mirror of https://github.com/vladmandic/automatic
263 lines
9.3 KiB
Python
263 lines
9.3 KiB
Python
import gc
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import sys
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import contextlib
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import torch
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from modules import cmd_args, shared, memstats
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if sys.platform == "darwin":
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from modules import mac_specific # pylint: disable=ungrouped-imports
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previous_oom = 0
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def has_mps() -> bool:
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if sys.platform != "darwin":
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return False
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else:
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return mac_specific.has_mps
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def extract_device_id(args, name): # pylint: disable=redefined-outer-name
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for x in range(len(args)):
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if name in args[x]:
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return args[x + 1]
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return None
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def get_cuda_device_string():
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if backend == 'ipex':
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if shared.cmd_opts.device_id is not None:
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return f"xpu:{shared.cmd_opts.device_id}"
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return "xpu"
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elif backend == 'directml' and torch.dml.is_available():
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if shared.cmd_opts.device_id is not None:
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return f"privateuseone:{shared.cmd_opts.device_id}"
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return torch.dml.get_device_string(torch.dml.default_device().index)
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else:
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if shared.cmd_opts.device_id is not None:
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return f"cuda:{shared.cmd_opts.device_id}"
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return "cuda"
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def get_optimal_device_name():
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if cuda_ok or backend == 'directml':
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return get_cuda_device_string()
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if has_mps():
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return "mps"
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return "cpu"
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def get_optimal_device():
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return torch.device(get_optimal_device_name())
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def get_device_for(task):
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if task in shared.cmd_opts.use_cpu:
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shared.log.debug(f'Forcing CPU for task: {task}')
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return cpu
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return get_optimal_device()
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def torch_gc(force=False):
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mem = memstats.memory_stats()
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gpu = mem.get('gpu', {})
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oom = gpu.get('oom', 0)
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used = round(100 * gpu.get('used', 0) / gpu.get('total', 1))
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global previous_oom # pylint: disable=global-statement
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if oom > previous_oom:
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previous_oom = oom
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shared.log.warning(f'GPU out-of-memory error: {mem}')
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if used > 95:
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shared.log.info(f'GPU high memory utilization: {used}% {mem}')
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force = True
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if backend == "directml":
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practical_used = round(100 * torch.cuda.memory_allocated() / (1 << 30) / gpu.get('total', 1))
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shared.log.info(f'Practical GPU memory utilization: {practical_used}%')
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if shared.opts.disable_gc and not force:
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return
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collected = gc.collect()
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if cuda_ok:
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try:
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with torch.cuda.device(get_cuda_device_string()):
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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except Exception:
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pass
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shared.log.debug(f'gc: collected={collected} device={torch.device(get_optimal_device_name())} {memstats.memory_stats()}')
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def test_fp16():
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if shared.cmd_opts.experimental:
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return True
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try:
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x = torch.tensor([[1.5,.0,.0,.0]]).to(device).half()
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layerNorm = torch.nn.LayerNorm(4, eps=0.00001, elementwise_affine=True, dtype=torch.float16, device=device)
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_y = layerNorm(x)
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shared.log.debug('Torch FP16 test passed')
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return True
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except Exception as e:
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shared.log.warning(f'Torch FP16 test failed: Forcing FP32 operations: {e}')
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shared.opts.cuda_dtype = 'FP32'
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shared.opts.no_half = True
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shared.opts.no_half_vae = True
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return False
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def test_bf16():
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if shared.cmd_opts.experimental:
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return True
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try:
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import torch.nn.functional as F
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image = torch.randn(1, 4, 32, 32).to(device=device, dtype=torch.bfloat16)
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_out = F.interpolate(image, size=(64, 64), mode="nearest")
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return True
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except Exception:
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shared.log.warning('Torch BF16 test failed: Fallback to FP16 operations')
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return False
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def set_cuda_params():
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shared.log.debug('Verifying Torch settings')
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if cuda_ok:
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try:
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = True
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torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = True
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except Exception:
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pass
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if torch.backends.cudnn.is_available():
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try:
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torch.backends.cudnn.benchmark = True
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if shared.opts.cudnn_benchmark:
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shared.log.debug('Torch enable cuDNN benchmark')
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torch.backends.cudnn.benchmark_limit = 0
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torch.backends.cudnn.allow_tf32 = True
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except Exception:
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pass
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global dtype, dtype_vae, dtype_unet, unet_needs_upcast # pylint: disable=global-statement
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if shared.opts.cuda_dtype == 'FP32':
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dtype = torch.float32
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dtype_vae = torch.float32
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dtype_unet = torch.float32
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if shared.opts.cuda_dtype == 'BF16' or dtype == torch.bfloat16:
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bf16_ok = test_bf16()
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dtype = torch.bfloat16 if bf16_ok else torch.float16
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dtype_vae = torch.bfloat16 if bf16_ok else torch.float16
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dtype_unet = torch.bfloat16 if bf16_ok else torch.float16
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if shared.opts.cuda_dtype == 'FP16' or dtype == torch.float16:
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fp16_ok = test_fp16()
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dtype = torch.float16 if fp16_ok else torch.float32
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dtype_vae = torch.float16 if fp16_ok else torch.float32
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dtype_unet = torch.float16 if fp16_ok else torch.float32
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else:
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pass
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if shared.opts.no_half:
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shared.log.info('Torch override dtype: no-half set')
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dtype = torch.float32
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dtype_vae = torch.float32
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dtype_unet = torch.float32
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if shared.opts.no_half_vae: # set dtype again as no-half-vae options take priority
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shared.log.info('Torch override VAE dtype: no-half set')
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dtype_vae = torch.float32
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unet_needs_upcast = shared.opts.upcast_sampling
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shared.log.debug(f'Desired Torch parameters: dtype={shared.opts.cuda_dtype} no-half={shared.opts.no_half} no-half-vae={shared.opts.no_half_vae} upscast={shared.opts.upcast_sampling}')
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shared.log.info(f'Setting Torch parameters: dtype={dtype} vae={dtype_vae} unet={dtype_unet}')
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shared.log.debug(f'Torch default device: {torch.device(get_optimal_device_name())}')
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args = cmd_args.parser.parse_args()
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if args.use_ipex or (hasattr(torch, 'xpu') and torch.xpu.is_available()):
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backend = 'ipex'
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from modules.intel.ipex import ipex_init
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ipex_init()
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elif args.use_directml:
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backend = 'directml'
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from modules.dml import directml_init
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directml_init()
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elif torch.cuda.is_available() and torch.version.cuda:
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backend = 'cuda'
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elif torch.cuda.is_available() and torch.version.hip:
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backend = 'rocm'
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elif sys.platform == 'darwin':
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backend = 'mps'
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else:
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backend = 'cpu'
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cuda_ok = torch.cuda.is_available()
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cpu = torch.device("cpu")
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device = device_interrogate = device_gfpgan = device_esrgan = device_codeformer = None
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dtype = torch.float16
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dtype_vae = torch.float16
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dtype_unet = torch.float16
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unet_needs_upcast = False
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def cond_cast_unet(tensor):
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return tensor.to(dtype_unet) if unet_needs_upcast else tensor
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def cond_cast_float(tensor):
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return tensor.float() if unet_needs_upcast else tensor
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def randn(seed, shape):
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torch.manual_seed(seed)
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if backend == 'ipex':
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torch.xpu.manual_seed_all(seed)
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if device.type == 'mps':
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return torch.randn(shape, device=cpu).to(device)
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return torch.randn(shape, device=device)
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def randn_without_seed(shape):
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if device.type == 'mps':
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return torch.randn(shape, device=cpu).to(device)
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return torch.randn(shape, device=device)
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def autocast(disable=False):
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if disable:
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return contextlib.nullcontext()
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if dtype == torch.float32 or shared.cmd_opts.precision == "Full":
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return contextlib.nullcontext()
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if shared.cmd_opts.use_directml:
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return torch.dml.amp.autocast(dtype)
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if cuda_ok:
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return torch.autocast("cuda")
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else:
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return torch.autocast("cpu")
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def without_autocast(disable=False):
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if disable:
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return contextlib.nullcontext()
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if shared.cmd_opts.use_directml:
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return torch.dml.amp.autocast(enabled=False) if torch.is_autocast_enabled() else contextlib.nullcontext() # pylint: disable=unexpected-keyword-arg
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if cuda_ok:
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return torch.autocast("cuda", enabled=False) if torch.is_autocast_enabled() else contextlib.nullcontext()
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else:
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return torch.autocast("cpu", enabled=False) if torch.is_autocast_enabled() else contextlib.nullcontext()
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class NansException(Exception):
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pass
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def test_for_nans(x, where):
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if shared.opts.disable_nan_check:
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return
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if not torch.all(torch.isnan(x)).item():
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return
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if where == "unet":
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message = "A tensor with all NaNs was produced in Unet."
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if not shared.opts.no_half:
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message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try setting the \"Upcast cross attention layer to float32\" option in Settings > Stable Diffusion or using the --no-half commandline argument to fix this."
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elif where == "vae":
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message = "A tensor with all NaNs was produced in VAE."
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if not shared.opts.no_half and not shared.opts.no_half_vae:
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message += " This could be because there's not enough precision to represent the picture. Try adding --no-half-vae commandline argument to fix this."
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else:
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message = "A tensor with all NaNs was produced."
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message += " Use --disable-nan-check commandline argument to disable this check."
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raise NansException(message)
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