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"""Benchmark numerically verified cached prefill/decode for dense and Q25."""
from __future__ import annotations
import argparse
from datetime import datetime, timezone
import gc
import json
from pathlib import Path
import sys
import time
import torch
from torch.nn.attention import SDPBackend, sdpa_kernel
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT / "src"))
sys.path.insert(0, str(ROOT / "scripts"))
from run_strata_headquotient_v1_1_frontier import construct_export # noqa: E402
from strata.experiments.compose_rf import load_dense_base # noqa: E402
from strata.modeling.compose import HeadQuotientLM # noqa: E402
from strata.training.lm_data import PackedLMDataset # noqa: E402
DEFAULT_CONFIG = ROOT / "configs/experiments/strata_headquotient_v1_1.json"
def parse_ints(value: str) -> tuple[int, ...]:
return tuple(int(item) for item in value.split(",") if item)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--config", type=Path, default=DEFAULT_CONFIG)
parser.add_argument("--device", default="cuda:0" if torch.cuda.is_available() else "cpu")
parser.add_argument("--prefix-lengths", type=parse_ints, default=(2048, 4096, 8160))
parser.add_argument("--batch-sizes", type=parse_ints, default=(1, 4, 8, 16))
parser.add_argument("--decode-steps", type=int, default=32)
parser.add_argument("--warmup", type=int, default=1)
parser.add_argument("--repeats", type=int, default=3)
parser.add_argument("--output", type=Path)
parser.add_argument("--smoke", action="store_true")
return parser.parse_args()
def synchronize(device: torch.device) -> None:
if device.type == "cuda":
torch.cuda.synchronize(device)
def load_models(config: dict, device: torch.device):
dense_base, model_config = load_dense_base(
ROOT / config["model_config"], Path(config["dense_checkpoint"]), device,
)
dense = HeadQuotientLM(dense_base, model_config, gamma_max=0.05)
for block in dense.base_model.blocks:
block.strip_graph_adapter()
dense.to(device).eval()
output = Path(config["output_root"])
plan = json.loads((output / "selection/Q25/plan.json").read_text(encoding="utf-8"))
scoped = json.loads((output / "scoped_adapter/result.json").read_text(encoding="utf-8"))
q25, _cfg, *_rest = construct_export(config, plan, scoped, device)
q25.load_state_dict(
torch.load(output / "frontier/Q25/exported_model.pt", map_location=device, weights_only=True),
strict=True,
)
q25.eval()
return dense, q25
@torch.inference_mode()
def verify_incremental(model, ids: torch.Tensor) -> dict[str, float | bool]:
prefix = ids[:, :-1]
final = ids[:, -1:]
with sdpa_kernel(SDPBackend.MATH):
full = model(ids, graph_enabled=False).logits[:, -1].float()
cached = model.prefill_cache(prefix, maximum_length=ids.shape[1])
incremental = model.decode_step(final, cached.cache).logits[:, -1].float()
difference = (full - incremental).abs()
probability_difference = (
full.softmax(-1) - incremental.softmax(-1)
).abs()
top_equal = bool(torch.equal(full.argmax(-1), incremental.argmax(-1)))
return {
"maximum_absolute_logit_difference": float(difference.max()),
"mean_absolute_logit_difference": float(difference.mean()),
"maximum_absolute_probability_difference": float(probability_difference.max()),
"top_token_exact": top_equal,
"numerically_equivalent": bool(
top_equal
and float(difference.max()) <= 0.15
and float(probability_difference.max()) <= 5e-3
),
}
@torch.inference_mode()
def profile_one(
model,
tokens: torch.Tensor,
*,
decode_steps: int,
warmup: int,
repeats: int,
device: torch.device,
) -> dict[str, float | int]:
prefix = tokens[:, :-decode_steps]
continuation = tokens[:, -decode_steps:]
maximum_length = tokens.shape[1]
for _ in range(warmup):
state = model.prefill_cache(prefix, maximum_length=maximum_length)
for step in range(decode_steps):
state = model.decode_step(continuation[:, step : step + 1], state.cache)
synchronize(device)
prefill_elapsed = 0.0
decode_elapsed = 0.0
first_token_elapsed = 0.0
peak = 0
persistent = 0
for _ in range(repeats):
gc.collect()
if device.type == "cuda":
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats(device)
started = time.perf_counter()
state = model.prefill_cache(prefix, maximum_length=maximum_length)
synchronize(device)
prefill_elapsed += time.perf_counter() - started
persistent = sum(
tensor.numel() * tensor.element_size()
for layer in state.cache
for tensor in (
layer.global_key, layer.global_value,
layer.local_key, layer.local_value,
)
)
started = time.perf_counter()
state = model.decode_step(continuation[:, :1], state.cache)
synchronize(device)
first_token_elapsed += time.perf_counter() - started
started = time.perf_counter()
for step in range(1, decode_steps):
state = model.decode_step(continuation[:, step : step + 1], state.cache)
synchronize(device)
decode_elapsed += time.perf_counter() - started
if device.type == "cuda":
peak = max(peak, int(torch.cuda.max_memory_allocated(device)))
batch = tokens.shape[0]
prefix_tokens = prefix.numel()
decoded_tokens = batch * decode_steps
total_decode_time = first_token_elapsed + decode_elapsed
return {
"batch_size": batch,
"prefix_length": prefix.shape[1],
"decode_steps": decode_steps,
"prefill_seconds": prefill_elapsed / repeats,
"prefill_tokens_per_second": repeats * prefix_tokens / prefill_elapsed,
"first_cached_token_ms": 1000 * first_token_elapsed / repeats,
"decode_seconds": total_decode_time / repeats,
"decode_tokens_per_second": repeats * decoded_tokens / total_decode_time,
"persistent_kv_bytes": persistent,
"peak_allocated_bytes": peak,
}
def main() -> None:
args = parse_args()
config = json.loads(args.config.read_text(encoding="utf-8"))
device = torch.device(args.device)
if device.type == "cuda":
torch.cuda.set_device(device)
if args.smoke:
args.prefix_lengths = (64,)
args.batch_sizes = (1,)
args.decode_steps = 4
args.repeats = 1
maximum = max(args.prefix_lengths) + args.decode_steps
if maximum > 8192:
raise ValueError("prefix plus decode steps exceeds true-8k context")
dataset = PackedLMDataset(config["heldout_corpus"], seq_len=maximum)
source = dataset[min(700, len(dataset) - 1)].unsqueeze(0).to(device)
dense, q25 = load_models(config, device)
verification_ids = source[:, : min(maximum, 96)]
verification = {
"dense": verify_incremental(dense, verification_ids),
"q25": verify_incremental(q25, verification_ids),
}
if not all(row["numerically_equivalent"] for row in verification.values()):
raise RuntimeError(f"cached decoding differs from full decoding: {verification}")
rows = []
for prefix_length in args.prefix_lengths:
length = prefix_length + args.decode_steps
for batch_size in args.batch_sizes:
tokens = source[:, :length].expand(batch_size, -1).contiguous()
values = {}
for name, model in (("dense", dense), ("q25", q25)):
try:
values[name] = profile_one(
model, tokens, decode_steps=args.decode_steps,
warmup=args.warmup, repeats=args.repeats, device=device,
)
except torch.OutOfMemoryError:
values[name] = {"out_of_memory": True}
if device.type == "cuda":
torch.cuda.empty_cache()
row = {
"prefix_length": prefix_length,
"batch_size": batch_size,
"dense": values["dense"],
"q25": values["q25"],
}
if not any(value.get("out_of_memory", False) for value in values.values()):
row["ratios"] = {
"prefill_throughput": (
values["q25"]["prefill_tokens_per_second"]
/ values["dense"]["prefill_tokens_per_second"]
),
"decode_throughput": (
values["q25"]["decode_tokens_per_second"]
/ values["dense"]["decode_tokens_per_second"]
),
"persistent_kv": (
values["q25"]["persistent_kv_bytes"]
/ values["dense"]["persistent_kv_bytes"]
),
}
rows.append(row)
print(json.dumps(row, sort_keys=True), flush=True)
payload = {
"program": "STRATA-HEADQUOTIENT-Q25-CACHED-DECODE-PROFILE",
"created_at": datetime.now(timezone.utc).isoformat(),
"device": str(device),
"torch_version": torch.__version__,
"graph_enabled": False,
"measurement": "inference-only KV cache; BF16 numerical equivalence is checked against full-sequence logits; prefill and autoregressive decode are reported separately",
"verification": verification,
"rows": rows,
}
output = args.output or Path(config["output_root"]) / "cached_decode/profile.json"
output.parent.mkdir(parents=True, exist_ok=True)
output.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8")
print(json.dumps({"output": str(output), "rows": len(rows)}, sort_keys=True))
if __name__ == "__main__":
main()
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