mirror of
https://github.com/vladmandic/automatic
synced 2026-09-05 04:20:44 +02:00
Disty0
93 lines
3.9 KiB
Python
93 lines
3.9 KiB
Python
import os
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import contextlib
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import torch
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import intel_extension_for_pytorch as ipex
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from modules import shared
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from .diffusers import ipex_diffusers
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from .hijacks import ipex_hijacks
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#ControlNet depth_leres++
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class DummyDataParallel(torch.nn.Module):
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def __new__(cls, module, device_ids=None, output_device=None, dim=0):
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if type(device_ids) is list and len(device_ids) > 1:
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shared.log.warning("IPEX backend doesn't support DataParallel on multiple XPU devices")
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return module.to(shared.device)
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def return_null_context(*args, **kwargs):
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return contextlib.nullcontext()
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def ipex_init():
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#Replace cuda with xpu:
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torch.cuda.current_device = torch.xpu.current_device
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torch.cuda.current_stream = torch.xpu.current_stream
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torch.cuda.device = torch.xpu.device
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torch.cuda.device_count = torch.xpu.device_count
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torch.cuda.device_of = torch.xpu.device_of
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torch.cuda.getDeviceIdListForCard = torch.xpu.getDeviceIdListForCard
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torch.cuda.get_device_name = torch.xpu.get_device_name
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torch.cuda.get_device_properties = torch.xpu.get_device_properties
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torch.cuda.init = torch.xpu.init
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torch.cuda.is_available = torch.xpu.is_available
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torch.cuda.is_initialized = torch.xpu.is_initialized
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torch.cuda.set_device = torch.xpu.set_device
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torch.cuda.stream = torch.xpu.stream
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torch.cuda.synchronize = torch.xpu.synchronize
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torch.cuda.Event = torch.xpu.Event
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torch.cuda.Stream = torch.xpu.Stream
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torch.cuda.FloatTensor = torch.xpu.FloatTensor
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torch.Tensor.cuda = torch.Tensor.xpu
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torch.Tensor.is_cuda = torch.Tensor.is_xpu
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#Memory:
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torch.cuda.empty_cache = torch.xpu.empty_cache
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torch.cuda.memory_stats = torch.xpu.memory_stats
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torch.cuda.memory_summary = torch.xpu.memory_summary
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torch.cuda.memory_snapshot = torch.xpu.memory_snapshot
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torch.cuda.memory_allocated = torch.xpu.memory_allocated
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torch.cuda.max_memory_allocated = torch.xpu.max_memory_allocated
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torch.cuda.memory_reserved = torch.xpu.memory_reserved
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torch.cuda.max_memory_reserved = torch.xpu.max_memory_reserved
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torch.cuda.reset_peak_memory_stats = torch.xpu.reset_peak_memory_stats
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torch.cuda.memory_stats_as_nested_dict = torch.xpu.memory_stats_as_nested_dict
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torch.cuda.reset_accumulated_memory_stats = torch.xpu.reset_accumulated_memory_stats
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#RNG:
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torch.cuda.get_rng_state = torch.xpu.get_rng_state
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torch.cuda.get_rng_state_all = torch.xpu.get_rng_state_all
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torch.cuda.set_rng_state = torch.xpu.set_rng_state
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torch.cuda.set_rng_state_all = torch.xpu.set_rng_state_all
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torch.cuda.manual_seed = torch.xpu.manual_seed
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torch.cuda.manual_seed_all = torch.xpu.manual_seed_all
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torch.cuda.seed = torch.xpu.seed
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torch.cuda.seed_all = torch.xpu.seed_all
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torch.cuda.initial_seed = torch.xpu.initial_seed
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#Training:
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try:
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torch.cuda.amp.GradScaler = torch.xpu.amp.GradScaler
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except Exception:
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torch.cuda.amp.GradScaler = ipex.cpu.autocast._grad_scaler.GradScaler
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#C
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torch._C._cuda_getCurrentRawStream = ipex._C._getCurrentStream
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ipex._C._DeviceProperties.major = 2023
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ipex._C._DeviceProperties.minor = 2
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#Fix functions with ipex:
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torch.cuda.mem_get_info = lambda device=None: [(torch.xpu.get_device_properties(device).total_memory - torch.xpu.memory_allocated(device)), torch.xpu.get_device_properties(device).total_memory]
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torch._utils._get_available_device_type = lambda: "xpu" # pylint: disable=protected-access
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torch.xpu.empty_cache = torch.xpu.empty_cache if "WSL2" not in os.popen("uname -a").read() else lambda: None
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torch.cuda.get_device_properties.major = 2023
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torch.cuda.get_device_properties.minor = 2
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torch.backends.cuda.sdp_kernel = return_null_context
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torch.nn.DataParallel = DummyDataParallel
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torch.cuda.ipc_collect = lambda: None
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torch.cuda.utilization = lambda: 0
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ipex_hijacks()
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ipex_diffusers()
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try:
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from .openvino import openvino_fx
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except Exception:
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pass
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