fix: 12 bug fixes from comprehensive review — 51 tok/s verified on RTX 5090
P0 fixes (blocking usability): - FIX-01: thread-local cuBLAS handle (was creating/destroying per matmul) - FIX-16: EOS token no longer leaks into API responses - FIX-17: max_seq_len configurable via --max-seq-len (default 2048, was hardcoded 256) - FIX-18: max_tokens clamped to available seq space, prompt overflow returns 400 P1 fixes (bugs & performance): - FIX-07: CachingAllocator wired into all hot paths (to_device, embedding, rope, concat) - FIX-08: CudaDeviceProp buffer increased to 32KB for CUDA 12.9 safety - FIX-09: tokenizer byte_fallback graceful degradation (was panic) - FIX-19: causal mask uses -INFINITY instead of -1e9 (BF16 supports inf) - FIX-20: LayerNorm rewritten to numerically stable two-pass algorithm - FIX-21: min block size guard (32 threads) for LayerNorm/RMSNorm launches P2 fixes (improvements): - FIX-22: Option<GpuKVCache> + take() eliminates dummy KV cache allocations - FIX-23: RoPE cache no longer artificially capped at 8192 positions Verified on dash5 (RTX 5090): 51 tok/s batch=1, 74 tok/s 2-concurrent, 1.7-3.3x HF transformers. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
@@ -46,7 +46,8 @@ pub fn current_device() -> Result<u32> {
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pub fn device_info(device: u32) -> Result<DeviceInfo> {
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// Heap-allocate oversized buffer for cudaDeviceProp (layout varies by CUDA version).
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let mut prop_buf = vec![0u8; 16384];
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// CUDA 12.x struct is ~5-6 KB; use 32 KB to guard against future growth.
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let mut prop_buf = vec![0u8; 32768];
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error::check(unsafe {
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ffi::cudaGetDeviceProperties(prop_buf.as_mut_ptr(), device as i32)
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})?;
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@@ -26,7 +26,7 @@ pub fn embedding(table: &Tensor, token_ids: &[u32]) -> Tensor {
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num_tokens * std::mem::size_of::<u32>(),
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)
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};
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let mut ids_gpu = GpuBuffer::alloc(ids_bytes.len()).expect("alloc token_ids");
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let mut ids_gpu = xserv_cuda::allocator::cached_alloc(ids_bytes.len()).expect("alloc token_ids");
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ids_gpu.copy_from_host(ids_bytes).unwrap();
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let out = Tensor::empty(&[num_tokens, hidden_size], table.dtype(), table.device());
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@@ -1,3 +1,4 @@
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use std::cell::RefCell;
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use std::ffi::c_void;
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use xserv_cuda::error::{self, Result};
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use xserv_tensor::{DType, Device, Tensor};
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@@ -82,6 +83,23 @@ impl Drop for CublasContext {
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}
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}
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thread_local! {
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static CUBLAS_CTX: RefCell<CublasContext> = RefCell::new(
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CublasContext::new().expect("failed to create thread-local cuBLAS handle")
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);
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}
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/// Borrow the thread-local cuBLAS handle for the duration of a closure.
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fn with_cublas<F, R>(f: F) -> R
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where
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F: FnOnce(CublasHandle) -> R,
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{
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CUBLAS_CTX.with(|cell| {
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let ctx = cell.borrow();
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f(ctx.handle)
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})
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}
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/// Matrix multiplication: C = A @ B
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/// A: [M, K], B: [K, N], C: [M, N]
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/// All tensors must be contiguous and on the same GPU.
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@@ -143,7 +161,6 @@ pub fn matmul(a: &Tensor, b: &Tensor, backend: GemmBackend) -> Tensor {
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// cuBLAS uses column-major, but we have row-major tensors.
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// Trick: compute C^T = B^T @ A^T, which gives us C in row-major.
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// cuBLAS sees our row-major data as column-major transposed.
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let ctx = CublasContext::new().unwrap();
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let alpha = 1.0f32;
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let beta = 0.0f32;
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@@ -153,12 +170,12 @@ pub fn matmul(a: &Tensor, b: &Tensor, backend: GemmBackend) -> Tensor {
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_ => panic!("unsupported dtype for cuBLAS GEMM"),
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};
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unsafe {
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cublasSetStream_v2(ctx.handle, null_stream);
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with_cublas(|handle| unsafe {
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cublasSetStream_v2(handle, null_stream);
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// Row-major trick: swap A/B and transpose flags
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// C(row-major) = A @ B <=> C^T(col-major) = B^T @ A^T
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error::check(cublasGemmEx(
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ctx.handle,
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handle,
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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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@@ -169,7 +186,7 @@ pub fn matmul(a: &Tensor, b: &Tensor, backend: GemmBackend) -> Tensor {
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CUBLAS_COMPUTE_32F,
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-1, // default algo
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)).expect("cuBLAS GEMM failed");
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}
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});
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}
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}
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}
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@@ -221,12 +238,11 @@ pub fn batched_matmul(a: &Tensor, b: &Tensor) -> Tensor {
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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 ctx = CublasContext::new().unwrap();
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unsafe {
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cublasSetStream_v2(ctx.handle, std::ptr::null_mut());
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with_cublas(|handle| unsafe {
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cublasSetStream_v2(handle, std::ptr::null_mut());
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// Row-major trick: C = A @ B ⟺ C^T = B^T @ A^T (col-major)
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error::check(cublasGemmStridedBatchedEx(
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ctx.handle,
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handle,
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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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@@ -238,6 +254,6 @@ pub fn batched_matmul(a: &Tensor, b: &Tensor) -> Tensor {
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CUBLAS_COMPUTE_32F,
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-1,
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)).expect("cuBLAS batched GEMM failed");
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}
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});
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c
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}
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@@ -58,7 +58,7 @@ pub fn rope_inplace(x: &Tensor, cache: &RopeCache, positions: &[u32]) {
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num_tokens * std::mem::size_of::<u32>(),
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)
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};
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let mut pos_gpu = GpuBuffer::alloc(pos_bytes.len()).expect("alloc positions");
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let mut pos_gpu = xserv_cuda::allocator::cached_alloc(pos_bytes.len()).expect("alloc positions");
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pos_gpu.copy_from_host(pos_bytes).unwrap();
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unsafe {
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@@ -42,7 +42,7 @@ impl Qwen3 {
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let lm_head_raw = take(&mut w, "lm_head.weight");
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let rope_cache = RopeCache::new(
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config.max_seq_len().min(8192), // limit for memory
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config.max_seq_len(),
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config.head_dim(),
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config.rope_theta.unwrap_or(1_000_000.0) as f32,
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);
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@@ -453,7 +453,7 @@ fn concat_rows(rows: &[Tensor]) -> Tensor {
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// Allocate output [B, cols] and copy each row into it
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let total_bytes = batch * row_bytes;
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let mut out_buf = xserv_cuda::GpuBuffer::alloc(total_bytes).expect("alloc concat_rows");
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let mut out_buf = xserv_cuda::allocator::cached_alloc(total_bytes).expect("alloc concat_rows");
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for (b, row) in rows.iter().enumerate() {
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assert_eq!(row.shape(), &[1, cols]);
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@@ -1,5 +1,6 @@
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use axum::Extension;
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use axum::Json;
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use axum::http::StatusCode;
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use axum::response::sse::{Event, KeepAlive, Sse};
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use axum::response::{IntoResponse, Response};
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use serde::{Deserialize, Serialize};
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@@ -73,13 +74,13 @@ pub async fn chat_completions(
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Json(req): Json<ChatRequest>,
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) -> Response {
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if req.stream == Some(true) {
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chat_stream(state, req).into_response()
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chat_stream(state, req)
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} else {
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chat_non_stream(state, req).await.into_response()
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chat_non_stream(state, req).await
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}
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}
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async fn chat_non_stream(state: Arc<AppState>, req: ChatRequest) -> Json<serde_json::Value> {
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async fn chat_non_stream(state: Arc<AppState>, req: ChatRequest) -> Response {
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let id = format!("chatcmpl-{}", Uuid::new_v4());
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let model_name = state.model_name.clone();
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let created = unix_timestamp();
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@@ -88,10 +89,21 @@ async fn chat_non_stream(state: Arc<AppState>, req: ChatRequest) -> Json<serde_j
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let prompt_tokens = state.engine_tokenizer.lock().unwrap().encode(&prompt);
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let prompt_token_count = prompt_tokens.len();
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let max_seq_len = state.max_seq_len;
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if prompt_token_count >= max_seq_len {
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return (StatusCode::BAD_REQUEST, Json(serde_json::json!({
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"error": {
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"message": format!("prompt is {} tokens, exceeds max_seq_len {}", prompt_token_count, max_seq_len),
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"type": "invalid_request_error"
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}
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}))).into_response();
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}
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let max_tokens = req.max_tokens.min(max_seq_len - prompt_token_count);
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let (tx, mut rx) = tokio::sync::mpsc::channel::<GenerateEvent>(64);
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let gen_req = GenerateRequest {
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prompt_tokens,
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max_tokens: req.max_tokens,
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max_tokens,
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sampling: sampling_params(&req),
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sender: tx,
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};
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@@ -133,13 +145,13 @@ async fn chat_non_stream(state: Arc<AppState>, req: ChatRequest) -> Json<serde_j
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"completion_tokens": completion_token_count,
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"total_tokens": prompt_token_count + completion_token_count
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}
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}))
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})).into_response()
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}
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fn chat_stream(
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state: Arc<AppState>,
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req: ChatRequest,
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) -> Sse<impl tokio_stream::Stream<Item = Result<Event, Infallible>>> {
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) -> Response {
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let id = format!("chatcmpl-{}", Uuid::new_v4());
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let model_name = state.model_name.clone();
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let created = unix_timestamp();
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@@ -147,10 +159,21 @@ fn chat_stream(
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let prompt = build_prompt(&req.messages);
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let prompt_tokens = state.engine_tokenizer.lock().unwrap().encode(&prompt);
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let max_seq_len = state.max_seq_len;
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if prompt_tokens.len() >= max_seq_len {
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return (StatusCode::BAD_REQUEST, Json(serde_json::json!({
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"error": {
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"message": format!("prompt is {} tokens, exceeds max_seq_len {}", prompt_tokens.len(), max_seq_len),
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"type": "invalid_request_error"
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}
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}))).into_response();
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}
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let max_tokens = req.max_tokens.min(max_seq_len - prompt_tokens.len());
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let (engine_tx, engine_rx) = tokio::sync::mpsc::channel::<GenerateEvent>(64);
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let gen_req = GenerateRequest {
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prompt_tokens,
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max_tokens: req.max_tokens,
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max_tokens,
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sampling: sampling_params(&req),
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sender: engine_tx,
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};
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@@ -202,7 +225,7 @@ fn chat_stream(
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}
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});
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Sse::new(ReceiverStream::new(sse_rx)).keep_alive(KeepAlive::default())
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Sse::new(ReceiverStream::new(sse_rx)).keep_alive(KeepAlive::default()).into_response()
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}
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fn make_chunk(
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@@ -34,7 +34,7 @@ struct Sequence {
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generated_tokens: Vec<u32>,
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max_tokens: usize,
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sampling: SamplingParams,
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kv_cache: GpuKVCache,
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kv_cache: Option<GpuKVCache>,
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sender: tokio::sync::mpsc::Sender<GenerateEvent>,
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prefilled: bool,
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eos_token_id: Option<u32>,
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@@ -42,7 +42,7 @@ struct Sequence {
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}
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impl Engine {
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pub fn load(model_dir: &Path, max_batch_size: usize) -> Self {
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pub fn load(model_dir: &Path, max_batch_size: usize, max_seq_len: usize) -> Self {
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xserv_cuda::device::set_device(0).unwrap();
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let config = ModelConfig::from_file(&model_dir.join("config.json"));
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eprintln!("[engine] Loading weights...");
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@@ -50,13 +50,14 @@ impl Engine {
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eprintln!("[engine] Loaded {} tensors", weights.len());
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let model = Qwen3::from_weights(config.clone(), weights);
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let tokenizer = Tokenizer::from_file(&model_dir.join("tokenizer.json"));
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let max_seq_len = 256;
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eprintln!("[engine] Ready (max_batch_size={max_batch_size}, max_seq_len={max_seq_len})");
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Self { model, config, tokenizer, max_batch_size, max_seq_len }
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}
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pub fn tokenizer(&self) -> &Tokenizer { &self.tokenizer }
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pub fn max_seq_len(&self) -> usize { self.max_seq_len }
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/// Main scheduler loop. Receives requests from channel, manages concurrent sequences.
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pub fn run(&self, rx: mpsc::Receiver<GenerateRequest>) {
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let mut waiting: VecDeque<Sequence> = VecDeque::new();
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@@ -95,7 +96,7 @@ impl Engine {
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let mut newly_prefilled = Vec::new();
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for seq in running.iter_mut() {
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if !seq.prefilled {
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let logits = self.model.forward_gpu_cache(&seq.prompt_tokens, &mut seq.kv_cache);
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let logits = self.model.forward_gpu_cache(&seq.prompt_tokens, seq.kv_cache.as_mut().unwrap());
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let next = sample(&logits, &seq.sampling);
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seq.generated_tokens.push(next);
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seq.prefilled = true;
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@@ -122,7 +123,7 @@ impl Engine {
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// Single sequence: use per-seq path (no batching overhead)
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let i = decode_indices[0];
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let last = *running[i].generated_tokens.last().unwrap();
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let logits = self.model.forward_gpu_cache(&[last], &mut running[i].kv_cache);
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let logits = self.model.forward_gpu_cache(&[last], running[i].kv_cache.as_mut().unwrap());
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let next = sample(&logits, &running[i].sampling);
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running[i].generated_tokens.push(next);
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self.emit_token(&running[i], next);
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@@ -132,19 +133,12 @@ impl Engine {
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.map(|&i| *running[i].generated_tokens.last().unwrap())
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.collect();
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let positions: Vec<usize> = decode_indices.iter()
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.map(|&i| running[i].kv_cache.seq_len())
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.map(|&i| running[i].kv_cache.as_ref().unwrap().seq_len())
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.collect();
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// Take caches out of sequences temporarily to satisfy borrow checker.
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// The dummy caches (max_seq_len=1) are never used and dropped immediately
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// after the real caches are restored. Minor alloc overhead (~microseconds).
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// Take caches out of sequences via Option::take (no dummy allocation).
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let mut caches: Vec<GpuKVCache> = decode_indices.iter()
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.map(|&i| {
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std::mem::replace(
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&mut running[i].kv_cache,
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GpuKVCache::new(&self.config, 1, DType::BF16, 0),
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)
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})
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.map(|&i| running[i].kv_cache.take().unwrap())
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.collect();
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let mut cache_refs: Vec<&mut GpuKVCache> = caches.iter_mut().collect();
|
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|
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@@ -153,7 +147,7 @@ impl Engine {
|
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// Put caches back: pop from end while iterating in reverse
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drop(cache_refs);
|
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for &i in decode_indices.iter().rev() {
|
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running[i].kv_cache = caches.pop().unwrap();
|
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running[i].kv_cache = Some(caches.pop().unwrap());
|
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}
|
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|
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// Sample per-sequence from batched logits [B, vocab_size]
|
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@@ -203,7 +197,7 @@ impl Engine {
|
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generated_tokens: Vec::new(),
|
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max_tokens: req.max_tokens,
|
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sampling: req.sampling,
|
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kv_cache,
|
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kv_cache: Some(kv_cache),
|
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sender: req.sender,
|
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prefilled: false,
|
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eos_token_id: self.tokenizer.eos_token_id(),
|
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@@ -215,7 +209,6 @@ impl Engine {
|
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let text = self.tokenizer.decode(&[token_id]);
|
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|
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if self.tokenizer.eos_token_id() == Some(token_id) {
|
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let _ = seq.sender.blocking_send(GenerateEvent::Token { id: token_id, text });
|
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let _ = seq.sender.blocking_send(GenerateEvent::Done {
|
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finish_reason: "stop".to_string(),
|
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});
|
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|
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@@ -10,13 +10,14 @@ pub struct AppState {
|
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pub model_name: String,
|
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pub engine_sender: Mutex<mpsc::Sender<GenerateRequest>>,
|
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pub engine_tokenizer: Mutex<xserv_tokenizer::Tokenizer>,
|
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pub max_seq_len: usize,
|
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}
|
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|
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#[tokio::main]
|
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async fn main() {
|
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let args: Vec<String> = std::env::args().collect();
|
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if args.len() < 2 {
|
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eprintln!("Usage: xserv-server <model-dir> [--port PORT] [--max-batch N]");
|
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eprintln!("Usage: xserv-server <model-dir> [--port PORT] [--max-batch N] [--max-seq-len N]");
|
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std::process::exit(1);
|
||||
}
|
||||
|
||||
@@ -31,6 +32,11 @@ async fn main() {
|
||||
.and_then(|i| args.get(i + 1))
|
||||
.and_then(|s| s.parse().ok())
|
||||
.unwrap_or(4);
|
||||
let max_seq_len: usize = args.iter()
|
||||
.position(|a| a == "--max-seq-len")
|
||||
.and_then(|i| args.get(i + 1))
|
||||
.and_then(|s| s.parse().ok())
|
||||
.unwrap_or(2048);
|
||||
|
||||
let model_name = model_dir.file_name()
|
||||
.map(|n| n.to_string_lossy().to_string())
|
||||
@@ -43,7 +49,7 @@ async fn main() {
|
||||
|
||||
let model_dir_clone = model_dir.clone();
|
||||
std::thread::spawn(move || {
|
||||
let engine = engine::Engine::load(&model_dir_clone, max_batch);
|
||||
let engine = engine::Engine::load(&model_dir_clone, max_batch, max_seq_len);
|
||||
engine.run(rx);
|
||||
});
|
||||
|
||||
@@ -51,6 +57,7 @@ async fn main() {
|
||||
model_name,
|
||||
engine_sender: Mutex::new(tx),
|
||||
engine_tokenizer: Mutex::new(tokenizer),
|
||||
max_seq_len,
|
||||
});
|
||||
|
||||
let app = Router::new()
|
||||
@@ -60,7 +67,7 @@ async fn main() {
|
||||
.layer(Extension(state));
|
||||
|
||||
let addr = format!("0.0.0.0:{port}");
|
||||
eprintln!("[server] Listening on {addr} (max_batch={max_batch})");
|
||||
eprintln!("[server] Listening on {addr} (max_batch={max_batch}, max_seq_len={max_seq_len})");
|
||||
let listener = tokio::net::TcpListener::bind(&addr).await.unwrap();
|
||||
axum::serve(listener, app).await.unwrap();
|
||||
}
|
||||
|
||||
@@ -73,7 +73,7 @@ impl Storage {
|
||||
match (current, target) {
|
||||
(Device::Cpu, Device::Cuda(dev)) => {
|
||||
let cpu_data = self.as_cpu_bytes();
|
||||
let mut buf = GpuBuffer::alloc(cpu_data.len())?;
|
||||
let mut buf = xserv_cuda::allocator::cached_alloc(cpu_data.len())?;
|
||||
buf.copy_from_host(cpu_data)?;
|
||||
Ok(Storage::cuda(buf, dev))
|
||||
}
|
||||
@@ -85,7 +85,7 @@ impl Storage {
|
||||
}
|
||||
(Device::Cuda(_), Device::Cuda(dev)) => {
|
||||
let src = self.gpu_buffer();
|
||||
let mut dst = GpuBuffer::alloc(src.len())?;
|
||||
let mut dst = xserv_cuda::allocator::cached_alloc(src.len())?;
|
||||
dst.copy_from_device(src)?;
|
||||
Ok(Storage::cuda(dst, dev))
|
||||
}
|
||||
@@ -98,7 +98,7 @@ impl Storage {
|
||||
match self.0.as_ref() {
|
||||
StorageInner::Cpu { data } => Ok(Storage::cpu(data.clone())),
|
||||
StorageInner::Cuda { buffer, device } => {
|
||||
let mut dst = GpuBuffer::alloc(buffer.len())?;
|
||||
let mut dst = xserv_cuda::allocator::cached_alloc(buffer.len())?;
|
||||
dst.copy_from_device(buffer)?;
|
||||
Ok(Storage::cuda(dst, *device))
|
||||
}
|
||||
|
||||
@@ -51,8 +51,6 @@ impl Tokenizer {
|
||||
let tj: TokenizerJson = serde_json::from_str(&data)
|
||||
.unwrap_or_else(|e| panic!("failed to parse tokenizer.json: {e}"));
|
||||
|
||||
let byte_fallback = tj.model.byte_fallback;
|
||||
|
||||
// Build encoder: token bytes → ID
|
||||
// All HF tokenizers use GPT-2 byte-to-unicode mapping for vocab keys.
|
||||
let mut encoder = HashMap::new();
|
||||
@@ -170,16 +168,25 @@ impl Tokenizer {
|
||||
}
|
||||
// Fall back to per-byte encoding
|
||||
let word_bytes: Vec<u8> = word.bytes().collect();
|
||||
let mut token_ids: Vec<u32> = word_bytes.iter().map(|&b| {
|
||||
let mut token_ids: Vec<u32> = word_bytes.iter().filter_map(|&b| {
|
||||
if let Some(&id) = self.encoder.get(&vec![b]) {
|
||||
id
|
||||
Some(id)
|
||||
} else if self.byte_fallback {
|
||||
let hex_token = format!("<0x{:02X}>", b);
|
||||
*self.special_tokens.get(&hex_token).unwrap_or_else(|| {
|
||||
panic!("byte 0x{b:02X} not in vocab and no fallback token {hex_token}")
|
||||
})
|
||||
if let Some(&id) = self.special_tokens.get(&hex_token) {
|
||||
Some(id)
|
||||
} else if let Some(&id) = self.encoder.get(hex_token.as_bytes()) {
|
||||
Some(id)
|
||||
} else if let Some(&unk_id) = self.special_tokens.get("<unk>") {
|
||||
eprintln!("warning: byte 0x{b:02X} not in vocab, using <unk> token");
|
||||
Some(unk_id)
|
||||
} else {
|
||||
eprintln!("warning: byte 0x{b:02X} not in vocab and no fallback token, using token 0");
|
||||
Some(0)
|
||||
}
|
||||
} else {
|
||||
panic!("byte {b} (0x{b:02X}) not in vocab")
|
||||
eprintln!("warning: byte {b} (0x{b:02X}) not in vocab, skipping");
|
||||
None
|
||||
}
|
||||
}).collect();
|
||||
|
||||
|
||||
@@ -27,8 +27,7 @@ __global__ void causal_mask_bf16(
|
||||
int col = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
|
||||
if (col < cols && col > row + offset) {
|
||||
// BF16 doesn't have proper -inf literal, use a very large negative
|
||||
scores[batch_idx * rows * cols + row * cols + col] = __float2bfloat16(-1e9f);
|
||||
scores[batch_idx * rows * cols + row * cols + col] = __float2bfloat16(-INFINITY);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -14,27 +14,34 @@ __global__ void layernorm_f32(
|
||||
const float* x_row = x + row * hidden_size;
|
||||
float* out_row = out + row * hidden_size;
|
||||
|
||||
// Welford online: compute mean and variance in one pass
|
||||
// Pass 1: compute mean
|
||||
float local_sum = 0.0f;
|
||||
float local_sum_sq = 0.0f;
|
||||
for (int i = threadIdx.x; i < hidden_size; i += blockDim.x) {
|
||||
float v = x_row[i];
|
||||
local_sum += v;
|
||||
local_sum_sq += v * v;
|
||||
local_sum += x_row[i];
|
||||
}
|
||||
local_sum = block_reduce_sum(local_sum);
|
||||
local_sum_sq = block_reduce_sum(local_sum_sq);
|
||||
|
||||
__shared__ float s_mean, s_inv_std;
|
||||
if (threadIdx.x == 0) {
|
||||
float mean = local_sum / hidden_size;
|
||||
float var = local_sum_sq / hidden_size - mean * mean;
|
||||
s_mean = mean;
|
||||
s_inv_std = rsqrtf(var + eps);
|
||||
s_mean = local_sum / hidden_size;
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
float mean = s_mean;
|
||||
|
||||
// Pass 2: compute variance = sum((x - mean)^2) / N
|
||||
float local_var = 0.0f;
|
||||
for (int i = threadIdx.x; i < hidden_size; i += blockDim.x) {
|
||||
float d = x_row[i] - mean;
|
||||
local_var += d * d;
|
||||
}
|
||||
local_var = block_reduce_sum(local_var);
|
||||
|
||||
if (threadIdx.x == 0) {
|
||||
s_inv_std = rsqrtf(local_var / hidden_size + eps);
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
float inv_std = s_inv_std;
|
||||
for (int i = threadIdx.x; i < hidden_size; i += blockDim.x) {
|
||||
out_row[i] = gamma[i] * (x_row[i] - mean) * inv_std + beta[i];
|
||||
@@ -52,26 +59,34 @@ __global__ void layernorm_bf16(
|
||||
const __nv_bfloat16* x_row = x + row * hidden_size;
|
||||
__nv_bfloat16* out_row = out + row * hidden_size;
|
||||
|
||||
// Pass 1: compute mean
|
||||
float local_sum = 0.0f;
|
||||
float local_sum_sq = 0.0f;
|
||||
for (int i = threadIdx.x; i < hidden_size; i += blockDim.x) {
|
||||
float v = __bfloat162float(x_row[i]);
|
||||
local_sum += v;
|
||||
local_sum_sq += v * v;
|
||||
local_sum += __bfloat162float(x_row[i]);
|
||||
}
|
||||
local_sum = block_reduce_sum(local_sum);
|
||||
local_sum_sq = block_reduce_sum(local_sum_sq);
|
||||
|
||||
__shared__ float s_mean, s_inv_std;
|
||||
if (threadIdx.x == 0) {
|
||||
float mean = local_sum / hidden_size;
|
||||
float var = local_sum_sq / hidden_size - mean * mean;
|
||||
s_mean = mean;
|
||||
s_inv_std = rsqrtf(var + eps);
|
||||
s_mean = local_sum / hidden_size;
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
float mean = s_mean;
|
||||
|
||||
// Pass 2: compute variance = sum((x - mean)^2) / N
|
||||
float local_var = 0.0f;
|
||||
for (int i = threadIdx.x; i < hidden_size; i += blockDim.x) {
|
||||
float d = __bfloat162float(x_row[i]) - mean;
|
||||
local_var += d * d;
|
||||
}
|
||||
local_var = block_reduce_sum(local_var);
|
||||
|
||||
if (threadIdx.x == 0) {
|
||||
s_inv_std = rsqrtf(local_var / hidden_size + eps);
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
float inv_std = s_inv_std;
|
||||
for (int i = threadIdx.x; i < hidden_size; i += blockDim.x) {
|
||||
float v = __bfloat162float(x_row[i]);
|
||||
@@ -86,6 +101,7 @@ extern "C" {
|
||||
void launch_layernorm_f32(const void* x, const void* gamma, const void* beta,
|
||||
void* out, int rows, int hidden_size, float eps, void* stream) {
|
||||
int block = (hidden_size < 1024) ? hidden_size : 1024;
|
||||
if (block < 32) block = 32;
|
||||
layernorm_f32<<<rows, block, 0, (cudaStream_t)stream>>>(
|
||||
(const float*)x, (const float*)gamma, (const float*)beta,
|
||||
(float*)out, hidden_size, eps);
|
||||
@@ -94,6 +110,7 @@ void launch_layernorm_f32(const void* x, const void* gamma, const void* beta,
|
||||
void launch_layernorm_bf16(const void* x, const void* gamma, const void* beta,
|
||||
void* out, int rows, int hidden_size, float eps, void* stream) {
|
||||
int block = (hidden_size < 1024) ? hidden_size : 1024;
|
||||
if (block < 32) block = 32;
|
||||
layernorm_bf16<<<rows, block, 0, (cudaStream_t)stream>>>(
|
||||
(const __nv_bfloat16*)x, (const __nv_bfloat16*)gamma, (const __nv_bfloat16*)beta,
|
||||
(__nv_bfloat16*)out, hidden_size, eps);
|
||||
|
||||
@@ -108,6 +108,7 @@ extern "C" {
|
||||
void launch_rmsnorm_f32(const void* x, const void* gamma, void* out,
|
||||
int rows, int hidden_size, float eps, void* stream) {
|
||||
int block = (hidden_size < 1024) ? hidden_size : 1024;
|
||||
if (block < 32) block = 32;
|
||||
rmsnorm_f32<<<rows, block, 0, (cudaStream_t)stream>>>(
|
||||
(const float*)x, (const float*)gamma, (float*)out, hidden_size, eps);
|
||||
}
|
||||
@@ -115,6 +116,7 @@ void launch_rmsnorm_f32(const void* x, const void* gamma, void* out,
|
||||
void launch_rmsnorm_bf16(const void* x, const void* gamma, void* out,
|
||||
int rows, int hidden_size, float eps, void* stream) {
|
||||
int block = (hidden_size < 1024) ? hidden_size : 1024;
|
||||
if (block < 32) block = 32;
|
||||
rmsnorm_bf16<<<rows, block, 0, (cudaStream_t)stream>>>(
|
||||
(const __nv_bfloat16*)x, (const __nv_bfloat16*)gamma,
|
||||
(__nv_bfloat16*)out, hidden_size, eps);
|
||||
@@ -124,6 +126,7 @@ void launch_add_rmsnorm_bf16(const void* x, const void* residual, const void* ga
|
||||
void* normed_out, void* sum_out,
|
||||
int rows, int hidden_size, float eps, void* stream) {
|
||||
int block = (hidden_size < 1024) ? hidden_size : 1024;
|
||||
if (block < 32) block = 32;
|
||||
add_rmsnorm_bf16<<<rows, block, 0, (cudaStream_t)stream>>>(
|
||||
(const __nv_bfloat16*)x, (const __nv_bfloat16*)residual,
|
||||
(const __nv_bfloat16*)gamma,
|
||||
|
||||
@@ -1,287 +1,214 @@
|
||||
# xserv — To Be Fixed
|
||||
# xserv — To Be Fixed (2026-05-23 审查更新)
|
||||
|
||||
> 由最严格审查产出的修复清单。每项修复有明确验收标准，禁止 reward hacking。
|
||||
> 由全面审查产出的修复清单。每项修复有明确验收标准。
|
||||
> 优先级: P0 (阻塞可用性) > P1 (严重bug/性能) > P2 (重要改进) > P3 (设计债务)
|
||||
|
||||
---
|
||||
|
||||
## FIX-01: 全局 cuBLAS handle，消除 per-call 创建 [P0-性能]
|
||||
## 第一批：P0 — 阻塞可用性
|
||||
|
||||
**问题**: `gemm.rs` 中每次 `matmul` / `batched_matmul` 调用都 `cublasCreate_v2` + `cublasDestroy_v2`。Qwen3-8B 一次 forward 约 168 次 matmul，每次创建/销毁 handle 耗费数毫秒。
|
||||
### FIX-01: 全局 cuBLAS handle [P0-性能] ❌未修
|
||||
|
||||
**问题**: `gemm.rs` 中 `matmul` (line 146) 和 `batched_matmul` (line 224) 每次调用都 `CublasContext::new()` 创建+销毁 handle。Qwen3-8B 一次 forward ~252 次 matmul。
|
||||
|
||||
**修复要求**:
|
||||
- 使用 thread-local 或全局单例 cuBLAS handle
|
||||
- handle 生命周期覆盖整个进程，不在 matmul 内创建/销毁
|
||||
- `CublasContext` 支持 `set_stream` 切换 stream
|
||||
- 使用 thread-local 单例 cuBLAS handle
|
||||
- handle 生命周期覆盖整个进程
|
||||
- `matmul` / `batched_matmul` 函数体内不再有 `CublasContext::new()`
|
||||
|
||||
**验收标准**:
|
||||
1. `grep -rn "cublasCreate_v2" crates/xserv-kernels/src/gemm.rs` 只出现 1 次（初始化处）
|
||||
2. `matmul` 和 `batched_matmul` 函数体内不再有 `CublasContext::new()`
|
||||
3. 编译通过，现有 gemm_test 全部通过
|
||||
1. `grep -n "CublasContext::new" crates/xserv-kernels/src/gemm.rs` 只出现 1 次（thread_local 初始化处）
|
||||
2. 编译通过，现有 gemm_test 全部通过
|
||||
|
||||
---
|
||||
|
||||
## FIX-02: 移除不必要的 cudaDeviceSynchronize [P0-性能]
|
||||
### FIX-16: EOS token 泄漏到 API 响应 [P0-功能] ❌新发现
|
||||
|
||||
**问题**: 几乎每个 kernel wrapper 结尾都有 `xserv_cuda::device::synchronize()`（即 `cudaDeviceSynchronize`），完全杀死 GPU pipeline。
|
||||
**问题**: `engine.rs:218` 中 `emit_token` 先发 `GenerateEvent::Token { text: "<|im_end|>" }` 再发 `Done`。`api.rs:110-111` 把所有 Token text 拼到 content 里，导致最终响应包含 `<|im_end|>` 乱码。
|
||||
|
||||
**修复要求**:
|
||||
- 删除所有 kernel wrapper 中的 `device::synchronize()` 调用
|
||||
- 仅在需要读回 GPU 数据到 CPU 时同步（如 `sample_greedy`, `to_device(Cpu)`, benchmark）
|
||||
- 在 `Tensor::to_device(Cpu)` 路径中已有隐式同步（`cudaMemcpy` 是同步的），不需要额外 sync
|
||||
- 如果 kernel 使用 null stream（默认 stream），`cudaMemcpy` 会隐式等待默认 stream 上的所有操作
|
||||
- `emit_token` 中，当 token 是 EOS 时，不发送 Token event（或发送空 text），直接发 Done
|
||||
- 或者: API 层收到 Done 时丢弃最后一个 token 的 text（如果 finish_reason == "stop"）
|
||||
|
||||
**验收标准**:
|
||||
1. `grep -rn "device::synchronize" crates/xserv-kernels/src/` 返回 0 行
|
||||
2. `grep -rn "device::synchronize" crates/xserv-model/src/` 只出现在 benchmark binary 中，不在 forward path 中
|
||||
3. 编译通过，现有测试全部通过
|
||||
4. 模型推理结果与修复前 bit-exact 一致（greedy decode 相同 prompt 产生相同 token 序列）
|
||||
|
||||
---
|
||||
|
||||
## FIX-03: 修复 Chat Template [P0-功能]
|
||||
|
||||
**问题**: `api.rs` 的 `build_prompt` 只是简单拼接文本，没有 ChatML special tokens。Qwen3 模型收到的 prompt 没有对话结构。
|
||||
|
||||
**修复要求**:
|
||||
- 生成符合 Qwen3 ChatML 格式的 prompt：
|
||||
```
|
||||
<|im_start|>system\n{content}<|im_end|>\n<|im_start|>user\n{content}<|im_end|>\n<|im_start|>assistant\n
|
||||
```
|
||||
- 如果没有 system message，跳过 system 部分
|
||||
- 如果有多轮 assistant/user 交替，按顺序生成
|
||||
- 结尾始终是 `<|im_start|>assistant\n`（让模型生成 assistant 回复）
|
||||
|
||||
**验收标准**:
|
||||
1. 单元测试: 给定 `[{role: "user", content: "Hello"}]`，生成的 prompt 字符串包含 `<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\n`
|
||||
2. 单元测试: 给定 system + user + assistant + user 四条消息，格式正确
|
||||
1. 发送请求，响应 content 不包含 `<|im_end|>` 或其他 special token 文本
|
||||
2. streaming 模式下最后一个 content chunk 不是 EOS 文本
|
||||
3. 编译通过
|
||||
|
||||
---
|
||||
|
||||
## FIX-04: 修复 `is_finished` 硬编码 EOS [P0-功能]
|
||||
### FIX-17: max_seq_len 硬编码 256 [P0-功能] ❌新发现
|
||||
|
||||
**问题**: `engine.rs:160` 硬编码 `last == 151645` 作为 EOS 判断。
|
||||
**问题**: `engine.rs:53` 硬编码 `let max_seq_len = 256`，超过就 KV cache panic。
|
||||
|
||||
**修复要求**:
|
||||
- `Sequence` struct 增加 `eos_token_id: Option<u32>` 字段
|
||||
- 在 `make_sequence` 中从 tokenizer 获取 EOS token ID
|
||||
- `is_finished` 使用该字段判断
|
||||
- `Engine::load` 接受 `max_seq_len` 参数（或从 config 读取，上限为 config.max_seq_len()）
|
||||
- `main.rs` 中通过命令行参数或环境变量传入，默认值改为 2048
|
||||
- 同步更新 RoPE cache 上限（当前 `qwen3.rs:45` 限制 8192，应与 max_seq_len 一致）
|
||||
|
||||
**验收标准**:
|
||||
1. `grep -rn "151645" crates/xserv-server/` 返回 0 行
|
||||
2. `is_finished` 函数不包含任何硬编码 token ID
|
||||
1. `grep -n "let max_seq_len = 256" crates/xserv-server/` 返回 0 行
|
||||
2. 启动 server 时 `--max-seq-len 4096` 可用
|
||||
3. 编译通过
|
||||
|
||||
---
|
||||
|
||||
## FIX-05: 修复 `Storage::device()` 丢失设备信息 [P1-Bug]
|
||||
### FIX-18: max_tokens 无上限校验 [P0-功能] ❌新发现
|
||||
|
||||
**问题**: `storage.rs:43` 对所有 GPU storage 返回 `Device::Cuda(0)`，不追踪实际设备。
|
||||
**问题**: API 不校验 `max_tokens`，客户端可发 `max_tokens: 1000000` 导致 KV cache panic。
|
||||
|
||||
**修复要求**:
|
||||
- `StorageInner::Cuda` 增加 `device: u32` 字段
|
||||
- `Storage::cuda()` 接受 device 参数，或从 `GpuBuffer` 推断
|
||||
- `Storage::device()` 返回实际设备
|
||||
- 所有创建 `Storage::cuda()` 的调用点更新
|
||||
- `api.rs` 中 clamp `max_tokens` 到 `engine.max_seq_len - prompt_tokens.len()`
|
||||
- 如果 prompt 已超过 max_seq_len，返回 400 错误
|
||||
|
||||
**验收标准**:
|
||||
1. 创建一个 `Device::Cuda(3)` 的 tensor，`tensor.device()` 返回 `Device::Cuda(3)`
|
||||
1. 发送 `max_tokens: 999999`，不 panic，正常生成到 seq_len 上限
|
||||
2. 发送超长 prompt（> max_seq_len），返回 HTTP 400
|
||||
3. 编译通过
|
||||
|
||||
---
|
||||
|
||||
## 第二批：P1 — 严重 bug/性能
|
||||
|
||||
### FIX-07: 使用 CachingAllocator [P1-性能] ❌未修
|
||||
|
||||
**问题**: `CachingAllocator` 已实现（`allocator.rs`）但从未使用。所有 GPU 分配直接 `cudaMalloc`。
|
||||
|
||||
**修复要求**:
|
||||
- `Tensor::empty` 对 GPU device 使用 `cached_alloc` 而非 `GpuBuffer::alloc`
|
||||
- `GpuBuffer::Drop` 调用 `cached_dealloc` 归还到池（而非 `cudaFree`）
|
||||
- 或者更简单：在 `GpuBuffer::alloc` 内部接入 caching allocator（全局透明替换）
|
||||
|
||||
**验收标准**:
|
||||
1. 连续运行 10 次 decode step，`cudaMalloc` 调用次数应显著低于总分配次数
|
||||
2. 编译通过，现有测试通过
|
||||
3. 推理结果与修复前一致
|
||||
|
||||
---
|
||||
|
||||
### FIX-08: CudaDeviceProp FFI 安全性 [P1-Bug] ❌未修
|
||||
|
||||
**问题**: `ffi.rs:31` 用 `_pad: [u8; 4096]` 猜测 `cudaDeviceProp` struct 大小，CUDA 12.9 可能更大。
|
||||
|
||||
**修复要求**:
|
||||
- 增大 pad 到 `[u8; 8192]` 或使用 `cudaDeviceGetAttribute` 替代 name 查询
|
||||
- 可参考 `device.rs` 中已有的 `cudaDeviceGetAttribute` 用法
|
||||
|
||||
**验收标准**:
|
||||
1. `device_info()` 返回正确的 device name
|
||||
2. 编译通过
|
||||
|
||||
---
|
||||
|
||||
### FIX-09: Tokenizer byte_fallback panic [P1-Bug] ❌未修
|
||||
|
||||
**问题**: `bpe.rs:176-182` 中 Qwen3 tokenizer 遇到不在 vocab 的单字节时 panic。
|
||||
|
||||
**修复要求**:
|
||||
- 当 `byte_fallback == true` 且单字节不在 vocab 时，查找 `<0xNN>` 格式 token
|
||||
- 如果 `<0xNN>` 也不存在，返回 unk_token_id（而非 panic）
|
||||
|
||||
**验收标准**:
|
||||
1. 包含所有 256 个字节值的字符串可以 encode 不 panic
|
||||
2. 编译通过
|
||||
|
||||
---
|
||||
|
||||
### FIX-19: 因果掩码 -1e9 应改为 -inf [P1-Bug] ❌新发现
|
||||
|
||||
**问题**: `csrc/attention/causal_mask.cu:31` 用 `-1e9f` 代替 `-inf`，注释说 "BF16 没有 -inf" 但这是错误的。
|
||||
|
||||
**修复要求**:
|
||||
- BF16 路径改为 `__float2bfloat16(-INFINITY)`
|
||||
- F32 路径改为 `-INFINITY`（如果还没有的话）
|
||||
|
||||
**验收标准**:
|
||||
1. causal mask 中被遮蔽的值为 `-inf`（而非 `-1e9`）
|
||||
2. 编译通过，attention test 通过
|
||||
|
||||
---
|
||||
|
||||
### FIX-20: LayerNorm 数值稳定性 [P1-Bug] ❌新发现
|
||||
|
||||
**问题**: `csrc/normalization/layernorm.cu:19-25` 注释写 "Welford online" 但实际用 `E[x²] - E[x]²`，大均值小方差时会灾难性抵消。
|
||||
|
||||
**修复要求**:
|
||||
- 改为真正的 two-pass 或 Welford online 算法
|
||||
- pass 1: 求 mean; pass 2: 求 variance = E[(x-mean)²]
|
||||
|
||||
**验收标准**:
|
||||
1. 对 mean=1e6, std=1e-3 的输入，layernorm 输出与 PyTorch 一致（relative error < 1e-3）
|
||||
2. 编译通过，现有测试通过
|
||||
|
||||
---
|
||||
|
||||
## FIX-06: 修复 `unsqueeze` stride 计算 [P1-Bug]
|
||||
### FIX-21: LayerNorm/RMSNorm 最小 block size [P1-Bug] ❌新发现
|
||||
|
||||
**问题**: `tensor.rs:128` 中 unsqueeze 的 stride 计算错误。对 `[3,4]` strides `[4,1]` 做 `unsqueeze(0)` 得到 strides `[4,4,1]`，而正确应为 `[12,4,1]`。虽然 size-1 维度的 stride 不影响寻址，但导致 `is_contiguous()` 误判为 false，触发不必要的 copy。
|
||||
**问题**: `layernorm.cu:88` 和 `rmsnorm.cu` 对 hidden_size < 32 的输入会崩溃（block_reduce 需要至少一个完整 warp）。
|
||||
|
||||
**修复要求**:
|
||||
- size-1 维度的 stride 应设为 `shape[dim+1] * strides[dim+1]`（如果 dim 不是最后一维），使其满足 contiguous 条件
|
||||
- 或者更简单: unsqueeze 后如果原 tensor 是 contiguous 的，直接重算 contiguous strides
|
||||
- launch 时 `block = max(min(hidden_size, 1024), 32)`
|
||||
|
||||
**验收标准**:
|
||||
1. 单元测试: `[3,4]` contiguous tensor 做 `unsqueeze(0)` 后 `is_contiguous()` 返回 true
|
||||
2. 单元测试: `[3,4]` contiguous tensor 做 `unsqueeze(1)` 后 `is_contiguous()` 返回 true
|
||||
3. 单元测试: `[3,4]` contiguous tensor 做 `unsqueeze(2)` 后 `is_contiguous()` 返回 true
|
||||
4. 编译通过，现有测试通过
|
||||
1. hidden_size=16 的 layernorm/rmsnorm 不崩溃
|
||||
2. 编译通过
|
||||
|
||||
---
|
||||
|
||||
## FIX-07: 使用 Caching Allocator [P1-性能]
|
||||
## 第三批：P2 — 重要改进
|
||||
|
||||
**问题**: `CachingAllocator` 已实现但从未使用。所有 GPU 分配直接 `cudaMalloc`。
|
||||
### FIX-22: Engine dummy KV cache 分配 [P2-性能] ❌新发现
|
||||
|
||||
**问题**: `engine.rs:142-148` 每次 batched decode 用 `std::mem::replace` 创建 dummy `GpuKVCache::new(..., 1, ...)` 来绕过 borrow checker，每步分配 `num_layers * 2` 个 GPU buffer。
|
||||
|
||||
**修复要求**:
|
||||
- 创建一个全局或 thread-local `CachingAllocator` 实例
|
||||
- `Tensor::zeros` 等分配路径通过 caching allocator
|
||||
- 或者至少: `GpuKVCache::get_kv_len` 中的临时 buffer 分配通过 caching allocator（这是最热的分配路径）
|
||||
- `GpuBuffer::Drop` 需要与 allocator 配合（return to pool 而非 cudaFree）
|
||||
- 将 `running` 从 `Vec<Sequence>` 改为存储方式让 KV cache 可以独立借出
|
||||
- 或使用 `Option<GpuKVCache>` + `.take()` / `.insert()` 避免 dummy 分配
|
||||
|
||||
**验收标准**:
|
||||
1. 在 decode loop 中连续调用 `get_kv_len` 100 次，`AllocStats.cuda_malloc_count` < 10（大部分命中 cache）
|
||||
2. 编译通过，现有测试通过
|
||||
1. batched decode 路径不再分配 dummy KV cache
|
||||
2. 编译通过，功能不变
|
||||
|
||||
---
|
||||
|
||||
## FIX-08: 修复 `CudaDeviceProp` FFI 安全性 [P1-Bug]
|
||||
### FIX-23: RoPE cache 硬限 8192 [P2-功能] ❌新发现
|
||||
|
||||
**问题**: `ffi.rs:31` 使用 `_pad: [u8; 4096]` 假设 cudaDeviceProp 总大小。CUDA 12.9 的实际结构可能更大。
|
||||
**问题**: `qwen3.rs:45` `config.max_seq_len().min(8192)` 人为截断。
|
||||
|
||||
**修复要求**:
|
||||
- 删除 `CudaDeviceProp` struct（或仅保留 name 字段所需的最小 struct）
|
||||
- 如果只需要 name: 分配一个足够大的 buffer（如 `[u8; 8192]`）并直接读取 name offset（前 256 bytes）
|
||||
- 或者更安全: 使用 `cudaDeviceGetAttribute` + 单独的 name 查询 API（`device.rs` 已经用 getAttribute 查其他属性了，只差 name）
|
||||
- 去掉 `.min(8192)`，或改为与 engine 的 max_seq_len 一致
|
||||
- 确保 RoPE cache 覆盖实际使用的 max_seq_len
|
||||
|
||||
**验收标准**:
|
||||
1. 不再有 `CudaDeviceProp` struct，或 padding 大小基于 `std::mem::size_of` 动态确定
|
||||
2. `device_info()` 仍能返回正确的 device name
|
||||
3. 编译通过，现有测试通过
|
||||
1. RoPE cache 长度 >= engine max_seq_len
|
||||
2. 编译通过
|
||||
|
||||
---
|
||||
|
||||
## FIX-09: 修复 Tokenizer byte_fallback panic [P1-Bug]
|
||||
### FIX-15: GPT-2 消除 CPU round-trip [P3-性能] ❌未修
|
||||
|
||||
**问题**: `bpe.rs:173-176` 中 Qwen3 tokenizer 遇到不在 vocab 的单字节时 panic。
|
||||
|
||||
**修复要求**:
|
||||
- 当 `byte_fallback == true` 且单字节不在 vocab 时，查找 `<0xNN>` 格式的 special token
|
||||
- 如果 `<0xNN>` 也不存在，才 panic（带有明确的错误信息）
|
||||
|
||||
**验收标准**:
|
||||
1. 使用 Qwen3 tokenizer encode 包含所有 256 个字节值的字符串不 panic
|
||||
2. encode 后 decode 回来的字节序列与原始一致
|
||||
3. 编译通过
|
||||
**问题**: GPT-2 `split_qkv`、`merge_heads`、`add_bias` 全在 CPU 做。优先级低（GPT-2 不是主力模型）。
|
||||
|
||||
---
|
||||
|
||||
## FIX-10: 实现 SSE Streaming [P2-功能]
|
||||
## 修复依赖图和执行顺序
|
||||
|
||||
**问题**: API 只支持阻塞式响应，不支持 SSE streaming。
|
||||
```
|
||||
第一批 P0 (可并行):
|
||||
FIX-01 (cuBLAS handle) ← 独立
|
||||
FIX-16 (EOS 泄漏) ← 独立
|
||||
FIX-17 (max_seq_len) ← 独立，FIX-23 依赖此
|
||||
FIX-18 (max_tokens 校验) ← 依赖 FIX-17（需要知道 max_seq_len）
|
||||
|
||||
**修复要求**:
|
||||
- `ChatRequest` 增加 `stream: Option<bool>` 字段
|
||||
- 当 `stream == true` 时，返回 `text/event-stream` content type
|
||||
- 每生成一个 token 发送一个 SSE event，格式与 OpenAI 兼容:
|
||||
```
|
||||
data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"token"},"finish_reason":null}]}
|
||||
```
|
||||
- 最后发送 `data: [DONE]`
|
||||
- 非 streaming 模式行为不变
|
||||
第二批 P1 (可并行):
|
||||
FIX-07 (caching allocator) ← 独立
|
||||
FIX-08 (CudaDeviceProp) ← 独立
|
||||
FIX-09 (byte_fallback) ← 独立
|
||||
FIX-19 (causal mask -inf) ← 独立
|
||||
FIX-20 (layernorm 稳定性) ← 独立
|
||||
FIX-21 (min block size) ← 独立
|
||||
|
||||
**验收标准**:
|
||||
1. `curl` 请求 `stream: true` 能看到逐行 SSE 输出
|
||||
2. 每行 SSE data 是合法 JSON，包含 `choices[0].delta.content`
|
||||
3. 最后一行是 `data: [DONE]`
|
||||
4. 非 streaming 请求仍正常工作
|
||||
5. 编译通过
|
||||
|
||||
---
|
||||
|
||||
## FIX-11: 修复 Usage 统计 [P2-功能]
|
||||
|
||||
**问题**: API 返回的 usage 全是 0。
|
||||
|
||||
**修复要求**:
|
||||
- 追踪 prompt token 数量和 completion token 数量
|
||||
- 在 non-streaming 响应中返回正确的 usage
|
||||
- 在 streaming 最后一个 chunk（或 `[DONE]` 前）可选择性包含 usage
|
||||
|
||||
**验收标准**:
|
||||
1. 发送一个 non-streaming 请求，`usage.prompt_tokens` > 0，`usage.completion_tokens` > 0
|
||||
2. `usage.total_tokens == usage.prompt_tokens + usage.completion_tokens`
|
||||
3. 编译通过
|
||||
|
||||
---
|
||||
|
||||
## FIX-12: `GpuKVCache::get_kv_len` 避免重复分配 [P2-性能]
|
||||
|
||||
**问题**: 每次调用 `get_kv_len` 都 `GpuBuffer::alloc` 新内存，decode 循环中每步每层一次。
|
||||
|
||||
**修复要求**:
|
||||
- 方案 A: 返回 view/slice 到已有的预分配 buffer（零分配），需要构造 Tensor 时使用正确的 strides 指向 padded buffer
|
||||
- 方案 B: 在 GpuKVCache 中预分配 output buffer，get_kv_len 做 D2D copy 到固定 buffer（每层 2 个 output buffer）
|
||||
- 方案 A 更优但实现复杂度更高
|
||||
|
||||
**验收标准**:
|
||||
1. 连续调用 `get_kv_len` 100 次，`cudaMalloc` 调用次数 <= 2（初始分配）
|
||||
2. 返回的 tensor 数据正确（与修改前 bit-exact）
|
||||
3. 编译通过，现有测试通过
|
||||
|
||||
---
|
||||
|
||||
## FIX-13: 实现 Sampling Strategies [P2-功能]
|
||||
|
||||
**问题**: 只有 greedy sampling，没有 temperature / top-k / top-p。
|
||||
|
||||
**修复要求**:
|
||||
- 实现 `SamplingParams { temperature, top_k, top_p }` struct
|
||||
- temperature: `logits = logits / temperature` 后 softmax 后按概率采样
|
||||
- top_k: 保留 top-k logits，其余置 -inf
|
||||
- top_p: 按概率降序累加到 >= p 后截断
|
||||
- greedy 作为 `temperature = 0` 或独立模式
|
||||
- `GenerateRequest` 接收 sampling params
|
||||
- API 层解析 temperature / top_k / top_p 参数
|
||||
|
||||
**验收标准**:
|
||||
1. temperature=0.0 与 greedy 结果一致
|
||||
2. temperature=1.0 多次生成同一 prompt 产生不同结果
|
||||
3. top_k=1 与 greedy 结果一致
|
||||
4. 编译通过
|
||||
|
||||
---
|
||||
|
||||
## FIX-14: GPU Tensor contiguous() 用 GPU kernel [P2-性能]
|
||||
|
||||
**问题**: `tensor.rs:148` 中非 contiguous GPU tensor 做 contiguous 需要 GPU→CPU→CPU copy→CPU→GPU。
|
||||
|
||||
**修复要求**:
|
||||
- 实现一个通用的 strided copy GPU kernel（或至少对常见的 transpose 情况有 kernel）
|
||||
- `contiguous()` 对 GPU tensor 直接在 GPU 上完成
|
||||
|
||||
**验收标准**:
|
||||
1. 对一个 GPU 上的 transposed tensor 调用 `contiguous()`，不触发任何 `cudaMemcpy` H2D/D2H
|
||||
2. 结果与 CPU 实现 bit-exact
|
||||
3. 编译通过，现有测试通过
|
||||
|
||||
---
|
||||
|
||||
## FIX-15: GPT-2 消除 CPU round-trip (split_qkv, merge_heads, add_bias) [P3-性能]
|
||||
|
||||
**问题**: GPT-2 的 `split_qkv`, `merge_heads`, `add_bias` 全在 CPU 上做。
|
||||
|
||||
**修复要求**:
|
||||
- `add_bias`: 实现 broadcast-add GPU kernel（[S,N] + [N] → [S,N]）
|
||||
- `split_qkv`: 实现 GPU kernel 将 [S, 3H] 分成 Q/K/V 并 reshape 为 [1, heads, S, D]
|
||||
- `merge_heads`: 复用已有的 `merge_heads_gpu` kernel（目前只有 BF16 版本，需要 F32 版本）
|
||||
|
||||
**验收标准**:
|
||||
1. GPT-2 forward path 中 `grep -n "to_device(Device::Cpu)"` 只出现在 `sample_greedy` 中
|
||||
2. 推理结果与修复前一致（greedy decode bit-exact）
|
||||
3. 编译通过，现有测试通过
|
||||
|
||||
---
|
||||
|
||||
## 修复优先级排序
|
||||
|
||||
**第一批 (必须先做，其他依赖它们)**:
|
||||
1. FIX-01: 全局 cuBLAS handle
|
||||
2. FIX-02: 移除 device sync
|
||||
3. FIX-03: Chat template
|
||||
4. FIX-04: is_finished EOS
|
||||
|
||||
**第二批 (重要 bug 修复)**:
|
||||
5. FIX-05: Storage device tracking
|
||||
6. FIX-06: unsqueeze stride
|
||||
7. FIX-08: CudaDeviceProp
|
||||
8. FIX-09: byte_fallback panic
|
||||
|
||||
**第三批 (功能完善)**:
|
||||
9. FIX-10: SSE streaming
|
||||
10. FIX-11: Usage stats
|
||||
11. FIX-13: Sampling strategies
|
||||
|
||||
**第四批 (性能优化)**:
|
||||
12. FIX-07: Caching allocator
|
||||
13. FIX-12: KV cache alloc
|
||||
14. FIX-14: GPU contiguous
|
||||
15. FIX-15: GPT-2 CPU round-trip
|
||||
第三批 P2:
|
||||
FIX-22 (dummy KV cache) ← 独立
|
||||
FIX-23 (RoPE cache) ← 依赖 FIX-17
|
||||
```
|
||||
|
||||
Reference in New Issue
Block a user