3581dd5435
Layer 1: hw_config.h (245 lines) — BI-V100 hardware descriptor + tuning tables Layer 2: moe_pipeline.py (461 lines) — MoE 7-step pipeline orchestrator Layer 3: attn_dispatch.py (270 lines) — Attention prefill/decode dispatch Layer 4: ilu_ops_api.h (182 lines) — Dispatch signature contract Layer 5: kernel_moe_ops.cpp (155 lines) — MoE kernel-level ops wrappers Layer 6: kernel_elem_ops.cpp (210 lines) — Element-wise kernel wrappers Layer 7: ixformer_infer.h (246 lines) — ixformer::infer namespace contract Layer 8: factor_topk_softmax.cu (456 lines) — MoE routing CUDA kernel Layer 9: factor_moe_compute_index.cu (174 lines) — Token index CUDA kernel Layer 10: factor_moe_combine.cu (154 lines) — Weighted combine CUDA kernel Total: 2553 lines across 10 files (h/cpp/cu/py) Upstream reference: 2787 lines across corresponding 10 xllm AST layers Each file follows the read-read-read-write pattern from upstream xllm, ds_vllm, and fla repos. No hand-written inference code — all kernel logic is cat-migrated from the upstream references.
271 lines
9.7 KiB
Python
271 lines
9.7 KiB
Python
"""
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ex_engine/factors/attn_dispatch.py
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Layer 3: Attention prefill/decode dispatch
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Upstream parallel: xllm_layers/ilu/attention.h + attention.cpp (82 + ~200 lines)
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→ batch_prefill() dispatches to ixinfer_flash_attn_unpad_with_block_tables
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→ batch_decode() dispatches to xllm_paged_attention
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The key dispatch decision:
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prefill (num_prefill_tokens > 0):
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→ flash attention with block tables (unpadded, variable-length)
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→ supports cu_seqlens for multi-request batching
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→ window attention via window_size_left/right
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decode (pure autoregressive, 1 token per sequence):
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→ paged attention v1/v2
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→ v1 vs v2 decision: total_tiles vs 2 × sm_count
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→ BI-V100: 16 SMs → V2 beneficial when seq_len > 1024
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GDN (GatedDeltaNet) layers [1,7,13,19] bypass this entirely —
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they use the gdn_dispatch module instead.
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Call chain:
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qwen3_5.py Qwen3_5DecoderLayer.forward()
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→ (full attention layers): attn_dispatch.dispatch_attention()
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→ prefill: flash_attn_with_block_tables
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→ decode: paged_attention_v1 or v2
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→ (GDN layers): gdn_dispatch.dispatch_gdn()
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"""
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import logging
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from typing import Optional, Tuple
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import torch
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logger = logging.getLogger("ex_engine.attn_dispatch")
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# BI-V100 dispatch thresholds
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# Source: SYSTEM_DESIGN.md, sub694 TPS analysis
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_SM_COUNT = 16
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_V2_THRESHOLD_FACTOR = 2 # V2 when total_tiles > 2 × SM_COUNT
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_PAGE_BLOCK_SIZE = 16 # default paged attention block size
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class AttnDispatchConfig:
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"""
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Attention dispatch configuration.
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Parallels xllm_layers/ilu/attention.h struct members:
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scale, is_causal, window_size_left, window_size_right, softcap
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"""
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__slots__ = (
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'num_heads', 'num_kv_heads', 'head_dim', 'scale',
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'is_causal', 'window_left', 'window_right',
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'block_size', 'max_context_len', 'softcap',
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)
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def __init__(
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self,
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num_heads: int = 28,
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num_kv_heads: int = 4,
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head_dim: int = 128,
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scale: Optional[float] = None,
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is_causal: bool = True,
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window_left: int = -1,
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window_right: int = -1,
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block_size: int = 16,
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max_context_len: int = 131072,
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softcap: float = 0.0,
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):
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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 = scale or (head_dim ** -0.5)
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self.is_causal = is_causal
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self.window_left = window_left
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self.window_right = window_right
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self.block_size = block_size
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self.max_context_len = max_context_len
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self.softcap = softcap
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def should_use_paged_v2(
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seq_len: int,
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num_kv_heads: int,
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block_size: int = 16,
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partition_size: int = 512,
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) -> bool:
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"""
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V1 vs V2 decision for paged attention.
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Upstream: vllm/attention/ops/paged_attn.py PagedAttention._use_v2()
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Rule: total_tiles = num_kv_heads × ceil(seq_len / partition_size)
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use V2 when total_tiles > 2 × SM_COUNT (BI-V100: 32)
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On BI-V100 with 16 SMs and 4 KV heads:
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V2 when seq_len > 512 × (2 × 16 / 4) = 4096
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In practice, V2 is better for seq_len > 1024 due to latency hiding.
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"""
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num_tiles = num_kv_heads * ((seq_len + partition_size - 1) // partition_size)
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return num_tiles > _V2_THRESHOLD_FACTOR * _SM_COUNT
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def dispatch_prefill(
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config: AttnDispatchConfig,
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query: torch.Tensor, # (total_q_tokens, num_heads, head_dim)
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key_cache: torch.Tensor, # (num_blocks, num_kv_heads, block_size, head_dim)
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value_cache: torch.Tensor, # (num_blocks, num_kv_heads, block_size, head_dim)
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block_tables: torch.Tensor, # (batch_size, max_blocks_per_seq)
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cu_seq_q: torch.Tensor, # (batch_size + 1,) int32 — query cumulative lengths
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cu_seq_k: torch.Tensor, # (batch_size + 1,) int32 — key cumulative lengths
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max_seq_q: int,
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max_seq_k: int,
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) -> torch.Tensor:
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"""
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Prefill attention via flash attention with block tables.
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Upstream: xllm::kernel::ilu::batch_prefill
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→ ixformer::infer::ixinfer_flash_attn_unpad_with_block_tables
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The BI-V100 ixformer implements this as a modified flash attention
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that reads KV from paged cache (block_tables → physical blocks).
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"""
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# Try ix_ops_dispatch first
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try:
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from ex_engine.python import ix_ops_dispatch
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output = ix_ops_dispatch.flash_attn_with_block_tables(
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query, key_cache, value_cache, block_tables,
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cu_seq_q, cu_seq_k, max_seq_q, max_seq_k,
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config.scale,
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is_causal=config.is_causal,
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window_left=config.window_left,
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window_right=config.window_right,
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softcap=config.softcap,
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)
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return output
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except (ImportError, RuntimeError, AttributeError) as e:
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logger.debug("flash_attn dispatch failed, using fallback: %s", e)
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# Try direct ixformer
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try:
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import ixformer.functions as ixf_F
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output = torch.empty_like(query)
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ixf_F.ixinfer_flash_attn_unpad_with_block_tables(
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query, key_cache, value_cache, output, block_tables,
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cu_seq_q, cu_seq_k, max_seq_q, max_seq_k,
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config.is_causal, config.window_left, config.window_right,
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config.scale, config.softcap, False, None, None, None)
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return output
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except (ImportError, AttributeError):
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pass
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# PyTorch fallback — SDPA (no block table support, for testing only)
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logger.warning("prefill: using PyTorch SDPA fallback (no block tables)")
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output = torch.nn.functional.scaled_dot_product_attention(
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query.unsqueeze(0), query.unsqueeze(0), query.unsqueeze(0),
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scale=config.scale, is_causal=config.is_causal)
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return output.squeeze(0)
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def dispatch_decode(
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config: AttnDispatchConfig,
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output: torch.Tensor, # (batch_size, num_heads, head_dim) preallocated
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query: torch.Tensor, # (batch_size, num_heads, head_dim)
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key_cache: torch.Tensor, # (num_blocks, num_kv_heads, block_size, head_dim)
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value_cache: torch.Tensor, # (num_blocks, num_kv_heads, block_size, head_dim)
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block_tables: torch.Tensor, # (batch_size, max_blocks_per_seq)
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context_lens: torch.Tensor, # (batch_size,) int32
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max_context_len: int,
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) -> None:
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"""
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Decode attention via paged attention v1/v2.
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Upstream: xllm::kernel::ilu::batch_decode
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→ ixformer::infer::xllm_paged_attention
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"""
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# Try ix_ops_dispatch
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try:
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from ex_engine.python import ix_ops_dispatch
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ix_ops_dispatch.paged_attention_v1(
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output, query, key_cache, value_cache,
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config.num_kv_heads, config.scale,
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block_tables, context_lens,
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config.block_size, max_context_len,
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window_left=config.window_left,
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window_right=config.window_right,
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softcap=config.softcap,
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)
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return
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except (ImportError, RuntimeError, AttributeError) as e:
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logger.debug("paged_attention dispatch failed: %s", e)
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# Try direct ixformer
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try:
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import ixformer.functions as ixf_F
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ixf_F.vllm_single_query_cached_kv_attention(
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output, query, key_cache, value_cache,
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config.num_kv_heads, config.scale,
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block_tables, context_lens,
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config.block_size, max_context_len, None)
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return
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except (ImportError, AttributeError):
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pass
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# PyTorch fallback — extremely slow, decode-only test path
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logger.warning("decode: using PyTorch fallback (very slow)")
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batch_size = query.shape[0]
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for b in range(batch_size):
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ctx_len = context_lens[b].item()
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q = query[b] # (num_heads, head_dim)
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# Reconstruct KV from cache
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blocks = block_tables[b]
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num_blocks_used = (ctx_len + config.block_size - 1) // config.block_size
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k_list, v_list = [], []
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for bi in range(num_blocks_used):
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block_idx = blocks[bi].item()
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tokens_in_block = min(config.block_size,
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ctx_len - bi * config.block_size)
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k_list.append(key_cache[block_idx, :, :tokens_in_block])
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v_list.append(value_cache[block_idx, :, :tokens_in_block])
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k = torch.cat(k_list, dim=1) # (kv_heads, ctx_len, head_dim)
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v = torch.cat(v_list, dim=1)
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# GQA: expand kv heads
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num_q_per_kv = config.num_heads // config.num_kv_heads
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k = k.repeat_interleave(num_q_per_kv, dim=0)
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v = v.repeat_interleave(num_q_per_kv, dim=0)
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# Standard attention
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scores = torch.einsum('hd,hsd->hs', q.float(), k.float())
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scores = scores * config.scale
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scores = torch.softmax(scores, dim=-1)
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out = torch.einsum('hs,hsd->hd', scores, v.float())
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output[b] = out.to(output.dtype)
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def dispatch_attention(
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config: AttnDispatchConfig,
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is_prefill: bool,
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query: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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block_tables: torch.Tensor,
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# Prefill-specific
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cu_seq_q: Optional[torch.Tensor] = None,
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cu_seq_k: Optional[torch.Tensor] = None,
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max_seq_q: int = 0,
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max_seq_k: int = 0,
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# Decode-specific
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context_lens: Optional[torch.Tensor] = None,
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max_context_len: int = 0,
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) -> torch.Tensor:
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"""
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Top-level attention dispatcher.
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Mirrors xllm's split between batch_prefill and batch_decode,
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routing to the correct kernel based on is_prefill flag.
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"""
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if is_prefill:
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return dispatch_prefill(
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config, query, key_cache, value_cache,
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block_tables, cu_seq_q, cu_seq_k,
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max_seq_q, max_seq_k)
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else:
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output = torch.empty_like(query)
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dispatch_decode(
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config, output, query, key_cache, value_cache,
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block_tables, context_lens, max_context_len)
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return output
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