467 lines
20 KiB
Python
467 lines
20 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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import bitsandbytes.functional as F
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from copy import deepcopy
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from itertools import chain
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from collections import defaultdict, abc as container_abcs
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class MockArgs(object):
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def __init__(self, initial_data):
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for key in initial_data:
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setattr(self, key, initial_data[key])
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class GlobalOptimManager(object):
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_instance = None
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def __init__(self):
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raise RuntimeError('Call get_instance() instead')
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def initialize(self):
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self.pid2config = {}
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self.index2config = {}
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self.optimizer = None
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self.uses_config_override = False
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@classmethod
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def get_instance(cls):
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if cls._instance is None:
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cls._instance = cls.__new__(cls)
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cls._instance.initialize()
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return cls._instance
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def register_parameters(self, params):
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param_groups = list(params)
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if not isinstance(param_groups[0], dict):
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param_groups = [{'params': param_groups}]
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for group_index, group in enumerate(param_groups):
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for p_index, p in enumerate(group['params']):
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if id(p) in self.pid2config:
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self.index2config[(group_index, p_index)] = self.pid2config[id(p)]
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def override_config(self, parameters, key=None, value=None, key_value_dict=None):
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'''
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Overrides initial optimizer config for specific parameters.
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The key-values of the optimizer config for the input parameters are overidden
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This can be both, optimizer parameters like "betas", or "lr" or it can be
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8-bit specific paramters like "optim_bits", "percentile_clipping".
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Parameters
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----------
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parameters : torch.Tensor or list(torch.Tensors)
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The input parameters.
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key : str
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The hyperparamter to override.
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value : object
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The value for the hyperparamters.
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key_value_dict : dict
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A dictionary with multiple key-values to override.
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'''
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self.uses_config_override = True
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if isinstance(parameters, torch.nn.Parameter):
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parameters = [parameters]
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if isinstance(parameters, torch.Tensor):
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parameters = [parameters]
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if key is not None and value is not None:
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assert key_value_dict is None
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key_value_dict = {key: value}
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if key_value_dict is not None:
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for p in parameters:
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if id(p) in self.pid2config:self.pid2config[id(p)].update(key_value_dict)
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else: self.pid2config[id(p)] = key_value_dict
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class Optimizer8bit(torch.optim.Optimizer):
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def __init__(self, params, defaults, optim_bits=32):
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super(Optimizer8bit, self).__init__(params, defaults)
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self.checked_if_on_gpu = False
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self.name2qmap = {}
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self.mng = GlobalOptimManager.get_instance()
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self.non_castable_tensor_keys = set(
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['qmap1', 'qmap2',
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'max1', 'max2',
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'new_max1', 'new_max2',
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'state1', 'state2',
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'gnorm_vec', 'absmax1', 'absmax2',
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'unorm_vec'])
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if optim_bits == 8: self.fill_qmap()
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def fill_qmap(self):
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self.name2qmap['dynamic'] = F.create_dynamic_map(signed=True)
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self.name2qmap['udynamic'] = F.create_dynamic_map(signed=False)
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def __setstate__(self, state):
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super(Optimizer8bit, self).__setstate__(state)
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def load_state_dict(self, state_dict):
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r"""Loads the optimizer state.
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Args:
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state_dict (dict): optimizer state. Should be an object returned
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from a call to :meth:`state_dict`.
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"""
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# deepcopy, to be consistent with module API
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state_dict = deepcopy(state_dict)
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# Validate the state_dict
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groups = self.param_groups
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saved_groups = state_dict['param_groups']
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if len(groups) != len(saved_groups):
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raise ValueError("loaded state dict has a different number of "
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"parameter groups")
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param_lens = (len(g['params']) for g in groups)
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saved_lens = (len(g['params']) for g in saved_groups)
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if any(p_len != s_len for p_len, s_len in zip(param_lens, saved_lens)):
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raise ValueError("loaded state dict contains a parameter group "
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"that doesn't match the size of optimizer's group")
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# Update the state
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id_map = {old_id: p for old_id, p in
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zip(chain.from_iterable((g['params'] for g in saved_groups)),
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chain.from_iterable((g['params'] for g in groups)))}
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def cast(param, value):
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r"""Make a deep copy of value, casting all tensors to device of param."""
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if isinstance(value, torch.Tensor):
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# Floating-point types are a bit special here. They are the only ones
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# that are assumed to always match the type of params.
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if param.is_floating_point() and value.dtype != torch.uint8:
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value = value.to(param.dtype)
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return value
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elif isinstance(value, dict):
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for k, v in value.items():
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if k in self.non_castable_tensor_keys:
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value[k] = v.to(param.device)
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else:
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value[k] = cast(param, v)
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return value
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elif isinstance(value, container_abcs.Iterable):
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return type(value)(cast(param, v) for v in value)
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else:
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return value
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# Copy state assigned to params (and cast tensors to appropriate types).
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# State that is not assigned to params is copied as is (needed for
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# backward compatibility).
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state = defaultdict(dict)
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for k, v in state_dict['state'].items():
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if k in id_map:
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param = id_map[k]
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state[param] = cast(param, v)
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else:
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state[k] = v
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# Update parameter groups, setting their 'params' value
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def update_group(group, new_group):
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new_group['params'] = group['params']
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return new_group
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param_groups = [
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update_group(g, ng) for g, ng in zip(groups, saved_groups)]
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self.__setstate__({'state': state, 'param_groups': param_groups})
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def to_gpu(self):
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self.checked_if_on_gpu = True
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for gindex, group in enumerate(self.param_groups):
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for pindex, p in enumerate(group['params']):
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if p in self.state:
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values = self.state[p]
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for k, v in values.items():
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if isinstance(v, torch.Tensor):
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self.state[p][k] = v.to(p.device)
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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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Arguments:
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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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overflows = []
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if not self.checked_if_on_gpu: self.to_gpu() # needed for fairseq pure fp16 training
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for gindex, group in enumerate(self.param_groups):
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for pindex, p in enumerate(group['params']):
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if p.grad is None:
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continue
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state = self.state[p]
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if len(state) == 0:
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self.init_state(group, p, gindex, pindex)
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self.update_step(group, p, gindex, pindex)
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return loss
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def get_config(self, gindex, pindex, group):
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config = {}
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config['betas'] = group['betas']
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config['eps'] = group['eps']
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config['weight_decay'] = group['weight_decay']
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config['lr'] = group['lr']
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config['optim_bits'] = self.args.optim_bits
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config['min_8bit_size'] = self.args.min_8bit_size
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config['percentile_clipping'] = self.args.percentile_clipping
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config['block_wise'] = self.args.block_wise
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config['max_unorm'] = self.args.max_unorm
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config['skip_zeros'] = self.args.skip_zeros
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if (gindex, pindex) in self.mng.index2config:
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config.update(self.mng.index2config[(gindex, pindex)])
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return config
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def init_state(self, group, p, gindex, pindex):
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raise NotImplementedError(f'init_state method needs to be overidden')
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def update_step(self, group, p, gindex, pindex):
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raise NotImplementedError(f'The update_step method needs to be overidden')
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class Optimizer2State(Optimizer8bit):
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def __init__(self, optimizer_name, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
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weight_decay=0.0, optim_bits=32, args=None,
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min_8bit_size=4096, percentile_clipping=100, block_wise=True, max_unorm=0.0,
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skip_zeros=False):
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if not 0.0 <= eps:
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raise ValueError("Invalid epsilon value: {}".format(eps))
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if isinstance(betas, str):
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betas = eval(betas)
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print(betas, 'parsed')
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for i in range(len(betas)):
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if not 0.0 <= betas[i] < 1.0:
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raise ValueError(f"Invalid beta parameter at index {i}: {betas[i]}")
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if not 0.0 <= weight_decay:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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defaults = dict(lr=lr, betas=betas, eps=eps,
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weight_decay=weight_decay)
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super(Optimizer2State, self).__init__(params, defaults, optim_bits)
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if args is None:
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args = {}
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args['optim_bits'] = optim_bits
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args['percentile_clipping'] = 100
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args['min_8bit_size'] = min_8bit_size
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args['percentile_clipping'] = percentile_clipping
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args['block_wise'] = block_wise
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args['max_unorm'] = max_unorm
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args['skip_zeros'] = skip_zeros
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self.args = MockArgs(args)
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else:
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self.args = args
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self.optimizer_name = optimizer_name
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@torch.no_grad()
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def init_state(self, group, p, gindex, pindex):
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config = self.get_config(gindex, pindex, group)
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if config['optim_bits'] == 32:
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dtype = torch.float32
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elif config['optim_bits'] == 8:
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dtype = torch.uint8
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else: raise NotImplementedError(f'Amount of optimizer bits not supported: {config["optim_bits"]}')
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if p.numel() < config['min_8bit_size']: dtype = torch.float32
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state = self.state[p]
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state['step'] = 0
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if dtype == torch.float32 or (dtype == torch.uint8 and p.numel() < 4096):
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state['state1'] = torch.zeros_like(p, memory_format=torch.preserve_format, dtype=torch.float32, device=p.device)
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state['state2'] = torch.zeros_like(p, memory_format=torch.preserve_format, dtype=torch.float32, device=p.device)
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elif dtype == torch.uint8:
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if state['step'] == 0:
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if 'dynamic' not in self.name2qmap: self.fill_qmap()
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self.name2qmap['dynamic'] = self.name2qmap['dynamic'].to(p.device)
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self.name2qmap['udynamic'] = self.name2qmap['udynamic'].to(p.device)
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state['state1'] = torch.zeros_like(p, memory_format=torch.preserve_format, dtype=torch.uint8, device=p.device)
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state['qmap1'] = self.name2qmap['dynamic']
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state['state2'] = torch.zeros_like(p, memory_format=torch.preserve_format, dtype=torch.uint8, device=p.device)
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state['qmap2'] = self.name2qmap['udynamic']
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if config['block_wise']:
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n = p.numel()
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blocks = n//2048
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blocks += 1 if n % 2048 > 0 else 0
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state['absmax1'] = torch.zeros((blocks,), dtype=torch.float32, device=p.device)
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state['absmax2'] = torch.zeros((blocks,), dtype=torch.float32, device=p.device)
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else:
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state['max1'] = torch.zeros((1,), dtype=torch.float32, device=p.device)
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state['new_max1'] = torch.zeros((1,), dtype=torch.float32, device=p.device)
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state['max2'] = torch.zeros((1,), dtype=torch.float32, device=p.device)
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state['new_max2'] = torch.zeros((1,), dtype=torch.float32, device=p.device)
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if config['percentile_clipping'] < 100:
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state['gnorm_vec'] = torch.zeros((100,), device=p.device)
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if config['max_unorm'] > 0.0:
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state['unorm_vec'] = torch.zeros((1,), device=p.device)
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@torch.no_grad()
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def update_step(self, group, p, gindex, pindex):
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state = self.state[p]
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grad = p.grad
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config = self.get_config(gindex, pindex, group)
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state['step'] += 1
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step = state['step']
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if config['percentile_clipping'] < 100:
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current_gnorm, clip_value, gnorm_scale = F.percentile_clipping(grad, state['gnorm_vec'], step, config['percentile_clipping'])
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else:
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gnorm_scale = 1.0
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if state['state1'].dtype == torch.float:
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F.optimizer_update_32bit(self.optimizer_name, grad, p, state['state1'], config['betas'][0], config['eps'], step, config['lr'],
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state['state2'], config['betas'][1], config['weight_decay'], gnorm_scale,
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state['unorm_vec'] if config['max_unorm'] > 0.0 else None, max_unorm=config['max_unorm'])
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elif state['state1'].dtype == torch.uint8 and not config['block_wise']:
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F.optimizer_update_8bit(self.optimizer_name, grad, p, state['state1'], state['state2'], config['betas'][0], config['betas'][1],
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config['eps'], step, config['lr'],
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state['qmap1'], state['qmap2'], state['max1'], state['max2'], state['new_max1'], state['new_max2'],
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config['weight_decay'], gnorm_scale=gnorm_scale,
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unorm_vec=state['unorm_vec'] if config['max_unorm'] > 0.0 else None, max_unorm=config['max_unorm'])
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# swap maxes
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state['max1'], state['new_max1'] = state['new_max1'], state['max1']
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state['max2'], state['new_max2'] = state['new_max2'], state['max2']
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elif state['state1'].dtype == torch.uint8 and config['block_wise']:
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F.optimizer_update_8bit_blockwise(self.optimizer_name, grad, p, state['state1'], state['state2'], config['betas'][0], config['betas'][1],
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config['eps'], step, config['lr'],
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state['qmap1'], state['qmap2'], state['absmax1'], state['absmax2'],
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config['weight_decay'], gnorm_scale=gnorm_scale)
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class Optimizer1State(Optimizer8bit):
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def __init__(self, optimizer_name, params, lr=1e-3, betas=(0.9, 0.0), eps=1e-8,
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weight_decay=0.0, optim_bits=32, args=None,
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min_8bit_size=4096, percentile_clipping=100, block_wise=True, max_unorm=0.0,
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skip_zeros=False):
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if not 0.0 <= eps:
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raise ValueError("Invalid epsilon value: {}".format(eps))
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for i in range(len(betas)):
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if not 0.0 <= betas[i] < 1.0:
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raise ValueError(f"Invalid beta parameter at index {i}: {betas[i]}")
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if not 0.0 <= weight_decay:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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defaults = dict(lr=lr, betas=betas, eps=eps,
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weight_decay=weight_decay)
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super(Optimizer1State, self).__init__(params, defaults, optim_bits)
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if args is None:
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args = {}
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args['optim_bits'] = optim_bits
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args['percentile_clipping'] = 100
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args['min_8bit_size'] = min_8bit_size
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args['percentile_clipping'] = percentile_clipping
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args['block_wise'] = block_wise
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args['max_unorm'] = max_unorm
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args['skip_zeros'] = skip_zeros
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self.args = MockArgs(args)
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else:
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self.args = args
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self.optimizer_name = optimizer_name
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@torch.no_grad()
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def init_state(self, group, p, gindex, pindex):
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config = self.get_config(gindex, pindex, group)
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if config['optim_bits'] == 32:
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dtype = torch.float32
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elif config['optim_bits'] == 8:
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dtype = torch.uint8
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else: raise NotImplementedError(f'Amount of optimizer bits not supported: {config["optim_bits"]}')
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if p.numel() < config['min_8bit_size']: dtype = torch.float32
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state = self.state[p]
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state['step'] = 0
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if dtype == torch.float32 or (dtype == torch.uint8 and p.numel() < 4096):
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state['state1'] = torch.zeros_like(p, memory_format=torch.preserve_format, dtype=torch.float32, device=p.device)
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elif dtype == torch.uint8:
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if state['step'] == 0:
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if 'dynamic' not in self.name2qmap: self.fill_qmap()
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self.name2qmap['dynamic'] = self.name2qmap['dynamic'].to(p.device)
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state['state1'] = torch.zeros_like(p, memory_format=torch.preserve_format, dtype=torch.uint8, device=p.device)
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state['qmap1'] = self.name2qmap['dynamic']
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if config['block_wise']:
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n = p.numel()
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blocks = n//2048
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blocks += 1 if n % 2048 > 0 else 0
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state['absmax1'] = torch.zeros((blocks,), dtype=torch.float32, device=p.device)
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else:
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state['max1'] = torch.zeros((1,), dtype=torch.float32, device=p.device)
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state['new_max1'] = torch.zeros((1,), dtype=torch.float32, device=p.device)
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if config['percentile_clipping'] < 100:
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state['gnorm_vec'] = torch.zeros((100,), device=p.device)
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if config['max_unorm'] > 0.0:
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state['unorm_vec'] = torch.zeros((1,), device=p.device)
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@torch.no_grad()
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def update_step(self, group, p, gindex, pindex):
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state = self.state[p]
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grad = p.grad
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config = self.get_config(gindex, pindex, group)
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state['step'] += 1
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step = state['step']
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if config['percentile_clipping'] < 100:
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current_gnorm, clip_value, gnorm_scale = F.percentile_clipping(grad, state['gnorm_vec'], step, config['percentile_clipping'])
|
|
else:
|
|
gnorm_scale = 1.0
|
|
|
|
if state['state1'].dtype == torch.float:
|
|
F.optimizer_update_32bit(self.optimizer_name, grad, p, state['state1'], config['betas'][0], config['eps'], step, config['lr'],
|
|
None, 0.0, config['weight_decay'], gnorm_scale,
|
|
state['unorm_vec'] if config['max_unorm'] > 0.0 else None, max_unorm=config['max_unorm'],
|
|
skip_zeros=False)
|
|
|
|
elif state['state1'].dtype == torch.uint8 and not config['block_wise']:
|
|
F.optimizer_update_8bit(self.optimizer_name, grad, p, state['state1'], None, config['betas'][0], config['betas'][1],
|
|
config['eps'], step, config['lr'], state['qmap1'], None, state['max1'], None, state['new_max1'], None,
|
|
config['weight_decay'], gnorm_scale,
|
|
state['unorm_vec'] if config['max_unorm'] > 0.0 else None, max_unorm=config['max_unorm'])
|
|
|
|
state['max1'], state['new_max1'] = state['new_max1'], state['max1']
|
|
elif state['state1'].dtype == torch.uint8 and config['block_wise']:
|
|
F.optimizer_update_8bit_blockwise(self.optimizer_name, grad, p, state['state1'], None, config['betas'][0], config['betas'][1],
|
|
config['eps'], step, config['lr'],
|
|
state['qmap1'], None, state['absmax1'], None,
|
|
config['weight_decay'], gnorm_scale=gnorm_scale, skip_zeros=False)
|