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