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Revert "feat: batched MoE expert GEMM — replaces Python for-loop"

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This reverts commit 06d7713db6.
This commit is contained in:
claude
2026-08-14 11:47:37 +00:00
parent 06d7713db6
commit 50a249e0a3
3 changed files with 14 additions and 160 deletions
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3
qwen3_6_scripts/build_xllm_kernels.sh
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@@ -67,8 +67,5 @@ build_kernel "xllm_cache" \
build_kernel "xllm_moe" \
"${CUDA_DIR}/moe/moe_fused_topk.cu" "${CUDA_DIR}/moe/moe_compute_index.cu" "${CUDA_DIR}/moe/moe_combine.cu" "${BIND_DIR}/xllm_moe_bind.cpp"
build_kernel "xllm_moe_gemm" \
"${CUDA_DIR}/moe_batched_gemm.cu"
echo "=== All kernels built ==="
ls -lh "${PREBUILT_DIR}"/xllm_*.so 2>/dev/null || echo "No .so files found"

57
qwen3_6_scripts/qwen3_5.py
View File

@@ -156,14 +156,6 @@ except ImportError:
except ImportError:
_xllm_moe = None
try:
from vllm import xllm_moe_gemm as _xllm_moe_gemm
except ImportError:
try:
import xllm_moe_gemm as _xllm_moe_gemm
except ImportError:
_xllm_moe_gemm = None
try:
from vllm import corex_gdn_chunk_recurrent as _corex_gdn_chunk_recurrent
except ImportError:
@@ -221,13 +213,8 @@ _USE_COREX_MOE_INDEX_COMBINE = (
_USE_XLLM_MOE = (
_xllm_moe is not None
and env_bool("BI100_MOE_XLLM", True))
_USE_XLLM_MOE_GEMM = (
_xllm_moe_gemm is not None
and env_bool("BI100_MOE_XLLM_GEMM", True))
if _USE_XLLM_MOE:
logger.info("xllm_moe ENABLED — fused_topk + compute_index + combine_result")
if _USE_XLLM_MOE_GEMM:
logger.info("xllm_moe_gemm ENABLED — batched expert GEMM (replaces Python loop)")
_USE_FUSED_MOE_ACTIVATION = env_bool("BI100_MOE_FUSED_ACTIVATION", True)
# ix_fused_moe: full 7-step fused MoE pipeline via ixformer C++ API
@@ -1821,37 +1808,21 @@ class Qwen3_5MoeSparseBlock(nn.Module):
expert_counts = torch.bincount(
flat_eids, minlength=w13.shape[0]).tolist()
if _USE_XLLM_MOE_GEMM:
# Batched expert GEMM via CUDA — eliminates Python for-loop
# Build expert_offsets from expert_counts (cumsum with leading 0)
expert_counts_t = torch.tensor(
expert_counts if isinstance(expert_counts, list)
else expert_counts.tolist(),
dtype=torch.int64, device=hidden_states.device)
expert_offsets = torch.zeros(
len(expert_counts) + 1, dtype=torch.int64,
device=hidden_states.device)
torch.cumsum(expert_counts_t, dim=0, out=expert_offsets[1:])
out = _xllm_moe_gemm.moe_experts_forward(
hidden_states, w13, w2,
sorted_tok_ids, sorted_weights.float(),
expert_offsets, self.top_k)
else:
start = 0
for eid, count in enumerate(expert_counts):
end = start + count
if count == 0:
start = end
continue
tok_ids = sorted_tok_ids[start:end]
tokens = hidden_states[tok_ids] # (n, H)
gate_up = F.linear(tokens, w13[eid]) # (n, 2*I)
gate, up = gate_up.chunk(2, dim=-1)
act = F.silu(gate) * up # (n, I)
expert_out = F.linear(act, w2[eid]) # (n, H)
weights = sorted_weights[start:end].unsqueeze(-1)
out.index_add_(0, tok_ids, (expert_out * weights).to(out.dtype))
start = 0
for eid, count in enumerate(expert_counts):
end = start + count
if count == 0:
start = end
continue
tok_ids = sorted_tok_ids[start:end]
tokens = hidden_states[tok_ids] # (n, H)
gate_up = F.linear(tokens, w13[eid]) # (n, 2*I)
gate, up = gate_up.chunk(2, dim=-1)
act = F.silu(gate) * up # (n, I)
expert_out = F.linear(act, w2[eid]) # (n, H)
weights = sorted_weights[start:end].unsqueeze(-1)
out.index_add_(0, tok_ids, (expert_out * weights).to(out.dtype))
start = end
return out # partial, all-reduce done in forward()