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import platform
from typing import NamedTuple, Callable, Optional
import torch
from modules.errors import log
from modules.sd_hijack_utils import CondFunc
memory_providers = ["None", "atiadlxx (AMD only)"]
default_memory_provider = "None"
if platform.system() == "Windows":
memory_providers.append("Performance Counter")
default_memory_provider = "Performance Counter"
do_nothing = lambda: None # pylint: disable=unnecessary-lambda-assignment
do_nothing_with_self = lambda self: None # pylint: disable=unnecessary-lambda-assignment
def _set_memory_provider():
from modules.shared import opts, cmd_opts
if opts.directml_memory_provider == "Performance Counter":
from .backend import pdh_mem_get_info
from .memory import MemoryProvider
torch.dml.mem_get_info = pdh_mem_get_info
if torch.dml.memory_provider is not None:
del torch.dml.memory_provider
torch.dml.memory_provider = MemoryProvider()
elif opts.directml_memory_provider == "atiadlxx (AMD only)":
device_name = torch.dml.get_device_name(cmd_opts.device_id)
if "AMD" not in device_name and "Radeon" not in device_name:
log.warning(f"Memory stats provider is changed to None because the current device is not AMDGPU. Current Device: {device_name}")
opts.directml_memory_provider = "None"
_set_memory_provider()
return
from .backend import amd_mem_get_info
torch.dml.mem_get_info = amd_mem_get_info
else:
from .backend import mem_get_info
torch.dml.mem_get_info = mem_get_info
torch.cuda.mem_get_info = torch.dml.mem_get_info
def directml_init():
try:
from modules.dml.backend import DirectML # pylint: disable=ungrouped-imports
# Alternative of torch.cuda for DirectML.
torch.dml = DirectML
torch.cuda.is_available = lambda: False
torch.cuda.device = torch.dml.device
torch.cuda.device_count = torch.dml.device_count
torch.cuda.current_device = torch.dml.current_device
torch.cuda.get_device_name = torch.dml.get_device_name
torch.cuda.get_device_properties = torch.dml.get_device_properties
torch.cuda.empty_cache = do_nothing
torch.cuda.ipc_collect = do_nothing
torch.cuda.memory_stats = torch.dml.memory_stats
torch.cuda.mem_get_info = torch.dml.mem_get_info
torch.cuda.memory_allocated = torch.dml.memory_allocated
torch.cuda.max_memory_allocated = torch.dml.max_memory_allocated
torch.cuda.reset_peak_memory_stats = torch.dml.reset_peak_memory_stats
torch.cuda.utilization = lambda: 0
torch.Tensor.directml = lambda self: self.to(torch.dml.current_device())
except Exception as e:
log.error(f'DirectML initialization failed: {e}')
return False, e
return True, None
def directml_do_hijack():
import modules.dml.hijack # pylint: disable=unused-import
from modules.devices import device
CondFunc('torch.Generator',
lambda orig_func, device: orig_func("cpu"),
lambda orig_func, device: True)
if not torch.dml.has_float64_support(device):
torch.Tensor.__str__ = do_nothing_with_self
CondFunc('torch.from_numpy',
lambda orig_func, *args, **kwargs: orig_func(args[0].astype('float32')),
lambda *args, **kwargs: args[1].dtype == float)
_set_memory_provider()
class OverrideItem(NamedTuple):
value: str
condition: Optional[Callable]
message: Optional[str]
opts_override_table = {
"diffusers_generator_device": OverrideItem("CPU", None, "DirectML does not support torch Generator API"),
"diffusers_model_cpu_offload": OverrideItem(False, None, "Diffusers model CPU offloading does not support DirectML devices"),
"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"),
}
def directml_override_opts():
from modules import shared
if shared.cmd_opts.experimental:
return
count = 0
for key in opts_override_table:
item = opts_override_table[key]
if getattr(shared.opts, key) != item.value and (item.condition is None or item.condition(shared.opts)):
count += 1
setattr(shared.opts, key, item.value)
shared.log.warning(f'Overriding: {key}={item.value} {item.message if item.message is not None else ""}')
if count > 0:
shared.log.info(f'Options override: count={count}. If you want to keep them from overriding, run with --experimental argument.')
_set_memory_provider()
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