124 lines
4.3 KiB
Python
124 lines
4.3 KiB
Python
"""CPU offloading wrapper for large models that don't fit in VRAM."""
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import torch
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import torch.nn as nn
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from typing import Any
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class CPUOffloadWrapper(nn.Module):
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"""
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Wraps a model to automatically offload to/from GPU during forward pass.
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This allows running models larger than VRAM by keeping them on CPU
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and only moving the active layers to GPU during computation.
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"""
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def __init__(self, model: nn.Module, device: torch.device):
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"""
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Initialize CPU offload wrapper.
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Args:
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model: The model to wrap (should be on CPU)
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device: Target CUDA device for computation
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"""
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super().__init__()
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self.model = model
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self.device = device
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self.execution_device = device if torch.cuda.is_available() else torch.device('cpu')
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# Keep model on CPU
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if self.model.training:
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self.model.eval()
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def forward(self, *args, **kwargs):
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"""
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Forward pass with automatic CPU offloading.
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Uses sequential execution with layer-by-layer GPU transfer to avoid OOM.
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"""
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# Move input tensors to GPU
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args = tuple(arg.to(self.execution_device) if isinstance(arg, torch.Tensor) else arg for arg in args)
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kwargs = {k: v.to(self.execution_device) if isinstance(v, torch.Tensor) else v for k, v in kwargs.items()}
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# Instead of moving entire model, use hooks to move layers on-demand
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# This is slower but avoids OOM for large models
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try:
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output = self._sequential_forward(*args, **kwargs)
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finally:
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# Ensure model is back on CPU
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if next(self.model.parameters()).device != torch.device('cpu'):
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self.model.to('cpu')
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torch.cuda.empty_cache()
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return output
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def _sequential_forward(self, *args, **kwargs):
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"""
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Run forward pass with CPU execution and tensor-level GPU offloading.
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For models >12GB, keeps model on CPU and only moves intermediate tensors.
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This is slower but works with limited VRAM.
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"""
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import gc
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# Model is too large (>12GB) to fit in VRAM, must run on CPU
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# Keep model on CPU, only inputs/outputs on GPU
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print(f"⚠️ Running model on CPU (model >12GB, VRAM limited)")
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# Ensure model is on CPU
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if next(self.model.parameters()).device != torch.device('cpu'):
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self.model.to('cpu')
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torch.cuda.empty_cache()
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gc.collect()
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# Get model dtype for consistent computation
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model_dtype = next(self.model.parameters()).dtype
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# Move inputs to CPU and convert to model dtype
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def convert_tensor(t):
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if isinstance(t, torch.Tensor):
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return t.to(device='cpu', dtype=model_dtype)
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return t
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args_cpu = tuple(convert_tensor(arg) for arg in args)
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kwargs_cpu = {k: convert_tensor(v) for k, v in kwargs.items()}
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# Run forward pass on CPU
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with torch.inference_mode():
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output = self.model(*args_cpu, **kwargs_cpu)
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# Move output back to GPU for further processing
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if isinstance(output, torch.Tensor):
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output = output.to(self.execution_device)
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elif isinstance(output, (list, tuple)):
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output = type(output)(o.to(self.execution_device) if isinstance(o, torch.Tensor) else o for o in output)
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elif isinstance(output, dict):
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output = {k: v.to(self.execution_device) if isinstance(v, torch.Tensor) else v for k, v in output.items()}
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return output
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def __call__(self, *args, **kwargs):
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"""Allow calling the wrapper like the original model."""
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return self.forward(*args, **kwargs)
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def to(self, device):
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"""Override to() to just update target device without moving model."""
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if str(device).startswith('cuda'):
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self.execution_device = device
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return self
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def cpu(self):
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"""Keep model on CPU."""
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return self
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def cuda(self, device=None):
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"""Update execution device but keep model on CPU."""
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self.execution_device = torch.device(f'cuda:{device}' if device is not None else 'cuda:0')
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return self
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def eval(self):
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"""Set model to eval mode."""
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self.model.eval()
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return self
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