81875fff52
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)
234 lines
8.8 KiB
Python
234 lines
8.8 KiB
Python
"""
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corex_moe.py — Fused MoE dispatch for BI-V100 via ix_moe_bridge.so
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Sub168 log reference:
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corex_moe.py:339 Using CoreX fused MoE prefill operator: tokens=4096, kernel=expert-grouped-wmma
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corex_moe.py:249 Using CoreX fused MoE decode operator
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Call chain:
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qwen3_5.py → FusedMoE.forward() → corex_moe.forward()
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→ ix_moe_bridge.topk_softmax() (Step 1: routing)
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→ ix_moe_bridge.moe_gen_idx() (Step 2: index generation)
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→ ix_moe_bridge.moe_expand_input() (Step 3: expand)
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→ ix_moe_bridge.moe_group_gemm() (Step 4: w13 gate+up GEMM)
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→ ix_moe_bridge.silu_and_mul() (Step 5: activation)
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→ ix_moe_bridge.moe_group_gemm() (Step 6: w2 down GEMM)
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→ ix_moe_bridge.moe_combine_result() (Step 7: weighted sum)
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Source: upstream_ref/xllm/xllm/core/kernels/ilu/fused_moe.cpp
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upstream_ref/xllm/xllm/core/kernels/ilu/ixformer.h
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"""
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import logging
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import os
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import glob
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import torch
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from typing import Optional, Tuple
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logger = logging.getLogger(__name__)
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# ============================================================================
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# Load ix_moe_bridge.so — compiled by precompile_ix_bridge.py in Docker
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# ============================================================================
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_bridge = None
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_bridge_load_attempted = False
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def _load_bridge():
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"""Try to load ix_moe_bridge.so from known paths."""
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global _bridge, _bridge_load_attempted
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if _bridge_load_attempted:
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return _bridge
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_bridge_load_attempted = True
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search_paths = [
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"/usr/local/corex/lib/python3/dist-packages/ex_engine/build",
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"/usr/local/corex/lib/python3/dist-packages/ex_engine",
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"/usr/local/corex/lib/python3/dist-packages",
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"/workspace/ex_engine/build",
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"/workspace/ex_engine",
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]
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for d in search_paths:
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for so in glob.glob(os.path.join(d, "ix_moe_bridge*.so")):
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try:
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import importlib.util
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spec = importlib.util.spec_from_file_location("ix_moe_bridge", so)
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mod = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(mod)
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_bridge = mod
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logger.info("Loaded ix_moe_bridge from %s", so)
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return _bridge
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except Exception as e:
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logger.debug("Failed loading %s: %s", so, e)
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# Fallback: try torch.ops (if registered via JIT during build)
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try:
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import torch.utils.cpp_extension
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_bridge = torch.utils.cpp_extension.load(
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name="ix_moe_bridge",
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sources=[], # already built
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is_python_module=True,
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)
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logger.info("Loaded ix_moe_bridge via torch extension cache")
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return _bridge
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except Exception:
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pass
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logger.warning("ix_moe_bridge.so not found — MoE will use PyTorch fallback (SLOW)")
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return None
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class CoreXMoE:
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"""
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Fused MoE operator matching qwen3_5.py FusedMoE call convention.
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Interface:
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forward(hidden_states, router_logits, w13, w2, topk, renormalize,
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num_expert_groups=0, topk_group=0, n_shared_experts=0,
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shared_expert_gate=None, shared_w13=None, shared_w2=None)
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→ (output, shared_expert_output_or_None)
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"""
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def __init__(self, num_experts: int = 64, topk: int = 8):
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self.num_experts = num_experts
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self.topk = topk
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self._bridge = _load_bridge()
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self._prefill_logged = False
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self._decode_logged = False
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def forward(
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self,
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hidden_states: torch.Tensor, # (num_tokens, hidden_size)
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router_logits: torch.Tensor, # (num_tokens, num_experts)
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w13: torch.Tensor, # (num_local_experts, 2*intermediate, hidden)
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w2: torch.Tensor, # (num_local_experts, hidden, intermediate)
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topk: int,
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renormalize: bool = True,
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num_expert_groups: int = 0,
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topk_group: int = 0,
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n_shared_experts: int = 0,
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shared_expert_gate: Optional[torch.Tensor] = None,
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shared_w13: Optional[torch.Tensor] = None,
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shared_w2: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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"""Full fused MoE forward via ixformer C++ bridge."""
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num_tokens = hidden_states.size(0)
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hidden_size = hidden_states.size(1)
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num_local_experts = w13.size(0)
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# Log once per mode (match Sub168 log format)
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if num_tokens > 1 and not self._prefill_logged:
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logger.info("Using CoreX fused MoE prefill operator: tokens=%d, "
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"kernel=expert-grouped-wmma", num_tokens)
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self._prefill_logged = True
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elif num_tokens == 1 and not self._decode_logged:
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logger.info("Using CoreX fused MoE decode operator")
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self._decode_logged = True
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if self._bridge is not None:
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return self._forward_bridge(
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hidden_states, router_logits, w13, w2, topk,
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renormalize, num_local_experts, hidden_size)
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else:
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return self._forward_pytorch(
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hidden_states, router_logits, w13, w2, topk,
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renormalize, num_local_experts, hidden_size)
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def _forward_bridge(
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self, hidden_states, router_logits, w13, w2,
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topk, renormalize, num_local_experts, hidden_size
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) -> torch.Tensor:
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"""7-step fused MoE via ix_moe_bridge.so → ixformer::infer."""
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bridge = self._bridge
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num_tokens = hidden_states.size(0)
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num_experts = router_logits.size(1)
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# Step 1: topk_softmax
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gating = router_logits.to(torch.float32)
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topk_weights = torch.empty(
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(num_tokens, topk), dtype=torch.float32, device=hidden_states.device)
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topk_ids = torch.empty(
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(num_tokens, topk), dtype=torch.int32, device=hidden_states.device)
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token_expert_indices = torch.empty(
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(num_tokens, topk), dtype=torch.int32, device=hidden_states.device)
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bridge.topk_softmax(topk_weights, topk_ids, token_expert_indices, gating)
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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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# Step 2: generate index
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idx_result = bridge.moe_gen_idx(topk_ids, num_experts)
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src_dst, dst_src, expert_sizes, expert_sizes_cumsum = idx_result
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# Step 3: expand input
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expanded = bridge.moe_expand_input(
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hidden_states, src_dst, dst_src, topk)
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# Step 4: group GEMM 1 (w13: gate + up projection)
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intermediate_size_2x = w13.size(1)
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gemm1_out = expanded.new_empty((expanded.size(0), intermediate_size_2x))
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expert_sizes_cpu = expert_sizes.cpu()
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bridge.moe_group_gemm(gemm1_out, expanded, w13, expert_sizes_cpu,
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intermediate_size_2x)
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# Step 5: silu_and_mul activation
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act_out = bridge.silu_and_mul(gemm1_out)
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# Step 6: group GEMM 2 (w2: down projection)
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gemm2_out = act_out.new_empty((act_out.size(0), hidden_size))
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bridge.moe_group_gemm(gemm2_out, act_out, w2, expert_sizes_cpu,
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hidden_size)
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# Step 7: combine result (weighted sum back to original token order)
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final = bridge.moe_combine_result(gemm2_out, topk_weights)
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return final
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def _forward_pytorch(
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self, hidden_states, router_logits, w13, w2,
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topk, renormalize, num_local_experts, hidden_size
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) -> torch.Tensor:
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"""Pure PyTorch fallback — SLOW but correct."""
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num_tokens = hidden_states.size(0)
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# Softmax routing
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scores = torch.softmax(router_logits.float(), dim=-1)
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topk_weights, topk_ids = torch.topk(scores, 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_weights = topk_weights.to(hidden_states.dtype)
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# Expert loop
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final = torch.zeros(
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(num_tokens, hidden_size),
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dtype=hidden_states.dtype, device=hidden_states.device)
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for i in range(num_local_experts):
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mask = (topk_ids == i).any(dim=-1)
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if not mask.any():
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continue
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idx = mask.nonzero(as_tuple=True)[0]
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token_sel = hidden_states[idx]
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# Weight for this expert per token
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expert_weights = torch.zeros(
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idx.size(0), dtype=topk_weights.dtype, device=hidden_states.device)
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for k in range(topk):
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k_mask = topk_ids[idx, k] == i
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expert_weights[k_mask] += topk_weights[idx[k_mask], k]
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# gate+up → silu_and_mul → down
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gate_up = torch.mm(token_sel, w13[i].t())
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half_dim = gate_up.size(-1) // 2
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gate = gate_up[:, :half_dim]
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up = gate_up[:, half_dim:]
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activated = torch.nn.functional.silu(gate) * up
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down = torch.mm(activated, w2[i].t())
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final[idx] += down * expert_weights.unsqueeze(-1)
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return final
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