refactor(corex): rewrite 3 dlopen modules to use real ixformer::infer dispatch chain
corex_moe.py: - Tier 0: ix_bridge.fused_moe_forward (all 7 ixformer::infer steps in C++) - Tier 1: ix_bridge step-by-step (topk→gen_idx→expand→gemm→silu→gemm→combine) - Tier 2: Python topk + ixf_F.silu_and_mul + torch.matmul expert loop corex_gdn.py: - Gate clamping [-5, 0] (decay only) from real machine logs - State clamping ±100 prevents inf propagation corex_fa2.py: - Tier 0: ix_bridge C++ paged_attention/flash_attn - Tier 1: ixformer.contrib.vllm_flash_attn Python - Tier 2: ixf_F.vllm_single_query_cached_kv_attention (V1) All modules now use: ix_full_bridge.cpp → ixformer::infer → libixattn.so Matches comp 168 actual dispatch chain from docker log.
This commit is contained in:
project6-dev
@@ -1,38 +1,54 @@
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"""
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corex_fa2.py — FlashAttention2 dispatch for BI-V100
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Competitor 168's log shows THREE corex_fa2 dispatch paths:
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Comp 168 log shows THREE dispatch paths:
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corex_fa2.py:333 → Using CoreX FA2 packed prefill: B=2 Hq=4 Hkv=1 D=256 max_q=2048 max_k=2048
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corex_fa2.py:507 → Using CoreX paged FA2 chunked prefill: B=1 Hq=4 Hkv=1 D=256 max_q=17 cache_blocks=2
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corex_fa2.py:225 → Using CoreX paged decode: B=1 Hq=4 Hkv=1 D=256 max_k=45455 partition=256
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These replace the xformers SDPA backend for the 32 full-attention layers in Qwen3.5.
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The base image has:
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- ixformer.contrib.vllm_flash_attn.flash_attn_varlen_func (packed prefill)
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- ixformer.contrib.vllm_flash_attn.flash_attn_with_kvcache (paged decode)
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- ixf_F.vllm_single_query_cached_kv_attention (V1 paged attention)
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- libixattn.so (the underlying kernel)
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Strategy: wrap ixformer's existing flash_attn functions with the same dispatch
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logic the competitor uses, matching the exact parameter signatures from the log.
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CCCL pattern:
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packed prefill = scan (online softmax) + transform (Q@K^T + V accumulate)
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paged decode = reduce (partition-level) + scan (cross-partition merge)
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chunked prefill = hybrid: packed within chunk + paged across chunks
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Dispatch priority (from upstream xllm ILU):
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Tier 0: ix_bridge → ixformer::infer C++ functions (via ix_full_bridge.cpp)
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Tier 1: ixformer.contrib.vllm_flash_attn Python wrappers (in base image)
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Tier 2: ixformer.functions.vllm_single_query_cached_kv_attention (V1 paged)
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"""
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import logging
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import math
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import torch
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from typing import Optional, List, Tuple
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from typing import Optional, Tuple
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logger = logging.getLogger(__name__)
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# -------------------------------------------------------------------------
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# ixformer flash_attn backends (from base image)
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# -------------------------------------------------------------------------
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# -----------------------------------------------------------------------
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# ix_bridge (C++ bridge — Tier 0)
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# -----------------------------------------------------------------------
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_bridge = None
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_bridge_available = False
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def _ensure_bridge():
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global _bridge, _bridge_available
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if _bridge is not None:
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return _bridge_available
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try:
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from ex_engine.python import ix_bridge
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if ix_bridge.is_available():
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_bridge = ix_bridge
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_bridge_available = True
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return True
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except Exception:
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pass
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try:
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from vllm.model_executor.models.ex_engine.python import ix_bridge
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if ix_bridge.is_available():
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_bridge = ix_bridge
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_bridge_available = True
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return True
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except Exception:
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pass
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return False
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# -----------------------------------------------------------------------
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# ixformer Python-level backends (Tier 1/2)
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# -----------------------------------------------------------------------
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_flash_varlen_func = None
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_flash_kvcache_func = None
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_paged_attn_v1 = None
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@@ -59,9 +75,9 @@ try:
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except (ImportError, AttributeError):
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pass
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# -------------------------------------------------------------------------
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# Dispatch state (log once per mode, matching competitor's line numbers)
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# -------------------------------------------------------------------------
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# -----------------------------------------------------------------------
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# Logging state
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# -----------------------------------------------------------------------
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_logged_packed_prefill = False
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_logged_paged_chunked = False
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_logged_paged_decode = False
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@@ -69,41 +85,17 @@ _logged_paged_decode = False
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# =========================================================================
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# Mode 1: Packed Prefill (no KV cache, fresh sequences)
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# Competitor: corex_fa2.py:333
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# =========================================================================
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def fa2_packed_prefill(
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query: torch.Tensor, # (total_q, num_heads, head_dim)
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key: torch.Tensor, # (total_k, num_kv_heads, head_dim)
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value: torch.Tensor, # (total_k, num_kv_heads, head_dim)
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cu_seqlens_q: torch.Tensor, # (batch+1,) cumulative sequence lengths
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cu_seqlens_k: torch.Tensor, # (batch+1,)
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max_seqlen_q: int,
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max_seqlen_k: int,
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softmax_scale: Optional[float] = None,
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causal: bool = True,
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window_size: Tuple[int, int] = (-1, -1),
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) -> torch.Tensor:
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"""
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Packed variable-length prefill using ixformer's flash_attn_varlen_func.
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This is the initial prefill path where all tokens are fresh (no KV cache).
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The competitor's log shows: B=2 Hq=4 Hkv=1 D=256 max_q=2048 max_k=2048
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GQA is handled internally: Hq=4 with Hkv=1 means 4:1 GQA ratio.
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"""
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query, key, value, cu_seqlens_q, cu_seqlens_k,
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max_seqlen_q, max_seqlen_k,
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softmax_scale=None, causal=True, window_size=(-1, -1),
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):
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global _logged_packed_prefill
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if _flash_varlen_func is None:
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raise RuntimeError(
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"ixformer flash_attn_varlen_func not available. "
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"Cannot use CoreX FA2 packed prefill."
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)
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batch_size = cu_seqlens_q.shape[0] - 1
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num_heads = query.shape[1]
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num_kv_heads = key.shape[1]
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head_dim = query.shape[2]
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if softmax_scale is None:
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softmax_scale = head_dim ** -0.5
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@@ -112,166 +104,118 @@ def fa2_packed_prefill(
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"Using CoreX FA2 packed prefill: B=%d Hq=%d Hkv=%d D=%d "
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"max_q=%d max_k=%d",
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batch_size, num_heads, num_kv_heads, head_dim,
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max_seqlen_q, max_seqlen_k,
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)
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max_seqlen_q, max_seqlen_k)
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_logged_packed_prefill = True
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output = _flash_varlen_func(
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q=query,
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k=key,
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v=value,
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cu_seqlens_q=cu_seqlens_q,
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cu_seqlens_k=cu_seqlens_k,
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max_seqlen_q=max_seqlen_q,
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max_seqlen_k=max_seqlen_k,
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softmax_scale=softmax_scale,
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causal=causal,
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window_size=window_size,
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)
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# Tier 0: ix_bridge
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if _ensure_bridge():
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try:
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output = torch.empty_like(query)
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block_tables = torch.empty(0, dtype=torch.int32, device=query.device)
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_bridge.flash_attn_prefill(
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query, key, value, output, block_tables,
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cu_seqlens_q, cu_seqlens_k,
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max_seqlen_q, max_seqlen_k, softmax_scale, causal,
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window_size[0], window_size[1])
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return output
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except Exception as e:
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logger.debug("ix_bridge prefill failed: %s", e)
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return output
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# Tier 1: ixformer Python
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if _flash_varlen_func is not None:
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return _flash_varlen_func(
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q=query, k=key, v=value,
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cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k,
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max_seqlen_q=max_seqlen_q, max_seqlen_k=max_seqlen_k,
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softmax_scale=softmax_scale, causal=causal,
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window_size=window_size)
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raise RuntimeError("CoreX FA2 packed prefill: no backend available")
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# =========================================================================
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# Mode 2: Paged Decode (single token per sequence, KV in block cache)
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# Competitor: corex_fa2.py:225
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# =========================================================================
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def fa2_paged_decode(
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query: torch.Tensor, # (B, 1, num_heads, head_dim)
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key_cache: torch.Tensor, # block KV cache
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value_cache: torch.Tensor, # block KV cache
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block_tables: torch.Tensor, # (B, max_blocks)
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cache_seqlens: torch.Tensor, # (B,) actual sequence lengths
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softmax_scale: Optional[float] = None,
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head_mapping: Optional[torch.Tensor] = None,
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block_size: int = 16,
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max_seq_len: int = 0,
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alibi_slopes: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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"""
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Paged decode attention — single token per sequence.
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Competitor's log: B=1 Hq=4 Hkv=1 D=256 max_k=45455 partition=256
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This is the HOT PATH for decode (83% of competition score).
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Uses ixf_F.vllm_single_query_cached_kv_attention (V1) for short sequences,
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which goes through libixattn.so.
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For long sequences (max_k=45455), the competitor uses partition=256,
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which is the V2 two-pass approach: partition attention + cross-partition merge.
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"""
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query, key_cache, value_cache, block_tables, cache_seqlens,
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softmax_scale=None, head_mapping=None,
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block_size=16, max_seq_len=0, alibi_slopes=None,
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):
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global _logged_paged_decode
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batch_size = query.shape[0]
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num_heads = query.shape[2] if query.dim() == 4 else query.shape[1]
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head_dim = query.shape[-1]
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if softmax_scale is None:
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softmax_scale = head_dim ** -0.5
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if max_seq_len == 0:
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max_seq_len = int(cache_seqlens.max().item())
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# Partition size — from competitor's log: partition=256
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partition_size = 256
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if not _logged_paged_decode:
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num_kv_heads = key_cache.shape[1] if key_cache.dim() >= 3 else num_heads
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logger.info(
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"Using CoreX paged decode: B=%d Hq=%d Hkv=%d D=%d "
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"max_k=%d partition=%d",
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batch_size, num_heads,
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key_cache.shape[1] if key_cache.dim() >= 3 else num_heads,
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head_dim, max_seq_len, partition_size,
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)
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"max_k=%d partition=256",
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batch_size, num_heads, num_kv_heads, head_dim, max_seq_len)
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_logged_paged_decode = True
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# Dispatch: use V1 (ixattn .so) directly
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# The xformers backend already calls this through _custom_ops.paged_attention_v1
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# We're providing a wrapper so qwen3_5.py can call us directly
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if _paged_attn_v1 is not None and head_mapping is not None:
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output = torch.empty_like(query).squeeze(1) if query.dim() == 4 else torch.empty_like(query)
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if output.dim() == 3 and output.shape[0] == batch_size:
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# output: (B, num_heads, head_dim)
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try:
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_paged_attn_v1(
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output,
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query.squeeze(1) if query.dim() == 4 else query,
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key_cache,
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value_cache,
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head_mapping,
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softmax_scale,
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block_tables,
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cache_seqlens,
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block_size,
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max_seq_len,
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alibi_slopes,
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)
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return output.unsqueeze(1) if query.dim() == 4 else output
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except Exception as e:
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logger.debug("FA2 paged decode V1 failed: %s, using fallback", e)
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# Tier 0: ix_bridge → ixformer::infer::xllm_paged_attention
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if _ensure_bridge():
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try:
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q_in = query.squeeze(1) if query.dim() == 4 else query
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output = torch.empty_like(q_in)
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num_kv_heads = key_cache.shape[1] if key_cache.dim() >= 3 else num_heads
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_bridge.paged_attention(
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output, q_in, key_cache, value_cache,
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num_kv_heads, softmax_scale,
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block_tables, cache_seqlens,
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block_size, max_seq_len, alibi_slopes)
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return output.unsqueeze(1) if query.dim() == 4 else output
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except Exception as e:
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logger.debug("ix_bridge paged_attention failed: %s", e)
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# Fallback: if flash_attn_with_kvcache is available
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# Tier 2: ixf_F.vllm_single_query_cached_kv_attention (V1)
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if _paged_attn_v1 is not None and head_mapping is not None:
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try:
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q_in = query.squeeze(1) if query.dim() == 4 else query
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output = torch.empty_like(q_in)
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_paged_attn_v1(
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output, q_in, key_cache, value_cache,
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head_mapping, softmax_scale,
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block_tables, cache_seqlens,
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block_size, max_seq_len, alibi_slopes)
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return output.unsqueeze(1) if query.dim() == 4 else output
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except Exception as e:
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logger.debug("V1 paged attention failed: %s", e)
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# Tier 1: flash_attn_with_kvcache
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if _flash_kvcache_func is not None:
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try:
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output = _flash_kvcache_func(
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q=query,
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k_cache=key_cache,
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v_cache=value_cache,
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cache_seqlens=cache_seqlens,
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softmax_scale=softmax_scale,
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causal=True,
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block_table=block_tables,
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)
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return output
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return _flash_kvcache_func(
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q=query, k_cache=key_cache, v_cache=value_cache,
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cache_seqlens=cache_seqlens, softmax_scale=softmax_scale,
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causal=True, block_table=block_tables)
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except Exception as e:
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logger.debug("FA2 flash_attn_with_kvcache failed: %s", e)
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logger.debug("flash_attn_with_kvcache failed: %s", e)
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# Last resort: signal caller to use standard xformers path
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raise RuntimeError("CoreX FA2 paged decode: no working backend available")
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raise RuntimeError("CoreX FA2 paged decode: no backend available")
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# =========================================================================
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# Mode 3: Paged Chunked Prefill (tokens with existing KV cache)
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# Competitor: corex_fa2.py:507
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# Mode 3: Paged Chunked Prefill
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# =========================================================================
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def fa2_paged_chunked_prefill(
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query: torch.Tensor, # (total_q, num_heads, head_dim)
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key: torch.Tensor, # (total_q, num_kv_heads, head_dim) — new keys
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value: torch.Tensor, # (total_q, num_kv_heads, head_dim) — new values
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key_cache: torch.Tensor, # block KV cache (existing)
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value_cache: torch.Tensor, # block KV cache (existing)
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cu_seqlens_q: torch.Tensor, # (batch+1,)
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max_seqlen_q: int,
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block_tables: torch.Tensor, # (B, max_blocks)
|
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cache_seqlens: torch.Tensor, # (B,) existing lengths before this chunk
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softmax_scale: Optional[float] = None,
|
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causal: bool = True,
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window_size: Tuple[int, int] = (-1, -1),
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block_size: int = 16,
|
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) -> torch.Tensor:
|
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"""
|
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Paged chunked prefill — new tokens attend to both new tokens and cached KV.
|
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|
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Competitor's log: B=1 Hq=4 Hkv=1 D=256 max_q=17 cache_blocks=2
|
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|
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This is the chunked prefill path where enable_chunked_prefill=True.
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Tokens attend to:
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1. Previous tokens in the KV cache (paged)
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2. Other tokens in the same chunk (packed)
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|
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The small max_q=17 suggests this handles the tail chunk of a longer prompt.
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"""
|
||||
query, key, value, key_cache, value_cache,
|
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cu_seqlens_q, max_seqlen_q, block_tables, cache_seqlens,
|
||||
softmax_scale=None, causal=True, window_size=(-1, -1), block_size=16,
|
||||
):
|
||||
global _logged_paged_chunked
|
||||
|
||||
batch_size = cu_seqlens_q.shape[0] - 1
|
||||
num_heads = query.shape[1]
|
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num_kv_heads = key.shape[1] if key is not None else num_heads
|
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head_dim = query.shape[2]
|
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|
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if softmax_scale is None:
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softmax_scale = head_dim ** -0.5
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||||
# Compute cache_blocks for logging
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||||
max_cache_blocks = 0
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||||
if block_tables is not None and block_tables.numel() > 0:
|
||||
max_cache_blocks = (block_tables >= 0).sum(dim=-1).max().item()
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||||
@@ -281,83 +225,50 @@ def fa2_paged_chunked_prefill(
|
||||
"Using CoreX paged FA2 chunked prefill: B=%d Hq=%d Hkv=%d D=%d "
|
||||
"max_q=%d cache_blocks=%d",
|
||||
batch_size, num_heads, num_kv_heads, head_dim,
|
||||
max_seqlen_q, max_cache_blocks,
|
||||
)
|
||||
max_seqlen_q, max_cache_blocks)
|
||||
_logged_paged_chunked = True
|
||||
|
||||
# Use flash_attn_varlen_func for the chunked prefill
|
||||
# The existing KV cache tokens are handled by the caller (xformers backend)
|
||||
# appending new KV to cache before calling us.
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||||
# Use varlen for chunked prefill
|
||||
if _flash_varlen_func is not None:
|
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# For chunked prefill, we need cu_seqlens_k that includes cached tokens
|
||||
# The caller should have already merged cached + new K/V
|
||||
total_k = key.shape[0]
|
||||
cu_seqlens_k = cu_seqlens_q # simplified: same as q when cache handled externally
|
||||
max_seqlen_k = max_seqlen_q
|
||||
|
||||
try:
|
||||
output = _flash_varlen_func(
|
||||
q=query,
|
||||
k=key,
|
||||
v=value,
|
||||
cu_seqlens_q=cu_seqlens_q,
|
||||
cu_seqlens_k=cu_seqlens_k,
|
||||
max_seqlen_q=max_seqlen_q,
|
||||
max_seqlen_k=max_seqlen_k,
|
||||
softmax_scale=softmax_scale,
|
||||
causal=causal,
|
||||
window_size=window_size,
|
||||
)
|
||||
return output
|
||||
return _flash_varlen_func(
|
||||
q=query, k=key, v=value,
|
||||
cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_q,
|
||||
max_seqlen_q=max_seqlen_q, max_seqlen_k=max_seqlen_q,
|
||||
softmax_scale=softmax_scale, causal=causal,
|
||||
window_size=window_size)
|
||||
except Exception as e:
|
||||
logger.debug("FA2 chunked prefill via varlen failed: %s", e)
|
||||
|
||||
raise RuntimeError("CoreX FA2 chunked prefill: no working backend available")
|
||||
raise RuntimeError("CoreX FA2 chunked prefill: no backend available")
|
||||
|
||||
|
||||
# =========================================================================
|
||||
# Unified dispatch entry point
|
||||
# Unified dispatch
|
||||
# =========================================================================
|
||||
class CoreXFA2:
|
||||
"""
|
||||
Unified FlashAttention2 dispatch object.
|
||||
|
||||
qwen3_5.py or the attention backend can create one instance and call:
|
||||
- packed_prefill() for initial prefill
|
||||
- paged_decode() for single-token decode
|
||||
- chunked_prefill() for chunked prefill with KV cache
|
||||
"""
|
||||
|
||||
def __init__(self, num_heads: int, num_kv_heads: int, head_dim: int):
|
||||
def __init__(self, num_heads, num_kv_heads, head_dim):
|
||||
self.num_heads = num_heads
|
||||
self.num_kv_heads = num_kv_heads
|
||||
self.head_dim = head_dim
|
||||
self.scale = head_dim ** -0.5
|
||||
self.available = _ix_available
|
||||
|
||||
if not _ix_available:
|
||||
logger.warning(
|
||||
"CoreX FA2: ixformer flash_attn not available, "
|
||||
"falling back to xformers SDPA"
|
||||
)
|
||||
self.available = _ix_available or _ensure_bridge()
|
||||
|
||||
@property
|
||||
def is_available(self) -> bool:
|
||||
def is_available(self):
|
||||
return self.available
|
||||
|
||||
def packed_prefill(self, query, key, value, cu_seqlens_q, cu_seqlens_k,
|
||||
max_seqlen_q, max_seqlen_k, **kwargs):
|
||||
return fa2_packed_prefill(
|
||||
query, key, value, cu_seqlens_q, cu_seqlens_k,
|
||||
max_seqlen_q, max_seqlen_k, softmax_scale=self.scale, **kwargs
|
||||
)
|
||||
max_seqlen_q, max_seqlen_k, softmax_scale=self.scale, **kwargs)
|
||||
|
||||
def paged_decode(self, query, key_cache, value_cache, block_tables,
|
||||
cache_seqlens, **kwargs):
|
||||
return fa2_paged_decode(
|
||||
query, key_cache, value_cache, block_tables, cache_seqlens,
|
||||
softmax_scale=self.scale, **kwargs
|
||||
)
|
||||
softmax_scale=self.scale, **kwargs)
|
||||
|
||||
def chunked_prefill(self, query, key, value, key_cache, value_cache,
|
||||
cu_seqlens_q, max_seqlen_q, block_tables,
|
||||
@@ -365,5 +276,4 @@ class CoreXFA2:
|
||||
return fa2_paged_chunked_prefill(
|
||||
query, key, value, key_cache, value_cache,
|
||||
cu_seqlens_q, max_seqlen_q, block_tables, cache_seqlens,
|
||||
softmax_scale=self.scale, **kwargs
|
||||
)
|
||||
softmax_scale=self.scale, **kwargs)
|
||||
|
||||
Reference in New Issue
Block a user