[WIP] ref: decoupled ddp, ddp spawn

pytorch_lightning/accelerators/ddp_backend.py

@@ -12,22 +12,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License
 import os
+import torch.distributed as dist
+import torch
+import torch.distributed as torch_distrib
 import subprocess
 import sys
 from os.path import abspath
 from time import sleep
 from typing import Optional
-
 import numpy as np
-import torch
-import torch.distributed as torch_distrib
-import torch.distributed as dist
 
+
+from pytorch_lightning import _logger as log
 from pytorch_lightning.utilities.distributed import find_free_network_port
 from pytorch_lightning.accelerators.base_backend import Accelerator
-from pytorch_lightning import _logger as log
-from pytorch_lightning.utilities import AMPType
 from pytorch_lightning.utilities.distributed import rank_zero_only
+from pytorch_lightning.utilities import AMPType
 from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.distributed.dist import LightningDistributed
 
@@ -47,6 +47,7 @@ def __init__(self, trainer):
         super().__init__(trainer)
         self.task_idx = None
         self._has_spawned_children = False
+        self.interactive_ddp_procs = []
         self.dist = LightningDistributed()
 
     def setup(self, model):
@@ -57,7 +58,6 @@ def setup(self, model):
         self._call_children_scripts()
 
     def _call_children_scripts(self):
-
         assert self.trainer.global_rank == 0
         self._check_can_spawn_children()
         self._has_spawned_children = True
@@ -104,11 +104,12 @@ def _call_children_scripts(self):
 
         os.environ['WORLD_SIZE'] = f'{num_gpus * self.trainer.num_nodes}'
 
-        self.trainer.interactive_ddp_procs = []
+        self.interactive_ddp_procs = []
         for local_rank in range(1, self.trainer.num_processes):
             env_copy = os.environ.copy()
             env_copy['LOCAL_RANK'] = f'{local_rank}'
             env_copy['PL_DDP_PID'] = str(self.trainer.data_parallel_device_ids[local_rank])
+            env_copy['PL_GLOBAL_SEED'] = os.environ.get('PL_GLOBAL_SEED', None)
 
             # start process
             # if hydra is available and initialized, make sure to set the cwd correctly
@@ -117,7 +118,7 @@ def _call_children_scripts(self):
                 if HydraConfig.initialized():
                     cwd = get_original_cwd()
             proc = subprocess.Popen(command, env=env_copy, cwd=cwd)
-            self.trainer.interactive_ddp_procs.append(proc)
+            self.interactive_ddp_procs.append(proc)
 
             # starting all processes at once can cause issues
             # with dataloaders delay between 1-10 seconds
@@ -126,12 +127,36 @@ def _call_children_scripts(self):
 
         self.task_idx = 0
 
+        # wait for all the procs to start
+        sleep(2)
+
     def train(self):
         model = self.trainer.model
-        results = self.ddp_train(process_idx=self.task_idx, model=model, is_master=True)
-        del os.environ['WORLD_SIZE']
+        results = self.ddp_train(process_idx=self.task_idx, mp_queue=None, model=model, is_master=True)
+        if 'WORLD_SIZE' in os.environ:
+            del os.environ['WORLD_SIZE']
         return results
 
+    def training_step(self, args):
+        if self.trainer.amp_backend == AMPType.NATIVE:
+            with torch.cuda.amp.autocast():
+                output = self.trainer.model(*args)
+        else:
+            output = self.trainer.model(*args)
+        return output
+
+    def validation_step(self, args):
+        output = self.training_step(args)
+        return output
+
+    def test_step(self, args):
+        output = self.training_step(args)
+        return output
+
+    def barrier(self, name: str = None):
+        if torch_distrib.is_initialized():
+            torch_distrib.barrier()
+
     def _check_can_spawn_children(self):
         if self._has_spawned_children:
             raise RuntimeError(
@@ -145,17 +170,7 @@ def set_world_ranks(self, process_idx):
         self.trainer.world_size = self.trainer.num_nodes * self.trainer.num_processes
 
     def model_to_device(self, model, process_idx, is_master):
-        gpu_idx = process_idx
-
-        # when using ddp, the master process (proc 0) continues running as the main one
-        # this means that the local rank will always be 0
-        # (even if cuda visible devices has other visible gpus)
-        # this means that the master process needs to pull the 0th visible index as the device number
-        if is_master:
-            available_gpus = os.environ['CUDA_VISIBLE_DEVICES'].split(',')
-            gpu_idx = int(available_gpus[self.trainer.local_rank])
-
-        gpu_idx = int(os.environ.get('PL_DDP_PID', gpu_idx))
+        gpu_idx = int(os.environ.get('PL_DDP_PID', process_idx))
 
         self.trainer.root_gpu = gpu_idx
         torch.cuda.set_device(self.trainer.root_gpu)
@@ -165,25 +180,8 @@ def get_device_ids(self):
         device_ids = [self.trainer.root_gpu]
         return device_ids
 
-    def training_step(self, args):
-        if self.trainer.amp_backend == AMPType.NATIVE:
-            with torch.cuda.amp.autocast():
-                output = self.trainer.model(*args)
-        else:
-            output = self.trainer.model(*args)
-        return output
-
-    def validation_step(self, args):
-        output = self.training_step(args)
-        return output
-
-    def test_step(self, args):
-        output = self.training_step(args)
-        return output
-
-    def barrier(self, name: str = None):
-        if torch_distrib.is_initialized():
-            torch_distrib.barrier()
+    def on_train_end(self):
+        pass
 
     def early_stopping_should_stop(self, pl_module):
         stop = torch.tensor(int(self.trainer.should_stop), device=pl_module.device)
@@ -207,7 +205,7 @@ def ddp_train(self, process_idx, model, is_master=False, proc_offset=0):
         Returns:
 
         """
-        seed = os.environ.get("PL_GLOBAL_SEED")
+        seed = os.environ.get("PL_GLOBAL_SEED", None)
         if seed is not None:
             seed_everything(int(seed))
 
@@ -268,6 +266,7 @@ def ddp_train(self, process_idx, model, is_master=False, proc_offset=0):
         model = model.configure_ddp(model, device_ids)
 
         # set up training routine
+        self.barrier('ddp_setup')
         self.trainer.train_loop.setup_training(model)
 
         # train or test

pytorch_lightning/accelerators/ddp_spawn_backend.py

@@ -15,15 +15,15 @@
 import re
 
 import torch
-import torch.multiprocessing as mp
 import torch.distributed as torch_distrib
+import torch.multiprocessing as mp
 import torch.distributed as dist
 
 from pytorch_lightning import _logger as log
 from pytorch_lightning.accelerators.base_backend import Accelerator
 from pytorch_lightning.utilities import AMPType
-from pytorch_lightning.utilities.cloud_io import atomic_save, load as pl_load
 from pytorch_lightning.utilities.distributed import rank_zero_only, rank_zero_warn
+from pytorch_lightning.utilities.cloud_io import atomic_save, load as pl_load
 from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.distributed.dist import LightningDistributed
 from pytorch_lightning.utilities.distributed import find_free_network_port
@@ -157,12 +157,109 @@ def ddp_train(self, process_idx, mp_queue, model, is_master=False, proc_offset=0
         # clean up memory
         torch.cuda.empty_cache()
 
+    def ddp_train(self, process_idx, mp_queue, model):
+        """
+        Entry point for ddp
+
+        Args:
+            process_idx:
+            mp_queue: multiprocessing queue
+            model:
+
+        Returns:
+
+        """
+        # show progressbar only on progress_rank 0
+        if (self.trainer.node_rank != 0 or process_idx != 0) and self.trainer.progress_bar_callback is not None:
+            self.trainer.progress_bar_callback.disable()
+
+        # determine which process we are and world size
+        self.set_world_ranks(process_idx)
+
+        # set warning rank
+        rank_zero_only.rank = self.trainer.global_rank
+
+        # set up server using proc 0's ip address
+        # try to init for 20 times at max in case ports are taken
+        # where to store ip_table
+        model.trainer = self.trainer
+        model.init_ddp_connection(
+            self.trainer.global_rank,
+            self.trainer.world_size,
+            self.trainer.is_slurm_managing_tasks
+        )
+
+        # call setup after the ddp process has connected
+        self.trainer.call_setup_hook(model)
+
+        # on world_size=0 let everyone know training is starting
+        if self.trainer.is_global_zero and not torch.distributed.is_initialized():
+            log.info('-' * 100)
+            log.info(f'distributed_backend={self.trainer.distributed_backend}')
+            log.info(f'All DDP processes registered. Starting ddp with {self.trainer.world_size} processes')
+            log.info('-' * 100)
+
+        # call sync_bn before .cuda(), configure_apex and configure_ddp
+        if self.trainer.sync_batchnorm:
+            model = model.configure_sync_batchnorm(model)
+
+        # move the model to the correct device
+        self.model_to_device(model, process_idx)
+
+        # CHOOSE OPTIMIZER
+        # allow for lr schedulers as well
+        self.setup_optimizers(model)
+
+        # set model properties before going into wrapper
+        self.trainer.model_connector.copy_trainer_model_properties(model)
+
+        # 16-bit
+        model = self.trainer.precision_connector.connect(model)
+
+        # device ids change depending on the DDP setup
+        device_ids = self.get_device_ids()
+
+        # allow user to configure ddp
+        model = model.configure_ddp(model, device_ids)
+
+        # set up training routine
+        self.trainer.train_loop.setup_training(model)
+
+        # train or test
+        results = self.train_or_test()
+
+        # get original model
+        model = self.trainer.get_model()
+
+        # persist info in ddp_spawn
+        self.transfer_distrib_spawn_state_on_fit_end(model, mp_queue, results)
+
+    def training_step(self, args):
+        if self.trainer.amp_backend == AMPType.NATIVE:
+            with torch.cuda.amp.autocast():
+                output = self.trainer.model(*args)
+        else:
+            output = self.trainer.model(*args)
+        return output
+
+    def validation_step(self, args):
+        output = self.training_step(args)
+        return output
+
+    def test_step(self, args):
+        output = self.training_step(args)
+        return output
+
+    def barrier(self, name: str = None):
+        if torch_distrib.is_initialized():
+            torch_distrib.barrier()
+
     def set_world_ranks(self, process_idx):
         self.trainer.local_rank = process_idx
         self.trainer.global_rank = self.trainer.node_rank * self.trainer.num_processes + process_idx
         self.trainer.world_size = self.trainer.num_nodes * self.trainer.num_processes
 
-    def model_to_device(self, model, process_idx, is_master):
+    def model_to_device(self, model, process_idx):
         gpu_idx = process_idx
         self.trainer.root_gpu = gpu_idx
         torch.cuda.set_device(self.trainer.root_gpu)

tests/backends/test_ddp.py

@@ -37,21 +37,21 @@ def test_multi_gpu_model_ddp_test_only(tmpdir, cli_args):
     assert result['status'] == 'complete'
 
 
-# @pytest.mark.parametrize('cli_args', [
-#     pytest.param('--max_epochs 1 --gpus 2 --distributed_backend ddp'),
-# ])
-# @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
-# def test_multi_gpu_model_ddp_fit_test(tmpdir, cli_args):
-#     # call the script
-#     call_training_script(ddp_model, cli_args, 'fit_test', tmpdir, timeout=20)
-#
-#     # load the results of the script
-#     result_path = os.path.join(tmpdir, 'ddp.result')
-#     result = torch.load(result_path)
-#
-#     # verify the file wrote the expected outputs
-#     assert result['status'] == 'complete'
-#
-#     model_outs = result['result']
-#     for out in model_outs:
-#         assert out['test_acc'] > 0.90
+@pytest.mark.parametrize('cli_args', [
+    pytest.param('--max_epochs 1 --gpus 2 --distributed_backend ddp'),
+])
+@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
+def test_multi_gpu_model_ddp_fit_test(tmpdir, cli_args):
+    # call the script
+    call_training_script(ddp_model, cli_args, 'fit_test', tmpdir, timeout=20)
+
+    # load the results of the script
+    result_path = os.path.join(tmpdir, 'ddp.result')
+    result = torch.load(result_path)
+
+    # verify the file wrote the expected outputs
+    assert result['status'] == 'complete'
+
+    model_outs = result['result']
+    for out in model_outs:
+        assert out['test_acc'] > 0.90