Files
pytorch/benchmarks/dynamo/common.py
Boyuan Feng 90b4e130d6 [Benchmark] cleanup torchbench models (#164816)
Prune models from TorchInductor dashboard to reduce ci cost. This PR prunes torchbench models according to the [doc](https://docs.google.com/document/d/1nLPNNAU-_M9Clx9FMrJ1ycdPxe-xRA54olPnsFzdpoU/edit?tab=t.0), which removes timm and huggingface models from torchbench.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164816
Approved by: https://github.com/anijain2305, https://github.com/seemethere, https://github.com/huydhn, https://github.com/malfet
2025-10-09 00:31:25 +00:00

4353 lines
152 KiB
Python

#!/usr/bin/env python3
from __future__ import annotations
import argparse
import collections
import contextlib
import copy
import csv
import dataclasses
import functools
import gc
import importlib
import itertools
import json
import logging
import os
import platform
import random
import shutil
import signal
import subprocess
import sys
import tempfile
import time
import weakref
from contextlib import contextmanager
from typing import Any, NamedTuple, Optional, overload, TYPE_CHECKING, TypeVar
from unittest.mock import MagicMock
import numpy as np
import pandas as pd
import psutil
import yaml
from scipy.stats import gmean, ttest_ind
from tqdm.auto import tqdm, trange
import torch
import torch._dynamo
import torch._dynamo.utils
import torch._export
import torch.distributed
import torch.multiprocessing as mp
from torch._C import _has_cuda as HAS_CUDA, _has_xpu as HAS_XPU
from torch._C._nativert import PyModelRunner
from torch._dynamo.profiler import fx_insert_profiling, Profiler
from torch._dynamo.testing import (
dummy_fx_compile,
format_speedup,
reset_rng_state,
same,
)
from torch._logging.scribe import open_source_signpost
try:
from torch._dynamo.utils import clone_inputs, graph_break_reasons
from torch._inductor.utils import fresh_cache
except ImportError:
from _dynamo.utils import clone_inputs, graph_break_reasons
from _inductor.utils import fresh_cache
import torch._functorch.config
from torch._functorch.aot_autograd import set_model_name
from torch._inductor import config as inductor_config, metrics
from torch._subclasses.fake_tensor import FakeTensorMode
from torch.utils import _pytree as pytree
from torch.utils._pytree import tree_map, tree_map_only
try:
import torch_xla
import torch_xla.core.xla_model as xm
# This is to workaround the backward issue https://github.com/pytorch/xla/issues/4174
torch_xla._XLAC._init_computation_client()
except ImportError:
# ignore the error if torch_xla is not installed
pass
if TYPE_CHECKING:
from collections.abc import Sequence
_D = TypeVar("_D", bound=dict[str, Any])
_T = TypeVar("_T")
log = logging.getLogger(__name__)
# We are primarily interested in TF32
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True)
# Suppress torch.profiler spam
os.environ["KINETO_LOG_LEVEL"] = "5"
current_name = ""
current_device = ""
current_backend = ""
current_mode = ""
current_dtype = ""
current_quantization = ""
current_settings = None
current_batch_size = None
output_filename = None
disable_output = False
MAX_DOWNLOAD_ATTEMPTS = 5
class CI(NamedTuple):
backend: str # aot_eager or inductor
training: bool
dynamic: bool = False
device: str = "cuda"
CI_SKIP_OPTIMIZER = {
# HF
"MobileBertForMaskedLM", # Stack issue in fx
}
try:
from .fb.common import INTERNAL_CI_SKIP_DYNAMIC_BATCH_ONLY
except ImportError:
INTERNAL_CI_SKIP_DYNAMIC_BATCH_ONLY = set()
try:
from pytorch.benchmark.fb.run_utils import trace_handler
except ImportError:
trace_handler = None
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",
"pyhpc_isoneutral_mixing",
"pyhpc_equation_of_state",
"pyhpc_turbulent_kinetic_energy",
"detectron2_fcos_r_50_fpn",
"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",
"Reformer",
"llama",
}.union(INTERNAL_CI_SKIP_DYNAMIC_BATCH_ONLY)
# These models currently fail accuracy with eager Adam optimizer
# so we use SGD when running the full benchmarks
# https://github.com/pytorch/pytorch/issues/115966
BENCHMARK_USE_SGD = {
# TorchBench
"BERT_pytorch",
"LearningToPaint",
"alexnet",
"dcgan",
"demucs",
"densenet121",
"dlrm",
"fastNLP_Bert",
"mobilenet_v2",
"phlippe_densenet",
"phlippe_resnet",
"pytorch_stargan",
"resnet18",
"shufflenet_v2_x1_0",
"speech_transformer",
"squeezenet1_1",
"stable_diffusion_text_encoder",
"vgg16",
# HF
"AlbertForMaskedLM",
"BartForCausalLM",
"ElectraForCausalLM",
"M2M100ForConditionalGeneration",
"MBartForCausalLM",
"OPTForCausalLM",
"PLBartForCausalLM",
"PegasusForCausalLM",
"TrOCRForCausalLM",
"XGLMForCausalLM",
# TIMM
"adv_inception_v3",
"tf_efficientnet_b0",
"ghostnet_100",
}
# These models OOM in CI
# due to the extra memory of Adam optimizer states,
# so we fall back to SGD in CI
CI_USE_SGD = {
"torchrec_dlrm",
"demucs",
"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_maskrcnn_r_101_c4",
"detectron2_maskrcnn_r_101_fpn",
"detectron2_maskrcnn_r_50_c4",
"detectron2_maskrcnn_r_50_fpn",
"llama_v2_7b_16h",
"mobilenet_v2_quantized_qat",
"phi_1_5 resnet50_quantized_qat",
"BlenderbotForCausalLM",
"DALLE2_pytorch",
"moco",
"timm_efficientdet",
"ghostnet_100",
"inception_v3",
"mobilevit_s",
"pytorch_CycleGAN_and_pix2pix",
"vision_maskrcnn",
"dlrm",
"resnet50",
"dm_nfnet_f0",
}
DO_NOT_CAST_INPUTS = {"stable_diffusion"}
# Maps a benchmark model name to a list of status codes. For any listed entry, we'll
# capture TORCH_COMPILE_DEBUG logs in CI runs and preserve them (i.e., for upload) if
# the result status matches one listed.
CI_PRESERVE_COMPILE_DEBUG = {
# For example:
# "mnasnet1_0": ["fail_accuracy"],
}
@functools.lru_cache(maxsize=1)
def load_yaml_file(filename):
filepath = os.path.join(os.path.dirname(__file__), filename)
with open(filepath) as f:
data = yaml.safe_load(f)
internal_file_path = os.path.join(os.path.dirname(__file__), "fb", filename)
if os.path.exists(internal_file_path):
with open(internal_file_path) as f:
internal_data = yaml.safe_load(f)
data.update(internal_data)
def flatten(lst):
for item in lst:
if isinstance(item, list):
yield from flatten(item)
else:
yield item
def maybe_list_to_set(obj):
if isinstance(obj, dict):
return {k: maybe_list_to_set(v) for k, v in obj.items()}
if isinstance(obj, list):
return set(flatten(obj))
return obj
return maybe_list_to_set(data)
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 write_outputs(filename, headers, row, upload_to_benchmark_db: bool = True):
"""
Write both CSV and JSON outputs using the original CSV output interface
"""
global disable_output
if disable_output:
return
output_csv(filename, headers, row)
if upload_to_benchmark_db:
output_json(filename, headers, row)
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 output_json(filename, headers, row):
"""
Write the result into JSON format, so that it can be uploaded to the benchmark database
to be displayed on OSS dashboard. The JSON format is defined at
https://github.com/pytorch/pytorch/wiki/How-to-integrate-with-PyTorch-OSS-benchmark-database
"""
origin = ""
if "torchbench" in filename:
origin = "torchbench"
elif "huggingface" in filename:
origin = "huggingface"
elif "timm_models" in filename:
origin = "timm_models"
extra_info = {
"device": current_device,
"quantization": current_quantization,
"batch_size": current_batch_size,
}
if current_settings:
extra_info.update(current_settings)
mapping_headers = {headers[i]: v for i, v in enumerate(row)}
with open(f"{os.path.splitext(filename)[0]}.json", "a") as f:
for header, value in mapping_headers.items():
# These headers are not metric names
if header in ("dev", "name", "batch_size"):
continue
# Make sure that the record is valid
if not current_name:
continue
record = {
"benchmark": {
"name": "TorchInductor",
"mode": current_mode,
"dtype": current_dtype,
"extra_info": extra_info,
},
"model": {
"name": current_name,
"type": "OSS model",
"backend": current_backend,
"origins": [origin],
},
}
# NB: When the metric is accuracy, its value is actually a string, i.e. pass, and
# not a number. ClickHouse doesn't support mix types atm. It has a Variant type
# https://clickhouse.com/docs/en/sql-reference/data-types/variant, but this isn't
# recommended by CH team themselves. The workaround here is to store that value
# in the extra_info field instead.
if isinstance(value, str):
record["metric"] = {
"name": header,
"extra_info": {"benchmark_values": [value]},
}
else:
record["metric"] = {
"name": header,
"benchmark_values": [value],
}
print(json.dumps(record), file=f)
def get_suite_from_model_iter_fn(model_iter_fn):
# TODO: This is a bit of a hack
suite = None
if (runner := getattr(model_iter_fn, "__self__", None)) and hasattr(
runner, "suite_name"
):
suite = runner.suite_name
return suite
def output_signpost(data, args, suite, error=None):
from torch.utils._stats import simple_call_counter
data = data.copy()
if "name" not in data:
data["name"] = current_name
if "dev" not in data:
data["dev"] = current_device
filtered_args = vars(args).copy()
# I generated this list by reading through all the configs and dropping
# ones that looked irrelevant or redundant
for k in [
"filter",
"exclude",
"exclude_exact",
"dump_raw_metrics",
"log_operator_inputs",
"distributed_master_port",
"skip_accuracy_check",
"generate_aot_autograd_stats",
"output",
"output_directory",
"disable_output",
"export_profiler_trace",
"profiler_trace_name",
"explain",
"stats",
"print_memory",
"print_compilation_time",
"print_dataframe_summary",
"print_graph_breaks",
"log_graph_breaks",
"timing",
"progress",
"timeout",
"per_process_memory_fraction",
"minify",
"verbose",
"quiet",
"print_fx",
"print_aten_ops",
"log_conv_args",
"recompile_profiler",
"find_batch_sizes",
# Redundant
"batch_size",
"batch_size_file",
"only",
"diff_branch",
"tag",
"coverage",
"overhead",
"speedup_dynamo_ts",
"speedup_fx2trt",
"speedup_fx2trt_fp16",
"accuracy",
"performance",
"tolerance",
]:
del filtered_args[k]
event_name = "unknown"
if args.accuracy:
event_name = "accuracy"
elif args.quantization:
event_name = "quantization"
elif args.performance:
event_name = "performance"
from torch._dynamo.utils import calculate_time_spent, compilation_time_metrics
wall_time_by_phase = calculate_time_spent()
open_source_signpost(
subsystem="dynamo_benchmark",
name=event_name,
parameters=json.dumps(
{
**data,
# TODO: Arguably the rest of these should be in the CSV too
"suite": suite,
# Better than using compile_times utils directly
# NB: Externally, compilation_metrics colloquially refers to
# the coarse-grained phase timings, even though internally
# they are called something else
"compilation_metrics": wall_time_by_phase,
"agg_compilation_metrics": {
k: sum(v) for k, v in compilation_time_metrics.items()
},
"detailed_compilation_metrics": compilation_time_metrics,
"simple_call_counter": simple_call_counter,
# NB: args has training vs inference
"args": filtered_args,
"error": error,
}
),
)
return wall_time_by_phase["total_wall_time"]
def nothing(f):
return f
@functools.cache
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
if HAS_CUDA:
import torch.cuda
if not torch.cuda._is_in_bad_fork():
torch.cuda.manual_seed_all(seed)
if HAS_XPU:
import torch.xpu
if not torch.xpu._is_in_bad_fork():
torch.xpu.manual_seed_all(seed)
return default_generator.manual_seed(seed)
torch.manual_seed = deterministic_torch_manual_seed
def empty_gpu_cache(device):
"""
Explicitly empty gpu cache to avoid OOM in subsequent run.
"""
if device not in ["cuda", "xpu", "mps"]:
log.warning(
"Trying to call the empty_gpu_cache for device: %s, which is not in list [cuda, xpu]",
device,
)
return
getattr(torch, device).empty_cache()
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:
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,
batch_size=None,
):
use_xla = tensor_is_on_xla(example_inputs)
synchronize()
if batch_size:
patch_torch_manual_seed()
if use_xla:
xm.mark_step()
xm.wait_device_ops()
def vary_batch(t: torch.Tensor, new_batch_size) -> torch.Tensor:
for i, s in enumerate(t.size()):
if s == batch_size:
# If new batch is smaller, we truncate
if new_batch_size < batch_size:
indexer = [slice(None)] * t.ndim
indexer[i] = slice(0, new_batch_size)
t = t[tuple(indexer)]
# If new batch is greater, we just duplicate the last row
# over and over until we hit the desired batch size
elif new_batch_size > batch_size:
indexer = [slice(None)] * t.ndim
indexer[i] = -1
last_slice = t[tuple(indexer)].unsqueeze(i)
repeat_shape = list(t.shape)
repeat_shape[i] = new_batch_size - batch_size
padding = last_slice.expand(*repeat_shape)
t = torch.cat([t, padding], dim=i)
break
return t
time_total = 0
# Dont collect outputs to correctly measure timing
for i in range(times):
# If batch_size is 1, it too often collides with other non batch size
# dimensions resulting in errors.
if batch_size and batch_size > 1:
# Calculate new batch size by varying the original batch size by up to 20%
# Ensure it's at least greater than 1
variation = random.uniform(0.8, 1.2)
new_batch_size = max(2, int(batch_size * variation))
example_inputs = tree_map_only(
torch.Tensor, lambda x: vary_batch(x, new_batch_size), example_inputs
)
# 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
@overload
def _normalize_bench_inputs(example_inputs: _D) -> tuple[tuple[()], _D]: ...
@overload
def _normalize_bench_inputs(
example_inputs: Sequence[_T],
) -> tuple[tuple[_T, ...], dict[str, Any]]: ...
def _normalize_bench_inputs(example_inputs):
# 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_leaves(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,
serialized_type_name=f"{output_type.__module__}.{output_type.__name__}",
)
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()
write_outputs(
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)
# TODO: CompilerProfiler is deprecated, remove this
def recompile_profiler_experiment(args, model_iter_fn, model, example_inputs):
prof = torch._dynamo.utils.CompilerProfiler()
opt_model_iter_fn = torch._dynamo.optimize(prof, nopython=args.nopython)(
model_iter_fn
)
opt_model_iter_fn(model, example_inputs)
write_outputs(
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 latency_experiment(args, model_iter_fn, model, example_inputs, mark, **kwargs):
"""
Measure latency on a specific backend.
"""
timings = np.zeros((args.repeat,), np.float64)
# if we randomize the input, we should also check the result is correct
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
with maybe_profile(args.export_profiler_trace, **args.profile_details) as p:
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)
with maybe_mark_profile(p=p, mark=mark):
timings[rep], actual_output = timed(
model,
model_iter_fn,
inputs,
return_result=True,
times=times,
collect_outputs=args.collect_outputs,
)
if args.export_profiler_trace:
name = args.profiler_trace_name + "_" + model.name
if hasattr(args, "rank"):
name += f"_rank_{args.rank}"
name += ".json"
name = os.path.join(torch._dynamo.config.base_dir, name)
p.export_chrome_trace(name)
return timings
# TODO: This seems to be specifically triggered by torchao testing
def latency_experiment_summary(suite_name, args, model, timings, **kwargs):
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 "cache_lookup_latency" in kwargs:
headers.append("cache_lookup_latency")
row.append(kwargs["cache_lookup_latency"])
if "dynamo_stats" in kwargs:
for k, v in kwargs["dynamo_stats"].items():
headers.append(k)
row.append(v)
write_outputs(
output_filename,
headers,
row,
)
c_headers, c_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}"
)
write_outputs(
output_filename[:-4] + "_compilation_metrics.csv",
first_headers + c_headers,
first_fields + c_data,
)
# Hypothetically you can use this from other places, but it's currently
# inaccessible, and when this assert fails you need to update the
# event_name here to account for the other cases you are using this
assert args.quantization is not None
output_signpost(
dict(zip(headers, row)),
args,
suite_name,
)
return msg
def speedup_experiment(args, model_iter_fn, model, example_inputs, **kwargs):
"""
Measure speedups over eager.
Writes to ./speedups.csv
"""
timings = np.zeros((args.repeat, 2), np.float64)
# if we randomize the input, we should also check the result is correct
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 instability 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, **args.profile_details) as p:
if args.export_aot_inductor:
frozen_model_iter_fn = export_aot_inductor(
model, example_inputs, args.inductor_compile_mode
)
elif args.export_nativert:
frozen_model_iter_fn = export_nativert(model, example_inputs)
elif args.torchscript_jit_trace:
frozen_model_iter_fn = torchscript_jit_trace(model, example_inputs)
else:
if kwargs["hf_llm"]:
# If it's an llm, we want to optimize model.forward, and use
# the generate function
model.forward = torch._dynamo.run(model)
frozen_model_iter_fn = 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"),
torch.compiler.set_stance("force_eager"),
):
timings[rep, 0], expected_output = timed(
model,
model_iter_fn,
inputs,
return_result=True,
times=times,
collect_outputs=args.collect_outputs,
batch_size=kwargs.get("batch_size"),
)
# 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 args.export_profiler_trace:
name = args.profiler_trace_name + "_" + model.name
if hasattr(args, "rank"):
name += f"_rank_{args.rank}"
if args.export_perfdoctor and trace_handler:
trace_handler(name, p)
else:
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 "cache_lookup_latency" in kwargs:
headers.append("cache_lookup_latency")
row.append(kwargs["cache_lookup_latency"])
if "dynamo_stats" in kwargs:
for k, v in kwargs["dynamo_stats"].items():
headers.append(k)
row.append(v)
write_outputs(
output_filename,
headers,
row,
)
c_headers, c_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}"
)
write_outputs(
output_filename[:-4] + "_compilation_metrics.csv",
first_headers + c_headers,
first_fields + c_data,
)
output_signpost(
dict(zip(headers, row)),
args,
get_suite_from_model_iter_fn(model_iter_fn),
)
return msg
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:]])
]
)
write_outputs(
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
write_outputs(
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
def _produce_dynamic_shapes_for_export(path, x):
# mark_dynamic() is ignored for export.
# use this to produce dynamic_shapes spec instead.
from torch.export.dynamic_shapes import Dim
if not isinstance(x, torch.Tensor):
return None
return dict.fromkeys(getattr(x, "_dynamo_dynamic_indices", {}), Dim.AUTO)
class AOTInductorModelCache:
cache: dict[weakref.ref, tuple[Any, float]] = {}
@classmethod
def load(cls, model, example_inputs, mode):
import torch._inductor
from torch.export.dynamic_shapes import _combine_args, _tree_map_with_path
key = weakref.ref(model)
if key not in cls.cache:
# Register the output dataclass to pytree
example_args, example_kwargs = _normalize_bench_inputs(example_inputs)
with torch.no_grad():
# copy.deepcopy is required to prevent any surprising side-effect,
# see https://github.com/pytorch/pytorch/issues/113029
# This will cause memory stats to be overshadowed by this eager run.
# To fix that, memory stats will be reset later.
example_outputs = copy.deepcopy(model)(*example_args, **example_kwargs)
if pytree.is_namedtuple_instance(example_outputs):
typ = type(example_outputs)
pytree._register_namedtuple(
typ,
serialized_type_name=f"{typ.__module__}.{typ.__name__}",
)
else:
_register_dataclass_output_as_pytree(example_outputs)
combined_args = _combine_args(model, example_args, example_kwargs)
dynamic_shapes = _tree_map_with_path(
_produce_dynamic_shapes_for_export, combined_args
)
# delete example_outputs and reset memory stats here
del example_outputs
if current_device == "cuda":
empty_gpu_cache(current_device)
torch.cuda.reset_peak_memory_stats()
pre_clone_memory_used = torch.cuda.max_memory_allocated()
elif current_device == "hpu":
torch.hpu.reset_peak_memory_stats()
pre_clone_memory_used = torch.hpu.max_memory_allocated()
# Clone the model pre-exporting. This prevents scenarios observed in a few
# models, where the forward pass modifies model state while exporting, and
# FakeTensors are thus saved as model data members. This invalidates model
# reuse in eager mode, so it's safest to export a model clone.
model_clone = copy.deepcopy(model)
# Since CPU doesn't monitor max memory allocation, anything measuring peak
# memory will miss our transient model clone on CPU anyway.
#
# The justification for tracking this value (in order to remove it from the
# AOTInductor memory measurements) is that normal usage of AOTInductor would
# not clone the model, since the eager model would be unused post-export.
clone_memory_used = 0.0
if current_device == "cuda":
clone_memory_used = (
torch.cuda.max_memory_allocated() - pre_clone_memory_used
) / 1e9
elif current_device == "hpu":
clone_memory_used = (
torch.hpu.max_memory_allocated() - pre_clone_memory_used
) / 1e9
inductor_configs = {}
if mode == "max-autotune":
inductor_configs["max_autotune"] = True
ep = torch.export.export(
model_clone,
example_args,
example_kwargs,
dynamic_shapes=dynamic_shapes,
strict=False,
)
with torch.no_grad():
package_path = torch._inductor.aoti_compile_and_package(
ep, inductor_configs=inductor_configs
) # type: ignore[arg-type]
cls.cache[key] = (
torch._inductor.aoti_load_package(package_path),
clone_memory_used,
)
return cls.cache[key][0]
@classmethod
def get_excess_memory(cls, model) -> float:
return cls.cache.get(weakref.ref(model), (None, 0.0))[1]
class NativeRTCache:
cache: dict[weakref.ref, Any] = {}
@classmethod
def load(cls, model, example_inputs):
from torch.export.dynamic_shapes import _combine_args, _tree_map_with_path
key = weakref.ref(model)
if key not in cls.cache:
example_args, example_kwargs = _normalize_bench_inputs(example_inputs)
example_outputs = model(*example_args, **example_kwargs)
_register_dataclass_output_as_pytree(example_outputs)
combined_args = _combine_args(model, example_args, example_kwargs)
dynamic_shapes = _tree_map_with_path(
_produce_dynamic_shapes_for_export, combined_args
)
ep = torch.export.export(
model, example_args, example_kwargs, dynamic_shapes=dynamic_shapes
)
ep = ep.run_decompositions({})
with tempfile.NamedTemporaryFile(delete=False) as f:
torch.export.pt2_archive._package.package_pt2(
f, exported_programs={"forward": ep}
)
filename = f.name
cls.cache[key] = PyModelRunner(filename, "forward")
return cls.cache[key]
class JitTracedCache:
cache: dict[weakref.ref, Any] = {}
@classmethod
def load(cls, model, example_inputs):
key = weakref.ref(model)
if key not in cls.cache:
example_args, example_kwargs = _normalize_bench_inputs(example_inputs)
if example_args:
jit_traced_module = torch.jit.trace(
model, example_inputs=example_args, strict=False
)
else:
jit_traced_module = torch.jit.trace(
model, example_kwarg_inputs=example_kwargs, strict=False
)
cls.cache[key] = jit_traced_module
return cls.cache[key]
def export(model, example_inputs):
from torch.export.dynamic_shapes import _combine_args, _tree_map_with_path
example_args, example_kwargs = _normalize_bench_inputs(example_inputs)
example_outputs = model(*example_args, **example_kwargs)
_register_dataclass_output_as_pytree(example_outputs)
combined_args = _combine_args(model, example_args, example_kwargs)
dynamic_shapes = _tree_map_with_path(
_produce_dynamic_shapes_for_export, combined_args
)
# NOTE: if args.export is ever enabled for --performance mode (rather than solely
# --accuracy), we'll need to clone the model and subtract out extra memory usage, as
# done in AOTInductorModelCache.
ep = torch.export.export(
model, example_args, example_kwargs, dynamic_shapes=dynamic_shapes, strict=True
)
def opt_export(_, example_inputs):
example_args, example_kwargs = _normalize_bench_inputs(example_inputs)
return ep.module()(*example_args, **example_kwargs)
return opt_export
def export_nativert(model, example_inputs):
optimized = NativeRTCache.load(model, example_inputs)
def opt_nativert(_, example_inputs, collect_outputs=False):
example_args, example_kwargs = _normalize_bench_inputs(example_inputs)
return optimized.run(*example_args, **example_kwargs)
return opt_nativert
def export_aot_inductor(model, example_inputs, mode):
optimized = AOTInductorModelCache.load(model, example_inputs, mode)
def opt_aot_inductor(_, example_inputs, collect_outputs=False):
example_args, example_kwargs = _normalize_bench_inputs(example_inputs)
return optimized(*example_args, **example_kwargs)
return opt_aot_inductor
def torchscript_jit_trace(model, example_inputs):
optimized = JitTracedCache.load(model, example_inputs)
def opt_jit_trace(_, example_inputs, collect_outputs=False):
example_args, example_kwargs = _normalize_bench_inputs(example_inputs)
return optimized(*example_args, **example_kwargs)
return opt_jit_trace
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( # noqa: B904
f"Failed to load model '{args}' with following error(s): {str(e)}."
)
return wrapper
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)
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"]),
"autograd_captures": torch._dynamo.utils.counters["compiled_autograd"][
"captures"
],
"autograd_compiles": torch._dynamo.utils.counters["compiled_autograd"][
"compiles"
],
"cudagraph_skips": torch._dynamo.utils.counters["inductor"][
"cudagraph_skips"
],
}
)
@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()
@contextmanager
def maybe_snapshot_memory(should_snapshot_memory, suffix):
# Enables Memory Snapshot tool for memory deep dives:
# https://pytorch.org/blog/understanding-gpu-memory-1/
try:
if should_snapshot_memory:
torch.cuda.memory._record_memory_history(max_entries=100000)
yield
finally:
if should_snapshot_memory:
try:
torch.cuda.memory._dump_snapshot(
os.path.join(
torch._dynamo.config.base_dir,
f"{output_filename.rstrip('.csv')}_{suffix}.pickle",
)
)
except Exception as e:
log.error("Failed to save memory snapshot, %s", e)
torch.cuda.memory._record_memory_history(enabled=None)
class BenchmarkRunner:
def __init__(self):
self.model_iter_fn = None
self.grad_scaler = DummyGradScaler()
self.autocast = contextlib.nullcontext
self.autocast_arg = {}
self.optimizer: Optional[torch.optim.Optimizer] = None
self._args = None
def setup_amp(self, current_device=None):
if self.args.only in self.fp32_only_models:
return
devices = [current_device] if current_device else self.args.devices
if self.args.amp:
# AMP training can lead to small loss values which can underflow
# 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.amp.GradScaler(device="cuda", init_scale=2.0)
self.autocast = functools.partial(
torch.amp.autocast, device_type=devices[0]
)
if self.args.amp_dtype:
amp_dtype = (
torch.float16
if self.args.amp_dtype == "float16"
else torch.bfloat16
)
self.autocast_arg["dtype"] = amp_dtype
def init_optimizer(self, name, device, params):
if device == "cuda" and self.args.training and name not in CI_SKIP_OPTIMIZER:
if (name in CI_USE_SGD and self.args.ci) or name in BENCHMARK_USE_SGD:
self.optimizer = torch.optim.SGD(params, lr=0.01, foreach=True)
# Disable multi_tensor_sgd for benchmarking, there isn't a large performance benefit (~1%) to compiling
# this optimizer because it is a single foreach add, and increases compile time.
# After autotuning and fake tensor caching lands, we can enable, because the compile time impact will be lower.
# Fake Tensor caching: https://github.com/pytorch/pytorch/pull/113873
# Autotuning: https://github.com/pytorch/pytorch/issues/117447
self.optimizer.step = torch._dynamo.disable(self.optimizer.step)
else:
self.optimizer = torch.optim.Adam(
params, lr=0.01, capturable=True, 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 skip_models_for_cpu_aarch64(self):
return set()
@property
def skip_models_for_freezing_cpu(self):
return set()
@property
def skip_models_for_freezing_cuda(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 force_fp16_for_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 skip_multiprocess_models(self):
return set()
@property
def skip_models_due_to_control_flow(self):
return set()
@property
def skip_models_due_to_export_not_supported(self):
return set()
@property
def disable_cudagraph_models(self):
return set()
@property
def guard_on_nn_module_models(self):
return set()
@property
def inline_inbuilt_nn_modules_models(self):
return set()
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 use_larger_multiplier_for_smaller_tensor(self, name):
return False
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()
elif self.args.only in self.force_fp16_for_bf16_models:
log.warning(
"Model %s does not support bfloat16, running with float16 instead",
self.args.only,
)
model, example_inputs = cast_to_fp16(model, example_inputs)
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 RuntimeError("Eager run failed") from e
def maybe_cast(self, model, 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:
empty_gpu_cache(current_device)
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, model_iter_fn):
n = self.args.iterations
for _ in range(n - 1):
model_iter_fn(mod, inputs, collect_outputs=False)
return model_iter_fn(mod, inputs, collect_outputs=True)
@torch._disable_dynamo(recursive=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 get_fsdp_auto_wrap_policy(self, model_name: str):
from diffusers.models.transformer_2d import Transformer2DModel
from torchbenchmark.models.nanogpt.model import Block
from transformers.models.llama.modeling_llama import LlamaDecoderLayer
from torch.distributed.fsdp.wrap import (
ModuleWrapPolicy,
size_based_auto_wrap_policy,
)
# handcrafted wrap policy
MODEL_FSDP_WRAP = {
"stable_diffusion_unet": (Transformer2DModel,),
"llama_v2_7b_16h": (LlamaDecoderLayer,),
"nanogpt": (Block,),
}
if model_name not in MODEL_FSDP_WRAP:
# default to using wrap policy based on module size
return functools.partial(
size_based_auto_wrap_policy, recurse=True, min_num_params=int(1e5)
)
return ModuleWrapPolicy(MODEL_FSDP_WRAP[model_name])
def deepcopy_and_maybe_parallelize(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,
)
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,
)
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=self.get_fsdp_auto_wrap_policy(self.args.only),
)
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)
o_headers = list(headers)
o_fields = list(fields)
dynamo_stats = get_dynamo_stats()
dynamo_stats.subtract(dynamo_start_stats)
for k, v in dynamo_stats.items():
headers.append(k)
fields.append(v)
total_wall_time = output_signpost(
dict(zip(o_headers, o_fields)),
self.args,
self.suite_name,
)
headers.append("compilation_latency")
fields.append(total_wall_time)
write_outputs(output_filename, headers, fields)
if self.args.print_compilation_time:
print(f"Compilation time (from dynamo_timed): {total_wall_time}")
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)
# Skip all accuracy check for the torchao backend
if self.args.backend == "torchao":
return record_status("pass_due_to_skip", dynamo_start_stats=start_stats)
with self.pick_grad(name, self.args.training):
# Collect the fp64 reference outputs to be used later for accuracy checking.
fp64_outputs = None
model_fp64 = None
inputs_fp64 = None
try:
model_fp64, inputs_fp64 = cast_to_fp64(
self.deepcopy_and_maybe_parallelize(model),
clone_inputs(example_inputs),
)
self.init_optimizer(name, current_device, model_fp64.parameters())
fp64_outputs = self.run_n_iterations(
model_fp64, inputs_fp64, self.model_iter_fn
)
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
finally:
del model_fp64, inputs_fp64
empty_gpu_cache(current_device)
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"
# Get results of native pytorch
reset_rng_state()
model_copy = None
try:
with torch.compiler.set_stance("force_eager"):
model_copy = self.deepcopy_and_maybe_parallelize(model)
self.init_optimizer(name, current_device, model_copy.parameters())
correct_result = self.run_n_iterations(
model_copy, clone_inputs(example_inputs), self.model_iter_fn
)
except Exception as e:
accuracy_status = (
"eager_1st_run_OOM"
if isinstance(e, torch.cuda.OutOfMemoryError)
else "eager_1st_run_fail"
)
log.exception("")
return record_status(accuracy_status, dynamo_start_stats=start_stats)
finally:
del model_copy
empty_gpu_cache(current_device)
# Rerun native pytorch
reset_rng_state()
model_copy = None
try:
with torch.compiler.set_stance("force_eager"):
model_copy = self.deepcopy_and_maybe_parallelize(model)
self.init_optimizer(name, current_device, model_copy.parameters())
correct_rerun_result = self.run_n_iterations(
model_copy, clone_inputs(example_inputs), self.model_iter_fn
)
except Exception as e:
accuracy_status = (
"eager_2nd_run_OOM"
if isinstance(e, torch.cuda.OutOfMemoryError)
else "eager_2nd_run_fail"
)
log.exception("")
return record_status(accuracy_status, dynamo_start_stats=start_stats)
finally:
del model_copy
empty_gpu_cache(current_device)
# Two eager runs should have exactly same result, within tolerance.
# TODO If we want the above to be true, then deterministic should be set.
# For example, MIOpen convolutions could be implemented with non-deterministic algos.
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=tolerance if torch.version.hip else 0,
equal_nan=self.equal_nan,
use_larger_multiplier_for_smaller_tensor=self.use_larger_multiplier_for_smaller_tensor(
name
),
)
):
is_same = False
except Exception:
# 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()
torch._dynamo.utils.counters.clear()
model_copy = None
try:
model_copy = self.deepcopy_and_maybe_parallelize(model)
self.init_optimizer(name, current_device, model_copy.parameters())
if (
self.args.export
or self.args.export_aot_inductor
or self.args.export_nativert
or self.args.torchscript_jit_trace
):
# 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(**self.autocast_arg):
optimized_model_iter_fn = optimize_ctx(
model_copy, example_inputs
)
new_result = optimized_model_iter_fn(model_copy, example_inputs)
else:
optimized_model_iter_fn = optimize_ctx(self.model_iter_fn)
new_result = self.run_n_iterations(
model_copy, example_inputs, optimized_model_iter_fn
)
except Exception as e:
log.exception("")
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)
finally:
del model_copy
if name in self.skip_accuracy_check_as_eager_non_deterministic:
return record_status("pass_due_to_skip", dynamo_start_stats=start_stats)
force_max_multiplier = False
if (
self.args.freezing
and self.args.bfloat16
and torch._dynamo.utils.counters["inductor"]["binary_folding_conv"] > 0
):
force_max_multiplier = True
try:
if self.args.training and self.args.amp:
if process_fn := self.get_output_amp_train_process_func.get(
name, None
):
correct_result = process_fn(correct_result)
new_result = process_fn(new_result)
fp64_outputs = process_fn(fp64_outputs)
if not same(
correct_result,
new_result,
fp64_outputs,
equal_nan=self.equal_nan,
use_larger_multiplier_for_smaller_tensor=self.use_larger_multiplier_for_smaller_tensor(
name
),
cos_similarity=cos_similarity,
tol=tolerance,
force_max_multiplier=force_max_multiplier,
):
is_same = False
except Exception:
# 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, self.model_iter_fn
)
# 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.model_iter_fn)
new_result = self.run_n_iterations(
model_copy, example_inputs, optimized_model_iter_fn
)
except Exception:
log.exception("")
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(),
]
write_outputs(output_filename, headers, fields)
return tolerance_status
def run_performance_test_non_alternate(
self, name, model, example_inputs, optimize_ctx, experiment, tag=None
):
"Run performance test in non-alternately."
assert experiment.func is latency_experiment, (
"Must run with latency_experiment."
)
def warmup(fn, model, example_inputs, mode, niters=10):
gc.collect()
peak_mem = 0
start_stats = get_dynamo_stats()
try:
if current_device == "cuda":
torch.cuda.reset_peak_memory_stats()
empty_gpu_cache(current_device)
elif current_device == "hpu":
torch.hpu.reset_peak_memory_stats()
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 == "hpu":
peak_mem = torch.hpu.max_memory_allocated() / 10**9
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)
write_csv_when_exception(
self.args, current_name, "warmup_failed", current_device
)
output_signpost({}, self.args, self.suite_name, error="warmup_failed")
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_parallelize(model)
if not hasattr(model, name):
model.name = name
self.init_optimizer(name, current_device, model.parameters())
# The self.autocast context is needed for the model we export with aot_compile,
# similar to what we do in the check_accuracy function
ctx = (
self.autocast(**self.autocast_arg)
if self.args.export_aot_inductor
else contextlib.nullcontext()
)
with self.pick_grad(name, self.args.training), ctx:
ok, total = Stats.reset_counters()
experiment_kwargs = {}
if tag is not None:
experiment_kwargs["tag"] = tag
results = []
with maybe_snapshot_memory(
self.args.snapshot_memory, f"eager_{self.args.only}"
):
eager_latency, eager_peak_mem, _ = warmup(
self.model_iter_fn, model, example_inputs, "eager"
)
if self.args.use_warm_peak_memory:
_, eager_peak_mem, _ = warmup(
self.model_iter_fn, model, example_inputs, "eager", niters=1
)
baseline_timings = experiment(
self.model_iter_fn,
model,
example_inputs,
mark="expected",
**experiment_kwargs,
)
if self.args.export_aot_inductor:
optimized_model_iter_fn = optimize_ctx
else:
optimized_model_iter_fn = optimize_ctx(self.model_iter_fn)
with maybe_snapshot_memory(
self.args.snapshot_memory, f"compiled_{self.args.only}"
):
dynamo_latency, dynamo_peak_mem, dynamo_stats = warmup(
optimized_model_iter_fn, model, example_inputs, "dynamo"
)
if self.args.use_warm_peak_memory:
_, dynamo_peak_mem, _ = warmup(
optimized_model_iter_fn,
model,
example_inputs,
"dynamo",
niters=1,
)
# If we use warm peak memory, the AOT model loading transient memory
# won't be present on the warm measurement. We only have to account for
# it when using cold memory.
elif self.args.export_aot_inductor:
dynamo_peak_mem -= AOTInductorModelCache.get_excess_memory(model)
if self.args.profile_dynamo_cache_lookup:
with torch.profiler.profile(
activities=[torch.profiler.ProfilerActivity.CPU]
) as prof:
warmup(optimized_model_iter_fn, model, example_inputs, "dynamo")
events = list(
filter(
lambda event: "TorchDynamo Cache Lookup" in event.key,
prof.key_averages(),
)
)
dynamo_cache_lookup_latency = events[0].self_cpu_time_total
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 self.args.print_compilation_time:
print(f"Compilation time: {compilation_time:.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 self.args.profile_dynamo_cache_lookup:
experiment_kwargs["cache_lookup_latency"] = (
dynamo_cache_lookup_latency
)
backend_timings = experiment(
self.model_iter_fn,
model,
example_inputs,
mark="expected",
**experiment_kwargs,
)
timings = np.stack((baseline_timings, backend_timings), axis=1)
result_summary = latency_experiment_summary(
self.suite_name, self.args, model, timings, **experiment_kwargs
)
results.append(result_summary)
return " ".join(map(str, results))
def run_performance_test(
self,
name,
model,
example_inputs,
optimize_ctx,
experiment,
tag=None,
batch_size=None,
):
niters = 5
if getattr(self, "hf_llm", False):
# If we're benchmarking an llm, we want to use the generate function
self.model_iter_fn = self.generate
niters = 1
if self.args.xla:
with self.pick_grad(name, self.args.training):
return experiment(
self.model_iter_fn, *self.maybe_cast(model, example_inputs)
)
def warmup(fn, model, example_inputs, mode, niters=5):
gc.collect()
peak_mem = 0
start_stats = get_dynamo_stats()
try:
if current_device == "cuda":
torch.cuda.reset_peak_memory_stats()
empty_gpu_cache(current_device)
elif current_device == "hpu":
torch.hpu.reset_peak_memory_stats()
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 == "hpu":
peak_mem = torch.hpu.max_memory_allocated() / 10**9
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)
write_csv_when_exception(
self.args, current_name, "warmup_failed", current_device
)
output_signpost({}, self.args, self.suite_name, error="warmup_failed")
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_parallelize(model)
if not hasattr(model, name):
model.name = name
self.init_optimizer(name, current_device, model.parameters())
# The self.autocast context is needed for the model we export with aot_compile,
# similar to what we do in the check_accuracy function
ctx = (
self.autocast(**self.autocast_arg)
if self.args.export_aot_inductor
else contextlib.nullcontext()
)
with self.pick_grad(name, self.args.training), ctx:
ok, total = Stats.reset_counters()
experiment_kwargs = {}
experiment_kwargs["batch_size"] = batch_size
if tag is not None:
experiment_kwargs["tag"] = tag
results = []
with maybe_snapshot_memory(
self.args.snapshot_memory, f"eager_{self.args.only}"
):
with torch.compiler.set_stance("force_eager"):
eager_latency, eager_peak_mem, _ = warmup(
self.model_iter_fn,
copy.deepcopy(model),
example_inputs,
"eager",
niters=niters,
)
if self.args.use_warm_peak_memory:
_, eager_peak_mem, _ = warmup(
self.model_iter_fn,
copy.deepcopy(model),
example_inputs,
"eager",
niters=1,
)
if (
self.args.export_aot_inductor
or self.args.export_nativert
or self.args.torchscript_jit_trace
):
optimized_model_iter_fn = optimize_ctx
else:
if getattr(self, "hf_llm", False):
# If it's an llm, we want to optimize model.forward, and use
# the generate function
model = optimize_ctx(model)
optimized_model_iter_fn = self.model_iter_fn
else:
optimized_model_iter_fn = optimize_ctx(self.model_iter_fn)
with maybe_snapshot_memory(
self.args.snapshot_memory, f"compiled_{self.args.only}"
):
dynamo_latency, dynamo_peak_mem, dynamo_stats = warmup(
optimized_model_iter_fn, model, example_inputs, "dynamo"
)
if self.args.use_warm_peak_memory:
_, dynamo_peak_mem, _ = warmup(
optimized_model_iter_fn,
model,
example_inputs,
"dynamo",
niters=1,
)
# If we use warm peak memory, the AOT model loading transient memory
# won't be present on the warm measurement. We only have to account for
# it when using cold memory.
elif self.args.export_aot_inductor:
dynamo_peak_mem -= AOTInductorModelCache.get_excess_memory(model)
if self.args.profile_dynamo_cache_lookup:
with torch.profiler.profile(
activities=[torch.profiler.ProfilerActivity.CPU]
) as prof:
warmup(optimized_model_iter_fn, model, example_inputs, "dynamo")
events = list(
filter(
lambda event: "TorchDynamo Cache Lookup" in event.key,
prof.key_averages(),
)
)
dynamo_cache_lookup_latency = events[0].self_cpu_time_total
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 self.args.print_compilation_time:
print(f"Compilation time: {compilation_time:.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 self.args.profile_dynamo_cache_lookup:
experiment_kwargs["cache_lookup_latency"] = (
dynamo_cache_lookup_latency
)
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"
)
experiment_kwargs["hf_llm"] = getattr(self, "hf_llm", False)
results.append(
experiment(
self.model_iter_fn, model, example_inputs, **experiment_kwargs
)
)
return " ".join(map(str, results))
def minify_model(
self,
name,
model,
example_inputs,
optimize_ctx,
experiment,
tag,
):
log.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:
log.error("Could not find repro script for model %s", name)
else:
log.info(
"Repro script for model %s with minified graph saved to %s",
name,
repro_dir,
)
def maybe_preserve_compile_debug(self, name, status):
if (
name in CI_PRESERVE_COMPILE_DEBUG
and status in CI_PRESERVE_COMPILE_DEBUG[name]
):
src_dir = torch._dynamo.utils.get_debug_dir()
if os.path.isdir(src_dir):
dbg_dir = os.path.join(
os.getcwd(), "test", "debug", "torch_compile_debug"
)
dst_dir = os.path.join(dbg_dir, os.path.basename(src_dir))
try:
os.makedirs(dbg_dir, exist_ok=True)
os.rename(src_dir, dst_dir)
log.warning("Moved %s to %s", src_dir, dst_dir)
except OSError:
log.exception("Failed to preserve %s", src_dir)
def run_one_model(
self,
name,
model,
example_inputs,
optimize_ctx,
experiment,
explain=False,
tag=None,
batch_size=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:
if self.args.backend == "torchao":
status = self.run_performance_test_non_alternate(
name, model, example_inputs, optimize_ctx, experiment, tag
)
else:
status = self.run_performance_test(
name,
model,
example_inputs,
optimize_ctx,
experiment,
tag,
batch_size=batch_size,
)
print(status)
empty_gpu_cache(current_device)
self.maybe_preserve_compile_debug(name, 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])
)
# NB: Don't upload them to the benchmark database as they are debugging
# information. There are also around a million records a day which is
# wasteful to store
write_outputs(
filename,
["model", "reason", "user_stack"],
[current_name, reason, user_stack],
False,
)
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, 16),
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, cuda or hpu"
)
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 iterations 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(
"--freezing", action="store_true", help="turn on freezing", default=False
)
parser.add_argument(
"--inductor-config",
"-c",
action="append",
help="key=value in torch._inductor.config",
)
parser.add_argument(
"--ci", action="store_true", help="Flag to tell that its a CI run"
)
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.
Doesn't recursively wrap, mainly useful for checking dynamo UnspecNNModule compatibility
""",
)
parser.add_argument(
"--optimize-ddp-mode",
type=str,
default="ddp_optimizer",
help="Specify the DDP optimization mode -- the value of torch._dynamo.config.optimize_ddp.",
)
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(
"--propagate-real-tensors",
action="store_true",
help="Capture as much data dependent as you can by unsoundly propagating real tensors",
)
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 many 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(
"--disable-output",
action="store_true",
help="Disable writing of output files, e.g., for warm-up runs",
)
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(
"--profile-details", action="store_true", help="More detailed profiler trace."
)
parser.add_argument(
"--export-perfdoctor",
action="store_true",
help="Export Chrome trace to perf doctor. (internal only)",
)
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(
"--use-warm-peak-memory",
"--use_warm_peak_memory",
action="store_true",
help="Measure peak memory using a warm run to reduce autotuning noise",
)
parser.add_argument(
"--print-memory",
action="store_true",
help="print extra memory statistics",
)
parser.add_argument(
"--print-compilation-time",
action="store_true",
help="print compilation latency",
)
parser.add_argument(
"--print-dataframe-summary",
action="store_true",
help="print dataframe result used for calculating accuracy",
)
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.",
)
parser.add_argument(
"--compiled-autograd",
action="store_true",
help="Enables compiled autograd on compiled benchmark",
)
parser.add_argument(
"--profile_dynamo_cache_lookup",
"--profile-dynamo-cache-lookup",
action="store_true",
help="profiles TorchDynamo cache lookup",
)
parser.add_argument(
"--snapshot-memory",
"--snapshot_memory",
action="store_true",
help="Enables Memory Snapshot tool for memory deep dives: https://pytorch.org/blog/understanding-gpu-memory-1/",
)
parser.add_argument(
"--retain-output",
action="store_true",
help="Enables appending to the already existing output file if it exists \
instead of deleting it and creating a new one.",
)
parser.add_argument(
"--caching-precompile",
action="store_true",
help="Enables caching precompile, serializing artifacts to DynamoCache between runs",
)
group_latency = parser.add_mutually_exclusive_group()
group_latency.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.",
)
group_latency.add_argument(
"--warm-start-latency",
"--warm_start_latency",
action="store_true",
help="Run model(s) twice and preserve caches in between to enable a 'warm start' on the 2nd run",
)
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"
)
parser.add_argument(
"--amp-dtype",
choices=("bfloat16", "float16"),
help="the data type used with 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(
"--quantization",
choices=[
"int8dynamic",
"int8weightonly",
"int4weightonly",
"autoquant",
"noquant",
],
default=None,
help="Measure speedup of torchao quantization with TorchInductor baseline",
)
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(
"--export-nativert",
action="store_true",
help="Measure pass rate with Export+NativeRT",
)
group.add_argument(
"--torchscript-jit-trace",
action="store_true",
help="Measure pass rate with TorchScript jit.trace",
)
group.add_argument(
"--xla", action="store_true", help="Compare TorchXLA to eager PyTorch"
)
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_caching_precompile():
"""
After every process_entry, save precompile artifacts to DynamoCache
"""
assert torch._dynamo.config.caching_precompile, (
"Caching precompile should be enabled with --caching-precompile"
)
from torch._dynamo.precompile_context import PrecompileContext
debug_info = PrecompileContext.save_to_dynamo_cache()
print(
f"Saved {len(debug_info['dynamo'])} precompile artifacts with {len(debug_info['backends'])} backends"
)
def process_entry(rank, runner, original_dir, args):
args.rank = rank
with maybe_init_distributed(
args.init_distributed,
rank=rank,
world_size=args.world_size,
port=args.distributed_master_port,
):
result = run(runner, args, original_dir)
if args.caching_precompile:
process_caching_precompile()
return result
def maybe_fresh_cache(args):
cache_dir_assigned = "TORCHINDUCTOR_CACHE_DIR" in os.environ
if not cache_dir_assigned and (
args.cold_start_latency or args.warm_start_latency or args.ci
):
return fresh_cache()
else:
return contextlib.nullcontext()
def main(runner, original_dir=None, args=None):
if original_dir:
os.chdir(original_dir)
args = parse_args() if not args else parse_args(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?"
)
with maybe_fresh_cache(args):
if args.caching_precompile:
os.environ["TORCH_CACHING_PRECOMPILE"] = "1"
torch._dynamo.config.caching_precompile = True
args.init_distributed = args.only and args.multiprocess
if args.init_distributed:
# 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
)
elif args.only and args.warm_start_latency:
# Warm start mode. Enable FX graph caching and perform back-to-back runs in
# separate processes (but ensure the inductor cache is preserved across runs).
env = os.environ.copy()
env["TORCHINDUCTOR_FX_GRAPH_CACHE"] = "1"
cmd = [sys.executable] + sys.argv
cmd.remove("--warm-start-latency")
print(f"Performing cold-start run for {args.only}")
warmup_cmd = cmd + ["--repeat=1", "--disable-output"]
subprocess.check_call(warmup_cmd, timeout=args.timeout, env=env)
print(f"Performing warm-start run for {args.only}")
subprocess.check_call(cmd, timeout=args.timeout, env=env)
else:
# single process path just uses the main process
args.world_size = 1
process_entry(0, runner, original_dir, args)
def write_csv_when_exception(args, name: str, status: str, device=None):
print(status)
placeholder_batch_size = 0
devices = [device] if device is not None else args.devices
if args.accuracy:
headers = ["dev", "name", "batch_size", "accuracy"]
rows = [[device, name, placeholder_batch_size, status] for device in 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 devices]
else:
headers = []
rows = [[device, name, placeholder_batch_size, 0.0] for device in devices]
for row in rows:
write_outputs(output_filename, headers, row)
def run(runner, args, original_dir=None):
# Pass the parsed args object to benchmark runner object
torch._dynamo.reset()
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.quantization:
assert args.backend is None
args.backend = "torchao"
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.compiled_autograd:
torch._dynamo.config.compiled_autograd = True
if args.propagate_real_tensors:
# TODO: Separate flag for data dependent
torch._dynamo.config.capture_scalar_outputs = True
torch._dynamo.config.capture_dynamic_output_shape_ops = True
torch._functorch.config.fake_tensor_propagate_real_tensors = True
if args.specialize_int:
torch._dynamo.config.specialize_int = True
if args.ci:
if args.accuracy:
# Run fewer iterations when checking accuracy
args.repeat = min(args.repeat, 2)
# Set translation validation on by default on CI accuracy runs.
torch.fx.experimental._config.translation_validation = True
if args.ddp:
assert args.training, "DDP benchmark requires --training mode"
torch._dynamo.config.optimize_ddp = args.optimize_ddp_mode
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",
"sam_fast",
"resnet50_quantized_qat",
"mobilenet_v2_quantized_qat",
"detectron2_maskrcnn",
"detectron2_maskrcnn_r_101_c4",
"detectron2_maskrcnn_r_101_fpn",
"detectron2_maskrcnn_r_50_c4",
"detectron2_maskrcnn_r_50_fpn",
"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",
}:
# some of the models do not support use_deterministic_algorithms
torch.use_deterministic_algorithms(True)
if args.devices == ["xpu"]:
torch.use_deterministic_algorithms(True, warn_only=True)
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
if args.only is not None and args.only in {
"nvidia_deeprecommender",
}:
# These seem unhappy with numerics of larger cuBLASLt workspace
torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False
torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.allow_tf32 = False
torch.backends.cudnn.benchmark = False
torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(False)
torch.backends.mkldnn.deterministic = True
# Remove randomness 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 and not args.multiprocess:
args.device_index = "0"
# Stricter check to disable fallbacks
args.suppress_errors = False
if not args.disable_cudagraphs:
runner.skip_models.update(
{
# xfail: https://github.com/pytorch/pytorch/issues/145773
"llama",
"cm3leon_generate",
}
)
if args.device_index is not None:
if args.multiprocess:
print("Cannot specify both --device_index and --multiprocess")
return sys.exit(-1)
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 (HAS_CUDA or HAS_XPU):
global synchronize
synchronize = torch.cuda.synchronize if HAS_CUDA else torch.xpu.synchronize
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._logging.set_logs(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"]:
arch = platform.machine()
runner.skip_models.update(runner.skip_models_for_cpu)
if arch == "aarch64":
runner.skip_models.update(runner.skip_models_for_cpu_aarch64)
elif args.devices == ["cuda"]:
runner.skip_models.update(runner.skip_models_for_cuda)
if not args.multiprocess:
runner.skip_models.update(runner.skip_multiprocess_models)
if args.freezing:
if args.devices == ["cpu"]:
runner.skip_models.update(runner.skip_models_for_freezing_cpu)
elif args.devices == ["cuda"]:
runner.skip_models.update(runner.skip_models_for_freezing_cuda)
if args.no_skip:
runner.skip_models.clear()
experiment = null_experiment
global \
current_name, \
current_device, \
current_batch_size, \
current_backend, \
current_mode, \
current_dtype, \
current_quantization, \
current_settings, \
output_filename, \
disable_output, \
optimize_ctx
optimize_ctx = contextlib.nullcontext()
if args.disable_output:
disable_output = True
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 = export
experiment = speedup_experiment
output_filename = "export.csv"
elif args.export_nativert:
optimize_ctx = export_nativert
experiment = speedup_experiment
output_filename = "export_nativert.csv"
elif args.torchscript_jit_trace:
optimize_ctx = torchscript_jit_trace
experiment = speedup_experiment
output_filename = "torchscript_jit_trace.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.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"
optimize_ctx = functools.partial(
export_aot_inductor, mode=args.inductor_compile_mode
)
# AOTInductor doesn't support control flow yet
runner.skip_models.update(runner.skip_models_due_to_control_flow)
runner.skip_models.update(runner.skip_models_due_to_export_not_supported)
elif args.backend == "torchao":
assert "cuda" in args.devices, "Quantization requires CUDA device."
assert args.bfloat16, "Quantization requires dtype bfloat16."
try:
from torchao_backend import setup_baseline, torchao_optimize_ctx
except ImportError:
try:
from .torchao_backend import setup_baseline, torchao_optimize_ctx
except ImportError:
from userbenchmark.dynamo.dynamobench.torchao_backend import (
setup_baseline,
torchao_optimize_ctx,
)
setup_baseline()
baseline_ctx = functools.partial(
torch.compile,
backend="inductor",
fullgraph=args.nopython,
mode=args.inductor_compile_mode,
)
model_iter_fn = baseline_ctx(runner.model_iter_fn)
# needed to avoid error that causes inconsistent timing due to:
# Unable to hit fast path of CUDAGraphs because of pending, uninvoked backwards
def model_iter_fn_and_mark_step(*args, **kwargs):
torch.compiler.cudagraph_mark_step_begin()
model_iter_fn(*args, **kwargs)
runner.model_iter_fn = model_iter_fn_and_mark_step
optimize_ctx = torchao_optimize_ctx(args.quantization)
else:
optimize_ctx = torch._dynamo.optimize(args.backend, nopython=args.nopython)
experiment = (
speedup_experiment if not args.backend == "torchao" else latency_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.only in runner.disable_cudagraph_models:
args.disable_cudagraphs = True
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
if args.inference:
inductor_config.freezing = args.freezing
if args.inductor_config:
for config in args.inductor_config:
key, value = config.split("=")
typ = type(inductor_config.__getattr__(key))
if issubclass(typ, bool):
assert value in ("0", "1", "True", "False")
value = value in ("1", "True")
elif issubclass(typ, (str, int, float)):
value = typ(value)
else:
raise NotImplementedError(typ)
inductor_config.__setattr__(key, value)
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)
write_outputs(output_filename, [], [args.only, batch_size])
return
args.profile_details = {}
if args.export_profiler_trace:
if args.profile_details:
args.profile_details = {
"record_shapes": True,
"profile_memory": True,
"with_stack": True,
"with_modules": True,
}
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.fx.experimental._config.translation_validation = False
experiment = functools.partial(experiment, args)
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:
name = model_name
try:
with tqdm(desc="loading model"):
extra_args = []
if hasattr(args, "rank") and hasattr(args, "world_size"):
extra_args += [
"--rank",
str(args.rank),
"--world_size",
str(args.world_size),
]
if args.part:
(
device,
name,
model,
example_inputs,
batch_size,
) = runner.load_model(
device,
model_name,
batch_size=batch_size,
part=args.part,
extra_args=extra_args,
)
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,
extra_args=extra_args,
)
else:
(
device,
name,
model,
example_inputs,
batch_size,
) = runner.load_model(
device,
model_name,
batch_size=batch_size,
extra_args=extra_args,
)
except Exception as e:
import traceback
mode = "train" if args.training else "eval"
print(f"{device:4} {mode:5} {name:34} ")
print(traceback.format_exc())
status = (
"model_fail_to_load"
if isinstance(e, NotImplementedError)
else "eager_fail_to_run"
)
write_csv_when_exception(args, name, status, device)
# NB: current_name/current_device not set, so pass
# explicitly
output_signpost(
{"name": name, "dev": device},
args,
runner.suite_name,
error=status,
)
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
current_backend = args.backend
current_mode = (
"training" if args.training else "inference" if args.inference else ""
)
if args.float16:
current_dtype = "float16"
elif args.bfloat16:
current_dtype = "bfloat16"
elif args.float32:
current_dtype = "float32"
elif args.amp:
current_dtype = "amp"
else:
current_dtype = ""
current_quantization = args.quantization
# Keep the remaining of the settings
current_settings = vars(args)
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.maybe_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
)
if model_name in DO_NOT_CAST_INPUTS:
model, _ = runner.cast_based_on_args(model, example_inputs)
else:
model, example_inputs = runner.cast_based_on_args(model, example_inputs)
runner.setup_amp(current_device)
guard_ctx = contextlib.nullcontext()
if name in runner.guard_on_nn_module_models:
guard_ctx = torch._dynamo.config.patch(guard_nn_modules=True)
inline_ctx = contextlib.nullcontext()
if name in runner.inline_inbuilt_nn_modules_models:
inline_ctx = torch._dynamo.config.patch(inline_inbuilt_nn_modules=True)
with guard_ctx:
with inline_ctx:
runner.run_one_model(
name,
model,
example_inputs,
optimize_ctx,
experiment,
explain=args.explain,
tag=args.tag,
batch_size=batch_size if args.dynamic_batch_only else None,
)
if args.generate_aot_autograd_stats:
stats_file = output_filename.split(".csv")[0] + "_stats.csv"
write_outputs(
stats_file,
("dev", "name", "batch_size", "total_aot_graphs", "ok_aot_graphs"),
[
current_device,
current_name,
current_batch_size,
*Stats.aot_summary(),
],
)
else:
metrics.purge_old_log_files()
if (
output_filename
and os.path.exists(output_filename)
and not args.retain_output
):
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
if args.progress:
print(f"Running model {i + 1}/{nmodels}", flush=True)
try:
timeout = args.timeout
if should_diff_branch(args):
timeout *= 2
env = os.environ.copy()
if args.ci and name in CI_PRESERVE_COMPILE_DEBUG:
env["TORCH_COMPILE_DEBUG"] = "1"
subprocess.check_call(
[sys.executable] + sys.argv + [f"--only={name}"],
timeout=timeout,
env=env,
)
except subprocess.TimeoutExpired:
write_csv_when_exception(args, name, "timeout")
# NB: device is potentially multiple here, though we should
# try our best to report in anyway TODO
output_signpost(
{"name": name}, args, runner.suite_name, error="timeout"
)
except subprocess.CalledProcessError as e:
print("Run failed with return code: ", e.returncode, file=sys.stderr)
print("Output: ", e.output, file=sys.stderr)
print("Error: ", e.stderr, file=sys.stderr)
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}")
try:
from .microbenchmarks.operator_inp_utils import OperatorInputsMode
except ImportError:
from microbenchmarks.operator_inp_utils import OperatorInputsMode
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 huggingface.py"
)