mirror of
https://github.com/NVIDIA/nvbench.git
synced 2026-03-14 20:27:24 +00:00
398 lines
13 KiB
Python
Executable File
398 lines
13 KiB
Python
Executable File
#!/usr/bin/env python
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import argparse
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import math
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import os
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import sys
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import jsondiff
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import tabulate
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from colorama import Fore
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from nvbench_json import reader
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# Parse version string into tuple, "x.y.z" -> (x, y, z)
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def version_tuple(v):
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return tuple(map(int, (v.split("."))))
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tabulate_version = version_tuple(tabulate.__version__)
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all_devices = []
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config_count = 0
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unknown_count = 0
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failure_count = 0
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pass_count = 0
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def find_matching_bench(needle, haystack):
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for hay in haystack:
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if hay["name"] == needle["name"] and hay["axes"] == needle["axes"]:
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return hay
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return None
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def find_device_by_id(device_id):
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for device in all_devices:
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if device["id"] == device_id:
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return device
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return None
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def format_int64_axis_value(axis_name, axis_value, axes):
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axis = next(filter(lambda ax: ax["name"] == axis_name, axes))
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axis_flags = axis["flags"]
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value = int(axis_value["value"])
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if axis_flags == "pow2":
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value = math.log2(value)
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return "2^%d" % value
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return "%d" % value
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def format_float64_axis_value(axis_name, axis_value, axes):
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return "%.5g" % float(axis_value["value"])
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def format_type_axis_value(axis_name, axis_value, axes):
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return "%s" % axis_value["value"]
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def format_string_axis_value(axis_name, axis_value, axes):
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return "%s" % axis_value["value"]
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def format_axis_value(axis_name, axis_value, axes):
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axis = next(filter(lambda ax: ax["name"] == axis_name, axes))
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axis_type = axis["type"]
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if axis_type == "int64":
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return format_int64_axis_value(axis_name, axis_value, axes)
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elif axis_type == "float64":
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return format_float64_axis_value(axis_name, axis_value, axes)
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elif axis_type == "type":
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return format_type_axis_value(axis_name, axis_value, axes)
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elif axis_type == "string":
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return format_string_axis_value(axis_name, axis_value, axes)
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def format_duration(seconds):
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if seconds >= 1:
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multiplier = 1.0
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units = "s"
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elif seconds >= 1e-3:
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multiplier = 1e3
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units = "ms"
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elif seconds >= 1e-6:
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multiplier = 1e6
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units = "us"
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else:
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multiplier = 1e6
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units = "us"
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return "%0.3f %s" % (seconds * multiplier, units)
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def format_percentage(percentage):
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# When there aren't enough samples for a meaningful noise measurement,
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# the noise is recorded as infinity. Unfortunately, JSON spec doesn't
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# allow for inf, so these get turned into null.
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if percentage is None:
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return "inf"
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return "%0.2f%%" % (percentage * 100.0)
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def compare_benches(ref_benches, cmp_benches, threshold, plot):
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if plot:
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import matplotlib.pyplot as plt
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import seaborn as sns
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sns.set()
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for cmp_bench in cmp_benches:
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ref_bench = find_matching_bench(cmp_bench, ref_benches)
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if not ref_bench:
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continue
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print("# %s\n" % (cmp_bench["name"]))
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device_ids = cmp_bench["devices"]
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axes = cmp_bench["axes"]
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ref_states = ref_bench["states"]
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cmp_states = cmp_bench["states"]
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axes = axes if axes else []
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headers = [x["name"] for x in axes]
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colalign = ["center"] * len(headers)
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headers.append("Ref Time")
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colalign.append("right")
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headers.append("Ref Noise")
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colalign.append("right")
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headers.append("Cmp Time")
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colalign.append("right")
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headers.append("Cmp Noise")
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colalign.append("right")
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headers.append("Diff")
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colalign.append("right")
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headers.append("%Diff")
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colalign.append("right")
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headers.append("Status")
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colalign.append("center")
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for device_id in device_ids:
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rows = []
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plot_data = {"cmp": {}, "ref": {}, "cmp_noise": {}, "ref_noise": {}}
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for cmp_state in cmp_states:
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cmp_state_name = cmp_state["name"]
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ref_state = next(
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filter(lambda st: st["name"] == cmp_state_name, ref_states), None
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)
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if not ref_state:
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continue
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axis_values = cmp_state["axis_values"]
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if not axis_values:
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axis_values = []
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row = []
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for axis_value in axis_values:
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axis_value_name = axis_value["name"]
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row.append(format_axis_value(axis_value_name, axis_value, axes))
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cmp_summaries = cmp_state["summaries"]
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ref_summaries = ref_state["summaries"]
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if not ref_summaries or not cmp_summaries:
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continue
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def lookup_summary(summaries, tag):
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return next(filter(lambda s: s["tag"] == tag, summaries), None)
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cmp_time_summary = lookup_summary(
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cmp_summaries, "nv/cold/time/gpu/mean"
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)
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ref_time_summary = lookup_summary(
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ref_summaries, "nv/cold/time/gpu/mean"
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)
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cmp_noise_summary = lookup_summary(
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cmp_summaries, "nv/cold/time/gpu/stdev/relative"
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)
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ref_noise_summary = lookup_summary(
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ref_summaries, "nv/cold/time/gpu/stdev/relative"
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)
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# TODO: Use other timings, too. Maybe multiple rows, with a
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# "Timing" column + values "CPU/GPU/Batch"?
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if not all(
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[
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cmp_time_summary,
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ref_time_summary,
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cmp_noise_summary,
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ref_noise_summary,
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]
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):
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continue
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def extract_value(summary):
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summary_data = summary["data"]
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value_data = next(
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filter(lambda v: v["name"] == "value", summary_data)
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)
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assert value_data["type"] == "float64"
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return value_data["value"]
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cmp_time = extract_value(cmp_time_summary)
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ref_time = extract_value(ref_time_summary)
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cmp_noise = extract_value(cmp_noise_summary)
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ref_noise = extract_value(ref_noise_summary)
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# Convert string encoding to expected numerics:
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cmp_time = float(cmp_time)
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ref_time = float(ref_time)
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diff = cmp_time - ref_time
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frac_diff = diff / ref_time
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if ref_noise and cmp_noise:
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ref_noise = float(ref_noise)
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cmp_noise = float(cmp_noise)
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min_noise = min(ref_noise, cmp_noise)
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elif ref_noise:
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ref_noise = float(ref_noise)
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min_noise = ref_noise
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elif cmp_noise:
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cmp_noise = float(cmp_noise)
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min_noise = cmp_noise
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else:
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min_noise = None # Noise is inf
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if plot:
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axis_name = []
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axis_value = "--"
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for aid in range(len(axis_values)):
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if axis_values[aid]["name"] != plot:
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axis_name.append(
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"{} = {}".format(
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axis_values[aid]["name"], axis_values[aid]["value"]
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)
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)
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else:
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axis_value = float(axis_values[aid]["value"])
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axis_name = ", ".join(axis_name)
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if axis_name not in plot_data["cmp"]:
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plot_data["cmp"][axis_name] = {}
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plot_data["ref"][axis_name] = {}
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plot_data["cmp_noise"][axis_name] = {}
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plot_data["ref_noise"][axis_name] = {}
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plot_data["cmp"][axis_name][axis_value] = cmp_time
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plot_data["ref"][axis_name][axis_value] = ref_time
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plot_data["cmp_noise"][axis_name][axis_value] = cmp_noise
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plot_data["ref_noise"][axis_name][axis_value] = ref_noise
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global config_count
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global unknown_count
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global pass_count
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global failure_count
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config_count += 1
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if not min_noise:
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unknown_count += 1
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status = Fore.YELLOW + "????" + Fore.RESET
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elif abs(frac_diff) <= min_noise:
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pass_count += 1
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status = Fore.BLUE + "SAME" + Fore.RESET
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elif diff < 0:
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failure_count += 1
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status = Fore.GREEN + "FAST" + Fore.RESET
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else:
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failure_count += 1
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status = Fore.RED + "SLOW" + Fore.RESET
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if abs(frac_diff) >= threshold:
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row.append(format_duration(ref_time))
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row.append(format_percentage(ref_noise))
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row.append(format_duration(cmp_time))
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row.append(format_percentage(cmp_noise))
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row.append(format_duration(diff))
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row.append(format_percentage(frac_diff))
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row.append(status)
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rows.append(row)
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if len(rows) == 0:
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continue
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device = find_device_by_id(device_id)
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print("## [%d] %s\n" % (device["id"], device["name"]))
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# colalign and github format require tabulate 0.8.3
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if tabulate_version >= (0, 8, 3):
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print(
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tabulate.tabulate(
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rows, headers=headers, colalign=colalign, tablefmt="github"
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)
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)
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else:
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print(tabulate.tabulate(rows, headers=headers, tablefmt="markdown"))
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print("")
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if plot:
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plt.xscale("log")
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plt.yscale("log")
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plt.xlabel(plot)
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plt.ylabel("time [s]")
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plt.title(device["name"])
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def plot_line(key, shape, label):
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x = [float(x) for x in plot_data[key][axis].keys()]
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y = list(plot_data[key][axis].values())
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noise = list(plot_data[key + "_noise"][axis].values())
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top = [y[i] + y[i] * noise[i] for i in range(len(x))]
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bottom = [y[i] - y[i] * noise[i] for i in range(len(x))]
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p = plt.plot(x, y, shape, marker="o", label=label)
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plt.fill_between(x, bottom, top, color=p[0].get_color(), alpha=0.1)
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for axis in plot_data["cmp"].keys():
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plot_line("cmp", "-", axis)
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plot_line("ref", "--", axis + " ref")
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plt.legend()
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plt.show()
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def main():
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help_text = "%(prog)s [reference.json compare.json | reference_dir/ compare_dir/]"
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parser = argparse.ArgumentParser(prog="nvbench_compare", usage=help_text)
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parser.add_argument(
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"--threshold-diff",
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type=float,
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dest="threshold",
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default=0.0,
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help="only show benchmarks where percentage diff is >= THRESHOLD",
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)
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parser.add_argument(
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"--plot-along", type=str, dest="plot", default=None, help="plot results"
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)
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args, files_or_dirs = parser.parse_known_args()
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print(files_or_dirs)
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if len(files_or_dirs) != 2:
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parser.print_help()
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sys.exit(1)
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# if provided two directories, find all the exactly named files
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# in both and treat them as the reference and compare
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to_compare = []
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if os.path.isdir(files_or_dirs[0]) and os.path.isdir(files_or_dirs[1]):
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for f in os.listdir(files_or_dirs[1]):
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if os.path.splitext(f)[1] != ".json":
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continue
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r = os.path.join(files_or_dirs[0], f)
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c = os.path.join(files_or_dirs[1], f)
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if (
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os.path.isfile(r)
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and os.path.isfile(c)
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and os.path.getsize(r) > 0
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and os.path.getsize(c) > 0
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):
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to_compare.append((r, c))
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else:
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to_compare = [(files_or_dirs[0], files_or_dirs[1])]
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for ref, comp in to_compare:
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ref_root = reader.read_file(ref)
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cmp_root = reader.read_file(comp)
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global all_devices
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all_devices = cmp_root["devices"]
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if ref_root["devices"] != cmp_root["devices"]:
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print("Device sections do not match.")
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print(
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jsondiff.diff(
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ref_root["devices"], cmp_root["devices"], syntax="symmetric"
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)
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)
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sys.exit(1)
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compare_benches(
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ref_root["benchmarks"], cmp_root["benchmarks"], args.threshold, args.plot
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)
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print("# Summary\n")
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print("- Total Matches: %d" % config_count)
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print(" - Pass (diff <= min_noise): %d" % pass_count)
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print(" - Unknown (infinite noise): %d" % unknown_count)
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print(" - Failure (diff > min_noise): %d" % failure_count)
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return failure_count
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if __name__ == "__main__":
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sys.exit(main())
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