[BUG] DeepSpeedDiffusersTransformerBlock doesn't support int8 forward

Describe the bug
A clear and concise description of what the bug is.

After diving within the codebase, I found that DeepSpeedDiffusersTransformerBlock supports int8 inference: https://github.com/microsoft/DeepSpeed/blob/master/deepspeed/module_inject/replace_module.py#L241

I replaced those lines by:

    def replace_attn_block(child, policy):
        config = Diffusers2DTransformerConfig(int8_quantization=False)
        return DeepSpeedDiffusersTransformerBlock(child, config)

resulting the following error:

nn.functional.linear(out_norm_3, self.ff1_w)
*** RuntimeError: expected scalar type Half but found Char

I wondered if this meant to work as I couldn't find tests related to it?

Here is my benchmarking script.

import os
import diffusers
import torch
import deepspeed
import argparse
from pytorch_lightning import seed_everything

def benchmark_fn(iters: int, warm_up_iters: int, function, *args, **kwargs) -> float:
    """
    Function for benchmarking a pytorch function.

    Parameters
    ----------
    iters: int
        Number of iterations.
    function: lambda function
        function to benchmark.
    args: Any type
        Args to function.
    Returns
    -------
    float
        Runtime per iteration in ms.
    """
    import torch

    results = []

    # Warm up
    for _ in range(warm_up_iters):
        function(*args, **kwargs)

    # Start benchmark.
    torch.cuda.synchronize()
    start_event = torch.cuda.Event(enable_timing=True)
    end_event = torch.cuda.Event(enable_timing=True)
    start_event.record()
    for _ in range(iters):
        results.append(function(*args, **kwargs))
    end_event.record()
    torch.cuda.synchronize()
    # in ms
    return (start_event.elapsed_time(end_event)) / iters, results


hf_auth_key = os.getenv("HF_AUTH_KEY")
if not hf_auth_key:
    raise ValueError("HF_AUTH_KEY is not set")

pipe = diffusers.StableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    use_auth_token=hf_auth_key,
    torch_dtype=torch.float16,
    revision="fp16")

pipe = deepspeed.init_inference(pipe.to("cuda"), dtype=torch.float16)

parser = argparse.ArgumentParser()

parser.add_argument(
    "--prompt",
    type=str,
    nargs="?",
    default="astronaut riding a horse, digital art, epic lighting, highly-detailed masterpiece trending HQ",
    help="the prompt to render"
)

parser.add_argument(
    "--init-img",
    type=str,
    nargs="?",
    help="path to the input image"
)
parser.add_argument(
    "--seed",
    type=int,
    default=42,
    help="the seed (for reproducible sampling)",
)
parser.add_argument(
    "--outdir",
    type=str,
    nargs="?",
    help="dir to write results to",
    default="./outputs",
)
opt = parser.parse_args()
os.makedirs(opt.outdir, exist_ok=True)
seed_everything(opt.seed)

t, results = benchmark_fn(10, 5, pipe, prompt=[opt.prompt])
print(t)

grid_count = len(os.listdir(opt.outdir)) - 1

for result in results:
    for image in result.images:
        image.save(os.path.join(opt.outdir, f'grid-hf-{grid_count:04}.png'))
        grid_count += 1

To Reproduce
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1. Simple inference script to reproduce
2. What packages are required and their versions
3. How to run the script
4. ...

Expected behavior
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ds_report output
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System info (please complete the following information):

• OS: [e.g. Ubuntu 18.04]
• GPU count and types [e.g. two machines with x8 A100s each]
• (if applicable) what DeepSpeed-MII version are you using
• (if applicable) Hugging Face Transformers/Accelerate/etc. versions
• Python version
• Any other relevant info about your setup

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Additional context
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Start in deepspeed/module_inject/replace_module.py around the DeepSpeedDiffusersTransformerBlock replacement at line 241, then inspect the block's int8 forward path. Run the provided Stable Diffusion benchmark with the reported configuration and verify that inference no longer fails with the Half-versus-Char type mismatch.