fix imports

generate_samples.py

@@ -19,16 +19,16 @@
 import time
 
 import torch
-# from transformers.tokenization_gpt2 import GPT2Tokenizer
+from transformers.tokenization_gpt2 import GPT2Tokenizer
 
 from src import mpu
 from src.arguments import get_args
 from src.fp16 import FP16_Module
 from src.model import DistributedDataParallel as DDP
 from src.model import GPT3Model
-from src.pretrain_gpt3 import generate
-from src.pretrain_gpt3 import initialize_distributed
-from src.pretrain_gpt3 import set_random_seed
+from .pretrain_gpt3 import generate
+from .pretrain_gpt3 import initialize_distributed
+from .pretrain_gpt3 import set_random_seed
 from src.utils import Timers
 from src.utils import export_to_huggingface_model
 from src.utils import print_rank_0
@@ -105,11 +105,11 @@ def generate_samples(model, tokenizer, args):
                     context_length = len(context_tokens)
 
                     if context_length >= args.seq_length // 2:
-                        print("\nContext length", context_length, \
+                        print("\nContext length", context_length,
                               "\nPlease give smaller context (half of the sequence length)!")
                         continue
             else:
-                context_tokens = tokenizer("EMPTY TEXT")['input_ids']
+                _ = tokenizer("EMPTY TEXT")['input_ids']
 
             terminate_runs_tensor = torch.cuda.LongTensor([terminate_runs])
             torch.distributed.broadcast(terminate_runs_tensor, mpu.get_model_parallel_src_rank(),
@@ -140,7 +140,7 @@ def generate_samples(model, tokenizer, args):
 
 
 def prepare_tokenizer(args):
-    tokenizer = GPT3Tokenizer.from_pretrained(args.tokenizer_path)
+    tokenizer = GPT2Tokenizer.from_pretrained(args.tokenizer_path)
     eod_token = tokenizer.encoder['<pad>']
     num_tokens = len(tokenizer)
 
@@ -166,7 +166,7 @@ def main():
     torch.backends.cudnn.enabled = False
 
     # Timer.
-    timers = Timers()
+    _ = Timers()
 
     # Arguments.
     args = get_args()