wehub-resource-sync
99 lines
4.1 KiB
Python
Executable File
99 lines
4.1 KiB
Python
Executable File
# Copyright (c) Microsoft Corporation.
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# SPDX-License-Identifier: Apache-2.0
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# DeepSpeed Team
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import torch
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from deepspeed.ops.op_builder import CPUAdagradBuilder
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from deepspeed.utils.logging import should_log_le
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class DeepSpeedCPUAdagrad(torch.optim.Optimizer):
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optimizer_id = 0
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def __init__(self, model_params, lr=1e-2, eps=1e-10, weight_decay=0, amsgrad=False, fp32_optimizer_states=True):
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default_args = dict(lr=lr, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad)
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super(DeepSpeedCPUAdagrad, self).__init__(model_params, default_args)
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self.opt_id = DeepSpeedCPUAdagrad.optimizer_id
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DeepSpeedCPUAdagrad.optimizer_id = DeepSpeedCPUAdagrad.optimizer_id + 1
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self.fp32_optimizer_states = fp32_optimizer_states
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self.ds_opt_adagrad = CPUAdagradBuilder().load()
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self.ds_opt_adagrad.create_adagrad(self.opt_id, lr, eps, weight_decay, should_log_le("info"))
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def __del__(self):
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# need to destroy the C++ object explicitly to avoid a memory leak when deepspeed.initialize
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# is used multiple times in the same process (notebook or pytest worker)
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self.ds_opt_adagrad.destroy_adagrad(self.opt_id)
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def __setstate__(self, state):
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super(DeepSpeedCPUAdagrad, self).__setstate__(state)
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for group in self.param_groups:
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group.setdefault('amsgrad', False)
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@torch.no_grad()
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def step(self, closure=None):
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"""Update the model parameters.
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.. note::
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This method will be called internally by ZeRO-Offload. DeepSpeed
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users should still use ``engine.step()`` as shown in the
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`Getting Started
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<https://www.deepspeed.ai/getting-started/#training>`_ guide.
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Args:
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closure (callable, optional): closure to compute the loss.
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Defaults to ``None``.
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Returns:
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loss: if ``closure`` is provided. Otherwise ``None``.
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"""
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loss = None
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if closure is not None:
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with torch.enable_grad():
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loss = closure()
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# intended device for step
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device = torch.device('cpu')
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for group_id, group in enumerate(self.param_groups):
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for param_id, p in enumerate(group['params']):
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if p.grad is None:
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continue
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assert p.device == device, f"CPUAdagrad param is on {p.device} and must be 'cpu', make " \
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"sure you enabled 'offload_optimizer': 'cpu' in your ZeRO config."
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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#print(f'group {group_id} param {param_id} = {p.numel()}')
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state['step'] = 0
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#use full precision by default unless self.fp32_optimizer_states is off
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state_dtype = torch.float if self.fp32_optimizer_states else p.dtype
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#memory_format=torch.preserve_format)
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# gradient variances
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state['exp_avg_sq'] = torch.zeros_like(p.data, dtype=state_dtype, device='cpu')
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#memory_format=torch.preserve_format)
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state['step'] += 1
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if p.grad.is_sparse == True:
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sparse_param = p.sparse_mask(p.grad)
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sparse_exp_avg_sq = state['exp_avg_sq'].sparse_mask(p.grad)
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self.ds_opt_adagrad.adagrad_update(self.opt_id, state['step'], group['lr'], group['eps'],
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group['weight_decay'], sparse_param.values(), p.grad.values(),
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sparse_exp_avg_sq.values())
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p[sparse_param.indices()] = sparse_param.values()
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state['exp_avg_sq'][sparse_exp_avg_sq.indices()] = sparse_exp_avg_sq.values()
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else:
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self.ds_opt_adagrad.adagrad_update(self.opt_id, state['step'], group['lr'], group['eps'],
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group['weight_decay'], p.data, p.grad.data, state['exp_avg_sq'])
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return loss
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