Upload apex-master/tests/distributed/amp_master_params/amp_master_params.py with huggingface_hub

apex-master/tests/distributed/amp_master_params/amp_master_params.py

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+import torch
+import argparse
+import os
+from apex import amp
+# FOR DISTRIBUTED: (can also use torch.nn.parallel.DistributedDataParallel instead)
+from apex.parallel import DistributedDataParallel
+
+parser = argparse.ArgumentParser()
+# FOR DISTRIBUTED:  Parse for the local_rank argument, which will be supplied
+# automatically by torch.distributed.launch.
+parser.add_argument("--local_rank", default=0, type=int)
+args = parser.parse_args()
+
+# FOR DISTRIBUTED:  If we are running under torch.distributed.launch,
+# the 'WORLD_SIZE' environment variable will also be set automatically.
+args.distributed = False
+if 'WORLD_SIZE' in os.environ:
+    args.distributed = int(os.environ['WORLD_SIZE']) > 1
+
+if args.distributed:
+    # FOR DISTRIBUTED:  Set the device according to local_rank.
+    torch.cuda.set_device(args.local_rank)
+
+    # FOR DISTRIBUTED:  Initialize the backend.  torch.distributed.launch will provide
+    # environment variables, and requires that you use init_method=`env://`.
+    torch.distributed.init_process_group(backend='nccl',
+                                         init_method='env://')
+
+    torch.manual_seed(torch.distributed.get_rank())
+
+torch.backends.cudnn.benchmark = True
+
+N, D_in, D_out = 64, 1024, 16
+
+# Each process receives its own batch of "fake input data" and "fake target data."
+# The "training loop" in each process just uses this fake batch over and over.
+# https://github.com/NVIDIA/apex/tree/master/examples/imagenet provides a more realistic
+# example of distributed data sampling for both training and validation.
+x = torch.randn(N, D_in, device='cuda')
+y = torch.randn(N, D_out, device='cuda')
+
+model = torch.nn.Linear(D_in, D_out).cuda()
+optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
+
+model, optimizer = amp.initialize(model, optimizer, opt_level="O2")
+
+if args.distributed:
+    # FOR DISTRIBUTED:  After amp.initialize, wrap the model with
+    # apex.parallel.DistributedDataParallel.
+    model = DistributedDataParallel(model)
+    # torch.nn.parallel.DistributedDataParallel is also fine, with some added args:
+    # model = torch.nn.parallel.DistributedDataParallel(model,
+    #                                                   device_ids=[args.local_rank],
+    #                                                   output_device=args.local_rank)
+
+loss_fn = torch.nn.MSELoss()
+
+for t in range(500):
+    optimizer.zero_grad()
+    y_pred = model(x)
+    loss = loss_fn(y_pred, y)
+    with amp.scale_loss(loss, optimizer) as scaled_loss:
+        scaled_loss.backward()
+    optimizer.step()
+
+if args.local_rank == 0:
+    print("final loss = ", loss)
+
+torch.save(list(model.parameters()), "rank{}model.pth".format(torch.distributed.get_rank()))
+torch.save(list(amp.master_params(optimizer)), "rank{}master.pth".format(torch.distributed.get_rank()))