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https://github.com/NVIDIA/nvbench.git
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6d8aa878cf
It is not emitted just yet, but the code becomes ready for it when it starts being emitted
1369 lines
45 KiB
Python
1369 lines
45 KiB
Python
#!/usr/bin/env python
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#
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# SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION.
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# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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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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from collections import Counter
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from dataclasses import dataclass
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from enum import Enum
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from typing import Any, Mapping
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import jsondiff
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import numpy as np
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import tabulate
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from colorama import Fore
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try:
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from nvbench_json import reader
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except ImportError:
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from scripts.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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GPU_TIME_MIN_TAG = "nv/cold/time/gpu/min"
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GPU_TIME_MAX_TAG = "nv/cold/time/gpu/max"
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GPU_TIME_MEAN_TAG = "nv/cold/time/gpu/mean"
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GPU_TIME_STDEV_TAG = "nv/cold/time/gpu/stdev/absolute"
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GPU_TIME_STDEV_RELATIVE_TAG = "nv/cold/time/gpu/stdev/relative"
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GPU_TIME_MEDIAN_TAG = "nv/cold/time/gpu/median"
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GPU_TIME_IR_TAG = "nv/cold/time/gpu/ir/absolute"
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GPU_TIME_IR_RELATIVE_TAG = "nv/cold/time/gpu/ir/relative"
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SAMPLE_TIMES_TAG = "nv/json/bin:nv/cold/sample_times"
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SAMPLE_FREQUENCIES_TAG = "nv/json/freqs-bin:nv/cold/sample_freqs"
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# These dataclasses are treated as parsed value objects. frozen=True prevents
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# accidental field reassignment but does not imply deep immutability.
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@dataclass(frozen=True)
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class GpuTimingData:
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minimum: float | None
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maximum: float | None
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mean: float | None
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stdev: float | None
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stdev_relative: float | None
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median: float | None
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interquartile_range: float | None
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interquartile_range_relative: float | None
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samples: np.ndarray | None = None
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frequencies: np.ndarray | None = None
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@dataclass(frozen=True)
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class TimeEstimate:
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center: float | None
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relative_dispersion: float | None
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class ComparisonStatus(str, Enum):
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UNKNOWN = "????"
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UNDECIDED = "UNDECIDED"
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SAME = "SAME"
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FAST = "FAST"
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SLOW = "SLOW"
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@dataclass(frozen=True)
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class SummaryComparison:
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ref_estimate: TimeEstimate
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cmp_estimate: TimeEstimate
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ref_time: float
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cmp_time: float
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ref_noise: float | None
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cmp_noise: float | None
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diff: float
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frac_diff: float
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max_noise: float | None
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status: ComparisonStatus
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@dataclass
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class ComparisonStats:
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config_count: int = 0
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pass_count: int = 0
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improvement_count: int = 0
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regression_count: int = 0
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undecided_count: int = 0
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unknown_count: int = 0
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def record(self, status: ComparisonStatus) -> None:
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self.config_count += 1
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if status == ComparisonStatus.UNKNOWN:
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self.unknown_count += 1
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elif status == ComparisonStatus.UNDECIDED:
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self.undecided_count += 1
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elif status == ComparisonStatus.SAME:
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self.pass_count += 1
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elif status == ComparisonStatus.FAST:
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self.improvement_count += 1
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else:
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self.regression_count += 1
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DeviceInfo = Mapping[str, Any]
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@dataclass(frozen=True)
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class ComparisonRunData:
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# Device metadata fields are treated as read-only; stats is intentionally
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# mutable and accumulates counts across one comparison run.
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stats: ComparisonStats
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ref_devices: tuple[DeviceInfo, ...]
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cmp_devices: tuple[DeviceInfo, ...]
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@dataclass(frozen=True)
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class BenchmarkFilterScope:
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benchmark_name: str
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axis_filters: list[dict]
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@dataclass(frozen=True)
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class BenchmarkFilterPlan:
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global_axis_filters: list[dict]
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benchmark_scopes: list[BenchmarkFilterScope]
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class OrderedBenchmarkFilterAction(argparse.Action):
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def __call__(self, parser, namespace, values, option_string=None):
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actions = getattr(namespace, self.dest, None)
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actions = [] if actions is None else list(actions)
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action_kind = "axis" if option_string in {"-a", "--axis"} else "benchmark"
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actions.append((action_kind, values))
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setattr(namespace, self.dest, actions)
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def state_match_key(state):
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device_prefix = f"Device={state['device']}"
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state_name = state["name"]
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if state_name == device_prefix:
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return ""
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if state_name.startswith(f"{device_prefix} "):
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return state_name[len(device_prefix) + 1 :]
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return state_name
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def group_states_by_match_key(states):
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grouped = {}
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for state in states:
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grouped.setdefault(state_match_key(state), []).append(state)
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return grouped
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def state_group_counts(grouped_states):
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return Counter(
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{state_name: len(states) for state_name, states in grouped_states.items()}
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)
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def format_device_ids(device_ids):
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return ", ".join(str(device_id) for device_id in device_ids)
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def parse_device_filter(device_arg, option_name):
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device_arg = device_arg.strip()
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if device_arg.lower() == "all":
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return None
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values = [value.strip() for value in device_arg.split(",")]
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if not all(values):
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raise ValueError(
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f"{option_name} must be 'all', a non-negative integer, "
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"or comma-separated non-negative integers"
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)
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try:
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device_ids = [int(value) for value in values]
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except ValueError as exc:
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raise ValueError(
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f"{option_name} must be 'all', a non-negative integer, "
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"or comma-separated non-negative integers"
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) from exc
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if any(device_id < 0 for device_id in device_ids):
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raise ValueError(
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f"{option_name} must be 'all', a non-negative integer, "
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"or comma-separated non-negative integers"
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)
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return device_ids
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def select_devices(all_devices, device_filter, option_name):
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if device_filter is None:
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return list(all_devices)
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devices_by_id = {device["id"]: device for device in all_devices}
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missing_ids = [
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device_id for device_id in device_filter if device_id not in devices_by_id
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]
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if missing_ids:
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raise ValueError(
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f"{option_name} requested device id(s) not present in input: "
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f"{format_device_ids(missing_ids)}"
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)
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return [devices_by_id[device_id] for device_id in device_filter]
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def resolve_benchmark_device_ids(bench, device_filter, option_name):
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if device_filter is None:
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return list(bench["devices"])
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benchmark_device_ids = set(bench["devices"])
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missing_ids = [
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device_id
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for device_id in device_filter
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if device_id not in benchmark_device_ids
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]
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if missing_ids:
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raise ValueError(
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f"benchmark {bench['name']!r} does not contain {option_name} "
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f"device id(s): {format_device_ids(missing_ids)}"
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)
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return device_filter
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def require_matching_device_sections(reference_device_filter, compare_device_filter):
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return reference_device_filter is None and compare_device_filter is None
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# TODO(opavlyk): replace with Emoji(StrEnum) after EOL of Python 3.10
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class Emoji(str, Enum):
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YELLOW = "\U0001f7e1"
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BLUE = "\U0001f535"
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GREEN = "\U0001f7e2"
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RED = "\U0001f534"
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NONE = ""
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def colorize(msg: str, fore: Fore, emoji: Emoji, no_color: bool) -> str:
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if no_color:
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prefix = ""
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if emoji_s := emoji.value:
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prefix = f"{emoji_s} "
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return f"{prefix}{msg}"
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else:
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return f"{fore}{msg}{Fore.RESET}"
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def lookup_summary(summaries, tag):
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return next((summary for summary in summaries if summary["tag"] == tag), None)
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def extract_summary_data_value(summary, name, expected_type):
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summary_tag = summary.get("tag", "<unknown>")
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for value_data in summary.get("data", []):
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if value_data.get("name") != name:
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continue
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value_type = value_data.get("type")
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if value_type != expected_type:
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raise ValueError(
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f"summary {summary_tag!r} field {name!r} has type "
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f"{value_type!r}; expected {expected_type!r}"
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)
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if "value" not in value_data:
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raise ValueError(f"summary {summary_tag!r} field {name!r} is missing value")
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return value_data["value"]
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raise ValueError(f"summary {summary_tag!r} is missing field {name!r}")
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def extract_summary_value(summary):
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return extract_summary_data_value(summary, "value", "float64")
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def normalize_float_value(value, *, null_value=None):
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if value is None:
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return null_value
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return float(value)
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def extract_summary_float(summaries, tag, *, null_value=None):
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summary = lookup_summary(summaries, tag)
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if summary is None:
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return None
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return normalize_float_value(extract_summary_value(summary), null_value=null_value)
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def extract_binary_filename(summary):
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value = extract_summary_data_value(summary, "filename", "string")
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if not isinstance(value, str):
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raise ValueError(
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f"summary {summary.get('tag', '<unknown>')!r} field 'filename' "
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"value must be a string"
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)
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return value
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def extract_binary_size(summary):
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value = extract_summary_data_value(summary, "size", "int64")
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try:
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return int(value)
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except (TypeError, ValueError) as exc:
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raise ValueError(
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f"summary {summary.get('tag', '<unknown>')!r} field 'size' "
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f"value {value!r} is not an int64"
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) from exc
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def extract_binary_meta(summaries, tag):
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summary = lookup_summary(summaries, tag)
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if summary is None:
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return None, None
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return extract_binary_size(summary), extract_binary_filename(summary)
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def resolve_binary_filename(json_dir, binary_filename):
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if os.path.isabs(binary_filename):
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return binary_filename
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json_relative_filename = os.path.join(json_dir, binary_filename)
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if os.path.exists(json_relative_filename):
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return json_relative_filename
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parent_relative_filename = os.path.join(os.path.dirname(json_dir), binary_filename)
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if os.path.exists(parent_relative_filename):
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return parent_relative_filename
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if os.path.exists(binary_filename):
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return binary_filename
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return json_relative_filename
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def read_float32_binary(count, filename, json_dir):
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if count is None or filename is None or json_dir is None:
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return None
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filename = resolve_binary_filename(json_dir, filename)
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try:
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values = np.fromfile(filename, dtype="<f4")
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except FileNotFoundError:
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return None
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if count != len(values):
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raise ValueError(f"expected {count} values in {filename}, found {len(values)}")
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return values
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def extract_sample_times(summaries, json_dir):
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sample_count, samples_filename = extract_binary_meta(summaries, SAMPLE_TIMES_TAG)
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return read_float32_binary(sample_count, samples_filename, json_dir)
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def extract_sample_frequencies(summaries, json_dir):
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frequency_count, frequencies_filename = extract_binary_meta(
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summaries, SAMPLE_FREQUENCIES_TAG
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)
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return read_float32_binary(frequency_count, frequencies_filename, json_dir)
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def extract_gpu_timing_data(summaries, json_dir=None):
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samples = extract_sample_times(summaries, json_dir)
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frequencies = extract_sample_frequencies(summaries, json_dir)
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if (
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samples is not None
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and frequencies is not None
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and len(samples) != len(frequencies)
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):
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raise ValueError(
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f"sample count ({len(samples)}) does not match "
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f"frequency count ({len(frequencies)})"
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)
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return GpuTimingData(
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minimum=extract_summary_float(summaries, GPU_TIME_MIN_TAG),
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maximum=extract_summary_float(summaries, GPU_TIME_MAX_TAG),
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mean=extract_summary_float(summaries, GPU_TIME_MEAN_TAG),
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stdev=extract_summary_float(summaries, GPU_TIME_STDEV_TAG, null_value=math.inf),
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stdev_relative=extract_summary_float(
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summaries, GPU_TIME_STDEV_RELATIVE_TAG, null_value=math.inf
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),
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median=extract_summary_float(summaries, GPU_TIME_MEDIAN_TAG),
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interquartile_range=extract_summary_float(
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summaries, GPU_TIME_IR_TAG, null_value=math.inf
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),
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interquartile_range_relative=extract_summary_float(
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summaries, GPU_TIME_IR_RELATIVE_TAG, null_value=math.inf
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),
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samples=samples,
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frequencies=frequencies,
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)
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def compute_relative_dispersion(dispersion, center):
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if (
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dispersion is None
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or center is None
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or center <= 0
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or not math.isfinite(center)
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or dispersion < 0
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or math.isnan(dispersion)
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):
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return None
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return dispersion / center
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def has_robust_estimate(summary):
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return summary.median is not None and (
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summary.interquartile_range_relative is not None
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or summary.interquartile_range is not None
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)
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def has_mean_estimate(summary):
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return summary.mean is not None and (
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summary.stdev_relative is not None or summary.stdev is not None
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)
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def select_relative_dispersion(relative_dispersion, absolute_dispersion, center):
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if relative_dispersion is not None:
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return relative_dispersion
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return compute_relative_dispersion(absolute_dispersion, center)
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def compute_common_time_estimates(ref_timing, cmp_timing):
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if has_robust_estimate(ref_timing) and has_robust_estimate(cmp_timing):
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return (
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TimeEstimate(
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center=ref_timing.median,
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relative_dispersion=select_relative_dispersion(
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ref_timing.interquartile_range_relative,
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ref_timing.interquartile_range,
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ref_timing.median,
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),
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),
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TimeEstimate(
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center=cmp_timing.median,
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relative_dispersion=select_relative_dispersion(
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cmp_timing.interquartile_range_relative,
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cmp_timing.interquartile_range,
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cmp_timing.median,
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),
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),
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)
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if has_mean_estimate(ref_timing) and has_mean_estimate(cmp_timing):
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return (
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TimeEstimate(
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center=ref_timing.mean,
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relative_dispersion=select_relative_dispersion(
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ref_timing.stdev_relative, ref_timing.stdev, ref_timing.mean
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),
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),
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TimeEstimate(
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center=cmp_timing.mean,
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relative_dispersion=select_relative_dispersion(
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cmp_timing.stdev_relative, cmp_timing.stdev, cmp_timing.mean
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),
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),
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)
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return (
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TimeEstimate(
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center=ref_timing.mean,
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relative_dispersion=compute_relative_dispersion(
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ref_timing.stdev, ref_timing.mean
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),
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),
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TimeEstimate(
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center=cmp_timing.mean,
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relative_dispersion=compute_relative_dispersion(
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cmp_timing.stdev, cmp_timing.mean
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),
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),
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)
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def compare_gpu_timings(ref_timing, cmp_timing):
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ref_estimate, cmp_estimate = compute_common_time_estimates(ref_timing, cmp_timing)
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cmp_time = cmp_estimate.center
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ref_time = ref_estimate.center
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if cmp_time is None or ref_time is None:
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return None
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if not math.isfinite(cmp_time) or not math.isfinite(ref_time):
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return None
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if cmp_time <= 0.0 or ref_time <= 0.0:
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return None
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cmp_noise = cmp_estimate.relative_dispersion
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ref_noise = ref_estimate.relative_dispersion
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diff = cmp_time - ref_time
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frac_diff = diff / ref_time
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if not has_finite_noise(ref_noise) or not has_finite_noise(cmp_noise):
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max_noise = None
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status = ComparisonStatus.UNKNOWN
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else:
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max_noise = max(ref_noise, cmp_noise)
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if abs(frac_diff) <= max_noise:
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status = ComparisonStatus.SAME
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elif diff < 0:
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status = ComparisonStatus.FAST
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else:
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status = ComparisonStatus.SLOW
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return SummaryComparison(
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ref_estimate=ref_estimate,
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cmp_estimate=cmp_estimate,
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ref_time=ref_time,
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cmp_time=cmp_time,
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ref_noise=ref_noise,
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cmp_noise=cmp_noise,
|
|
diff=diff,
|
|
frac_diff=frac_diff,
|
|
max_noise=max_noise,
|
|
status=status,
|
|
)
|
|
|
|
|
|
def find_matching_bench(needle, haystack):
|
|
for hay in haystack:
|
|
if hay["name"] == needle["name"]:
|
|
return hay
|
|
return None
|
|
|
|
|
|
def find_device_by_id(device_id, all_devices):
|
|
for device in all_devices:
|
|
if device["id"] == device_id:
|
|
return device
|
|
return None
|
|
|
|
|
|
def format_int64_axis_value(axis_name, axis_value, axes):
|
|
axis = next(filter(lambda ax: ax["name"] == axis_name, axes))
|
|
axis_flags = axis["flags"]
|
|
value = int(axis_value["value"])
|
|
if axis_flags == "pow2":
|
|
value = math.log2(value)
|
|
return f"2^{value:.0f}"
|
|
return f"{value:d}"
|
|
|
|
|
|
def format_float64_axis_value(axis_name, axis_value, axes):
|
|
return "%.5g" % float(axis_value["value"])
|
|
|
|
|
|
def format_type_axis_value(axis_name, axis_value, axes):
|
|
return f"{axis_value['value']}"
|
|
|
|
|
|
def format_string_axis_value(axis_name, axis_value, axes):
|
|
return f"{axis_value['value']}"
|
|
|
|
|
|
def format_axis_value(axis_name, axis_value, axes):
|
|
axis = next(filter(lambda ax: ax["name"] == axis_name, axes))
|
|
axis_type = axis["type"]
|
|
if axis_type == "int64":
|
|
return format_int64_axis_value(axis_name, axis_value, axes)
|
|
elif axis_type == "float64":
|
|
return format_float64_axis_value(axis_name, axis_value, axes)
|
|
elif axis_type == "type":
|
|
return format_type_axis_value(axis_name, axis_value, axes)
|
|
elif axis_type == "string":
|
|
return format_string_axis_value(axis_name, axis_value, axes)
|
|
|
|
|
|
def make_display(name: str, display_values: list[str]) -> str:
|
|
open_bracket, close_bracket = ("[", "]") if len(display_values) > 1 else ("", "")
|
|
joined_values = ",".join(display_values)
|
|
return f"{name}={open_bracket}{joined_values}{close_bracket}"
|
|
|
|
|
|
def parse_axis_filters(axis_args):
|
|
filters = []
|
|
for axis_arg in axis_args:
|
|
if "=" not in axis_arg:
|
|
raise ValueError(f"Axis filter must be NAME=VALUE: {axis_arg}")
|
|
name, value = axis_arg.split("=", 1)
|
|
name = name.strip()
|
|
value = value.strip()
|
|
if not name or not value:
|
|
raise ValueError(f"Axis filter must be NAME=VALUE: {axis_arg}")
|
|
|
|
values = []
|
|
if value.startswith("[") and value.endswith("]"):
|
|
inner = value[1:-1].strip()
|
|
values = [
|
|
stripped for item in inner.split(",") if (stripped := item.strip())
|
|
]
|
|
else:
|
|
values = [value]
|
|
display_values = list(values)
|
|
|
|
if name.endswith("[pow2]"):
|
|
name = name[: -len("[pow2]")].strip()
|
|
if not name:
|
|
raise ValueError(f"Axis filter missing name before [pow2]: {axis_arg}")
|
|
try:
|
|
exponents = [int(v) for v in values]
|
|
except ValueError as exc:
|
|
raise ValueError(
|
|
f"Axis filter [pow2] value must be integer: {axis_arg}"
|
|
) from exc
|
|
values = [str(2**exponent) for exponent in exponents]
|
|
display_values = [f"2^{exponent}" for exponent in exponents]
|
|
|
|
if not values:
|
|
raise ValueError(f"Axis filter must specify at least one value: {axis_arg}")
|
|
|
|
display = make_display(name, display_values)
|
|
filters.append(
|
|
{
|
|
"name": name,
|
|
"values": values,
|
|
"display": display,
|
|
}
|
|
)
|
|
return filters
|
|
|
|
|
|
def build_benchmark_filter_plan(filter_actions):
|
|
global_axis_args = []
|
|
benchmark_scopes = []
|
|
current_scope = None
|
|
|
|
for action_kind, action_value in filter_actions or []:
|
|
if action_kind == "benchmark":
|
|
current_scope = {"benchmark_name": action_value, "axis_args": []}
|
|
benchmark_scopes.append(current_scope)
|
|
elif current_scope is None:
|
|
global_axis_args.append(action_value)
|
|
else:
|
|
current_scope["axis_args"].append(action_value)
|
|
|
|
return BenchmarkFilterPlan(
|
|
global_axis_filters=parse_axis_filters(global_axis_args),
|
|
benchmark_scopes=[
|
|
BenchmarkFilterScope(
|
|
benchmark_name=scope["benchmark_name"],
|
|
axis_filters=parse_axis_filters(scope["axis_args"]),
|
|
)
|
|
for scope in benchmark_scopes
|
|
],
|
|
)
|
|
|
|
|
|
def benchmark_is_selected(benchmark_name, filter_plan):
|
|
return not filter_plan.benchmark_scopes or any(
|
|
scope.benchmark_name == benchmark_name for scope in filter_plan.benchmark_scopes
|
|
)
|
|
|
|
|
|
def axis_filter_groups_for_benchmark(benchmark_name, filter_plan):
|
|
if not filter_plan.benchmark_scopes:
|
|
return [filter_plan.global_axis_filters]
|
|
|
|
matching_scopes = [
|
|
scope
|
|
for scope in filter_plan.benchmark_scopes
|
|
if scope.benchmark_name == benchmark_name
|
|
]
|
|
return [
|
|
filter_plan.global_axis_filters + scope.axis_filters
|
|
for scope in matching_scopes
|
|
]
|
|
|
|
|
|
def matches_axis_filters(state, axis_filters):
|
|
if not axis_filters:
|
|
return True
|
|
|
|
axis_values = state.get("axis_values") or []
|
|
for axis_filter in axis_filters:
|
|
filter_name = axis_filter["name"]
|
|
filter_values = axis_filter["values"]
|
|
matched = False
|
|
for axis_value in axis_values:
|
|
if axis_value.get("name") != filter_name:
|
|
continue
|
|
value = axis_value.get("value")
|
|
if value is None:
|
|
continue
|
|
if str(value) in filter_values:
|
|
matched = True
|
|
break
|
|
if not matched:
|
|
return False
|
|
return True
|
|
|
|
|
|
def matches_axis_filter_groups(state, axis_filter_groups):
|
|
return any(
|
|
matches_axis_filters(state, axis_filters) for axis_filters in axis_filter_groups
|
|
)
|
|
|
|
|
|
def matching_axis_filters(state, axis_filter_groups):
|
|
return next(
|
|
(
|
|
axis_filters
|
|
for axis_filters in axis_filter_groups
|
|
if matches_axis_filters(state, axis_filters)
|
|
),
|
|
[],
|
|
)
|
|
|
|
|
|
def format_duration(seconds):
|
|
if seconds >= 1:
|
|
multiplier = 1.0
|
|
units = "s"
|
|
elif seconds >= 1e-3:
|
|
multiplier = 1e3
|
|
units = "ms"
|
|
elif seconds >= 1e-6:
|
|
multiplier = 1e6
|
|
units = "us"
|
|
else:
|
|
multiplier = 1e6
|
|
units = "us"
|
|
return f"{seconds * multiplier:0.3f} {units}"
|
|
|
|
|
|
def format_percentage(percentage):
|
|
if percentage is None:
|
|
return "n/a"
|
|
if math.isnan(percentage):
|
|
return "n/a"
|
|
if math.isinf(percentage):
|
|
return "inf"
|
|
return f"{percentage * 100.0:0.2f}%"
|
|
|
|
|
|
def has_finite_noise(noise):
|
|
return noise is not None and math.isfinite(noise)
|
|
|
|
|
|
def colorize_comparison_status(status, no_color):
|
|
if status == ComparisonStatus.UNKNOWN:
|
|
return colorize(status.value, Fore.YELLOW, Emoji.YELLOW, no_color)
|
|
if status == ComparisonStatus.UNDECIDED:
|
|
return colorize(status.value, Fore.YELLOW, Emoji.YELLOW, no_color)
|
|
if status == ComparisonStatus.SAME:
|
|
return colorize(status.value, Fore.BLUE, Emoji.BLUE, no_color)
|
|
if status == ComparisonStatus.FAST:
|
|
return colorize(status.value, Fore.GREEN, Emoji.GREEN, no_color)
|
|
return colorize(status.value, Fore.RED, Emoji.RED, no_color)
|
|
|
|
|
|
def format_axis_values(axis_values, axes, axis_filters=None):
|
|
if not axis_values:
|
|
return ""
|
|
filtered_names = set()
|
|
if axis_filters:
|
|
filtered_names = {
|
|
axis_filter["name"]
|
|
for axis_filter in axis_filters
|
|
if len(axis_filter["values"]) == 1
|
|
}
|
|
parts = []
|
|
for axis_value in axis_values:
|
|
axis_name = axis_value["name"]
|
|
if axis_name in filtered_names:
|
|
continue
|
|
formatted = format_axis_value(axis_name, axis_value, axes)
|
|
parts.append(f"{axis_name}={formatted}")
|
|
return " ".join(parts)
|
|
|
|
|
|
def plot_comparison_entries(entries, title=None, dark=False):
|
|
if not entries:
|
|
print("No comparison data to plot.")
|
|
return 1
|
|
|
|
if not os.environ.get("DISPLAY"):
|
|
import matplotlib
|
|
|
|
matplotlib.use("Agg")
|
|
|
|
import matplotlib.pyplot as plt
|
|
from matplotlib.ticker import PercentFormatter
|
|
|
|
labels, values, statuses, bench_names = map(list, zip(*entries))
|
|
|
|
status_colors = {
|
|
"SLOW": "red",
|
|
"FAST": "green",
|
|
"SAME": "blue",
|
|
}
|
|
colors = [status_colors.get(status, "gray") for status in statuses]
|
|
|
|
fig_height = max(4.0, 0.3 * len(entries) + 1.5)
|
|
fig, ax = plt.subplots(figsize=(10, fig_height))
|
|
if dark:
|
|
fig.patch.set_facecolor("black")
|
|
ax.set_facecolor("black")
|
|
ax.tick_params(colors="white")
|
|
ax.xaxis.label.set_color("white")
|
|
ax.yaxis.label.set_color("white")
|
|
ax.title.set_color("white")
|
|
for spine in ax.spines.values():
|
|
spine.set_color("white")
|
|
|
|
y_pos = range(len(labels))
|
|
ax.barh(y_pos, values, color=colors)
|
|
ax.set_yticks(y_pos)
|
|
ax.set_yticklabels(labels)
|
|
ax.invert_yaxis()
|
|
ax.set_ylim(len(labels) - 0.5, -0.5)
|
|
|
|
separator_color = "white" if dark else "gray"
|
|
ax.axvline(0, color=separator_color, linewidth=1, alpha=0.6)
|
|
for index in range(1, len(bench_names)):
|
|
if bench_names[index] != bench_names[index - 1]:
|
|
ax.axhline(index - 0.5, color=separator_color, linewidth=0.6, alpha=0.4)
|
|
ax.xaxis.set_major_formatter(PercentFormatter(1.0))
|
|
|
|
if title:
|
|
ax.set_title(title)
|
|
|
|
min_val = min(values)
|
|
max_val = max(values)
|
|
if min_val == max_val:
|
|
pad = 0.05 if min_val == 0 else abs(min_val) * 0.1
|
|
ax.set_xlim(min_val - pad, max_val + pad)
|
|
else:
|
|
pad = (max_val - min_val) * 0.1
|
|
ax.set_xlim(min_val - pad, max_val + pad)
|
|
|
|
fig.tight_layout()
|
|
|
|
if not os.environ.get("DISPLAY"):
|
|
output = "nvbench_compare.png"
|
|
fig.savefig(output, dpi=150)
|
|
print(f"Saved comparison plot to {output}")
|
|
else:
|
|
plt.show()
|
|
return 0
|
|
|
|
|
|
def compare_benches(
|
|
run_data: ComparisonRunData,
|
|
ref_benches,
|
|
cmp_benches,
|
|
threshold,
|
|
plot_along,
|
|
plot,
|
|
dark,
|
|
filter_plan,
|
|
no_color,
|
|
reference_device_filter=None,
|
|
compare_device_filter=None,
|
|
ref_json_dir=None,
|
|
cmp_json_dir=None,
|
|
):
|
|
if plot_along:
|
|
import matplotlib.pyplot as plt
|
|
import seaborn as sns
|
|
|
|
sns.set_theme()
|
|
|
|
comparison_entries = []
|
|
comparison_device_names = set()
|
|
for cmp_bench in cmp_benches:
|
|
ref_bench = find_matching_bench(cmp_bench, ref_benches)
|
|
if not ref_bench:
|
|
continue
|
|
if not benchmark_is_selected(cmp_bench["name"], filter_plan):
|
|
continue
|
|
axis_filter_groups = axis_filter_groups_for_benchmark(
|
|
cmp_bench["name"], filter_plan
|
|
)
|
|
|
|
cmp_device_ids = resolve_benchmark_device_ids(
|
|
cmp_bench, compare_device_filter, "--compare-devices"
|
|
)
|
|
ref_device_ids = resolve_benchmark_device_ids(
|
|
ref_bench, reference_device_filter, "--reference-devices"
|
|
)
|
|
if len(cmp_device_ids) != len(ref_device_ids):
|
|
raise ValueError(
|
|
f"benchmark {cmp_bench['name']!r} has {len(ref_device_ids)} "
|
|
f"reference device(s) but {len(cmp_device_ids)} compare device(s); "
|
|
"nvbench_compare pairs devices by position, so each compared "
|
|
"benchmark must contain the same number of devices"
|
|
)
|
|
|
|
print(f"""# {cmp_bench["name"]}\n""")
|
|
|
|
axes = cmp_bench["axes"]
|
|
ref_states = ref_bench["states"]
|
|
cmp_states = cmp_bench["states"]
|
|
|
|
axes = axes if axes else []
|
|
|
|
headers = [x["name"] for x in axes]
|
|
colalign = ["center"] * len(headers)
|
|
|
|
headers.append("Ref Time")
|
|
colalign.append("right")
|
|
headers.append("Ref Noise")
|
|
colalign.append("right")
|
|
headers.append("Cmp Time")
|
|
colalign.append("right")
|
|
headers.append("Cmp Noise")
|
|
colalign.append("right")
|
|
headers.append("Diff")
|
|
colalign.append("right")
|
|
headers.append("%Diff")
|
|
colalign.append("right")
|
|
headers.append("Status")
|
|
colalign.append("center")
|
|
|
|
for cmp_device_index, cmp_device_id in enumerate(cmp_device_ids):
|
|
ref_device_id = ref_device_ids[cmp_device_index]
|
|
ref_device_states = [
|
|
state
|
|
for state in ref_states
|
|
if state["device"] == ref_device_id
|
|
and matches_axis_filter_groups(state, axis_filter_groups)
|
|
]
|
|
cmp_device_states = [
|
|
state
|
|
for state in cmp_states
|
|
if state["device"] == cmp_device_id
|
|
and matches_axis_filter_groups(state, axis_filter_groups)
|
|
]
|
|
ref_states_by_name = group_states_by_match_key(ref_device_states)
|
|
cmp_states_by_name = group_states_by_match_key(cmp_device_states)
|
|
ref_state_counts = state_group_counts(ref_states_by_name)
|
|
cmp_state_counts = state_group_counts(cmp_states_by_name)
|
|
if ref_state_counts != cmp_state_counts:
|
|
raise ValueError(
|
|
f"benchmark {cmp_bench['name']!r} device pair "
|
|
f"ref={ref_device_id} cmp={cmp_device_id} has mismatched "
|
|
f"state occurrences: ref={dict(ref_state_counts)}, "
|
|
f"cmp={dict(cmp_state_counts)}"
|
|
)
|
|
|
|
rows = []
|
|
plot_data: dict[str, dict[str, dict[float, float | None]]] = {
|
|
"cmp": {},
|
|
"ref": {},
|
|
"cmp_noise": {},
|
|
"ref_noise": {},
|
|
}
|
|
counters: dict[str, int] = {}
|
|
|
|
for cmp_state in cmp_device_states:
|
|
cmp_state_name = state_match_key(cmp_state)
|
|
occurrence = counters.get(cmp_state_name, 0)
|
|
counters[cmp_state_name] = occurrence + 1
|
|
# Duplicate state names are matched by occurrence order within
|
|
# the filtered device section.
|
|
ref_state = ref_states_by_name[cmp_state_name][occurrence]
|
|
axis_values = cmp_state["axis_values"]
|
|
if not axis_values:
|
|
axis_values = []
|
|
|
|
row = []
|
|
for axis_value in axis_values:
|
|
axis_value_name = axis_value["name"]
|
|
row.append(format_axis_value(axis_value_name, axis_value, axes))
|
|
|
|
cmp_summaries = cmp_state["summaries"]
|
|
ref_summaries = ref_state["summaries"]
|
|
|
|
if not ref_summaries or not cmp_summaries:
|
|
continue
|
|
|
|
# TODO: Use other timings, too. Maybe multiple rows, with a
|
|
# "Timing" column + values "CPU/GPU/Batch"?
|
|
cmp_gpu_time = extract_gpu_timing_data(cmp_summaries, cmp_json_dir)
|
|
ref_gpu_time = extract_gpu_timing_data(ref_summaries, ref_json_dir)
|
|
comparison = compare_gpu_timings(ref_gpu_time, cmp_gpu_time)
|
|
if comparison is None:
|
|
continue
|
|
|
|
if plot_along:
|
|
axis_name_parts = []
|
|
axis_value = None
|
|
for av in axis_values:
|
|
if av["name"] != plot_along:
|
|
axis_name_parts.append(f"""{av["name"]} = {av["value"]}""")
|
|
else:
|
|
axis_value = float(av["value"])
|
|
if axis_value is not None:
|
|
axis_name = ", ".join(axis_name_parts)
|
|
|
|
if axis_name not in plot_data["cmp"]:
|
|
plot_data["cmp"][axis_name] = {}
|
|
plot_data["ref"][axis_name] = {}
|
|
plot_data["cmp_noise"][axis_name] = {}
|
|
plot_data["ref_noise"][axis_name] = {}
|
|
|
|
plot_data["cmp"][axis_name][axis_value] = comparison.cmp_time
|
|
plot_data["ref"][axis_name][axis_value] = comparison.ref_time
|
|
plot_data["cmp_noise"][axis_name][axis_value] = (
|
|
comparison.cmp_noise
|
|
)
|
|
plot_data["ref_noise"][axis_name][axis_value] = (
|
|
comparison.ref_noise
|
|
)
|
|
|
|
run_data.stats.record(comparison.status)
|
|
status = colorize_comparison_status(comparison.status, no_color)
|
|
|
|
if abs(comparison.frac_diff) >= threshold:
|
|
axis_filters = matching_axis_filters(cmp_state, axis_filter_groups)
|
|
row.append(format_duration(comparison.ref_time))
|
|
row.append(format_percentage(comparison.ref_noise))
|
|
row.append(format_duration(comparison.cmp_time))
|
|
row.append(format_percentage(comparison.cmp_noise))
|
|
row.append(format_duration(comparison.diff))
|
|
row.append(format_percentage(comparison.frac_diff))
|
|
row.append(status)
|
|
|
|
rows.append(row)
|
|
if plot:
|
|
axis_label = format_axis_values(axis_values, axes, axis_filters)
|
|
if axis_label:
|
|
label = f"""{cmp_bench["name"]} | {axis_label}"""
|
|
else:
|
|
label = cmp_bench["name"]
|
|
cmp_device = find_device_by_id(
|
|
cmp_state["device"], run_data.cmp_devices
|
|
)
|
|
if cmp_device:
|
|
comparison_device_names.add(cmp_device["name"])
|
|
comparison_entries.append(
|
|
(
|
|
label,
|
|
comparison.frac_diff,
|
|
comparison.status.value,
|
|
cmp_bench["name"],
|
|
)
|
|
)
|
|
|
|
if len(rows) == 0:
|
|
continue
|
|
|
|
cmp_device = find_device_by_id(cmp_device_id, run_data.cmp_devices)
|
|
ref_device = find_device_by_id(ref_device_id, run_data.ref_devices)
|
|
if ref_device is None or cmp_device is None:
|
|
raise ValueError(
|
|
f"benchmark {cmp_bench['name']!r} references device pair "
|
|
f"ref={ref_device_id} cmp={cmp_device_id}, but device metadata is missing"
|
|
)
|
|
|
|
if cmp_device == ref_device:
|
|
print(f"## [{cmp_device['id']}] {cmp_device['name']}\n")
|
|
else:
|
|
print(
|
|
f"## [{ref_device['id']}] {ref_device['name']} vs. "
|
|
f"[{cmp_device['id']}] {cmp_device['name']}\n"
|
|
)
|
|
# colalign and github format require tabulate 0.8.3
|
|
if tabulate_version >= (0, 8, 3):
|
|
print(
|
|
tabulate.tabulate(
|
|
rows, headers=headers, colalign=colalign, tablefmt="github"
|
|
)
|
|
)
|
|
else:
|
|
print(tabulate.tabulate(rows, headers=headers, tablefmt="markdown"))
|
|
|
|
print("")
|
|
|
|
if plot_along:
|
|
fig = plt.figure()
|
|
try:
|
|
plt.xscale("log")
|
|
plt.yscale("log")
|
|
plt.xlabel(plot_along)
|
|
plt.ylabel("time [s]")
|
|
plt.title(cmp_device["name"])
|
|
|
|
def plot_line(key, shape, label, data_axis, data=plot_data):
|
|
axis_times = data[key][data_axis]
|
|
if not axis_times:
|
|
return
|
|
axis_noise = data[key + "_noise"][data_axis]
|
|
series = sorted(
|
|
(
|
|
(
|
|
float(axis_value),
|
|
axis_times[axis_value],
|
|
axis_noise[axis_value],
|
|
)
|
|
for axis_value in axis_times
|
|
),
|
|
key=lambda item: item[0],
|
|
)
|
|
x, y, noise = map(list, zip(*series, strict=True))
|
|
|
|
p = plt.plot(x, y, shape, marker="o", label=label)
|
|
|
|
def plot_confidence_band(first, last):
|
|
if last - first < 2:
|
|
return
|
|
|
|
band_x = x[first:last]
|
|
band_y = y[first:last]
|
|
band_noise = noise[first:last]
|
|
top = [
|
|
band_y[i] + band_y[i] * band_noise[i]
|
|
for i in range(len(band_x))
|
|
]
|
|
bottom = [
|
|
max(
|
|
band_y[i] - band_y[i] * band_noise[i],
|
|
band_y[i] * 0.001,
|
|
)
|
|
for i in range(len(band_x))
|
|
]
|
|
plt.fill_between(
|
|
band_x, bottom, top, color=p[0].get_color(), alpha=0.1
|
|
)
|
|
|
|
start = None
|
|
for i, noise_value in enumerate(noise):
|
|
if has_finite_noise(noise_value) and start is None:
|
|
start = i
|
|
if not has_finite_noise(noise_value) and start is not None:
|
|
plot_confidence_band(start, i)
|
|
start = None
|
|
|
|
if start is not None:
|
|
plot_confidence_band(start, len(x))
|
|
|
|
for axis in plot_data["cmp"].keys():
|
|
plot_line("cmp", "-", axis, axis)
|
|
plot_line("ref", "--", axis + " ref", axis)
|
|
|
|
plt.legend()
|
|
plt.show()
|
|
finally:
|
|
plt.close(fig)
|
|
|
|
if plot:
|
|
title = "%SOL Bandwidth change"
|
|
if len(comparison_device_names) == 1:
|
|
title = f"{title} - {next(iter(comparison_device_names))}"
|
|
if filter_plan.global_axis_filters:
|
|
axis_label = ", ".join(
|
|
axis_filter["display"]
|
|
for axis_filter in filter_plan.global_axis_filters
|
|
if len(axis_filter["values"]) == 1
|
|
)
|
|
if axis_label:
|
|
title = f"{title} ({axis_label})"
|
|
plot_comparison_entries(comparison_entries, title=title, dark=dark)
|
|
|
|
|
|
def main() -> int:
|
|
"""
|
|
Returns a process exit code.
|
|
- 0 means the comparison completed successfully.
|
|
- 1 signals an error has occurred.
|
|
|
|
The number of detected regressions is reported in the summary output.
|
|
"""
|
|
help_text = "%(prog)s [reference.json compare.json | reference_dir/ compare_dir/]"
|
|
parser = argparse.ArgumentParser(prog="nvbench_compare", usage=help_text)
|
|
parser.add_argument(
|
|
"--ignore-devices",
|
|
dest="ignore_devices",
|
|
default=False,
|
|
help="Ignore differences in the device sections and compare anyway",
|
|
action="store_true",
|
|
)
|
|
parser.add_argument(
|
|
"--threshold-diff",
|
|
type=float,
|
|
dest="threshold",
|
|
default=0.0,
|
|
help="only show benchmarks where percentage diff is >= THRESHOLD",
|
|
)
|
|
parser.add_argument(
|
|
"--plot-along", type=str, dest="plot_along", default=None, help="plot results"
|
|
)
|
|
parser.add_argument(
|
|
"--plot",
|
|
dest="plot",
|
|
default=False,
|
|
help="plot comparison summary",
|
|
action="store_true",
|
|
)
|
|
parser.add_argument(
|
|
"--dark",
|
|
action="store_true",
|
|
help="Use dark theme (black background, white text)",
|
|
)
|
|
parser.add_argument(
|
|
"--no-color",
|
|
dest="no_color",
|
|
action="store_true",
|
|
help="Use emoji instead of ANSI color codes (useful for GitHub issues/PRs)",
|
|
)
|
|
parser.add_argument(
|
|
"--reference-devices",
|
|
default="all",
|
|
help="Reference devices to compare: all, a non-negative integer id, or comma-separated ids",
|
|
)
|
|
parser.add_argument(
|
|
"--compare-devices",
|
|
default="all",
|
|
help="Compare devices to compare: all, a non-negative integer id, or comma-separated ids",
|
|
)
|
|
parser.add_argument(
|
|
"-a",
|
|
"--axis",
|
|
dest="filter_actions",
|
|
action=OrderedBenchmarkFilterAction,
|
|
help=(
|
|
"Filter on axis value, e.g. -a Elements{io}=2^20. Applies to the "
|
|
"most recent --benchmark, or all benchmarks if specified before any "
|
|
"--benchmark arguments."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"-b",
|
|
"--benchmark",
|
|
dest="filter_actions",
|
|
action=OrderedBenchmarkFilterAction,
|
|
help="Filter by benchmark name (can repeat)",
|
|
)
|
|
|
|
args, files_or_dirs = parser.parse_known_args()
|
|
try:
|
|
filter_plan = build_benchmark_filter_plan(args.filter_actions)
|
|
reference_device_filter = parse_device_filter(
|
|
args.reference_devices, "--reference-devices"
|
|
)
|
|
compare_device_filter = parse_device_filter(
|
|
args.compare_devices, "--compare-devices"
|
|
)
|
|
except ValueError as exc:
|
|
print(str(exc))
|
|
return 1
|
|
|
|
if len(files_or_dirs) != 2:
|
|
parser.print_help()
|
|
return 1
|
|
|
|
# if provided two directories, find all the exactly named files
|
|
# in both and treat them as the reference and compare
|
|
to_compare = []
|
|
if os.path.isdir(files_or_dirs[0]) and os.path.isdir(files_or_dirs[1]):
|
|
for f in os.listdir(files_or_dirs[1]):
|
|
if os.path.splitext(f)[1] != ".json":
|
|
continue
|
|
r = os.path.join(files_or_dirs[0], f)
|
|
c = os.path.join(files_or_dirs[1], f)
|
|
if (
|
|
os.path.isfile(r)
|
|
and os.path.isfile(c)
|
|
and os.path.getsize(r) > 0
|
|
and os.path.getsize(c) > 0
|
|
):
|
|
to_compare.append((r, c))
|
|
else:
|
|
to_compare = [(files_or_dirs[0], files_or_dirs[1])]
|
|
|
|
stats = ComparisonStats()
|
|
|
|
for ref, comp in to_compare:
|
|
ref_root = reader.read_file(ref)
|
|
cmp_root = reader.read_file(comp)
|
|
|
|
try:
|
|
selected_ref_devices = select_devices(
|
|
ref_root["devices"], reference_device_filter, "--reference-devices"
|
|
)
|
|
selected_cmp_devices = select_devices(
|
|
cmp_root["devices"], compare_device_filter, "--compare-devices"
|
|
)
|
|
except ValueError as exc:
|
|
print(str(exc))
|
|
return 1
|
|
|
|
if len(selected_ref_devices) != len(selected_cmp_devices):
|
|
print(
|
|
f"--reference-devices selected {len(selected_ref_devices)} device(s), "
|
|
f"but --compare-devices selected {len(selected_cmp_devices)} device(s)"
|
|
)
|
|
return 1
|
|
|
|
if selected_ref_devices != selected_cmp_devices:
|
|
warn_fore = Fore.YELLOW if args.ignore_devices else Fore.RED
|
|
msg_text = "Device sections do not match"
|
|
print(colorize(msg_text, warn_fore, Emoji.NONE, args.no_color), end="")
|
|
print(": ", end="")
|
|
|
|
print(
|
|
jsondiff.diff(
|
|
selected_ref_devices, selected_cmp_devices, syntax="symmetric"
|
|
)
|
|
)
|
|
if not args.ignore_devices and require_matching_device_sections(
|
|
reference_device_filter, compare_device_filter
|
|
):
|
|
return 1
|
|
|
|
run_data = ComparisonRunData(
|
|
stats=stats,
|
|
ref_devices=tuple(selected_ref_devices),
|
|
cmp_devices=tuple(selected_cmp_devices),
|
|
)
|
|
|
|
try:
|
|
compare_benches(
|
|
run_data,
|
|
ref_root["benchmarks"],
|
|
cmp_root["benchmarks"],
|
|
args.threshold,
|
|
args.plot_along,
|
|
args.plot,
|
|
args.dark,
|
|
filter_plan,
|
|
args.no_color,
|
|
reference_device_filter,
|
|
compare_device_filter,
|
|
os.path.dirname(ref),
|
|
os.path.dirname(comp),
|
|
)
|
|
except ValueError as exc:
|
|
print(str(exc))
|
|
return 1
|
|
|
|
print("# Summary\n")
|
|
print(f"- Total Matches: {stats.config_count}")
|
|
print(f" - Pass (abs(%Diff) <= max_noise): {stats.pass_count}")
|
|
print(
|
|
" - Improvement (abs(%Diff) > max_noise, %Diff < 0): "
|
|
f"{stats.improvement_count}"
|
|
)
|
|
print(
|
|
f" - Regression (abs(%Diff) > max_noise, %Diff > 0): {stats.regression_count}"
|
|
)
|
|
print(
|
|
f" - Undecided (comparison requires more evidence): {stats.undecided_count}"
|
|
)
|
|
print(f" - Unknown (infinite or unavailable noise): {stats.unknown_count}")
|
|
return 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
sys.exit(main())
|