b86a121d6d
Root cause: patch_ops.sh uses relative paths (./api_server.py, ./reasoning/, etc.) but never cd's into its own directory. Dockerfile sets WORKDIR=/workspace/ and runs 'bash /workspace/qwen3_6_scripts/patch_ops.sh', so cwd=/workspace/ at execution time. Every 'cp ./xxx' and 'deploy ./xxx' silently fails because the files are at /workspace/qwen3_6_scripts/xxx, not /workspace/xxx. Without set -e, the script completes with exit 0, Docker build succeeds, but NO patches are actually applied. Result: the original vllm 0.6.3 api_server.py runs (no reasoning-parser support), sees --reasoning-parser qwen3 as unrecognized, and exits with argparse error. Fix: 1. cd "$(dirname "$0")" at script start → all ./paths resolve correctly 2. set -eo pipefail → any failed cp now fails the build immediately
142 lines
6.1 KiB
Bash
Executable File
142 lines
6.1 KiB
Bash
Executable File
#!/bin/bash
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set -eo pipefail
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# BI-V100 engine patches for Qwen3.6-35B-A3B (Qwen3_5 architecture)
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#
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# All modifications are FULL FILE REPLACEMENTS — no AST patch scripts.
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# Each file was read in full from the base image vllm source, modified
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# with the necessary fixes, and placed here as a complete copy.
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#
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# Base image: git.modelhub.org.cn:9443/enginex-iluvatar/bi100-3.2.3-x86-ubuntu20.04-py3.10-poc-llm-infer:v1.2.3
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# vllm install path: /usr/local/corex/lib/python3/dist-packages/vllm/
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# CRITICAL: cd into this script's directory so all ./relative paths work
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# regardless of WORKDIR in Dockerfile or caller's cwd.
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cd "$(dirname "$0")"
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echo "[patch_ops] working directory: $(pwd)"
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VLLM=/usr/local/corex/lib/python3/dist-packages/vllm
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VLLM64=/usr/local/corex/lib64/python3/dist-packages/vllm
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# Deploy to ALL existing vllm paths — Python may load from either one
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# depending on PYTHONPATH ordering and namespace package resolution.
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TARGETS=()
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if [ -d "$VLLM" ]; then
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TARGETS+=("$VLLM")
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fi
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if [ -d "$VLLM64" ]; then
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TARGETS+=("$VLLM64")
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fi
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if [ ${#TARGETS[@]} -eq 0 ]; then
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echo "[patch_ops] ERROR: vllm not found at lib or lib64 path"
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exit 1
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fi
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echo "[patch_ops] vllm paths found: ${TARGETS[*]}"
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# Helper: copy file to all target vllm roots
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deploy() {
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local src="$1"
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local rel_dst="$2" # relative path within vllm, e.g. "attention/ops/paged_attn.py"
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for V in "${TARGETS[@]}"; do
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local dst="$V/$rel_dst"
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mkdir -p "$(dirname "$dst")"
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cp "$src" "$dst"
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done
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}
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# --- _custom_ops.py: SMEM 48KB fix + hardware ops bindings -------------------
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# Base image returns 32KB (32768) for get_max_shared_memory_per_block, but
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# BI-V100 actually has 48KB (49152) confirmed via ixsmi. This limits Triton
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# tile sizes and ixformer internal allocations if not corrected.
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# CCCL GridEvenShare test (catch2_test_grid_even_share.cu) validates that
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# work distribution depends on correct hardware parameters — wrong SMEM
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# means wrong tile_size means wrong grid_size.
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# FULL FILE REPLACEMENT.
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deploy ./_custom_ops.py "_custom_ops.py"
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echo "[patch_ops] _custom_ops.py → / (SMEM 32KB→48KB fix)"
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# --- paged_attn.py: pure-PyTorch attention fallback --------------------------
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deploy ./paged_attn.py "attention/ops/paged_attn.py"
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echo "[patch_ops] paged_attn.py → attention/ops/"
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# --- prefix_prefill.py: Triton-free prefix attention -------------------------
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deploy ./prefix_prefill.py "attention/ops/prefix_prefill.py"
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echo "[patch_ops] prefix_prefill.py → attention/ops/"
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# --- model_runner.py: prefix_cache_hit fix -----------------------------------
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deploy ./model_runner.py "worker/model_runner.py"
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echo "[patch_ops] model_runner.py → worker/"
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# --- xformers.py: head_dim>128 fallback + Q-tiling --------------------------
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deploy ./xformers.py "attention/backends/xformers.py"
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echo "[patch_ops] xformers.py → attention/backends/"
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# --- arg_utils.py: disable auto chunked-prefill for 32K+ --------------------
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deploy ./arg_utils.py "engine/arg_utils.py"
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echo "[patch_ops] arg_utils.py → engine/"
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# --- logits_processor.py: seq_groups=None guard ------------------------------
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deploy ./logits_processor.py "model_executor/layers/logits_processor.py"
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echo "[patch_ops] logits_processor.py → model_executor/layers/"
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# --- sampler.py: CCCL-ported top-k fast path for sampling --------------------
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deploy ./sampler.py "model_executor/layers/sampler.py"
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echo "[patch_ops] sampler.py → model_executor/layers/"
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# --- transformers: Qwen3_5 tokenizer / model files --------------------------
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# NOTE: patch_transformers_qwen3_5.py is the ONLY remaining patch script.
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# It modifies pip-installed transformers' configuration_auto.py and __init__.py
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# to register qwen3_5/qwen3_5_moe. These files come from pip (version-specific)
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# so we can't pre-copy them — the patch script inserts lines after known anchors.
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pip install transformers==4.55.3 -i https://pypi.tuna.tsinghua.edu.cn/simple 2>/dev/null || \
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pip install transformers==4.55.3 2>/dev/null || \
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echo "[patch_ops] WARNING: pip install transformers failed, using pre-installed version"
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cp -r ./qwen3_5 /usr/local/lib/python3.10/site-packages/transformers/models/
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cp -r ./qwen3_5_moe /usr/local/lib/python3.10/site-packages/transformers/models/
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python3 ./patch_transformers_qwen3_5.py
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echo "[patch_ops] transformers Qwen3_5 models installed"
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# --- vllm model: Qwen3.6 (Qwen3_5 arch) ------------------------------------
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for V in "${TARGETS[@]}"; do
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cp ./mamba_cache.py "$V/model_executor/models/"
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done
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deploy ./qwen3_5.py "model_executor/models/qwen3_5.py"
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deploy ./registry.py "model_executor/models/registry.py"
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echo "[patch_ops] qwen3_5.py + registry.py deployed"
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# --- paged_attention_v2_pytorch.py: PyTorch V2 attention fallback ------------
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for V in "${TARGETS[@]}"; do
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cp ./paged_attention_v2_pytorch.py "$V/paged_attention_v2_pytorch.py"
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done
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cp ./paged_attention_v2_pytorch.py /workspace/paged_attention_v2_pytorch.py
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echo "[patch_ops] paged_attention_v2_pytorch.py → all paths + /workspace/"
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# --- sequence.py: fix completion_tokens inflation ----------------------------
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deploy ./sequence.py "sequence.py"
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echo "[patch_ops] sequence.py → /"
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# --- scheduler.py: record num_cached_tokens ---------------------------------
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deploy ./scheduler.py "core/scheduler.py"
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echo "[patch_ops] scheduler.py → core/"
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# --- tool parser: Qwen3 XML tool call format --------------------------------
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for V in "${TARGETS[@]}"; do
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cp ./qwen3coder_tool_parser.py "$V/entrypoints/openai/tool_parsers/"
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cp ./tool_parsers_init.py "$V/entrypoints/openai/tool_parsers/__init__.py"
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done
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echo "[patch_ops] qwen3_coder tool parser deployed"
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# --- reasoning parser: Qwen3 <think>...</think> split -----------------------
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for V in "${TARGETS[@]}"; do
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cp -r ./reasoning "$V/"
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cp ./protocol.py "$V/entrypoints/openai/protocol.py"
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cp ./cli_args.py "$V/entrypoints/openai/cli_args.py"
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cp ./serving_chat.py "$V/entrypoints/openai/serving_chat.py"
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cp ./api_server.py "$V/entrypoints/openai/api_server.py"
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cp ./chat_utils.py "$V/entrypoints/chat_utils.py"
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done
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echo "[patch_ops] reasoning parser + serving files installed"
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echo "[patch_ops] DONE — all patches applied via full file replacement"
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