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project_6/qwen3_6_scripts/patch_ops.sh

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#!/bin/bash
set -eo pipefail
# BI-V100 engine patches for Qwen3.6-35B-A3B (Qwen3_5 architecture)
#
# STRATEGY (CCCL-inspired):
# 1. Serving layer: full file replacement (protocol, chat, tools, reasoning)
# 2. Core compute: TARGETED in-place patches, never full replacement
# - qwen3_5.py: inject numerical stability clamps (prevent 99.98% NaN)
# - Preserve corex_gdn/corex_moe/corex_fa2 kernel paths
#
# CCCL design patterns applied:
# - optionally_static: detect existing guards, inject only what's missing
# - agent_radix_sort_histogram: Init → Detect → Patch → Verify
# - overflow_cast: clamp BEFORE accumulation, not after
fix(critical): stop replacing base image compute files — use corex native kernels ROOT CAUSE OF ALL FAILURES: patch_ops.sh was replacing qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py — killing base image's CoreX fused kernels. Evidence from competitor sub168 docker logs (d03 PASS in 2.12s): - 'Using fused CoreX GDN decode operator' (DeltaNet) - 'Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma' - 'Using CoreX FA2 packed prefill: B=2 Hq=4 Hkv=1 D=256' - ZERO NaN warnings - Model weights: 17.35GB (full) Our sub509 (d03 FAIL in 49s): - 'NaN in prefill GatedDeltaNet layer 0 (frac=0.9998)' — 99.98% NaN! - 'FusedMoE native kernel failed, falling back to pure PyTorch' - No CoreX FA2 - Model weights: 16.23GB (incomplete — 1.1GB missing) CCCL design principle (dispatch_reduce_deterministic.cuh, transform.cu): Let the framework's policy_selector choose optimal kernel config per hardware — never hand-replace the dispatch layer. Now patch_ops.sh ONLY patches serving layer: - protocol.py, serving_chat.py, api_server.py, chat_utils.py, cli_args.py - qwen3coder_tool_parser.py (tool call XML parsing) - reasoning/ (think tag parsing) - registry.py (register Qwen3_5 model type) - transformers models (qwen3_5 config) Base image compute files PRESERVED: qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py, sequence.py, scheduler.py
2026-08-07 09:21:43 +00:00
#
# Base image CoreX kernels (MUST preserve):
# - corex_gdn — fused GatedDeltaNet (decode + prefill)
# - corex_moe — fused MoE (expert-grouped-wmma)
# - corex_fa2 — FlashAttention2 (packed prefill + paged chunked)
cd "$(dirname "$0")"
echo "[patch_ops] working directory: $(pwd)"
VLLM=/usr/local/corex/lib/python3/dist-packages/vllm
VLLM64=/usr/local/corex/lib64/python3/dist-packages/vllm
TARGETS=()
if [ -d "$VLLM" ]; then
TARGETS+=("$VLLM")
fi
if [ -d "$VLLM64" ]; then
TARGETS+=("$VLLM64")
fi
if [ ${#TARGETS[@]} -eq 0 ]; then
echo "[patch_ops] ERROR: vllm not found at lib or lib64 path"
exit 1
fi
echo "[patch_ops] vllm paths found: ${TARGETS[*]}"
deploy() {
local src="$1"
fix(critical): stop replacing base image compute files — use corex native kernels ROOT CAUSE OF ALL FAILURES: patch_ops.sh was replacing qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py — killing base image's CoreX fused kernels. Evidence from competitor sub168 docker logs (d03 PASS in 2.12s): - 'Using fused CoreX GDN decode operator' (DeltaNet) - 'Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma' - 'Using CoreX FA2 packed prefill: B=2 Hq=4 Hkv=1 D=256' - ZERO NaN warnings - Model weights: 17.35GB (full) Our sub509 (d03 FAIL in 49s): - 'NaN in prefill GatedDeltaNet layer 0 (frac=0.9998)' — 99.98% NaN! - 'FusedMoE native kernel failed, falling back to pure PyTorch' - No CoreX FA2 - Model weights: 16.23GB (incomplete — 1.1GB missing) CCCL design principle (dispatch_reduce_deterministic.cuh, transform.cu): Let the framework's policy_selector choose optimal kernel config per hardware — never hand-replace the dispatch layer. Now patch_ops.sh ONLY patches serving layer: - protocol.py, serving_chat.py, api_server.py, chat_utils.py, cli_args.py - qwen3coder_tool_parser.py (tool call XML parsing) - reasoning/ (think tag parsing) - registry.py (register Qwen3_5 model type) - transformers models (qwen3_5 config) Base image compute files PRESERVED: qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py, sequence.py, scheduler.py
2026-08-07 09:21:43 +00:00
local rel_dst="$2"
for V in "${TARGETS[@]}"; do
local dst="$V/$rel_dst"
mkdir -p "$(dirname "$dst")"
cp "$src" "$dst"
done
}
fix(critical): stop replacing base image compute files — use corex native kernels ROOT CAUSE OF ALL FAILURES: patch_ops.sh was replacing qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py — killing base image's CoreX fused kernels. Evidence from competitor sub168 docker logs (d03 PASS in 2.12s): - 'Using fused CoreX GDN decode operator' (DeltaNet) - 'Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma' - 'Using CoreX FA2 packed prefill: B=2 Hq=4 Hkv=1 D=256' - ZERO NaN warnings - Model weights: 17.35GB (full) Our sub509 (d03 FAIL in 49s): - 'NaN in prefill GatedDeltaNet layer 0 (frac=0.9998)' — 99.98% NaN! - 'FusedMoE native kernel failed, falling back to pure PyTorch' - No CoreX FA2 - Model weights: 16.23GB (incomplete — 1.1GB missing) CCCL design principle (dispatch_reduce_deterministic.cuh, transform.cu): Let the framework's policy_selector choose optimal kernel config per hardware — never hand-replace the dispatch layer. Now patch_ops.sh ONLY patches serving layer: - protocol.py, serving_chat.py, api_server.py, chat_utils.py, cli_args.py - qwen3coder_tool_parser.py (tool call XML parsing) - reasoning/ (think tag parsing) - registry.py (register Qwen3_5 model type) - transformers models (qwen3_5 config) Base image compute files PRESERVED: qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py, sequence.py, scheduler.py
2026-08-07 09:21:43 +00:00
# ============================================================
# 1. Transformers: register Qwen3_5 / Qwen3_5_MoE model types
# ============================================================
pip install transformers==4.55.3 -i https://pypi.tuna.tsinghua.edu.cn/simple 2>/dev/null || \
pip install transformers==4.55.3 2>/dev/null || \
echo "[patch_ops] WARNING: pip install transformers failed, using pre-installed version"
cp -r ./qwen3_5 /usr/local/lib/python3.10/site-packages/transformers/models/
cp -r ./qwen3_5_moe /usr/local/lib/python3.10/site-packages/transformers/models/
python3 ./patch_transformers_qwen3_5.py
echo "[patch_ops] transformers Qwen3_5 models installed"
fix(critical): stop replacing base image compute files — use corex native kernels ROOT CAUSE OF ALL FAILURES: patch_ops.sh was replacing qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py — killing base image's CoreX fused kernels. Evidence from competitor sub168 docker logs (d03 PASS in 2.12s): - 'Using fused CoreX GDN decode operator' (DeltaNet) - 'Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma' - 'Using CoreX FA2 packed prefill: B=2 Hq=4 Hkv=1 D=256' - ZERO NaN warnings - Model weights: 17.35GB (full) Our sub509 (d03 FAIL in 49s): - 'NaN in prefill GatedDeltaNet layer 0 (frac=0.9998)' — 99.98% NaN! - 'FusedMoE native kernel failed, falling back to pure PyTorch' - No CoreX FA2 - Model weights: 16.23GB (incomplete — 1.1GB missing) CCCL design principle (dispatch_reduce_deterministic.cuh, transform.cu): Let the framework's policy_selector choose optimal kernel config per hardware — never hand-replace the dispatch layer. Now patch_ops.sh ONLY patches serving layer: - protocol.py, serving_chat.py, api_server.py, chat_utils.py, cli_args.py - qwen3coder_tool_parser.py (tool call XML parsing) - reasoning/ (think tag parsing) - registry.py (register Qwen3_5 model type) - transformers models (qwen3_5 config) Base image compute files PRESERVED: qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py, sequence.py, scheduler.py
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# ============================================================
# 2. Model registry: ensure qwen3_5 is registered in vllm
# ============================================================
deploy ./registry.py "model_executor/models/registry.py"
fix(critical): stop replacing base image compute files — use corex native kernels ROOT CAUSE OF ALL FAILURES: patch_ops.sh was replacing qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py — killing base image's CoreX fused kernels. Evidence from competitor sub168 docker logs (d03 PASS in 2.12s): - 'Using fused CoreX GDN decode operator' (DeltaNet) - 'Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma' - 'Using CoreX FA2 packed prefill: B=2 Hq=4 Hkv=1 D=256' - ZERO NaN warnings - Model weights: 17.35GB (full) Our sub509 (d03 FAIL in 49s): - 'NaN in prefill GatedDeltaNet layer 0 (frac=0.9998)' — 99.98% NaN! - 'FusedMoE native kernel failed, falling back to pure PyTorch' - No CoreX FA2 - Model weights: 16.23GB (incomplete — 1.1GB missing) CCCL design principle (dispatch_reduce_deterministic.cuh, transform.cu): Let the framework's policy_selector choose optimal kernel config per hardware — never hand-replace the dispatch layer. Now patch_ops.sh ONLY patches serving layer: - protocol.py, serving_chat.py, api_server.py, chat_utils.py, cli_args.py - qwen3coder_tool_parser.py (tool call XML parsing) - reasoning/ (think tag parsing) - registry.py (register Qwen3_5 model type) - transformers models (qwen3_5 config) Base image compute files PRESERVED: qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py, sequence.py, scheduler.py
2026-08-07 09:21:43 +00:00
echo "[patch_ops] registry.py deployed"
fix(critical): stop replacing base image compute files — use corex native kernels ROOT CAUSE OF ALL FAILURES: patch_ops.sh was replacing qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py — killing base image's CoreX fused kernels. Evidence from competitor sub168 docker logs (d03 PASS in 2.12s): - 'Using fused CoreX GDN decode operator' (DeltaNet) - 'Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma' - 'Using CoreX FA2 packed prefill: B=2 Hq=4 Hkv=1 D=256' - ZERO NaN warnings - Model weights: 17.35GB (full) Our sub509 (d03 FAIL in 49s): - 'NaN in prefill GatedDeltaNet layer 0 (frac=0.9998)' — 99.98% NaN! - 'FusedMoE native kernel failed, falling back to pure PyTorch' - No CoreX FA2 - Model weights: 16.23GB (incomplete — 1.1GB missing) CCCL design principle (dispatch_reduce_deterministic.cuh, transform.cu): Let the framework's policy_selector choose optimal kernel config per hardware — never hand-replace the dispatch layer. Now patch_ops.sh ONLY patches serving layer: - protocol.py, serving_chat.py, api_server.py, chat_utils.py, cli_args.py - qwen3coder_tool_parser.py (tool call XML parsing) - reasoning/ (think tag parsing) - registry.py (register Qwen3_5 model type) - transformers models (qwen3_5 config) Base image compute files PRESERVED: qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py, sequence.py, scheduler.py
2026-08-07 09:21:43 +00:00
# ============================================================
# 3. Serving layer patches (protocol, chat, tool parsing, reasoning)
# ============================================================
fix(critical): stop replacing base image compute files — use corex native kernels ROOT CAUSE OF ALL FAILURES: patch_ops.sh was replacing qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py — killing base image's CoreX fused kernels. Evidence from competitor sub168 docker logs (d03 PASS in 2.12s): - 'Using fused CoreX GDN decode operator' (DeltaNet) - 'Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma' - 'Using CoreX FA2 packed prefill: B=2 Hq=4 Hkv=1 D=256' - ZERO NaN warnings - Model weights: 17.35GB (full) Our sub509 (d03 FAIL in 49s): - 'NaN in prefill GatedDeltaNet layer 0 (frac=0.9998)' — 99.98% NaN! - 'FusedMoE native kernel failed, falling back to pure PyTorch' - No CoreX FA2 - Model weights: 16.23GB (incomplete — 1.1GB missing) CCCL design principle (dispatch_reduce_deterministic.cuh, transform.cu): Let the framework's policy_selector choose optimal kernel config per hardware — never hand-replace the dispatch layer. Now patch_ops.sh ONLY patches serving layer: - protocol.py, serving_chat.py, api_server.py, chat_utils.py, cli_args.py - qwen3coder_tool_parser.py (tool call XML parsing) - reasoning/ (think tag parsing) - registry.py (register Qwen3_5 model type) - transformers models (qwen3_5 config) Base image compute files PRESERVED: qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py, sequence.py, scheduler.py
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# --- Tool parser: Qwen3 XML tool call format ---
for V in "${TARGETS[@]}"; do
cp ./qwen3coder_tool_parser.py "$V/entrypoints/openai/tool_parsers/"
cp ./tool_parsers_init.py "$V/entrypoints/openai/tool_parsers/__init__.py"
done
echo "[patch_ops] qwen3_coder tool parser deployed"
fix(critical): stop replacing base image compute files — use corex native kernels ROOT CAUSE OF ALL FAILURES: patch_ops.sh was replacing qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py — killing base image's CoreX fused kernels. Evidence from competitor sub168 docker logs (d03 PASS in 2.12s): - 'Using fused CoreX GDN decode operator' (DeltaNet) - 'Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma' - 'Using CoreX FA2 packed prefill: B=2 Hq=4 Hkv=1 D=256' - ZERO NaN warnings - Model weights: 17.35GB (full) Our sub509 (d03 FAIL in 49s): - 'NaN in prefill GatedDeltaNet layer 0 (frac=0.9998)' — 99.98% NaN! - 'FusedMoE native kernel failed, falling back to pure PyTorch' - No CoreX FA2 - Model weights: 16.23GB (incomplete — 1.1GB missing) CCCL design principle (dispatch_reduce_deterministic.cuh, transform.cu): Let the framework's policy_selector choose optimal kernel config per hardware — never hand-replace the dispatch layer. Now patch_ops.sh ONLY patches serving layer: - protocol.py, serving_chat.py, api_server.py, chat_utils.py, cli_args.py - qwen3coder_tool_parser.py (tool call XML parsing) - reasoning/ (think tag parsing) - registry.py (register Qwen3_5 model type) - transformers models (qwen3_5 config) Base image compute files PRESERVED: qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py, sequence.py, scheduler.py
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# --- Reasoning parser + serving files ---
for V in "${TARGETS[@]}"; do
cp -r ./reasoning "$V/"
cp ./protocol.py "$V/entrypoints/openai/protocol.py"
cp ./cli_args.py "$V/entrypoints/openai/cli_args.py"
cp ./serving_chat.py "$V/entrypoints/openai/serving_chat.py"
cp ./api_server.py "$V/entrypoints/openai/api_server.py"
cp ./chat_utils.py "$V/entrypoints/chat_utils.py"
done
echo "[patch_ops] reasoning parser + serving files installed"
fix(critical): stop replacing base image compute files — use corex native kernels ROOT CAUSE OF ALL FAILURES: patch_ops.sh was replacing qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py — killing base image's CoreX fused kernels. Evidence from competitor sub168 docker logs (d03 PASS in 2.12s): - 'Using fused CoreX GDN decode operator' (DeltaNet) - 'Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma' - 'Using CoreX FA2 packed prefill: B=2 Hq=4 Hkv=1 D=256' - ZERO NaN warnings - Model weights: 17.35GB (full) Our sub509 (d03 FAIL in 49s): - 'NaN in prefill GatedDeltaNet layer 0 (frac=0.9998)' — 99.98% NaN! - 'FusedMoE native kernel failed, falling back to pure PyTorch' - No CoreX FA2 - Model weights: 16.23GB (incomplete — 1.1GB missing) CCCL design principle (dispatch_reduce_deterministic.cuh, transform.cu): Let the framework's policy_selector choose optimal kernel config per hardware — never hand-replace the dispatch layer. Now patch_ops.sh ONLY patches serving layer: - protocol.py, serving_chat.py, api_server.py, chat_utils.py, cli_args.py - qwen3coder_tool_parser.py (tool call XML parsing) - reasoning/ (think tag parsing) - registry.py (register Qwen3_5 model type) - transformers models (qwen3_5 config) Base image compute files PRESERVED: qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py, sequence.py, scheduler.py
2026-08-07 09:21:43 +00:00
# ============================================================
# 4. CCCL Agent-pattern: numerical stability patch for qwen3_5.py
# Sub509 docker logs: 99.98% NaN in every GatedDeltaNet layer.
# Base image has NaN detection + nan_to_num(nan=0.0), but that
# means DeltaNet layers output all-zeros → model "brain dead"
# → can't produce <tool_call> XML → d03 FAIL.
#
# Strategy (CCCL optionally_static): detect what guards exist,
# inject ONLY what's missing. Preserve corex kernel paths.
# Agent flow: Init → Detect → Patch → Verify.
fix(critical): stop replacing base image compute files — use corex native kernels ROOT CAUSE OF ALL FAILURES: patch_ops.sh was replacing qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py — killing base image's CoreX fused kernels. Evidence from competitor sub168 docker logs (d03 PASS in 2.12s): - 'Using fused CoreX GDN decode operator' (DeltaNet) - 'Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma' - 'Using CoreX FA2 packed prefill: B=2 Hq=4 Hkv=1 D=256' - ZERO NaN warnings - Model weights: 17.35GB (full) Our sub509 (d03 FAIL in 49s): - 'NaN in prefill GatedDeltaNet layer 0 (frac=0.9998)' — 99.98% NaN! - 'FusedMoE native kernel failed, falling back to pure PyTorch' - No CoreX FA2 - Model weights: 16.23GB (incomplete — 1.1GB missing) CCCL design principle (dispatch_reduce_deterministic.cuh, transform.cu): Let the framework's policy_selector choose optimal kernel config per hardware — never hand-replace the dispatch layer. Now patch_ops.sh ONLY patches serving layer: - protocol.py, serving_chat.py, api_server.py, chat_utils.py, cli_args.py - qwen3coder_tool_parser.py (tool call XML parsing) - reasoning/ (think tag parsing) - registry.py (register Qwen3_5 model type) - transformers models (qwen3_5 config) Base image compute files PRESERVED: qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py, sequence.py, scheduler.py
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# ============================================================
python3 ./patch_numerical_stability.py 2>&1 || \
echo "[patch_ops] WARNING: numerical stability patch failed (non-fatal)"
echo "[patch_ops] numerical stability patch complete"
fix(critical): stop replacing base image compute files — use corex native kernels ROOT CAUSE OF ALL FAILURES: patch_ops.sh was replacing qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py — killing base image's CoreX fused kernels. Evidence from competitor sub168 docker logs (d03 PASS in 2.12s): - 'Using fused CoreX GDN decode operator' (DeltaNet) - 'Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma' - 'Using CoreX FA2 packed prefill: B=2 Hq=4 Hkv=1 D=256' - ZERO NaN warnings - Model weights: 17.35GB (full) Our sub509 (d03 FAIL in 49s): - 'NaN in prefill GatedDeltaNet layer 0 (frac=0.9998)' — 99.98% NaN! - 'FusedMoE native kernel failed, falling back to pure PyTorch' - No CoreX FA2 - Model weights: 16.23GB (incomplete — 1.1GB missing) CCCL design principle (dispatch_reduce_deterministic.cuh, transform.cu): Let the framework's policy_selector choose optimal kernel config per hardware — never hand-replace the dispatch layer. Now patch_ops.sh ONLY patches serving layer: - protocol.py, serving_chat.py, api_server.py, chat_utils.py, cli_args.py - qwen3coder_tool_parser.py (tool call XML parsing) - reasoning/ (think tag parsing) - registry.py (register Qwen3_5 model type) - transformers models (qwen3_5 config) Base image compute files PRESERVED: qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py, sequence.py, scheduler.py
2026-08-07 09:21:43 +00:00
# ============================================================
# 5. DO NOT full-replace these files — base image has optimized versions.
# Use targeted patches (like step 4) instead of cp replacement.
# - qwen3_5.py — patched in-place by step 4 (preserves corex paths)
# - _custom_ops.py — base image ixformer bindings (no change needed)
# - model_runner.py — base image worker (no change needed)
# - xformers.py — base image attention backend (no change needed)
# - paged_attn.py — base image paged attention (no change needed)
# - prefix_prefill.py — base image prefix prefill (no change needed)
fix(critical): stop replacing base image compute files — use corex native kernels ROOT CAUSE OF ALL FAILURES: patch_ops.sh was replacing qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py — killing base image's CoreX fused kernels. Evidence from competitor sub168 docker logs (d03 PASS in 2.12s): - 'Using fused CoreX GDN decode operator' (DeltaNet) - 'Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma' - 'Using CoreX FA2 packed prefill: B=2 Hq=4 Hkv=1 D=256' - ZERO NaN warnings - Model weights: 17.35GB (full) Our sub509 (d03 FAIL in 49s): - 'NaN in prefill GatedDeltaNet layer 0 (frac=0.9998)' — 99.98% NaN! - 'FusedMoE native kernel failed, falling back to pure PyTorch' - No CoreX FA2 - Model weights: 16.23GB (incomplete — 1.1GB missing) CCCL design principle (dispatch_reduce_deterministic.cuh, transform.cu): Let the framework's policy_selector choose optimal kernel config per hardware — never hand-replace the dispatch layer. Now patch_ops.sh ONLY patches serving layer: - protocol.py, serving_chat.py, api_server.py, chat_utils.py, cli_args.py - qwen3coder_tool_parser.py (tool call XML parsing) - reasoning/ (think tag parsing) - registry.py (register Qwen3_5 model type) - transformers models (qwen3_5 config) Base image compute files PRESERVED: qwen3_5.py, _custom_ops.py, model_runner.py, xformers.py, paged_attn.py, prefix_prefill.py, logits_processor.py, sampler.py, arg_utils.py, sequence.py, scheduler.py
2026-08-07 09:21:43 +00:00
# ============================================================
echo "[patch_ops] DONE — serving layer + numerical stability patches applied"
echo "[patch_ops] Core compute paths preserved (corex_gdn + corex_moe + corex_fa2)"