Added benchmarks for larger regex fsm and runtime benahcmarks for the…

… same

tests/benchmark/test_benchmark_regex_fsm.py

@@ -1,10 +1,13 @@
 import pytest
+from transformers import AutoTokenizer
 
 import outlines
+from outlines.fsm.fsm import RegexFSM
+from outlines.fsm.guide import RegexGuide  # noqa: E402
+from outlines.models.transformers import TransformerTokenizer
 
 outlines.disable_cache()
 
-from outlines.fsm.guide import RegexGuide  # noqa: E402
 
 regex_samples = {
     "email": r"[a-z0-9!#$%&'*+/=?^_`{|}~-]+(?:\.[a-z0-9!#$%&'*+/=?^_`{|}~-]+)*@(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?",
@@ -16,6 +19,7 @@
     "url": r"(https?:\/\/)?([\da-z\.-]+)\.([a-z\.]{2,6})([\/\w \.-]*)*\/?",
     "ssn": r"\d{3}-\d{2}-\d{4}",
     "complex_span_constrained_relation_extraction": "(['\"\\ ,]?((?:of|resulting|case|which|cultures|a|core|extreme|selflessness|spiritual|various|However|both|vary|in|other|secular|the|religious|among|moral|and|It|object|worldviews|altruism|traditional|material|aspect|or|life|beings|virtue|is|however|opposite|concern|an|practice|it|for|s|quality|religions|In|Altruism|animals|happiness|many|become|principle|human|selfishness|may|synonym)['\"\\ ,]?)+['\"\\ ,]?\\s\\|\\s([^|\\(\\)\n]{1,})\\s\\|\\s['\"\\ ,]?((?:of|resulting|case|which|cultures|a|core|extreme|selflessness|spiritual|various|However|both|vary|in|other|secular|the|religious|among|moral|and|It|object|worldviews|altruism|traditional|material|aspect|or|life|beings|virtue|is|however|opposite|concern|an|practice|it|for|s|quality|religions|In|Altruism|animals|happiness|many|become|principle|human|selfishness|may|synonym)['\"\\ ,]?)+['\"\\ ,]?(\\s\\|\\s\\(([^|\\(\\)\n]{1,})\\s\\|\\s([^|\\(\\)\n]{1,})\\))*\\n)*",
+    "high_ram_consumption": r"A: [\w \.\*\-=\+,\?/]{50,85}\. The answer is [1-9][0-9]{0,9}\.\n",
 }
 
 
@@ -30,3 +34,32 @@ def test_benchmark_regex_to_fsm(
         args=(regex_str, tokenizer),
         rounds=8,
     )
+
+
+def setup_large_fsm():
+    regex_str = r"A: [\w \.\*\-=\+,\?/]{50,65}\. The answer is [1-9][0-9]{0,9}\.\n"
+    tokenizer_name = "gpt2"
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+    tokenizer = TransformerTokenizer(tokenizer)
+    fsm = RegexFSM(regex_str, tokenizer)
+    return (fsm,), {}
+
+
+def test_benchmark_regex_fsm_token_ids(benchmark, tokenizer, ensure_numba_compiled):
+    def dict_run_through(fsm):
+        for start_state in fsm.states_to_token_maps.keys():
+            _ = fsm.allowed_token_ids(start_state)
+
+    benchmark.pedantic(dict_run_through, rounds=8, setup=setup_large_fsm)
+
+
+def test_benchmark_regex_fsm_states(benchmark, tokenizer, ensure_numba_compiled):
+    def dict_run_through(fsm):
+        for start_state in fsm.states_to_token_maps.keys():
+            for token in fsm.allowed_token_ids(start_state):
+                # Access every end state individually, to get an amortized
+                # cost of dictionary accesses.
+                next_state = fsm.next_state(start_state, token)
+                assert next_state is not None
+
+    benchmark.pedantic(dict_run_through, rounds=8, setup=setup_large_fsm)