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from functools import partial
from jax import random
import jax.numpy as np
from jax.scipy.linalg import block_diag
import wandb
from .train_helpers import create_train_state, reduce_lr_on_plateau,\
linear_warmup, cosine_annealing, constant_lr, train_epoch, validate
from .dataloading import Datasets
from .seq_model import BatchClassificationModel, RetrievalModel
from .ssm import init_S5SSM
from .ssm_init import make_DPLR_HiPPO
def train(args):
"""
Main function to train over a certain number of epochs
"""
best_test_loss = 100000000
best_test_acc = -10000.0
if args.USE_WANDB:
# Make wandb config dictionary
wandb.init(project=args.wandb_project, job_type='model_training', config=vars(args), entity=args.wandb_entity)
else:
wandb.init(mode='offline')
ssm_size = args.ssm_size_base
ssm_lr = args.ssm_lr_base
# determine the size of initial blocks
block_size = int(ssm_size / args.blocks)
wandb.log({"block_size": block_size})
# Set global learning rate lr (e.g. encoders, etc.) as function of ssm_lr
lr = args.lr_factor * ssm_lr
# Set randomness...
print("[*] Setting Randomness...")
key = random.PRNGKey(args.jax_seed)
init_rng, train_rng = random.split(key, num=2)
# Get dataset creation function
create_dataset_fn = Datasets[args.dataset]
# Dataset dependent logic
if args.dataset in ["imdb-classification", "listops-classification", "aan-classification"]:
padded = True
if args.dataset in ["aan-classification"]:
# Use retreival model for document matching
retrieval = True
print("Using retrieval model for document matching")
else:
retrieval = False
else:
padded = False
retrieval = False
# For speech dataset
if args.dataset in ["speech35-classification"]:
speech = True
print("Will evaluate on both resolutions for speech task")
else:
speech = False
# Create dataset...
init_rng, key = random.split(init_rng, num=2)
trainloader, valloader, testloader, aux_dataloaders, n_classes, seq_len, in_dim, train_size = \
create_dataset_fn(args.dir_name, seed=args.jax_seed, bsz=args.bsz)
print(f"[*] Starting S5 Training on `{args.dataset}` =>> Initializing...")
# Initialize state matrix A using approximation to HiPPO-LegS matrix
Lambda, _, B, V, B_orig = make_DPLR_HiPPO(block_size)
if args.conj_sym:
block_size = block_size // 2
ssm_size = ssm_size // 2
Lambda = Lambda[:block_size]
V = V[:, :block_size]
Vc = V.conj().T
# If initializing state matrix A as block-diagonal, put HiPPO approximation
# on each block
Lambda = (Lambda * np.ones((args.blocks, block_size))).ravel()
V = block_diag(*([V] * args.blocks))
Vinv = block_diag(*([Vc] * args.blocks))
print("Lambda.shape={}".format(Lambda.shape))
print("V.shape={}".format(V.shape))
print("Vinv.shape={}".format(Vinv.shape))
ssm_init_fn = init_S5SSM(H=args.d_model,
P=ssm_size,
Lambda_re_init=Lambda.real,
Lambda_im_init=Lambda.imag,
V=V,
Vinv=Vinv,
C_init=args.C_init,
discretization=args.discretization,
dt_min=args.dt_min,
dt_max=args.dt_max,
conj_sym=args.conj_sym,
clip_eigs=args.clip_eigs,
bidirectional=args.bidirectional)
if retrieval:
# Use retrieval head for AAN task
print("Using Retrieval head for {} task".format(args.dataset))
model_cls = partial(
RetrievalModel,
ssm=ssm_init_fn,
d_output=n_classes,
d_model=args.d_model,
n_layers=args.n_layers,
padded=padded,
activation=args.activation_fn,
dropout=args.p_dropout,
prenorm=args.prenorm,
batchnorm=args.batchnorm,
bn_momentum=args.bn_momentum,
)
else:
model_cls = partial(
BatchClassificationModel,
ssm=ssm_init_fn,
d_output=n_classes,
d_model=args.d_model,
n_layers=args.n_layers,
padded=padded,
activation=args.activation_fn,
dropout=args.p_dropout,
mode=args.mode,
prenorm=args.prenorm,
batchnorm=args.batchnorm,
bn_momentum=args.bn_momentum,
)
# initialize training state
state = create_train_state(model_cls,
init_rng,
padded,
retrieval,
in_dim=in_dim,
bsz=args.bsz,
seq_len=seq_len,
weight_decay=args.weight_decay,
batchnorm=args.batchnorm,
opt_config=args.opt_config,
ssm_lr=ssm_lr,
lr=lr,
dt_global=args.dt_global)
# Training Loop over epochs
best_loss, best_acc, best_epoch = 100000000, -100000000.0, 0 # This best loss is val_loss
count, best_val_loss = 0, 100000000 # This line is for early stopping purposes
lr_count, opt_acc = 0, -100000000.0 # This line is for learning rate decay
step = 0 # for per step learning rate decay
steps_per_epoch = int(train_size/args.bsz)
for epoch in range(args.epochs):
print(f"[*] Starting Training Epoch {epoch + 1}...")
if epoch < args.warmup_end:
print("using linear warmup for epoch {}".format(epoch+1))
decay_function = linear_warmup
end_step = steps_per_epoch * args.warmup_end
elif args.cosine_anneal:
print("using cosine annealing for epoch {}".format(epoch+1))
decay_function = cosine_annealing
# for per step learning rate decay
end_step = steps_per_epoch * args.epochs - (steps_per_epoch * args.warmup_end)
else:
print("using constant lr for epoch {}".format(epoch+1))
decay_function = constant_lr
end_step = None
# TODO: Switch to letting Optax handle this.
# Passing this around to manually handle per step learning rate decay.
lr_params = (decay_function, ssm_lr, lr, step, end_step, args.opt_config, args.lr_min)
train_rng, skey = random.split(train_rng)
state, train_loss, step = train_epoch(state,
skey,
model_cls,
trainloader,
seq_len,
in_dim,
args.batchnorm,
lr_params)
if valloader is not None:
print(f"[*] Running Epoch {epoch + 1} Validation...")
val_loss, val_acc = validate(state,
model_cls,
valloader,
seq_len,
in_dim,
args.batchnorm)
print(f"[*] Running Epoch {epoch + 1} Test...")
test_loss, test_acc = validate(state,
model_cls,
testloader,
seq_len,
in_dim,
args.batchnorm)
print(f"\n=>> Epoch {epoch + 1} Metrics ===")
print(
f"\tTrain Loss: {train_loss:.5f} -- Val Loss: {val_loss:.5f} --Test Loss: {test_loss:.5f} --"
f" Val Accuracy: {val_acc:.4f}"
f" Test Accuracy: {test_acc:.4f}"
)
else:
# else use test set as validation set (e.g. IMDB)
print(f"[*] Running Epoch {epoch + 1} Test...")
val_loss, val_acc = validate(state,
model_cls,
testloader,
seq_len,
in_dim,
args.batchnorm)
print(f"\n=>> Epoch {epoch + 1} Metrics ===")
print(
f"\tTrain Loss: {train_loss:.5f} --Test Loss: {val_loss:.5f} --"
f" Test Accuracy: {val_acc:.4f}"
)
# For early stopping purposes
if val_loss < best_val_loss:
count = 0
best_val_loss = val_loss
else:
count += 1
if val_acc > best_acc:
# Increment counters etc.
count = 0
best_loss, best_acc, best_epoch = val_loss, val_acc, epoch
if valloader is not None:
best_test_loss, best_test_acc = test_loss, test_acc
else:
best_test_loss, best_test_acc = best_loss, best_acc
# Do some validation on improvement.
if speech:
# Evaluate on resolution 2 val and test sets
print(f"[*] Running Epoch {epoch + 1} Res 2 Validation...")
val2_loss, val2_acc = validate(state,
model_cls,
aux_dataloaders['valloader2'],
int(seq_len // 2),
in_dim,
args.batchnorm,
step_rescale=2.0)
print(f"[*] Running Epoch {epoch + 1} Res 2 Test...")
test2_loss, test2_acc = validate(state, model_cls, aux_dataloaders['testloader2'], int(seq_len // 2), in_dim, args.batchnorm, step_rescale=2.0)
print(f"\n=>> Epoch {epoch + 1} Res 2 Metrics ===")
print(
f"\tVal2 Loss: {val2_loss:.5f} --Test2 Loss: {test2_loss:.5f} --"
f" Val Accuracy: {val2_acc:.4f}"
f" Test Accuracy: {test2_acc:.4f}"
)
# For learning rate decay purposes:
input = lr, ssm_lr, lr_count, val_acc, opt_acc
lr, ssm_lr, lr_count, opt_acc = reduce_lr_on_plateau(input, factor=args.reduce_factor, patience=args.lr_patience, lr_min=args.lr_min)
# Print best accuracy & loss so far...
print(
f"\tBest Val Loss: {best_loss:.5f} -- Best Val Accuracy:"
f" {best_acc:.4f} at Epoch {best_epoch + 1}\n"
f"\tBest Test Loss: {best_test_loss:.5f} -- Best Test Accuracy:"
f" {best_test_acc:.4f} at Epoch {best_epoch + 1}\n"
)
if valloader is not None:
if speech:
wandb.log(
{
"Training Loss": train_loss,
"Val loss": val_loss,
"Val Accuracy": val_acc,
"Test Loss": test_loss,
"Test Accuracy": test_acc,
"Val2 loss": val2_loss,
"Val2 Accuracy": val2_acc,
"Test2 Loss": test2_loss,
"Test2 Accuracy": test2_acc,
"count": count,
"Learning rate count": lr_count,
"Opt acc": opt_acc,
"lr": state.opt_state.inner_states['regular'].inner_state.hyperparams['learning_rate'],
"ssm_lr": state.opt_state.inner_states['ssm'].inner_state.hyperparams['learning_rate']
}
)
else:
wandb.log(
{
"Training Loss": train_loss,
"Val loss": val_loss,
"Val Accuracy": val_acc,
"Test Loss": test_loss,
"Test Accuracy": test_acc,
"count": count,
"Learning rate count": lr_count,
"Opt acc": opt_acc,
"lr": state.opt_state.inner_states['regular'].inner_state.hyperparams['learning_rate'],
"ssm_lr": state.opt_state.inner_states['ssm'].inner_state.hyperparams['learning_rate']
}
)
else:
wandb.log(
{
"Training Loss": train_loss,
"Val loss": val_loss,
"Val Accuracy": val_acc,
"count": count,
"Learning rate count": lr_count,
"Opt acc": opt_acc,
"lr": state.opt_state.inner_states['regular'].inner_state.hyperparams['learning_rate'],
"ssm_lr": state.opt_state.inner_states['ssm'].inner_state.hyperparams['learning_rate']
}
)
wandb.run.summary["Best Val Loss"] = best_loss
wandb.run.summary["Best Val Accuracy"] = best_acc
wandb.run.summary["Best Epoch"] = best_epoch
wandb.run.summary["Best Test Loss"] = best_test_loss
wandb.run.summary["Best Test Accuracy"] = best_test_acc
if count > args.early_stop_patience:
break
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