Make BERT benchmark code more robust

benchmark/benchmark_bert_tokenizer.py

@@ -14,29 +14,36 @@ def benchmark_bert_tokenizer(args):
     tt_tokenizer = tt_bert_tokenizer(VOCAB_FILE, return_tokens=True)
     hf_tokenizer_slow = hf_bert_tokenizer_slow.from_pretrained("bert-base-uncased")
     hf_tokenizer_fast = hf_tokenizer_lib.from_pretrained("bert-base-uncased")
-    dp = EnWik9().header(args.num_samples)
+    dp = EnWik9().header(args.num_samples).batch(args.batch_size)
     samples = list(dp)
 
     with Timer("Running TorchText BERT Tokenizer on non-batched input"):
-        for s in samples:
-            tt_tokenizer(s)
+        for batch in samples:
+            for s in batch:
+                tt_tokenizer(s)
 
     with Timer("Running HF BERT Tokenizer (slow) on non-batched input"):
-        for s in samples:
-            hf_tokenizer_slow.tokenize(s)
+        for batch in samples:
+            for s in batch:
+                hf_tokenizer_slow.tokenize(s)
 
     with Timer("Running HF BERT Tokenizer (fast) on non-batched input"):
-        for s in samples:
-            hf_tokenizer_fast.encode(s)
+        for batch in samples:
+            for s in batch:
+                hf_tokenizer_fast.encode(s)
 
     with Timer("Running TorchText BERT Tokenizer on batched input"):
-        tt_tokenizer(samples)
+        for batch in samples:
+            tt_tokenizer(batch)
 
     with Timer("Running HF BERT Tokenizer (fast) on batched input"):
-        hf_tokenizer_fast.encode_batch(samples)
+        for batch in samples:
+            hf_tokenizer_fast.encode_batch(batch)
 
 
 if __name__ == "__main__":
     parser = ArgumentParser()
-    parser.add_argument("--num-samples", default=1000, type=int)
+    parser.add_argument("--num-samples", default=10000, type=int)
+    parser.add_argument("--batch-size", default=100, type=int)
+
     benchmark_bert_tokenizer(parser.parse_args())