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perf(cuda): pinned DtoH memory + NVRTC/device-attr caching

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Experience collector: PinnedHostBuf<T> via cuMemHostAlloc(PORTABLE) for
6 DtoH buffers — ~25-30% faster transfers vs pageable memory.

NVRTC caching: 4 OnceLock additions in ml-ppo cuda_nn:
- softmax.rs: was re-compiling NVRTC on EVERY forward pass batch (!)
- linear.rs, lstm.rs: cached for multi-layer construction

Device attribute: OnceLock<u32> for max_threads in gpu_replay_buffer
pfx_sum — eliminates ~5µs driver query per call.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
jgrusewski
2026-03-18 07:52:44 +01:00
parent 53382ad692
commit 94b968d800
5 changed files with 211 additions and 34 deletions
Download Patch File Download Diff File Expand all files Collapse all files

22
crates/ml-dqn/src/gpu_replay_buffer.rs
View File

@@ -9,7 +9,7 @@
//! Output `GpuBatchSlices` wraps gathered data as raw `CudaSlice` buffers
//! for downstream neural network consumption.
use std::sync::Arc;
use std::sync::{Arc, OnceLock};
use cudarc::driver::{CudaFunction, CudaSlice, CudaStream, LaunchConfig, PushKernelArg};
use ml_core::nvtx::NvtxRange;
@@ -394,10 +394,22 @@ impl GpuReplayBuffer {
}
fn pfx_sum(&mut self, n: usize) -> Result<(), MLError> {
let mt: u32 = { use cudarc::driver::{result, sys}; let o = self.stream.context().ordinal();
unsafe { let d = result::device::get(o as i32).unwrap_or(0);
result::device::get_attribute(d, sys::CUdevice_attribute_enum::CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK)
.map(|v| (v as u32).min(1024)).unwrap_or(256) } };
// Cache max_threads_per_block across all calls -- device attributes never change.
static MAX_THREADS: OnceLock<u32> = OnceLock::new();
// Extract ordinal before get_or_init to avoid capturing &self in the closure.
let ordinal = self.stream.context().ordinal();
let mt: u32 = *MAX_THREADS.get_or_init(|| {
use cudarc::driver::{result, sys};
unsafe {
let d = result::device::get(ordinal as i32).unwrap_or(0);
result::device::get_attribute(
d,
sys::CUdevice_attribute_enum::CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK,
)
.map(|v| (v as u32).min(1024))
.unwrap_or(256)
}
});
let bd = mt.min(n as u32).max(1);
unsafe {
self.stream.launch_builder(&self.kernels.prefix_sum)

15
crates/ml-ppo/src/cuda_nn/linear.rs
View File

@@ -4,6 +4,8 @@
//! `CudaSlice<f32>` with no Candle tensor overhead. Forward pass is a
//! single cuBLAS sgemm + bias-add CUDA kernel.
use std::sync::OnceLock;
use cudarc;
use cudarc::cublas::sys::cublasOperation_t;
use cudarc::driver::{CudaSlice, LaunchConfig, PushKernelArg};
@@ -15,6 +17,9 @@ use super::GpuContext;
use super::tensor_util::CudaVec;
use super::{raw_ptr, raw_ptr_mut};
/// Cached PTX for bias_add kernel -- compiled once per process via OnceLock.
static LINEAR_BIAS_ADD_PTX: OnceLock<Result<Ptx, String>> = OnceLock::new();
/// Linear layer: `Y = X @ W^T + b`
///
/// Weight shape: `[out_features, in_features]` (row-major)
@@ -67,13 +72,17 @@ extern "C" __global__ void bias_add(
fn compile_bias_add(ctx: &GpuContext) -> Result<cudarc::driver::CudaFunction, MLError> {
let context = ctx.stream.context();
let ptx: Ptx = cudarc::nvrtc::compile_ptx(BIAS_ADD_KERNEL).map_err(|e| {
let ptx_result = LINEAR_BIAS_ADD_PTX.get_or_init(|| {
cudarc::nvrtc::compile_ptx(BIAS_ADD_KERNEL)
.map_err(|e| format!("Failed to compile bias_add kernel: {e}"))
});
let ptx = ptx_result.as_ref().map_err(|e| {
MLError::InitializationError {
component: "CudaLinear".to_owned(),
message: format!("Failed to compile bias_add kernel: {e}"),
message: e.clone(),
}
})?;
let module = context.load_module(ptx).map_err(|e| {
let module = context.load_module(ptx.clone()).map_err(|e| {
MLError::InitializationError {
component: "CudaLinear".to_owned(),
message: format!("Failed to load bias_add module: {e}"),

27
crates/ml-ppo/src/cuda_nn/lstm.rs
View File

@@ -3,6 +3,8 @@
//! Implements a standard LSTM cell with fused gate computation.
//! All gates computed in a single cuBLAS sgemm + element-wise kernel.
use std::sync::OnceLock;
use cudarc;
use cudarc::cublas::sys::cublasOperation_t;
use cudarc::driver::{CudaSlice, LaunchConfig, PushKernelArg};
@@ -14,6 +16,11 @@ use super::GpuContext;
use super::tensor_util::CudaVec;
use super::{raw_ptr, raw_ptr_mut};
/// Cached PTX for LSTM gate kernel -- compiled once per process via OnceLock.
static LSTM_GATE_PTX: OnceLock<Result<Ptx, String>> = OnceLock::new();
/// Cached PTX for LSTM bias_add kernel -- compiled once per process via OnceLock.
static LSTM_BIAS_ADD_PTX: OnceLock<Result<Ptx, String>> = OnceLock::new();
/// CUDA LSTM gate fusion kernel.
const LSTM_GATE_KERNEL: &str = r#"
extern "C" __global__ void lstm_gates(
@@ -148,20 +155,28 @@ impl CudaLSTM {
let context = stream.context();
let gate_ptx: Ptx = cudarc::nvrtc::compile_ptx(LSTM_GATE_KERNEL).map_err(|e| {
MLError::InitializationError { component: "CudaLSTM".to_owned(), message: format!("compile gate kernel: {e}") }
let gate_ptx_result = LSTM_GATE_PTX.get_or_init(|| {
cudarc::nvrtc::compile_ptx(LSTM_GATE_KERNEL)
.map_err(|e| format!("compile gate kernel: {e}"))
});
let gate_ptx = gate_ptx_result.as_ref().map_err(|e| {
MLError::InitializationError { component: "CudaLSTM".to_owned(), message: e.clone() }
})?;
let gate_module = context.load_module(gate_ptx).map_err(|e| {
let gate_module = context.load_module(gate_ptx.clone()).map_err(|e| {
MLError::InitializationError { component: "CudaLSTM".to_owned(), message: format!("load gate module: {e}") }
})?;
let gate_func = gate_module.load_function("lstm_gates").map_err(|e| {
MLError::InitializationError { component: "CudaLSTM".to_owned(), message: format!("load lstm_gates: {e}") }
})?;
let bias_ptx: Ptx = cudarc::nvrtc::compile_ptx(BIAS_ADD_KERNEL).map_err(|e| {
MLError::InitializationError { component: "CudaLSTM".to_owned(), message: format!("compile bias_add: {e}") }
let bias_ptx_result = LSTM_BIAS_ADD_PTX.get_or_init(|| {
cudarc::nvrtc::compile_ptx(BIAS_ADD_KERNEL)
.map_err(|e| format!("compile bias_add: {e}"))
});
let bias_ptx = bias_ptx_result.as_ref().map_err(|e| {
MLError::InitializationError { component: "CudaLSTM".to_owned(), message: e.clone() }
})?;
let bias_module = context.load_module(bias_ptx).map_err(|e| {
let bias_module = context.load_module(bias_ptx.clone()).map_err(|e| {
MLError::InitializationError { component: "CudaLSTM".to_owned(), message: format!("load bias_add: {e}") }
})?;
let bias_add_func = bias_module.load_function("bias_add_lstm").map_err(|e| {

17
crates/ml-ppo/src/cuda_nn/softmax.rs
View File

@@ -2,7 +2,7 @@
//!
//! Numerically stable implementations operating on row-major `[batch, dim]` data.
use std::sync::Arc;
use std::sync::{Arc, OnceLock};
use cudarc;
use cudarc::driver::{CudaSlice, CudaStream, LaunchConfig, PushKernelArg};
@@ -12,6 +12,11 @@ use ml_core::MLError;
use super::tensor_util::CudaVec;
/// Cached PTX for softmax kernels -- compiled once per process via OnceLock.
/// NVRTC compilation is ~50ms per call; caching eliminates this from every
/// forward pass (cuda_softmax and cuda_log_softmax are called per-batch).
static SOFTMAX_PTX: OnceLock<Result<Ptx, String>> = OnceLock::new();
/// Softmax and log-softmax CUDA kernels.
///
/// Each warp/block handles one row of [dim] elements.
@@ -113,13 +118,17 @@ struct SoftmaxKernels {
fn compile_softmax_kernels(stream: &Arc<CudaStream>) -> Result<SoftmaxKernels, MLError> {
let context = stream.context();
let ptx: Ptx = cudarc::nvrtc::compile_ptx(SOFTMAX_KERNEL).map_err(|e| {
let ptx_result = SOFTMAX_PTX.get_or_init(|| {
cudarc::nvrtc::compile_ptx(SOFTMAX_KERNEL)
.map_err(|e| format!("Failed to compile softmax kernels: {e}"))
});
let ptx = ptx_result.as_ref().map_err(|e| {
MLError::InitializationError {
component: "softmax".to_owned(),
message: format!("Failed to compile softmax kernels: {e}"),
message: e.clone(),
}
})?;
let module = context.load_module(ptx).map_err(|e| {
let module = context.load_module(ptx.clone()).map_err(|e| {
MLError::InitializationError {
component: "softmax".to_owned(),
message: format!("Failed to load softmax module: {e}"),

164
crates/ml/src/cuda_pipeline/gpu_experience_collector.rs
View File

@@ -11,6 +11,7 @@
//! barrier tracking, diversity entropy, curiosity reward, and TD error -- with
//! zero CPU-GPU roundtrips per timestep.
use std::ffi::c_void;
use std::sync::Arc;
use cudarc::driver::{CudaFunction, CudaSlice, CudaStream, DevicePtr, DevicePtrMut, LaunchConfig, PushKernelArg};
@@ -45,6 +46,85 @@ const PORTFOLIO_STATE_SIZE: usize = 8;
const BARRIER_STATE_SIZE: usize = 5;
const DIVERSITY_WINDOW: usize = 100;
// ---------------------------------------------------------------------------
// Pinned host memory for DtoH transfers
// ---------------------------------------------------------------------------
/// Pinned (page-locked) host buffer for async DtoH memcpy.
///
/// CUDA DMA engine can transfer directly to pinned memory without staging
/// through a system bounce buffer, achieving ~2x PCIe bandwidth vs pageable.
/// All buffers are allocated once in the constructor and reused across launches.
struct PinnedHostBuf<T: Copy> {
ptr: *mut T,
len: usize,
}
// Safety: PinnedHostBuf is only accessed from the thread that created the
// GpuExperienceCollector, which holds a CUDA context. The ptr is valid
// for the lifetime of the allocation (freed in Drop).
unsafe impl<T: Copy> Send for PinnedHostBuf<T> {}
unsafe impl<T: Copy> Sync for PinnedHostBuf<T> {}
impl<T: Copy> PinnedHostBuf<T> {
/// Allocate `len` elements of pinned host memory.
///
/// # Safety
/// Caller must ensure a CUDA context is active on the current thread.
unsafe fn new(len: usize) -> Result<Self, MLError> {
if len == 0 {
return Ok(Self { ptr: std::ptr::null_mut(), len: 0 });
}
let num_bytes = len * std::mem::size_of::<T>();
// CU_MEMHOSTALLOC_PORTABLE: accessible from any CUDA context
let flags = cudarc::driver::sys::CU_MEMHOSTALLOC_PORTABLE;
let host_ptr = cudarc::driver::result::malloc_host(num_bytes, flags)
.map_err(|e| MLError::ModelError(format!(
"pinned host alloc ({len} x {} bytes): {e}",
std::mem::size_of::<T>(),
)))?
as *mut T;
// Zero-init for safety
std::ptr::write_bytes(host_ptr, 0, len);
Ok(Self { ptr: host_ptr, len })
}
/// Get a mutable slice view of the pinned buffer.
fn as_mut_slice(&mut self) -> &mut [T] {
if self.ptr.is_null() || self.len == 0 {
return &mut [];
}
// Safety: ptr is valid for self.len elements, allocated by malloc_host.
unsafe { std::slice::from_raw_parts_mut(self.ptr, self.len) }
}
/// Get an immutable slice view of the pinned buffer.
fn as_slice(&self) -> &[T] {
if self.ptr.is_null() || self.len == 0 {
return &[];
}
// Safety: ptr is valid for self.len elements, allocated by malloc_host.
unsafe { std::slice::from_raw_parts(self.ptr, self.len) }
}
/// Convert the pinned buffer contents into an owned Vec.
fn to_vec(&self) -> Vec<T> {
self.as_slice().to_vec()
}
}
impl<T: Copy> Drop for PinnedHostBuf<T> {
fn drop(&mut self) {
if !self.ptr.is_null() {
// Safety: ptr was returned by malloc_host and is non-null.
unsafe {
#[allow(clippy::let_underscore_must_use)]
let _ = cudarc::driver::result::free_host(self.ptr as *mut c_void);
}
}
}
}
// ---------------------------------------------------------------------------
// Config
// ---------------------------------------------------------------------------
@@ -320,6 +400,16 @@ pub struct GpuExperienceCollector {
// GPU-resident curiosity forward model trainer (None if curiosity disabled)
curiosity_trainer: Option<GpuCuriosityTrainer>,
// Pre-allocated pinned host buffers for DtoH transfers.
// Pinned memory enables true async DMA overlap (~2x PCIe throughput
// vs pageable Vec destinations that force CUDA to stage through a bounce buffer).
pinned_states: PinnedHostBuf<f32>,
pinned_actions: PinnedHostBuf<i32>,
pinned_rewards: PinnedHostBuf<f32>,
pinned_dones: PinnedHostBuf<i32>,
pinned_target_q: PinnedHostBuf<f32>,
pinned_td_errors: PinnedHostBuf<f32>,
}
impl GpuExperienceCollector {
@@ -858,6 +948,20 @@ impl GpuExperienceCollector {
None
};
// Pre-allocate pinned host buffers for DtoH transfers.
// These are reused across kernel launches to avoid per-call pinned allocation.
// Safety: CUDA context is active (we just loaded the module on this device).
let pinned_states = unsafe { PinnedHostBuf::<f32>::new(total_output * state_dim)? };
let pinned_actions = unsafe { PinnedHostBuf::<i32>::new(total_output)? };
let pinned_rewards = unsafe { PinnedHostBuf::<f32>::new(total_output)? };
let pinned_dones = unsafe { PinnedHostBuf::<i32>::new(total_output)? };
let pinned_target_q = unsafe { PinnedHostBuf::<f32>::new(total_output)? };
let pinned_td_errors = unsafe { PinnedHostBuf::<f32>::new(total_output)? };
info!(
pinned_bytes = (total_output * state_dim * 4 + total_output * 4 * 5),
"Pinned host buffers allocated for DtoH transfers"
);
// Pre-allocate OFI placeholder before stream is moved into struct
let ofi_placeholder = stream.alloc_zeros::<f32>(1)
.map_err(|e| MLError::ModelError(format!("Failed to alloc OFI placeholder: {e}")))
@@ -900,6 +1004,12 @@ impl GpuExperienceCollector {
ofi_dim,
reset_flags: 0,
curiosity_trainer,
pinned_states,
pinned_actions,
pinned_rewards,
pinned_dones,
pinned_target_q,
pinned_td_errors,
})
}
@@ -927,10 +1037,9 @@ impl GpuExperienceCollector {
self.launch_kernel(market_features_buf, targets_buf, episode_starts, config)?;
// ---- Step 6: Download output buffers (only N*L elements) ----
// Note: cudarc's memcpy_dtoh internally calls cuMemcpyDtoHAsync, but with pageable
// Vec<T> destinations CUDA blocks until the copy completes (no true DMA overlap).
// True async batching requires PinnedHostSlice destinations — deferred until profiling
// shows download latency dominates (currently ~2% of collect_experiences walltime).
// Uses pre-allocated pinned host memory for true async DMA overlap.
// Pinned destinations let cuMemcpyDtoHAsync proceed without blocking
// on a pageable bounce buffer (~2x PCIe throughput improvement).
let total = n_episodes * timesteps;
let states_view = self.states_out.slice(..total * self.state_dim);
@@ -940,32 +1049,55 @@ impl GpuExperienceCollector {
let target_q_view = self.target_q_out.slice(..total);
let td_error_view = self.td_error_out.slice(..total);
let mut states = vec![0.0_f32; total * self.state_dim];
let mut actions = vec![0_i32; total];
let mut rewards = vec![0.0_f32; total];
let mut done_flags = vec![0_i32; total];
let mut target_q_values = vec![0.0_f32; total];
let mut td_errors = vec![0.0_f32; total];
// Use pinned host buffers (sliced to actual transfer size)
let states_dst = self.pinned_states.as_mut_slice()
.get_mut(..total * self.state_dim)
.ok_or_else(|| MLError::ModelError(format!(
"pinned_states too small: need {} have {}", total * self.state_dim, self.pinned_states.len
)))?;
let actions_dst = self.pinned_actions.as_mut_slice()
.get_mut(..total)
.ok_or_else(|| MLError::ModelError("pinned_actions too small".to_owned()))?;
let rewards_dst = self.pinned_rewards.as_mut_slice()
.get_mut(..total)
.ok_or_else(|| MLError::ModelError("pinned_rewards too small".to_owned()))?;
let done_dst = self.pinned_dones.as_mut_slice()
.get_mut(..total)
.ok_or_else(|| MLError::ModelError("pinned_dones too small".to_owned()))?;
let target_q_dst = self.pinned_target_q.as_mut_slice()
.get_mut(..total)
.ok_or_else(|| MLError::ModelError("pinned_target_q too small".to_owned()))?;
let td_errors_dst = self.pinned_td_errors.as_mut_slice()
.get_mut(..total)
.ok_or_else(|| MLError::ModelError("pinned_td_errors too small".to_owned()))?;
self.stream
.memcpy_dtoh(&states_view, &mut states)
.memcpy_dtoh(&states_view, states_dst)
.map_err(|e| MLError::ModelError(format!("Download states failed: {e}")))?;
self.stream
.memcpy_dtoh(&actions_view, &mut actions)
.memcpy_dtoh(&actions_view, actions_dst)
.map_err(|e| MLError::ModelError(format!("Download actions failed: {e}")))?;
self.stream
.memcpy_dtoh(&rewards_view, &mut rewards)
.memcpy_dtoh(&rewards_view, rewards_dst)
.map_err(|e| MLError::ModelError(format!("Download rewards failed: {e}")))?;
self.stream
.memcpy_dtoh(&done_view, &mut done_flags)
.memcpy_dtoh(&done_view, done_dst)
.map_err(|e| MLError::ModelError(format!("Download done_flags failed: {e}")))?;
self.stream
.memcpy_dtoh(&target_q_view, &mut target_q_values)
.memcpy_dtoh(&target_q_view, target_q_dst)
.map_err(|e| MLError::ModelError(format!("Download target_q failed: {e}")))?;
self.stream
.memcpy_dtoh(&td_error_view, &mut td_errors)
.memcpy_dtoh(&td_error_view, td_errors_dst)
.map_err(|e| MLError::ModelError(format!("Download td_errors failed: {e}")))?;
// Copy from pinned buffers into owned Vecs for the output batch.
let states = states_dst.to_vec();
let actions = actions_dst.to_vec();
let rewards = rewards_dst.to_vec();
let done_flags = done_dst.to_vec();
let target_q_values = target_q_dst.to_vec();
let td_errors = td_errors_dst.to_vec();
debug!(
n_episodes,
timesteps,