| #!/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 re |
| import shutil |
| import subprocess |
| from collections import defaultdict |
| from datetime import datetime |
| from os.path import abspath, exists |
| from random import randint |
| |
| import matplotlib.pyplot as plt |
| |
| import numpy as np |
| import pandas as pd |
| import torch |
| |
| import torch._dynamo |
| from matplotlib import rcParams |
| from scipy.stats import gmean |
| from tabulate import tabulate |
| |
| 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 ", |
| "aot_cudagraphs": "--training --backend=aot_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 ", |
| }, |
| "inference": { |
| "ts_nnc": "--speedup-ts", |
| "ts_nvfuser": "-n100 --speedup-ts --nvfuser", |
| "trt": "-n100 --speedup-trt", |
| "ts_nvfuser_cudagraphs": "--backend=cudagraphs_ts", |
| "inductor": "-n50 --inductor", |
| }, |
| } |
| |
| INFERENCE_COMPILERS = tuple(TABLE["inference"].keys()) |
| TRAINING_COMPILERS = tuple(TABLE["training"].keys()) |
| |
| DEFAULTS = { |
| "training": [ |
| "eager", |
| "aot_eager", |
| "aot_cudagraphs", |
| "nvprims_nvfuser", |
| "inductor", |
| "inductor_no_cudagraphs", |
| ], |
| "inference": ["ts_nvfuser_cudagraphs", "inductor"], |
| "flag_compilers": { |
| "training": ["inductor", "inductor_no_cudagraphs"], |
| "inference": ["inductor"], |
| }, |
| "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 |
| |
| |
| FLAG_FNS = { |
| "speedup": flag_speedup, |
| "compilation_latency": flag_compilation_latency, |
| "compression_ratio": flag_compression_ratio, |
| } |
| |
| |
| 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, |
| ) |
| |
| # Choose either generation of commands, pretty parsing or e2e runs |
| group = parser.add_mutually_exclusive_group(required=False) |
| group.add_argument( |
| "--print_run_commands", |
| action="store_true", |
| help="Generate commands and saves them to run.sh", |
| ) |
| group.add_argument( |
| "--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( |
| "--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( |
| "--update-dashboard", |
| action="store_true", |
| default=False, |
| help="Updates to dashboard", |
| ) |
| 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", |
| ) |
| args = parser.parse_args() |
| return args |
| |
| |
| def get_mode(args): |
| if args.inference: |
| return "inference" |
| return "training" |
| |
| |
| def get_skip_tests(suite): |
| """ |
| Generate -x seperated 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) |
| |
| if hasattr(module, "SKIP"): |
| skip_tests.update(module.SKIP) |
| if hasattr(module, "SKIP_TRAIN"): |
| skip_tests.update(module.SKIP_TRAIN) |
| |
| skip_tests = map(lambda name: f"-x {name}", 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) |
| with open("run.sh", "w") as runfile: |
| lines = [] |
| |
| lines.append("# Setup the output directory") |
| lines.append(f"rm -rf {output_dir}") |
| lines.append(f"mkdir {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)}" |
| cmd = f"python benchmarks/dynamo/{suite}.py --{testing} --{dtype} -d{device} --output={output_filename}" |
| cmd = f"{cmd} {base_cmd} {args.extra_args} --no-skip --dashboard" |
| |
| skip_tests_str = get_skip_tests(suite) |
| 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 testing == "performance" and compiler in ( |
| "inductor", |
| "inductor_no_cudagraphs", |
| ): |
| cmd = f"{cmd} --cold_start_latency" |
| lines.append(cmd) |
| lines.append("") |
| runfile.writelines([line + "\n" for line in lines]) |
| |
| |
| 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(): |
| import git |
| |
| out_io = io.StringIO() |
| |
| def print_commit_hash(path, name): |
| if exists(path): |
| repo = git.Repo(path, search_parent_directories=True) |
| sha = repo.head.object.hexsha |
| out_io.write(f"{name} commit: {sha}\n") |
| else: |
| out_io.write(f"{name} Absent\n") |
| |
| def env_var(name): |
| out_io.write(f"{name} = {os.environ[name]}\n") |
| |
| out_io.write("## Commit hashes ##\n") |
| print_commit_hash(".", "torch._dynamo") |
| print_commit_hash("../pytorch", "pytorch") |
| print_commit_hash("../functorch", "functorch") |
| 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") |
| |
| 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.lru_cache(None) |
| 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: |
| day = datetime.today().strftime("%j") |
| prefix = datetime.today().strftime(f"day_{day}_%d_%m_%y") |
| return day, prefix |
| |
| |
| @functools.lru_cache(None) |
| def default_archive_name(dtype): |
| _, prefix = archive_data(None) |
| return f"{prefix}_performance_{dtype}_{randint(100, 999)}" |
| |
| |
| 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}") |
| |
| |
| 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, "r") 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 |
| ): |
| 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() |
| |
| 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) |
| ): |
| assert batch_a == batch_b or batch_a == 0 or batch_b == 0, print( |
| f"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"]) |
| |
| 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] not in ( |
| "pass", |
| "pass_due_to_skip", |
| ): |
| perf_row[compiler] = 0.0 |
| 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).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): |
| description = ( |
| "We evaluate different backends " |
| "across three benchmark suites - torchbench, huggingface and timm. We run " |
| "these experiments on A100 GPUs. Each experiment runs one iteration of forward " |
| "and backward pass. 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 get_metric_title(self, 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") |
| |
| def generate_warnings(self): |
| title = "## Warnings ##" |
| body = "" |
| for metric in [ |
| "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(self.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(self.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) |
| print(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) |
| print(warnings_body) |
| |
| str_io = io.StringIO() |
| for suite in self.suites: |
| str_io.write(self.prepare_message(suite)) |
| str_io.write("\n") |
| print(str_io.getvalue()) |
| 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() |
| |
| parser_class = ParsePerformanceLogs |
| parser = parser_class( |
| suites, devices, dtypes, compilers, flag_compilers, mode, output_dir |
| ) |
| 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") |
| |
| |
| class AccuracyRegressionTracker: |
| """ |
| Compares the most recent 2 accuracy benchmarks to find previously |
| passing models that now fail. |
| """ |
| |
| def __init__(self, args): |
| self.args = args |
| self.lookup_file = os.path.join(self.args.dashboard_archive_path, "lookup.csv") |
| assert os.path.exists(self.lookup_file) |
| |
| def find_last_2(self, suite, device, dtype, compiler): |
| df = pd.read_csv(self.lookup_file, names=("day", "mode", "prec", "path")) |
| df = df[df["mode"] == "performance"] |
| df = df[df["prec"] == dtype] |
| df = df[::-1] |
| parsers = [] |
| for path in df["path"]: |
| output_dir = os.path.join(self.args.dashboard_archive_path, path) |
| if os.path.exists( |
| os.path.join( |
| output_dir, |
| generate_csv_name( |
| self.args, dtype, suite, device, compiler, "accuracy" |
| ), |
| ) |
| ): |
| parsers.append( |
| ParsePerformanceLogs( |
| [suite], |
| [device], |
| [dtype], |
| [compiler], |
| [compiler], |
| get_mode(self.args), |
| output_dir, |
| ) |
| ) |
| if len(parsers) >= 2: |
| return parsers |
| return None |
| |
| def generate_comment(self): |
| title = "## Accuracy Regressions ##\n" |
| body = "" |
| dtype = self.args.dtypes[0] |
| device = self.args.devices[0] |
| for suite in self.args.suites: |
| dfs = [] |
| for compiler in self.args.flag_compilers: |
| last2 = self.find_last_2(suite, device, dtype, compiler) |
| if last2 is None: |
| continue |
| |
| df_cur, df_prev = [ |
| last2[i].untouched_parsed_frames[suite]["accuracy"] for i in (0, 1) |
| ] |
| df_merge = df_cur.merge(df_prev, on="name", suffixes=("_cur", "_prev")) |
| flag = np.logical_and( |
| df_merge[compiler + "_prev"].apply(lambda x: "pass" in x), |
| df_merge[compiler + "_cur"].apply(lambda x: "pass" not in 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 |
| tabform = tabulate(df, headers="keys", tablefmt="pretty", showindex="never") |
| str_io = io.StringIO() |
| str_io.write("\n") |
| str_io.write(f"Accuracy regressions for {suite}\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) |
| |
| with open(f"{self.args.output_dir}/gh_accuracy_regression.txt", "w") as gh_fh: |
| gh_fh.write(comment) |
| print(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") |
| assert os.path.exists(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)) |
| |
| assert len(log_infos) >= self.k |
| 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() |
| 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} : \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"]: |
| 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 |
| ) |
| assert os.path.exists(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": |
| 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") |
| assert os.path.exists(self.lookup_file) |
| |
| 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, |
| ) |
| day, _ = archive_data(self.args.archive_name) |
| target_dir = ( |
| default_archive_name(dtype) |
| if self.args.archive_name is None |
| else self.args.archive_name |
| ) |
| |
| # Update lookup csv the folder to arhived logs |
| subprocess.check_call( |
| f'echo "{day},performance,{dtype},{target_dir}" >> {self.lookup_file}', |
| shell=True, |
| ) |
| |
| def upload_graphs(self): |
| title = "## Performance graphs ##\n" |
| str_io = io.StringIO() |
| 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} : \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_warnings.txt", |
| "gh_regression.txt", |
| "gh_accuracy_regression.txt", |
| "gh_training.txt", |
| "gh_graphs.txt", |
| ] |
| all_lines = [] |
| for f in files: |
| try: |
| with open(os.path.join(self.output_dir, f), "r") 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 commment to dashboard |
| """ |
| subprocess.check_call( |
| [ |
| self.args.dashboard_gh_cli_path, |
| "issue", |
| "comment", |
| "--repo=https://github.com/pytorch/torchdynamo.git", |
| "681", |
| "-b", |
| comment, |
| ] |
| ) |
| |
| def update(self): |
| self.upload_graphs() |
| AccuracyRegressionTracker(self.args).generate_comment() |
| try: |
| RegressionTracker(self.args).diff() |
| except Exception as e: |
| logging.exception(e) |
| with open(f"{self.args.output_dir}/gh_regression.txt", "w") as gh_fh: |
| gh_fh.write("") |
| |
| comment = self.gen_comment() |
| self.comment_on_gh(comment) |
| |
| 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: |
| assert args.training |
| 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 |
| args.flag_compilers = flag_compilers |
| args.suites = suites |
| |
| if args.print_run_commands: |
| generate_commands(args, dtypes, suites, devices, compilers, output_dir) |
| elif args.visualize_logs: |
| parse_logs(args, dtypes, suites, devices, compilers, flag_compilers, output_dir) |
| elif args.run: |
| generate_commands(args, dtypes, suites, devices, compilers, output_dir) |
| # TODO - Do we need to worry about segfaults |
| try: |
| os.system("bash run.sh") |
| except Exception as e: |
| print( |
| "Running commands failed. Please run manually (bash run.sh) and inspect the errors." |
| ) |
| raise e |
| if not args.log_operator_inputs: |
| archive( |
| output_dir, args.dashboard_archive_path, args.archive_name, dtypes[0] |
| ) |
| parse_logs( |
| args, dtypes, suites, devices, compilers, flag_compilers, output_dir |
| ) |
| |
| if args.update_dashboard: |
| DashboardUpdater(args).update() |
