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train_util.py
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import copy
import functools
import os
import blobfile as bf
import numpy as np
import torch as th
import torch.distributed as dist
from torch.nn.parallel.distributed import DistributedDataParallel as DDP
from torch.optim import AdamW
import io
from diffuseq.utils import dist_util, logger
from diffuseq.utils.fp16_util import (
make_master_params,
master_params_to_model_params,
model_grads_to_master_grads,
unflatten_master_params,
zero_grad,
)
from diffuseq.utils.nn import update_ema
from diffuseq.step_sample import LossAwareSampler, UniformSampler
# For ImageNet experiments, this was a good default value.
# We found that the lg_loss_scale quickly climbed to
# 20-21 within the first ~1K steps of training.
INITIAL_LOG_LOSS_SCALE = 20.0
class TrainLoop:
def __init__(
self,
*,
model,
diffusion,
data,
batch_size,
microbatch,
lr,
ema_rate,
log_interval,
save_interval,
resume_checkpoint,
use_fp16=False,
fp16_scale_growth=1e-3,
schedule_sampler=None,
weight_decay=0.0,
learning_steps=0,
checkpoint_path='',
gradient_clipping=-1.,
eval_data=None,
eval_interval=-1,
):
self.model = model
self.diffusion = diffusion
self.data = data
self.eval_data = eval_data
self.batch_size = batch_size
self.microbatch = microbatch if microbatch > 0 else batch_size
self.lr = lr
self.ema_rate = (
[ema_rate]
if isinstance(ema_rate, float)
else [float(x) for x in ema_rate.split(",")]
)
self.log_interval = log_interval
self.eval_interval = eval_interval
self.save_interval = save_interval
self.resume_checkpoint = resume_checkpoint
self.use_fp16 = use_fp16
self.fp16_scale_growth = fp16_scale_growth
self.schedule_sampler = schedule_sampler or UniformSampler(diffusion)
self.weight_decay = weight_decay
self.learning_steps = learning_steps
self.gradient_clipping = gradient_clipping
self.step = 0
self.resume_step = 0
self.global_batch = self.batch_size * dist.get_world_size()
self.model_params = list(self.model.parameters())
self.master_params = self.model_params
self.lg_loss_scale = INITIAL_LOG_LOSS_SCALE
self.sync_cuda = th.cuda.is_available()
self.checkpoint_path = checkpoint_path # DEBUG **
self._load_and_sync_parameters()
if self.use_fp16:
self._setup_fp16()
self.opt = AdamW(self.master_params, lr=self.lr, weight_decay=self.weight_decay)
if self.resume_step:
# self._load_optimizer_state()
frac_done = (self.step + self.resume_step) / self.learning_steps
lr = self.lr * (1 - frac_done)
self.opt = AdamW(self.master_params, lr=lr, weight_decay=self.weight_decay)
# Model was resumed, either due to a restart or a checkpoint
# being specified at the command line.
self.ema_params = [
self._load_ema_parameters(rate) for rate in self.ema_rate
]
else:
self.ema_params = [
copy.deepcopy(self.master_params) for _ in range(len(self.ema_rate))
]
if th.cuda.is_available(): # DEBUG **
self.use_ddp = True
print(dist_util.dev())
self.ddp_model = DDP(
self.model,
device_ids=[dist_util.dev()],
output_device=dist_util.dev(),
broadcast_buffers=False,
bucket_cap_mb=128,
find_unused_parameters=False,
)
else:
if dist.get_world_size() > 1:
logger.warn(
"Distributed training requires CUDA. "
"Gradients will not be synchronized properly!"
)
self.use_ddp = False
self.ddp_model = self.model
def _load_and_sync_parameters(self):
resume_checkpoint = find_resume_checkpoint() or self.resume_checkpoint
if resume_checkpoint[-3:] == '.pt':
self.resume_step = parse_resume_step_from_filename(resume_checkpoint)
if dist.get_rank() == 0:
logger.log(f"loading model from checkpoint: {resume_checkpoint}...")
self.model.load_state_dict(
dist_util.load_state_dict(
actual_model_path(resume_checkpoint), map_location=dist_util.dev()
)
)
dist_util.sync_params(self.model.parameters())
def _load_ema_parameters(self, rate):
ema_params = copy.deepcopy(self.master_params)
main_checkpoint = find_resume_checkpoint() or self.resume_checkpoint
ema_checkpoint = find_ema_checkpoint(main_checkpoint, self.resume_step, rate)
if ema_checkpoint:
if dist.get_rank() == 0:
logger.log(f"loading EMA from checkpoint: {ema_checkpoint}...")
state_dict = dist_util.load_state_dict(
actual_model_path(ema_checkpoint), map_location=dist_util.dev()
)
ema_params = self._state_dict_to_master_params(state_dict)
dist_util.sync_params(ema_params)
return ema_params
def _load_optimizer_state(self):
main_checkpoint = find_resume_checkpoint() or self.resume_checkpoint
if bf.exists(main_checkpoint):
logger.log(f"loading optimizer state from checkpoint: {main_checkpoint}")
state_dict = dist_util.load_state_dict(
actual_model_path(main_checkpoint), map_location=dist_util.dev()
)
self.opt.load_state_dict(state_dict)
def _setup_fp16(self):
self.master_params = make_master_params(self.model_params)
self.model.convert_to_fp16()
def run_loop(self):
while (
not self.learning_steps
or self.step + self.resume_step < self.learning_steps
):
batch, cond = next(self.data)
self.run_step(batch, cond)
if self.step % self.log_interval == 0:
logger.dumpkvs()
if self.eval_data is not None and self.step % self.eval_interval == 0:
batch_eval, cond_eval = next(self.eval_data)
self.forward_only(batch_eval, cond_eval)
print('eval on validation set')
logger.dumpkvs()
if self.step > 0 and self.step % self.save_interval == 0:
self.save()
# Run for a finite amount of time in integration tests.
if os.environ.get("DIFFUSION_TRAINING_TEST", "") and self.step > 0:
return
self.step += 1
# Save the last checkpoint if it wasn't already saved.
if (self.step - 1) % self.save_interval != 0:
self.save()
def run_step(self, batch, cond):
self.forward_backward(batch, cond)
if self.use_fp16:
self.optimize_fp16()
else:
self.optimize_normal()
self.log_step()
def forward_only(self, batch, cond):
with th.no_grad():
zero_grad(self.model_params)
for i in range(0, batch.shape[0], self.microbatch):
micro = batch[i: i + self.microbatch].to(dist_util.dev())
micro_cond = {
k: v[i: i + self.microbatch].to(dist_util.dev())
for k, v in cond.items()
}
last_batch = (i + self.microbatch) >= batch.shape[0]
t, weights = self.schedule_sampler.sample(micro.shape[0], dist_util.dev())
# print(micro_cond.keys())
compute_losses = functools.partial(
self.diffusion.training_losses,
self.ddp_model,
micro,
t,
model_kwargs=micro_cond,
)
if last_batch or not self.use_ddp:
losses = compute_losses()
else:
with self.ddp_model.no_sync():
losses = compute_losses()
log_loss_dict(
self.diffusion, t, {f"eval_{k}": v * weights for k, v in losses.items()}
)
def forward_backward(self, batch, cond):
zero_grad(self.model_params)
for i in range(0, batch.shape[0], self.microbatch):
micro = batch[i : i + self.microbatch].to(dist_util.dev())
micro_cond = {
k: v[i : i + self.microbatch].to(dist_util.dev())
for k, v in cond.items()
}
last_batch = (i + self.microbatch) >= batch.shape[0]
t, weights = self.schedule_sampler.sample(micro.shape[0], dist_util.dev())
# print(micro_cond.keys())
compute_losses = functools.partial(
self.diffusion.training_losses,
self.ddp_model,
micro,
t,
model_kwargs=micro_cond,
)
if last_batch or not self.use_ddp:
losses = compute_losses()
else:
with self.ddp_model.no_sync():
losses = compute_losses()
if isinstance(self.schedule_sampler, LossAwareSampler):
self.schedule_sampler.update_with_local_losses(
t, losses["loss"].detach()
)
loss = (losses["loss"] * weights).mean()
log_loss_dict(
self.diffusion, t, {k: v * weights for k, v in losses.items()}
)
if self.use_fp16:
loss_scale = 2 ** self.lg_loss_scale
(loss * loss_scale).backward()
else:
loss.backward()
def optimize_fp16(self):
if any(not th.isfinite(p.grad).all() for p in self.model_params):
self.lg_loss_scale -= 1
logger.log(f"Found NaN, decreased lg_loss_scale to {self.lg_loss_scale}")
return
model_grads_to_master_grads(self.model_params, self.master_params)
self.master_params[0].grad.mul_(1.0 / (2 ** self.lg_loss_scale))
self._log_grad_norm()
self._anneal_lr()
self.opt.step()
for rate, params in zip(self.ema_rate, self.ema_params):
update_ema(params, self.master_params, rate=rate)
master_params_to_model_params(self.model_params, self.master_params)
self.lg_loss_scale += self.fp16_scale_growth
def grad_clip(self):
# print('doing gradient clipping')
max_grad_norm=self.gradient_clipping #3.0
if hasattr(self.opt, "clip_grad_norm"):
# Some optimizers (like the sharded optimizer) have a specific way to do gradient clipping
self.opt.clip_grad_norm(max_grad_norm)
# else:
# assert False
# elif hasattr(self.model, "clip_grad_norm_"):
# # Some models (like FullyShardedDDP) have a specific way to do gradient clipping
# self.model.clip_grad_norm_(args.max_grad_norm)
else:
# Revert to normal clipping otherwise, handling Apex or full precision
th.nn.utils.clip_grad_norm_(
self.model.parameters(), #amp.master_params(self.opt) if self.use_apex else
max_grad_norm,
)
def optimize_normal(self):
if self.gradient_clipping > 0:
self.grad_clip()
self._log_grad_norm()
self._anneal_lr()
self.opt.step()
for rate, params in zip(self.ema_rate, self.ema_params):
update_ema(params, self.master_params, rate=rate)
def _log_grad_norm(self):
sqsum = 0.0
# cnt = 0
for p in self.master_params:
# print(cnt, p) ## DEBUG
# print(cnt, p.grad)
# cnt += 1
if p.grad != None:
sqsum += (p.grad ** 2).sum().item()
logger.logkv_mean("grad_norm", np.sqrt(sqsum))
def _anneal_lr(self):
if not self.learning_steps:
return
frac_done = (self.step + self.resume_step) / self.learning_steps
lr = self.lr * (1 - frac_done)
for param_group in self.opt.param_groups:
param_group["lr"] = lr
def log_step(self):
logger.logkv("step", self.step + self.resume_step)
logger.logkv("samples", (self.step + self.resume_step + 1) * self.global_batch)
if self.use_fp16:
logger.logkv("lg_loss_scale", self.lg_loss_scale)
def save(self):
def save_checkpoint(rate, params):
state_dict = self._master_params_to_state_dict(params)
if dist.get_rank() == 0:
logger.log(f"saving model {rate}...")
if not rate:
filename = f"model{(self.step+self.resume_step):06d}.pt"
else:
filename = f"ema_{rate}_{(self.step+self.resume_step):06d}.pt"
print('writing to', bf.join(get_blob_logdir(), filename))
print('writing to', bf.join(self.checkpoint_path, filename))
# with bf.BlobFile(bf.join(get_blob_logdir(), filename), "wb") as f:
# th.save(state_dict, f)
with bf.BlobFile(bf.join(self.checkpoint_path, filename), "wb") as f: # DEBUG **
th.save(state_dict, f) # save locally
# pass # save empty
# save_checkpoint(0, self.master_params)
for rate, params in zip(self.ema_rate, self.ema_params):
save_checkpoint(rate, params)
dist.barrier()
def _master_params_to_state_dict(self, master_params):
if self.use_fp16:
master_params = unflatten_master_params(
list(self.model.parameters()), master_params # DEBUG **
)
state_dict = self.model.state_dict()
for i, (name, _value) in enumerate(self.model.named_parameters()):
assert name in state_dict
state_dict[name] = master_params[i]
return state_dict
def _state_dict_to_master_params(self, state_dict):
params = [state_dict[name] for name, _ in self.model.named_parameters()]
if self.use_fp16:
return make_master_params(params)
else:
return params
def parse_resume_step_from_filename(filename):
"""
Parse filenames of the form path/to/modelNNNNNN.pt, where NNNNNN is the
checkpoint's number of steps.
"""
if filename[-3:] == '.pt':
return int(filename[-9:-3])
else:
return 0
def get_blob_logdir():
return os.environ.get("DIFFUSION_BLOB_LOGDIR", logger.get_dir())
def find_resume_checkpoint():
# On your infrastructure, you may want to override this to automatically
# discover the latest checkpoint on your blob storage, etc.
return None
def find_ema_checkpoint(main_checkpoint, step, rate):
if main_checkpoint is None:
return None
filename = f"ema_{rate}_{(step):06d}.pt"
path = bf.join(bf.dirname(main_checkpoint), filename)
if bf.exists(path):
return path
return None
def log_loss_dict(diffusion, ts, losses):
for key, values in losses.items():
logger.logkv_mean(key, values.mean().item())
# Log the quantiles (four quartiles, in particular).
for sub_t, sub_loss in zip(ts.cpu().numpy(), values.detach().cpu().numpy()):
quartile = int(4 * sub_t / diffusion.num_timesteps)
logger.logkv_mean(f"{key}_q{quartile}", sub_loss)
def actual_model_path(model_path):
return model_path