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

This reverts commit 06d7713db6.

ex_engine/xllm_kernels/cuda/moe_batched_gemm.cu

@@ -1,114 +0,0 @@
-// moe_batched_gemm.cu — Fused MoE expert GEMM for BI-V100
-//
-// Replaces the Python for-loop over 256 experts with:
-//   1. Gather tokens by expert (sorted by moe_compute_index)
-//   2. Per-expert GEMM via cublas (torch::mm)
-//   3. Fused silu activation
-//   4. Per-expert down GEMM
-//   5. Weighted scatter-add back to output
-//
-// This eliminates Python loop overhead (~256 iterations) and reduces
-// kernel launch overhead by batching small GEMMs.
-//
-// Dimensions (Qwen3.5-35B-A3B, TP=4):
-//   w13: (256, 256, 2048)  -> E=256, 2*I=256, H=2048
-//   w2:  (256, 2048, 128)  -> E=256, H=2048, I=128
-
-#include <torch/extension.h>
-#include <c10/cuda/CUDAGuard.h>
-#include <ATen/cuda/CUDAContext.h>
-
-namespace xllm::kernel::cuda {
-
-// Fused SiLU-and-mul kernel (gate_up → act)
-// gate_up: (N, 2*I), output: (N, I)
-__global__ void silu_and_mul_inplace_kernel(
-    const __half* __restrict__ gate_up,
-    __half* __restrict__ output,
-    int64_t N, int64_t I) {
-    int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;
-    if (idx >= N * I) return;
-
-    int64_t row = idx / I;
-    int64_t col = idx % I;
-
-    float gate = __half2float(gate_up[row * 2 * I + col]);
-    float up = __half2float(gate_up[row * 2 * I + I + col]);
-    float silu_gate = gate / (1.0f + expf(-gate));
-    output[idx] = __float2half(silu_gate * up);
-}
-
-// Main function: batched expert forward
-// Called from Python with pre-sorted token indices
-torch::Tensor moe_experts_forward(
-    torch::Tensor hidden_states,     // (T, H) — all tokens
-    torch::Tensor w13,               // (E, 2*I, H) — gate+up weights
-    torch::Tensor w2,                // (E, H, I) — down weights
-    torch::Tensor sorted_tok_ids,    // (T*topk,) — which token for each slot
-    torch::Tensor sorted_weights,    // (T*topk,) — routing weight for each slot
-    torch::Tensor expert_offsets,    // (E+1,) — cumsum of expert_sizes, expert_offsets[0]=0
-    int64_t topk) {
-
-    auto stream = at::cuda::getCurrentCUDAStream();
-    int64_t T = hidden_states.size(0);
-    int64_t H = hidden_states.size(1);
-    int64_t E = w13.size(0);
-    int64_t two_I = w13.size(1);  // 2*I
-    int64_t I = two_I / 2;
-
-    auto out = torch::zeros({T, H}, hidden_states.options());
-
-    // Get expert offsets on CPU for loop control
-    auto offsets_cpu = expert_offsets.to(torch::kCPU, torch::kInt64);
-    auto offsets_ptr = offsets_cpu.data_ptr<int64_t>();
-
-    for (int64_t eid = 0; eid < E; ++eid) {
-        int64_t start = offsets_ptr[eid];
-        int64_t end = offsets_ptr[eid + 1];
-        int64_t count = end - start;
-        if (count == 0) continue;
-
-        // Gather tokens for this expert
-        auto tok_ids = sorted_tok_ids.slice(0, start, end);  // (count,)
-        auto tokens = hidden_states.index_select(0, tok_ids); // (count, H)
-
-        // GEMM 1: gate+up projection
-        // tokens (count, H) × w13[eid].T (H, 2*I) → (count, 2*I)
-        auto gate_up = torch::mm(tokens, w13[eid].t());  // (count, 2*I)
-
-        // Fused SiLU activation
-        auto act = torch::empty({count, I}, hidden_states.options());
-        if (hidden_states.dtype() == torch::kFloat16) {
-            int64_t total = count * I;
-            int block = 256;
-            int grid = (total + block - 1) / block;
-            silu_and_mul_inplace_kernel<<<grid, block, 0, stream>>>(
-                gate_up.data_ptr<at::Half>(),
-                act.data_ptr<at::Half>(),
-                count, I);
-        } else {
-            auto chunks = gate_up.chunk(2, /*dim=*/1);
-            act = torch::silu(chunks[0]) * chunks[1];
-        }
-
-        // GEMM 2: down projection
-        // act (count, I) × w2[eid].T (I, H) → (count, H)
-        auto expert_out = torch::mm(act, w2[eid].t());  // (count, H)
-
-        // Weighted scatter-add
-        auto weights = sorted_weights.slice(0, start, end).unsqueeze(1); // (count, 1)
-        out.index_add_(0, tok_ids, (expert_out * weights).to(out.dtype()));
-    }
-
-    return out;
-}
-
-}  // namespace xllm::kernel::cuda
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("moe_experts_forward", &xllm::kernel::cuda::moe_experts_forward,
-          "Batched MoE expert forward (gather → GEMM → silu → GEMM → scatter)",
-          py::arg("hidden_states"), py::arg("w13"), py::arg("w2"),
-          py::arg("sorted_tok_ids"), py::arg("sorted_weights"),
-          py::arg("expert_offsets"), py::arg("topk"));
-}

qwen3_6_scripts/build_xllm_kernels.sh

@@ -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"

qwen3_6_scripts/qwen3_5.py

@@ -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:
+            start = 0
-                # Batched expert GEMM via CUDA — eliminates Python for-loop
+            for eid, count in enumerate(expert_counts):
-                # Build expert_offsets from expert_counts (cumsum with leading 0)
+                end = start + count
-                expert_counts_t = torch.tensor(
+                if count == 0:
-                    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 = 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()