6f6b7e959b
Dockerfile/qwen3_6_scripts/ex_engine/computility-run.yaml 全部 还原到26e6cb40的精确内容。删除所有26e6cb40不存在的新增文件 (prebuilt/*.so, wheels/*.whl, vendor_overrides/, 新增.cu/.sh等)。 目的：确认26e6cb40的文件内容在当前git状态下仍能通过竞赛平台build。 如果通过，说明问题在新增文件中；如果不通过，说明问题在git仓库层面。
280 lines
10 KiB
Python
280 lines
10 KiB
Python
"""
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corex_fa2.py — FlashAttention2 dispatch for BI-V100
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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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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 torch
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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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# 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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_ix_available = False
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try:
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from ixformer.contrib.vllm_flash_attn import (
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flash_attn_varlen_func as _flash_varlen_func,
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)
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_ix_available = True
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except ImportError:
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pass
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try:
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from ixformer.contrib.vllm_flash_attn import (
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flash_attn_with_kvcache as _flash_kvcache_func,
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)
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except ImportError:
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pass
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try:
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import ixformer.functions as ixf_F
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_paged_attn_v1 = ixf_F.vllm_single_query_cached_kv_attention
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except (ImportError, AttributeError):
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pass
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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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# =========================================================================
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# Mode 1: Packed Prefill (no KV cache, fresh sequences)
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# =========================================================================
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def fa2_packed_prefill(
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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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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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if not _logged_packed_prefill:
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logger.info(
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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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_logged_packed_prefill = True
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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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# 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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# =========================================================================
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def fa2_paged_decode(
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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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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=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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# 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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# 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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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("flash_attn_with_kvcache failed: %s", e)
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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
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# =========================================================================
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def fa2_paged_chunked_prefill(
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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,
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softmax_scale=None, causal=True, window_size=(-1, -1), block_size=16,
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):
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global _logged_paged_chunked
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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] if key is not None else num_heads
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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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max_cache_blocks = 0
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if block_tables is not None and block_tables.numel() > 0:
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max_cache_blocks = (block_tables >= 0).sum(dim=-1).max().item()
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if not _logged_paged_chunked:
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logger.info(
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"Using CoreX paged FA2 chunked prefill: B=%d Hq=%d Hkv=%d D=%d "
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"max_q=%d cache_blocks=%d",
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batch_size, num_heads, num_kv_heads, head_dim,
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max_seqlen_q, max_cache_blocks)
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_logged_paged_chunked = True
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# Use varlen for chunked prefill
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if _flash_varlen_func is not None:
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try:
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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_q,
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max_seqlen_q=max_seqlen_q, max_seqlen_k=max_seqlen_q,
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softmax_scale=softmax_scale, causal=causal,
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window_size=window_size)
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except Exception as e:
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logger.debug("FA2 chunked prefill via varlen failed: %s", e)
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raise RuntimeError("CoreX FA2 chunked prefill: no backend available")
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# =========================================================================
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# Unified dispatch
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# =========================================================================
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class CoreXFA2:
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def __init__(self, num_heads, num_kv_heads, head_dim):
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self.num_heads = num_heads
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self.num_kv_heads = num_kv_heads
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self.head_dim = head_dim
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self.scale = head_dim ** -0.5
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self.available = _ix_available or _ensure_bridge()
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@property
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def is_available(self):
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return self.available
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def packed_prefill(self, query, key, value, cu_seqlens_q, cu_seqlens_k,
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max_seqlen_q, max_seqlen_k, **kwargs):
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return fa2_packed_prefill(
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query, key, value, cu_seqlens_q, cu_seqlens_k,
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max_seqlen_q, max_seqlen_k, softmax_scale=self.scale, **kwargs)
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def paged_decode(self, query, key_cache, value_cache, block_tables,
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cache_seqlens, **kwargs):
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return fa2_paged_decode(
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query, key_cache, value_cache, block_tables, cache_seqlens,
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softmax_scale=self.scale, **kwargs)
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def chunked_prefill(self, query, key, value, key_cache, value_cache,
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cu_seqlens_q, max_seqlen_q, block_tables,
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cache_seqlens, **kwargs):
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return fa2_paged_chunked_prefill(
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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,
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softmax_scale=self.scale, **kwargs)
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