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import os
import time
import torch
import torch.cuda.amp as amp
from loguru import logger
def _count_parameters(model):
total = sum(p.numel() for p in model.parameters())
trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
return total, trainable
def _cuda_available():
return torch.cuda.is_available()
def _sync_cuda():
if _cuda_available():
torch.cuda.synchronize()
def _format_optional(value, precision=2):
if value is None:
return "NA"
return f"{value:.{precision}f}"
def _append_csv(summary, csv_path):
if not csv_path:
return
csv_dir = os.path.dirname(csv_path)
if csv_dir:
os.makedirs(csv_dir, exist_ok=True)
fields = [
"model_name",
"parameters_m",
"flops_g",
"macs_g",
"inference_time_ms",
"trainable_parameters_m",
"batch_size",
"warmup",
"repeat",
]
write_header = not os.path.exists(csv_path) or os.path.getsize(csv_path) == 0
with open(csv_path, "a", newline="") as f:
writer = csv.DictWriter(f, fieldnames=fields)
if write_header:
writer.writeheader()
writer.writerow({key: summary.get(key) for key in fields})
def _profile_flops(forward_fn, use_amp):
try:
from torch.profiler import ProfilerActivity, profile
except Exception as exc:
logger.warning(f"FLOPs profiling is unavailable: {exc}")
return None
activities = [ProfilerActivity.CPU]
if _cuda_available():
activities.append(ProfilerActivity.CUDA)
try:
with torch.no_grad(), profile(activities=activities, with_flops=True) as prof:
with amp.autocast(enabled=use_amp and _cuda_available()):
forward_fn()
_sync_cuda()
except Exception as exc:
logger.warning(f"FLOPs profiling failed: {exc}")
return None
flops = sum((getattr(evt, "flops", 0) or 0) for evt in prof.key_averages())
if flops <= 0:
logger.warning(
"FLOPs profiler returned 0. PyTorch only estimates FLOPs for supported ops."
)
return None
return flops
def _profile_single_forward(forward_fn, use_amp, profile_flops):
flops = None
if not profile_flops:
elapsed_ms = _measure_latency_ms(forward_fn, use_amp, warmup=0, repeat=1)
return elapsed_ms, flops
try:
from torch.profiler import ProfilerActivity, profile
except Exception as exc:
logger.warning(f"FLOPs profiling is unavailable: {exc}")
elapsed_ms = _measure_latency_ms(forward_fn, use_amp, warmup=0, repeat=1)
return elapsed_ms, flops
activities = [ProfilerActivity.CPU]
if _cuda_available():
activities.append(ProfilerActivity.CUDA)
try:
_sync_cuda()
if _cuda_available():
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
else:
start_time = time.perf_counter()
with torch.no_grad(), profile(activities=activities, with_flops=True) as prof:
if _cuda_available():
start.record()
with amp.autocast(enabled=use_amp and _cuda_available()):
forward_fn()
if _cuda_available():
end.record()
_sync_cuda()
if _cuda_available():
elapsed_ms = start.elapsed_time(end)
else:
elapsed_ms = (time.perf_counter() - start_time) * 1000.0
flops = sum((getattr(evt, "flops", 0) or 0) for evt in prof.key_averages())
if flops <= 0:
logger.warning(
"FLOPs profiler returned 0. PyTorch only estimates FLOPs for supported ops."
)
flops = None
return elapsed_ms, flops
except Exception as exc:
logger.warning(f"Single-forward profiling failed: {exc}")
elapsed_ms = _measure_latency_ms(forward_fn, use_amp, warmup=0, repeat=1)
return elapsed_ms, flops
def _measure_latency_ms(forward_fn, use_amp, warmup, repeat):
warmup = max(int(warmup), 0)
repeat = max(int(repeat), 1)
with torch.no_grad():
for _ in range(warmup):
with amp.autocast(enabled=use_amp and _cuda_available()):
forward_fn()
_sync_cuda()
if _cuda_available():
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
with torch.no_grad():
for _ in range(repeat):
with amp.autocast(enabled=use_amp and _cuda_available()):
forward_fn()
end.record()
_sync_cuda()
return start.elapsed_time(end)
start_time = time.perf_counter()
with torch.no_grad():
for _ in range(repeat):
with amp.autocast(enabled=False):
forward_fn()
return (time.perf_counter() - start_time) * 1000.0
def profile_model_complexity(
model,
forward_fn,
batch_size,
model_name,
warmup=10,
repeat=50,
use_amp=True,
profile_flops=True,
csv_path=None,
):
model.eval()
batch_size = max(int(batch_size), 1)
warmup = max(int(warmup), 0)
repeat = max(int(repeat), 1)
total_params, trainable_params = _count_parameters(model)
if warmup == 0 and repeat == 1:
elapsed_ms, flops = _profile_single_forward(forward_fn, use_amp, profile_flops)
else:
elapsed_ms = _measure_latency_ms(forward_fn, use_amp, warmup, repeat)
flops = None
inference_time_ms = elapsed_ms / (repeat * batch_size)
if profile_flops and flops is None and not (warmup == 0 and repeat == 1):
flops = _profile_flops(forward_fn, use_amp)
flops_g = flops / batch_size / 1e9 if flops is not None else None
macs_g = flops_g / 2.0 if flops_g is not None else None
summary = {
"model_name": model_name,
"parameters_m": total_params / 1e6,
"flops_g": flops_g,
"macs_g": macs_g,
"inference_time_ms": inference_time_ms,
"trainable_parameters_m": trainable_params / 1e6,
"batch_size": batch_size,
"warmup": warmup,
"repeat": repeat,
}
logger.info("#########################################")
logger.info("Model complexity and inference performance")
logger.info(f"Model Name: {summary['model_name']}")
logger.info(f"Parameters (M): {summary['parameters_m']:.2f}")
logger.info(f"FLOPs (G, estimated): {_format_optional(summary['flops_g'])}")
logger.info(f"MACs (G, estimated): {_format_optional(summary['macs_g'])}")
logger.info(f"Inference Time (ms/sample): {summary['inference_time_ms']:.2f}")
logger.info(f"Trainable Parameters (M): {summary['trainable_parameters_m']:.2f}")
logger.info("#########################################")
print(
"COMPLEXITY_RESULT "
f"model_name={summary['model_name']} "
f"parameters_m={summary['parameters_m']:.2f} "
f"flops_g={_format_optional(summary['flops_g'])} "
f"macs_g={_format_optional(summary['macs_g'])} "
f"inference_time_ms={summary['inference_time_ms']:.2f} "
f"trainable_parameters_m={summary['trainable_parameters_m']:.2f}"
)
_append_csv(summary, csv_path)
if csv_path:
logger.info(f"Appended complexity result to: {csv_path}")
return summary
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