OpenAI Chat Completions Stream

.gitignore

@@ -2,4 +2,5 @@
 runpod.toml
 *.pyc
 .env
-test/*
+test/*
+vllm-base/vllm-*

src/engine.py

@@ -125,44 +125,21 @@ class vLLMEngine:
 
         response_generator = await self.openai_engine.create_chat_completion(chat_completion_request, DummyRequest())
         if not stream:
-            yield json.loads(response_generator.model_dump_json())
+            yield response_generator
         else: 
-            batch_contents = {}
-            batch_latest_choices = {}
+            batch = ""
             batch_token_counter = 0
-            last_chunk = {}
             
             async for chunk_str in response_generator:
-                try:
-                    chunk = json.loads(chunk_str.removeprefix("data: ").rstrip("\n\n")) 
-                except:
-                    continue
-                
-                if "choices" in chunk:
-                    for choice in chunk["choices"]:
-                        choice_index = choice["index"]
-                        if "delta" in choice and "content" in choice["delta"]:
-                            batch_contents[choice_index] =  batch_contents.get(choice_index, []) + [choice["delta"]["content"]]
-                            batch_latest_choices[choice_index] = choice
-                            batch_token_counter += 1
-                    last_chunk = chunk
-                
-                if batch_token_counter >= batch_size:
-                    for choice_index in batch_latest_choices:
-                        batch_latest_choices[choice_index]["delta"]["content"] = batch_contents[choice_index]
-                    last_chunk["choices"] = list(batch_latest_choices.values())
-                    yield last_chunk
-                    
-                    batch_contents = {}
-                    batch_latest_choices = {}
-                    batch_token_counter = 0
-
-            if batch_contents:
-                for choice_index in batch_latest_choices:
-                    batch_latest_choices[choice_index]["delta"]["content"] = batch_contents[choice_index]
-                last_chunk["choices"] = list(batch_latest_choices.values())
-                yield last_chunk
-    
+                if "data" in chunk_str:
+                    batch += chunk_str
+                    batch_token_counter += 1
+                    if batch_token_counter >= batch_size:
+                        yield batch
+                        batch = ""
+                        batch_token_counter = 0
+                        
+               
     def _initialize_config(self):
         quantization = self._get_quantization()
         model, download_dir = self._get_model_name_and_path()

src/test_openai_stream.py

@@ -0,0 +1,44 @@
+import os
+from utils import JobInput
+from engine import vLLMEngine
+
+os.environ["MODEL_NAME"] = "facebook/opt-125m"
+os.environ["CUSTOM_CHAT_TEMPLATE"] = "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"
+
+vllm_engine = vLLMEngine()
+
+async def handler(job):
+    job_input = JobInput(job["input"])
+    results_generator = vllm_engine.generate(job_input)
+    async for batch in results_generator:
+        yield batch
+        
+test_payload = {
+    "input": {
+        "messages":  [
+            {"role": "user", "content": "Write me a 3000 word long and detailed essay about how the french revolution impacted the rest of europe over the 18th century."},
+        ],
+        "batch_size": 2, # How many tokens to yield per batch
+        "apply_chat_template": True,
+        "sampling_params": {
+            "max_tokens": 10,
+            "temperature": 0,
+            "ignore_eos": True,
+            "n":1
+        },
+        "stream": True,
+        "use_openai_format": True
+    }
+}
+
+async def test_handler():
+    print("Start of output")
+    print("=" *50)
+    async for batch in handler(test_payload):
+        print(batch, end="")
+    print("=" *50)
+    print("End of output")
+    
+import asyncio
+
+asyncio.run(test_handler())