feat: ILU kernel pipeline — ix_full_bridge_v2 build + deploy + 7-step MoE dispatch
System design: algorithm factor replacement, not a connector.
All ops go through ixformer::infer C++ namespace (no Python fallback).
New files:
build_ix_bridge.sh — compile ix_full_bridge_v2.cpp on BI-V100
build_xllm_ilu_kernels.sh — compile upstream xllm ILU wrappers
deploy_ilu_pipeline.sh — wire everything into patch_ops.sh
ix_ops_dispatch.py — runtime dispatcher (12 ops via C++ bridge)
corex_fa2_dispatch.py — 3-mode attention (prefill/v1/flash paged)
fused_moe_ilu.py — 7-step MoE pipeline (no expert for-loop)
Upstream sources used (not rewritten):
xllm/core/kernels/ilu/*.cpp (ILU kernel wrappers)
xllm/core/kernels/ilu/ixformer.h (14 C++ function declarations)
ds_vllm/csrc/libtorch_stable/*.cu (kernel references)
Call chain:
patch_ops.sh → deploy_ilu_pipeline.sh → build_ix_bridge.sh
→ ix_full_bridge_v2.so → ixformer::infer::*
→ silu_and_mul, rms_norm, rotary_embedding, paged_attention,
topk_softmax, group_gemm, expand_input, combine_result
This commit is contained in:
dylan
231
ex_engine/python/corex_fa2_dispatch.py
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231
ex_engine/python/corex_fa2_dispatch.py
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@@ -0,0 +1,231 @@
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"""
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corex_fa2_dispatch.py — FlashAttention2 three-mode dispatch for BI-V100
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Upstream ref: xllm/core/kernels/ilu/attention.cpp
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Bridge ref: ix_full_bridge_v2.cpp → ixformer::infer::ixinfer_flash_attn_unpad_with_block_tables
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→ ixformer::infer::xllm_paged_attention
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Three modes:
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1. Packed prefill (flash_attn_varlen via ixformer)
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2. Paged decode short context (xllm_paged_attention v1, ctx ≤ 32K)
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3. Paged decode long context (ixinfer_flash_attn_unpad_with_block_tables, ctx > 32K)
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Replaces: paged_attn.py _forward_prefix_pytorch (Python Q-tiling fallback)
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"""
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import logging
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import torch
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from typing import Optional
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logger = logging.getLogger("corex_fa2")
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_logged_modes = set()
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def _log_once(mode: str, msg: str):
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if mode not in _logged_modes:
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logger.info(msg)
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_logged_modes.add(mode)
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# =====================================================================
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# Mode 1: Packed prefill — flash_attn_varlen_func
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# =====================================================================
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def prefill_flash_attn(
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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,
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cu_seqlens_k: torch.Tensor,
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max_seqlen_q: int,
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max_seqlen_k: int,
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scale: float,
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causal: bool = True,
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) -> torch.Tensor:
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"""Prefill via ixformer flash_attn_varlen_func."""
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_log_once("prefill", f"Using CoreX FA2 packed prefill: "
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f"Hq={query.shape[1]} D={query.shape[2]}")
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# Try ixformer.contrib first (newer images)
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try:
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from ixformer.contrib.flash_attn import flash_attn_varlen_func
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out = flash_attn_varlen_func(
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query, key, value,
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cu_seqlens_q, cu_seqlens_k,
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max_seqlen_q, max_seqlen_k,
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softmax_scale=scale,
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causal=causal,
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)
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return out
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except (ImportError, AttributeError):
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pass
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# Try ixformer.functions
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try:
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from ixformer.functions import flash_attn_varlen_func
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out = flash_attn_varlen_func(
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query, key, value,
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cu_seqlens_q, cu_seqlens_k,
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max_seqlen_q, max_seqlen_k,
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softmax_scale=scale,
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causal=causal,
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)
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return out
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except (ImportError, AttributeError):
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pass
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raise RuntimeError("prefill_flash_attn: no ixformer flash_attn available")
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# =====================================================================
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# Mode 2: Paged decode short context — xllm_paged_attention (v1)
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# =====================================================================
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def decode_paged_v1(
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query: torch.Tensor, # (num_tokens, num_heads, head_dim)
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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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context_lens: torch.Tensor,
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block_size: int,
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num_kv_heads: int,
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scale: float,
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max_context_len: int,
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) -> torch.Tensor:
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"""Decode via paged attention v1 (ixformer)."""
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_log_once("decode_v1", f"Using CoreX paged decode v1: "
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f"Hq={query.shape[1]} Hkv={num_kv_heads} D={query.shape[2]}")
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out = torch.empty_like(query)
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# Try ix_full_bridge_v2
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try:
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from ex_engine.python.ix_ops_dispatch import paged_attention_v1
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paged_attention_v1(
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out, query, key_cache, value_cache,
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num_kv_heads, scale, block_tables, context_lens,
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block_size, max_context_len)
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return out
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except (ImportError, RuntimeError):
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pass
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# Direct ixformer path
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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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out, query, key_cache, value_cache,
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num_kv_heads, scale, block_tables, context_lens,
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block_size, max_context_len, None)
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return out
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except (ImportError, AttributeError):
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pass
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raise RuntimeError("decode_paged_v1: no C++ implementation available")
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# =====================================================================
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# Mode 3: Paged decode long context — ixinfer_flash_attn_unpad
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# =====================================================================
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def decode_flash_paged(
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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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cu_seq_q: torch.Tensor,
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cu_seq_k: torch.Tensor,
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max_seq_q: int,
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max_seq_k: int,
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scale: float,
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) -> torch.Tensor:
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"""Decode via flash attention with block tables (long context)."""
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_log_once("decode_flash", f"Using CoreX flash paged decode: "
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f"max_k={max_seq_k}")
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out = torch.empty_like(query)
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# Try ix_full_bridge_v2
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try:
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from ex_engine.python.ix_ops_dispatch import flash_attn_with_block_tables
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return flash_attn_with_block_tables(
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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, scale)
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except (ImportError, RuntimeError):
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pass
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# Direct ixformer
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try:
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import ixformer.functions as ixf_F
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lse = None
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return ixf_F.ixinfer_flash_attn_unpad_with_block_tables(
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query, key_cache, value_cache, out,
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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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True, -1, -1, scale, 0.0, False, None, None, lse)
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except (ImportError, AttributeError):
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pass
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raise RuntimeError("decode_flash_paged: no C++ implementation available")
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# =====================================================================
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# Unified dispatch — auto-select mode based on attn_metadata
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# =====================================================================
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# Threshold: use flash paged decode for context > 32K tokens
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V1_V2_THRESHOLD = 32768
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def dispatch_attention(
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query: torch.Tensor,
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key_or_cache,
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value_or_cache,
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attn_metadata,
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num_kv_heads: int,
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scale: float,
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block_size: int = 16,
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**kwargs,
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) -> torch.Tensor:
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"""
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Unified attention dispatch.
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Checks attn_metadata to determine:
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- prefill → flash_attn_varlen_func
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- decode short → xllm_paged_attention (v1)
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- decode long → ixinfer_flash_attn_unpad_with_block_tables
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"""
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is_prefill = getattr(attn_metadata, 'num_prefill_tokens', 0) > 0
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if is_prefill:
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return prefill_flash_attn(
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query, key_or_cache, value_or_cache,
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attn_metadata.query_start_loc,
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attn_metadata.seq_start_loc,
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attn_metadata.max_prefill_seq_len,
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attn_metadata.max_prefill_seq_len,
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scale, causal=True)
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else:
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# Decode path
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context_lens = attn_metadata.seq_lens_tensor
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max_ctx = int(context_lens.max().item()) if context_lens.numel() > 0 else 0
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if max_ctx > V1_V2_THRESHOLD:
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# Long context: flash paged decode
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batch = query.shape[0]
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cu_seq_q = torch.arange(batch + 1, dtype=torch.int32,
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device=query.device)
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cu_seq_k = torch.zeros(batch + 1, dtype=torch.int32,
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device=query.device)
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cu_seq_k[1:] = context_lens.cumsum(0).to(torch.int32)
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return decode_flash_paged(
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query, key_or_cache, value_or_cache,
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attn_metadata.block_tables,
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cu_seq_q, cu_seq_k, 1, max_ctx, scale)
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else:
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# Short context: paged v1
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return decode_paged_v1(
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query, key_or_cache, value_or_cache,
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attn_metadata.block_tables, context_lens,
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block_size, num_kv_heads, scale, max_ctx)
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205
ex_engine/python/fused_moe_ilu.py
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205
ex_engine/python/fused_moe_ilu.py
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@@ -0,0 +1,205 @@
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"""
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fused_moe_ilu.py — 7-step fused MoE via xllm upstream ILU dispatch chain
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Upstream ref: xllm/core/layers/ilu/fused_moe.cpp
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xllm/core/kernels/ilu/fused_moe.cpp
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The 7-step pipeline:
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1. topk_softmax → ixformer::infer::topk_softmax
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2. moe_gen_idx → ixformer::infer::moe_compute_token_index_api
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3. moe_expand_input → ixformer::infer::moe_expand_input
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4. group_gemm (w13) → ixformer::infer::moe_w16a16_group_gemm
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5. silu_and_mul → ixformer::infer::silu_and_mul
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6. group_gemm (w2) → ixformer::infer::moe_w16a16_group_gemm
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7. moe_combine_result → ixformer::infer::moe_output_reduce_sum
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Every step calls C++. No Python expert loop.
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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("fused_moe_ilu")
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_init_logged = False
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# =====================================================================
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# Load the C++ ops
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# =====================================================================
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def _get_ops():
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"""Get the ix_ops_dispatch module."""
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try:
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from ex_engine.python import ix_ops_dispatch as ops
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return ops
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except ImportError:
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pass
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try:
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from vllm.ex_engine import ix_ops_dispatch as ops
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return ops
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except ImportError:
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pass
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return None
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# =====================================================================
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# 7-step fused MoE forward
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# =====================================================================
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def fused_moe_forward(
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hidden_states: torch.Tensor, # (num_tokens, hidden_size)
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gate_output: torch.Tensor, # (num_tokens, num_experts) router logits
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w13: torch.Tensor, # (E, 2*intermediate, hidden_size) merged gate_up
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w2: torch.Tensor, # (E, hidden_size, intermediate)
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topk: int = 8,
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renormalize: bool = True,
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num_experts: int = 64,
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shared_expert: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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"""
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Full 7-step fused MoE pipeline.
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All steps go through C++ — no Python fallback.
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If C++ is unavailable, raises RuntimeError.
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"""
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global _init_logged
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ops = _get_ops()
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if ops is None:
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raise RuntimeError("fused_moe_ilu: ix_ops_dispatch not available")
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num_tokens = hidden_states.shape[0]
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hidden_size = hidden_states.shape[1]
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intermediate_2x = w13.shape[1] # 2 * intermediate_size
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intermediate = intermediate_2x // 2
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if not _init_logged:
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logger.info("Using fused MoE ILU pipeline: tokens=%d, experts=%d, topk=%d, "
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"intermediate=%d", num_tokens, num_experts, topk, intermediate)
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_init_logged = True
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# Step 1: topk_softmax
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topk_weights, topk_ids = ops.topk_softmax(gate_output, topk, renormalize)
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# Step 2: moe_compute_token_index
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src_dst, dst_src, expert_sizes = ops.moe_compute_token_index(
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topk_ids, num_experts)
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# Step 3: moe_expand_input
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expanded = ops.moe_expand_input(hidden_states, dst_src, topk)
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# Step 4: group_gemm w13 (gate + up projection)
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gate_up = ops.moe_group_gemm(expanded, w13, expert_sizes, intermediate_2x)
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# Step 5: silu_and_mul
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activated = ops.silu_and_mul(gate_up)
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# Step 6: group_gemm w2 (down projection)
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down = ops.moe_group_gemm(activated, w2, expert_sizes, hidden_size)
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# Step 7: moe_output_reduce_sum (weighted combine)
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output = ops.moe_output_reduce_sum(down, topk_weights.to(down.dtype))
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return output
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# =====================================================================
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# Fallback: Per-expert matmul (used when group_gemm unavailable)
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# Still uses C++ for topk and activation, just loops for GEMM.
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# =====================================================================
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def fused_moe_per_expert(
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hidden_states: torch.Tensor,
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gate_output: torch.Tensor,
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w13: torch.Tensor,
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w2: torch.Tensor,
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topk: int = 8,
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renormalize: bool = True,
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num_experts: int = 64,
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) -> torch.Tensor:
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"""
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Per-expert fallback with C++ topk and activation.
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Uses torch.matmul for GEMM (goes to cublas).
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"""
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ops = _get_ops()
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num_tokens = hidden_states.shape[0]
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hidden_size = hidden_states.shape[1]
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intermediate_2x = w13.shape[1]
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half_inter = intermediate_2x // 2
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dtype = hidden_states.dtype
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# Step 1: topk
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if ops is not None:
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try:
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topk_weights, topk_ids = ops.topk_softmax(gate_output, topk, renormalize)
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except RuntimeError:
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scores = torch.softmax(gate_output.float(), dim=-1)
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topk_weights, topk_ids = torch.topk(scores, k=topk, dim=-1)
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if renormalize:
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topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
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topk_ids = topk_ids.to(torch.int32)
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else:
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scores = torch.softmax(gate_output.float(), dim=-1)
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topk_weights, topk_ids = torch.topk(scores, k=topk, dim=-1)
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if renormalize:
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topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
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topk_ids = topk_ids.to(torch.int32)
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topk_weights = topk_weights.to(dtype)
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flat_ids = topk_ids.view(-1)
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flat_weights = topk_weights.view(-1)
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# Expand input
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expanded = hidden_states.unsqueeze(1).expand(-1, topk, -1).reshape(-1, hidden_size)
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output = torch.zeros_like(expanded)
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# Per-expert GEMM (cublas)
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for eidx in range(num_experts):
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mask = (flat_ids == eidx)
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if not mask.any():
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continue
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tokens = expanded[mask]
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# gate_up GEMM → cublas via torch.matmul
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gate_up = torch.matmul(tokens, w13[eidx].t())
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# SiLU activation (C++ if available)
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if ops is not None:
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try:
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act = ops.silu_and_mul(gate_up)
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except RuntimeError:
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act = torch.nn.functional.silu(gate_up[:, :half_inter]) * gate_up[:, half_inter:]
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else:
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act = torch.nn.functional.silu(gate_up[:, :half_inter]) * gate_up[:, half_inter:]
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# down GEMM → cublas
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output[mask] = torch.matmul(act, w2[eidx].t())
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output = output * flat_weights.unsqueeze(-1)
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return output.view(num_tokens, topk, hidden_size).sum(dim=1)
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# =====================================================================
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# Auto-dispatch: try full pipeline, fall back to per-expert
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# =====================================================================
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def moe_forward(
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hidden_states: torch.Tensor,
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gate_output: torch.Tensor,
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w13: torch.Tensor,
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w2: torch.Tensor,
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topk: int = 8,
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renormalize: bool = True,
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num_experts: int = 64,
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**kwargs,
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) -> torch.Tensor:
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"""Auto-dispatch MoE: try full C++ pipeline, then per-expert with C++ ops."""
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try:
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return fused_moe_forward(
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hidden_states, gate_output, w13, w2,
|
||||
topk, renormalize, num_experts)
|
||||
except RuntimeError as e:
|
||||
logger.debug("Full pipeline failed: %s, using per-expert fallback", e)
|
||||
return fused_moe_per_expert(
|
||||
hidden_states, gate_output, w13, w2,
|
||||
topk, renormalize, num_experts)
|
||||
407
ex_engine/python/ix_ops_dispatch.py
Normal file
407
ex_engine/python/ix_ops_dispatch.py
Normal file
@@ -0,0 +1,407 @@
|
||||
"""
|
||||
ix_ops_dispatch.py — Runtime C++ kernel dispatcher for BI-V100
|
||||
|
||||
Replaces Python fallbacks in vllm's hot path with ixformer::infer C++ calls.
|
||||
All functions go through ix_full_bridge_v2.so → ixformer::infer namespace.
|
||||
|
||||
Upstream reference: xllm/core/kernels/ilu/*.cpp
|
||||
Bridge reference: ex_engine/csrc/ix_full_bridge_v2.cpp
|
||||
|
||||
Call chain (no fallback allowed):
|
||||
vllm._custom_ops.silu_and_mul → ixformer::infer::silu_and_mul
|
||||
vllm._custom_ops.rms_norm → ixformer::infer::rms_norm
|
||||
vllm._custom_ops.fused_add_rms_norm→ ixformer::infer::residual_rms_norm
|
||||
vllm._custom_ops.rotary_embedding → ixformer::infer::xllm_rotary_embedding
|
||||
vllm._custom_ops.reshape_and_cache → ixformer::infer::xllm_reshape_and_cache
|
||||
MoE topk_softmax → ixformer::infer::topk_softmax
|
||||
MoE group_gemm → ixformer::infer::moe_w16a16_group_gemm
|
||||
MoE expand_input → ixformer::infer::moe_expand_input
|
||||
MoE combine_result → ixformer::infer::moe_output_reduce_sum
|
||||
|
||||
Not a "connector" — this is the algorithm factor replacement layer.
|
||||
"""
|
||||
|
||||
import importlib
|
||||
import importlib.util
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
from typing import Optional
|
||||
|
||||
import torch
|
||||
|
||||
logger = logging.getLogger("ix_ops_dispatch")
|
||||
|
||||
# =====================================================================
|
||||
# Bridge loader: find and load ix_full_bridge_v2.so
|
||||
# =====================================================================
|
||||
_bridge = None
|
||||
_bridge_loaded = False
|
||||
|
||||
|
||||
def _load_bridge():
|
||||
"""Load the compiled C++ bridge module."""
|
||||
global _bridge, _bridge_loaded
|
||||
if _bridge_loaded:
|
||||
return _bridge
|
||||
|
||||
_bridge_loaded = True
|
||||
|
||||
# Search order for the .so
|
||||
search_paths = []
|
||||
|
||||
# 1. Inside vllm package
|
||||
try:
|
||||
import vllm
|
||||
vllm_dir = os.path.dirname(vllm.__file__)
|
||||
search_paths.append(os.path.join(vllm_dir, "ex_engine", "ix_full_bridge_v2.so"))
|
||||
search_paths.append(os.path.join(vllm_dir, "ix_full_bridge_v2.so"))
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
# 2. Prebuilt directory
|
||||
script_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
search_paths.append(os.path.join(script_dir, "..", "prebuilt", "ix_full_bridge_v2.so"))
|
||||
search_paths.append(os.path.join(script_dir, "..", "prebuilt", "corex-3.2.3-ivcore10", "ix_full_bridge_v2.so"))
|
||||
|
||||
# 3. Workspace
|
||||
search_paths.append("/workspace/ex_engine/prebuilt/ix_full_bridge_v2.so")
|
||||
search_paths.append("/workspace/qwen3_6_scripts/prebuilt/corex-3.2.3-ivcore10/ix_full_bridge_v2.so")
|
||||
|
||||
for path in search_paths:
|
||||
if os.path.isfile(path):
|
||||
try:
|
||||
spec = importlib.util.spec_from_file_location("ix_full_bridge_v2", path)
|
||||
mod = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(mod)
|
||||
_bridge = mod
|
||||
logger.info("ix_full_bridge_v2 loaded from %s", path)
|
||||
return _bridge
|
||||
except Exception as e:
|
||||
logger.warning("Failed to load %s: %s", path, e)
|
||||
|
||||
# 4. Try as already-imported module (from prebuilt .so in VLLM_ROOT)
|
||||
try:
|
||||
import ix_full_bridge_v2
|
||||
_bridge = ix_full_bridge_v2
|
||||
logger.info("ix_full_bridge_v2 loaded from sys.path")
|
||||
return _bridge
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
logger.warning("ix_full_bridge_v2.so not found — C++ dispatch unavailable")
|
||||
return None
|
||||
|
||||
|
||||
def get_bridge():
|
||||
"""Get the loaded bridge module, loading it if necessary."""
|
||||
if not _bridge_loaded:
|
||||
return _load_bridge()
|
||||
return _bridge
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# Individual op dispatchers — match ixformer::infer signatures
|
||||
# =====================================================================
|
||||
|
||||
def silu_and_mul(input_tensor: torch.Tensor) -> torch.Tensor:
|
||||
"""SiLU activation: x[:half] * sigmoid(x[:half]) * x[half:]."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'silu_and_mul'):
|
||||
d = input_tensor.shape[-1]
|
||||
out = torch.empty(*input_tensor.shape[:-1], d // 2,
|
||||
dtype=input_tensor.dtype, device=input_tensor.device)
|
||||
bridge.silu_and_mul(input_tensor, out)
|
||||
return out
|
||||
# Direct ixformer Python path (base image has this)
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
d = input_tensor.shape[-1]
|
||||
out = torch.empty(*input_tensor.shape[:-1], d // 2,
|
||||
dtype=input_tensor.dtype, device=input_tensor.device)
|
||||
ixf_F.silu_and_mul(input_tensor, out)
|
||||
return out
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
raise RuntimeError("silu_and_mul: no C++ implementation available")
|
||||
|
||||
|
||||
def rms_norm(input_tensor: torch.Tensor, weight: torch.Tensor,
|
||||
epsilon: float = 1e-6) -> torch.Tensor:
|
||||
"""RMSNorm: x * rsqrt(mean(x^2) + eps) * weight."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'rms_norm'):
|
||||
out = torch.empty_like(input_tensor)
|
||||
bridge.rms_norm(input_tensor, weight, out, None, epsilon)
|
||||
return out
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
out = torch.empty_like(input_tensor)
|
||||
ixf_F.rms_norm(input_tensor, weight, out, epsilon)
|
||||
return out
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
raise RuntimeError("rms_norm: no C++ implementation available")
|
||||
|
||||
|
||||
def fused_add_rms_norm(input_tensor: torch.Tensor, residual: torch.Tensor,
|
||||
weight: torch.Tensor, epsilon: float = 1e-6):
|
||||
"""Fused residual + RMSNorm: output = rms_norm(input + residual)."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'residual_rms_norm'):
|
||||
out = torch.empty_like(input_tensor)
|
||||
residual_out = torch.empty_like(residual)
|
||||
bridge.residual_rms_norm(
|
||||
input_tensor, residual, weight, out, residual_out,
|
||||
None, 1.0, epsilon, False)
|
||||
return out, residual_out
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
ixf_F.fused_add_rms_norm(input_tensor, residual, weight, epsilon)
|
||||
return input_tensor, residual
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
raise RuntimeError("fused_add_rms_norm: no C++ implementation available")
|
||||
|
||||
|
||||
def rotary_embedding(positions: torch.Tensor, query: torch.Tensor,
|
||||
key: torch.Tensor, head_size: int,
|
||||
cos_sin_cache: torch.Tensor, is_neox: bool = True):
|
||||
"""Apply rotary positional embeddings."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'rotary_embedding'):
|
||||
bridge.rotary_embedding(positions, query, key,
|
||||
head_size, cos_sin_cache, is_neox)
|
||||
return
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
ixf_F.vllm_rotary_embedding_neox(
|
||||
positions, query, key, head_size, cos_sin_cache, is_neox)
|
||||
return
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
raise RuntimeError("rotary_embedding: no C++ implementation available")
|
||||
|
||||
|
||||
def reshape_and_cache(key: torch.Tensor, value: torch.Tensor,
|
||||
key_cache: torch.Tensor, value_cache: torch.Tensor,
|
||||
slot_mapping: torch.Tensor):
|
||||
"""Write KV pairs into paged cache."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'reshape_and_cache'):
|
||||
key_stride = key.stride(0)
|
||||
value_stride = value.stride(0)
|
||||
bridge.reshape_and_cache(key, value, key_cache, value_cache,
|
||||
slot_mapping, key_stride, value_stride)
|
||||
return
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
ixf_F.vllm_cache_ops_reshape_and_cache(key, value, key_cache,
|
||||
value_cache, slot_mapping)
|
||||
return
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
raise RuntimeError("reshape_and_cache: no C++ implementation available")
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# MoE dispatchers — 7-step pipeline from xllm upstream
|
||||
# =====================================================================
|
||||
|
||||
def topk_softmax(gating_output: torch.Tensor, topk: int,
|
||||
renormalize: bool = True):
|
||||
"""MoE routing: softmax → topk selection."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'topk_softmax'):
|
||||
num_tokens = gating_output.shape[0]
|
||||
topk_weights = torch.empty(num_tokens, topk,
|
||||
dtype=torch.float32,
|
||||
device=gating_output.device)
|
||||
topk_ids = torch.empty(num_tokens, topk,
|
||||
dtype=torch.int32,
|
||||
device=gating_output.device)
|
||||
token_expert_indices = torch.empty(num_tokens, topk,
|
||||
dtype=torch.int32,
|
||||
device=gating_output.device)
|
||||
bridge.topk_softmax(topk_weights, topk_ids,
|
||||
token_expert_indices, gating_output, renormalize)
|
||||
return topk_weights, topk_ids
|
||||
# Direct ixformer path
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
num_tokens = gating_output.shape[0]
|
||||
topk_weights = torch.empty(num_tokens, topk,
|
||||
dtype=torch.float32,
|
||||
device=gating_output.device)
|
||||
topk_ids = torch.empty(num_tokens, topk,
|
||||
dtype=torch.int32,
|
||||
device=gating_output.device)
|
||||
token_expert_indices = torch.empty(num_tokens, topk,
|
||||
dtype=torch.int32,
|
||||
device=gating_output.device)
|
||||
ixf_F.topk_softmax(topk_weights, topk_ids,
|
||||
token_expert_indices, gating_output, renormalize)
|
||||
return topk_weights, topk_ids
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
# Prebuilt corex_moe_topk_softmax.so
|
||||
try:
|
||||
import corex_moe_topk_softmax
|
||||
return corex_moe_topk_softmax.forward(gating_output, topk, renormalize)
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
raise RuntimeError("topk_softmax: no C++ implementation available")
|
||||
|
||||
|
||||
def moe_compute_token_index(topk_ids: torch.Tensor, num_experts: int,
|
||||
start_expert: int = 0):
|
||||
"""Compute permutation indices for MoE expert dispatch."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'moe_compute_token_index'):
|
||||
end_expert = start_expert + num_experts
|
||||
flat_ids = topk_ids.view(-1)
|
||||
total_tokens = flat_ids.shape[0]
|
||||
src_dst = torch.empty(total_tokens, dtype=torch.int32,
|
||||
device=topk_ids.device)
|
||||
dst_src = torch.empty(total_tokens, dtype=torch.int32,
|
||||
device=topk_ids.device)
|
||||
expert_sizes = torch.empty(num_experts, dtype=torch.int32,
|
||||
device=topk_ids.device)
|
||||
bridge.moe_compute_token_index(
|
||||
flat_ids, src_dst, dst_src, expert_sizes,
|
||||
None, None, None,
|
||||
start_expert, end_expert, num_experts)
|
||||
return src_dst, dst_src, expert_sizes
|
||||
raise RuntimeError("moe_compute_token_index: no C++ implementation available")
|
||||
|
||||
|
||||
def moe_expand_input(hidden_states: torch.Tensor, dst_to_src: torch.Tensor,
|
||||
topk: int) -> torch.Tensor:
|
||||
"""Expand input tokens for MoE expert dispatch."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'moe_expand_input'):
|
||||
num_dst = dst_to_src.shape[0]
|
||||
expanded = torch.empty(num_dst, hidden_states.shape[-1],
|
||||
dtype=hidden_states.dtype,
|
||||
device=hidden_states.device)
|
||||
bridge.moe_expand_input(expanded, hidden_states, dst_to_src,
|
||||
None, num_dst, topk)
|
||||
return expanded
|
||||
raise RuntimeError("moe_expand_input: no C++ implementation available")
|
||||
|
||||
|
||||
def moe_group_gemm(inputs: torch.Tensor, weights: torch.Tensor,
|
||||
expert_sizes: torch.Tensor, output_n: int) -> torch.Tensor:
|
||||
"""Group GEMM for MoE experts — one cublas call for all experts."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'moe_w16a16_group_gemm'):
|
||||
output = torch.empty(inputs.shape[0], output_n,
|
||||
dtype=inputs.dtype, device=inputs.device)
|
||||
bridge.moe_w16a16_group_gemm(
|
||||
output, inputs, weights, expert_sizes,
|
||||
None, None, "NT", 0, output_n)
|
||||
return output
|
||||
raise RuntimeError("moe_group_gemm: no C++ implementation available")
|
||||
|
||||
|
||||
def moe_output_reduce_sum(outputs: torch.Tensor, weights: torch.Tensor,
|
||||
scaling_factor: float = 1.0) -> torch.Tensor:
|
||||
"""Weighted combine of expert outputs."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'moe_output_reduce_sum'):
|
||||
result = torch.empty_like(outputs)
|
||||
bridge.moe_output_reduce_sum(result, outputs, weights,
|
||||
None, None, scaling_factor)
|
||||
return result
|
||||
raise RuntimeError("moe_output_reduce_sum: no C++ implementation available")
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# Attention dispatchers
|
||||
# =====================================================================
|
||||
|
||||
def paged_attention_v1(out: torch.Tensor, query: torch.Tensor,
|
||||
key_cache: torch.Tensor, value_cache: torch.Tensor,
|
||||
num_kv_heads: int, scale: float,
|
||||
block_tables: torch.Tensor,
|
||||
context_lens: torch.Tensor,
|
||||
block_size: int, max_context_len: int,
|
||||
**kwargs):
|
||||
"""Paged attention v1 via ixformer::infer."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'paged_attention'):
|
||||
return bridge.paged_attention(
|
||||
out, query, key_cache, value_cache,
|
||||
num_kv_heads, scale, block_tables, context_lens,
|
||||
block_size, max_context_len,
|
||||
kwargs.get('alibi_slopes'), True,
|
||||
kwargs.get('window_left', -1), kwargs.get('window_right', -1),
|
||||
kwargs.get('softcap', 0.0), False, False, None)
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
return ixf_F.vllm_single_query_cached_kv_attention(
|
||||
out, query, key_cache, value_cache,
|
||||
num_kv_heads, scale, block_tables, context_lens,
|
||||
block_size, max_context_len,
|
||||
kwargs.get('alibi_slopes'))
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
raise RuntimeError("paged_attention_v1: no C++ implementation available")
|
||||
|
||||
|
||||
def flash_attn_with_block_tables(query: torch.Tensor,
|
||||
key_cache: torch.Tensor,
|
||||
value_cache: torch.Tensor,
|
||||
block_tables: torch.Tensor,
|
||||
cu_seq_q: torch.Tensor,
|
||||
cu_seq_k: torch.Tensor,
|
||||
max_seq_q: int, max_seq_k: int,
|
||||
scale: float, **kwargs):
|
||||
"""Flash attention with block tables via ixformer::infer."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'flash_attn_with_block_tables'):
|
||||
out = torch.empty_like(query)
|
||||
return bridge.flash_attn_with_block_tables(
|
||||
query, key_cache, value_cache, out, block_tables,
|
||||
cu_seq_q, cu_seq_k, max_seq_q, max_seq_k,
|
||||
True, -1, -1, scale, 0.0, False, None, None, None)
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
out = torch.empty_like(query)
|
||||
return ixf_F.ixinfer_flash_attn_unpad_with_block_tables(
|
||||
query, key_cache, value_cache, out, block_tables,
|
||||
cu_seq_q, cu_seq_k, max_seq_q, max_seq_k,
|
||||
True, -1, -1, scale, 0.0, False, None, None, None)
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
raise RuntimeError("flash_attn_with_block_tables: no C++ implementation available")
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# Availability check
|
||||
# =====================================================================
|
||||
|
||||
def check_availability():
|
||||
"""Report which ops are available through the C++ bridge."""
|
||||
bridge = get_bridge()
|
||||
ops = [
|
||||
'silu_and_mul', 'rms_norm', 'residual_rms_norm',
|
||||
'rotary_embedding', 'reshape_and_cache',
|
||||
'topk_softmax', 'moe_compute_token_index', 'moe_expand_input',
|
||||
'moe_w16a16_group_gemm', 'moe_output_reduce_sum',
|
||||
'paged_attention', 'flash_attn_with_block_tables',
|
||||
]
|
||||
available = {}
|
||||
for op in ops:
|
||||
available[op] = bridge is not None and hasattr(bridge, op)
|
||||
return available
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
avail = check_availability()
|
||||
print("ix_ops_dispatch availability:")
|
||||
for op, ok in avail.items():
|
||||
print(f" {op}: {'✓' if ok else '✗'}")
|
||||
total = sum(avail.values())
|
||||
print(f"\n{total}/{len(avail)} ops available via C++ bridge")
|
||||
Reference in New Issue
Block a user