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Running
on
A10G
| from itertools import repeat | |
| import collections.abc | |
| import torch | |
| from torch import nn as nn | |
| from torchvision.ops.misc import FrozenBatchNorm2d | |
| def freeze_batch_norm_2d(module, module_match={}, name=''): | |
| """ | |
| Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is | |
| itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and | |
| returned. Otherwise, the module is walked recursively and submodules are converted in place. | |
| Args: | |
| module (torch.nn.Module): Any PyTorch module. | |
| module_match (dict): Dictionary of full module names to freeze (all if empty) | |
| name (str): Full module name (prefix) | |
| Returns: | |
| torch.nn.Module: Resulting module | |
| Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 | |
| """ | |
| res = module | |
| is_match = True | |
| if module_match: | |
| is_match = name in module_match | |
| if is_match and isinstance(module, (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm)): | |
| res = FrozenBatchNorm2d(module.num_features) | |
| res.num_features = module.num_features | |
| res.affine = module.affine | |
| if module.affine: | |
| res.weight.data = module.weight.data.clone().detach() | |
| res.bias.data = module.bias.data.clone().detach() | |
| res.running_mean.data = module.running_mean.data | |
| res.running_var.data = module.running_var.data | |
| res.eps = module.eps | |
| else: | |
| for child_name, child in module.named_children(): | |
| full_child_name = '.'.join([name, child_name]) if name else child_name | |
| new_child = freeze_batch_norm_2d(child, module_match, full_child_name) | |
| if new_child is not child: | |
| res.add_module(child_name, new_child) | |
| return res | |
| # From PyTorch internals | |
| def _ntuple(n): | |
| def parse(x): | |
| if isinstance(x, collections.abc.Iterable): | |
| return x | |
| return tuple(repeat(x, n)) | |
| return parse | |
| to_1tuple = _ntuple(1) | |
| to_2tuple = _ntuple(2) | |
| to_3tuple = _ntuple(3) | |
| to_4tuple = _ntuple(4) | |
| to_ntuple = lambda n, x: _ntuple(n)(x) | |
| # Replaces all linear layers with linear_replacement | |
| # TODO: add int8 support for other linear layers including attn and convnets | |
| def replace_linear(model, linear_replacement, include_modules=['c_fc', 'c_proj'], copy_weights=True): | |
| for name, module in model.named_children(): | |
| if len(list(module.children())) > 0: | |
| replace_linear(module, linear_replacement, include_modules, copy_weights) | |
| if isinstance(module, torch.nn.Linear) and name in include_modules: | |
| old_module = model._modules[name] | |
| model._modules[name] = linear_replacement( | |
| module.in_features, | |
| module.out_features, | |
| module.bias is not None, | |
| ) | |
| if copy_weights: | |
| model._modules[name].weight.data.copy_(old_module.weight.data) | |
| if model._modules[name].bias is not None: | |
| model._modules[name].bias.data.copy_(old_module.bias) | |
| return model | |
| def convert_int8_model_to_inference_mode(model): | |
| for m in model.modules(): | |
| if hasattr(m, 'prepare_for_eval'): | |
| int8_original_dtype = m.weight.dtype | |
| m.prepare_for_eval() | |
| m.int8_original_dtype = int8_original_dtype |