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android / platform / external / pytorch / b535ed2c1a / . / benchmarks / dynamo / common.py
blob: 5d8b43a1dfc44ce9531294c8d455554b36e9bd81 [file] [log] [blame]
#!/usr/bin/env python3
from __future__ import annotations
import argparse
import collections
import contextlib
import copy
import csv
import dataclasses
import functools
import importlib
import itertools
import logging
import os
import pathlib
import random
import shutil
import signal
import subprocess
import sys
import time
from contextlib import contextmanager
from typing import Any, Callable, Mapping, NamedTuple, Optional, Tuple, Type
from unittest.mock import MagicMock
import numpy as np
import pandas as pd
import psutil
import torch
import torch._dynamo
import torch._dynamo.utils
import torch._export
import torch.distributed
import torch.fx._pytree as fx_pytree
import torch.multiprocessing as mp
from scipy.stats import gmean, ttest_ind
from torch._dynamo.profiler import fx_insert_profiling, Profiler
from torch._dynamo.testing import dummy_fx_compile, format_speedup, same
from torch._dynamo.utils import clone_inputs, graph_break_reasons
from torch._functorch.aot_autograd import set_model_name
from torch._inductor import config as inductor_config
from torch._inductor.utils import fresh_inductor_cache
from torch._subclasses.fake_tensor import FakeTensorMode
from torch.utils import _pytree as pytree
from torch.utils._pytree import tree_map, tree_map_only
from tqdm.auto import tqdm, trange
try:
from .microbenchmarks.operator_inp_utils import OperatorInputsMode
except ImportError:
from microbenchmarks.operator_inp_utils import OperatorInputsMode
try:
import torch_xla.core.xla_model as xm
except ImportError:
# ignore the error if torch_xla is not installed
pass
log = logging.getLogger(__name__)
# We are primarily interested in TF32
torch.backends.cuda.matmul.allow_tf32 = True
# Suppress torch.profiler spam
os.environ["KINETO_LOG_LEVEL"] = "5"
current_name = ""
current_device = ""
current_onnx_compiler = ""
current_batch_size = None
output_filename = None
MAX_DOWNLOAD_ATTEMPTS = 5
class CI(NamedTuple):
backend: str # aot_eager or inductor
training: bool
dynamic: bool = False
device: str = "cuda"
CI_SKIP = collections.defaultdict(list)
# Skips for dynamic=False
# Here eager really means dynamo+eager
CI_SKIP[CI("eager", training=False)] = [
# TorchBench
"DALLE2_pytorch", # AttributeError: text_encodings
"hf_BigBird", # fail_accuracy
# TypeError: pad_center() takes 1 positional argument but 2 were given
"tacotron2",
# Huggingface
"DebertaV2ForQuestionAnswering", # OOM
]
CI_SKIP[CI("eager", training=True)] = [
*CI_SKIP[CI("eager", training=False)],
# TorchBench
"BERT_pytorch", # accuracy
"Background_Matting", # fp64_OOM
"hf_BigBird", # fp64_OOM
"hf_T5_base", # fp64_OOM
"llama", # Accuracy failed: allclose not within tol=0.001
"vision_maskrcnn", # The size of tensor a (29) must match the size of tensor b (33) (doesn't repro)
# Huggingface
"XGLMForCausalLM", # OOM
# TIMM
"cait_m36_384", # fp64_OOM
"convit_base", # fp64_OOM
"mobilenetv2_100", # accuracy
"xcit_large_24_p8_224", # fp64_OOM,
]
CI_SKIP[CI("aot_eager", training=False)] = [
*CI_SKIP[CI("eager", training=False)],
# all dynamic shapes errors for detectron variants
"demucs", # OOM
"detectron2_fasterrcnn_r_101_c4",
"detectron2_fasterrcnn_r_101_dc5",
"detectron2_fasterrcnn_r_101_fpn",
"detectron2_fasterrcnn_r_50_c4",
"detectron2_fasterrcnn_r_50_dc5",
"detectron2_fasterrcnn_r_50_fpn",
"detectron2_fcos_r_50_fpn",
"detectron2_maskrcnn_r_101_c4",
"detectron2_maskrcnn_r_101_fpn",
"detectron2_maskrcnn_r_50_c4",
"detectron2_maskrcnn_r_50_fpn",
"hf_BigBird", # OOM
"tacotron2", # AssertionError: Deduped args out of bounds
# Huggingface
"BartForConditionalGeneration", # OOM
"DebertaV2ForQuestionAnswering", # OOM
# Torchbench
"speech_transformer", # https://github.com/pytorch/pytorch/issues/99893
"pyhpc_isoneutral_mixing", # https://github.com/pytorch/pytorch/issues/99893
"pyhpc_turbulent_kinetic_energy", # https://github.com/pytorch/pytorch/issues/99893
]
CI_SKIP[CI("aot_eager", training=True)] = [
*CI_SKIP[CI("aot_eager", training=False)],
# TorchBench
"Background_Matting", # fp64_OOM
"hf_T5_base", # fp64_OOM
"mobilenet_v2_quantized_qat", # fp64_OOM
"resnet50_quantized_qat", # fp64_OOM
"pytorch_struct",
# Huggingface
"MBartForConditionalGeneration", # OOM
"M2M100ForConditionalGeneration", # OOM
"XGLMForCausalLM", # OOM
# TIMM
"cait_m36_384", # fp64_OOM
"convit_base", # fp64_OOM
"fbnetv3_b", # Accuracy (blocks.2.2.bn1.weight.grad)
"levit_128", # Accuracy (patch_embed.0.c.weight.grad)
"lcnet_050", # Accuracy (blocks.1.0.bn2.weight.grad)
"sebotnet33ts_256", # Accuracy (stem.conv1.conv.weight.grad)
"xcit_large_24_p8_224", # fp64_OOM,
]
CI_SKIP[CI("inductor", training=False)] = [
# TorchBench
"DALLE2_pytorch", # AttributeError: text_encodings
"demucs", # OOM
"detectron2_fasterrcnn_r_101_c4",
"detectron2_fasterrcnn_r_101_dc5",
"detectron2_fasterrcnn_r_101_fpn",
"detectron2_fasterrcnn_r_50_c4",
"detectron2_fasterrcnn_r_50_dc5",
"detectron2_fasterrcnn_r_50_fpn",
"detectron2_fcos_r_50_fpn",
"detectron2_maskrcnn_r_101_c4",
"detectron2_maskrcnn_r_101_fpn",
"detectron2_maskrcnn_r_50_c4",
"detectron2_maskrcnn_r_50_fpn",
# TorchBench
"detectron2",
"densenet121", # flaky accuracy
"hf_T5", # accuracy
"hf_BigBird", # accuracy
"hf_GPT2_large", # OOM
"maml", # accuracy
"mobilenet_v2_quantized_qat", # The eval test only supports CPU
"pytorch_struct", # Test eval is not implemented
"pyhpc_equation_of_state", # Accuracy
"pyhpc_turbulent_kinetic_energy", # Accuracy
"tacotron2",
]
CI_SKIP[CI("inductor", training=False, device="cpu")] = [
# TorchBench
"drq", # Need to update torchbench
"detectron2_fasterrcnn_r_101_c4",
"detectron2_fasterrcnn_r_101_dc5",
"detectron2_fasterrcnn_r_101_fpn",
"detectron2_fasterrcnn_r_50_c4",
"detectron2_fasterrcnn_r_50_dc5",
"detectron2_fasterrcnn_r_50_fpn",
"detectron2_fcos_r_50_fpn",
"detectron2_maskrcnn_r_101_c4",
"detectron2_maskrcnn_r_101_fpn",
"detectron2_maskrcnn_r_50_c4",
"detectron2_maskrcnn_r_50_fpn",
"doctr_det_predictor", # requires newer gcc
"doctr_reco_predictor", # requires newer gcc
"gat", # does not work with fp32
"gcn", # does not work with fp32
"hf_Bert_large", # OOM
"hf_GPT2_large", # Intermittent failure on CI
"hf_T5_base", # OOM
"mobilenet_v2_quantized_qat",
"pyhpc_turbulent_kinetic_energy",
"resnet50_quantized_qat", # Eager model failed to run(Quantize only works on Float Tensor, got Double)
"sage", # does not work with fp32
# Huggingface
"MBartForConditionalGeneration", # Accuracy https://github.com/pytorch/pytorch/issues/94793
"PLBartForConditionalGeneration", # Accuracy https://github.com/pytorch/pytorch/issues/94794
# TIMM
"cait_m36_384", # Accuracy
"pnasnet5large", # OOM
"xcit_large_24_p8_224", # OOM https://github.com/pytorch/pytorch/issues/95984
"opacus_cifar10", # Fails to run https://github.com/pytorch/pytorch/issues/99201
]
CI_SKIP[CI("inductor", training=True)] = [
*CI_SKIP[CI("inductor", training=False)],
# TorchBench
"Background_Matting", # fp64_OOM
"hf_T5_base", # accuracy
"mobilenet_v3_large", # accuracy
"resnet50_quantized_qat", # Eager model failed to run
"AlbertForQuestionAnswering", # accuracy
"crossvit_9_240", # fails to run on timm 0.8.22 with cudagraphs, mempools
"deit_base_distilled_patch16_224", # fails to run in timm 0.8.22, cudagraphs
"mobilevit_s",
"pit_b_224",
"twins_pcpvt_base",
"visformer_small",
"vit_base_patch16_224",
"xcit_large_24_p8_224",
]
# Skips for dynamic=True
CI_SKIP[CI("aot_eager", training=False, dynamic=True)] = [
*CI_SKIP[CI("aot_eager", training=False)],
"vision_maskrcnn", # accuracy failure on boxes, after https://github.com/pytorch/pytorch/issues/101093
# https://github.com/pytorch/pytorch/issues/103760
"hf_T5_generate",
"hf_Bert", # Error: RelaxedUnspecConstraint(L['input_ids'].size()[0]) - inferred constant (4)
]
CI_SKIP[CI("aot_eager", training=True, dynamic=True)] = [
*CI_SKIP[CI("aot_eager", training=True)],
*CI_SKIP[CI("aot_eager", training=False, dynamic=True)],
"llama", # AssertionError: cannot compute free_symbols of True
"torchrec_dlrm", # RuntimeError: mat1 and mat2 must have the same dtype, but got Float and BFloat16
]
CI_SKIP[CI("inductor", training=False, dynamic=True)] = [
*CI_SKIP[CI("aot_eager", training=False, dynamic=True)],
*CI_SKIP[CI("inductor", training=False)],
"nanogpt_generate", # Assertion `index out of bounds: 0 <= tmp0 < 64` failed.
]
CI_SKIP[CI("inductor", training=True, dynamic=True)] = [
# NB: Intentionally omitting for symmetry with dynamic=False
# *CI_SKIP[CI("aot_eager", training=True, dynamic=True)],
*CI_SKIP[CI("inductor", training=False, dynamic=True)],
*CI_SKIP[CI("inductor", training=True)],
"levit_128", # Accuracy fails on A10G, passes on A100
"sebotnet33ts_256", # Flaky accuracy failed
]
CI_SKIP[CI("inductor", training=False, dynamic=True, device="cpu")] = [
*CI_SKIP[CI("inductor", training=False, device="cpu")],
"pyhpc_isoneutral_mixing",
"dpn107",
]
CI_SKIP_OPTIMIZER = {
# TIMM
"convmixer_768_32", # accuracy
"hrnet_w18", # Stack issue in fx
# HF
"pnasnet5large", # Stack issue in fx
"MobileBertForMaskedLM", # Stack issue in fx
"MobileBertForQuestionAnswering", # Stack issue in fx
"PegasusForConditionalGeneration", # OOM
}
CI_SKIP_DYNAMIC_BATCH_ONLY = {
"sam",
# See https://github.com/mindee/doctr/blob/f2114758d529ed8d3d0030581638f0520b6b98d8/doctr/models/detection/core.py#L89
# It iterates over the batch, which is dynamic, and dynamo chokes
# We should be able to graphbreak there.
"doctr_det_predictor",
"dlrm",
}
def model_specified_by_path(path_and_class_str):
return ":" in path_and_class_str
def load_model_from_path(path_and_class_str):
configs = {}
for kvstr in path_and_class_str.split(","):
k, v = kvstr.split(":")
configs[k] = v
for name in ["path", "class"]:
if name not in configs:
raise RuntimeError(
"Invalid --only arguments. Check help message for the correct format"
)
path = configs["path"]
class_name = configs["class"]
if path[:1] != "/":
raise RuntimeError(
"Use absolute path since dynamo may change the current working directory which makes using relative path tricky"
)
spec = importlib.util.spec_from_file_location("module_name", path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
model_class = getattr(module, class_name)
assert issubclass(model_class, torch.nn.Module)
model = model_class()
assert hasattr(model, "get_example_inputs")
inputs = model.get_example_inputs()
return model, inputs
def output_csv(filename, headers, row):
if os.path.exists(filename):
with open(filename) as fd:
lines = list(csv.reader(fd)) or [[]]
if headers and len(headers) > len(lines[0]):
# if prior results failed the header might not be filled in yet
lines[0] = headers
else:
headers = lines[0]
else:
lines = [headers]
lines.append([(f"{x:.6f}" if isinstance(x, float) else x) for x in row])
with open(filename, "w") as fd:
writer = csv.writer(fd, lineterminator="\n")
for line in lines:
writer.writerow(list(line) + ["0"] * (len(headers) - len(line)))
def nothing(f):
return f
@functools.lru_cache(None)
def patch_torch_manual_seed():
"""Make torch manual seed deterministic. Helps with accuracy testing."""
def deterministic_torch_manual_seed(*args, **kwargs):
from torch._C import default_generator
seed = 1337
import torch.cuda
if not torch.cuda._is_in_bad_fork():
torch.cuda.manual_seed_all(seed)
return default_generator.manual_seed(seed)
torch.manual_seed = deterministic_torch_manual_seed
def synchronize():
pass
def summarize_graph_break(filename):
"""
Sorts and de-dupes the graphs breaks on the reason string. Note that this
function is just a best effort to reduce the logging information. We could
miss some graph breaks because of de-duping. We can further refine this
function as need arises.
"""
log_file = f"{filename.rstrip('.csv')}_graph_breaks.csv"
if os.path.exists(log_file):
df = pd.read_csv(log_file)
df = df.sort_values("reason").drop_duplicates(subset="reason")
# Specialize for multi tensor sgd as reason is not identical
multi_tensor_sgd_row = df.loc[df["reason"].str.contains("_multi_tensor_sgd")]
if len(multi_tensor_sgd_row):
df = df[
~df["reason"].str.contains("_multi_tensor_sgd")
] # Drop all sgd rows
df = pd.concat(
[df, pd.DataFrame([multi_tensor_sgd_row.iloc[0]])], axis=0
) # Add back a single row
df.to_csv(f"{log_file.rstrip('.csv')}_deduped.csv", index=False)
def print_summary(filename, print_dataframe=False):
if not (filename and os.path.exists(filename)):
return
data = pd.read_csv(filename)
if "tag" in data.columns:
for tag in data.tag.unique():
if tag == "0.0000":
continue # This happens for failed runs
print(f"\nSummary for tag={tag}:")
print_summary_table(data[data.tag == tag], print_dataframe=print_dataframe)
else:
print_summary_table(data, print_dataframe=print_dataframe)
summarize_graph_break(filename)
def print_summary_table(data, print_dataframe=False):
if print_dataframe:
pd.options.display.max_rows = 1000
pd.options.display.max_columns = 1000
pd.options.display.width = 2000
print(data)
width = max(map(len, data.columns))
for col in data.columns:
try:
if col in ("dev", "name", "batch_size", "tag"):
continue
elif col in ("pct_ops", "pct_time"):
print(col.ljust(width), f"{data[col].mean():.3%}")
elif col in ("graphs", "graph_calls", "captured_ops", "total_ops"):
print(col.ljust(width), f"{data[col].mean():.3f}")
elif col in ("compilation_latency"):
print(col.ljust(width), f"mean={data[col].mean():.3f} seconds")
elif col in ("compression_ratio"):
print(col.ljust(width), f"mean={data[col].mean():.3f}x")
elif col in ("accuracy"):
pass_rate = (data[col] == "pass").mean()
print(col.ljust(width), f"pass_rate={100*pass_rate:.2f}%")
else:
cdata = data[col]
print(
col.ljust(width),
f"gmean={gmean(cdata):.2f}x mean={cdata.mean():.3f}x",
)
except Exception as e:
pass
def tensor_is_on_xla(tensors):
def visit(x: torch.Tensor):
nonlocal result
if x.device.type == "xla":
result = True
result = False
tree_map_only(torch.Tensor, visit, tensors)
return result
def timed(
model,
model_iter_fn,
example_inputs,
times=1,
return_result=False,
collect_outputs=False,
):
use_xla = tensor_is_on_xla(example_inputs)
synchronize()
if use_xla:
xm.mark_step()
xm.wait_device_ops()
time_total = 0
# Dont collect outputs to correctly measure timing
for _ in range(times):
# Put this call inside the loop to reset the seed for each iteration.
# Don't include reset_rng_state() to correctly measure timing
reset_rng_state(use_xla)
t_iter_begin = time.perf_counter()
result = model_iter_fn(model, example_inputs, collect_outputs=collect_outputs)
# instead of calling sync on result_list, we should call mark_step.
# In training case, result_list may be empty, but we want to
# send all the pending graphs for compilation.
if use_xla:
# For the model running on regular torchxla (baseline), we need the
# mark step to send the accumulated graph for compilation.
#
# For the model running with dynamo/torchxla bridge, in training case,
# we need the mark step to send the optimizer graph out for
# compilation.
xm.mark_step()
t_iter_end = time.perf_counter()
time_total += t_iter_end - t_iter_begin
t_0 = time.perf_counter()
if use_xla:
xm.wait_device_ops()
synchronize()
t_1 = time.perf_counter()
time_total += t_1 - t_0
return (time_total, result) if return_result else time_total
def _normalize_bench_inputs(example_inputs) -> Tuple[Tuple[Any], Mapping[str, Any]]:
# NOTE(bowbao): For huggingface benchmark, example_inputs are formatted as dictionary,
# and consumed like `model(**example_inputs)`.
# For other benchmarks, example_inputs are formatted as tuple and consumed
# like `model(*example_inputs)`.
if isinstance(example_inputs, dict):
return (), example_inputs
else:
return tuple(example_inputs), {}
def _register_dataclass_output_as_pytree(example_outputs) -> None:
# NOTE(angelayi): For huggingface benchmark, some example outputs are
# formatted as a dataclass which pytree cannot consume. So we want
# to register the pytree implementation here
example_outputs_flat, _ = pytree.tree_flatten(example_outputs)
output_dataclass_types = [
type(out) for out in example_outputs_flat if dataclasses.is_dataclass(type(out))
]
for output_type in output_dataclass_types:
from torch._export.utils import register_dataclass_as_pytree_node
register_dataclass_as_pytree_node(output_type)
class Stats:
totals = collections.defaultdict(collections.Counter)
@classmethod
def reset_counters(cls):
for k, v in torch._dynamo.utils.counters.items():
cls.totals[k].update(v)
ok = torch._dynamo.utils.counters["frames"]["ok"]
total = torch._dynamo.utils.counters["frames"]["total"]
torch._dynamo.utils.counters.clear()
return ok, total
@classmethod
def print_summary(cls):
for k, v in sorted(cls.totals.items()):
lines = "\n ".join(map(str, v.most_common(50)))
print(f"STATS {k}\n {lines}")
@classmethod
def aot_summary(cls):
return [cls.totals["aot_autograd"]["total"], cls.totals["aot_autograd"]["ok"]]
def coverage_experiment(args, model_iter_fn, model, example_inputs):
"""
Test operator/model coverage of TorchDynamo and record statistics
taken from a profiler. This target is mainly intended to check
correctness.
Writes to ./coverage.csv
"""
profiler = Profiler()
frozen_model_iter_fn = torch._dynamo.run(model_iter_fn)
with profiler.prof:
frozen_model_iter_fn(model, example_inputs)
coverage_result = profiler.results()
output_csv(
output_filename,
(
"dev",
"name",
"batch_size",
"graphs",
"graph_calls",
"captured_ops",
"total_ops",
"pct_ops",
"pct_time",
),
[
current_device,
current_name,
current_batch_size,
]
+ coverage_result.tocsv(),
)
return coverage_result
def speedup_experiment_fx2trt(args, model_iter_fn, model, example_inputs):
"""
Measure speedups over eager using the trt inference backend. TRT backend is based fx graph
generated by torch._dynamo.
Writes to ./speedups_fx2trt.csv
"""
return speedup_experiment(args, model_iter_fn, model, example_inputs)
def recompile_profiler_experiment(args, model_iter_fn, model, example_inputs):
with torch._dynamo.utils.CompileProfiler() as prof:
opt_model_iter_fn = torch._dynamo.optimize(prof, nopython=args.nopython)(
model_iter_fn
)
opt_model_iter_fn(model, example_inputs)
output_csv(
output_filename, ["model", "profiler report"], [current_name, prof.report()]
)
met = prof.get_metrics()
guard_failures = len(met["guard_failures"])
return [guard_failures]
def randomize_input(inputs):
if isinstance(inputs, (list, tuple)):
return type(inputs)([randomize_input(x) for x in inputs])
elif isinstance(inputs, torch.Tensor):
if inputs.dtype in (torch.float32, torch.float64):
torch._dynamo.utils.counters["randomize_input"]["times"] += 1
return torch.randn_like(inputs)
elif inputs.dtype == torch.int64:
# Note: we can not simply tune integer tensors as follows
# `return torch.randint_like(inputs, high=inputs.max().item())`
# This may break some invariants between tensors.
# E.g. in embedding lookup case, one tensor is the length
# and another is an indices tensor.
return inputs
else:
raise RuntimeError(
f"randomize_input need support tensor of type {inputs.dtype}"
)
else:
raise RuntimeError(
f"randomize_input can not handle input of type {type(inputs)}"
)
def maybe_mark_step(args):
if args.trace_on_xla:
xm.mark_step()
def speedup_experiment(args, model_iter_fn, model, example_inputs, **kwargs):
"""
Measure speedups over eager.
Writes to ./speedups.csv
"""
# if args.dynamic_shapes:
# return speedup_experiment_ds(args, model_iter_fn, model, example_inputs)
timings = np.zeros((args.repeat, 2), np.float64)
# if we randomize the input, we should also check the result is correct
should_check_result = should_randomize_input = args.randomize_input
import contextlib
from torch._inductor.utils import maybe_profile
@contextlib.contextmanager
def maybe_mark_profile(*args, **kwargs):
prof: torch.profiler.profile = kwargs.pop("p", None)
mark = kwargs.pop("mark", None)
if prof:
with torch.profiler.record_function(mark):
yield
else:
yield
times = args.iterations_per_run
# Use higher tolerance for XLA since XLA cause numerical unstability when
# graph size changes
tolerance = args.xla_tolerance if args.trace_on_xla else 1e-4
torch._dynamo.config.repro_tolerance = tolerance
with maybe_profile(args.export_profiler_trace) as p:
if args.export_aot_inductor:
frozen_model_iter_fn = export_aot_inductor(model_iter_fn)
else:
frozen_model_iter_fn = torch._dynamo.run(model_iter_fn)
for rep in trange(args.repeat, desc="running benchmark"):
inputs = (
randomize_input(copy.deepcopy(example_inputs))
if should_randomize_input
else example_inputs
)
# need call mark_step to perform the computation
# on randomize_input. Otherwise the first call using the
# inputs will incur high penalty then the next one.
maybe_mark_step(args)
# interleave the runs to handle frequency scaling and load changes
with maybe_mark_profile(p=p, mark="expected"):
timings[rep, 0], expected_output = timed(
model,
model_iter_fn,
inputs,
return_result=True,
times=times,
collect_outputs=args.collect_outputs,
)
# call mark_step between the 2 calls to make the comparison fair.
maybe_mark_step(args)
with maybe_mark_profile(p=p, mark="actual"):
timings[rep, 1], actual_output = timed(
model,
frozen_model_iter_fn,
inputs,
return_result=True,
times=times,
collect_outputs=args.collect_outputs,
)
if should_check_result:
is_correct = is_correct and same(
expected_output, actual_output, tol=tolerance
)
if args.export_profiler_trace:
name = args.profiler_trace_name + "_" + model.name + ".json"
name = os.path.join(torch._dynamo.config.base_dir, name)
p.export_chrome_trace(name)
median = np.median(timings, axis=0)
speedup = median[0] / median[1]
if args.dump_raw_metrics:
np.save(
f"{output_filename[:-4]}-raw_timings-{current_name}-{current_device}.npy",
timings,
)
first_headers = ["dev", "name", "batch_size"]
first_fields = [current_device, current_name, current_batch_size]
if "tag" in kwargs:
first_headers.append("tag")
first_fields.append(kwargs["tag"])
headers = first_headers + ["speedup", "abs_latency"]
row = first_fields + [float(speedup), median[1] * 1000]
msg = f"{speedup:.3f}x"
if args.baseline:
headers.extend(
[
"baseline",
"speedup_vs_baseline",
]
)
df = pd.read_csv(args.baseline)
try:
baseline_speedup = df[df["name"] == current_name]["speedup"].item()
row.extend([baseline_speedup, speedup / baseline_speedup])
msg = f"{baseline_speedup:.3f}x -> {speedup:.3f}x [{speedup / baseline_speedup:.3f}x]"
except (KeyError, ZeroDivisionError):
row.extend(
[
0.0,
0.0,
]
)
if "compilation_latency" in kwargs:
headers += [
"compilation_latency",
"compression_ratio",
"eager_peak_mem",
"dynamo_peak_mem",
]
row.append(kwargs["compilation_latency"])
row.append(kwargs["compression_ratio"])
row.append(kwargs["eager_peak_mem"])
row.append(kwargs["dynamo_peak_mem"])
if "dynamo_stats" in kwargs:
for k, v in kwargs["dynamo_stats"].items():
headers.append(k)
row.append(v)
output_csv(
output_filename,
headers,
row,
)
headers, data = torch._dynamo.utils.compile_times(repr="csv", aggregate=True)
assert (
output_filename.find(".csv") > 0
), f"expected output_filename to be a .csv, but got {output_filename}"
output_csv(
output_filename[:-4] + "_compilation_metrics.csv",
first_headers + headers,
first_fields + data,
)
return msg
def speedup_experiment_ds(args, model_iter_fn, model, example_inputs):
"""
Run dynamic shapes benchmarks.
Requires dynamic shape compatible models, which provide a list of example inputs.
Warms up using the first input example and then iterates the inputs,
measuring (and expecting minimal) variance between the runtime for different examples.
"""
timings = np.zeros((args.repeat, len(example_inputs), 2), np.float64)
if args.repeat > 5:
print(
f"\ndynamic shapes experiments are slow, consider setting --repeat less than {args.repeat}\n"
)
nwarmup = 4
for rep in range(args.repeat):
# Start each rep fresh, e.g. only warmup on example 0
torch._dynamo.reset()
optimized_model_iter_fn = optimize_ctx(model_iter_fn)
for _ in range(nwarmup):
optimized_model_iter_fn(model, example_inputs[0])
for input_idx, inputs in enumerate(example_inputs):
# interleave the runs to handle frequency scaling and load changes
timings[rep, input_idx, 0] = timed(
model, model_iter_fn, inputs, return_result=False
)
# different from regular speedup_experiment, we _DO_ want to allow recompilation
timings[rep, input_idx, 1] = timed(
model, optimized_model_iter_fn, inputs, return_result=False
)
medians = np.median(timings, axis=0)
speedups = list(medians[:, 0] / medians[:, 1])
speedups_mean = np.mean(speedups)
speedups_median = np.median(speedups)
speedups_var = np.var(speedups)
# TODO this x[0] is not going to work in general but bert only has 1 input
shapes = [x[0].shape for x in example_inputs]
shape_keys = sorted(set(shapes))
shape_speedups = {
shape: [
it[1] for it in filter(lambda it: it[0] == shape, zip(shapes, speedups))
]
for shape in shape_keys
}
output_str = (
f"mean: {speedups_mean:.3f}, median: {speedups_median:.3f}, var: {speedups_var:.3f}"
+ "\nSpeedups by shape: "
+ "\n".join(
[
f"{shape}: "
+ ", ".join([f"{speedup: .3g}" for speedup in shape_speedups[shape]])
for shape in shape_keys
]
)
)
output_csv(
output_filename,
("dev", "name", "batch_size", "speedup mean", "speedup median", "speedup var"),
[
current_device,
current_name,
current_batch_size,
speedups_mean,
speedups_median,
speedups_var,
],
)
return output_str
def speedup_experiment_onnx(
onnx_model_cls: Type[OnnxModelFromTorchScript],
args,
model_iter_fn,
model,
example_inputs,
**kwargs,
):
"""
Measure speedups over eager.
This function is responsible for the following:
1. Creation of OnnxModel, which handles export, ort initialization.
2. Creating iobinding with OnnxModel if device is CUDA, which is essential for perf measurement.
3. Running ORT with OnnxModel.
Writes to ./{output_filename}, which should be
`pathlib.Path(self.output_dir) / f"{self.compiler}_{suite}_{self.dtype}_{self.mode}_{self.device}_{self.testing}.csv".
TODO(bowbao): Record export time and export peak memory usage.
"""
timings = np.zeros((args.repeat, 2), np.float64)
is_correct = True
should_randomize_input = args.randomize_input
times = args.iterations_per_run
onnx_model = onnx_model_cls(
args.output_directory or ".", model, copy.deepcopy(example_inputs)
)
def create_onnx_input_binded_fn(
onnx_model: OnnxModelFromTorchScript, pt_inputs, example_outputs
):
# Goal is to move the iobinding creation outside of the timer function.
iobinding, outputs = onnx_model.create_iobinding(pt_inputs, example_outputs)
def onnxrt_model_iter_fn(model, inputs, collect_outputs=True):
onnx_model.run_with_iobinding(iobinding, outputs)
if collect_outputs:
return outputs
return onnxrt_model_iter_fn
def create_onnx_fn(onnx_model: OnnxModelFromTorchScript, pt_inputs):
def onnxrt_model_iter_fn(model, inputs, collect_outputs=True):
return onnx_model.run(pt_inputs)
return onnxrt_model_iter_fn
for rep in range(args.repeat):
inputs = (
randomize_input(copy.deepcopy(example_inputs))
if should_randomize_input
else example_inputs
)
timings[rep, 0], expected_output = timed(
model,
model_iter_fn,
inputs,
return_result=True,
times=times,
collect_outputs=args.collect_outputs,
)
if current_device == "cpu":
onnxrt_model_iter_fn = create_onnx_fn(onnx_model, inputs)
else:
onnxrt_model_iter_fn = create_onnx_input_binded_fn(
onnx_model, inputs, expected_output
)
timings[rep, 1], actual_output = timed(
model,
onnxrt_model_iter_fn,
inputs,
return_result=True,
times=times,
collect_outputs=args.collect_outputs,
)
pvalue = ttest_ind(timings[:, 0], timings[:, 1]).pvalue
median = np.median(timings, axis=0)
speedup = median[0] / median[1]
if args.dump_raw_metrics:
np.save(
f"{output_filename[:-4]}-raw_timings-{current_name}-{current_device}.npy",
timings,
)
headers = ["dev", "name", "batch_size", "speedup", "abs_latency"]
row = [
current_device,
current_name,
current_batch_size,
float(speedup),
median[1] * 1000,
]
if "compilation_latency" in kwargs:
headers = headers + ["compilation_latency", "compression_ratio"]
row.append(kwargs["compilation_latency"])
row.append(kwargs["compression_ratio"])
output_csv(
output_filename,
headers,
row,
)
headers, data = torch._dynamo.utils.compile_times(repr="csv", aggregate=True)
assert (
output_filename.find(".csv") > 0
), f"expected output_filename to be a .csv, but got {output_filename}"
output_csv(
output_filename[:-4] + "_compilation_metrics.csv",
["dev", "name", "batch_size"] + headers,
[current_device, current_name, current_batch_size] + data,
)
return format_speedup(speedup, pvalue, is_correct=is_correct)
def overhead_experiment(*args, model_iter_fn):
"""
Measure overheads of TorchDynamo by running with no backend (only
eager+FX), and reporting speedup/slowdown over eager.
Writes to ./overheads.csv
"""
return speedup_experiment(*args, model_iter_fn)
def print_fx(gm, example_inputs):
print(gm.graph)
return gm
def print_aten_ops(gm, example_inputs):
from functorch.compile import aot_module
def trace_printer(gm, _):
print(gm.graph)
return gm
return aot_module(gm, fw_compiler=trace_printer, bw_compiler=trace_printer)
def baselines(models, model_iter_fn, example_inputs, args):
"""
Common measurement code across all baseline experiments.
"""
models = list(models)
for idx, (name, model) in enumerate(models):
if idx == 0:
result0 = model_iter_fn(model, example_inputs)
elif model is not None:
try:
result = model_iter_fn(model, example_inputs)
if same(result0, result):
continue
print(name, "is INCORRECT")
except Exception:
log.exception("error checking %s", name)
models[idx] = (name, None)
timings = np.zeros((args.repeat, len(models)), np.float64)
timings.fill(1.0e10)
for rep in range(args.repeat):
for idx, (name, model) in enumerate(models):
if model is not None:
try:
timings[rep, idx] = timed(model, model_iter_fn, example_inputs)
except Exception:
pass
pvalue = [
ttest_ind(timings[:, 0], timings[:, i]).pvalue
for i in range(1, timings.shape[1])
]
median = np.median(timings, axis=0)
speedup = median[0] / median[1:]
for idx, (name, model) in enumerate(models[1:]):
if model is None:
speedup[idx] = 0.0
result = " ".join(
[
format_speedup(s, p, m is not None)
for s, p, m in zip(speedup, pvalue, [m for n, m in models[1:]])
]
)
output_csv(
output_filename,
("dev", "name", "batch_size") + tuple(n for n, m in models[1:]),
[current_device, current_name, current_batch_size]
+ [f"{x:.4f}" for x in speedup],
)
return result
def xla(args, model_iter_fn, model, example_inputs):
xla_dev = xm.xla_device(devkind=current_device)
model_xla = copy.deepcopy(model).to("cpu").to(device=xla_dev)
example_inputs_xla = tree_map_only(
torch.Tensor, lambda x: x.to("cpu").to(device=xla_dev), example_inputs
)
for _ in range(3): # warmup
timed(model, model_iter_fn, example_inputs)
timed(model_xla, model_iter_fn, example_inputs_xla)
timings = np.zeros((args.repeat, 2), np.float64)
timings.fill(1.0e10)
for rep in range(args.repeat):
timings[rep, 0] = timed(model, model_iter_fn, example_inputs)
timings[rep, 1] = timed(model_xla, model_iter_fn, example_inputs_xla)
pvalue = ttest_ind(timings[:, 0], timings[:, 1]).pvalue
time_baseline, time_xla = np.median(timings, axis=0)
speedup = time_baseline / time_xla
output_csv(
output_filename,
("dev", "name", "batch_size", "speedup", "time_baseline", "time_xla"),
[
current_device,
current_name,
current_batch_size,
speedup,
time_baseline,
time_xla,
],
)
return format_speedup(speedup, pvalue)
def try_script(model, example_inputs):
try:
return torch.jit.script(model)
except Exception:
return None
class AOTInductorModelCache:
cache = dict()
@classmethod
def load(cls, model, example_inputs, eager_forward):
key = id(model)
if key not in cls.cache:
# Register the output dataclass to pytree
example_outputs = eager_forward(
copy.deepcopy(model), clone_inputs(example_inputs)
)
_register_dataclass_output_as_pytree(example_outputs)
example_args, example_kwargs = _normalize_bench_inputs(example_inputs)
example_inputs = torch._export.combine_args_kwargs(
example_args, example_kwargs
)
so_path, exported = torch._export.aot_compile(
model, example_args, example_kwargs
)
output_node = list(exported.graph.nodes)[-1]
output_tensors = [
torch.empty(
node.meta["val"].size(),
dtype=node.meta["val"].dtype,
layout=node.meta["val"].layout,
device=node.meta["val"].device,
)
for node in output_node.args[0]
]
# Use a utility function for easier benchmarking
source = """
#include <torch/csrc/inductor/aot_inductor_model_container.h>
torch::aot_inductor::AOTInductorModelContainer model(1);
void run(
const std::vector<at::Tensor>& input_tensors,
std::vector<at::Tensor>& output_tensors) {
model.run(input_tensors, output_tensors, at::cuda::getCurrentCUDAStream());
}
"""
module = torch.utils.cpp_extension.load_inline(
name="aot_inductor",
cpp_sources=[source],
functions=["run"],
extra_ldflags=[so_path],
with_cuda=True,
)
value = {
"module": module,
"exported": exported,
"output_tensors": output_tensors,
"output_spec": exported.call_spec.out_spec,
}
cls.cache[key] = value
return (
cls.cache[key]["module"],
cls.cache[key]["exported"],
cls.cache[key]["output_tensors"],
cls.cache[key]["output_spec"],
)
def export_aot_inductor(forward: Callable):
eager_forward = forward
def opt_aot_inductor(model, example_inputs, collect_outputs=False):
module, exported, output_tensors, output_spec = AOTInductorModelCache.load(
model, example_inputs, eager_forward
)
param_buffer_values = list(exported.state_dict.values())
example_args, example_kwargs = _normalize_bench_inputs(example_inputs)
example_inputs = torch._export.combine_args_kwargs(example_args, example_kwargs)
flat_example_inputs = fx_pytree.tree_flatten_spec(
example_inputs, exported.call_spec.in_spec
)
all_args = (*param_buffer_values, *flat_example_inputs)
module.run(all_args, output_tensors)
return pytree.tree_unflatten(output_tensors, output_spec)
return opt_aot_inductor
def download_retry_decorator(download_fn):
"""
Decorator function for applying retry logic to a download function.
The wrapped function will be called up to 5 times and raises an exception if the function fails each time.
After each unsuccessful attempt, there is a delay before the next attempt, which is increased linearly with the number of tries.
Usage:
@download_retry_decorator
def download_function(model_name: str):
# download logic goes here
"""
@functools.wraps(download_fn)
def wrapper(self, *args, **kwargs) -> Any:
tries = 0
total_allowed_tries = MAX_DOWNLOAD_ATTEMPTS
while tries <= total_allowed_tries:
try:
model = download_fn(self, *args, **kwargs)
return model
except Exception as e:
tries += 1
if tries <= total_allowed_tries:
wait = tries * 30
print(
f"Failed to load model: {e}. Trying again ({tries}/{total_allowed_tries}) after {wait}s"
)
time.sleep(wait)
else:
raise RuntimeError(
f"Failed to load model '{args}' with following error(s): {str(e)}."
)
return wrapper
class OnnxModelFromTorchScript:
"""TorchScript based onnx export. `torch.onnx.export`
TODO(bowbao):
* large model export failed.
Onnx Model is larger than 2GB, but exporter makes decision based pt model size, which is
smaller than 2GB.
* OOM on slightly larger model.
Both pt model and ort inference session are on gpu. Attempt has been made to move ORT to
cuda:1, however ORT perf drop significantly.
For now running everything with batch_size 1 set in launch script.
"""
TORCH_TO_NUMPY_DTYPE = {
torch.float16: np.float16,
torch.float32: np.float32,
torch.float64: np.float64,
torch.uint8: np.uint8,
torch.int8: np.int8,
torch.int16: np.int16,
torch.int32: np.int32,
torch.int64: np.longlong,
torch.bool: np.bool_,
}
def __init__(self, output_directory, model, example_inputs):
self.model_path = self._generate_onnx_model_path(output_directory)
self._export(
model,
example_inputs,
self.model_path,
opset_version=17,
do_constant_folding=False,
verbose=False,
)
self.onnx_session = self._init_ort_session(self.model_path)
def _generate_onnx_model_path(
self, output_directory: str, onnx_model_folder_name: str = "bench_onnx_models"
) -> str:
# Hack to get model name.
from torch._functorch import aot_autograd
model_name = aot_autograd.model_name
model_path = pathlib.Path(output_directory, onnx_model_folder_name, model_name)
if model_path.exists() and model_path.is_dir():
shutil.rmtree(model_path)
model_path.mkdir(parents=True, exist_ok=True)
return str(model_path / "model.onnx")
def _export(self, model, example_inputs, output_path: str, /, **kwargs) -> None:
# Hack for huggingface models (kwargs only).
if isinstance(example_inputs, dict):
class WrapperModel(torch.nn.Module):
def __init__(self, model, keys):
super().__init__()
self.model = model
self.keys = keys
def forward(self, *args):
return self.model(**dict(zip(self.keys, args)))
model = WrapperModel(model, list(example_inputs.keys()))
torch.onnx.export(
model,
self.format_pt_inputs(example_inputs),
output_path,
**kwargs,
)
def _init_ort_session(self, model_path: str):
import onnxruntime
if current_device == "cpu":
ort_providers = ["CPUExecutionProvider"]
else:
# NOTE(bowbao): Reduce OOM by running ORT on another gpu.
# TODO(bowbao): This works to avoid OOM, but performance is surprisingly very bad.
# cuda_provider_options = {
# "device_id": 1 if torch.cuda.device_count() > 1 else 0,
# }
# ort_providers = [("CUDAExecutionProvider", cuda_provider_options)]
ort_providers = ["CUDAExecutionProvider"]
ort_session = onnxruntime.InferenceSession(
self.model_path,
providers=ort_providers,
)
return ort_session
def format_pt_inputs(self, pt_inputs):
# NOTE(bowbao): For huggingface benchmark, pt_inputs are formatted as dictionary,
# and consumed like `model(**pt_inputs)`.
# For other benchmarks, pt_inputs are formatted as tuple and consumed
# like `model(*pt_inputs)`.
if isinstance(pt_inputs, dict):
pt_inputs = list(pt_inputs.values())
if isinstance(pt_inputs, torch.Tensor):
pt_inputs = (pt_inputs,)
return tuple(arg.contiguous() for arg in pt_inputs)
def format_pt_outputs(self, pt_outputs):
if isinstance(pt_outputs, torch.Tensor):
pt_outputs = (pt_outputs,)
pt_outputs, _ = pytree.tree_flatten(pt_outputs)
# Hack for huggingface model outputs
try:
from transformers import modeling_outputs
except ImportError:
pass
else:
def _to_tuple(x):
if isinstance(x, modeling_outputs.ModelOutput):
return x.to_tuple()
return x
pt_outputs = pytree.tree_map(_to_tuple, pt_outputs)
pt_outputs, _ = pytree.tree_flatten(pt_outputs)
return pt_outputs
def create_outputs(self, *example_outputs):
return tuple(torch.empty_like(x) for x in example_outputs)
def create_iobinding(self, pt_inputs, example_outputs):
pt_inputs = self.format_pt_inputs(pt_inputs)
example_outputs = self.format_pt_outputs(example_outputs)
iobinding = self.onnx_session.io_binding()
args = [arg.contiguous() for arg in pt_inputs]
for ort_input, arg in zip(self.onnx_session.get_inputs(), args):
# NOTE: Small hack to reduce OOM issue by running ORT on another device.
# Disabled due to ORT perf regression.
# if torch.cuda.device_count() > 1:
# arg = arg.detach().to("cuda:1")
device = arg.device
iobinding.bind_input(
ort_input.name,
device.type,
device.index or 0,
self.TORCH_TO_NUMPY_DTYPE[arg.dtype],
arg.size(),
arg.data_ptr(),
)
outputs = self.create_outputs(*example_outputs)
for ort_output, output in zip(self.onnx_session.get_outputs(), outputs):
# if torch.cuda.device_count() > 1:
# output = output.detach().to("cuda:1")
device = output.device
iobinding.bind_output(
ort_output.name,
device.type,
device.index or 0,
self.TORCH_TO_NUMPY_DTYPE[output.dtype],
output.size(),
output.data_ptr(),
)
return iobinding, outputs
def run_with_iobinding(self, iobinding, outputs):
# 'outputs' are torch empty tensors binded to 'iobinding'.
self.onnx_session.run_with_iobinding(iobinding)
return outputs
def run(self, pt_inputs):
# NOTE: For CUDA performance testing, use `run_with_iobinding` to exclude memory
# copying overhead for inputs/outputs between cpu and gpu.
# Otherwise perf number is inaccurate.
pt_inputs = self.format_pt_inputs(pt_inputs)
onnx_inputs = {
ort_input.name: pt_input.cpu().numpy()
for ort_input, pt_input in zip(self.onnx_session.get_inputs(), pt_inputs)
}
ort_outputs = self.onnx_session.run(None, onnx_inputs)
pt_outputs = [
torch.from_numpy(ort_output).to(current_device)
for ort_output in ort_outputs
]
if len(pt_outputs) == 1:
return pt_outputs[0]
return pt_outputs
class OnnxModelFromDynamo(OnnxModelFromTorchScript):
"""Dynamo and Fx based export. `torch.onnx.dynamo_export`."""
def __init__(self, output_directory, model, example_inputs):
self.model_path = self._generate_onnx_model_path(
output_directory, "bench_dynamo_onnx_model"
)
self._export_output = self._export(model, example_inputs, self.model_path)
self.onnx_session = self._init_ort_session(self.model_path)
def _export(
self, model, example_inputs, output_path: str
) -> torch.onnx.ExportOutput:
example_args, example_kwargs = _normalize_bench_inputs(example_inputs)
options = torch.onnx.ExportOptions()
export_output = torch.onnx.dynamo_export(
model, *example_args, **example_kwargs, export_options=options
)
export_output.save(output_path)
return export_output
def format_pt_inputs(self, pt_inputs):
pt_args, pt_kwargs = _normalize_bench_inputs(pt_inputs)
return self._export_output.adapt_torch_inputs_to_onnx(*pt_args, **pt_kwargs)
def format_pt_outputs(self, pt_outputs):
return self._export_output.adapt_torch_outputs_to_onnx(pt_outputs)
def optimize_onnx_ctx(
output_directory: str,
onnx_model_cls: Type[OnnxModelFromTorchScript],
run_n_iterations: Callable,
) -> Callable:
# NOTE(bowbao): This function creates and returns the onnx version of 'run_n_iterations',
# which does the following:
# 1. Export and cache model.
# 2. Create iobinding for ORT.
# 3. Run ORT for n iterations.
onnx_model: Optional[OnnxModelFromTorchScript] = None
def run_n_iterations_onnx(model, inputs, n=2):
from _onnx import reporter
from torch.onnx._internal import exporter
from torch.onnx._internal.fx import diagnostics
# NOTE(bowbao): Capture all export & ort errors and diagnostics.
# Serialize to csv, to be parsed and summarized later by '._onnx/reporter.py'.
# TODO: Accuracy mismatch is not reported here in csv.
assert (
output_filename.find(".csv") > 0
), f"expected output_filename to be a .csv, but got {output_filename}"
output_error_filename = output_filename[:-4] + "_export_error.csv"
parser = reporter.ExportErrorParser(
current_device, current_name, current_batch_size
)
try:
nonlocal onnx_model
if onnx_model is None:
onnx_model = onnx_model_cls(
output_directory, model, copy.deepcopy(inputs)
)
for _ in range(n - 1):
onnx_model.run(inputs)
return onnx_model.run(inputs)
except exporter.OnnxExporterError as e:
# `torch.onnx.dynamo_export` raises error that encloses diagnostics.
diagnostic_context = e.export_output.diagnostic_context
for parsed_error in parser.parse_diagnostic_context(diagnostic_context):
output_csv(
output_error_filename, parsed_error.headers, parsed_error.row
)
# Check also the raw exception that caused export failure.
# Skip if it is already analyzed by diagnostics.
cause_of_exception = e.__cause__
if not isinstance(
cause_of_exception, diagnostics.RuntimeErrorWithDiagnostic
):
parsed_error = parser.parse_exception(cause_of_exception)
output_csv(
output_error_filename, parsed_error.headers, parsed_error.row
)
raise
except Exception as e:
# `torch.onnx.export` errors.
# ORT errors.
parsed_error = parser.parse_exception(e)
output_csv(output_error_filename, parsed_error.headers, parsed_error.row)
raise
return run_n_iterations_onnx
def read_batch_size_from_file(args, filename, model_name):
batch_size = None
if os.path.exists("benchmarks"):
filename = os.path.join("benchmarks", filename)
assert os.path.exists(filename), filename
with open(filename) as f:
lines = f.readlines()
lines = [i.split(",") for i in lines if len(i.strip()) > 0]
for val in lines:
cur_name, b = val
if model_name == cur_name:
batch_size = int(b)
if batch_size is None:
log.warning("Could not find batch size for %s", model_name)
elif batch_size == -1:
raise RuntimeError(
f"Batch size is unset for {model_name} in {args.batch_size_file}"
)
print(f"batch size: {batch_size}")
return batch_size
class TimeOutException(Exception):
pass
def alarm_handler(signum, frame):
raise TimeOutException()
def exit_after(s):
"""
Decorator to raise TimeoutException if the fn is taking more than s seconds
to run.
"""
def outer(fn):
def inner(*args, **kwargs):
signal.signal(signal.SIGALRM, alarm_handler)
signal.alarm(s)
try:
result = fn(*args, **kwargs)
finally:
signal.alarm(0)
return result
return inner
return outer
def get_peak_memory():
return torch.cuda.max_memory_allocated() / 10**9
def null_experiment(args, model_iter_fn, model, example_inputs):
"""
A no-op experiment useful for making sure TorchBenchark alone works properly.
"""
return []
def cast_to(dtype, model, inputs):
# cast model and inputs to fp16
if dtype == torch.float16:
model = model.half()
else:
model = model.to(dtype)
inputs = tree_map(
lambda x: x.to(dtype)
if isinstance(x, torch.Tensor) and x.is_floating_point()
else x,
inputs,
)
return model, inputs
def cast_to_bf16(model, inputs):
return cast_to(torch.bfloat16, model, inputs)
def cast_to_fp16(model, inputs):
return cast_to(torch.float16, model, inputs)
def cast_to_fp64(model, inputs):
return cast_to(torch.float64, model, inputs)
def cast_to_fp32(model, inputs):
return cast_to(torch.float32, model, inputs)
def reset_rng_state(use_xla=False):
torch.manual_seed(1337)
random.seed(1337)
np.random.seed(1337)
if use_xla:
xm.set_rng_state(1337, str(xm.xla_device()))
class DummyGradScaler:
def scale(self, loss):
return loss
def get_dynamo_stats():
# TODO: consider deepcopy'ing the entire counters struct and
# adding a helper to do subtraction on it
return collections.Counter(
{
"calls_captured": torch._dynamo.utils.counters["stats"]["calls_captured"],
"unique_graphs": torch._dynamo.utils.counters["stats"]["unique_graphs"],
"graph_breaks": sum(torch._dynamo.utils.counters["graph_break"].values()),
# NB: The plus removes zero counts
"unique_graph_breaks": len(+torch._dynamo.utils.counters["graph_break"]),
}
)
def maybe_fresh_cache(fn, is_cold_start):
def inner(*args, **kwargs):
cache_minder = contextlib.nullcontext()
if is_cold_start:
cache_entries = {}
cache_minder = fresh_inductor_cache(cache_entries)
try:
with cache_minder:
return fn(*args, **kwargs)
finally:
dump_cache = False
if dump_cache and is_cold_start:
output_csv(
output_filename[:-4] + "_triton_cache.csv",
["dev", "name", "batch_size", "triton_cache"],
[
current_device,
current_name,
current_batch_size,
cache_entries,
],
)
return inner
@contextmanager
def maybe_init_distributed(should_init_distributed, rank, world_size, port="6789"):
try:
if should_init_distributed:
torch.cuda.set_device(rank)
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = port
torch.distributed.init_process_group(
"nccl", rank=rank, world_size=world_size
)
yield
finally:
if should_init_distributed:
torch.distributed.destroy_process_group()
class BenchmarkRunner:
def __init__(self):
self.model_iter_fn = None
self.grad_scaler = DummyGradScaler()
self.autocast = contextlib.nullcontext
self.optimizer = None
self._args = None
def setup_amp(self):
if self.args.only in self.fp32_only_models:
return
if self.args.amp and self.args.devices == ["cuda"]:
# AMP training can lead to small loss values which can undeflow
# gradient values returning in zero gradients. To solve this
# problem, PyTorch introduces GradScaler. GradScaler is a stateful
# structure, that scales the loss values to prevent underflow. Loss
# values are big at the beginning of training (therefore not
# requiring scaling), while loss value tends to be small as network
# starts getting better (requiring scaling). GradScaler manages all
# of this fine tuning, checking the gradients are turning to inf,
# discarding such batches.
# Since we are not running a long iteration, default value of
# init_scale 65536 is going to turn all gradients to inf. Therefore,
# we just use a init_scale of 2.0 for benchmarking purpose.
# Disabling Gradscaler because
# 1) Benchmark setup runs 2 iterations of fwd-bwd. So, not useful.
# 2) Current setup shares grad_scaler for eager and dynamo model,
# which is bad as Gradscaler has state and can adjust the scaling
# factor between eager and dynamo run, making accuracy check
# harder.
# self.grad_scaler = torch.cuda.amp.GradScaler(init_scale=2.0)
self.autocast = torch.cuda.amp.autocast
elif (self.args.bfloat16 or self.args.amp) and self.args.devices == ["cpu"]:
self.autocast = torch.cpu.amp.autocast
def init_optimizer(self, name, device, params):
if device == "cuda" and self.args.training and name not in CI_SKIP_OPTIMIZER:
self.optimizer = torch.optim.SGD(params, lr=0.01, foreach=True)
else:
self.optimizer = None
@property
def args(self):
return self._args
@args.setter
def args(self, args):
self._args = args
@property
def skip_models(self):
return set()
@property
def skip_models_for_cuda(self):
return set()
@property
def skip_models_for_cpu(self):
return set()
@property
def slow_models(self):
return set()
@property
def very_slow_models(self):
return set()
@property
def non_deterministic_models(self):
return set()
@property
def fp32_only_models(self):
return set()
@property
def force_amp_for_fp16_bf16_models(self):
return set()
@property
def skip_not_suitable_for_training_models(self):
return set()
@property
def failing_torchinductor_models(self):
return set()
@property
def failing_fx2trt_models(self):
return set()
@property
def skip_accuracy_checks_large_models_dashboard(self):
return set()
@property
def skip_accuracy_check_as_eager_non_deterministic(self):
return set()
@property
def get_tolerance_and_cosine_flag(self, is_training, current_device, name):
raise NotImplementedError()
@property
def equal_nan(self):
equal_nan = True
if self.args.float32:
equal_nan = False
return equal_nan
def iter_models(self, args):
for model_name in self.iter_model_names(args):
for device in args.devices:
try:
yield self.load_model(
device,
model_name,
batch_size=args.batch_size,
)
except NotImplementedError:
continue # bad benchmark implementation
def deepcopy_model(self, model):
return copy.deepcopy(model)
def cast_based_on_args(self, model, example_inputs):
if self.args.float32 or self.args.only in self.fp32_only_models:
if not self.args.float32:
log.warning("Model %s supports float32 only", self.args.only)
model, example_inputs = cast_to_fp32(model, example_inputs)
elif self.args.float16:
if self.args.only in self.force_amp_for_fp16_bf16_models:
log.warning(
"Model %s does not support float16, running with amp instead",
self.args.only,
)
self.args.amp = True
self.setup_amp()
else:
model, example_inputs = cast_to_fp16(model, example_inputs)
elif self.args.bfloat16:
if self.args.only in self.force_amp_for_fp16_bf16_models:
log.warning(
"Model %s does not support bfloat16, running with amp instead",
self.args.only,
)
self.args.amp = True
self.setup_amp()
else:
model, example_inputs = cast_to_bf16(model, example_inputs)
return model, example_inputs
def validate_model(self, model, example_inputs):
"""
Runs the eager model with example inputs to ensure that eager passes.
"""
model = self.deepcopy_model(model)
example_inputs = clone_inputs(example_inputs)
model, example_inputs = self.cast_based_on_args(model, example_inputs)
try:
self.model_iter_fn(model, example_inputs)
except Exception as e:
raise NotImplementedError("Eager model failed to run") from e
def maybe_cast(self, model, example_inputs):
model = self.deepcopy_model(model)
example_inputs = clone_inputs(example_inputs)
model, example_inputs = self.cast_based_on_args(model, example_inputs)
return model, example_inputs
def decay_batch_exp(self, batch_size, factor=0.5, divisor=2):
out_batch_size = batch_size * factor
if out_batch_size > divisor:
out_batch_size = (out_batch_size + 1) // divisor * divisor
else:
out_batch_size = batch_size - 1
return max(0, int(out_batch_size))
def batch_size_finder(self, device, model_name, initial_batch_size=1024):
batch_size = initial_batch_size
while batch_size >= 1:
torch.cuda.empty_cache()
try:
device, name, model, example_inputs, _ = self.load_model(
device,
model_name,
batch_size,
)
self.model_iter_fn(model, example_inputs)
return batch_size
except RuntimeError as e:
error_str = str(e)
if "channels_last" in error_str:
break
batch_size = self.decay_batch_exp(batch_size)
return 1
def run_n_iterations(self, mod, inputs):
n = self.args.iterations
for _ in range(n - 1):
self.model_iter_fn(mod, inputs, collect_outputs=False)
return self.model_iter_fn(mod, inputs, collect_outputs=True)
def optimizer_zero_grad(self, mod):
if self.optimizer is not None:
self.optimizer.zero_grad(True)
else:
mod.zero_grad(True)
def optimizer_step(self):
if self.optimizer is not None:
self.optimizer.step()
def get_benchmark_indices(self, length):
start = self._args.partition_id * (length // self._args.total_partitions)
end = (
(self._args.partition_id + 1) * (length // self._args.total_partitions)
if self._args.partition_id < self._args.total_partitions - 1
else length
)
return start, end
def deepcopy_and_maybe_ddp(self, model):
model = self.deepcopy_model(model)
if self.args.ddp:
assert (
torch.distributed.is_available()
), "Can't use DDP without a distributed enabled build"
from torch.nn.parallel import DistributedDataParallel as DDP
model = DDP(model, find_unused_parameters=True)
elif self.args.fsdp:
assert (
torch.distributed.is_available()
), "Can't use FSDP without a distributed enabled build"
from torch.distributed.fsdp import (
FullyShardedDataParallel as FSDP,
MixedPrecision,
)
from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy
if self.args.float16:
dtype = torch.float16
elif self.args.bfloat16:
dtype = torch.bfloat16
else:
dtype = torch.float32
mp_policy = MixedPrecision(
param_dtype=dtype,
# Gradient communication precision.
reduce_dtype=dtype,
# Buffer precision.
buffer_dtype=dtype,
)
my_auto_wrap_policy = functools.partial(
size_based_auto_wrap_policy, recurse=True, min_num_params=int(1e5)
)
model = FSDP(
model,
use_orig_params=True,
device_id=torch.cuda.current_device()
if self.args.devices[-1] == "cuda"
else None,
mixed_precision=mp_policy,
limit_all_gathers=True,
auto_wrap_policy=my_auto_wrap_policy,
)
if torch._inductor.config.triton.cudagraphs:
log.warning("Disabling cudagraphs for FSDP compatibility")
torch._inductor.config.triton.cudagraphs = False
return model
def check_accuracy(
self, name, model, example_inputs, optimize_ctx, experiment, tag
):
"""
Checks accuracy.
1) Collect the outputs with fp64 datatype. This is useful for error checking.
2) Checks if eager itself has variations.
"""
start_stats = get_dynamo_stats()
def record_status(accuracy_status, dynamo_start_stats):
"""
Records the status in the csv file
"""
if current_name in self.non_deterministic_models:
if accuracy_status in (
"pass",
"eager_two_runs_differ",
"fail_accuracy",
):
accuracy_status = "pass"
headers = ["dev", "name", "batch_size", "accuracy"]
fields = [current_device, current_name, current_batch_size, accuracy_status]
if tag is not None:
headers.insert(3, "tag")
fields.insert(3, tag)
dynamo_stats = get_dynamo_stats()
dynamo_stats.subtract(dynamo_start_stats)
for k, v in dynamo_stats.items():
headers.append(k)
fields.append(v)
output_csv(output_filename, headers, fields)
return accuracy_status
if name in self.skip_accuracy_checks_large_models_dashboard:
return record_status("pass_due_to_skip", dynamo_start_stats=start_stats)
# Collect the fp64 reference outputs to be used later for accuracy checking.
fp64_outputs = None
try:
model_fp64, inputs_fp64 = cast_to_fp64(
self.deepcopy_and_maybe_ddp(model),
clone_inputs(example_inputs),
)
self.init_optimizer(name, current_device, model_fp64.parameters())
fp64_outputs = self.run_n_iterations(model_fp64, inputs_fp64)
fp64_outputs = tree_map(
lambda x: x.to(torch.float64)
if isinstance(x, torch.Tensor) and x.is_floating_point()
else x,
fp64_outputs,
)
except Exception:
log.warning(
"fp64 golden ref were not generated for %s. Setting accuracy check to cosine",
name,
)
self.args.cosine = True
fp64_outputs = None
tolerance, cos_similarity = self.get_tolerance_and_cosine_flag(
self.args.training, current_device, name
)
# Cast the model to float16/float32 as necessary
model, example_inputs = self.maybe_cast(model, example_inputs)
accuracy_status = "pass"
with self.pick_grad(name, self.args.training):
# Get results of native pytorch
reset_rng_state()
try:
model_copy = self.deepcopy_and_maybe_ddp(model)
self.init_optimizer(name, current_device, model_copy.parameters())
correct_result = self.run_n_iterations(
model_copy, clone_inputs(example_inputs)
)
except Exception as e:
accuracy_status = (
"eager_1st_run_OOM"
if isinstance(e, torch.cuda.OutOfMemoryError)
else "eager_1st_run_fail"
)
log.exception(e)
return record_status(accuracy_status, dynamo_start_stats=start_stats)
# Rerun native pytorch
reset_rng_state()
try:
model_copy = self.deepcopy_and_maybe_ddp(model)
self.init_optimizer(name, current_device, model_copy.parameters())
correct_rerun_result = self.run_n_iterations(
model_copy, clone_inputs(example_inputs)
)
except Exception as e:
accuracy_status = (
"eager_2nd_run_OOM"
if isinstance(e, torch.cuda.OutOfMemoryError)
else "eager_2nd_run_fail"
)
return record_status(accuracy_status, dynamo_start_stats=start_stats)
# Two eager runs should have exactly same result
is_same = True
try:
if (
name not in self.skip_accuracy_check_as_eager_non_deterministic
and not same(
correct_result,
correct_rerun_result,
fp64_ref=None,
cos_similarity=False,
tol=0,
equal_nan=self.equal_nan,
)
):
is_same = False
except Exception as e:
# Sometimes torch.allclose may throw RuntimeError
is_same = False
if not is_same:
accuracy_status = "eager_two_runs_differ"
return record_status(accuracy_status, dynamo_start_stats=start_stats)
correct_rerun_result = None
# Run with Dynamo
reset_rng_state()
torch._dynamo.reset()
try:
model_copy = self.deepcopy_and_maybe_ddp(model)
self.init_optimizer(name, current_device, model_copy.parameters())
if self.args.export:
# TB and TIMM use list example_inputs
# HF use dict example_inputs
example_args, example_kwargs = _normalize_bench_inputs(
example_inputs
)
# Register the output dataclass to pytree
example_outputs = model_copy(*example_args, **example_kwargs)
_register_dataclass_output_as_pytree(example_outputs)
# apply export on module directly
# no need for n iterations
# the logic should be the same to self.model_iter_fn (forward_pass)
with self.autocast():
optimized_model_iter_fn = optimize_ctx(
model_copy, example_args, example_kwargs
)
new_result = optimized_model_iter_fn(
*example_args, **example_kwargs
)
else:
optimized_model_iter_fn = optimize_ctx(self.run_n_iterations)
new_result = optimized_model_iter_fn(model_copy, example_inputs)
except Exception as e:
log.exception(e)
print(
"TorchDynamo optimized model failed to run because of following error"
)
accuracy_status = (
"OOM"
if isinstance(e, torch.cuda.OutOfMemoryError)
else "fail_to_run"
)
return record_status(accuracy_status, dynamo_start_stats=start_stats)
if name in self.skip_accuracy_check_as_eager_non_deterministic:
return record_status("pass_due_to_skip", dynamo_start_stats=start_stats)
# Workaround for ONNX for non-tensor outputs
if (
current_onnx_compiler == "torchscript"
or current_onnx_compiler == "dynamo"
):
from _onnx import patch
(
correct_result,
new_result,
fp64_outputs,
) = patch.patch_non_tensor_outputs(
correct_result, new_result, fp64_outputs
)
try:
if not same(
correct_result,
new_result,
fp64_outputs,
equal_nan=self.equal_nan,
cos_similarity=cos_similarity,
tol=tolerance,
):
is_same = False
except Exception as e:
# Sometimes torch.allclose may throw RuntimeError
is_same = False
if not is_same:
if self.args.skip_accuracy_check:
accuracy_status = "pass_due_to_skip"
else:
accuracy_status = "fail_accuracy"
return record_status(accuracy_status, dynamo_start_stats=start_stats)
return record_status(accuracy_status, dynamo_start_stats=start_stats)
def check_tolerance(
self, name, model, example_inputs, optimize_ctx, base_device="cpu"
):
"""
Checks tolerance based on https://pytorch.org/docs/stable/generated/torch.allclose.html.
"""
tolerance_status = "pass"
if name in self.skip_accuracy_checks_large_models_dashboard:
tolerance_status = "pass_due_to_skip"
return tolerance_status
# Cast the model to float16/float32 as necessary
model, example_inputs = self.maybe_cast(model, example_inputs)
with self.pick_grad(name, self.args.training):
# Get results of native pytorch
reset_rng_state()
model_copy = copy.deepcopy(model)
model_copy = model_copy.to(base_device)
example_inputs_copy = copy.deepcopy(example_inputs)
example_inputs_copy = tree_map(
lambda x: x.to(base_device), example_inputs_copy
)
self.init_optimizer(name, base_device, model_copy.parameters())
correct_result = self.run_n_iterations(model_copy, example_inputs_copy)
# Run with Dynamo
# Sometime CI fails with random triton compilation failure which will be skipped for now
# TODO: revisit this after switching to new Triton runtime
reset_rng_state()
torch._dynamo.reset()
try:
self.init_optimizer(name, current_device, model.parameters())
optimized_model_iter_fn = optimize_ctx(self.run_n_iterations)
new_result = optimized_model_iter_fn(model, example_inputs)
except Exception as e:
log.exception(e)
if (
self.args.ci
and isinstance(e, BackendCompilerFailed)
and (
"Internal Triton PTX codegen error" in str(e)
or "cubin" in str(e)
)
):
return "pass_due_to_skip"
else:
print(
"TorchDynamo optimized model failed to run because of following error"
)
return "fail_to_run"
def dump_max_mean_values(tol, ref, res):
if isinstance(ref, (list, tuple, torch.nn.ParameterList, torch.Size)):
for refi, resi in zip(ref, res):
dump_max_mean_values(tol, refi, resi)
elif isinstance(ref, dict):
for k in ref.keys():
dump_max_mean_values(tol, ref[k], res[k])
elif isinstance(ref, torch.Tensor):
res = res.to(base_device)
t = torch.abs(ref - res) / (1 + torch.abs(ref))
tol.append(t.flatten().to(torch.float32))
return tol
tol = []
dump_max_mean_values(tol, correct_result, new_result)
tol = torch.cat(tol)
tol = torch.tensor(tol)
max = torch.max(tol)
mean = torch.mean(tol)
div = torch.std(tol)
headers = ["dev", "name", "batch_size", "max", "mean", "std"]
fields = [
current_device,
current_name,
current_batch_size,
max.item(),
mean.item(),
div.item(),
]
output_csv(output_filename, headers, fields)
return tolerance_status
def run_performance_test(
self, name, model, example_inputs, optimize_ctx, experiment, tag=None
):
if self.args.xla:
with self.pick_grad(name, self.args.training):
return experiment(*self.maybe_cast(model, example_inputs))
def warmup(fn, model, example_inputs, mode, niters=5):
peak_mem = 0
start_stats = get_dynamo_stats()
try:
if current_device == "cuda":
torch.cuda.reset_peak_memory_stats()
torch.cuda.empty_cache()
t0 = time.perf_counter()
for _ in range(niters):
fn(model, example_inputs)
t1 = time.perf_counter()
latency = t1 - t0
if current_device == "cuda":
peak_mem = get_peak_memory()
elif current_device == "cpu":
total = psutil.virtual_memory().total
percentage = psutil.Process(os.getpid()).memory_percent()
peak_mem = percentage * total / 10**9
except Exception:
log.exception("Backend %s failed in warmup()", mode)
return sys.exit(-1)
dynamo_stats = get_dynamo_stats()
dynamo_stats.subtract(start_stats)
return latency, peak_mem, dynamo_stats
# Cast the model to float16/float32 as necessary
model, example_inputs = self.maybe_cast(model, example_inputs)
# Use distributed wrapping as necessary
model = self.deepcopy_and_maybe_ddp(model)
self.init_optimizer(name, current_device, model.parameters())
with self.pick_grad(name, self.args.training):
ok, total = Stats.reset_counters()
experiment_kwargs = {}
if tag is not None:
experiment_kwargs["tag"] = tag
results = []
eager_latency, eager_peak_mem, _ = warmup(
self.model_iter_fn, model, example_inputs, "eager"
)
optimized_model_iter_fn = optimize_ctx(self.model_iter_fn)
dynamo_latency, dynamo_peak_mem, dynamo_stats = warmup(
optimized_model_iter_fn, model, example_inputs, "dynamo"
)
compilation_time = dynamo_latency - eager_latency
compression_ratio = (
eager_peak_mem / dynamo_peak_mem if dynamo_peak_mem else 0.0
)
if self.args.print_memory:
print(
f"memory: eager: {eager_peak_mem:.2f} GB, "
f"dynamo: {dynamo_peak_mem:.2f} GB, "
f"ratio: {compression_ratio:.2f}"
)
if experiment.func is speedup_experiment:
experiment_kwargs["compilation_latency"] = compilation_time
experiment_kwargs["compression_ratio"] = compression_ratio
experiment_kwargs["eager_peak_mem"] = eager_peak_mem
experiment_kwargs["dynamo_peak_mem"] = dynamo_peak_mem
experiment_kwargs["dynamo_stats"] = dynamo_stats
if experiment.func is coverage_experiment:
ok, total = Stats.reset_counters()
results = []
# run with torch._dynamo few times to populate the cache
for _ in range(3):
optimized_model_iter_fn(model, example_inputs)
_, frames_second_pass = Stats.reset_counters() # should be 0
if frames_second_pass > 0:
optimized_model_iter_fn(model, example_inputs)
_, frames_third_pass = Stats.reset_counters() # should be 0
else:
frames_third_pass = 0
results.append(
f"{ok:3}/{total:3} +{frames_third_pass} frames {compilation_time:3.0f}s"
)
if not hasattr(model, name):
model.name = name
results.append(experiment(model, example_inputs, **experiment_kwargs))
return " ".join(map(str, results))
def minify_model(
self,
name,
model,
example_inputs,
optimize_ctx,
experiment,
tag,
):
logging.info("Minifying %s...", name)
os.environ["TORCH_COMPILE_DEBUG"] = "1"
os.environ["TORCHDYNAMO_REPRO_AFTER"] = "dynamo"
os.environ["TORCHDYNAMO_REPRO_LEVEL"] = "4"
self.check_accuracy(name, model, example_inputs, optimize_ctx, experiment, tag)
if self.args.output_directory:
repro_dir = self.args.output_directory
else:
repro_dir = torch._dynamo.config.base_dir
try:
shutil.move("repro.py", f"{repro_dir}/{name}_repro.py")
except OSError as e:
logging.error("Could not find repro script for model %s", name)
else:
logging.info(
"Repro script for model %s with minified graph saved to %s",
name,
repro_dir,
)
def run_one_model(
self,
name,
model,
example_inputs,
optimize_ctx,
experiment,
explain=False,
tag=None,
):
mode = "train" if self.args.training else "eval"
msg = f"{current_device:4} {mode:5} {current_name:34} "
if tag:
msg += f" {tag:26}"
print(msg, flush=True)
start_stats = get_dynamo_stats()
if self.args.accuracy:
status = self.check_accuracy(
name, model, example_inputs, optimize_ctx, experiment, tag
)
print(status)
if status == "fail_accuracy" and self.args.minify:
self.minify_model(
name, model, example_inputs, optimize_ctx, experiment, tag
)
elif self.args.tolerance:
status = self.check_tolerance(name, model, example_inputs, optimize_ctx)
print(status)
elif self.args.performance:
status = self.run_performance_test(
name, model, example_inputs, optimize_ctx, experiment, tag
)
print(status)
if self.args.timing:
from torch._dynamo.utils import op_count, print_time_report
from torch.utils._stats import simple_call_counter
print_time_report()
stats = "STATS: "
stats = stats + " | ".join(
itertools.chain(
[f"call_* op count: {op_count}"],
(f"{key}:{value}" for key, value in simple_call_counter.items()),
)
)
print(stats)
stats = get_dynamo_stats()
stats.subtract(start_stats)
if explain:
print(
f"Dynamo produced {stats['unique_graphs']} graphs "
f"covering {stats['calls_captured']} ops with "
f"{stats['graph_breaks']} graph breaks ({stats['unique_graph_breaks']} unique)"
)
if explain or self.args.log_graph_breaks or self.args.print_graph_breaks:
filename = f"{output_filename.rstrip('.csv')}_graph_breaks.csv"
def add_double_quotes(x):
# Delimiter because reason could have comma
return f'"{x}"'
for graph_break in graph_break_reasons:
reason = add_double_quotes(graph_break.reason)
user_stack = add_double_quotes(
", ".join([str(x) for x in graph_break.user_stack])
)
output_csv(
filename,
["model", "reason", "user_stack"],
[current_name, reason, user_stack],
)
if self.args.stats:
Stats.print_summary()
def help(fn):
return fn.__doc__
diff_branch_default = "DIFF-BRANCH-DEFAULT"
def should_diff_branch(args):
return args.diff_branch != diff_branch_default
def parse_args(args=None):
parser = argparse.ArgumentParser()
parser.add_argument(
"--filter", "-k", action="append", help="filter benchmarks with regexp"
)
parser.add_argument(
"--exclude", "-x", action="append", help="filter benchmarks with regexp"
)
parser.add_argument(
"--exclude-exact", action="append", help="filter benchmarks with exact match"
)
parser.add_argument(
"--total-partitions",
type=int,
default=1,
choices=range(1, 10),
help="Total number of partitions we want to divide the benchmark suite into",
)
parser.add_argument(
"--partition-id",
type=int,
default=0,
help="ID of the benchmark suite partition to be run. Used to divide CI tasks",
)
parser.add_argument(
"--devices", "--device", "-d", action="append", help="cpu or cuda"
)
parser.add_argument("--device-index", help="CUDA device index")
parser.add_argument(
"--repeat", "-n", type=int, default=30, help="number of timing runs"
)
iterations_per_run_help = """
Run this may iterations for each time measurement. This is mainly used for
XLA training. We want to run multiple iterations per measurement so the
tracing and computation for different iteartions can overlap with each
other. This makes sure we have an accurate xla baseline.
"""
parser.add_argument(
"--iterations-per-run", type=int, default=1, help=iterations_per_run_help
)
parser.add_argument(
"--randomize-input",
action="store_true",
help="Whether to randomize the input values. Dimensions will be kept the same.",
)
parser.add_argument(
"--threads",
"-t",
type=int,
help="number of threads to use for eager and inductor",
)
parser.add_argument(
"--nopython", action="store_true", help="Turn graph breaks into errors"
)
parser.add_argument(
"--no-skip",
action="store_true",
help="run models that are in the global SKIP list",
)
parser.add_argument(
"--prims-nvfuser", action="store_true", help="user prims + nvfuser backend"
)
parser.add_argument(
"--dump-raw-metrics",
action="store_true",
help="dump raw timing metrics from speedup experiment",
)
parser.add_argument(
"--log-operator-inputs",
action="store_true",
default=False,
)
parser.add_argument(
"--channels-last",
action="store_true",
default=False,
help="use channels last format",
)
parser.add_argument(
"--batch-size", "--batch_size", type=int, help="batch size for benchmarking"
)
parser.add_argument(
"--iterations", type=int, default=2, help="how many iterations to run"
)
parser.add_argument(
"--batch-size-file", type=str, help="String to load batch size from"
)
parser.add_argument("--cosine", action="store_true", help="use cosine similarity")
parser.add_argument(
"--cpp-wrapper", action="store_true", help="turn on cpp/cuda wrapper codegen"
)
parser.add_argument(
"--freezing", action="store_true", help="turn on freezing", default=False
)
parser.add_argument(
"--ci", action="store_true", help="Flag to tell that its a CI run"
)
parser.add_argument(
"--dynamic-ci-skips-only",
action="store_true",
help=(
"Run only the models that would have been skipped in CI "
"if dynamic-shapes, compared to running without dynamic-shapes. "
"This is useful for checking if more models are now "
"successfully passing with dynamic shapes. "
"Implies --dynamic-shapes and --ci"
),
)
parser.add_argument(
"--dashboard", action="store_true", help="Flag to tell that its a Dashboard run"
)
parser.add_argument(
"--skip-fp64-check", action="store_true", help="skip accuracy check using fp64"
)
parser.add_argument(
"--fast", "-f", action="store_true", help="skip slow benchmarks"
)
parser.add_argument(
"--only",
help="""Run just one model from torchbench. Or
specify the path and class name of the model in format like:
--only=path:<MODEL_FILE_PATH>,class:<CLASS_NAME>
Due to the fact that dynamo changes current working directory,
the path should be an absolute path.
The class should have a method get_example_inputs to return the inputs
for the model. An example looks like
```
class LinearModel(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(10, 10)
def forward(self, x):
return self.linear(x)
def get_example_inputs(self):
return (torch.randn(2, 10),)
```
""",
)
parser.add_argument(
"--multiprocess",
action="store_true",
help="Create n processes based on the number of devices (distributed use case).",
)
parser.add_argument(
"--ddp",
action="store_true",
help="Wraps model in DDP before running it, and uses dynamo DDPOptmizer (graph breaks) by default.",
)
parser.add_argument(
"--fsdp",
action="store_true",
help="""Wraps model in FSDP before running it. Disables cudagraphs by default.
Doesn't recursively wrap, mainly useful for checking dynamo UnspecNNModule compatibility
""",
)
parser.add_argument(
"--no-optimize-ddp",
action="store_true",
help="Disables dynamo DDPOptimizer (graph breaks). (Applies only when using --ddp benchmark mode).",
)
parser.add_argument(
"--distributed-master-port",
default="6789",
help="Port to bind for for torch.distributed. Use the default unless it's conflicting with another user",
)
parser.add_argument(
"--dynamic-shapes",
action="store_true",
help="Runs a dynamic shapes version of the benchmark, if available.",
)
parser.add_argument(
"--dynamic-batch-only",
action="store_true",
help="Only assume batch dimension is dynamic. Implies --dynamic-shapes",
)
parser.add_argument(
"--specialize-int", action="store_true", help="Run with specialize_int=True."
)
parser.add_argument(
"--use-eval-mode",
action="store_true",
help="sets model.eval() to reduce randomness",
)
parser.add_argument(
"--skip-accuracy-check",
action="store_true",
help="keeps running even when accuracy fails",
)
parser.add_argument(
"--generate-aot-autograd-stats",
action="store_true",
help="Generates AOT Autograd stats like how mnay graphs are sent to AOT",
)
parser.add_argument(
"--inductor-settings",
action="store_true",
help="Use same settings as --inductor for baseline comparisons",
)
parser.add_argument(
"--suppress-errors",
action="store_true",
help="Suppress errors instead of raising them",
)
parser.add_argument(
"--output",
help="Overrides the output filename",
)
parser.add_argument(
"--output-directory",
help="Overrides the directory to place output files.",
)
parser.add_argument(
"--baseline",
help="Compare with a prior --output",
)
parser.add_argument(
"--part",
default=None,
help="Specify the part of the model to run.",
)
parser.add_argument(
"--export-profiler-trace",
action="store_true",
help="exports trace of kineto profiler",
)
parser.add_argument(
"--profiler-trace-name",
"--profiler_trace_name",
help="Overwrites exported trace name",
)
parser.add_argument(
"--diff-branch",
default=diff_branch_default,
help="delta current branch against given branch.",
)
parser.add_argument(
"--tag", default=None, help="Specify a tag to be included in csv files."
)
parser.add_argument(
"--explain",
action="store_true",
help="print some graph/op statistics during the run, similar to .explain()",
)
parser.add_argument(
"--stats",
action="store_true",
help="print graph counter stats",
)
parser.add_argument(
"--print-memory",
action="store_true",
help="print extra memory statistics",
)
parser.add_argument(
"--print-dataframe-summary",
action="store_true",
help="print dataframe result used for calculating accuracy",
)
parser.add_argument(
"--cold-start-latency",
"--cold_start_latency",
action="store_true",
help="Use a fresh triton cachedir when running each model, to force cold-start compile.",
)
parser.add_argument(
"--disable-cudagraphs",
action="store_true",
help="Disables cudagraphs for Inductor",
)
parser.add_argument(
"--disable-split-reductions",
action="store_true",
help="Disables split reductions for Inductor",
)
parser.add_argument(
"--disable-persistent-reductions",
action="store_true",
help="Disables split reductions for Inductor",
)
parser.add_argument(
"--disable-divisible-by-16",
action="store_true",
help="Disables divisible by 16 hint to Triton for Inductor",
)
parser.add_argument(
"--inductor-compile-mode",
default=None,
help="torch.compile mode argument for inductor runs.",
)
parser.add_argument(
"--print-graph-breaks",
action="store_true",
help="Show a warning whenever graph break",
)
parser.add_argument(
"--log-graph-breaks",
action="store_true",
help="log graph breaks in a file",
)
parser.add_argument(
"--trace-on-xla",
action="store_true",
help="Whether to trace the model on XLA or on eager device",
)
parser.add_argument(
"--xla-tolerance",
type=float,
default=1e-2,
help="XLA needs a loose tolerance to pass the correctness check",
)
parser.add_argument(
"--collect-outputs",
action="store_true",
help="""Whether to collect outputs for training. Set this to true if we
want to verify the numerical correctness of graidents. But that may
cause time measurement not accurate""",
)
parser.add_argument(
"--enable-activation-checkpointing",
action="store_true",
help="Enables activation checkpointing for HF models",
)
parser.add_argument("--timing", action="store_true", help="Emits phase timing")
parser.add_argument(
"--progress",
action="store_true",
help="Print n/k models message between each model run.",
)
parser.add_argument(
"--timeout",
type=int,
default=2000,
help="timeout (second) for benchmarking.",
)
parser.add_argument(
"--per_process_memory_fraction",
type=float,
default=1,
help="Set per-process GPU memory fraction (limit) for reducing usable size and reproducing OOMs",
)
parser.add_argument(
"--no-translation-validation",
action="store_true",
help="Disable translation validation for accuracy builds.",
)
parser.add_argument(
"--minify",
action="store_true",
help="Enable minification when failure is below tolerance. Save repro script for each model.",
)
group_fuser = parser.add_mutually_exclusive_group()
# --nvfuser is now the default, keep the option to not break scripts
group_fuser.add_argument("--nvfuser", action="store_true", help=argparse.SUPPRESS)
group_fuser.add_argument("--nnc", action="store_true", help="enable NNC for GPUs")
group_prec = parser.add_mutually_exclusive_group()
group_prec.add_argument("--float16", action="store_true", help="cast model to fp16")
group_prec.add_argument(
"--bfloat16", action="store_true", help="cast model to bf16"
)
group_prec.add_argument("--float32", action="store_true", help="cast model to fp32")
group_prec.add_argument(
"--amp", action="store_true", help="use automatic mixed precision"
)
group_printout = parser.add_mutually_exclusive_group()
group_printout.add_argument(
"--verbose", "-v", action="store_true", help="enable verbose debug printouts"
)
group_printout.add_argument(
"--quiet", "-q", action="store_true", help="suppress debug printouts"
)
group = parser.add_mutually_exclusive_group()
group.add_argument(
"--coverage", action="store_true", help="(default) " + help(coverage_experiment)
)
group.add_argument(
"--overhead", action="store_true", help=help(overhead_experiment)
)
group.add_argument(
"--speedup-dynamo-ts",
action="store_true",
help="TorchDynamo frontend with torchscript backend",
)
group.add_argument(
"--speedup-fx2trt", action="store_true", help=help(speedup_experiment_fx2trt)
)
group.add_argument(
"--speedup-fx2trt-fp16",
action="store_true",
help=help(speedup_experiment_fx2trt),
)
group.add_argument(
"--print-fx",
action="store_true",
help="Print fx traces captured from model",
)
group.add_argument(
"--print-aten-ops",
action="store_true",
help="Print traces of aten ops captured by AOT autograd",
)
group.add_argument(
"--inductor",
action="store_true",
help="Measure speedup with TorchInductor",
)
group.add_argument(
"--export",
action="store_true",
help="Measure pass rate with export",
)
group.add_argument(
"--export-aot-inductor",
action="store_true",
help="Measure pass rate with Export+AOTInductor",
)
group.add_argument(
"--xla", action="store_true", help="Compare TorchXLA to eager PyTorch"
)
group.add_argument(
"--torchscript-onnx",
"--torchscript_onnx",
action="store_true",
help="Measure speedup with TorchScript ONNX, i.e. `torch.onnx.export`",
)
group.add_argument(
"--dynamo-onnx",
"--dynamo_onnx",
action="store_true",
help="Measure speedup with Dynamo ONNX, i.e. `torch.onnx.dynamo_export`",
)
group.add_argument(
"--backend",
choices=torch._dynamo.list_backends(exclude_tags=None),
help="measure speedup with a given backend",
)
group.add_argument("--nothing", action="store_true", help=help(null_experiment))
group.add_argument(
"--log-conv-args",
action="store_true",
help="Dump convolution input/weight/bias's shape/stride/dtype and other options to json",
)
group.add_argument(
"--recompile-profiler",
"--recompile_profiler",
action="store_true",
help="Run the dynamo recompilation profiler on each model.",
)
group.add_argument(
"--find-batch-sizes",
action="store_true",
help="finds the largest batch size that could fit on GPUs",
)
mode_group = parser.add_mutually_exclusive_group(required=True)
mode_group.add_argument(
"--accuracy",
action="store_true",
help="Checks accuracy with small batch size and eval mode",
)
mode_group.add_argument(
"--performance", action="store_true", help="Measures performance speedup"
)
mode_group.add_argument(
"--tolerance",
action="store_true",
help="extracts the tolerance for each model with small batch size and eval mode",
)
run_mode_group = parser.add_mutually_exclusive_group(required=True)
run_mode_group.add_argument(
"--training",
action="store_true",
help="Performs training",
)
run_mode_group.add_argument(
"--inference", action="store_true", help="Performs inference"
)
return parser.parse_args(args)
def process_entry(rank, runner, original_dir, args):
args.rank = rank
with maybe_init_distributed(
args.use_distributed,
rank=rank,
world_size=args.world_size,
port=args.distributed_master_port,
):
return maybe_fresh_cache(
run, (args.cold_start_latency and args.only) or args.ci
)(runner, args, original_dir)
def main(runner, original_dir=None):
if original_dir:
os.chdir(original_dir)
args = parse_args()
if args.baseline:
args.baseline = os.path.abspath(args.baseline)
if should_diff_branch(args):
import git
# We do this here so we error out earlier if there's an issue
repo = git.Repo()
if repo.is_dirty():
raise RuntimeError(
"--diff-branch called on dirty branch. Commit, stash, or reset."
)
main_branch = repo.active_branch.name
if main_branch == args.diff_branch:
raise RuntimeError(
f"--diff-branch: current branch is same as {args.diff_branch} branch, what are you diffing?"
)
args.use_distributed = (args.ddp or args.fsdp) and args.only
if args.multiprocess:
# NB: Do NOT query device count before CUDA initialization; we're
# going to overwrite CUDA_VISIBLE_DEVICES and this will result in
# https://github.com/pytorch/pytorch/issues/107300
device_count = torch.cuda.device_count()
if device_count <= 1:
log.warning(
"The use multiprocess flag is set but there are <= 1 devices available."
)
# multiprocess path
args.world_size = device_count
mp.spawn(process_entry, args=(runner, original_dir, args), nprocs=device_count)
else:
# single process path just uses the main process
args.world_size = 1
process_entry(0, runner, original_dir, args)
def run(runner, args, original_dir=None):
# Pass the parsed args object to benchmark runner object
runner.args = args
args.filter = args.filter or [r"."]
args.exclude = args.exclude or [r"^$"]
args.exclude_exact = args.exclude_exact or []
if args.inductor:
assert args.backend is None
args.backend = "inductor"
if args.dynamic_ci_skips_only:
args.dynamic_shapes = True
args.ci = True
if args.dynamic_batch_only:
args.dynamic_shapes = True
torch._dynamo.config.assume_static_by_default = True
if args.dynamic_shapes:
if not args.dynamic_batch_only:
torch._dynamo.config.assume_static_by_default = False
if args.specialize_int:
torch._dynamo.config.specialize_int = True
if args.ci:
if args.accuracy:
# Run fewer iterations when checking accuracy
args.repeat = 2
# Set translation validation on by default on CI accuracy runs.
torch._dynamo.config.translation_validation = True
if args.dynamic_ci_skips_only:
# Test only the incremental set of jobs whose skipped was
# caused solely by turning on dynamic shapes
assert args.dynamic_shapes
ci = functools.partial(CI, args.backend, training=args.training)
args.filter = list(
set(CI_SKIP[ci(dynamic=True)]) - set(CI_SKIP[ci(dynamic=False)])
)
else:
ci = functools.partial(
CI, args.backend, training=args.training, dynamic=args.dynamic_shapes
)
for device in args.devices:
args.exclude_exact.extend(CI_SKIP[ci(device=device)])
if args.ddp:
# TODO: we could also hook DDP bench up to --speedup bench, _not_ for mgpu e2e perf,
# but just to measure impact on singlenode of performing graph-breaks.
# Left it as a follow up to keep this PR isolated.
assert (
args.accuracy
), "DDP benchmark is currently only hooked up to --accuracy bench"
assert args.training, "DDP benchmark requires --training mode"
if args.no_optimize_ddp:
torch._dynamo.config.optimize_ddp = False
else:
# TODO(whc) after enabling DDPOptimizer by default this could be removed or assert
torch._dynamo.config.optimize_ddp = True
if args.only == "dlrm":
log.error(
"DLRM+DDP is unsupported as it requires sharding the embedding layer separately from DDP"
)
return sys.exit(-1)
if args.accuracy:
# Use small batch size. We use >1 batch size to ensure we test
# batch_norm type of operators that work on batch dims.
# TODO - Go through the failures for batch size = 2
if args.batch_size is None:
if runner.suite_name == "huggingface":
args.batch_size = 1
elif runner.suite_name == "torchbench":
args.batch_size = 4
else:
# Larger batch size of TIMM models to have stable batch_norm
assert runner.suite_name == "timm_models"
args.batch_size = 8
# Remove sources of randomness
if runner.suite_name not in ("timm_models", "huggingface"):
# TODO - Using train mode for timm_models and HF models. Move to train mode for Torchbench as well.
args.use_eval_mode = True
inductor_config.fallback_random = True
if args.only is not None and args.only not in {
"alexnet",
"Background_Matting",
"pytorch_CycleGAN_and_pix2pix",
"pytorch_unet",
"Super_SloMo",
"vgg16",
# https://github.com/pytorch/pytorch/issues/96724
"Wav2Vec2ForCTC",
"Wav2Vec2ForPreTraining",
"sam",
}:
# some of the models do not support use_deterministic_algorithms
torch.use_deterministic_algorithms(True)
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.allow_tf32 = False
torch.backends.cudnn.benchmark = False
torch.backends.cuda.matmul.allow_tf32 = False
# Remove randomeness when torch manual seed is called
patch_torch_manual_seed()
# Some models e.g. yolov3 assert batch size on n_gpus
if "CUDA_VISIBLE_DEVICES" not in os.environ:
args.device_index = "0"
# Stricter check to disable fallbacks
args.suppress_errors = False
if args.device_index is not None:
os.environ["CUDA_VISIBLE_DEVICES"] = args.device_index
elif args.performance:
# Ensure that we test on real scenarios
args.use_eval_mode = False
if args.partition_id > args.total_partitions or args.partition_id < 0:
print("Invalid partition id")
return sys.exit(-1)
if not args.devices:
if torch.cuda.is_available():
args.devices = ["cuda"]
else:
log.warning("torch.cuda.is_available() == False, using CPU")
args.devices = ["cpu"]
if args.devices != ["cpu"] and torch.cuda.is_available():
global synchronize
synchronize = torch.cuda.synchronize
if (
args.devices == ["cuda"]
and torch.cuda.get_device_properties(0).total_memory < 25 * 2**30
):
# OOM errors on an RTX 3090 with 24gb RAM
runner.skip_models.update(
{
# torchbench
"hf_Longformer",
"timm_nfnet",
"timm_efficientdet",
}
)
if args.training:
runner.skip_models.add("hf_T5")
if args.nnc:
torch._C._jit_override_can_fuse_on_cpu(True)
torch._C._jit_override_can_fuse_on_gpu(True)
torch._C._jit_set_texpr_fuser_enabled(True)
torch._C._jit_set_nvfuser_enabled(False)
if args.threads:
torch.set_num_threads(args.threads)
if args.verbose:
torch._logging.set_logs(dynamo=logging.DEBUG)
if args.print_graph_breaks:
torch._dynamo.config.print_graph_breaks = True
if args.quiet:
torch._logging.set_logs(dynamo=logging.ERROR)
torch._dynamo.config.suppress_errors = args.suppress_errors
if args.training:
runner.model_iter_fn = runner.forward_and_backward_pass
runner.skip_models.update(runner.skip_not_suitable_for_training_models)
else:
runner.model_iter_fn = runner.forward_pass
if args.fast:
runner.skip_models.update(runner.slow_models)
if args.devices == ["cpu"]:
runner.skip_models.update(runner.very_slow_models)
runner.skip_models.update(runner.skip_models_for_cpu)
elif args.devices == ["cuda"]:
runner.skip_models.update(runner.skip_models_for_cuda)
if args.no_skip:
runner.skip_models.clear()
experiment = null_experiment
global current_name, current_device, current_batch_size, output_filename, optimize_ctx, current_onnx_compiler
optimize_ctx = contextlib.nullcontext()
if args.overhead:
optimize_ctx = torch._dynamo.optimize(dummy_fx_compile, nopython=args.nopython)
experiment = speedup_experiment
output_filename = "overheads.csv"
elif args.inductor:
inductor_config.debug = args.verbose
if args.threads:
inductor_config.cpp.threads = args.threads
optimize_ctx = functools.partial(
torch.compile,
backend="inductor",
fullgraph=args.nopython,
mode=args.inductor_compile_mode,
)
experiment = speedup_experiment
output_filename = "inductor.csv"
elif args.export:
optimize_ctx = torch._export.export
experiment = speedup_experiment
output_filename = "export.csv"
elif args.xla:
(dev,) = args.devices
os.environ["PJRT_DEVICE"] = {"cuda": "GPU", "cpu": "CPU"}[dev]
torch._dynamo.mark_dynamic = MagicMock()
experiment = xla
output_filename = "xla.csv"
elif args.torchscript_onnx:
optimize_ctx = functools.partial(
optimize_onnx_ctx, args.output_directory or ".", OnnxModelFromTorchScript
)
experiment = functools.partial(
speedup_experiment_onnx, OnnxModelFromTorchScript
)
output_filename = "torchscript_onnx.csv"
current_onnx_compiler = "torchscript"
elif args.dynamo_onnx:
optimize_ctx = functools.partial(
optimize_onnx_ctx, args.output_directory or ".", OnnxModelFromDynamo
)
experiment = functools.partial(speedup_experiment_onnx, OnnxModelFromDynamo)
output_filename = "dynamo_onnx.csv"
current_onnx_compiler = "dynamo"
elif args.speedup_dynamo_ts:
optimize_ctx = torch._dynamo.optimize("ts", nopython=args.nopython)
experiment = speedup_experiment
output_filename = "speedup_dynamo_ts.csv"
elif args.prims_nvfuser:
optimize_ctx = torch._dynamo.optimize("prims_nvfuser", nopython=args.nopython)
experiment = speedup_experiment
backend_str = "prims_nvfuser"
output_filename = f"accuracy_aot_{backend_str}.csv"
elif args.print_fx:
optimize_ctx = torch._dynamo.optimize(
print_fx,
nopython=args.nopython,
)
elif args.print_aten_ops:
optimize_ctx = torch._dynamo.optimize(
print_aten_ops,
nopython=args.nopython,
)
elif args.nothing:
optimize_ctx = nothing
experiment = speedup_experiment
output_filename = "nothing.csv"
elif args.backend or args.export_aot_inductor:
if args.export_aot_inductor:
assert not args.training, "AOTInductor only supports inference"
assert args.devices == ["cuda"], "AOTInductor only tested for CUDA"
optimize_ctx = export_aot_inductor
else:
optimize_ctx = torch._dynamo.optimize(args.backend, nopython=args.nopython)
experiment = speedup_experiment
if args.accuracy:
output_filename = f"accuracy_{args.backend}.csv"
elif args.tolerance:
output_filename = f"tolerance_{args.backend}.csv"
else:
output_filename = f"speedup_{args.backend}.csv"
elif args.recompile_profiler:
output_filename = "recompile_profiler_log.csv"
experiment = recompile_profiler_experiment
else:
optimize_ctx = torch._dynamo.optimize(
fx_insert_profiling, nopython=args.nopython
)
experiment = coverage_experiment
output_filename = "coverage.csv"
if args.inductor or args.backend == "inductor" or args.export_aot_inductor:
inductor_config.triton.cudagraphs = not args.disable_cudagraphs
inductor_config.triton.persistent_reductions = (
not args.disable_persistent_reductions
)
inductor_config.split_reductions = not args.disable_split_reductions
inductor_config.triton.divisible_by_16 = not args.disable_divisible_by_16
inductor_config.cpp_wrapper = args.cpp_wrapper
if args.inference:
inductor_config.freezing = args.freezing
runner.setup_amp()
if args.output:
output_filename = args.output
if output_filename:
if args.output_directory:
output_filename = os.path.join(args.output_directory, output_filename)
else:
output_filename = os.path.join(
torch._dynamo.config.base_dir, output_filename
)
if args.find_batch_sizes and args.only:
for device in args.devices:
batch_size = runner.batch_size_finder(device, args.only)
print(args.only, batch_size)
output_csv(output_filename, [], [args.only, batch_size])
return
if args.export_profiler_trace:
if args.profiler_trace_name is None:
if args.backend:
args.profiler_trace_name = args.backend
elif args.inductor:
args.profiler_trace_name = "inductor"
else:
args.profiler_trace_name = "profile"
else:
args.profiler_trace_name = args.profiler_trace_name
if args.no_translation_validation:
# Overwrite 'translation_validation' config, if specified.
torch._dynamo.config.translation_validation = False
experiment = functools.partial(experiment, args, runner.model_iter_fn)
if args.only and should_diff_branch(args):
import git
repo = git.Repo()
main_branch = repo.active_branch.name
try:
# Adding diff-branch again to the args will override previous value
call_args = (
[sys.executable] + sys.argv + [f"--diff-branch={diff_branch_default}"]
)
# Run for main branch
subprocess.check_call(call_args + [f"--tag={main_branch}"])
# Run for comparison branch
repo.git.checkout(args.diff_branch)
subprocess.check_call(call_args + [f"--tag={args.diff_branch}"])
finally:
# Go back to main branch
repo.git.checkout(main_branch)
elif args.only:
model_name = args.only
for device in args.devices:
batch_size = args.batch_size
if args.batch_size_file:
batch_size = read_batch_size_from_file(
args, args.batch_size_file, model_name
)
if model_specified_by_path(args.only):
model, example_inputs = load_model_from_path(args.only)
name = model.__class__.__name__
model = model.to(device=device)
example_inputs = tree_map_only(
torch.Tensor, lambda x: x.to(device=device), example_inputs
)
else:
try:
with tqdm(desc="loading model"):
if args.part:
(
device,
name,
model,
example_inputs,
batch_size,
) = runner.load_model(
device,
model_name,
batch_size=batch_size,
part=args.part,
)
else:
if args.fsdp:
# Always load model on cpu for fsdp
# When initializing FSDP, we will use the cuda device if args.cuda is set
(
_,
name,
model,
example_inputs,
batch_size,
) = runner.load_model(
"cpu", model_name, batch_size=batch_size
)
else:
(
device,
name,
model,
example_inputs,
batch_size,
) = runner.load_model(
device, model_name, batch_size=batch_size
)
except NotImplementedError as e:
print(e)
import traceback
print(traceback.format_exc())
logging.warning("%s failed to load", args.only)
continue # bad benchmark implementation
if args.trace_on_xla:
xla_dev = xm.xla_device()
model = model.to(device=xla_dev)
example_inputs = tree_map_only(
torch.Tensor, lambda x: x.to(device=xla_dev), example_inputs
)
current_name = name
current_device = device
current_batch_size = batch_size
set_model_name(name)
# Look for stuff that looks like batch size, and mark it dynamic.
# Better integration would integrate directly with benchmark suite
# but cannot conveniently do this
# NB: This must be done late enough so that we don't do more
# conversions on the inputs
# NB: Assumes only the first batch-y like dimension is the batch
marked = False
def detect_and_mark_batch(t):
nonlocal marked
for i, s in enumerate(t.size()):
if s == batch_size:
torch._dynamo.mark_dynamic(t, i)
marked = True
break
if (
args.dynamic_batch_only
and batch_size > 1
and model_name not in CI_SKIP_DYNAMIC_BATCH_ONLY
):
tree_map_only(torch.Tensor, detect_and_mark_batch, example_inputs)
assert marked, f"nothing in example_inputs had a dim with {batch_size}"
if args.log_operator_inputs:
log_operator_inputs(
model, example_inputs, runner.model_iter_fn, name, args
)
continue
if args.per_process_memory_fraction != 1:
torch.cuda.set_per_process_memory_fraction(
args.per_process_memory_fraction
)
model, example_inputs = runner.cast_based_on_args(model, example_inputs)
runner.run_one_model(
name,
model,
example_inputs,
optimize_ctx,
experiment,
explain=args.explain,
tag=args.tag,
)
if args.generate_aot_autograd_stats:
stats_file = output_filename.split(".csv")[0] + "_stats.csv"
output_csv(
stats_file,
("dev", "name", "batch_size", "total_aot_graphs", "ok_aot_graphs"),
[
current_device,
current_name,
current_batch_size,
*Stats.aot_summary(),
],
)
else:
if output_filename and os.path.exists(output_filename):
os.unlink(output_filename)
if original_dir:
os.chdir(original_dir)
model_names = list(runner.iter_model_names(args))
nmodels = len(model_names)
for i, name in enumerate(model_names):
current_name = name
placeholder_batch_size = 0
if args.progress:
print(f"Running model {i+1}/{nmodels}", flush=True)
def write_csv(status):
if args.accuracy:
headers = ["dev", "name", "batch_size", "accuracy"]
rows = [
[device, name, placeholder_batch_size, status]
for device in args.devices
]
elif args.performance:
headers = ["dev", "name", "batch_size", "speedup", "abs_latency"]
rows = [
[device, name, placeholder_batch_size, 0.0, 0.0]
for device in args.devices
]
else:
headers = []
rows = [
[device, name, placeholder_batch_size, 0.0]
for device in args.devices
]
for row in rows:
output_csv(output_filename, headers, row)
try:
timeout = args.timeout
if should_diff_branch(args):
timeout *= 2
subprocess.check_call(
[sys.executable] + sys.argv + [f"--only={name}"], timeout=timeout
)
except subprocess.TimeoutExpired:
print("TIMEOUT", file=sys.stderr)
write_csv("timeout")
except subprocess.SubprocessError:
print("ERROR", file=sys.stderr)
write_csv("infra_error")
print_summary(output_filename, print_dataframe=args.print_dataframe_summary)
def log_operator_inputs(model, example_inputs, model_iter_fn, name, args):
mode = "training" if args.training else "eval"
output = os.path.join(os.path.dirname(args.output), f"{name}_{mode}.txt")
# TODO - add option for coalescing inputs over multiple runs
if os.path.exists(output):
print(f"Skipping {name}, {output} already exists")
return
print(f"Running {name}")
operator_mode = OperatorInputsMode()
fake_tensor_mode = FakeTensorMode()
with torch._subclasses.fake_tensor.FakeCopyMode(fake_tensor_mode):
model_fake = copy.deepcopy(model)
example_inputs_fake = copy.deepcopy(example_inputs)
try:
with fake_tensor_mode, operator_mode:
model_iter_fn(model_fake, example_inputs_fake, collect_outputs=False)
except Exception as e:
print(f"{name} failed to run with fake tensors, trying real. Exception: {e}")
operator_mode = OperatorInputsMode()
try:
with operator_mode:
model_iter_fn(model, example_inputs, collect_outputs=False)
except Exception as e2:
print(f"{name} failed to run with real. Exception: {e2}")
raise
print(f"Writing output to {output}")
operator_mode.log_to_file(output)
if __name__ == "__main__":
raise RuntimeError(
f"You shouldn't run {sys.argv[0]} directly, instead try timm_model.py, torchbench.py or hugginface.py"
)