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| from functools import wraps | |
| from typing import Any, Dict, Iterable, Optional, Tuple, TypeVar | |
| import torch | |
| T = TypeVar("T", bound=callable) | |
| ref_map = { | |
| torch.float64: [torch.float16,torch.float32,torch.bfloat16,torch.half], | |
| torch.float32: [torch.float16,torch.bfloat16,torch.half], | |
| torch.float16: [], | |
| torch.bfloat16: [], |
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| import numpy as np | |
| import torch | |
| from torchvision.transforms import functional as F | |
| from PIL import Image | |
| image_path = "./117.webp" | |
| num_images = 32 | |
| total_rot = 360 | |
| rot_step = total_rot / num_images |
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| from typing import List, Any | |
| import enum | |
| from cuda import cudart | |
| CUDART_VERSION = 12020 | |
| CUDA_EGL_MAX_PLANES = 3 | |
| CUDA_IPC_HANDLE_SIZE = 64 |
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| import torch | |
| torch.set_printoptions(precision=4, sci_mode=False) | |
| import triton | |
| import triton.language as tl | |
| from torch import Tensor | |
| def quanitze_fp8_tensorwise(x: torch.Tensor, dtype=torch.float8_e4m3fn): | |
| scale = x.abs().max() / torch.finfo(dtype).max |
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