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import argparse
import collections
import gc
import json
import math
import statistics
import sys
import time
import tomllib
from pathlib import Path
ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(ROOT / "src"))
import torch
from vimeml.training.data import make_loader, write_json
from vimeml.training.data_v2 import PrefixCropWindowDataset
from vimeml.training.model_factory import model_from_checkpoint
from vimeml.training.runtime_v2 import compile_backbone
from vimeml.training.train import learning_rate, optimizer_for, worker_init
def build_loader(config, batch_size, workers, cuda):
settings = config["training"]
dataset = PrefixCropWindowDataset(
ROOT / config["token_dir"],
ROOT / config["index_dir"],
seed=settings["seed"],
probability=settings["prefix_crop_probability"],
min_remaining_tokens=settings["prefix_crop_min_remaining_tokens"],
)
loader = make_loader(
dataset,
batch_size,
workers,
settings["seed"],
pin_memory=cuda,
bucket_multiplier=settings["bucket_multiplier"],
worker_init_fn=worker_init,
)
loader.batch_sampler.set_epoch(settings.get("data_epoch_offset", 0))
return dataset, loader
def compilation_counts():
# Diagnostic counters are optional and vary across PyTorch versions.
try:
from torch._dynamo.utils import counters
except ImportError:
return {}
return {
f"{group}/{key}": value
for group, values in counters.items()
for key, value in values.items()
if group in {"stats", "frames", "inductor", "graph_break"}
and isinstance(value, (int, float))
}
def measure(config, saved, batch_size, workers, warmup, steps, variant, plan_only):
settings = {**config["training"]}
dataset, loader = build_loader(config, batch_size, workers, not plan_only)
settings["max_steps"] = settings["epochs"] * math.ceil(len(dataset) / batch_size)
model = optimizer = iterator = loss = inputs = labels = None
device = torch.device("cuda")
if not plan_only:
torch.manual_seed(settings["seed"])
model = model_from_checkpoint(saved).to(device).train()
optimizer = optimizer_for(model, settings, device)
if variant == "compiled":
compile_backbone(model)
torch.cuda.reset_peak_memory_stats(device)
before_counts = compilation_counts()
records = []
shapes = collections.Counter()
try:
for index in range(warmup + steps):
started = time.perf_counter()
if iterator is None:
iterator = iter(loader)
batch = next(iterator)
data_seconds = time.perf_counter() - started
tokens = int(batch["lengths"].sum())
positions = batch["input_ids"].numel()
shape = list(batch["input_ids"].shape)
shapes["x".join(map(str, shape))] += 1
if not plan_only:
rate = learning_rate(index + 1, settings)
for group in optimizer.param_groups:
group["lr"] = rate
optimizer.zero_grad(set_to_none=True)
inputs = batch["input_ids"].to(device, non_blocking=True)
labels = batch["labels"].to(device, non_blocking=True)
with torch.autocast("cuda", dtype=torch.bfloat16):
loss = model(inputs, labels)["loss_sum"] / tokens
loss.backward()
torch.nn.utils.clip_grad_norm_(
model.parameters(), settings["grad_clip"], error_if_nonfinite=True
)
optimizer.step()
torch.cuda.synchronize(device)
if not math.isfinite(float(loss)):
raise FloatingPointError("Non-finite benchmark loss.")
records.append(
{
"step": index + 1,
"warmup": index < warmup,
"shape": shape,
"tokens": tokens,
"positions": positions,
"data_seconds": data_seconds,
"update_seconds": time.perf_counter() - started,
}
)
if not plan_only and ((index + 1) % 16 == 0 or index == 0):
print(
f"{variant} batch={batch_size} step={index + 1}/{warmup + steps} "
f"seconds={records[-1]['update_seconds']:.3f}",
flush=True,
)
measured = records[warmup:]
seconds = sum(row["update_seconds"] for row in measured)
counts = compilation_counts()
result = {
"variant": variant,
"batch_size": batch_size,
"workers": workers,
"warmup_steps": warmup,
"measured_steps": steps,
"warmup_seconds": sum(row["update_seconds"] for row in records[:warmup]),
"measured_seconds": seconds,
"shape_counts": dict(shapes),
"padding_fraction": 1
- sum(row["tokens"] for row in measured)
/ sum(row["positions"] for row in measured),
"p50_update_ms": statistics.median(
row["update_seconds"] for row in measured
)
* 1000,
"p95_update_ms": sorted(row["update_seconds"] for row in measured)[
math.ceil(0.95 * steps) - 1
]
* 1000,
"compilation_counter_delta": {
key: value - before_counts.get(key, 0)
for key, value in counts.items()
if value != before_counts.get(key, 0)
},
"records": records,
}
if not plan_only:
result.update(
{
"loader_wait_fraction": sum(row["data_seconds"] for row in measured)
/ seconds,
"effective_tokens_per_second": sum(
row["tokens"] for row in measured
)
/ seconds,
"samples_per_second": sum(row["shape"][0] for row in measured)
/ seconds,
"peak_allocated_mib": torch.cuda.max_memory_allocated(device)
/ 1024**2,
"peak_reserved_mib": torch.cuda.max_memory_reserved(device)
/ 1024**2,
}
)
else:
result["gpu_throughput_measured"] = False
result["cpu_data_fraction"] = (
sum(row["data_seconds"] for row in measured) / seconds
)
return result
finally:
iterator = loader = optimizer = model = loss = inputs = labels = None
dataset.close()
if not plan_only:
gc.collect()
torch.cuda.empty_cache()
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--config", type=Path, default=ROOT / "configs/train-v21.toml")
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--batch-sizes", type=int, nargs="+", default=[256, 512])
parser.add_argument("--warmup", type=int, default=32)
parser.add_argument("--steps", type=int, default=128)
parser.add_argument("--workers", type=int)
parser.add_argument(
"--plan-only",
action="store_true",
help="Read real batches on CPU; no model load or GPU updates.",
)
args = parser.parse_args()
if args.output.exists():
parser.error("Use a fresh output directory.")
if args.steps < 1 or args.warmup < 1 or any(size < 1 for size in args.batch_sizes):
parser.error("Batch sizes, steps and warmup must be positive.")
config = tomllib.loads(args.config.read_text(encoding="utf-8"))
settings = config["training"]
workers = settings["num_workers"] if args.workers is None else args.workers
if (
workers < 0
or settings["precision"] != "bf16"
or settings["gradient_accumulation"] != 1
):
parser.error(
"This benchmark expects nonnegative workers, BF16 and accumulation=1."
)
torch.set_num_threads(settings["cpu_threads"])
saved = None
if not args.plan_only:
if not torch.cuda.is_available() or not torch.cuda.is_bf16_supported():
parser.error(
"CUDA BF16 is required for measurements; use --plan-only on CPU."
)
saved = torch.load(
ROOT / config["initialization"]["checkpoint"],
map_location="cpu",
weights_only=True,
)
args.output.mkdir(parents=True)
report = {
"format": "vimeml_v21_real_data_performance_v1",
"status": "running",
"torch_version": str(torch.__version__),
"gpu": None if args.plan_only else torch.cuda.get_device_name(),
"config": str(args.config),
"data_epoch": settings.get("data_epoch_offset", 0),
"formal_training_started": False,
"saved_model_or_optimizer": False,
"plan_only": args.plan_only,
"cache_policy": "Existing disk caches retained; first-use and warmup are reported separately.",
"timing_scope": "Real loader, prefix crop, H2D, forward/backward, clipping, fused AdamW and synchronization; no epoch evaluation/checkpoint/W&B.",
"interpretation": "A short prefix of the real epoch, not full-epoch or thermal steady-state performance; measured-region recompilation is not discarded.",
"results": [],
}
try:
for size in args.batch_sizes:
variants = ("loader_only",) if args.plan_only else ("eager", "compiled")
for variant in variants:
try:
item = measure(
config,
saved,
size,
workers,
args.warmup,
args.steps,
variant,
args.plan_only,
)
except torch.cuda.OutOfMemoryError:
item = {
"variant": variant,
"batch_size": size,
"status": "unavailable_cuda_oom",
}
report["results"].append(item)
write_json(args.output / "report.json", report)
if not args.plan_only:
eager, compiled = report["results"][-2:]
if any(
item.get("status") == "unavailable_cuda_oom"
for item in (eager, compiled)
):
continue
if [(r["shape"], r["tokens"]) for r in eager["records"]] != [
(r["shape"], r["tokens"]) for r in compiled["records"]
]:
raise ValueError("Eager/compiled batch sequence summaries differ.")
compiled["speedup_vs_same_batch_eager"] = (
eager["measured_seconds"] / compiled["measured_seconds"]
)
report["status"] = "complete"
except BaseException as error:
report.update(status="failed", error=str(error))
raise
finally:
write_json(args.output / "report.json", report)
print(
json.dumps(
{
**report,
"results": [
{k: v for k, v in item.items() if k != "records"}
for item in report["results"]
],
},
ensure_ascii=False,
indent=2,
)
)
if __name__ == "__main__":
main()
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