mirror of
https://github.com/vladmandic/automatic
synced 2026-08-31 17:41:06 +02:00
Seunghoon Lee
d4aa840a77
DirectML reuses GPU memory instead of returning it. So prints "practical" GPU memory utilization too.
48 lines
1.8 KiB
Python
48 lines
1.8 KiB
Python
import os
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import torch
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from modules.sd_hijack_utils import CondFunc
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do_nothing = lambda: None
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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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mem_bound = os.environ.get("DML_GPU_MEMORY_BOUND", None)
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if mem_bound is not None:
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torch.dml.set_gpu_memory_bound(int(mem_bound))
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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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def directml_override_opts():
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from modules import shared
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if shared.backend == shared.Backend.DIFFUSERS:
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shared.opts.diffusers_generator_device = "cpu" # DirectML does not support torch.Generator API.
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