[BUG] GPT-J InferenceEngine Initialization Failure: `RuntimeError`

Describe the bug
Initializing an InferenceEngine for GPT-J fails with the following error:

RuntimeError: view size is not compatible with input tensor's size and stride (at 
least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.

To Reproduce
Steps to reproduce the behavior:

1. Install packages:

pip3 install torch==1.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
pip install transformers==4.18.0
pip install deepspeed==0.6.4

2. Run the following:

import os
import deepspeed
from transformers import AutoModelForCausalLM, AutoTokenizer

# Get local gpu rank from torch.distributed/deepspeed launcher
local_rank = int(os.getenv('LOCAL_RANK', '0'))
world_size = int(os.getenv('WORLD_SIZE', '1'))

model = AutoModelForCausalLM.from_pretrained(
    "EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16, low_cpu_mem_usage=True
)
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")

model = deepspeed.init_inference(model,
                                 mp_size=1,
                                 dtype=torch.float16,
                                 replace_method='auto',
                                 replace_with_kernel_inject=True)

Expected behavior
This should initialize a DeepSpeed GPT-J InferenceEngine.

ds_report output

--------------------------------------------------
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
--------------------------------------------------
cpu_adam ............... [NO] ....... [OKAY]
cpu_adagrad ............ [NO] ....... [OKAY]
fused_adam ............. [NO] ....... [OKAY]
fused_lamb ............. [NO] ....... [OKAY]
 [WARNING]  please install triton==1.0.0 if you want to use sparse attention
sparse_attn ............ [NO] ....... [NO]
transformer ............ [NO] ....... [OKAY]
stochastic_transformer . [NO] ....... [OKAY]
 [WARNING]  async_io requires the dev libaio .so object and headers but these were not found.
 [WARNING]  async_io: please install the libaio-devel package with yum
 [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]
utils .................. [NO] ....... [OKAY]
quantizer .............. [NO] ....... [OKAY]
transformer_inference .. [NO] ....... [OKAY]
--------------------------------------------------
DeepSpeed general environment info:
torch install path ............... ['/home/ec2-user/anaconda3/envs/pytorch_p38/lib/python3.8/site-packages/torch']
torch version .................... 1.11.0+cu113
torch cuda version ............... 11.3
torch hip version ................ None
nvcc version ..................... 11.1
deepspeed install path ........... ['/home/ec2-user/anaconda3/envs/pytorch_p38/lib/python3.8/site-packages/deepspeed']
deepspeed info ................... 0.6.4, unknown, unknown
deepspeed wheel compiled w. ...... torch 1.10, cuda 11.1

System info (please complete the following information):

• OS: AWS SageMaker Notebook instance
• GPU count and types: 1 Nvidia 16GB T4 GPU.
• Python version: 3.8

Launcher context
No launcher, just running in a notebook.

Additional context
This exact code was working until recently. I'm currently rolling back versions.

Start with the provided GPT-J reproduction and the deepspeed.init_inference call, using the listed torch, transformers, and deepspeed versions. Compare the failing initialization with the versions that previously worked and trace the RuntimeError to the inference initialization path. Done means the reproduction initializes a DeepSpeed GPT-J InferenceEngine without the view-size error.