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https://github.com/saymrwulf/onnxruntime.git
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0c1a5098dc
### Description Disable two PERF* rules in ruff to allow better readability. Rational commented inline. This change also removes the unused noqa directives because of the rule change. ### Motivation and Context Readability
85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
import argparse
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import csv
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import os # noqa: F401
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import numpy as np # noqa: F401
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parser = argparse.ArgumentParser()
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parser.add_argument("--input", type=str)
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args = parser.parse_args()
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def get_gpu_lines(path):
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lines = []
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with open(path, newline="") as f:
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reader = csv.reader(f, delimiter=",")
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for row in reader:
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if row[2].find("TotalDurationNs") < 0:
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lines.append(row)
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return lines
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activities = [
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("nccl", lambda x: x.find("nccl") >= 0),
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("gemm", lambda x: x.find("Cijk_") >= 0),
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("memcpy", lambda x: x.find("CUDA mem") >= 0),
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("adam", lambda x: x.lower().find("adam") >= 0),
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("lamb", lambda x: x.lower().find("lamb") >= 0 or x.lower().find("multi_tensor_apply") >= 0),
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("dropout", lambda x: x.lower().find("dropout") >= 0 or x.find("curand") >= 0),
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("layernorm", lambda x: x.find("LayerNorm") >= 0 or x.find("cuCompute") >= 0),
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("reduce", lambda x: x.find("reduce") >= 0),
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("softmax", lambda x: x.lower().find("softmax") >= 0),
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("transpose", lambda x: x.lower().find("transpose") >= 0),
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("element-wise", lambda x: x.lower().find("elementwise") >= 0 or x.find("DivGrad") >= 0),
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("jit", lambda x: x.startswith("kernel_")),
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("misc", lambda x: True),
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]
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def group_gpu_activity(lines):
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groups = {name: [] for name, _ in activities}
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for line in lines:
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for name, check in activities:
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if check(line[0]):
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groups[name].append(line)
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break
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return groups
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def get_seconds(time):
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return float(time.replace("us", "")) / (1000.0 * 1000.0 * 1000.0)
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def gpu_percent_time(activities):
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return sum([float(a[4].replace("%", "")) for a in activities])
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def gpu_absolute_time(activities):
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return sum([get_seconds(a[2]) for a in activities])
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def gpu_kernel_calls(activities):
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return sum([int(a[1]) for a in activities])
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lines = get_gpu_lines(args.input)
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groups = group_gpu_activity(lines)
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for name in groups:
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activities = groups[name]
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print(
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"{}: N={}, calls={}, absolute={:.3f}s, percent={:.2f}%".format(
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name,
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len(activities),
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gpu_kernel_calls(activities),
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gpu_absolute_time(activities),
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gpu_percent_time(activities),
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)
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)
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total = [item for name in groups for item in groups[name]]
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print(
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"Total: N={}, calls={}, absolute={:.3f}s, percent={:.2f}%".format(
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len(total), gpu_kernel_calls(total), gpu_absolute_time(total), gpu_percent_time(total)
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)
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)
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