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Running
on
Zero
File size: 2,886 Bytes
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import argparse
import csv
import gc
import os
from dataclasses import dataclass
from typing import Dict, List, Union
import torch
import torch.utils.benchmark as benchmark
GITHUB_SHA = os.getenv("GITHUB_SHA", None)
BENCHMARK_FIELDS = [
"pipeline_cls",
"ckpt_id",
"batch_size",
"num_inference_steps",
"model_cpu_offload",
"run_compile",
"time (secs)",
"memory (gbs)",
"actual_gpu_memory (gbs)",
"github_sha",
]
PROMPT = "ghibli style, a fantasy landscape with castles"
BASE_PATH = os.getenv("BASE_PATH", ".")
TOTAL_GPU_MEMORY = float(os.getenv("TOTAL_GPU_MEMORY", torch.cuda.get_device_properties(0).total_memory / (1024**3)))
REPO_ID = "diffusers/benchmarks"
FINAL_CSV_FILE = "collated_results.csv"
@dataclass
class BenchmarkInfo:
time: float
memory: float
def flush():
"""Wipes off memory."""
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
def bytes_to_giga_bytes(bytes):
return f"{(bytes / 1024 / 1024 / 1024):.3f}"
def benchmark_fn(f, *args, **kwargs):
t0 = benchmark.Timer(
stmt="f(*args, **kwargs)",
globals={"args": args, "kwargs": kwargs, "f": f},
num_threads=torch.get_num_threads(),
)
return f"{(t0.blocked_autorange().mean):.3f}"
def generate_csv_dict(
pipeline_cls: str, ckpt: str, args: argparse.Namespace, benchmark_info: BenchmarkInfo
) -> Dict[str, Union[str, bool, float]]:
"""Packs benchmarking data into a dictionary for latter serialization."""
data_dict = {
"pipeline_cls": pipeline_cls,
"ckpt_id": ckpt,
"batch_size": args.batch_size,
"num_inference_steps": args.num_inference_steps,
"model_cpu_offload": args.model_cpu_offload,
"run_compile": args.run_compile,
"time (secs)": benchmark_info.time,
"memory (gbs)": benchmark_info.memory,
"actual_gpu_memory (gbs)": f"{(TOTAL_GPU_MEMORY):.3f}",
"github_sha": GITHUB_SHA,
}
return data_dict
def write_to_csv(file_name: str, data_dict: Dict[str, Union[str, bool, float]]):
"""Serializes a dictionary into a CSV file."""
with open(file_name, mode="w", newline="") as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=BENCHMARK_FIELDS)
writer.writeheader()
writer.writerow(data_dict)
def collate_csv(input_files: List[str], output_file: str):
"""Collates multiple identically structured CSVs into a single CSV file."""
with open(output_file, mode="w", newline="") as outfile:
writer = csv.DictWriter(outfile, fieldnames=BENCHMARK_FIELDS)
writer.writeheader()
for file in input_files:
with open(file, mode="r") as infile:
reader = csv.DictReader(infile)
for row in reader:
writer.writerow(row)
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