 GlyphRunner System
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ae13f78c22
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Initial commit: 2125_GCE project
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2026-07-09 12:54:44 -04:00 |
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GlyphRunner System
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b4ba84c1d2
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Refine XIC v1 to Symbolic Extension Only (No GPU Code)
Removed GPU-related code per specification:
- Deleted xic_extensions/gpu_runtime.py
- Removed GPU logic from op_RUN_PROMPT and op_STREAM
- Removed demo_gpu.gx.json
Kept pure symbolic extension:
- 5 new instructions: STREAM, CHAIN, CALL_GLYPH, SET_CONTEXT, LOG
- Symbolic execution mode via SET_MODE "symbolic"
- run_symbolic_prompt() integration with LAIN cognition layer
- demo_symbolic.gx.json for testing
Implementation now focuses exclusively on:
- Extending instruction set (9 total ops)
- Adding symbolic routing to cognition layer
- Preserving backward compatibility (zero breaking changes)
- No external GPU dependencies
All validation tests pass:
✅ OP_TABLE coverage (9 operations)
✅ XICContext.symbolic_mode field
✅ run_symbolic_prompt() callable
✅ Backward compatibility (demo_chat unchanged)
✅ Symbolic mode execution (LAIN pipeline)
✅ SET_CONTEXT, CHAIN, RUN_PROMPT routing
Constraints met:
✅ No breaking changes
✅ No XIC v2 binary format
✅ No GPU-related code
✅ Strict v1 JSON + .gx architecture
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2026-05-21 01:23:48 -04:00 |
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GlyphRunner System
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69c97e125a
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Extend XIC v1 Engine with Symbolic Mode, 5 New Ops, GPU Path, Cognition Integration
New instructions:
- STREAM: Line-by-line execution and output
- CHAIN: Named execution boundaries
- CALL_GLYPH: Invoke glyph-aware cognition
- SET_CONTEXT: Set symbolic/cognitive context metadata
- LOG: Structured logging
Symbolic execution mode:
- SET_MODE "symbolic" routes prompts through LAIN 8-lane cognition pipeline
- run_symbolic_prompt() compresses prompt, builds manifest, executes via execute_symbolic()
- Full integration with glyphos/cognitive_kernel.py
GPU-accelerated path:
- xic_extensions/gpu_runtime.py: has_gpu() probes torch.cuda, run_on_gpu() executes
- SET_PARAM "use_gpu" true enables GPU (auto-fallback to CPU if unavailable)
- No required GPU dependencies; system works equally on CPU
Demo programs:
- demo_symbolic.gx.json: Shows symbolic mode through LAIN pipeline
- demo_gpu.gx.json: Shows GPU mode with CPU fallback
Backward compatibility:
- All 4 original ops unchanged; 5 new ops added to OP_TABLE
- xic_vm.py, xic_executor.py: No changes (pure dispatcher pattern holds)
- demo_chat.gx.json: Still executes identically
- All existing GlyphRunner commands: Unchanged behavior
Architecture:
- Lazy imports prevent circular dependencies (xic_ops, glyphos, xic_extensions)
- Clean separation: XIC is client of cognition layer
- Zero breaking changes; additive extension only
- No XIC v2 binary format; all within v1 JSON+.gx architecture
Validation:
- 10 integration tests: all passing
- Backward compat verified with original demo
- Symbolic and GPU modes tested end-to-end
- No external dependencies required (GPU optional)
Co-contributors: LAIN cognition engine, gx_compiler GSZ3, glyphos event system
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2026-05-21 01:19:40 -04:00 |
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GlyphRunner System
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43887931cc
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Complete GlyphRunner Implementation: All Subsystems & Integration Tests
This commit includes the complete implementation of the GlyphRunner system:
SUBSYSTEMS CREATED:
1. xic_extensions (5 modules)
- gsz3_decompressor: Compression/decompression with checksum validation
- segment_runtime: Multi-segment execution with namespace merging
- execution_tracer: Execution tracing with event capture
- profiler: Lightweight segment profiling (duration, memory, counts)
- compressed_engine: High-level orchestration (simulate/execute modes)
2. gx_compiler (5 modules)
- segmenter: Deterministic source code segmentation
- compressor: GSZ3 compression wrapper
- manifest_builder: XIC/GX manifest generation
- gx_packer: Binary .gx file format (XIC header + manifest + payload)
- compiler: High-level compilation pipeline
3. runtime_executor (6 modules)
- gx_loader: .gx file loading and parsing
- execution_plan: Execution plan building from manifest
- context: Runtime execution context management
- runner: Core execution engine with tracing/profiling
- events: Runtime event system and event bus
- integration: High-level API (run_gx_with_summary)
4. gx_cli (5 modules)
- commands: Command implementations (compile, run, inspect, summary)
- parser: argparse-based argument parsing
- dispatcher: Command routing and execution
- main: CLI entry point with exception handling
5. codex_lineage (6 modules)
- lineage_model: Data structures (EpochInfo, ContributorInfo, etc.)
- epoch_mapper: Version string parsing (v1, v2.5-beta, etc.)
- contributor_index: In-memory contributor registry
- lineage_resolver: Manifest → CodexEntry resolution
- grammar_hooks: Human-readable report generation
- inspector: High-level .gx file inspection utility
INTEGRATION TESTS (7 test files)
- test_compile: Compilation pipeline tests
- test_run: Execution verification tests
- test_inspect: Inspection and manifest tests
- test_summary: Summary generation tests
- test_errors: Error handling and graceful failure
- test_determinism: Reproducibility and determinism
- run_all_tests: Master test runner
ARCHITECTURE HIGHLIGHTS:
✓ Zero circular imports
✓ Pure functions where possible
✓ Explicit error handling
✓ No global side effects
✓ Only stdlib dependencies
✓ Deterministic output
✓ Production-ready code
PIPELINE:
sample.py → [gx_compiler] → sample.gx (960 bytes, XIC format)
→ [runtime_executor] → Execution (6 segments)
→ [codex_lineage] → Human-readable lineage report
CLI COMMANDS:
gx compile <source.py> [-o output.gx]
gx run <file.gx>
gx inspect <file.gx>
gx summary <file.gx>
VERIFICATION:
✓ All 5 subsystems created and tested
✓ Full pipeline: compile → inspect → execute
✓ Codex lineage fully integrated with gx_cli
✓ 25+ integration test cases
✓ End-to-end testing successful
✓ No external dependencies beyond Python stdlib
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
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2026-05-20 10:54:44 -04:00 |
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