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compress_model_parallel.py
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compress_model_parallel.py
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IMAGE_SIZE = (64, 64)
TRAIN_SIZE = 50000
VALIDATION_SIZE = 10000
BATCH_SIZE_PER_GPU = 512
global_batch_size = (BATCH_SIZE_PER_GPU * 1)
NUM_CLASSES = 10
TEST = 1
EPOCHS = 20 if TEST == 1 else 2
NUM_PROC = 2
EARLY_STOPPING = False
SUMMARY_PATH = ""
OG = None
ARCH = 'resnet'
MODEL_PATH = ""
AUG = True
TARGET_FILE = ""
DATA_SET="cifar10"
import os
#os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import math
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
os.environ['CUDA_VISIBLE_DEVICES'] = str(rank)
import tensorflow as tf
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
for i in range(len(physical_devices)):
tf.config.experimental.set_memory_growth(physical_devices[i], True)
import tensorflow_datasets as tfds
import numpy as np
import math
import time
import os
import gc
def train_layer(target, train_dataset, test_dataset, rank=0):
"""Trains a replacement layer given a target
Args:
target: A dictionary containing {'name': layer.name, 'layer': i}
Returns:
target: Updated dictionary
"""
layer_start = time.time()
writer = tf.summary.create_file_writer(SUMMARY_PATH + f"{target['name']}")
with writer.as_default():
print(f"training layer {target['name']}")
tf.keras.backend.clear_session()
print("cleared backend")
model = tf.keras.models.load_model(MODEL_PATH)
print("model loaded")
in_layer = target['layer']
get_output = tf.keras.Model(inputs=model.input, outputs=[model.layers[in_layer - 1].output,
model.layers[in_layer].output])
replacement_layers = build_replacement(get_output, layers=2)
replacement_len = len(replacement_layers.layers)
layer_train_gen = LayerBatch(get_output, train_dataset, TRAIN_SIZE, global_batch_size)
layer_test_gen = LayerBatch(get_output, test_dataset, VALIDATION_SIZE, global_batch_size)
MSE = tf.losses.MeanSquaredError()
starting_lr = 2e-3
initial_learning_rate = 0.1
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
starting_lr,
decay_steps=3000,
decay_rate=0.96,
staircase=True)
optimizer=tf.keras.optimizers.RMSprop(lr_schedule)
replacement_layers.compile(loss=MSE, optimizer=optimizer)
target['score'] = (0, 0)
replacement_layers.save(f'/tmp/layer_{rank}.h5')
print('epochs started')
for epoch in range(EPOCHS):
if epoch % 5 == 0:
del replacement_layers, layer_train_gen, layer_test_gen
gc.collect()
tf.keras.backend.clear_session()
model = tf.keras.models.load_model(MODEL_PATH)
in_layer = target['layer']
get_output = tf.keras.Model(inputs=model.input, outputs=[model.layers[in_layer - 1].output,
model.layers[in_layer].output])
layer_train_gen = LayerBatch(get_output, train_dataset, TRAIN_SIZE, global_batch_size)
layer_test_gen = LayerBatch(get_output, test_dataset, VALIDATION_SIZE, global_batch_size)
replacement_layers = tf.keras.models.load_model(f'/tmp/layer_{rank}.h5')
print('training started')
history = replacement_layers.fit(x=layer_train_gen,
epochs=1,
steps_per_epoch=math.ceil(TRAIN_SIZE / global_batch_size / TEST),
validation_data=layer_test_gen,
shuffle=False,
validation_steps=math.ceil(VALIDATION_SIZE / global_batch_size / TEST),
verbose=2)
tf.summary.scalar(name='rep_loss', data=history.history['loss'][0], step=epoch)
tf.summary.scalar(name='val_loss', data=history.history['val_loss'][0], step=epoch)
if epoch % 5 == 0:
replacement_layers.save(f'/tmp/layer_{rank}.h5')
weights = [replacement_layers.layers[1].get_weights(), replacement_layers.layers[3].get_weights()]
tf.keras.backend.clear_session()
model = tf.keras.models.load_model(MODEL_PATH)
layer_name = target['name']
layer_pos = target['layer']
filters = model.layers[layer_pos].output.shape[-1]
new_model = replace_layer(model, layer_name, lambda x: replac(x, filters))
new_model.layers[layer_pos].set_weights(weights[0])
new_model.layers[layer_pos + 2].set_weights(weights[1])
new_model.compile(optimizer=tf.keras.optimizers.SGD(.1), loss="sparse_categorical_crossentropy", metrics=['accuracy'])
score = new_model.evaluate(test_dataset, steps=math.ceil(VALIDATION_SIZE / global_batch_size / TEST))
del new_model
gc.collect()
if score[1] > target['score'][1]:
target['score'] = score
target['weights'] = weights
tf.summary.scalar(name='model_acc', data=score[1], step=epoch)
tf.summary.scalar(name='model_loss', data=score[0], step=epoch)
print(f"epoch: {epoch}, rep loss {history.history['loss']}, val loss {history.history['val_loss']}, model acc {score[1]}")
if EARLY_STOPPING:
if OG[1] - target['score'][1] < 0.002:
print('stoping early')
break
writer.flush()
layer_end = time.time()
layer_time = layer_end - layer_start
target['run_time'] = layer_time
target['rank'] = rank
del replacement_layers, layer_train_gen, layer_test_gen, model, train_dataset, test_dataset
gc.collect()
return target
if __name__ == '__main__':
import json
import functools
import operator
import tensorflow_datasets as tfds
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-im", "--image_size", type=int,
help="dataset image size", default=32)
parser.add_argument("-ts", "--train_size", type=int,
help="dataset training split size", default=50000)
parser.add_argument("-vs", "--val_size", type=int,
help="dataset validation split size", default=10000)
parser.add_argument("-bs", "--batch_size", type=int,
help="batch size", default=256)
parser.add_argument("-nc", "--num_classes", type=int, default=10)
parser.add_argument("-ep", "--epochs", type=int, default=40)
parser.add_argument("-es", "--early_stopping", type=bool, default=False)
parser.add_argument("-tm", "--test_multiplier", type=int, default=1,
help="multipler to speed up training when testing")
parser.add_argument("-sp", "--summary_path", type=str, default="./summarys/vgg/")
parser.add_argument("-tp", "--timing_path", type=str, help="file name and path for saving timing data")
parser.add_argument("-sd", "--schedule", type=str, help="file name and path for schedule")
parser.add_argument("-ar", "--arch", type=str,
help="model architecture being compressed ex. vgg, resnet",
choices=['vgg', 'resnet', 'vgg19'], default='resnet')
parser.add_argument("-mp", "--model_path", type=str, help="file path to saved model file", default='cifar10.h5')
parser.add_argument('-aug', "--augment_data", type=bool, default=True, help="Whether or not to augement images or cache them")
parser.add_argument("-tf", "--target_file_path", type=str, help="path to target file", default='targets_resnet.json')
parser.add_argument("-fr", "--freeze_layers", type=bool, default=False, help="whether to freeze unreplaced layers")
parser.add_argument("-ds", "--data_set", type=str, choices=['cifar10', 'beans', 'bean_scrap'], default="cifar10")
args = parser.parse_args()
IMAGE_SIZE = (args.image_size, args.image_size)
TRAIN_SIZE = args.train_size
VALIDATION_SIZE = args.val_size
global_batch_size = args.batch_size
NUM_CLASSES = args.num_classes
EPOCHS = args.epochs
EARLY_STOPPING = args.early_stopping
TEST = args.test_multiplier
SUMMARY_PATH = args.summary_path
timing_path = args.timing_path
ARCH = args.arch
MODEL_PATH = args.model_path
AUG = args.augment_data
freeze = args.freeze_layers
DATA_SET = args.data_set
schedule = args.schedule
TARGET_FILE = args.target_file_path
if ARCH == 'resnet':
from utils_resnet import *
elif ARCH == 'vgg':
from utils import *
else:
from utils_beans import *
with open(TARGET_FILE, 'r') as f:
targets = json.load(f)
if DATA_SET != "bean_scrap":
dataset, info = tfds.load(DATA_SET, with_info=True)
options = tf.data.Options()
options.experimental_threading.max_intra_op_parallelism = 1
train = dataset['train'].with_options(options)
test = dataset['test'].with_options(options)
else:
dataset, info = tfds.load("beans", with_info=True)
options = tf.data.Options()
options.experimental_threading.max_intra_op_parallelism = 1
test = dataset['test'].with_options(options)
train = dataset['train'].with_options(options)
list_ds = tf.data.Dataset.list_files(str('/tf/notebooks/bean_data/*/*'))
aug = list_ds.map(parse_image).cache()
train = aug.concatenate(train)
if AUG:
train = train.map(lambda x: load_image_train(x, IMAGE_SIZE, NUM_CLASSES), num_parallel_calls=4)
else:
train = train.map(lambda x: load_image_test(x, IMAGE_SIZE, NUM_CLASSES), num_parallel_calls=4)
train = train.cache()
train_dataset = train.shuffle(1000).batch(global_batch_size).repeat()
train_dataset = train_dataset.prefetch(buffer_size=2)
test_dataset = test.map(lambda x: load_image_test(x, IMAGE_SIZE, NUM_CLASSES), num_parallel_calls=4)
#test_dataset = test_dataset.cache()
test_dataset = test_dataset.batch(global_batch_size).repeat()
test_dataset = test_dataset.prefetch(buffer_size=2)
model = tf.keras.models.load_model(MODEL_PATH)
if freeze:
for layer in model.layers:
layer.trainable = False
model.compile(optimizer=tf.optimizers.SGD(learning_rate=.01, momentum=.9, nesterov=True), loss='mse', metrics=['acc'])
OG = model.evaluate(test_dataset, steps=math.ceil(VALIDATION_SIZE/global_batch_size/TEST))
del model
tf.keras.backend.clear_session()
print(schedule)
my_layers = []
# either load a schedule from json for each GPU or
# assign one based on cyclic assignment
if schedule is not None:
print("load schedule")
with open(schedule, 'r') as f:
schedules = json.load(f)
my_layers = schedules[str(rank)]
else:
my_layers = [targets[i]['layer'] for i in range(rank, len(targets), size)]
print(my_layers)
if rank == 0:
tik = time.time()
print(f"OG IS : {OG}\n")
print(my_layers)
targets = [train_layer(target, train_dataset, test_dataset, rank) for target in targets if target['layer'] in my_layers]
targets = comm.gather(targets, root=0)
del train_dataset
if rank == 0:
tok = time.time()
total_time = tok - tik
targets = functools.reduce(operator.iconcat, targets, [])
list.sort(targets, key=lambda target: target['layer'])
tf.keras.backend.clear_session()
model = tf.keras.models.load_model(MODEL_PATH)
writer = tf.summary.create_file_writer(SUMMARY_PATH + "final_model")
with writer.as_default():
model.save('cifar10_resnet_modified.h5')
for target in targets[::-1]:
target['replaced'] = False
if OG[1] - target['score'][1] < 0.15:
print(f'replacing layer {target["name"]}')
target['replaced'] = True
layer_name = target['name']
layer_pos = target['layer']
filters = model.layers[layer_pos].output.shape[-1]
new_model = replace_layer(model, layer_name, lambda x: replac(x, filters))
new_model.layers[layer_pos].set_weights(target['weights'][0])
new_model.layers[layer_pos + 2].set_weights(target['weights'][1])
new_model.save('cifar10_resnet_modified.h5')
tf.keras.backend.clear_session()
model = tf.keras.models.load_model('cifar10_resnet_modified.h5')
tf.keras.backend.clear_session()
test_dataset = dataset['test'].map(lambda x: load_image_test(x, IMAGE_SIZE, NUM_CLASSES), num_parallel_calls=tf.data.experimental.AUTOTUNE)
test_dataset = test_dataset.batch(global_batch_size).repeat()
test_dataset = test_dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
train = dataset['train']
if AUG:
train = train.map(lambda x: load_image_train(x, IMAGE_SIZE, NUM_CLASSES), num_parallel_calls=4)
else:
train = train.map(lambda x: load_image_test(x, IMAGE_SIZE, NUM_CLASSES), num_parallel_calls=4)
train = train.cache()
train_dataset = train.batch(global_batch_size).repeat()
train_dataset = train_dataset.prefetch(buffer_size=2)
fine_tune_epochs = 60
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
.00063,
decay_steps= math.ceil(TRAIN_SIZE / global_batch_size / TEST ) * fine_tune_epochs // 3,
decay_rate=0.96,
staircase=False)
from tensorflow.keras.callbacks import ModelCheckpoint
checkpoint = ModelCheckpoint('cifar10_resnet_modified_fine_tune.h5', monitor='val_accuracy', verbose=1, save_best_only=True)
model = tf.keras.models.load_model('cifar10_resnet_modified.h5')
model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=lr_schedule, momentum=.9, nesterov=True), loss="sparse_categorical_crossentropy", metrics=['accuracy'])
final = model.evaluate(test_dataset, steps=math.ceil(VALIDATION_SIZE / global_batch_size / TEST))
fine_tune = model.fit(
x=train_dataset,
epochs=fine_tune_epochs,
steps_per_epoch=math.ceil(TRAIN_SIZE / global_batch_size / TEST),
validation_data=test_dataset,
shuffle=False,
validation_steps=math.ceil(VALIDATION_SIZE / global_batch_size / TEST),
verbose=1,
callbacks=[checkpoint])
model = tf.keras.models.load_model('cifar10_resnet_modified_fine_tune.h5')
final_fine_tune = model.evaluate(test_dataset, steps=math.ceil(VALIDATION_SIZE / global_batch_size / TEST))
#new_model.save('cifar10_resnet_modified_fine_tune.h5')
tf.summary.scalar(name='model_acc', data=final[1], step=0)
tf.summary.scalar(name='model_loss', data=final[0], step=0)
tf.summary.scalar(name='model_acc_fine_tune', data=final_fine_tune[1], step=0)
tf.summary.scalar(name='model_loss_fine_tune', data=final_fine_tune[0], step=0)
if timing_path is not None:
timing_dump = [{'name': target['name'], 'layer': target['layer'], 'run_time': target['run_time'], 'rank': target['rank'], 'replaced': target['replaced'], 'score': target['score']} for target in targets]
timing_dump.append({'total_time': total_time})
timing_dump.append({'final_acc': final[1]})
timing_dump.append({'fine_tune_acc': final_fine_tune[1]})
with open(timing_path, 'w') as f:
json.dump(timing_dump, f, indent='\t')
writer.flush()