PaddleX/paddlex/inference/utils/benchmark.py

# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import csv
import functools
from types import GeneratorType
import time
from pathlib import Path
import numpy as np
from prettytable import PrettyTable

from ...utils.flags import INFER_BENCHMARK, INFER_BENCHMARK_OUTPUT
from ...utils.misc import Singleton
from ...utils import logging


class Benchmark(metaclass=Singleton):
    def __init__(self):
        self._components = {}
        self._warmup_start = None
        self._warmup_elapse = None
        self._warmup_num = None
        self._e2e_tic = None
        self._e2e_elapse = None

    def attach(self, component):
        self._components[component.name] = component

    def start(self):
        self._warmup_start = time.time()
        self._reset()

    def warmup_stop(self, warmup_num):
        self._warmup_elapse = (time.time() - self._warmup_start) * 1000
        self._warmup_num = warmup_num
        self._reset()

    def _reset(self):
        for name, cmp in self.iterate_cmp(self._components):
            cmp.timer.reset()
        self._e2e_tic = time.time()

    def iterate_cmp(self, cmps):
        if cmps is None:
            return
        for name, cmp in cmps.items():
            if hasattr(cmp, "benchmark"):
                yield from self.iterate_cmp(cmp.benchmark)
            yield name, cmp

    def gather(self, e2e_num):
        # lazy import for avoiding circular import
        from ...utils.flags import NEW_PREDICTOR

        if NEW_PREDICTOR:
            from ..new_models.base import BasePaddlePredictor
        else:
            from ..models.common_components.paddle_predictor import BasePaddlePredictor

        detail = []
        summary = {"preprocess": 0, "inference": 0, "postprocess": 0}
        op_tag = "preprocess"
        for name, cmp in self._components.items():
            if isinstance(cmp, BasePaddlePredictor):
                # TODO(gaotingquan): show by hierarchy. Now dont show xxxPredictor benchmark info to ensure mutual exclusivity between components.
                for name, sub_cmp in cmp.benchmark.items():
                    times = sub_cmp.timer.logs
                    counts = len(times)
                    avg = np.mean(times) * 1000
                    total = np.sum(times) * 1000
                    detail.append((name, total, counts, avg))
                    summary["inference"] += total
                op_tag = "postprocess"
            else:
                # TODO(gaotingquan): support sub_cmps for others
                # if hasattr(cmp, "benchmark"):
                times = cmp.timer.logs
                counts = len(times)
                avg = np.mean(times) * 1000
                total = np.sum(times) * 1000
                detail.append((name, total, counts, avg))
                summary[op_tag] += total

        summary = [
            (
                "PreProcess",
                summary["preprocess"],
                e2e_num,
                summary["preprocess"] / e2e_num,
            ),
            (
                "Inference",
                summary["inference"],
                e2e_num,
                summary["inference"] / e2e_num,
            ),
            (
                "PostProcess",
                summary["postprocess"],
                e2e_num,
                summary["postprocess"] / e2e_num,
            ),
            ("End2End", self._e2e_elapse, e2e_num, self._e2e_elapse / e2e_num),
        ]
        if self._warmup_elapse:
            warmup_elapse, warmup_num, warmup_avg = (
                self._warmup_elapse,
                self._warmup_num,
                self._warmup_elapse / self._warmup_num,
            )
        else:
            warmup_elapse, warmup_num, warmup_avg = 0, 0, 0
        summary.append(
            (
                "WarmUp",
                warmup_elapse,
                warmup_num,
                warmup_avg,
            )
        )
        return detail, summary

    def collect(self, e2e_num):
        self._e2e_elapse = (time.time() - self._e2e_tic) * 1000
        detail, summary = self.gather(e2e_num)

        detail_head = [
            "Component",
            "Total Time (ms)",
            "Number of Calls",
            "Avg Time Per Call (ms)",
        ]
        table = PrettyTable(detail_head)
        table.add_rows(
            [
                (name, f"{total:.8f}", cnts, f"{avg:.8f}")
                for name, total, cnts, avg in detail
            ]
        )
        logging.info(table)

        summary_head = [
            "Stage",
            "Total Time (ms)",
            "Number of Instances",
            "Avg Time Per Instance (ms)",
        ]
        table = PrettyTable(summary_head)
        table.add_rows(
            [
                (name, f"{total:.8f}", cnts, f"{avg:.8f}")
                for name, total, cnts, avg in summary
            ]
        )
        logging.info(table)

        if INFER_BENCHMARK_OUTPUT:
            save_dir = Path(INFER_BENCHMARK_OUTPUT)
            save_dir.mkdir(parents=True, exist_ok=True)
            csv_data = [detail_head, *detail]
            with open(Path(save_dir) / "detail.csv", "w", newline="") as file:
                writer = csv.writer(file)
                writer.writerows(csv_data)

            csv_data = [summary_head, *summary]
            with open(Path(save_dir) / "summary.csv", "w", newline="") as file:
                writer = csv.writer(file)
                writer.writerows(csv_data)


class Timer:
    def __init__(self, component):
        from ..new_models.base import BaseComponent

        assert isinstance(component, BaseComponent)
        benchmark.attach(component)
        component.apply = self.watch_func(component.apply)
        self._tic = None
        self._elapses = []

    def watch_func(self, func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            tic = time.time()
            output = func(*args, **kwargs)
            if isinstance(output, GeneratorType):
                return self.watch_generator(output)
            else:
                self._update(time.time() - tic)
                return output

        return wrapper

    def watch_generator(self, generator):
        @functools.wraps(generator)
        def wrapper():
            while 1:
                try:
                    tic = time.time()
                    item = next(generator)
                    self._update(time.time() - tic)
                    yield item
                except StopIteration:
                    break

        return wrapper()

    def reset(self):
        self._tic = None
        self._elapses = []

    def _update(self, elapse):
        self._elapses.append(elapse)

    @property
    def logs(self):
        return self._elapses


benchmark = Benchmark() if INFER_BENCHMARK else None