e8f0948fe1
Architecture (CCCL dispatch pattern):
base_image ixformer::infer → ix_full_bridge.so → ix_ops.py → vllm patches
New files:
ex_engine/python/ix_ops.py — Python API for all 14 ixformer::infer ops
ex_engine/python/patch_vllm_ops.py — monkey-patch vllm GemmaRMSNorm, SiluAndMul
ex_engine/deploy_ix_bridge.sh — build-time deployment script
Modified:
qwen3_6_scripts/patch_ops.sh — integrated ix_bridge deployment + startup hook
Call chain: DecoderLayer.forward → GemmaRMSNorm → ix_ops.fused_add_rms_norm
→ ixformer::infer::residual_rms_norm (fused C++ kernel)
344 lines
15 KiB
Bash
Executable File
344 lines
15 KiB
Bash
Executable File
#!/usr/bin/env bash
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# BI-V100 patch script for Qwen3.6-35B-A3B (Qwen3_5 MoE architecture)
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#
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# Triton situation on BI-V100:
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# - Standard Triton 2.3.1 is already present in the image.
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# - HAS_TRITON = False (hardcoded in vendor vllm), but Triton is still used
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# for TP-mode cache management (custom_cache_manager / libentry).
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# - The vendor's triton_utils/__init__.py, custom_cache_manager.py, libentry.py
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# are already correct for standard Triton 2.3.1 — do NOT overwrite them.
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# - DO NOT install BI-V150 corex Triton 2.1.0 (pkgs/triton): that causes
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# GPU hang on BI-V100 because the Triton CUDA PTX kernels are incompatible.
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# Recommended server start command for TP=4 support 256K, needs chunked prefill
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# CUDA_VISIBLE_DEVICES="4,5,6,7" VLLM_ENGINE_ITERATION_TIMEOUT_S=3600 python3 -m vllm.entrypoints.openai.api_server \
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# --model /workspace/models/Qwen3.6-35B-A3B --port 1111 --served-model-name llm \
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# --max-model-len 262144 --trust-remote-code -tp 4 --gpu-memory-utilization 0.90 \
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# --max-num-seqs 1 --disable-log-requests --disable-frontend-multiprocessing \
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# --max-num-batched-tokens 8192 --enable-chunked-prefill --enable-prefix-caching \
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# --max-seq-len-to-capture 32768 --enable-auto-tool-choice \
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# --tool-call-parser qwen3_coder --reasoning-parser qwen3
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#
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# With prefix caching (GDN align-mode, requires chunked prefill):
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# CUDA_VISIBLE_DEVICES="4,5,6,7" VLLM_ENGINE_ITERATION_TIMEOUT_S=3600 python3 -m vllm.entrypoints.openai.api_server \
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# --model /workspace/models/Qwen3.6-35B-A3B --port 1111 --served-model-name llm \
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# --max-model-len 262144 --trust-remote-code -tp 4 --gpu-memory-utilization 0.90 \
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# --max-num-seqs 1 --disable-log-requests --disable-frontend-multiprocessing \
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# --max-num-batched-tokens 8192 --enable-chunked-prefill --enable-prefix-caching \
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# --max-seq-len-to-capture 32768 --enable-auto-tool-choice \
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# --tool-call-parser qwen3_coder --reasoning-parser qwen3
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set -eo pipefail
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# cd into this script's directory so ./relative paths work
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cd "$(dirname "${BASH_SOURCE[0]}")"
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echo "[patch_ops] working directory: $(pwd)"
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build_stage() { printf '[BI100 BUILD] %s\n' "$1" >&2; }
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require_file() {
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local path=$1
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[[ -f "$path" ]] || {
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printf 'required patch source is missing: %s\n' "$path" >&2
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exit 2
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}
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}
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install_patch_file() {
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local source=$1
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local target=$2
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require_file "$source"
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mkdir -p "$(dirname "$target")"
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install -m 0644 "$source" "$target"
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}
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build_stage "patch script entered"
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build_stage "checking offline transformers dependency"
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# --- transformers: Qwen3_5 tokenizer / model files --------------------------
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TRANSFORMERS_REQUIRED_VERSION="4.55.3"
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if ! python3 - "$TRANSFORMERS_REQUIRED_VERSION" <<'PY'
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import importlib.metadata
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import sys
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required = sys.argv[1]
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try:
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installed = importlib.metadata.version("transformers")
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except importlib.metadata.PackageNotFoundError:
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raise SystemExit(1)
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raise SystemExit(0 if installed == required else 1)
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PY
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then
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WHEEL_DIR="./wheels"
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if ! ls "${WHEEL_DIR}/transformers-${TRANSFORMERS_REQUIRED_VERSION}"*.whl >/dev/null 2>&1; then
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echo "transformers ${TRANSFORMERS_REQUIRED_VERSION} is required, but no offline wheel was found in ${WHEEL_DIR}" >&2
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exit 2
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fi
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python3 -m pip install --no-index --no-deps --find-links="${WHEEL_DIR}" \
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"transformers==${TRANSFORMERS_REQUIRED_VERSION}"
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fi
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python3 - "$TRANSFORMERS_REQUIRED_VERSION" <<'PY'
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import importlib.metadata
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import sys
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required = sys.argv[1]
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installed = importlib.metadata.version("transformers")
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if installed != required:
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raise SystemExit(
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f"transformers version mismatch: expected {required}, got {installed}")
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print(f"[ok] transformers {installed}")
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PY
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build_stage "discovering Python package roots"
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python3 - <<'PY' > /tmp/qwen36_patch_paths.env
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from patch_utils import package_root, shell_env_line
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print(shell_env_line("VLLM_ROOT", package_root("vllm")))
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print(shell_env_line("TRANSFORMERS_ROOT", package_root("transformers")))
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PY
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source /tmp/qwen36_patch_paths.env
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echo "VLLM_ROOT=${VLLM_ROOT}"
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echo "TRANSFORMERS_ROOT=${TRANSFORMERS_ROOT}"
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[[ -d "$VLLM_ROOT" ]] || {
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printf 'vLLM root does not exist: %s\n' "$VLLM_ROOT" >&2
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exit 2
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}
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VLLM_OVERRIDE_ROOT="./vendor_overrides/vllm"
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[[ -d "$VLLM_OVERRIDE_ROOT" ]] || {
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printf 'vLLM override directory missing: %s\n' "$VLLM_OVERRIDE_ROOT" >&2
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exit 2
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}
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build_stage "installing authoritative vLLM core block overrides"
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install_patch_file \
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"${VLLM_OVERRIDE_ROOT}/core/evictor_v2.py" \
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"${VLLM_ROOT}/core/evictor_v2.py"
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install_patch_file \
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"${VLLM_OVERRIDE_ROOT}/core/block/cpu_kv_content_cache.py" \
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"${VLLM_ROOT}/core/block/cpu_kv_content_cache.py"
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install_patch_file \
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"${VLLM_OVERRIDE_ROOT}/core/block/cpu_gpu_block_allocator.py" \
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"${VLLM_ROOT}/core/block/cpu_gpu_block_allocator.py"
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install_patch_file \
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"${VLLM_OVERRIDE_ROOT}/core/block/prefix_caching_block.py" \
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"${VLLM_ROOT}/core/block/prefix_caching_block.py"
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install_patch_file \
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"${VLLM_OVERRIDE_ROOT}/core/block/block_table.py" \
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"${VLLM_ROOT}/core/block/block_table.py"
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install_patch_file \
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"${VLLM_OVERRIDE_ROOT}/core/block_manager_v2.py" \
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"${VLLM_ROOT}/core/block_manager_v2.py"
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install_patch_file \
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"${VLLM_OVERRIDE_ROOT}/sampling_params.py" \
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"${VLLM_ROOT}/sampling_params.py"
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install_patch_file \
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"${VLLM_OVERRIDE_ROOT}/model_executor/sampling_metadata.py" \
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"${VLLM_ROOT}/model_executor/sampling_metadata.py"
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install_patch_file \
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"${VLLM_OVERRIDE_ROOT}/model_executor/layers/sampler.py" \
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"${VLLM_ROOT}/model_executor/layers/sampler.py"
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build_stage "installing hash-pinned CoreX 3.2.3 extensions (16 prebuilt .so)"
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bash ./install_prebuilt_corex.sh "${VLLM_ROOT}"
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build_stage "installing BI100 runtime modules"
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cp ./bi100_env.py "${VLLM_ROOT}/bi100_env.py"
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cp ./bi100_profile.py "${VLLM_ROOT}/bi100_profile.py"
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cp ./block_major_kv_cache.py "${VLLM_ROOT}/block_major_kv_cache.py"
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cp ./gdn_prefix.py "${VLLM_ROOT}/gdn_prefix.py"
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build_stage "installing CoreX paged-KV swap compatibility"
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python3 ./patch_corex_swap_blocks.py
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python3 ./patch_block_major_cache_engine.py
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python3 ./patch_worker_cache_transfer_order.py
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# --- paged_attn.py: replace forward_prefix with pure-PyTorch fallback -------
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# The Triton context_attention_fwd kernel hangs BI-V100 GPUs permanently
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# (standard Triton 2.3.1 PTX is not supported by the corex runtime either).
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# Our paged_attn.py bypasses it entirely via _forward_prefix_pytorch, which
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# utilizes K-tiling techniques, and also have _forward_decode_pytorch to bypass kernel
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# when context length is high
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cp ./paged_attn.py "${VLLM_ROOT}/attention/ops/paged_attn.py"
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# --- model_runner.py: fix prefix_cache_hit stays True in chunked-prefill chunk 2+ ---
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# Bug: _compute_for_prefix_cache_hit Case 1 (prefix_cache_len <= context_len)
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# leaves prefix_cache_hit=True. Then _add_seq_group uses block_table=computed_block_nums
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# (only the original prefix blocks), ignoring chunk-1 KV cache blocks.
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# _forward_prefix_pytorch then gets an undersized block_tables and crashes with
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# "amax(): Expected reduction dim -1 to have non-zero size" on the 2nd tile.
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# Fix: set prefix_cache_hit=False for Case 1 so the full block_tables is used.
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python3 ./patch_model_runner.py
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build_stage "installing executor startup diagnostics"
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python3 ./patch_executor_startup_debug.py
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python3 ./patch_worker_startup_profile_guard.py
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python3 ./patch_block_major_worker_capacity.py
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build_stage "installing transformers Qwen3.5 model support"
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cp -r ./qwen3_5 "${TRANSFORMERS_ROOT}/models/"
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cp -r ./qwen3_5_moe "${TRANSFORMERS_ROOT}/models/"
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python3 ./patch_transformers_qwen3_5.py
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build_stage "installing vLLM Qwen3.6 model implementation"
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# --- vllm model: Qwen3.6-35B-A3B (Qwen3_5 MoE arch) -------------------------
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cp ./mamba_cache.py "${VLLM_ROOT}/model_executor/models/"
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cp ./qwen3_5.py "${VLLM_ROOT}/model_executor/models/qwen3_5.py"
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cp ./ix_fused_moe.py "${VLLM_ROOT}/model_executor/models/ix_fused_moe.py" || true
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python3 ./patch_vllm_qwen3_5.py
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# --- Deploy prebuilt .so into vllm package for import -----------------------
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PREBUILT_DIR="./prebuilt/corex-3.2.3-ivcore10"
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if [ -d "$PREBUILT_DIR" ]; then
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for so_file in "$PREBUILT_DIR"/*.so; do
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base=$(basename "$so_file" .so)
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# Deploy corex_*.so as vllm submodules (import from vllm import corex_xxx)
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cp "$so_file" "${VLLM_ROOT}/${base}.so" 2>/dev/null || true
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echo "[patch_ops] deployed ${base}.so → ${VLLM_ROOT}/"
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done
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fi
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# --- Deploy ix_bridge Python integration layer --------------------------------
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build_stage "deploying ix_bridge operator replacements"
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EX_ENGINE_DIR="$(cd "$(dirname "$0")/../ex_engine" 2>/dev/null && pwd || echo "")"
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if [ -z "$EX_ENGINE_DIR" ] || [ ! -d "$EX_ENGINE_DIR" ]; then
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EX_ENGINE_DIR="$(cd "$(dirname "$0")" && pwd)/../ex_engine"
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fi
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if [ -d "$EX_ENGINE_DIR/python" ]; then
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# Create ex_engine package inside vllm
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mkdir -p "${VLLM_ROOT}/ex_engine/csrc"
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echo '"""ex_engine — Algorithm factor replacement for BI-V100."""' > "${VLLM_ROOT}/ex_engine/__init__.py"
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# Deploy Python modules
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cp "$EX_ENGINE_DIR/python/ix_ops.py" "${VLLM_ROOT}/ex_engine/ix_ops.py"
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cp "$EX_ENGINE_DIR/python/patch_vllm_ops.py" "${VLLM_ROOT}/ex_engine/patch_vllm_ops.py"
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echo "[patch_ops] deployed ix_ops.py + patch_vllm_ops.py → ${VLLM_ROOT}/ex_engine/"
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# Deploy bridge C++ source for JIT fallback
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for cpp in "$EX_ENGINE_DIR"/csrc/ix_full_bridge*.cpp "$EX_ENGINE_DIR"/csrc/ix_moe_bridge.cpp; do
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[ -f "$cpp" ] && cp "$cpp" "${VLLM_ROOT}/ex_engine/csrc/" && \
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echo "[patch_ops] deployed $(basename $cpp) for JIT fallback"
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done
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# Create startup hook that patches vllm ops at import time
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cat > "${VLLM_ROOT}/ix_startup_patch.py" << 'STARTUP_EOF'
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"""Apply ix_ops patches at vllm startup."""
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import logging
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_logger = logging.getLogger("ix_startup_patch")
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def apply():
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try:
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from vllm.ex_engine.patch_vllm_ops import apply_all_patches
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n = apply_all_patches()
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if n > 0:
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_logger.info("ix_startup_patch: %d patches applied", n)
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return n
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except Exception as e:
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_logger.warning("ix_startup_patch failed: %s", e)
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return 0
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_n_patches = apply()
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STARTUP_EOF
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echo "[patch_ops] deployed ix_startup_patch.py"
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# Hook into vllm __init__.py to auto-apply patches on import
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VLLM_INIT="${VLLM_ROOT}/__init__.py"
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if [ -f "$VLLM_INIT" ]; then
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if ! grep -q "ix_startup_patch" "$VLLM_INIT" 2>/dev/null; then
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echo "" >> "$VLLM_INIT"
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echo "# Auto-apply ix_bridge operator patches" >> "$VLLM_INIT"
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echo "try:" >> "$VLLM_INIT"
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echo " from vllm import ix_startup_patch" >> "$VLLM_INIT"
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echo "except Exception:" >> "$VLLM_INIT"
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echo " pass" >> "$VLLM_INIT"
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echo "[patch_ops] hooked ix_startup_patch into vllm/__init__.py"
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fi
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fi
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else
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echo "[patch_ops] WARN: ex_engine/python not found, skip ix_bridge deployment"
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fi
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# --- sequence.py: fix completion_tokens inflation under chunked prefill ------
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# Bug: get_output_token_ids_to_return(delta=True) with num_new_tokens=0
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# returns _cached_all_token_ids[-0:] == [0:] (the ENTIRE prompt+output list).
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# Each prefill chunk step adds prompt_len to previous_num_tokens, so a 10K
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# prompt processed in 3 chunks inflates completion_tokens by ~30K.
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# Also adds num_cached_tokens field to RequestMetrics for prefix-cache stats.
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cp ./sequence.py "${VLLM_ROOT}/sequence.py"
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# --- scheduler.py: record num_cached_tokens in RequestMetrics ----------------
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# Reports only the longest prefix backed by both live KV blocks and an exact
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# GDN restore state. Raw KV-only hits must not inflate cached_tokens.
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# serving_chat.py exposes the value in the OpenAI-compatible usage details.
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cp ./scheduler.py "${VLLM_ROOT}/core/scheduler.py"
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build_stage "installing diagnostic initial allocation trace"
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python3 ./patch_block_manager_cache_trace.py
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build_stage "installing scheduler and attention patches"
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# --- xformers: bypass cudnnFlashAttnForward (head_dim=256 > 128 limit) ------
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# Injects _run_sdpa_fallback (pure matmul+softmax) into xformers.py.
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# Required because head_dim=256 > 128 and ixformer flash attention either
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# crashes (is_causal=True) or produces wrong output (attn_mask path).
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# The fallback uses query_start_loc to derive actual query lengths, so it
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# works correctly during profiling runs with chunked-prefill-style batches.
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# also bypasses auto chunked prefill on
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python3 ./patch_xformers_sdpa_seq.py
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python3 ./patch_xformers_profile.py
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build_stage "installing API parsers and serving modules"
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# --- tool parser: Qwen3 XML tool call format ---------------------------------
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# Registers "qwen3_coder" parser for Qwen3.6 XML-style tool calls:
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# <tool_call><function=name><parameter=key>\nvalue\n</parameter></function></tool_call>
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# Use at server start: --tool-call-parser qwen3_coder --enable-auto-tool-choice
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cp ./qwen3coder_tool_parser.py "${VLLM_ROOT}/entrypoints/openai/tool_parsers/"
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python3 ./patch_vllm_tool_parser.py
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# --- reasoning parser: Qwen3 <think>...</think> split ------------------------
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# Adds --reasoning-parser qwen3 support.
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# Routes thinking tokens to reasoning_content, rest to content in the delta.
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# Works together with --tool-call-parser qwen3_coder (think → tool call flow).
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cp -r ./reasoning "${VLLM_ROOT}/"
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cp ./protocol.py "${VLLM_ROOT}/entrypoints/openai/protocol.py"
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cp ./cli_args.py "${VLLM_ROOT}/entrypoints/openai/cli_args.py"
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cp ./serving_chat.py "${VLLM_ROOT}/entrypoints/openai/serving_chat.py"
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cp ./serving_tokenization.py \
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"${VLLM_ROOT}/entrypoints/openai/serving_tokenization.py"
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cp ./api_server.py "${VLLM_ROOT}/entrypoints/openai/api_server.py"
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cp ./chat_utils.py "${VLLM_ROOT}/entrypoints/chat_utils.py"
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python3 - ./api_server.py \
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"${VLLM_ROOT}/entrypoints/openai/api_server.py" <<'PY'
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from pathlib import Path
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import sys
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source = Path(sys.argv[1]).read_bytes()
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installed = Path(sys.argv[2]).read_bytes()
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if source != installed:
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raise SystemExit("runtime api_server overlay identity mismatch")
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PY
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# --- protocol.py identity check: ensure max_completion_tokens is accepted ---
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python3 - ./protocol.py \
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"${VLLM_ROOT}/entrypoints/openai/protocol.py" <<'PY'
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from pathlib import Path
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import sys
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source = Path(sys.argv[1]).read_bytes()
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installed = Path(sys.argv[2]).read_bytes()
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if source != installed:
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raise SystemExit("runtime protocol overlay identity mismatch")
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# Verify max_completion_tokens field is declared (not just extra=allow)
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if b"max_completion_tokens" not in installed:
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raise SystemExit("protocol.py missing max_completion_tokens field")
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PY
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build_stage "compiling submission Python sources"
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find . -path './wheels' -prune -o -name '*.py' -print0 | xargs -0 python3 -m py_compile
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build_stage "verifying dlopen chain"
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python3 ./verify_dlopen_chain.py --vllm-root "${VLLM_ROOT}" || {
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echo "[WARN] dlopen chain verification found issues (non-fatal)"
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}
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build_stage "patch script completed"
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