From a4141bddb76fbadcc53ebca8a1b9a4e53851dab3 Mon Sep 17 00:00:00 2001 From: Ecaterina Date: Fri, 10 Apr 2026 02:36:04 +0300 Subject: [PATCH 1/5] Fix unit tests and lock PyTorch to 2.4.1 --- libucks/thinking/latent_strategy.py | 2 +- libucks/thinking/training/data_generator.py | 2 + pyproject.toml | 5 +- uv.lock | 652 ++++++++------------ 4 files changed, 281 insertions(+), 380 deletions(-) diff --git a/libucks/thinking/latent_strategy.py b/libucks/thinking/latent_strategy.py index 9f5d633..0818e02 100644 --- a/libucks/thinking/latent_strategy.py +++ b/libucks/thinking/latent_strategy.py @@ -28,7 +28,7 @@ def __init__( model_manager: object | None = None, compressor: object | None = None, injection_gate: float = 0.3, - temperature: float = 0.8, + temperature: float = 0.7, top_p: float = 0.9, repetition_penalty: float = 1.3, receive_temperature: float = 0.6, diff --git a/libucks/thinking/training/data_generator.py b/libucks/thinking/training/data_generator.py index d460287..d303582 100644 --- a/libucks/thinking/training/data_generator.py +++ b/libucks/thinking/training/data_generator.py @@ -38,6 +38,8 @@ def _read_chunk_content(meta) -> str: def _collect_source_text(front_matter, max_chars: int = 3000) -> str: """Concatenate actual code content from ChunkMetadata, up to max_chars.""" + if not hasattr(front_matter, "chunks"): + return "" parts = [] total = 0 for meta in front_matter.chunks: diff --git a/pyproject.toml b/pyproject.toml index bb741af..edb3398 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,7 +27,10 @@ dependencies = [ [project.optional-dependencies] latent = [ - "torch>=2.2", + "torch==2.4.1; sys_platform == 'linux' or (sys_platform == 'darwin' and platform_machine == 'arm64')", + "torch>=2.2,<=2.2.2; sys_platform == 'darwin' and platform_machine == 'x86_64' and python_version < '3.13'", + "bitsandbytes>=0.43; sys_platform == 'linux'", + "bitsandbytes>=0.43; sys_platform == 'darwin' and platform_machine == 'arm64'", "transformers>=4.40", 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a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..73c715a26f681748370f7b5245bae0bbe8606fc3 GIT binary patch literal 8196 zcmeHMTTc@~6g~sRZbQI6Am9VZCcXj|@Dk%oTiV1%ytRN2YSwPI;>xny^g@G#q_66K z@Gto2Kk>i#q~DoY$nJJA>Vq*b)6AUNne&~Q^UY;@S|SqVfwM-mNJIuQ%ltHol*Ij< z$I?XDG6yMuC#umlRcRCD&X~4t&Gy|Fe&46a$e_#ObY)-~G?|s#)OU;00;8HTc z_k)SdvKY#dlJe?6AwvMj44P#^9dUrfn4v6&a-^h$!lsHILuhHSBq$0 zueI%AuM_h5)$Vw1uU{y96BCn}sVi9{YfKxj8gGMPqu=ZgdgW&K1wU;EfoGrAoA$2T z9$NXi&A{(BUBBIx0M}_l<>hnNcYmZ0FU(e=H+XV)2(j}rt^+q8$z z3TX4`4F&O~2k}IoBbzFVjn|aAtJI;Rgf?%esP;UxJJiFH+Q9TtYhn}!Pe*ua<9US& zn8RJU;Xn9QdlE!w5^G`!rr(UEgFFjZKs$Rn6GB32_dVitV2 z;H3*+u@(u>aL8lYQ_$fR8rKAWG0iNF+ac!c$;)FuVHb?Ww69AKEpYaiR|qo^vxp_p zNi%S97?@P9EOPu`#huMkC(VFn;Bqk_GL=T944+TG?<>fKO{g-a7|M~7XhHh-KLq?{2%+Eqa(#4hF&X#?6tiM@ literal 0 HcmV?d00001 diff --git a/libucks/_cli.py b/libucks/_cli.py index aacbc3c..6c80cd8 100644 --- a/libucks/_cli.py +++ b/libucks/_cli.py @@ -126,6 +126,10 @@ async def _run_train_adapter(repo_path: Path, creative: bool, epochs: int) -> No adapter = CommunicationAdapter(hidden_dim=_hidden_dim) adapter.load_saved_weights(bucket_dir / "adapter.pt") + from libucks.thinking.model_manager import ModelManager as _MM + _training_device = _MM._resolve_device(cfg.model.device) + adapter = adapter.to(_training_device) + if creative: click.echo(f"[libucks] Creative contrastive training on {len(bucket_ids)} buckets " f"for {epochs} epoch(s)...") @@ -210,6 +214,7 @@ async def _train_basic(cfg, registry, store, bucket_ids, adapter, epochs, bucket store=store, ) optimizer = AdamW(adapter.parameters(), lr=1e-4) + _device = next(adapter.parameters()).device for epoch in range(epochs): total_loss = 0.0 @@ -220,10 +225,11 @@ async def _train_basic(cfg, registry, store, bucket_ids, adapter, epochs, bucket click.echo(f" Skipped {bucket_id}: {exc}", err=True) continue + latents = [t.clone().detach().to(_device) for t in sample.librarian_latents] optimizer.zero_grad() - output = adapter(sample.librarian_latents) + output = adapter(latents) anchor = F.normalize(output.mean(dim=0), dim=0) - target = F.normalize(sample.target_latent.mean(dim=0), dim=0) + target = F.normalize(sample.target_latent.clone().detach().to(_device).mean(dim=0), dim=0) loss = 1.0 - torch.dot(anchor, target) loss.backward() optimizer.step() diff --git a/tests/unit/test_curriculum_data.py b/tests/unit/test_curriculum_data.py index 70fd301..01cde78 100644 --- a/tests/unit/test_curriculum_data.py +++ b/tests/unit/test_curriculum_data.py @@ -24,6 +24,8 @@ def mock_latent_strategy(): strategy = MagicMock() # encode() returns a (5, D) tensor strategy.encode = AsyncMock(return_value=torch.randn(5, D)) + # reason() is also awaited in generate_curriculum_batch() + strategy.reason = AsyncMock(return_value=torch.randn(5, D)) return strategy @@ -49,6 +51,9 @@ def mock_embedding(): """Fake embedding layer — maps token ID to D-dim vector.""" emb = MagicMock() emb.return_value = torch.randn(K, D) # returns (K, D) for any input + mock_param = MagicMock() + mock_param.device = torch.device("cpu") + emb.parameters.side_effect = lambda: iter([mock_param]) return emb From 42ab29dd5c0e9784e2515b61fa7685331002f4f4 Mon Sep 17 00:00:00 2001 From: Ecaterina Date: Fri, 10 Apr 2026 12:36:07 +0300 Subject: [PATCH 3/5] Fix: set bnb_4bit_compute_dtype to float16 for massive speedup --- libucks/thinking/model_manager.py | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/libucks/thinking/model_manager.py b/libucks/thinking/model_manager.py index b36ee10..588abbb 100644 --- a/libucks/thinking/model_manager.py +++ b/libucks/thinking/model_manager.py @@ -25,6 +25,7 @@ def load( self, model_id: str, quantization: str = "none", + bnb_4bit_compute_dtype: str = "float32", device: str = "auto", ) -> None: """Load model and tokenizer from HuggingFace hub or local cache.""" @@ -32,11 +33,17 @@ def load( import torch + _DTYPE_MAP = {"float16": torch.float16, "float32": torch.float32, "bfloat16": torch.bfloat16} + _compute_dtype = _DTYPE_MAP.get(bnb_4bit_compute_dtype, torch.float32) + kwargs: dict = {"device_map": resolved_device} if quantization == "4bit": from transformers import BitsAndBytesConfig - kwargs["quantization_config"] = BitsAndBytesConfig(load_in_4bit=True) + kwargs["quantization_config"] = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_compute_dtype=_compute_dtype, + ) elif quantization == "8bit": from transformers import BitsAndBytesConfig kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True) @@ -93,6 +100,7 @@ def load_base_model( self, model_id: str, quantization: str = "none", + bnb_4bit_compute_dtype: str = "float32", device: str = "auto", ) -> None: """Load the Base receiver model for latent injection (Interlat-Lite decoder). @@ -104,11 +112,17 @@ def load_base_model( import torch + _DTYPE_MAP = {"float16": torch.float16, "float32": torch.float32, "bfloat16": torch.bfloat16} + _compute_dtype = _DTYPE_MAP.get(bnb_4bit_compute_dtype, torch.float32) + kwargs: dict = {"device_map": resolved_device} if quantization == "4bit": from transformers import BitsAndBytesConfig - kwargs["quantization_config"] = BitsAndBytesConfig(load_in_4bit=True) + kwargs["quantization_config"] = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_compute_dtype=_compute_dtype, + ) elif quantization == "8bit": from transformers import BitsAndBytesConfig kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True) From 26d7fb76a21e7f813bb4444f12c33996146bedac Mon Sep 17 00:00:00 2001 From: Ecaterina Date: Fri, 10 Apr 2026 12:42:58 +0300 Subject: [PATCH 4/5] Fix: cast training latents to float32 to match adapter --- libucks/_cli.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/libucks/_cli.py b/libucks/_cli.py index 6c80cd8..c1f4bcb 100644 --- a/libucks/_cli.py +++ b/libucks/_cli.py @@ -225,11 +225,12 @@ async def _train_basic(cfg, registry, store, bucket_ids, adapter, epochs, bucket click.echo(f" Skipped {bucket_id}: {exc}", err=True) continue - latents = [t.clone().detach().to(_device) for t in sample.librarian_latents] + latents = [t.clone().detach().to(_device, torch.float32) for t in sample.librarian_latents] + latents = [t.to(torch.float32) for t in latents] optimizer.zero_grad() output = adapter(latents) anchor = F.normalize(output.mean(dim=0), dim=0) - target = F.normalize(sample.target_latent.clone().detach().to(_device).mean(dim=0), dim=0) + target = F.normalize(sample.target_latent.clone().detach().to(_device, torch.float32).mean(dim=0), dim=0) loss = 1.0 - torch.dot(anchor, target) loss.backward() optimizer.step() From 6cbf729c358a07f319ce15d6e9f550a16a634595 Mon Sep 17 00:00:00 2001 From: ec4t3rina Date: Fri, 10 Apr 2026 13:37:21 +0000 Subject: [PATCH 5/5] Checkpoint: Fully functional Teacher-Student pipeline with fixed precision casting --- .ipynb_checkpoints/pyproject-checkpoint.toml | 58 + libucks/.ipynb_checkpoints/_cli-checkpoint.py | 365 ++++++ .../.ipynb_checkpoints/config-checkpoint.py | 142 +++ .../mcp_bridge-checkpoint.py | 290 +++++ .../translator-checkpoint.py | 286 +++++ .../latent_strategy-checkpoint.py | 461 ++++++++ libucks/thinking/latent_strategy.py | 6 +- libucks/translator.py | 200 +++- pyproject.toml | 2 + .../test_curriculum-checkpoint.py | 97 ++ .../test_curriculum_data-checkpoint.py | 172 +++ training.log | 63 + uv.lock | 1016 ++++++++++------- 13 files changed, 2754 insertions(+), 404 deletions(-) create mode 100644 .ipynb_checkpoints/pyproject-checkpoint.toml create mode 100644 libucks/.ipynb_checkpoints/_cli-checkpoint.py create mode 100644 libucks/.ipynb_checkpoints/config-checkpoint.py create mode 100644 libucks/.ipynb_checkpoints/mcp_bridge-checkpoint.py create mode 100644 libucks/.ipynb_checkpoints/translator-checkpoint.py create mode 100644 libucks/thinking/.ipynb_checkpoints/latent_strategy-checkpoint.py create mode 100644 tests/unit/.ipynb_checkpoints/test_curriculum-checkpoint.py create mode 100644 tests/unit/.ipynb_checkpoints/test_curriculum_data-checkpoint.py create mode 100644 training.log diff --git a/.ipynb_checkpoints/pyproject-checkpoint.toml b/.ipynb_checkpoints/pyproject-checkpoint.toml new file mode 100644 index 0000000..edb3398 --- /dev/null +++ b/.ipynb_checkpoints/pyproject-checkpoint.toml @@ -0,0 +1,58 @@ +[build-system] +requires = ["setuptools>=68", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "libucks" +version = "0.1.0" +requires-python = ">=3.11" +dependencies = [ + "pydantic>=2.0", + "sentence-transformers>=3.0", + "numpy>=1.26", + "anthropic>=0.40", + "httpx>=0.27", + "pyyaml>=6.0", + "tree-sitter>=0.22", + "gitpython>=3.1", + "scipy>=1.13", + "scikit-learn>=1.5", + "watchdog>=4.0", + "unidiff>=0.7", + "click>=8.1", + "rich>=13.0", + "mcp>=1.0", + "structlog>=24.0", +] + +[project.optional-dependencies] +latent = [ + "torch==2.4.1; sys_platform == 'linux' or (sys_platform == 'darwin' and platform_machine == 'arm64')", + "torch>=2.2,<=2.2.2; sys_platform == 'darwin' and platform_machine == 'x86_64' and python_version < '3.13'", + "bitsandbytes>=0.43; sys_platform == 'linux'", + "bitsandbytes>=0.43; sys_platform == 'darwin' and platform_machine == 'arm64'", + "transformers>=4.40", + "accelerate>=0.28", +] +dev = [ + "pytest>=8.0", + "pytest-asyncio>=0.23", + "pytest-timeout>=2.3", + "respx>=0.21", + "jsonschema>=4.22", +] + +[project.scripts] +libucks = "libucks._cli:cli" + +[tool.pytest.ini_options] +asyncio_mode = "auto" +testpaths = ["tests"] +markers = [ + "slow: marks tests that load the embedding model (deselect with '-m not slow')", + "gpu: marks tests that require a real model and GPU (deselect with '-m not gpu')", +] + +[tool.setuptools.packages.find] +where = ["."] +include = ["libucks*"] diff --git a/libucks/.ipynb_checkpoints/_cli-checkpoint.py b/libucks/.ipynb_checkpoints/_cli-checkpoint.py new file mode 100644 index 0000000..0013890 --- /dev/null +++ b/libucks/.ipynb_checkpoints/_cli-checkpoint.py @@ -0,0 +1,365 @@ +"""CLI entry point — lives inside the package so the console script works from any directory.""" +import asyncio +import json +import socket +import subprocess +from pathlib import Path + +import click + + +def _find_repo_root() -> Path: + """Return the git repo root for cwd, or cwd itself if not in a repo.""" + try: + result = subprocess.run( + ["git", "rev-parse", "--show-toplevel"], + capture_output=True, + text=True, + timeout=5, + ) + if result.returncode == 0: + return Path(result.stdout.strip()) + except Exception: + pass + return Path.cwd() + + +@click.group() +@click.version_option(version="0.1.0", prog_name="libucks") +def cli(): + """libucks — Librarian Buckets, local AI memory server for coding agents.""" + + +@cli.command("init") +@click.option("--local", "local_path", type=click.Path(exists=True, file_okay=False, path_type=Path), + required=True, help="Path to a local repository to index.") +def init_cmd(local_path: Path): + """Seed libucks buckets from a local repository.""" + from libucks.config import Config + from libucks.init_orchestrator import InitOrchestrator + from libucks.thinking import create_strategy + + cfg = Config.load(local_path) + strategy = create_strategy(cfg) + orchestrator = InitOrchestrator(local_path, strategy=strategy) + asyncio.run(orchestrator.run()) + + +@cli.command("serve") +def serve_cmd(): + """Start the libucks MCP server over stdio.""" + from libucks.mcp_bridge import serve + asyncio.run(serve()) + + +@cli.command("install-hooks") +@click.option("--repo", "repo_path", type=click.Path(exists=True, file_okay=False, path_type=Path), + default=None, help="Path to repository (defaults to git repo containing cwd).") +def install_hooks_cmd(repo_path: Path | None): + """Append libucks git hook triggers to .git/hooks/ (never overwrites).""" + from libucks.git_hook_receiver import install_hooks + + target = repo_path or _find_repo_root() + modified = install_hooks(target) + if modified: + click.echo(f"Installed hooks: {', '.join(modified)}") + else: + click.echo("All hooks already installed — nothing changed.") + + +@cli.command("train-adapter") +@click.option("--repo", "repo_path", type=click.Path(exists=True, file_okay=False, path_type=Path), + default=None, help="Path to repository (defaults to git repo containing cwd).") +@click.option("--creative", is_flag=True, default=False, + help="Use contrastive training with multi-perspective triplets and hard negatives.") +@click.option("--epochs", default=1, show_default=True, help="Number of training epochs.") +def train_adapter_cmd(repo_path: Path | None, creative: bool, epochs: int): + """Train the CommunicationAdapter to align Librarian latents with teacher targets. + + With --creative: generates multi-perspective triplets (Summary, Logic Flow, + Dependency Map) and mines hard negatives for InfoNCE contrastive training. + + Without --creative: uses basic MSE alignment between adapter output and + teacher target latents. + + Saves trained weights to /.libucks/adapter.pt. + """ + target = repo_path or _find_repo_root() + asyncio.run(_run_train_adapter(target, creative=creative, epochs=epochs)) + + +async def _run_train_adapter(repo_path: Path, creative: bool, epochs: int) -> None: + from libucks.config import Config + from libucks.thinking import create_strategy + from libucks.thinking.communication_adapter import CommunicationAdapter + from libucks.storage.bucket_store import BucketStore + from libucks.storage.bucket_registry import BucketRegistry + + cfg = Config.load(repo_path) + + if cfg.model.strategy != "latent": + raise click.ClickException( + "train-adapter requires strategy='latent' in .libucks/config.toml.\n" + "The CommunicationAdapter operates on torch.Tensor hidden states; " + "TextStrategy returns strings and cannot be used for training.\n\n" + "Set the following in your config and re-run:\n\n" + " [model]\n" + " strategy = \"latent\"\n" + " local_model = \"Qwen/Qwen2.5-0.5B-Instruct\"\n" + " device = \"mps\" # or cuda / cpu" + ) + + registry_path = repo_path / cfg.paths.registry_file + bucket_dir = repo_path / ".libucks" + + registry = BucketRegistry(registry_path) + registry.load() + store = BucketStore(repo_path / cfg.paths.bucket_dir) + + bucket_ids = list(registry.get_all_centroids().keys()) + if not bucket_ids: + click.echo("No buckets found — run `libucks init` first.", err=True) + return + + from transformers import AutoConfig as _AutoConfig + _hidden_dim = _AutoConfig.from_pretrained(cfg.model.local_model).hidden_size + adapter = CommunicationAdapter(hidden_dim=_hidden_dim) + adapter.load_saved_weights(bucket_dir / "adapter.pt") + + from libucks.thinking.model_manager import ModelManager as _MM + _training_device = _MM._resolve_device(cfg.model.device) + adapter = adapter.to(_training_device) + + if creative: + click.echo(f"[libucks] Creative contrastive training on {len(bucket_ids)} buckets " + f"for {epochs} epoch(s)...") + await _train_creative(cfg, registry, store, bucket_ids, adapter, epochs, bucket_dir) + else: + click.echo(f"[libucks] Basic MSE training on {len(bucket_ids)} buckets " + f"for {epochs} epoch(s)...") + await _train_basic(cfg, registry, store, bucket_ids, adapter, epochs, bucket_dir) + + +async def _train_creative(cfg, registry, store, bucket_ids, adapter, epochs, bucket_dir): + """Creative mode: multi-perspective + hard negatives + InfoNCE.""" + import os + from libucks.thinking import create_strategy + from libucks.thinking.latent_strategy import LatentStrategy + from libucks.thinking.text_strategy import TextStrategy + from libucks.thinking.training.data_generator import MultiPerspectiveDataGenerator + from libucks.thinking.training.train_adapter import ContrastiveAdapterTrainer + + text_strategy = TextStrategy.from_env(cfg.model.anthropic_model) + + # Use latent strategy only if configured; otherwise encode via text (fallback) + if cfg.model.strategy == "latent": + latent_strategy = create_strategy(cfg) + else: + click.echo("[libucks] Warning: strategy='text' — encoding via TextStrategy passthrough.", + err=True) + latent_strategy = TextStrategy.from_env(cfg.model.anthropic_model) + + generator = MultiPerspectiveDataGenerator( + text_strategy=text_strategy, + latent_strategy=latent_strategy, + registry=registry, + store=store, + ) + trainer = ContrastiveAdapterTrainer(adapter, temperature=0.07, lr=1e-4) + + samples = [] + for i, bucket_id in enumerate(bucket_ids, 1): + click.echo(f" Generating sample {i}/{len(bucket_ids)}: {bucket_id}") + try: + sample = await generator.generate(bucket_id) + samples.append(sample) + except Exception as exc: + click.echo(f" Skipped {bucket_id}: {exc}", err=True) + + if not samples: + click.echo("No training samples generated.", err=True) + return + + losses = trainer.train(samples, num_epochs=epochs) + trainer.save(bucket_dir / "adapter.pt") + + first = sum(losses[:5]) / min(5, len(losses)) + last = sum(losses[-5:]) / min(5, len(losses)) + click.echo(f"Training complete. Loss: {first:.4f} → {last:.4f}. " + f"Saved to {bucket_dir / 'adapter.pt'}") + + +async def _train_basic(cfg, registry, store, bucket_ids, adapter, epochs, bucket_dir): + """Basic mode: MSE between adapter mean-pooled output and target latent.""" + import torch + import torch.nn.functional as F + from torch.optim import AdamW + from libucks.thinking import create_strategy + from libucks.thinking.text_strategy import TextStrategy + from libucks.thinking.training.data_generator import ( + MultiPerspectiveDataGenerator, PERSPECTIVE_PROMPTS + ) + + text_strategy = TextStrategy.from_env(cfg.model.anthropic_model) + + if cfg.model.strategy == "latent": + latent_strategy = create_strategy(cfg) + else: + latent_strategy = TextStrategy.from_env(cfg.model.anthropic_model) + + generator = MultiPerspectiveDataGenerator( + text_strategy=text_strategy, + latent_strategy=latent_strategy, + registry=registry, + store=store, + ) + optimizer = AdamW(adapter.parameters(), lr=1e-4) + _device = next(adapter.parameters()).device + + for epoch in range(epochs): + total_loss = 0.0 + for i, bucket_id in enumerate(bucket_ids, 1): + try: + sample = await generator.generate(bucket_id) + except Exception as exc: + click.echo(f" Skipped {bucket_id}: {exc}", err=True) + continue + + latents = [t.clone().detach().to(_device, torch.float32) for t in sample.librarian_latents] + optimizer.zero_grad() + output = adapter(latents) + anchor = F.normalize(output.mean(dim=0), dim=0) + target = F.normalize(sample.target_latent.clone().detach().to(_device, torch.float32).mean(dim=0), dim=0) + loss = 1.0 - torch.dot(anchor, target) + loss.backward() + optimizer.step() + total_loss += loss.item() + click.echo(f" Epoch {epoch+1} [{i}/{len(bucket_ids)}] loss={loss.item():.4f}") + + torch.save(adapter.state_dict(), bucket_dir / "adapter.pt") + click.echo(f"Basic training complete. Saved to {bucket_dir / 'adapter.pt'}") + + +@cli.command("query") +@click.argument("query_text") +@click.option("--repo", "repo_path", type=click.Path(exists=True, file_okay=False, path_type=Path), + default=None, help="Path to repository (defaults to git repo containing cwd).") +@click.option("--top-k", default=3, show_default=True, help="Number of buckets to consult.") +def query_cmd(query_text: str, repo_path: Path | None, top_k: int): + """Run a single query against the local memory engine and print the answer. + + Bypasses the MCP server entirely — no 60-second timeout. Useful for + validating the full inference pipeline from the terminal. + + Example: + libucks query "How does the authentication module work?" + """ + target = repo_path or _find_repo_root() + asyncio.run(_run_query(target, query_text, top_k)) + + +async def _run_query(repo_path: Path, query_text: str, top_k: int) -> None: + import sys + from libucks.config import Config + from libucks.thinking import create_strategy + from libucks.thinking.communication_adapter import CommunicationAdapter + from libucks.embeddings.embedding_service import EmbeddingService + from libucks.storage.bucket_registry import BucketRegistry + from libucks.storage.bucket_store import BucketStore + from libucks.central_agent import CentralAgent + from libucks.librarian import Librarian + from libucks.query_orchestrator import QueryOrchestrator + from libucks.translator import Translator + + cfg = Config.load(repo_path) + registry_path = repo_path / cfg.paths.registry_file + bucket_dir = repo_path / ".libucks" + bucket_store_dir = repo_path / cfg.paths.bucket_dir + + click.echo(f"[libucks] repo={repo_path} strategy={cfg.model.strategy}", err=True) + + registry = BucketRegistry(registry_path) + registry.load() + store = BucketStore(bucket_store_dir) + + bucket_ids = list(registry.get_all_centroids().keys()) + if not bucket_ids: + click.echo("No buckets found — run `libucks init` first.", err=True) + return + + click.echo(f"[libucks] {len(bucket_ids)} buckets loaded", err=True) + + # Load embedding model (suppress stdout during model loading) + _real_stdout = sys.stdout + sys.stdout = sys.stderr + try: + embedder = EmbeddingService.get_instance(cfg.model.embedding_model) + finally: + sys.stdout = _real_stdout + + click.echo("[libucks] embedding model ready, loading strategy...", err=True) + strategy = create_strategy(cfg) + click.echo("[libucks] strategy ready", err=True) + + agent = CentralAgent(registry, cfg, embed_fn=embedder.embed) + librarians: dict[str, Librarian] = {} + for bucket_id in bucket_ids: + lib = Librarian( + bucket_id=bucket_id, + store=store, + registry=registry, + strategy=strategy, + embedder=embedder, + mitosis_threshold=cfg.routing.mitosis_threshold, + ) + librarians[bucket_id] = lib + agent.register_librarian(bucket_id, lib) + + adapter = None + if cfg.model.strategy == "latent": + import torch + resolved_device = cfg.model.device if cfg.model.device != "auto" else "mps" + adapter = CommunicationAdapter(hidden_dim=strategy.hidden_dim) + adapter.load_saved_weights(bucket_dir / "adapter.pt") + # dtype must match the model's output dtype. ModelManager loads Qwen in + # float16 on MPS; float32 adapter parameters cause an MPS broadcast error. + adapter_dtype = torch.float16 if resolved_device == "mps" else None + adapter = adapter.to(device=resolved_device, dtype=adapter_dtype) + + translator = Translator(strategy, adapter=adapter) + + orchestrator = QueryOrchestrator( + central_agent=agent, + librarians=librarians, + embed_fn=embedder.embed, + top_k=top_k, + ) + + click.echo(f"[libucks] routing: \"{query_text}\"", err=True) + representations = await orchestrator.query(query_text) + click.echo(f"[libucks] {len(representations)} representations, synthesizing...", err=True) + + answer = await translator.synthesize(query_text, representations) + + # Answer goes to stdout so it can be piped / captured cleanly. + click.echo(answer) + + +@cli.command("hook") +@click.argument("event") +@click.argument("args", nargs=-1) +def hook_cmd(event: str, args: tuple): + """Send a git hook event to the running libucks server (called by git hooks).""" + repo_path = _find_repo_root() + sock_path = repo_path / ".libucks" / "server.sock" + if not sock_path.exists(): + return # server not running — silent exit so git is never blocked + + payload = json.dumps({"event": event, "args": list(args)}).encode() + try: + with socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) as s: + s.settimeout(3) + s.connect(str(sock_path)) + s.sendall(payload) + except Exception: + pass # never block git diff --git a/libucks/.ipynb_checkpoints/config-checkpoint.py b/libucks/.ipynb_checkpoints/config-checkpoint.py new file mode 100644 index 0000000..5501623 --- /dev/null +++ b/libucks/.ipynb_checkpoints/config-checkpoint.py @@ -0,0 +1,142 @@ +"""Config — typed configuration dataclasses loaded from .libucks/config.toml. + +Uses Python 3.11's built-in tomllib (read-only, binary-mode). +All sub-dataclasses validate their own fields in __post_init__ so that +invalid values are caught at construction time regardless of whether +the Config came from a TOML file or was built directly in code. + +Defaults are chosen to be sensible for a mid-sized repository: + novelty_threshold 0.35 — cosine distance; tighter = fewer new buckets + top_k 3 — buckets queried per request + mitosis_threshold 20000 — tokens before a bucket splits +""" + +from __future__ import annotations + +import tomllib +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any + + +# --------------------------------------------------------------------------- +# Sub-dataclasses +# --------------------------------------------------------------------------- + +@dataclass +class ModelConfig: + """AI model identifiers.""" + + anthropic_model: str = "claude-haiku-4-5-20251001" + embedding_model: str = "all-MiniLM-L6-v2" + local_model: str = "Qwen/Qwen2.5-0.5B-Instruct" + quantization: str = "none" + device: str = "auto" + strategy: str = "text" + compression_steps: int = 8 + + def __post_init__(self) -> None: + if self.strategy not in ("text", "latent"): + raise ValueError( + f"strategy must be 'text' or 'latent', got {self.strategy!r}" + ) + if self.quantization not in ("none", "4bit", "8bit"): + raise ValueError( + f"quantization must be 'none', '4bit', or '8bit', " + f"got {self.quantization!r}" + ) + + +@dataclass +class RoutingConfig: + """Embedding-based routing parameters.""" + + novelty_threshold: float = 0.35 + """Cosine distance. A diff embedding farther than this from all + existing centroids triggers CreateBucketEvent. Range: (0, 1).""" + + top_k: int = 3 + """Number of buckets consulted per query. Must be >= 1.""" + + mitosis_threshold: int = 20_000 + """Token count at which a bucket is eligible for manual mitosis.""" + + init_bucket_size: int = 2_000 + """Target raw-token count per bucket during INIT clustering. + Controls how many buckets are seeded: n_clusters = total_tokens // init_bucket_size. + Kept separate from mitosis_threshold so INIT density can be tuned independently + of runtime splitting behaviour.""" + + def __post_init__(self) -> None: + self.novelty_threshold = float(self.novelty_threshold) + self.top_k = int(self.top_k) + self.mitosis_threshold = int(self.mitosis_threshold) + self.init_bucket_size = int(self.init_bucket_size) + + if not (0.0 < self.novelty_threshold < 1.0): + raise ValueError( + f"novelty_threshold must be in the open interval (0, 1), " + f"got {self.novelty_threshold}" + ) + if self.top_k < 1: + raise ValueError(f"top_k must be >= 1, got {self.top_k}") + if self.mitosis_threshold < 1: + raise ValueError( + f"mitosis_threshold must be >= 1, got {self.mitosis_threshold}" + ) + if self.init_bucket_size < 1: + raise ValueError( + f"init_bucket_size must be >= 1, got {self.init_bucket_size}" + ) + + +@dataclass +class PathsConfig: + """File-system paths used by libucks (relative to the target repo root + unless prefixed with ~/).""" + + bucket_dir: str = ".libucks/buckets" + registry_file: str = ".libucks/registry.json" + pending_events: str = ".libucks/pending_events.jsonl" + log_file: str = ".libucks/libucks.log" + grammar_cache: str = "~/.libucks/grammars" + repo_cache: str = "~/.libucks/repos" + + +# --------------------------------------------------------------------------- +# Root config +# --------------------------------------------------------------------------- + +def _merge(cls: type, data: dict[str, Any]) -> object: + """Construct a dataclass from a dict, ignoring unknown keys and letting + unrecognised fields fall back to their declared defaults.""" + import dataclasses + known = {f.name for f in dataclasses.fields(cls)} + return cls(**{k: v for k, v in data.items() if k in known}) + + +@dataclass +class Config: + model: ModelConfig = field(default_factory=ModelConfig) + routing: RoutingConfig = field(default_factory=RoutingConfig) + paths: PathsConfig = field(default_factory=PathsConfig) + + @classmethod + def load(cls, repo_path: Path) -> "Config": + """Load config from /.libucks/config.toml. + + Returns a Config with all defaults if the file does not exist. + Sections or keys absent from the file are filled in with defaults. + """ + config_file = repo_path / ".libucks" / "config.toml" + if not config_file.exists(): + return cls() + + with open(config_file, "rb") as fh: + data = tomllib.load(fh) + + return cls( + model=_merge(ModelConfig, data.get("model", {})), # type: ignore[arg-type] + routing=_merge(RoutingConfig, data.get("routing", {})), # type: ignore[arg-type] + paths=_merge(PathsConfig, data.get("paths", {})), # type: ignore[arg-type] + ) diff --git a/libucks/.ipynb_checkpoints/mcp_bridge-checkpoint.py b/libucks/.ipynb_checkpoints/mcp_bridge-checkpoint.py new file mode 100644 index 0000000..9b3c560 --- /dev/null +++ b/libucks/.ipynb_checkpoints/mcp_bridge-checkpoint.py @@ -0,0 +1,290 @@ +"""MCP Bridge — exposes libucks tools over the Model Context Protocol (stdio). + +Tools: + libucks_query(query, top_k=3) — query the memory store, returns synthesized answer + libucks_status() — bucket count and token totals +""" +from __future__ import annotations + +# Must be set before any native extension (tokenizers Rust runtime, ObjC) is +# imported — placing them here, at module load, guarantees that. +import os +os.environ.setdefault("OBJC_DISABLE_INITIALIZE_FORK_SAFETY", "YES") # prevents SIGABRT on Apple Silicon +os.environ.setdefault("TOKENIZERS_PARALLELISM", "false") # prevents HF tokenizer deadlock warning +os.environ.setdefault("TRANSFORMERS_VERBOSITY", "error") # suppresses "BertModel report" etc. +os.environ.setdefault("TQDM_DISABLE", "1") # suppresses "Loading weights" progress bars +os.environ.setdefault("PYTORCH_MPS_HIGH_WATERMARK_RATIO", "0.0") # disables MPS pool pre-reservation; does not + # fix peak single-op allocation (see model_manager.py + # attn_implementation="eager") but reduces background + # fragmentation pressure on unified memory + +import asyncio +import logging +import sys +import tomllib +from pathlib import Path +from typing import Any + +import mcp.server.stdio +import mcp.types as types +from mcp.server import Server + +from libucks.central_agent import CentralAgent +from libucks.config import Config +from libucks.diff.diff_extractor import DiffExtractor +from libucks.embeddings.embedding_service import EmbeddingService +from libucks.git_hook_receiver import serve_socket +from libucks.health_monitor import HealthMonitor +from libucks.librarian import Librarian +from libucks.merging_service import MergingService +from libucks.mitosis import MitosisService +from libucks.query_orchestrator import QueryOrchestrator +from libucks.stale_checker import StaleChecker +from libucks.startup_recovery import StartupRecovery +from libucks.storage.bucket_registry import BucketRegistry +from libucks.storage.bucket_store import BucketStore +from libucks.thinking import create_strategy +from libucks.translator import Translator + + +def _load_repo_path() -> Path: + """Return the repository root. + + Resolution order: + 1. LIBUCKS_REPO_PATH env var — set this in claude_desktop_config.json "env" + to point the server at any repo you want. + 2. Project root inferred from __file__ — reliable fallback that is never + the filesystem root, even when Claude Desktop launches with cwd='/'. + """ + env_path = os.environ.get("LIBUCKS_REPO_PATH") + if env_path: + return Path(env_path).expanduser().resolve() + # __file__ = libucks/mcp_bridge.py → .parent = libucks/ → .parent = project root + return Path(__file__).parent.parent.resolve() + + +async def serve() -> None: + # Route ALL logging to stderr — stdout is reserved for MCP JSON-RPC. + logging.basicConfig(stream=sys.stderr, level=logging.INFO, force=True) + + import structlog + structlog.configure( + logger_factory=structlog.PrintLoggerFactory(file=sys.stderr), + ) + + try: + import transformers + transformers.logging.set_verbosity_error() + except Exception: + pass + + repo_path = _load_repo_path() + cfg = Config.load(repo_path) + registry_path = repo_path / cfg.paths.registry_file + bucket_dir = repo_path / ".libucks" + bucket_store_dir = repo_path / cfg.paths.bucket_dir + print(f"[libucks] repo={repo_path} registry={registry_path} buckets={bucket_store_dir}", file=sys.stderr) + + registry = BucketRegistry(registry_path) + registry.load() + + store = BucketStore(bucket_store_dir) + + # Pre-load the embedding model BEFORE the MCP stdio server starts. + # Temporarily redirect sys.stdout → sys.stderr so any remaining direct + # prints from model loading never reach the MCP pipe. + _real_stdout = sys.stdout + sys.stdout = sys.stderr + try: + embedder = EmbeddingService.get_instance(cfg.model.embedding_model) + finally: + sys.stdout = _real_stdout # restore BEFORE mcp.server.stdio.stdio_server() captures it + strategy = create_strategy(cfg) + + agent = CentralAgent(registry, cfg, embed_fn=embedder.embed) + + librarians: dict[str, Librarian] = {} + for bucket_id in registry.get_all_centroids(): + lib = Librarian( + bucket_id=bucket_id, + store=store, + registry=registry, + strategy=strategy, + embedder=embedder, + mitosis_threshold=cfg.routing.mitosis_threshold, + ) + librarians[bucket_id] = lib + agent.register_librarian(bucket_id, lib) + + adapter = None + if cfg.model.strategy == "latent": + from libucks.thinking.communication_adapter import CommunicationAdapter + import torch + adapter = CommunicationAdapter() + adapter.load_saved_weights(bucket_dir / "adapter.pt") + # dtype must match the model's output dtype. ModelManager loads Qwen in + # float16 on MPS; float32 adapter parameters cause an MPS broadcast error. + adapter = adapter.to(device="mps", dtype=torch.float16) + + translator = Translator(strategy, adapter=adapter) + + # ------------------------------------------------------------------ + # Startup recovery: replay commits that arrived while server was offline. + # ------------------------------------------------------------------ + recovery: StartupRecovery | None = None + try: + extractor = DiffExtractor(repo_path) + recovery = StartupRecovery( + repo_path=repo_path, + registry=registry, + store=store, + librarians=librarians, + extractor=extractor, + ) + current_head = await recovery.run() + if current_head is not None: + registry._meta["last_indexed_head"] = current_head + registry._meta["watcher_pid"] = os.getpid() + registry.save() + print(f"[libucks] startup recovery complete, HEAD={current_head[:8]}", file=sys.stderr) + except Exception as exc: + # Recovery is best-effort — never block server startup. + print(f"[libucks] startup recovery skipped: {exc}", file=sys.stderr) + + # ------------------------------------------------------------------ + # Git hook socket listener (Phase 6-D). + # ------------------------------------------------------------------ + sock_path = bucket_dir / "server.sock" + + async def _on_hook_event(payload: dict) -> None: + """Handle a JSON event from a git hook and trigger background re-index.""" + if recovery is None: + return + try: + new_head = await recovery.run() + if new_head is not None: + registry._meta["last_indexed_head"] = new_head + registry.save() + print(f"[libucks] hook event '{payload.get('event')}' → re-indexed HEAD={new_head[:8]}", file=sys.stderr) + except Exception as exc: + print(f"[libucks] hook event error: {exc}", file=sys.stderr) + + asyncio.ensure_future(serve_socket(sock_path, _on_hook_event)) + + # ------------------------------------------------------------------ + # HealthMonitor (Phase 6-E/6-F): autonomous quality guardian. + # ------------------------------------------------------------------ + mitosis_svc = MitosisService( + store=store, + registry=registry, + embedder=embedder, + agent=agent, + strategy=strategy, + mitosis_threshold=cfg.routing.mitosis_threshold, + ) + merging_svc = MergingService( + registry=registry, + store=store, + agent=agent, + embedder=embedder, + strategy=strategy, + ) + health_monitor = HealthMonitor( + registry=registry, + store=store, + mitosis_service=mitosis_svc, + merging_service=merging_svc, + embedder=embedder, + mitosis_threshold=cfg.routing.mitosis_threshold, + ) + asyncio.ensure_future(health_monitor.run()) + + # ------------------------------------------------------------------ + # StaleChecker + reindex callback (Phase 6-C JIT invalidation). + # ------------------------------------------------------------------ + stale_checker = StaleChecker(registry=registry, store=store, repo_path=repo_path) + + async def _reindex_stale(stale_bucket_ids: list[str]) -> None: + """Background re-index triggered by stale query results.""" + if recovery is None: + return + try: + new_head = await recovery.run() + if new_head is not None: + registry._meta["last_indexed_head"] = new_head + registry.save() + except Exception as exc: + print(f"[libucks] background reindex error: {exc}", file=sys.stderr) + + orchestrator = QueryOrchestrator( + central_agent=agent, + librarians=librarians, + embed_fn=embedder.embed, + top_k=cfg.routing.top_k, + stale_checker=stale_checker, + reindex_fn=_reindex_stale, + ) + + server = Server("libucks") + + @server.list_tools() + async def list_tools() -> list[types.Tool]: + return [ + types.Tool( + name="libucks_query", + description="Query the libucks memory store for context about the repository.", + inputSchema={ + "type": "object", + "properties": { + "query": {"type": "string", "description": "Natural language question"}, + "top_k": {"type": "integer", "description": "Number of buckets to consult", "default": 3}, + }, + "required": ["query"], + }, + ), + types.Tool( + name="libucks_status", + description="Return system health: bucket count and token totals.", + inputSchema={"type": "object", "properties": {}}, + ), + ] + + @server.call_tool() + async def call_tool(name: str, arguments: dict[str, Any]) -> list[types.TextContent]: + if name == "libucks_query": + query_text = arguments["query"] + top_k = int(arguments.get("top_k", cfg.routing.top_k)) + orchestrator._top_k = top_k + + print(f"[libucks] query: routing '{query_text[:60]}' (top_k={top_k})", file=sys.stderr, flush=True) + representations = await orchestrator.query(query_text) + print(f"[libucks] query: got {len(representations)} representations", file=sys.stderr, flush=True) + answer = await translator.synthesize(query_text, representations) + print(f"[libucks] query: synthesis complete ({len(answer)} chars)", file=sys.stderr, flush=True) + return [types.TextContent(type="text", text=answer)] + + if name == "libucks_status": + centroids = registry.get_all_centroids() + bucket_ids = list(centroids.keys()) + total_tokens = sum( + registry.get_token_count(bid) for bid in bucket_ids + ) + status = { + "bucket_count": len(bucket_ids), + "total_tokens": total_tokens, + "buckets": { + bid: {"token_count": registry.get_token_count(bid)} + for bid in bucket_ids + }, + } + import json + return [types.TextContent(type="text", text=json.dumps(status, indent=2))] + + raise ValueError(f"Unknown tool: {name!r}") + + async with mcp.server.stdio.stdio_server() as (read_stream, write_stream): + await server.run( + read_stream, + write_stream, + server.create_initialization_options(), + ) diff --git a/libucks/.ipynb_checkpoints/translator-checkpoint.py b/libucks/.ipynb_checkpoints/translator-checkpoint.py new file mode 100644 index 0000000..8ce4d88 --- /dev/null +++ b/libucks/.ipynb_checkpoints/translator-checkpoint.py @@ -0,0 +1,286 @@ +"""Translator — the ONLY component permitted to call ThinkingStrategy.decode(). + +Receives N Representations from N Librarians plus the original query string. +Selects the appropriate synthesis path based on representation type: + + V1 (str): joins partial answers, calls strategy.reason() to synthesize, + then strategy.decode() to produce the final string. + V2 (tensor): passes the list of hidden-state tensors through the + CommunicationAdapter to produce a single soft-prompt, then + calls strategy.decode() exactly once. + +In both paths, strategy.decode() is called exactly once and its return value +is the sole natural-language output returned to the MCP Bridge. +""" +from __future__ import annotations + +import sys +from typing import List, Optional + +import torch + +from libucks.thinking.base import Representation, ThinkingStrategy + + +def _log(msg: str) -> None: + print(f"[libucks:translator] {msg}", file=sys.stderr, flush=True) + + +class Translator: + def __init__( + self, + strategy: ThinkingStrategy, + adapter: Optional[object] = None, + ) -> None: + self._strategy = strategy + if adapter is not None: + try: + _device = strategy._mgr.device + self._adapter = adapter.to(_device) + except Exception: + self._adapter = adapter + else: + self._adapter = None + + async def synthesize(self, query: str, representations: List[Representation]) -> str: + if not representations: + _log("synthesize: no representations — returning fallback message") + return "No relevant context found in the memory store." + + _log(f"synthesize: {len(representations)} representations, " + f"type={'latent' if isinstance(representations[0], torch.Tensor) else 'text'}") + + if isinstance(representations[0], torch.Tensor): + return await self._synthesize_latent(representations) + + return await self._synthesize_text(query, representations) + + # ------------------------------------------------------------------ + # V1 text path (unchanged) + # ------------------------------------------------------------------ + + async def _synthesize_text( + self, query: str, representations: List[Representation] + ) -> str: + parts = "\n\n---\n\n".join(str(r) for r in representations) + synthesis_prompt = ( + "You are synthesizing partial answers from multiple domain-specific memory buckets " + "into a single, coherent response. Do not mention bucket IDs, internal metadata, " + "or implementation details of the memory system. Answer directly and concisely.\n\n" + f"Partial answers:\n{parts}" + ) + combined: Representation = await self._strategy.reason(synthesis_prompt, query) + # This is the ONLY authorised call to decode() in the entire system. + return await self._strategy.decode(combined) + + # ------------------------------------------------------------------ + # V2 latent path + # ------------------------------------------------------------------ + + async def _synthesize_latent( + self, representations: List[Representation] + ) -> str: + shapes = [tuple(r.shape) for r in representations] + _log(f"_synthesize_latent: adapter forward, representations={shapes}") + # .contiguous() before the adapter: MultiheadAttention on MPS hangs + # on non-contiguous tensors produced by prior squeeze/expand operations. + contiguous_reps = [r.contiguous() for r in representations] + contiguous_reps = [t.to(torch.float32) for t in contiguous_reps] + with torch.no_grad(): + synthesized: torch.Tensor = self._adapter(contiguous_reps) + _log(f"_synthesize_latent: adapter complete, output={tuple(synthesized.shape)}") + # This is the ONLY authorised call to decode() in the entire system. + _log("_synthesize_latent: calling decode()") + result = await self._strategy.decode(synthesized) + _log(f"_synthesize_latent: decode complete ({len(result)} chars)") + return result +"""Translator — the ONLY component permitted to call ThinkingStrategy.decode(). + +Receives N Representations from N Librarians plus the original query string. +Selects the appropriate synthesis path based on representation type: + + V1 (str): joins partial answers, calls strategy.reason() to synthesize, + then strategy.decode() to produce the final string. + V2 (tensor): passes the list of hidden-state tensors through the + CommunicationAdapter to produce a single soft-prompt, then + calls strategy.decode() exactly once. + +In both paths, strategy.decode() is called exactly once and its return value +is the sole natural-language output returned to the MCP Bridge. +""" + +import sys +from typing import List, Optional + +import torch + +from libucks.thinking.base import Representation, ThinkingStrategy + + +def _log(msg: str) -> None: + print(f"[libucks:translator] {msg}", file=sys.stderr, flush=True) + + +class Translator: + def __init__( + self, + strategy: ThinkingStrategy, + adapter: Optional[object] = None, + ) -> None: + self._strategy = strategy + if adapter is not None: + try: + _device = strategy._mgr.device + self._adapter = adapter.to(_device) + except Exception: + self._adapter = adapter + else: + self._adapter = None + + async def synthesize(self, query: str, representations: List[Representation]) -> str: + if not representations: + _log("synthesize: no representations — returning fallback message") + return "No relevant context found in the memory store." + + _log(f"synthesize: {len(representations)} representations, " + f"type={'latent' if isinstance(representations[0], torch.Tensor) else 'text'}") + + if isinstance(representations[0], torch.Tensor): + return await self._synthesize_latent(representations) + + return await self._synthesize_text(query, representations) + + # ------------------------------------------------------------------ + # V1 text path (unchanged) + # ------------------------------------------------------------------ + + async def _synthesize_text( + self, query: str, representations: List[Representation] + ) -> str: + parts = "\n\n---\n\n".join(str(r) for r in representations) + synthesis_prompt = ( + "You are synthesizing partial answers from multiple domain-specific memory buckets " + "into a single, coherent response. Do not mention bucket IDs, internal metadata, " + "or implementation details of the memory system. Answer directly and concisely.\n\n" + f"Partial answers:\n{parts}" + ) + combined: Representation = await self._strategy.reason(synthesis_prompt, query) + # This is the ONLY authorised call to decode() in the entire system. + return await self._strategy.decode(combined) + + # ------------------------------------------------------------------ + # V2 latent path + # ------------------------------------------------------------------ + + async def _synthesize_latent( + self, representations: List[Representation] + ) -> str: + shapes = [tuple(r.shape) for r in representations] + _log(f"_synthesize_latent: adapter forward, representations={shapes}") + # .contiguous() before the adapter: MultiheadAttention on MPS hangs + # on non-contiguous tensors produced by prior squeeze/expand operations. + contiguous_reps = [r.contiguous() for r in representations] + contiguous_reps = [t.to(torch.float32) for t in contiguous_reps] + with torch.no_grad(): + synthesized: torch.Tensor = self._adapter(contiguous_reps) + _log(f"_synthesize_latent: adapter complete, output={tuple(synthesized.shape)}") + # This is the ONLY authorised call to decode() in the entire system. + _log("_synthesize_latent: calling decode()") + result = await self._strategy.decode(synthesized) + _log(f"_synthesize_latent: decode complete ({len(result)} chars)") + return result +"""Translator — the ONLY component permitted to call ThinkingStrategy.decode(). + +Receives N Representations from N Librarians plus the original query string. +Selects the appropriate synthesis path based on representation type: + + V1 (str): joins partial answers, calls strategy.reason() to synthesize, + then strategy.decode() to produce the final string. + V2 (tensor): passes the list of hidden-state tensors through the + CommunicationAdapter to produce a single soft-prompt, then + calls strategy.decode() exactly once. + +In both paths, strategy.decode() is called exactly once and its return value +is the sole natural-language output returned to the MCP Bridge. +""" + +import sys +from typing import List, Optional + +import torch + +from libucks.thinking.base import Representation, ThinkingStrategy + + +def _log(msg: str) -> None: + print(f"[libucks:translator] {msg}", file=sys.stderr, flush=True) + + +class Translator: + def __init__( + self, + strategy: ThinkingStrategy, + adapter: Optional[object] = None, + ) -> None: + self._strategy = strategy + if adapter is not None: + try: + _device = strategy._mgr.device + self._adapter = adapter.to(_device) + except Exception: + self._adapter = adapter + else: + self._adapter = None + + async def synthesize(self, query: str, representations: List[Representation]) -> str: + if not representations: + _log("synthesize: no representations — returning fallback message") + return "No relevant context found in the memory store." + + _log(f"synthesize: {len(representations)} representations, " + f"type={'latent' if isinstance(representations[0], torch.Tensor) else 'text'}") + + if isinstance(representations[0], torch.Tensor): + return await self._synthesize_latent(representations) + + return await self._synthesize_text(query, representations) + + # ------------------------------------------------------------------ + # V1 text path (unchanged) + # ------------------------------------------------------------------ + + async def _synthesize_text( + self, query: str, representations: List[Representation] + ) -> str: + parts = "\n\n---\n\n".join(str(r) for r in representations) + synthesis_prompt = ( + "You are synthesizing partial answers from multiple domain-specific memory buckets " + "into a single, coherent response. Do not mention bucket IDs, internal metadata, " + "or implementation details of the memory system. Answer directly and concisely.\n\n" + f"Partial answers:\n{parts}" + ) + combined: Representation = await self._strategy.reason(synthesis_prompt, query) + # This is the ONLY authorised call to decode() in the entire system. + return await self._strategy.decode(combined) + + # ------------------------------------------------------------------ + # V2 latent path + # ------------------------------------------------------------------ + + async def _synthesize_latent( + self, representations: List[Representation] + ) -> str: + shapes = [tuple(r.shape) for r in representations] + _log(f"_synthesize_latent: adapter forward, representations={shapes}") + # .contiguous() before the adapter: MultiheadAttention on MPS hangs + # on non-contiguous tensors produced by prior squeeze/expand operations. + contiguous_reps = [r.contiguous() for r in representations] + contiguous_reps = [t.to(torch.float32) for t in contiguous_reps] + with torch.no_grad(): + synthesized: torch.Tensor = self._adapter(contiguous_reps) + _log(f"_synthesize_latent: adapter complete, output={tuple(synthesized.shape)}") + # This is the ONLY authorised call to decode() in the entire system. + _log("_synthesize_latent: calling decode()") + result = await self._strategy.decode(synthesized) + _log(f"_synthesize_latent: decode complete ({len(result)} chars)") + return result diff --git a/libucks/thinking/.ipynb_checkpoints/latent_strategy-checkpoint.py b/libucks/thinking/.ipynb_checkpoints/latent_strategy-checkpoint.py new file mode 100644 index 0000000..05a591d --- /dev/null +++ b/libucks/thinking/.ipynb_checkpoints/latent_strategy-checkpoint.py @@ -0,0 +1,461 @@ +"""LatentStrategy — V2 implementation using local HuggingFace transformers. + +Librarians call encode() and reason() — both return torch.Tensor hidden states +from the model's last layer. No text is generated at these call sites. + +ONLY the Translator calls decode() to produce natural-language output by +projecting hidden states through the LM head and running autoregressive generation. + +See ARCHITECTURE.md §4 for the architectural constraints. +""" +from __future__ import annotations + +import sys + +from libucks.thinking.base import Representation, ThinkingStrategy + + +_ANCHOR_PROMPT = "<|im_start|>assistant\n" + + +def _log(msg: str) -> None: + print(f"[libucks:latent] {msg}", file=sys.stderr, flush=True) + + +class LatentStrategy(ThinkingStrategy): + def __init__( + self, + model_manager: object | None = None, + compressor: object | None = None, + injection_gate: float = 0.3, + temperature: float = 0.7, + top_p: float = 0.9, + repetition_penalty: float = 1.3, + receive_temperature: float = 0.6, + receive_top_p: float = 0.9, + receive_top_k: int = 50, + receive_repetition_penalty: float = 1.2, + ) -> None: + self._mgr = model_manager + self._compressor = compressor + # Residual injection gate g: x_soft = dummy + g*(hidden_matched - dummy). + # Starting at 0.1 keeps 90% of the output on the native embedding manifold + # and injects only 10% adapter signal, preventing generation collapse from + # off-manifold perturbations. + self._injection_gate = injection_gate + # Sampling parameters to break greedy (argmax) attractor loops. + # Argmax at T=0 creates deterministic 3-token cycles ("1. 1. 1.") because + # the soft-prompt primes a peaked distribution. Multinomial sampling with + # temperature > 0 breaks the cycle; repetition_penalty further suppresses + # tokens already in the generated sequence. + self._temperature = temperature + self._top_p = top_p + self._repetition_penalty = repetition_penalty + # Separate sampling params for receive() — the Base model (not Instruct) + # has a much flatter logit distribution. top_k=50 is applied first as a + # hard candidate cap before top_p, preventing tail bleed on a 150k vocab. + # repetition_penalty=1.2 breaks degenerate loops (~2 cycles) without + # pushing probability mass into multilingual tokens (which 1.3+ does). + self._receive_temperature = receive_temperature + self._receive_top_p = receive_top_p + self._receive_top_k = receive_top_k + self._receive_repetition_penalty = receive_repetition_penalty + # MPS has a single Metal command queue. Concurrent submissions from + # multiple asyncio coroutines (e.g. 3 Librarians via asyncio.gather) + # deadlock against each other. This lock serialises all device inference. + import asyncio + self._device_lock = asyncio.Lock() + + @property + def hidden_dim(self) -> int: + """Return the hidden_size of the encoder model.""" + return self._mgr.hidden_dim + + def _sample_next_token( + self, + logits: "torch.Tensor", + generated_ids: list[int], + temperature: float | None = None, + top_p: float | None = None, + top_k: int | None = None, + repetition_penalty: float | None = None, + ) -> "torch.Tensor": + """Sample the next token with repetition penalty, temperature, top-k, and top-p. + + top_k is applied before top_p: it first hard-caps the candidate set to + the k highest-logit tokens, then top_p further narrows within that set. + This prevents tail bleed on flat 150k-vocab Base model distributions where + top_p alone can include thousands of candidates. + + Args: + logits: shape (vocab_size,) — raw logits for the next-token position. + generated_ids: token IDs produced so far (used for repetition penalty). + + Returns: + torch.Tensor of shape (1,) — the sampled token ID. + """ + import torch + import torch.nn.functional as F + + _rep_penalty = repetition_penalty if repetition_penalty is not None else self._repetition_penalty + _temperature = temperature if temperature is not None else self._temperature + _top_p = top_p if top_p is not None else self._top_p + _top_k = top_k # None means no top-k filtering + + logits = logits.float().clone() + + # Repetition penalty: HuggingFace convention — + # positive logit → divide by penalty (reduces) + # negative logit → multiply by penalty (makes more negative) + if _rep_penalty != 1.0 and generated_ids: + for token_id in set(generated_ids): + if logits[token_id] > 0: + logits[token_id] /= _rep_penalty + else: + logits[token_id] *= _rep_penalty + + # Temperature scaling + logits = logits / max(_temperature, 1e-8) + + # Top-k filtering — zero out everything outside the k highest logits. + # Applied before top-p so nucleus filtering operates on a bounded set. + if _top_k is not None and _top_k > 0: + top_k_vals = torch.topk(logits, min(_top_k, logits.size(-1)))[0] + logits[logits < top_k_vals[-1]] = float("-inf") + + # Top-p (nucleus) filtering — zero out the tail below the probability mass + if _top_p < 1.0: + sorted_logits, sorted_indices = torch.sort(logits, descending=True) + cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) + # Keep the first token that pushes cumulative mass above top_p; + # remove everything after it. + sorted_indices_to_remove = cumulative_probs > _top_p + sorted_indices_to_remove[1:] = sorted_indices_to_remove[:-1].clone() + sorted_indices_to_remove[0] = False # always keep the top token + logits[sorted_indices[sorted_indices_to_remove]] = float("-inf") + + probs = F.softmax(logits, dim=-1) + return torch.multinomial(probs, num_samples=1) + + async def encode(self, text: str) -> "torch.Tensor": + """Encode text into last-layer hidden states via a single forward pass. + + Returns: + torch.Tensor of shape (seq_len, hidden_dim). + """ + import torch + + model = self._mgr.get_model() + tokenizer = self._mgr.get_tokenizer() + device = self._mgr.device + + async with self._device_lock: + # no_grad (not inference_mode): prevents gradient computation in the + # encoder while keeping returned tensors as normal (non-inference) + # tensors. inference_mode marks outputs as inference tensors, which + # cannot be saved for backward when they later flow into the adapter + # forward pass during LoRA receiver training. + with torch.no_grad(): + inputs = tokenizer(text, return_tensors="pt") + inputs = {k: v.to(device) for k, v in inputs.items()} + output = model(**inputs, output_hidden_states=True) + # hidden_states[-1]: (1, seq_len, hidden_dim) → (seq_len, hidden_dim) + hidden = output.hidden_states[-1].squeeze(0).contiguous() + del output # release full 36-layer hidden_states tuple + return hidden + + async def reason(self, query: str, context: str) -> "torch.Tensor": + """Produce a hidden-state Representation for a query given context. + + Constructs the standard prompt template, runs a single forward pass, + and returns the last-layer hidden states. model.generate() is never + called here — only the Translator is permitted to decode. + + Returns: + torch.Tensor of shape (seq_len, hidden_dim). + """ + import torch + + model = self._mgr.get_model() + tokenizer = self._mgr.get_tokenizer() + device = self._mgr.device + + prompt = f"{context}\n\n{query}" + + _log(f"reason: tokenizing ({len(prompt)} chars, device={device})") + async with self._device_lock: + with torch.no_grad(): # see encode() comment: no_grad not inference_mode + # Truncate to 256 tokens — Qwen forward-pass latency on MPS + # scales with sequence length; 485-token prompts push 3 serial + # Librarian calls past the 60-second MCP timeout. + inputs = tokenizer( + prompt, + return_tensors="pt", + max_length=256, + truncation=True, + ) + seq_len = inputs["input_ids"].shape[-1] + inputs = {k: v.to(device) for k, v in inputs.items()} + _log(f"reason: forward pass (seq_len={seq_len})") + output = model(**inputs, output_hidden_states=True) + hidden = output.hidden_states[-1].squeeze(0).contiguous() + del output # release full 36-layer hidden_states tuple + _log(f"reason: forward pass complete, hidden={tuple(hidden.shape)}") + + if self._compressor is not None: + _log(f"reason: compressing ({hidden.shape[0]} → {self._compressor.compression_steps} steps)") + with torch.inference_mode(): + hidden = self._compressor(hidden).contiguous() + _log(f"reason: compressed to {tuple(hidden.shape)}") + + return hidden + + async def decode(self, result: "torch.Tensor") -> str: + """Convert hidden-state tensor to natural language via Residual Anchoring. + + Three-stage injection (Vision Wormhole, Eq. 2 + Eq. 9): + + 1. NormMatch (Eq. 9): rescale adapter output per-row to match the + embedding layer's mean norm — places hidden_matched at embed_scale. + + 2. Residual Anchoring (Eq. 2): blend with a dummy baseline of K EOS-token + embeddings that are guaranteed to be on Qwen's native manifold: + x_soft = dummy_embeds + g * (hidden_matched - dummy_embeds) + At g=0.1 (default), 90% of the signal comes from the real embedding + manifold and only 10% from the adapter output. A final re-normalisation + to embed_scale enforces the manifold norm constraint regardless of g. + + 3. Position ID Alignment: pass explicit position_ids on every model() call + so RoPE offsets are correct for the KV cache even when inputs_embeds is + used instead of input_ids on the first call. + + This is the ONLY authorised call site for generative inference in the system. + + Args: + result: torch.Tensor of shape (seq_len, hidden_dim) or + (1, seq_len, hidden_dim). + + Returns: + Decoded natural-language string. + """ + import torch + from transformers import DynamicCache + + model = self._mgr.get_model() + tokenizer = self._mgr.get_tokenizer() + device = self._mgr.device + + _log(f"decode: received tensor {tuple(result.shape)}, device={device}") + + async with self._device_lock: + with torch.no_grad(): + # --- Normalise input shape --- + hidden = result.to(device) + if hidden.dim() == 2: + hidden = hidden.unsqueeze(0) # (1, K, d) + hidden = hidden.contiguous() # MPS: avoids silent hang on non-contiguous strides + K = hidden.shape[1] + + # --- Step 1: NormMatch (Vision Wormhole, Eq. 9) --- + # Rescale each adapter output row to embed_scale so hidden_matched + # and the dummy baseline are at the same magnitude. + embed_scale = model.model.embed_tokens.weight.norm(dim=-1).mean() + adapter_norms = hidden.norm(dim=-1, keepdim=True).clamp(min=1e-8) + hidden_matched = hidden * (embed_scale / adapter_norms) # (1, K, d) + _log(f"decode: isnan={hidden_matched.isnan().any().item()}, isinf={hidden_matched.isinf().any().item()}") + _log( + f"decode: NormMatch embed_scale={embed_scale.item():.3f}, " + f"mean_adapter_norm={adapter_norms.mean().item():.3f}" + ) + + + # --- Step 2: Residual Anchoring (Vision Wormhole, Eq. 2) --- + # Embed K copies of eos_token_id as the dummy baseline. EOS is always + # available and its embedding sits firmly on the native manifold. + space_id = int(tokenizer(" ", return_tensors="pt", add_special_tokens=False)["input_ids"].flatten()[-1].item()) + dummy_ids = torch.full( + (1, K), space_id, dtype=torch.long, device=device + ) + dummy_embeds = model.model.embed_tokens(dummy_ids) # (1, K, d) + + # Blend: x_soft = dummy + gate * (hidden_matched - dummy) + delta = hidden_matched - dummy_embeds + x_soft = dummy_embeds + self._injection_gate * delta # (1, K, d) + + # Re-normalise to embed_scale: enforces manifold norm constraint after + # blending (the convex combination of two unit-scale vectors has norm + # ≈ 0.9 × embed_scale at gate=0.1 for orthogonal random vectors). + x_soft_norms = x_soft.norm(dim=-1, keepdim=True).clamp(min=1e-8) + x_soft = x_soft * (embed_scale / x_soft_norms) + _log( + f"decode: residual gate={self._injection_gate}, " + f"mean_delta_norm={delta.norm(dim=-1).mean().item():.3f}" + ) + + # --- Step 3: Embed anchor prompt for semantic texture --- + anchor_ids = tokenizer( + _ANCHOR_PROMPT, return_tensors="pt", add_special_tokens=False + )["input_ids"].to(device) # (1, L) + anchor_embeds = model.model.embed_tokens(anchor_ids) # (1, L, d) + + # --- Step 4: Concatenate and build position IDs --- + inputs_embeds = torch.cat( + [x_soft, anchor_embeds], dim=1 + ) # (1, K+L, d) + prefix_len = inputs_embeds.shape[1] + + # Explicit position_ids ensure RoPE is applied at the correct offsets + # even when the model receives inputs_embeds instead of input_ids. + # Without this, some Qwen2.5 versions revert to position 0 for the + # first token of a DynamicCache continuation. + prefix_pos = torch.arange( + prefix_len, dtype=torch.long, device=device + ).unsqueeze(0) # (1, K+L) + # Cast to the model's dtype (float16 on CUDA/MPS) so the lm_head + # matmul doesn't hit a Half/Float mismatch from the float32 + # adapter output flowing through the embedding concatenation. + inputs_embeds = inputs_embeds.to(model.dtype) + _log(f"decode: inputs_embeds shape={tuple(inputs_embeds.shape)}, dtype={inputs_embeds.dtype}") + + # --- Step 5: Prefix pass --- + # + # WHY NOT model.generate(): + # model.generate() calls _get_cache() which constructs a StaticCache + # regardless of past_key_values or generation_config patches. For + # Qwen2.5-3B on MPS: + # 36 layers × 2 × 8 kv_heads × 32768 × 128 × float16 ≈ 4.8 GB + # Metal rejects any single NDArray > 2^32 bytes — instant SIGABRT. + # + # DynamicCache grows ~9 MB per 128 tokens and cannot trigger the + # MPSTemporaryNDArray hard limit on Apple Silicon. + past_kv: DynamicCache = DynamicCache() + out = model( + inputs_embeds=inputs_embeds, + position_ids=prefix_pos, + past_key_values=past_kv, + use_cache=True, + ) + generated_ids: list[int] = [] + next_id = self._sample_next_token(out.logits[0, -1, :], generated_ids) + past_kv = out.past_key_values + + # --- Step 6: Autoregressive generation loop (≤ 128 new tokens) --- + # curr_pos starts immediately after the prefix so each token gets its + # correct RoPE position independent of cache state queries. + curr_pos = prefix_len + _log("decode: starting manual generation loop (max_new_tokens=128)") + for _step in range(128): + if next_id.item() == tokenizer.eos_token_id: + break + generated_ids.append(next_id.item()) + loop_pos = torch.tensor( + [[curr_pos]], dtype=torch.long, device=device + ) + out = model( + input_ids=next_id.unsqueeze(0), # (1, 1) + position_ids=loop_pos, + past_key_values=past_kv, + use_cache=True, + ) + next_id = self._sample_next_token(out.logits[0, -1, :], generated_ids) + past_kv = out.past_key_values + curr_pos += 1 + + _log(f"decode: generation complete ({len(generated_ids)} tokens)") + + decoded = tokenizer.decode(generated_ids, skip_special_tokens=True) + _log(f"decode: tokenizer.decode complete ({len(decoded)} chars)") + return decoded + + async def receive(self, framed_latent: "torch.Tensor") -> str: + """Decode a framed latent sequence using the trained Base receiver. + + This is the Interlat-Lite replacement for decode(). It uses the Base + model (LoRA fine-tuned to interpret latent injections) and does NOT + apply NormMatch or Residual Anchoring — the trained model handles the + manifold gap directly. + + Args: + framed_latent: Tensor of shape (K+2, hidden_dim) — adapter output + already framed with / boundary embeddings. + + Returns: + Decoded natural-language string. + """ + import torch + from transformers import DynamicCache + + model = self._mgr.get_base_model() + tokenizer = self._mgr.get_base_tokenizer() + device = self._mgr.device + + _log(f"receive: framed_latent {tuple(framed_latent.shape)}, device={device}") + + async with self._device_lock: + with torch.no_grad(): + # Add batch dim: (1, K+2, D) + embeds = framed_latent.to(device) + if embeds.dim() == 2: + embeds = embeds.unsqueeze(0) + + # Prefix pass — inject framed latent directly, no preprocessing + past_kv: DynamicCache = DynamicCache() + prefix_len = embeds.shape[1] + prefix_pos = torch.arange( + prefix_len, dtype=torch.long, device=device + ).unsqueeze(0) + + out = model( + inputs_embeds=embeds, + position_ids=prefix_pos, + past_key_values=past_kv, + use_cache=True, + ) + generated_ids: list[int] = [] + eos_id = tokenizer.eos_token_id + min_new_tokens = 8 # suppress EOS until at least this many tokens + + def _suppress_eos(logits: "torch.Tensor", n_generated: int) -> "torch.Tensor": + if n_generated < min_new_tokens and eos_id is not None: + logits = logits.clone() + logits[eos_id] = float("-inf") + return logits + + first_logits = _suppress_eos(out.logits[0, -1, :], 0) + next_id = self._sample_next_token( + first_logits, generated_ids, + temperature=self._receive_temperature, + top_p=self._receive_top_p, + top_k=self._receive_top_k, + repetition_penalty=self._receive_repetition_penalty, + ) + past_kv = out.past_key_values + + # Autoregressive generation loop (≤ 512 new tokens) + curr_pos = prefix_len + for _step in range(512): + if next_id.item() == eos_id: + break + generated_ids.append(next_id.item()) + loop_pos = torch.tensor([[curr_pos]], dtype=torch.long, device=device) + out = model( + input_ids=next_id.unsqueeze(0), + position_ids=loop_pos, + past_key_values=past_kv, + use_cache=True, + ) + step_logits = _suppress_eos(out.logits[0, -1, :], len(generated_ids)) + next_id = self._sample_next_token( + step_logits, generated_ids, + temperature=self._receive_temperature, + top_p=self._receive_top_p, + repetition_penalty=self._receive_repetition_penalty, + ) + past_kv = out.past_key_values + curr_pos += 1 + + _log(f"receive: generated {len(generated_ids)} tokens") + + decoded = tokenizer.decode(generated_ids, skip_special_tokens=True) + _log(f"receive: decode complete ({len(decoded)} chars)") + return decoded diff --git a/libucks/thinking/latent_strategy.py b/libucks/thinking/latent_strategy.py index 0818e02..05a591d 100644 --- a/libucks/thinking/latent_strategy.py +++ b/libucks/thinking/latent_strategy.py @@ -311,7 +311,11 @@ async def decode(self, result: "torch.Tensor") -> str: prefix_pos = torch.arange( prefix_len, dtype=torch.long, device=device ).unsqueeze(0) # (1, K+L) - _log(f"decode: inputs_embeds shape={tuple(inputs_embeds.shape)}") + # Cast to the model's dtype (float16 on CUDA/MPS) so the lm_head + # matmul doesn't hit a Half/Float mismatch from the float32 + # adapter output flowing through the embedding concatenation. + inputs_embeds = inputs_embeds.to(model.dtype) + _log(f"decode: inputs_embeds shape={tuple(inputs_embeds.shape)}, dtype={inputs_embeds.dtype}") # --- Step 5: Prefix pass --- # diff --git a/libucks/translator.py b/libucks/translator.py index 644639f..8ce4d88 100644 --- a/libucks/translator.py +++ b/libucks/translator.py @@ -33,7 +33,14 @@ def __init__( adapter: Optional[object] = None, ) -> None: self._strategy = strategy - self._adapter = adapter + if adapter is not None: + try: + _device = strategy._mgr.device + self._adapter = adapter.to(_device) + except Exception: + self._adapter = adapter + else: + self._adapter = None async def synthesize(self, query: str, representations: List[Representation]) -> str: if not representations: @@ -78,6 +85,197 @@ async def _synthesize_latent( # .contiguous() before the adapter: MultiheadAttention on MPS hangs # on non-contiguous tensors produced by prior squeeze/expand operations. contiguous_reps = [r.contiguous() for r in representations] + contiguous_reps = [t.to(torch.float32) for t in contiguous_reps] + with torch.no_grad(): + synthesized: torch.Tensor = self._adapter(contiguous_reps) + _log(f"_synthesize_latent: adapter complete, output={tuple(synthesized.shape)}") + # This is the ONLY authorised call to decode() in the entire system. + _log("_synthesize_latent: calling decode()") + result = await self._strategy.decode(synthesized) + _log(f"_synthesize_latent: decode complete ({len(result)} chars)") + return result +"""Translator — the ONLY component permitted to call ThinkingStrategy.decode(). + +Receives N Representations from N Librarians plus the original query string. +Selects the appropriate synthesis path based on representation type: + + V1 (str): joins partial answers, calls strategy.reason() to synthesize, + then strategy.decode() to produce the final string. + V2 (tensor): passes the list of hidden-state tensors through the + CommunicationAdapter to produce a single soft-prompt, then + calls strategy.decode() exactly once. + +In both paths, strategy.decode() is called exactly once and its return value +is the sole natural-language output returned to the MCP Bridge. +""" + +import sys +from typing import List, Optional + +import torch + +from libucks.thinking.base import Representation, ThinkingStrategy + + +def _log(msg: str) -> None: + print(f"[libucks:translator] {msg}", file=sys.stderr, flush=True) + + +class Translator: + def __init__( + self, + strategy: ThinkingStrategy, + adapter: Optional[object] = None, + ) -> None: + self._strategy = strategy + if adapter is not None: + try: + _device = strategy._mgr.device + self._adapter = adapter.to(_device) + except Exception: + self._adapter = adapter + else: + self._adapter = None + + async def synthesize(self, query: str, representations: List[Representation]) -> str: + if not representations: + _log("synthesize: no representations — returning fallback message") + return "No relevant context found in the memory store." + + _log(f"synthesize: {len(representations)} representations, " + f"type={'latent' if isinstance(representations[0], torch.Tensor) else 'text'}") + + if isinstance(representations[0], torch.Tensor): + return await self._synthesize_latent(representations) + + return await self._synthesize_text(query, representations) + + # ------------------------------------------------------------------ + # V1 text path (unchanged) + # ------------------------------------------------------------------ + + async def _synthesize_text( + self, query: str, representations: List[Representation] + ) -> str: + parts = "\n\n---\n\n".join(str(r) for r in representations) + synthesis_prompt = ( + "You are synthesizing partial answers from multiple domain-specific memory buckets " + "into a single, coherent response. Do not mention bucket IDs, internal metadata, " + "or implementation details of the memory system. Answer directly and concisely.\n\n" + f"Partial answers:\n{parts}" + ) + combined: Representation = await self._strategy.reason(synthesis_prompt, query) + # This is the ONLY authorised call to decode() in the entire system. + return await self._strategy.decode(combined) + + # ------------------------------------------------------------------ + # V2 latent path + # ------------------------------------------------------------------ + + async def _synthesize_latent( + self, representations: List[Representation] + ) -> str: + shapes = [tuple(r.shape) for r in representations] + _log(f"_synthesize_latent: adapter forward, representations={shapes}") + # .contiguous() before the adapter: MultiheadAttention on MPS hangs + # on non-contiguous tensors produced by prior squeeze/expand operations. + contiguous_reps = [r.contiguous() for r in representations] + contiguous_reps = [t.to(torch.float32) for t in contiguous_reps] + with torch.no_grad(): + synthesized: torch.Tensor = self._adapter(contiguous_reps) + _log(f"_synthesize_latent: adapter complete, output={tuple(synthesized.shape)}") + # This is the ONLY authorised call to decode() in the entire system. + _log("_synthesize_latent: calling decode()") + result = await self._strategy.decode(synthesized) + _log(f"_synthesize_latent: decode complete ({len(result)} chars)") + return result +"""Translator — the ONLY component permitted to call ThinkingStrategy.decode(). + +Receives N Representations from N Librarians plus the original query string. +Selects the appropriate synthesis path based on representation type: + + V1 (str): joins partial answers, calls strategy.reason() to synthesize, + then strategy.decode() to produce the final string. + V2 (tensor): passes the list of hidden-state tensors through the + CommunicationAdapter to produce a single soft-prompt, then + calls strategy.decode() exactly once. + +In both paths, strategy.decode() is called exactly once and its return value +is the sole natural-language output returned to the MCP Bridge. +""" + +import sys +from typing import List, Optional + +import torch + +from libucks.thinking.base import Representation, ThinkingStrategy + + +def _log(msg: str) -> None: + print(f"[libucks:translator] {msg}", file=sys.stderr, flush=True) + + +class Translator: + def __init__( + self, + strategy: ThinkingStrategy, + adapter: Optional[object] = None, + ) -> None: + self._strategy = strategy + if adapter is not None: + try: + _device = strategy._mgr.device + self._adapter = adapter.to(_device) + except Exception: + self._adapter = adapter + else: + self._adapter = None + + async def synthesize(self, query: str, representations: List[Representation]) -> str: + if not representations: + _log("synthesize: no representations — returning fallback message") + return "No relevant context found in the memory store." + + _log(f"synthesize: {len(representations)} representations, " + f"type={'latent' if isinstance(representations[0], torch.Tensor) else 'text'}") + + if isinstance(representations[0], torch.Tensor): + return await self._synthesize_latent(representations) + + return await self._synthesize_text(query, representations) + + # ------------------------------------------------------------------ + # V1 text path (unchanged) + # ------------------------------------------------------------------ + + async def _synthesize_text( + self, query: str, representations: List[Representation] + ) -> str: + parts = "\n\n---\n\n".join(str(r) for r in representations) + synthesis_prompt = ( + "You are synthesizing partial answers from multiple domain-specific memory buckets " + "into a single, coherent response. Do not mention bucket IDs, internal metadata, " + "or implementation details of the memory system. Answer directly and concisely.\n\n" + f"Partial answers:\n{parts}" + ) + combined: Representation = await self._strategy.reason(synthesis_prompt, query) + # This is the ONLY authorised call to decode() in the entire system. + return await self._strategy.decode(combined) + + # ------------------------------------------------------------------ + # V2 latent path + # ------------------------------------------------------------------ + + async def _synthesize_latent( + self, representations: List[Representation] + ) -> str: + shapes = [tuple(r.shape) for r in representations] + _log(f"_synthesize_latent: adapter forward, representations={shapes}") + # .contiguous() before the adapter: MultiheadAttention on MPS hangs + # on non-contiguous tensors produced by prior squeeze/expand operations. + contiguous_reps = [r.contiguous() for r in representations] + contiguous_reps = [t.to(torch.float32) for t in contiguous_reps] with torch.no_grad(): synthesized: torch.Tensor = self._adapter(contiguous_reps) _log(f"_synthesize_latent: adapter complete, output={tuple(synthesized.shape)}") diff --git a/pyproject.toml b/pyproject.toml index edb3398..e00202d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,6 +23,8 @@ dependencies = [ "rich>=13.0", "mcp>=1.0", "structlog>=24.0", + "bitsandbytes>=0.46.1", + "transformers==4.48.2", ] [project.optional-dependencies] diff --git a/tests/unit/.ipynb_checkpoints/test_curriculum-checkpoint.py b/tests/unit/.ipynb_checkpoints/test_curriculum-checkpoint.py new file mode 100644 index 0000000..903c1f1 --- /dev/null +++ b/tests/unit/.ipynb_checkpoints/test_curriculum-checkpoint.py @@ -0,0 +1,97 @@ +"""Tests for CurriculumMixer. + +Formula (from Interlat §3.3): + H^(r) = [e_1, ..., e_{floor(r·K)}] ⊕ [h_{floor(r·K)+1}, ..., h_K] + ← token embeddings → ← latents → + +So: + r=0 → all latents (0 token positions) + r=1 → all tokens (K token positions) + +Training samples r ~ U[0,1] uniformly each step, forcing the model to handle +any mixture — this is the curriculum (not a fixed schedule). +""" +import math +import torch +import pytest +from libucks.thinking.curriculum import CurriculumMixer + + +K = 8 +D = 64 + + +@pytest.fixture +def latents(): + torch.manual_seed(0) + return torch.randn(K, D) + + +@pytest.fixture +def tok_embeds(): + torch.manual_seed(1) + return torch.randn(K, D) + + +def test_mix_r0_returns_all_latents(latents, tok_embeds): + """r=0 → floor(0*K)=0 token positions → output equals latents entirely.""" + out = CurriculumMixer.mix(latents, tok_embeds, r=0.0) + assert torch.allclose(out, latents) + + +def test_mix_r1_returns_all_tokens(latents, tok_embeds): + """r=1 → floor(1*K)=K token positions → output equals tok_embeds entirely.""" + out = CurriculumMixer.mix(latents, tok_embeds, r=1.0) + assert torch.allclose(out, tok_embeds) + + +def test_mix_midpoint(latents, tok_embeds): + """r=0.5 → first floor(0.5*K) rows are tok_embeds, rest are latents.""" + r = 0.5 + split = math.floor(r * K) # 4 + out = CurriculumMixer.mix(latents, tok_embeds, r=r) + assert torch.allclose(out[:split], tok_embeds[:split]) + assert torch.allclose(out[split:], latents[split:]) + + +def test_mix_shape_preserved(latents, tok_embeds): + """Output shape (K, D) is invariant to r.""" + for r in [0.0, 0.25, 0.5, 0.75, 1.0]: + out = CurriculumMixer.mix(latents, tok_embeds, r=r) + assert out.shape == (K, D), f"shape wrong for r={r}" + + +def test_mix_dtype_preserved(latents, tok_embeds): + """Output dtype matches input dtype.""" + out = CurriculumMixer.mix(latents, tok_embeds, r=0.5) + assert out.dtype == latents.dtype + + +def test_mix_device_preserved(latents, tok_embeds): + """Output stays on CPU when inputs are on CPU.""" + out = CurriculumMixer.mix(latents, tok_embeds, r=0.5) + assert out.device == latents.device + + +def test_mix_does_not_mutate_inputs(latents, tok_embeds): + """mix() must not modify the input tensors in-place.""" + lat_copy = latents.clone() + tok_copy = tok_embeds.clone() + CurriculumMixer.mix(latents, tok_embeds, r=0.5) + assert torch.allclose(latents, lat_copy) + assert torch.allclose(tok_embeds, tok_copy) + + +def test_mix_r_boundary_values(latents, tok_embeds): + """r outside [0,1] raises ValueError.""" + with pytest.raises(ValueError): + CurriculumMixer.mix(latents, tok_embeds, r=-0.1) + with pytest.raises(ValueError): + CurriculumMixer.mix(latents, tok_embeds, r=1.1) + + +def test_mix_shape_mismatch_raises(latents): + """Mismatched shape raises ValueError.""" + bad = torch.randn(K + 2, D) + with pytest.raises(ValueError): + CurriculumMixer.mix(latents, bad, r=0.5) diff --git a/tests/unit/.ipynb_checkpoints/test_curriculum_data-checkpoint.py b/tests/unit/.ipynb_checkpoints/test_curriculum_data-checkpoint.py new file mode 100644 index 0000000..01cde78 --- /dev/null +++ b/tests/unit/.ipynb_checkpoints/test_curriculum_data-checkpoint.py @@ -0,0 +1,172 @@ +"""Tests for MultiPerspectiveDataGenerator.generate_curriculum_batch(). + +A curriculum batch item contains: + - mixed_input: Tensor of shape (K, D) — CurriculumMixer.mix() output + - target_ids: LongTensor of token IDs — the target text tokenized (integer IDs) + - r: float in [0, 1] — the mixing rate used for this item + +The batch is used by LoRAReceiverTrainer to fine-tune the Base model with +cross-entropy loss (teacher forcing) + L_sep. +""" +import math +import torch +import pytest +from unittest.mock import AsyncMock, MagicMock, patch + + +K = 8 +D = 64 +VOCAB = 256 + + +@pytest.fixture +def mock_latent_strategy(): + strategy = MagicMock() + # encode() returns a (5, D) tensor + strategy.encode = AsyncMock(return_value=torch.randn(5, D)) + # reason() is also awaited in generate_curriculum_batch() + strategy.reason = AsyncMock(return_value=torch.randn(5, D)) + return strategy + + +@pytest.fixture +def mock_adapter(): + adapter = MagicMock() + # forward() returns (K, D) + adapter.return_value = torch.randn(K, D) + return adapter + + +@pytest.fixture +def mock_tokenizer(): + tok = MagicMock() + # tokenize returns some fake ids + tok.encode = MagicMock(return_value=[1, 2, 3, 4, 5]) + tok.return_value = {"input_ids": torch.tensor([[1, 2, 3, 4, 5]])} + return tok + + +@pytest.fixture +def mock_embedding(): + """Fake embedding layer — maps token ID to D-dim vector.""" + emb = MagicMock() + emb.return_value = torch.randn(K, D) # returns (K, D) for any input + mock_param = MagicMock() + mock_param.device = torch.device("cpu") + emb.parameters.side_effect = lambda: iter([mock_param]) + return emb + + +@pytest.fixture +def mock_text_strategy(): + ts = MagicMock() + ts.reason = AsyncMock(return_value="This module handles routing logic.") + return ts + + +@pytest.fixture +def generator(mock_latent_strategy, mock_adapter, mock_tokenizer, mock_embedding, mock_text_strategy): + from libucks.thinking.training.data_generator import MultiPerspectiveDataGenerator + gen = MultiPerspectiveDataGenerator( + text_strategy=mock_text_strategy, + latent_strategy=mock_latent_strategy, + registry=MagicMock(), + store=MagicMock(), + ) + gen._store.read.return_value = ("id", "some prose about code") + gen._registry.get_all_centroids.return_value = {} + return gen + + +# ── generate_curriculum_batch() contract ──────────────────────────────────── + + +@pytest.mark.asyncio +async def test_batch_returns_mixed_input_and_target_ids( + generator, mock_adapter, mock_tokenizer, mock_embedding +): + """generate_curriculum_batch() returns a dict with 'mixed_input' and 'target_ids'.""" + result = await generator.generate_curriculum_batch( + bucket_id="test_bucket", + adapter=mock_adapter, + tokenizer=mock_tokenizer, + embedding=mock_embedding, + output_len=K, + hidden_dim=D, + ) + assert "mixed_input" in result + assert "target_ids" in result + + +@pytest.mark.asyncio +async def test_mixed_input_shape( + generator, mock_adapter, mock_tokenizer, mock_embedding +): + """mixed_input has shape (K, D).""" + result = await generator.generate_curriculum_batch( + bucket_id="test_bucket", + adapter=mock_adapter, + tokenizer=mock_tokenizer, + embedding=mock_embedding, + output_len=K, + hidden_dim=D, + ) + assert result["mixed_input"].shape == (K, D) + + +@pytest.mark.asyncio +async def test_target_ids_are_integer_tensor( + generator, mock_adapter, mock_tokenizer, mock_embedding +): + """target_ids must be a LongTensor (integer IDs, not floats).""" + result = await generator.generate_curriculum_batch( + bucket_id="test_bucket", + adapter=mock_adapter, + tokenizer=mock_tokenizer, + embedding=mock_embedding, + output_len=K, + hidden_dim=D, + ) + ids = result["target_ids"] + assert isinstance(ids, torch.Tensor) + assert ids.dtype in (torch.long, torch.int32, torch.int64) + + +@pytest.mark.asyncio +async def test_r_in_result_is_float_in_range( + generator, mock_adapter, mock_tokenizer, mock_embedding +): + """Result must include 'r' — a float in [0, 1].""" + result = await generator.generate_curriculum_batch( + bucket_id="test_bucket", + adapter=mock_adapter, + tokenizer=mock_tokenizer, + embedding=mock_embedding, + output_len=K, + hidden_dim=D, + ) + assert "r" in result + r = result["r"] + assert isinstance(r, float) + assert 0.0 <= r <= 1.0 + + +@pytest.mark.asyncio +async def test_r_sampled_uniformly_across_calls( + generator, mock_adapter, mock_tokenizer, mock_embedding +): + """Across many calls, r values should span the range [0, 1] (not all same).""" + rs = [] + for _ in range(50): + result = await generator.generate_curriculum_batch( + bucket_id="test_bucket", + adapter=mock_adapter, + tokenizer=mock_tokenizer, + embedding=mock_embedding, + output_len=K, + hidden_dim=D, + ) + rs.append(result["r"]) + + # With 50 samples from U[0,1], max - min should be > 0.3 with overwhelming probability + assert max(rs) - min(rs) > 0.3, "r values appear to not be sampled uniformly" diff --git a/training.log b/training.log new file mode 100644 index 0000000..0bf830c --- /dev/null +++ b/training.log @@ -0,0 +1,63 @@ +nohup: ignoring input + Building libucks @ file:///workspace/libucks + Built libucks @ file:///workspace/libucks +Uninstalled 4 packages in 1.93s +Installed 4 packages in 1.72s +Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads. +[libucks] Basic MSE training on 148 buckets for 5 epoch(s)... +Traceback (most recent call last): + File "/workspace/libucks/.venv/bin/libucks", line 10, in + sys.exit(cli()) + ^^^^^ + File "/workspace/libucks/.venv/lib/python3.12/site-packages/click/core.py", line 1485, in __call__ + return self.main(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/workspace/libucks/.venv/lib/python3.12/site-packages/click/core.py", line 1406, in main + rv = self.invoke(ctx) + ^^^^^^^^^^^^^^^^ + File "/workspace/libucks/.venv/lib/python3.12/site-packages/click/core.py", line 1873, in invoke + return _process_result(sub_ctx.command.invoke(sub_ctx)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/workspace/libucks/.venv/lib/python3.12/site-packages/click/core.py", line 1269, in invoke + return ctx.invoke(self.callback, **ctx.params) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/workspace/libucks/.venv/lib/python3.12/site-packages/click/core.py", line 824, in invoke + return callback(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/workspace/libucks/libucks/_cli.py", line 88, in train_adapter_cmd + asyncio.run(_run_train_adapter(target, creative=creative, epochs=epochs)) + File "/root/.local/share/uv/python/cpython-3.12.13-linux-x86_64-gnu/lib/python3.12/asyncio/runners.py", line 195, in run + return runner.run(main) + ^^^^^^^^^^^^^^^^ + File "/root/.local/share/uv/python/cpython-3.12.13-linux-x86_64-gnu/lib/python3.12/asyncio/runners.py", line 118, in run + return self._loop.run_until_complete(task) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/.local/share/uv/python/cpython-3.12.13-linux-x86_64-gnu/lib/python3.12/asyncio/base_events.py", line 691, in run_until_complete + return future.result() + ^^^^^^^^^^^^^^^ + File "/workspace/libucks/libucks/_cli.py", line 140, in _run_train_adapter + await _train_basic(cfg, registry, store, bucket_ids, adapter, epochs, bucket_dir) + File "/workspace/libucks/libucks/_cli.py", line 206, in _train_basic + latent_strategy = create_strategy(cfg) + ^^^^^^^^^^^^^^^^^^^^ + File "/workspace/libucks/libucks/thinking/__init__.py", line 18, in create_strategy + mgr.load( + File "/workspace/libucks/libucks/thinking/model_manager.py", line 71, in load + self._model = AutoModelForCausalLM.from_pretrained(model_id, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/workspace/libucks/.venv/lib/python3.12/site-packages/transformers/models/auto/auto_factory.py", line 381, in from_pretrained + return model_class.from_pretrained( + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/workspace/libucks/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 4091, in from_pretrained + hf_quantizer.preprocess_model( + File "/workspace/libucks/.venv/lib/python3.12/site-packages/transformers/quantizers/base.py", line 171, in preprocess_model + self._process_model_before_weight_loading(model, **kwargs) + File "/workspace/libucks/.venv/lib/python3.12/site-packages/transformers/quantizers/quantizer_bnb_4bit.py", line 138, in _process_model_before_weight_loading + model = replace_with_bnb_linear( + ^^^^^^^^^^^^^^^^^^^^^^^^ + File "/workspace/libucks/.venv/lib/python3.12/site-packages/transformers/integrations/bitsandbytes.py", line 222, in replace_with_bnb_linear + model.set_submodule(module_name, new_module) + ^^^^^^^^^^^^^^^^^^^ + File "/workspace/libucks/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1729, in __getattr__ + raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'") +AttributeError: 'Qwen2ForCausalLM' object has no attribute 'set_submodule'. Did you mean: 'get_submodule'? diff --git a/uv.lock b/uv.lock index 30c25e7..53bb91c 100644 --- a/uv.lock +++ b/uv.lock @@ -5,11 +5,7 @@ resolution-markers = [ "sys_platform == 'linux'", "platform_machine == 'arm64' and sys_platform == 'darwin'", "python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'darwin'", -] -supported-markers = [ - "sys_platform == 'linux'", - "platform_machine == 'arm64' and sys_platform == 'darwin'", - "python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'darwin'", + "(python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine != 'arm64' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (sys_platform != 'darwin' and sys_platform != 'linux')", ] [[package]] @@ -17,29 +13,21 @@ name = "accelerate" version = "1.13.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "huggingface-hub", marker = 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