Updating GPT2-TF2 Scripts

scripts/gpt2-tf2/gpt2_1step.py

@@ -0,0 +1,58 @@
+import sys
+
+import numpy as np
+import jsonlines as jsonl
+from transformers import GPT2TokenizerFast, TFGPT2LMHeadModel
+import tensorflow as tf
+from tensorflow.keras import metrics 
+
+BATCH_SIZE=1
+
+def get_dataset(fil):
+    data = []
+    with jsonl.open(fil) as reader:
+        for line in reader:
+            data.append(line['text'])
+    return data
+
+if len(sys.argv) == 1:
+    model_size = "Small"
+    data_dir = '/dockerx/data/'
+else:
+    model_size = sys.argv[1]
+    data_dir = sys.argv[2]
+
+if model_size == "Small":
+    model_name = "gpt2"
+    train_file = data_dir+'small-117M.train.jsonl'
+    test_file = data_dir+'small-117M.test.jsonl'
+elif model_size == "Medium":
+    model_name = "gpt2-medium"
+    train_file = data_dir+'medium-345M.train.jsonl'
+    test_file = data_dir+'medium-345M.test.jsonl'
+elif model_size == "Large":
+    model_name = "gpt2-large"
+    train_file = data_dir+'large-762M.train.jsonl'
+    test_file = data_dir+'large-762M.test.jsonl'
+elif model_size == "XL":
+    model_name = 'gpt2-xl'
+    train_file = data_dir+'xl-1542M.train.jsonl'
+    test_file = data_dir+'xl-1542M.test.jsonl'
+print("Profiling model " + model_name)
+
+tokenizer = GPT2TokenizerFast.from_pretrained(model_name)
+tokenizer.pad_token = tokenizer.eos_token
+def tokenize(data):
+    data = tokenizer(data[0], return_tensors='tf', padding=True, truncation=True)
+    return tf.data.Dataset.from_tensor_slices((dict(data), data['input_ids']))
+
+train_dataset = tokenize(get_dataset(train_file)).batch(BATCH_SIZE)
+model = TFGPT2LMHeadModel.from_pretrained(model_name)
+#Supresses the past_key_values from being expressed in the progress bar
+model.config.use_cache=False
+optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5)
+loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+metric = metrics.SparseCategoricalAccuracy(name='Accuracy')
+model.compile(optimizer=optimizer, loss=[loss, *[None] * model.config.n_layer])
+model.fit(train_dataset, batch_size=1, epochs=1)
+

scripts/gpt2-tf2/gpt2_profile.py

@@ -0,0 +1,5 @@
+import sys
+import pandas as pd
+profile_dir = sys.argv[1]
+df = pd.read_csv(profile_dir+'results.stats.csv')
+print('Total time for one step GPT2', sum(df["TotalDurationNs"])*1e-9, 's')

scripts/gpt2-tf2/gpt2_train.py

@@ -1,9 +1,10 @@
 import sys
+
 import numpy as np
+import jsonlines as jsonl
 from transformers import GPT2TokenizerFast, TFGPT2LMHeadModel
 import tensorflow as tf
 from tensorflow.keras import metrics 
-import jsonlines as jsonl
 
 BATCH_SIZE=1
 
@@ -69,8 +70,7 @@ def tokenize(data, truncate=False):
 print("========================= Compiling Model ============================")
 model.compile(optimizer=optimizer, loss=[loss, *[None] * model.config.n_layer], metrics=[metric])
 print("========================= Finetuning Model ==================================")
-model.fit(train_dataset, batch_size=64, epochs=num_epochs)#, testation_data=test_dataset)
+model.fit(train_dataset, batch_size=64, epochs=num_epochs)
 print("========================= Evaluating Model ==================================")
 info = model.evaluate(test_dataset, verbose=2)
-#print("========================= Saving Model ======================================")
-#model.save(model_name+'finetuned')
+

scripts/gpt2-tf2/gpt2_train_distributed.py

@@ -0,0 +1,83 @@
+import sys
+
+import numpy as np
+import jsonlines as jsonl
+from transformers import GPT2TokenizerFast, TFGPT2LMHeadModel
+import tensorflow as tf
+from tensorflow.keras import metrics 
+
+
+def get_dataset(fil):
+    data = []
+    with jsonl.open(fil) as reader:
+        for line in reader:
+            data.append(line['text'])
+    return data
+
+if len(sys.argv) == 1:
+    model_size = "Small"
+    data_dir = '/dockerx/data/tf-gpt-2/data/'
+    num_epochs = 1
+    num_gpus = len(tf.config.list_physical_devices(device_type='GPU'))
+    truncate = True
+else:
+    model_size = sys.argv[1]
+    data_dir = sys.argv[2]
+    num_epochs = int(sys.argv[3])
+    num_gpus = int(sys.argv[4])
+    if int(sys.argv[5]) == 1:
+        truncate = True
+    else:
+        truncate = False
+
+if model_size == "Small":
+    model_name = "gpt2"
+    train_file = data_dir+'small-117M-k40.train.jsonl'
+    valid_file = data_dir+'small-117M-k40.valid.jsonl'
+elif model_size == "Medium":
+    model_name = "gpt2-medium"
+    train_file = data_dir+'medium-345M-k40.train.jsonl'
+    valid_file = data_dir+'medium-345M-k40.valid.jsonl'
+elif model_size == "Large":
+    model_name = "gpt2-large"
+    train_file = data_dir+'large-762M-k40.train.jsonl'
+    valid_file = data_dir+'large-762M-k40.valid.jsonl'
+elif model_size == "XL":
+    model_name = 'gpt2-xl'
+    train_file = data_dir+'xl-1542M-k40.train.jsonl'
+    valid_file = data_dir+'xl-1542M-k40.valid.jsonl'
+print("Finetuning model " + model_name)
+print("With dataset "+train_file)
+
+def tokenize(data, tokenizer, truncate=False):
+    if truncate:
+        data = tokenizer(data[:1000], return_tensors='tf', padding=True, truncation=True)
+    else:
+        data = tokenizer(data, return_tensors='tf', padding=True, truncation=True)
+    return tf.data.Dataset.from_tensor_slices((dict(data), data['input_ids']))
+
+print("============================ Creating Distributed Strategy ===========================")
+devices = []
+for i in range(num_gpus):
+    devices.append("GPU:"+str(i))
+strategy = tf.distribute.MirroredStrategy(devices=devices)
+print('Number of devices: {}'.format(strategy.num_replicas_in_sync))
+print("============================ Loading model from pretrained and compiling ===========================")
+with strategy.scope():
+    tokenizer = GPT2TokenizerFast.from_pretrained(model_name)
+    tokenizer.pad_token = tokenizer.eos_token
+    print("========================= Loading dataset ========================")
+    train_dataset = tokenize(get_dataset(train_file),tokenizer, truncate).batch(num_gpus)
+    valid_dataset = tokenize(get_dataset(valid_file),tokenizer, truncate).batch(num_gpus)
+    model = TFGPT2LMHeadModel.from_pretrained(model_name)
+    #Disable past key values
+    model.config.use_cache=False
+    optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5)
+    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+    metric = metrics.SparseCategoricalAccuracy(name='Accuracy')
+    model.compile(optimizer=optimizer, loss=[loss, *[None] * model.config.n_layer], metrics=[metric])
+print("========================= Finetuning Model ==================================")
+model.fit(train_dataset, batch_size=64, epochs=num_epochs)
+print("========================= Evaluating Model ==================================")
+model.evaluate(valid_dataset)
+

scripts/gpt2-tf2/profile_gpt2_train.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+model_size=$1
+echo $model_size
+model_dir=$2
+profile_dir=$3
+rocprof --stats python3 gpt2_1step.py $model_size $model_dir
+python3 gpt2_profile.py $profile_dir