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ff7b988 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 | # Utils for fp16 training.
import importlib
import math
import numpy as np
import torch
import jukebox.utils.dist_adapter as dist
from torch.optim import Optimizer
from torch._utils import _flatten_dense_tensors
from jukebox.utils.dist_utils import allreduce
def adam_step(p: torch.Tensor, out_p: torch.Tensor, exp_avg: torch.Tensor, exp_avg_sq: torch.Tensor, grad: torch.Tensor,
lr: float, beta1: float, beta2: float, eps: float, scale: float, step: int, eps_mode: int, bias_correction: int, weight_decay: float):
assert bias_correction == 1
assert eps_mode == 1
grad = grad.float()
grad.div_(scale)
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
denom = exp_avg_sq.sqrt().add_(eps)
bias_correction1 = 1 - beta1 ** step
bias_correction2 = 1 - beta2 ** step
step_size = lr * math.sqrt(bias_correction2) / bias_correction1
p.add_(exp_avg/denom + weight_decay*p.float(), alpha=-step_size)
# Import fused_adam if we have apex, otherwise use regular adam
try:
fused_adam_cuda = importlib.import_module("fused_adam_cuda")
fused_adam_step = fused_adam_cuda.adam
print("Using apex fused_adam_cuda")
except ModuleNotFoundError:
fused_adam_step = adam_step
def backward(loss, params, scalar, fp16, logger):
# Perform backward
if not fp16:
scale = 1.0
loss.backward()
gn = grad_norm(params, scale)
return loss, scale, gn, False, False
else:
scale = scalar.get_scale()
loss = (loss.float())*scale
overflow_loss = check_overflow(loss.item())
overflow_loss = allreduce(int(overflow_loss), op=dist.ReduceOp.MAX) > 0
if not overflow_loss:
loss.backward()
gn = grad_norm(params, scale)
overflow_grad = check_overflow(gn)
overflow_grad = allreduce(int(overflow_grad), op=dist.ReduceOp.MAX) > 0
scalar.update_scale(overflow_grad)
else:
gn = 0.0
overflow_grad = True
loss = (loss.detach().float()) / scale # Should delete computation graph for overflow
if logger.rank == 0:
if loss > 12.: print(f"\nWarning. Loss is {loss}")
if overflow_loss: print(f"\nOverflow in forward. Loss {loss}, lgscale {np.log2(scale)}. Skipping batch completely (no backward, scale update)")
elif overflow_grad: print(f"\nOverflow in backward. Loss {loss}, grad norm {gn}, lgscale {np.log2(scale)}, new lgscale {np.log2(scalar.get_scale())}")
return loss, scale, gn, overflow_loss, overflow_grad
# Automatic loss scaling
class LossScalar(object):
def __init__(self,
loss_scale,
init_scale=2. ** 16,
scale_factor=2. ** (1. / 1000),
scale_window=1):
if loss_scale == None:
# Use dynamic loss scaling
self.dynamic = True
self.loss_scale = init_scale
else:
self.dynamic = False
self.loss_scale = loss_scale
self.max_loss_scale = 2.**24
self.scale_factor = scale_factor
self.scale_window = scale_window
self.unskipped = 0
self.overflow = False
def get_scale(self):
return self.loss_scale
def update_scale(self, overflow):
if overflow and self.dynamic:
self.loss_scale /= 2.
self.unskipped = 0
else:
self.unskipped += 1
if self.unskipped == self.scale_window and self.dynamic:
self.loss_scale = min(self.max_loss_scale, self.loss_scale * self.scale_factor)
self.unskipped = 0
def check_overflow(val):
return (val == float('inf')) or (val == -float('inf')) or (val != val)
def grad_norm(params, scale, flat=False):
params = list(params)
if flat:
# Faster but more memory
fp16_grads = [p.grad for p in params if p.grad is not None and p.data.dtype == torch.float16]
fp16_norm = 0.0 if len(fp16_grads) == 0 else float(_flatten_dense_tensors(fp16_grads).norm(p=2, dtype=torch.float32))
fp32_grads = [p.grad for p in params if p.grad is not None and p.data.dtype != torch.float16]
fp32_norm = 0.0 if len(fp32_grads) == 0 else float(_flatten_dense_tensors(fp32_grads).norm(p=2))
grad_norm = (fp16_norm**2 + fp32_norm**2)**0.5
else:
# Slightly slower but less memory
grad_norm = 0.0
for p in params:
if p.grad is not None:
grad_norm += p.grad.norm(p=2, dtype=torch.float32)**2
grad_norm = float(grad_norm**0.5)
return grad_norm / scale
def clipped_grad_scale(grad_norm, max_grad_norm, scale):
clip = grad_norm / max_grad_norm
if clip > 1:
scale = clip * scale
return scale
class FP16FusedAdam(Optimizer):
def __init__(
self,
params,
lr=1e-3,
bias_correction=True,
betas=(0.9, 0.999),
eps=1e-8,
eps_inside_sqrt=False,
weight_decay=0.0,
amsgrad=False,
):
if amsgrad:
raise RuntimeError("FusedAdam does not support the AMSGrad variant.")
defaults = dict(
lr=lr, bias_correction=bias_correction, betas=betas, eps=eps, weight_decay=weight_decay
)
super(FP16FusedAdam, self).__init__(params, defaults)
self.eps_mode = 0 if eps_inside_sqrt else 1
self.FLOAT16_MAX = 65504.0
self.init_state()
def init_state(self):
for group in self.param_groups:
for p in group["params"]:
assert p.requires_grad == True
state = self.state[p]
if len(state) == 0:
state["step"] = 0
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state["exp_avg_sq"] = torch.zeros_like(p.data)
if p.data.dtype == torch.float16:
state["scale_exp_avg"] = 1.0
state["scale_exp_avg_sq"] = 1.0
def step(self, closure=None, scale=1.0):
"""Performs a single optimization step. Scales gradients down by scale
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
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 group in self.param_groups:
bias_correction = 1 if group["bias_correction"] else 0
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad.data
state = self.state[p]
if p.data.dtype == torch.float16:
exp_avg, exp_avg_sq = (
state["exp_avg"].float() * state["scale_exp_avg"],
state["exp_avg_sq"].float() * state["scale_exp_avg_sq"],
)
else:
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
beta1, beta2 = group["betas"]
state["step"] += 1
out_p = torch.tensor([], dtype=torch.float)
fused_adam_step(
p.data,
out_p,
exp_avg,
exp_avg_sq,
grad,
group["lr"],
beta1,
beta2,
group["eps"],
scale,
state["step"],
self.eps_mode,
bias_correction,
group["weight_decay"],
)
if p.data.dtype == torch.float16:
state["scale_exp_avg"] = (
1e-8 + float(torch.norm(exp_avg, float("inf"))) / self.FLOAT16_MAX
)
state["scale_exp_avg_sq"] = (
1e-8 + float(torch.norm(exp_avg_sq, float("inf"))) / self.FLOAT16_MAX
)
state["exp_avg"] = (exp_avg / state["scale_exp_avg"]).half()
state["exp_avg_sq"] = (exp_avg_sq / state["scale_exp_avg_sq"]).half()
return loss
class FusedAdam(Optimizer):
def __init__(
self,
params,
lr=1e-3,
bias_correction=True,
betas=(0.9, 0.999),
eps=1e-8,
eps_inside_sqrt=False,
weight_decay=0.0,
amsgrad=False,
):
if amsgrad:
raise RuntimeError("FusedAdam does not support the AMSGrad variant.")
defaults = dict(
lr=lr, bias_correction=bias_correction, betas=betas, eps=eps, weight_decay=weight_decay
)
super(FusedAdam, self).__init__(params, defaults)
self.eps_mode = 0 if eps_inside_sqrt else 1
def step(self, closure=None, scale=1.0):
"""Performs a single optimization step. Scales gradients down by scale
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
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 group in self.param_groups:
bias_correction = 1 if group["bias_correction"] else 0
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad.data
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.data).float()
# Exponential moving average of squared gradient values
state["exp_avg_sq"] = torch.zeros_like(p.data).float()
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
beta1, beta2 = group["betas"]
state["step"] += 1
out_p = torch.tensor([], dtype=torch.float)
fused_adam_step(
p.data,
out_p,
exp_avg,
exp_avg_sq,
grad,
group["lr"],
beta1,
beta2,
group["eps"],
scale,
state["step"],
self.eps_mode,
bias_correction,
group["weight_decay"],
)
return loss
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