| |
| from typing import Any, List, Sequence |
|
|
| import torch |
|
|
| from pytorch_lightning import Callback, Trainer, LightningModule |
| from pytorch_lightning.utilities import rank_zero_only |
| from pytorch_lightning.utilities.parsing import AttributeDict |
|
|
| from src.utils.flops import has_deepspeed_profiling, has_fvcore_profiling |
| from src.utils.flops import profile_deepspeed, profile_fvcore |
|
|
|
|
| class FlopCount(Callback): |
| """Counter the number of FLOPs used by the model |
| """ |
| def __init__(self, profilers: List[str] = ['fvcore', 'deepspeed'], |
| input_size: tuple = (3, 224, 224), input_dtype=torch.float32, device=None): |
| if not isinstance(profilers, Sequence): |
| profilers = [profilers] |
| if any(p not in ['fvcore', 'deepspeed'] for p in profilers): |
| raise NotImplementedError('Only support fvcore and deepspeed profilers') |
| if 'fvcore' in profilers and not has_fvcore_profiling: |
| raise ImportError('fvcore is not installed. Install it by running `pip install fvcore`') |
| elif 'deepspeed' in profilers and not has_deepspeed_profiling: |
| raise ImportError('deepspeed is not installed') |
| super().__init__() |
| self.profilers = profilers |
| self.input_size = tuple(input_size) |
| self.input_dtype = input_dtype |
| self.device = device |
|
|
| @rank_zero_only |
| def on_fit_start(self, trainer: Trainer, pl_module: LightningModule) -> None: |
| if 'fvcore' in self.profilers: |
| _, macs, _, acts = profile_fvcore(pl_module.to(self.device), input_size=self.input_size, |
| input_dtype=self.input_dtype, detailed=True) |
| trainer.logger.log_hyperparams({'GMACs': macs * 1e-9, 'MActs': acts * 1e-6}) |
| if 'deepspeed' in self.profilers: |
| macs, _= profile_deepspeed(pl_module.to(self.device), input_size=self.input_size, |
| input_dtype=self.input_dtype, detailed=True) |
| if 'fvcore' not in self.profilers: |
| trainer.logger.log_hyperparams({'GMACs': macs * 1e-9}) |
|
|
