Add QKV fusion to the Hunyuan Video transformer

[Ednaordinary]

What does this PR do?

This adds QKV fusion to Hunyuan Video. At the moment, this gives minimal/no improvement:

	QKV	No QKV
Time (sec)	522.18	547.21
VRAM (GiB)	4.17	3.88

The biggest improvement is expected in combination with torchao, though that currently errors out due to torchao tensors not having the ability to be concatenated:

NotImplementedError: AffineQuantizedTensor dispatch: attempting to run unimplemented operator/function: func=<OpOverload(op='aten.cat', overload='default')>, types=(<class 'torchao.dtypes.affine_quantized_tensor.AffineQuantizedTensor'>,), arg_types=(<class 'list'>,), kwarg_types={}

BitsAndBytes also errors out (relevant but somewhat dated discussion):

RuntimeError: Only Tensors of floating point and complex dtype can require gradients

There's a slight hack in HunyuanVideoIndividualTokenRefinerBlock, since attention with qkv fusion seems to become tuple(tensor, None) instead of just a tensor

Reproducible script

import torch
from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel
import imageio as iio
import math
import numpy as np
import io
import time

quant_div = 3
quant_mod = 3
full_dtype = torch.bfloat16
cast_dtype = torch.float8_e4m3fn

torch.manual_seed(42)

def export_to_video_bytes(fps, frames):
    request = iio.core.Request("<bytes>", mode="w", extension=".mp4")
    pyavobject = iio.plugins.pyav.PyAVPlugin(request)
    if isinstance(frames, np.ndarray):
        frames = (np.array(frames) * 255).astype('uint8')
    else:
        frames = np.array(frames)
    new_bytes = pyavobject.write(frames, codec="libx264", fps=fps)
    out_bytes = io.BytesIO(new_bytes)
    return out_bytes

def export_to_video(frames, path, fps):
    video_bytes = export_to_video_bytes(fps, frames)
    video_bytes.seek(0)
    with open(path, "wb") as f:
        f.write(video_bytes.getbuffer())

model_id = "tencent/HunyuanVideo"

print("Loading transformer")
transformer = HunyuanVideoTransformer3DModel.from_pretrained(
    model_id, subfolder="transformer", torch_dtype=torch.bfloat16, revision="refs/pr/18"
)
transformer.fuse_qkv_projections()

pipe = HunyuanVideoPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=torch.float16, revision="refs/pr/18")
pipe.scheduler._shift = 7.0
pipe.vae.enable_tiling()
#pipe.enable_model_cpu_offload()
pipe.enable_sequential_cpu_offload()

start_time = time.perf_counter()
output = pipe(
    prompt="a cat walks along the sidewalk of a city. The camera follows the cat at knee level. The city has many people and cars moving around, with advertisement billboards in the background",
    #height=544, #544,
    #width=960, #960,
    height = 544,
    width=960,
    num_frames=45,
    num_inference_steps=20,
).frames[0]
export_to_video(output, "output.mp4", fps=15)
print("Time:", round(time.perf_counter() - start_time, 2), "seconds")
print("Max vram:", round(torch.cuda.max_memory_allocated(device="cuda") / 1024 ** 3, 3), "GiB")

Comparison

QKV fusion:

output_qkv.mp4

No fusion:

output.mp4

Results are different but comparable.

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Who can review?

@a-r-r-o-w @DN6

[a-r-r-o-w]

In my experience, QKV fusion does not really help much with eiter time or memory requirements, even with quantization. In fact, there is even slow downs at times depending on the quantization technique applied.

Not sure if it would be beneficial adding but since we do support it for some other things, it makes sense to do so in the interest of consistency. Will ask @yiyixuxu to make the final call