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engram / scripts /demo_agent_session.py
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"""
ENGRAM Protocol — Demo Agent Session
End-to-end demonstration:
1. Load model via llama-cpp-python (D1)
2. Generate with a prompt → measure cold TTFT
3. Extract KV cache → compress → serialize to .eng
4. Index in EGR manifold index
5. Reset model → restore from .eng → measure cached TTFT
6. Print speedup ratio
D6: Target >10x TTFT reduction at 16K context on Llama 3.1 8B.
Cold baseline: ~1,500-5,000ms. Cached target: <500ms.
Anything below 4x at 16K is a failure.
"""
from __future__ import annotations
import argparse
import sys
import time
from pathlib import Path
def _run_dry_run(args: argparse.Namespace) -> int:
"""Run full pipeline with synthetic tensors — no model file needed."""
import os
import tempfile
import torch
from kvcos.core.cache_spec import LLAMA_3_1_8B
from kvcos.core.serializer import EngramSerializer
from kvcos.core.types import CompressionMethod, StateExtractionMode
from kvcos.core.manifold_index import IndexEntry, ManifoldIndex
from kvcos.core.state_extractor import MARStateExtractor
from kvcos.storage.local import LocalStorageBackend
spec = LLAMA_3_1_8B
ctx_len = args.context
model_name = spec["model_id"]
# ── Synthetic KV tensors ──────────────────────────────────
torch.manual_seed(42)
shape = (spec["n_layers"], spec["n_kv_heads"], ctx_len, spec["head_dim"])
keys = torch.randn(shape, dtype=torch.float16)
values = torch.randn(shape, dtype=torch.float16)
tensor_mb = keys.numel() * keys.element_size() / 1024 / 1024
with tempfile.TemporaryDirectory() as tmp:
tmp_dir = Path(tmp)
# ── Serialize to .eng ────────────────────────────────
serializer = EngramSerializer()
eng_path = tmp_dir / "dry_run.eng"
t0 = time.perf_counter()
result = serializer.serialize(
keys=keys, values=values,
agent_id="dry-run-agent",
task_description="dry run benchmark",
model_id=model_name,
output_path=eng_path,
compression=CompressionMethod.Q8_0,
)
serialize_ms = (time.perf_counter() - t0) * 1000
# ── Load back ────────────────────────────────────────
t0 = time.perf_counter()
k_out, v_out, meta = serializer.deserialize(eng_path)
deserialize_ms = (time.perf_counter() - t0) * 1000
assert k_out.shape == keys.shape, f"Shape mismatch: {k_out.shape} vs {keys.shape}"
# ── EGR granular timing ──────────────────────────────
extractor = MARStateExtractor(
mode=StateExtractionMode.SVD_PROJECT,
rank=min(160, spec["head_dim"]),
)
dim = extractor.output_dim(spec)
index = ManifoldIndex(dim=dim)
storage = LocalStorageBackend(data_dir=tmp_dir)
# Index: extract + serialize + store + add
t0 = time.perf_counter()
extraction = extractor.extract(keys, spec)
t_extract = time.perf_counter()
eng2 = tmp_dir / "indexed.eng"
serializer.serialize(
keys=keys, values=values,
agent_id="dry-run-agent",
task_description="dry run benchmark",
model_id=model_name,
output_path=eng2,
compression=CompressionMethod.Q8_0,
cache_id="dry-run-001",
)
t_serialize = time.perf_counter()
idx_meta = serializer.read_metadata_only(eng2)
storage.store_file("dry-run-001", eng2, idx_meta)
t_store = time.perf_counter()
from datetime import datetime, timezone
entry = IndexEntry(
cache_id="dry-run-001",
task_description="dry run benchmark",
model_id=model_name,
created_at=datetime.now(timezone.utc).isoformat(),
context_len=ctx_len,
l2_norm=extraction.l2_norm,
)
index.add(extraction.state_vec, entry)
t_add = time.perf_counter()
extract_ms = (t_extract - t0) * 1000
ser_ms = (t_serialize - t_extract) * 1000
store_ms = (t_store - t_serialize) * 1000
add_ms = (t_add - t_store) * 1000
index_ms = (t_add - t0) * 1000
# Retrieve: extract query + search + load
torch.manual_seed(99)
query_keys = torch.randn(shape, dtype=torch.float16)
t0 = time.perf_counter()
q_ext = extractor.extract(query_keys, spec)
t_qext = time.perf_counter()
results = index.search(q_ext.state_vec, top_k=1)
t_search = time.perf_counter()
# Load matched engram
stored_path = storage.get_path("dry-run-001")
k_loaded, v_loaded, _ = serializer.deserialize(stored_path)
t_load = time.perf_counter()
q_extract_ms = (t_qext - t0) * 1000
search_ms = (t_search - t_qext) * 1000
load_ms = (t_load - t_search) * 1000
retrieve_ms = (t_load - t0) * 1000
# ── Simulate TTFT estimates ──────────────────────────
cold_ms = ctx_len * 0.1 # simulated
cached_ms = deserialize_ms
egr_overhead = extract_ms + search_ms # overhead added to warm path
speedup = cold_ms / cached_ms if cached_ms > 0 else float("inf")
eng_size_mb = os.path.getsize(eng_path) / 1024 / 1024
# ── Output ───────────────────────────────────────────
sep = "=" * 35
print(sep)
print("ENGRAM Protocol \u2014 EGR Demo")
print(f"Model: {model_name}")
print(f"Context: {ctx_len} tokens")
print(sep)
print(f"Cold TTFT: {cold_ms:.1f}ms (simulated)")
print(f"Cached TTFT: {cached_ms:.1f}ms (deserialize)")
print(f"Speedup: {speedup:.1f}x")
print(f"D6 target: >10x at 16K tokens")
status = "PASS" if speedup > 10 else "FAIL"
print(f"Status: {status}")
print(f"EGR overhead: {egr_overhead:.1f}ms (extract+search)")
print(f".eng file: {eng_path.name} ({eng_size_mb:.1f}MB)")
print(f"Tensor shape: {list(shape)} ({tensor_mb:.0f}MB per K/V)")
print(sep)
print()
print("Index breakdown:")
print(f" SVD extract: {extract_ms:8.1f}ms")
print(f" Serialize .eng: {ser_ms:8.1f}ms")
print(f" Store backend: {store_ms:8.1f}ms")
print(f" FAISS add(): {add_ms:8.1f}ms")
print(f" TOTAL: {index_ms:8.1f}ms")
print()
print("Retrieve breakdown:")
print(f" SVD extract: {q_extract_ms:8.1f}ms")
print(f" FAISS search(): {search_ms:8.1f}ms")
print(f" Load+deser: {load_ms:8.1f}ms")
print(f" TOTAL: {retrieve_ms:8.1f}ms")
print()
print("Verification:")
print(f" Round-trip shape: {'OK' if k_out.shape == keys.shape else 'FAIL'}")
print(f" Retrieval result: {'OK' if len(results) >= 1 else 'FAIL'}")
print(f" .eng valid: {'OK' if eng_path.exists() else 'FAIL'}")
return 0 if speedup > 10 else 1
def main():
parser = argparse.ArgumentParser(
description="ENGRAM Protocol — Demo Agent Session",
epilog="D6: >10x TTFT reduction at 16K context on Llama 3.1 8B",
)
parser.add_argument(
"--model", "-m", default=None,
help="Path to GGUF model file (required unless --dry-run)",
)
parser.add_argument(
"--context", "-c", type=int, default=4096,
help="Context length to fill (tokens). Default: 4096",
)
parser.add_argument(
"--n-ctx", type=int, default=16384,
help="Max context window for model. Default: 16384",
)
parser.add_argument(
"--data-dir", type=str, default=None,
help="ENGRAM data directory. Default: ~/.engram/data",
)
parser.add_argument(
"--dry-run", action="store_true",
help="Run full pipeline with synthetic tensors (no model needed)",
)
parser.add_argument(
"--verbose", "-v", action="store_true",
help="Enable verbose output",
)
args = parser.parse_args()
if args.dry_run:
return _run_dry_run(args)
if not args.model:
parser.error("--model is required unless --dry-run is specified")
print("=" * 70)
print("ENGRAM Protocol — Demo Agent Session")
print("KV cache fingerprinting for persistent semantic retrieval")
print("=" * 70)
print()
# ── Setup ─────────────────────────────────────────────────
from kvcos.core.config import get_config
from kvcos.core.serializer import EngramSerializer
from kvcos.core.types import CompressionMethod, StateExtractionMode
from kvcos.core.manifold_index import ManifoldIndex
from kvcos.core.retriever import EGRRetriever
from kvcos.core.state_extractor import MARStateExtractor
from kvcos.storage.local import LocalStorageBackend
from integrations.llama_cpp_bridge import LlamaCppBridge
config = get_config()
data_dir = Path(args.data_dir) if args.data_dir else config.data_dir
# ── Step 1: Load Model ────────────────────────────────────
print(f"[1/6] Loading model: {args.model}")
bridge = LlamaCppBridge(
model_path=args.model,
n_ctx=args.n_ctx,
n_gpu_layers=0, # D1
verbose=args.verbose,
)
spec = bridge.load_model()
print(f" Model: {spec['model_id']}")
print(f" Architecture: {spec['n_layers']}L / {spec['n_heads']}H / {spec['n_kv_heads']}KV / {spec['head_dim']}D")
print(f" Context window: {args.n_ctx}")
print()
# ── Step 2: Generate + Cold TTFT ──────────────────────────
filler = "The quick brown fox jumps over the lazy dog. " * 100
target_tokens = args.context
prompt = filler[:target_tokens * 4]
print(f"[2/6] Cold prefill ({target_tokens} target tokens)...")
t0 = time.perf_counter()
cold = bridge.measure_cold_ttft(prompt)
print(f" Cold TTFT: {cold.ttft_ms:.1f}ms ({cold.context_len} tokens)")
print()
# ── Step 3: Extract + Serialize ───────────────────────────
print("[3/6] Extracting KV cache...")
try:
parsed = bridge.extract_kv_cache()
print(f" Keys shape: {list(parsed.keys.shape)}")
print(f" Values shape: {list(parsed.values.shape)}")
print(f" Cells: {parsed.n_cells}")
except Exception as e:
print(f" KV extraction failed: {e}")
print(" This is expected if the blob format doesn't match.")
print(" Falling back to save_state/load_state raw blob path.")
parsed = None
print()
print("[3b/6] Saving raw state blob...")
raw_state = bridge.llm.save_state()
raw_blob = bytes(raw_state.llama_state)
print(f" Raw state size: {len(raw_blob) / 1024 / 1024:.1f} MB")
if parsed is not None:
print("[3c/6] Serializing to .eng format...")
serializer = EngramSerializer()
eng_path = data_dir / "demo" / "session_001.eng"
result = serializer.serialize(
keys=parsed.keys,
values=parsed.values,
agent_id="demo-agent",
task_description="demo session - cold prefill benchmark",
model_id=spec["model_id"],
output_path=eng_path,
compression=CompressionMethod.Q8_0,
)
print(f" .eng file: {result['path']}")
print(f" Size: {result['size_bytes'] / 1024 / 1024:.1f} MB")
print(f" Compression ratio: {result['compression_ratio']:.2f}x")
print()
# ── Step 4: Index in EGR ──────────────────────────────────
if parsed is not None:
print("[4/6] Indexing in EGR manifold index...")
storage = LocalStorageBackend(data_dir=data_dir)
extractor = MARStateExtractor(
mode=StateExtractionMode.SVD_PROJECT,
rank=min(160, spec["head_dim"]),
)
dim = extractor.output_dim(spec)
index = ManifoldIndex(dim=dim)
retriever = EGRRetriever(extractor, index, storage)
cache_id = retriever.index_engram(
keys=parsed.keys,
values=parsed.values,
spec=spec,
agent_id="demo-agent",
task_description="demo session - cold prefill benchmark",
model_id=spec["model_id"],
)
print(f" Indexed: {cache_id}")
print(f" State vector dim: {dim}")
print(f" Index entries: {index.n_entries}")
else:
print("[4/6] Skipped (KV extraction failed)")
print()
# ── Step 5: Restore + Cached TTFT ─────────────────────────
print("[5/6] Restoring from cached state...")
t0 = time.perf_counter()
cached = bridge.measure_cached_ttft(raw_blob)
print(f" Cached TTFT: {cached.ttft_ms:.1f}ms")
print()
# ── Step 6: Results ───────────────────────────────────────
cold_ms = cold.ttft_ms
cached_ms = cached.ttft_ms
speedup = cold_ms / cached_ms if cached_ms > 0 else float("inf")
eng_path_str = result["path"] if parsed else "N/A"
eng_size_kb = result["size_bytes"] / 1024 if parsed else 0
sep = "=" * 35
print(sep)
print("ENGRAM Protocol — EGR Demo")
print(f"Model: {spec['model_id']}")
print(f"Context: {cold.context_len} tokens")
print(sep)
print(f"Cold TTFT: {cold_ms:.1f}ms")
print(f"Cached TTFT: {cached_ms:.1f}ms")
print(f"Speedup: {speedup:.1f}x")
print(f"D6 target: >10x at 16K tokens")
status = "PASS" if speedup > 10 else "FAIL"
print(f"Status: {status}")
print(f".eng file: {eng_path_str} ({eng_size_kb:.1f}KB)")
print(sep)
return 0 if speedup >= 4 else 1
if __name__ == "__main__":
sys.exit(main())