forked from mrq/bitsandbytes-rocm
116 lines
4.7 KiB
Python
116 lines
4.7 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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from torch.optim import Optimizer
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from bitsandbytes.optim.optimizer import Optimizer1State
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class LARS(Optimizer1State):
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def __init__(self, params, lr, momentum=0, dampening=0,
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weight_decay=0, nesterov=False, optim_bits=32, args=None,
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min_8bit_size=4096, percentile_clipping=100, max_unorm=0.02):
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if momentum == 0:
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raise NotImplementedError(f'LARS without momentum is not supported!')
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super(LARS, self).__init__('lars', params, lr, (momentum, dampening), 0.0,
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weight_decay, optim_bits, args, min_8bit_size, percentile_clipping, max_unorm=max_unorm, block_wise=False)
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class LARS8bit(Optimizer1State):
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def __init__(self, params, lr, momentum=0, dampening=0,
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weight_decay=0, nesterov=False, args=None,
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min_8bit_size=4096, percentile_clipping=100, max_unorm=0.02):
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if momentum == 0:
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raise NotImplementedError(f'LARS without momentum is not supported!')
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super(LARS8bit, self).__init__('lars', params, lr, (momentum, dampening), 0.0,
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weight_decay, 8, args, min_8bit_size, percentile_clipping, max_unorm=max_unorm, block_wise=False)
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class LARS32bit(Optimizer1State):
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def __init__(self, params, lr, momentum=0, dampening=0,
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weight_decay=0, nesterov=False, args=None,
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min_8bit_size=4096, percentile_clipping=100, max_unorm=0.02):
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if momentum == 0:
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raise NotImplementedError(f'LARS without momentum is not supported!')
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super(LARS32bit, self).__init__('lars', params, lr, (momentum, dampening), 0.0,
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weight_decay, 32, args, min_8bit_size, percentile_clipping, max_unorm=max_unorm, block_wise=False)
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class PytorchLARS(Optimizer):
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def __init__(self, params, lr=0.01, momentum=0, dampening=0,
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weight_decay=0, nesterov=False, max_unorm=0.02):
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if lr < 0.0:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if momentum < 0.0:
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raise ValueError("Invalid momentum value: {}".format(momentum))
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if weight_decay < 0.0:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
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weight_decay=weight_decay, nesterov=nesterov, max_unorm=max_unorm)
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if nesterov and (momentum <= 0 or dampening != 0):
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raise ValueError("Nesterov momentum requires a momentum and zero dampening")
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super(PytorchLARS, self).__init__(params, defaults)
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def __setstate__(self, state):
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super(PytorchLARS, self).__setstate__(state)
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for group in self.param_groups:
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group.setdefault('nesterov', False)
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@torch.no_grad()
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def step(self, closure=None):
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"""Performs a single optimization step.
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Args:
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closure (callable, optional): A closure that reevaluates the model
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and returns the loss.
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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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for group in self.param_groups:
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params_with_grad = []
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d_p_list = []
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momentum_buffer_list = []
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weight_decay = group['weight_decay']
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momentum = group['momentum']
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dampening = group['dampening']
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nesterov = group['nesterov']
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max_unorm = group['max_unorm']
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lr = group['lr']
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for p in group['params']:
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if p.grad is None: continue
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state = self.state[p]
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d_p = p.grad
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if weight_decay != 0:
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d_p = d_p.add(param, alpha=weight_decay)
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if momentum != 0:
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buf = state.get('momentum_buffer', None)
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if buf is None:
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buf = torch.clone(d_p).detach()
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state['momentum_buffer']= buf
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else:
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buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
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if nesterov:
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update = d_p + buf*momentum
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else:
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update = buf
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update_scale = 1.0
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if max_unorm > 0.0:
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assert p.dtype == torch.float32
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pnorm = torch.norm(p.detach())
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unorm = torch.norm(update)
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if unorm > max_unorm*pnorm:
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update_scale = max_unorm*pnorm/unorm
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p.add_(update, alpha=-lr*update_scale)
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return loss
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