208cee4cf5
- Task 9: memcpy_stod_inplace → memcpy_htod (cudarc 0.17 API) - Task 9: Fix transposed actions_history layout — accumulate CPU-side in window-major [window][step] layout, upload once before metrics kernel - Task 9: Replace non-existent Tensor::from_raw_buffer with download- and-reupload pattern (temporary, replaced by Task 11 GPU gather kernel) - Task 10: DqnOptimizer → DQNTrainer (hyperopt adapter) (actual struct name) - Task 10: internal_trainer type → &InternalDQNTrainer (avoids name conflict with adapter's own DQNTrainer alias) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2250 lines
84 KiB
Markdown
2250 lines
84 KiB
Markdown
# CUDA Backtest & GPU-Resident Training — Implementation Plan
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> **For agentic workers:** REQUIRED: Use superpowers:subagent-driven-development (if subagents available) or superpowers:executing-plans to implement this plan. Steps use checkbox (`- [ ]`) syntax for tracking.
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**Goal:** Eliminate all GPU→CPU roundtrips from DQN training and port walk-forward backtesting to CUDA.
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**Architecture:** Three phases — (1) seal remaining GPU→CPU gaps in the training loop, (2) vectorized CUDA backtest kernel for hyperopt evaluation, (3) general-purpose GPU backtester for standalone evaluation.
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**Tech Stack:** Rust, cudarc 0.17 (via `candle_core::cuda_backend::cudarc`), NVRTC, Candle tensors, CUDA C kernels
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**Spec:** `docs/plans/2026-03-11-cuda-backtest-gpu-residency.md`
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---
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## Chunk 1: Phase 1 — Seal the Training Loop
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### Task 1: GPU-persistent epoch-boundary state on GpuExperienceCollector
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Eliminate `cudaStreamSynchronize` between epochs by keeping vol EMA, portfolio, and DSR state on GPU.
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**Files:**
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- Modify: `crates/ml/src/cuda_pipeline/gpu_experience_collector.rs:256-304` (struct fields)
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- Modify: `crates/ml/src/cuda_pipeline/dqn_experience_kernel.cu` (kernel read/write epoch state)
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- Test: `SQLX_OFFLINE=true cargo test -p ml --lib -- gpu_experience_collector --no-capture`
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- [ ] **Step 1: Add persistent epoch state buffers to GpuExperienceCollector**
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In `crates/ml/src/cuda_pipeline/gpu_experience_collector.rs`, add fields after line 304 (`td_error_out`):
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```rust
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// Persistent epoch state — survives across kernel launches.
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// Eliminates CPU↔GPU sync at epoch boundaries.
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// Layout: [vol_ema, median_vol, portfolio_value, portfolio_position,
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// portfolio_cash, dsr_mean, dsr_var, step_count]
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epoch_state: CudaSlice<f32>, // [8]
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/// Bitfield: bit 0 = reset portfolio, bit 1 = reset DSR, bit 2 = reset vol EMA
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reset_flags: u32,
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```
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- [ ] **Step 2: Allocate epoch_state buffer in GpuExperienceCollector::new()**
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Find the allocation section in `new()` (after `rng_states` allocation) and add:
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```rust
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// Persistent epoch state — initialized to defaults, updated by kernel
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let epoch_state_init: Vec<f32> = vec![
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0.01, // vol_ema (initial EMA estimate)
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0.01, // median_vol
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initial_capital, // portfolio_value
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0.0, // portfolio_position
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initial_capital, // portfolio_cash
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0.0, // dsr_mean
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1.0, // dsr_var (avoid div-by-zero)
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0.0, // step_count
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];
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let epoch_state = stream.memcpy_stod(&epoch_state_init)
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.map_err(|e| MLError::ModelError(format!("epoch_state alloc: {e}")))?;
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```
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- [ ] **Step 3: Add kernel argument for epoch_state in collect_experiences / collect_experiences_gpu**
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In the kernel launch argument list (both methods), add `epoch_state` and `reset_flags` as additional kernel args:
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```rust
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builder
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// ... existing args ...
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.arg(&self.epoch_state)
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.arg(&(self.reset_flags as i32))
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```
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- [ ] **Step 4: Add epoch state read/write to dqn_experience_kernel.cu**
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At kernel entry, thread 0 of block 0 applies reset flags directly to global memory.
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All threads then read epoch state from global memory (8 floats, L1-cached).
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**IMPORTANT:** Do NOT use shared memory for epoch state — shared memory is per-block,
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so block N-1 cannot see block 0's shmem. Global memory with `__threadfence()` is correct.
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```c
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// In dqn_experience_kernel.cu, inside the kernel function
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// Epoch state lives in global memory — visible to all blocks.
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if (threadIdx.x == 0 && blockIdx.x == 0) {
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// Apply reset flags (only once, before any block reads)
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if (reset_flags & 1) { // reset portfolio
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epoch_state[2] = initial_capital;
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epoch_state[3] = 0.0f;
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epoch_state[4] = initial_capital;
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}
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if (reset_flags & 2) { // reset DSR
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epoch_state[5] = 0.0f;
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epoch_state[6] = 1.0f;
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}
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if (reset_flags & 4) { // reset vol EMA
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epoch_state[0] = 0.01f;
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epoch_state[1] = 0.01f;
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}
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__threadfence(); // Ensure all blocks see updated epoch_state
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}
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// Grid-level sync: launch reset as separate 1-block pre-kernel if needed,
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// or use cooperative groups. Simplest: split into two kernel launches —
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// reset_epoch_state_kernel (1 block, 1 thread) + main experience kernel.
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// The reset kernel is a no-op when reset_flags == 0.
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// All threads read epoch state from global memory (8 floats, L1-cached)
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float vol_ema = epoch_state[0];
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float median_vol = epoch_state[1];
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float port_value = epoch_state[2];
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float port_pos = epoch_state[3];
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float port_cash = epoch_state[4];
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float dsr_mean = epoch_state[5];
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float dsr_var = epoch_state[6];
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float step_count = epoch_state[7];
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```
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At kernel exit, last thread of last active episode writes final state back:
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```c
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// Thread 0 of the last block writes updated epoch state back.
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// Only vol_ema, dsr_mean, dsr_var, step_count are updated by kernel logic.
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// Portfolio state is per-episode, not per-epoch — it stays in episode buffers.
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if (threadIdx.x == 0 && blockIdx.x == gridDim.x - 1) {
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epoch_state[0] = updated_vol_ema;
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epoch_state[1] = updated_median_vol;
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epoch_state[5] = updated_dsr_mean;
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epoch_state[6] = updated_dsr_var;
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epoch_state[7] = step_count + (float)steps_this_launch;
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}
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```
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- [ ] **Step 5: Add public method to set reset_flags**
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```rust
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impl GpuExperienceCollector {
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/// Set epoch-boundary reset flags for next kernel launch.
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/// Bit 0: reset portfolio to initial_capital. Bit 1: reset DSR normalizer.
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/// Bit 2: reset vol EMA.
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pub fn set_reset_flags(&mut self, flags: u32) {
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self.reset_flags = flags;
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}
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/// Clear reset flags (called automatically after kernel launch).
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pub fn clear_reset_flags(&mut self) {
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self.reset_flags = 0;
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}
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}
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```
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- [ ] **Step 6: Run existing GPU experience collector tests**
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Run: `SQLX_OFFLINE=true cargo test -p ml --lib -- gpu_experience --no-capture`
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Expected: PASS (existing tests still work, new fields are additive)
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- [ ] **Step 7: Commit**
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```bash
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git add crates/ml/src/cuda_pipeline/gpu_experience_collector.rs crates/ml/src/cuda_pipeline/dqn_experience_kernel.cu
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git commit -m "feat(cuda): add GPU-persistent epoch state to GpuExperienceCollector
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Eliminates cudaStreamSynchronize between epochs by keeping vol EMA,
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portfolio state, and DSR normalizer in persistent CudaSlice buffers.
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Kernel reads initial state at launch, writes final state at exit."
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```
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---
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### Task 2: Monitoring reduction kernel — batch stats per epoch instead of per-launch
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Replace per-kernel-launch monitoring downloads with a single epoch-end reduction.
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**Files:**
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- Create: `crates/ml/src/cuda_pipeline/monitoring_kernel.cu`
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- Create: `crates/ml/src/cuda_pipeline/gpu_monitoring.rs`
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- Modify: `crates/ml/src/cuda_pipeline/mod.rs:20-29` (add module)
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- Modify: `crates/ml/src/cuda_pipeline/gpu_experience_collector.rs:225-244` (GpuExperienceBatch)
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- Test: `SQLX_OFFLINE=true cargo test -p ml --lib -- gpu_monitoring --no-capture`
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- [ ] **Step 1: Write the monitoring reduction kernel**
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Create `crates/ml/src/cuda_pipeline/monitoring_kernel.cu`:
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```c
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// Custom atomic min/max for float (must be defined BEFORE the kernel).
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__device__ float atomicMin_float(float* addr, float val) {
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int* addr_as_int = (int*)addr;
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int old = *addr_as_int, assumed;
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do {
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assumed = old;
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old = atomicCAS(addr_as_int, assumed,
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__float_as_int(fminf(val, __int_as_float(assumed))));
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} while (assumed != old);
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return __int_as_float(old);
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}
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__device__ float atomicMax_float(float* addr, float val) {
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int* addr_as_int = (int*)addr;
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int old = *addr_as_int, assumed;
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do {
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assumed = old;
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old = atomicCAS(addr_as_int, assumed,
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__float_as_int(fmaxf(val, __int_as_float(assumed))));
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} while (assumed != old);
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return __int_as_float(old);
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}
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// Reduce per-experience rewards and actions into a compact summary.
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// One block, parallel reduction across N elements.
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extern "C" __global__ void monitoring_reduce(
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const float* __restrict__ rewards, // [N]
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const int* __restrict__ actions, // [N]
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float* summary, // [12]: mean, std, min, max, sharpe, counts[5], total, _pad
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int N
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) {
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__shared__ float s_sum;
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__shared__ float s_sq_sum;
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__shared__ float s_min;
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__shared__ float s_max;
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__shared__ int s_counts[5];
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int tid = threadIdx.x;
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int stride = blockDim.x;
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// Init shared memory
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if (tid == 0) {
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s_sum = 0.0f; s_sq_sum = 0.0f;
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s_min = 1e30f; s_max = -1e30f;
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for (int i = 0; i < 5; i++) s_counts[i] = 0;
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}
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__syncthreads();
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// Thread-local accumulators
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float local_sum = 0.0f, local_sq = 0.0f;
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float local_min = 1e30f, local_max = -1e30f;
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int local_counts[5] = {0, 0, 0, 0, 0};
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for (int i = tid; i < N; i += stride) {
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float r = rewards[i];
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local_sum += r;
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local_sq += r * r;
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local_min = fminf(local_min, r);
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local_max = fmaxf(local_max, r);
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int a = actions[i];
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if (a >= 0 && a < 5) local_counts[a]++;
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}
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// Warp reduction then atomic to shared
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atomicAdd(&s_sum, local_sum);
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atomicAdd(&s_sq_sum, local_sq);
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atomicMin_float(&s_min, local_min); // Custom atomicMin for float
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atomicMax_float(&s_max, local_max);
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for (int i = 0; i < 5; i++) atomicAdd(&s_counts[i], local_counts[i]);
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__syncthreads();
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// Thread 0 writes summary
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if (tid == 0) {
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float mean = s_sum / (float)N;
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float var = s_sq_sum / (float)N - mean * mean;
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float std = sqrtf(fmaxf(var, 0.0f));
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summary[0] = mean;
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summary[1] = std;
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summary[2] = s_min;
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summary[3] = s_max;
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summary[4] = (std > 1e-8f) ? mean / std : 0.0f; // Sharpe estimate
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for (int i = 0; i < 5; i++) summary[5 + i] = (float)s_counts[i];
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summary[10] = (float)N;
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summary[11] = 0.0f; // padding
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}
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}
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```
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- [ ] **Step 2: Write the Rust wrapper**
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Create `crates/ml/src/cuda_pipeline/gpu_monitoring.rs`:
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```rust
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#![allow(unsafe_code)]
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//! GPU monitoring reduction — aggregates per-experience rewards/actions
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//! into a compact summary without downloading full arrays.
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use std::sync::Arc;
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use candle_core::cuda_backend::cudarc;
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use cudarc::driver::{CudaFunction, CudaSlice, CudaStream, LaunchConfig, PushKernelArg};
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use cudarc::nvrtc::Ptx;
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use crate::MLError;
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/// Compact monitoring summary from GPU reduction (48 bytes).
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#[derive(Debug, Clone, Default)]
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pub struct MonitoringSummary {
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pub mean_reward: f32,
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pub reward_std: f32,
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pub min_reward: f32,
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pub max_reward: f32,
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pub sharpe_estimate: f32,
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pub action_counts: [usize; 5],
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pub total_experiences: usize,
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}
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/// GPU monitoring reducer.
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#[allow(missing_debug_implementations)]
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pub struct GpuMonitoringReducer {
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stream: Arc<CudaStream>,
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kernel_func: CudaFunction,
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summary_buf: CudaSlice<f32>, // [12]
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}
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impl GpuMonitoringReducer {
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pub fn new(stream: &Arc<CudaStream>) -> Result<Self, MLError> {
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let context = stream.context();
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let kernel_src = include_str!("monitoring_kernel.cu");
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let ptx: Ptx = cudarc::nvrtc::compile_ptx(kernel_src)
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.map_err(|e| MLError::ModelError(format!("monitoring kernel compile: {e}")))?;
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let module = context.load_module(ptx)
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.map_err(|e| MLError::ModelError(format!("monitoring module load: {e}")))?;
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let kernel_func = module.load_function("monitoring_reduce")
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.map_err(|e| MLError::ModelError(format!("monitoring_reduce load: {e}")))?;
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let summary_buf = stream.alloc_zeros::<f32>(12)
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.map_err(|e| MLError::ModelError(format!("monitoring summary alloc: {e}")))?;
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Ok(Self { stream: Arc::clone(stream), kernel_func, summary_buf })
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}
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/// Launch reduction over rewards/actions buffers already on GPU.
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/// Does NOT synchronize — caller must sync before reading result.
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pub fn reduce(
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&mut self,
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rewards: &CudaSlice<f32>,
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actions: &CudaSlice<i32>,
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n: usize,
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) -> Result<(), MLError> {
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let config = LaunchConfig {
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grid_dim: (1, 1, 1),
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block_dim: (256, 1, 1),
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shared_mem_bytes: 0,
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};
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unsafe {
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self.stream
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.launch_builder(&self.kernel_func)
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.arg(rewards)
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.arg(actions)
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.arg(&self.summary_buf)
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.arg(&(n as i32))
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.launch(config)
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.map_err(|e| MLError::ModelError(format!("monitoring_reduce launch: {e}")))?;
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}
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Ok(())
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}
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/// Download summary from GPU (single 48-byte transfer).
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pub fn download_summary(&self) -> Result<MonitoringSummary, MLError> {
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let mut raw = vec![0.0_f32; 12];
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self.stream.memcpy_dtoh(&self.summary_buf, &mut raw)
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.map_err(|e| MLError::ModelError(format!("monitoring download: {e}")))?;
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Ok(MonitoringSummary {
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mean_reward: raw[0],
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reward_std: raw[1],
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min_reward: raw[2],
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max_reward: raw[3],
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sharpe_estimate: raw[4],
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action_counts: [
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raw[5] as usize, raw[6] as usize, raw[7] as usize,
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raw[8] as usize, raw[9] as usize,
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],
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total_experiences: raw[10] as usize,
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})
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}
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}
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```
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- [ ] **Step 3: Register module in mod.rs**
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In `crates/ml/src/cuda_pipeline/mod.rs`, after line 28 (`gpu_training_guard`):
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```rust
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#[cfg(feature = "cuda")]
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pub mod gpu_monitoring;
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```
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- [ ] **Step 4: Write unit test**
|
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|
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Add to `crates/ml/src/cuda_pipeline/gpu_monitoring.rs`:
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|
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```rust
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#[cfg(test)]
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mod tests {
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use super::*;
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|
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#[test]
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fn test_monitoring_summary_default() {
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let s = MonitoringSummary::default();
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assert_eq!(s.total_experiences, 0);
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assert_eq!(s.action_counts, [0; 5]);
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}
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}
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```
|
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|
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- [ ] **Step 5: Run test**
|
||
|
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Run: `SQLX_OFFLINE=true cargo test -p ml --lib -- gpu_monitoring --no-capture`
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Expected: PASS
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|
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- [ ] **Step 6: Commit**
|
||
|
||
```bash
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git add crates/ml/src/cuda_pipeline/monitoring_kernel.cu crates/ml/src/cuda_pipeline/gpu_monitoring.rs crates/ml/src/cuda_pipeline/mod.rs
|
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git commit -m "feat(cuda): add monitoring reduction kernel
|
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|
||
Replaces per-launch rewards/actions download with single epoch-end
|
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reduction. monitoring_reduce kernel computes mean, std, min, max,
|
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Sharpe estimate, and per-action counts via parallel reduction.
|
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Single 48-byte download instead of N*8 bytes per kernel launch."
|
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```
|
||
|
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---
|
||
|
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### Task 3: Wire monitoring reducer into trainer, remove per-launch downloads
|
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|
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Replace `rewards_cpu`/`actions_cpu` downloads in `collect_experiences_gpu` with deferred reduction.
|
||
|
||
**Files:**
|
||
- Modify: `crates/ml/src/cuda_pipeline/gpu_experience_collector.rs:772-858` (collect_experiences_gpu)
|
||
- Modify: `crates/ml/src/trainers/dqn/trainer.rs:2208-2242` (GPU experience collection path)
|
||
- Modify: `crates/ml/src/trainers/dqn/trainer.rs:3300-3340` (epoch-end monitoring)
|
||
- Test: `SQLX_OFFLINE=true cargo test -p ml --lib -- dqn_trainer --no-capture`
|
||
|
||
- [ ] **Step 1: Add GpuMonitoringReducer field to DQNTrainer**
|
||
|
||
In `crates/ml/src/trainers/dqn/trainer.rs`, find the `DQNTrainer` struct and add after `training_guard`:
|
||
|
||
```rust
|
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/// GPU monitoring reducer — accumulates reward/action stats across kernel launches
|
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#[cfg(feature = "cuda")]
|
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gpu_monitoring: Option<crate::cuda_pipeline::gpu_monitoring::GpuMonitoringReducer>,
|
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```
|
||
|
||
Initialize as `None` in `DQNTrainer::new()`.
|
||
|
||
- [ ] **Step 2: Add public getters for GPU reward/action buffers**
|
||
|
||
In `gpu_experience_collector.rs`, add accessor methods (the underlying fields are private):
|
||
|
||
```rust
|
||
impl GpuExperienceCollector {
|
||
/// GPU-resident rewards buffer for monitoring reducer (no CPU download).
|
||
pub fn rewards_gpu(&self) -> &CudaSlice<f32> { &self.rewards_out }
|
||
/// GPU-resident actions buffer for monitoring reducer (no CPU download).
|
||
pub fn actions_gpu(&self) -> &CudaSlice<i32> { &self.actions_out }
|
||
}
|
||
```
|
||
|
||
- [ ] **Step 2b: Remove rewards_cpu/actions_cpu from GpuExperienceBatch**
|
||
|
||
In `gpu_experience_collector.rs:225-244`, keep the GPU tensor fields but remove:
|
||
|
||
```rust
|
||
// REMOVE these two fields:
|
||
// pub rewards_cpu: Vec<f32>,
|
||
// pub actions_cpu: Vec<i32>,
|
||
```
|
||
|
||
And remove the `memcpy_dtoh` calls at lines 820-823 in `collect_experiences_gpu()`.
|
||
Update all downstream consumers that unpack these fields — search for `rewards_cpu`
|
||
and `actions_cpu` in `trainer.rs` and replace with the monitoring reducer path.
|
||
|
||
- [ ] **Step 3: Update trainer GPU collection path**
|
||
|
||
In `trainer.rs:2208-2242`, replace the monitoring loop with monitoring reducer call:
|
||
|
||
```rust
|
||
if use_gpu_per {
|
||
match collector.collect_experiences_gpu(
|
||
features_buf, targets_buf, &episode_starts, &config, &self.device,
|
||
) {
|
||
Ok(gpu_batch) => {
|
||
let count = gpu_batch.n_episodes * gpu_batch.timesteps;
|
||
info!("GPU collected {} experiences (zero-roundtrip)", count);
|
||
|
||
// Deferred monitoring: reduce on GPU, download at epoch end
|
||
#[cfg(feature = "cuda")]
|
||
if let Some(ref mut mon) = self.gpu_monitoring {
|
||
let _ = mon.reduce(collector.rewards_gpu(), collector.actions_gpu(), count);
|
||
}
|
||
|
||
if count > 0 {
|
||
let agent = self.agent.read().await;
|
||
agent.insert_batch_tensors(
|
||
&gpu_batch.states, &gpu_batch.next_states,
|
||
&gpu_batch.actions, &gpu_batch.rewards, &gpu_batch.dones,
|
||
).map_err(|e| anyhow::anyhow!("GPU PER insert_batch: {e}"))?;
|
||
}
|
||
true
|
||
}
|
||
Err(e) => { /* existing fallback */ false }
|
||
}
|
||
}
|
||
```
|
||
|
||
- [ ] **Step 4: Add epoch-end monitoring summary download**
|
||
|
||
In the epoch-end section of `train_epoch()` (around line 3300), add:
|
||
|
||
```rust
|
||
// Epoch-end: download monitoring summary (48 bytes, one transfer)
|
||
#[cfg(feature = "cuda")]
|
||
if let Some(ref mon) = self.gpu_monitoring {
|
||
if let Ok(summary) = mon.download_summary() {
|
||
// Use GPU-computed Sharpe estimate directly — do NOT push mean_reward
|
||
// N times into pnl_history (that would give zero std and infinite/NaN Sharpe).
|
||
// The MonitoringSummary already has mean, std, and sharpe_estimate from
|
||
// the full reward distribution computed on GPU.
|
||
info!(
|
||
"GPU epoch summary: mean_reward={:.6}, std={:.6}, sharpe={:.3}, actions={:?}",
|
||
summary.mean_reward, summary.reward_std, summary.sharpe_estimate,
|
||
summary.action_counts
|
||
);
|
||
// Update epoch metrics tracking (self.epoch_metrics is the existing monitoring struct)
|
||
self.epoch_metrics.gpu_mean_reward = Some(summary.mean_reward as f64);
|
||
self.epoch_metrics.gpu_sharpe = Some(summary.sharpe_estimate as f64);
|
||
self.epoch_metrics.action_distribution = summary.action_counts;
|
||
}
|
||
}
|
||
```
|
||
|
||
Note: The `epoch_metrics` fields above are new fields added to whatever monitoring struct the
|
||
trainer uses. Implementer should search for the existing epoch-end monitoring code and add these
|
||
fields alongside the existing ones.
|
||
|
||
- [ ] **Step 5: Run tests**
|
||
|
||
Run: `SQLX_OFFLINE=true cargo test -p ml --lib -- dqn --no-capture 2>&1 | tail -5`
|
||
Expected: all existing DQN tests PASS
|
||
|
||
- [ ] **Step 6: Commit**
|
||
|
||
```bash
|
||
git add crates/ml/src/cuda_pipeline/gpu_experience_collector.rs crates/ml/src/trainers/dqn/trainer.rs
|
||
git commit -m "perf(dqn): replace per-launch monitoring download with epoch-end GPU reduction
|
||
|
||
Eliminates N*8 bytes of memcpy_dtoh per experience kernel launch.
|
||
MonitoringReducer accumulates stats on GPU, single 48-byte download
|
||
at epoch boundary. Zero cudaStreamSynchronize during experience collection."
|
||
```
|
||
|
||
---
|
||
|
||
### Task 4: Eliminate to_scalar readbacks from train_step_single_batch
|
||
|
||
Make the GPU training guard the exclusive path for loss/grad-norm on CUDA.
|
||
|
||
**Files:**
|
||
- Modify: `crates/ml/src/trainers/dqn/trainer.rs:4703-4852` (train_step_single_batch)
|
||
- Test: `SQLX_OFFLINE=true cargo test -p ml --lib -- train_step --no-capture`
|
||
|
||
- [ ] **Step 1: Remove CPU fallback from train_step_single_batch**
|
||
|
||
In `trainer.rs:4783-4818` (the `else` branch of `if let Some(ref mut guard) = self.training_guard`):
|
||
|
||
Replace the CPU fallback with a hard requirement when CUDA:
|
||
|
||
```rust
|
||
} else {
|
||
// CUDA build without training guard should not happen — init is lazy,
|
||
// but if it failed, we must fall back to single batched readback.
|
||
let stacked = candle_core::Tensor::cat(
|
||
&[&gpu_result.loss_gpu.unsqueeze(0)?, &gpu_result.grad_norm_gpu.unsqueeze(0)?], 0)?;
|
||
let readback = stacked.to_vec1::<f32>()?;
|
||
let loss_f32 = readback.first().copied().unwrap_or(0.0);
|
||
let grad_norm_f32 = readback.get(1).copied().unwrap_or(0.0);
|
||
agent.log_diagnostics(grad_norm_f32)?;
|
||
let loss_clipped_val = loss_f32.min(1e6_f32);
|
||
(loss_clipped_val as f64, grad_norm_f32 as f64)
|
||
}
|
||
```
|
||
|
||
Note: This preserves the fallback but still reduces to a single batched readback (2 floats) instead of separate `to_scalar` calls.
|
||
|
||
- [ ] **Step 2: Add Q-value accumulator methods to GpuTrainingGuard**
|
||
|
||
In `gpu_training_guard.rs`, add accumulation support. Uses the same mapped-pinned-memory
|
||
pattern as the existing loss/grad accumulator:
|
||
|
||
```rust
|
||
impl GpuTrainingGuard {
|
||
/// Accumulate a Q-value mean on GPU (zero sync). Uses running Welford accumulator.
|
||
pub fn accumulate_q_value(&mut self, avg_q_tensor: &Tensor) -> Result<(), MLError> {
|
||
// Stack with existing accumulator tensor, run Candle add on device
|
||
self.q_count += 1;
|
||
let delta = avg_q_tensor.sub(&self.q_mean_tensor)?;
|
||
let count_f = Tensor::new(self.q_count as f32, avg_q_tensor.device())?;
|
||
self.q_mean_tensor = self.q_mean_tensor.add(&delta.div(&count_f)?)?;
|
||
Ok(())
|
||
}
|
||
|
||
/// Read accumulated Q-value mean at epoch end (single scalar download).
|
||
pub fn read_q_accumulator(&self) -> Result<f64, MLError> {
|
||
if self.q_count == 0 { return Ok(0.0); }
|
||
Ok(self.q_mean_tensor.to_scalar::<f32>()? as f64)
|
||
}
|
||
|
||
/// Reset Q-value accumulator for new epoch.
|
||
pub fn reset_q_accumulator(&mut self, device: &Device) -> Result<(), MLError> {
|
||
self.q_count = 0;
|
||
self.q_mean_tensor = Tensor::zeros((), DType::F32, device)?;
|
||
Ok(())
|
||
}
|
||
}
|
||
```
|
||
|
||
Add fields `q_count: usize` and `q_mean_tensor: Tensor` to the struct, initialized to 0 and
|
||
`Tensor::zeros((), DType::F32, device)` respectively.
|
||
|
||
- [ ] **Step 3: Eliminate Q-value to_scalar at line 5419**
|
||
|
||
In the `estimate_avg_q_value` method (around line 5416-5421), the `mean_all().to_scalar::<f32>()` is only called every 50 steps. Replace with GPU guard accumulator:
|
||
|
||
```rust
|
||
// GPU path: accumulate Q-value in training guard (zero sync)
|
||
#[cfg(feature = "cuda")]
|
||
if let Some(ref mut guard) = self.training_guard {
|
||
let avg_q_tensor = max_q_values.mean_all()?;
|
||
guard.accumulate_q_value(&avg_q_tensor)?;
|
||
// Read at epoch boundary via guard.read_q_accumulator()
|
||
return Ok(0.0); // Placeholder — real value read at epoch end
|
||
}
|
||
|
||
// CPU/non-CUDA fallback: single to_scalar
|
||
let avg_q = max_q_values.mean_all()?.to_scalar::<f32>()? as f64;
|
||
Ok(avg_q)
|
||
```
|
||
|
||
- [ ] **Step 4: Run tests**
|
||
|
||
Run: `SQLX_OFFLINE=true cargo test -p ml --lib -- train_step --no-capture`
|
||
Expected: PASS
|
||
|
||
- [ ] **Step 5: Commit**
|
||
|
||
```bash
|
||
git add crates/ml/src/trainers/dqn/trainer.rs crates/ml/src/cuda_pipeline/gpu_training_guard.rs
|
||
git commit -m "perf(dqn): eliminate per-step to_scalar readbacks from train_step
|
||
|
||
GPU training guard is now exclusive path for loss/grad-norm on CUDA.
|
||
Q-value estimation accumulates on GPU via Welford running mean, read
|
||
at epoch boundary. Reduces per-step cudaStreamSynchronize from 3 to 0."
|
||
```
|
||
|
||
---
|
||
|
||
### Task 5: Wire epoch-boundary state resets to GPU experience collector
|
||
|
||
Connect DQNTrainer epoch reset logic to `GpuExperienceCollector::set_reset_flags()`.
|
||
|
||
**Files:**
|
||
- Modify: `crates/ml/src/trainers/dqn/trainer.rs:1680-1700` (epoch boundary resets)
|
||
- Test: `SQLX_OFFLINE=true cargo test -p ml --lib -- dqn --no-capture`
|
||
|
||
- [ ] **Step 1: Replace CPU resets with GPU reset flags**
|
||
|
||
At the epoch boundary section (lines 1680-1696), add:
|
||
|
||
```rust
|
||
// GPU-persistent epoch state: set reset flags instead of CPU state mutation
|
||
#[cfg(feature = "cuda")]
|
||
if let Some(ref mut collector) = self.gpu_experience_collector {
|
||
let mut flags: u32 = 0;
|
||
if self.hyperparams.use_dsr {
|
||
flags |= 1; // reset portfolio
|
||
flags |= 2; // reset DSR normalizer
|
||
}
|
||
// Vol EMA: never reset between epochs (continuous tracking)
|
||
collector.set_reset_flags(flags);
|
||
}
|
||
```
|
||
|
||
- [ ] **Step 2: Run tests**
|
||
|
||
Run: `SQLX_OFFLINE=true cargo test -p ml --lib -- dqn --no-capture 2>&1 | tail -5`
|
||
Expected: PASS
|
||
|
||
- [ ] **Step 3: Commit**
|
||
|
||
```bash
|
||
git add crates/ml/src/trainers/dqn/trainer.rs
|
||
git commit -m "perf(dqn): wire epoch-boundary state resets to GPU experience collector
|
||
|
||
DSR portfolio reset and normalizer reset now happen via kernel flags
|
||
instead of CPU state mutation. Eliminates cudaStreamSynchronize at
|
||
epoch boundaries."
|
||
```
|
||
|
||
---
|
||
|
||
### Task 6: Epoch-end Q-value diagnostics — move to_vec2 to GPU kernel
|
||
|
||
Replace the CPU `to_vec2` readback in `compute_epoch_q_diagnostics` with GPU reduction.
|
||
|
||
**Files:**
|
||
- Modify: `crates/ml/src/trainers/dqn/trainer.rs:5435-5498` (compute_epoch_q_diagnostics)
|
||
- Modify: `crates/ml/src/cuda_pipeline/gpu_training_guard.rs` (add Q-diagnostics kernel)
|
||
- Test: `SQLX_OFFLINE=true cargo test -p ml --lib -- q_diagnostics --no-capture`
|
||
|
||
- [ ] **Step 1: Add Q-diagnostics method to GpuTrainingGuard**
|
||
|
||
In `gpu_training_guard.rs`, add a method that computes gap stats and per-action averages on GPU:
|
||
|
||
This is a free function (not on GpuTrainingGuard — it uses only Candle tensor ops,
|
||
no cudarc primitives or guard fields). Place it in the trainer or a utility module.
|
||
|
||
```rust
|
||
/// Compute Q-value gap and per-action averages on GPU.
|
||
/// Returns (mean_gap, min_gap, max_gap, per_action_avgs[5]).
|
||
/// Single 8-float readback at epoch end.
|
||
fn compute_q_diagnostics_gpu(
|
||
q_values: &Tensor, // [batch, 5]
|
||
) -> Result<((f64, f64, f64), [f64; 5]), MLError> {
|
||
// sort_last_dim returns (sorted_values, indices) — destructure the tuple
|
||
let (sorted, _indices) = q_values.sort_last_dim(true)?; // descending
|
||
let best = sorted.narrow(1, 0, 1)?;
|
||
let second = sorted.narrow(1, 1, 1)?;
|
||
let gaps = best.sub(&second)?;
|
||
|
||
// Batch all gap stats into a single tensor to minimize readbacks:
|
||
// [mean_gap, min_gap, max_gap] — one to_vec1 instead of three to_scalar
|
||
// Note: In candle-core (git 671de1d), min(D)/max(D) return Result<Tensor>,
|
||
// NOT Result<(Tensor, Tensor)>. Flatten first for scalar reduction.
|
||
let gaps_flat = gaps.flatten_all()?;
|
||
let mean_gap = gaps_flat.mean_all()?; // scalar tensor
|
||
let min_gap = gaps_flat.min(0)?; // scalar tensor
|
||
let max_gap = gaps_flat.max(0)?; // scalar tensor
|
||
let gap_stats = Tensor::cat(
|
||
&[&mean_gap.unsqueeze(0)?, &min_gap.unsqueeze(0)?, &max_gap.unsqueeze(0)?], 0
|
||
)?;
|
||
|
||
// Per-action means: mean along batch dim [5]
|
||
let per_action = q_values.mean(0)?;
|
||
|
||
// Single batched readback: [3 gap stats + 5 per-action means] = 8 floats
|
||
let combined = Tensor::cat(&[&gap_stats, &per_action], 0)?;
|
||
let vals = combined.to_vec1::<f32>()?;
|
||
|
||
let mean_g = vals.first().copied().unwrap_or(0.0) as f64;
|
||
let min_g = vals.get(1).copied().unwrap_or(0.0) as f64;
|
||
let max_g = vals.get(2).copied().unwrap_or(0.0) as f64;
|
||
let mut avgs = [0.0_f64; 5];
|
||
for (i, &v) in vals.iter().skip(3).enumerate().take(5) {
|
||
avgs[i] = v as f64;
|
||
}
|
||
|
||
Ok(((mean_g, min_g, max_g), avgs))
|
||
}
|
||
```
|
||
|
||
Single 8-float (32-byte) readback at epoch end — acceptable.
|
||
|
||
- [ ] **Step 2: Update compute_epoch_q_diagnostics to use GPU path**
|
||
|
||
In `trainer.rs:5435`, replace the method body:
|
||
|
||
```rust
|
||
async fn compute_epoch_q_diagnostics(&self) -> Option<((f64, f64, f64), [f64; 5])> {
|
||
// ... (keep existing batch sampling and forward pass logic) ...
|
||
|
||
// GPU path: compute diagnostics on-device (free function, no guard needed)
|
||
#[cfg(feature = "cuda")]
|
||
if self.device.is_cuda() {
|
||
return compute_q_diagnostics_gpu(&batch_q_values).ok();
|
||
}
|
||
|
||
// CPU fallback: existing to_vec2 path
|
||
let q_2d: Vec<Vec<f32>> = batch_q_values.to_vec2::<f32>().ok()?;
|
||
// ... (keep existing CPU computation) ...
|
||
}
|
||
```
|
||
|
||
- [ ] **Step 3: Run tests**
|
||
|
||
Run: `SQLX_OFFLINE=true cargo test -p ml --lib -- epoch_q --no-capture`
|
||
Expected: PASS
|
||
|
||
- [ ] **Step 4: Commit**
|
||
|
||
```bash
|
||
git add crates/ml/src/trainers/dqn/trainer.rs
|
||
git commit -m "perf(dqn): move epoch Q-value diagnostics to GPU reduction
|
||
|
||
compute_epoch_q_diagnostics now uses compute_q_diagnostics_gpu() free
|
||
function with Candle tensor ops. Batches gap stats + per-action means
|
||
into single 8-float readback instead of full N×5 to_vec2 download."
|
||
```
|
||
|
||
---
|
||
|
||
## Chunk 2: Phase 2 — Vectorized CUDA Backtest Kernel
|
||
|
||
### Task 7: Backtest environment step kernel
|
||
|
||
Core CUDA kernel that executes trade actions, updates portfolio state, and computes step rewards across parallel walk-forward windows.
|
||
|
||
**Files:**
|
||
- Create: `crates/ml/src/cuda_pipeline/backtest_env_kernel.cu`
|
||
- Test: Compilation test via NVRTC in Task 9
|
||
|
||
- [ ] **Step 1: Write backtest_env_kernel.cu**
|
||
|
||
Create `crates/ml/src/cuda_pipeline/backtest_env_kernel.cu`:
|
||
|
||
```c
|
||
// Vectorized backtest environment step kernel.
|
||
// One thread per walk-forward window. Each thread steps sequentially.
|
||
//
|
||
// Portfolio state layout per window [8 floats]:
|
||
// [0] value - current portfolio value
|
||
// [1] position - current position size (-1.0 to +1.0)
|
||
// [2] cash - cash balance
|
||
// [3] entry_price - entry price of current position (0 if flat)
|
||
// [4] max_equity - peak equity for drawdown tracking
|
||
// [5] step_pnl - PnL this step (for reward)
|
||
// [6] cum_return - cumulative log return
|
||
// [7] step_count - number of completed steps
|
||
|
||
#define PORTFOLIO_STATE_SIZE 8
|
||
|
||
extern "C" __global__ void backtest_env_step(
|
||
// Market data (read-only, uploaded once)
|
||
const float* __restrict__ prices, // [n_windows * max_len * 4] (OHLC)
|
||
const int* __restrict__ window_lens, // [n_windows]
|
||
|
||
// Actions from model for current step
|
||
const int* __restrict__ actions, // [n_windows] (0-4: Short100..Long100)
|
||
|
||
// Portfolio state (read-write, persistent across steps)
|
||
float* portfolio_state, // [n_windows * PORTFOLIO_STATE_SIZE]
|
||
|
||
// Step outputs
|
||
float* step_rewards, // [n_windows]
|
||
float* step_returns, // [n_windows * max_len] (accumulated)
|
||
int* done_flags, // [n_windows]
|
||
|
||
// Config
|
||
int n_windows,
|
||
int max_len,
|
||
float max_position,
|
||
float tx_cost_bps,
|
||
float spread_cost,
|
||
int current_step
|
||
) {
|
||
int w = blockIdx.x * blockDim.x + threadIdx.x;
|
||
if (w >= n_windows) return;
|
||
if (done_flags[w]) return;
|
||
|
||
int wlen = window_lens[w];
|
||
if (current_step >= wlen) {
|
||
done_flags[w] = 1;
|
||
return;
|
||
}
|
||
|
||
// Read current prices
|
||
int price_base = (w * max_len + current_step) * 4;
|
||
float open = prices[price_base + 0];
|
||
float high = prices[price_base + 1];
|
||
float low = prices[price_base + 2];
|
||
float close = prices[price_base + 3];
|
||
|
||
// Read portfolio state
|
||
int ps = w * PORTFOLIO_STATE_SIZE;
|
||
float value = portfolio_state[ps + 0];
|
||
float position = portfolio_state[ps + 1];
|
||
float cash = portfolio_state[ps + 2];
|
||
float entry_price = portfolio_state[ps + 3];
|
||
float max_equity = portfolio_state[ps + 4];
|
||
float cum_return = portfolio_state[ps + 6];
|
||
|
||
// Map action (0-4) to target exposure
|
||
float target_exposure;
|
||
switch (actions[w]) {
|
||
case 0: target_exposure = -1.0f; break; // Short100
|
||
case 1: target_exposure = -0.5f; break; // Short50
|
||
case 2: target_exposure = 0.0f; break; // Flat
|
||
case 3: target_exposure = 0.5f; break; // Long50
|
||
case 4: target_exposure = 1.0f; break; // Long100
|
||
default: target_exposure = 0.0f; break;
|
||
}
|
||
target_exposure *= max_position;
|
||
|
||
// Execute trade if position changes
|
||
float delta = target_exposure - position;
|
||
float trade_cost = 0.0f;
|
||
if (fabsf(delta) > 0.001f && close > 0.0f) {
|
||
trade_cost = fabsf(delta) * close * tx_cost_bps * 0.0001f
|
||
+ fabsf(delta) * spread_cost * 0.5f;
|
||
cash -= trade_cost;
|
||
|
||
// Mark-to-market old position
|
||
if (fabsf(position) > 0.001f && entry_price > 0.0f) {
|
||
float pnl = position * (close - entry_price);
|
||
cash += pnl;
|
||
}
|
||
|
||
position = target_exposure;
|
||
entry_price = close;
|
||
}
|
||
|
||
// Mark-to-market current position
|
||
float unrealized = 0.0f;
|
||
if (fabsf(position) > 0.001f && entry_price > 0.0f) {
|
||
unrealized = position * (close - entry_price);
|
||
}
|
||
float new_value = cash + unrealized;
|
||
|
||
// Step return
|
||
float step_ret = (value > 0.0f) ? (new_value - value) / value : 0.0f;
|
||
float new_cum_return = cum_return + step_ret;
|
||
|
||
// Update max equity for drawdown
|
||
float new_max = fmaxf(max_equity, new_value);
|
||
|
||
// Write portfolio state
|
||
portfolio_state[ps + 0] = new_value;
|
||
portfolio_state[ps + 1] = position;
|
||
portfolio_state[ps + 2] = cash;
|
||
portfolio_state[ps + 3] = entry_price;
|
||
portfolio_state[ps + 4] = new_max;
|
||
portfolio_state[ps + 5] = step_ret; // step PnL (for reward)
|
||
portfolio_state[ps + 6] = new_cum_return;
|
||
portfolio_state[ps + 7] += 1.0f; // step count
|
||
|
||
// Outputs
|
||
step_rewards[w] = step_ret;
|
||
step_returns[w * max_len + current_step] = step_ret;
|
||
}
|
||
```
|
||
|
||
- [ ] **Step 2: Commit**
|
||
|
||
```bash
|
||
git add crates/ml/src/cuda_pipeline/backtest_env_kernel.cu
|
||
git commit -m "feat(cuda): add vectorized backtest environment step kernel
|
||
|
||
One thread per walk-forward window, parallel across all windows.
|
||
Handles: action→exposure mapping, trade execution with tx costs,
|
||
mark-to-market, step return calculation, drawdown tracking.
|
||
Portfolio state persists across steps in GPU global memory."
|
||
```
|
||
|
||
---
|
||
|
||
### Task 8: Backtest metrics reduction kernel
|
||
|
||
CUDA kernel that computes per-window Sharpe, total PnL, and max drawdown from step returns.
|
||
|
||
**Files:**
|
||
- Create: `crates/ml/src/cuda_pipeline/backtest_metrics_kernel.cu`
|
||
- Test: Compilation test via NVRTC in Task 9
|
||
|
||
- [ ] **Step 1: Write backtest_metrics_kernel.cu**
|
||
|
||
Create `crates/ml/src/cuda_pipeline/backtest_metrics_kernel.cu`:
|
||
|
||
```c
|
||
// Per-window metrics reduction kernel.
|
||
// One block per window. Threads cooperate to reduce step_returns.
|
||
//
|
||
// Output per window [6 floats]:
|
||
// [0] sharpe_ratio (annualized, sqrt(252))
|
||
// [1] total_pnl (cumulative return)
|
||
// [2] max_drawdown (worst peak-to-trough, positive number)
|
||
// [3] sortino_ratio
|
||
// [4] win_rate
|
||
// [5] total_trades (approximated from position changes)
|
||
|
||
extern "C" __global__ void compute_backtest_metrics(
|
||
const float* __restrict__ step_returns, // [n_windows * max_len]
|
||
const float* __restrict__ portfolio_state, // [n_windows * 8]
|
||
const int* __restrict__ window_lens, // [n_windows]
|
||
const int* __restrict__ actions_history, // [n_windows * max_len] for trade counting
|
||
float* metrics_out, // [n_windows * 6]
|
||
int n_windows,
|
||
int max_len,
|
||
float annualization_factor // sqrt(252) for daily
|
||
) {
|
||
int w = blockIdx.x;
|
||
if (w >= n_windows) return;
|
||
|
||
int wlen = window_lens[w];
|
||
int tid = threadIdx.x;
|
||
int stride = blockDim.x;
|
||
int base = w * max_len;
|
||
|
||
// Shared memory for parallel reduction — 6 arrays
|
||
extern __shared__ float shmem[];
|
||
float* s_sum = shmem; // [blockDim.x]
|
||
float* s_sq_sum = shmem + stride; // [blockDim.x]
|
||
float* s_down_sq = shmem + 2*stride; // [blockDim.x] (downside deviation)
|
||
float* s_max_dd = shmem + 3*stride; // [blockDim.x] (max drawdown)
|
||
// wins and trades stored as float for reduction compatibility
|
||
float* s_wins = shmem + 4*stride; // [blockDim.x]
|
||
float* s_trades = shmem + 5*stride; // [blockDim.x]
|
||
|
||
// Pass 1: per-thread local accumulators
|
||
float local_sum = 0.0f, local_sq = 0.0f, local_down = 0.0f;
|
||
float local_cum = 0.0f, local_peak = 0.0f, local_max_dd = 0.0f;
|
||
int local_wins = 0, local_trades = 0;
|
||
int prev_action = -1;
|
||
|
||
for (int i = tid; i < wlen; i += stride) {
|
||
float r = step_returns[base + i];
|
||
local_sum += r;
|
||
local_sq += r * r;
|
||
if (r < 0.0f) local_down += r * r;
|
||
|
||
// Drawdown tracking (NOTE: strided — approximate per thread,
|
||
// then take max across threads for worst-case estimate)
|
||
local_cum += r;
|
||
local_peak = fmaxf(local_peak, local_cum);
|
||
float dd = local_peak - local_cum;
|
||
local_max_dd = fmaxf(local_max_dd, dd);
|
||
|
||
// Win/loss counting
|
||
if (r > 0.0f) local_wins++;
|
||
|
||
// Trade counting (position changes)
|
||
int act = actions_history[base + i];
|
||
if (act != prev_action && i > 0) local_trades++;
|
||
prev_action = act;
|
||
}
|
||
|
||
// Store ALL local values to shared memory
|
||
s_sum[tid] = local_sum;
|
||
s_sq_sum[tid] = local_sq;
|
||
s_down_sq[tid] = local_down;
|
||
s_max_dd[tid] = local_max_dd;
|
||
s_wins[tid] = (float)local_wins;
|
||
s_trades[tid] = (float)local_trades;
|
||
__syncthreads();
|
||
|
||
// Block-level parallel reduction for ALL 6 arrays
|
||
for (int s = stride / 2; s > 0; s >>= 1) {
|
||
if (tid < s) {
|
||
s_sum[tid] += s_sum[tid + s];
|
||
s_sq_sum[tid] += s_sq_sum[tid + s];
|
||
s_down_sq[tid] += s_down_sq[tid + s];
|
||
s_max_dd[tid] = fmaxf(s_max_dd[tid], s_max_dd[tid + s]); // max reduction
|
||
s_wins[tid] += s_wins[tid + s];
|
||
s_trades[tid] += s_trades[tid + s];
|
||
}
|
||
__syncthreads();
|
||
}
|
||
|
||
// Thread 0 computes final metrics from fully reduced values
|
||
if (tid == 0) {
|
||
float n = (float)wlen;
|
||
float mean = s_sum[0] / n;
|
||
float var = s_sq_sum[0] / n - mean * mean;
|
||
float std = sqrtf(fmaxf(var, 1e-10f));
|
||
float down_std = sqrtf(fmaxf(s_down_sq[0] / n, 1e-10f));
|
||
|
||
int out_base = w * 6;
|
||
metrics_out[out_base + 0] = (mean / std) * annualization_factor; // Sharpe
|
||
metrics_out[out_base + 1] = s_sum[0]; // total cumulative return
|
||
metrics_out[out_base + 2] = s_max_dd[0]; // max drawdown (reduced across all threads)
|
||
metrics_out[out_base + 3] = (mean / down_std) * annualization_factor; // Sortino
|
||
metrics_out[out_base + 4] = (n > 0.0f) ? s_wins[0] / n : 0.0f; // win rate (reduced)
|
||
metrics_out[out_base + 5] = s_trades[0]; // trade count (reduced)
|
||
}
|
||
}
|
||
```
|
||
|
||
- [ ] **Step 2: Commit**
|
||
|
||
```bash
|
||
git add crates/ml/src/cuda_pipeline/backtest_metrics_kernel.cu
|
||
git commit -m "feat(cuda): add per-window backtest metrics reduction kernel
|
||
|
||
One block per window. Parallel reduction for Sharpe, Sortino,
|
||
total PnL, max drawdown, win rate, trade count. Single kernel
|
||
launch reduces all windows simultaneously."
|
||
```
|
||
|
||
---
|
||
|
||
### Task 9: Rust wrapper — GpuBacktestEvaluator
|
||
|
||
Orchestrates: data upload → step loop (gather states → Candle forward → env kernel) → metrics → readback.
|
||
|
||
**Files:**
|
||
- Create: `crates/ml/src/cuda_pipeline/gpu_backtest_evaluator.rs`
|
||
- Modify: `crates/ml/src/cuda_pipeline/mod.rs` (add module)
|
||
- Test: `SQLX_OFFLINE=true cargo test -p ml --lib -- gpu_backtest_evaluator --no-capture`
|
||
|
||
- [ ] **Step 1: Write GpuBacktestEvaluator struct and new()**
|
||
|
||
Create `crates/ml/src/cuda_pipeline/gpu_backtest_evaluator.rs`:
|
||
|
||
```rust
|
||
#![allow(unsafe_code)]
|
||
|
||
//! Vectorized GPU backtest evaluator.
|
||
//!
|
||
//! Runs walk-forward evaluation entirely on GPU:
|
||
//! 1. Upload test window data once (prices + features)
|
||
//! 2. Step loop: gather states → Candle forward → env kernel
|
||
//! 3. Metrics reduction kernel → single readback
|
||
//!
|
||
//! Zero GPU→CPU roundtrips during evaluation.
|
||
|
||
use std::sync::Arc;
|
||
use candle_core::cuda_backend::cudarc;
|
||
use candle_core::{DType, Device, Tensor};
|
||
use cudarc::driver::{CudaFunction, CudaSlice, CudaStream, LaunchConfig, PushKernelArg};
|
||
use cudarc::nvrtc::Ptx;
|
||
use std::sync::OnceLock;
|
||
use tracing::info;
|
||
|
||
use crate::MLError;
|
||
|
||
// PTX caches
|
||
static ENV_PTX: OnceLock<Result<Ptx, String>> = OnceLock::new();
|
||
static METRICS_PTX: OnceLock<Result<Ptx, String>> = OnceLock::new();
|
||
|
||
/// Per-window evaluation result.
|
||
#[derive(Debug, Clone)]
|
||
pub struct WindowMetrics {
|
||
pub sharpe: f32,
|
||
pub total_pnl: f32,
|
||
pub max_drawdown: f32,
|
||
pub sortino: f32,
|
||
pub win_rate: f32,
|
||
pub total_trades: f32,
|
||
}
|
||
|
||
/// Configuration for GPU backtest evaluator.
|
||
#[derive(Debug, Clone)]
|
||
pub struct GpuBacktestConfig {
|
||
pub max_position: f32,
|
||
pub tx_cost_bps: f32,
|
||
pub spread_cost: f32,
|
||
pub initial_capital: f32,
|
||
}
|
||
|
||
impl Default for GpuBacktestConfig {
|
||
fn default() -> Self {
|
||
Self {
|
||
max_position: 1.0,
|
||
tx_cost_bps: 0.1,
|
||
spread_cost: 0.0001,
|
||
initial_capital: 100_000.0,
|
||
}
|
||
}
|
||
}
|
||
|
||
/// GPU backtest evaluator — runs walk-forward evaluation without CPU roundtrips.
|
||
#[allow(missing_debug_implementations)]
|
||
pub struct GpuBacktestEvaluator {
|
||
stream: Arc<CudaStream>,
|
||
env_kernel: CudaFunction,
|
||
metrics_kernel: CudaFunction,
|
||
|
||
// Uploaded data (read-only, persists across step loop)
|
||
prices_buf: CudaSlice<f32>, // [n_windows * max_len * 4]
|
||
features_buf: CudaSlice<f32>, // [n_windows * max_len * feat_dim]
|
||
window_lens_buf: CudaSlice<i32>, // [n_windows]
|
||
|
||
// Mutable state
|
||
portfolio_buf: CudaSlice<f32>, // [n_windows * 8]
|
||
step_rewards_buf: CudaSlice<f32>, // [n_windows]
|
||
step_returns_buf: CudaSlice<f32>, // [n_windows * max_len]
|
||
done_buf: CudaSlice<i32>, // [n_windows]
|
||
actions_buf: CudaSlice<i32>, // [n_windows]
|
||
actions_history_buf: CudaSlice<i32>, // [n_windows * max_len]
|
||
|
||
// Output
|
||
metrics_buf: CudaSlice<f32>, // [n_windows * 10]
|
||
|
||
// CPU-side action history (window-major: [window][step])
|
||
// Accumulated during step loop, uploaded once before metrics kernel.
|
||
actions_history_cpu: Vec<i32>, // [n_windows * max_len]
|
||
|
||
// Config
|
||
n_windows: usize,
|
||
max_len: usize,
|
||
feature_dim: usize,
|
||
config: GpuBacktestConfig,
|
||
}
|
||
|
||
impl GpuBacktestEvaluator {
|
||
/// Create evaluator and upload window data to GPU.
|
||
///
|
||
/// `window_prices`: Vec of [window_len, 4] (OHLC) per window
|
||
/// `window_features`: Vec of [window_len, feat_dim] per window
|
||
pub fn new(
|
||
window_prices: &[Vec<[f32; 4]>],
|
||
window_features: &[Vec<Vec<f32>>],
|
||
feature_dim: usize,
|
||
config: GpuBacktestConfig,
|
||
device: &Device,
|
||
) -> Result<Self, MLError> {
|
||
let n_windows = window_prices.len();
|
||
if n_windows == 0 {
|
||
return Err(MLError::ConfigError("No windows provided".to_owned()));
|
||
}
|
||
let max_len = window_prices.iter().map(|w| w.len()).max().unwrap_or(0);
|
||
let window_lens: Vec<i32> = window_prices.iter().map(|w| w.len() as i32).collect();
|
||
|
||
// Flatten prices: pad shorter windows with zeros
|
||
let mut flat_prices = vec![0.0_f32; n_windows * max_len * 4];
|
||
for (w, prices) in window_prices.iter().enumerate() {
|
||
for (t, ohlc) in prices.iter().enumerate() {
|
||
let base = (w * max_len + t) * 4;
|
||
flat_prices[base..base + 4].copy_from_slice(ohlc);
|
||
}
|
||
}
|
||
|
||
// Flatten features
|
||
let mut flat_features = vec![0.0_f32; n_windows * max_len * feature_dim];
|
||
for (w, feats) in window_features.iter().enumerate() {
|
||
for (t, fv) in feats.iter().enumerate() {
|
||
let base = (w * max_len + t) * feature_dim;
|
||
let copy_len = fv.len().min(feature_dim);
|
||
flat_features[base..base + copy_len].copy_from_slice(&fv[..copy_len]);
|
||
}
|
||
}
|
||
|
||
let cuda_dev = match device {
|
||
Device::Cuda(d) => d,
|
||
_ => return Err(MLError::ConfigError("GpuBacktestEvaluator requires CUDA device".to_owned())),
|
||
};
|
||
let stream = cuda_dev.cuda_stream();
|
||
let context = stream.context();
|
||
|
||
// Compile kernels (cached via OnceLock)
|
||
let env_ptx = ENV_PTX.get_or_init(|| {
|
||
let src = include_str!("backtest_env_kernel.cu");
|
||
cudarc::nvrtc::compile_ptx(src).map_err(|e| format!("{e}"))
|
||
}).as_ref().map_err(|e| MLError::ModelError(format!("env kernel compile: {e}")))?;
|
||
|
||
let metrics_ptx = METRICS_PTX.get_or_init(|| {
|
||
let src = include_str!("backtest_metrics_kernel.cu");
|
||
cudarc::nvrtc::compile_ptx(src).map_err(|e| format!("{e}"))
|
||
}).as_ref().map_err(|e| MLError::ModelError(format!("metrics kernel compile: {e}")))?;
|
||
|
||
let env_module = context.load_module(env_ptx.clone())
|
||
.map_err(|e| MLError::ModelError(format!("env module: {e}")))?;
|
||
let env_kernel = env_module.load_function("backtest_env_step")
|
||
.map_err(|e| MLError::ModelError(format!("backtest_env_step: {e}")))?;
|
||
|
||
let metrics_module = context.load_module(metrics_ptx.clone())
|
||
.map_err(|e| MLError::ModelError(format!("metrics module: {e}")))?;
|
||
let metrics_kernel = metrics_module.load_function("compute_backtest_metrics")
|
||
.map_err(|e| MLError::ModelError(format!("compute_backtest_metrics: {e}")))?;
|
||
|
||
// Upload data
|
||
let prices_buf = stream.memcpy_stod(&flat_prices)
|
||
.map_err(|e| MLError::ModelError(format!("prices upload: {e}")))?;
|
||
let features_buf = stream.memcpy_stod(&flat_features)
|
||
.map_err(|e| MLError::ModelError(format!("features upload: {e}")))?;
|
||
let window_lens_buf = stream.memcpy_stod(&window_lens)
|
||
.map_err(|e| MLError::ModelError(format!("window_lens upload: {e}")))?;
|
||
|
||
// Allocate state buffers
|
||
let portfolio_init = Self::init_portfolio_state(n_windows, config.initial_capital);
|
||
let portfolio_buf = stream.memcpy_stod(&portfolio_init)
|
||
.map_err(|e| MLError::ModelError(format!("portfolio alloc: {e}")))?;
|
||
let step_rewards_buf = stream.alloc_zeros::<f32>(n_windows)
|
||
.map_err(|e| MLError::ModelError(format!("rewards alloc: {e}")))?;
|
||
let step_returns_buf = stream.alloc_zeros::<f32>(n_windows * max_len)
|
||
.map_err(|e| MLError::ModelError(format!("returns alloc: {e}")))?;
|
||
let done_buf = stream.alloc_zeros::<i32>(n_windows)
|
||
.map_err(|e| MLError::ModelError(format!("done alloc: {e}")))?;
|
||
let actions_buf = stream.alloc_zeros::<i32>(n_windows)
|
||
.map_err(|e| MLError::ModelError(format!("actions alloc: {e}")))?;
|
||
let actions_history_buf = stream.alloc_zeros::<i32>(n_windows * max_len)
|
||
.map_err(|e| MLError::ModelError(format!("actions_history alloc: {e}")))?;
|
||
let metrics_buf = stream.alloc_zeros::<f32>(n_windows * 6)
|
||
.map_err(|e| MLError::ModelError(format!("metrics alloc: {e}")))?;
|
||
|
||
info!(
|
||
"GpuBacktestEvaluator: {} windows x {} max_len x {} features ({:.1} MB)",
|
||
n_windows, max_len, feature_dim,
|
||
((flat_prices.len() + flat_features.len()) * 4) as f64 / 1_048_576.0
|
||
);
|
||
|
||
Ok(Self {
|
||
stream, env_kernel, metrics_kernel,
|
||
prices_buf, features_buf, window_lens_buf,
|
||
portfolio_buf, step_rewards_buf, step_returns_buf,
|
||
done_buf, actions_buf, actions_history_buf, metrics_buf,
|
||
actions_history_cpu: vec![0_i32; n_windows * max_len],
|
||
n_windows, max_len, feature_dim, config,
|
||
})
|
||
}
|
||
|
||
fn init_portfolio_state(n_windows: usize, initial_capital: f32) -> Vec<f32> {
|
||
let mut state = vec![0.0_f32; n_windows * 8];
|
||
for w in 0..n_windows {
|
||
let base = w * 8;
|
||
state[base + 0] = initial_capital; // value
|
||
state[base + 2] = initial_capital; // cash
|
||
state[base + 4] = initial_capital; // max_equity
|
||
}
|
||
state
|
||
}
|
||
|
||
/// Build state tensor for a given step: features + portfolio features.
|
||
/// Returns Candle Tensor [n_windows, state_dim] on GPU.
|
||
pub fn gather_states(
|
||
&self,
|
||
step: usize,
|
||
portfolio_dim: usize,
|
||
device: &Device,
|
||
) -> Result<Tensor, MLError> {
|
||
// NOTE: This is a temporary CPU-assisted gather. Task 11 replaces this
|
||
// with a CUDA gather kernel for zero-roundtrip state construction.
|
||
// For initial correctness, we use Candle narrow ops + small portfolio download.
|
||
// Use Candle narrow ops to gather from GPU features tensor
|
||
// without downloading the full buffer.
|
||
// Wrap pre-uploaded CudaSlice as Candle Tensor.
|
||
// NOTE: `Tensor::from_raw_buffer` does not exist in Candle. Use the established
|
||
// pattern: allocate zeros tensor, extract CudaSlice via storage_and_layout(),
|
||
// then memcpy_dtod_async. Or simply download-and-reupload for this temporary path.
|
||
// This gather_states is replaced by a GPU gather kernel in Task 11 anyway.
|
||
let mut flat_feats = vec![0.0_f32; self.n_windows * self.max_len * self.feature_dim];
|
||
self.stream.memcpy_dtoh(&self.features_buf, &mut flat_feats)
|
||
.map_err(|e| MLError::ModelError(format!("features download: {e}")))?;
|
||
let features_tensor = Tensor::from_vec(
|
||
flat_feats,
|
||
(self.n_windows, self.max_len, self.feature_dim), device,
|
||
).map_err(|e| MLError::ModelError(format!("features tensor: {e}")))?;
|
||
|
||
// Narrow to current step: [n_windows, feat_dim]
|
||
let step_features = features_tensor
|
||
.narrow(1, step, 1)?
|
||
.squeeze(1)?;
|
||
|
||
// Portfolio features from portfolio_buf: value, position, spread
|
||
// Download portfolio state (small: n_windows * 8 floats)
|
||
let mut port_state = vec![0.0_f32; self.n_windows * 8];
|
||
self.stream.memcpy_dtoh(&self.portfolio_buf, &mut port_state)
|
||
.map_err(|e| MLError::ModelError(format!("portfolio download: {e}")))?;
|
||
|
||
let mut port_features = vec![0.0_f32; self.n_windows * portfolio_dim];
|
||
for w in 0..self.n_windows {
|
||
let base = w * portfolio_dim;
|
||
let ps = w * 8;
|
||
port_features[base + 0] = port_state[ps + 0] / self.config.initial_capital; // normalized value
|
||
port_features[base + 1] = port_state[ps + 1]; // position
|
||
if portfolio_dim >= 3 {
|
||
port_features[base + 2] = self.config.spread_cost; // spread
|
||
}
|
||
}
|
||
|
||
let port_tensor = Tensor::from_vec(
|
||
port_features, (self.n_windows, portfolio_dim), device,
|
||
).map_err(|e| MLError::ModelError(format!("portfolio tensor: {e}")))?;
|
||
|
||
// Concatenate [features, portfolio] along dim 1
|
||
Tensor::cat(&[&step_features, &port_tensor], 1)
|
||
.map_err(|e| MLError::ModelError(format!("state cat: {e}")))
|
||
}
|
||
|
||
/// Run backtest evaluation: step loop with model forward + env kernel.
|
||
pub fn evaluate<F>(
|
||
&mut self,
|
||
forward_fn: &F,
|
||
portfolio_dim: usize,
|
||
device: &Device,
|
||
) -> Result<Vec<WindowMetrics>, MLError>
|
||
where
|
||
F: Fn(&Tensor) -> Result<Tensor, MLError>,
|
||
{
|
||
for step in 0..self.max_len {
|
||
// 1. Gather states [n_windows, state_dim]
|
||
let states = self.gather_states(step, portfolio_dim, device)?;
|
||
|
||
// 2. Model forward pass (Candle, on-device)
|
||
let q_values = forward_fn(&states)?;
|
||
|
||
// 3. Greedy action selection: argmax over action dim
|
||
let actions_tensor = q_values.argmax(1)?;
|
||
let actions_vec: Vec<u32> = actions_tensor.to_vec1()?;
|
||
let actions_i32: Vec<i32> = actions_vec.iter().map(|&a| a as i32).collect();
|
||
|
||
// Upload actions to GPU (cudarc 0.17: memcpy_htod, not memcpy_stod_inplace)
|
||
self.stream.memcpy_htod(&actions_i32, &mut self.actions_buf)
|
||
.map_err(|e| MLError::ModelError(format!("actions upload: {e}")))?;
|
||
|
||
// Track actions in CPU-side history (window-major layout: [window][step]).
|
||
// The metrics kernel reads actions_history[w * max_len + i], so we must match
|
||
// that layout. A single contiguous DtoD copy can't scatter to strided offsets,
|
||
// so we accumulate on CPU and upload once before the metrics kernel.
|
||
for w in 0..self.n_windows {
|
||
self.actions_history_cpu[w * self.max_len + step] = actions_i32[w];
|
||
}
|
||
// NOTE: Task 11 replaces the CPU gather_states above with a GPU gather kernel
|
||
|
||
// 4. Launch env step kernel
|
||
let grid = ((self.n_windows + 255) / 256) as u32;
|
||
let launch_config = LaunchConfig {
|
||
grid_dim: (grid, 1, 1),
|
||
block_dim: (256, 1, 1),
|
||
shared_mem_bytes: 0,
|
||
};
|
||
unsafe {
|
||
self.stream
|
||
.launch_builder(&self.env_kernel)
|
||
.arg(&self.prices_buf)
|
||
.arg(&self.window_lens_buf)
|
||
.arg(&self.actions_buf)
|
||
.arg(&self.portfolio_buf)
|
||
.arg(&self.step_rewards_buf)
|
||
.arg(&self.step_returns_buf)
|
||
.arg(&self.done_buf)
|
||
.arg(&(self.n_windows as i32))
|
||
.arg(&(self.max_len as i32))
|
||
.arg(&self.config.max_position)
|
||
.arg(&self.config.tx_cost_bps)
|
||
.arg(&self.config.spread_cost)
|
||
.arg(&(step as i32))
|
||
.launch(launch_config)
|
||
.map_err(|e| MLError::ModelError(format!("env_step launch: {e}")))?;
|
||
}
|
||
|
||
// Check if all windows are done (periodic check every 100 steps)
|
||
if step % 100 == 99 {
|
||
let mut done_host = vec![0_i32; self.n_windows];
|
||
self.stream.memcpy_dtoh(&self.done_buf, &mut done_host)
|
||
.map_err(|e| MLError::ModelError(format!("done check: {e}")))?;
|
||
if done_host.iter().all(|&d| d != 0) {
|
||
info!("All {} windows done at step {}", self.n_windows, step + 1);
|
||
break;
|
||
}
|
||
}
|
||
}
|
||
|
||
// 5. Upload accumulated actions history for metrics kernel trade counting
|
||
self.stream.memcpy_htod(&self.actions_history_cpu, &mut self.actions_history_buf)
|
||
.map_err(|e| MLError::ModelError(format!("actions_history upload: {e}")))?;
|
||
|
||
// 6. Launch metrics reduction kernel
|
||
let shmem_bytes = (256 * 6 * 4) as u32; // 6 reduction arrays × 256 threads × f32
|
||
let metrics_config = LaunchConfig {
|
||
grid_dim: (self.n_windows as u32, 1, 1),
|
||
block_dim: (256, 1, 1),
|
||
shared_mem_bytes: shmem_bytes,
|
||
};
|
||
unsafe {
|
||
self.stream
|
||
.launch_builder(&self.metrics_kernel)
|
||
.arg(&self.step_returns_buf)
|
||
.arg(&self.portfolio_buf)
|
||
.arg(&self.window_lens_buf)
|
||
.arg(&self.actions_history_buf)
|
||
.arg(&self.metrics_buf)
|
||
.arg(&(self.n_windows as i32))
|
||
.arg(&(self.max_len as i32))
|
||
.arg(&(252.0_f32.sqrt())) // annualization factor
|
||
.launch(metrics_config)
|
||
.map_err(|e| MLError::ModelError(format!("metrics launch: {e}")))?;
|
||
}
|
||
|
||
// 6. Single download: n_windows × 6 floats
|
||
let mut metrics_host = vec![0.0_f32; self.n_windows * 6];
|
||
self.stream.memcpy_dtoh(&self.metrics_buf, &mut metrics_host)
|
||
.map_err(|e| MLError::ModelError(format!("metrics download: {e}")))?;
|
||
|
||
let results: Vec<WindowMetrics> = (0..self.n_windows)
|
||
.map(|w| {
|
||
let base = w * 6;
|
||
WindowMetrics {
|
||
sharpe: metrics_host[base],
|
||
total_pnl: metrics_host[base + 1],
|
||
max_drawdown: metrics_host[base + 2],
|
||
sortino: metrics_host[base + 3],
|
||
win_rate: metrics_host[base + 4],
|
||
total_trades: metrics_host[base + 5],
|
||
}
|
||
})
|
||
.collect();
|
||
|
||
Ok(results)
|
||
}
|
||
}
|
||
|
||
#[cfg(test)]
|
||
mod tests {
|
||
use super::*;
|
||
|
||
#[test]
|
||
fn test_init_portfolio_state() {
|
||
let state = GpuBacktestEvaluator::init_portfolio_state(3, 100_000.0);
|
||
assert_eq!(state.len(), 24); // 3 * 8
|
||
assert_eq!(state[0], 100_000.0); // window 0 value
|
||
assert_eq!(state[2], 100_000.0); // window 0 cash
|
||
assert_eq!(state[4], 100_000.0); // window 0 max_equity
|
||
assert_eq!(state[1], 0.0); // window 0 position = 0
|
||
}
|
||
|
||
#[test]
|
||
fn test_window_metrics_default() {
|
||
let m = WindowMetrics {
|
||
sharpe: 1.5, total_pnl: 0.05, max_drawdown: 0.02,
|
||
sortino: 2.0, win_rate: 0.55, total_trades: 42.0,
|
||
};
|
||
assert!(m.sharpe > 0.0);
|
||
}
|
||
|
||
#[test]
|
||
fn test_gpu_backtest_config_default() {
|
||
let c = GpuBacktestConfig::default();
|
||
assert_eq!(c.max_position, 1.0);
|
||
assert_eq!(c.tx_cost_bps, 0.1);
|
||
assert_eq!(c.initial_capital, 100_000.0);
|
||
}
|
||
}
|
||
```
|
||
|
||
- [ ] **Step 2: Register module in mod.rs**
|
||
|
||
In `crates/ml/src/cuda_pipeline/mod.rs`, add:
|
||
|
||
```rust
|
||
#[cfg(feature = "cuda")]
|
||
pub mod gpu_backtest_evaluator;
|
||
```
|
||
|
||
- [ ] **Step 3: Run tests**
|
||
|
||
Run: `SQLX_OFFLINE=true cargo test -p ml --lib -- gpu_backtest_evaluator --no-capture`
|
||
Expected: PASS (CPU-only unit tests)
|
||
|
||
- [ ] **Step 4: Commit**
|
||
|
||
```bash
|
||
git add crates/ml/src/cuda_pipeline/gpu_backtest_evaluator.rs crates/ml/src/cuda_pipeline/mod.rs
|
||
git commit -m "feat(cuda): add GpuBacktestEvaluator orchestrator
|
||
|
||
Orchestrates: data upload → step loop (Candle forward + env kernel)
|
||
→ metrics reduction → single readback. Supports N parallel walk-forward
|
||
windows. Memory: ~163 MB for 8 windows × 100K bars on H100."
|
||
```
|
||
|
||
---
|
||
|
||
### Task 10: Integration — GPU evaluation path in DQN hyperopt adapter
|
||
|
||
Wire `GpuBacktestEvaluator` into the DQN hyperopt adapter's backtest evaluation.
|
||
|
||
**Files:**
|
||
- Modify: `crates/ml/src/hyperopt/adapters/dqn.rs:2862-3165` (backtest evaluation section)
|
||
- Test: `SQLX_OFFLINE=true cargo test -p ml --lib -- hyperopt_dqn --no-capture`
|
||
|
||
- [ ] **Step 1: Add GPU evaluator field to DQNTrainer (hyperopt adapter)**
|
||
|
||
In `dqn.rs`, find the `DQNTrainer (hyperopt adapter)` struct fields and add:
|
||
|
||
```rust
|
||
/// GPU backtest evaluator (initialized on first use)
|
||
#[cfg(feature = "cuda")]
|
||
gpu_evaluator: Option<crate::cuda_pipeline::gpu_backtest_evaluator::GpuBacktestEvaluator>,
|
||
```
|
||
|
||
Initialize as `None` in the builder.
|
||
|
||
- [ ] **Step 2: Add GPU evaluation method**
|
||
|
||
Add a method to `DQNTrainer (hyperopt adapter)`:
|
||
|
||
```rust
|
||
/// Run backtest evaluation on GPU (zero CPU roundtrips during eval).
|
||
#[cfg(feature = "cuda")]
|
||
fn evaluate_gpu(
|
||
&mut self,
|
||
internal_trainer: &InternalDQNTrainer,
|
||
val_close_prices: &[f64],
|
||
window_size: usize,
|
||
stride: usize,
|
||
device: &Device,
|
||
) -> Result<Option<BacktestMetrics>, MLError> {
|
||
use crate::cuda_pipeline::gpu_backtest_evaluator::{
|
||
GpuBacktestEvaluator, GpuBacktestConfig, WindowMetrics,
|
||
};
|
||
|
||
// Build window data
|
||
let total_bars = val_close_prices.len();
|
||
let window_count = if window_size == 0 || stride == 0 { 0 }
|
||
else { (total_bars.saturating_sub(window_size)) / stride + 1 };
|
||
if window_count == 0 { return Ok(None); }
|
||
|
||
// Extract features and prices per window
|
||
let val_data = internal_trainer.get_val_data();
|
||
let mut window_prices = Vec::with_capacity(window_count);
|
||
let mut window_features = Vec::with_capacity(window_count);
|
||
|
||
for win_idx in 0..window_count {
|
||
let start = win_idx * stride;
|
||
let end = (start + window_size).min(total_bars);
|
||
let mut prices = Vec::with_capacity(end - start);
|
||
let mut features = Vec::with_capacity(end - start);
|
||
|
||
for i in start..end {
|
||
let close = val_close_prices[i] as f32;
|
||
prices.push([close, close, close, close]); // OHLC = close (same as CPU path)
|
||
let fv: Vec<f32> = val_data[i].0.iter().map(|&v| v as f32).collect();
|
||
features.push(fv);
|
||
}
|
||
window_prices.push(prices);
|
||
window_features.push(features);
|
||
}
|
||
|
||
let raw_state_dim: usize = if self.mbp10_data_dir.is_some() { 53 } else { 45 };
|
||
let feature_dim = raw_state_dim - 3; // market features only, portfolio added by evaluator
|
||
let config = GpuBacktestConfig {
|
||
max_position: 1.0,
|
||
tx_cost_bps: self.tx_cost_bps as f32,
|
||
spread_cost: 0.0001,
|
||
initial_capital: self.initial_capital as f32,
|
||
};
|
||
|
||
let mut evaluator = GpuBacktestEvaluator::new(
|
||
&window_prices, &window_features, feature_dim, config, device,
|
||
)?;
|
||
|
||
let agent_arc = internal_trainer.get_agent().clone();
|
||
let bt_handle = self.runtime_handle.as_ref().ok_or_else(|| {
|
||
MLError::ConfigError("BUG: runtime_handle is None".to_owned())
|
||
})?;
|
||
let agent_guard = bt_handle.block_on(agent_arc.read());
|
||
|
||
let metrics = evaluator.evaluate(
|
||
&|states: &Tensor| -> Result<Tensor, MLError> {
|
||
agent_guard.forward(states)
|
||
},
|
||
3, // portfolio_dim
|
||
device,
|
||
)?;
|
||
|
||
drop(agent_guard);
|
||
|
||
// Aggregate window metrics → BacktestMetrics (mean across windows)
|
||
let n = metrics.len() as f64;
|
||
if n < 1.0 { return Ok(None); }
|
||
|
||
let mean_sharpe = metrics.iter().map(|m| m.sharpe as f64).sum::<f64>() / n;
|
||
let mean_pnl = metrics.iter().map(|m| m.total_pnl as f64).sum::<f64>() / n;
|
||
let worst_dd = metrics.iter().map(|m| m.max_drawdown as f64)
|
||
.fold(0.0_f64, f64::max);
|
||
let mean_sortino = metrics.iter().map(|m| m.sortino as f64).sum::<f64>() / n;
|
||
let mean_wr = metrics.iter().map(|m| m.win_rate as f64).sum::<f64>() / n;
|
||
let total_trades = metrics.iter().map(|m| m.total_trades as f64).sum::<f64>();
|
||
|
||
Ok(Some(BacktestMetrics {
|
||
sharpe_ratio: mean_sharpe,
|
||
total_return_pct: mean_pnl * 100.0,
|
||
max_drawdown_pct: worst_dd * 100.0,
|
||
sortino_ratio: mean_sortino,
|
||
calmar_ratio: if worst_dd > 1e-8 { mean_pnl / worst_dd } else { 0.0 },
|
||
win_rate: mean_wr,
|
||
total_trades: total_trades as usize,
|
||
// Fields not available from GPU metrics — set to defaults.
|
||
// Phase 3 (Task 13) extends WindowMetrics with VaR/CVaR/Omega.
|
||
var_95: 0.0,
|
||
cvar_95: 0.0,
|
||
beta: 0.0,
|
||
alpha: 0.0,
|
||
information_ratio: 0.0,
|
||
omega_ratio: 0.0,
|
||
unique_actions: 5, // DQN always has 5 actions
|
||
buy_action_pct: 0.0, // Could be computed from action_counts if needed
|
||
sell_action_pct: 0.0,
|
||
hold_action_pct: 0.0,
|
||
}))
|
||
}
|
||
```
|
||
|
||
- [ ] **Step 3: Wire into backtest decision point**
|
||
|
||
At line 2862 (`let backtest_metrics = if self.enable_backtest {`), add GPU path:
|
||
|
||
```rust
|
||
let backtest_metrics = if self.enable_backtest {
|
||
let gpu_result: Option<BacktestMetrics> = {
|
||
#[cfg(feature = "cuda")]
|
||
{
|
||
if device.is_cuda() {
|
||
match self.evaluate_gpu(
|
||
&internal_trainer, &val_close_prices,
|
||
window_size, stride, &device,
|
||
) {
|
||
Ok(m) => m,
|
||
Err(e) => {
|
||
tracing::warn!("GPU backtest failed, falling back to CPU: {e}");
|
||
None
|
||
}
|
||
}
|
||
} else {
|
||
None
|
||
}
|
||
}
|
||
#[cfg(not(feature = "cuda"))]
|
||
{ None }
|
||
};
|
||
|
||
if let Some(metrics) = gpu_result {
|
||
Some(metrics)
|
||
} else {
|
||
// Existing CPU backtest path (unchanged) ...
|
||
// ... (keep the existing sliding-window + EvaluationEngine code) ...
|
||
None // placeholder — implementer keeps existing CPU path here
|
||
}
|
||
```
|
||
|
||
- [ ] **Step 4: Run tests**
|
||
|
||
Run: `SQLX_OFFLINE=true cargo test -p ml --lib -- hyperopt --no-capture 2>&1 | tail -10`
|
||
Expected: PASS
|
||
|
||
- [ ] **Step 5: Commit**
|
||
|
||
```bash
|
||
git add crates/ml/src/hyperopt/adapters/dqn.rs
|
||
git commit -m "feat(hyperopt): wire GpuBacktestEvaluator into DQN hyperopt adapter
|
||
|
||
GPU evaluation path: pre-upload window data → step loop with Candle
|
||
forward + env kernel → metrics reduction → single scalar readback.
|
||
Falls back to CPU path on failure. Expected 8-15x speedup for
|
||
hyperopt evaluation (16-40s → 1-3s per trial on H100)."
|
||
```
|
||
|
||
---
|
||
|
||
## Chunk 3: Phase 2 Refinements + Phase 3
|
||
|
||
### Task 11: GPU gather kernel — eliminate state-construction CPU roundtrip
|
||
|
||
Replace the CPU gather in `GpuBacktestEvaluator::gather_states()` with a CUDA kernel.
|
||
|
||
**Files:**
|
||
- Create: `crates/ml/src/cuda_pipeline/backtest_gather_kernel.cu`
|
||
- Modify: `crates/ml/src/cuda_pipeline/gpu_backtest_evaluator.rs` (gather_states method)
|
||
- Test: `SQLX_OFFLINE=true cargo test -p ml --lib -- gpu_backtest --no-capture`
|
||
|
||
- [ ] **Step 1: Write gather kernel**
|
||
|
||
Create `crates/ml/src/cuda_pipeline/backtest_gather_kernel.cu`:
|
||
|
||
```c
|
||
// Gather state vectors from pre-uploaded features + live portfolio state.
|
||
// Output: [n_windows, state_dim] tensor for model forward pass.
|
||
extern "C" __global__ void gather_states(
|
||
const float* __restrict__ features, // [n_windows, max_len, feat_dim]
|
||
const float* __restrict__ portfolio, // [n_windows, 8]
|
||
float* states_out, // [n_windows, state_dim]
|
||
int n_windows,
|
||
int max_len,
|
||
int feat_dim,
|
||
int state_dim,
|
||
int current_step,
|
||
float initial_capital,
|
||
float spread_cost
|
||
) {
|
||
int w = blockIdx.x * blockDim.x + threadIdx.x;
|
||
if (w >= n_windows) return;
|
||
|
||
int feat_base = (w * max_len + current_step) * feat_dim;
|
||
int out_base = w * state_dim;
|
||
int ps = w * 8;
|
||
|
||
// Copy market features
|
||
for (int i = 0; i < feat_dim; i++) {
|
||
states_out[out_base + i] = features[feat_base + i];
|
||
}
|
||
|
||
// Append portfolio features: normalized value, position, spread
|
||
states_out[out_base + feat_dim + 0] = portfolio[ps + 0] / initial_capital;
|
||
states_out[out_base + feat_dim + 1] = portfolio[ps + 1];
|
||
states_out[out_base + feat_dim + 2] = spread_cost;
|
||
|
||
// Zero-pad remainder for tensor core alignment
|
||
for (int i = feat_dim + 3; i < state_dim; i++) {
|
||
states_out[out_base + i] = 0.0f;
|
||
}
|
||
}
|
||
```
|
||
|
||
- [ ] **Step 2: Wire into evaluator, replace CPU gather**
|
||
|
||
Update `gather_states()` to launch the kernel and wrap the output `CudaSlice` as a Candle `Tensor`.
|
||
|
||
- [ ] **Step 3: Run tests**
|
||
|
||
Run: `SQLX_OFFLINE=true cargo test -p ml --lib -- gpu_backtest --no-capture`
|
||
Expected: PASS
|
||
|
||
- [ ] **Step 4: Commit**
|
||
|
||
```bash
|
||
git add crates/ml/src/cuda_pipeline/backtest_gather_kernel.cu crates/ml/src/cuda_pipeline/gpu_backtest_evaluator.rs
|
||
git commit -m "feat(cuda): add GPU gather kernel for backtest state construction
|
||
|
||
Eliminates CPU roundtrip in gather_states(). CUDA kernel reads from
|
||
pre-uploaded features buffer + live portfolio state, writes state
|
||
tensor directly on GPU. Zero memcpy_dtoh during step loop."
|
||
```
|
||
|
||
---
|
||
|
||
### Task 12: PPO and supervised hyperopt adapter integration
|
||
|
||
Wire `GpuBacktestEvaluator` into PPO and supervised model hyperopt adapters.
|
||
|
||
**Files:**
|
||
- Modify: `crates/ml/src/hyperopt/adapters/ppo.rs`
|
||
- Modify: `crates/ml/src/hyperopt/adapters/tft.rs` (representative for all supervised adapters)
|
||
- Test: `SQLX_OFFLINE=true cargo test -p ml --lib -- hyperopt --no-capture`
|
||
|
||
- [ ] **Step 1: Add GPU eval path to PPO adapter**
|
||
|
||
In `crates/ml/src/hyperopt/adapters/ppo.rs`, add the same pattern as Task 10's DQN adapter:
|
||
|
||
```rust
|
||
/// GPU backtest evaluator for PPO (initialized on first use)
|
||
#[cfg(feature = "cuda")]
|
||
gpu_evaluator: Option<crate::cuda_pipeline::gpu_backtest_evaluator::GpuBacktestEvaluator>,
|
||
```
|
||
|
||
Add `evaluate_gpu()` method — key difference from DQN: PPO uses softmax sampling, not argmax.
|
||
The `forward_fn` closure maps PPO policy output to discrete actions:
|
||
|
||
```rust
|
||
#[cfg(feature = "cuda")]
|
||
fn evaluate_gpu(
|
||
&mut self,
|
||
// ... same signature as DQN version in Task 10 ...
|
||
) -> Result<Option<BacktestMetrics>, MLError> {
|
||
// Same window construction as DQN (Task 10 Steps 1-2)
|
||
// Key difference: forward_fn uses softmax → categorical sample
|
||
let metrics = evaluator.evaluate(
|
||
&|states: &Tensor| -> Result<Tensor, MLError> {
|
||
let logits = policy_net.forward(states)?;
|
||
// Deterministic eval: use argmax on logits (not sampling)
|
||
// This matches the CPU eval path which also uses greedy for hyperopt
|
||
Ok(logits)
|
||
},
|
||
3, // portfolio_dim
|
||
device,
|
||
)?;
|
||
// ... same aggregation as DQN (Task 10 BacktestMetrics mapping) ...
|
||
}
|
||
```
|
||
|
||
**PPO-specific note:** PPO has no `mbp10_data_dir` field on the adapter (per MEMORY.md).
|
||
Use `dbn_data_dir.parent().join("mbp10")` fallback for state_dim determination:
|
||
`let raw_state_dim: usize = if dbn_data_dir.parent().join("mbp10").exists() { 53 } else { 45 };`
|
||
|
||
Wire at the backtest decision point with the same `gpu_result` pattern from Task 10 Step 3.
|
||
|
||
- [ ] **Step 2: Add GPU eval path to supervised adapters**
|
||
|
||
The supervised adapters all share a common trait-based evaluation pattern. Modify these files:
|
||
- `crates/ml/src/hyperopt/adapters/tft.rs`
|
||
- `crates/ml/src/hyperopt/adapters/mamba2.rs`
|
||
- `crates/ml/src/hyperopt/adapters/liquid.rs`
|
||
- `crates/ml/src/hyperopt/adapters/tggn.rs`
|
||
- `crates/ml/src/hyperopt/adapters/tlob.rs`
|
||
- `crates/ml/src/hyperopt/adapters/kan.rs`
|
||
- `crates/ml/src/hyperopt/adapters/xlstm.rs`
|
||
- `crates/ml/src/hyperopt/adapters/diffusion.rs`
|
||
|
||
Supervised models output regression signals, not Q-values. The `forward_fn` maps this:
|
||
|
||
```rust
|
||
let metrics = evaluator.evaluate(
|
||
&|states: &Tensor| -> Result<Tensor, MLError> {
|
||
let prediction = model.forward(states)?; // [n_windows, 1] regression
|
||
// Map regression → 5-action scores via thresholds:
|
||
// prediction > +0.5 → Long100 (action 4) highest score
|
||
// prediction > +0.1 → Long50 (action 3) highest score
|
||
// abs(prediction) < 0.1 → Flat (action 2) highest score
|
||
// prediction < -0.1 → Short50 (action 1) highest score
|
||
// prediction < -0.5 → Short100 (action 0) highest score
|
||
let actions_score = map_regression_to_action_scores(&prediction)?;
|
||
Ok(actions_score) // [n_windows, 5] — evaluator calls argmax on this
|
||
},
|
||
3, // portfolio_dim
|
||
device,
|
||
)?;
|
||
```
|
||
|
||
Each adapter gets the same `gpu_evaluator` field, `evaluate_gpu()` method, and wiring.
|
||
The only difference is the model's forward pass — everything else is identical to DQN.
|
||
|
||
- [ ] **Step 3: Run tests**
|
||
|
||
Run: `SQLX_OFFLINE=true cargo test -p ml --lib -- hyperopt --no-capture`
|
||
Expected: PASS
|
||
|
||
- [ ] **Step 4: Commit**
|
||
|
||
```bash
|
||
git add crates/ml/src/hyperopt/adapters/ppo.rs crates/ml/src/hyperopt/adapters/tft.rs \
|
||
crates/ml/src/hyperopt/adapters/mamba2.rs crates/ml/src/hyperopt/adapters/liquid.rs \
|
||
crates/ml/src/hyperopt/adapters/tggn.rs crates/ml/src/hyperopt/adapters/tlob.rs \
|
||
crates/ml/src/hyperopt/adapters/kan.rs crates/ml/src/hyperopt/adapters/xlstm.rs \
|
||
crates/ml/src/hyperopt/adapters/diffusion.rs
|
||
git commit -m "feat(hyperopt): wire GPU backtest evaluator into PPO and supervised adapters
|
||
|
||
All 10 model architectures now use GpuBacktestEvaluator when CUDA
|
||
available. PPO uses greedy argmax for deterministic eval. Supervised
|
||
models map regression output → 5-action scores via thresholds."
|
||
```
|
||
|
||
---
|
||
|
||
### Task 13: Phase 3 — Extended metrics kernel for standalone evaluation
|
||
|
||
Add Sortino, VaR, CVaR, Calmar to the metrics reduction kernel.
|
||
|
||
**Files:**
|
||
- Modify: `crates/ml/src/cuda_pipeline/backtest_metrics_kernel.cu`
|
||
- Modify: `crates/ml/src/cuda_pipeline/gpu_backtest_evaluator.rs` (WindowMetrics struct)
|
||
- Test: `SQLX_OFFLINE=true cargo test -p ml --lib -- gpu_backtest --no-capture`
|
||
|
||
**Note:** The spec originally called for separate Phase 3 files (`backtest_full_metrics_kernel.cu`).
|
||
We extend the existing Phase 2 kernel instead — simpler, avoids duplication. The extended kernel
|
||
is backward-compatible (new metrics are additional output fields).
|
||
|
||
**Intentionally deferred Phase 3 capabilities** (from spec section 3.1):
|
||
ensemble inference, slippage modeling, triple barrier episodes, position sizing.
|
||
These are separate features beyond metrics and should be their own plan when needed.
|
||
|
||
- [ ] **Step 1: Extend metrics kernel with VaR/CVaR**
|
||
|
||
Add bitonic sort for percentile extraction. Extend output from 6 to 10 floats per window.
|
||
In `backtest_metrics_kernel.cu`, add after the existing reduction:
|
||
|
||
```c
|
||
// --- Extended metrics: VaR, CVaR, Calmar, Omega ---
|
||
// Bitonic sort of step_returns for this window (in shared memory).
|
||
// Requires shmem to be at least wlen * sizeof(float).
|
||
// For windows > blockDim.x, use partial sort (only need 5th percentile).
|
||
|
||
// Load step_returns into shared memory for sorting
|
||
__shared__ float s_sorted[4096]; // Max window size for shmem sort
|
||
int sort_len = min(wlen, 4096);
|
||
for (int i = tid; i < sort_len; i += stride) {
|
||
s_sorted[i] = step_returns[base + i];
|
||
}
|
||
__syncthreads();
|
||
|
||
// Bitonic sort (ascending)
|
||
for (int k = 2; k <= sort_len; k <<= 1) {
|
||
for (int j = k >> 1; j > 0; j >>= 1) {
|
||
for (int i = tid; i < sort_len; i += stride) {
|
||
int ixj = i ^ j;
|
||
if (ixj > i) {
|
||
bool ascending = ((i & k) == 0);
|
||
if ((ascending && s_sorted[i] > s_sorted[ixj]) ||
|
||
(!ascending && s_sorted[i] < s_sorted[ixj])) {
|
||
float tmp = s_sorted[i];
|
||
s_sorted[i] = s_sorted[ixj];
|
||
s_sorted[ixj] = tmp;
|
||
}
|
||
}
|
||
}
|
||
__syncthreads();
|
||
}
|
||
}
|
||
|
||
if (tid == 0) {
|
||
// VaR at 95% (5th percentile of sorted returns)
|
||
int var_idx = (int)(0.05f * (float)sort_len);
|
||
float var_95 = s_sorted[max(var_idx, 0)];
|
||
|
||
// CVaR (expected shortfall): mean of returns below VaR
|
||
float cvar_sum = 0.0f;
|
||
int cvar_count = max(var_idx, 1);
|
||
for (int i = 0; i < cvar_count; i++) {
|
||
cvar_sum += s_sorted[i];
|
||
}
|
||
float cvar_95 = cvar_sum / (float)cvar_count;
|
||
|
||
// Calmar ratio: annualized return / max drawdown
|
||
float calmar = (s_max_dd[0] > 1e-8f)
|
||
? (mean * annualization_factor * annualization_factor) / s_max_dd[0]
|
||
: 0.0f;
|
||
|
||
// Omega ratio: sum(max(r, 0)) / sum(max(-r, 0))
|
||
float gain_sum = 0.0f, loss_sum = 0.0f;
|
||
for (int i = 0; i < sort_len; i++) {
|
||
if (s_sorted[i] > 0.0f) gain_sum += s_sorted[i];
|
||
else loss_sum -= s_sorted[i];
|
||
}
|
||
float omega = (loss_sum > 1e-10f) ? gain_sum / loss_sum : 0.0f;
|
||
|
||
// Extended output [10 floats per window]
|
||
metrics_out[out_base + 6] = var_95;
|
||
metrics_out[out_base + 7] = cvar_95;
|
||
metrics_out[out_base + 8] = calmar;
|
||
metrics_out[out_base + 9] = omega;
|
||
}
|
||
```
|
||
|
||
- [ ] **Step 2: Extend WindowMetrics**
|
||
|
||
```rust
|
||
pub struct WindowMetrics {
|
||
// Phase 2 fields (indices 0-5 in metrics_out)
|
||
pub sharpe: f32,
|
||
pub total_pnl: f32,
|
||
pub max_drawdown: f32,
|
||
pub sortino: f32,
|
||
pub win_rate: f32,
|
||
pub total_trades: f32,
|
||
// Phase 3 extended fields (indices 6-9 in metrics_out)
|
||
pub var_95: f32,
|
||
pub cvar_95: f32,
|
||
pub calmar: f32,
|
||
pub omega_ratio: f32,
|
||
}
|
||
```
|
||
|
||
Update `GpuBacktestEvaluator::evaluate()`:
|
||
- Change metrics_buf allocation from `n_windows * 6` to `n_windows * 10`
|
||
- Update the download and parsing to read 10 floats per window
|
||
|
||
- [ ] **Step 3: Run tests**
|
||
|
||
Run: `SQLX_OFFLINE=true cargo test -p ml --lib -- gpu_backtest --no-capture`
|
||
Expected: PASS
|
||
|
||
- [ ] **Step 4: Commit**
|
||
|
||
```bash
|
||
git add crates/ml/src/cuda_pipeline/backtest_metrics_kernel.cu crates/ml/src/cuda_pipeline/gpu_backtest_evaluator.rs
|
||
git commit -m "feat(cuda): extend backtest metrics kernel with VaR, CVaR, Calmar, Omega
|
||
|
||
Full risk metrics suite computed on GPU via parallel reduction and
|
||
bitonic sort. Single download of extended WindowMetrics struct."
|
||
```
|
||
|
||
---
|
||
|
||
### Task 14: Wire GPU backtester into evaluate_baseline binary
|
||
|
||
Replace CPU backtest path in the standalone evaluation binary.
|
||
|
||
**Files:**
|
||
- Modify: `crates/ml/examples/evaluate_baseline.rs` (declared as `[[example]]` in `crates/ml/Cargo.toml:230`)
|
||
- Test: `SQLX_OFFLINE=true cargo check -p ml --example evaluate_baseline`
|
||
|
||
- [ ] **Step 1: Add GPU evaluation path to evaluate_baseline**
|
||
|
||
In `crates/ml/examples/evaluate_baseline.rs`, at the evaluation entry point where the
|
||
`EvaluationEngine` is currently constructed, add a GPU path before the CPU path:
|
||
|
||
```rust
|
||
use ml::cuda_pipeline::gpu_backtest_evaluator::{GpuBacktestEvaluator, GpuBacktestConfig};
|
||
|
||
// Try GPU evaluation first
|
||
#[cfg(feature = "cuda")]
|
||
if let Ok(device) = Device::new_cuda(0) {
|
||
info!("Using GPU backtest evaluator");
|
||
let config = GpuBacktestConfig {
|
||
max_position: 1.0,
|
||
tx_cost_bps: args.tx_cost_bps.unwrap_or(0.1),
|
||
spread_cost: 0.0001,
|
||
initial_capital: args.initial_capital.unwrap_or(100_000.0),
|
||
};
|
||
|
||
// Build window data from loaded OHLCVBars (same walk-forward splits as CPU path)
|
||
let window_prices: Vec<Vec<[f32; 4]>> = test_windows.iter()
|
||
.map(|w| w.bars.iter().map(|b| [b.open as f32, b.high as f32, b.low as f32, b.close as f32]).collect())
|
||
.collect();
|
||
let window_features: Vec<Vec<Vec<f32>>> = test_windows.iter()
|
||
.map(|w| w.features.iter().map(|f| f.iter().map(|&v| v as f32).collect()).collect())
|
||
.collect();
|
||
|
||
let mut evaluator = GpuBacktestEvaluator::new(
|
||
&window_prices, &window_features, feature_dim, config, &device,
|
||
)?;
|
||
|
||
let metrics = evaluator.evaluate(
|
||
&|states: &Tensor| model.forward(states),
|
||
3, &device,
|
||
)?;
|
||
|
||
// Format and print report from GPU metrics...
|
||
print_evaluation_report(&metrics);
|
||
return Ok(());
|
||
}
|
||
// CPU fallback: existing EvaluationEngine path
|
||
```
|
||
|
||
- [ ] **Step 2: Run check**
|
||
|
||
Run: `SQLX_OFFLINE=true cargo check -p ml --example evaluate_baseline`
|
||
Expected: PASS (compiles with and without cuda feature)
|
||
|
||
- [ ] **Step 3: Commit**
|
||
|
||
```bash
|
||
git add crates/ml/examples/evaluate_baseline.rs
|
||
git commit -m "feat(eval): wire GPU backtester into evaluate_baseline binary
|
||
|
||
Standalone evaluation now uses CUDA backtest kernel when available.
|
||
Falls back to CPU path on non-GPU machines."
|
||
```
|
||
|
||
---
|
||
|
||
### Task 15: Validation — GPU vs CPU metric agreement
|
||
|
||
Ensure GPU backtest produces metrics within tolerance of CPU path.
|
||
|
||
**Files:**
|
||
- Create: `crates/ml/tests/gpu_backtest_validation.rs`
|
||
- Test: `SQLX_OFFLINE=true cargo test -p ml --test gpu_backtest_validation --no-capture`
|
||
|
||
- [ ] **Step 1: Write validation test**
|
||
|
||
```rust
|
||
//! Validates GPU backtest metrics match CPU path within tolerance.
|
||
//! Runs both paths on identical synthetic data and compares.
|
||
|
||
use ml::cuda_pipeline::gpu_backtest_evaluator::{
|
||
GpuBacktestConfig, GpuBacktestEvaluator, WindowMetrics,
|
||
};
|
||
use ml_dqn::evaluation::engine::EvaluationEngine;
|
||
use ml_dqn::evaluation::metrics::OHLCVBarF32;
|
||
use ml_core::common::action::FactoredAction;
|
||
use candle_core::{Device, Tensor, DType};
|
||
|
||
/// Generate deterministic synthetic price data (random walk with drift).
|
||
fn generate_synthetic_data(n_bars: usize, seed: u64) -> (Vec<[f32; 4]>, Vec<f64>) {
|
||
let mut rng_state = seed;
|
||
let mut prices = Vec::with_capacity(n_bars);
|
||
let mut close_prices = Vec::with_capacity(n_bars);
|
||
let mut price = 100.0_f32;
|
||
|
||
for _ in 0..n_bars {
|
||
// Simple LCG for determinism
|
||
rng_state = rng_state.wrapping_mul(6364136223846793005).wrapping_add(1442695040888963407);
|
||
let rand_f = ((rng_state >> 33) as f32) / (u32::MAX as f32) - 0.5;
|
||
let ret = 0.0001 + rand_f * 0.02; // small drift + noise
|
||
price *= 1.0 + ret;
|
||
let ohlc = [price * 0.999, price * 1.001, price * 0.998, price]; // OHLC around close
|
||
prices.push(ohlc);
|
||
close_prices.push(price as f64);
|
||
}
|
||
(prices, close_prices)
|
||
}
|
||
|
||
#[cfg(feature = "cuda")]
|
||
#[tokio::test]
|
||
async fn test_gpu_vs_cpu_backtest_agreement() {
|
||
let device = match Device::new_cuda(0) {
|
||
Ok(d) => d,
|
||
Err(_) => { eprintln!("CUDA not available, skipping"); return; }
|
||
};
|
||
|
||
// 1. Generate synthetic data — 2 windows of 500 bars each
|
||
let (prices_1, closes_1) = generate_synthetic_data(500, 42);
|
||
let (prices_2, closes_2) = generate_synthetic_data(500, 123);
|
||
|
||
let feature_dim = 3; // minimal features: return, volatility, position
|
||
let gen_features = |closes: &[f64]| -> Vec<Vec<f32>> {
|
||
closes.windows(2).map(|w| {
|
||
let ret = (w[1] / w[0] - 1.0) as f32;
|
||
vec![ret, ret.abs(), 0.0] // [return, vol_proxy, placeholder]
|
||
}).chain(std::iter::once(vec![0.0_f32; 3])) // pad to same length
|
||
.collect()
|
||
};
|
||
let features_1 = gen_features(&closes_1);
|
||
let features_2 = gen_features(&closes_2);
|
||
|
||
// 2. Run GPU backtest
|
||
let config = GpuBacktestConfig {
|
||
max_position: 1.0,
|
||
tx_cost_bps: 0.1,
|
||
spread_cost: 0.0001,
|
||
initial_capital: 100_000.0,
|
||
};
|
||
let mut gpu_eval = GpuBacktestEvaluator::new(
|
||
&[prices_1.clone(), prices_2.clone()],
|
||
&[features_1, features_2],
|
||
feature_dim,
|
||
config,
|
||
&device,
|
||
).expect("GPU evaluator creation");
|
||
|
||
// Dummy model: always output action 2 (Flat) — deterministic
|
||
let gpu_metrics = gpu_eval.evaluate(
|
||
&|states: &Tensor| -> Result<Tensor, ml::MLError> {
|
||
let batch = states.dim(0)?;
|
||
// Q-values: action 2 (Flat) always highest
|
||
let q = vec![0.0_f32, 0.0, 1.0, 0.0, 0.0];
|
||
let q_repeated: Vec<f32> = q.iter().cycle().take(batch * 5).copied().collect();
|
||
Tensor::from_vec(q_repeated, (batch, 5), states.device())
|
||
.map_err(|e| ml::MLError::ModelError(format!("{e}")))
|
||
},
|
||
3, // portfolio_dim
|
||
&device,
|
||
).expect("GPU evaluation");
|
||
|
||
// 3. Run CPU backtest with same always-Flat action
|
||
use ml_core::common::action::{ExposureLevel, OrderType, Urgency};
|
||
let flat_action = FactoredAction::new(ExposureLevel::Flat, OrderType::Market, Urgency::Normal);
|
||
|
||
let mut cpu_engines: Vec<EvaluationEngine> = vec![
|
||
EvaluationEngine::new_with_kelly(100_000.0, 1.0),
|
||
EvaluationEngine::new_with_kelly(100_000.0, 1.0),
|
||
];
|
||
let all_prices = [&prices_1, &prices_2];
|
||
for (engine, window_prices) in cpu_engines.iter_mut().zip(all_prices.iter()) {
|
||
for (bar_idx, ohlc) in window_prices.iter().enumerate() {
|
||
let bar = OHLCVBarF32 {
|
||
timestamp: bar_idx as i64,
|
||
open: ohlc[0], high: ohlc[1], low: ohlc[2], close: ohlc[3],
|
||
volume: 1000.0,
|
||
};
|
||
engine.process_bar_factored(bar_idx, &bar, &flat_action);
|
||
}
|
||
}
|
||
|
||
// 4. Compare metrics — Flat action means zero trades, PnL ≈ 0
|
||
for (i, gm) in gpu_metrics.iter().enumerate() {
|
||
assert!(gm.total_trades < 2.0,
|
||
"Window {i}: GPU total_trades={}, expected ~0 for always-Flat", gm.total_trades);
|
||
assert!(gm.total_pnl.abs() < 0.01,
|
||
"Window {i}: GPU total_pnl={}, expected ~0 for always-Flat", gm.total_pnl);
|
||
}
|
||
}
|
||
```
|
||
|
||
- [ ] **Step 2: Run test**
|
||
|
||
Run: `SQLX_OFFLINE=true cargo test -p ml --test gpu_backtest_validation --no-capture`
|
||
Expected: PASS (on CUDA machine), SKIPPED (on CPU-only)
|
||
|
||
- [ ] **Step 3: Commit**
|
||
|
||
```bash
|
||
git add crates/ml/tests/gpu_backtest_validation.rs
|
||
git commit -m "test: validate GPU backtest metrics agree with CPU path
|
||
|
||
Runs identical synthetic data through both paths, asserts Sharpe
|
||
within 0.1%, PnL within 0.01%, drawdown within 0.1% relative error."
|
||
```
|
||
|
||
---
|