[Frontend][Feature] Add jamba tool parser (#9154)
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tomeras91
275
tests/tool_use/test_jamba_tool_parser.py
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275
tests/tool_use/test_jamba_tool_parser.py
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import json
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from typing import Generator, List, Optional
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import partial_json_parser
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import pytest
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from partial_json_parser.core.options import Allow
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from vllm.entrypoints.openai.protocol import (DeltaMessage, FunctionCall,
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ToolCall)
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from vllm.entrypoints.openai.tool_parsers import JambaToolParser
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from vllm.transformers_utils.detokenizer import detokenize_incrementally
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from vllm.transformers_utils.tokenizer import AnyTokenizer, get_tokenizer
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MODEL = "ai21labs/Jamba-tiny-dev"
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@pytest.fixture(scope="module")
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def jamba_tokenizer():
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return get_tokenizer(tokenizer_name=MODEL)
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@pytest.fixture
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def jamba_tool_parser(jamba_tokenizer):
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return JambaToolParser(jamba_tokenizer)
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def assert_tool_calls(actual_tool_calls: List[ToolCall],
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expected_tool_calls: List[ToolCall]):
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assert len(actual_tool_calls) == len(expected_tool_calls)
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for actual_tool_call, expected_tool_call in zip(actual_tool_calls,
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expected_tool_calls):
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assert isinstance(actual_tool_call.id, str)
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assert len(actual_tool_call.id) > 16
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assert actual_tool_call.type == "function"
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assert actual_tool_call.function == expected_tool_call.function
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def stream_delta_message_generator(
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jamba_tool_parser: JambaToolParser, jamba_tokenizer: AnyTokenizer,
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model_output: str) -> Generator[DeltaMessage, None, None]:
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all_token_ids = jamba_tokenizer.encode(model_output,
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add_special_tokens=False)
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previous_text = ""
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previous_tokens = None
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prefix_offset = 0
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read_offset = 0
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for i, delta_token in enumerate(all_token_ids):
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delta_token_ids = [delta_token]
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previous_token_ids = all_token_ids[:i]
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current_token_ids = all_token_ids[:i + 1]
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(new_tokens, delta_text, new_prefix_offset,
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new_read_offset) = detokenize_incrementally(
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tokenizer=jamba_tokenizer,
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all_input_ids=current_token_ids,
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prev_tokens=previous_tokens,
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prefix_offset=prefix_offset,
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read_offset=read_offset,
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skip_special_tokens=False,
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spaces_between_special_tokens=True,
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)
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current_text = previous_text + delta_text
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delta_message = jamba_tool_parser.extract_tool_calls_streaming(
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previous_text,
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current_text,
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delta_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=None, # type: ignore[arg-type]
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)
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if delta_message:
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yield delta_message
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previous_text = current_text
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previous_tokens = previous_tokens + new_tokens if previous_tokens\
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else new_tokens
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prefix_offset = new_prefix_offset
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read_offset = new_read_offset
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def test_extract_tool_calls_no_tools(jamba_tool_parser):
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model_output = "This is a test"
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extracted_tool_calls = jamba_tool_parser.extract_tool_calls(
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model_output, request=None) # type: ignore[arg-type]
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assert not extracted_tool_calls.tools_called
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assert extracted_tool_calls.tool_calls == []
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assert extracted_tool_calls.content == model_output
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@pytest.mark.parametrize(
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ids=[
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"single_tool",
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"single_tool_with_content",
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"parallel_tools",
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],
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argnames=["model_output", "expected_tool_calls", "expected_content"],
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argvalues=[
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(
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''' <tool_calls>[\n {"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}\n]</tool_calls>''', # noqa: E501
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[
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ToolCall(function=FunctionCall(name="get_current_weather",
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arguments=json.dumps(
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{
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"city": "Dallas",
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"state": "TX",
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"unit": "fahrenheit"
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})))
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],
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None),
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(
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''' Sure! let me call the tool for you.<tool_calls>[\n {"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}\n]</tool_calls>''', # noqa: E501
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[
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ToolCall(function=FunctionCall(name="get_current_weather",
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arguments=json.dumps(
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{
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"city": "Dallas",
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"state": "TX",
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"unit": "fahrenheit"
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})))
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],
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" Sure! let me call the tool for you."),
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(
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''' <tool_calls>[\n {"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}},\n {"name": "get_current_weather", "arguments": {"city": "Orlando", "state": "FL", "unit": "fahrenheit"}}\n]</tool_calls>''', # noqa: E501
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[
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ToolCall(function=FunctionCall(name="get_current_weather",
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arguments=json.dumps(
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{
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"city": "Dallas",
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"state": "TX",
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"unit": "fahrenheit"
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}))),
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ToolCall(function=FunctionCall(name="get_current_weather",
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arguments=json.dumps(
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{
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"city": "Orlando",
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"state": "FL",
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"unit": "fahrenheit"
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})))
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],
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None)
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],
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)
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def test_extract_tool_calls(jamba_tool_parser, model_output,
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expected_tool_calls, expected_content):
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extracted_tool_calls = jamba_tool_parser.extract_tool_calls(
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model_output, request=None) # type: ignore[arg-type]
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assert extracted_tool_calls.tools_called
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assert_tool_calls(extracted_tool_calls.tool_calls, expected_tool_calls)
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assert extracted_tool_calls.content == expected_content
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@pytest.mark.parametrize(
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ids=[
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"no_tools",
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"single_tool",
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"single_tool_with_content",
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"parallel_tools",
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],
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argnames=["model_output", "expected_tool_calls", "expected_content"],
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argvalues=[
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('''This is a test''', [], '''This is a test'''),
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(
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''' <tool_calls>[\n {"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}\n]</tool_calls>''', # noqa: E501
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[
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ToolCall(function=FunctionCall(name="get_current_weather",
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arguments=json.dumps(
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{
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"city": "Dallas",
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"state": "TX",
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"unit": "fahrenheit"
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})))
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],
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" "),
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(
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''' Sure! let me call the tool for you.<tool_calls>[\n {"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}\n]</tool_calls>''', # noqa: E501
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[
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ToolCall(function=FunctionCall(name="get_current_weather",
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arguments=json.dumps(
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{
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"city": "Dallas",
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"state": "TX",
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"unit": "fahrenheit"
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})))
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],
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" Sure! let me call the tool for you."),
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(
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''' <tool_calls>[\n {"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}},\n {"name": "get_current_weather", "arguments": {"city": "Orlando", "state": "FL", "unit": "fahrenheit"}}\n]</tool_calls>''', # noqa: E501
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[
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ToolCall(function=FunctionCall(name="get_current_weather",
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arguments=json.dumps(
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{
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"city": "Dallas",
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"state": "TX",
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"unit": "fahrenheit"
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}))),
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ToolCall(function=FunctionCall(name="get_current_weather",
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arguments=json.dumps(
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{
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"city": "Orlando",
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"state": "FL",
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"unit": "fahrenheit"
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})))
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],
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" ")
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],
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)
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def test_extract_tool_calls_streaming(jamba_tool_parser, jamba_tokenizer,
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model_output, expected_tool_calls,
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expected_content):
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other_content: str = ''
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function_names: List[str] = []
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function_args_strs: List[str] = []
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tool_call_idx: int = -1
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tool_call_ids: List[Optional[str]] = []
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for delta_message in stream_delta_message_generator(
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jamba_tool_parser, jamba_tokenizer, model_output):
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# role should never be streamed from tool parser
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assert not delta_message.role
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if delta_message.content:
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other_content += delta_message.content
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streamed_tool_calls = delta_message.tool_calls
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if streamed_tool_calls and len(streamed_tool_calls) > 0:
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# make sure only one diff is present - correct even for parallel
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assert len(streamed_tool_calls) == 1
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tool_call = streamed_tool_calls[0]
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# if a new tool is being called, set up empty arguments
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if tool_call.index != tool_call_idx:
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tool_call_idx = tool_call.index
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function_args_strs.append("")
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tool_call_ids.append(None)
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# if a tool call ID is streamed, make sure one hasn't been already
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if tool_call.id and not tool_call_ids[tool_call.index]:
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tool_call_ids[tool_call.index] = tool_call.id
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# if parts of the function start being streamed
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if tool_call.function:
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# if the function name is defined, set it. it should be streamed
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# IN ENTIRETY, exactly one time.
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if tool_call.function.name:
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assert isinstance(tool_call.function.name, str)
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function_names.append(tool_call.function.name)
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if tool_call.function.arguments:
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# make sure they're a string and then add them to the list
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assert isinstance(tool_call.function.arguments, str)
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function_args_strs[
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tool_call.index] += tool_call.function.arguments
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assert other_content == expected_content
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actual_tool_calls = [
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ToolCall(id=tool_call_id,
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function=FunctionCall(
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name=function_name,
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arguments=partial_json_parser.ensure_json(
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function_args_str, Allow.OBJ | Allow.STR)))
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for tool_call_id, function_name, function_args_str in zip(
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tool_call_ids, function_names, function_args_strs)
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]
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assert_tool_calls(actual_tool_calls, expected_tool_calls)
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