emilandClaude Opus 4.7
ee8a9fc5bc
Two bugs were blocking memba's main promise (load .memb in a fresh process → model continues with full recalled context): 1. llama_state_set_data() restores the C-level KV-cache + SSM hidden state, but llama-cpp-python's Python wrapper still reports n_tokens=0. The next eval() then decodes new tokens at offset 0 and overwrites the loaded state. Fix: extend MEMB format with an optional 12-byte trailer appended after the CRC32. It carries the wrapper's n_tokens. The C library reads up to CRC and ignores anything past it, so files stay backward-compatible with libmemba; only the Python loader uses it. 2. Llama.__call__ / create_chat_completion / generate all re-tokenise the prompt on every call and clear the KV-cache when the new tokens don't prefix-match input_ids. That destroys any state we just loaded. Fix: rewrite Session.chat() to use raw tokenize → eval → sample. eval() appends tokens to the live state without resetting, and we handle stop-token detection ourselves. Verified end-to-end on Nemotron-3-Nano-4B (hybrid 21x Mamba-2 + 4x attention) — see experiments/README.md for the full findings log. diag_session_nemotron.py, mood_batch_poc.py, mood_stream_poc.py and recall_poc.py now all pass their cross-process tests; Falcon-Mamba still fails because the trained model itself can't do cross-turn recall — that was the original misdiagnosis. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
92 lines
3.5 KiB
Python
92 lines
3.5 KiB
Python
"""
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mood_stream_poc.py — streaming sentiment, then save/load across processes.
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This is the REAL product test:
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1. Open memba session
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2. Feed 15 chat messages ONE AT A TIME as "observations"
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3. Save state, exit process
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4. In a fresh process: load state, query mood
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"""
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from __future__ import annotations
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import sys, argparse, time
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "python"))
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from memba import Session
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MAMBA = "/home/emil/Desktop/Coding/AI/Memba/NVIDIA-Nemotron3-Nano-4B-Q4_K_M.gguf"
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STATE_DIR = "/tmp/mood_stream_test"
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SESSION = "mood_stream"
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CHAT_LOG = [
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"Morning team! Coffee in hand, ready to tackle the auth refactor today.",
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"Just pushed PR #234 fixing the token validation bug. Should be a quick merge.",
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"Code review comments came in fast, all good catches. Iterating now.",
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"Basic flow working locally, tests passing. Feeling good about this.",
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"Heading to lunch, hopefully wrap this up by EOD.",
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"Back. CI is failing on something unrelated, looking into it.",
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"OK the 'unrelated' thing is actually related. Auth tests use a stale fixture.",
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"Why does the fixture rebuild take 12 minutes. Every. Single. Time.",
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"Cancelled the run twice now. Going to bypass and run tests locally.",
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"Local passes, CI fails. Classic.",
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"Two hours gone on this fixture issue. Not even what I was supposed to be doing.",
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"Now there's a merge conflict with main because someone restructured migrations.",
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"Whoever shipped those migrations on a Friday afternoon, I will find you.",
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"Closing the laptop. Will fight this tomorrow.",
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"Actually no. One more try before I sleep.",
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]
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QUESTIONS = [
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"Briefly: what is this person's current emotional state? One sentence.",
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"Has their mood changed during this monitoring session? One sentence.",
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"Roughly when did they start having a hard time?",
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]
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def build():
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state_path = Path(STATE_DIR) / f"{SESSION}.memb"
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if state_path.exists():
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state_path.unlink()
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print(f"[build] cleared previous state")
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s = Session(
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model_path=MAMBA, session_id=SESSION, state_dir=STATE_DIR,
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n_gpu_layers=-1, n_ctx=4096, chat_format="chatml",
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)
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print(f"[build] init state {s.state_size:,} B")
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# Stream-feed: each message wrapped as if WE'RE TELLING the model
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# "here's a new message you're observing"
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for i, msg in enumerate(CHAT_LOG, 1):
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observation = f"You are silently observing one person's chat messages. New message just arrived:\n[msg {i}] {msg}\nReply with just 'noted'."
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ack = s.chat(observation, max_tokens=4)
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print(f"[build] msg {i:>2}: {msg[:50]:<50} → ack={ack!r}")
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print(f"[build] state after streaming: {s.state_size:,} B")
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s.save()
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print(f"[build] saved → {state_path}")
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def query():
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state_path = Path(STATE_DIR) / f"{SESSION}.memb"
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if not state_path.exists():
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print("[query] no state — run build first"); return 1
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print(f"[query] loading state {state_path.stat().st_size:,} B")
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s = Session(
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model_path=MAMBA, session_id=SESSION, state_dir=STATE_DIR,
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n_gpu_layers=-1, n_ctx=4096, chat_format="chatml",
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)
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for i, q in enumerate(QUESTIONS, 1):
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print(f"\n[Q{i}] {q}")
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print(f"[A{i}] {s.chat(q, max_tokens=120)}")
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if __name__ == "__main__":
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p = argparse.ArgumentParser()
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p.add_argument("cmd", choices=["build", "query"])
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args = p.parse_args()
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if args.cmd == "build":
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build()
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else:
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query()
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