Issue with FSDP does not reduce memory footprint  when scaling up GPUs
Labels: oncall: distributed, triaged, module: fsdp

### 🐛 Describe the bug

We are running FSDP with a model using the size_based_auto wrap policy with A800 gpus. Scaling up GPUs, by extending to multi nodes , we are expecting to have smaller shard on each and overall proportional memory reduction. On the contrary, as the number of GPU cards increases, we have not observed a decrease in VRAM usage. The VRAM usage of a single graphics card actually increases with the increase in the number of cards.

![image](https://github.com/pytorch/pytorch/assets/82354186/c15b872e-1b7a-4a8c-b41e-b0354aa5f5c3)



We get the allocated memory using "torch.cuda.memory_allocated()".

We have defined a test model that does not have any practical meaning itself，below is the code we used for testing.

```
import os
import argparse
import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms


from torch.optim.lr_scheduler import StepLR

import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data.distributed import DistributedSampler
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import (
    CPUOffload,
    BackwardPrefetch,
)
from torch.distributed.fsdp.wrap import (
    size_based_auto_wrap_policy,
    enable_wrap,
    wrap,
)
from loguru import logger
def setup():
    # initialize the process group
    dist.init_process_group()

def cleanup():
    dist.destroy_process_group()
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.temp=nn.ModuleList([nn.Linear(9216,9216)for _ in range(40)])
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):

        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = torch.flatten(x, 1)
        for sub in self.temp:
            x=sub(x)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output

def train(args, model, rank, world_size, train_loader, optimizer, epoch, sampler=None):
    model.train()
    ddp_loss = torch.zeros(2).to(rank)
    if sampler:
        sampler.set_epoch(epoch)
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(rank), target.to(rank)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target, reduction='sum')
        loss.backward()
        optimizer.step()
        ddp_loss[0] += loss.item()
        ddp_loss[1] += len(data)

    dist.all_reduce(ddp_loss, op=dist.ReduceOp.SUM)
    if rank == 0:
        print('Train Epoch: {} \tLoss: {:.6f}'.format(epoch, ddp_loss[0] / ddp_loss[1]))

def test(model, rank, world_size, test_loader):
    model.eval()
    correct = 0
    ddp_loss = torch.zeros(3).to(rank)
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(rank), target.to(rank)
            output = model(data)
            ddp_loss[0] += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
            ddp_loss[1] += pred.eq(target.view_as(pred)).sum().item()
            ddp_loss[2] += len(data)

    dist.all_reduce(ddp_loss, op=dist.ReduceOp.SUM)

    if rank == 0:
        test_loss = ddp_loss[0] / ddp_loss[2]
        print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n'.format(
            test_loss, int(ddp_loss[1]), int(ddp_loss[2]),
            100. * ddp_loss[1] / ddp_loss[2]))
def fsdp_main(world_size, args):
    setup()
    rank=int(os.environ["LOCAL_RANK"])
    transform=transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
    ])

    dataset1 = datasets.MNIST('../data', train=True, download=True,
                        transform=transform)
    dataset2 = datasets.MNIST('../data', train=False,
                        transform=transform)

    sampler1 = DistributedSampler(dataset1, rank=rank, num_replicas=world_size, shuffle=True)
    sampler2 = DistributedSampler(dataset2, rank=rank, num_replicas=world_size)

    train_kwargs = {'batch_size': args.batch_size, 'sampler': sampler1}
    test_kwargs = {'batch_size': args.test_batch_size, 'sampler': sampler2}
    cuda_kwargs = {'num_workers': world_size,
                    'pin_memory': True,
                    'shuffle': False}
    train_kwargs.update(cuda_kwargs)
    test_kwargs.update(cuda_kwargs)

    train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)
    test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
    my_auto_wrap_policy = functools.partial(
        size_based_auto_wrap_policy,recurse=True ,min_num_params=100
    )
    torch.cuda.set_device(rank)


    init_start_event = torch.cuda.Event(enable_timing=True)
    init_end_event = torch.cuda.Event(enable_timing=True)

    model = Net().to(rank)
    topara=0
    for item in model.parameters():
        topara+=item.numel()

    logger.info('total memory cost for param is %r G'%(topara*4/1024**3))
    be=torch.cuda.memory_allocated(rank)
    model = FSDP(model,sharding_strategy=torch.distributed.fsdp.ShardingStrategy.FULL_SHARD,auto_wrap_policy=my_auto_wrap_policy,device_id=torch.cuda.current_device())

    af=torch.cuda.memory_allocated(rank)
    logger.info('memory_allocated  %r G for parameters'%((af-be)/1024**3))

    optimizer = optim.Adadelta(model.parameters(), lr=args.lr)

    scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
    init_start_event.record()
    for epoch in range(1, args.epochs + 1):
        train(args, model, rank, world_size, train_loader, optimizer, epoch, sampler=sampler1)
        test(model, rank, world_size, test_loader)
        scheduler.step()

    init_end_event.record()

    if rank == 0:
        print(f"CUDA event elapsed time: {init_start_event.elapsed_time(init_end_event) / 1000}sec")
        print(f"{model}")

    if args.save_model:
        # use a barrier to make sure training is done on all ranks
        dist.barrier()
        # state_dict for FSDP model is only available on Nightlies for now
        states = model.state_dict()
        if rank == 0:
            torch.save(states, "mnist_cnn.pt")

    cleanup()
if __name__ == '__main__':
    # Training settings
    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                        help='input batch size for training (default: 64)')
    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                        help='input batch size for testing (default: 1000)')
    parser.add_argument('--epochs', type=int, default=10, metavar='N',
                        help='number of epochs to train (default: 14)')
    parser.add_argument('--lr', type=float, default=1.0, metavar='LR',
                        help='learning rate (default: 1.0)')
    parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
                        help='Learning rate step gamma (default: 0.7)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--save-model', action='store_true', default=False,
                        help='For Saving the current Model')
    args = parser.parse_args()

    torch.manual_seed(args.seed)

    WORLD_SIZE = torch.cuda.device_count()
    fsdp_main(world_size=WORLD_SIZE,args=args)
```
We run our test code by torchrun.
```
torchrun --standalone  --nnodes=1 --nproc-per-node=$CARDNUM test.py
```
We noticed that a previous [issue](https://github.com/pytorch/pytorch/issues/82001) had raised a similar question, but the problem was not resolved satisfactorily.How should fsdp be used in order to reduce the VRAM usage of the model as the number of GPU cards increases? We would like to inquire whether this is a problem with the fsdp code or if it is related to our usage.


### Versions

PyTorch version: 2.0.1+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.2 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: Could not collect
CMake version: version 3.26.4
Libc version: glibc-2.35

Python version: 3.10.11 (main, May 16 2023, 00:28:57) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-3.10.0-1160.83.1.el7.x86_64-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A800-SXM4-80GB
GPU 1: NVIDIA A800-SXM4-80GB
GPU 2: NVIDIA A800-SXM4-80GB
GPU 3: NVIDIA A800-SXM4-80GB
GPU 4: NVIDIA A800-SXM4-80GB
GPU 5: NVIDIA A800-SXM4-80GB
GPU 6: NVIDIA A800-SXM4-80GB
GPU 7: NVIDIA A800-SXM4-80GB

Nvidia driver version: 525.85.12
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Address sizes:                   46 bits physical, 57 bits virtual
Byte Order:                      Little Endian
CPU(s):                          128
On-line CPU(s) list:             0-127
Vendor ID:                       GenuineIntel
BIOS Vendor ID:                  Intel(R) Corporation
Model name:                      Intel(R) Xeon(R) Platinum 8350C CPU @ 2.60GHz
BIOS Model name:                 Intel(R) Xeon(R) Platinum 8350C CPU @ 2.60GHz
CPU family:                      6
Model:                           106
Thread(s) per core:              2
Core(s) per socket:              32
Socket(s):                       2
Stepping:                        6
Frequency boost:                 enabled
CPU max MHz:                     2601.0000
CPU min MHz:                     800.0000
BogoMIPS:                        5200.00
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch epb cat_l3 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq md_clear pconfig spec_ctrl intel_stibp flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       3 MiB (64 instances)
L1i cache:                       2 MiB (64 instances)
L2 cache:                        80 MiB (64 instances)
L3 cache:                        96 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-31,64-95
NUMA node1 CPU(s):               32-63,96-127
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Vulnerable
Vulnerability Retbleed:          Not affected
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1:        Vulnerable: Load fences, __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2:        Vulnerable
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.25.0
[pip3] torch==2.0.1
[pip3] torchaudio==2.0.2
[pip3] torchvision==0.15.2
[pip3] triton==2.0.0
[conda] numpy                     1.25.0                   pypi_0    pypi
[conda] torch                     2.0.1                    pypi_0    pypi
[conda] torchaudio                2.0.2                    pypi_0    pypi
[conda] torchvision               0.15.2                   pypi_0    pypi
[conda] triton                    2.0.0                    pypi_0    pypi

cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu