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Delete deepspeed test

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SeanNaren 2021-03-16 16:47:18 +00:00
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tests/plugins/test_deepspeed_plugin.py
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import json
import os
from unittest import mock
import pytest
import torch
from torch import Tensor
from torch.optim import Optimizer
from pytorch_lightning import Trainer
from pytorch_lightning.plugins import DeepSpeedPlugin, DeepSpeedPrecisionPlugin
from pytorch_lightning.plugins.training_type.deepspeed import LightningDeepSpeedModule
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from tests.helpers.boring_model import BoringModel
from tests.helpers.runif import RunIf
def test_deepspeed_lightning_module(tmpdir):
"""
Test to ensure that a model wrapped in `LightningDeepSpeedModule` moves types and device correctly.
"""
model = BoringModel()
module = LightningDeepSpeedModule(model, precision=16)
module.half()
assert module.dtype == torch.half
assert model.dtype == torch.half
module.to(torch.double)
assert module.dtype == torch.double
assert model.dtype == torch.double
@RunIf(min_gpus=1)
def test_deepspeed_lightning_module_precision(tmpdir):
"""
Test to ensure that a model wrapped in `LightningDeepSpeedModule` moves tensors to half when precision 16.
"""
model = BoringModel()
module = LightningDeepSpeedModule(model, precision=16)
module.cuda().half()
assert module.dtype == torch.half
assert model.dtype == torch.half
x = torch.randn((1, 32), dtype=torch.float).cuda()
out = module(x)
assert out.dtype == torch.half
module.to(torch.double)
assert module.dtype == torch.double
assert model.dtype == torch.double
@pytest.fixture
def deepspeed_config():
return {
"optimizer": {
"type": "SGD",
"params": {
"lr": 3e-5,
},
},
'scheduler': {
"type": "WarmupLR",
"params": {
"last_batch_iteration": -1,
"warmup_min_lr": 0,
"warmup_max_lr": 3e-5,
"warmup_num_steps": 100,
}
}
}
@pytest.fixture
def deepspeed_zero_config(deepspeed_config):
return {**deepspeed_config, 'zero_allow_untested_optimizer': True, 'zero_optimization': {'stage': 2}}
@RunIf(deepspeed=True)
@pytest.mark.parametrize("input", ("deepspeed", DeepSpeedPlugin))
def test_deepspeed_plugin_string(tmpdir, input):
"""
Test to ensure that the plugin can be passed via string or instance, and parallel devices is correctly set.
"""
trainer = Trainer(
fast_dev_run=True,
default_root_dir=tmpdir,
plugins=input if isinstance(input, str) else input(),
)
assert isinstance(trainer.accelerator.training_type_plugin, DeepSpeedPlugin)
assert trainer.accelerator.training_type_plugin.parallel_devices == [torch.device('cpu')]
@RunIf(deepspeed=True)
def test_deepspeed_plugin_env(tmpdir, monkeypatch, deepspeed_config):
"""
Test to ensure that the plugin can be passed via a string with an environment variable.
"""
config_path = os.path.join(tmpdir, 'temp.json')
with open(config_path, 'w') as f:
f.write(json.dumps(deepspeed_config))
monkeypatch.setenv("PL_DEEPSPEED_CONFIG_PATH", config_path)
trainer = Trainer(
fast_dev_run=True,
default_root_dir=tmpdir,
plugins='deepspeed',
)
plugin = trainer.accelerator.training_type_plugin
assert isinstance(plugin, DeepSpeedPlugin)
assert plugin.parallel_devices == [torch.device('cpu')]
assert plugin.config == deepspeed_config
@RunIf(amp_native=True, deepspeed=True)
@pytest.mark.parametrize(
"amp_backend", [
pytest.param("native", marks=RunIf(amp_native=True)),
pytest.param("apex", marks=RunIf(amp_apex=True)),
]
)
def test_deepspeed_precision_choice(amp_backend, tmpdir):
"""
Test to ensure precision plugin is also correctly chosen.
DeepSpeed handles precision via Custom DeepSpeedPrecisionPlugin
"""
trainer = Trainer(
fast_dev_run=True,
default_root_dir=tmpdir,
plugins='deepspeed',
amp_backend=amp_backend,
precision=16,
)
assert isinstance(trainer.accelerator.training_type_plugin, DeepSpeedPlugin)
assert isinstance(trainer.accelerator.precision_plugin, DeepSpeedPrecisionPlugin)
assert trainer.accelerator.precision_plugin.precision == 16
@RunIf(deepspeed=True)
def test_deepspeed_with_invalid_config_path(tmpdir):
"""
Test to ensure if we pass an invalid config path we throw an exception.
"""
with pytest.raises(
MisconfigurationException, match="You passed in a path to a DeepSpeed config but the path does not exist"
):
DeepSpeedPlugin(config='invalid_path.json')
@RunIf(deepspeed=True)
def test_deepspeed_with_env_path(tmpdir, monkeypatch, deepspeed_config):
"""
Test to ensure if we pass an env variable, we load the config from the path.
"""
config_path = os.path.join(tmpdir, 'temp.json')
with open(config_path, 'w') as f:
f.write(json.dumps(deepspeed_config))
monkeypatch.setenv("PL_DEEPSPEED_CONFIG_PATH", config_path)
plugin = DeepSpeedPlugin()
assert plugin.config == deepspeed_config
@RunIf(deepspeed=True)
def test_deepspeed_defaults(tmpdir):
"""
Ensure that defaults are correctly set as a config for DeepSpeed if no arguments are passed.
"""
plugin = DeepSpeedPlugin()
assert plugin.config is not None
assert isinstance(plugin.config["zero_optimization"], dict)
@RunIf(min_gpus=1, deepspeed=True, special=True)
def test_invalid_deepspeed_defaults_no_precision(dist_initialized_mock, tmpdir):
"""Test to ensure that using defaults, if precision is not set to 16, we throw an exception."""
model = BoringModel()
trainer = Trainer(
default_root_dir=tmpdir,
fast_dev_run=True,
plugins='deepspeed',
)
with pytest.raises(
MisconfigurationException, match='To use DeepSpeed ZeRO Optimization, you must set precision=16.'
):
trainer.fit(model)
@RunIf(min_gpus=1, deepspeed=True, special=True)
def test_warn_deepspeed_override_backward(tmpdir):
"""Test to ensure that if the backward hook in the LightningModule is overridden, we throw a warning."""
class TestModel(BoringModel):
def backward(self, loss: Tensor, optimizer: Optimizer, optimizer_idx: int, *args, **kwargs) -> None:
return loss.backward()
model = TestModel()
trainer = Trainer(
fast_dev_run=True,
default_root_dir=tmpdir,
plugins=DeepSpeedPlugin(),
gpus=1,
precision=16,
)
with pytest.warns(UserWarning, match='Overridden backward hook in the LightningModule will be ignored'):
trainer.fit(model)
@RunIf(min_gpus=1, deepspeed=True, special=True)
def test_deepspeed_run_configure_optimizers(tmpdir):
"""Test end to end that deepspeed works with defaults (without ZeRO as that requires compilation),
whilst using configure_optimizers for optimizers and schedulers."""
class TestModel(BoringModel):
def on_train_start(self) -> None:
from deepspeed.runtime.zero.stage2 import FP16_DeepSpeedZeroOptimizer
assert isinstance(self.trainer.optimizers[0], FP16_DeepSpeedZeroOptimizer)
assert isinstance(self.trainer.optimizers[0].optimizer, torch.optim.SGD)
assert self.trainer.lr_schedulers == [] # DeepSpeed manages LR scheduler internally
# Ensure DeepSpeed engine has initialized with our optimizer/lr_scheduler
assert isinstance(self.trainer.model.lr_scheduler, torch.optim.lr_scheduler.StepLR)
model = TestModel()
trainer = Trainer(
plugins=DeepSpeedPlugin(), # disable ZeRO so our optimizers are not wrapped
default_root_dir=tmpdir,
gpus=1,
fast_dev_run=True,
precision=16,
)
trainer.fit(model)
_assert_save_model_is_equal(model, tmpdir, trainer)
@RunIf(min_gpus=1, deepspeed=True, special=True)
def test_deepspeed_config(tmpdir, deepspeed_zero_config):
"""
Test to ensure deepspeed works correctly when passed a DeepSpeed config object including optimizers/schedulers
and saves the model weights to load correctly.
"""
class TestModel(BoringModel):
def on_train_start(self) -> None:
from deepspeed.runtime.lr_schedules import WarmupLR
from deepspeed.runtime.zero.stage2 import FP16_DeepSpeedZeroOptimizer
assert isinstance(self.trainer.optimizers[0], FP16_DeepSpeedZeroOptimizer)
assert isinstance(self.trainer.optimizers[0].optimizer, torch.optim.SGD)
assert self.trainer.lr_schedulers == [] # DeepSpeed manages LR scheduler internally
# Ensure DeepSpeed engine has initialized with our optimizer/lr_scheduler
assert isinstance(self.trainer.model.lr_scheduler, WarmupLR)
model = TestModel()
trainer = Trainer(
plugins=[DeepSpeedPlugin(config=deepspeed_zero_config)],
default_root_dir=tmpdir,
gpus=1,
fast_dev_run=True,
precision=16,
)
trainer.fit(model)
trainer.test(model)
_assert_save_model_is_equal(model, tmpdir, trainer)
@RunIf(min_gpus=1, deepspeed=True, special=True)
def test_deepspeed_custom_precision_params(tmpdir):
"""Ensure if we modify the FP16 parameters via the DeepSpeedPlugin, the deepspeed config contains these changes."""
class TestModel(BoringModel):
def on_train_start(self) -> None:
assert self.trainer.training_type_plugin.config['fp16']['loss_scale'] == 10
assert self.trainer.training_type_plugin.config['fp16']['initial_scale_power'] == 10
assert self.trainer.training_type_plugin.config['fp16']['loss_scale_window'] == 10
assert self.trainer.training_type_plugin.config['fp16']['hysteresis'] == 10
assert self.trainer.training_type_plugin.config['fp16']['min_loss_scale'] == 10
raise SystemExit()
model = TestModel()
ds = DeepSpeedPlugin(loss_scale=10, initial_scale_power=10, loss_scale_window=10, hysteresis=10, min_loss_scale=10)
trainer = Trainer(default_root_dir=tmpdir, plugins=[ds], precision=16, gpus=1)
with pytest.raises(SystemExit):
trainer.fit(model)
@RunIf(min_gpus=1, deepspeed=True, special=True)
def test_deepspeed_assert_config_zero_offload_disabled(tmpdir, deepspeed_zero_config):
"""Ensure if we use a config and turn off cpu_offload, that this is set to False within the config."""
deepspeed_zero_config['zero_optimization']['cpu_offload'] = False
class TestModel(BoringModel):
def on_train_start(self) -> None:
assert self.trainer.training_type_plugin.config['zero_optimization']['cpu_offload'] is False
raise SystemExit()
model = TestModel()
trainer = Trainer(
plugins=[DeepSpeedPlugin(config=deepspeed_zero_config)],
precision=16,
gpus=1,
default_root_dir=tmpdir,
)
with pytest.raises(SystemExit):
trainer.fit(model)
@RunIf(min_gpus=2, special=True, deepspeed=True)
def test_deepspeed_multigpu(tmpdir, deepspeed_config):
"""
Test to ensure that DeepSpeed with multiple GPUs works, without ZeRO Optimization as this requires compilation.
"""
model = BoringModel()
trainer = Trainer(
plugins=[DeepSpeedPlugin()],
default_root_dir=tmpdir,
gpus=2,
fast_dev_run=True,
precision=16,
)
trainer.fit(model)
trainer.test(model)
_assert_save_model_is_equal(model, tmpdir, trainer)
def _assert_save_model_is_equal(model, tmpdir, trainer):
checkpoint_path = os.path.join(tmpdir, 'model.pt')
trainer.save_checkpoint(checkpoint_path)
# carry out the check only on rank 0
if trainer.global_rank == 0:
saved_model = BoringModel.load_from_checkpoint(checkpoint_path)
if model.dtype == torch.half:
saved_model = saved_model.half() # model is loaded in float32 as default, move it to float16
model = model.cpu()
# Assert model parameters are identical after loading
for orig_param, trained_model_param in zip(model.parameters(), saved_model.parameters()):
assert torch.equal(orig_param, trained_model_param)