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train.py
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train.py
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import itertools
import os
import pickle
import time
from dataclasses import dataclass
from typing import Callable, Optional
import datasets
import numpy as np
import torch
import torch_optimizer as toptim
from transformers.modeling_utils import load_sharded_checkpoint
import weak_to_strong.logger as logger
from weak_to_strong.common import clear_mem
from weak_to_strong.eval import eval_model_acc
from weak_to_strong.loss import xent_loss
from weak_to_strong.model import TransformerWithHead
@dataclass
class ModelConfig:
name: str
default_lr: float
eval_batch_size: int
custom_kwargs: Optional[dict] = None
gradient_checkpointing: bool = False
model_parallel: bool = False
default_optimizer: str = "adam"
def train_model(
model: torch.nn.Module,
ds: datasets.Dataset,
batch_size: int,
lr: float = 1e-5,
loss_fn: Callable = xent_loss,
log_every: int = 10,
eval_every: int = 100,
eval_batch_size: int = 256,
minibatch_size: int = 8,
eval_ds: Optional[datasets.Dataset] = None,
gradient_checkpointing: bool = False,
train_with_dropout: bool = False,
epochs: int = 1,
lr_schedule: str = "cosine_anneal",
optimizer_name: str = "adam",
):
print("LR", lr, "batch_size", batch_size, "minibatch_size", minibatch_size)
assert batch_size % minibatch_size == 0, "batch size must be divisible by minibatch size"
# we purposefully turn off dropout, for determinism
# this seems to help for 1 epoch finetuning anyways
if train_with_dropout:
model.train()
else:
model.eval()
if gradient_checkpointing:
(
model if hasattr(model, "gradient_checkpointing_enable") else model.module
).gradient_checkpointing_enable()
nsteps = len(ds) * epochs // batch_size
def lr_schedule_fn(step):
if lr_schedule == "constant":
return 1
else:
assert False, f"invalid lr schedule, {lr_schedule}, must be constant or cosine_anneal"
if optimizer_name.lower() == "adam":
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
elif optimizer_name.lower() == "adafactor":
optimizer = toptim.Adafactor(model.parameters(), lr=lr)
else:
assert False, f"invalid optimizer {optimizer_name}, must be adam or adafactor"
if lr_schedule == "cosine_anneal":
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, nsteps)
else:
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_schedule_fn)
step = 0
it = itertools.chain.from_iterable(itertools.repeat(ds, epochs))
losses = []
accuracies = []
eval_acc_dict = {}
# If the model is wrapped by DataParallel, it doesn't have a device. In this case,
# we use GPU 0 as the output device. This sadly means that this device will store
# a bit more data than other ones, but hopefully should not be too big of a deal.
io_device = model.device if hasattr(model, "device") else 0
while step < nsteps:
loss_tot = 0
if eval_every and (step + 1) % eval_every == 0:
eval_results = eval_model_acc(model, eval_ds, eval_batch_size)
if gradient_checkpointing:
(
model if hasattr(model, "gradient_checkpointing_enable") else model.module
).gradient_checkpointing_enable()
if train_with_dropout:
model.train()
eval_accs = np.mean([r["acc"] for r in eval_results])
eval_acc_dict[step] = eval_accs
logger.logkv("eval_accuracy", eval_accs)
all_logits = []
all_labels = []
for i in range(batch_size // minibatch_size):
try:
mbatch = [next(it) for _ in range(minibatch_size)]
except StopIteration:
break
input_ids = (
torch.nn.utils.rnn.pad_sequence([torch.tensor(ex["input_ids"]) for ex in mbatch])
.transpose(
0,
1,
)
.to(io_device)
)
labels = torch.tensor([ex["soft_label"] for ex in mbatch]).to(io_device)
logits = model(input_ids)
all_logits.extend(logits.to(io_device))
all_labels.extend(labels)
all_logits = torch.stack(all_logits)
all_labels = torch.stack(all_labels)
loss = loss_fn(all_logits, all_labels, step_frac=step / nsteps)
loss_tot += loss.item()
loss.backward()
losses.append(loss_tot)
accuracies.append(
torch.mean(
(torch.argmax(all_logits, dim=1) == torch.argmax(all_labels, dim=1)).to(
torch.float32
)
).item()
)
logger.logkvs(
{
"step": step,
"progress": step / nsteps,
"loss": loss_tot,
"train_accuracy": accuracies[-1],
"lr": lr_scheduler.get_last_lr()[0],
}
)
optimizer.step()
optimizer.zero_grad()
lr_scheduler.step()
if log_every and step % log_every == 0:
print(
f"Step: {step}/{nsteps} Recent losses: {np.mean(losses)} {np.mean(accuracies)} {len(losses)}"
)
losses = []
accuracies = []
step += 1
logger.dumpkvs()
final_eval_results = None
if eval_every:
print("Final evaluation:")
final_eval_results = eval_model_acc(model, eval_ds, eval_batch_size)
logger.logkv("eval_accuracy", np.mean([r["acc"] for r in final_eval_results]))
logger.dumpkvs()
return final_eval_results
def train_and_save_model(
model_config: ModelConfig,
train_ds: datasets.Dataset,
test_ds: datasets.Dataset,
inference_ds: Optional[datasets.Dataset] = None,
*,
batch_size: int,
lr: float,
epochs: int,
eval_batch_size: Optional[int] = None,
minibatch_size_per_device: Optional[int] = None,
save_path: Optional[str] = None,
loss_fn: Callable = xent_loss,
label: str = "default",
force_retrain: bool = False,
train_with_dropout: bool = False,
linear_probe: bool = False,
lr_schedule: str = "constant",
optimizer_name: str = "adam",
eval_every: Optional[int] = None,
):
if eval_batch_size is None:
eval_batch_size = batch_size
if minibatch_size_per_device is None:
minibatch_size_per_device = 1
gradient_checkpointing = model_config.gradient_checkpointing
custom_kwargs = model_config.custom_kwargs or {}
def maybe_load_model(model):
if os.path.exists(os.path.join(save_path, "results.pkl")) and not force_retrain:
print("loading from", save_path)
checkpoint_path = os.path.join(save_path, "pytorch_model.bin")
if not os.path.exists(checkpoint_path):
# Assume this means we have a sharded checkpoint, and load it appropriately
load_sharded_checkpoint(model, checkpoint_path)
else:
state_dict = torch.load(os.path.join(save_path, "pytorch_model.bin"))
state_dict = {
k.replace("transformer.module", "transformer"): v
for (k, v) in state_dict.items()
}
custom_kwargs["state_dict"] = state_dict
return True
return False
already_trained = False
# Load the model
if model_config.model_parallel:
assert torch.cuda.device_count() > 1, f"you might want more gpus for {model_config.name}"
model = TransformerWithHead.from_pretrained(
model_config.name,
num_labels=2,
device_map="auto",
linear_probe=linear_probe,
**custom_kwargs,
)
already_trained = maybe_load_model(model)
# slight misnomer, more like minibatch_size_per_dp_replica
minibatch_size = minibatch_size_per_device
else:
model = TransformerWithHead.from_pretrained(
model_config.name, num_labels=2, linear_probe=linear_probe, **custom_kwargs
).to("cuda")
already_trained = maybe_load_model(model)
# data parallel: currently not supported with model parallel
minibatch_size = min(minibatch_size_per_device * torch.cuda.device_count(), batch_size)
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model, output_device=0)
print(
"Using",
torch.cuda.device_count(),
"GPUs, setting minibatch_size to",
minibatch_size,
)
else:
minibatch_size = minibatch_size_per_device
if already_trained:
test_results = eval_model_acc(model, test_ds, eval_batch_size)
else:
start = time.time()
test_results = train_model(
model,
train_ds,
batch_size,
lr=lr,
epochs=epochs,
eval_ds=test_ds,
gradient_checkpointing=gradient_checkpointing,
loss_fn=loss_fn,
eval_batch_size=eval_batch_size,
eval_every=eval_every,
minibatch_size=minibatch_size,
train_with_dropout=train_with_dropout,
lr_schedule=lr_schedule,
optimizer_name=optimizer_name,
)
print("Model training took", time.time() - start, "seconds")
if save_path:
# Note: If the model is wrapped by DataParallel, we need to unwrap it before saving
(model if hasattr(model, "save_pretrained") else model.module).save_pretrained(
save_path
)
print("saved", save_path)
inference_results = None
if inference_ds:
inference_results = eval_model_acc(model, inference_ds, eval_batch_size)
logger.logkv("inference_accuracy", np.mean([r["acc"] for r in inference_results]))
if save_path:
with open(os.path.join(save_path, "results.pkl"), "wb") as f:
pickle.dump(
{
"avg_acc_test": float(np.mean([r["acc"] for r in test_results])),
"avg_acc_inference": float(
np.mean([r["acc"] for r in inference_results] if inference_results else [])
),
"test_results": test_results,
"inference_results": inference_results if inference_results else [],
},
f,
)
# try to clean up memory
clear_mem()
logger.shutdown()
return test_results, inference_results