Adding the benchmarking with TorchBench

benchmarks/benchmark_experiment.py

@@ -0,0 +1,183 @@
+from collections import OrderedDict
+import logging
+import os
+import torch
+import torch._dynamo as dynamo
+
+try:
+  from .util import is_xla_device_available, get_accelerator_model
+except ImportError:
+  from util import is_xla_device_available, get_accelerator_model
+
+try:
+  import torch_xla.core.xla_model as xm
+except ImportError:
+  # ignore the error if torch_xla is not installed
+  pass
+
+logger = logging.getLogger(__name__)
+
+
+class ExperimentLoader:
+
+  def __init__(self, args):
+    self._args = args
+    self.experiment_name = self._args.experiment_name
+
+  def expand_config_choices(self, config_choices):
+    configs = [{}]
+
+    for key, choices in config_choices.items():
+      tmp_configs = []
+      for config in configs:
+        for choice in choices:
+          tmp_config = config.copy()
+          tmp_config[key] = choice
+          tmp_configs.append(tmp_config)
+      configs = tmp_configs
+
+    return configs
+
+  def list_experiment_configs(self):
+    if self.experiment_name == "run_all":
+      config_choices = {
+          "accelerator": ["cpu", "gpu", "tpu"],
+          "xla": [None, "PJRT", "XRT"],
+          "dynamo": [
+              None, "inductor", "torchxla_trace_once", "aot_torchxla_trace_once"
+          ],
+          "test": ["eval", "train"],
+      }
+
+      if self._args.accelerator:
+        config_choices["accelerator"] = list(set(self._args.accelerator))
+      if self._args.xla:
+        config_choices["xla"] = list(set(self._args.xla))
+        config_choices["xla"] = [
+            x if x != "None" else None for x in config_choices["xla"]
+        ]
+      if self._args.dynamo:
+        config_choices["dynamo"] = list(set(self._args.dynamo))
+        config_choices["dynamo"] = [
+            x if x != "None" else None for x in config_choices["dynamo"]
+        ]
+      if self._args.test:
+        config_choices["test"] = list(set(self._args.test))
+    else:
+      raise NotImplementedError
+
+    experiment_configs = []
+    for experiment_config in self.expand_config_choices(config_choices):
+      if not self.is_available(experiment_config):
+        continue
+
+      self._add_experiment_env(experiment_config)
+      experiment_configs.append(experiment_config)
+    return experiment_configs
+
+  def is_available(self, experiment_config):
+    if experiment_config["dynamo"] and experiment_config[
+        "dynamo"] not in dynamo.list_backends(exclude_tags=()):
+      return False
+    if experiment_config["dynamo"] == "inductor" and not (
+        experiment_config["accelerator"] == "gpu" and
+        not experiment_config["xla"]):
+      return False
+    if experiment_config["dynamo"] == "torchxla_trace_once" and not (
+        experiment_config["xla"] and experiment_config["test"] == "eval"):
+      return False
+    if experiment_config["dynamo"] == "aot_torchxla_trace_once" and not (
+        experiment_config["xla"] and experiment_config["test"] == "train"):
+      return False
+    if (experiment_config["xla"] and
+        not is_xla_device_available(experiment_config["accelerator"].upper())):
+      return False
+    if (experiment_config["accelerator"] == "tpu" and
+        not experiment_config["xla"]):
+      return False
+    if (experiment_config["accelerator"] == "gpu" and
+        not experiment_config["xla"] and not torch.cuda.is_available()):
+      return False
+    return True
+
+  def _add_experiment_env(self, experiment_config):
+    process_env = None
+    if experiment_config["xla"]:
+      # remove env vars that would interfere with subprocess settings
+      os.environ.pop("PJRT_DEVICE", None)
+      os.environ.pop("XRT_TPU_CONFIG", None)
+    if experiment_config["xla"] == "PJRT":
+      process_env = os.environ.copy()
+      process_env["PJRT_DEVICE"] = experiment_config["accelerator"].upper()
+    elif experiment_config["xla"] == "XRT":
+      process_env = os.environ.copy()
+      if is_xla_device_available("TPU"):
+        process_env["TPU_NUM_DEVICES"] = "1"
+        process_env["XRT_TPU_CONFIG"] = "localservice;0;localhost:51011"
+      elif is_xla_device_available("GPU"):
+        process_env["GPU_NUM_DEVICES"] = "1"
+    elif not experiment_config["xla"] and is_xla_device_available(
+        experiment_config["accelerator"].upper()):
+      # In non-xla CPU training experiments, an env var is still needed if an
+      # xla device exists, or there will be "Missing XLA configuration" error.
+      process_env = os.environ.copy()
+      process_env["PJRT_DEVICE"] = experiment_config["accelerator"].upper()
+
+    experiment_config["process_env"] = process_env
+
+  def load_experiment(self, experiment_config, dummy=False):
+    experiment_name = self.experiment_name
+    accelerator = experiment_config["accelerator"]
+    xla = experiment_config["xla"]
+    dynamo = experiment_config["dynamo"]
+    test = experiment_config["test"]
+    batch_size = experiment_config.get("batch_size", self._args.batch_size)
+    benchmark_experiment = BenchmarkExperiment(
+        experiment_name=experiment_name,
+        accelerator=accelerator,
+        xla=xla,
+        dynamo=dynamo,
+        test=test,
+        batch_size=batch_size)
+
+    return benchmark_experiment
+
+
+class BenchmarkExperiment:
+
+  def __init__(self, experiment_name, accelerator, xla, dynamo, test,
+               batch_size):
+    self.experiment_name = experiment_name
+    self.accelerator = accelerator
+    self.xla = xla
+    self.dynamo = dynamo
+    self.test = test
+    self.batch_size = batch_size
+    self.accelerator_model = get_accelerator_model(self.accelerator)
+
+  def get_device(self):
+    if self.xla:
+      device = xm.xla_device(devkind=self.accelerator.upper())
+    elif self.accelerator == "cpu":
+      device = torch.device("cpu")
+    elif self.accelerator == "gpu":
+      device = torch.device("cuda")
+    else:
+      raise NotImplementedError
+
+    return device
+
+  @property
+  def filename_str(self):
+    return "-".join(self.to_dict().values())
+
+  def to_dict(self):
+    d = OrderedDict()
+    d["experiment_name"] = self.experiment_name
+    d["accelerator"] = self.accelerator
+    d["accelerator_model"] = self.accelerator_model
+    d["xla"] = self.xla
+    d["dynamo"] = self.dynamo
+    d["test"] = self.test
+    d["batch_size"] = self.batch_size
+    return d

benchmarks/benchmark_model.py

@@ -0,0 +1,172 @@
+from collections import OrderedDict
+import logging
+import re
+import torch
+import torch.nn as nn
+import torch._dynamo as dynamo
+from torch._dynamo.testing import collect_results
+import types
+
+try:
+  from .util import move_to_device
+except ImportError:
+  from util import move_to_device
+
+logger = logging.getLogger(__name__)
+
+
+class ModelLoader:
+
+  def __init__(self, args):
+    self._args = args
+    self.suite_name = self._args.suite_name
+    self.benchmark_model_class = BenchmarkModel
+
+  def list_model_configs(self):
+    model_configs = [
+        {
+            "model_name": "dummy"
+        },
+    ]
+
+    return model_configs
+
+  def is_compatible(self, dummy_benchmark_model, benchmark_experiment):
+    return True
+
+  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 skip_model(self, model_name):
+    return (not re.search("|".join(self._args.filter), model_name, re.I) or
+            re.search("|".join(self._args.exclude), model_name, re.I))
+
+  def load_model(self, model_config, benchmark_experiment, dummy=False):
+    suite_name = self.suite_name
+    model_name = model_config["model_name"]
+    benchmark_model = self.benchmark_model_class(
+        suite_name=suite_name,
+        model_name=model_name,
+        benchmark_experiment=benchmark_experiment,
+    )
+
+    if not dummy:
+      benchmark_model.set_up()
+      benchmark_model.prepare_for_experiment()
+
+    return benchmark_model
+
+
+class BenchmarkModel:
+
+  def __init__(self, suite_name, model_name, benchmark_experiment):
+    self.suite_name = suite_name
+    self.model_name = model_name
+    self.benchmark_experiment = benchmark_experiment
+
+  def set_up(self):
+    """Set up module, actual batch_size, example_inputs, and optimizer_class
+
+    This is model suite specific.
+    """
+    if self.model_name != "dummy":
+      raise NotImplementedError
+
+    self.module = nn.Sequential(
+        nn.Linear(32, 512),
+        nn.ReLU(),
+        nn.Linear(512, 512),
+        nn.ReLU(),
+        nn.Linear(512, 32),
+        nn.Softmax(dim=1),
+    )
+
+    self.benchmark_experiment.batch_size = 16
+    self.example_inputs = (torch.rand(self.benchmark_experiment.batch_size,
+                                      32),)
+    self.optimizer_class = torch.optim.Adam
+
+  def _prepare_for_eval(self):
+    self.module.eval()
+    self.model_iter_fn = self.eval
+
+  def _prepare_for_train(self):
+    self.module.train()
+    self.model_iter_fn = self.train
+    if self.benchmark_experiment.dynamo == "aot_torchxla_trace_once":
+      # TODO: dynamo aot_torchxla_trace_once would fail if there is an
+      # optimizer.
+      # This makes the aot_torchxla_trace_once results not comparable
+      # with other training results
+      self.optimizer = None
+    else:
+      if not hasattr(self, "optimizer"):
+        # For some special models, self.set_up() may have initialized an
+        # optimizer to use. So only initialize it when there is none existing.
+        self.optimizer = self.optimizer_class(self.module.parameters(), lr=0.01)
+
+  def prepare_for_experiment(self):
+    self.device = self.benchmark_experiment.get_device()
+    self.module = self.module.to(self.device)
+    self.example_inputs = move_to_device(self.example_inputs, self.device)
+
+    if self.benchmark_experiment.test == "eval":
+      self._prepare_for_eval()
+    elif self.benchmark_experiment.test == "train":
+      self._prepare_for_train()
+    else:
+      raise NotImplementedError
+
+    if self.benchmark_experiment.dynamo:
+      self.model_iter_fn = torch.compile(self.model_iter_fn,
+                                         backend=self.benchmark_experiment.dynamo)
+
+  def pick_grad(self):
+    if self.benchmark_experiment.test == "eval":
+      return torch.no_grad()
+    elif self.benchmark_experiment.test == "train":
+      return torch.enable_grad()
+
+  def _optimizer_zero_grad(self):
+    if self.optimizer is not None:
+      self.optimizer.zero_grad(True)
+    else:
+      self.module.zero_grad(True)
+
+  def _optimizer_step(self):
+    if self.optimizer is not None:
+      self.optimizer.step()
+
+  def compute_loss(self, pred):
+    raise NotImplementedError
+
+  def train(self, inputs, collect_full_output=False):
+    self._optimizer_zero_grad()
+    pred = self.module(*inputs)
+    loss = self.compute_loss(pred)
+    loss.backward()
+    self._optimizer_step()
+    if collect_full_output:
+      return collect_results(self.module, pred, loss, inputs)
+    # return loss.detach()
+    # TODO: dynamo inductor would fail if .detach() is used
+    return None
+
+  def eval(self, inputs, collect_full_output=False):
+    pred = self.module(*inputs)
+    return pred
+
+  @property
+  def filename_str(self):
+    return "-".join(self.to_dict().values())
+
+  def to_dict(self):
+    d = OrderedDict()
+    d["suite_name"] = self.suite_name
+    d["model_name"] = self.model_name
+    return d