Replaces ddp .spawn with subprocess (#2029)

* replace ddp spawn with subprocess

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Author: williamFalcon

.run_local_tests.sh

@@ -12,8 +12,8 @@ rm -rf ./tests/cometruns*
 rm -rf ./tests/wandb*
 rm -rf ./tests/tests/*
 rm -rf ./lightning_logs
-python -m coverage run --source pytorch_lightning -m py.test pytorch_lightning tests pl_examples -v --doctest-modules --flake8
+python -m coverage run --source pytorch_lightning -m py.test pytorch_lightning tests pl_examples -v --doctest-modules --flake8 --durations=0
 python -m coverage report -m
 
 # specific file
-# python -m coverage run --source pytorch_lightning -m py.test -k test_trainer.py --flake8
+# python -m coverage run --source pytorch_lightning -m py.test -k test_trainer.py --flake8 --durations=0

pl_examples/basic_examples/cpu_template.py

@@ -10,25 +10,23 @@
 import pytorch_lightning as pl
 from pl_examples.models.lightning_template import LightningTemplateModel
 
-SEED = 2334
-torch.manual_seed(SEED)
-np.random.seed(SEED)
+pl.seed_everything(234)
 
 
-def main(hparams):
+def main(args):
     """
     Main training routine specific for this project
-    :param hparams:
+    :param args:
     """
     # ------------------------
     # 1 INIT LIGHTNING MODEL
     # ------------------------
-    model = LightningTemplateModel(hparams)
+    model = LightningTemplateModel(**vars(args))
 
     # ------------------------
     # 2 INIT TRAINER
     # ------------------------
-    trainer = pl.Trainer(max_epochs=hparams.epochs, overfit_pct=0.01, early_stop_callback=True)
+    trainer = pl.Trainer.from_argparse_args(args)
 
     # ------------------------
     # 3 START TRAINING
@@ -46,9 +44,10 @@ def main(hparams):
 
     # each LightningModule defines arguments relevant to it
     parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
-    hyperparams = parser.parse_args()
+    parser = pl.Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
 
     # ---------------------
     # RUN TRAINING
     # ---------------------
-    main(hyperparams)
+    main(args)

pytorch_lightning/core/lightning.py

@@ -957,7 +957,7 @@ def init_ddp_connection(
                         f"is not equal to the computed world size ({world_size}). Ignored.")
 
         torch_backend = "nccl" if self.trainer.on_gpu else "gloo"
-        log.info(f"initializing proc_rank {proc_rank} world {world_size}")
+        log.info(f"initializing ddp: LOCAL_RANK: {proc_rank}/{world_size - 1} WORLD_SIZE:{world_size}")
         torch_distrib.init_process_group(torch_backend, rank=proc_rank, world_size=world_size)
 
     def configure_apex(

pytorch_lightning/trainer/distrib_data_parallel.py

@@ -117,6 +117,11 @@ def train_fx(trial_hparams, cluster_manager, _):
 import re
 from abc import ABC, abstractmethod
 from typing import Union
+import subprocess
+import sys
+from time import sleep
+import numpy as np
+from os.path import abspath
 
 import torch
 from pytorch_lightning import _logger as log
@@ -311,7 +316,7 @@ def set_nvidia_flags(self, is_slurm_managing_tasks, data_parallel_device_ids):
         os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
 
         # when slurm is managing the task it sets the visible devices
-        if not is_slurm_managing_tasks:
+        if not is_slurm_managing_tasks and 'CUDA_VISIBLE_DEVICES' not in os.environ:
             if isinstance(data_parallel_device_ids, int):
                 id_str = ','.join(str(x) for x in list(range(data_parallel_device_ids)))
                 os.environ["CUDA_VISIBLE_DEVICES"] = id_str
@@ -322,7 +327,74 @@ def set_nvidia_flags(self, is_slurm_managing_tasks, data_parallel_device_ids):
         # don't make this debug... this is good UX
         log.info(f'CUDA_VISIBLE_DEVICES: [{os.environ["CUDA_VISIBLE_DEVICES"]}]')
 
-    def ddp_train(self, process_idx, model):
+    def __set_random_port(self):
+        """
+        When running DDP NOT managed by SLURM, the ports might collide
+        :return:
+        """
+        try:
+            default_port = os.environ['MASTER_PORT']
+        except Exception:
+            import random
+            default_port = random.randint(10000, 19000)
+            os.environ['MASTER_PORT'] = str(default_port)
+
+    def spawn_ddp_children(self, model):
+        self.__set_random_port()
+        port = os.environ['MASTER_PORT']
+
+        master_address = '127.0.0.1' if 'MASTER_ADDR' not in os.environ else os.environ['MASTER_ADDR']
+        os.environ['MASTER_PORT'] = f'{port}'
+        os.environ['MASTER_ADDR'] = f'{master_address}'
+
+        # allow the user to pass the node rank
+        node_rank = '0'
+        if 'NODE_RANK' in os.environ:
+            node_rank = os.environ['NODE_RANK']
+        if 'GROUP_RANK' in os.environ:
+            node_rank = os.environ['GROUP_RANK']
+
+        os.environ['NODE_RANK'] = node_rank
+        os.environ['LOCAL_RANK'] = '0'
+
+        # pull out the commands used to run the script and resolve the abs file path
+        command = sys.argv
+        full_path = abspath(command[0])
+        command[0] = full_path
+        command = ['python'] + command
+
+        # since this script sets the visible devices we replace the gpus flag with a number
+        num_gpus = os.environ['CUDA_VISIBLE_DEVICES'].split(',').__len__()
+
+        # if script called without a flag, pass in a flag anyhow
+        if '--gpus' not in command:
+            arg_gpus = len(self.gpus) if isinstance(self.gpus, list) else self.gpus
+            command += ['--gpus', arg_gpus]
+
+        gpu_flag_idx = command.index('--gpus')
+        command[gpu_flag_idx + 1] = f'{num_gpus}'
+
+        os.environ['WORLD_SIZE'] = f'{num_gpus * self.num_nodes}'
+
+        self.interactive_ddp_procs = []
+        for local_rank in range(1, self.num_processes):
+            env_copy = os.environ.copy()
+            env_copy['LOCAL_RANK'] = f'{local_rank}'
+
+            # import pdb; pdb.set_trace()
+            # start process
+            proc = subprocess.Popen(command, env=env_copy)
+            self.interactive_ddp_procs.append(proc)
+
+            # starting all processes at once can cause issues
+            # with dataloaders delay between 1-10 seconds
+            delay = np.random.uniform(1, 5, 1)[0]
+            sleep(delay)
+
+        local_rank = 0
+        self.ddp_train(local_rank, model, is_master=True)
+
+    def ddp_train(self, process_idx, model, is_master=False):
         """
         Entry point into a DP thread
         :param gpu_idx:
@@ -359,7 +431,14 @@ def ddp_train(self, process_idx, model):
         # MODEL
         # copy model to each gpu
         if self.on_gpu:
-            self.root_gpu = process_idx
+            gpu_idx = process_idx
+            if is_master:
+                # source of truth is cuda for gpu idx
+                gpus = os.environ['CUDA_VISIBLE_DEVICES'].split(',')
+                local_rank = int(os.environ['LOCAL_RANK'])
+                gpu_idx = int(gpus[local_rank])
+
+            self.root_gpu = gpu_idx
             torch.cuda.set_device(self.root_gpu)
             model.cuda(self.root_gpu)
 
@@ -388,9 +467,6 @@ def ddp_train(self, process_idx, model):
         # continue training routine
         self.run_pretrain_routine(model)
 
-        # when ddp ends, we save the model
-        self.save_spawn_weights(model)
-
     def save_spawn_weights(self, model):
         """
         Dump a temporary checkpoint after ddp ends to get weights out of the process

pytorch_lightning/trainer/distrib_parts.py

@@ -685,8 +685,18 @@ def sanitize_gpu_ids(gpus):
     :return: unmodified gpus variable
     """
     all_available_gpus = get_all_available_gpus()
+    misconfig = False
     for gpu in gpus:
         if gpu not in all_available_gpus:
+            misconfig = True
+
+    if misconfig:
+        # sometimes auto ddp might have different flags
+        # but this is not what the user intended
+        # correct for the user
+        if len(gpus) == len(all_available_gpus):
+            gpus = all_available_gpus
+        else:
             raise MisconfigurationException(f"""
                 You requested GPUs: {gpus}
                 But your machine only has: {all_available_gpus}

pytorch_lightning/trainer/trainer.py

@@ -35,7 +35,6 @@
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities import rank_zero_warn, parsing
 
-
 try:
     from apex import amp
 except ImportError:
@@ -119,7 +118,7 @@ def __init__(
             distributed_backend: Optional[str] = None,
             precision: int = 32,
             print_nan_grads: bool = False,  # backward compatible, todo: remove in v0.9.0
-            weights_summary: Optional[str] = 'full',
+            weights_summary: Optional[str] = 'top',
             weights_save_path: Optional[str] = None,
             num_sanity_val_steps: int = 2,
             truncated_bptt_steps: Optional[int] = None,
@@ -494,6 +493,7 @@ def __init__(
         # init flags for SLURM+ddp to work
         self.proc_rank = 0
         self.world_size = 1
+        self.interactive_ddp_procs = []
         self.configure_slurm_ddp(self.num_nodes)
         self.node_rank = self.determine_ddp_node_rank()
 
@@ -871,16 +871,12 @@ def fit(
                 task = int(os.environ['LOCAL_RANK'])
                 self.ddp_train(task, model)
 
-            else:
-                self.__set_random_port()
-                # track for predict
+            elif self.distributed_backend == 'cpu_ddp':
                 self.model = model
-                # train
                 mp.spawn(self.ddp_train, nprocs=self.num_processes, args=(model,))
-                # load weights if not interrupted
-                if self.on_colab_kaggle:
-                    self.load_spawn_weights(model)
-                    self.model = model
+
+            elif self.distributed_backend == 'ddp':
+                self.spawn_ddp_children(model)
 
         # 1 gpu or dp option triggers training using DP module
         # easier to avoid NCCL issues
@@ -928,18 +924,6 @@ def fit(
         # used for testing or when we need to know that training succeeded
         return 1
 
-    def __set_random_port(self):
-        """
-        When running DDP NOT managed by SLURM, the ports might collide
-        :return:
-        """
-        try:
-            default_port = os.environ['MASTER_PORT']
-        except Exception:
-            import random
-            default_port = random.randint(10000, 19000)
-            os.environ['MASTER_PORT'] = str(default_port)
-
     def __attach_dataloaders(self, model, train_dataloader=None, val_dataloaders=None, test_dataloaders=None):
         # when dataloader is passed via fit, patch the train_dataloader
         # functions to overwrite with these implementations
@@ -1046,7 +1030,10 @@ def run_pretrain_routine(self, model: LightningModule):
 
         # clear cache before training
         if self.on_gpu:
-            torch.cuda.empty_cache()
+            # use context because of:
+            # https://discuss.pytorch.org/t/out-of-memory-when-i-use-torch-cuda-empty-cache/57898
+            with torch.cuda.device(f'cuda:{self.root_gpu}'):
+                torch.cuda.empty_cache()
 
         # CORE TRAINING LOOP
         self.train()
@@ -1096,7 +1083,10 @@ def test(
         if model is not None:
             self.model = model
             self.fit(model)
-        elif self.use_ddp or self.use_tpu:  # pragma: no-cover
+
+        # on tpu, .spawn means we don't have a trained model
+        # TODO: remove TPU spawn
+        elif self.use_tpu:  # pragma: no-cover
             # attempt to load weights from a spawn
             path = os.path.join(self.default_root_dir, '__temp_weight_ddp_end.ckpt')
             test_model = self.model

pytorch_lightning/trainer/training_loop.py

@@ -158,6 +158,7 @@ def training_step(self, batch, batch_idx):
 from pytorch_lightning.trainer.supporters import TensorRunningAccum, CombinedLoaderIterator
 from pytorch_lightning.utilities import rank_zero_warn
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
+import subprocess
 
 try:
     from apex import amp
@@ -305,13 +306,13 @@ def has_arg(self, *args):
 
     def train(self):
         # add signal handlers for process kills
-        def _signal_kill_handler(*args):
-            return TrainerTrainLoopMixin.run_training_teardown(self)
-
-        orig_signal_handlers = {}
-        for sig_name in SIGNAL_TERMINATE:
-            orig_signal_handlers[sig_name] = signal.signal(getattr(signal, sig_name),
-                                                           _signal_kill_handler)
+        # def _signal_kill_handler(*args):
+        #     return TrainerTrainLoopMixin.run_training_teardown(self)
+        #
+        # orig_signal_handlers = {}
+        # for sig_name in SIGNAL_TERMINATE:
+        #     orig_signal_handlers[sig_name] = signal.signal(getattr(signal, sig_name),
+        #                                                    _signal_kill_handler)
 
         # get model
         model = self.get_model()
@@ -384,15 +385,17 @@ def _signal_kill_handler(*args):
 
             self.run_training_teardown()
 
-            # reset signal handlers
-            for sig_name in SIGNAL_TERMINATE:
-                signal.signal(getattr(signal, sig_name), orig_signal_handlers[sig_name])
-
         except KeyboardInterrupt:
-            if self.proc_rank == 0:
-                log.info('Detected KeyboardInterrupt, attempting graceful shutdown...')
-            self.interrupted = True
-            self.run_training_teardown()
+            rank_zero_warn('Detected KeyboardInterrupt, attempting graceful shutdown...')
+
+            # user could press ctrl+c many times... only shutdown once
+            if not self.interrupted:
+                self.interrupted = True
+
+                for proc in self.interactive_ddp_procs:
+                    subprocess.Popen.kill(proc)
+
+                self.run_training_teardown()
 
     def run_training_epoch(self):
 
@@ -681,7 +684,7 @@ def _get_optimizers_iterable(self):
         opt_idx = np.argmax(optimizer_freq_cumsum > current_place_in_loop)
         return [(opt_idx, self.optimizers[opt_idx])]
 
-    @atexit.register
+    # @atexit.register
     def run_training_teardown(self):
         if hasattr(self, '_teardown_already_run') and self._teardown_already_run:
             return

tests/base/model_utilities.py

@@ -12,7 +12,7 @@ def dataloader(self, train):
         loader = DataLoader(
             dataset=dataset,
             batch_size=self.batch_size,
-            # test and valid shall not be shuffled
+            num_workers=3,
             shuffle=train,
         )
         return loader

tests/base/utils.py

@@ -25,7 +25,7 @@ def assert_speed_parity(pl_times, pt_times, num_epochs):
         f"lightning was slower than PT (threshold {max_diff_per_epoch})"
 
 
-def run_model_test_without_loggers(trainer_options, model, min_acc=0.50):
+def run_model_test_without_loggers(trainer_options, model, min_acc=0.30):
     reset_seed()
 
     # fit model
@@ -155,7 +155,7 @@ def load_model_from_checkpoint(root_weights_dir, module_class=EvalModelTemplate)
     return trained_model
 
 
-def run_prediction(dataloader, trained_model, dp=False, min_acc=0.5):
+def run_prediction(dataloader, trained_model, dp=False, min_acc=0.3):
     # run prediction on 1 batch
     for batch in dataloader:
         break