Spaces:
Running
Running
File size: 7,422 Bytes
c61ccee |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
from typing import Dict, List, Optional, Tuple
import torch
import torch.optim._functional as F
from torch import Tensor
__all__: List[str] = []
# Define a TorchScript compatible Functional Adam Optimizer
# where we use these optimizer in a functional way.
# Instead of using the `param.grad` when updating parameters,
# we explicitly allow the distributed optimizer pass gradients to
# the `step` function. In this way, we could separate the gradients
# and parameters and allow multithreaded trainer to update the
# parameters without data traces on accumulating to the same .grad.
# NOTE: This should be only used by distributed optimizer internals
# and not meant to expose to the user.
@torch.jit.script
class _FunctionalAdam:
def __init__(
self,
params: List[Tensor],
lr: float = 1e-3,
betas: Tuple[float, float] = (0.9, 0.999),
eps: float = 1e-8,
weight_decay: float = 0.0,
amsgrad: bool = False,
maximize: bool = False,
foreach: bool = False,
fused: bool = False,
_allow_empty_param_list: bool = False,
):
if not 0.0 <= lr:
raise ValueError(f"Invalid learning rate: {lr}")
if not 0.0 <= eps:
raise ValueError(f"Invalid epsilon value: {eps}")
if not 0.0 <= betas[0] < 1.0:
raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
if not 0.0 <= betas[1] < 1.0:
raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
if not 0.0 <= weight_decay:
raise ValueError(f"Invalid weight_decay value: {weight_decay}")
self.defaults = {
"lr": lr,
"eps": eps,
"beta1": betas[0],
"beta2": betas[1],
"weight_decay": weight_decay,
}
self.amsgrad = amsgrad
self.maximize = maximize
self.foreach = foreach
self.fused = fused
self.state = torch.jit.annotate(Dict[torch.Tensor, Dict[str, torch.Tensor]], {})
if len(params) == 0 and not _allow_empty_param_list:
raise ValueError("optimizer got an empty parameter list")
# NOTE: we only have one param_group and don't allow user to add additional
# param group as it's not a common use case.
self.param_group = {"params": params}
def step_param(self, param: Tensor, grad: Optional[Tensor]):
"""
Similar to step, but operates on a single parameter and optionally a
gradient tensor.
"""
params_with_grad = []
grads = []
exp_avgs = []
exp_avg_sqs = []
max_exp_avg_sqs = []
state_steps: List[Tensor] = []
has_complex = torch.is_complex(param)
if grad is not None:
params_with_grad.append(param)
grads.append(grad)
if param not in self.state:
self.state[param] = {}
state = self.state[param]
state["step"] = torch.tensor(0.0)
state["exp_avg"] = torch.zeros_like(
param, memory_format=torch.preserve_format
)
state["exp_avg_sq"] = torch.zeros_like(
param, memory_format=torch.preserve_format
)
if self.amsgrad:
state["max_exp_avg_sq"] = torch.zeros_like(
param, memory_format=torch.preserve_format
)
state = self.state[param]
exp_avgs.append(state["exp_avg"])
exp_avg_sqs.append(state["exp_avg_sq"])
if self.amsgrad:
max_exp_avg_sqs.append(state["max_exp_avg_sq"])
state_steps.append(state["step"])
with torch.no_grad():
F.adam(
params_with_grad,
grads,
exp_avgs,
exp_avg_sqs,
max_exp_avg_sqs,
state_steps,
amsgrad=self.amsgrad,
has_complex=has_complex,
maximize=self.maximize,
beta1=self.defaults["beta1"],
beta2=self.defaults["beta2"],
lr=self.defaults["lr"],
weight_decay=self.defaults["weight_decay"],
eps=self.defaults["eps"],
foreach=self.foreach,
fused=self.fused,
grad_scale=None,
found_inf=None,
)
def step(self, gradients: List[Optional[Tensor]]):
params = self.param_group["params"]
params_with_grad = []
grads = []
exp_avgs = []
exp_avg_sqs = []
max_exp_avg_sqs = []
state_steps: List[Tensor] = []
has_complex = False
if len(params) != len(gradients):
raise ValueError(
"the gradients passed in does not equal to the size of the parameters!"
+ f"Params length: {len(params)}. "
+ f"Gradients length: {len(gradients)}"
)
for param, gradient in zip(self.param_group["params"], gradients):
if gradient is not None:
has_complex |= torch.is_complex(param)
params_with_grad.append(param)
grads.append(gradient)
# Lazy state initialization
if param not in self.state:
self.state[param] = {}
state = self.state[param]
state["step"] = torch.tensor(0.0)
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(
param, memory_format=torch.preserve_format
)
# Exponential moving average of squared gradient values
state["exp_avg_sq"] = torch.zeros_like(
param, memory_format=torch.preserve_format
)
if self.amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state["max_exp_avg_sq"] = torch.zeros_like(
param, memory_format=torch.preserve_format
)
state = self.state[param]
exp_avgs.append(state["exp_avg"])
exp_avg_sqs.append(state["exp_avg_sq"])
if self.amsgrad:
max_exp_avg_sqs.append(state["max_exp_avg_sq"])
state_steps.append(state["step"])
with torch.no_grad():
F.adam(
params_with_grad,
grads,
exp_avgs,
exp_avg_sqs,
max_exp_avg_sqs,
state_steps,
amsgrad=self.amsgrad,
has_complex=has_complex,
maximize=self.maximize,
beta1=self.defaults["beta1"],
beta2=self.defaults["beta2"],
lr=self.defaults["lr"],
weight_decay=self.defaults["weight_decay"],
eps=self.defaults["eps"],
foreach=self.foreach,
fused=self.fused,
grad_scale=None,
found_inf=None,
)
|