DL-Art-School/codes/trainer/batch_size_optimizer.py
James Betker e16af944c0 BSO fix
2022-02-12 20:01:04 -07:00

133 lines
6.2 KiB
Python

import math
import random
import torch
from torch import distributed
from torch._C._distributed_c10d import ReduceOp
from utils.util import opt_get
def create_batch_size_optimizer(opt_train):
if 'batch_size_optimizer' in opt_train.keys():
if opt_train['batch_size_optimizer']['type'] == 'gradient_direction':
return GradientDirectionOptimizer(opt_train)
return MegabatchBatchSizeOptimizer(opt_train)
def grad(p):
if p.grad is None:
return torch.tensor(0)
return p.grad.detach().clone()
# Base class for BatchSizeOptimizers.
class BatchSizeOptimizer:
def focus(self, optimizer):
pass
def should_step(self, it):
raise NotImplementedError
def get_statistics(self):
return {}
# BatchSizeOptimizer that just steps every megabatch.
class MegabatchBatchSizeOptimizer(BatchSizeOptimizer):
def __init__(self, opt_train):
pass
def should_step(self, it):
return True
# BatchSizeOptimizer that uses the gradient direction of a few parameters to determine when to step.
# Very similar to what is described in https://aclanthology.org/2020.acl-main.323.pdf
# Special note: this class will ALWAYS accumulate, at a minimum, 3 batches. Plan accordingly.
class GradientDirectionOptimizer(BatchSizeOptimizer):
def __init__(self, opt_train):
self.opt = opt_train['batch_size_optimizer']
self.max_full_batches = opt_get(self.opt, ['max_full_batches'], 10)
self.parameters_to_poll = opt_get(self.opt, ['poll_parameters'], 8)
self.recalculate_directions_every = opt_get(self.opt, ['recalculate_directions_steps'], 1)
self.current_model = None
# Metrics
self.steps_taken = 0
self.last_number_iterations = torch.zeros((128,))
self.last_number_iterations_i = 0
self.last_number_iterations_filled = False
def vector_angle(self, v1, v2):
if torch.all(v1 == 0) or torch.all(v2 == 0):
return torch.tensor(0, device=v1.device)
with torch.no_grad():
v1 = v1.flatten()
v2 = v2.flatten()
v1_norm = (v1 ** 2).sum().sqrt()
v2_norm = (v2 ** 2).sum().sqrt()
angle = torch.arccos((torch.dot(v1, v2)) / (v1_norm * v2_norm))
return angle
def focus(self, model):
if not hasattr(model, '_gradient_direction_optimizer_finished') or model._gradient_direction_optimizer_finished:
all_params = list(filter(lambda t: '.weight' in t[0] and not hasattr(t[1].requires_grad, 'DO_NOT_TRAIN'),
list(model.named_parameters()))) # Extracts weight parameters. Who cares about biases anyways? :)
num_params = min(len(all_params), self.parameters_to_poll)
model._gradient_direction_optimizer_params = random.sample(all_params, num_params)
model._gradient_direction_optimizer_prior_directions = [0 for _ in range(num_params)]
model._gradient_direction_optimizer_stopped_decreasing = [False for _ in range(num_params)]
model._gradient_direction_optimizer_prior_grads = None
model._gradient_direction_optimizer_step = 0
model._gradient_direction_optimizer_finished = False
self.current_model = model
def should_step(self, it):
model = self.current_model
model._gradient_direction_optimizer_step += 1
cur_grads = [grad(p) for k, p in model._gradient_direction_optimizer_params]
for cg in cur_grads:
if torch.any(torch.isnan(cg)):
print("BSO: found NaN. Passing it off to the GradScaler..")
return True
if model._gradient_direction_optimizer_prior_grads is not None:
cur_dir = [self.vector_angle(lgrad, cgrad) for lgrad, cgrad in zip(model._gradient_direction_optimizer_prior_grads, cur_grads)]
delta_dir = [(cdir - ldir) for cdir, ldir in zip(cur_dir, model._gradient_direction_optimizer_prior_directions)]
model._gradient_direction_optimizer_prior_directions = cur_dir
model._gradient_direction_optimizer_stopped_decreasing = [sd or dd < 0 for sd, dd in zip(model._gradient_direction_optimizer_stopped_decreasing, delta_dir)]
all_finished = all(model._gradient_direction_optimizer_stopped_decreasing)
# For distributed optimizers, like ZeroRedundancyAdam, we need to reach a consensus as to whether or not to reduce.
if distributed.is_initialized() and distributed.get_world_size() > 1:
all_finished = torch.tensor(all_finished)
distributed.all_reduce(all_finished, ReduceOp.BAND)
all_finished = torch.all(all_finished)
if all_finished or model._gradient_direction_optimizer_step >= self.max_full_batches:
# <0 means the gradient direction is getting larger. Halt batch accumulation here.
model._gradient_direction_optimizer_finished = True
self.record_number_steps(model._gradient_direction_optimizer_step)
# Fix the gradients. We've accumulated _gradient_direction_optimizer_step steps total, so we need to divide the grads by that.
for p in model.parameters():
if p.requires_grad:
p.grad = p.grad / model._gradient_direction_optimizer_step
return True
model._gradient_direction_optimizer_prior_grads = cur_grads
return False
def record_number_steps(self, steps):
self.last_number_iterations[self.last_number_iterations_i] = steps
if self.last_number_iterations_i == self.last_number_iterations.shape[0]-1:
self.last_number_iterations_filled = True
self.last_number_iterations_i = (self.last_number_iterations_i + 1) % self.last_number_iterations.shape[0]
self.steps_taken += 1
def get_statistics(self):
res = {"batch_size_opt_total_steps": self.steps_taken}
if self.last_number_iterations_filled:
res["batch_size_opt_avg_iterations_per_step"] = self.last_number_iterations.mean().item()
else:
res["batch_size_opt_avg_iterations_per_step"] = self.last_number_iterations[:self.last_number_iterations_i].mean().item()
return res