Update tortoise/utils/devices.py vram issue
Added line 85 to set the name variable as it was 'None' causing vram to be incorrect
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aJoe
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815ae5d707
commit
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@ -1,127 +1,128 @@
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import torch
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import psutil
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import importlib
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DEVICE_OVERRIDE = None
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DEVICE_BATCH_SIZE_MAP = [(14, 16), (10,8), (7,4)]
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from inspect import currentframe, getframeinfo
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import gc
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def do_gc():
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gc.collect()
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try:
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torch.cuda.empty_cache()
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except Exception as e:
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pass
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def print_stats(collect=False):
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cf = currentframe().f_back
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msg = f'{getframeinfo(cf).filename}:{cf.f_lineno}'
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if collect:
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do_gc()
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tot = torch.cuda.get_device_properties(0).total_memory / (1024 ** 3)
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res = torch.cuda.memory_reserved(0) / (1024 ** 3)
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alloc = torch.cuda.memory_allocated(0) / (1024 ** 3)
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print("[{}] Total: {:.3f} | Reserved: {:.3f} | Allocated: {:.3f} | Free: {:.3f}".format( msg, tot, res, alloc, tot-res ))
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def has_dml():
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loader = importlib.find_loader('torch_directml')
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if loader is None:
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return False
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import torch_directml
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return torch_directml.is_available()
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def set_device_name(name):
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global DEVICE_OVERRIDE
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DEVICE_OVERRIDE = name
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def get_device_name(attempt_gc=True):
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global DEVICE_OVERRIDE
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if DEVICE_OVERRIDE is not None and DEVICE_OVERRIDE != "":
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return DEVICE_OVERRIDE
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name = 'cpu'
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if torch.cuda.is_available():
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name = 'cuda'
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if attempt_gc:
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torch.cuda.empty_cache() # may have performance implications
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elif has_dml():
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name = 'dml'
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return name
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def get_device(verbose=False):
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name = get_device_name()
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if verbose:
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if name == 'cpu':
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print("No hardware acceleration is available, falling back to CPU...")
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else:
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print(f"Hardware acceleration found: {name}")
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if name == "dml":
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import torch_directml
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return torch_directml.device()
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return torch.device(name)
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def get_device_vram( name=get_device_name() ):
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available = 1
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if name == "cuda":
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_, available = torch.cuda.mem_get_info()
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elif name == "cpu":
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available = psutil.virtual_memory()[4]
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return available / (1024 ** 3)
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def get_device_batch_size(name=None):
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vram = get_device_vram(name)
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if vram > 14:
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return 16
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elif vram > 10:
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return 8
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elif vram > 7:
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return 4
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"""
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for k, v in DEVICE_BATCH_SIZE_MAP:
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if vram > k:
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return v
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"""
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return 1
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def get_device_count(name=get_device_name()):
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if name == "cuda":
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return torch.cuda.device_count()
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if name == "dml":
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import torch_directml
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return torch_directml.device_count()
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return 1
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if has_dml():
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_cumsum = torch.cumsum
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_repeat_interleave = torch.repeat_interleave
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_multinomial = torch.multinomial
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_Tensor_new = torch.Tensor.new
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_Tensor_cumsum = torch.Tensor.cumsum
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_Tensor_repeat_interleave = torch.Tensor.repeat_interleave
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_Tensor_multinomial = torch.Tensor.multinomial
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torch.cumsum = lambda input, *args, **kwargs: ( _cumsum(input.to("cpu"), *args, **kwargs).to(input.device) )
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torch.repeat_interleave = lambda input, *args, **kwargs: ( _repeat_interleave(input.to("cpu"), *args, **kwargs).to(input.device) )
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torch.multinomial = lambda input, *args, **kwargs: ( _multinomial(input.to("cpu"), *args, **kwargs).to(input.device) )
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torch.Tensor.new = lambda self, *args, **kwargs: ( _Tensor_new(self.to("cpu"), *args, **kwargs).to(self.device) )
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torch.Tensor.cumsum = lambda self, *args, **kwargs: ( _Tensor_cumsum(self.to("cpu"), *args, **kwargs).to(self.device) )
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torch.Tensor.repeat_interleave = lambda self, *args, **kwargs: ( _Tensor_repeat_interleave(self.to("cpu"), *args, **kwargs).to(self.device) )
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import torch
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import psutil
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import importlib
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DEVICE_OVERRIDE = None
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DEVICE_BATCH_SIZE_MAP = [(14, 16), (10,8), (7,4)]
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from inspect import currentframe, getframeinfo
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import gc
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def do_gc():
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gc.collect()
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try:
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torch.cuda.empty_cache()
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except Exception as e:
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pass
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def print_stats(collect=False):
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cf = currentframe().f_back
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msg = f'{getframeinfo(cf).filename}:{cf.f_lineno}'
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if collect:
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do_gc()
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tot = torch.cuda.get_device_properties(0).total_memory / (1024 ** 3)
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res = torch.cuda.memory_reserved(0) / (1024 ** 3)
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alloc = torch.cuda.memory_allocated(0) / (1024 ** 3)
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print("[{}] Total: {:.3f} | Reserved: {:.3f} | Allocated: {:.3f} | Free: {:.3f}".format( msg, tot, res, alloc, tot-res ))
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def has_dml():
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loader = importlib.find_loader('torch_directml')
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if loader is None:
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return False
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import torch_directml
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return torch_directml.is_available()
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def set_device_name(name):
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global DEVICE_OVERRIDE
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DEVICE_OVERRIDE = name
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def get_device_name(attempt_gc=True):
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global DEVICE_OVERRIDE
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if DEVICE_OVERRIDE is not None and DEVICE_OVERRIDE != "":
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return DEVICE_OVERRIDE
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name = 'cpu'
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if torch.cuda.is_available():
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name = 'cuda'
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if attempt_gc:
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torch.cuda.empty_cache() # may have performance implications
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elif has_dml():
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name = 'dml'
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return name
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def get_device(verbose=False):
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name = get_device_name()
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if verbose:
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if name == 'cpu':
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print("No hardware acceleration is available, falling back to CPU...")
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else:
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print(f"Hardware acceleration found: {name}")
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if name == "dml":
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import torch_directml
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return torch_directml.device()
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return torch.device(name)
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def get_device_vram( name=get_device_name() ):
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available = 1
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if name == "cuda":
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_, available = torch.cuda.mem_get_info()
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elif name == "cpu":
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available = psutil.virtual_memory()[4]
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return available / (1024 ** 3)
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def get_device_batch_size(name=None):
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name = get_device_name()
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vram = get_device_vram(name)
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if vram > 14:
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return 16
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elif vram > 10:
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return 8
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elif vram > 7:
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return 4
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"""
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for k, v in DEVICE_BATCH_SIZE_MAP:
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if vram > k:
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return v
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"""
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return 1
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def get_device_count(name=get_device_name()):
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if name == "cuda":
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return torch.cuda.device_count()
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if name == "dml":
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import torch_directml
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return torch_directml.device_count()
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return 1
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if has_dml():
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_cumsum = torch.cumsum
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_repeat_interleave = torch.repeat_interleave
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_multinomial = torch.multinomial
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_Tensor_new = torch.Tensor.new
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_Tensor_cumsum = torch.Tensor.cumsum
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_Tensor_repeat_interleave = torch.Tensor.repeat_interleave
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_Tensor_multinomial = torch.Tensor.multinomial
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torch.cumsum = lambda input, *args, **kwargs: ( _cumsum(input.to("cpu"), *args, **kwargs).to(input.device) )
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torch.repeat_interleave = lambda input, *args, **kwargs: ( _repeat_interleave(input.to("cpu"), *args, **kwargs).to(input.device) )
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torch.multinomial = lambda input, *args, **kwargs: ( _multinomial(input.to("cpu"), *args, **kwargs).to(input.device) )
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torch.Tensor.new = lambda self, *args, **kwargs: ( _Tensor_new(self.to("cpu"), *args, **kwargs).to(self.device) )
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torch.Tensor.cumsum = lambda self, *args, **kwargs: ( _Tensor_cumsum(self.to("cpu"), *args, **kwargs).to(self.device) )
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torch.Tensor.repeat_interleave = lambda self, *args, **kwargs: ( _Tensor_repeat_interleave(self.to("cpu"), *args, **kwargs).to(self.device) )
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torch.Tensor.multinomial = lambda self, *args, **kwargs: ( _Tensor_multinomial(self.to("cpu"), *args, **kwargs).to(self.device) )
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