Gpu idx

pytorch_lightning/accelerator_backends/__init__.py

@@ -3,3 +3,4 @@
 from pytorch_lightning.accelerator_backends.dp_backend import DataParallelBackend
 from pytorch_lightning.accelerator_backends.ddp_spawn_backend import DDPSpawnBackend
 from pytorch_lightning.accelerator_backends.cpu_backend import CPUBackend
+from pytorch_lightning.accelerator_backends.ddp_backend_temp import DDPBackendTemp

pytorch_lightning/accelerator_backends/ddp_backend_temp.py

@@ -0,0 +1,213 @@
+import os
+import torch
+import subprocess
+import sys
+from time import sleep
+import numpy as np
+from os.path import abspath
+from pytorch_lightning.utilities import NATIVE_AMP_AVALAIBLE
+from pytorch_lightning.utilities.distributed import rank_zero_only
+from pytorch_lightning import _logger as log
+from typing import Optional
+
+try:
+    from hydra.utils import to_absolute_path, get_original_cwd
+    from hydra.core.hydra_config import HydraConfig
+except ImportError:
+    HYDRA_AVAILABLE = False
+else:
+    HYDRA_AVAILABLE = True
+
+try:
+    from apex import amp
+except ImportError:
+    APEX_AVAILABLE = False
+else:
+    APEX_AVAILABLE = True
+
+
+class DDPBackendTemp(object):
+
+    def __init__(self, trainer):
+        self.trainer = trainer
+
+    def spawn_ddp_children(self, model):
+        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'
+
+        # when user is using hydra find the absolute path
+        path_lib = abspath if not HYDRA_AVAILABLE else to_absolute_path
+
+        # pull out the commands used to run the script and resolve the abs file path
+        command = sys.argv
+        try:
+            full_path = path_lib(command[0])
+        except Exception as e:
+            full_path = abspath(command[0])
+
+        command[0] = full_path
+        # use the same python interpreter and actually running
+        command = [sys.executable] + 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 '--gpus' in command:
+            gpu_flag_idx = command.index('--gpus')
+            command[gpu_flag_idx + 1] = f'{num_gpus}'
+
+        os.environ['WORLD_SIZE'] = f'{num_gpus * self.trainer.num_nodes}'
+
+        self.trainer.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}'
+
+            # start process
+            # if hydra is available and initialized, make sure to set the cwd correctly
+            cwd: Optional[str] = None
+            if HYDRA_AVAILABLE:
+                if HydraConfig.initialized():
+                    cwd = get_original_cwd()
+            proc = subprocess.Popen(command, env=env_copy, cwd=cwd)
+            self.trainer.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
+        results = self.ddp_train(local_rank, mp_queue=None, model=model, is_master=True)
+        del os.environ['WORLD_SIZE']
+
+        return results
+
+    def ddp_train(self, process_idx, mp_queue, model, is_master=False, proc_offset=0):
+        """
+        Entry point for ddp
+
+        Args:
+            process_idx:
+            mp_queue: multiprocessing queue
+            model:
+            is_master:
+            proc_offset:
+
+        Returns:
+
+        """
+        # offset the process id if requested
+        process_idx = process_idx + proc_offset
+
+        # 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
+        if self.trainer.use_ddp:
+            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
+
+        elif self.trainer.use_ddp2:
+            self.trainer.local_rank = self.trainer.node_rank
+            self.trainer.global_rank = self.trainer.node_rank
+            self.trainer.world_size = self.trainer.num_nodes
+
+        # 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
+        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:
+            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)
+
+        # CHOOSE OPTIMIZER
+        # allow for lr schedulers as well
+        optimizers, lr_schedulers, optimizer_frequencies = self.trainer.init_optimizers(model)
+        self.trainer.optimizers = optimizers
+        self.trainer.lr_schedulers = lr_schedulers
+        self.trainer.optimizer_frequencies = optimizer_frequencies
+
+        # MODEL
+        # copy model to each gpu
+        if self.trainer.on_gpu:
+            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])
+
+            self.trainer.root_gpu = gpu_idx
+            torch.cuda.set_device(self.trainer.root_gpu)
+            model.cuda(self.trainer.root_gpu)
+
+        # set model properties before going into wrapper
+        self.trainer.copy_trainer_model_properties(model)
+
+        # AMP
+        # run through amp wrapper before going to distributed DP
+        # TODO: remove with dropping NVIDIA AMP support
+        if self.trainer.use_amp and not NATIVE_AMP_AVALAIBLE:
+            model, optimizers = model.configure_apex(amp, model, self.trainer.optimizers, self.trainer.amp_level)
+            self.trainer.optimizers = optimizers
+            self.trainer.reinit_scheduler_properties(self.trainer.optimizers, self.trainer.lr_schedulers)
+
+        # DDP2 uses all GPUs on the machine
+        if self.trainer.distributed_backend == 'ddp' or self.trainer.distributed_backend == 'ddp_spawn':
+            device_ids = [self.trainer.root_gpu]
+        elif self.trainer.use_ddp2:
+            device_ids = self.trainer.data_parallel_device_ids
+        else:  # includes ddp_cpu
+            device_ids = None
+
+        # allow user to configure ddp
+        model = model.configure_ddp(model, device_ids)
+
+        # continue training routine
+        results = self.trainer.run_pretrain_routine(model)
+
+        # get original model
+        model = self.trainer.get_model()
+
+        # persist info in ddp_spawn
+        self.trainer.transfer_distrib_spawn_state_on_fit_end(model, mp_queue, results)
+
+        # clean up memory
+        torch.cuda.empty_cache()
+
+        if self.trainer.global_rank == 0 and self.trainer.distributed_backend not in ['ddp_spawn', 'ddp_cpu']:
+            return results

pytorch_lightning/accelerator_backends/ddp_spawn_backend.py

@@ -60,23 +60,18 @@ def teardown(self, model):
         self.trainer.model = model
         return results
 
-    def ddp_train(self, process_idx, mp_queue, model, is_master=False, proc_offset=0):
+    def ddp_train(self, process_idx, mp_queue, model):
         """
         Entry point for ddp
 
         Args:
             process_idx:
             mp_queue: multiprocessing queue
             model:
-            is_master:
-            proc_offset:
 
         Returns:
 
         """
-        # offset the process id if requested
-        process_idx = process_idx + proc_offset
-
         # 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()
@@ -126,11 +121,6 @@ def ddp_train(self, process_idx, mp_queue, model, is_master=False, proc_offset=0
         # copy model to each gpu
         if self.trainer.on_gpu:
             gpu_idx = process_idx
-            if is_master:
-                # source of truth is cuda for gpu idx
-                gpus = os.environ['CUDA_VISIBLE_DEVICES'].split(',')
-                gpu_idx = int(gpus[self.trainer.local_rank])
-
             self.trainer.root_gpu = gpu_idx
             torch.cuda.set_device(self.trainer.root_gpu)
             model.cuda(self.trainer.root_gpu)