9f9ecff4cd
Signed-off-by: Max de Bayser <maxdebayser@gmail.com> Signed-off-by: Max de Bayser <mbayser@br.ibm.com>
361 lines
12 KiB
Python
361 lines
12 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import json
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import random
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from typing import Any
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import openai
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import pytest
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from transformers import AutoTokenizer
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from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
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from vllm.entrypoints.openai.engine.protocol import (
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DeltaMessage,
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)
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from vllm.tool_parsers.granite4_tool_parser import Granite4ToolParser
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from ....utils import RemoteOpenAIServer
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MODEL = "ibm-granite/granite-4.0-h-tiny"
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@pytest.fixture(scope="module")
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def server():
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model = MODEL
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args_for_model = [
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"--enforce-eager",
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"--enable-auto-tool-choice",
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"--tool-call-parser",
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"granite4",
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"--tokenizer",
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"ibm-granite/granite-4.0-h-tiny",
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"--max-model-len",
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"4096",
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"--max-num-seqs",
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"2",
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]
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with RemoteOpenAIServer(model, args_for_model, max_wait_seconds=480) as server:
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yield server
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def create_complex_input(create_string_args: bool):
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coord_arg: dict | str = {
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"coordinates": [[23.54, 43.1], [-12.2, 54.3], [4, 5]],
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"coordinate_type": "latlong",
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}
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if create_string_args:
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# test granite behavior
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coord_arg = json.dumps(coord_arg)
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return [
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{"name": "find_bbox", "arguments": coord_arg},
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{
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"name": "get_stock_price",
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"arguments": {
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"symbol": "AAPL",
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"start_date": "2021-01-01",
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"end_date": "2021-12-31",
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},
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},
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{"name": "find_bbox", "arguments": coord_arg},
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]
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def random_chunks(s: str, min_len: int, max_len: int):
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chunks = []
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i = 0
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n = len(s)
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while i < n:
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size = random.randint(min_len, max_len)
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chunks.append(s[i : i + size])
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i += size
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return chunks
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@pytest.fixture(scope="module")
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def tokenizer():
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return AutoTokenizer.from_pretrained(MODEL)
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# create a variety of input chunk sizes
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@pytest.mark.parametrize(
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"min_chunk, max_chunk",
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[
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(1, 1),
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(1, 2),
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(5, 7),
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(6, 20),
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],
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)
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def test_tool_call_parser_complex(min_chunk: int, max_chunk: int, tokenizer):
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input_dicts = create_complex_input(True)
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formatted_tcs = [
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"<tool_call> " + json.dumps(call) + " </tool_call>" for call in input_dicts
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]
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text_messages = [
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"Here goes the bbox call: \n",
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" Now the stock price call: \n ",
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" Now another bbox call: \n ",
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" See? I'm a helpful assistant.",
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]
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test_input = (
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text_messages[0]
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+ formatted_tcs[0]
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+ text_messages[1]
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+ formatted_tcs[1]
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+ text_messages[2]
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+ formatted_tcs[2]
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+ text_messages[3]
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)
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any_chat_request = ChatCompletionRequest(
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seed=42,
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model=MODEL,
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messages=[],
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)
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parser = Granite4ToolParser(tokenizer=tokenizer)
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delta_messages = list[DeltaMessage]()
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for text in random_chunks(test_input, min_chunk, max_chunk):
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delta = parser.extract_tool_calls_streaming(
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previous_text="",
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current_text="",
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delta_text=text,
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previous_token_ids=[],
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current_token_ids=[],
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delta_token_ids=[],
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request=any_chat_request,
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)
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if delta is not None:
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delta_messages.append(delta)
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content = ""
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tool_calls = list[dict[str, Any]]()
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current_name = "__start__"
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current_args = ""
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for msg in delta_messages:
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if msg.content:
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content += msg.content
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for tool_call in msg.tool_calls:
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if delta_func := tool_call.function:
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if delta_func.name is not None:
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if current_name == "__start__":
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current_name = delta_func.name
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if delta_func.name != current_name:
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tool_calls.append(
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{
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"name": current_name,
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"arguments": json.loads(current_args),
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}
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)
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current_name = delta_func.name
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current_args = ""
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if delta_func.arguments:
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current_args += delta_func.arguments
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if current_name != "__start__":
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tool_calls.append({"name": current_name, "arguments": json.loads(current_args)})
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assert content == "".join(text_messages)
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assert tool_calls == create_complex_input(False)
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_acme_region_name_for_transaction_id",
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"description": "Returns ACME transaction/transaction ID information"
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" including ACME regions\n\nArgs:\n start_time "
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"(str): Start date and time in datetime format "
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'"%Y-%m-%dT%H:%M:%S.%f"\n end_time (str): End '
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"date and time in datetime format "
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'"%Y-%m-%dT%H:%M:%S.%f"\n size (int, optional): '
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"Number of ACME Transaction IDs to return\n "
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"order (str, optional): Sort by most run "
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"transaction IDs. The value can be 'asc' for "
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"ascending or 'desc' for descending\n "
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"transaction_id (str, optional): ACME Transaction "
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"ID to filter on\n acme_region (str, optional): "
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"ACME Region to filter on\nReturns:\n - A "
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"dictionary containing a list of ACME transaction "
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"ids and the ACME regions they run in:\n {\n"
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' "Number of transaction IDs" : int,\n'
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' "Total transaction IDs available": int'
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',\n "ACME Transaction IDs": [\n '
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' {\n "Transaction ID": '
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'str,\n "Number of runs": int,\n'
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' "ACME Regions": [str],\n '
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" },\n ...\n ],"
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'\n "Start time" : datetime,\n '
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' "End time" : datetime,\n '
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' "Order" : str\n }\n '
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" - If no ACME region found for transaction id, "
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'returns:\n {"Success": "No ACME region '
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'found for transaction id."}\n - If an error '
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'occurs, returns:\n {"Error": "{exception'
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' message}"}',
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"parameters": {
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"properties": {
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"start_time": {},
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"end_time": {},
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"size": {"default": 500},
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"order": {"default": "desc"},
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"transaction_id": {"default": None},
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"acme_region": {"default": None},
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},
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"required": ["start_time", "end_time"],
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"type": "object",
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},
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},
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}
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]
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tools2 = [
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{
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"description": "Get the current weather",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"description": "The city and state, e.g. San Francisco, CA",
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"type": "string",
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}
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},
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"required": ["location"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "get_stock_price",
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"description": "Retrieves the current stock price for a given "
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"ticker symbol. The ticker symbol must be a valid "
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"symbol for a publicly traded company on a major US"
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" stock exchange like NYSE or NASDAQ. The tool will"
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" return the latest trade price in USD. It should "
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"be used when the user asks about the current or "
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"most recent price of a specific stock. It will not"
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" provide any other information about the stock or"
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" company.",
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"parameters": {
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"type": "object",
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"properties": {
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"ticker": {
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"description": "The stock ticker symbol, e.g."
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" AAPL for Apple Inc.",
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"type": "string",
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}
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},
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},
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},
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},
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]
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messages = [
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{
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"content": "\n\nSystem: You are a helpful, precise, and methodical AI"
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" assistant that uses tool outputs provided inline.\nAlways"
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" assume the current datetime is 2026-01-29T13:59:09.238901"
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"+00:00.\n\nIf you receive a ToolMessage with `tool_call_id"
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'` equal to "get_time_range" (or "time_range_tool"), you '
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"MUST:\n 1. Parse that JSON and use the values `start` and"
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" `end` directly when calling other tools.\n 2. Do not "
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"re-call or re-compute the time range.\n 3. Pass resolved "
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"values (ISO strings) as arguments to any subsequent tool "
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"(do not pass function metadata or placeholders).\n 4. If "
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"a tool requires datetime objects rather than strings, "
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"convert the ISO strings into language-native datetime "
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"objects before invoking.\n\nAlways return fully resolved "
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"arguments in correct types (e.g., ISO datetime strings or"
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" datetime objects) and never include placeholders like "
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'"<start>".\n\n',
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"role": "system",
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},
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{
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"content": "What are the transaction IDs that ran in the"
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" ACME region A9345 over the last two months?",
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"role": "user",
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},
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{
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"content": '["2026-01-26T09: 51: 55.467722Z", "2026-01-27T09: 51: 55.467722Z"]',
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"role": "tool",
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"tool_call_id": "time_range_tool",
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},
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]
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messages2 = [{"role": "user", "content": "What's stock price for IBM?"}]
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messages3 = [{"role": "user", "content": "What's the current weather in New York?"}]
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def get_args(client: openai.OpenAI, _tools, _messages, _stop):
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response = client.chat.completions.create(
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model=MODEL,
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messages=_messages,
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temperature=0,
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tools=_tools,
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max_tokens=200,
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stop=_stop,
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tool_choice="auto",
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)
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return response.choices[0].message.tool_calls[0].function.arguments
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async def get_args_streaming(
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async_client: openai.AsyncOpenAI, _tools, _messages, _stop
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):
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stream = await async_client.chat.completions.create(
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model=MODEL,
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messages=_messages,
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temperature=0,
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tools=_tools,
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max_tokens=200,
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stop=_stop,
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tool_choice="auto",
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stream=True,
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)
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full_call = []
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async for chunk in stream:
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tc = chunk.choices[0].delta.tool_calls
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if tc and tc[0].function.arguments:
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full_call.append(tc[0].function.arguments)
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return "".join(full_call)
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async def run_scenario(server: RemoteOpenAIServer, _tools, _messages, _stop):
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non_streaming = get_args(server.get_client(), _tools, _messages, _stop)
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json.loads(non_streaming) # verify that it is json loadable
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streaming = await get_args_streaming(
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server.get_async_client(), _tools, _messages, _stop
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)
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json.loads(streaming)
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assert non_streaming == streaming, f"{non_streaming=}, {streaming=}"
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@pytest.mark.asyncio
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async def test_stop_sequence_interference(server: RemoteOpenAIServer):
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print("Testing scenario 1")
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await run_scenario(server, tools, messages, "veroniqueprattyushveroniqueprattyush")
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print("Testing scenario 2")
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await run_scenario(
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server, tools2, messages2, "veroniqueprattyushveroniqueprattyush"
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)
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print("Testing scenario 3")
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await run_scenario(server, tools2, messages3, "prattyush")
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