Captures the ENTIRE per-step training pipeline into ONE cuGraphLaunch,
eliminating ~150 individual kernel launches and ~143ms of host-side
Rust overhead per step.
Phase 1: Lobsim → raw_launch
- apply_snapshot_from_device inlined as 4× raw_memcpy_dtod + raw_launch
- step_fill_from_market_targets inlined as 2× raw_launch
- LobSimRawPtrs trait method caches 30+ device pointers
Phase 2: LR controller → GPU kernel
- New rl_lr_from_mapped_pinned.cu reads losses from device pointers
- New adamw_step_isv_lr kernel reads LR from ISV instead of scalar arg
- All 12 Adam .step() calls use step_isv_lr() in mega-graph mode
Phase 3: PER → main stream
- mega_graph_single_stream flag routes PER to self.raw_stream
- Cross-stream events skipped, K forced to 1
Phase 4: Mega-graph capture/replay
- Three-state machine: warmup → capture → replay
- enable_mega_graph() propagates to perception trainer
- Perception sub-graph guards prevent sub-captures inside mega-capture
- grad_h_accumulate_scaled_isv reads lambda from ISV on-device
Validated: 500 steps, 68-76 sps sustained, no NaN, all losses finite.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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