Tpu save

CHANGELOG.md

@@ -95,6 +95,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
+- Added feature to move tensors to CPU before saving ([#4309](https://github.com/PyTorchLightning/pytorch-lightning/pull/4309))
+
 
 
 ## [1.0.8] - 2020-11-24

dockers/tpu-tests/tpu_test_cases.jsonnet

@@ -21,7 +21,7 @@ local tputests = base.BaseTest {
   command: utils.scriptCommand(
     |||
       cd pytorch-lightning
-      coverage run --source=pytorch_lightning -m pytest tests/models/test_tpu.py -v
+      coverage run --source=pytorch_lightning -m pytest tests/models/test_tpu.py tests/backends/test_tpu_backend.py pytorch_lightning/utilities/xla_device_utils.py -v
       test_exit_code=$?
       echo "\n||| END PYTEST LOGS |||\n"
       coverage xml

docs/source/amp.rst

@@ -88,7 +88,7 @@ TPU 16-bit
 16-bit on TPUs is much simpler. To use 16-bit with TPUs set precision to 16 when using the TPU flag
 
 .. testcode::
-    :skipif: not XLA_AVAILABLE
+    :skipif: not TPU_AVAILABLE
 
     # DEFAULT
     trainer = Trainer(tpu_cores=8, precision=32)

docs/source/conf.py

@@ -362,6 +362,7 @@ def package_list_from_file(file):
     NATIVE_AMP_AVAILABLE,
     APEX_AVAILABLE,
     XLA_AVAILABLE,
+    TPU_AVAILABLE,
 )
 TORCHVISION_AVAILABLE = importlib.util.find_spec("torchvision") is not None
 

docs/source/trainer.rst

@@ -1100,7 +1100,7 @@ Your effective batch size is batch_size * total tpu cores.
 
 This parameter can be either 1 or 8.
 
-.. testcode::
+Example::
 
     # your_trainer_file.py
 

pytorch_lightning/accelerators/accelerator.py

@@ -241,6 +241,9 @@ def __setstate__(self, d):
         self.dist = d['dist']
         self.ddp_plugin = d['ddp_plugin']
 
+    def on_save(self, checkpoint):
+        return checkpoint
+
 
 # TODO: allow user to compare with string even internaly we shall use these Enum to prevent typos...
 class BackendType(Enum):

pytorch_lightning/accelerators/accelerator_connector.py

@@ -20,7 +20,7 @@
 from pytorch_lightning.accelerators.accelerator import Accelerator
 from pytorch_lightning.cluster_environments.slurm_environment import SLURMEnvironment
 from pytorch_lightning.cluster_environments.torchelastic_environment import TorchElasticEnvironment
-from pytorch_lightning.utilities import XLA_AVAILABLE, device_parser, rank_zero_only
+from pytorch_lightning.utilities import XLA_AVAILABLE, device_parser, rank_zero_only, TPU_AVAILABLE
 from pytorch_lightning.utilities.distributed import rank_zero_info, rank_zero_warn
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
@@ -350,7 +350,7 @@ def set_distributed_mode(self):
 
         rank_zero_info(f'GPU available: {torch.cuda.is_available()}, used: {self.trainer.on_gpu}')
         num_cores = self.trainer.tpu_cores if self.trainer.tpu_cores is not None else 0
-        rank_zero_info(f'TPU available: {XLA_AVAILABLE}, using: {num_cores} TPU cores')
+        rank_zero_info(f'TPU available: {TPU_AVAILABLE}, using: {num_cores} TPU cores')
 
         if torch.cuda.is_available() and not self.trainer.on_gpu:
             rank_zero_warn('GPU available but not used. Set the --gpus flag when calling the script.')

pytorch_lightning/accelerators/tpu_accelerator.py

@@ -23,7 +23,7 @@
 from pytorch_lightning import _logger as log
 from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
 from pytorch_lightning.core import LightningModule
-from pytorch_lightning.utilities import TPU_AVAILABLE, rank_zero_info, rank_zero_only, rank_zero_warn
+from pytorch_lightning.utilities import TPU_AVAILABLE, rank_zero_info, rank_zero_only, rank_zero_warn, move_data_to_device
 from pytorch_lightning.utilities.cloud_io import atomic_save
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
@@ -344,7 +344,8 @@ def transfer_distrib_spawn_state_on_fit_end(self, model, mp_queue, results):
             last_path = None
             if not self.trainer.testing and best_model_path is not None and len(best_model_path) > 0:
                 last_path = re.sub('.ckpt', '.tmp_end.ckpt', best_model_path)
-                atomic_save(model.state_dict(), last_path)
+                state_dict = move_data_to_device(model.state_dict(), torch.device("cpu"))
+                atomic_save(state_dict, last_path)
             mp_queue.put(last_path)
 
     def broadcast(self, obj, src=0):
@@ -366,3 +367,11 @@ def sync_tensor(self,
     @property
     def norm_clipping_epsilon(self):
         return 1e-6
+
+    def on_save(self, checkpoint):
+        """
+        Move XLA tensors to CPU before saving
+        Recommended on XLA Guide:
+        https://github.com/pytorch/xla/blob/master/API_GUIDE.md#saving-and-loading-xla-tensors
+        """
+        return move_data_to_device(checkpoint, torch.device("cpu"))

pytorch_lightning/core/lightning.py

@@ -20,7 +20,6 @@
 import os
 import re
 import tempfile
-import types
 from abc import ABC
 from argparse import Namespace
 from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Tuple, Union
@@ -31,14 +30,12 @@
 from torch.optim.optimizer import Optimizer
 
 from pytorch_lightning import _logger as log
-from pytorch_lightning.callbacks import Callback
 from pytorch_lightning.core.grads import GradInformation
 from pytorch_lightning.core.hooks import CheckpointHooks, DataHooks, ModelHooks
 from pytorch_lightning.core.memory import ModelSummary
-from pytorch_lightning.core.optimizer import LightningOptimizer
 from pytorch_lightning.core.saving import ALLOWED_CONFIG_TYPES, PRIMITIVE_TYPES, ModelIO
 from pytorch_lightning.core.step_result import Result
-from pytorch_lightning.utilities import TPU_AVAILABLE, AMPType, rank_zero_warn
+from pytorch_lightning.utilities import TPU_AVAILABLE, rank_zero_warn
 from pytorch_lightning.utilities.device_dtype_mixin import DeviceDtypeModuleMixin
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.parsing import AttributeDict, collect_init_args, get_init_args
@@ -1239,7 +1236,7 @@ def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx,
             model hook don't forget to add the call to it before ``optimizer.zero_grad()`` yourself.
 
         """
-        if on_tpu:
+        if on_tpu and TPU_AVAILABLE:
             xm.optimizer_step(optimizer, optimizer_args={'closure': optimizer_closure, **kwargs})
 
         elif self.trainer.amp_backend is not None:

pytorch_lightning/core/optimizer.py

@@ -11,38 +11,14 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import collections
-import copy
-import inspect
-import os
-import re
-import tempfile
 import types
-from abc import ABC
-from argparse import Namespace
-from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Tuple, Union
+from typing import Any, Callable, Optional
 from weakref import proxy
 
-import torch
-from torch import ScriptModule, Tensor
-from torch.nn import Module
-from torch.optim import SGD
 from torch.optim.optimizer import Optimizer
 
-from pytorch_lightning import _logger as log
-from pytorch_lightning.callbacks import Callback
-from pytorch_lightning.core.grads import GradInformation
-from pytorch_lightning.core.hooks import CheckpointHooks, DataHooks, ModelHooks
-from pytorch_lightning.core.memory import ModelSummary
-from pytorch_lightning.core.saving import ALLOWED_CONFIG_TYPES, PRIMITIVE_TYPES, ModelIO
-from pytorch_lightning.core.step_result import Result
-from pytorch_lightning.utilities import AMPType, rank_zero_warn
-from pytorch_lightning.utilities.device_dtype_mixin import DeviceDtypeModuleMixin
+from pytorch_lightning.utilities import TPU_AVAILABLE
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from pytorch_lightning.utilities.parsing import AttributeDict, collect_init_args, get_init_args
-from pytorch_lightning.utilities.xla_device_utils import XLADeviceUtils
-
-TPU_AVAILABLE = XLADeviceUtils.tpu_device_exists()
 
 if TPU_AVAILABLE:
     import torch_xla.core.xla_model as xm

pytorch_lightning/trainer/connectors/checkpoint_connector.py

@@ -227,6 +227,9 @@ def hpc_save(self, folderpath: str, logger):
 
         model.on_hpc_save(checkpoint)
 
+        if self.trainer.accelerator_backend:
+            checkpoint = self.trainer.accelerator_backend.on_save(checkpoint)
+
         # do the actual save
         # TODO: fix for anything with multiprocess DP, DDP, DDP2
         try:
@@ -380,6 +383,8 @@ def save_checkpoint(self, filepath, weights_only: bool = False):
 
         if self.trainer.is_global_zero:
             # write the checkpoint dictionary on the file
+            if self.trainer.accelerator_backend:
+                checkpoint = self.trainer.accelerator_backend.on_save(checkpoint)
             try:
                 atomic_save(checkpoint, filepath)
             except AttributeError as err:

pytorch_lightning/trainer/trainer.py

@@ -785,7 +785,7 @@ def __test_using_best_weights(self, ckpt_path, test_dataloaders):
                     f'specify a path for a checkpoint .test(ckpt_path=PATH)'
                 )
                 return {}
-            if self.accelerator_backend is not None:
+            if self.accelerator_backend is not None and not self.use_tpu:
                 self.accelerator_backend.barrier()
 
             ckpt = pl_load(ckpt_path, map_location=lambda storage, loc: storage)

pytorch_lightning/utilities/cloud_io.py

@@ -14,11 +14,11 @@
 
 import io
 from distutils.version import LooseVersion
-from typing import Union, IO
 from pathlib import Path
-from urllib.parse import urlparse
-import torch
+from typing import IO, Union
+
 import fsspec
+import torch
 
 
 def load(path_or_url: Union[str, IO, Path], map_location=None):
@@ -52,6 +52,7 @@ def atomic_save(checkpoint, filepath: str):
         filepath: The path to which the checkpoint will be saved.
             This points to the file that the checkpoint will be stored in.
     """
+
     bytesbuffer = io.BytesIO()
     # Can't use the new zipfile serialization for 1.6.0 because there's a bug in
     # torch.hub.load_state_dict_from_url() that prevents it from loading the new files.

pytorch_lightning/utilities/device_parser.py

@@ -13,6 +13,8 @@
 # limitations under the License.
 import torch
 from typing import Union, Any, List, Optional, MutableSequence
+
+from pytorch_lightning.utilities import TPU_AVAILABLE
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 
@@ -104,6 +106,9 @@ def parse_tpu_cores(tpu_cores: Union[int, str, List]) -> Optional[Union[List[int
     if not _tpu_cores_valid(tpu_cores):
         raise MisconfigurationException("`tpu_cores` can only be 1, 8 or [<1-8>]")
 
+    if tpu_cores is not None and not TPU_AVAILABLE:
+        raise MisconfigurationException('No TPU devices were found.')
+
     return tpu_cores
 
 

pytorch_lightning/utilities/xla_device_utils.py

@@ -14,6 +14,7 @@
 import functools
 import importlib
 import queue as q
+import traceback
 from multiprocessing import Process, Queue
 
 import torch
@@ -28,8 +29,6 @@ def inner_f(queue, func, *args, **kwargs):  # pragma: no cover
     try:
         queue.put(func(*args, **kwargs))
     except Exception:
-        import traceback
-
         traceback.print_exc()
         queue.put(None)
 
@@ -40,10 +39,11 @@ def wrapper(*args, **kwargs):
         queue = Queue()
         proc = Process(target=inner_f, args=(queue, func, *args), kwargs=kwargs)
         proc.start()
-        proc.join(10)
+        proc.join(20)
         try:
             return queue.get_nowait()
         except q.Empty:
+            traceback.print_exc()
             return False
 
     return wrapper
@@ -81,10 +81,20 @@ def _is_device_tpu() -> bool:
             device_type = XLADeviceUtils._fetch_xla_device_type(device)
             return device_type == "TPU"
 
+    @staticmethod
+    def xla_available() -> bool:
+        """
+        Check if XLA library is installed
+
+        Return:
+            A boolean value indicating if a XLA is installed
+        """
+        return XLA_AVAILABLE
+
     @staticmethod
     def tpu_device_exists() -> bool:
         """
-        Public method to check if TPU is available
+        Runs XLA device check within a separate process
 
         Return:
             A boolean value indicating if a TPU device exists on the system

tests/backends/test_tpu_backend.py

@@ -0,0 +1,58 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License
+
+import pytest
+import torch
+
+from pytorch_lightning import Trainer
+from pytorch_lightning.utilities.xla_device_utils import XLADeviceUtils
+from tests.base.boring_model import BoringModel
+from tests.base.develop_utils import pl_multi_process_test
+
+
+@pytest.mark.skipif(not XLADeviceUtils.tpu_device_exists(), reason="test requires TPU machine")
+@pl_multi_process_test
+def test_resume_training_on_cpu(tmpdir):
+    """ Checks if training can be resumed from a saved checkpoint on CPU"""
+
+    # Train a model on TPU
+    model = BoringModel()
+    trainer = Trainer(checkpoint_callback=True, max_epochs=1, tpu_cores=8,)
+    trainer.fit(model)
+
+    model_path = trainer.checkpoint_callback.best_model_path
+
+    # Verify saved Tensors are on CPU
+    ckpt = torch.load(model_path)
+    weight_tensor = list(ckpt["state_dict"].values())[0]
+    assert weight_tensor.device == torch.device("cpu")
+
+    # Verify that training is resumed on CPU
+    trainer = Trainer(resume_from_checkpoint=model_path, checkpoint_callback=True, max_epochs=1, default_root_dir=tmpdir)
+    result = trainer.fit(model)
+
+    assert result == 1
+
+
+@pytest.mark.skipif(not XLADeviceUtils.tpu_device_exists(), reason="test requires TPU machine")
+@pl_multi_process_test
+def test_if_test_works_after_train(tmpdir):
+    """ Ensure that .test() works after .fit() """
+
+    # Train a model on TPU
+    model = BoringModel()
+    trainer = Trainer(checkpoint_callback=True, max_epochs=1, tpu_cores=8, default_root_dir=tmpdir)
+    trainer.fit(model)
+
+    assert trainer.test() == 1