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from __future__ import annotations
import argparse
from dataclasses import asdict
import json
from pathlib import Path
import sys
import traceback
import torch
from pcm.planner.canonical import CANONICAL_P_PROTOCOL, CanonicalPStore
from pcm.planner.compatibility import CompatibilityKind, resolve_compatibility
from pcm.planner.interactive_runtimes import (
GenerationResult,
GemmaInteractiveRuntime,
PythiaInteractiveRuntime,
canonical_route,
)
from pcm.planner.interactive_session import (
CanonicalStateManager,
PersonalityManager,
SessionRecorder,
git_commit,
utc_now,
)
from pcm.planner.memory_review import PostTurnMemoryReviewer
from pcm.planner.representation import train_and_probe_representation
HELP = """Chat normally by typing any message.
Commands:
/help show this help
/state show active canonical P-cache entries
/personality show promoted personality entries and the last retrieval
/events show the most recent Planner Cache events
/save checkpoint P-cache and P-package state
/quit save and exit
Memory extraction is automatic. These explicit forms are also recognized:
The silver key belongs to Alice
Alice owns the silver key
The silver key is currently in Paris
The current status of the silver key is garden
remember: silver key.owner=Alice
invalidate: silver key.owner
Pythia consumes accepted state through its TTL. Gemma uses an LTL for accepted
lexical values. Rejected routes remain inert and conversation continues normally.
"""
def parser() -> argparse.ArgumentParser:
root = Path(__file__).resolve().parents[3]
result = argparse.ArgumentParser(description="Planner Cache interactive terminal")
result.add_argument("runtime", choices=("pythia", "gemma"))
result.add_argument("--repo-root", type=Path, default=root)
result.add_argument("--model", type=Path, required=True)
result.add_argument("--adapter", type=Path, required=True)
result.add_argument("--router", type=Path, required=True)
result.add_argument("--llama-cpp-dir", type=Path)
result.add_argument("--tokenizer-bundle", type=Path)
result.add_argument("--ppkg", type=Path)
result.add_argument("--session-root", type=Path, required=True)
result.add_argument("--p-cache", type=Path)
result.add_argument("--slots", type=int, default=128)
result.add_argument("--context-tokens", type=int)
result.add_argument("--max-new-tokens", type=int, default=96)
result.add_argument("--temperature", type=float, default=0.7)
result.add_argument("--top-p", type=float, default=0.9)
result.add_argument("--seed", type=int, default=1234)
result.add_argument("--gpu-layers", type=int, default=12)
result.add_argument("--threads", type=int, default=8)
result.add_argument("--llama-pid-file", type=Path)
result.add_argument("--review-model", type=Path)
result.add_argument("--review-llama-cpp-dir", type=Path)
result.add_argument("--review-gpu-layers", type=int, default=0)
result.add_argument("--review-pid-file", type=Path)
result.add_argument("--no-logging", action="store_true")
return result
def require_file(path: Path, label: str) -> Path:
if not path.is_file():
raise FileNotFoundError(f"{label} not found: {path}")
return path
def validate_args(args: argparse.Namespace) -> None:
if args.runtime == "pythia":
if not args.model.is_dir():
raise FileNotFoundError(f"pythia model not found: {args.model}")
review_model = getattr(args, "review_model", None)
review_llama_cpp_dir = getattr(args, "review_llama_cpp_dir", None)
if review_model is not None:
require_file(review_model, "structured review model")
if review_llama_cpp_dir is None:
raise ValueError(
"--review-llama-cpp-dir is required with --review-model"
)
require_file(
review_llama_cpp_dir / "build/bin/llama-server",
"review llama-server",
)
else:
require_file(args.model, "gemma model")
expected = ".ttl" if args.runtime == "pythia" else ".ltl"
require_file(args.adapter, f"{expected} compatibility artifact")
if args.adapter.suffix != expected:
raise ValueError(f"{args.runtime} requires a {expected} compatibility artifact")
resolution = resolve_compatibility(args.adapter)
expected_kind = (
CompatibilityKind.TTL if args.runtime == "pythia" else CompatibilityKind.LTL
)
if resolution.kind is not expected_kind:
raise ValueError(
f"{args.runtime} cannot use {resolution.kind.value} compatibility"
)
require_file(args.router, ".router artifact")
if args.p_cache is not None:
require_file(args.p_cache, "P-cache snapshot")
if args.runtime == "gemma":
if args.llama_cpp_dir is None:
raise ValueError("--llama-cpp-dir is required for Gemma")
if args.tokenizer_bundle is None or not args.tokenizer_bundle.is_dir():
raise FileNotFoundError("--tokenizer-bundle is required for Gemma")
require_file(args.llama_cpp_dir / "build/bin/llama-server", "llama-server")
require_file(args.llama_cpp_dir / "build/bin/llama-cli", "llama-cli")
def build_runtime(args: argparse.Namespace):
common = {
"model_path": args.model,
"adapter_path": args.adapter,
"router_path": args.router,
"max_new_tokens": args.max_new_tokens,
"temperature": args.temperature,
"top_p": args.top_p,
"seed": args.seed,
}
if args.runtime == "pythia":
return PythiaInteractiveRuntime(
**common,
max_context_tokens=args.context_tokens or 1024,
review_model_path=getattr(args, "review_model", None),
review_llama_cpp_dir=getattr(args, "review_llama_cpp_dir", None),
review_gpu_layers=getattr(args, "review_gpu_layers", 0),
review_pid_file=getattr(args, "review_pid_file", None),
)
return GemmaInteractiveRuntime(
**common,
llama_cpp_dir=args.llama_cpp_dir,
tokenizer_bundle=args.tokenizer_bundle,
max_context_tokens=args.context_tokens or 4096,
gpu_layers=args.gpu_layers,
threads=args.threads,
pid_file=args.llama_pid_file,
)
def display_json(value: object) -> None:
print(json.dumps(value, indent=2, sort_keys=True, ensure_ascii=False))
def save_session_state(
recorder: SessionRecorder,
state: CanonicalStateManager,
personality: PersonalityManager | None,
*,
reason: str,
) -> None:
snapshot = recorder.directory / "p-cache.safetensors"
if recorder.enabled:
state.store.save(snapshot)
state.save_runtime_metadata(recorder.directory / "p-cache-runtime.json")
checksum = personality.checkpoint() if personality is not None else None
recorder.save_event(
reason,
p_cache_snapshot=str(snapshot) if recorder.enabled else None,
ppkg_checksum=checksum,
)
def final_payload(
state: CanonicalStateManager,
personality: PersonalityManager | None,
) -> dict[str, object]:
personality_page = (
{
"entries": [], "total_active": 0, "returned": 0,
"limit": 100, "offset": 0, "truncated": False,
}
if personality is None
else personality.visible_entry_page(limit=100)
)
return {
"canonical_p_protocol": CANONICAL_P_PROTOCOL,
"active_p_cache_entries": state.snapshot(),
"ppkg_path": None if personality is None else str(personality.path),
"ppkg_mutations": [] if personality is None else personality.mutations,
"promoted_personality_entries": personality_page.pop("entries"),
"promoted_personality_page": personality_page,
}
class PlannerChatSession:
"""One active Planner Cache session shared by terminal and web front ends."""
def __init__(self, args: argparse.Namespace) -> None:
validate_args(args)
self.args = args
representation, _config, probe = train_and_probe_representation()
store = (
CanonicalPStore.load(args.p_cache, dtype=torch.float32)
if args.p_cache is not None else None
)
self.state = CanonicalStateManager(representation, slots=args.slots, store=store)
if args.p_cache is not None:
self.state.load_runtime_metadata(
args.p_cache.with_name("p-cache-runtime.json")
)
self.personality = (
PersonalityManager(args.ppkg, representation)
if args.ppkg is not None else None
)
try:
self.runtime = build_runtime(args)
except Exception:
if self.personality is not None:
self.personality.close()
raise
self.reviewer = PostTurnMemoryReviewer(self.runtime.review_memory)
metadata = {
**self.runtime.metadata(),
"canonical_p_protocol_version": CANONICAL_P_PROTOCOL,
"ppkg_path": (
None if self.personality is None
else str(self.personality.path.resolve())
),
"git_commit": git_commit(args.repo_root),
"launch_arguments": vars(args) | {
key: None if value is None else str(value)
for key, value in vars(args).items() if isinstance(value, Path)
},
"canonical_representation": {
"recipe": "train_and_probe_representation",
"seed": 97,
"training_steps": 600,
"held_out_p_only_state_recovery": probe["p_only_state_recovery"],
},
}
self.recorder = SessionRecorder(
args.session_root, args.runtime, metadata,
enabled=not args.no_logging,
)
self.history: list[tuple[str, str]] = []
self._pending_review: tuple[str, str, list[tuple[str, str]]] | None = None
self.closed = False
@property
def title(self) -> str:
return (
"Planner Cache — Pythia-1.4B"
if self.args.runtime == "pythia"
else "Planner Cache — Gemma4 E4B"
)
def command(
self,
command: str,
*,
source: str = "terminal",
personality_limit: int = 100,
personality_offset: int = 0,
) -> object:
command = command.strip().casefold()
self.recorder.event("SESSION_COMMAND", source=source, command=command)
if command == "/help":
return HELP
if command == "/state":
return self.state.snapshot()
if command == "/personality":
page = (
{
"entries": [], "total_active": 0, "returned": 0,
"limit": personality_limit, "offset": personality_offset,
"truncated": False,
}
if self.personality is None
else self.personality.visible_entry_page(
limit=personality_limit, offset=personality_offset,
)
)
return {
"promoted": page.pop("entries"),
"page": page,
"last_retrieval": (
None
if self.personality is None
or self.personality.last_selection is None
else {
"route": asdict(self.personality.last_selection.route),
"entries": [
asdict(entry)
for entry in self.personality.last_selection.entries
],
}
),
}
if command == "/events":
return list(self.recorder.recent_events)
if command == "/save":
save_session_state(
self.recorder, self.state, self.personality, reason="explicit"
)
return "Session state saved."
if command == "/quit":
return "quit"
return "Unknown command. Type /help."
def chat(
self,
message: str,
*,
raw_messages: list[dict[str, object]] | None = None,
) -> GenerationResult:
if self._pending_review is not None:
raise RuntimeError("previous post-turn memory review is incomplete")
self.recorder.turn += 1
self.recorder.transcript(
role="user", text=message, model=self.runtime.model_id,
runtime=self.runtime.runtime,
)
self.recorder.event(
"P_STATE_BEFORE", source="p_cache", entries=self.state.snapshot()
)
mutations = self.state.extract_manual_mutations(message)
if mutations:
self.state.apply(mutations, self.recorder)
else:
self.recorder.event(
"P_IGNORE", source="p_cache",
reason="no deterministic manual override before generation",
)
if self.personality is not None:
evidence = self.personality.extract_evidence(
message, turn=self.recorder.turn, timestamp=utc_now(),
)
if evidence is not None:
self.personality.ingest(evidence, self.recorder)
personality_store = self.personality.query(message, self.recorder)
else:
personality_store = None
query = self.state.infer_query(message)
if (
query.entity is None
and personality_store is not None
and personality_store.cache.occupied
):
query = type(query)(
entity="user", relation_id=0, relation="response_style",
reason="accepted context-specific P-package state",
)
open_values = bool(getattr(self.runtime, "supports_open_values", False))
translation_store = self.state.translation_store(
include_open_values=open_values
)
if query.entity is not None and query.relation_id is not None:
full_route, full_candidates = canonical_route(
self.runtime.router, self.runtime.encoder, self.state.store, query,
)
if full_route is not None and full_route.has_valid:
selected_slot = int(full_route.indices[0, 0])
accepted = bool(full_route.accepted[0])
if (
accepted
and not open_values
and not self.state.translator_compatible.get(selected_slot, True)
):
self.recorder.event(
"ROUTER_QUERY", source="canonical_compatibility_precheck",
entity=query.entity, relation=query.relation,
)
self.recorder.event(
"ROUTER_CANDIDATES", source="p_cache",
candidates=full_candidates,
)
self.recorder.event(
"ROUTER_ACCEPT", source="p_cache",
score=float(full_route.scores[0, 0].detach()),
selected_state=self.state.entry(selected_slot),
)
self.recorder.event(
"TTL_DISABLE", source="ttl",
reason=(
"canonical value is outside this TTL's supported vocabulary"
),
selected_state=self.state.entry(selected_slot),
)
self.recorder.event(
"MODEL_GENERATION_START", source="model", model=self.runtime.model_id,
runtime=self.runtime.runtime, query=asdict(query),
)
started = utc_now()
try:
result = self.runtime.generate(
message, self.history, translation_store, personality_store, query,
emit=self.recorder.event if self.recorder.enabled else None,
raw_messages=raw_messages,
)
except Exception as error:
self.recorder.event(
"ERROR", source="model", error_type=type(error).__name__,
message=str(error), traceback=traceback.format_exc(),
)
raise
self.recorder.event(
"MODEL_GENERATION_END", source="model", started_at=started,
latency_seconds=result.latency_seconds,
input_tokens=result.input_tokens, output_tokens=result.output_tokens,
diagnostics=result.diagnostics,
)
self.recorder.transcript(
role="assistant", text=result.text, model=self.runtime.model_id,
runtime=self.runtime.runtime, latency_seconds=result.latency_seconds,
input_tokens=result.input_tokens, output_tokens=result.output_tokens,
)
model_candidates = self.state.extract_mutations(result.text)
for candidate in model_candidates:
self.recorder.event(
"P_IGNORE", source="model_output", candidate=asdict(candidate),
reason="model-generated state is not authoritative without user evidence",
)
self._pending_review = (message, result.text, list(self.history))
return result
def complete_turn_review(self) -> None:
"""Finish the hidden review before another visible turn may start."""
if self._pending_review is None:
return
message, response, prior_history = self._pending_review
self._pending_review = None
self.reviewer.run(
user_message=message,
assistant_response=response,
recent_context=prior_history,
state=self.state,
recorder=self.recorder,
)
self.history.append((message, response))
self.recorder.event(
"P_STATE_AFTER", source="p_cache", entries=self.state.snapshot()
)
def close(self, *, reason: str = "normal") -> None:
if self.closed:
return
self.closed = True
if self._pending_review is not None:
self.complete_turn_review()
try:
self.runtime.close()
except Exception as error:
self.recorder.event(
"ERROR", source="runtime_cleanup",
error_type=type(error).__name__, message=str(error),
)
try:
save_session_state(
self.recorder, self.state, self.personality, reason="final"
)
except Exception as error:
self.recorder.event(
"ERROR", source="session_save",
error_type=type(error).__name__, message=str(error),
)
try:
payload = final_payload(self.state, self.personality)
except Exception as error:
self.recorder.event(
"ERROR", source="final_state",
error_type=type(error).__name__, message=str(error),
)
payload = {
"canonical_p_protocol": CANONICAL_P_PROTOCOL,
"active_p_cache_entries": self.state.snapshot(),
"ppkg_mutations": [],
"final_state_error": str(error),
}
self.recorder.finalize(payload, reason=reason)
if self.personality is not None:
try:
self.personality.close()
except Exception:
pass
def run(args: argparse.Namespace) -> int:
session = None
reason = "normal"
try:
session = PlannerChatSession(args)
title = (
"Planner Cache — Pythia-1.4B"
if args.runtime == "pythia"
else "Planner Cache — Gemma4 E4B"
)
print(f"\n{title}\n")
while True:
try:
message = input("You: ")
except EOFError:
reason = "eof"
break
stripped = message.strip()
if not stripped:
continue
if stripped.startswith("/"):
command = stripped.casefold()
if command == "/quit":
session.command(command)
reason = "quit"
break
value = session.command(command)
if isinstance(value, str):
print(value)
else:
display_json(value)
continue
try:
result = session.chat(message)
except KeyboardInterrupt:
reason = "ctrl-c"
raise
except Exception as error:
print(f"Generation failed: {error}", file=sys.stderr)
continue
print(f"Assistant: {result.text}")
session.complete_turn_review()
except KeyboardInterrupt:
reason = "ctrl-c"
print("\nStopping.")
finally:
if session is not None:
session.close(reason=reason)
return 0
def main() -> None:
try:
raise SystemExit(run(parser().parse_args()))
except (FileNotFoundError, ValueError, RuntimeError) as error:
print(f"Planner Cache startup failed: {error}", file=sys.stderr)
raise SystemExit(2) from error
if __name__ == "__main__":
main()
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