2023-11-27 13:08:12 +00:00
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import torch
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2024-04-26 17:19:55 +00:00
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2024-04-26 13:48:58 +00:00
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def is_xpu_available():
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try:
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import intel_extension_for_pytorch
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except ImportError:
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return False
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return hasattr(torch, "xpu") and torch.xpu.is_available()
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2024-04-26 17:19:55 +00:00
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2024-05-13 10:44:30 +00:00
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def get_cuda_free_memory(device, memory_fraction):
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total_free_memory, _ = torch.cuda.mem_get_info(device)
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total_gpu_memory = torch.cuda.get_device_properties(device).total_memory
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free_memory = max(0, total_free_memory - (1 - memory_fraction) * total_gpu_memory)
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return free_memory
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2024-05-23 12:11:08 +00:00
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def get_xpu_free_memory(device, memory_fraction):
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2024-05-13 10:44:30 +00:00
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total_gpu_memory = torch.xpu.get_device_properties(device).total_memory
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free_memory = int(total_gpu_memory * 0.5)
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return free_memory
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SYSTEM = None
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if torch.version.hip is not None:
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SYSTEM = "rocm"
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empty_cache = torch.cuda.empty_cache
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synchronize = torch.cuda.synchronize
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get_free_memory = get_cuda_free_memory
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elif torch.version.cuda is not None and torch.cuda.is_available():
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SYSTEM = "cuda"
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empty_cache = torch.cuda.empty_cache
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synchronize = torch.cuda.synchronize
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get_free_memory = get_cuda_free_memory
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elif is_xpu_available():
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SYSTEM = "xpu"
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empty_cache = torch.xpu.empty_cache
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synchronize = torch.xpu.synchronize
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get_free_memory = get_xpu_free_memory
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else:
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SYSTEM = "cpu"
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def noop(*args, **kwargs):
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pass
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empty_cache = noop
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synchronize = noop
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get_free_memory = noop
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