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| """ |
| Utilities to create common models |
| """ |
|
|
| from functools import lru_cache |
| from typing import Optional, Tuple |
|
|
| import torch |
| import torch.distributed as dist |
| from torch import nn |
|
|
|
|
| @lru_cache |
| def is_rank0() -> int: |
| return (not dist.is_initialized()) or (dist.get_rank() == 0) |
|
|
|
|
| def print_gpu_memory_usage(prefix: str = "GPU memory usage") -> None: |
| """Report the current GPU VRAM usage.""" |
| if is_rank0(): |
| free_mem, total_mem = torch.cuda.mem_get_info() |
| print(f"{prefix}: {(total_mem - free_mem) / (1024**3):.2f} GB / {total_mem / (1024**3):.2f} GB.") |
|
|
|
|
| def _get_model_size(model: nn.Module, scale: str = "auto") -> Tuple[float, str]: |
| """Compute the model size.""" |
| n_params = sum(p.numel() for p in model.parameters()) |
|
|
| if scale == "auto": |
| if n_params > 1e9: |
| scale = "B" |
| elif n_params > 1e6: |
| scale = "M" |
| elif n_params > 1e3: |
| scale = "K" |
| else: |
| scale = "" |
|
|
| if scale == "B": |
| n_params = n_params / 1e9 |
| elif scale == "M": |
| n_params = n_params / 1e6 |
| elif scale == "K": |
| n_params = n_params / 1e3 |
| elif scale == "": |
| pass |
| else: |
| raise NotImplementedError(f"Unknown scale {scale}.") |
|
|
| return n_params, scale |
|
|
|
|
| def print_model_size(model: nn.Module, name: Optional[str] = None) -> None: |
| """Print the model size.""" |
| if is_rank0(): |
| n_params, scale = _get_model_size(model, scale="auto") |
| if name is None: |
| name = model.__class__.__name__ |
|
|
| print(f"{name} contains {n_params:.2f}{scale} parameters.") |
|
|
