| | from contextlib import contextmanager |
| |
|
| | import deepspeed |
| | import torch |
| | import torch.nn as nn |
| | from deepspeed.runtime.zero import GatheredParameters |
| |
|
| |
|
| | class EMADeepspeed(nn.Module): |
| | """ migrated from https://github.com/microsoft/DeepSpeed/issues/2056 |
| | """ |
| |
|
| | def __init__(self, model, decay=0.9999, use_num_updates=True): |
| | super().__init__() |
| | if decay < 0.0 or decay > 1.0: |
| | raise ValueError('Decay must be between 0 and 1') |
| |
|
| | self.m_name2s_name = {} |
| | self.decay = decay |
| | self.num_updates = 0 if use_num_updates else -1 |
| |
|
| | with GatheredParameters(model.parameters(), fwd_module=self): |
| | for name, p in model.named_parameters(): |
| | if p.requires_grad: |
| | |
| | s_name = name.replace('.', '') |
| | self.m_name2s_name.update({name: s_name}) |
| | self.register_buffer(s_name, p.clone().detach().data) |
| | |
| | self.collected_params = [] |
| |
|
| | def forward(self, model): |
| | decay = self.decay |
| |
|
| | if self.num_updates >= 0: |
| | self.num_updates += 1 |
| | decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates)) |
| |
|
| | one_minus_decay = 1.0 - decay |
| | shadow_params = dict(self.named_buffers()) |
| |
|
| | with torch.no_grad(): |
| | with GatheredParameters(model.parameters()): |
| | if deepspeed.comm.get_rank() == 0: |
| | m_param = dict(model.named_parameters()) |
| |
|
| | for key in m_param: |
| | if m_param[key].requires_grad: |
| | sname = self.m_name2s_name[key] |
| | shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) |
| | shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) |
| | else: |
| | assert key not in self.m_name2s_name |
| |
|
| | def copy_to(self, model): |
| | shadow_params = dict(self.named_buffers()) |
| | with GatheredParameters(model.parameters(), modifier_rank=0): |
| | if deepspeed.comm.get_rank() == 0: |
| | m_param = dict(model.named_parameters()) |
| | for key in m_param: |
| | if m_param[key].requires_grad: |
| | m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) |
| | else: |
| | assert key not in self.m_name2s_name |
| |
|
| | def store(self, model): |
| | """ |
| | Save the current parameters for restoring later. |
| | Args: |
| | model: A model that parameters will be stored |
| | """ |
| | with GatheredParameters(model.parameters()): |
| | if deepspeed.comm.get_rank() == 0: |
| | parameters = model.parameters() |
| | self.collected_params = [param.clone() for param in parameters] |
| |
|
| | def restore(self, model): |
| | """ |
| | Restore the parameters stored with the `store` method. |
| | Useful to validate the model with EMA parameters without affecting the |
| | original optimization process. Store the parameters before the |
| | `copy_to` method. After validation (or model saving), use this to |
| | restore the former parameters. |
| | Args: |
| | model: A model that to restore its parameters. |
| | """ |
| | with GatheredParameters(model.parameters(), modifier_rank=0): |
| | if deepspeed.comm.get_rank() == 0: |
| | parameters = model.parameters() |
| | for c_param, param in zip(self.collected_params, parameters): |
| | param.data.copy_(c_param.data) |
| |
|
| | @contextmanager |
| | def activate(self, model): |
| | try: |
| | self.store(model) |
| | self.copy_to(model) |
| | yield |
| | finally: |
| | self.restore(model) |
| |
|
