Add fp16 support of Qwen1.5MoE models (A2.7B) to DeepSpeed-FastGen

[ZonePG]

This PR adds support for Qwen1.5MoE-A2.7B models.

support for https://github.com/microsoft/DeepSpeed-MII/issues/457

Test Code

for mii pipeline:

import mii

pipe = mii.pipeline("/data/zonepg/models/Qwen/Qwen1.5-MoE-A2.7B")
responses = pipe("DeepSpeed is", max_new_tokens=128, do_sample=False)
if pipe.is_rank_0:
    print(responses[0])

for huggingface:

import mii

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
import torch
tokenizer = AutoTokenizer.from_pretrained("/data/zonepg/models/Qwen/Qwen1.5-MoE-A2.7B")
model = AutoModelForCausalLM.from_pretrained("/data/zonepg/models/Qwen/Qwen1.5-MoE-A2.7B", device_map="auto", torch_dtype=torch.float16, trust_remote_code=True).eval()
print(model)
inputs = tokenizer('DeepSpeed is', return_tensors='pt')
inputs = inputs.to(model.device)
pred = model.generate(**inputs, max_new_tokens=128, do_sample=False, repetition_penalty=1.0)
test = tokenizer.decode(pred.cpu()[0], skip_special_tokens=False)
print(test)

Qwen1.5-MoE-A2.7B

Huggingface output with prompt "DeepSpeed is":

 a deep learning framework that is designed to accelerate the training of large-scale neural networks. It is built on top of PyTorch and provides a set of tools and techniques for optimizing the performance of deep learning models.

DeepSpeed supports a variety of hardware accelerators, including GPUs, TPUs, and FPGAs, and can be used to train models on distributed systems, such as clusters of GPUs or TPUs.

One of the key features of DeepSpeed is its ability to automatically parallelize the training of deep learning models across multiple GPUs or TPUs. This can significantly reduce the time required to train large models, as it allows the

DeepSpeed-FastGen output with prompt "DeepSpeed is":

 a deep learning framework that is designed to accelerate the training of large-scale neural networks. It is built on top of PyTorch and provides a set of tools and techniques for optimizing the performance of deep learning models.

DeepSpeed supports a variety of hardware accelerators, including GPUs, TPUs, and FPGAs, and can be used to train models on distributed systems, such as clusters of GPUs or TPUs.

One of the key features of DeepSpeed is its ability to automatically parallelize the training of deep learning models across multiple GPUs or TPUs. This can significantly reduce the time required to train large models, as it allows the

DeepSpeed-FastGen output with prompt "DeepSpeed is" with 8-way sharding:

 a deep learning framework that is designed to accelerate the training of large-scale neural networks. It is built on top of PyTorch and provides a set of tools and techniques for optimizing the performance of deep learning models.

DeepSpeed supports a variety of hardware accelerators, including GPUs, TPUs, and FPGAs, and can be used to train models on distributed systems, such as clusters of GPUs or TPUs.

One of the key features of DeepSpeed is its ability to automatically parallelize the training of deep learning models across multiple GPUs or TPUs. This can significantly reduce the time required to train large models, as it allows the