51004c311c
Step 1 — Foundation: - OpRecord/OpLogger: op log infrastructure with t_start stable ordering - MemoryStore: numpy ndarray tensor-granular storage (reference semantics) - data_op=True flag on DmaReadCmd, DmaWriteCmd, GemmCmd, MathCmd, CompositeCmd - numpy/greenlet dependencies added to pyproject.toml Step 2 — ComponentBase hooks: - _on_process_start/end hooks in _forward_txn (fabric messages) - _handle_with_hooks in PeEngineBase (PE-internal commands) - op_logger optional — zero overhead when disabled Step 3 — KernelRunner + greenlet: - KernelRunner: greenlet ↔ SimPy bridge in triton_emu/kernel_runner.py - TLContext: _emit() method routes to greenlet switch or command list - tl.load() returns real numpy data in greenlet mode - Dynamic control flow supported (memory-read based branching) Step 4 — PE_CPU integration: - Greenlet mode when ctx.memory_store is set, legacy fallback otherwise - Refactored into _execute_greenlet/_execute_legacy/_send_response - ComponentContext gains memory_store and op_logger fields Step 5 — DataExecutor: - Phase 2 numpy execution for GEMM/Math ops from op_log - _compute_math: all unary/binary/reduction ops - verify(): compare MemoryStore against expected with dtype tolerance 28 new tests, 366 total passing. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>