b902090fb2
Source: cccl_upstream/cub/test/catch2_test_grid_even_share.cu (random pick) GridEvenShare test validates: grid_size = min(max_grid, ceil_div(N, tile_size)) If SMEM is reported as 32KB instead of 48KB, tile_size is 33% smaller, grid_size is 50% larger, and every kernel launch wastes occupancy. Base image _custom_ops.py: get_max_shared_memory_per_block → 32*1024 = 32768 Our fix: → 49152 (confirmed 48KB via ixsmi on Phanthy Cloud) This affects ALL kernel launches that query SMEM limits: - Triton JIT tile sizing (prefix_prefill, flash_attn) - ixformer internal SMEM allocation - paged_attention block_size calculations Was modified in vllm/_custom_ops.py but NEVER added to qwen3_6_scripts/ for Docker deployment. Now deployed.
124 lines
6.0 KiB
Bash
Executable File
124 lines
6.0 KiB
Bash
Executable File
# 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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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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# Detect which lib path exists
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if [ -d "$VLLM" ]; then
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V=$VLLM
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elif [ -d "$VLLM64" ]; then
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V=$VLLM64
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else
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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 path: $V"
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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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cp ./_custom_ops.py $V/_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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# Bypasses Triton context_attention_fwd (hangs BI-V100 permanently).
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# Uses K-tiling Flash Attention online softmax for prefix attention.
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# Uses pure-PyTorch decode for seq_len > 32K.
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# CCCL-ported: adaptive tile sizing from dispatch_reduce.cuh GridEvenShare.
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cp ./paged_attn.py $V/attention/ops/paged_attn.py
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echo "[patch_ops] paged_attn.py → attention/ops/"
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# --- model_runner.py: prefix_cache_hit fix -----------------------------------
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# Bug: Case 1 (prefix_cache_len <= context_len) leaves prefix_cache_hit=True,
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# causing undersized block_tables in chunked prefill chunk 2+.
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# Fix: set prefix_cache_hit=False for Case 1.
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# FULL FILE REPLACEMENT — no patch_model_runner.py script.
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cp ./model_runner.py $V/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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# Injects _run_sdpa_fallback (pure matmul+softmax) for head_dim=256.
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# ixformer flash attention crashes (is_causal=True) or gives wrong output.
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# Also disables auto chunked-prefill (Q-tiling handles long context).
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# FULL FILE REPLACEMENT — no patch_xformers_sdpa_seq.py script.
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cp ./xformers.py $V/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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# Q-tiling in _run_sdpa_fallback handles long-context memory.
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# FULL FILE REPLACEMENT.
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cp ./arg_utils.py $V/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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# Prevents crash when seq_groups is None during intermediate chunked-prefill.
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# FULL FILE REPLACEMENT.
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cp ./logits_processor.py $V/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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# When all sequences use top_p=1.0, skip full sort+cumsum and use torch.topk.
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# CCCL partition/flagged.cu insight: radix select is O(N×bits_per_pass) vs
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# full sort O(N log N). For vocab=152064: topk ~11 passes vs sort ~17 passes.
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# FULL FILE REPLACEMENT.
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cp ./sampler.py $V/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
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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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# FULL FILE REPLACEMENT of registry.py with Qwen3_5 entries pre-added.
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# No more patch_vllm_qwen3_5.py script.
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cp ./mamba_cache.py $V/model_executor/models/
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cp ./qwen3_5.py $V/model_executor/models/qwen3_5.py
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cp ./registry.py $V/model_executor/models/registry.py
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echo "[patch_ops] qwen3_5.py + registry.py deployed"
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# --- sequence.py: fix completion_tokens inflation ----------------------------
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cp ./sequence.py $V/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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cp ./scheduler.py $V/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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# FULL FILE REPLACEMENT of __init__.py with Qwen3CoderToolParser pre-added.
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# No more patch_vllm_tool_parser.py script.
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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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echo "[patch_ops] qwen3_coder tool parser deployed"
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# --- reasoning parser: Qwen3 <think>...</think> split -----------------------
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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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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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