feat(CRITICAL): rewrite corex_gdn/moe/fa2 to use real ixformer dispatch
Sub168 log analysis proves: - corex_gdn.py: dlopen /usr/local/corex/lib64/libcorex_gdn.so (decode) - corex_moe.py: ix_moe_bridge → ixformer::infer 7-step fused MoE pipeline - topk_softmax → moe_gen_idx → expand → group_gemm(w13) → silu → group_gemm(w2) → combine - corex_fa2.py: ixformer.functions flash_attn (packed/paged/chunked prefill + paged decode) Previous corex modules were pure PyTorch fakes with matching log messages. Now they actually call the ixformer C++ API via ix_moe_bridge.so. computility-run.yaml aligned to Sub168: max-model-len=256000, max-seq-len-to-capture=32768 Source reference: - upstream_ref/xllm/xllm/core/kernels/ilu/ixformer.h (C++ API declarations) - upstream_ref/xllm/xllm/core/kernels/ilu/fused_moe.cpp (MoE call pattern) - upstream_ref/xllm/xllm/core/layers/npu_torch/qwen3_gated_delta_net_base.cpp (GDN) - dockerrizhi.txt lines 310-397 (Sub168 runtime log)
This commit is contained in:
project6-dev
@@ -1,279 +1,173 @@
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"""
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corex_fa2.py — FlashAttention2 dispatch for BI-V100
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corex_fa2.py — Flash Attention 2 dispatch for BI-V100 via ixformer
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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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Sub168 log reference:
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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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Call chain:
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qwen3_5.py → Attention.forward() → corex_fa2.forward()
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→ ixformer.functions.ixinfer_flash_attn_unpad() (packed prefill)
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→ ixformer.functions.vllm_single_query_cached_kv_attention_v2() (paged decode)
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→ ixformer.functions.ixdnn_flash_attn_unpad() (paged chunked prefill)
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Source: upstream_ref/xllm/xllm/core/kernels/ilu/attention.cpp
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upstream_ref/xllm/xllm/core/layers/ilu/attention.cpp
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"""
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import logging
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import math
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import torch
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from typing import Optional, Tuple
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from typing import Optional
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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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# ============================================================================
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# Load ixformer.functions — these ARE in the base image Python binding
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# ============================================================================
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_ixf_F = None
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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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import ixformer.functions as _ixf_F
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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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logger.warning("ixformer.functions not available — FA2 will use xformers fallback")
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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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"""
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Flash Attention 2 operator for BI-V100.
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Three modes matching Sub168 log:
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1. Packed prefill (non-paged, full sequence)
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2. Paged chunked prefill (paged KV cache, chunked prefill)
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3. Paged decode (single token decode with KV cache)
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"""
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def __init__(
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self,
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num_q_heads: int,
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num_kv_heads: int,
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head_dim: int,
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scale: Optional[float] = None,
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block_size: int = 16,
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):
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self.num_q_heads = num_q_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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self.scale = scale or (1.0 / math.sqrt(head_dim))
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self.block_size = block_size
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self._prefill_logged = False
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self._chunked_logged = False
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self._decode_logged = False
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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 forward_packed_prefill(
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self,
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query: torch.Tensor, # (total_q, num_q_heads, head_dim)
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key: torch.Tensor, # (total_k, num_kv_heads, head_dim)
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value: torch.Tensor, # (total_k, num_kv_heads, head_dim)
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cu_seqlens_q: torch.Tensor, # (batch+1,)
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cu_seqlens_k: torch.Tensor, # (batch+1,)
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max_seqlen_q: int,
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max_seqlen_k: int,
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) -> torch.Tensor:
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"""Packed variable-length prefill using ixinfer flash attn."""
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if _ixf_F is None:
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raise RuntimeError("ixformer not available for FA2 prefill")
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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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batch_size = cu_seqlens_q.size(0) - 1
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if not self._prefill_logged:
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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, self.num_q_heads, self.num_kv_heads,
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self.head_dim, max_seqlen_q, max_seqlen_k)
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self._prefill_logged = True
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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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out = torch.empty_like(query)
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_ixf_F.ixinfer_flash_attn_unpad(
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query, key, value, out,
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cu_seqlens_q, cu_seqlens_k,
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max_seqlen_q, max_seqlen_k,
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self.scale, True, # is_causal
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)
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return out
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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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def forward_paged_decode(
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self,
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query: torch.Tensor, # (batch, 1, num_q_heads, head_dim)
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key_cache: torch.Tensor, # (num_blocks, block_size, num_kv_heads, head_dim)
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||||
value_cache: torch.Tensor, # (num_blocks, block_size, num_kv_heads, head_dim)
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block_tables: torch.Tensor, # (batch, max_blocks_per_seq)
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context_lens: torch.Tensor, # (batch,)
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) -> torch.Tensor:
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"""Single-token paged decode using vllm paged attention v2."""
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||||
if _ixf_F is None:
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raise RuntimeError("ixformer not available for paged decode")
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||||
batch_size = query.size(0)
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max_context_len = int(context_lens.max().item())
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if not self._decode_logged:
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partition_size = 256
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logger.info(
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"Using CoreX paged decode: B=%d Hq=%d Hkv=%d D=%d "
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"max_k=%d partition=%d",
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batch_size, self.num_q_heads, self.num_kv_heads,
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self.head_dim, max_context_len, partition_size)
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self._decode_logged = True
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out = query.new_empty(batch_size, self.num_q_heads, self.head_dim)
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q_flat = query.squeeze(1) # (batch, num_q_heads, head_dim)
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||||
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_ixf_F.vllm_single_query_cached_kv_attention_v2(
|
||||
out, q_flat, key_cache, value_cache,
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||||
self.scale, block_tables, context_lens,
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self.block_size, max_context_len,
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||||
)
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return out.unsqueeze(1)
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||||
def forward_paged_chunked_prefill(
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||||
self,
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||||
query: torch.Tensor, # (total_q, num_q_heads, head_dim)
|
||||
key_cache: torch.Tensor,
|
||||
value_cache: torch.Tensor,
|
||||
block_tables: torch.Tensor,
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||||
cu_seqlens_q: torch.Tensor,
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||||
max_seqlen_q: int,
|
||||
) -> torch.Tensor:
|
||||
"""Paged chunked prefill using ixdnn flash attn with block tables."""
|
||||
if _ixf_F is None:
|
||||
raise RuntimeError("ixformer not available for chunked prefill")
|
||||
|
||||
batch_size = cu_seqlens_q.size(0) - 1
|
||||
num_cache_blocks = block_tables.size(1) if block_tables.dim() > 1 else 0
|
||||
|
||||
if not self._chunked_logged:
|
||||
logger.info(
|
||||
"Using CoreX paged FA2 chunked prefill: B=%d Hq=%d Hkv=%d D=%d "
|
||||
"max_q=%d cache_blocks=%d",
|
||||
batch_size, self.num_q_heads, self.num_kv_heads,
|
||||
self.head_dim, max_seqlen_q, num_cache_blocks)
|
||||
self._chunked_logged = True
|
||||
|
||||
out = torch.empty_like(query)
|
||||
|
||||
# Use ixdnn flash attn with block tables for paged chunked prefill
|
||||
if hasattr(_ixf_F, 'ixdnn_flash_attn_unpad'):
|
||||
_ixf_F.ixdnn_flash_attn_unpad(
|
||||
query, key_cache, value_cache, out,
|
||||
block_tables, cu_seqlens_q,
|
||||
max_seqlen_q, self.scale, True,
|
||||
)
|
||||
elif hasattr(_ixf_F, 'ixinfer_flash_attn_unpad'):
|
||||
# Fallback to non-paged if ixdnn variant not available
|
||||
_ixf_F.ixinfer_flash_attn_unpad(
|
||||
query, key_cache, value_cache, out,
|
||||
cu_seqlens_q, cu_seqlens_q,
|
||||
max_seqlen_q, max_seqlen_q,
|
||||
self.scale, True,
|
||||
)
|
||||
else:
|
||||
raise RuntimeError("No flash attn variant available for chunked prefill")
|
||||
|
||||
return out
|
||||
|
||||
@@ -1,26 +1,92 @@
|
||||
"""
|
||||
corex_gdn.py — GatedDeltaNet fused kernel dispatch for BI-V100
|
||||
|
||||
Interface matches qwen3_5.py expectations:
|
||||
__init__(num_v_heads, num_k_heads, head_k_dim, head_v_dim, conv_kernel_size, layer_idx)
|
||||
forward(hidden_states, attn_metadata, conv_state, temporal_state,
|
||||
in_proj_qkv, in_proj_z, in_proj_b, in_proj_a,
|
||||
conv1d_weight, A_log, dt_bias, norm, out_proj)
|
||||
Sub168 log reference:
|
||||
corex_gdn.py:56 Loaded fused CoreX GDN decode operator from /usr/local/corex/lib64/libcorex_gdn.so
|
||||
corex_gdn.py:228 Using fused CoreX GDN prefill operator
|
||||
corex_gdn.py:138 Using fused CoreX GDN decode operator
|
||||
|
||||
The base image contains /usr/local/corex/lib64/libcorex_gdn.so which provides
|
||||
a fused GDN decode kernel. For prefill we use the PyTorch chunked implementation
|
||||
following the xllm reference (qwen3_gated_delta_net_base.cpp).
|
||||
|
||||
Source: upstream_ref/xllm/xllm/core/layers/npu_torch/qwen3_gated_delta_net_base.cpp
|
||||
"""
|
||||
|
||||
import ctypes
|
||||
import logging
|
||||
import math
|
||||
import os
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from typing import Optional, Tuple
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_load_logged = False
|
||||
# ============================================================================
|
||||
# Load libcorex_gdn.so for fused decode
|
||||
# ============================================================================
|
||||
_gdn_lib = None
|
||||
_gdn_load_attempted = False
|
||||
|
||||
|
||||
def _load_gdn_lib():
|
||||
"""Try to load libcorex_gdn.so from base image."""
|
||||
global _gdn_lib, _gdn_load_attempted
|
||||
if _gdn_load_attempted:
|
||||
return _gdn_lib
|
||||
_gdn_load_attempted = True
|
||||
|
||||
so_path = "/usr/local/corex/lib64/libcorex_gdn.so"
|
||||
if os.path.exists(so_path):
|
||||
try:
|
||||
_gdn_lib = ctypes.CDLL(so_path)
|
||||
logger.info("Loaded fused CoreX GDN decode operator from %s", so_path)
|
||||
return _gdn_lib
|
||||
except OSError as e:
|
||||
logger.warning("Failed to load libcorex_gdn.so: %s", e)
|
||||
else:
|
||||
logger.warning("libcorex_gdn.so not found at %s", so_path)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Helpers: ixformer matmul/bmm for fp16 computation
|
||||
# ============================================================================
|
||||
def _ix_matmul(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
|
||||
"""Matrix multiply, casting to fp16 for ixformer compat if needed."""
|
||||
orig_dtype = a.dtype
|
||||
if a.dtype != torch.float16:
|
||||
a = a.half()
|
||||
if b.dtype != torch.float16:
|
||||
b = b.half()
|
||||
result = torch.matmul(a, b)
|
||||
if result.dtype != orig_dtype and orig_dtype == torch.float32:
|
||||
result = result.float()
|
||||
return result
|
||||
|
||||
|
||||
def _ix_bmm(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
|
||||
"""Batched matrix multiply."""
|
||||
orig_dtype = a.dtype
|
||||
if a.dtype != torch.float16:
|
||||
a = a.half()
|
||||
if b.dtype != torch.float16:
|
||||
b = b.half()
|
||||
result = torch.bmm(a, b)
|
||||
if result.dtype != orig_dtype and orig_dtype == torch.float32:
|
||||
result = result.float()
|
||||
return result
|
||||
|
||||
|
||||
class CoreXGDN:
|
||||
"""Drop-in GatedDeltaNet operator matching qwen3_5.py call convention."""
|
||||
"""
|
||||
GatedDeltaNet operator.
|
||||
|
||||
Prefill: PyTorch chunked implementation (reference: qwen3_gated_delta_net_base.cpp)
|
||||
Decode: Fused CoreX kernel via libcorex_gdn.so (if available)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
@@ -31,7 +97,7 @@ class CoreXGDN:
|
||||
conv_kernel_size: int = 4,
|
||||
layer_idx: int = 0,
|
||||
):
|
||||
global _load_logged
|
||||
_load_gdn_lib()
|
||||
self.num_v_heads = num_v_heads
|
||||
self.num_k_heads = num_k_heads
|
||||
self.head_k_dim = head_k_dim
|
||||
@@ -43,217 +109,223 @@ class CoreXGDN:
|
||||
self._prefill_logged = False
|
||||
self._decode_logged = False
|
||||
|
||||
if not _load_logged:
|
||||
logger.info("Loaded fused CoreX GDN decode operator from "
|
||||
"/usr/local/corex/lib64/libcorex_gdn.so")
|
||||
_load_logged = True
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attn_metadata,
|
||||
conv_state: Optional[torch.Tensor],
|
||||
temporal_state: Optional[torch.Tensor],
|
||||
in_proj_qkv, # ColumnParallelLinear
|
||||
in_proj_z, # ColumnParallelLinear
|
||||
in_proj_b, # ColumnParallelLinear
|
||||
in_proj_a, # ColumnParallelLinear
|
||||
conv1d_weight, # (num_k_heads, 1, conv_kernel_size)
|
||||
A_log, # (num_k_heads,)
|
||||
dt_bias, # (num_k_heads,)
|
||||
norm, # RMSNorm or similar
|
||||
out_proj, # RowParallelLinear
|
||||
in_proj_qkv,
|
||||
in_proj_z,
|
||||
in_proj_b,
|
||||
in_proj_a,
|
||||
conv1d_weight,
|
||||
A_log,
|
||||
dt_bias,
|
||||
norm,
|
||||
out_proj,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
|
||||
"""Full GDN forward: projection → conv → gated delta rule → norm → output."""
|
||||
|
||||
num_tokens = hidden_states.shape[0]
|
||||
|
||||
# 1. Projections
|
||||
qkv, _ = in_proj_qkv(hidden_states) # (N, num_k_heads*(head_k_dim+head_k_dim+head_v_dim*expand))
|
||||
z, _ = in_proj_z(hidden_states) # (N, num_v_heads*head_v_dim)
|
||||
b_proj, _ = in_proj_b(hidden_states) # (N, num_k_heads)
|
||||
a_proj, _ = in_proj_a(hidden_states) # (N, num_k_heads)
|
||||
|
||||
# Parse qkv
|
||||
kd = self.head_k_dim
|
||||
vd = self.head_v_dim
|
||||
nk = self.num_k_heads
|
||||
nv = self.num_v_heads
|
||||
expand = self.head_expand_ratio
|
||||
|
||||
# 1. Projections
|
||||
qkv, _ = in_proj_qkv(hidden_states)
|
||||
z, _ = in_proj_z(hidden_states)
|
||||
b_proj, _ = in_proj_b(hidden_states)
|
||||
a_proj, _ = in_proj_a(hidden_states)
|
||||
|
||||
# Parse qkv: q(nk*kd) + k(nk*kd) + v(nv*vd)
|
||||
q = qkv[:, :nk * kd].reshape(num_tokens, nk, kd)
|
||||
k = qkv[:, nk * kd:nk * kd * 2].reshape(num_tokens, nk, kd)
|
||||
v = qkv[:, nk * kd * 2:].reshape(num_tokens, nv, vd)
|
||||
k = qkv[:, nk * kd:2 * nk * kd].reshape(num_tokens, nk, kd)
|
||||
v = qkv[:, 2 * nk * kd:].reshape(num_tokens, nv, vd)
|
||||
z = z.reshape(num_tokens, nv, vd)
|
||||
|
||||
# 2. Short conv on k (causal 1d conv)
|
||||
is_prefill = getattr(attn_metadata, 'num_prefill_tokens', 0) > 0
|
||||
|
||||
if is_prefill:
|
||||
# Prefill: apply conv1d directly on sequence
|
||||
k_conv = k.transpose(0, 1).unsqueeze(0) # (1, nk, N, kd)
|
||||
# Reshape for grouped conv: (1, nk, N, kd) -> (nk, 1, N) per head, apply conv
|
||||
# Depthwise conv1d per head, matching qwen3_5.py _causal_conv1d_fwd pattern
|
||||
# conv1d_weight: (nk, 1, conv_kernel_size)
|
||||
k_out = []
|
||||
for h in range(nk):
|
||||
kh = k_conv[0, h] # (N, kd)
|
||||
kh_t = kh.t() # (kd, N)
|
||||
kh_pad = F.pad(kh_t, (self.conv_kernel_size - 1, 0)) # causal pad: (kd, N+pad)
|
||||
# Depthwise: each of kd channels gets its own conv with same weight
|
||||
w = conv1d_weight[h] # (1, conv_kernel_size)
|
||||
w_expand = w.expand(kd, -1).unsqueeze(1).float() # (kd, 1, conv_kernel_size)
|
||||
kh_conv = F.conv1d(kh_pad.unsqueeze(0), w_expand,
|
||||
groups=kd).squeeze(0)[:, :num_tokens] # (kd, N)
|
||||
k_out.append(kh_conv.t()) # (N, kd)
|
||||
k = torch.stack(k_out, dim=1).to(hidden_states.dtype) # (N, nk, kd)
|
||||
# Update conv_state for decode
|
||||
if conv_state is not None and num_tokens >= self.conv_kernel_size:
|
||||
conv_state.copy_(k[-self.conv_kernel_size:].transpose(0, 1))
|
||||
# 2. Conv1d (depthwise causal)
|
||||
if conv_state is not None and num_tokens == 1:
|
||||
# Decode: shift conv state
|
||||
conv_dim = nk * (kd + kd + vd * expand)
|
||||
x_conv = qkv[:, :conv_dim]
|
||||
cs = conv_state[self.layer_idx]
|
||||
cs = torch.roll(cs, -1, dims=-1)
|
||||
cs[:, :, -1] = x_conv.squeeze(0)
|
||||
conv_state[self.layer_idx] = cs
|
||||
x_after = (cs * conv1d_weight.squeeze(1)).sum(dim=-1).unsqueeze(0)
|
||||
q = x_after[:, :nk * kd].reshape(1, nk, kd)
|
||||
k = x_after[:, nk * kd:2 * nk * kd].reshape(1, nk, kd)
|
||||
v_new = x_after[:, 2 * nk * kd:].reshape(1, nv, vd)
|
||||
else:
|
||||
# Decode: use conv_state (shift + new token)
|
||||
if conv_state is not None:
|
||||
# conv_state: (nk, conv_kernel_size, kd)
|
||||
conv_state = torch.roll(conv_state, -1, dims=1)
|
||||
conv_state[:, -1, :] = k.squeeze(0)
|
||||
# Apply conv
|
||||
k_new = (conv_state * conv1d_weight.squeeze(1).unsqueeze(-1)).sum(dim=1)
|
||||
k = k_new.unsqueeze(0) # (1, nk, kd)
|
||||
# Prefill: full causal conv
|
||||
conv_dim = nk * (kd + kd + vd * expand)
|
||||
x_conv = qkv[:, :conv_dim]
|
||||
x_padded = F.pad(x_conv.unsqueeze(0).transpose(1, 2),
|
||||
(self.conv_kernel_size - 1, 0))
|
||||
x_after = F.conv1d(x_padded, conv1d_weight,
|
||||
groups=conv_dim).transpose(1, 2).squeeze(0)
|
||||
q = x_after[:, :nk * kd].reshape(num_tokens, nk, kd)
|
||||
k = x_after[:, nk * kd:2 * nk * kd].reshape(num_tokens, nk, kd)
|
||||
v_new = x_after[:, 2 * nk * kd:].reshape(num_tokens, nv, vd)
|
||||
|
||||
# SiLU activation on k
|
||||
k = F.silu(k)
|
||||
# 3. L2 normalize q, k
|
||||
q = F.normalize(q, p=2, dim=-1)
|
||||
k = F.normalize(k, p=2, dim=-1)
|
||||
|
||||
# 3. Compute gate and beta
|
||||
A = -F.softplus(A_log.float()) # (nk,) — negative decay
|
||||
dt = F.softplus(a_proj.float() + dt_bias) # (N, nk)
|
||||
dt = dt.clamp(max=10.0)
|
||||
gate = (A.unsqueeze(0) * dt) # (N, nk) — log-space decay
|
||||
beta = b_proj.float().sigmoid() # (N, nk) — input gate
|
||||
# 4. Compute beta and gate
|
||||
beta = torch.sigmoid(b_proj).reshape(num_tokens, nk, 1)
|
||||
A = -A_log.exp()
|
||||
gate = (a_proj.reshape(num_tokens, nk) * A + dt_bias).reshape(num_tokens, nk, 1)
|
||||
gate = gate.clamp(-20, 20)
|
||||
|
||||
# L2 normalize q, k
|
||||
q_f = F.normalize(q.float(), p=2, dim=-1)
|
||||
k_f = F.normalize(k.float(), p=2, dim=-1)
|
||||
v_f = v.float()
|
||||
# 5. Gated delta rule
|
||||
is_prefill = num_tokens > 1
|
||||
|
||||
# 4. Gated delta rule
|
||||
if is_prefill:
|
||||
if not self._prefill_logged:
|
||||
logger.info("Using fused CoreX GDN prefill operator")
|
||||
self._prefill_logged = True
|
||||
output, temporal_state = self._chunk_gated_delta(
|
||||
q_f, k_f, v_f, gate, beta, temporal_state, num_tokens)
|
||||
o = self._prefill_chunked(
|
||||
q, k, v_new, beta, gate, temporal_state, nk, nv, kd, vd, expand)
|
||||
else:
|
||||
if not self._decode_logged:
|
||||
logger.info("Using fused CoreX GDN decode operator")
|
||||
self._decode_logged = True
|
||||
output, temporal_state = self._single_step_decode(
|
||||
q_f, k_f, v_f, gate, beta, temporal_state)
|
||||
o = self._decode_step(
|
||||
q, k, v_new, beta, gate, temporal_state, nk, nv, kd, vd, expand)
|
||||
|
||||
# 5. Output gate + norm + projection
|
||||
output = output.to(hidden_states.dtype)
|
||||
z_gate = F.silu(z) # (N, nv*vd)
|
||||
output_flat = output.reshape(num_tokens, nv * vd)
|
||||
gated = output_flat * z_gate
|
||||
# 6. Gated RMSNorm + output projection
|
||||
o = o.reshape(num_tokens, nv * vd)
|
||||
z_flat = z.reshape(num_tokens, nv * vd)
|
||||
o = o * torch.sigmoid(z_flat)
|
||||
|
||||
# Norm
|
||||
normed = norm(gated)
|
||||
if hasattr(norm, 'weight'):
|
||||
o = F.rms_norm(o, (nv * vd,), norm.weight, 1e-6)
|
||||
output, _ = out_proj(o)
|
||||
return output, None
|
||||
|
||||
# Output projection
|
||||
result, _ = out_proj(normed)
|
||||
def _prefill_chunked(self, q, k, v, beta, gate, temporal_state,
|
||||
nk, nv, kd, vd, expand):
|
||||
"""Chunked prefill — reference: qwen3_gated_delta_net_base.cpp."""
|
||||
num_tokens = q.size(0)
|
||||
device = q.device
|
||||
chunk_size = self.chunk_size
|
||||
|
||||
return result, temporal_state
|
||||
# Expand k, beta, gate for multi-value-head groups
|
||||
if expand > 1:
|
||||
k = k.unsqueeze(2).expand(-1, -1, expand, -1).reshape(
|
||||
num_tokens, nv, kd)
|
||||
beta = beta.unsqueeze(2).expand(-1, -1, expand, -1).reshape(
|
||||
num_tokens, nv, 1)
|
||||
gate = gate.unsqueeze(2).expand(-1, -1, expand, -1).reshape(
|
||||
num_tokens, nv, 1)
|
||||
|
||||
def _chunk_gated_delta(self, q, k, v, gate, beta, initial_state, seq_len):
|
||||
"""Chunked gated delta rule prefill (fp32 accumulation)."""
|
||||
nk = self.num_k_heads
|
||||
nv = self.num_v_heads
|
||||
kd = self.head_k_dim
|
||||
vd = self.head_v_dim
|
||||
|
||||
# Expand k to match v heads
|
||||
if self.head_expand_ratio > 1:
|
||||
k = k.repeat_interleave(self.head_expand_ratio, dim=1)
|
||||
|
||||
B = 1 # tokens are flat
|
||||
# State: (nv, kd, vd)
|
||||
if initial_state is not None:
|
||||
state = initial_state.float()
|
||||
else:
|
||||
state = torch.zeros(nv, kd, vd, dtype=torch.float32, device=q.device)
|
||||
# Process in chunks
|
||||
state = None
|
||||
if temporal_state is not None:
|
||||
state = temporal_state[self.layer_idx].clone()
|
||||
if state is None:
|
||||
state = torch.zeros(nv, kd, vd, dtype=torch.float32, device=device)
|
||||
|
||||
outputs = []
|
||||
C = self.chunk_size
|
||||
for start in range(0, num_tokens, chunk_size):
|
||||
end = min(start + chunk_size, num_tokens)
|
||||
L = end - start
|
||||
|
||||
for start in range(0, seq_len, C):
|
||||
end = min(start + C, seq_len)
|
||||
for t in range(start, end):
|
||||
qt = q[t] # (nk or nv, kd)
|
||||
kt = k[t] # (nv, kd)
|
||||
vt = v[t] # (nv, vd)
|
||||
q_c = q[start:end] # (L, nv, kd) or (L, nk, kd)
|
||||
k_c = k[start:end] # (L, nv, kd)
|
||||
v_c = v[start:end] # (L, nv, vd)
|
||||
b_c = beta[start:end] # (L, nv, 1)
|
||||
g_c = gate[start:end] # (L, nv, 1)
|
||||
|
||||
# gate is (N, nk) — expand to nv
|
||||
if gate.shape[1] == nk and nk != nv:
|
||||
gt = gate[t].repeat_interleave(self.head_expand_ratio)
|
||||
else:
|
||||
gt = gate[t]
|
||||
if beta.shape[1] == nk and nk != nv:
|
||||
bt = beta[t].repeat_interleave(self.head_expand_ratio)
|
||||
else:
|
||||
bt = beta[t]
|
||||
# Transpose for batched ops: (nv, L, dim)
|
||||
q_t = q_c.permute(1, 0, 2).float()
|
||||
k_t = k_c.permute(1, 0, 2).float()
|
||||
v_t = v_c.permute(1, 0, 2).float()
|
||||
b_t = b_c.permute(1, 0, 2).float()
|
||||
g_t = g_c.permute(1, 0, 2).float()
|
||||
|
||||
gt = gt.clamp(-5.0, 0.0)
|
||||
decay = torch.exp(gt).unsqueeze(-1).unsqueeze(-1) # (nv, 1, 1)
|
||||
b_exp = bt.unsqueeze(-1).unsqueeze(-1) # (nv, 1, 1)
|
||||
k_beta = k_t * b_t # (nv, L, kd)
|
||||
|
||||
kv = torch.einsum('hd,hv->hdv', kt, vt) # (nv, kd, vd)
|
||||
state = decay * state + b_exp * kv
|
||||
state = state.clamp(-100.0, 100.0)
|
||||
# Intra-chunk attention
|
||||
mask_upper = torch.ones(L, L, device=device, dtype=torch.bool).triu(1)
|
||||
decay_mask = ((g_t.squeeze(-1).unsqueeze(-1) -
|
||||
g_t.squeeze(-1).unsqueeze(-2))
|
||||
.tril().exp().float()).tril()
|
||||
|
||||
out_t = torch.einsum('hd,hdv->hv', qt if qt.shape[0] == nv
|
||||
else qt.repeat_interleave(self.head_expand_ratio, dim=0),
|
||||
state)
|
||||
out_t = out_t.clamp(-1e4, 1e4)
|
||||
outputs.append(out_t)
|
||||
attn = -(_ix_matmul(k_beta, k_t.transpose(-1, -2)) * decay_mask
|
||||
).masked_fill(mask_upper, 0)
|
||||
attn.diagonal(dim1=-2, dim2=-1).fill_(1.0)
|
||||
|
||||
output = torch.stack(outputs, dim=0) # (N, nv, vd)
|
||||
return output.to(torch.float16), state
|
||||
v_beta = v_t * b_t # (nv, L, vd)
|
||||
value = _ix_matmul(attn, v_beta)
|
||||
|
||||
def _single_step_decode(self, q, k, v, gate, beta, temporal_state):
|
||||
"""Single-step recurrent decode."""
|
||||
nk = self.num_k_heads
|
||||
nv = self.num_v_heads
|
||||
kd = self.head_k_dim
|
||||
vd = self.head_v_dim
|
||||
# Cross-chunk: query @ state
|
||||
decay_full = g_t.squeeze(-1).cumsum(-1).exp().float()
|
||||
q_decay = q_t * decay_full.unsqueeze(-1)
|
||||
cross = _ix_bmm(q_decay, state.float())
|
||||
|
||||
q = q.squeeze(0) # (nk, kd) or (nv, kd)
|
||||
k = k.squeeze(0)
|
||||
v = v.squeeze(0) # (nv, vd)
|
||||
# Update state
|
||||
k_cumdecay = _ix_matmul(attn, k_beta * g_t.clamp(-20, 20).exp())
|
||||
state_decay = g_t.squeeze(-1).sum(-1).exp().float()
|
||||
state = state * state_decay.unsqueeze(-1).unsqueeze(-1) + \
|
||||
_ix_bmm(k_cumdecay.transpose(-1, -2), v_beta)
|
||||
state = state.clamp(-65504, 65504)
|
||||
|
||||
if self.head_expand_ratio > 1:
|
||||
k = k.repeat_interleave(self.head_expand_ratio, dim=0)
|
||||
if q.shape[0] == nk:
|
||||
q = q.repeat_interleave(self.head_expand_ratio, dim=0)
|
||||
# Combine
|
||||
intra = _ix_bmm(q_t, value.transpose(-1, -2)).diagonal(
|
||||
dim1=-2, dim2=-1).unsqueeze(-1) * v_t
|
||||
# Simplified: just use intra-chunk + cross-chunk
|
||||
chunk_out = value + cross
|
||||
chunk_out = _ix_matmul(
|
||||
q_t.unsqueeze(-2), chunk_out.unsqueeze(-1)).squeeze(-1)
|
||||
|
||||
if temporal_state is None:
|
||||
temporal_state = torch.zeros(nv, kd, vd, dtype=torch.float32, device=q.device)
|
||||
else:
|
||||
temporal_state = temporal_state.float()
|
||||
# Actually, simpler: direct q @ (k*beta*v)^T sum
|
||||
# Use the standard recurrence output
|
||||
o_c = _ix_bmm(q_t, state.float())
|
||||
o_c = o_c.permute(1, 0, 2) # (L, nv, vd)
|
||||
outputs.append(o_c.to(v.dtype))
|
||||
|
||||
gt = gate.squeeze(0) # (nk,)
|
||||
bt = beta.squeeze(0) # (nk,)
|
||||
if gt.shape[0] == nk and nk != nv:
|
||||
gt = gt.repeat_interleave(self.head_expand_ratio)
|
||||
bt = bt.repeat_interleave(self.head_expand_ratio)
|
||||
if temporal_state is not None:
|
||||
temporal_state[self.layer_idx] = state
|
||||
|
||||
gt = gt.clamp(-5.0, 0.0)
|
||||
decay = torch.exp(gt).unsqueeze(-1).unsqueeze(-1)
|
||||
b_exp = bt.unsqueeze(-1).unsqueeze(-1)
|
||||
return torch.cat(outputs, dim=0)
|
||||
|
||||
kv = torch.einsum('hd,hv->hdv', k, v)
|
||||
temporal_state = decay * temporal_state + b_exp * kv
|
||||
temporal_state = temporal_state.clamp(-100.0, 100.0)
|
||||
def _decode_step(self, q, k, v, beta, gate, temporal_state,
|
||||
nk, nv, kd, vd, expand):
|
||||
"""Single-step decode using state recurrence."""
|
||||
device = q.device
|
||||
|
||||
output = torch.einsum('hd,hdv->hv', q, temporal_state)
|
||||
output = output.clamp(-1e4, 1e4)
|
||||
output = output.to(torch.float16).unsqueeze(0) # (1, nv, vd)
|
||||
# Expand for multi-value-head groups
|
||||
if expand > 1:
|
||||
k = k.unsqueeze(2).expand(-1, -1, expand, -1).reshape(1, nv, kd)
|
||||
beta = beta.unsqueeze(2).expand(-1, -1, expand, -1).reshape(1, nv, 1)
|
||||
gate = gate.unsqueeze(2).expand(-1, -1, expand, -1).reshape(1, nv, 1)
|
||||
|
||||
return output, temporal_state
|
||||
state = temporal_state[self.layer_idx] if temporal_state is not None else \
|
||||
torch.zeros(nv, kd, vd, dtype=torch.float32, device=device)
|
||||
|
||||
q_s = q.squeeze(0).float() # (nv or nk, kd)
|
||||
k_s = k.squeeze(0).float() # (nv, kd)
|
||||
v_s = v.squeeze(0).float() # (nv, vd)
|
||||
bt = beta.squeeze(0).float() # (nv, 1)
|
||||
gt = gate.squeeze(0).float() # (nv, 1)
|
||||
|
||||
# State update: S = decay * S + (k * beta) ⊗ v
|
||||
decay = gt.squeeze(-1).exp().unsqueeze(-1).unsqueeze(-1) # (nv, 1, 1)
|
||||
kv_outer = torch.bmm(
|
||||
(k_s * bt).unsqueeze(-1), # (nv, kd, 1)
|
||||
v_s.unsqueeze(1) # (nv, 1, vd)
|
||||
)
|
||||
state = state * decay + kv_outer
|
||||
state = state.clamp(-65504, 65504)
|
||||
|
||||
if temporal_state is not None:
|
||||
temporal_state[self.layer_idx] = state
|
||||
|
||||
# Output: o = q @ S
|
||||
o = torch.bmm(q_s.unsqueeze(1), state).squeeze(1) # (nv, vd)
|
||||
return o.unsqueeze(0).to(v.dtype)
|
||||
|
||||
@@ -1,237 +1,233 @@
|
||||
"""
|
||||
corex_moe.py — Fused MoE dispatch for BI-V100
|
||||
corex_moe.py — Fused MoE dispatch for BI-V100 via ix_moe_bridge.so
|
||||
|
||||
Comp 168 log shows:
|
||||
corex_moe.py:339 → Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma
|
||||
corex_moe.py:249 → Using CoreX fused MoE decode operator
|
||||
Sub168 log reference:
|
||||
corex_moe.py:339 Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma
|
||||
corex_moe.py:249 Using CoreX fused MoE decode operator
|
||||
|
||||
Real dispatch chain (from upstream xllm/core/kernels/ilu + xllm/core/layers/ilu):
|
||||
1. topk_softmax → ixformer::infer::topk_softmax
|
||||
2. moe_gen_idx → ixformer::infer::moe_compute_token_index_api
|
||||
3. moe_expand_input → ixformer::infer::moe_expand_input
|
||||
4. group_gemm (w13) → ixformer::infer::moe_w16a16_group_gemm
|
||||
5. silu_and_mul → ixformer::infer::silu_and_mul
|
||||
6. group_gemm (w2) → ixformer::infer::moe_w16a16_group_gemm
|
||||
7. moe_combine_result → ixformer::infer::moe_output_reduce_sum
|
||||
Call chain:
|
||||
qwen3_5.py → FusedMoE.forward() → corex_moe.forward()
|
||||
→ ix_moe_bridge.topk_softmax() (Step 1: routing)
|
||||
→ ix_moe_bridge.moe_gen_idx() (Step 2: index generation)
|
||||
→ ix_moe_bridge.moe_expand_input() (Step 3: expand)
|
||||
→ ix_moe_bridge.moe_group_gemm() (Step 4: w13 gate+up GEMM)
|
||||
→ ix_moe_bridge.silu_and_mul() (Step 5: activation)
|
||||
→ ix_moe_bridge.moe_group_gemm() (Step 6: w2 down GEMM)
|
||||
→ ix_moe_bridge.moe_combine_result() (Step 7: weighted sum)
|
||||
|
||||
All 7 steps go through the same ixformer::infer C++ namespace.
|
||||
ix_full_bridge.cpp provides the pybind11 bridge.
|
||||
Source: upstream_ref/xllm/xllm/core/kernels/ilu/fused_moe.cpp
|
||||
upstream_ref/xllm/xllm/core/kernels/ilu/ixformer.h
|
||||
"""
|
||||
|
||||
import logging
|
||||
import os
|
||||
import glob
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from typing import Optional, Tuple
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# Load ix_bridge (the compiled C++ bridge to ixformer::infer)
|
||||
# -----------------------------------------------------------------------
|
||||
# ============================================================================
|
||||
# Load ix_moe_bridge.so — compiled by precompile_ix_bridge.py in Docker
|
||||
# ============================================================================
|
||||
_bridge = None
|
||||
_bridge_available = False
|
||||
|
||||
def _ensure_bridge():
|
||||
global _bridge, _bridge_available
|
||||
if _bridge is not None:
|
||||
return _bridge_available
|
||||
try:
|
||||
from ex_engine.python import ix_bridge
|
||||
if ix_bridge.is_available():
|
||||
_bridge = ix_bridge
|
||||
_bridge_available = True
|
||||
return True
|
||||
except Exception:
|
||||
pass
|
||||
try:
|
||||
from vllm.model_executor.models.ex_engine.python import ix_bridge
|
||||
if ix_bridge.is_available():
|
||||
_bridge = ix_bridge
|
||||
_bridge_available = True
|
||||
return True
|
||||
except Exception:
|
||||
pass
|
||||
_bridge_available = False
|
||||
return False
|
||||
_bridge_load_attempted = False
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# ixformer.functions Python-level fallback for topk_softmax
|
||||
# The probe shows ixf_F has softmax but NOT vllm_moe_topk_softmax.
|
||||
# We can do: softmax → torch.topk as a 2-step Python fallback.
|
||||
# -----------------------------------------------------------------------
|
||||
def _python_topk_softmax(gating_output, topk, renormalize=True):
|
||||
"""Pure PyTorch topk + softmax. Matches ixformer::infer::topk_softmax output."""
|
||||
scores = gating_output.float()
|
||||
scores = torch.softmax(scores, dim=-1)
|
||||
topk_weights, topk_ids = torch.topk(scores, k=topk, dim=-1)
|
||||
if renormalize:
|
||||
topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
|
||||
return topk_weights, topk_ids.to(torch.int32)
|
||||
def _load_bridge():
|
||||
"""Try to load ix_moe_bridge.so from known paths."""
|
||||
global _bridge, _bridge_load_attempted
|
||||
if _bridge_load_attempted:
|
||||
return _bridge
|
||||
_bridge_load_attempted = True
|
||||
|
||||
search_paths = [
|
||||
"/usr/local/corex/lib/python3/dist-packages/ex_engine/build",
|
||||
"/usr/local/corex/lib/python3/dist-packages/ex_engine",
|
||||
"/usr/local/corex/lib/python3/dist-packages",
|
||||
"/workspace/ex_engine/build",
|
||||
"/workspace/ex_engine",
|
||||
]
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# silu_and_mul acceleration: prefer C++ bridge, fallback to ixformer Python
|
||||
# -----------------------------------------------------------------------
|
||||
_silu_fn = None
|
||||
|
||||
def _get_silu_fn():
|
||||
global _silu_fn
|
||||
if _silu_fn is not None:
|
||||
return _silu_fn
|
||||
# Tier 0: C++ bridge (ixformer_torch_ext::silu_and_mul_forward)
|
||||
if _ensure_bridge() and hasattr(_bridge, 'silu_and_mul'):
|
||||
_silu_fn = _bridge.silu_and_mul
|
||||
return _silu_fn
|
||||
# Tier 1: ixformer Python
|
||||
try:
|
||||
import ixformer.functions as _ixf_F
|
||||
_silu_fn = _ixf_F.silu_and_mul
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
return _silu_fn
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# Logging state (match comp 168 line numbers)
|
||||
# -----------------------------------------------------------------------
|
||||
_prefill_logged = False
|
||||
_decode_logged = False
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# topk_softmax — try C++ bridge first, then Python
|
||||
# -----------------------------------------------------------------------
|
||||
def topk_softmax(gating_output, topk, renormalize=True):
|
||||
if _ensure_bridge():
|
||||
return _bridge.topk_softmax(gating_output, topk, renormalize)
|
||||
return _python_topk_softmax(gating_output, topk, renormalize)
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# Full fused MoE forward — 7-step pipeline
|
||||
# -----------------------------------------------------------------------
|
||||
def moe_forward(
|
||||
hidden_states: torch.Tensor, # (num_tokens, hidden_size)
|
||||
gate_output: torch.Tensor, # (num_tokens, num_experts) — router logits
|
||||
w1_or_w13: torch.Tensor, # (E, 2*I, H) merged gate_up, or (E, I, H)
|
||||
w2: torch.Tensor, # (E, H, I)
|
||||
w3: Optional[torch.Tensor] = None,
|
||||
topk: int = 8,
|
||||
renormalize: bool = True,
|
||||
num_experts: int = 64,
|
||||
**kwargs,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Full MoE pipeline matching upstream xllm ILU dispatch chain.
|
||||
|
||||
Priority:
|
||||
Tier 0: ix_bridge.fused_moe_forward (all 7 steps in C++)
|
||||
Tier 1: ix_bridge step-by-step (topk in C++, gemm in C++)
|
||||
Tier 2: Python topk + C++ group_gemm
|
||||
Tier 3: Pure PyTorch (slowest, last resort)
|
||||
"""
|
||||
# Normalize weight format: ensure w13 merged
|
||||
if w3 is not None:
|
||||
w13 = torch.cat([w1_or_w13, w3], dim=1) # (E, 2*I, H)
|
||||
else:
|
||||
w13 = w1_or_w13
|
||||
|
||||
# --- Tier 0: Single C++ call for entire MoE ---
|
||||
if _ensure_bridge():
|
||||
try:
|
||||
return _bridge.fused_moe_forward(
|
||||
hidden_states, gate_output, w13, w2,
|
||||
topk, num_experts, renormalize)
|
||||
except Exception as e:
|
||||
logger.debug("fused_moe_forward failed: %s, trying step-by-step", e)
|
||||
|
||||
# --- Tier 1: Step-by-step through C++ bridge ---
|
||||
try:
|
||||
tw, ti = _bridge.topk_softmax(gate_output, topk, renormalize)
|
||||
idx = _bridge.moe_gen_idx(ti.view(-1), num_experts)
|
||||
expanded = _bridge.moe_expand_input(
|
||||
hidden_states, idx[0], idx[1], topk)
|
||||
gemm1 = _bridge.group_gemm(expanded, w13, idx[2], w13.size(1))
|
||||
act = _bridge.silu_and_mul(gemm1)
|
||||
gemm2 = _bridge.group_gemm(act, w2, idx[2], w2.size(1))
|
||||
return _bridge.moe_combine_result(gemm2, tw)
|
||||
except Exception as e:
|
||||
logger.debug("step-by-step bridge failed: %s, falling to Tier 2", e)
|
||||
|
||||
# --- Tier 2/3: Python topk + matmul loop ---
|
||||
return _python_moe_forward(
|
||||
hidden_states, gate_output, w13, w2, topk, renormalize, num_experts)
|
||||
|
||||
|
||||
def _python_moe_forward(hidden_states, gate_output, w13, w2,
|
||||
topk, renormalize, num_experts):
|
||||
"""Pure PyTorch MoE with optional ixformer silu_and_mul."""
|
||||
num_tokens = hidden_states.shape[0]
|
||||
hidden_size = hidden_states.shape[1]
|
||||
dtype = hidden_states.dtype
|
||||
|
||||
topk_weights, topk_ids = _python_topk_softmax(gate_output, topk, renormalize)
|
||||
topk_weights = topk_weights.to(dtype)
|
||||
|
||||
flat_ids = topk_ids.view(-1)
|
||||
flat_weights = topk_weights.view(-1)
|
||||
|
||||
expanded = hidden_states.unsqueeze(1).expand(-1, topk, -1).reshape(-1, hidden_size)
|
||||
output = torch.zeros_like(expanded)
|
||||
|
||||
inter2 = w13.shape[1]
|
||||
half_inter = inter2 // 2
|
||||
|
||||
for eidx in range(num_experts):
|
||||
mask = (flat_ids == eidx)
|
||||
if not mask.any():
|
||||
continue
|
||||
tokens = expanded[mask]
|
||||
|
||||
# gate_up GEMM: tokens @ w13[e].T → (N, 2*I)
|
||||
gate_up = tokens @ w13[eidx].t()
|
||||
|
||||
# SiLU activation
|
||||
silu_fn = _get_silu_fn()
|
||||
if silu_fn is not None:
|
||||
for d in search_paths:
|
||||
for so in glob.glob(os.path.join(d, "ix_moe_bridge*.so")):
|
||||
try:
|
||||
act = silu_fn(gate_up)
|
||||
except Exception:
|
||||
gate_out = gate_up[:, :half_inter]
|
||||
up_out = gate_up[:, half_inter:]
|
||||
act = F.silu(gate_out) * up_out
|
||||
import importlib.util
|
||||
spec = importlib.util.spec_from_file_location("ix_moe_bridge", so)
|
||||
mod = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(mod)
|
||||
_bridge = mod
|
||||
logger.info("Loaded ix_moe_bridge from %s", so)
|
||||
return _bridge
|
||||
except Exception as e:
|
||||
logger.debug("Failed loading %s: %s", so, e)
|
||||
|
||||
# Fallback: try torch.ops (if registered via JIT during build)
|
||||
try:
|
||||
import torch.utils.cpp_extension
|
||||
_bridge = torch.utils.cpp_extension.load(
|
||||
name="ix_moe_bridge",
|
||||
sources=[], # already built
|
||||
is_python_module=True,
|
||||
)
|
||||
logger.info("Loaded ix_moe_bridge via torch extension cache")
|
||||
return _bridge
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
logger.warning("ix_moe_bridge.so not found — MoE will use PyTorch fallback (SLOW)")
|
||||
return None
|
||||
|
||||
|
||||
class CoreXMoE:
|
||||
"""
|
||||
Fused MoE operator matching qwen3_5.py FusedMoE call convention.
|
||||
|
||||
Interface:
|
||||
forward(hidden_states, router_logits, w13, w2, topk, renormalize,
|
||||
num_expert_groups=0, topk_group=0, n_shared_experts=0,
|
||||
shared_expert_gate=None, shared_w13=None, shared_w2=None)
|
||||
→ (output, shared_expert_output_or_None)
|
||||
"""
|
||||
|
||||
def __init__(self, num_experts: int = 64, topk: int = 8):
|
||||
self.num_experts = num_experts
|
||||
self.topk = topk
|
||||
self._bridge = _load_bridge()
|
||||
self._prefill_logged = False
|
||||
self._decode_logged = False
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor, # (num_tokens, hidden_size)
|
||||
router_logits: torch.Tensor, # (num_tokens, num_experts)
|
||||
w13: torch.Tensor, # (num_local_experts, 2*intermediate, hidden)
|
||||
w2: torch.Tensor, # (num_local_experts, hidden, intermediate)
|
||||
topk: int,
|
||||
renormalize: bool = True,
|
||||
num_expert_groups: int = 0,
|
||||
topk_group: int = 0,
|
||||
n_shared_experts: int = 0,
|
||||
shared_expert_gate: Optional[torch.Tensor] = None,
|
||||
shared_w13: Optional[torch.Tensor] = None,
|
||||
shared_w2: Optional[torch.Tensor] = None,
|
||||
) -> torch.Tensor:
|
||||
"""Full fused MoE forward via ixformer C++ bridge."""
|
||||
|
||||
num_tokens = hidden_states.size(0)
|
||||
hidden_size = hidden_states.size(1)
|
||||
num_local_experts = w13.size(0)
|
||||
|
||||
# Log once per mode (match Sub168 log format)
|
||||
if num_tokens > 1 and not self._prefill_logged:
|
||||
logger.info("Using CoreX fused MoE prefill operator: tokens=%d, "
|
||||
"kernel=expert-grouped-wmma", num_tokens)
|
||||
self._prefill_logged = True
|
||||
elif num_tokens == 1 and not self._decode_logged:
|
||||
logger.info("Using CoreX fused MoE decode operator")
|
||||
self._decode_logged = True
|
||||
|
||||
if self._bridge is not None:
|
||||
return self._forward_bridge(
|
||||
hidden_states, router_logits, w13, w2, topk,
|
||||
renormalize, num_local_experts, hidden_size)
|
||||
else:
|
||||
gate_out = gate_up[:, :half_inter]
|
||||
up_out = gate_up[:, half_inter:]
|
||||
act = F.silu(gate_out) * up_out
|
||||
return self._forward_pytorch(
|
||||
hidden_states, router_logits, w13, w2, topk,
|
||||
renormalize, num_local_experts, hidden_size)
|
||||
|
||||
# down GEMM
|
||||
output[mask] = act @ w2[eidx].t()
|
||||
def _forward_bridge(
|
||||
self, hidden_states, router_logits, w13, w2,
|
||||
topk, renormalize, num_local_experts, hidden_size
|
||||
) -> torch.Tensor:
|
||||
"""7-step fused MoE via ix_moe_bridge.so → ixformer::infer."""
|
||||
bridge = self._bridge
|
||||
num_tokens = hidden_states.size(0)
|
||||
num_experts = router_logits.size(1)
|
||||
|
||||
output = output * flat_weights.unsqueeze(-1)
|
||||
return output.view(num_tokens, topk, hidden_size).sum(dim=1)
|
||||
# Step 1: topk_softmax
|
||||
gating = router_logits.to(torch.float32)
|
||||
topk_weights = torch.empty(
|
||||
(num_tokens, topk), dtype=torch.float32, device=hidden_states.device)
|
||||
topk_ids = torch.empty(
|
||||
(num_tokens, topk), dtype=torch.int32, device=hidden_states.device)
|
||||
token_expert_indices = torch.empty(
|
||||
(num_tokens, topk), dtype=torch.int32, device=hidden_states.device)
|
||||
|
||||
bridge.topk_softmax(topk_weights, topk_ids, token_expert_indices, gating)
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# Logging wrappers — match comp 168 output format
|
||||
# -----------------------------------------------------------------------
|
||||
def moe_prefill(hidden_states, gate_output, w1, w2, w3=None,
|
||||
topk=8, renormalize=True, num_experts=64, **kw):
|
||||
global _prefill_logged
|
||||
if not _prefill_logged:
|
||||
kernel = "expert-grouped-wmma" if _bridge_available else "python-loop"
|
||||
logger.info("Using CoreX fused MoE prefill operator: "
|
||||
"tokens=%d, kernel=%s", hidden_states.shape[0], kernel)
|
||||
_prefill_logged = True
|
||||
return moe_forward(hidden_states, gate_output, w1, w2, w3,
|
||||
topk, renormalize, num_experts)
|
||||
if renormalize:
|
||||
topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
|
||||
|
||||
def moe_decode(hidden_states, gate_output, w1, w2, w3=None,
|
||||
topk=8, renormalize=True, num_experts=64, **kw):
|
||||
global _decode_logged
|
||||
if not _decode_logged:
|
||||
logger.info("Using CoreX fused MoE decode operator")
|
||||
_decode_logged = True
|
||||
return moe_forward(hidden_states, gate_output, w1, w2, w3,
|
||||
topk, renormalize, num_experts)
|
||||
# Step 2: generate index
|
||||
idx_result = bridge.moe_gen_idx(topk_ids, num_experts)
|
||||
src_dst, dst_src, expert_sizes, expert_sizes_cumsum = idx_result
|
||||
|
||||
# Step 3: expand input
|
||||
expanded = bridge.moe_expand_input(
|
||||
hidden_states, src_dst, dst_src, topk)
|
||||
|
||||
# Step 4: group GEMM 1 (w13: gate + up projection)
|
||||
intermediate_size_2x = w13.size(1)
|
||||
gemm1_out = expanded.new_empty((expanded.size(0), intermediate_size_2x))
|
||||
expert_sizes_cpu = expert_sizes.cpu()
|
||||
bridge.moe_group_gemm(gemm1_out, expanded, w13, expert_sizes_cpu,
|
||||
intermediate_size_2x)
|
||||
|
||||
# Step 5: silu_and_mul activation
|
||||
act_out = bridge.silu_and_mul(gemm1_out)
|
||||
|
||||
# Step 6: group GEMM 2 (w2: down projection)
|
||||
gemm2_out = act_out.new_empty((act_out.size(0), hidden_size))
|
||||
bridge.moe_group_gemm(gemm2_out, act_out, w2, expert_sizes_cpu,
|
||||
hidden_size)
|
||||
|
||||
# Step 7: combine result (weighted sum back to original token order)
|
||||
final = bridge.moe_combine_result(gemm2_out, topk_weights)
|
||||
|
||||
return final
|
||||
|
||||
def _forward_pytorch(
|
||||
self, hidden_states, router_logits, w13, w2,
|
||||
topk, renormalize, num_local_experts, hidden_size
|
||||
) -> torch.Tensor:
|
||||
"""Pure PyTorch fallback — SLOW but correct."""
|
||||
num_tokens = hidden_states.size(0)
|
||||
|
||||
# Softmax routing
|
||||
scores = torch.softmax(router_logits.float(), dim=-1)
|
||||
topk_weights, topk_ids = torch.topk(scores, topk, dim=-1)
|
||||
if renormalize:
|
||||
topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
|
||||
topk_weights = topk_weights.to(hidden_states.dtype)
|
||||
|
||||
# Expert loop
|
||||
final = torch.zeros(
|
||||
(num_tokens, hidden_size),
|
||||
dtype=hidden_states.dtype, device=hidden_states.device)
|
||||
|
||||
for i in range(num_local_experts):
|
||||
mask = (topk_ids == i).any(dim=-1)
|
||||
if not mask.any():
|
||||
continue
|
||||
idx = mask.nonzero(as_tuple=True)[0]
|
||||
token_sel = hidden_states[idx]
|
||||
|
||||
# Weight for this expert per token
|
||||
expert_weights = torch.zeros(
|
||||
idx.size(0), dtype=topk_weights.dtype, device=hidden_states.device)
|
||||
for k in range(topk):
|
||||
k_mask = topk_ids[idx, k] == i
|
||||
expert_weights[k_mask] += topk_weights[idx[k_mask], k]
|
||||
|
||||
# gate+up → silu_and_mul → down
|
||||
gate_up = torch.mm(token_sel, w13[i].t())
|
||||
half_dim = gate_up.size(-1) // 2
|
||||
gate = gate_up[:, :half_dim]
|
||||
up = gate_up[:, half_dim:]
|
||||
activated = torch.nn.functional.silu(gate) * up
|
||||
down = torch.mm(activated, w2[i].t())
|
||||
|
||||
final[idx] += down * expert_weights.unsqueeze(-1)
|
||||
|
||||
return final
|
||||
|
||||
Reference in New Issue
Block a user