[BUG] Enabling drop_tokens in MoE layer causes inference to hang

Describe the bug
When I enable drop_tokens for deepspeed.moe.layer.MoE the program hangs. If I use eval_capacity_factor it works but I would not like to overshoot the capacity factor to prevent token dropping, as I have noticed that deepspeed takes memory accordingly to the capacity factor set.

To Reproduce

# https://proceedings.mlr.press/v162/rajbhandari22a/rajbhandari22a.pdf
# https://github.com/microsoft/DeepSpeed

import torch
import torch.nn as nn
import sys
import os 
import time
import csv
from tqdm import tqdm
from torch.utils.data import DataLoader, DistributedSampler
from transformers import AutoTokenizer, AutoModel
from datasets import load_dataset
import argparse 
import deepspeed

parser = argparse.ArgumentParser()
parser.add_argument("--local_rank", default=0, type=int) 
parser.add_argument("--world_size", default=8, type=int)
args = parser.parse_args()

def setup():
    os.environ["HF_HOME"] = "/cache"
    os.environ["HF_DATASETS_CACHE"] = "/cache"
    os.environ["TRITON_HOME"] = "/.triton"
    os.environ["TOKENIZERS_PARALLELISM"] = "false"

    deepspeed.init_distributed()

    torch.cuda.set_device(args.local_rank)

def run_inference_workload():
    setup()

    model_name = f"google/switch-base-128"
    model = AutoModel.from_pretrained(model_name, cache_dir="/cache")

    class MLPWrapper(nn.Module):
        def __init__(self, child):
            super().__init__()
            self.child = child
        
        def forward(self, x):
            x = self.child(x)
            return x[0], (x[1], x[2])

    # Update to add DeepspeedMoE to it
    def add_deepspeed_moe_model(module):
        if type(module).__name__ == "SwitchTransformersLayerFF":
            for child_name, child in module.named_children():
                if type(child).__name__ == "SwitchTransformersSparseMLP":
                    router = getattr(child, "router")
                    experts = getattr(child, "experts")
                    if type(experts) == nn.ModuleDict:
                        experts = list(experts.values())
                    
                    num_experts_per_gpu = 128 // args.world_size

                    experts = experts[args.local_rank*num_experts_per_gpu:(args.local_rank+1)*num_experts_per_gpu]

                    new = deepspeed.moe.layer.MoE(
                        hidden_size=768,
                        expert=experts[0],
                        num_experts=128,
                        ep_size=args.world_size,
                        k=1,
                        eval_capacity_factor=10.0,
                        # drop_tokens=False,
                        use_tutel=True,
                        top2_2nd_expert_sampling=False,
                        use_rts=False,
                    )

                    with torch.no_grad():
                        new.deepspeed_moe.gate.wg.weight.copy_(router.classifier.weight)
                        for i in range(len(experts)):
                            new.deepspeed_moe.experts.deepspeed_experts[i].wi.weight.copy_(experts[i].wi.weight)
                            new.deepspeed_moe.experts.deepspeed_experts[i].wo.weight.copy_(experts[i].wo.weight)

                    setattr(module, child_name, MLPWrapper(new))
        else:
            for child in module.children():
                add_deepspeed_moe_model(child)

    add_deepspeed_moe_model(model)

    model.eval()
    model.cuda()

    ds_engine = deepspeed.init_inference(
        model,
        dtype=torch.float,
        replace_with_kernel_inject=False,
        moe={
            "enabled": True,
            "ep_size": args.world_size, 
            "moe_experts": [128],
        }
    )

    tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir="/cache")

    dataset = load_dataset(
        "bookcorpus/bookcorpus", 
        split=f"train[:3200]", 
        streaming=False, 
        trust_remote_code=True, 
        cache_dir="/cache"
    )
    sampler = DistributedSampler(
        dataset, 
        num_replicas=args.world_size, 
        rank=args.local_rank, 
        shuffle=True, 
        seed=49
    )
    def collate_fn(batch):
        texts = ["summarize: " + item["text"] for item in batch]
        
        tokenized = tokenizer(
            texts, 
            padding=True, 
            truncation=True, 
            max_length=120,
            return_tensors="pt"
        )
        return {
            **tokenized,
            "decoder_input_ids": torch.tensor([[tokenizer.pad_token_id]]*len(batch))
        }
    loader = DataLoader(
        dataset, 
        sampler=sampler, 
        batch_size=100,
        collate_fn=collate_fn,
    )

    run_standard_experiment(ds_engine, loader)


def run_standard_experiment(ds_engine, loader):    
    with torch.no_grad():
        for batch in tqdm(loader):
            batch = {k: v.cuda() for k, v in batch.items()}
            ds_engine(
                input_ids=batch["input_ids"], 
                attention_mask=batch["attention_mask"],
                decoder_input_ids=batch["decoder_input_ids"],
            )

if __name__ == "__main__":
    run_inference_workload()

    print("All done :)")

Steps to reproduce the behavior:

1. deepspeed --num_gpus 8 script.py
2. It will run no problem but if you uncomment the drop_tokens, the program halts

Expected behavior
I expect that the program runs.

ds_report output

[2024-11-29 16:34:46,436] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)
--------------------------------------------------
DeepSpeed C++/CUDA extension op report
--------------------------------------------------
NOTE: Ops not installed will be just-in-time (JIT) compiled at
      runtime if needed. Op compatibility means that your system
      meet the required dependencies to JIT install the op.
--------------------------------------------------
JIT compiled ops requires ninja
ninja .................. [OKAY]
--------------------------------------------------
op name ................ installed .. compatible
--------------------------------------------------
 [WARNING]  async_io requires the dev libaio .so object and headers but these were not found.
 [WARNING]  async_io: please install the libaio-dev package with apt
 [WARNING]  If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
async_io ............... [NO] ....... [NO]
fused_adam ............. [NO] ....... [OKAY]
cpu_adam ............... [NO] ....... [OKAY]
cpu_adagrad ............ [NO] ....... [OKAY]
cpu_lion ............... [NO] ....... [OKAY]
 [WARNING]  Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
evoformer_attn ......... [NO] ....... [NO]
 [WARNING]  NVIDIA Inference is only supported on Ampere and newer architectures
fp_quantizer ........... [NO] ....... [NO]
fused_lamb ............. [NO] ....... [OKAY]
fused_lion ............. [NO] ....... [OKAY]
 [WARNING]  gds requires the dev libaio .so object and headers but these were not found.
 [WARNING]  gds: please install the libaio-dev package with apt
 [WARNING]  If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
gds .................... [NO] ....... [NO]
transformer_inference .. [NO] ....... [OKAY]
inference_core_ops ..... [NO] ....... [OKAY]
cutlass_ops ............ [NO] ....... [OKAY]
quantizer .............. [NO] ....... [OKAY]
ragged_device_ops ...... [NO] ....... [OKAY]
ragged_ops ............. [NO] ....... [OKAY]
random_ltd ............. [NO] ....... [OKAY]
 [WARNING]  sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.4
 [WARNING]  using untested triton version (3.0.0), only 1.0.0 is known to be compatible
sparse_attn ............ [NO] ....... [NO]
spatial_inference ...... [NO] ....... [OKAY]
transformer ............ [NO] ....... [OKAY]
stochastic_transformer . [NO] ....... [OKAY]
--------------------------------------------------
DeepSpeed general environment info:
torch install path ............... ['/usr/local/lib/python3.10/dist-packages/torch']
torch version .................... 2.4.0a0+f70bd71a48.nv24.06
deepspeed install path ........... ['/usr/local/lib/python3.10/dist-packages/deepspeed']
deepspeed info ................... 0.15.3, unknown, unknown
torch cuda version ............... 12.5
torch hip version ................ None
nvcc version ..................... 12.5
deepspeed wheel compiled w. ...... torch 2.4, cuda 12.5
shared memory (/dev/shm) size .... 251.87 GB

System info (please complete the following information):

• Ubuntu 22.04
• 1 Node DGX1 8xV100
• Transformers 4.46.0
• DeepSpeed 0.15.3
• Python 3.10.12

Docker context
nvcr.io/nvidia/pytorch:24.06-py3

Start with the provided script.py, focusing on deepspeed.moe.layer.MoE and deepspeed.init_inference, then reproduce with deepspeed --num_gpus 8 script.py while comparing drop_tokens settings. Done means inference completes across all eight GPUs with drop_tokens enabled instead of hanging.