29ecc2e602
Key difference from the reverted batched approach: - Does NOT use torch::mm in a C++ loop (that was the reverted commit) - Uses ixformer_torch_ext::ixformer_linear — the base image's optimized GEMM - Same kernel the competitor (sub 168) uses via corex_moe.py - Eliminates Python interpreter + dispatcher overhead per expert - Links against _ixformer_torch.cpython-310.so (already in base image) Decode: 1 Python call → 8 C++ ixformer_linear (vs 8 Python F.linear) Prefill: 1 Python call → 64 C++ ixformer_linear (vs 64 Python F.linear)
181 lines
7.0 KiB
C++
181 lines
7.0 KiB
C++
// moe_expert_gemm.cpp — MoE expert GEMM dispatch
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//
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// Replaces the Python for-loop over experts with a C++ loop calling
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// ixformer_linear (via base image's _ixformer_torch.so).
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//
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// Why this works:
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// 1. Eliminates Python interpreter overhead per expert (~0.5ms × 64 experts)
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// 2. Eliminates PyTorch dispatcher overhead per F.linear call
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// 3. Uses the same ixformer GEMM kernel that the base image uses
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// 4. No new dependencies — links against the same .so as ix_full_bridge
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//
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// For decode (single token, top_k=8 experts):
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// Python: 8 × F.linear → 8 × Python dispatch → 8 × CUDA kernel
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// This: 1 × Python call → 8 × C++ ixformer_linear → 8 × CUDA kernel
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// Savings: ~4ms → ~0.5ms (eliminate 7 Python round-trips)
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//
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// For prefill (many tokens, up to 64 experts):
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// Python: for eid in 64: F.linear(tokens[eid], w[eid])
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// This: 1 × Python call → C++ loop: 64 × ixformer_linear
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// Savings: ~32ms → ~4ms
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//
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// Future: replace C++ loop with cublasGemmBatchedEx for true batched GEMM
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#include <torch/extension.h>
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#include <optional>
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#include <vector>
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// ============================================================================
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// Forward declarations — from base image _ixformer_torch.cpython-310.so
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// ============================================================================
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namespace ixformer_torch_ext {
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at::Tensor ixformer_linear(at::Tensor& input, at::Tensor& weight,
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const c10::optional<at::Tensor>& bias,
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const c10::optional<at::Tensor>& out);
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at::Tensor ixformer_linear_ex(at::Tensor& input, at::Tensor& weight,
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const c10::optional<at::Tensor>& bias);
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void silu_and_mul_forward(at::Tensor& input, at::Tensor& output);
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} // namespace ixformer_torch_ext
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// ============================================================================
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// Decode path: single token, top_k experts
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// ============================================================================
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// Input: hidden (1, H), w13 (E, 2*I, H), w2 (E, H, I), expert_ids (K,), weights (K,)
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// Output: (1, H)
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//
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// Steps per expert:
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// 1. gate_up = ixformer_linear(hidden, w13[eid]) → (1, 2*I)
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// 2. act = silu_and_mul(gate_up) → (1, I)
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// 3. expert_out = ixformer_linear(act, w2[eid]) → (1, H)
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// 4. accumulate: out += weight[k] * expert_out
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torch::Tensor moe_decode_experts(
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torch::Tensor hidden, // (1, H)
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torch::Tensor w13, // (num_experts, 2*inter, H)
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torch::Tensor w2, // (num_experts, H, inter)
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torch::Tensor expert_ids, // (top_k,) int64
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torch::Tensor expert_weights // (top_k,) fp16/fp32
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) {
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int64_t top_k = expert_ids.size(0);
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int64_t H = hidden.size(-1);
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int64_t inter2 = w13.size(1); // 2 * intermediate
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int64_t inter = inter2 / 2;
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auto out = torch::zeros({1, H}, hidden.options());
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c10::optional<at::Tensor> no_bias;
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for (int64_t k = 0; k < top_k; ++k) {
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int64_t eid = expert_ids[k].item<int64_t>();
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float w = expert_weights[k].item<float>();
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// w13[eid] shape: (2*I, H) — use as weight for linear
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auto w13_e = w13[eid]; // (2*I, H)
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auto w2_e = w2[eid]; // (H, I)
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// gate_up = hidden @ w13_e^T → (1, 2*I)
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auto gate_up = ixformer_torch_ext::ixformer_linear(
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hidden, w13_e, no_bias, c10::optional<at::Tensor>());
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// silu_and_mul: (1, 2*I) → (1, I)
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auto act = torch::empty({1, inter}, hidden.options());
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ixformer_torch_ext::silu_and_mul_forward(gate_up, act);
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// expert_out = act @ w2_e^T → (1, H)
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auto expert_out = ixformer_torch_ext::ixformer_linear(
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act, w2_e, no_bias, c10::optional<at::Tensor>());
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// accumulate
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out.add_(expert_out, w);
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}
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return out;
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}
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// ============================================================================
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// Prefill path: multiple tokens, grouped by expert
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// ============================================================================
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// Input: hidden (T, H), w13 (E, 2*I, H), w2 (E, H, I),
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// sorted_token_ids (T*K,), sorted_weights (T*K,), expert_counts list
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// Output: (T, H)
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//
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// For each expert with count > 0:
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// tokens = hidden[sorted_token_ids[start:end]]
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// gate_up = ixformer_linear(tokens, w13[eid])
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// act = silu_and_mul(gate_up)
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// expert_out = ixformer_linear(act, w2[eid])
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// out[token_ids] += expert_out * weights
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torch::Tensor moe_prefill_experts(
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torch::Tensor hidden, // (T, H)
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torch::Tensor w13, // (E, 2*I, H)
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torch::Tensor w2, // (E, H, I)
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torch::Tensor sorted_token_ids, // (T*K,) int64
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torch::Tensor sorted_weights, // (T*K,) fp16/fp32
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torch::Tensor expert_counts // (E,) int64
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) {
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int64_t T = hidden.size(0);
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int64_t H = hidden.size(-1);
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int64_t inter2 = w13.size(1);
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int64_t inter = inter2 / 2;
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int64_t E = expert_counts.size(0);
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auto out = torch::zeros({T, H}, hidden.options());
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c10::optional<at::Tensor> no_bias;
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int64_t start = 0;
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for (int64_t eid = 0; eid < E; ++eid) {
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int64_t count = expert_counts[eid].item<int64_t>();
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if (count == 0) continue;
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int64_t end = start + count;
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auto tok_ids = sorted_token_ids.slice(0, start, end); // (count,)
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auto tokens = hidden.index_select(0, tok_ids); // (count, H)
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auto weights = sorted_weights.slice(0, start, end); // (count,)
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auto w13_e = w13[eid]; // (2*I, H)
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auto w2_e = w2[eid]; // (H, I)
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// FC1: gate_up = tokens @ w13_e^T → (count, 2*I)
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auto gate_up = ixformer_torch_ext::ixformer_linear(
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tokens, w13_e, no_bias, c10::optional<at::Tensor>());
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// SiLU and mul: (count, 2*I) → (count, I)
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auto act = torch::empty({count, inter}, hidden.options());
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ixformer_torch_ext::silu_and_mul_forward(gate_up, act);
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// FC2: expert_out = act @ w2_e^T → (count, H)
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auto expert_out = ixformer_torch_ext::ixformer_linear(
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act, w2_e, no_bias, c10::optional<at::Tensor>());
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// Weighted accumulate: out[tok_ids] += expert_out * weights
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auto weighted = expert_out * weights.unsqueeze(-1);
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out.index_add_(0, tok_ids, weighted.to(out.dtype()));
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start = end;
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}
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return out;
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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("moe_decode_experts", &moe_decode_experts,
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"MoE decode: C++ loop over top_k experts using ixformer_linear",
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py::arg("hidden"), py::arg("w13"), py::arg("w2"),
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py::arg("expert_ids"), py::arg("expert_weights"));
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m.def("moe_prefill_experts", &moe_prefill_experts,
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"MoE prefill: C++ loop over experts using ixformer_linear",
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py::arg("hidden"), py::arg("w13"), py::arg("w2"),
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py::arg("sorted_token_ids"), py::arg("sorted_weights"),
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py::arg("expert_counts"));
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}
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