Aug 15 04:00:05 pop-os bash[2065048]: [109B blob data] +Aug 15 04:00:05 pop-os bash[2065048]: [110B blob data] +Aug 15 04:00:05 pop-os bash[2065048]: [110B blob data] +Aug 15 04:00:06 pop-os bash[2065048]: [109B blob data] +Aug 15 04:00:17 pop-os bash[2065048]: [110B blob data] +Aug 15 04:00:18 pop-os bash[2065048]: [109B blob data] +Aug 15 04:00:34 pop-os bash[2065048]: [109B blob data] +Aug 15 04:00:38 pop-os bash[2065048]: [110B blob data] +Aug 15 04:00:42 pop-os bash[2065048]: [110B blob data] +Aug 15 04:00:54 pop-os bash[2065048]: [110B blob data] +Aug 15 04:00:57 pop-os bash[2065048]: [110B blob data] +Aug 15 04:01:01 pop-os bash[2065048]: [110B blob data] +Aug 15 04:01:14 pop-os bash[2065048]: [110B blob data] +Aug 15 04:01:15 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04:02:56 pop-os bash[2065048]: [110B blob data] +Aug 15 04:03:00 pop-os bash[2065048]: [110B blob data] +Aug 15 04:03:05 pop-os bash[2065048]: [110B blob data] +Aug 15 04:03:10 pop-os bash[2065048]: [110B blob data] +Aug 15 04:03:14 pop-os bash[2065048]: [110B blob data] +Aug 15 04:03:19 pop-os bash[2065048]: [110B blob data] +Aug 15 04:03:24 pop-os bash[2065048]: [110B blob data] +Aug 15 04:03:30 pop-os bash[2065048]: [110B blob data] +Aug 15 04:03:35 pop-os bash[2065048]: [110B blob data] +Aug 15 04:03:40 pop-os bash[2065048]: [110B blob data] +Aug 15 04:03:45 pop-os bash[2065048]: [110B blob data] +Aug 15 04:03:50 pop-os bash[2065048]: [110B blob data] +Aug 15 04:03:56 pop-os bash[2065048]: [110B blob data] +Aug 15 04:04:00 pop-os bash[2065048]: [110B blob data] +Aug 15 04:04:06 pop-os bash[2065048]: [110B blob data] +Aug 15 04:04:11 pop-os bash[2065048]: [110B blob data] +Aug 15 04:04:16 pop-os bash[2065048]: [110B blob data] +Aug 15 04:04:21 pop-os bash[2065048]: [110B blob data] +Aug 15 04:04:26 pop-os bash[2065048]: [110B blob data] +Aug 15 04:04:31 pop-os bash[2065048]: [110B blob data] +Aug 15 04:04:36 pop-os bash[2065048]: [110B blob data] +Aug 15 04:04:41 pop-os bash[2065048]: [110B blob data] +Aug 15 04:04:47 pop-os bash[2065048]: [110B blob data] +Aug 15 04:04:52 pop-os bash[2065048]: [110B blob data] +Aug 15 04:04:57 pop-os bash[2065048]: [110B blob data] +Aug 15 04:05:02 pop-os bash[2065048]: [110B blob data] +Aug 15 04:05:07 pop-os bash[2065048]: [110B blob data] +Aug 15 04:05:12 pop-os bash[2065048]: [110B blob data] +Aug 15 04:05:17 pop-os bash[2065048]: [110B blob data] +Aug 15 04:05:18 pop-os bash[2065048]: [110B blob data] +Aug 15 04:05:18 pop-os bash[2065048]: [110B blob data] +Aug 15 04:05:18 pop-os bash[2065048]: (Worker_TP0 pid=342) +Aug 15 04:05:18 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:05:18 [default_loader.py:465] Loading weights took 313.45 seconds +Aug 15 04:05:28 pop-os bash[2065048]: [rank1]:[W815 08:05:28.639545923 CUDACachingAllocator.cpp:508] expandable_segments: memory mapping failed with OOM on device 1 while trying to map 20971520 bytes (free: 55967744, total: 102011043840). +Aug 15 04:05:28 pop-os bash[2065048]: [rank0]:[W815 08:05:28.708741958 CUDACachingAllocator.cpp:508] expandable_segments: memory mapping failed with OOM on device 0 while trying to map 20971520 bytes (free: 3801088, total: 102014189568). +Aug 15 04:05:28 pop-os bash[2065048]: [rank3]:[W815 08:05:28.714168564 CUDACachingAllocator.cpp:508] expandable_segments: memory mapping failed with OOM on device 3 while trying to map 20971520 bytes (free: 7995392, total: 102014189568). +Aug 15 04:05:28 pop-os bash[2065048]: [rank2]:[W815 08:05:28.826173273 CUDACachingAllocator.cpp:508] expandable_segments: memory mapping failed with OOM on device 2 while trying to map 20971520 bytes (free: 7995392, total: 102014189568). +Aug 15 04:05:33 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:05:33 [model_runner.py:463] Model loading took 73.91 GiB and 330.479583 seconds +Aug 15 04:05:33 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:05:33 [model_runner.py:463] Model loading took 73.91 GiB and 330.477916 seconds +Aug 15 04:05:33 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:05:33 [model_runner.py:463] Model loading took 73.91 GiB and 330.548260 seconds +Aug 15 04:05:33 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:05:33 [topk_topp_sampler.py:62] Using FlashInfer for top-p & top-k sampling. +Aug 15 04:05:34 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:05:34 [model_runner.py:463] Model loading took 73.91 GiB and 330.850954 seconds +Aug 15 04:05:34 pop-os bash[2065048]: (EngineCore pid=280) WARNING 08-15 08:05:34 [torch_utils.py:251] OMP_NUM_THREADS=16 is set; leaving Torch threads at 16 for serving. Multi-threaded torch CPU ops during serving can degrade performance through spin-wait contention and cgroup CPU-quota throttling. +Aug 15 04:05:35 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:05:35 [exl3.py:5257] GLM SQG full-W4A8 runtime planned: capacity=2304 topk=8 ubatches=1 TP=4 rank=0 local_intermediate=512 direct-E4M3, no A16 fallback +Aug 15 04:05:36 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:05:36 [model_runner.py:994] Profiled V1 prompt-logprobs workspace with chunk size 1024 +Aug 15 04:05:37 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:05:37 [selector.py:190] Using FLASH_ATTN MLA prefill backend. +Aug 15 04:05:37 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:05:37 [kernel_warmup.py:112] Warming up ll_bf16 router GEMM kernels for shapes: ((6144, 256),). +Aug 15 04:05:38 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:05:38 [kernel_warmup.py:395] Warmed up 1 fused MLA BF16/MXFP8 query variants. +Aug 15 04:05:38 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:05:38 [kernel_warmup.py:395] Warmed up 1 fused MLA BF16/MXFP8 query variants. +Aug 15 04:05:38 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:05:38 [kernel_warmup.py:395] Warmed up 1 fused MLA BF16/MXFP8 query variants. +Aug 15 04:05:38 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:05:38 [kernel_warmup.py:395] Warmed up 1 fused MLA BF16/MXFP8 query variants. +Aug 15 04:05:38 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:05:38 [gpu_worker.py:574] Initial free memory 93.92 GiB, reserved 0.25 GiB memory for KV Cache as specified by kv_cache_memory_bytes config and skipped memory profiling. This does not respect the gpu_memory_utilization config. Only use kv_cache_memory_bytes config when you want manual control of KV cache memory size. If OOM'ed, check the difference of initial free memory between the current run and the previous run where kv_cache_memory_bytes is suggested and update it correspondingly. +Aug 15 04:05:38 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:05:38 [gpu_worker.py:574] Initial free memory 93.84 GiB, reserved 0.25 GiB memory for KV Cache as specified by kv_cache_memory_bytes config and skipped memory profiling. This does not respect the gpu_memory_utilization config. Only use kv_cache_memory_bytes config when you want manual control of KV cache memory size. If OOM'ed, check the difference of initial free memory between the current run and the previous run where kv_cache_memory_bytes is suggested and update it correspondingly. +Aug 15 04:05:38 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:05:38 [gpu_worker.py:574] Initial free memory 92.71 GiB, reserved 0.25 GiB memory for KV Cache as specified by kv_cache_memory_bytes config and skipped memory profiling. This does not respect the gpu_memory_utilization config. Only use kv_cache_memory_bytes config when you want manual control of KV cache memory size. If OOM'ed, check the difference of initial free memory between the current run and the previous run where kv_cache_memory_bytes is suggested and update it correspondingly. +Aug 15 04:05:38 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:05:38 [kernel_warmup.py:430] Deferring runtime-dependent kernel warmup until KV cache initialization. +Aug 15 04:05:38 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:05:38 [gpu_worker.py:574] Initial free memory 93.86 GiB, reserved 0.25 GiB memory for KV Cache as specified by kv_cache_memory_bytes config and skipped memory profiling. This does not respect the gpu_memory_utilization config. Only use kv_cache_memory_bytes config when you want manual control of KV cache memory size. If OOM'ed, check the difference of initial free memory between the current run and the previous run where kv_cache_memory_bytes is suggested and update it correspondingly. +Aug 15 04:05:38 pop-os bash[2065048]: (EngineCore pid=280) INFO 08-15 08:05:38 [kv_cache_utils.py:2451] Overriding num_gpu_blocks=77 with num_gpu_blocks_override=64 +Aug 15 04:05:38 pop-os bash[2065048]: (EngineCore pid=280) INFO 08-15 08:05:38 [kv_cache_utils.py:2451] Overriding num_gpu_blocks=77 with num_gpu_blocks_override=64 +Aug 15 04:05:38 pop-os bash[2065048]: (EngineCore pid=280) INFO 08-15 08:05:38 [kv_cache_utils.py:2451] Overriding num_gpu_blocks=77 with num_gpu_blocks_override=64 +Aug 15 04:05:38 pop-os bash[2065048]: (EngineCore pid=280) INFO 08-15 08:05:38 [kv_cache_utils.py:2451] Overriding num_gpu_blocks=77 with num_gpu_blocks_override=64 +Aug 15 04:05:38 pop-os bash[2065048]: (EngineCore pid=280) INFO 08-15 08:05:38 [kv_cache_utils.py:2135] GPU KV cache size: 4,096 tokens, Maximum concurrency for 2,304 tokens per request: 1.78x +Aug 15 04:05:38 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:05:38 [indexer.py:686] DSA indexer decode path: use_flattening=False use_varlen=False (next_n=1, use_fp4_indexer_cache=False) +Aug 15 04:06:40 pop-os bash[2065048]: (EngineCore pid=280) INFO 08-15 08:06:40 [shm_broadcast.py:801] No available shared memory broadcast block found in 60 seconds. This typically happens when some processes are hanging or doing some time-consuming work (e.g. compilation, weight/kv cache quantization). +Aug 15 04:06:53 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:06:53 [kernel_warmup.py:457] Skipping FlashInfer autotune because no FlashInfer compute kernels are active. +Aug 15 04:06:54 pop-os bash[2065048]: (Worker_TP1 pid=433) INFO 08-15 08:06:54 [jit_monitor.py:79] Kernel JIT monitor activated; monitored JIT compilations during inference will use mode=warn. +Aug 15 04:06:54 pop-os bash[2065048]: (Worker_TP3 pid=785) INFO 08-15 08:06:54 [jit_monitor.py:79] Kernel JIT monitor activated; monitored JIT compilations during inference will use mode=warn. +Aug 15 04:06:54 pop-os bash[2065048]: (Worker_TP2 pid=680) INFO 08-15 08:06:54 [jit_monitor.py:79] Kernel JIT monitor activated; monitored JIT compilations during inference will use mode=warn. +Aug 15 04:06:54 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:06:54 [jit_monitor.py:79] Kernel JIT monitor activated; monitored JIT compilations during inference will use mode=warn. +Aug 15 04:06:55 pop-os bash[2065048]: (Worker_TP0 pid=342) WARNING 08-15 08:06:55 [torch_utils.py:251] OMP_NUM_THREADS=16 is set; leaving Torch threads at 16 for serving. Multi-threaded torch CPU ops during serving can degrade performance through spin-wait contention and cgroup CPU-quota throttling. +Aug 15 04:06:55 pop-os bash[2065048]: (EngineCore pid=280) INFO 08-15 08:06:55 [core.py:356] init engine (profile, create kv cache, warmup model) took 81.11 s +Aug 15 04:07:03 pop-os bash[2065048]: (EngineCore pid=280) WARNING 08-15 08:07:03 [vllm.py:1296] Enforce eager set, disabling torch.compile and CUDAGraphs. This is equivalent to setting -cc.mode=none -cc.cudagraph_mode=none +Aug 15 04:07:03 pop-os bash[2065048]: (EngineCore pid=280) WARNING 08-15 08:07:03 [vllm.py:1329] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored. +Aug 15 04:07:03 pop-os bash[2065048]: (EngineCore pid=280) INFO 08-15 08:07:03 [kernel.py:310] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['vllm_c', 'native'], fused_add_rms_norm=['vllm_c', 'native']) +Aug 15 04:07:03 pop-os bash[2065048]: (EngineCore pid=280) INFO 08-15 08:07:03 [vllm.py:1508] Cudagraph is disabled under eager mode +Aug 15 04:07:03 pop-os bash[2065048]: (EngineCore pid=280) INFO 08-15 08:07:03 [compilation.py:329] Enabled custom fusions: norm_quant, act_quant +Aug 15 04:07:15 pop-os bash[2065048]: INFO 08-15 08:07:15 [hf.py:540] Detected the chat template content format to be 'openai'. You can set `--chat-template-content-format` to override this. +Aug 15 04:07:17 pop-os bash[2065048]: [143B blob data] +Aug 15 04:07:17 pop-os bash[2065048]: [344B blob data] +Aug 15 04:07:17 pop-os bash[2065048]: (Worker_TP0 pid=342) WARNING 08-15 08:07:17 [jit_monitor.py:135] Triton kernel JIT compilation during inference: _gather_shared_paged_supertile_kernel. This causes a latency spike; consider extending warmup to cover this shape/config. +Aug 15 04:07:17 pop-os bash[2065048]: (Worker_TP0 pid=342) WARNING 08-15 08:07:17 [jit_monitor.py:135] Triton kernel JIT compilation during inference: _pack_topk_routes_post_prefix_kernel. This causes a latency spike; consider extending warmup to cover this shape/config. +Aug 15 04:07:18 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:07:18 [model_runner.py:1769] V2 pre-LM-head capture hook: directory=/results/hidden-raw requests=1 rows=2048 hidden_shape=(2048, 6144) +Aug 15 04:07:18 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:07:18 [model_runner.py:1826] Saved V2 pre-LM-head rows [0, 2048) shape=(2048, 6144) to /results/hidden-raw/0-811bfa3b/hidden.rows-000000-002048.safetensors +Aug 15 04:07:46 pop-os bash[2065048]: [414B blob data] +Aug 15 04:08:13 pop-os bash[2065048]: {"mean_kld": 0.1401771516114036, "median": 0.0015440876595675945, "trimmed_mean": 0.1401771516114036, "p99": 2.480538845062256, "max": 8.928885459899902, "nonfinite": 0, "positions": 2047} +Aug 15 04:08:13 pop-os bash[2065048]: INFO 08-15 08:08:13 [core_client.py:689] [shutdown] MPClient: start timeout=60.0s +Aug 15 04:08:13 pop-os bash[2065048]: INFO 08-15 08:08:13 [core_client.py:691] [shutdown] MPClient: stopping engine manager +Aug 15 04:08:13 pop-os bash[2065048]: INFO 08-15 08:08:13 [utils.py:615] [shutdown] Process manager: send sigterm to process EngineCore +Aug 15 04:08:13 pop-os bash[2065048]: (EngineCore pid=280) INFO 08-15 08:08:13 [core.py:1355] [shutdown] EngineCore: trigger received signal=SIGTERM +Aug 15 04:08:13 pop-os bash[2065048]: (EngineCore pid=280) INFO 08-15 08:08:13 [core.py:1491] [shutdown] EngineCore: start mode=abort timeout=0s +Aug 15 04:08:13 pop-os bash[2065048]: (EngineCore pid=280) INFO 08-15 08:08:13 [core.py:1522] [shutdown] EngineCore: request processing complete; starting resource teardown +Aug 15 04:08:13 pop-os bash[2065048]: (EngineCore pid=280) INFO 08-15 08:08:13 [core.py:1368] [shutdown] EngineCore: exiting busy loop +Aug 15 04:08:13 pop-os bash[2065048]: (EngineCore pid=280) INFO 08-15 08:08:13 [multiproc_executor.py:470] [shutdown] Executor: waiting for worker exit count=4 +Aug 15 04:08:13 pop-os bash[2065048]: (Worker_TP0 pid=342) INFO 08-15 08:08:13 [multiproc_executor.py:834] Parent process exited, terminating worker queues +Aug 15 04:08:18 pop-os bash[2065048]: (EngineCore pid=280) WARNING 08-15 08:08:18 [multiproc_executor.py:482] [shutdown] Executor: workers still running after grace period; sending SIGTERM count=4 +Aug 15 04:08:22 pop-os bash[2065048]: (EngineCore pid=280) WARNING 08-15 08:08:22 [multiproc_executor.py:492] [shutdown] Executor: workers still running after SIGTERM; sending SIGKILL count=4 +Aug 15 04:08:28 pop-os bash[2065048]: INFO 08-15 08:08:28 [core_client.py:693] [shutdown] MPClient: engine manager stopped +Aug 15 04:08:28 pop-os bash[2065048]: INFO 08-15 08:08:28 [core_client.py:694] [shutdown] MPClient: cleaning up background resources +Aug 15 04:08:28 pop-os bash[2065048]: INFO 08-15 08:08:28 [core_client.py:696] [shutdown] MPClient: complete +Aug 15 04:08:32 pop-os bash[2063897]: GLM hidden replay: 2026-08-15T04:08:32-04:00 sealing the raw [2048,6144] capture +Aug 15 04:08:33 pop-os bash[2233301]: { +Aug 15 04:08:33 pop-os bash[2233301]: "complete": true, +Aug 15 04:08:33 pop-os bash[2233301]: "created_utc": "2026-08-15T08:08:33.506748+00:00", +Aug 15 04:08:33 pop-os bash[2233301]: "dtype": "BF16", +Aug 15 04:08:33 pop-os bash[2233301]: "file": "hidden_states.safetensors", +Aug 15 04:08:33 pop-os bash[2233301]: "file_sha256": "50781f1192645e13899d11f2181717ea05e028688db0201ede6d7e88360ee874", +Aug 15 04:08:33 pop-os bash[2233301]: "full_kld_receipt": "/capture/capture-full-kld.json", +Aug 15 04:08:33 pop-os bash[2233301]: "full_kld_receipt_sha256": "a67e896ffc1f879cdb3320042c50981532ce7837e0375fe34bcb0729fe685fc7", +Aug 15 04:08:33 pop-os bash[2233301]: "hidden_width": 6144, +Aug 15 04:08:33 pop-os bash[2233301]: "interpretation": "Raw prompt capture retains all 2,048 rows; rows 0..2,046 are scored against next-token reference logits and row 2,047 is retained but not scored because its continuation target is absent.", +Aug 15 04:08:33 pop-os bash[2233301]: "key": "hidden_states", +Aug 15 04:08:33 pop-os bash[2233301]: "raw_chunks": [ +Aug 15 04:08:33 pop-os bash[2233301]: { +Aug 15 04:08:33 pop-os bash[2233301]: "file": "0-811bfa3b/hidden.rows-000000-002048.safetensors", +Aug 15 04:08:33 pop-os bash[2233301]: "row_end": 2048, +Aug 15 04:08:33 pop-os bash[2233301]: "row_start": 0, +Aug 15 04:08:33 pop-os bash[2233301]: "sha256": "0bc36a066b48662418e2f5b92c469b1bfbbbd22e0130b5617450a974635e2977", +Aug 15 04:08:33 pop-os bash[2233301]: "size_bytes": 25166048 +Aug 15 04:08:33 pop-os bash[2233301]: } +Aug 15 04:08:33 pop-os bash[2233301]: ], +Aug 15 04:08:33 pop-os bash[2233301]: "raw_rows": 2048, +Aug 15 04:08:33 pop-os bash[2233301]: "request_id": "0-811bfa3b", +Aug 15 04:08:33 pop-os bash[2233301]: "schema": "glm52-pre-lm-head-hidden-one-context-v1", +Aug 15 04:08:33 pop-os bash[2233301]: "scored_rows": 2047, +Aug 15 04:08:33 pop-os bash[2233301]: "semantic_point": "after_final_rmsnorm_before_lm_head", +Aug 15 04:08:33 pop-os bash[2233301]: "shape": [ +Aug 15 04:08:33 pop-os bash[2233301]: 2048, +Aug 15 04:08:33 pop-os bash[2233301]: 6144 +Aug 15 04:08:33 pop-os bash[2233301]: ], +Aug 15 04:08:33 pop-os bash[2233301]: "size_bytes": 25166120, +Aug 15 04:08:33 pop-os bash[2233301]: "token_file": "/tokens.json", +Aug 15 04:08:33 pop-os bash[2233301]: "token_file_sha256": "d0be87a4909ad00c311e36fc5c81c7d6942216c16a8fbbbf77778edb273346f1", +Aug 15 04:08:33 pop-os bash[2233301]: "token_sequence_sha256": "d0be87a4909ad00c311e36fc5c81c7d6942216c16a8fbbbf77778edb273346f1" +Aug 15 04:08:33 pop-os bash[2233301]: } +Aug 15 04:08:33 pop-os bash[2063897]: GLM hidden replay: 2026-08-15T04:08:33-04:00 exporting and comparing the unchanged source/candidate BF16 LM head +Aug 15 04:08:43 pop-os bash[2234034]: { +Aug 15 04:08:43 pop-os bash[2234034]: "candidate_checkpoint": "/model", +Aug 15 04:08:43 pop-os bash[2234034]: "candidate_index_sha256": "98d8c371ba5ec8aaf2e326b228bcd36a5df4cad600085a8f4dd2e098f2474e9f", +Aug 15 04:08:43 pop-os bash[2234034]: "candidate_shard": "direct-bf16-00001-of-00282.safetensors", +Aug 15 04:08:43 pop-os bash[2234034]: "candidate_shard_sha256": "32448fb3c149fb8024efd99c3d0e0df7f33ae09f0f3173234f32fcbf4b6f2316", +Aug 15 04:08:43 pop-os bash[2234034]: "checkpoint_tensor_key": "lm_head.weight", +Aug 15 04:08:43 pop-os bash[2234034]: "complete": true, +Aug 15 04:08:43 pop-os bash[2234034]: "dtype": "BF16", +Aug 15 04:08:43 pop-os bash[2234034]: "file": "weight.safetensors", +Aug 15 04:08:43 pop-os bash[2234034]: "file_sha256": "f56e1020446f4eaf73b418dd62ef853eb51e4f5c502d09ec0301086a90557969", +Aug 15 04:08:43 pop-os bash[2234034]: "key": "weight", +Aug 15 04:08:43 pop-os bash[2234034]: "raw_tensor_sha256": "a012be05e7716292407d418b408222de256d4dbe2fe2143a44d27d8e3553bfba", +Aug 15 04:08:43 pop-os bash[2234034]: "schema": "glm52-canonical-bf16-lm-head-v1", +Aug 15 04:08:43 pop-os bash[2234034]: "shape": [ +Aug 15 04:08:43 pop-os bash[2234034]: 154880, +Aug 15 04:08:43 pop-os bash[2234034]: 6144 +Aug 15 04:08:43 pop-os bash[2234034]: ], +Aug 15 04:08:43 pop-os bash[2234034]: "size_bytes": 1903165672, +Aug 15 04:08:43 pop-os bash[2234034]: "source_candidate_byte_identity": true, +Aug 15 04:08:43 pop-os bash[2234034]: "source_checkpoint": "/source", +Aug 15 04:08:43 pop-os bash[2234034]: "source_index_sha256": "62182715e5ef3eb0069b38ff710a38e7a118154278768a8c7ab8efed60c501f5", +Aug 15 04:08:43 pop-os bash[2234034]: "source_shard": "direct-bf16-00001-of-00282.safetensors", +Aug 15 04:08:43 pop-os bash[2234034]: "source_shard_sha256": "32448fb3c149fb8024efd99c3d0e0df7f33ae09f0f3173234f32fcbf4b6f2316" +Aug 15 04:08:43 pop-os bash[2234034]: } +Aug 15 04:08:43 pop-os bash[2063897]: GLM hidden replay: 2026-08-15T04:08:43-04:00 running deterministic two-pass full-vocabulary hidden replay +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 32, "row_start": 0, "running_mean_kld": 0.1320747888315506} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 64, "row_start": 32, "running_mean_kld": 0.07156199915512751} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 96, "row_start": 64, "running_mean_kld": 0.19599888532661483} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 128, "row_start": 96, "running_mean_kld": 0.23064505423388437} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 160, "row_start": 128, "running_mean_kld": 0.19038956334716053} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 192, "row_start": 160, "running_mean_kld": 0.20586478606043115} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 224, "row_start": 192, "running_mean_kld": 0.22730622972272682} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 256, "row_start": 224, "running_mean_kld": 0.22912096337153048} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 288, "row_start": 256, "running_mean_kld": 0.21703097320934342} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 320, "row_start": 288, "running_mean_kld": 0.21636183728805056} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 352, "row_start": 320, "running_mean_kld": 0.213994527887553} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 384, "row_start": 352, "running_mean_kld": 0.20697230463279828} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 416, "row_start": 384, "running_mean_kld": 0.20086003425462123} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 448, "row_start": 416, "running_mean_kld": 0.19057563535035685} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 480, "row_start": 448, "running_mean_kld": 0.19815402535063567} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 512, "row_start": 480, "running_mean_kld": 0.1909978603361607} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 544, "row_start": 512, "running_mean_kld": 0.19412334152575428} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 576, "row_start": 544, "running_mean_kld": 0.20521973982553426} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 608, "row_start": 576, "running_mean_kld": 0.20304159259580692} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 640, "row_start": 608, "running_mean_kld": 0.19301092994012484} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 672, "row_start": 640, "running_mean_kld": 0.18623711352594996} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 704, "row_start": 672, "running_mean_kld": 0.17823384080017385} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 736, "row_start": 704, "running_mean_kld": 0.17425421925042048} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 768, "row_start": 736, "running_mean_kld": 0.18810168168940314} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 800, "row_start": 768, "running_mean_kld": 0.18904574346801162} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 832, "row_start": 800, "running_mean_kld": 0.1833706657449123} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 864, "row_start": 832, "running_mean_kld": 0.1844963607771715} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 896, "row_start": 864, "running_mean_kld": 0.18413197591774816} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 928, "row_start": 896, "running_mean_kld": 0.18285820646185602} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 960, "row_start": 928, "running_mean_kld": 0.18463141003848407} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 992, "row_start": 960, "running_mean_kld": 0.1791737490057187} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1024, "row_start": 992, "running_mean_kld": 0.17819018976652395} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1056, "row_start": 1024, "running_mean_kld": 0.17516982900832068} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1088, "row_start": 1056, "running_mean_kld": 0.17179948972138892} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1120, "row_start": 1088, "running_mean_kld": 0.17225032119377903} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1152, "row_start": 1120, "running_mean_kld": 0.17098779922888263} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1184, "row_start": 1152, "running_mean_kld": 0.16662371715065905} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1216, "row_start": 1184, "running_mean_kld": 0.16504932963831648} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1248, "row_start": 1216, "running_mean_kld": 0.1632315909892594} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1280, "row_start": 1248, "running_mean_kld": 0.16141113922695496} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1312, "row_start": 1280, "running_mean_kld": 0.1581804981658824} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1344, "row_start": 1312, "running_mean_kld": 0.15708988787534645} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1376, "row_start": 1344, "running_mean_kld": 0.15345914801836377} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1408, "row_start": 1376, "running_mean_kld": 0.1499967823223507} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1440, "row_start": 1408, "running_mean_kld": 0.1470506677682473} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1472, "row_start": 1440, "running_mean_kld": 0.1451751866353017} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1504, "row_start": 1472, "running_mean_kld": 0.14331461181489144} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1536, "row_start": 1504, "running_mean_kld": 0.14059897157707937} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1568, "row_start": 1536, "running_mean_kld": 0.13994411455567635} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1600, "row_start": 1568, "running_mean_kld": 0.14022389457391343} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1632, "row_start": 1600, "running_mean_kld": 0.1425312863512129} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1664, "row_start": 1632, "running_mean_kld": 0.14254466245191397} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1696, "row_start": 1664, "running_mean_kld": 0.14000674816889289} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1728, "row_start": 1696, "running_mean_kld": 0.14017521428420407} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1760, "row_start": 1728, "running_mean_kld": 0.14129541177537155} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1792, "row_start": 1760, "running_mean_kld": 0.13927929265791456} +Aug 15 04:08:47 pop-os bash[2237107]: {"row_end": 1824, "row_start": 1792, "running_mean_kld": 0.13921650738940683} +Aug 15 04:08:48 pop-os bash[2237107]: {"row_end": 1856, "row_start": 1824, "running_mean_kld": 0.13970360492513043} +Aug 15 04:08:48 pop-os bash[2237107]: {"row_end": 1888, "row_start": 1856, "running_mean_kld": 0.1439954975554077} +Aug 15 04:08:48 pop-os bash[2237107]: {"row_end": 1920, "row_start": 1888, "running_mean_kld": 0.14225627316924197} +Aug 15 04:08:48 pop-os bash[2237107]: {"row_end": 1952, "row_start": 1920, "running_mean_kld": 0.14200141136579367} +Aug 15 04:08:48 pop-os bash[2237107]: {"row_end": 1984, "row_start": 1952, "running_mean_kld": 0.14088418008520182} +Aug 15 04:08:48 pop-os bash[2237107]: {"row_end": 2016, "row_start": 1984, "running_mean_kld": 0.14158455351143526} +Aug 15 04:08:48 pop-os bash[2237107]: {"row_end": 2047, "row_start": 2016, "running_mean_kld": 0.14017626494580232} +Aug 15 04:08:49 pop-os bash[2237107]: {"hidden_vs_baseline_mean_abs_delta": 8.866656012740393e-07, "mean_kld": 0.1401762649458023, "qualification_pass": false, "top1_agreement": 0.9174401563263312} +Aug 15 04:08:50 pop-os systemd[1796]: glm52-k96tail-exact-r11-hidden-replay.service: Main process exited, code=exited, status=2/INVALIDARGUMENT +Aug 15 04:08:50 pop-os systemd[1796]: glm52-k96tail-exact-r11-hidden-replay.service: Failed with result 'exit-code'. diff --git a/reproduction/closure/evidence/final-exact-ii-r11/hidden-replay/runtime-evidence/pp-00-pid-342.json 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The vendor registration correction states that **vendor account V-441 corresponds to Mirel Instrument, headquartered in Estonia** (not Latvia, which is where the similarly named Mirel Industrial, located).", + "output_text": "", + "reasoning_text": "", + "content_text": "", + "error": "", + "hit_max_tokens": false, + "estimated_tokens": false, + "cancelled": false, + "item_id": "", + "category": "", + "expected_answer": "" + }, + { + "run_index": 2, + "phase": "profile", + "concurrency": 1, + "ok": true, + "correct": true, + "completion_tokens": 2783, + "prompt_tokens": 133186, + "elapsed": 64.26823912700638, + "gen_elapsed": 63.38291544698586, + "ttft": 0.885146569009521, + "time_to_second_token": 0.03246501299145166, + "gen_tok_s": 43.90773097724939, + "e2e_tok_s": 43.30288238487844, + "finish_reason": "stop", + "final_answer": "Estonia", + "score_label": "pass", + "score_detail": "", + "parsed_answer": "", + "output_excerpt": "Estonia", + "output_text": "", + "reasoning_text": "", + "content_text": "", + "error": "", + "hit_max_tokens": false, + "estimated_tokens": false, + "cancelled": false, + "item_id": "", + "category": "", + "expected_answer": "" + }, + { + "run_index": 3, + "phase": "profile", + "concurrency": 1, + "ok": true, + "correct": true, + "completion_tokens": 2980, + "prompt_tokens": 133186, + "elapsed": 69.5352602010098, + "gen_elapsed": 68.63255907100393, + "ttft": 0.9020906579971779, + "time_to_second_token": 0.01949478501046542, + "gen_tok_s": 43.419625325598545, + "e2e_tok_s": 42.85595525759928, + "finish_reason": "stop", + "final_answer": "Estonia.", + "score_label": "pass", + "score_detail": "", + "parsed_answer": "", + "output_excerpt": "Estonia.", + "output_text": "", + "reasoning_text": "", + "content_text": "", + "error": "", + "hit_max_tokens": false, + "estimated_tokens": false, + "cancelled": false, + "item_id": "", + "category": "", + "expected_answer": "" + }, + { + "run_index": 4, + "phase": "profile", + "concurrency": 1, + "ok": true, + "correct": true, + "completion_tokens": 5947, + "prompt_tokens": 133186, + "elapsed": 146.53665814099077, + "gen_elapsed": 145.64214676400297, + "ttft": 0.8942597729910631, + "time_to_second_token": 0.03797443999792449, + "gen_tok_s": 40.83296032182537, + "e2e_tok_s": 40.58370154912413, + "finish_reason": "stop", + "final_answer": "The manufacturer of the material used by the Glass Current salinity bench is headquartered in **Estonia**. (The vendor registration correction notes that vendor account V-441 corresponds to Mirel Instrument, which is headquartered in Estonia, not Latvia as previously recorded.)", + "score_label": "pass", + "score_detail": "", + "parsed_answer": "", + "output_excerpt": "The manufacturer of the material used by the Glass Current salinity bench is headquartered in **Estonia**. (The vendor registration correction notes that vendor account V-441 corresponds to Mirel Instrument, which is headquartered in Estoni", + "output_text": "", + "reasoning_text": "", + "content_text": "", + "error": "", + "hit_max_tokens": false, + "estimated_tokens": false, + "cancelled": false, + "item_id": "", + "category": "", + "expected_answer": "" + }, + { + "run_index": 5, + "phase": "profile", + "concurrency": 1, + "ok": true, + "correct": true, + "completion_tokens": 3751, + "prompt_tokens": 133186, + "elapsed": 88.25072755399742, + "gen_elapsed": 87.33457387999806, + "ttft": 0.9159076880023349, + "time_to_second_token": 0.01803984199068509, + "gen_tok_s": 42.94977158935997, + "e2e_tok_s": 42.50389887952934, + "finish_reason": "stop", + "final_answer": "5. Vendor account **V-441** corresponds to **Mirel Instrument**, which is headquartered in **Estonia** (not Latvia, which is where the similarly named vendor Mirel Industrial, vendor account V-447, is headquartered).", + "score_label": "pass", + "score_detail": "", + "parsed_answer": "", + "output_excerpt": "5. Vendor account **V-441** corresponds to **Mirel Instrument**, which is headquartered in **Estonia** (not Latvia, which is where the similarly named vendor Mirel Industrial, vendor account V-447, is headquartered).", + "output_text": "", + "reasoning_text": "", + "content_text": "", + "error": "", + "hit_max_tokens": false, + "estimated_tokens": false, + "cancelled": false, + "item_id": "", + "category": "", + "expected_answer": "" + } + ], + "methodology": { + "name": "Completion-token statistics", + "prefill": "A single max_tokens=1 scout request is sent first to populate the server prefix cache. Measured requests use the exact same prompt, so engines with prefix caching can reuse prefill while decode is sampled in parallel.", + "adaptive_concurrency": "If --profile-concurrency is set, the benchmark sends exactly the configured number of measured profile requests with that many concurrent workers. Otherwise it probes increasing concurrency levels and keeps the fastest aggregate generation-token throughput, then collects additional runs at the selected concurrency until min_results is reached.", + "token_need": "completion_tokens is taken from OpenAI stream usage. If usage is missing, streamed content chunks are used as an estimated fallback and the run is marked estimated_tokens=true.", + "correctness": "By default correctness is scored by applying the regex to the final non-empty answer line, matching the GLM dense-MLA vs NSA comparison." + } +} diff --git a/reproduction/closure/evidence/final-exact-ii-r11/qualification-5x/exact-r11-tp4dcp4mtp3-20260815T084427Z/estonia-c1-r5.log.gz b/reproduction/closure/evidence/final-exact-ii-r11/qualification-5x/exact-r11-tp4dcp4mtp3-20260815T084427Z/estonia-c1-r5.log.gz new file mode 100644 index 0000000000000000000000000000000000000000..bca1985fa61ed5e02ce41be83de04ddb45b20216 --- /dev/null +++ b/reproduction/closure/evidence/final-exact-ii-r11/qualification-5x/exact-r11-tp4dcp4mtp3-20260815T084427Z/estonia-c1-r5.log.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9106f61e022b4fbfad9e0081329f86a243a44c33fcdf492b59f6f3c8eefdba6c +size 1519 diff --git 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Override with completion-stats options if you intentionally want scout behavior.", + "adaptive_concurrency": "If --profile-concurrency is set, the benchmark sends exactly the configured number of measured profile requests with that many concurrent workers. Otherwise it probes increasing concurrency levels and keeps the fastest aggregate generation-token throughput, then collects additional runs at the selected concurrency until min_results is reached.", + "token_need": "completion_tokens is taken from OpenAI stream usage. If usage is missing, streamed content chunks are used as an estimated fallback and the run is marked estimated_tokens=true.", + "correctness": "The final answer is parsed from the end of the response as two numbers: ticket count and hours. EXACT is 72, 46.0; NEAR is within tolerance; FAIL is outside tolerance or unparseable. 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"bc77337e26f93db4cabaea7938041c0984327a0faa9fbe53022296b06ce8baae", + "inner_manifest": "layers-003-077.sha256", + "inner_manifest_sha256": "7a132925fa4bc6e01e5a44112fb236889a13f30a6fba875d485c24ea2f1c6250", + "files": 225 + }, + "k96_parity": { + "path": "parity-004-077.tar.gz", + "sha256": "c2834976a7211bb29668e25a0f8e5011ed9dd649e6f7134598562fa2c2b4b371", + "inner_manifest": "parity-004-077.sha256", + "inner_manifest_sha256": "cf9360ddee41bc5c9dca5813dd12e36bea677a32717413cff0588b803a011e5a", + "files": 74 + } + }, + "assembly": { + "path": "COUPLED_REENCODE_MANIFEST.json", + "manifest_id": "8e12c1d9d98878e45ef8f1de8fb690756cae4deac2550269698708d9fb776e36", + "sha256": "5b4309289c69dc618da03d34e10f826b8a6e7ba3fc67969582cf5b81b825a123", + "complete": true + }, + "codec": { + "path": "model_codec_validation.json", + "sha256": "a2d0648abc05e883e90d1c0ba0bbbb4464bbe8658fcd77ea5400849a8582bf14", + "routed_layer_count": 76, + "coupled_layer_count": 75, + "per_layer_census_ok": true, + "mtp_layer_78_preserved": true, + "pass": true + } +} diff --git a/reproduction/closure/evidence/final-mechanical/README.md b/reproduction/closure/evidence/final-mechanical/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c0c91347cdf1383ab1796b5dafb029b94fec33fe --- /dev/null +++ b/reproduction/closure/evidence/final-mechanical/README.md @@ -0,0 +1,25 @@ +# Final mechanical evidence + +This directory binds the completed non-tensor mechanical campaign closure. +It contains exact source bytes inside deterministic gzip-compressed tar +archives, compact inner SHA-256 manifests, the assembly manifest, and the +authoritative model codec validation receipt. + +The mechanical domain is 75 target layers, 3 through 77. All 75 have a +complete layer manifest, quality receipt, and passing B12X runtime oracle. +Exact scorer/encoder byte parity covers the 74 K96 layers 4 through 77. Layer +3 is the sealed K48 exception and has no K96 parity receipt. + +The model codec receipt covers all 76 routed layers, including preserved +source-SQG MTP layer 78. It reports 177,613 indexed tensors, the same number on +disk, a passing per-layer census, and no problems. + +Verify the closure from `coupled_hadamard_k96tail/` with: + +```bash +python3 verify_source_sync.py +``` + +The verifier checks archive hashes, rejects unsafe archive members, validates +every inner file hash, and enforces the 75-oracle, 74-parity, layer-3-exception +boundary. diff --git a/reproduction/closure/evidence/final-mechanical/layer-003-k48-exception.json b/reproduction/closure/evidence/final-mechanical/layer-003-k48-exception.json new file mode 100644 index 0000000000000000000000000000000000000000..a156494a78e3cf3256e525689eb0ab30245a84e0 --- /dev/null +++ b/reproduction/closure/evidence/final-mechanical/layer-003-k48-exception.json @@ -0,0 +1,16 @@ +{ + "schema": "glm52-coupled-k96tail-layer3-exception-v1", + "complete": true, + "layer": 3, + "rate_policy": "preserve_sealed_k48_exception", + "k3": 720, + "k4": 48, + "total": 768, + "bits_per_weight": 3.0625, + "mechanical_manifest_sha256": "07f4b1ab1a122f84b1ad948055f4b74bef8a9b4fb22ffe268df126f4435d4303", + "quality_receipt_sha256": "d2d7728383292dd7a8fb928bcb2df754c86ee6bd3308de31c33c7fb4eec34908", + "runtime_oracle_sha256": "b21353178925fa67f55d665ec6cef3830b4a00a750120807b3fd5ce73cf88865", + "k96_score_encode_parity_receipt": null, + "parity_disposition": "not_applicable_layer3_is_k48_not_k96", + "claim_boundary": "mechanical seal and runtime oracle only" +} diff --git a/reproduction/closure/evidence/final-mechanical/layers-003-077.sha256 b/reproduction/closure/evidence/final-mechanical/layers-003-077.sha256 new file mode 100644 index 0000000000000000000000000000000000000000..02d35ef57cbe80ce34ccf7575fd9a624263eae60 --- /dev/null +++ b/reproduction/closure/evidence/final-mechanical/layers-003-077.sha256 @@ -0,0 +1,225 @@ +07f4b1ab1a122f84b1ad948055f4b74bef8a9b4fb22ffe268df126f4435d4303 r7-experts-layer-003.json +c8de971a8dccdfa107175b17548f1c838387a11052f72fb3ae4e7303940d4e20 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/dev/null +++ b/reproduction/closure/evidence/snapshot_2026-08-15T002639-0400/README.md @@ -0,0 +1,28 @@ +# K96 coupled-Hadamard frozen evidence snapshot + +This directory freezes the measured local state at +`2026-08-15T00:26:39-04:00`. It is not a live status feed and does not claim a +finished model. + +- `status.json` is the compact campaign frontier and final-result disposition. +- `layers_003_046/` holds the 44 sealed non-tensor manifests, quality receipts, + and passing native B12X runtime oracles. +- `parity_layers_004_046/` holds 43 exact scorer/encoder payload-parity proofs, + each covering 256 experts and 768 projections. +- `allocations_layers_004_046/` holds the base and KLD/route-guarded K96 + allocations for every sealed K96 layer. All 43 guarded allocations remain + production eligible. +- `recipe_layers_004_046/` holds compact no-shortcut recipe, beta, + preregistration, final-profile binding, selection, and holdout lineage. +- `score_ledger.json` aggregates every complete score layer 4--50. Its + `score_receipts_layers_004_050.sha256` companion binds all 12,032 expert JSON + receipts and 12,032 paired row-SSE arrays without copying those payloads. + The 24,064 bound files total 2,154,247,428 bytes; all embedded row-SSE hashes + were checked against their arrays. +- `wave_archives.sha256` binds 11 omitted candidate-metadata archives totaling + 78,249,961 bytes. `campaign.log` is the 99,034-byte frozen controller log. + +No `.safetensors`, row-SSE `.npz`, activation capture, Hessian payload, cache, +container layer, credential, SSH endpoint, token, or provider secret is copied. +This historical snapshot predates final assembly and KLD measurement. Current +sealed results are in `../final-mechanical/` and `../final-exact-ii-r11/`. diff --git a/reproduction/closure/evidence/snapshot_2026-08-15T002639-0400/allocations_layers_004_046/layer_004.kld-route-v1-k096.allocation.json b/reproduction/closure/evidence/snapshot_2026-08-15T002639-0400/allocations_layers_004_046/layer_004.kld-route-v1-k096.allocation.json new file mode 100644 index 0000000000000000000000000000000000000000..e41f364d1f4e2d465e9187d8f6030279cc557ce7 --- /dev/null +++ b/reproduction/closure/evidence/snapshot_2026-08-15T002639-0400/allocations_layers_004_046/layer_004.kld-route-v1-k096.allocation.json @@ -0,0 +1 @@ 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diff --git a/reproduction/closure/evidence/snapshot_2026-08-15T002639-0400/campaign.log b/reproduction/closure/evidence/snapshot_2026-08-15T002639-0400/campaign.log new file mode 100644 index 0000000000000000000000000000000000000000..37d083d0141913384f31b77eb745f0d028036817 --- /dev/null +++ b/reproduction/closure/evidence/snapshot_2026-08-15T002639-0400/campaign.log @@ -0,0 +1,925 @@ +[campaign] 2026-08-13T10:49:13-04:00 starting hybrid K48-layer3 / K96-layers4-78 campaign waves 3..75 +[campaign] 2026-08-13T10:49:15-04:00 ensuring pinned saved inputs for wave-003-006 + Fetching 1079 files: 0%| | 0/1079 [00:00 + main() + File "/home/brandonmusic/KLC_SANDBOXES/glm52_fresh_sqg_3p0625/scripts/build_kld_route_guarded_allocation.py", line 118, in main + raise ValueError("base K96, score, or KLD contract differs") +ValueError: base K96, score, or KLD contract differs +[campaign] 2026-08-13T13:54:22-04:00 starting hybrid K48-layer3 / K96-layers4-78 campaign waves 3..75 +[campaign] 2026-08-13T13:54:23-04:00 ensuring pinned saved inputs for wave-003-006 + Fetching 1079 files: 0%| | 0/1079 [00:00