Implement generate_until for LMEvalORTGenAIEvaluator

olive/evaluator/lmeval_ort.py

@@ -509,7 +509,10 @@ def __init__(
                 self.max_length = max_length
             else:
                 self.max_length = genai_config["search"]["max_length"]
-            self._eot_token_id = genai_config["model"]["eos_token_id"]
+            eot = genai_config["model"]["eos_token_id"]
+            # eos_token_id can be a list (e.g. [1, 106] for Gemma4) or a scalar.
+            # Use the first element for loglikelihood evaluation.
+            self._eot_token_id = eot[0] if isinstance(eot, list) else eot
         self.params = og.GeneratorParams(self.model)
         self.params.set_search_options(max_length=self.max_length, past_present_share_buffer=False)
 
@@ -575,3 +578,70 @@ def model_call(self, input_ids: torch.Tensor, cont_len: int = 0) -> torch.Tensor
 
     def complete(self):
         pass
+
+    def generate_until(self, requests, disable_tqdm: bool = False) -> list[str]:
+        """Generate text until a stop sequence is reached.
+
+        Used by benchmarks like MMLU Pro (CoT variant) that score by generating
+        chain-of-thought text and extracting the answer with a regex filter.
+        """
+        results = []
+        for request in tqdm(requests, disable=disable_tqdm, desc="Running generate_until requests"):
+            context = request.args[0]
+            gen_kwargs = request.args[1]
+
+            until = gen_kwargs.get("until", [])
+            max_gen_toks = gen_kwargs.get("max_gen_toks", 256)
+            if isinstance(until, str):
+                until = [until]
+
+            input_ids = self.tok_encode(context)
+            max_new_tokens = min(max_gen_toks, self.max_length - len(input_ids))
+            if max_new_tokens <= 0:
+                results.append("")
+                continue
+
+            params = og.GeneratorParams(self.model)
+            params.set_search_options(
+                max_length=len(input_ids) + max_new_tokens,
+                past_present_share_buffer=False,
+                batch_size=1,
+            )
+            if gen_kwargs.get("temperature", 0.0) == 0.0:
+                params.set_search_options(do_sample=False)
+            else:
+                params.set_search_options(
+                    do_sample=True,
+                    temperature=gen_kwargs["temperature"],
+                )
+
+            generator = og.Generator(self.model, params)
+            generator.append_tokens([input_ids])
+
+            eos_ids = self._eot_token_id if isinstance(self._eot_token_id, (list, tuple)) else [self._eot_token_id]
+
+            generated_ids = []
+            # Decode periodically to check for stop sequences
+            decode_interval = 16
+            while not generator.is_done():
+                generator.generate_next_token()
+                token_id = generator.get_next_tokens()[0]
+                generated_ids.append(token_id)
+                if token_id in eos_ids:
+                    break
+                # Check stop sequences periodically by decoding
+                if until and len(generated_ids) % decode_interval == 0:
+                    partial_text = self.tokenizer.decode(generated_ids)
+                    if any(stop_seq in partial_text for stop_seq in until):
+                        break
+
+            generated_text = self.tokenizer.decode(generated_ids)
+
+            # Truncate at the first stop sequence
+            for stop_seq in until:
+                idx = generated_text.find(stop_seq)
+                if idx != -1:
+                    generated_text = generated_text[:idx]
+
+            results.append(generated_text)
+        return results