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# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
PyTorch utilities: Utilities related to PyTorch
"""
from __future__ import annotations
import functools
import os
from typing import Callable, ParamSpec, TypeVar
from . import logging
from .import_utils import is_torch_available, is_torch_mlu_available, is_torch_npu_available, is_torch_version
T = TypeVar("T")
P = ParamSpec("P")
if is_torch_available():
import torch
from torch.fft import fftn, fftshift, ifftn, ifftshift
BACKEND_SUPPORTS_TRAINING = {"cuda": True, "xpu": True, "cpu": True, "mps": False, "default": True}
BACKEND_EMPTY_CACHE = {
"cuda": torch.cuda.empty_cache,
"xpu": torch.xpu.empty_cache,
"cpu": None,
"mps": torch.mps.empty_cache,
"default": None,
}
BACKEND_DEVICE_COUNT = {
"cuda": torch.cuda.device_count,
"xpu": torch.xpu.device_count,
"cpu": lambda: 0,
"mps": lambda: 0,
"default": 0,
}
BACKEND_MANUAL_SEED = {
"cuda": torch.cuda.manual_seed,
"xpu": torch.xpu.manual_seed,
"cpu": torch.manual_seed,
"mps": torch.mps.manual_seed,
"default": torch.manual_seed,
}
BACKEND_RESET_PEAK_MEMORY_STATS = {
"cuda": torch.cuda.reset_peak_memory_stats,
"xpu": getattr(torch.xpu, "reset_peak_memory_stats", None),
"cpu": None,
"mps": None,
"default": None,
}
BACKEND_RESET_MAX_MEMORY_ALLOCATED = {
"cuda": torch.cuda.reset_max_memory_allocated,
"xpu": getattr(torch.xpu, "reset_peak_memory_stats", None),
"cpu": None,
"mps": None,
"default": None,
}
BACKEND_MAX_MEMORY_ALLOCATED = {
"cuda": torch.cuda.max_memory_allocated,
"xpu": getattr(torch.xpu, "max_memory_allocated", None),
"cpu": 0,
"mps": 0,
"default": 0,
}
BACKEND_SYNCHRONIZE = {
"cuda": torch.cuda.synchronize,
"xpu": getattr(torch.xpu, "synchronize", None),
"cpu": None,
"mps": None,
"default": None,
}
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
try:
from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):
def maybe_allow_in_graph(cls):
return cls
# This dispatches a defined function according to the accelerator from the function definitions.
def _device_agnostic_dispatch(device: str, dispatch_table: dict[str, callable], *args, **kwargs):
if device not in dispatch_table:
return dispatch_table["default"](*args, **kwargs)
fn = dispatch_table[device]
# Some device agnostic functions return values. Need to guard against 'None' instead at
# user level
if not callable(fn):
return fn
return fn(*args, **kwargs)
# These are callables which automatically dispatch the function specific to the accelerator
def backend_manual_seed(device: str, seed: int):
return _device_agnostic_dispatch(device, BACKEND_MANUAL_SEED, seed)
def backend_synchronize(device: str):
return _device_agnostic_dispatch(device, BACKEND_SYNCHRONIZE)
def backend_empty_cache(device: str):
return _device_agnostic_dispatch(device, BACKEND_EMPTY_CACHE)
def backend_device_count(device: str):
return _device_agnostic_dispatch(device, BACKEND_DEVICE_COUNT)
def backend_reset_peak_memory_stats(device: str):
return _device_agnostic_dispatch(device, BACKEND_RESET_PEAK_MEMORY_STATS)
def backend_reset_max_memory_allocated(device: str):
return _device_agnostic_dispatch(device, BACKEND_RESET_MAX_MEMORY_ALLOCATED)
def backend_max_memory_allocated(device: str):
return _device_agnostic_dispatch(device, BACKEND_MAX_MEMORY_ALLOCATED)
# These are callables which return boolean behaviour flags and can be used to specify some
# device agnostic alternative where the feature is unsupported.
def backend_supports_training(device: str):
if not is_torch_available():
return False
if device not in BACKEND_SUPPORTS_TRAINING:
device = "default"
return BACKEND_SUPPORTS_TRAINING[device]
def randn_tensor(
shape: tuple | list,
generator: list["torch.Generator"] | "torch.Generator" | None = None,
device: str | "torch.device" | None = None,
dtype: "torch.dtype" | None = None,
layout: "torch.layout" | None = None,
):
"""A helper function to create random tensors on the desired `device` with the desired `dtype`. When
passing a list of generators, you can seed each batch size individually. If CPU generators are passed, the tensor
is always created on the CPU.
"""
# device on which tensor is created defaults to device
if isinstance(device, str):
device = torch.device(device)
rand_device = device
batch_size = shape[0]
layout = layout or torch.strided
device = device or torch.device("cpu")
if generator is not None:
gen_device_type = generator.device.type if not isinstance(generator, list) else generator[0].device.type
if gen_device_type != device.type and gen_device_type == "cpu":
rand_device = "cpu"
if device != "mps":
logger.info(
f"The passed generator was created on 'cpu' even though a tensor on {device} was expected."
f" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"
f" slightly speed up this function by passing a generator that was created on the {device} device."
)
elif gen_device_type != device.type and gen_device_type == "cuda":
raise ValueError(f"Cannot generate a {device} tensor from a generator of type {gen_device_type}.")
# make sure generator list of length 1 is treated like a non-list
if isinstance(generator, list) and len(generator) == 1:
generator = generator[0]
if isinstance(generator, list):
shape = (1,) + shape[1:]
latents = [
torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype, layout=layout)
for i in range(batch_size)
]
latents = torch.cat(latents, dim=0).to(device)
else:
latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype, layout=layout).to(device)
return latents
def is_compiled_module(module) -> bool:
"""Check whether the module was compiled with torch.compile()"""
if is_torch_version("<", "2.0.0") or not hasattr(torch, "_dynamo"):
return False
return isinstance(module, torch._dynamo.eval_frame.OptimizedModule)
def unwrap_module(module):
"""Unwraps a module if it was compiled with torch.compile()"""
return module._orig_mod if is_compiled_module(module) else module
def fourier_filter(x_in: "torch.Tensor", threshold: int, scale: int) -> "torch.Tensor":
"""Fourier filter as introduced in FreeU (https://huggingface.co/papers/2309.11497).
This version of the method comes from here:
https://github.com/huggingface/diffusers/pull/5164#issuecomment-1732638706
"""
x = x_in
B, C, H, W = x.shape
# Non-power of 2 images must be float32
if (W & (W - 1)) != 0 or (H & (H - 1)) != 0:
x = x.to(dtype=torch.float32)
# fftn does not support bfloat16, and produces the experimental ComplexHalf
# dtype (torch.complex32) when given float16, which is numerically unstable
# and triggers a UserWarning. Upcast any non-float32 dtype to float32.
elif x.dtype != torch.float32:
x = x.to(dtype=torch.float32)
# FFT
x_freq = fftn(x, dim=(-2, -1))
x_freq = fftshift(x_freq, dim=(-2, -1))
B, C, H, W = x_freq.shape
mask = torch.ones((B, C, H, W), device=x.device)
crow, ccol = H // 2, W // 2
mask[..., crow - threshold : crow + threshold, ccol - threshold : ccol + threshold] = scale
x_freq = x_freq * mask
# IFFT
x_freq = ifftshift(x_freq, dim=(-2, -1))
x_filtered = ifftn(x_freq, dim=(-2, -1)).real
return x_filtered.to(dtype=x_in.dtype)
def apply_freeu(
resolution_idx: int, hidden_states: "torch.Tensor", res_hidden_states: "torch.Tensor", **freeu_kwargs
) -> tuple["torch.Tensor", "torch.Tensor"]:
"""Applies the FreeU mechanism as introduced in https:
//arxiv.org/abs/2309.11497. Adapted from the official code repository: https://github.com/ChenyangSi/FreeU.
Args:
resolution_idx (`int`): Integer denoting the UNet block where FreeU is being applied.
hidden_states (`torch.Tensor`): Inputs to the underlying block.
res_hidden_states (`torch.Tensor`): Features from the skip block corresponding to the underlying block.
s1 (`float`): Scaling factor for stage 1 to attenuate the contributions of the skip features.
s2 (`float`): Scaling factor for stage 2 to attenuate the contributions of the skip features.
b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
"""
if resolution_idx == 0:
num_half_channels = hidden_states.shape[1] // 2
hidden_states[:, :num_half_channels] = hidden_states[:, :num_half_channels] * freeu_kwargs["b1"]
res_hidden_states = fourier_filter(res_hidden_states, threshold=1, scale=freeu_kwargs["s1"])
if resolution_idx == 1:
num_half_channels = hidden_states.shape[1] // 2
hidden_states[:, :num_half_channels] = hidden_states[:, :num_half_channels] * freeu_kwargs["b2"]
res_hidden_states = fourier_filter(res_hidden_states, threshold=1, scale=freeu_kwargs["s2"])
return hidden_states, res_hidden_states
def get_torch_cuda_device_capability():
if torch.cuda.is_available():
device = torch.device("cuda")
compute_capability = torch.cuda.get_device_capability(device)
compute_capability = f"{compute_capability[0]}.{compute_capability[1]}"
return float(compute_capability)
else:
return None
@functools.lru_cache
def get_device():
if torch.cuda.is_available():
return "cuda"
elif is_torch_npu_available():
return "npu"
elif hasattr(torch, "xpu") and torch.xpu.is_available():
return "xpu"
elif torch.backends.mps.is_available():
return "mps"
elif is_torch_mlu_available():
return "mlu"
else:
return "cpu"
def empty_device_cache(device_type: str | None = None):
if device_type is None:
device_type = get_device()
if device_type in ["cpu"]:
return
device_mod = getattr(torch, device_type, torch.cuda)
device_mod.empty_cache()
def device_synchronize(device_type: str | None = None):
if device_type is None:
device_type = get_device()
device_mod = getattr(torch, device_type, torch.cuda)
device_mod.synchronize()
def enable_full_determinism():
"""
Helper function for reproducible behavior during distributed training. See
- https://pytorch.org/docs/stable/notes/randomness.html for pytorch
"""
# Enable PyTorch deterministic mode. This potentially requires either the environment
# variable 'CUDA_LAUNCH_BLOCKING' or 'CUBLAS_WORKSPACE_CONFIG' to be set,
# depending on the CUDA version, so we set them both here
os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
torch.use_deterministic_algorithms(True)
# Enable CUDNN deterministic mode
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.backends.cuda.matmul.allow_tf32 = False
def disable_full_determinism():
os.environ["CUDA_LAUNCH_BLOCKING"] = "0"
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ""
torch.use_deterministic_algorithms(False)
@functools.wraps(functools.lru_cache)
def lru_cache_unless_export(maxsize=128, typed=False):
def outer_wrapper(fn: Callable[P, T]):
cached = functools.lru_cache(maxsize=maxsize, typed=typed)(fn)
if is_torch_version("<", "2.7.0"):
return cached
@functools.wraps(fn)
def inner_wrapper(*args: P.args, **kwargs: P.kwargs):
compiler = getattr(torch, "compiler", None)
is_exporting = bool(compiler and hasattr(compiler, "is_exporting") and compiler.is_exporting())
is_compiling = bool(compiler and hasattr(compiler, "is_compiling") and compiler.is_compiling())
# Fallback for older builds where compiler.is_compiling is unavailable.
if not is_compiling:
dynamo = getattr(torch, "_dynamo", None)
if dynamo is not None and hasattr(dynamo, "is_compiling"):
is_compiling = dynamo.is_compiling()
if is_exporting or is_compiling:
return fn(*args, **kwargs)
return cached(*args, **kwargs)
return inner_wrapper
return outer_wrapper
if is_torch_available():
torch_device = get_device()