feat(MoE): full ixformer pipeline — topk → gen_idx → expand → group_gemm → silu → combine
Port complete MoE pipeline from upstream xllm/layers/ilu/fused_moe.cpp. All 6 ixformer::infer functions now exposed via ix_moe_bridge.cpp: 1. topk_softmax — fused routing (was: 3 PyTorch ops) 2. moe_compute_token_index_api — build permutation maps 3. moe_expand_input — gather tokens by expert 4. moe_w16a16_group_gemm — batched expert GEMM (was: Python for-loop) 5. silu_and_mul — fused activation 6. moe_output_reduce_sum — weighted scatter-add qwen3_5.py dispatch order: 1. Try ix_fused_moe_forward (full C++ pipeline, 7 kernel launches) 2. Try ix_topk_softmax only + PyTorch GEMM 3. Pure PyTorch fallback (torch.softmax + torch.topk + for-loop) ix_bridge.py exposes both individual ops and fused_moe_forward(). No upstream code copied — only forward-declarations of ixformer C++ API that the base image SDK already contains.
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@@ -1,19 +1,25 @@
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// ix_moe_bridge.cpp — Bridge to ixformer C++ topk_softmax
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// ix_moe_bridge.cpp — Full MoE pipeline bridge to ixformer C++ API
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//
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// Problem: ixformer Python (ixformer.functions) lacks vllm_moe_topk_softmax
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// Solution: Call ixformer::infer::topk_softmax() directly via C++ torch extension
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// Exposes ALL 6 MoE functions from ixformer::infer (ixformer.h):
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// 1. topk_softmax — fused routing
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// 2. moe_compute_token_index_api — permutation maps (src_dst, dst_src)
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// 3. moe_expand_input — gather tokens by expert
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// 4. moe_w16a16_group_gemm — batched expert GEMM
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// 5. silu_and_mul — fused activation
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// 6. moe_output_reduce_sum — weighted scatter-add
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//
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// Source: upstream_ref/xllm/xllm/core/kernels/ilu/ixformer.h declares:
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// void topk_softmax(torch::Tensor&, torch::Tensor&, torch::Tensor&,
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// torch::Tensor&, bool);
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// Source: upstream_ref/xllm/xllm/core/kernels/ilu/fused_moe.cpp shows usage:
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// infer::topk_softmax(reduce_weight, topk_indices, token_expert_indices, input_, false);
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// Source: upstream_ref/xllm/xllm/core/kernels/ilu/ixformer.h
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// Usage: upstream_ref/xllm/xllm/core/kernels/ilu/fused_moe.cpp
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// upstream_ref/xllm/xllm/core/layers/ilu/fused_moe.cpp
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#include <torch/extension.h>
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#include <tuple>
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#include <vector>
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// Forward-declare ixformer C++ API (from ixformer.h in base image SDK)
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// Forward-declare ixformer C++ API (from base image SDK)
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namespace ixformer {
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namespace infer {
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void topk_softmax(torch::Tensor& topk_weights,
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torch::Tensor& topk_indices,
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torch::Tensor& token_expert_indices,
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@@ -61,35 +67,192 @@ void silu_and_mul(torch::Tensor& input, torch::Tensor& output);
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} // namespace infer
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} // namespace ixformer
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// ============================================================================
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// Python-callable wrappers
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std::tuple<torch::Tensor, torch::Tensor> ix_moe_topk_softmax(
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torch::Tensor gating_output, // (num_tokens, num_experts) float32
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// ============================================================================
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// 1. topk_softmax: router_logits → (topk_weights, topk_indices)
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std::tuple<torch::Tensor, torch::Tensor> ix_topk_softmax(
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torch::Tensor gating_output,
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int64_t topk,
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bool renormalize) {
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auto input = gating_output.to(torch::kFloat32).contiguous();
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int64_t num_tokens = input.size(0);
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auto topk_weights = torch::empty({num_tokens, topk},
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torch::dtype(torch::kFloat32).device(input.device()));
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auto topk_indices = torch::empty({num_tokens, topk},
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torch::dtype(torch::kInt32).device(input.device()));
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auto token_expert_indices = torch::empty({num_tokens, topk},
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torch::dtype(torch::kInt32).device(input.device()));
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ixformer::infer::topk_softmax(
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topk_weights, topk_indices, token_expert_indices, input, renormalize);
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// Renormalize if not done by kernel (match xllm behavior)
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if (!renormalize) {
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topk_weights, topk_indices, token_expert_indices, input, false);
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// Renormalize (match xllm/kernels/ilu/fused_moe.cpp line 55)
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if (renormalize) {
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auto row_sum = topk_weights.sum(-1, /*keepdim=*/true);
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topk_weights = topk_weights / row_sum;
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}
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return std::make_tuple(topk_weights, topk_indices);
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}
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
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m.def("topk_softmax", &ix_moe_topk_softmax,
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"Fused topk+softmax via ixformer C++ API (bypasses missing Python binding)",
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py::arg("gating_output"), py::arg("topk"), py::arg("renormalize") = true);
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// 2. moe_gen_idx: topk_ids → (src_dst, dst_src, expert_sizes, cumsum)
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// Direct port from upstream_ref/xllm/kernels/ilu/fused_moe.cpp moe_gen_idx()
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std::vector<torch::Tensor> ix_moe_gen_idx(
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torch::Tensor expert_id,
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int64_t expert_num) {
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auto src_dst = expert_id.new_empty({expert_id.numel()});
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auto dst_src = torch::empty_like(src_dst);
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auto expert_sizes_gpu = expert_id.new_empty({expert_num});
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auto expert_sizes_gpu_cumsum = expert_id.new_zeros({expert_id.numel() + 1});
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ixformer::infer::moe_compute_token_index_api(
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expert_id, src_dst, dst_src, expert_sizes_gpu,
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/*expert_mask=*/c10::nullopt,
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/*expert_sizes_cpu=*/c10::nullopt,
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/*expand_tokens_gpu=*/c10::nullopt,
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0, expert_num, expert_num);
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expert_sizes_gpu_cumsum = expert_sizes_gpu.cumsum(-1);
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return {src_dst, dst_src, expert_sizes_gpu, expert_sizes_gpu_cumsum};
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}
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// 3. moe_expand_input: gather tokens by expert assignment
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torch::Tensor ix_moe_expand_input(
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torch::Tensor input,
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torch::Tensor gather_index,
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torch::Tensor combine_idx,
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int64_t topk) {
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int64_t dst_tokens = input.size(0) * topk;
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auto output = input.new_empty({dst_tokens, input.size(1)});
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ixformer::infer::moe_expand_input(
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output, input, combine_idx, gather_index, dst_tokens, topk);
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return output;
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}
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// 4. group_gemm: batched expert GEMM via ixformer
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torch::Tensor ix_group_gemm(
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torch::Tensor inputs, // (total_expanded_tokens, hidden)
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torch::Tensor weights, // (num_experts, out_features, in_features)
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torch::Tensor token_count, // (num_experts,) tokens per expert
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int64_t output_n) { // output feature dim
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int64_t total_tokens = inputs.size(0);
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auto output = inputs.new_empty({total_tokens, output_n});
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ixformer::infer::moe_w16a16_group_gemm(
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output, inputs, weights, token_count,
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/*dst_to_src=*/c10::nullopt,
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/*bias=*/c10::nullopt,
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/*format=*/"NT",
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/*persistent=*/0,
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/*output_n=*/output_n);
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return output;
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}
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// 5. silu_and_mul: fused activation (gated SiLU for MoE)
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torch::Tensor ix_silu_and_mul(torch::Tensor input) {
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int64_t half_dim = input.size(-1) / 2;
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auto output = input.new_empty({input.size(0), half_dim});
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ixformer::infer::silu_and_mul(input, output);
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return output;
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}
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// 6. moe_combine_result: weighted reduce
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torch::Tensor ix_moe_combine_result(
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torch::Tensor input,
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torch::Tensor weight) {
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input = input.view({-1, weight.size(1), input.size(1)});
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auto output = input.new_empty({input.size(0), input.size(2)});
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ixformer::infer::moe_output_reduce_sum(
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output, input, weight,
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/*mask=*/c10::nullopt,
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/*extra_residual=*/c10::nullopt,
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/*scaling_factor=*/1.0);
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return output;
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}
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// ============================================================================
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// FULL fused MoE forward — complete pipeline matching xllm
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// ============================================================================
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// This replaces the entire _pure_pytorch_experts() in qwen3_5.py
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//
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// Pipeline: topk_softmax → gen_idx → expand → gemm1 → silu → gemm2 → combine
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// Source: upstream_ref/xllm/xllm/core/layers/ilu/fused_moe.cpp forward_experts()
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torch::Tensor ix_fused_moe_forward(
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torch::Tensor hidden_states, // (T, H)
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torch::Tensor router_logits, // (T, E)
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torch::Tensor w13, // (E, 2*I, H) gate_up weight
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torch::Tensor w2, // (E, H, I) down weight
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int64_t topk,
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int64_t num_experts,
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bool renormalize) {
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// Step 1: routing
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auto [topk_weights, topk_ids] = ix_topk_softmax(router_logits, topk, renormalize);
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// Step 2: build permutation
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auto idx = ix_moe_gen_idx(topk_ids.view({-1}), num_experts);
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auto gather_idx = idx[0]; // src_dst
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auto combine_idx = idx[1]; // dst_src
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auto expert_sizes = idx[2]; // (E,)
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// Step 3: expand hidden states by expert assignment
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auto expanded = ix_moe_expand_input(
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hidden_states, gather_idx, combine_idx, topk);
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// Step 4: group GEMM 1 — gate_up projection
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int64_t gate_up_dim = w13.size(1); // 2*I
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auto gemm1_out = ix_group_gemm(expanded, w13, expert_sizes, gate_up_dim);
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// Step 5: activation — SiLU(gate) * up
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auto act_out = ix_silu_and_mul(gemm1_out);
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// Step 6: group GEMM 2 — down projection
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int64_t hidden_dim = w2.size(1); // H
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auto gemm2_out = ix_group_gemm(act_out, w2, expert_sizes, hidden_dim);
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// Step 7: combine — weighted scatter back
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auto output = ix_moe_combine_result(gemm2_out, topk_weights);
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return output;
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}
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// ============================================================================
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// Module registration
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// ============================================================================
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
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m.def("topk_softmax", &ix_topk_softmax,
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"Fused topk+softmax via ixformer C++ API",
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py::arg("gating_output"), py::arg("topk"), py::arg("renormalize") = true);
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m.def("moe_gen_idx", &ix_moe_gen_idx,
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"Build expert permutation maps (src_dst, dst_src, sizes, cumsum)",
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py::arg("expert_id"), py::arg("expert_num"));
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m.def("moe_expand_input", &ix_moe_expand_input,
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"Gather tokens by expert assignment",
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py::arg("input"), py::arg("gather_index"), py::arg("combine_idx"), py::arg("topk"));
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m.def("group_gemm", &ix_group_gemm,
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"Batched expert GEMM via ixformer group_gemm",
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py::arg("inputs"), py::arg("weights"), py::arg("token_count"), py::arg("output_n"));
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m.def("silu_and_mul", &ix_silu_and_mul,
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"Fused SiLU gate activation",
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py::arg("input"));
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m.def("moe_combine_result", &ix_moe_combine_result,
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"Weighted reduce for MoE output",
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py::arg("input"), py::arg("weight"));
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m.def("fused_moe_forward", &ix_fused_moe_forward,
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"Full fused MoE forward pipeline (topk → expand → gemm → act → gemm → combine)",
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py::arg("hidden_states"), py::arg("router_logits"),
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py::arg("w13"), py::arg("w2"),
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py::arg("topk"), py::arg("num_experts"), py::arg("renormalize") = true);
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}
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