model: fused GPU MoE kernel — eliminate CPU roundtrip
Replace the per-token CPU-routed MoE forward with an all-GPU path: 1. moe_topk_softmax: GPU top-k + softmax (was CPU sort + softmax) 2. moe_replicate: broadcast input to all local experts 3. cublasGemmStridedBatchedEx: batched expert matmul (was per-expert cuBLAS) 4. moe_weighted_sum: FP32-accumulated weighted sum on GPU (was GPU→CPU→F32→BF16→GPU) Expert weights stored as contiguous 3D tensors for strided batched GEMM. Zero CPU↔GPU transfers per MoE layer (was ~40 per token per layer). Also: configurable geglu_alpha, LayerNorm bias auto-detect, unused-weight diagnostic at load time. GSM8K 30-problem: 11/30 → 23/30 (76.7%) vs llama.cpp 30/30 (100%). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Gahow Wang
@@ -29,6 +29,7 @@ fn main() {
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.file("../../csrc/attention/flash_attention.cu")
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.file("../../csrc/attention/paged_attention.cu")
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.file("../../csrc/attention/reshape_and_cache.cu")
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.file("../../csrc/moe/moe_kernels.cu")
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.compile("xserv_kernels");
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println!("cargo:rerun-if-changed=../../csrc/");
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@@ -5,6 +5,7 @@ pub mod dispatch;
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pub mod embedding;
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pub mod gemm;
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pub mod layernorm;
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pub mod moe;
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pub mod rmsnorm;
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pub mod rope;
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pub mod softmax;
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223
crates/xserv-kernels/src/moe.rs
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223
crates/xserv-kernels/src/moe.rs
Normal file
@@ -0,0 +1,223 @@
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use std::ffi::c_void;
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use xserv_tensor::{DType, Device, Tensor};
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use crate::gemm::{cublas_handle, CublasHandle};
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unsafe extern "C" {
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fn launch_moe_topk_softmax_bf16(
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router_logits: *const c_void,
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topk_ids: *mut c_void, topk_weights: *mut c_void,
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num_tokens: i32, num_experts: i32, top_k: i32,
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stream: *mut c_void,
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);
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fn launch_moe_replicate_bf16(
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x: *const c_void, x_rep: *mut c_void,
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num_tokens: i32, hidden: i32, local_experts: i32,
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stream: *mut c_void,
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);
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fn launch_moe_bias_add_3d_bf16(
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x: *mut c_void, bias: *const c_void,
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batch: i32, num_tokens: i32, dim: i32,
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stream: *mut c_void,
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);
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fn launch_moe_weighted_sum_bf16(
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expert_out: *const c_void,
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topk_ids: *const c_void, topk_weights: *const c_void,
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out: *mut c_void,
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num_tokens: i32, hidden: i32, top_k: i32,
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expert_start: i32, local_experts: i32,
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stream: *mut c_void,
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);
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fn cublasGemmStridedBatchedEx(
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handle: CublasHandle,
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transa: i32, transb: i32,
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m: i32, n: i32, k: i32,
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alpha: *const c_void,
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a: *const c_void, a_type: i32, lda: i32, stride_a: i64,
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b: *const c_void, b_type: i32, ldb: i32, stride_b: i64,
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beta: *const c_void,
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c: *mut c_void, c_type: i32, ldc: i32, stride_c: i64,
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batch_count: i32,
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compute_type: i32,
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algo: i32,
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) -> i32;
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fn cublasSetStream_v2(handle: CublasHandle, stream: *mut c_void) -> i32;
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}
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const CUDA_R_16BF: i32 = 14;
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const CUBLAS_COMPUTE_32F: i32 = 68;
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const CUBLAS_GEMM_DEFAULT: i32 = -1;
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/// GPU top-k selection + softmax over router logits.
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///
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/// Input: router_logits [num_tokens, num_experts] BF16 on GPU
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/// Output: (topk_ids [num_tokens, top_k] i32, topk_weights [num_tokens, top_k] f32)
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pub fn moe_topk_softmax(
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router_logits: &Tensor,
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num_experts: usize,
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top_k: usize,
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) -> (Tensor, Tensor) {
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assert_eq!(router_logits.ndim(), 2);
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assert_eq!(router_logits.dtype(), DType::BF16);
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assert!(router_logits.is_contiguous());
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let num_tokens = router_logits.shape()[0];
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assert_eq!(router_logits.shape()[1], num_experts);
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let topk_ids = Tensor::empty(&[num_tokens, top_k], DType::F32, router_logits.device());
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let topk_weights = Tensor::empty(&[num_tokens, top_k], DType::F32, router_logits.device());
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unsafe {
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launch_moe_topk_softmax_bf16(
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router_logits.data_ptr() as *const c_void,
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topk_ids.data_ptr() as *mut c_void,
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topk_weights.data_ptr() as *mut c_void,
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num_tokens as i32, num_experts as i32, top_k as i32,
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std::ptr::null_mut(),
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);
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}
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(topk_ids, topk_weights)
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}
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/// Replicate x [num_tokens, hidden] → [local_experts, num_tokens, hidden].
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pub fn moe_replicate(x: &Tensor, local_experts: usize) -> Tensor {
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assert_eq!(x.ndim(), 2);
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assert_eq!(x.dtype(), DType::BF16);
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assert!(x.is_contiguous());
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let num_tokens = x.shape()[0];
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let hidden = x.shape()[1];
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let out = Tensor::empty(&[local_experts, num_tokens, hidden], DType::BF16, x.device());
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unsafe {
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launch_moe_replicate_bf16(
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x.data_ptr() as *const c_void,
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out.data_ptr() as *mut c_void,
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num_tokens as i32, hidden as i32, local_experts as i32,
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std::ptr::null_mut(),
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);
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}
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out
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}
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/// In-place 3D bias add: x [batch, num_tokens, dim] += bias [batch, dim].
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pub fn moe_bias_add_3d(x: &Tensor, bias: &Tensor) {
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assert_eq!(x.ndim(), 3);
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assert_eq!(bias.ndim(), 2);
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assert_eq!(x.dtype(), DType::BF16);
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assert!(x.is_contiguous());
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let batch = x.shape()[0];
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let num_tokens = x.shape()[1];
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let dim = x.shape()[2];
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assert_eq!(bias.shape(), &[batch, dim]);
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unsafe {
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launch_moe_bias_add_3d_bf16(
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x.data_ptr() as *mut c_void,
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bias.data_ptr() as *const c_void,
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batch as i32, num_tokens as i32, dim as i32,
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std::ptr::null_mut(),
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);
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}
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}
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/// Weighted sum of expert outputs → [num_tokens, hidden].
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///
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/// expert_out: [local_experts, num_tokens, hidden] BF16
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/// topk_ids: [num_tokens, top_k] i32 (global expert indices)
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/// topk_weights: [num_tokens, top_k] f32
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pub fn moe_weighted_sum(
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expert_out: &Tensor,
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topk_ids: &Tensor,
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topk_weights: &Tensor,
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expert_start: usize,
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local_experts: usize,
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top_k: usize,
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) -> Tensor {
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assert_eq!(expert_out.ndim(), 3);
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assert_eq!(expert_out.dtype(), DType::BF16);
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let num_tokens = expert_out.shape()[1];
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let hidden = expert_out.shape()[2];
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let out = Tensor::empty(&[num_tokens, hidden], DType::BF16, expert_out.device());
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unsafe {
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launch_moe_weighted_sum_bf16(
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expert_out.data_ptr() as *const c_void,
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topk_ids.data_ptr() as *const c_void,
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topk_weights.data_ptr() as *const c_void,
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out.data_ptr() as *mut c_void,
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num_tokens as i32, hidden as i32, top_k as i32,
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expert_start as i32, local_experts as i32,
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std::ptr::null_mut(),
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);
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}
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out
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}
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/// Strided batched GEMM for MoE expert forward.
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/// C[b] = A[b] @ B[b] for b in 0..batch
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///
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/// A: [batch, M, K] BF16 contiguous
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/// B: [batch, K, N] BF16 contiguous
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/// Returns C: [batch, M, N] BF16
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#[allow(clippy::too_many_arguments)]
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pub fn batched_gemm_strided(a: &Tensor, b: &Tensor) -> Tensor {
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assert_eq!(a.ndim(), 3);
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assert_eq!(b.ndim(), 3);
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assert_eq!(a.dtype(), DType::BF16);
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assert_eq!(b.dtype(), DType::BF16);
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assert!(a.is_contiguous() && b.is_contiguous());
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assert_eq!(a.shape()[0], b.shape()[0]);
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assert_eq!(a.shape()[2], b.shape()[1]);
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let batch = a.shape()[0];
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let m = a.shape()[1];
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let k = a.shape()[2];
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let n = b.shape()[2];
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let c = Tensor::empty(&[batch, m, n], DType::BF16, a.device());
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let alpha: f32 = 1.0;
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let beta: f32 = 0.0;
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// cuBLAS column-major: we compute C^T = B^T @ A^T
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// A is [batch, M, K] row-major → A^T is [K, M] col-major, lda=K
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// B is [batch, K, N] row-major → B^T is [N, K] col-major, ldb=N? No...
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//
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// Actually for row-major: A[M,K] in memory = col-major A^T[K,M] with lda=K.
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// So we call cublasGemmStridedBatchedEx with:
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// transa=N, transb=N
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// m=N, n=M, k=K (because cuBLAS sees col-major)
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// A_cublas = B_row (pointer), lda=N
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// B_cublas = A_row (pointer), ldb=K
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// C_cublas = C_row (pointer), ldc=N
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let stride_a = (m * k) as i64;
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let stride_b = (k * n) as i64;
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let stride_c = (m * n) as i64;
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let handle = cublas_handle();
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unsafe {
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cublasSetStream_v2(handle, std::ptr::null_mut());
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let status = cublasGemmStridedBatchedEx(
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handle,
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0, 0, // CUBLAS_OP_N, CUBLAS_OP_N
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n as i32, m as i32, k as i32,
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&alpha as *const f32 as *const c_void,
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b.data_ptr() as *const c_void, CUDA_R_16BF, n as i32, stride_b,
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a.data_ptr() as *const c_void, CUDA_R_16BF, k as i32, stride_a,
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&beta as *const f32 as *const c_void,
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c.data_ptr() as *mut c_void, CUDA_R_16BF, n as i32, stride_c,
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batch as i32,
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CUBLAS_COMPUTE_32F,
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CUBLAS_GEMM_DEFAULT,
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);
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assert_eq!(status, 0, "cublasGemmStridedBatchedEx failed: {status}");
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}
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c
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}
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@@ -73,6 +73,10 @@ pub struct ModelConfig {
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pub rope_scaling: Option<RopeScaling>,
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#[serde(default)]
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pub swiglu_limit: Option<f64>,
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#[serde(default)]
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pub geglu_alpha: Option<f64>,
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#[serde(default)]
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pub hidden_act: Option<String>,
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}
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impl ModelConfig {
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@@ -144,4 +148,8 @@ impl ModelConfig {
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pub fn window_size(&self) -> usize {
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self.sliding_window.unwrap_or(0)
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}
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pub fn geglu_alpha(&self) -> f32 {
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self.geglu_alpha.unwrap_or(1.702) as f32
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}
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}
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@@ -12,15 +12,18 @@ pub struct GptOss {
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embed_tokens: Tensor,
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layers: Vec<GptOssBlock>,
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norm: Tensor,
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norm_bias: Option<Tensor>,
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lm_head_t: Tensor,
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rope_cache: RopeCache,
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tp: Option<std::sync::Arc<xserv_distributed::TpContext>>,
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local_num_heads: usize,
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local_num_kv_heads: usize,
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has_norm_bias: bool,
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}
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struct GptOssBlock {
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input_norm: Tensor,
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input_norm_bias: Option<Tensor>,
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// Attention (with bias)
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q_proj_wt: Tensor,
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q_proj_bias: Tensor,
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@@ -36,12 +39,15 @@ struct GptOssBlock {
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window_size: usize,
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// MoE MLP
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post_norm: Tensor,
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post_norm_bias: Option<Tensor>,
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router_wt: Tensor,
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router_bias: Tensor,
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expert_gate_up_wt: Vec<Tensor>,
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expert_gate_up_bias: Vec<Tensor>,
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expert_down_wt: Vec<Tensor>,
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expert_down_bias: Vec<Tensor>,
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// 3D expert weights for batched GEMM (contiguous on GPU)
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expert_gate_up_wt: Tensor, // [local_experts, hidden, 2*inter]
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expert_gate_up_bias: Tensor, // [local_experts, 2*inter]
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expert_down_wt: Tensor, // [local_experts, inter, hidden]
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expert_down_bias: Tensor, // [local_experts, hidden]
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local_experts: usize,
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// Activation params
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glu_alpha: f32,
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glu_limit: f32,
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@@ -80,6 +86,7 @@ impl GptOss {
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let embed_tokens = repl(take(&mut w, "model.embed_tokens.weight"));
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let norm = repl(take(&mut w, "model.norm.weight"));
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let norm_bias = w.remove("model.norm.bias").map(|t| repl(t));
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let lm_head_t = repl(take(&mut w, "lm_head.weight")).transpose(0, 1).contiguous();
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let head_dim = config.head_dim();
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@@ -106,13 +113,13 @@ impl GptOss {
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let num_layers = config.num_layers();
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let num_experts = config.num_experts();
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let glu_alpha = 1.702f32;
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let glu_alpha = config.geglu_alpha();
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let glu_limit = config.swiglu_limit.unwrap_or(7.0) as f32;
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let mut layers = Vec::with_capacity(num_layers);
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if rank == 0 {
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eprintln!(
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"Loading gpt-oss weights: {} layers, {} experts, world={world}...",
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"Loading gpt-oss weights: {} layers, {} experts, world={world}, glu_alpha={glu_alpha}...",
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num_layers, num_experts
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);
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}
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@@ -152,34 +159,26 @@ impl GptOss {
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let local_experts = num_experts / world;
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let expert_start = rank * local_experts;
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let mut expert_gate_up_wt = Vec::with_capacity(local_experts);
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let mut expert_gate_up_bias = Vec::with_capacity(local_experts);
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let mut expert_down_wt = Vec::with_capacity(local_experts);
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let mut expert_down_bias = Vec::with_capacity(local_experts);
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let inter2 = gate_up_3d.shape()[2]; // 2 * intermediate_size
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let hidden = gate_up_3d.shape()[1];
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let inter = down_3d.shape()[1]; // intermediate_size
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for local_e in 0..local_experts {
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let e = expert_start + local_e;
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let gu_slice = slice_expert_3d(&gate_up_3d, e, hidden, inter2);
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expert_gate_up_wt.push(gu_slice.to_device(dev));
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let gu_bias = slice_expert_2d(&gate_up_bias_2d, e, inter2);
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expert_gate_up_bias.push(gu_bias.to_device(dev));
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let d_slice = slice_expert_3d(&down_3d, e, inter, hidden);
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expert_down_wt.push(d_slice.to_device(dev));
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let d_bias = slice_expert_2d(&down_bias_2d, e, hidden);
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expert_down_bias.push(d_bias.to_device(dev));
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}
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// Slice the rank's range of experts as contiguous 3D tensors on GPU
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let expert_gate_up_wt = slice_expert_range_3d(&gate_up_3d, expert_start, local_experts, hidden, inter2).to_device(dev);
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let expert_gate_up_bias = slice_expert_range_2d(&gate_up_bias_2d, expert_start, local_experts, inter2).to_device(dev);
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let expert_down_wt = slice_expert_range_3d(&down_3d, expert_start, local_experts, inter, hidden).to_device(dev);
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let expert_down_bias = slice_expert_range_2d(&down_bias_2d, expert_start, local_experts, hidden).to_device(dev);
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xserv_cuda::allocator::cached_trim();
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let input_norm = repl(take(&mut w, &format!("{p}.input_layernorm.weight")));
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let input_norm_bias = w.remove(&format!("{p}.input_layernorm.bias")).map(|t| repl(t));
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let post_norm = repl(take(&mut w, &format!("{p}.post_attention_layernorm.weight")));
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let post_norm_bias = w.remove(&format!("{p}.post_attention_layernorm.bias")).map(|t| repl(t));
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layers.push(GptOssBlock {
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input_norm: repl(take(&mut w, &format!("{p}.input_layernorm.weight"))),
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input_norm,
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input_norm_bias,
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q_proj_wt,
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q_proj_bias,
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k_proj_wt,
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@@ -191,13 +190,15 @@ impl GptOss {
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sinks,
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is_sliding,
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window_size,
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post_norm: repl(take(&mut w, &format!("{p}.post_attention_layernorm.weight"))),
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post_norm,
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post_norm_bias,
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router_wt,
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router_bias,
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expert_gate_up_wt,
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expert_gate_up_bias,
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expert_down_wt,
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expert_down_bias,
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local_experts,
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glu_alpha,
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glu_limit,
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});
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@@ -206,16 +207,35 @@ impl GptOss {
|
||||
let local_num_heads = config.num_heads() / world;
|
||||
let local_num_kv_heads = config.num_kv_heads() / world;
|
||||
|
||||
let has_norm_bias = norm_bias.is_some();
|
||||
if rank == 0 {
|
||||
if has_norm_bias {
|
||||
eprintln!("gpt-oss: detected LayerNorm bias — using LayerNorm instead of RMSNorm");
|
||||
}
|
||||
}
|
||||
|
||||
// Warn about unused weights that the model didn't consume
|
||||
if rank == 0 && !w.is_empty() {
|
||||
eprintln!("WARNING: {} unused weight(s) in model:", w.len());
|
||||
let mut keys: Vec<_> = w.keys().collect();
|
||||
keys.sort();
|
||||
for k in &keys {
|
||||
eprintln!(" {k}");
|
||||
}
|
||||
}
|
||||
|
||||
Self {
|
||||
config,
|
||||
embed_tokens,
|
||||
layers,
|
||||
norm,
|
||||
norm_bias,
|
||||
lm_head_t,
|
||||
rope_cache,
|
||||
tp,
|
||||
local_num_heads,
|
||||
local_num_kv_heads,
|
||||
has_norm_bias,
|
||||
}
|
||||
}
|
||||
|
||||
@@ -229,6 +249,34 @@ impl GptOss {
|
||||
}
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn norm(x: &Tensor, weight: &Tensor, bias: &Option<Tensor>, eps: f32) -> Tensor {
|
||||
match bias {
|
||||
Some(b) => layernorm(x, weight, b, eps),
|
||||
None => rmsnorm(x, weight, eps),
|
||||
}
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn add_norm(x: &Tensor, residual: &Tensor, weight: &Tensor, bias: &Option<Tensor>, eps: f32) -> (Tensor, Tensor) {
|
||||
match bias {
|
||||
Some(b) => {
|
||||
let sum = xserv_kernels::add(x, residual);
|
||||
let normed = layernorm(&sum, weight, b, eps);
|
||||
(normed, sum)
|
||||
}
|
||||
None => add_rmsnorm(x, residual, weight, eps),
|
||||
}
|
||||
}
|
||||
|
||||
fn norm_eps(&self) -> f32 {
|
||||
if self.has_norm_bias {
|
||||
self.config.ln_eps()
|
||||
} else {
|
||||
self.config.rms_norm_eps.unwrap_or(1e-5) as f32
|
||||
}
|
||||
}
|
||||
|
||||
/// Paged decode: process one token per sequence using paged KV cache.
|
||||
pub fn forward_decode_paged(
|
||||
&self,
|
||||
@@ -245,7 +293,7 @@ impl GptOss {
|
||||
let num_heads = self.local_num_heads;
|
||||
let num_kv_heads = self.local_num_kv_heads;
|
||||
let head_dim = self.config.head_dim();
|
||||
let eps = self.config.rms_norm_eps.unwrap_or(1e-5) as f32;
|
||||
let eps = self.norm_eps();
|
||||
|
||||
let kv_lens: Vec<i32> = positions.iter().map(|&p| (p + 1) as i32).collect();
|
||||
for (b, &slot) in seq_slots.iter().enumerate() {
|
||||
@@ -263,7 +311,7 @@ impl GptOss {
|
||||
|
||||
for (layer_idx, layer) in self.layers.iter().enumerate() {
|
||||
let residual = x.clone();
|
||||
let normed = rmsnorm(&x, &layer.input_norm, eps);
|
||||
let normed = Self::norm(&x, &layer.input_norm, &layer.input_norm_bias, eps);
|
||||
|
||||
// Q/K/V projections with bias
|
||||
let q_all = add_bias(&matmul_2d(&normed, &layer.q_proj_wt), &layer.q_proj_bias);
|
||||
@@ -304,13 +352,11 @@ impl GptOss {
|
||||
|
||||
|
||||
// Residual + post-norm
|
||||
let (normed, x_new) = add_rmsnorm(&attn_proj, &residual, &layer.post_norm, eps);
|
||||
|
||||
let (normed, x_new) = Self::add_norm(&attn_proj, &residual, &layer.post_norm, &layer.post_norm_bias, eps);
|
||||
|
||||
let residual = x_new;
|
||||
let normed = normed.contiguous();
|
||||
|
||||
|
||||
// MoE MLP
|
||||
let moe_out = self.moe_forward(&normed, layer, batch);
|
||||
x = xserv_kernels::add(&residual, &moe_out);
|
||||
@@ -322,7 +368,7 @@ impl GptOss {
|
||||
}
|
||||
|
||||
unsafe { xserv_cuda::ffi::cudaDeviceSynchronize(); }
|
||||
let x = rmsnorm(&x, &self.norm, eps);
|
||||
let x = Self::norm(&x, &self.norm, &self.norm_bias, eps);
|
||||
unsafe { xserv_cuda::ffi::cudaDeviceSynchronize(); }
|
||||
let logits = matmul_2d(&x, &self.lm_head_t);
|
||||
unsafe { xserv_cuda::ffi::cudaDeviceSynchronize(); }
|
||||
@@ -341,7 +387,7 @@ impl GptOss {
|
||||
let num_heads = self.local_num_heads;
|
||||
let num_kv_heads = self.local_num_kv_heads;
|
||||
let head_dim = self.config.head_dim();
|
||||
let eps = self.config.rms_norm_eps.unwrap_or(1e-5) as f32;
|
||||
let eps = self.norm_eps();
|
||||
|
||||
paged_cache.ensure_capacity(slot, pos_offset + new_tokens);
|
||||
paged_cache.advance_seq_len(slot, new_tokens);
|
||||
@@ -351,7 +397,7 @@ impl GptOss {
|
||||
|
||||
for (layer_idx, layer) in self.layers.iter().enumerate() {
|
||||
let residual = x.clone();
|
||||
let normed = rmsnorm(&x, &layer.input_norm, eps);
|
||||
let normed = Self::norm(&x, &layer.input_norm, &layer.input_norm_bias, eps);
|
||||
|
||||
let q = add_bias(&matmul_2d(&normed, &layer.q_proj_wt), &layer.q_proj_bias);
|
||||
let k = add_bias(&matmul_2d(&normed, &layer.k_proj_wt), &layer.k_proj_bias);
|
||||
@@ -381,7 +427,7 @@ impl GptOss {
|
||||
self.all_reduce(&attn_proj);
|
||||
let attn_proj = add_bias(&attn_proj, &layer.o_proj_bias);
|
||||
|
||||
let (normed, x_new) = add_rmsnorm(&attn_proj, &residual, &layer.post_norm, eps);
|
||||
let (normed, x_new) = Self::add_norm(&attn_proj, &residual, &layer.post_norm, &layer.post_norm_bias, eps);
|
||||
let residual = x_new;
|
||||
|
||||
// MoE MLP
|
||||
@@ -389,90 +435,65 @@ impl GptOss {
|
||||
x = xserv_kernels::add(&residual, &moe_out);
|
||||
}
|
||||
|
||||
let x = rmsnorm(&x, &self.norm, eps);
|
||||
let x = Self::norm(&x, &self.norm, &self.norm_bias, eps);
|
||||
let logits = matmul_2d(&x, &self.lm_head_t);
|
||||
unsafe { xserv_cuda::ffi::cudaDeviceSynchronize(); }
|
||||
logits
|
||||
}
|
||||
|
||||
/// MoE forward pass for one layer with expert parallelism.
|
||||
/// Each rank owns `num_experts / world` experts. Tokens routed to non-local
|
||||
/// experts get zero contribution from this rank; AllReduce sums all ranks.
|
||||
/// Input: [tokens, hidden], Output: [tokens, hidden]
|
||||
/// MoE forward pass — fully on GPU via batched GEMM.
|
||||
///
|
||||
/// Each rank owns `local_experts` experts. The input is replicated across all
|
||||
/// local experts, processed via two batched cuBLAS GEMMs (gate_up and down),
|
||||
/// and the selected experts' outputs are weighted-summed on GPU. Non-selected
|
||||
/// experts contribute zero (via the routing weights), so no scatter/gather is
|
||||
/// needed. AllReduce sums partial results across TP ranks.
|
||||
fn moe_forward(&self, x: &Tensor, layer: &GptOssBlock, num_tokens: usize) -> Tensor {
|
||||
let hidden = self.config.hidden();
|
||||
let num_experts = self.config.num_experts();
|
||||
let top_k = self.config.experts_per_token();
|
||||
let world = self.tp.as_ref().map(|tp| tp.world).unwrap_or(1);
|
||||
let rank = self.tp.as_ref().map(|tp| tp.rank).unwrap_or(0);
|
||||
let local_experts = num_experts / world;
|
||||
let local_experts = layer.local_experts;
|
||||
let expert_start = rank * local_experts;
|
||||
|
||||
// Router: [tokens, hidden] @ [hidden, num_experts] + bias → [tokens, num_experts]
|
||||
unsafe { xserv_cuda::ffi::cudaDeviceSynchronize(); }
|
||||
// 1. Router: [tokens, hidden] @ [hidden, num_experts] + bias → [tokens, num_experts]
|
||||
let router_logits = add_bias(
|
||||
&matmul_2d(x, &layer.router_wt),
|
||||
&layer.router_bias,
|
||||
);
|
||||
unsafe { xserv_cuda::ffi::cudaDeviceSynchronize(); }
|
||||
let router_cpu = router_logits.to_device(Device::Cpu);
|
||||
let router_data = router_cpu.as_slice::<bf16>();
|
||||
let x_cpu = x.to_device(Device::Cpu);
|
||||
let x_data = x_cpu.as_slice::<bf16>();
|
||||
|
||||
let mut output_acc = vec![0.0f32; num_tokens * hidden];
|
||||
// 2. GPU top-k + softmax
|
||||
let (topk_ids, topk_weights) = xserv_kernels::moe::moe_topk_softmax(
|
||||
&router_logits, num_experts, top_k,
|
||||
);
|
||||
|
||||
for t in 0..num_tokens {
|
||||
let row = &router_data[t * num_experts..(t + 1) * num_experts];
|
||||
// 3. Replicate input: [tokens, hidden] → [local_experts, tokens, hidden]
|
||||
let x_rep = xserv_kernels::moe::moe_replicate(x, local_experts);
|
||||
|
||||
// Find top-k expert indices (global)
|
||||
let mut indices: Vec<(usize, f32)> = row.iter()
|
||||
.enumerate()
|
||||
.map(|(i, &v)| (i, v.to_f32()))
|
||||
.collect();
|
||||
indices.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
|
||||
let top_indices: Vec<(usize, f32)> = indices[..top_k].to_vec();
|
||||
// 4. Batched GEMM gate_up: [E, tokens, hidden] @ [E, hidden, 2*inter] → [E, tokens, 2*inter]
|
||||
let gate_up = xserv_kernels::moe::batched_gemm_strided(&x_rep, &layer.expert_gate_up_wt);
|
||||
|
||||
// Softmax over top-k logits
|
||||
let max_val = top_indices.iter().map(|x| x.1).fold(f32::NEG_INFINITY, f32::max);
|
||||
let exp_sum: f32 = top_indices.iter().map(|x| (x.1 - max_val).exp()).sum();
|
||||
let weights: Vec<f32> = top_indices.iter()
|
||||
.map(|x| (x.1 - max_val).exp() / exp_sum)
|
||||
.collect();
|
||||
// 5. Bias add: gate_up += expert_gate_up_bias (in-place)
|
||||
xserv_kernels::moe::moe_bias_add_3d(&gate_up, &layer.expert_gate_up_bias);
|
||||
|
||||
// Fresh GPU upload of token data — immune to cached allocator buffer reuse
|
||||
let token_slice = &x_data[t * hidden..(t + 1) * hidden];
|
||||
let token_tensor = Tensor::from_slice(token_slice, &[1, hidden]).to_device(x.device());
|
||||
// 6. GLU activation: treat [E * tokens, 2*inter] → [E * tokens, inter]
|
||||
let inter2 = gate_up.shape()[2];
|
||||
let flat_rows = local_experts * num_tokens;
|
||||
let gate_up_flat = gate_up.reshape(&[flat_rows, inter2]);
|
||||
let activated_flat = gpt_oss_glu(&gate_up_flat, layer.glu_alpha, layer.glu_limit);
|
||||
let inter = inter2 / 2;
|
||||
let activated = activated_flat.reshape(&[local_experts, num_tokens, inter]);
|
||||
|
||||
// 7. Batched GEMM down: [E, tokens, inter] @ [E, inter, hidden] → [E, tokens, hidden]
|
||||
let down = xserv_kernels::moe::batched_gemm_strided(&activated, &layer.expert_down_wt);
|
||||
|
||||
for (k_idx, &(expert_id, _)) in top_indices.iter().enumerate() {
|
||||
// Only process experts owned by this rank
|
||||
if expert_id < expert_start || expert_id >= expert_start + local_experts {
|
||||
continue;
|
||||
}
|
||||
let local_id = expert_id - expert_start;
|
||||
let weight = weights[k_idx];
|
||||
// 8. Bias add: down += expert_down_bias (in-place)
|
||||
xserv_kernels::moe::moe_bias_add_3d(&down, &layer.expert_down_bias);
|
||||
|
||||
let gate_up_raw = matmul_2d(&token_tensor, &layer.expert_gate_up_wt[local_id]);
|
||||
let gate_up = add_bias(&gate_up_raw, &layer.expert_gate_up_bias[local_id]);
|
||||
|
||||
let activated = gpt_oss_glu(&gate_up, layer.glu_alpha, layer.glu_limit);
|
||||
|
||||
let down_raw = matmul_2d(&activated, &layer.expert_down_wt[local_id]);
|
||||
let down = add_bias(&down_raw, &layer.expert_down_bias[local_id]);
|
||||
|
||||
|
||||
let down_cpu = down.to_device(Device::Cpu);
|
||||
let down_data = down_cpu.as_slice::<bf16>();
|
||||
for d in 0..hidden {
|
||||
output_acc[t * hidden + d] += weight * down_data[d].to_f32();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Convert accumulated output to BF16 tensor on GPU
|
||||
let output_bf16: Vec<bf16> = output_acc.iter().map(|&v| bf16::from_f32(v)).collect();
|
||||
let moe_out = Tensor::from_slice(&output_bf16, &[num_tokens, hidden]).to_device(x.device());
|
||||
// 9. Weighted sum across experts → [tokens, hidden]
|
||||
let moe_out = xserv_kernels::moe::moe_weighted_sum(
|
||||
&down, &topk_ids, &topk_weights,
|
||||
expert_start, local_experts, top_k,
|
||||
);
|
||||
|
||||
self.all_reduce(&moe_out);
|
||||
moe_out
|
||||
@@ -495,8 +516,15 @@ fn add_bias(x: &Tensor, bias: &Tensor) -> Tensor {
|
||||
let cols = x.shape()[1];
|
||||
assert_eq!(bias.shape()[0], cols, "bias size {} != cols {}", bias.shape()[0], cols);
|
||||
|
||||
// Broadcast bias to each row using GPU kernels.
|
||||
// Tile bias [cols] into [rows, cols] by repeating rows, then add element-wise.
|
||||
let x_c = x.contiguous();
|
||||
|
||||
if rows == 1 {
|
||||
// Fast path: reshape bias [cols] → [1, cols] (zero-copy), add directly on GPU
|
||||
let bias_2d = bias.reshape(&[1, cols]);
|
||||
return xserv_kernels::add(&x_c, &bias_2d);
|
||||
}
|
||||
|
||||
// General path: tile bias to [rows, cols] via CPU, then add on GPU
|
||||
let bias_cpu = bias.to_device(Device::Cpu);
|
||||
let bias_data = bias_cpu.as_slice::<bf16>();
|
||||
let mut tiled = Vec::with_capacity(rows * cols);
|
||||
@@ -504,7 +532,6 @@ fn add_bias(x: &Tensor, bias: &Tensor) -> Tensor {
|
||||
tiled.extend_from_slice(bias_data);
|
||||
}
|
||||
let bias_tiled = Tensor::from_slice(&tiled, &[rows, cols]).to_device(x.device());
|
||||
let x_c = x.contiguous();
|
||||
xserv_kernels::add(&x_c, &bias_tiled)
|
||||
}
|
||||
|
||||
@@ -554,23 +581,23 @@ fn shard_1d(t: &Tensor, rank: usize, world: usize) -> Tensor {
|
||||
Tensor::from_slice(&shard, &[local])
|
||||
}
|
||||
|
||||
/// Extract expert `e` from a [num_experts, rows, cols] 3D tensor → [rows, cols] 2D
|
||||
fn slice_expert_3d(t: &Tensor, e: usize, rows: usize, cols: usize) -> Tensor {
|
||||
/// Extract experts [start..start+count) from a [num_experts, rows, cols] 3D tensor
|
||||
fn slice_expert_range_3d(t: &Tensor, start: usize, count: usize, rows: usize, cols: usize) -> Tensor {
|
||||
assert_eq!(t.ndim(), 3);
|
||||
let host = t.to_device(Device::Cpu);
|
||||
let data = host.as_slice::<bf16>();
|
||||
let stride = rows * cols;
|
||||
let start = e * stride;
|
||||
let slice = data[start..start + stride].to_vec();
|
||||
Tensor::from_slice(&slice, &[rows, cols])
|
||||
let offset = start * stride;
|
||||
let slice = data[offset..offset + count * stride].to_vec();
|
||||
Tensor::from_slice(&slice, &[count, rows, cols])
|
||||
}
|
||||
|
||||
/// Extract expert `e` from a [num_experts, dim] 2D tensor → [dim] 1D
|
||||
fn slice_expert_2d(t: &Tensor, e: usize, dim: usize) -> Tensor {
|
||||
/// Extract experts [start..start+count) from a [num_experts, dim] 2D tensor
|
||||
fn slice_expert_range_2d(t: &Tensor, start: usize, count: usize, dim: usize) -> Tensor {
|
||||
assert_eq!(t.ndim(), 2);
|
||||
let host = t.to_device(Device::Cpu);
|
||||
let data = host.as_slice::<bf16>();
|
||||
let start = e * dim;
|
||||
let slice = data[start..start + dim].to_vec();
|
||||
Tensor::from_slice(&slice, &[dim])
|
||||
let offset = start * dim;
|
||||
let slice = data[offset..offset + count * dim].to_vec();
|
||||
Tensor::from_slice(&slice, &[count, dim])
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user