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# 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.
from enum import Enum, auto
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple
import torch
if TYPE_CHECKING:
from torch.optim.optimizer import _params_t
else:
_params_t = Any
try:
from fairscale import fused_adam_cuda # type: ignore
class Precision(Enum):
FULL_PRECISION = auto()
MIXED_PRECISION = auto()
MEMORY_EFFICIENT_MIXED_PRECISION = auto()
PURE_FP16 = auto()
class _MultiDeviceReplicator(object):
"""
Lazily serves copies of a tensor to requested devices. Copies are cached per-device.
"""
def __init__(self, master_tensor: torch.Tensor):
assert master_tensor.is_cuda
self.master = master_tensor
self._per_device_tensors: Dict[torch.device, torch.Tensor] = {}
def get(self, device: torch.device) -> torch.Tensor:
retval = self._per_device_tensors.get(device, None)
if retval is None:
retval = self.master.to(device=device, non_blocking=True, copy=True)
self._per_device_tensors[device] = retval
return retval
class Adam(torch.optim.Optimizer):
state: dict
defaults: dict
"""
Implements Adam algorithm. Currently GPU-only.
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
Compared to the original version in Apex, the fairseq version casts grads
and params to FP32 internally to support ``--memory-efficient-fp16``.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups.
lr (float, optional): learning rate. (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square. (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability. (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: False) NOT SUPPORTED in FusedAdam!
eps_inside_sqrt (boolean, optional): in the 'update parameters' step,
adds eps to the bias-corrected second moment estimate before
evaluating square root instead of adding it to the square root of
second moment estimate as in the original paper. (default: False)
precision (Precision, optional): One of Precision.FULL_PRECISION,
Precision.MIXED_PRECISION, Precision.MEMORY_EFFICIENT_MIXED_PRECISION
or Precision.PURE_FP16. Inferred based on model parameter precision if
None. (default: None)
.. _Adam: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
def __init__(
self,
params: _params_t,
lr: Optional[float] = 1e-3,
bias_correction: Optional[bool] = True,
betas: Optional[Tuple[float, float]] = (0.9, 0.999),
eps: Optional[float] = 1e-8,
eps_inside_sqrt: Optional[bool] = False,
weight_decay: Optional[float] = 0.0,
max_grad_norm: Optional[float] = 0.0,
amsgrad: Optional[bool] = False,
precision: Optional[Precision] = None,
):
parameters: List[Any] = list(params)
self.precision = precision
if self.precision is None:
self.precision = (
Precision.FULL_PRECISION if parameters[0].dtype == torch.float32 else Precision.MIXED_PRECISION
)
if self.precision is not Precision.FULL_PRECISION:
assert parameters[0].dtype == torch.float16
self.optim_type = torch.float16 if precision is Precision.PURE_FP16 else torch.float32
self._optim_scale = float(2**16) if precision is Precision.PURE_FP16 else 1.0
self._steps_since_optim_scale_change = 0
self._optim_scale_update_freq = 2000 # This is the value that GradScaler uses by default
self._overflow_buf = torch.cuda.IntTensor([0]) # type: ignore
if amsgrad:
raise RuntimeError("FusedAdam does not support the AMSGrad variant.")
defaults = {
"lr": lr,
"bias_correction": bias_correction,
"betas": betas,
"eps": eps,
"weight_decay": weight_decay,
"max_grad_norm": max_grad_norm,
}
super().__init__(parameters, defaults)
self.eps_mode = 0 if eps_inside_sqrt else 1
self.fp32_param_groups: List[Any] = []
if self.mixed_precision:
self._build_fp32_params(parameters)
def _build_fp32_params(self, params: Any) -> None:
# create FP32 copy of parameters and grads
fp32_params = []
for p in params:
p32 = torch.nn.Parameter(p.data.float()).to(p.device)
p32.grad = torch.zeros_like(p32.data)
fp32_params.append(p32)
params = fp32_params
self.fp32_param_groups = []
param_groups = list(params)
if not isinstance(param_groups[0], dict):
param_groups = [{"params": param_groups}]
for param_group in param_groups:
params = param_group["params"]
if isinstance(params, torch.Tensor):
param_group["params"] = [params]
else:
param_group["params"] = list(params)
for name, default in self.defaults.items():
param_group.setdefault(name, default)
params = param_group["params"]
param_set = set()
for group in self.param_groups:
param_set.update(set(group["params"]))
self.fp32_param_groups.append(param_group)
@property
def supports_memory_efficient_fp16(self) -> bool:
return True
@property
def _step_supports_amp_scaling(self) -> bool:
return False
@property
def mixed_precision(self) -> bool:
return self.precision is Precision.MIXED_PRECISION
def state_dict(self) -> Dict[str, Any]:
d = super().state_dict()
d["optim_scale"] = self._optim_scale
return d
def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
super().load_state_dict(state_dict)
self._optim_scale = state_dict["optim_scale"]
# TODO: Optimizer state gets cast to FP16 and back to FP32 for
# mixed-precision and memory-efficient mixed-precision. Eventually
# we want to fix this, as some precision may be lost
for group in self.param_groups:
for p in group["params"]:
self.state[p]["exp_avg"] = self.state[p]["exp_avg"].type(self.optim_type)
self.state[p]["exp_avg_sq"] = self.state[p]["exp_avg_sq"].type(self.optim_type)
def step(self, closure: Optional[Callable[[], float]] = None) -> Optional[float]:
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
grads (list of tensors, optional): weight gradient to use for the
optimizer update. If gradients have type torch.half, parameters
are expected to be in type torch.float. (default: None)
output params (list of tensors, optional): A reduced precision copy
of the updated weights written out in addition to the regular
updated weights. Have to be of same type as gradients. (default: None)
scale (float, optional): factor to divide gradient tensor values
by before applying to weights. (default: 1)
"""
loss = None
if closure is not None:
loss = closure()
for i in range(len(self.param_groups)):
group = self.param_groups[i]
bias_correction = 1 if group["bias_correction"] else 0
tensorlists: Dict[torch.device, List[List[torch.Tensor]]] = dict()
for j in range(len(group["params"])):
p = group["params"][j]
# note: p.grad should not ever be set for correct
# operation of mixed precision optimizer that sometimes
# sends None gradients
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError(
"FusedAdam does not support sparse gradients, " "please consider SparseAdam instead"
)
state = self.state[p]
# State initialization
if len(state) == 0:
state["step"] = 0
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(p, dtype=self.optim_type)
# Exponential moving average of squared gradient values
state["exp_avg_sq"] = torch.zeros_like(p, dtype=self.optim_type)
exp_avg = state["exp_avg"]
exp_avg_sq = state["exp_avg_sq"]
beta1, beta2 = group["betas"]
state["step"] += 1
out_p = p.data if self.mixed_precision else torch.tensor([])
param = self.fp32_param_groups[i]["params"][j] if self.mixed_precision else p
scale = 1.0
if self.mixed_precision:
pl = [param.data, exp_avg, exp_avg_sq, grad, out_p]
if p.device not in tensorlists:
tensorlists[p.device] = [[], [], [], [], []]
for tl, t in zip(tensorlists[p.device], pl):
tl.append(t)
else:
pl = [param.data, exp_avg, exp_avg_sq, grad]
if p.device not in tensorlists:
tensorlists[p.device] = [[], [], [], []]
for tl, t in zip(tensorlists[p.device], pl):
tl.append(t)
found_inf = torch.full((1,), 0.0, dtype=torch.float32, device=list(tensorlists.keys())[0])
per_device_found_inf = _MultiDeviceReplicator(found_inf)
for tensordevice, tensorlist in tensorlists.items():
with torch.cuda.device(tensordevice):
fused_adam_cuda.adam(
2048 * 32,
self._overflow_buf,
tensorlist,
group["lr"],
beta1,
beta2,
group["eps"],
scale,
self._optim_scale,
per_device_found_inf.get(tensordevice),
state["step"],
self.eps_mode,
bias_correction,
group["weight_decay"],
)
if sum(v.item() for v in per_device_found_inf._per_device_tensors.values()):
self._steps_since_optim_scale_change = 0
self._optim_scale /= 2
if self._optim_scale < 1.0:
raise RuntimeError("Optimizer state scale < 1. This may mean that gradients are exploding")
for group in self.param_groups:
for p in group["params"]:
self.state[p]["exp_avg"] = torch.zeros_like(p, dtype=self.optim_type)
self.state[p]["exp_avg_sq"] = torch.zeros_like(p, dtype=self.optim_type)
else:
self._steps_since_optim_scale_change += 1
if self._steps_since_optim_scale_change == self._optim_scale_update_freq:
self._steps_since_optim_scale_change = 0
if self._optim_scale < 2**16:
self._optim_scale *= 2
return loss
except ImportError:
pass
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