mirror of https://github.com/vladmandic/automatic
103 lines
4.1 KiB
Python
103 lines
4.1 KiB
Python
from platform import system
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import torch
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from typing import NamedTuple, Callable, Optional
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from modules.sd_hijack_utils import CondFunc
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memory_providers = ["None", "atiadlxx (AMD only)"]
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default_memory_provider = "None"
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if system() == "Windows":
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memory_providers.append("Performance Counter")
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default_memory_provider = "Performance Counter"
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do_nothing = lambda: None
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def _set_memory_provider():
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from modules.shared import opts, cmd_opts, log
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if opts.directml_memory_provider == "Performance Counter":
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from .backend import pdh_mem_get_info
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from .memory import MemoryProvider
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torch.dml.mem_get_info = pdh_mem_get_info
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if torch.dml.memory_provider is not None:
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del torch.dml.memory_provider
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torch.dml.memory_provider = MemoryProvider()
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elif opts.directml_memory_provider == "atiadlxx (AMD only)":
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device_name = torch.dml.get_device_name(cmd_opts.device_id)
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if "AMD" not in device_name and "Radeon" not in device_name:
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log.warning(f"Memory stats provider is changed to None because the current device is not AMDGPU. Current Device: {device_name}")
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opts.directml_memory_provider = "None"
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_set_memory_provider()
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return
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from .backend import amd_mem_get_info
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torch.dml.mem_get_info = amd_mem_get_info
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else:
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from .backend import mem_get_info
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torch.dml.mem_get_info = mem_get_info
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torch.cuda.mem_get_info = torch.dml.mem_get_info
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def directml_init():
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from modules.dml.backend import DirectML # pylint: disable=ungrouped-imports
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# Alternative of torch.cuda for DirectML.
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torch.dml = DirectML
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torch.cuda.is_available = lambda: False
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torch.cuda.device = torch.dml.device
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torch.cuda.device_count = torch.dml.device_count
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torch.cuda.current_device = torch.dml.current_device
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torch.cuda.get_device_name = torch.dml.get_device_name
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torch.cuda.get_device_properties = torch.dml.get_device_properties
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torch.cuda.empty_cache = do_nothing
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torch.cuda.ipc_collect = do_nothing
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torch.cuda.memory_stats = torch.dml.memory_stats
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torch.cuda.mem_get_info = torch.dml.mem_get_info
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torch.cuda.memory_allocated = torch.dml.memory_allocated
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torch.cuda.max_memory_allocated = torch.dml.max_memory_allocated
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torch.cuda.reset_peak_memory_stats = torch.dml.reset_peak_memory_stats
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torch.cuda.utilization = lambda: 0
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torch.Tensor.directml = lambda self: self.to(torch.dml.current_device())
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def directml_do_hijack():
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import modules.dml.hijack
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from modules.devices import device
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if not torch.dml.has_float64_support(device):
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CondFunc('torch.from_numpy',
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lambda orig_func, *args, **kwargs: orig_func(args[0].astype('float32')),
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lambda *args, **kwargs: args[1].dtype == float)
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_set_memory_provider()
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class OverrideItem(NamedTuple):
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value: str
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condition: Optional[Callable]
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message: Optional[str]
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opts_override_table = {
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"diffusers_generator_device": OverrideItem("cpu", None, "DirectML does not support torch Generator API."),
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"diffusers_model_cpu_offload": OverrideItem(False, None, "Diffusers' model CPU offloading does not support DirectML devices."),
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"diffusers_seq_cpu_offload": OverrideItem(False, lambda opts: opts.diffusers_pipeline != "Stable Diffusion XL", "Diffusers' sequential CPU offloading is available only on StableDiffusionXLPipeline with DirectML devices."),
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}
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def directml_override_opts():
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from modules import shared
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if shared.cmd_opts.experimental:
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return
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count = 0
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for key in opts_override_table:
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item = opts_override_table[key]
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if getattr(shared.opts, key) != item.value and (item.condition is None or item.condition(shared.opts)):
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count += 1
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setattr(shared.opts, key, item.value)
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if item.message is not None:
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shared.log.warning(item.message)
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shared.log.warning(f'{key} is automatically overriden to {item.value}.')
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if count > 0:
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shared.log.info(f'{count} options are automatically overriden. If you want to keep them from overriding, run with --experimental argument.')
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_set_memory_provider()
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