[python] Update vllm rolling batch to return only deltas

engines/python/setup/djl_python/rolling_batch/rolling_batch_vllm_utils.py

@@ -91,47 +91,26 @@ def update_request_cache_with_output(request_cache: OrderedDict,
 
 def update_multiple_sequences(cache, request_output, vllm_request_output):
     for completion_output in vllm_request_output.outputs:
-
         sequence_index = completion_output.index
-        if f"sequence_index_{sequence_index}" not in cache:
-            cache[f"sequence_index_{sequence_index}"] = {
-                "curr_length": 0,
-                "num_generated_tokens": 0
-            }
 
         if sequence_index not in request_output.sequences:
             request_output.sequences[sequence_index] = Sequence()
 
-        # set token of the sequence
-        # previous length of token ids generated
-        prev_len = cache[f"sequence_index_{sequence_index}"][
-            'num_generated_tokens']
-        # curr length of the token ids generated so far
-        cur_len = len(completion_output.token_ids)
-        cache[f"sequence_index_{sequence_index}"][
-            "num_generated_tokens"] = cur_len
-
         # get the newly generated token_ids
-        new_token_ids = completion_output.token_ids[
-            prev_len:
-            cur_len] if prev_len < cur_len else completion_output.token_ids
+        new_token_ids = completion_output.token_ids
 
         # get the newly generated token texts for speculative decoding
         output_token_texts = []
         if hasattr(completion_output, "output_token_texts"):
-            output_token_texts = completion_output.output_token_texts[
-                prev_len:
-                cur_len] if prev_len < cur_len else completion_output.output_token_texts
+            output_token_texts = completion_output.output_token_texts
 
         top_tokens = []
         token_texts = []
         # calculate log probs and token_texts
         if completion_output.logprobs:
-            new_logprobs_list = completion_output.logprobs[
-                prev_len:
-                cur_len] if prev_len < cur_len else completion_output.logprobs
             new_logprobs = []
-            for token_id, logprobs in zip(new_token_ids, new_logprobs_list):
+            for token_id, logprobs in zip(new_token_ids,
+                                          completion_output.logprobs):
                 new_logprobs.append(logprobs[token_id].logprob)
                 decoded_token = logprobs[token_id].decoded_token if logprobs[
                     token_id].decoded_token else ""
@@ -141,13 +120,10 @@ def update_multiple_sequences(cache, request_output, vllm_request_output):
                         Token(id=token_id_key,
                               text=logprob.decoded_token,
                               log_prob=logprob.logprob))
-
         elif new_token_ids:
             # TODO: Test and remove this. logprobs is always set 1. This case should never happen.
             new_logprobs = [None] * len(new_token_ids)
-            curr_length = cache[f"sequence_index_{sequence_index}"][
-                "curr_length"]
-            token_texts.append(completion_output.text[curr_length:])
+            token_texts.append(completion_output.text)
 
         if not output_token_texts:
             if len(token_texts) != len(new_token_ids):
@@ -186,9 +162,6 @@ def update_multiple_sequences(cache, request_output, vllm_request_output):
         request_output.sequences[sequence_index].set_next_top_tokens(
             top_tokens)
 
-        cache[f"sequence_index_{sequence_index}"]["curr_length"] = len(
-            completion_output.text)
-
 
 def get_speculative_decoding_metrics_record(
         completion_output: CompletionOutput,

engines/python/setup/djl_python/rolling_batch/vllm_rolling_batch.py

@@ -13,6 +13,7 @@
 from collections import OrderedDict, defaultdict
 
 from vllm import LLMEngine, SamplingParams
+from vllm.sampling_params import RequestOutputKind
 from vllm.utils import random_uuid, AtomicCounter
 
 from djl_python.request import Request
@@ -128,7 +129,8 @@ def inference(self, new_requests: List[Request]) -> List:
             request_id = random_uuid()
             prompt_inputs = get_prompt_inputs(request)
             params = self.translate_vllm_params(request.parameters)
-            sampling_params = SamplingParams(**params)
+            sampling_params = SamplingParams(
+                output_kind=RequestOutputKind.DELTA, **params)
             request_params = dict()
             if request.adapter is not None:
                 adapter_name = request.adapter.get_property("name")