#!/usr/bin/env python3

"""
A wrapper over the benchmark infrastructure to generate commonly used commands,
parse results and generate csv/graphs.

The script works on manually written TABLE (see below). We can add more commands
in the future.

One example usage is
-> python benchmarks/runner.py --suites=torchbench --inference
This command will generate the commands for the default compilers (see DEFAULTS
below) for inference, run them and visualize the logs.

If you want to just print the commands, you could use the following command
-> python benchmarks/runner.py --print-run-commands --suites=torchbench --inference

Similarly, if you want to just visualize the already finished logs
-> python benchmarks/runner.py --visualize-logs --suites=torchbench --inference

If you want to test float16
-> python benchmarks/runner.py --suites=torchbench --inference --dtypes=float16

"""

import argparse
import dataclasses
import functools
import glob
import importlib
import io
import itertools
import logging
import os
import platform
import re
import shutil
import subprocess
import sys
import tempfile
from collections import defaultdict
from datetime import datetime, timedelta, timezone
from os.path import abspath, exists
from random import randint

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib import rcParams
from scipy.stats import gmean
from tabulate import tabulate

import torch
import torch._dynamo


rcParams.update({"figure.autolayout": True})
plt.rc("axes", axisbelow=True)

DEFAULT_OUTPUT_DIR = "benchmark_logs"


log = logging.getLogger(__name__)

TABLE = {
    "training": {
        "ts_nnc": "--training --speedup-ts ",
        "ts_nvfuser": "--training --nvfuser --speedup-dynamo-ts ",
        "eager": "--training --backend=eager ",
        "aot_eager": "--training --backend=aot_eager ",
        "cudagraphs": "--training --backend=cudagraphs ",
        "aot_nvfuser": "--training --nvfuser --backend=aot_ts_nvfuser ",
        "nvprims_nvfuser": "--training --backend=nvprims_nvfuser ",
        "inductor": "--training --inductor ",
        "inductor_no_cudagraphs": "--training --inductor --disable-cudagraphs ",
        "inductor_max_autotune": "--training --inductor --inductor-compile-mode max-autotune ",
        "inductor_max_autotune_no_cudagraphs": (
            "--training --inductor --inductor-compile-mode max-autotune-no-cudagraphs --disable-cudagraphs "
        ),
    },
    "inference": {
        "aot_eager": "--inference --backend=aot_eager ",
        "eager": "--inference --backend=eager ",
        "ts_nnc": "--inference --speedup-ts ",
        "ts_nvfuser": "--inference -n100 --speedup-ts --nvfuser ",
        "trt": "--inference -n100 --speedup-trt ",
        "ts_nvfuser_cudagraphs": "--inference --backend=cudagraphs_ts ",
        "inductor": "--inference -n50 --inductor ",
        "inductor_no_cudagraphs": "--inference -n50 --inductor --disable-cudagraphs ",
        "inductor_max_autotune": "--inference -n50 --inductor --inductor-compile-mode max-autotune ",
        "inductor_max_autotune_no_cudagraphs": (
            "--inference -n50 --inductor --inductor-compile-mode max-autotune-no-cudagraphs --disable-cudagraphs "
        ),
    },
}

INFERENCE_COMPILERS = tuple(TABLE["inference"].keys())
TRAINING_COMPILERS = tuple(TABLE["training"].keys())

DEFAULTS = {
    "training": [
        "eager",
        "aot_eager",
        "inductor",
        "inductor_no_cudagraphs",
    ],
    "inference": [
        "eager",
        "aot_eager",
        "inductor",
        "inductor_no_cudagraphs",
    ],
    "flag_compilers": {
        "training": ["inductor", "inductor_no_cudagraphs"],
        "inference": ["inductor", "inductor_no_cudagraphs"],
    },
    "dtypes": [
        "float32",
    ],
    "suites": ["torchbench", "huggingface", "timm_models"],
    "devices": [
        "cuda",
    ],
    "quick": {
        "torchbench": '-k "resnet..$"',
        "huggingface": "-k Albert",
        "timm_models": ' -k "^resnet" -k "^inception"',
    },
}


DASHBOARD_DEFAULTS = {
    "dashboard_image_uploader": "/fsx/users/anijain/bin/imgur.sh",
    "dashboard_archive_path": "/data/home/anijain/cluster/cron_logs",
    "dashboard_gh_cli_path": "/data/home/anijain/miniconda/bin/gh",
}


def flag_speedup(x):
    return x < 0.95


def flag_compilation_latency(x):
    return x > 120


def flag_compression_ratio(x):
    return x < 0.9


def flag_accuracy(x):
    return "pass" not in x


FLAG_FNS = {
    "speedup": flag_speedup,
    "compilation_latency": flag_compilation_latency,
    "compression_ratio": flag_compression_ratio,
    "accuracy": flag_accuracy,
}


def percentage(part, whole, decimals=2):
    if whole == 0:
        return 0
    return round(100 * float(part) / float(whole), decimals)


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--devices", action="append", help="cpu or cuda")
    parser.add_argument("--dtypes", action="append", help="float16/float32/amp")
    parser.add_argument("--suites", action="append", help="huggingface/torchbench/timm")
    parser.add_argument(
        "--compilers",
        action="append",
        help=f"For --inference, options are {INFERENCE_COMPILERS}. For --training, options are {TRAINING_COMPILERS}",
    )

    parser.add_argument(
        "--flag-compilers",
        action="append",
        help="List of compilers to flag issues. Same format as --compilers.",
    )
    parser.add_argument(
        "--quick", action="store_true", help="Just runs one model. Helps in debugging"
    )
    parser.add_argument(
        "--output-dir",
        help="Choose the output directory to save the logs",
        default=DEFAULT_OUTPUT_DIR,
    )
    parser.add_argument(
        "--keep-output-dir",
        action="store_true",
        help="Do not cleanup the output directory before running",
    )

    # Choose either generation of commands, pretty parsing or e2e runs
    group = parser.add_mutually_exclusive_group(required=False)
    group.add_argument(
        "--print-run-commands",
        "--print_run_commands",
        action="store_true",
        help="Generate commands and saves them to run.sh",
    )
    group.add_argument(
        "--visualize-logs",
        "--visualize_logs",
        action="store_true",
        help="Pretty print the log files and draw graphs",
    )
    group.add_argument(
        "--run",
        action="store_true",
        default=True,
        help="Generate commands, run and parses the files",
    )

    parser.add_argument(
        "--log-operator-inputs",
        action="store_true",
        default=False,
        help="Log operator inputs",
    )
    parser.add_argument(
        "--include-slowdowns",
        "--include_slowdowns",
        action="store_true",
        default=False,
        help="Include slowdowns in geomean performance speedup report. By default, slowdowns are ignored. "
        "This is because one can always use eager if compile is not speeding things up",
    )

    parser.add_argument(
        "--extra-args", default="", help="Append commandline with these args"
    )

    # Choose either inference or training
    group_mode = parser.add_mutually_exclusive_group(required=True)
    group_mode.add_argument(
        "--inference", action="store_true", help="Only run inference related tasks"
    )
    group_mode.add_argument(
        "--training", action="store_true", help="Only run training related tasks"
    )

    parser.add_argument(
        "--base-sha",
        help="commit id for the tested pytorch",
    )
    parser.add_argument(
        "--total-partitions",
        type=int,
        help="Total number of partitions, to be passed to the actual benchmark script",
    )
    parser.add_argument(
        "--partition-id",
        type=int,
        help="ID of partition, to be passed to the actual benchmark script",
    )

    parser.add_argument(
        "--update-dashboard",
        action="store_true",
        default=False,
        help="Updates to dashboard",
    )
    parser.add_argument(
        "--no-graphs",
        action="store_true",
        default=False,
        help="Do not generate and upload metric graphs",
    )
    parser.add_argument(
        "--no-update-archive",
        action="store_true",
        default=False,
        help="Do not update lookup.csv or the log archive",
    )
    parser.add_argument(
        "--no-gh-comment",
        action="store_true",
        default=False,
        help="Do not write a comment to github",
    )
    parser.add_argument(
        "--no-detect-regressions",
        action="store_true",
        default=False,
        help="Do not compare to previous runs for regressions or metric graphs.",
    )
    parser.add_argument(
        "--update-dashboard-test",
        action="store_true",
        default=False,
        help="does all of --no-graphs, --no-update-archive, and --no-gh-comment",
    )
    parser.add_argument(
        "--dashboard-image-uploader",
        default=DASHBOARD_DEFAULTS["dashboard_image_uploader"],
        help="Image uploader command",
    )
    parser.add_argument(
        "--dashboard-archive-path",
        default=DASHBOARD_DEFAULTS["dashboard_archive_path"],
        help="Archived directory path",
    )
    parser.add_argument(
        "--archive-name",
        help="Directory name under dashboard-archive-path to copy output-dir to. "
        "If not provided, a generated name is used.",
    )
    parser.add_argument(
        "--dashboard-gh-cli-path",
        default=DASHBOARD_DEFAULTS["dashboard_gh_cli_path"],
        help="Github CLI path",
    )
    parser.add_argument(
        "--batch-size",
        "--batch_size",
        type=int,
        default=None,
        help="batch size for benchmarking",
    )
    parser.add_argument(
        "--threads",
        "-t",
        type=int,
        default=None,
        help="number of threads to use for eager and inductor.",
    )
    launcher_group = parser.add_argument_group("CPU Launcher Parameters")
    launcher_group.add_argument(
        "--enable-cpu-launcher",
        "--enable_cpu_launcher",
        action="store_true",
        default=False,
        help="Use torch.backends.xeon.run_cpu to get the peak performance on Intel(R) Xeon(R) Scalable Processors.",
    )
    launcher_group.add_argument(
        "--cpu-launcher-args",
        "--cpu_launcher_args",
        type=str,
        default="",
        help="Provide the args of torch.backends.xeon.run_cpu. "
        "To look up what optional arguments this launcher offers: python -m torch.backends.xeon.run_cpu --help",
    )
    parser.add_argument(
        "--no-cold-start-latency",
        action="store_true",
        default=False,
        help="Do not include --cold-start-latency on inductor benchmarks",
    )
    parser.add_argument(
        "--inductor-compile-mode",
        default=None,
        help="torch.compile mode argument for inductor runs.",
    )
    args = parser.parse_args()
    return args


def get_mode(args):
    if args.inference:
        return "inference"
    return "training"


def get_skip_tests(suite, device, is_training: bool):
    """
    Generate -x separated string to skip the unusual setup training tests
    """
    skip_tests = set()
    original_dir = abspath(os.getcwd())
    module = importlib.import_module(suite)
    os.chdir(original_dir)
    arch = platform.machine()

    if suite == "torchbench":
        skip_tests.update(module.TorchBenchmarkRunner().skip_models)
        if is_training:
            skip_tests.update(
                module.TorchBenchmarkRunner().skip_not_suitable_for_training_models
            )
        if device == "cpu":
            skip_tests.update(module.TorchBenchmarkRunner().skip_models_for_cpu)
            if arch == "aarch64":
                skip_tests.update(
                    module.TorchBenchmarkRunner().skip_models_for_cpu_aarch64
                )
        elif device == "cuda":
            skip_tests.update(module.TorchBenchmarkRunner().skip_models_for_cuda)

    skip_tests = (f"-x {name}" for name in skip_tests)
    skip_str = " ".join(skip_tests)
    return skip_str


def generate_csv_name(args, dtype, suite, device, compiler, testing):
    mode = get_mode(args)
    return f"{compiler}_{suite}_{dtype}_{mode}_{device}_{testing}.csv"


def generate_commands(args, dtypes, suites, devices, compilers, output_dir):
    mode = get_mode(args)
    suites_str = "_".join(suites)
    devices_str = "_".join(devices)
    dtypes_str = "_".join(dtypes)
    compilers_str = "_".join(compilers)
    generated_file = (
        f"run_{mode}_{devices_str}_{dtypes_str}_{suites_str}_{compilers_str}.sh"
    )
    with open(generated_file, "w") as runfile:
        lines = []

        lines.append("#!/bin/bash")
        lines.append("set -x")
        lines.append("# Setup the output directory")
        if not args.keep_output_dir:
            lines.append(f"rm -rf {output_dir}")
        # It's ok if the output directory already exists
        lines.append(f"mkdir -p {output_dir}")
        lines.append("")

        for testing in ["performance", "accuracy"]:
            for iter in itertools.product(suites, devices, dtypes):
                suite, device, dtype = iter
                lines.append(
                    f"# Commands for {suite} for device={device}, dtype={dtype} for {mode} and for {testing} testing"
                )
                info = TABLE[mode]
                for compiler in compilers:
                    base_cmd = info[compiler]
                    output_filename = f"{output_dir}/{generate_csv_name(args, dtype, suite, device, compiler, testing)}"
                    launcher_cmd = "python"
                    if args.enable_cpu_launcher:
                        launcher_cmd = f"python -m torch.backends.xeon.run_cpu {args.cpu_launcher_args}"
                    cmd = f"{launcher_cmd} benchmarks/dynamo/{suite}.py --{testing} --{dtype} -d{device} --output={output_filename}"
                    cmd = f"{cmd} {base_cmd} {args.extra_args} --dashboard"
                    skip_tests_str = get_skip_tests(suite, device, args.training)
                    cmd = f"{cmd} {skip_tests_str}"

                    if args.log_operator_inputs:
                        cmd = f"{cmd} --log-operator-inputs"

                    if args.quick:
                        filters = DEFAULTS["quick"][suite]
                        cmd = f"{cmd} {filters}"

                    if (
                        compiler
                        in (
                            "inductor",
                            "inductor_no_cudagraphs",
                        )
                        and not args.no_cold_start_latency
                    ):
                        cmd = f"{cmd} --cold-start-latency"

                    if args.batch_size is not None:
                        cmd = f"{cmd} --batch-size {args.batch_size}"

                    if args.threads is not None:
                        cmd = f"{cmd} --threads {args.threads}"

                    if args.total_partitions is not None:
                        cmd = f"{cmd} --total-partitions {args.total_partitions}"

                    if args.partition_id is not None:
                        cmd = f"{cmd} --partition-id {args.partition_id}"

                    if args.inductor_compile_mode is not None:
                        cmd = f"{cmd} --inductor-compile-mode {args.inductor_compile_mode}"
                    lines.append(cmd)
                lines.append("")
        runfile.writelines([line + "\n" for line in lines])
    return generated_file


def generate_dropdown_comment(title, body):
    str_io = io.StringIO()
    str_io.write(f"{title}\n")
    str_io.write("<details>\n")
    str_io.write("<summary>see more</summary>\n")
    str_io.write(f"{body}")
    str_io.write("\n")
    str_io.write("</details>\n\n")
    return str_io.getvalue()


def build_summary(args):
    out_io = io.StringIO()

    def print_commit_hash(path, name):
        if args.base_sha is not None:
            if name == "pytorch":
                out_io.write(f"{name} commit: {args.base_sha}\n")
        elif exists(path):
            import git

            repo = git.Repo(path, search_parent_directories=True)
            sha = repo.head.object.hexsha
            date = repo.head.object.committed_datetime
            out_io.write(f"{name} commit: {sha}\n")
            out_io.write(f"{name} commit date: {date}\n")
        else:
            out_io.write(f"{name} Absent\n")

    def env_var(name):
        if name in os.environ:
            out_io.write(f"{name} = {os.environ[name]}\n")
        else:
            out_io.write(f"{name} = {None}\n")

    out_io.write("\n")
    out_io.write("### Run name ###\n")
    out_io.write(get_archive_name(args, args.dtypes[0]))
    out_io.write("\n")

    out_io.write("\n")
    out_io.write("### Commit hashes ###\n")
    print_commit_hash("../pytorch", "pytorch")
    print_commit_hash("../torchbenchmark", "torchbench")

    out_io.write("\n")
    out_io.write("### TorchDynamo config flags ###\n")
    for key in dir(torch._dynamo.config):
        val = getattr(torch._dynamo.config, key)
        if not key.startswith("__") and isinstance(val, bool):
            out_io.write(f"torch._dynamo.config.{key} = {val}\n")

    out_io.write("\n")
    out_io.write("### Torch version ###\n")
    out_io.write(f"torch: {torch.__version__}\n")

    out_io.write("\n")
    out_io.write("### Environment variables ###\n")
    env_var("TORCH_CUDA_ARCH_LIST")
    env_var("CUDA_HOME")
    env_var("USE_LLVM")

    if "cuda" in args.devices:
        out_io.write("\n")
        out_io.write("### GPU details ###\n")
        out_io.write(f"CUDNN VERSION: {torch.backends.cudnn.version()}\n")
        out_io.write(f"Number CUDA Devices: {torch.cuda.device_count()}\n")
        out_io.write(f"Device Name: {torch.cuda.get_device_name(0)}\n")
        out_io.write(
            f"Device Memory [GB]: {torch.cuda.get_device_properties(0).total_memory / 1e9}\n"
        )

    title = "## Build Summary"
    comment = generate_dropdown_comment(title, out_io.getvalue())
    with open(f"{output_dir}/gh_build_summary.txt", "w") as gh_fh:
        gh_fh.write(comment)


@functools.cache
def archive_data(archive_name):
    if archive_name is not None:
        prefix_match = re.search(r"\w+(?=_performance)", archive_name)
        if prefix_match is not None:
            prefix = prefix_match.group(0)
        else:
            prefix = ""
        day_match = re.search(r"day_(\d+)_", archive_name)
        if day_match is not None:
            day = day_match.group(1)
        else:
            day = "000"
    else:
        now = datetime.now(tz=timezone(timedelta(hours=-8)))
        day = now.strftime("%j")
        prefix = now.strftime(f"day_{day}_%d_%m_%y")
    return day, prefix


@functools.cache
def default_archive_name(dtype):
    _, prefix = archive_data(None)
    return f"{prefix}_performance_{dtype}_{randint(100, 999)}"


def get_archive_name(args, dtype):
    return (
        default_archive_name(dtype) if args.archive_name is None else args.archive_name
    )


def archive(src_dir, dest_dir_prefix, archive_name, dtype):
    if archive_name is None:
        archive_name = default_archive_name(dtype)
    # Copy the folder to archived location
    dest = os.path.join(dest_dir_prefix, archive_name)
    shutil.copytree(src_dir, dest, dirs_exist_ok=True)
    print(f"copied contents of {src_dir} to {dest}")


def get_metric_title(metric):
    if metric == "speedup":
        return "Performance speedup"
    elif metric == "accuracy":
        return "Accuracy"
    elif metric == "compilation_latency":
        return "Compilation latency (sec)"
    elif metric == "compression_ratio":
        return "Peak Memory Compression Ratio"
    elif metric == "abs_latency":
        return "Absolute latency (ms)"
    raise RuntimeError("unknown metric")


class Parser:
    def __init__(
        self, suites, devices, dtypes, compilers, flag_compilers, mode, output_dir
    ):
        self.suites = suites
        self.devices = devices
        self.dtypes = dtypes
        self.compilers = compilers
        self.flag_compilers = flag_compilers
        self.output_dir = output_dir
        self.mode = mode

    def has_header(self, output_filename):
        header_present = False
        with open(output_filename) as f:
            line = f.readline()
            if "dev" in line:
                header_present = True
        return header_present


class ParsePerformanceLogs(Parser):
    def __init__(
        self,
        suites,
        devices,
        dtypes,
        compilers,
        flag_compilers,
        mode,
        output_dir,
        include_slowdowns=False,
    ):
        super().__init__(
            suites,
            devices,
            dtypes,
            compilers,
            flag_compilers,
            mode,
            output_dir,
        )
        self.parsed_frames = defaultdict(lambda: defaultdict(None))
        self.untouched_parsed_frames = defaultdict(lambda: defaultdict(None))
        self.metrics = [
            "speedup",
            "abs_latency",
            "compilation_latency",
            "compression_ratio",
        ]
        self.bottom_k = 50
        self.parse()
        self.include_slowdowns = include_slowdowns

    def plot_graph(self, df, title):
        labels = df.columns.values.tolist()
        labels = labels[3:]
        df.plot(
            x="name",
            y=labels,
            kind="bar",
            width=0.65,
            title=title,
            ylabel="Speedup over eager",
            xlabel="",
            grid=True,
            figsize=(max(len(df.index) / 4, 5), 10),
            edgecolor="black",
        )
        plt.tight_layout()
        plt.savefig(f"{self.output_dir}/{title}.png")

    def read_csv(self, output_filename):
        if self.has_header(output_filename):
            return pd.read_csv(output_filename)
        else:
            return pd.read_csv(
                output_filename,
                names=[
                    "dev",
                    "name",
                    "batch_size",
                    "speedup",
                    "abs_latency",
                    "compilation_latency",
                    "compression_ratio",
                ],
                header=None,
                engine="python",
            )

    def parse(self):
        self.extract_df("accuracy", "accuracy")
        for metric in self.metrics:
            self.extract_df(metric, "performance")

    def clean_batch_sizes(self, frames):
        # Clean up batch sizes when its 0
        if len(frames) == 1:
            return frames
        batch_sizes = frames[0]["batch_size"].to_list()
        for frame in frames[1:]:
            frame_batch_sizes = frame["batch_size"].to_list()
            for idx, (batch_a, batch_b) in enumerate(
                zip(batch_sizes, frame_batch_sizes)
            ):
                if not (batch_a == batch_b or batch_a == 0 or batch_b == 0):
                    raise AssertionError(
                        f"batch size mismatch: a={batch_a}, b={batch_b}"
                    )
                batch_sizes[idx] = max(batch_a, batch_b)
        for frame in frames:
            frame["batch_size"] = batch_sizes
        return frames

    def extract_df(self, metric, testing):
        for iter in itertools.product(self.suites, self.devices, self.dtypes):
            suite, device, dtype = iter
            frames = []
            for compiler in self.compilers:
                output_filename = f"{self.output_dir}/{compiler}_{suite}_{dtype}_{self.mode}_{device}_{testing}.csv"
                df = self.read_csv(output_filename)
                if metric not in df:
                    df.insert(len(df.columns), metric, np.nan)
                df = df[["dev", "name", "batch_size", metric]]
                df.rename(columns={metric: compiler}, inplace=True)
                df["batch_size"] = df["batch_size"].astype(int)
                frames.append(df)

            # Merge the results
            frames = self.clean_batch_sizes(frames)
            if len(self.compilers) == 1:
                df = frames[0]
            else:
                # Merge data frames
                df = pd.merge(frames[0], frames[1], on=["dev", "name", "batch_size"])
                for idx in range(2, len(frames)):
                    df = pd.merge(df, frames[idx], on=["dev", "name", "batch_size"])

            if testing == "performance":
                for compiler in self.compilers:
                    df[compiler] = pd.to_numeric(df[compiler], errors="coerce").fillna(
                        0
                    )

            df_copy = df.copy()
            df_copy = df_copy.sort_values(
                by=list(reversed(self.compilers)), ascending=False
            )
            if "inductor" in self.compilers:
                df_copy = df_copy.sort_values(by="inductor", ascending=False)
            self.untouched_parsed_frames[suite][metric] = df_copy

            if testing == "performance":
                df_accuracy = self.parsed_frames[suite]["accuracy"]
                perf_rows = []
                for model_name in df["name"]:
                    perf_row = df[df["name"] == model_name].copy()
                    acc_row = df_accuracy[df_accuracy["name"] == model_name]
                    for compiler in self.compilers:
                        if not perf_row.empty:
                            if acc_row.empty:
                                perf_row[compiler] = 0.0
                            elif acc_row[compiler].iloc[0] in (
                                "model_fail_to_load",
                                "eager_fail_to_run",
                            ):
                                perf_row = pd.DataFrame()
                            elif acc_row[compiler].iloc[0] not in (
                                "pass",
                                "pass_due_to_skip",
                            ):
                                perf_row[compiler] = 0.0
                    if not perf_row.empty:
                        perf_rows.append(perf_row)
                df = pd.concat(perf_rows)
            df = df.sort_values(by=list(reversed(self.compilers)), ascending=False)

            if "inductor" in self.compilers:
                df = df.sort_values(by="inductor", ascending=False)
            self.parsed_frames[suite][metric] = df

    def get_passing_entries(self, compiler, df):
        return df[compiler][df[compiler] > 0]

    def comp_time(self, compiler, df):
        df = self.get_passing_entries(compiler, df)
        # df = df.sort_values(by=compiler, ascending=False)[compiler][: self.bottom_k]
        if df.empty:
            return "0.0"

        return f"{df.mean():.2f}"

    def geomean(self, compiler, df):
        cleaned_df = self.get_passing_entries(compiler, df)
        if not self.include_slowdowns:
            cleaned_df = cleaned_df.clip(1)
        if cleaned_df.empty:
            return "0.0x"
        return f"{gmean(cleaned_df):.2f}x"

    def passrate(self, compiler, df):
        total = len(df.index)
        passing = df[df[compiler] > 0.0][compiler].count()
        perc = int(percentage(passing, total, decimals=0))
        return f"{perc}%, {passing}/{total}"

    def memory(self, compiler, df):
        df = self.get_passing_entries(compiler, df)
        df = df.fillna(0)
        df = df[df > 0]
        if df.empty:
            return "0.0x"
        return f"{df.mean():.2f}x"

    def exec_summary_df(self, fn, metric):
        """
        Generate a table with passrate and geomean perf
        """
        cols = {}
        cols["Compiler"] = self.compilers
        for suite in self.suites:
            df = self.parsed_frames[suite][metric]
            # speedups = [self.geomean(compiler, df) for compiler in self.compilers]
            speedups = [fn(compiler, df) for compiler in self.compilers]
            col = pd.Series(data=speedups, index=self.compilers)
            cols[suite] = col
        df = pd.DataFrame(cols)
        df = df.fillna(0)
        df.to_csv(os.path.join(self.output_dir, f"{fn.__name__}.csv"))
        return df

    def exec_summary_text(self, caption, fn, metric):
        df = self.exec_summary_df(fn, metric)
        tabform = tabulate(df, headers="keys", tablefmt="pretty", showindex="never")

        str_io = io.StringIO()
        str_io.write(f"{caption}")
        str_io.write("~~~\n")
        str_io.write(f"{tabform}\n")
        str_io.write("~~~\n")
        return str_io.getvalue()

    def generate_executive_summary(self):
        machine = "A100 GPUs"
        if "cpu" in self.devices:
            get_machine_cmd = "lscpu| grep 'Model name' | awk -F':' '{print $2}'"
            machine = subprocess.getstatusoutput(get_machine_cmd)[1].strip()
        description = (
            "We evaluate different backends "
            "across three benchmark suites - torchbench, huggingface and timm. We run "
            "these experiments on "
            + machine
            + ". Each experiment runs one iteration of forward pass "
            "and backward pass for training and forward pass only for inference. "
            "For accuracy, we check the numerical correctness of forward pass outputs and gradients "
            "by comparing with native pytorch. We measure speedup "
            "by normalizing against the performance of native pytorch. We report mean "
            "compilation latency numbers and peak memory footprint reduction ratio. \n\n"
            "Caveats\n"
            "1) Batch size has been reduced to workaround OOM errors. Work is in progress to "
            "reduce peak memory footprint.\n"
            "2) Experiments do not cover dynamic shapes.\n"
            "3) Experimental setup does not have optimizer.\n\n"
        )
        comment = generate_dropdown_comment("", description)
        str_io = io.StringIO()
        str_io.write("\n")
        str_io.write("## Executive Summary ##\n")
        str_io.write(comment)

        speedup_caption = "Geometric mean speedup \n"
        speedup_summary = self.exec_summary_text(
            speedup_caption, self.geomean, "speedup"
        )

        passrate_caption = "Passrate\n"
        passrate_summary = self.exec_summary_text(
            passrate_caption, self.passrate, "speedup"
        )

        comp_time_caption = "Mean compilation time (seconds)\n"
        comp_time_summary = self.exec_summary_text(
            comp_time_caption, self.comp_time, "compilation_latency"
        )

        peak_memory_caption = (
            "Peak memory footprint compression ratio (higher is better)\n"
        )
        peak_memory_summary = self.exec_summary_text(
            peak_memory_caption, self.memory, "compression_ratio"
        )

        str_io.write(
            "To measure performance, compilation latency and memory footprint reduction, "
            "we remove the models that fail accuracy checks.\n\n"
        )
        str_io.write(passrate_summary)
        str_io.write(speedup_summary)
        str_io.write(comp_time_summary)
        str_io.write(peak_memory_summary)
        self.executive_summary = str_io.getvalue()

    def flag_bad_entries(self, suite, metric, flag_fn):
        df = self.untouched_parsed_frames[suite][metric]
        df = df.drop("dev", axis=1)
        df = df.rename(columns={"batch_size": "bs"})
        # apply flag_fn elementwise to flag_compilers columns,
        # if one element fails, the entire row is flagged
        flag = np.logical_or.reduce(
            df[self.flag_compilers].applymap(flag_fn),
            axis=1,
        )
        df = df[flag]
        df = df.assign(suite=suite)
        return df.reindex(columns=["suite", "name"] + self.flag_compilers)

    def generate_warnings(self):
        title = "## Warnings ##"
        body = (
            "We flag models where:\n\n"
            " - accuracy fails\n"
            " - speedup < 0.95x (NOTE: 0.0 speedup typically signifies a failure in the performance test)\n"
            " - compilation latency > 120 sec.\n"
            " - compression ratio < 0.9\n"
            "\n"
        )
        for metric in [
            "accuracy",
            "speedup",
            "compilation_latency",
            "compression_ratio",
        ]:
            dfs = []
            for suite in self.suites:
                dfs.append(self.flag_bad_entries(suite, metric, FLAG_FNS[metric]))
            df = pd.concat(dfs, axis=0)
            if df.empty:
                continue
            tabform = tabulate(df, headers="keys", tablefmt="pretty", showindex="never")
            str_io = io.StringIO()
            str_io.write("\n")
            str_io.write(get_metric_title(metric) + " warnings\n")
            str_io.write("~~~\n")
            str_io.write(f"{tabform}\n")
            str_io.write("~~~\n")
            body += str_io.getvalue()

        comment = generate_dropdown_comment(title, body)
        return comment

    def prepare_message(self, suite):
        title = f"## {suite} suite with {self.dtypes[0]} precision ##"
        body = ""
        for metric in [
            "speedup",
            "accuracy",
            "compilation_latency",
            "compression_ratio",
            "abs_latency",
        ]:
            df = self.untouched_parsed_frames[suite][metric]
            df = df.drop("dev", axis=1)
            df = df.rename(columns={"batch_size": "bs"})
            tabform = tabulate(df, headers="keys", tablefmt="pretty", showindex="never")
            str_io = io.StringIO()
            str_io.write("\n")
            str_io.write(get_metric_title(metric) + "\n")
            str_io.write("~~~\n")
            str_io.write(f"{tabform}\n")
            str_io.write("~~~\n")
            body += str_io.getvalue()

        comment = generate_dropdown_comment(title, body)
        return comment

    def gen_summary_files(self):
        self.generate_executive_summary()
        for suite in self.suites:
            self.plot_graph(
                self.untouched_parsed_frames[suite]["speedup"],
                f"{suite}_{self.dtypes[0]}",
            )

        with open(f"{self.output_dir}/gh_title.txt", "w") as gh_fh:
            str_io = io.StringIO()
            str_io.write("\n")
            str_io.write(f"# Performance Dashboard for {self.dtypes[0]} precision ##\n")
            str_io.write("\n")
            gh_fh.write(str_io.getvalue())

        with open(f"{self.output_dir}/gh_executive_summary.txt", "w") as gh_fh:
            gh_fh.write(self.executive_summary)

        with open(f"{self.output_dir}/gh_warnings.txt", "w") as gh_fh:
            warnings_body = self.generate_warnings()
            gh_fh.write(warnings_body)

        str_io = io.StringIO()
        for suite in self.suites:
            str_io.write(self.prepare_message(suite))
        str_io.write("\n")
        with open(f"{self.output_dir}/gh_{self.mode}.txt", "w") as gh_fh:
            gh_fh.write(str_io.getvalue())


def parse_logs(args, dtypes, suites, devices, compilers, flag_compilers, output_dir):
    mode = get_mode(args)
    build_summary(args)
    include_slowdowns = args.include_slowdowns

    parser_class = ParsePerformanceLogs
    parser = parser_class(
        suites,
        devices,
        dtypes,
        compilers,
        flag_compilers,
        mode,
        output_dir,
        include_slowdowns,
    )
    parser.gen_summary_files()
    return


@dataclasses.dataclass
class LogInfo:
    # Day of the year this log was generated
    day: str

    # Directory path where all logs are present
    dir_path: str


def get_date(log_info):
    return datetime.strptime(f"{log_info.day}", "%j").strftime("%m-%d")


def find_last_2_with_filenames(lookup_file, dashboard_archive_path, dtype, filenames):
    df = pd.read_csv(lookup_file, names=("day", "mode", "prec", "path"))
    df = df[df["mode"] == "performance"]
    df = df[df["prec"] == dtype]
    df = df[::-1]
    last2 = []
    for path in df["path"]:
        output_dir = os.path.join(dashboard_archive_path, path)
        fullpaths = [
            os.path.join(dashboard_archive_path, path, name) for name in filenames
        ]
        if all(os.path.exists(fullpath) for fullpath in fullpaths):
            last2.append(output_dir)
        if len(last2) >= 2:
            return last2
    return None


class SummaryStatDiffer:
    def __init__(self, args):
        self.args = args
        self.lookup_file = os.path.join(self.args.dashboard_archive_path, "lookup.csv")
        if not os.path.exists(self.lookup_file):
            raise AssertionError(f"lookup file not found: {self.lookup_file}")

    def generate_diff(self, last2, filename, caption):
        df_cur, df_prev = (pd.read_csv(os.path.join(path, filename)) for path in last2)
        df_merge = df_cur.merge(df_prev, on="Compiler", suffixes=("_cur", "_prev"))
        data = {col: [] for col in ("compiler", "suite", "prev_value", "cur_value")}
        for _, row in df_merge.iterrows():
            if row["Compiler"] in self.args.flag_compilers:
                for suite in self.args.suites:
                    if suite + "_prev" not in row or suite + "_cur" not in row:
                        continue
                    data["compiler"].append(row["Compiler"])
                    data["suite"].append(suite)
                    data["prev_value"].append(row[suite + "_prev"])
                    data["cur_value"].append(row[suite + "_cur"])

        df = pd.DataFrame(data)
        tabform = tabulate(df, headers="keys", tablefmt="pretty", showindex="never")
        str_io = io.StringIO()
        str_io.write("\n")
        str_io.write(f"{caption}\n")
        str_io.write("~~~\n")
        str_io.write(f"{tabform}\n")
        str_io.write("~~~\n")
        return str_io.getvalue()

    def generate_comment(self):
        title = "## Summary Statistics Diff ##\n"
        body = (
            "For each relevant compiler, we compare the summary statistics "
            "for the most 2 recent reports that actually run the compiler.\n\n"
        )
        dtype = self.args.dtypes[0]
        last2 = find_last_2_with_filenames(
            self.lookup_file,
            self.args.dashboard_archive_path,
            dtype,
            ["geomean.csv", "passrate.csv"],
        )

        if last2 is None:
            body += "Could not find most 2 recent reports.\n\n"
        else:
            for state, path in zip(("Current", "Previous"), last2):
                body += f"{state} report name: {path}\n\n"
            body += self.generate_diff(last2, "passrate.csv", "Passrate diff")
            body += self.generate_diff(
                last2, "geomean.csv", "Geometric mean speedup diff"
            )

        comment = generate_dropdown_comment(title, body)

        with open(f"{self.args.output_dir}/gh_summary_diff.txt", "w") as gh_fh:
            gh_fh.write(comment)


class RegressionDetector:
    """
    Compares the most recent 2 benchmarks to find previously unflagged models
    that are now flagged.
    """

    def __init__(self, args):
        self.args = args
        self.lookup_file = os.path.join(self.args.dashboard_archive_path, "lookup.csv")
        if not os.path.exists(self.lookup_file):
            raise AssertionError(f"lookup file not found: {self.lookup_file}")

    def generate_comment(self):
        title = "## Recent Regressions ##\n"
        body = (
            "For each relevant compiler, we compare the most recent 2 reports "
            "(that actually run the compiler) to find previously unflagged "
            "models that are now flagged as problematic (according to the "
            "'Warnings' section).\n\n"
        )
        dtype = self.args.dtypes[0]
        device = self.args.devices[0]
        for suite in self.args.suites:
            body += f"### Regressions for {suite} ###\n"
            last2 = {}

            for compiler in self.args.flag_compilers:
                filenames = [
                    generate_csv_name(
                        self.args, dtype, suite, device, compiler, testing
                    )
                    for testing in ["performance", "accuracy"]
                ]
                compiler_last2 = find_last_2_with_filenames(
                    self.lookup_file, self.args.dashboard_archive_path, dtype, filenames
                )
                if compiler_last2 is not None:
                    last2[compiler] = [
                        ParsePerformanceLogs(
                            [suite],
                            [device],
                            [dtype],
                            [compiler],
                            [compiler],
                            get_mode(self.args),
                            output_dir,
                        )
                        for output_dir in compiler_last2
                    ]
                    for state, path in zip(("Current", "Previous"), compiler_last2):
                        body += (
                            f"{state} report name (compiler: {compiler}, "
                            f"suite: {suite}): {path}\n\n"
                        )

            regressions_present = False
            for metric in [
                "accuracy",
                "speedup",
                "compilation_latency",
                "compression_ratio",
            ]:
                dfs = []
                for compiler in self.args.flag_compilers:
                    if last2[compiler] is None:
                        continue

                    df_cur, df_prev = (
                        last2[compiler][i].untouched_parsed_frames[suite][metric]
                        for i in (0, 1)
                    )
                    df_merge = df_cur.merge(
                        df_prev, on="name", suffixes=("_cur", "_prev")
                    )
                    flag_fn = FLAG_FNS[metric]
                    flag = np.logical_and(
                        df_merge[compiler + "_prev"].apply(
                            lambda x: not pd.isna(x) and not flag_fn(x)
                        ),
                        df_merge[compiler + "_cur"].apply(
                            lambda x: not pd.isna(x) and flag_fn(x)
                        ),
                    )
                    df_bad = df_merge[flag]
                    dfs.append(
                        pd.DataFrame(
                            data={
                                "compiler": compiler,
                                "name": df_bad["name"],
                                "prev_status": df_bad[compiler + "_prev"],
                                "cur_status": df_bad[compiler + "_cur"],
                            }
                        )
                    )

                if not dfs:
                    continue
                df = pd.concat(dfs, axis=0)
                if df.empty:
                    continue
                regressions_present = True
                tabform = tabulate(
                    df, headers="keys", tablefmt="pretty", showindex="never"
                )
                str_io = io.StringIO()
                str_io.write("\n")
                str_io.write(f"{get_metric_title(metric)} regressions\n")
                str_io.write("~~~\n")
                str_io.write(f"{tabform}\n")
                str_io.write("~~~\n")
                body += str_io.getvalue()

            if not regressions_present:
                body += "No regressions found.\n"

        comment = generate_dropdown_comment(title, body)

        with open(f"{self.args.output_dir}/gh_metric_regression.txt", "w") as gh_fh:
            gh_fh.write(comment)


class RegressionTracker:
    """
    Plots progress of different metrics over time to detect regressions.
    """

    def __init__(self, args):
        self.args = args
        self.suites = self.args.suites
        self.lookup_file = os.path.join(self.args.dashboard_archive_path, "lookup.csv")
        if not os.path.exists(self.lookup_file):
            raise AssertionError(f"lookup file not found: {self.lookup_file}")
        self.k = 10

    def find_last_k(self):
        """
        Find the last k pairs of (day number, log_path)
        """
        dtype = self.args.dtypes[0]
        df = pd.read_csv(self.lookup_file, names=("day", "mode", "prec", "path"))
        df = df[df["mode"] == "performance"]
        df = df[df["prec"] == dtype]
        log_infos = []
        for day, path in zip(df["day"], df["path"]):
            log_infos.append(LogInfo(day, path))

        if len(log_infos) < self.k:
            raise AssertionError(
                f"expected at least {self.k} log entries, got {len(log_infos)}"
            )
        log_infos = log_infos[len(log_infos) - self.k :]
        return log_infos

    def generate_comment(self):
        title = "## Metrics over time ##\n"
        str_io = io.StringIO()
        if not self.args.update_dashboard_test and not self.args.no_graphs:
            for name in glob.glob(self.args.output_dir + "/*over_time.png"):
                output = (
                    subprocess.check_output([self.args.dashboard_image_uploader, name])
                    .decode("ascii")
                    .rstrip()
                )
                str_io.write(f"\n{name} : ![]({output})\n")
        comment = generate_dropdown_comment(title, str_io.getvalue())

        with open(f"{self.args.output_dir}/gh_regression.txt", "w") as gh_fh:
            gh_fh.write(comment)

    def diff(self):
        log_infos = self.find_last_k()

        for metric in ["geomean", "passrate", "comp_time", "memory"]:
            fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 5))
            for idx, suite in enumerate(self.suites):
                dfs = []
                for log_info in log_infos:
                    dir_path = os.path.join(
                        self.args.dashboard_archive_path, log_info.dir_path
                    )
                    if not os.path.exists(dir_path):
                        raise AssertionError(f"directory not found: {dir_path}")
                    gmean_filename = os.path.join(dir_path, f"{metric}.csv")
                    if not os.path.exists(gmean_filename):
                        continue
                    df = pd.read_csv(gmean_filename)
                    if suite not in df:
                        continue
                    if metric == "geomean" or metric == "memory":
                        df[suite] = df[suite].str.replace("x", "").astype(float)
                    elif metric == "passrate":
                        df[suite] = df[suite].str.split("%").str[0].astype(float)
                    df.insert(0, "day", get_date(log_info))
                    df = df.pivot(index="day", columns="Compiler", values=suite)

                    # Interim stage when both inductor_cudagraphs and inductor exist
                    df = df.rename(columns={"inductor_cudagraphs": "inductor"})
                    for col_name in df.columns:
                        if col_name not in self.args.compilers:
                            df = df.drop(columns=[col_name])
                    dfs.append(df)

                df = pd.concat(dfs)
                df = df.interpolate(method="linear")
                ax = df.plot(
                    ax=axes[idx],
                    kind="line",
                    ylabel=metric,
                    xlabel="Date",
                    grid=True,
                    ylim=0 if metric == "passrate" else 0.8,
                    title=suite,
                    style=".-",
                    legend=False,
                )
                ax.legend(loc="lower right", ncol=2)

            plt.tight_layout()
            plt.savefig(os.path.join(output_dir, f"{metric}_over_time.png"))

        self.generate_comment()


class DashboardUpdater:
    """
    Aggregates the information and makes a comment to Performance Dashboard.
    https://github.com/pytorch/torchdynamo/issues/681
    """

    def __init__(self, args):
        self.args = args
        self.output_dir = args.output_dir
        self.lookup_file = os.path.join(self.args.dashboard_archive_path, "lookup.csv")
        if not os.path.exists(self.lookup_file):
            raise AssertionError(f"lookup file not found: {self.lookup_file}")
        try:
            if not self.args.update_dashboard_test and not self.args.no_update_archive:
                self.update_lookup_file()
        except subprocess.CalledProcessError:
            sys.stderr.write("failed to update lookup file\n")

    def update_lookup_file(self):
        dtype = self.args.dtypes[0]
        day, _ = archive_data(self.args.archive_name)
        target_dir = get_archive_name(self.args, dtype)
        # Update lookup csv the folder to archived logs
        subprocess.check_call(
            f'echo "{day},performance,{dtype},{target_dir}" >> {self.lookup_file}',
            shell=True,
        )

    def archive(self):
        dtype = self.args.dtypes[0]
        # Copy the folder to archived location
        archive(
            self.output_dir,
            self.args.dashboard_archive_path,
            self.args.archive_name,
            dtype,
        )

    def upload_graphs(self):
        title = "## Performance graphs ##\n"
        str_io = io.StringIO()
        if not self.args.update_dashboard_test and not self.args.no_graphs:
            for name in glob.glob(self.output_dir + "/*png"):
                if "over_time" not in name:
                    output = (
                        subprocess.check_output(
                            [self.args.dashboard_image_uploader, name]
                        )
                        .decode("ascii")
                        .rstrip()
                    )
                    str_io.write(f"\n{name} : ![]({output})\n")
        comment = generate_dropdown_comment(title, str_io.getvalue())

        with open(f"{self.output_dir}/gh_graphs.txt", "w") as gh_fh:
            gh_fh.write(comment)

    def gen_comment(self):
        files = [
            "gh_title.txt",
            "gh_executive_summary.txt",
            "gh_summary_diff.txt",
            "gh_warnings.txt",
            "gh_regression.txt",
            "gh_metric_regression.txt",
            "gh_training.txt" if self.args.training else "gh_inference.txt",
            "gh_graphs.txt",
            "gh_build_summary.txt",
        ]
        all_lines = []
        for f in files:
            try:
                with open(os.path.join(self.output_dir, f)) as fh:
                    all_lines.extend(fh.readlines())
            except FileNotFoundError:
                pass

        return "\n".join([x.rstrip() for x in all_lines])

    def comment_on_gh(self, comment):
        """
        Send a comment to dashboard
        """
        with tempfile.NamedTemporaryFile(mode="w", delete=False) as f:
            f.write(comment)
            filename = f.name

        issue_number = "93794"
        if self.args.dtypes[0] == "float32":
            issue_number = "93518"

        subprocess.check_call(
            [
                self.args.dashboard_gh_cli_path,
                "issue",
                "comment",
                "--repo=https://github.com/pytorch/pytorch.git",
                issue_number,
                "-F",
                filename,
            ]
        )

        os.remove(filename)

    def update(self):
        self.upload_graphs()
        if not self.args.no_detect_regressions:
            SummaryStatDiffer(self.args).generate_comment()
            RegressionDetector(self.args).generate_comment()
            try:
                RegressionTracker(self.args).diff()
            except Exception:
                log.exception("")
                with open(f"{self.args.output_dir}/gh_regression.txt", "w") as gh_fh:
                    gh_fh.write("")

        comment = self.gen_comment()
        print(comment)

        if not self.args.update_dashboard_test:
            if not self.args.no_gh_comment:
                self.comment_on_gh(comment)
            if not self.args.no_update_archive:
                self.archive()


if __name__ == "__main__":
    args = parse_args()

    def extract(key):
        return DEFAULTS[key] if getattr(args, key, None) is None else getattr(args, key)

    dtypes = extract("dtypes")
    suites = extract("suites")
    devices = extract("devices")

    if args.inference:
        compilers = DEFAULTS["inference"] if args.compilers is None else args.compilers
        flag_compilers = (
            DEFAULTS["flag_compilers"]["inference"]
            if args.flag_compilers is None
            else args.flag_compilers
        )
    else:
        if not args.training:
            raise AssertionError("expected args.training to be True")
        compilers = DEFAULTS["training"] if args.compilers is None else args.compilers
        flag_compilers = (
            DEFAULTS["flag_compilers"]["training"]
            if args.flag_compilers is None
            else args.flag_compilers
        )

    output_dir = args.output_dir
    args.compilers = compilers
    args.devices = devices
    args.dtypes = dtypes
    flag_compilers = list(set(flag_compilers) & set(compilers))
    args.flag_compilers = flag_compilers
    args.suites = suites

    if args.print_run_commands:
        generated_file = generate_commands(
            args, dtypes, suites, devices, compilers, output_dir
        )
        print(
            f"Running commands are generated in file {generated_file}. Please run (bash {generated_file})."
        )
    elif args.visualize_logs:
        parse_logs(args, dtypes, suites, devices, compilers, flag_compilers, output_dir)
    elif args.run:
        generated_file = generate_commands(
            args, dtypes, suites, devices, compilers, output_dir
        )
        # generate memoized archive name now so that the date is reflective
        # of when the run started
        get_archive_name(args, dtypes[0])
        # TODO - Do we need to worry about segfaults
        try:
            os.system(f"bash {generated_file}")
        except Exception as e:
            print(
                f"Running commands failed. Please run manually (bash {generated_file}) and inspect the errors."
            )
            raise e
        if not args.log_operator_inputs:
            if not args.no_update_archive:
                archive(
                    output_dir,
                    args.dashboard_archive_path,
                    args.archive_name,
                    dtypes[0],
                )
            parse_logs(
                args, dtypes, suites, devices, compilers, flag_compilers, output_dir
            )
            if not args.no_update_archive:
                archive(
                    output_dir,
                    args.dashboard_archive_path,
                    args.archive_name,
                    dtypes[0],
                )

    if args.update_dashboard:
        DashboardUpdater(args).update()
