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[raysgd] Cleanup User API (#7384)

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* Init fp16

* fp16 and schedulers

* scheduler linking and fp16

* to fp16

* loss scaling and documentation

* more documentation

* add tests, refactor config

* moredocs

* more docs

* fix logo, add test mode, add fp16 flag

* fix tests

* fix scheduler

* fix apex

* improve safety

* fix tests

* fix tests

* remove pin memory default

* rm

* fix

* Update doc/examples/doc_code/raysgd_torch_signatures.py

* fix

* migrate changes from other PR

* ok thanks

* pass

* signatures

* lint'

* Update python/ray/experimental/sgd/pytorch/utils.py

* Apply suggestions from code review

Co-Authored-By: Edward Oakes <ed.nmi.oakes@gmail.com>

* should address most comments

* comments

* fix this ci

* first_pass

* add overrides

* override

* fixing up operators

* format

* sgd

* constants

* rm

* revert

* save

* failures

* fixes

* trainer

* run test

* operator

* code

* op

* ok done

* operator

* sgd test fixes

* ok

* trainer

* format

* Apply suggestions from code review

Co-Authored-By: Edward Oakes <ed.nmi.oakes@gmail.com>

* Update doc/source/raysgd/raysgd_pytorch.rst

* docstring

* dcgan

* doc

* commits

* nit

* testing

* revert

* Start renaming pytorch to torch

* Rename PyTorchTrainer to TorchTrainer

* Rename PyTorch runners to Torch runners

* Finish renaming API

* Rename to torch in tests

* Finish renaming docs + tests

* Run format + fix DeprecationWarning

* fix

* move tests up

* benchmarks

* rename

* remove some args

* better metrics output

* fix up the benchmark

* benchmark-yaml

* horovod-benchmark

* benchmarks

* Remove benchmark code for cleanups

* makedatacreator

* relax

* metrics

* autosetsampler

* profile

* movements

* OK

* smoothen

* fix

* nitdocs

* loss

* comments

* fix

* fix

* runner_tests

* codes

* example

* fix_test

* fix

* tests

Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com>
Co-authored-by: Maksim Smolin <maximsmol@gmail.com>
This commit is contained in:
Richard Liaw
2020-03-10 08:41:42 -07:00
committed by GitHub
parent 89ec4adb72
commit d192ef0611
16 changed files with 840 additions and 478 deletions
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ci/jenkins_tests/run_tune_tests.sh
+5 -5
View File
@@ -38,31 +38,31 @@ $SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE}
python -m pytest /ray/python/ray/util/sgd/tests
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} --memory-swap=-1 $DOCKER_SHA \
python /ray/doc/examples/doc_code/raysgd_torch_signatures.py
python /ray/python/ray/util/sgd/torch/examples/raysgd_torch_signatures.py
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} --memory-swap=-1 $DOCKER_SHA \
python /ray/python/ray/util/sgd/torch/examples/train_example.py
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} --memory-swap=-1 $DOCKER_SHA \
python /ray/python/ray/util/sgd/torch/examples/train_example.py --num-replicas=2
python /ray/python/ray/util/sgd/torch/examples/train_example.py --num-workers=2
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} --memory-swap=-1 $DOCKER_SHA \
python /ray/python/ray/util/sgd/torch/examples/tune_example.py
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} --memory-swap=-1 $DOCKER_SHA \
python /ray/python/ray/util/sgd/torch/examples/tune_example.py --num-replicas=2
python /ray/python/ray/util/sgd/torch/examples/tune_example.py --num-workers=2
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} --memory-swap=-1 $DOCKER_SHA \
python /ray/python/ray/util/sgd/torch/examples/cifar_pytorch_example.py --smoke-test
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} --memory-swap=-1 $DOCKER_SHA \
python /ray/python/ray/util/sgd/torch/examples/cifar_pytorch_example.py --smoke-test --num-replicas=2
python /ray/python/ray/util/sgd/torch/examples/cifar_pytorch_example.py --smoke-test --num-workers=2
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} --memory-swap=-1 $DOCKER_SHA \
python /ray/python/ray/util/sgd/torch/examples/cifar_pytorch_example.py --smoke-test --tune
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} --memory-swap=-1 $DOCKER_SHA \
python /ray/python/ray/util/sgd/torch/examples/dcgan.py --smoke-test --num-replicas=2
python /ray/python/ray/util/sgd/torch/examples/dcgan.py --smoke-test --num-workers=2
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} --memory-swap=-1 $DOCKER_SHA \
python /ray/python/ray/util/sgd/tf/examples/tensorflow_train_example.py
doc/source/raysgd/raysgd_pytorch.rst
+84 -40
View File
@@ -19,7 +19,7 @@ Setting up training
The ``TorchTrainer`` can be constructed with functions that wrap components of the training script. Specifically, it requires constructors for the Model, Data, Optimizer, Loss, and ``lr_scheduler`` to create replicated copies across different devices and machines.
.. literalinclude:: ../../examples/doc_code/raysgd_torch_signatures.py
.. literalinclude:: ../../../python/ray/util/sgd/torch/examples/raysgd_torch_signatures.py
:language: python
:start-after: __torch_trainer_start__
:end-before: __torch_trainer_end__
@@ -31,7 +31,7 @@ Model Creator
This is the signature needed for ``TorchTrainer(model_creator=...)``.
.. literalinclude:: ../../examples/doc_code/raysgd_torch_signatures.py
.. literalinclude:: ../../../python/ray/util/sgd/torch/examples/raysgd_torch_signatures.py
:language: python
:start-after: __torch_model_start__
:end-before: __torch_model_end__
@@ -42,31 +42,55 @@ Optimizer Creator
This is the signature needed for ``TorchTrainer(optimizer_creator=...)``.
.. literalinclude:: ../../examples/doc_code/raysgd_torch_signatures.py
.. literalinclude:: ../../../python/ray/util/sgd/torch/examples/raysgd_torch_signatures.py
:language: python
:start-after: __torch_optimizer_start__
:end-before: __torch_optimizer_end__
Data Creator
~~~~~~~~~~~~
This is the signature needed for ``TorchTrainer(data_creator=...)``.
.. literalinclude:: ../../examples/doc_code/raysgd_torch_signatures.py
.. literalinclude:: ../../../python/ray/util/sgd/torch/examples/raysgd_torch_signatures.py
:language: python
:start-after: __torch_data_start__
:end-before: __torch_data_end__
.. tip:: Setting the batch size: Using a provided ``ray.util.sgd.utils.BATCH_SIZE`` variable, you can provide a global batch size that will be divided among all workers automatically.
.. code-block:: python
from torch.utils.data import DataLoader
from ray.util.sgd.utils import BATCH_SIZE
def data_creator(config):
# config[BATCH_SIZE] == provided BATCH_SIZE // num_workers
train_dataset, val_dataset = LinearDataset(2, 5), LinearDataset(2, 5)
train_loader = DataLoader(train_dataset, batch_size=config[BATCH_SIZE])
val_loader = DataLoader(val_dataset, batch_size=config[BATCH_SIZE])
return train_loader, val_loader
trainer = Trainer(
model_creator=model_creator,
optimizer_creator=optimizer_creator,
data_creator=batch_data_creator
config={BATCH_SIZE: 1024},
num_workers=128
)
# Each worker will process 1024 // 128 samples per batch
stats = Trainer.train()
Loss Creator
~~~~~~~~~~~~
This is the signature needed for ``TorchTrainer(loss_creator=...)``.
.. literalinclude:: ../../examples/doc_code/raysgd_torch_signatures.py
.. literalinclude:: ../../../python/ray/util/sgd/torch/examples/raysgd_torch_signatures.py
:language: python
:start-after: __torch_loss_start__
:end-before: __torch_loss_end__
@@ -78,7 +102,7 @@ Scheduler Creator
Optionally, you can provide a creator function for the learning rate scheduler. This is the signature needed
for ``TorchTrainer(scheduler_creator=...)``.
.. literalinclude:: ../../examples/doc_code/raysgd_torch_signatures.py
.. literalinclude:: ../../../python/ray/util/sgd/torch/examples/raysgd_torch_signatures.py
:language: python
:start-after: __torch_scheduler_start__
:end-before: __torch_scheduler_end__
@@ -91,14 +115,14 @@ Putting things together
Before instantiating the trainer, first start or connect to a Ray cluster:
.. literalinclude:: ../../examples/doc_code/raysgd_torch_signatures.py
.. literalinclude:: ../../../python/ray/util/sgd/torch/examples/raysgd_torch_signatures.py
:language: python
:start-after: __torch_ray_start__
:end-before: __torch_ray_end__
Instantiate the trainer object:
.. literalinclude:: ../../examples/doc_code/raysgd_torch_signatures.py
.. literalinclude:: ../../../python/ray/util/sgd/torch/examples/raysgd_torch_signatures.py
:language: python
:start-after: __torch_trainer_start__
:end-before: __torch_trainer_end__
@@ -109,9 +133,9 @@ You can also set the number of workers and whether the workers will use GPUs:
:emphasize-lines: 8,9
trainer = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=nn.MSELoss,
scheduler_creator=scheduler_creator,
config={"lr": 0.001},
@@ -128,7 +152,27 @@ Now that the trainer is constructed, here's how to train the model.
val_metrics = trainer.validate()
Each ``train`` call makes one pass over the training data, and each ``validate`` call runs the model on the validation data passed in by the ``data_creator``. Provide a custom training operator (:ref:`raysgd-custom-training`) to calculate custom metrics or customize the training/validation process.
Each ``train`` call makes one pass over the training data, and each ``validate`` call runs the model on the validation data passed in by the ``data_creator``.
You can also obtain profiling information:
.. code-block:: python
>>> from ray.tune.logger import pretty_print
>>> print(pretty_print(trainer.train(profile=True)))
batch_count: 16
epoch: 1
last_train_loss: 0.15574650466442108
mean_train_loss: 7.475177114367485
num_samples: 1000
profile:
mean_apply_s: 2.639293670654297e-05
mean_fwd_s: 0.00012960433959960938
mean_grad_s: 0.00016553401947021483
train_epoch_s: 0.023712158203125
Provide a custom training operator (:ref:`raysgd-custom-training`) to calculate custom metrics or customize the training/validation process.
After training, you may want to reappropriate the Ray cluster. To release Ray resources obtained by the Trainer:
@@ -238,10 +282,10 @@ Below is a partial example of a custom ``TrainingOperator`` that provides a ``tr
}
trainer = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
nn.BCELoss,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=nn.BCELoss,
training_operator_cls=GANOperator,
num_replicas=num_replicas,
config=config,
@@ -270,9 +314,9 @@ Use the ``initialization_hook`` parameter to initialize state on each worker pro
os.environ["NCCL_DEBUG"] = "INFO"
trainer = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=nn.MSELoss,
initialization_hook=initialization_hook,
config={"lr": 0.001}
@@ -291,9 +335,9 @@ and ``trainer.load``, which wraps the relevant ``torch.save`` and ``torch.load``
trainer_1.save(checkpoint_path)
trainer_2 = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=nn.MSELoss,
num_replicas=num_replicas)
trainer_2.restore(checkpoint_path)
@@ -318,9 +362,9 @@ You can enable mixed precision training for PyTorch with the ``use_fp16`` flag.
:emphasize-lines: 7
trainer = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=nn.MSELoss,
num_replicas=4,
use_fp16=True
@@ -335,9 +379,9 @@ To specify particular parameters for ``amp.initialize``, you can use the ``apex_
:emphasize-lines: 7-12
trainer = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=nn.MSELoss,
num_replicas=4,
use_fp16=True,
@@ -369,9 +413,9 @@ After connecting, you can scale up the number of workers seamlessly across multi
.. code-block:: python
trainer = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=nn.MSELoss,
num_replicas=100
)
@@ -389,7 +433,7 @@ For distributed deep learning, jobs are often run on infrastructure where nodes
trainer.train(max_retries=N)
During each ``train`` method, each parallel worker iterates through the dataset, synchronizing gradients and parameters at each batch. These synchronization primitives can hang when one or more of the parallel workers becomes unresponsive (i.e., when a node is lost). To address this, we've implemented the following protocol.
During each ``train`` method, each parallel worker iterates through the iterable, synchronizing gradients and parameters at each batch. These synchronization primitives can hang when one or more of the parallel workers becomes unresponsive (i.e., when a node is lost). To address this, we've implemented the following protocol.
1. If any worker node is lost, Ray will mark the training task as complete (``ray.wait`` will return).
2. Ray will throw ``RayActorException`` when fetching the result for any worker, so the Trainer class will call ``ray.get`` on the "finished" training task.
@@ -400,7 +444,7 @@ During each ``train`` method, each parallel worker iterates through the dataset,
Note that we assume the Trainer itself is not on a pre-emptible node. It is currently not possible to recover from a Trainer node failure.
Users can set ``checkpoint="auto"`` to always checkpoint the current model before executing a pass over the training dataset.
Users can set ``checkpoint="auto"`` to always checkpoint the current model before executing a pass over the training iterable.
.. code-block:: python
@@ -461,7 +505,7 @@ You can see the `DCGAN script <https://github.com/ray-project/ray/blob/master/py
return discriminator_opt, generator_opt
class CustomOperator(TrainingOperator):
def train_epoch(self, dataloader, info):
def train_epoch(self, iterator, info):
result = {}
for i, (model, optimizer) in enumerate(
zip(self.models, self.optimizers)):
@@ -469,17 +513,17 @@ You can see the `DCGAN script <https://github.com/ray-project/ray/blob/master/py
model=model,
criterion=self.criterion,
optimizer=optimizer,
dataloader=dataloader)
dataloader=iterator)
return result
trainer = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=nn.BCELoss,
training_operator_cls=CustomOperator)
trainer.train()
stats = trainer.train()
Feature Requests
python/ray/autoscaler/updater.py
+2 -1
View File
@@ -2,6 +2,7 @@ try: # py3
from shlex import quote
except ImportError: # py2
from pipes import quote
import click
import hashlib
import logging
import os
@@ -274,7 +275,7 @@ class SSHCommandRunner:
except subprocess.CalledProcessError:
if exit_on_fail:
quoted_cmd = " ".join(final_cmd[:-1] + [quote(final_cmd[-1])])
raise Exception(
raise click.ClickException(
"Command failed: \n\n {}\n".format(quoted_cmd))
else:
raise
python/ray/util/sgd/tests/test_torch.py
+236 -78
View File
@@ -1,7 +1,7 @@
import os
import tempfile
from unittest.mock import patch
import numpy as np
import pytest
import time
import torch
@@ -11,9 +11,11 @@ import torch.distributed as dist
import ray
from ray import tune
from ray.util.sgd.torch import TorchTrainer, TorchTrainable
from ray.util.sgd.torch.training_operator import _TestingOperator
from ray.util.sgd.torch.constants import BATCH_COUNT, SCHEDULER_STEP
from ray.util.sgd.utils import check_for_failure
from ray.util.sgd.torch.training_operator import (_TestingOperator,
_TestMetricsOperator)
from ray.util.sgd.torch.constants import SCHEDULER_STEP
from ray.util.sgd.utils import (check_for_failure, NUM_SAMPLES, BATCH_COUNT,
BATCH_SIZE)
from ray.util.sgd.torch.examples.train_example import (
model_creator, optimizer_creator, data_creator, LinearDataset)
@@ -29,11 +31,11 @@ def ray_start_2_cpus():
def test_single_step(ray_start_2_cpus): # noqa: F811
trainer = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=lambda config: nn.MSELoss(),
num_replicas=1)
num_workers=1)
metrics = trainer.train(num_steps=1)
assert metrics[BATCH_COUNT] == 1
@@ -41,31 +43,29 @@ def test_single_step(ray_start_2_cpus): # noqa: F811
assert val_metrics[BATCH_COUNT] == 1
@pytest.mark.parametrize("num_replicas", [1, 2]
if dist.is_available() else [1])
def test_train(ray_start_2_cpus, num_replicas): # noqa: F811
@pytest.mark.parametrize("num_workers", [1, 2] if dist.is_available() else [1])
def test_train(ray_start_2_cpus, num_workers): # noqa: F811
trainer = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=lambda config: nn.MSELoss(),
num_replicas=num_replicas)
num_workers=num_workers)
for i in range(3):
train_loss1 = trainer.train()["mean_train_loss"]
validation_loss1 = trainer.validate()["mean_validation_loss"]
validation_loss1 = trainer.validate()["mean_val_loss"]
for i in range(3):
train_loss2 = trainer.train()["mean_train_loss"]
validation_loss2 = trainer.validate()["mean_validation_loss"]
validation_loss2 = trainer.validate()["mean_val_loss"]
assert train_loss2 <= train_loss1, (train_loss2, train_loss1)
assert validation_loss2 <= validation_loss1, (validation_loss2,
validation_loss1)
@pytest.mark.parametrize("num_replicas", [1, 2]
if dist.is_available() else [1])
def test_multi_model(ray_start_2_cpus, num_replicas):
@pytest.mark.parametrize("num_workers", [1, 2] if dist.is_available() else [1])
def test_multi_model(ray_start_2_cpus, num_workers):
def train(*, model=None, criterion=None, optimizer=None, dataloader=None):
model.train()
train_loss = 0
@@ -108,13 +108,13 @@ def test_multi_model(ray_start_2_cpus, num_replicas):
return opts[0], opts[1]
trainer1 = TorchTrainer(
multi_model_creator,
data_creator,
multi_optimizer_creator,
model_creator=multi_model_creator,
data_creator=data_creator,
optimizer_creator=multi_optimizer_creator,
loss_creator=lambda config: nn.MSELoss(),
config={"custom_func": train_epoch},
training_operator_cls=_TestingOperator,
num_replicas=num_replicas)
num_workers=num_workers)
trainer1.train()
filename = os.path.join(tempfile.mkdtemp(), "checkpoint")
@@ -125,13 +125,13 @@ def test_multi_model(ray_start_2_cpus, num_replicas):
trainer1.shutdown()
trainer2 = TorchTrainer(
multi_model_creator,
data_creator,
multi_optimizer_creator,
model_creator=multi_model_creator,
data_creator=data_creator,
optimizer_creator=multi_optimizer_creator,
loss_creator=lambda config: nn.MSELoss(),
config={"custom_func": train_epoch},
training_operator_cls=_TestingOperator,
num_replicas=num_replicas)
num_workers=num_workers)
trainer2.restore(filename)
os.remove(filename)
@@ -151,9 +151,8 @@ def test_multi_model(ray_start_2_cpus, num_replicas):
trainer2.shutdown()
@pytest.mark.parametrize("num_replicas", [1, 2]
if dist.is_available() else [1])
def test_multi_model_matrix(ray_start_2_cpus, num_replicas): # noqa: F811
@pytest.mark.parametrize("num_workers", [1, 2] if dist.is_available() else [1])
def test_multi_model_matrix(ray_start_2_cpus, num_workers): # noqa: F811
def train_epoch(self, iterator, info):
if self.config.get("models", 1) > 1:
assert len(self.models) == self.config["models"], self.config
@@ -194,13 +193,13 @@ def test_multi_model_matrix(ray_start_2_cpus, num_replicas): # noqa: F811
for optimizer_count in range(1, 3):
for scheduler_count in range(1, 3):
trainer = TorchTrainer(
multi_model_creator,
data_creator,
multi_optimizer_creator,
model_creator=multi_model_creator,
data_creator=data_creator,
optimizer_creator=multi_optimizer_creator,
loss_creator=nn.MSELoss,
scheduler_creator=multi_scheduler_creator,
training_operator_cls=_TestingOperator,
num_replicas=num_replicas,
num_workers=num_workers,
config={
"models": model_count,
"optimizers": optimizer_count,
@@ -222,9 +221,9 @@ def test_scheduler_freq(ray_start_2_cpus, scheduler_freq): # noqa: F811
optimizer, step_size=30, gamma=0.1)
trainer = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=lambda config: nn.MSELoss(),
config={"custom_func": train_epoch},
training_operator_cls=_TestingOperator,
@@ -236,13 +235,168 @@ def test_scheduler_freq(ray_start_2_cpus, scheduler_freq): # noqa: F811
trainer.shutdown()
def test_profiling(ray_start_2_cpus): # noqa: F811
trainer = TorchTrainer(
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=lambda config: nn.MSELoss())
stats = trainer.train(profile=True)
assert "profile" in stats
stats = trainer.validate(profile=True)
assert "profile" in stats
trainer.shutdown()
def test_split_batch(ray_start_2_cpus):
if not dist.is_available():
return
def data_creator(config):
"""Returns training dataloader, validation dataloader."""
train_dataset = LinearDataset(2, 5, size=config["data_size"])
return torch.utils.data.DataLoader(
train_dataset,
batch_size=config[BATCH_SIZE],
)
data_size = 600
batch_size = 21
trainer = TorchTrainer(
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=lambda config: nn.MSELoss(),
num_workers=2,
config={
BATCH_SIZE: batch_size,
"data_size": data_size,
})
stats = trainer.train()
assert trainer.config[BATCH_SIZE] == (batch_size - 1)
assert stats[NUM_SAMPLES] == 600
assert stats[BATCH_COUNT] == (data_size // 20)
def test_reduce_result(ray_start_2_cpus):
if not dist.is_available():
return
def data_creator(config):
"""Returns training dataloader, validation dataloader."""
train_dataset = LinearDataset(2, 5, size=config["data_size"])
return torch.utils.data.DataLoader(train_dataset, batch_size=1)
data_size = 600
trainer = TorchTrainer(
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=lambda config: nn.MSELoss(),
num_workers=2,
config={"data_size": data_size})
list_stats = trainer.train(reduce_results=False, profile=True)
assert len(list_stats) == 2
assert [stats[NUM_SAMPLES] == data_size for stats in list_stats]
assert [stats[BATCH_COUNT] == (data_size // 2) for stats in list_stats]
@pytest.mark.parametrize("num_workers", [1, 2] if dist.is_available() else [1])
def test_metrics(ray_start_2_cpus, num_workers):
data_size, val_size = 600, 500
batch_size = 4
num_train_steps = int(data_size / batch_size)
num_val_steps = int(val_size / batch_size)
train_scores = [1] + ([0] * num_train_steps)
val_scores = [1] + ([0] * num_val_steps)
trainer = TorchTrainer(
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=lambda config: nn.MSELoss(),
num_workers=num_workers,
config={
"scores": train_scores,
"val_scores": val_scores,
"key": "score",
"batch_size": batch_size,
"data_size": data_size,
"val_size": val_size
},
training_operator_cls=_TestMetricsOperator)
stats = trainer.train(num_steps=num_train_steps)
# Test that we output mean and last of custom metrics in an epoch
assert "mean_score" in stats
assert stats["last_score"] == 0
assert stats[NUM_SAMPLES] == num_train_steps * batch_size
expected_score = num_workers * (sum(train_scores) /
(num_train_steps * batch_size))
assert np.allclose(stats["mean_score"], expected_score)
val_stats = trainer.validate()
# Test that we output mean and last of custom metrics in validation
assert val_stats["last_score"] == 0
expected_score = (sum(val_scores) /
(num_val_steps * batch_size)) * num_workers
assert np.allclose(val_stats["mean_score"], expected_score)
assert val_stats[BATCH_COUNT] == np.ceil(num_val_steps / num_workers)
assert val_stats[NUM_SAMPLES] == num_val_steps * batch_size
assert val_stats[NUM_SAMPLES] == val_size
trainer.shutdown()
@pytest.mark.parametrize("num_workers", [1, 2] if dist.is_available() else [1])
def test_metrics_nan(ray_start_2_cpus, num_workers):
data_size, val_size = 100, 100
batch_size = 10
num_train_steps = int(data_size / batch_size)
num_val_steps = int(val_size / batch_size)
train_scores = [np.nan] + ([0] * num_train_steps)
val_scores = [np.nan] + ([0] * num_val_steps)
trainer = TorchTrainer(
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=lambda config: nn.MSELoss(),
num_workers=num_workers,
config={
"scores": train_scores,
"val_scores": val_scores,
"key": "score",
"batch_size": batch_size,
"data_size": data_size,
"val_size": val_size
},
training_operator_cls=_TestMetricsOperator)
stats = trainer.train(num_steps=num_train_steps)
assert "mean_score" in stats
assert stats["last_score"] == 0
assert np.isnan(stats["mean_score"])
stats = trainer.validate()
assert "mean_score" in stats
assert stats["last_score"] == 0
assert np.isnan(stats["mean_score"])
def test_scheduler_validate(ray_start_2_cpus): # noqa: F811
from torch.optim.lr_scheduler import ReduceLROnPlateau
trainer = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=lambda config: nn.MSELoss(),
scheduler_creator=lambda optimizer, cfg: ReduceLROnPlateau(optimizer),
training_operator_cls=_TestingOperator)
@@ -254,20 +408,19 @@ def test_scheduler_validate(ray_start_2_cpus): # noqa: F811
trainer.shutdown()
@pytest.mark.parametrize("num_replicas", [1, 2]
if dist.is_available() else [1])
def test_tune_train(ray_start_2_cpus, num_replicas): # noqa: F811
@pytest.mark.parametrize("num_workers", [1, 2] if dist.is_available() else [1])
def test_tune_train(ray_start_2_cpus, num_workers): # noqa: F811
config = {
"model_creator": model_creator,
"data_creator": data_creator,
"optimizer_creator": optimizer_creator,
"loss_creator": lambda config: nn.MSELoss(),
"num_replicas": num_replicas,
"num_workers": num_workers,
"use_gpu": False,
"batch_size": 512,
"backend": "gloo",
"config": {
"batch_size": 512,
"lr": 0.001
}
}
@@ -283,22 +436,21 @@ def test_tune_train(ray_start_2_cpus, num_replicas): # noqa: F811
for path, df in analysis.trial_dataframes.items():
mean_train_loss1 = df.loc[0, "mean_train_loss"]
mean_train_loss2 = df.loc[1, "mean_train_loss"]
mean_validation_loss1 = df.loc[0, "mean_validation_loss"]
mean_validation_loss2 = df.loc[1, "mean_validation_loss"]
mean_val_loss1 = df.loc[0, "mean_val_loss"]
mean_val_loss2 = df.loc[1, "mean_val_loss"]
assert mean_train_loss2 <= mean_train_loss1
assert mean_validation_loss2 <= mean_validation_loss1
assert mean_val_loss2 <= mean_val_loss1
@pytest.mark.parametrize("num_replicas", [1, 2]
if dist.is_available() else [1])
def test_save_and_restore(ray_start_2_cpus, num_replicas): # noqa: F811
@pytest.mark.parametrize("num_workers", [1, 2] if dist.is_available() else [1])
def test_save_and_restore(ray_start_2_cpus, num_workers): # noqa: F811
trainer1 = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=lambda config: nn.MSELoss(),
num_replicas=num_replicas)
num_workers=num_workers)
trainer1.train()
filename = os.path.join(tempfile.mkdtemp(), "checkpoint")
@@ -309,11 +461,11 @@ def test_save_and_restore(ray_start_2_cpus, num_replicas): # noqa: F811
trainer1.shutdown()
trainer2 = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=lambda config: nn.MSELoss(),
num_replicas=num_replicas)
num_workers=num_workers)
trainer2.restore(filename)
os.remove(filename)
@@ -334,7 +486,9 @@ def test_fail_with_recover(ray_start_2_cpus): # noqa: F811
return
def single_loader(config):
return LinearDataset(2, 5, size=1000000)
dataset = LinearDataset(2, 5, size=1000000)
return torch.utils.data.DataLoader(
dataset, batch_size=config.get("batch_size", 32))
def step_with_fail(self, *args, **kwargs):
worker_stats = [
@@ -348,12 +502,12 @@ def test_fail_with_recover(ray_start_2_cpus): # noqa: F811
with patch.object(TorchTrainer, "_train_epoch", step_with_fail):
trainer1 = TorchTrainer(
model_creator,
single_loader,
optimizer_creator,
batch_size=100000,
model_creator=model_creator,
data_creator=single_loader,
optimizer_creator=optimizer_creator,
loss_creator=lambda config: nn.MSELoss(),
num_replicas=2)
config={"batch_size": 100000},
num_workers=2)
with pytest.raises(RuntimeError):
trainer1.train(max_retries=1)
@@ -364,7 +518,9 @@ def test_resize(ray_start_2_cpus): # noqa: F811
return
def single_loader(config):
return LinearDataset(2, 5, size=1000000)
dataset = LinearDataset(2, 5, size=1000000)
return torch.utils.data.DataLoader(
dataset, batch_size=config.get("batch_size", 32))
def step_with_fail(self, *args, **kwargs):
worker_stats = [
@@ -378,12 +534,12 @@ def test_resize(ray_start_2_cpus): # noqa: F811
with patch.object(TorchTrainer, "_train_epoch", step_with_fail):
trainer1 = TorchTrainer(
model_creator,
single_loader,
optimizer_creator,
batch_size=100000,
model_creator=model_creator,
data_creator=single_loader,
optimizer_creator=optimizer_creator,
config={"batch_size": 100000},
loss_creator=lambda config: nn.MSELoss(),
num_replicas=2)
num_workers=2)
@ray.remote
def try_test():
@@ -400,7 +556,9 @@ def test_fail_twice(ray_start_2_cpus): # noqa: F811
return
def single_loader(config):
return LinearDataset(2, 5, size=1000000)
dataset = LinearDataset(2, 5, size=1000000)
return torch.utils.data.DataLoader(
dataset, batch_size=config.get("batch_size", 32))
def step_with_fail(self, *args, **kwargs):
worker_stats = [
@@ -414,12 +572,12 @@ def test_fail_twice(ray_start_2_cpus): # noqa: F811
with patch.object(TorchTrainer, "_train_epoch", step_with_fail):
trainer1 = TorchTrainer(
model_creator,
single_loader,
optimizer_creator,
batch_size=100000,
model_creator=model_creator,
data_creator=single_loader,
optimizer_creator=optimizer_creator,
config={"batch_size": 100000},
loss_creator=lambda config: nn.MSELoss(),
num_replicas=2)
num_workers=2)
trainer1.train(max_retries=2)
python/ray/util/sgd/tests/test_torch_runner.py
+3 -3
View File
@@ -61,11 +61,11 @@ class TestTorchRunner(unittest.TestCase):
runner.setup()
runner.train_epoch()
runner.train_epoch()
runner.train_epoch()
result = runner.train_epoch()
self.assertEqual(runner.training_operator.validate.call_count, 0)
runner.validate()
self.assertTrue(runner.training_operator.validate.called)
self.assertEqual(runner.stats()["epoch"], 3)
self.assertEqual(result["epoch"], 3)
def testtrain_epoch(self):
class MockOperator(TrainingOperator):
@@ -88,7 +88,7 @@ class TestTorchRunner(unittest.TestCase):
result = runner.train_epoch()
self.assertEqual(runner.training_operator.count, 3)
self.assertEqual(result["count"], 3)
self.assertEqual(runner.stats()["epoch"], 3)
self.assertEqual(result["epoch"], 3)
def testGivens(self):
class MockOperator(TrainingOperator):
python/ray/util/sgd/torch/constants.py
-1
View File
@@ -1,5 +1,4 @@
USE_FP16 = "__use_fp16__"
BATCH_COUNT = "batch_count"
SCHEDULER_STEP = "scheduler_step"
SCHEDULER_STEP_BATCH = "batch"
SCHEDULER_STEP_EPOCH = "epoch"
python/ray/util/sgd/torch/distributed_torch_runner.py
+41 -36
View File
@@ -1,11 +1,11 @@
import collections
from filelock import FileLock
import logging
import os
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.utils.data
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from ray.util.sgd.torch.torch_runner import TorchRunner
@@ -39,17 +39,15 @@ class DistributedTorchRunner(TorchRunner):
self._setup_training()
def _setup_distributed_pytorch(self, url, world_rank, world_size):
with self._timers["setup_proc"]:
self.world_rank = world_rank
logger.debug(
"Connecting to {} world_rank: {} world_size: {}".format(
url, world_rank, world_size))
logger.debug("using {}".format(self.backend))
dist.init_process_group(
backend=self.backend,
init_method=url,
rank=world_rank,
world_size=world_size)
self.world_rank = world_rank
logger.debug("Connecting to {} world_rank: {} world_size: {}".format(
url, world_rank, world_size))
logger.debug("using {}".format(self.backend))
dist.init_process_group(
backend=self.backend,
init_method=url,
rank=world_rank,
world_size=world_size)
def _setup_training(self):
logger.debug("Creating model")
@@ -68,32 +66,12 @@ class DistributedTorchRunner(TorchRunner):
self.optimizers = [self.optimizers]
self._create_schedulers_if_available()
self._try_setup_apex()
# This needs to happen after apex
self.models = [DistributedDataParallel(model) for model in self.models]
logger.debug("Creating loss.")
self._create_loss()
logger.debug("Creating dataset.")
with FileLock(os.path.expanduser("~/.ray_data.lock")):
datasets = self.data_creator(self.config)
train_set, val_set = self._validate_datasets(datasets)
train_loader_config = self.dataloader_config.copy()
train_loader_config.update(
sampler=torch.utils.data.distributed.DistributedSampler(train_set),
shuffle=False)
self.train_loader = torch.utils.data.DataLoader(
train_set, batch_size=self.batch_size, **train_loader_config)
self.validation_loader = None
if val_set:
self.validation_loader = torch.utils.data.DataLoader(
val_set, batch_size=self.batch_size, **self.dataloader_config)
self._initialize_dataloaders()
self.training_operator = self.training_operator_cls(
self.config,
@@ -103,12 +81,39 @@ class DistributedTorchRunner(TorchRunner):
schedulers=self.schedulers,
use_fp16=self.use_fp16)
def _initialize_dataloaders(self):
super(DistributedTorchRunner, self)._initialize_dataloaders()
def with_sampler(loader):
# Automatically set the DistributedSampler
data_loader_args = {
"dataset": loader.dataset,
"batch_size": loader.batch_size,
"shuffle": False,
"num_workers": loader.num_workers,
"collate_fn": loader.collate_fn,
"pin_memory": loader.pin_memory,
"drop_last": loader.drop_last,
"timeout": loader.timeout,
"worker_init_fn": loader.worker_init_fn,
"sampler": DistributedSampler(loader.dataset)
}
return DataLoader(**data_loader_args)
if isinstance(self.train_loader, DataLoader):
self.train_loader = with_sampler(self.train_loader)
if self.validation_loader and isinstance(self.validation_loader,
DataLoader):
self.validation_loader = with_sampler(self.validation_loader)
def train_epoch(self, **kwargs):
"""Runs a training epoch and updates the model parameters.
Automatically sets epoch of sampler if possible.
"""
if hasattr(self.train_loader.sampler, "set_epoch"):
if hasattr(self.train_loader, "sampler") and hasattr(
self.train_loader.sampler, "set_epoch"):
self.train_loader.sampler.set_epoch(self.epochs)
return super(DistributedTorchRunner, self).train_epoch(**kwargs)
python/ray/util/sgd/torch/examples/cifar_pytorch_example.py
+24 -20
View File
@@ -3,13 +3,14 @@ import torch
import torch.nn as nn
import argparse
from ray import tune
import torch.utils.data
from torch.utils.data import DataLoader, Subset
import torchvision
import torchvision.transforms as transforms
import ray
from ray.util.sgd.torch import (TorchTrainer, TorchTrainable)
from ray.util.sgd.torch.resnet import ResNet18
from ray.util.sgd.utils import BATCH_SIZE
def initialization_hook():
@@ -40,11 +41,14 @@ def cifar_creator(config):
root="~/data", train=False, download=False, transform=transform_test)
if config.get("test_mode"):
train_dataset = torch.utils.data.Subset(train_dataset, list(range(64)))
validation_dataset = torch.utils.data.Subset(validation_dataset,
list(range(64)))
train_dataset = Subset(train_dataset, list(range(64)))
validation_dataset = Subset(validation_dataset, list(range(64)))
return train_dataset, validation_dataset
train_loader = DataLoader(
train_dataset, batch_size=config[BATCH_SIZE], num_workers=2)
validation_loader = DataLoader(
validation_dataset, batch_size=config[BATCH_SIZE], num_workers=2)
return train_loader, validation_loader
def optimizer_creator(model, config):
@@ -57,25 +61,25 @@ def scheduler_creator(optimizer, config):
optimizer, milestones=[150, 250, 350], gamma=0.1)
def train_example(num_replicas=1,
def train_example(num_workers=1,
num_epochs=5,
use_gpu=False,
use_fp16=False,
test_mode=False):
trainer1 = TorchTrainer(
ResNet18,
cifar_creator,
optimizer_creator,
nn.CrossEntropyLoss,
model_creator=ResNet18,
data_creator=cifar_creator,
optimizer_creator=optimizer_creator,
loss_creator=nn.CrossEntropyLoss,
scheduler_creator=scheduler_creator,
initialization_hook=initialization_hook,
num_replicas=num_replicas,
num_workers=num_workers,
config={
"lr": 0.01,
"test_mode": test_mode
"test_mode": test_mode,
BATCH_SIZE: 128,
},
use_gpu=use_gpu,
batch_size=16 if test_mode else 512,
backend="nccl" if use_gpu else "gloo",
scheduler_step_freq="epoch",
use_fp16=use_fp16)
@@ -89,18 +93,18 @@ def train_example(num_replicas=1,
print("success!")
def tune_example(num_replicas=1, use_gpu=False, test_mode=False):
def tune_example(num_workers=1, use_gpu=False, test_mode=False):
config = {
"model_creator": ResNet18,
"data_creator": cifar_creator,
"optimizer_creator": optimizer_creator,
"loss_creator": nn.CrossEntropyLoss,
"num_replicas": num_replicas,
"num_workers": num_workers,
"initialization_hook": initialization_hook,
"use_gpu": use_gpu,
"batch_size": 16 if test_mode else 512,
"config": {
"lr": tune.choice([1e-4, 1e-3]),
BATCH_SIZE: 128,
"test_mode": test_mode
},
"backend": "nccl" if use_gpu else "gloo"
@@ -124,11 +128,11 @@ if __name__ == "__main__":
type=str,
help="the address to use for Redis")
parser.add_argument(
"--num-replicas",
"--num-workers",
"-n",
type=int,
default=1,
help="Sets number of replicas for training.")
help="Sets number of workers for training.")
parser.add_argument(
"--num-epochs", type=int, default=5, help="Number of epochs to train.")
parser.add_argument(
@@ -155,12 +159,12 @@ if __name__ == "__main__":
if args.tune:
tune_example(
num_replicas=args.num_replicas,
num_workers=args.num_workers,
use_gpu=args.use_gpu,
test_mode=args.smoke_test)
else:
train_example(
num_replicas=args.num_replicas,
num_workers=args.num_workers,
num_epochs=args.num_epochs,
use_gpu=args.use_gpu,
use_fp16=args.fp16,
python/ray/util/sgd/torch/examples/dcgan.py
+26 -16
View File
@@ -31,7 +31,9 @@ def data_creator(config):
]))
if config.get("test_mode"):
dataset = torch.utils.data.Subset(dataset, list(range(64)))
return dataset
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=config.get("batch_size", 32))
return dataloader
class Generator(nn.Module):
@@ -137,12 +139,15 @@ class GANOperator(TrainingOperator):
N = len(imgs)
dataloader = torch.utils.data.DataLoader(imgs, batch_size=batch_size)
up = nn.Upsample(
size=(28, 28), mode="bilinear").type(torch.FloatTensor)
size=(28, 28),
mode="bilinear",
align_corners=False # This is to reduce user warnings from torch.
).type(torch.FloatTensor)
def get_pred(x):
x = up(x)
x = self.classifier(x)
return F.softmax(x).data.cpu().numpy()
return F.softmax(x, dim=1).data.cpu().numpy()
# Obtain predictions for the fake provided images
preds = np.zeros((N, 10))
@@ -218,27 +223,32 @@ class GANOperator(TrainingOperator):
}
def train_example(num_replicas=1, use_gpu=False, test_mode=False):
def train_example(num_workers=1, use_gpu=False, test_mode=False):
config = {
"test_mode": test_mode,
"batch_size": 16 if test_mode else 512 // num_workers,
"classification_model_path": os.path.join(
os.path.dirname(ray.__file__),
"util/sgd/torch/examples/mnist_cnn.pt")
}
trainer = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
nn.BCELoss,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=nn.BCELoss,
training_operator_cls=GANOperator,
num_replicas=num_replicas,
num_workers=num_workers,
config=config,
use_gpu=use_gpu,
batch_size=16 if test_mode else 512,
backend="nccl" if use_gpu else "gloo")
for i in range(5):
from tabulate import tabulate
for itr in range(5):
stats = trainer.train()
print(stats)
formatted = tabulate([stats], headers="keys")
if itr > 0: # Get the last line of the stats.
formatted = formatted.split("\n")[-1]
print(formatted)
return trainer
@@ -253,21 +263,21 @@ if __name__ == "__main__":
type=str,
help="the address to use to connect to a cluster.")
parser.add_argument(
"--num-replicas",
"--num-workers",
"-n",
type=int,
default=1,
help="Sets number of replicas for training.")
help="Sets number of workers for training.")
parser.add_argument(
"--use-gpu",
action="store_true",
default=False,
help="Enables GPU training")
args, _ = parser.parse_known_args()
args = parser.parse_args()
ray.init(address=args.address)
trainer = train_example(
num_replicas=args.num_replicas,
num_workers=args.num_workers,
use_gpu=args.use_gpu,
test_mode=args.smoke_test)
models = trainer.get_model()
doc/examples/doc_code/raysgd_torch_signatures.py → python/ray/util/sgd/torch/examples/raysgd_torch_signatures.py
+14 -11
View File
@@ -1,6 +1,5 @@
# flake8: noqa
"""
This file holds code for the torch Trainer creator signatures.
"""This file holds code for the TorchTrainer creator signatures.
It ignores yapf because yapf doesn't allow comments right after code blocks,
but we put comments right after code blocks to prevent large white spaces
@@ -46,22 +45,26 @@ def optimizer_creator(model, config):
# __torch_data_start__
from torch.utils.data import DataLoader
from ray.util.sgd.torch.examples.train_example import LinearDataset
def data_creator(config):
"""Constructs torch.utils.data.Dataset objects.
"""Constructs Iterables for training and validation.
Note that even though two Dataset objects can be returned,
only one dataset will be used for training.
Note that even though two Iterable objects can be returned,
only one Iterable will be used for training.
Args:
config: Configuration dictionary passed into ``TorchTrainer``
Returns:
One or Two Dataset objects. If only one Dataset object is provided,
One or Two Iterable objects. If only one Iterable object is provided,
``trainer.validate()`` will throw a ValueError.
"""
return LinearDataset(2, 5), LinearDataset(2, 5, size=400)
train_dataset, val_dataset = LinearDataset(2, 5), LinearDataset(2, 5)
train_loader = DataLoader(train_dataset, batch_size=config["batch_size"])
val_loader = DataLoader(val_dataset, batch_size=config["batch_size"])
return train_loader, val_loader
# __torch_data_end__
# __torch_loss_start__
@@ -111,11 +114,11 @@ ray.init()
from ray.util.sgd import TorchTrainer
trainer = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=nn.MSELoss,
scheduler_creator=scheduler_creator,
config={"lr": 0.001})
config={"lr": 0.001, "batch_size": 64})
# __torch_trainer_end__
python/ray/util/sgd/torch/examples/train_example.py
+24 -13
View File
@@ -1,5 +1,4 @@
"""
This file holds code for a Training guide for PytorchSGD in the documentation.
"""Example code for RaySGD Torch in the documentation.
It ignores yapf because yapf doesn't allow comments right after code blocks,
but we put comments right after code blocks to prevent large white spaces
@@ -55,20 +54,32 @@ def scheduler_creator(optimizer, config):
def data_creator(config):
"""Returns training dataloader, validation dataloader."""
return LinearDataset(2, 5), LinearDataset(2, 5, size=400)
train_dataset = LinearDataset(2, 5, size=config.get("data_size", 1000))
val_dataset = LinearDataset(2, 5, size=config.get("val_size", 400))
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config.get("batch_size", 32),
)
validation_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=config.get("batch_size", 32))
return train_loader, validation_loader
def train_example(num_replicas=1, use_gpu=False):
def train_example(num_workers=1, use_gpu=False):
trainer1 = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=nn.MSELoss,
scheduler_creator=scheduler_creator,
num_replicas=num_replicas,
num_workers=num_workers,
use_gpu=use_gpu,
batch_size=num_replicas * 4,
config={"lr": 1e-2, "hidden_size": 1},
config={
"lr": 1e-2, # used in optimizer_creator
"hidden_size": 1, # used in model_creator
"batch_size": 4, # used in data_creator
},
backend="gloo",
scheduler_step_freq="epoch")
for i in range(5):
@@ -91,11 +102,11 @@ if __name__ == "__main__":
type=str,
help="the address to use for Ray")
parser.add_argument(
"--num-replicas",
"--num-workers",
"-n",
type=int,
default=1,
help="Sets number of replicas for training.")
help="Sets number of workers for training.")
parser.add_argument(
"--use-gpu",
action="store_true",
@@ -109,4 +120,4 @@ if __name__ == "__main__":
import ray
ray.init(address=args.address)
train_example(num_replicas=args.num_replicas, use_gpu=args.use_gpu)
train_example(num_workers=args.num_workers, use_gpu=args.use_gpu)
python/ray/util/sgd/torch/examples/tune_example.py
+20 -13
View File
@@ -44,18 +44,27 @@ def optimizer_creator(model, config):
def data_creator(config):
"""Returns training dataloader, validation dataloader."""
return LinearDataset(2, 5), LinearDataset(2, 5, size=400)
train_dataset = LinearDataset(2, 5)
val_dataset = LinearDataset(2, 5, size=400)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config["batch_size"],
)
validation_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=config["batch_size"])
return train_loader, validation_loader
def tune_example(num_replicas=1, use_gpu=False):
def tune_example(num_workers=1, use_gpu=False):
config = {
"model_creator": tune.function(model_creator),
"data_creator": tune.function(data_creator),
"optimizer_creator": tune.function(optimizer_creator),
"loss_creator": tune.function(nn.MSELoss),
"num_replicas": num_replicas,
"model_creator": model_creator,
"data_creator": data_creator,
"optimizer_creator": optimizer_creator,
"loss_creator": nn.MSELoss,
"num_workers": num_workers,
"use_gpu": use_gpu,
"batch_size": 512,
"config": {"batch_size": 512 // num_workers},
"backend": "gloo"
}
@@ -77,20 +86,18 @@ if __name__ == "__main__":
type=str,
help="the address to use for Ray")
parser.add_argument(
"--num-replicas",
"--num-workers",
"-n",
type=int,
default=1,
help="Sets number of replicas for training.")
help="Sets number of workers for training.")
parser.add_argument(
"--use-gpu",
action="store_true",
default=False,
help="Enables GPU training")
parser.add_argument(
"--tune", action="store_true", default=False, help="Tune training")
args, _ = parser.parse_known_args()
ray.init(address=args.address)
tune_example(num_replicas=args.num_replicas, use_gpu=args.use_gpu)
tune_example(num_workers=args.num_workers, use_gpu=args.use_gpu)
python/ray/util/sgd/torch/torch_runner.py
+64 -70
View File
@@ -4,9 +4,8 @@ import logging
import inspect
import itertools
import os
import tempfile
import torch
import torch.utils.data
from torch.utils.data import Dataset
import ray
from ray.util.sgd.torch.constants import USE_FP16, SCHEDULER_STEP
@@ -27,16 +26,15 @@ class TorchRunner:
"""Manages a PyTorch model for training.
Args:
model_creator (dict -> *): see torch_trainer.py
data_creator (dict -> Dataset, Dataset): see torch_trainer.py.
optimizer_creator (models, dict -> optimizers): see torch_trainer.py.
loss_creator (dict -> loss | Loss class): see torch_trainer.py.
scheduler_creator (optimizers, dict -> schedulers): see
model_creator (dict -> Model(s)): see torch_trainer.py
data_creator (dict -> Iterable(s)): see torch_trainer.py.
optimizer_creator ((models, dict) -> optimizers): see torch_trainer.py.
loss_creator (torch.nn.*Loss class | dict -> loss):
see torch_trainer.py.
scheduler_creator ((optimizers, dict) -> scheduler): see
torch_trainer.py.
training_operator_cls: see torch_trainer.py
config (dict): see torch_trainer.py.
dataloader_config (dict): See torch_trainer.py.
batch_size (int): see torch_trainer.py.
use_fp16 (bool): see torch_trainer.py.
apex_args (dict|None): see torch_trainer.py.
scheduler_step_freq (str): see torch_trainer.py.
@@ -46,36 +44,23 @@ class TorchRunner:
model_creator,
data_creator,
optimizer_creator,
loss_creator,
loss_creator=None,
scheduler_creator=None,
training_operator_cls=None,
config=None,
dataloader_config=None,
batch_size=16,
use_fp16=False,
apex_args=None,
scheduler_step_freq="batch"):
self.model_creator = model_creator
self.data_creator = data_creator
self.optimizer_creator = optimizer_creator
self.loss_creator = loss_creator
self.data_creator = data_creator
self.scheduler_creator = scheduler_creator
self.training_operator_cls = training_operator_cls or TrainingOperator
self.config = {} if config is None else config
self.dataloader_config = {
"num_workers": 2
} if dataloader_config is None else dataloader_config
self.batch_size = batch_size
self.verbose = True
self.timers = utils.TimerCollection()
self.epochs = 0
self._timers = {
k: utils.TimerStat(window_size=1)
for k in [
"setup_proc", "setup_model", "get_state", "set_state",
"validation", "training"
]
}
self.models = None
self.optimizers = None
self.criterion = None
@@ -90,23 +75,40 @@ class TorchRunner:
"https://www.github.com/nvidia/apex to use fp16 training.")
self.scheduler_step_freq = scheduler_step_freq
def _validate_datasets(self, dataset):
assert dataset, "Datasets need to be returned in data_creator."
if issubclass(type(dataset), Dataset):
return dataset, None
elif len(dataset) == 2 and issubclass(type(dataset[0]), Dataset):
return dataset
else:
raise ValueError("Datasets must be <= 2. Got {}".format(dataset))
def _validate_loaders(self, loaders):
assert loaders, "Loaders need to be returned in data_creator."
if isinstance(loaders, (tuple, list)):
if len(loaders) == 1:
return loaders, None
elif len(loaders) == 2:
return loaders
else:
raise ValueError(
"Number of loaders must be <= 2. Got {}".format(loaders))
# No great way of checking type otherwise
return loaders, None
def _initialize_dataloaders(self):
logger.debug("Instantiating dataloaders.")
# When creating loaders, a filelock will be used to ensure no
# race conditions in data downloading among different workers.
with FileLock(os.path.join(tempfile.gettempdir(), ".ray_data.lock")):
loaders = self.data_creator(self.config)
train_loader, val_loader = self._validate_loaders(loaders)
self.train_loader, self.validation_loader = train_loader, val_loader
def _create_loss(self):
if not self.loss_creator:
return
logger.debug("Creating loss.")
if inspect.isclass(self.loss_creator) and issubclass(
self.loss_creator, torch.nn.modules.loss._Loss):
self.criterion = self.loss_creator()
else:
self.criterion = self.loss_creator(self.config)
if torch.cuda.is_available():
if torch.cuda.is_available() and hasattr("cuda", self.criterion):
self.criterion = self.criterion.cuda()
def _create_schedulers_if_available(self):
@@ -142,22 +144,7 @@ class TorchRunner:
self._create_schedulers_if_available()
self._try_setup_apex()
self._create_loss()
logger.debug("Creating dataset")
# When creating datasets, a filelock will be used to ensure no
# race conditions in data downloading among different workers.
with FileLock(os.path.expanduser("~/.ray_data.lock")):
datasets = self.data_creator(self.config)
train_set, val_set = self._validate_datasets(datasets)
self.train_loader = torch.utils.data.DataLoader(
train_set, batch_size=self.batch_size, **self.dataloader_config)
self.validation_loader = None
if val_set:
self.validation_loader = torch.utils.data.DataLoader(
val_set, batch_size=self.batch_size, **self.dataloader_config)
self._initialize_dataloaders()
self.training_operator = self.training_operator_cls(
self.config,
models=self.models,
@@ -174,47 +161,55 @@ class TorchRunner:
"""Finds a free port on the current node."""
return utils.find_free_port()
def train_epoch(self, num_steps=None, info=None):
def train_epoch(self, num_steps=None, profile=False, info=None):
"""Runs a training epoch and updates the model parameters."""
logger.debug("Begin Training Step {}".format(self.epochs + 1))
info = info or {}
self._toggle_profiling(profile=profile)
info.update({
USE_FP16: self.use_fp16,
SCHEDULER_STEP: self.scheduler_step_freq
})
with self._timers["training"]:
with self.timers.record("train_epoch"):
iterator = self.train_loader
if num_steps:
iterator = itertools.islice(iter(self.train_loader), num_steps)
train_stats = self.training_operator.train_epoch(iterator, info)
self.epochs += 1
train_stats.update(self.stats())
return train_stats
# This is so that `epochs` is first in ordering.
stats = dict(epoch=self.epochs, **train_stats)
if profile:
stats.update(profile=self.timers.stats())
return stats
def validate(self, num_steps=None, info=None):
def validate(self, num_steps=None, profile=False, info=None):
"""Evaluates the model on the validation data set."""
if self.validation_loader is None:
raise ValueError("No validation dataloader provided.")
info = info or {}
with self._timers["validation"]:
self._toggle_profiling(profile=profile)
with self.timers.record("validation"):
iterator = self.validation_loader
if num_steps:
iterator = itertools.islice(
iter(self.validation_loader), num_steps)
validation_stats = self.training_operator.validate(iterator, info)
validation_stats.update(self.stats())
validation_stats = self.training_operator.validate(
iterator, info=info)
if profile:
validation_stats.update(profile=self.timers.stats())
return validation_stats
def stats(self):
"""Returns a dictionary of statistics collected."""
stats = {"epoch": self.epochs}
for k, t in self._timers.items():
stats[k + "_time_mean"] = t.mean
stats[k + "_time_total"] = t.sum
t.reset()
return stats
def _toggle_profiling(self, profile=False):
"""Enables/Disables and resets timing profiles."""
if profile:
self.timers.enable()
self.timers.reset()
else:
self.timers.disable()
self.training_operator._set_timers(self.timers)
def _get_model_state_dicts(self):
# This is so that we create a duplicate of weights into CPU rather than
@@ -237,8 +232,7 @@ class TorchRunner:
"epoch": self.epochs,
"operator": self.training_operator.state_dict(),
"models": self._get_model_state_dicts(),
"optimizers": [opt.state_dict() for opt in self.optimizers],
"stats": self.stats()
"optimizers": [opt.state_dict() for opt in self.optimizers]
}
if self.schedulers:
state.update({
@@ -264,7 +258,7 @@ class TorchRunner:
if self.use_fp16 and "amp" in state and amp:
amp.load_state_dict(state["amp"])
self.epochs = state["stats"]["epoch"]
self.epochs = state["epoch"]
self.training_operator.load_state_dict(state_dict)
def apply(self, fn):
python/ray/util/sgd/torch/torch_trainer.py
+131 -99
View File
@@ -14,6 +14,7 @@ from ray.tune.trial import Resources
from ray.util.sgd.torch.distributed_torch_runner import (
DistributedTorchRunner)
from ray.util.sgd import utils
from ray.util.sgd.utils import NUM_SAMPLES, BATCH_SIZE
from ray.util.sgd.torch.torch_runner import TorchRunner
from ray.util.sgd.torch.constants import VALID_SCHEDULER_STEP
@@ -37,6 +38,8 @@ class TorchTrainer:
.. code-block:: python
ray.init()
def model_creator(config):
return nn.Linear(1, 1)
@@ -47,13 +50,19 @@ class TorchTrainer:
def data_creator(config):
return LinearDataset(2, 5), LinearDataset(2, 5, size=400)
batch_size = config["batch_size"]
train_data, val_data = LinearDataset(2, 5), LinearDataset(2, 5)
train_loader = DataLoader(train_data, batch_size=batch_size)
val_loader = DataLoader(val_data, batch_size=batch_size)
return train_loader, val_loader
trainer = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,
model_creator=model_creator,
data_creator=data_creator,
optimizer_creator=optimizer_creator,
loss_creator=nn.MSELoss,
config={"batch_size": 32},
use_gpu=True
)
for i in range(4):
@@ -67,12 +76,12 @@ class TorchTrainer:
a ``training_operator_cls`` must be specified. You do not need to
handle GPU/devices in this function; RaySGD will do that under
the hood.
data_creator (dict -> Dataset(s)): Constructor function
data_creator (dict -> Iterable(s)): Constructor function
that takes in the passed config and returns one or
two ``torch.utils.data.Dataset`` objects.
Note that even though two Dataset objects can be returned,
only one dataset will be used for training. RaySGD
will automatically wrap the objects with a ``DataLoader``.
two Iterable objects. Note that even though two Iterable objects
can be returned, only one will be used for training, and the
other will be used for validation. If not provided, you must
provide a custom TrainingOperator.
optimizer_creator ((models, dict) -> optimizers): Constructor
function that takes in the return values from
``model_creator`` and the passed config and returns One or
@@ -83,7 +92,8 @@ class TorchTrainer:
takes in the provided config for customization or a subclass
of ``torch.nn.modules.loss._Loss``, which is most Pytorch
loss classes. For example, ``loss_creator=torch.nn.BCELoss``.
scheduler_creator (optimizers, dict -> loss):
If not provided, you must provide a custom TrainingOperator.
scheduler_creator ((optimizers, dict) -> scheduler):
A constructor function for the torch scheduler. This is
a function that takes in the generated optimizers (from
``optimizer_creator``) provided config for customization.
@@ -96,20 +106,12 @@ class TorchTrainer:
TrainingOperator.
config (dict): Custom configuration value to be passed to
all creator and operator constructors.
dataloader_config (dict): Configuration values to be passed into
the ``torch.utils.data.DataLoader`` object that wraps
the dataset on each parallel worker for both training
and validation. Note that if ``num_replicas``
is greater than 1, ``shuffle`` and ``sampler`` will be
automatically set. See the available arguments
here https://pytorch.org/docs/stable/data.html.
num_replicas (int): the number of workers used in distributed
training.
num_workers (int): the number of workers used in distributed
training. If 1, the worker will not be wrapped with
DistributedDataParallel.
use_gpu (bool): Sets resource allocation for workers to 1 GPU
if true, and automatically moves both the model and optimizer
to the available CUDA device.
batch_size (int): Total batch size for each minibatch. This
value is divided among all workers and rounded.
backend (string): backend used by distributed PyTorch. Currently
support "nccl", "gloo", and "auto". If "auto", RaySGD will
automatically use "nccl" if `use_gpu` is True, and "gloo"
@@ -130,50 +132,53 @@ class TorchTrainer:
"""
def __init__(self,
model_creator,
data_creator,
optimizer_creator,
loss_creator,
*,
model_creator=None,
data_creator=None,
optimizer_creator=None,
loss_creator=None,
scheduler_creator=None,
training_operator_cls=None,
initialization_hook=None,
config=None,
dataloader_config=None,
num_replicas=1,
num_workers=1,
use_gpu=False,
batch_size=16,
backend="auto",
use_fp16=False,
apex_args=None,
scheduler_step_freq="batch"):
if num_replicas > 1 and not dist.is_available():
if num_workers > 1 and not dist.is_available():
raise ValueError(
("Distributed PyTorch is not supported on macOS. "
"To run without distributed PyTorch, set 'num_replicas=1'. "
"To run without distributed PyTorch, set 'num_workers=1'. "
"For more information, see "
"https://github.com/pytorch/examples/issues/467."))
if not (model_creator and optimizer_creator and data_creator):
raise ValueError("Must provide a Model, Optimizer, Data creator.")
self.model_creator = model_creator
self.data_creator = data_creator
self.optimizer_creator = optimizer_creator
self.loss_creator = loss_creator
self.data_creator = data_creator
self.scheduler_creator = scheduler_creator
self.training_operator_cls = training_operator_cls
if not training_operator_cls and not loss_creator:
raise ValueError("If a loss_creator is not provided, you must "
"provide a custom training operator.")
self.initialization_hook = initialization_hook
self.config = {} if config is None else config
self.dataloader_config = dataloader_config
self.optimizer_timer = utils.TimerStat(window_size=1)
if backend == "auto":
backend = "nccl" if use_gpu else "gloo"
logger.info("Using {} as backend.".format(backend))
logger.debug("Using {} as backend.".format(backend))
self.backend = backend
# TODO: Have an auto "use_gpu" option to detect and use GPUs.
self.use_gpu = use_gpu
self.batch_size = batch_size
self.max_replicas = num_replicas
self.max_replicas = num_workers
self.use_fp16 = use_fp16
@@ -190,24 +195,46 @@ class TorchTrainer:
self._start_workers(self.max_replicas)
def _start_workers(self, num_replicas):
logger.info(f"start_workers: Setting %d replicas." % num_replicas)
if num_replicas == 1:
def _configure_and_split_batch(self, num_workers):
"""If sgd.utils.BATCH_SIZE is provided, split among workers."""
if BATCH_SIZE not in self.config:
return
# Compute batch size per worker
logger.debug("BATCH_SIZE parameter detected. Splitting among workers.")
batch_size = self.config[BATCH_SIZE]
batch_size_per_worker = batch_size // num_workers
if batch_size % num_workers > 0:
new_batch_size = batch_size_per_worker * num_workers
logger.warning(
("Changing batch size from {old_batch_size} to "
"{new_batch_size} to evenly distribute batches across "
"{num_workers} workers.").format(
old_batch_size=batch_size,
new_batch_size=new_batch_size,
num_workers=num_workers))
self.config[BATCH_SIZE] = new_batch_size
return batch_size_per_worker
def _start_workers(self, num_workers):
logger.debug(f"start_workers: Setting %d workers." % num_workers)
worker_config = self.config.copy()
batch_size_per_worker = self._configure_and_split_batch(num_workers)
if batch_size_per_worker:
worker_config[BATCH_SIZE] = batch_size_per_worker
if num_workers == 1:
# Generate actor class
Runner = ray.remote(
num_cpus=1, num_gpus=int(self.use_gpu))(TorchRunner)
# Start workers
self.workers = [
Runner.remote(
self.model_creator,
self.data_creator,
self.optimizer_creator,
self.loss_creator,
self.scheduler_creator,
model_creator=self.model_creator,
data_creator=self.data_creator,
optimizer_creator=self.optimizer_creator,
loss_creator=self.loss_creator,
scheduler_creator=self.scheduler_creator,
training_operator_cls=self.training_operator_cls,
config=self.config,
dataloader_config=self.dataloader_config,
batch_size=self.batch_size,
config=worker_config,
use_fp16=self.use_fp16,
apex_args=self.apex_args,
scheduler_step_freq=self.scheduler_step_freq,
@@ -221,34 +248,21 @@ class TorchTrainer:
# Generate actor class
Runner = ray.remote(
num_cpus=1, num_gpus=int(self.use_gpu))(DistributedTorchRunner)
# Compute batch size per replica
batch_size_per_replica = self.batch_size // num_replicas
if self.batch_size % num_replicas > 0:
new_batch_size = batch_size_per_replica * num_replicas
logger.warning(
("Changing batch size from {old_batch_size} to "
"{new_batch_size} to evenly distribute batches across "
"{num_replicas} replicas.").format(
old_batch_size=self.batch_size,
new_batch_size=new_batch_size,
num_replicas=num_replicas))
# Start workers
self.workers = [
Runner.remote(
self.model_creator,
self.data_creator,
self.optimizer_creator,
self.loss_creator,
self.scheduler_creator,
model_creator=self.model_creator,
data_creator=self.data_creator,
optimizer_creator=self.optimizer_creator,
loss_creator=self.loss_creator,
scheduler_creator=self.scheduler_creator,
backend=self.backend,
training_operator_cls=self.training_operator_cls,
config=self.config,
dataloader_config=self.dataloader_config,
batch_size=batch_size_per_replica,
config=worker_config,
use_fp16=self.use_fp16,
apex_args=self.apex_args,
scheduler_step_freq=self.scheduler_step_freq)
for i in range(num_replicas)
for i in range(num_workers)
]
if self.initialization_hook:
self.apply_all_workers(self.initialization_hook)
@@ -265,18 +279,28 @@ class TorchTrainer:
def train(self,
num_steps=None,
profile=False,
reduce_results=True,
max_retries=0,
checkpoint="auto",
info=None):
"""Runs a training epoch.
Runs an average over all values returned from workers. Set
`max_retries` to enable fault handling in case of instance preemption.
Calls `operator.train_epoch()` on N parallel workers simultaneously
underneath the hood.
Set `max_retries` to enable fault handling in case of
instance preemption.
Args:
num_steps (int): Number of batches to compute update steps on.
This corresponds also to the number of times
``TrainingOperator.train_batch`` is called.
profile (bool): Returns time stats for the training procedure.
reduce_results (bool): Whether to average all metrics across
all workers into one dict. If a metric is a non-numerical
value (or nested dictionaries), one value will be randomly
selected among the workers. If False, returns a list of dicts.
max_retries (int): Must be non-negative. If set to N, will
kill all current workers, query the Ray global state for
total available resources, and re-launch up to the
@@ -289,9 +313,11 @@ class TorchTrainer:
operator for ``train_epoch`` and ``train_batch``.
Returns:
A dictionary of metrics for training.
(dict | list) A dictionary of metrics for training.
You can provide custom metrics by passing in a custom
``training_operator_cls``.
``training_operator_cls``. If ``reduce_results=False``,
this will return a list of metric dictionaries whose
length will be equal to ``num_workers``.
"""
assert max_retries >= 0, "`max_retries` must be non-negative."
if max_retries:
@@ -306,37 +332,46 @@ class TorchTrainer:
logger.info("Resize opportunity detected. Attempting to scale up.")
self._resize_workers(checkpoint=checkpoint)
with self.optimizer_timer:
success, worker_stats = self._train_epoch(
num_steps=num_steps, profile=profile, info=info)
# Fault handling
for i in range(max_retries):
if success:
break
else:
self._num_failures += 1
self._resize_workers(checkpoint=checkpoint)
logger.info(
"Retrying training step with %d workers." % len(self.workers))
success, worker_stats = self._train_epoch(
num_steps=num_steps, info=info)
# Fault handling
for i in range(max_retries):
if success:
break
else:
self._num_failures += 1
self._resize_workers(checkpoint=checkpoint)
logger.info("Retrying training step with %d workers." % len(
self.workers))
success, worker_stats = self._train_epoch(
num_steps=num_steps, info=info)
num_steps=num_steps, profile=profile, info=info)
if not success:
raise RuntimeError("Training run failed.")
worker_stats = ray.get(worker_stats)
if reduce_results:
return self._process_stats(worker_stats)
else:
return worker_stats
def _process_stats(self, worker_stats):
stats = {
NUM_SAMPLES: sum(
stats.pop(NUM_SAMPLES, np.nan) for stats in worker_stats)
}
train_stats = {}
for stat_key in worker_stats[0]:
if isinstance(worker_stats[0], numbers.Number):
train_stats[stat_key] = np.nanmean(
stats[stat_key] = np.nanmean(
[s.get(stat_key, np.nan) for s in worker_stats])
else:
train_stats[stat_key] = worker_stats[0][stat_key]
return train_stats
stats[stat_key] = worker_stats[0][stat_key]
return stats
def _train_epoch(self, num_steps=None, info=None):
def _train_epoch(self, num_steps=None, profile=False, info=None):
worker_stats = [
w.train_epoch.remote(num_steps=num_steps, info=info)
w.train_epoch.remote(
num_steps=num_steps, profile=profile, info=info)
for w in self.workers
]
success = utils.check_for_failure(worker_stats)
@@ -367,13 +402,14 @@ class TorchTrainer:
"""
return ray.get([w.apply_operator.remote(fn) for w in self.workers])
def validate(self, num_steps=None, info=None):
def validate(self, num_steps=None, profile=False, info=None):
"""Evaluates the model on the validation data set.
Args:
num_steps (int): Number of batches to compute update steps on.
This corresponds also to the number of times
``TrainingOperator.validate_batch`` is called.
profile (bool): Returns time stats for the evaluation procedure.
info (dict): Optional dictionary passed to the training
operator for `validate` and `validate_batch`.
@@ -383,15 +419,11 @@ class TorchTrainer:
``training_operator_cls``.
"""
worker_stats = ray.get([
w.validate.remote(num_steps=num_steps, info=info)
w.validate.remote(num_steps=num_steps, profile=profile, info=info)
for w in self.workers
])
validation_stats = {}
for stat_key in worker_stats[0]:
validation_stats[stat_key] = np.nanmean(
[s.get(stat_key, np.nan) for s in worker_stats])
return validation_stats
return self._process_stats(worker_stats)
def update_scheduler(self, metric):
"""Calls ``scheduler.step(metric)`` on all schedulers.
@@ -492,8 +524,8 @@ class TorchTrainable(Trainable):
return Resources(
cpu=0,
gpu=0,
extra_cpu=config["num_replicas"],
extra_gpu=int(config["use_gpu"]) * config["num_replicas"])
extra_cpu=config["num_workers"],
extra_gpu=int(config["use_gpu"]) * config["num_workers"])
def _setup(self, config):
self._trainer = TorchTrainer(**config)
python/ray/util/sgd/torch/training_operator.py
+97 -71
View File
@@ -1,9 +1,10 @@
import collections
import torch
from ray.util.sgd.utils import TimerStat, AverageMeter
from ray.util.sgd.torch.constants import (
SCHEDULER_STEP_EPOCH, SCHEDULER_STEP_BATCH, SCHEDULER_STEP, BATCH_COUNT)
from ray.util.sgd.utils import (TimerCollection, AverageMeterCollection,
NUM_SAMPLES)
from ray.util.sgd.torch.constants import (SCHEDULER_STEP_EPOCH,
SCHEDULER_STEP_BATCH, SCHEDULER_STEP)
amp = None
@@ -48,15 +49,10 @@ class TrainingOperator:
config,
models,
optimizers,
criterion,
criterion=None,
schedulers=None,
use_fp16=False):
# You are not expected to override this method.
self.timers = {
k: TimerStat()
for k in ["fwd", "grad", "apply", "epoch_time"]
}
self._validated_customization = False
self._models = models # List of models
assert isinstance(models, collections.Iterable), (
"Components need to be iterable. Got: {}".format(type(models)))
@@ -80,9 +76,13 @@ class TrainingOperator:
"Need to provide a custom operator subclassing "
"TrainingOperator if using multi-scheduler, "
"multi-model or multi-optimizer training/validation.")
self.timers = TimerCollection()
self.setup(config)
def _set_timers(self, timers):
"""Passes in the timers from the Runner."""
self.timers = timers
def setup(self, config):
"""Override this method to implement custom operator setup.
@@ -93,17 +93,35 @@ class TrainingOperator:
pass
def train_epoch(self, iterator, info):
"""Runs one standard training pass over the train_iterator.
"""Runs one standard training pass over the training dataloader.
By default, this method will iterate over the given iterator and
call ``self.train_batch`` over each batch.
If ``scheduler_step_freq`` is set, this class will also step the
scheduler accordingly.
call ``self.train_batch`` over each batch. If ``scheduler_step_freq``
is set, this default method will also step the scheduler accordingly.
You do not need to call ``train_batch`` in this method if you plan
to implement a custom optimization/training routine here.
You may find ``ray.util.sgd.utils.AverageMeterCollection`` useful
when overriding this method. See example below:
.. code-block:: python
def train_epoch(self, ...):
meter_collection = AverageMeterCollection()
self.model.train()
for batch in iterator:
# do some processing
metrics = {"metric_1": 1, "metric_2": 3} # dict of metrics
# This keeps track of all metrics across multiple batches
meter_collection.update(metrics, n=len(batch))
# Returns stats of the meters.
stats = meter_collection.summary()
return stats
Args:
iterator (iter): Iterator over the training data for the entire
epoch. This iterator is expected to be entirely consumed.
@@ -113,41 +131,28 @@ class TrainingOperator:
Returns:
A dict of metrics from training.
"""
self._losses = AverageMeter()
metric_meters = AverageMeterCollection()
self.model.train()
with self.timers["epoch_time"]:
for batch_idx, batch in enumerate(iterator):
batch_info = {
"batch_idx": batch_idx,
"global_step": self.global_step
}
batch_info.update(info)
metrics = self.train_batch(batch, batch_info=batch_info)
for batch_idx, batch in enumerate(iterator):
batch_info = {
"batch_idx": batch_idx,
"global_step": self.global_step
}
batch_info.update(info)
metrics = self.train_batch(batch, batch_info=batch_info)
if self.scheduler and batch_info.get(
SCHEDULER_STEP) == SCHEDULER_STEP_BATCH:
self.scheduler.step()
if self.scheduler and batch_info.get(
SCHEDULER_STEP) == SCHEDULER_STEP_BATCH:
self.scheduler.step()
if "loss" in metrics:
self._losses.update(
metrics["loss"], n=metrics.get("num_samples", 1))
self.global_step += 1
metric_meters.update(metrics, n=metrics.pop(NUM_SAMPLES, 1))
self.global_step += 1
if self.scheduler and info.get(SCHEDULER_STEP) == SCHEDULER_STEP_EPOCH:
self.scheduler.step()
stats = {
BATCH_COUNT: batch_idx + 1,
"mean_train_loss": self._losses.avg,
"last_train_loss": self._losses.val,
"epoch_time": self.timers["epoch_time"].last
}
stats.update({
timer_tag: timer.mean
for timer_tag, timer in self.timers.items()
})
return stats
return metric_meters.summary()
def train_batch(self, batch, batch_info):
"""Computes loss and updates the model over one batch.
@@ -176,6 +181,9 @@ class TrainingOperator:
By default, this dictionary contains "loss" and "num_samples".
"num_samples" corresponds to number of datapoints in the batch.
However, you can provide any number of other values.
Consider returning "num_samples" in the metrics because
by default, ``train_epoch`` uses "num_samples" to
calculate averages.
"""
features, target = batch
@@ -185,12 +193,12 @@ class TrainingOperator:
target = target.cuda(non_blocking=True)
# Compute output.
with self.timers["fwd"]:
with self.timers.record("fwd"):
output = self.model(features)
loss = self.criterion(output, target)
# Compute gradients in a backward pass.
with self.timers["grad"]:
with self.timers.record("grad"):
self.optimizer.zero_grad()
if self.use_fp16:
with amp.scale_loss(loss, self.optimizer) as scaled_loss:
@@ -199,35 +207,34 @@ class TrainingOperator:
loss.backward()
# Call step of optimizer to update model params.
with self.timers["apply"]:
with self.timers.record("apply"):
self.optimizer.step()
return {"loss": loss.item(), "num_samples": features.size(0)}
return {"train_loss": loss.item(), NUM_SAMPLES: features.size(0)}
def validate(self, val_iterator, info):
"""Runs one standard validation pass over the val_iterator.
This will call ``model.eval()`` and ``torch.no_grad`` when iterating
over the validation dataset.
over the validation dataloader.
If overriding this method, you can access model, criterion via
``self.model`` and ``self.criterion``. You also do not need to call
``validate_batch`` if overriding this method.
Args:
val_iterator (iter): Iterable constructed over the
validation dataset.
val_iterator (iter): Iterable constructed from the
validation dataloader.
info: (dict): Dictionary for information to be used for custom
validation operations.
Returns:
A dict of metrics from the evaluation.
By default, returns "mean_accuracy" and "mean_validation_loss"
By default, returns "mean_accuracy" and "mean_val_loss"
which is computed by aggregating "loss" and "correct" values
from ``validate_batch`` and dividing it by the sum of
``num_samples`` from all calls to ``self.validate_batch``.
"""
losses = AverageMeter()
total_correct = 0
metric_meters = AverageMeterCollection()
# switch to evaluate mode
self.model.eval()
@@ -236,19 +243,9 @@ class TrainingOperator:
batch_info = {"batch_idx": batch_idx}
batch_info.update(info)
metrics = self.validate_batch(batch, batch_info)
if "loss" in metrics:
losses.update(
metrics["loss"], n=metrics.get("num_samples", 1))
metric_meters.update(metrics, n=metrics.pop(NUM_SAMPLES, 1))
if "num_correct" in metrics:
total_correct += metrics["num_correct"]
stats = {
"batch_count": batch_idx + 1,
"mean_validation_loss": losses.avg,
"mean_accuracy": total_correct / losses.count
}
return stats
return metric_meters.summary()
def validate_batch(self, batch, batch_info):
"""Calcuates the loss and accuracy over a given batch.
@@ -262,7 +259,11 @@ class TrainingOperator:
Returns:
A dict of metrics.
By default, returns "loss", "num_correct", and "num_samples".
By default, returns "val_loss", "val_accuracy", and
"num_samples". When overriding, consider returning
"num_samples" in the metrics because
by default, ``validate`` uses "num_samples" to
calculate averages.
"""
features, target = batch
if torch.cuda.is_available():
@@ -270,14 +271,18 @@ class TrainingOperator:
target = target.cuda(non_blocking=True)
# compute output
output = self.model(features)
loss = self.criterion(output, target)
_, predicted = torch.max(output.data, 1)
with self.timers.record("eval_fwd"):
output = self.model(features)
loss = self.criterion(output, target)
_, predicted = torch.max(output.data, 1)
num_correct = (predicted == target).sum().item()
num_samples = target.size(0)
return {
"loss": loss.item(),
"num_correct": (predicted == target).sum().item(),
"num_samples": target.size(0)
"val_loss": loss.item(),
"val_accuracy": num_correct / num_samples,
NUM_SAMPLES: num_samples
}
def state_dict(self):
@@ -341,3 +346,24 @@ class _TestingOperator(TrainingOperator):
if callable(func):
return func(self, iterator, info)
return {"done": 1}
class _TestMetricsOperator(TrainingOperator):
def setup(self, config):
self._train_scores = config["scores"].copy()
self._val_scores = config["val_scores"].copy()
self.key = config["key"]
def train_batch(self, batch, batch_info=None):
metrics = super(_TestMetricsOperator, self).train_batch(
batch, batch_info)
num_samples = metrics[NUM_SAMPLES]
metrics.update({self.key: self._train_scores.pop(0) / num_samples})
return metrics
def validate_batch(self, batch, batch_info=None):
metrics = super(_TestMetricsOperator, self).validate_batch(
batch, batch_info)
num_samples = metrics[NUM_SAMPLES]
metrics.update({self.key: self._val_scores.pop(0) / num_samples})
return metrics
python/ray/util/sgd/utils.py
+69 -1
View File
@@ -1,4 +1,5 @@
from contextlib import closing
import collections
from contextlib import closing, contextmanager
import logging
import numpy as np
import socket
@@ -9,6 +10,10 @@ from ray.exceptions import RayActorError
logger = logging.getLogger(__name__)
BATCH_COUNT = "batch_count"
NUM_SAMPLES = "num_samples"
BATCH_SIZE = "*batch_size"
class TimerStat:
"""A running stat for conveniently logging the duration of a code block.
@@ -103,6 +108,46 @@ class TimerStat:
self.count = 0
@contextmanager
def _nullcontext(enter_result=None):
"""Used for mocking timer context."""
yield enter_result
class TimerCollection:
"""A grouping of Timers."""
def __init__(self):
self._timers = collections.defaultdict(TimerStat)
self._enabled = True
def disable(self):
self._enabled = False
def enable(self):
self._enabled = True
def reset(self):
for timer in self._timers.values():
timer.reset()
def record(self, key):
if self._enabled:
return self._timers[key]
else:
return _nullcontext()
def stats(self, mean=True, last=False):
aggregates = {}
for k, t in self._timers.items():
if t.count > 0:
if mean:
aggregates["mean_%s_s" % k] = t.mean
if last:
aggregates["last_%s_s" % k] = t.last
return aggregates
def find_free_port():
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
s.bind(("", 0))
@@ -129,6 +174,29 @@ class AverageMeter:
self.avg = self.sum / self.count
class AverageMeterCollection:
"""A grouping of AverageMeters."""
def __init__(self):
self._batch_count = 0
self.n = 0
self._meters = collections.defaultdict(AverageMeter)
def update(self, metrics, n=1):
self._batch_count += 1
self.n += n
for metric, value in metrics.items():
self._meters[metric].update(value, n=n)
def summary(self):
"""Returns a dict of average and most recent values for each metric."""
stats = {BATCH_COUNT: self._batch_count, NUM_SAMPLES: self.n}
for metric, meter in self._meters.items():
stats["mean_" + str(metric)] = meter.avg
stats["last_" + str(metric)] = meter.val
return stats
def check_for_failure(remote_values):
"""Checks remote values for any that returned and failed.