[BUG] Inference predictions dont match Huggingface for GPT-J

Describe the bug

hf_output [{'generated_text': 'Try without sampling the data.\n\nA:\n\nYou can use the following code to get the data from the database.\n$sql = "SELECT * FROM `table`";\n$result = mysqli_query($conn,'}]

ds output [{'generated_text': 'Try without sampling the ( � hub ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ('}]

To Reproduce Steps to reproduce the behavior:

import torch
from transformers import pipeline
import deepspeed

query_text = "Try without sampling"
from transformers import GPTJForCausalLM
model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B",
                revision="float16",
                torch_dtype=torch.float16,
                low_cpu_mem_usage=True)

pipe = pipeline("text-generation", model=model, tokenizer="EleutherAI/gpt-j-6B", device=0, framework="pt")

pipe.model.half()

hf_output = pipe(query_text, do_sample=False)

pipe.model = deepspeed.init_inference(
    pipe.model,
    mp_size=1,
    dtype=torch.half,
    replace_method="auto",
    replace_with_kernel_inject=True,
)

ds_output = pipe(query_text, do_sample=False)

print('HUGGINGFACE:', hf_output[0])
print('DEEPSPEED:', ds_output[0])

Expected behavior Output predictions match HF predictions

ds_report output

oot@2f0b3a15b3d0:/fsx/huilgolr/inference/rubik# ds_report
--------------------------------------------------
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]
sparse_attn ............ [NO] ....... [OKAY]
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-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]
utils .................. [NO] ....... [OKAY]
quantizer .............. [NO] ....... [OKAY]
transformer_inference .. [NO] ....... [OKAY]
--------------------------------------------------
DeepSpeed general environment info:
torch install path ............... ['/opt/conda/lib/python3.8/site-packages/torch']
torch version .................... 1.11.0+cu113
torch cuda version ............... 11.3
torch hip version ................ None
nvcc version ..................... 11.3
deepspeed install path ........... ['/deepspeed']
deepspeed info ................... 0.7.1+8b2a6371, 8b2a6371, master
deepspeed wheel compiled w. ...... torch 1.11, cuda 11.3

Screenshots If applicable, add screenshots to help explain your problem. Screenshots NA

System info (please complete the following information):

OS: [e.g. Ubuntu 18.04] Ubuntu
GPU count and types A100 GPU
Interconnects (if applicable) [e.g., two machines connected with 100 Gbps IB] N/A
Python version 3.8.3
Any other relevant info about your setup

Launcher context inference, single process

[rahul003]

Created a separate issue for the low_cpu_mem_usage flag issue. https://github.com/microsoft/DeepSpeed/issues/2275

Let’s use this for tracking the multi gpu correctness for GPT-J seen above

[AlexWortega]

HUGGINGFACE: [{‘generated_text’: “Try without sampling.\n\nI’m not sure if I’m doing it right.\n\nI’m not sure if I’m doing it right.\n\nI’m not sure if I’m doing it right.\n\nI’m not sure if”}]

with kernels [{‘generated_text’: “Try without sampling.\n\nI’m not sure if I’m doing it right.\n\n.\n\n.\n\n.\n\n.\n\n.\n\n as”}] without [{‘generated_text’: “Try without sampling.\n\nI’m not sure if I’m doing it right.\n\nI’m not sure if I’m doing it right.\n\nI’m not sure if I’m doing it right.\n\nI’m not sure if”}]