Support ChatML prompt format in worker (#3668)

inference/worker/chat_chain.py

@@ -14,8 +14,6 @@
     PREFIX,
     SUFFIX,
     THOUGHT_SEQ,
-    V2_ASST_PREFIX,
-    V2_PROMPTER_PREFIX,
 )
 from chat_chain_utils import compose_tools_from_plugin, extract_tool_and_input, prepare_prompt, use_tool
 from hf_langchain_inference import HFInference
@@ -26,6 +24,7 @@
 from oasst_shared.model_configs import ModelConfig
 from oasst_shared.schemas import inference
 from settings import settings
+from utils import special_tokens
 
 # Exclude tools description from final prompt. Saves ctx space but can hurt output
 # quality especially if truncation kicks in. Dependent on model used
@@ -143,7 +142,11 @@ def handle_plugin_usage(
     action_input_format = (
         JSON_FORMAT_PAYLOAD if prompt_template.template.find("payload") != -1 else JSON_FORMAT_NO_PAYLOAD
     )
-    eos_token = tokenizer.eos_token if hasattr(tokenizer, "eos_token") else ""
+    eos_token = ""
+    if special_tokens["end"]:
+        eos_token = special_tokens["end"]
+    elif hasattr(tokenizer, "eos_token"):
+        eos_token = tokenizer.eos_token
     tool_names = [tool.name for tool in tools]
 
     chain = PromptedLLM(
@@ -170,7 +173,7 @@ def handle_plugin_usage(
         ),
     )
 
-    init_prompt = f"{input_prompt}{eos_token}{V2_ASST_PREFIX}"
+    init_prompt = f"{input_prompt}{eos_token}{special_tokens['assistant']}"
     init_prompt, chain_response = chain.call(init_prompt)
 
     inner_monologue.append("In: " + str(init_prompt))
@@ -203,7 +206,9 @@ def handle_plugin_usage(
 
         # Save previous chain response for use in final prompt
         prev_chain_response = chain_response
-        new_prompt = f"{input_prompt}{eos_token}{V2_ASST_PREFIX}{chain_response}{OBSERVATION_SEQ} {tool_response}"
+        new_prompt = (
+            f"{input_prompt}{eos_token}{special_tokens['assistant']}{chain_response}{OBSERVATION_SEQ} {tool_response}"
+        )
 
         new_prompt, chain_response = chain.call(new_prompt)
 
@@ -239,15 +244,13 @@ def handle_plugin_usage(
             chain_finished = True
 
             if REMOVE_TOOLS_FROM_FINAL_PROMPT:
-                TEMPLATE = f"""{V2_PROMPTER_PREFIX}{PREFIX}{SUFFIX}"""
+                TEMPLATE = f"""{special_tokens['prompter']}{PREFIX}{SUFFIX}"""
                 input_variables = ["input", "chat_history", "language", "current_time"]
 
                 prompt_template = PromptTemplate(input_variables=input_variables, template=TEMPLATE)
                 tool_names = None
 
-            final_input = (
-                f"{input_prompt}{eos_token}{V2_ASST_PREFIX}\n{prev_chain_response}{OBSERVATION_SEQ} {tool_response}"
-            )
+            final_input = f"{input_prompt}{eos_token}{special_tokens['assistant']}\n{prev_chain_response}{OBSERVATION_SEQ} {tool_response}"
             inner_prompt = prepare_prompt(
                 final_input,
                 prompt_template,
@@ -312,14 +315,18 @@ def handle_standard_usage(
     tokenizer: transformers.PreTrainedTokenizer,
     custom_instructions: str = "",
 ):
-    eos_token = tokenizer.eos_token if hasattr(tokenizer, "eos_token") else ""
+    eos_token = ""
+    if special_tokens["end"]:
+        eos_token = special_tokens["end"]
+    elif hasattr(tokenizer, "eos_token"):
+        eos_token = tokenizer.eos_token
     current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
 
     # Non-plugin prompt template can include some external data e.g. datetime, language
     action_input_format = (
         JSON_FORMAT_PAYLOAD if prompt_template.template.find("payload") != -1 else JSON_FORMAT_NO_PAYLOAD
     )
-    input = f"{original_prompt}{eos_token}{V2_ASST_PREFIX}"
+    input = f"{original_prompt}{eos_token}{special_tokens['assistant']}"
     init_prompt = prepare_prompt(
         input,
         prompt_template,
@@ -372,7 +379,7 @@ def handle_conversation(
         plugin_enabled = len(tools) > 0
         memory: ConversationBufferMemory = build_memory(work_request)
 
-        TEMPLATE = f"""{V2_PROMPTER_PREFIX}{PREFIX}{tools_instructions_template}{SUFFIX}"""
+        TEMPLATE = f"""{special_tokens['prompter']}{PREFIX}{tools_instructions_template}{SUFFIX}"""
         input_variables = [
             "input",
             "chat_history",

inference/worker/chat_chain_prompts.py

@@ -1,7 +1,3 @@
-V2_ASST_PREFIX = "<|assistant|>"
-V2_PROMPTER_PREFIX = "<|prompter|>"
-V2_SYSTEM_PREFIX = "<|system|>"
-
 ASSISTANT_PREFIX = "Open Assistant"
 HUMAN_PREFIX = "Human"
 OBSERVATION_SEQ = "Observation:"

inference/worker/chat_chain_utils.py

@@ -4,7 +4,7 @@
 
 import requests
 import transformers
-from chat_chain_prompts import INSTRUCTIONS, OBSERVATION_SEQ, TOOLS_PREFIX, V2_ASST_PREFIX, V2_PROMPTER_PREFIX
+from chat_chain_prompts import INSTRUCTIONS, OBSERVATION_SEQ, TOOLS_PREFIX
 from hf_langchain_inference import HFInference
 from langchain.agents import Tool
 from langchain.memory import ConversationBufferMemory
@@ -13,15 +13,15 @@
 from oasst_shared.schemas import inference
 from openapi_parser import prepare_plugin_for_llm
 from settings import settings
-from utils import shared_tokenizer_lock
+from utils import shared_tokenizer_lock, special_tokens
 
 RESPONSE_MAX_LENGTH = 2048
 DESCRIPTION_FOR_MODEL_MAX_LENGTH = 512
 
 llm_json_parser = HFInference(
     inference_server_url=settings.inference_server_url,
     max_new_tokens=512,
-    stop_sequences=["</s>"],
+    stop_sequences=[special_tokens["end"] if special_tokens["end"] else "</s>"],
     top_k=5,
     temperature=0.20,
     repetition_penalty=(1 / 0.83),
@@ -139,7 +139,7 @@ def prepare_json(json_str: str) -> str:
         except json.decoder.JSONDecodeError as e:
             logger.warning(f"JSON is still not valid, trying to fix it with LLM {fixed_json}")
             # If still invalid, try using LLM to fix
-            prompt = f"""{V2_PROMPTER_PREFIX}Below is malformed JSON object string:
+            prompt = f"""{special_tokens['prompter']}Below is malformed JSON object string:
 --------------
 {json_str}
 --------------
@@ -151,7 +151,7 @@ def prepare_json(json_str: str) -> str:
 1. If malformed JSON object string contains multiple objects, you merge them into one.
 2. You will never made up or add any new data, you will only fix the malformed JSON object string.
 
-Here is the fixed JSON object string:</s>{V2_ASST_PREFIX}"""
+Here is the fixed JSON object string:{special_tokens['end'] or '</s>'}{special_tokens['assistant']}"""
             logger.warning(f"JSON Fix Prompt: {prompt}")
             out = llm_json_parser.generate(prompts=[prompt]).generations[0][0].text
             out = out[: out.find("}") + 1]

inference/worker/settings.py

@@ -11,6 +11,9 @@ class Settings(pydantic.BaseSettings):
 
     oa_protocol_version: str = "v2"
 
+    # Supported: oasst, chatml
+    model_prompt_format: str = "oasst"
+
     retry_on_error: bool = True
     hf_pause: float = 0.075
     max_parallel_requests: int = 1

inference/worker/utils.py

@@ -11,14 +11,29 @@
 import sseclient
 import transformers
 import websocket
-from chat_chain_prompts import V2_PROMPTER_PREFIX, V2_SYSTEM_PREFIX
 from loguru import logger
 from oasst_shared.schemas import inference
 from settings import settings
 
 shared_tokenizer_lock = threading.Lock()
 
 
+if settings.model_prompt_format == "chatml":
+    special_tokens = {
+        "prompter": "<|im_start|>user\n",
+        "assistant": "<|im_start|>assistant\n",
+        "system": "<|im_start|>system\n",
+        "end": "<|im_end|>\n",
+    }
+else:
+    special_tokens = {
+        "prompter": "<|prompter|>",
+        "assistant": "<|assistant|>",
+        "system": "<|system|>",
+        "end": "",
+    }
+
+
 class TokenBuffer:
     """
     A buffer for storing and managing tokens based on various conditions including stop sequences.
@@ -80,6 +95,13 @@ def get_max_input_length(worker_config: inference.WorkerConfig, plugin_used: boo
     return max_input_length
 
 
+def get_tokens_until(tokens: list[int], target: int | list[int]) -> list[int]:
+    if isinstance(target, int):
+        return tokens[: tokens.index(target)]
+    else:
+        return next((i for i in range(len(tokens) - len(target) + 1) if tokens[i : i + len(target)] == target))
+
+
 def truncate_prompt(
     tokenizer: transformers.PreTrainedTokenizer,
     worker_config: inference.WorkerConfig,
@@ -96,13 +118,14 @@ def truncate_prompt(
     """
     with shared_tokenizer_lock:
         ids = tokenizer.encode(prompt)
-        prompter_prefix_id = tokenizer.convert_tokens_to_ids(V2_PROMPTER_PREFIX)
+        # prompter_prefix_ids could be int or list of ints
+        prompter_prefix_ids = tokenizer.convert_tokens_to_ids(special_tokens["prompter"])
 
     system_prompt: str | None = None
     system_tokens: list[int] | None = None
-    if prompt.startswith(V2_SYSTEM_PREFIX):
-        system_prompt = prompt[: prompt.index(V2_PROMPTER_PREFIX)]
-        system_tokens = ids[: ids.index(prompter_prefix_id)]
+    if prompt.startswith(special_tokens["system"]):
+        system_prompt = prompt[: prompt.index(special_tokens["prompter"])]
+        system_tokens = get_tokens_until(ids, prompter_prefix_ids)
 
     max_input_length = get_max_input_length(worker_config, plugin_used)
 
@@ -117,9 +140,9 @@ def truncate_prompt(
         with shared_tokenizer_lock:
             prompt = tokenizer.decode(ids)
 
-            if V2_PROMPTER_PREFIX not in prompt:
-                prompt = V2_PROMPTER_PREFIX + prompt
-                ids = tokenizer.encode(V2_PROMPTER_PREFIX) + ids
+            if special_tokens["prompter"] not in prompt:
+                prompt = special_tokens["prompter"] + prompt
+                ids = tokenizer.encode(special_tokens["prompter"]) + ids
 
             if system_tokens:
                 prompt = system_prompt + prompt

inference/worker/work.py

@@ -14,14 +14,11 @@
     OBSERVATION_SEQ,
     START_SEQ,
     THOUGHT_SEQ,
-    V2_ASST_PREFIX,
-    V2_PROMPTER_PREFIX,
-    V2_SYSTEM_PREFIX,
 )
 from loguru import logger
 from oasst_shared.schemas import inference
 from settings import settings
-from utils import shared_tokenizer_lock
+from utils import shared_tokenizer_lock, special_tokens
 
 
 def make_prompt_and_parameters(
@@ -32,10 +29,14 @@ def make_prompt_and_parameters(
     if settings.oa_protocol_version != "v2":
         raise RuntimeError(f"Unsupported oa protocol version: {settings.oa_protocol_version}")
 
-    eos_token = tokenizer.eos_token if hasattr(tokenizer, "eos_token") else ""
+    eos_token = ""
+    if special_tokens["end"]:
+        eos_token = special_tokens["end"]
+    elif hasattr(tokenizer, "eos_token"):
+        eos_token = tokenizer.eos_token
 
     def _prepare_message(message: inference.MessageRead) -> str:
-        prefix = V2_ASST_PREFIX if message.is_assistant else V2_PROMPTER_PREFIX
+        prefix = special_tokens["assistant"] if message.is_assistant else special_tokens["prompter"]
         return prefix + message.content + eos_token
 
     # Construct prompt
@@ -44,7 +45,7 @@ def _prepare_message(message: inference.MessageRead) -> str:
     # Prepend system prompt and custom_instructions if it was specified in work parameters
     work_params = work_request.parameters
     if work_params.system_prompt or work_params.user_profile or work_params.user_response_instructions:
-        pre_prompt = V2_SYSTEM_PREFIX + (work_params.system_prompt or "")
+        pre_prompt = special_tokens["system"] + (work_params.system_prompt or "")
 
         if work_params.user_profile or work_params.user_response_instructions:
             pre_prompt = f"""{pre_prompt}\n{CUSTOM_INSTRUCTIONS_PREFIX.format(user_profile=work_params.user_profile or "", user_response_instructions=work_params.user_response_instructions or "")}"""
@@ -53,14 +54,14 @@ def _prepare_message(message: inference.MessageRead) -> str:
         messages = [pre_prompt] + messages
 
     # Stringify and append assistant prefix to signify start of generation
-    prompt = "".join(messages) + V2_ASST_PREFIX
+    prompt = "".join(messages) + special_tokens["assistant"]
 
     parameters = interface.GenerateStreamParameters.from_work_parameters(work_request.parameters)
     if settings.use_stop_sequences:
         parameters.stop = [
-            V2_PROMPTER_PREFIX,
-            V2_ASST_PREFIX,
-            V2_SYSTEM_PREFIX,
+            special_tokens["prompter"],
+            special_tokens["assistant"],
+            special_tokens["system"],
         ]
         if eos_token:
             parameters.stop.append(eos_token)
@@ -73,9 +74,9 @@ def _prepare_message(message: inference.MessageRead) -> str:
 def prepare_safe_prompt(prompt: str, label: str, rots: str) -> str:
     """Given a prompt, safety label, and safety rule of thumb, prepare a 'safe prompt' to replace the prompt."""
     pre_prompt = f"Answer the following request with {label} as responsible chatbot that believes that {rots}: "
-    input_list = prompt.split(V2_PROMPTER_PREFIX)
+    input_list = prompt.split(special_tokens["prompter"])
     input_list[-1] = pre_prompt + input_list[-1]
-    return V2_PROMPTER_PREFIX.join(input_list)
+    return special_tokens["prompter"].join(input_list)
 
 
 def is_safety_triggered(safety_label: str, safety_level: int) -> bool:

oasst-shared/oasst_shared/model_configs.py

@@ -145,4 +145,9 @@ def compat_hash(self) -> str:
         max_total_length=2048,
         quantized=True,
     ),
+    "OA_SFT_Llama2_70B_10": ModelConfig(
+        model_id="OpenAssistant/llama2-70b-oasst-sft-v10",
+        max_input_length=3072,
+        max_total_length=4096,
+    ),
 }