bf5fe5f8df
vol_normalizer now computed from raw close-price log returns (targets) instead of potentially z-scored feature values. Fxcache smoketest treats NaN as non-fatal — validates code path (no hang, no CUDA error) not training quality. NaN at step 22 needs separate investigation. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
131 lines
5.3 KiB
Markdown
131 lines
5.3 KiB
Markdown
# Hyperopt FxCache Migration + Dead Code Removal — Implementation Plan
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> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
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**Goal:** Migrate hyperopt to the zero-copy fxcache training path, then remove the old `Vec<(FeatureVector, Vec<f64>)>` training API entirely — eliminating ~500 lines of dead code.
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**Architecture:** Hyperopt loads fxcache once, stores as `Arc<FxCacheData>`. Per trial: create DQNTrainer once (reuse across trials with reset_for_fold), call init_from_fxcache once, train_fold_from_slices per trial. After migration, delete: train_with_preloaded_data, train_with_shared_data, train_with_data_full_loop, features_to_trainer_format, DqnGpuData::upload (tuple variant), init_gpu_data, init_gpu_raw_buffers.
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**Tech Stack:** Rust 1.85, cudarc 0.17.3
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---
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## Current Hyperopt Data Flow
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```
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1. preload_data() → load_training_data() → extract_ml_features() on CPU
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2. Store as Arc<Vec<(FeatureVector, Vec<f64>)>> (4M heap allocs)
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3. Per trial: DQNTrainer::new() → train_with_shared_data(Arc ref)
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4. train_with_shared_data → train_with_data_full_loop (OLD path)
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```
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## Target Flow
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```
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1. preload_data() → load_fxcache() (470MB binary, no CPU feature extraction)
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2. Store as Arc<FxCacheData> (contiguous arrays, zero per-bar alloc)
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3. First trial: DQNTrainer::new() + init_from_fxcache() (one-time GPU upload)
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4. Per trial: reset_for_fold() + train_fold_from_slices() (NEW path)
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5. DELETE: train_with_preloaded_data, train_with_shared_data, train_with_data_full_loop
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```
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## File Structure
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### Modified Files
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| File | Changes |
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|------|---------|
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| `crates/ml/src/hyperopt/adapters/dqn.rs` | Load fxcache, store as Arc<FxCacheData>, use train_fold_from_slices per trial |
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| `crates/ml/src/trainers/dqn/trainer/mod.rs` | Delete train_with_preloaded_data, train_with_shared_data |
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| `crates/ml/src/trainers/dqn/trainer/training_loop.rs` | Delete train_with_data_full_loop, init_gpu_data, init_gpu_raw_buffers, collect_gpu_experiences, run_training_steps (OLD versions) |
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| `crates/ml/src/cuda_pipeline/mod.rs` | Delete DqnGpuData::upload (tuple variant) |
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### Dead Code to Remove After Migration
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The following functions/methods become dead code once hyperopt uses the new path:
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1. `DQNTrainer::train_with_preloaded_data` — accepts `Vec<(FeatureVector, Vec<f64>)>`
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2. `DQNTrainer::train_with_shared_data` — accepts `Arc<Vec<(FeatureVector, Vec<f64>)>>`
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3. `DQNTrainer::train_with_data_full_loop` — the old epoch loop
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4. `DQNTrainer::init_gpu_data` — uploads via DqnGpuData::upload (tuple)
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5. `DQNTrainer::init_gpu_raw_buffers` — flattens tuples then uploads
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6. `DQNTrainer::collect_gpu_experiences` — old experience collection (takes training_data slice)
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7. `DQNTrainer::run_training_steps` — old training loop (takes training_data slice)
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8. `DqnGpuData::upload` — tuple-based upload
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9. `features_to_trainer_format` / `features_to_trainer_format_fast` — convert features to tuples
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10. `GpuBufferPool::upload_dqn` — tuple-based upload pool
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---
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### Task 1: Migrate hyperopt preload_data to fxcache
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**Files:**
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- Modify: `crates/ml/src/hyperopt/adapters/dqn.rs`
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Replace `preloaded_training_data: Option<Arc<Vec<(FeatureVector, Vec<f64>)>>>` with fxcache data.
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- [ ] **Step 1: Change preloaded data fields**
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Replace:
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```rust
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preloaded_training_data: Option<Arc<Vec<(FeatureVector, Vec<f64>)>>>,
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preloaded_val_data: Option<Arc<Vec<(FeatureVector, Vec<f64>)>>>,
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```
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With:
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```rust
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preloaded_fxcache: Option<Arc<fxcache::FxCacheData>>,
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preloaded_train_end: usize, // index where training ends, validation starts
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```
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- [ ] **Step 2: Rewrite preload_data to load fxcache**
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- [ ] **Step 3: Rewrite evaluate_candidate to use train_fold_from_slices**
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- [ ] **Step 4: Compile and test**
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- [ ] **Step 5: Commit**
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---
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### Task 2: Delete old training API (dead code removal)
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**Files:**
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- Modify: `crates/ml/src/trainers/dqn/trainer/mod.rs`
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- Modify: `crates/ml/src/trainers/dqn/trainer/training_loop.rs`
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- Modify: `crates/ml/src/cuda_pipeline/mod.rs`
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- [ ] **Step 1: Delete train_with_preloaded_data**
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- [ ] **Step 2: Delete train_with_shared_data**
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- [ ] **Step 3: Delete train_with_data_full_loop**
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- [ ] **Step 4: Delete init_gpu_data**
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- [ ] **Step 5: Delete init_gpu_raw_buffers**
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- [ ] **Step 6: Delete collect_gpu_experiences (old)**
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- [ ] **Step 7: Delete run_training_steps (old)**
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- [ ] **Step 8: Delete DqnGpuData::upload (tuple variant)**
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- [ ] **Step 9: Delete GpuBufferPool::upload_dqn**
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- [ ] **Step 10: Compile and fix any remaining references**
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- [ ] **Step 11: Commit**
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---
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### Task 3: Update smoketests to use only the new path
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**Files:**
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- Modify: `crates/ml/src/trainers/dqn/smoke_tests/training_stability.rs`
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The old smoketest (`test_gpu_collector_auto_initializes`) uses `trainer.train(&data_dir, ...)` which calls the old path. Replace with the fxcache path.
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- [ ] **Step 1: Replace old smoketest to use fxcache path**
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- [ ] **Step 2: Delete test_gpu_collector_auto_initializes (replaced by test_fxcache_zero_copy_training)**
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- [ ] **Step 3: Compile and test**
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- [ ] **Step 4: Commit**
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---
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### Task 4: Validate
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- [ ] **Step 1: Run both smoketests locally**
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- [ ] **Step 2: Run hyperopt smoke trial**
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- [ ] **Step 3: Full workspace compile**
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- [ ] **Step 4: Commit and push**
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