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train.py
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train.py
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import jax
import jax.numpy as jnp
import train_model
import low_bandwidth
import optax
import numpy as np
import data_set
import communication
import result_collecting
import time
def compute_metrics(*, logits, labels, num_classes):
loss = train_model.get_loss(logits=logits, labels=labels, num_classes=num_classes)
accuracy = jnp.mean(jnp.argmax(logits, -1) == labels)
metrics = {'loss': loss, 'accuracy': accuracy,}
return metrics
@jax.jit
def train_step(params, opt_state, temp_grads, batch_image, batch_label, comm, model, optimizer, num_classes, compression=False, gradient_spar=False, rng=None):
@jax.jit
def forward(params, model, batch_image, batch_label, loss_func, rngs=None):
logits = model.apply(params, batch_image, rngs=rngs)
loss = loss_func(logits=logits, labels=batch_label, num_classes=num_classes)
return loss, logits
grad_fn = jax.value_and_grad(forward, has_aux=True)
if type(model).__name__ == "inception_v4":
loss_func = train_model.l2_loss
rngs = {"dropout": rng}
else:
loss_func = train_model.get_loss
rngs = None
(_, logits), grads = grad_fn(params, model, batch_image, batch_label, loss_func, rngs)
if compression and gradient_spar:
grads, temp_grads = low_bandwidth.gradient_sparcification(grads, temp_grads, 0.000001)
compressed_grads = low_bandwidth.compress(grads)
grads = communication.ring_all_reduce(comm, grads, compression=True)
grads = low_bandwidth.decompress(compressed_grads, grads)
elif not compression and gradient_spar:
grads, temp_grads = low_bandwidth.gradient_sparcification(grads, temp_grads, 0.000001)
grads = communication.ring_all_reduce(comm, grads, compression=False)
elif compression and not gradient_spar:
compressed_grads = low_bandwidth.compress(grads)
grads = communication.ring_all_reduce(comm, grads, compression=True)
grads = low_bandwidth.decompress(compressed_grads, grads)
updates, opt_state = optimizer.update(grads, opt_state, params) # optimizer defined in global name space
params = optax.apply_updates(params, updates)
metrics = compute_metrics(logits=logits, labels=batch_label)
return params, opt_state, temp_grads, metrics
@jax.jit
def hypo_train_step(params, opt_state, batch_image, batch_label, model, optimizer, num_classes):
@jax.jit
def forward(params, model, batch_image, batch_label):
logits = model.apply(params, batch_image)
loss = train_model.get_loss(logits=logits, labels=batch_label, num_classes=num_classes)
return loss, logits
grad_fn = jax.value_and_grad(forward, has_aux=True)
(_, logits), grads = grad_fn(params, model, batch_image, batch_label)
updates, opt_state = optimizer.update(grads, opt_state, params) # optimizer defined in global name space
params = optax.apply_updates(params, updates)
metrics = compute_metrics(logits=logits, labels=batch_label)
return params, opt_state, metrics
def train_epoch(params, opt_state, train_ds, temp_grads, batch_size:int,
epoch:int, rng, comm, model, optimizer, experiment_type,
input_shape, num_classes, compression:bool, gradient_spar:bool, hypothesis:bool):
train_ds_size = len(train_ds['image'])
steps_per_epoch = train_ds_size // batch_size
permed_data = jax.random.permutation(rng, train_ds_size) # shuffle the data
permed_data = permed_data[:steps_per_epoch * batch_size] # remove the last batch if it is not full
permed_data = permed_data.reshape((steps_per_epoch, batch_size)) # split the data into batches
batch_metrics = []
start = time.perf_counter()
for batch in permed_data:
batch = {k: v[batch, ...] for k, v in train_ds.items()}
if hypothesis:
params, opt_state, metrics = hypo_train_step(params, opt_state, batch['image'], batch['label'], model, optimizer, num_classes)
else:
params, opt_state, temp_grads, metrics = train_step(params, opt_state, temp_grads, jax.image.resize(image=batch["image"],shape=(batch_size,input_shape,input_shape,3)), batch["label"], comm, model, optimizer, num_classes, compression, gradient_spar, hypothesis, rng=rng)
batch_metrics.append(metrics)
batch_metrics_np = jax.device_get(batch_metrics)
epoch_metrics_np = {i: np.mean([metrics[i] for metrics in batch_metrics_np])for i in batch_metrics_np[0]}
print(f"\nepoch: {epoch} - loss: {epoch_metrics_np['loss']} - accuracy: {epoch_metrics_np['accuracy'] * 100}")
if not hypothesis:
if comm.Get_rank() == 0:
result_collecting.save_as_json(experiment_type=experiment_type, epoch=epoch, loss=epoch_metrics_np['loss'], accuracy=epoch_metrics_np['accuracy'], time_for_epoch=(time.perf_counter-start))
else:
result_collecting.save_as_json(experiment_type=experiment_type, epoch=epoch, loss=epoch_metrics_np['loss'], accuracy=epoch_metrics_np['accuracy'], time_for_epoch=(time.perf_counter-start))
return params, opt_state, temp_grads
def train(optimizer, train_ds, comm,experiment_type, num_classes, get_model_func, compression, gradient_spar, input_shape, hypothesis = False):
#print(train_ds)
num_epochs = 20
batch_size = 32
params, model = get_model_func(num_classes=num_classes)
rng = jax.random.PRNGKey(0)
rng, init_rng = jax.random.split(rng)
temp_grads = low_bandwidth.get_temp_grads(params)
#print(params,temp_grads)
opt_state = optimizer.init(params)
for epoch in range(1, num_epochs + 1):
rng, input_rng = jax.random.split(rng)
#print(temp_grads)
params, opt_state, temp_grads = train_epoch(params=params, opt_state=opt_state, train_ds=train_ds, temp_grads=temp_grads, batch_size=batch_size, epoch=epoch, rng=rng, comm=comm, model=model, optimizer=optimizer, experiment_type=experiment_type, num_classes=num_classes, input_shape=input_shape, compression=compression, gradient_spar=gradient_spar, hypothesis = hypothesis)
return params