cb0b443274
Signed-off-by: Ben Browning <bbrownin@redhat.com> Co-authored-by: Chauncey <chaunceyjiang@gmail.com>
1571 lines
49 KiB
Python
1571 lines
49 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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from collections.abc import Generator
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import pytest
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from vllm.entrypoints.openai.chat_completion.protocol import (
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ChatCompletionRequest,
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ChatCompletionToolsParam,
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)
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from vllm.entrypoints.openai.engine.protocol import (
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DeltaMessage,
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FunctionCall,
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ToolCall,
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)
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from vllm.tokenizers import TokenizerLike, get_tokenizer
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from vllm.tokenizers.detokenizer_utils import detokenize_incrementally
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from vllm.tool_parsers.step3p5_tool_parser import Step3p5ToolParser
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MODEL = "stepfun-ai/Step-3.5-Flash"
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@pytest.fixture(scope="module")
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def step3p5_tokenizer():
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return get_tokenizer(tokenizer_name=MODEL)
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@pytest.fixture
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def step3p5_tool_parser(step3p5_tokenizer, sample_tools):
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return Step3p5ToolParser(step3p5_tokenizer, tools=sample_tools)
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@pytest.fixture
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def sample_tools():
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return [
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ChatCompletionToolsParam(
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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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"city": {"type": "string", "description": "The city name"},
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"state": {"type": "string", "description": "The state code"},
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"unit": {"type": "string", "enum": ["fahrenheit", "celsius"]},
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},
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"required": ["city", "state"],
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},
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},
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),
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ChatCompletionToolsParam(
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type="function",
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function={
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"name": "calculate_area",
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"description": "Calculate area of a shape",
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"parameters": {
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"type": "object",
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"properties": {
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"shape": {"type": "string"},
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"dimensions": {"type": "object"},
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"precision": {"type": "integer"},
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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 assert_tool_calls(
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actual_tool_calls: list[ToolCall], expected_tool_calls: list[ToolCall]
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):
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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(
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actual_tool_calls, expected_tool_calls
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):
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assert actual_tool_call.type == "function"
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assert actual_tool_call.function.name == expected_tool_call.function.name
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assert json.loads(actual_tool_call.function.arguments) == json.loads(
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expected_tool_call.function.arguments
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)
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def stream_delta_message_generator(
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step3p5_tool_parser,
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step3p5_tokenizer: TokenizerLike,
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model_output: str,
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request: ChatCompletionRequest | None = None,
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) -> Generator[DeltaMessage, None, None]:
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all_token_ids = step3p5_tokenizer.encode(model_output, 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, new_read_offset) = (
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detokenize_incrementally(
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tokenizer=step3p5_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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)
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current_text = previous_text + delta_text
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delta_message = step3p5_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=request,
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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 = (
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previous_tokens + new_tokens if previous_tokens else new_tokens
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)
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prefix_offset = new_prefix_offset
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read_offset = new_read_offset
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def stream_delta_message_generator_from_chunks(
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step3p5_tool_parser,
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step3p5_tokenizer: TokenizerLike,
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delta_text_chunks: list[str],
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request: ChatCompletionRequest | None = None,
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) -> Generator[DeltaMessage, None, None]:
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previous_text = ""
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previous_token_ids: list[int] = []
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for delta_text in delta_text_chunks:
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delta_token_ids = step3p5_tokenizer.encode(delta_text, add_special_tokens=False)
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current_text = previous_text + delta_text
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current_token_ids = previous_token_ids + delta_token_ids
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delta_message = step3p5_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=request,
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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_token_ids = current_token_ids
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def test_extract_tool_calls_no_tools(step3p5_tool_parser):
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model_output = "This is a test response without any tool calls"
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extracted_tool_calls = step3p5_tool_parser.extract_tool_calls(
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model_output, request=None
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) # 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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"single_tool_multiline_param",
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"parallel_tools",
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"tool_with_typed_params",
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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_call>
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<function=get_current_weather>
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<parameter=city>
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Dallas
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</parameter>
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<parameter=state>
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TX
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</parameter>
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<parameter=unit>
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fahrenheit
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</parameter>
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</function>
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</tool_call>""",
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[
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ToolCall(
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function=FunctionCall(
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name="get_current_weather",
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arguments=json.dumps(
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{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}
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),
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)
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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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"""Sure! Let me check the weather for you.<tool_call>
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<function=get_current_weather>
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<parameter=city>
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Dallas
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</parameter>
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<parameter=state>
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TX
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</parameter>
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<parameter=unit>
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fahrenheit
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</parameter>
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</function>
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</tool_call>""",
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[
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ToolCall(
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function=FunctionCall(
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name="get_current_weather",
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arguments=json.dumps(
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{"city": "Dallas", "state": "TX", "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 check the weather for you.",
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),
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(
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"""<tool_call>
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<function=calculate_area>
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<parameter=shape>
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rectangle
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</parameter>
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<parameter=dimensions>
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{"width": 10,
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"height": 20}
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</parameter>
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<parameter=precision>
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2
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</parameter>
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</function>
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</tool_call>""",
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[
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ToolCall(
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function=FunctionCall(
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name="calculate_area",
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arguments=json.dumps(
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{
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"shape": "rectangle",
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"dimensions": {"width": 10, "height": 20},
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"precision": 2,
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}
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),
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)
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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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"""<tool_call>
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<function=get_current_weather>
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<parameter=city>
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Dallas
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</parameter>
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<parameter=state>
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TX
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</parameter>
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<parameter=unit>
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fahrenheit
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</parameter>
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</function>
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</tool_call>
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<tool_call>
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<function=get_current_weather>
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<parameter=city>
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Orlando
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</parameter>
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<parameter=state>
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FL
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</parameter>
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<parameter=unit>
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fahrenheit
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</parameter>
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</function>
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</tool_call>""",
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[
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ToolCall(
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function=FunctionCall(
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name="get_current_weather",
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arguments=json.dumps(
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{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}
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),
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)
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),
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ToolCall(
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function=FunctionCall(
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name="get_current_weather",
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arguments=json.dumps(
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{"city": "Orlando", "state": "FL", "unit": "fahrenheit"}
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),
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)
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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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"""Let me calculate that area for you.<tool_call>
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<function=calculate_area>
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<parameter=shape>
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circle
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</parameter>
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<parameter=dimensions>
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{"radius": 15.5}
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</parameter>
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<parameter=precision>
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3
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</parameter>
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</function>
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</tool_call>""",
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[
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ToolCall(
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function=FunctionCall(
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name="calculate_area",
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arguments=json.dumps(
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{
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"shape": "circle",
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"dimensions": {"radius": 15.5},
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"precision": 3,
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}
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),
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)
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)
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],
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"Let me calculate that area for you.",
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),
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],
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)
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def test_extract_tool_calls(
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step3p5_tool_parser,
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sample_tools,
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model_output,
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expected_tool_calls,
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expected_content,
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):
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request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
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extracted_tool_calls = step3p5_tool_parser.extract_tool_calls(
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model_output, request=request
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)
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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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def test_extract_tool_calls_fallback_no_tags(step3p5_tool_parser, sample_tools):
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"""Test fallback parsing when XML tags are missing"""
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model_output = """<function=get_current_weather>
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<parameter=city>
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Dallas
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</parameter>
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<parameter=state>
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TX
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</parameter>
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</function>"""
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request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
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extracted_tool_calls = step3p5_tool_parser.extract_tool_calls(
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model_output, request=request
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)
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assert extracted_tool_calls.tools_called
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assert len(extracted_tool_calls.tool_calls) == 1
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assert extracted_tool_calls.tool_calls[0].function.name == "get_current_weather"
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def test_extract_tool_calls_type_conversion(step3p5_tokenizer):
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"""Test parameter type conversion based on tool schema"""
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tools = [
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ChatCompletionToolsParam(
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type="function",
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function={
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"name": "test_types",
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"parameters": {
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"type": "object",
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"properties": {
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"int_param": {"type": "integer"},
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"float_param": {"type": "float"},
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"bool_param": {"type": "boolean"},
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"str_param": {"type": "string"},
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"obj_param": {"type": "object"},
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},
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},
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},
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)
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]
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model_output = """<tool_call>
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<function=test_types>
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<parameter=int_param>
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42
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</parameter>
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<parameter=float_param>
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3.14
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</parameter>
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<parameter=bool_param>
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true
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</parameter>
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<parameter=str_param>
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hello world
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</parameter>
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<parameter=obj_param>
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{"key": "value"}
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</parameter>
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</function>
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</tool_call>"""
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parser = Step3p5ToolParser(step3p5_tokenizer, tools=tools)
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request = ChatCompletionRequest(model=MODEL, messages=[], tools=tools)
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extracted_tool_calls = parser.extract_tool_calls(model_output, request=request)
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args = json.loads(extracted_tool_calls.tool_calls[0].function.arguments)
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assert args["int_param"] == 42
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assert args["float_param"] == 3.14
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assert args["bool_param"] is True
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assert args["str_param"] == "hello world"
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assert args["obj_param"] == {"key": "value"}
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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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"single_tool_multiline_param",
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"parallel_tools",
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"tool_with_typed_params", # Added this test case
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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 without tools", [], "This is a test without tools"),
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(
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"""<tool_call>
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<function=get_current_weather>
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<parameter=city>
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Dallas
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</parameter>
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<parameter=state>
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TX
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</parameter>
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<parameter=unit>
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fahrenheit
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</parameter>
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</function>
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</tool_call>""",
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[
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ToolCall(
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function=FunctionCall(
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name="get_current_weather",
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arguments=json.dumps(
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{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}
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),
|
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)
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)
|
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],
|
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None,
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),
|
|
(
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"""Sure! Let me check the weather for you.<tool_call>
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<function=get_current_weather>
|
|
<parameter=city>
|
|
Dallas
|
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</parameter>
|
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<parameter=state>
|
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TX
|
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</parameter>
|
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<parameter=unit>
|
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fahrenheit
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</parameter>
|
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</function>
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</tool_call>""",
|
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[
|
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ToolCall(
|
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function=FunctionCall(
|
|
name="get_current_weather",
|
|
arguments=json.dumps(
|
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{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}
|
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),
|
|
)
|
|
)
|
|
],
|
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"Sure! Let me check the weather for you.",
|
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),
|
|
(
|
|
"""<tool_call>
|
|
<function=calculate_area>
|
|
<parameter=shape>
|
|
rectangle
|
|
</parameter>
|
|
<parameter=dimensions>
|
|
{"width": 10,
|
|
"height": 20}
|
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</parameter>
|
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<parameter=precision>
|
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2
|
|
</parameter>
|
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</function>
|
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</tool_call>""",
|
|
[
|
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ToolCall(
|
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function=FunctionCall(
|
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name="calculate_area",
|
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arguments=json.dumps(
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{
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"shape": "rectangle",
|
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"dimensions": {"width": 10, "height": 20},
|
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"precision": 2,
|
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}
|
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),
|
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)
|
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)
|
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],
|
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None,
|
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),
|
|
(
|
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"""<tool_call>
|
|
<function=get_current_weather>
|
|
<parameter=city>
|
|
Dallas
|
|
</parameter>
|
|
<parameter=state>
|
|
TX
|
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</parameter>
|
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<parameter=unit>
|
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fahrenheit
|
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</parameter>
|
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</function>
|
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</tool_call>
|
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<tool_call>
|
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<function=get_current_weather>
|
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<parameter=city>
|
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Orlando
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</parameter>
|
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<parameter=state>
|
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FL
|
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</parameter>
|
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<parameter=unit>
|
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celsius
|
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</parameter>
|
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</function>
|
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</tool_call>""",
|
|
[
|
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ToolCall(
|
|
function=FunctionCall(
|
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name="get_current_weather",
|
|
arguments=json.dumps(
|
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{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}
|
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),
|
|
)
|
|
),
|
|
ToolCall(
|
|
function=FunctionCall(
|
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name="get_current_weather",
|
|
arguments=json.dumps(
|
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{"city": "Orlando", "state": "FL", "unit": "celsius"}
|
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),
|
|
)
|
|
),
|
|
],
|
|
None,
|
|
),
|
|
# Added tool_with_typed_params test case
|
|
(
|
|
"""Let me calculate that area for you.<tool_call>
|
|
<function=calculate_area>
|
|
<parameter=shape>
|
|
circle
|
|
</parameter>
|
|
<parameter=dimensions>
|
|
{"radius": 15.5}
|
|
</parameter>
|
|
<parameter=precision>
|
|
3
|
|
</parameter>
|
|
</function>
|
|
</tool_call>""",
|
|
[
|
|
ToolCall(
|
|
function=FunctionCall(
|
|
name="calculate_area",
|
|
arguments=json.dumps(
|
|
{
|
|
"shape": "circle",
|
|
"dimensions": {"radius": 15.5},
|
|
"precision": 3,
|
|
}
|
|
),
|
|
)
|
|
)
|
|
],
|
|
"Let me calculate that area for you.",
|
|
),
|
|
],
|
|
)
|
|
def test_extract_tool_calls_streaming(
|
|
step3p5_tool_parser,
|
|
step3p5_tokenizer,
|
|
sample_tools,
|
|
model_output,
|
|
expected_tool_calls,
|
|
expected_content,
|
|
):
|
|
"""Test incremental streaming behavior including typed parameters"""
|
|
request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
|
|
|
|
other_content = ""
|
|
tool_states = {} # Track state per tool index
|
|
|
|
for delta_message in stream_delta_message_generator(
|
|
step3p5_tool_parser, step3p5_tokenizer, model_output, request
|
|
):
|
|
# role should never be streamed from tool parser
|
|
assert not delta_message.role
|
|
|
|
if delta_message.content:
|
|
other_content += delta_message.content
|
|
|
|
if delta_message.tool_calls:
|
|
for tool_call in delta_message.tool_calls:
|
|
idx = tool_call.index
|
|
|
|
# Initialize state for new tool
|
|
if idx not in tool_states:
|
|
tool_states[idx] = {
|
|
"id": None,
|
|
"name": None,
|
|
"arguments": "",
|
|
"type": None,
|
|
}
|
|
|
|
# First chunk should have id, name, and type
|
|
if tool_call.id:
|
|
tool_states[idx]["id"] = tool_call.id
|
|
|
|
if tool_call.type:
|
|
assert tool_call.type == "function"
|
|
tool_states[idx]["type"] = tool_call.type
|
|
|
|
if tool_call.function:
|
|
if tool_call.function.name:
|
|
# Should only be set once
|
|
assert tool_states[idx]["name"] is None
|
|
tool_states[idx]["name"] = tool_call.function.name
|
|
|
|
if tool_call.function.arguments is not None:
|
|
# Accumulate arguments incrementally
|
|
tool_states[idx]["arguments"] += tool_call.function.arguments
|
|
|
|
# Verify final content
|
|
assert other_content == (expected_content or "") # Handle None case
|
|
|
|
# Verify we got all expected tool calls
|
|
assert len(tool_states) == len(expected_tool_calls)
|
|
|
|
# Verify each tool call
|
|
for idx, expected_tool in enumerate(expected_tool_calls):
|
|
state = tool_states[idx]
|
|
assert state["id"] is not None
|
|
assert state["type"] == "function"
|
|
assert state["name"] == expected_tool.function.name
|
|
|
|
# Parse accumulated arguments
|
|
arguments_str = state["arguments"]
|
|
assert arguments_str is not None
|
|
actual_args = json.loads(arguments_str)
|
|
expected_args = json.loads(expected_tool.function.arguments)
|
|
assert actual_args == expected_args
|
|
|
|
|
|
def test_extract_tool_calls_missing_closing_parameter_tag(
|
|
step3p5_tool_parser, sample_tools
|
|
):
|
|
"""Test handling of missing closing </parameter> tag"""
|
|
# Using get_current_weather from sample_tools but with malformed XML
|
|
model_output = """Let me check the weather for you:
|
|
<tool_call>
|
|
<function=get_current_weather>
|
|
<parameter=city>
|
|
Dallas
|
|
<parameter=state>
|
|
TX
|
|
</parameter>
|
|
<parameter=unit>
|
|
fahrenheit
|
|
</parameter>
|
|
</function>
|
|
</tool_call>"""
|
|
|
|
request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
|
|
extracted_tool_calls = step3p5_tool_parser.extract_tool_calls(
|
|
model_output, request=request
|
|
)
|
|
|
|
# The parser should handle the malformed XML gracefully
|
|
assert extracted_tool_calls.tools_called
|
|
assert len(extracted_tool_calls.tool_calls) == 1
|
|
|
|
# Verify the function name is correct
|
|
assert extracted_tool_calls.tool_calls[0].function.name == "get_current_weather"
|
|
|
|
# Verify the arguments are parsed despite the missing closing tag
|
|
args = json.loads(extracted_tool_calls.tool_calls[0].function.arguments)
|
|
assert "city" in args
|
|
assert args["city"] == "Dallas"
|
|
assert args["state"] == "TX"
|
|
assert args["unit"] == "fahrenheit"
|
|
|
|
# Check that content before the tool call is preserved
|
|
assert "Let me check the weather for you:" in extracted_tool_calls.content
|
|
|
|
|
|
def test_extract_tool_calls_streaming_missing_closing_tag(
|
|
step3p5_tool_parser, step3p5_tokenizer, sample_tools
|
|
):
|
|
"""Test streaming with missing closing </parameter> tag"""
|
|
# Using get_current_weather from sample_tools but with malformed XML
|
|
model_output = """Let me check the weather for you:
|
|
<tool_call>
|
|
<function=get_current_weather>
|
|
<parameter=city>
|
|
Dallas
|
|
<parameter=state>
|
|
TX
|
|
</parameter>
|
|
<parameter=unit>
|
|
fahrenheit
|
|
</parameter>
|
|
</function>
|
|
</tool_call>"""
|
|
|
|
request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
|
|
|
|
other_content = ""
|
|
tool_states = {}
|
|
|
|
for delta_message in stream_delta_message_generator(
|
|
step3p5_tool_parser, step3p5_tokenizer, model_output, request
|
|
):
|
|
if delta_message.content:
|
|
other_content += delta_message.content
|
|
|
|
if delta_message.tool_calls:
|
|
for tool_call in delta_message.tool_calls:
|
|
idx = tool_call.index
|
|
|
|
if idx not in tool_states:
|
|
tool_states[idx] = {
|
|
"id": None,
|
|
"name": None,
|
|
"arguments": "",
|
|
"type": None,
|
|
}
|
|
|
|
if tool_call.id:
|
|
tool_states[idx]["id"] = tool_call.id
|
|
|
|
if tool_call.type:
|
|
assert tool_call.type == "function"
|
|
tool_states[idx]["type"] = tool_call.type
|
|
|
|
if tool_call.function:
|
|
if tool_call.function.name:
|
|
tool_states[idx]["name"] = tool_call.function.name
|
|
|
|
if tool_call.function.arguments is not None:
|
|
tool_states[idx]["arguments"] += tool_call.function.arguments
|
|
|
|
# Verify content was streamed
|
|
assert "Let me check the weather for you:" in other_content
|
|
|
|
# Verify we got the tool call
|
|
assert len(tool_states) == 1
|
|
state = tool_states[0]
|
|
assert state["id"] is not None
|
|
assert state["type"] == "function"
|
|
assert state["name"] == "get_current_weather"
|
|
|
|
# Verify arguments were parsed correctly despite missing closing tag
|
|
assert state["arguments"] is not None
|
|
args = json.loads(state["arguments"])
|
|
assert args["city"] == "Dallas"
|
|
assert args["state"] == "TX"
|
|
assert args["unit"] == "fahrenheit"
|
|
|
|
|
|
def test_extract_tool_calls_streaming_incremental(
|
|
step3p5_tool_parser, step3p5_tokenizer, sample_tools
|
|
):
|
|
"""Test that streaming is truly incremental"""
|
|
model_output = """I'll check the weather.<tool_call>
|
|
<function=get_current_weather>
|
|
<parameter=city>
|
|
Dallas
|
|
</parameter>
|
|
<parameter=state>
|
|
TX
|
|
</parameter>
|
|
</function>
|
|
</tool_call>"""
|
|
|
|
request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
|
|
|
|
chunks = []
|
|
for delta_message in stream_delta_message_generator(
|
|
step3p5_tool_parser, step3p5_tokenizer, model_output, request
|
|
):
|
|
chunks.append(delta_message)
|
|
|
|
# Should have multiple chunks
|
|
assert len(chunks) > 3
|
|
|
|
# First chunk(s) should be content
|
|
assert chunks[0].content is not None
|
|
assert chunks[0].tool_calls is None or chunks[0].tool_calls == []
|
|
|
|
# Should have a chunk with tool header (id, name, type)
|
|
header_found = False
|
|
for chunk in chunks:
|
|
if chunk.tool_calls and chunk.tool_calls[0].id:
|
|
header_found = True
|
|
assert chunk.tool_calls[0].function.name == "get_current_weather"
|
|
assert chunk.tool_calls[0].type == "function"
|
|
# Empty initially
|
|
assert chunk.tool_calls[0].function.arguments == ""
|
|
break
|
|
assert header_found
|
|
|
|
# Should have chunks with incremental arguments
|
|
arg_chunks = []
|
|
for chunk in chunks:
|
|
if chunk.tool_calls and chunk.tool_calls[0].function.arguments:
|
|
arg_chunks.append(chunk.tool_calls[0].function.arguments)
|
|
|
|
# Arguments should be streamed incrementally
|
|
assert len(arg_chunks) > 1
|
|
|
|
# Concatenated arguments should form valid JSON
|
|
full_args = "".join(arg_chunks)
|
|
parsed_args = json.loads(full_args)
|
|
assert parsed_args["city"] == "Dallas"
|
|
assert parsed_args["state"] == "TX"
|
|
|
|
|
|
def test_extract_tool_calls_complex_type_with_single_quote(step3p5_tokenizer):
|
|
"""Test parameter type conversion based on tool schema"""
|
|
tools = [
|
|
ChatCompletionToolsParam(
|
|
type="function",
|
|
function={
|
|
"name": "test_types",
|
|
"parameters": {
|
|
"type": "object",
|
|
"properties": {
|
|
"int_param": {"type": "integer"},
|
|
"float_param": {"type": "float"},
|
|
"bool_param": {"type": "boolean"},
|
|
"str_param": {"type": "string"},
|
|
"obj_param": {"type": "object"},
|
|
},
|
|
},
|
|
},
|
|
)
|
|
]
|
|
|
|
model_output = """<tool_call>
|
|
<function=test_types>
|
|
<parameter=obj_param>
|
|
{'key': 'value'}
|
|
</parameter>
|
|
</function>
|
|
</tool_call>"""
|
|
|
|
parser = Step3p5ToolParser(step3p5_tokenizer, tools=tools)
|
|
request = ChatCompletionRequest(model=MODEL, messages=[], tools=tools)
|
|
extracted_tool_calls = parser.extract_tool_calls(model_output, request=request)
|
|
|
|
args = json.loads(extracted_tool_calls.tool_calls[0].function.arguments)
|
|
assert args["obj_param"] == {"key": "value"}
|
|
|
|
|
|
def test_extract_tool_calls_streaming_mixed_content_and_multiple_tool_calls(
|
|
step3p5_tool_parser, step3p5_tokenizer, sample_tools
|
|
):
|
|
"""Test mixed content with multiple complete tool calls.
|
|
|
|
Scenario: Model outputs "hello" + complete tool call + "hi" + complete tool call.
|
|
Expected: "hello" as content, first tool call parsed (index=0), "hi" as content,
|
|
second tool call parsed (index=1).
|
|
"""
|
|
# Model output: hello + complete tool call + hi + complete tool call
|
|
model_output = """hello<tool_call>
|
|
<function=get_current_weather>
|
|
<parameter=city>
|
|
Dallas
|
|
</parameter>
|
|
<parameter=state>
|
|
TX
|
|
</parameter>
|
|
</function>
|
|
</tool_call>hi<tool_call>
|
|
<function=calculate_area>
|
|
<parameter=shape>
|
|
rectangle
|
|
</parameter>
|
|
<parameter=dimensions>
|
|
{"width": 10, "height": 5}
|
|
</parameter>
|
|
</function>
|
|
</tool_call>"""
|
|
|
|
request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
|
|
|
|
other_content = ""
|
|
tool_states = {}
|
|
|
|
for delta_message in stream_delta_message_generator(
|
|
step3p5_tool_parser, step3p5_tokenizer, model_output, request
|
|
):
|
|
if delta_message.content:
|
|
other_content += delta_message.content
|
|
|
|
if delta_message.tool_calls:
|
|
for tool_call in delta_message.tool_calls:
|
|
idx = tool_call.index
|
|
|
|
if idx not in tool_states:
|
|
tool_states[idx] = {
|
|
"id": None,
|
|
"name": None,
|
|
"arguments": "",
|
|
"type": None,
|
|
}
|
|
|
|
if tool_call.id:
|
|
tool_states[idx]["id"] = tool_call.id
|
|
|
|
if tool_call.type:
|
|
assert tool_call.type == "function"
|
|
tool_states[idx]["type"] = tool_call.type
|
|
|
|
if tool_call.function:
|
|
if tool_call.function.name:
|
|
tool_states[idx]["name"] = tool_call.function.name
|
|
|
|
if tool_call.function.arguments is not None:
|
|
tool_states[idx]["arguments"] += tool_call.function.arguments
|
|
|
|
# Should have exactly two complete tool calls
|
|
assert len(tool_states) == 2, "Should have exactly two complete tool calls"
|
|
|
|
# Verify the first tool call (index=0)
|
|
assert tool_states[0]["name"] == "get_current_weather"
|
|
assert tool_states[0]["arguments"]
|
|
args_dict_0 = json.loads(tool_states[0]["arguments"])
|
|
assert args_dict_0["city"] == "Dallas"
|
|
assert args_dict_0["state"] == "TX"
|
|
|
|
# Verify the second tool call (index=1)
|
|
assert tool_states[1]["name"] == "calculate_area"
|
|
assert tool_states[1]["arguments"]
|
|
args_dict_1 = json.loads(tool_states[1]["arguments"])
|
|
assert args_dict_1["shape"] == "rectangle"
|
|
assert isinstance(args_dict_1["dimensions"], dict), "dimensions should be a dict"
|
|
assert args_dict_1["dimensions"]["width"] == 10
|
|
assert args_dict_1["dimensions"]["height"] == 5
|
|
# Verify content: should contain "hello", "hi"
|
|
assert "hello" in other_content, "Should contain 'hello' as content"
|
|
assert "hi" in other_content, "Should contain 'hi' as content"
|
|
|
|
# Verify the order: hello should come first, then hi
|
|
hello_index = other_content.find("hello")
|
|
hi_index = other_content.find("hi")
|
|
|
|
assert hello_index >= 0, "'hello' should be in content"
|
|
assert hi_index > hello_index, "'hi' should come after 'hello'"
|
|
|
|
# Verify that tool call tags are NOT in the content
|
|
# We should not see complete tool call structures in content
|
|
assert "<function=get_current_weather>" not in other_content, (
|
|
"First tool call should not be in content"
|
|
)
|
|
assert "<function=calculate_area>" not in other_content, (
|
|
"Second tool call should not be in content"
|
|
)
|
|
|
|
|
|
def test_extract_tool_calls_non_streaming_mixed_content_and_multiple_tool_calls(
|
|
step3p5_tool_parser, sample_tools
|
|
):
|
|
"""Test non-streaming extraction with mixed content and multiple tool calls.
|
|
|
|
Scenario: Model outputs "hello" + complete tool call + "hi" + complete tool call.
|
|
Expected: "hello" as content, first tool call parsed (index=0), "hi" as content,
|
|
second tool call parsed (index=1)
|
|
"""
|
|
# Model output: hello + complete tool call + hi + complete tool call
|
|
model_output = """hello<tool_call>
|
|
<function=get_current_weather>
|
|
<parameter=city>
|
|
Dallas
|
|
</parameter>
|
|
<parameter=state>
|
|
TX
|
|
</parameter>
|
|
</function>
|
|
</tool_call>hi<tool_call>
|
|
<function=calculate_area>
|
|
<parameter=shape>
|
|
rectangle
|
|
</parameter>
|
|
<parameter=dimensions>
|
|
{"width": 10, "height": 5}
|
|
</parameter>
|
|
</function>
|
|
</tool_call>"""
|
|
|
|
request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
|
|
|
|
extracted_tool_calls = step3p5_tool_parser.extract_tool_calls(
|
|
model_output, request=request
|
|
)
|
|
|
|
# Should have exactly two complete tool calls
|
|
assert extracted_tool_calls.tools_called
|
|
assert len(extracted_tool_calls.tool_calls) == 2, (
|
|
"Should have exactly two complete tool calls"
|
|
)
|
|
|
|
# Verify the first tool call (index=0)
|
|
assert extracted_tool_calls.tool_calls[0].function.name == "get_current_weather"
|
|
args_dict_0 = json.loads(extracted_tool_calls.tool_calls[0].function.arguments)
|
|
assert args_dict_0["city"] == "Dallas"
|
|
assert args_dict_0["state"] == "TX"
|
|
|
|
# Verify the second tool call (index=1)
|
|
assert extracted_tool_calls.tool_calls[1].function.name == "calculate_area"
|
|
args_dict_1 = json.loads(extracted_tool_calls.tool_calls[1].function.arguments)
|
|
assert args_dict_1["shape"] == "rectangle"
|
|
assert isinstance(args_dict_1["dimensions"], dict), "dimensions should be a dict"
|
|
assert args_dict_1["dimensions"]["width"] == 10
|
|
assert args_dict_1["dimensions"]["height"] == 5
|
|
|
|
# Verify content: should contain "hello", "hi"
|
|
assert extracted_tool_calls.content is not None
|
|
assert "hello" in extracted_tool_calls.content, "Should contain 'hello' as content"
|
|
assert "hi" in extracted_tool_calls.content, "Should contain 'hi' as content"
|
|
|
|
# Verify the order: hello should come first, then hi
|
|
hello_index = extracted_tool_calls.content.find("hello")
|
|
hi_index = extracted_tool_calls.content.find("hi")
|
|
|
|
assert hello_index >= 0, "'hello' should be in content"
|
|
assert hi_index > hello_index, "'hi' should come after 'hello'"
|
|
|
|
# Verify that tool call tags are NOT in the content
|
|
assert "<function=get_current_weather>" not in extracted_tool_calls.content, (
|
|
"First tool call should not be in content"
|
|
)
|
|
assert "<function=calculate_area>" not in extracted_tool_calls.content, (
|
|
"Second tool call should not be in content"
|
|
)
|
|
|
|
|
|
def test_extract_tool_calls_streaming_full_input_mixed_content_and_multiple_tool_calls(
|
|
step3p5_tool_parser, step3p5_tokenizer, sample_tools
|
|
):
|
|
"""Test streaming with entire input as single delta_text.
|
|
|
|
Scenario: Model outputs "hello" + complete tool call + "hi" + complete tool call.
|
|
This test simulates the case where the entire input is sent as a single delta_text.
|
|
Expected: "hello" as content, first tool call parsed (index=0), "hi" as content,
|
|
second tool call parsed (index=1).
|
|
"""
|
|
# Model output: hello + complete tool call + hi + complete tool call
|
|
model_output = """hello<tool_call>
|
|
<function=get_current_weather>
|
|
<parameter=city>
|
|
Dallas
|
|
</parameter>
|
|
<parameter=state>
|
|
TX
|
|
</parameter>
|
|
</function>
|
|
</tool_call>hi<tool_call>
|
|
<function=calculate_area>
|
|
<parameter=shape>
|
|
rectangle
|
|
</parameter>
|
|
<parameter=dimensions>
|
|
{"width": 10, "height": 5}
|
|
</parameter>
|
|
</function>
|
|
</tool_call>"""
|
|
|
|
request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
|
|
|
|
other_content = ""
|
|
tool_states = {}
|
|
|
|
# Encode all content tokens at once
|
|
all_token_ids = step3p5_tokenizer.encode(model_output, add_special_tokens=False)
|
|
eos_token_id = step3p5_tokenizer.eos_token_id
|
|
|
|
# Include EOS token in delta_token_ids if available
|
|
if eos_token_id is not None:
|
|
delta_token_ids = all_token_ids + [eos_token_id]
|
|
else:
|
|
delta_token_ids = all_token_ids
|
|
|
|
# current_token_ids includes all content tokens (EOS is not part of the text)
|
|
current_token_ids = all_token_ids
|
|
previous_token_ids: list[int] = []
|
|
|
|
# Decode all tokens to get the full text
|
|
current_text = step3p5_tokenizer.decode(
|
|
current_token_ids, skip_special_tokens=False
|
|
)
|
|
previous_text = ""
|
|
delta_text = current_text
|
|
|
|
# Call parser once with all tokens including EOS
|
|
delta_result = step3p5_tool_parser.extract_tool_calls_streaming(
|
|
previous_text,
|
|
current_text,
|
|
delta_text,
|
|
previous_token_ids,
|
|
current_token_ids,
|
|
delta_token_ids,
|
|
request=request,
|
|
)
|
|
|
|
# Process delta result
|
|
if delta_result:
|
|
if delta_result.content:
|
|
other_content += delta_result.content
|
|
if delta_result.tool_calls:
|
|
for tool_call in delta_result.tool_calls:
|
|
idx = tool_call.index
|
|
if idx not in tool_states:
|
|
tool_states[idx] = {
|
|
"id": None,
|
|
"name": None,
|
|
"arguments": "",
|
|
"type": None,
|
|
}
|
|
if tool_call.id:
|
|
tool_states[idx]["id"] = tool_call.id
|
|
if tool_call.type:
|
|
tool_states[idx]["type"] = tool_call.type
|
|
if tool_call.function:
|
|
if tool_call.function.name:
|
|
tool_states[idx]["name"] = tool_call.function.name
|
|
if tool_call.function.arguments is not None:
|
|
tool_states[idx]["arguments"] += tool_call.function.arguments
|
|
|
|
# Should have exactly two complete tool calls
|
|
assert len(tool_states) == 2, "Should have exactly two complete tool calls"
|
|
|
|
# Verify the first tool call (index=0)
|
|
assert tool_states[0]["name"] == "get_current_weather"
|
|
assert tool_states[0]["arguments"]
|
|
args_dict_0 = json.loads(tool_states[0]["arguments"])
|
|
assert args_dict_0["city"] == "Dallas"
|
|
assert args_dict_0["state"] == "TX"
|
|
|
|
# Verify the second tool call (index=1)
|
|
assert tool_states[1]["name"] == "calculate_area"
|
|
assert tool_states[1]["arguments"]
|
|
args_dict_1 = json.loads(tool_states[1]["arguments"])
|
|
assert args_dict_1["shape"] == "rectangle"
|
|
assert isinstance(args_dict_1["dimensions"], dict), "dimensions should be a dict"
|
|
assert args_dict_1["dimensions"]["width"] == 10
|
|
assert args_dict_1["dimensions"]["height"] == 5
|
|
|
|
# Verify content: should contain "hello", "hi"
|
|
assert "hello" in other_content, "Should contain 'hello' as content"
|
|
assert "hi" in other_content, "Should contain 'hi' as content"
|
|
|
|
# Verify the order: hello should come first, then hi
|
|
hello_index = other_content.find("hello")
|
|
hi_index = other_content.find("hi")
|
|
|
|
assert hello_index >= 0, "'hello' should be in content"
|
|
assert hi_index > hello_index, "'hi' should come after 'hello'"
|
|
|
|
# Verify that tool call tags are NOT in the content
|
|
assert "<function=get_current_weather>" not in other_content, (
|
|
"First tool call should not be in content"
|
|
)
|
|
assert "<function=calculate_area>" not in other_content, (
|
|
"Second tool call should not be in content"
|
|
)
|
|
|
|
|
|
def test_extract_tool_calls_streaming_multiple_tool_calls_no_content_between(
|
|
step3p5_tool_parser, step3p5_tokenizer, sample_tools
|
|
):
|
|
"""Test multiple tool calls with no content between them.
|
|
|
|
Scenario: Model outputs "hello" + tool call + tool call
|
|
Expected: "hello" as content, first tool call parsed (index=0),
|
|
second tool call parsed (index=1).
|
|
No content should appear between the two tool calls.
|
|
"""
|
|
# Model output: hello + tool call + tool call (no content between tool calls)
|
|
model_output = """hello<tool_call>
|
|
<function=get_current_weather>
|
|
<parameter=city>
|
|
Dallas
|
|
</parameter>
|
|
<parameter=state>
|
|
TX
|
|
</parameter>
|
|
</function>
|
|
</tool_call><tool_call>
|
|
<function=calculate_area>
|
|
<parameter=shape>
|
|
rectangle
|
|
</parameter>
|
|
<parameter=dimensions>
|
|
{"width": 10, "height": 5}
|
|
</parameter>
|
|
</function>
|
|
</tool_call>"""
|
|
|
|
request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
|
|
|
|
other_content = ""
|
|
tool_states = {}
|
|
|
|
for delta_message in stream_delta_message_generator(
|
|
step3p5_tool_parser, step3p5_tokenizer, model_output, request
|
|
):
|
|
if delta_message.content:
|
|
other_content += delta_message.content
|
|
|
|
if delta_message.tool_calls:
|
|
for tool_call in delta_message.tool_calls:
|
|
idx = tool_call.index
|
|
|
|
if idx not in tool_states:
|
|
tool_states[idx] = {
|
|
"id": None,
|
|
"name": None,
|
|
"arguments": "",
|
|
"type": None,
|
|
}
|
|
|
|
if tool_call.id:
|
|
tool_states[idx]["id"] = tool_call.id
|
|
|
|
if tool_call.type:
|
|
assert tool_call.type == "function"
|
|
tool_states[idx]["type"] = tool_call.type
|
|
|
|
if tool_call.function:
|
|
if tool_call.function.name:
|
|
tool_states[idx]["name"] = tool_call.function.name
|
|
|
|
if tool_call.function.arguments is not None:
|
|
tool_states[idx]["arguments"] += tool_call.function.arguments
|
|
|
|
# Should have exactly two complete tool calls
|
|
assert len(tool_states) == 2, "Should have exactly two complete tool calls"
|
|
|
|
# Verify the first tool call (index=0)
|
|
assert tool_states[0]["name"] == "get_current_weather"
|
|
assert tool_states[0]["arguments"]
|
|
args_dict_0 = json.loads(tool_states[0]["arguments"])
|
|
assert args_dict_0["city"] == "Dallas"
|
|
assert args_dict_0["state"] == "TX"
|
|
|
|
# Verify the second tool call (index=1)
|
|
assert tool_states[1]["name"] == "calculate_area"
|
|
assert tool_states[1]["arguments"]
|
|
args_dict_1 = json.loads(tool_states[1]["arguments"])
|
|
assert args_dict_1["shape"] == "rectangle"
|
|
assert isinstance(args_dict_1["dimensions"], dict), "dimensions should be a dict"
|
|
assert args_dict_1["dimensions"]["width"] == 10
|
|
assert args_dict_1["dimensions"]["height"] == 5
|
|
|
|
assert "hello" in other_content, "Should contain 'hello' as content"
|
|
|
|
# Verify that tool call tags are NOT in the content
|
|
assert "<function=get_current_weather>" not in other_content, (
|
|
"First tool call should not be in content"
|
|
)
|
|
assert "<function=calculate_area>" not in other_content, (
|
|
"Second tool call should not be in content"
|
|
)
|
|
|
|
|
|
def test_extract_tool_calls_streaming_multi_token_chunk_boundary(
|
|
step3p5_tool_parser, step3p5_tokenizer, sample_tools
|
|
):
|
|
"""Ensure fallback doesn't close a new tool_call when boundary is in one chunk."""
|
|
request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
|
|
delta_text_chunks = [
|
|
"""<tool_call>
|
|
<function=get_current_weather>
|
|
<parameter=city>
|
|
Sys""",
|
|
"""
|
|
</parameter>
|
|
</function>
|
|
""",
|
|
"""</tool_call><tool_call>
|
|
<""",
|
|
"""function=calculate_area>
|
|
<parameter=shape>
|
|
rectangle""",
|
|
"""</parameter>
|
|
</function>
|
|
</tool_call>""",
|
|
]
|
|
boundary_chunk = delta_text_chunks[1]
|
|
assert len(step3p5_tokenizer.encode(boundary_chunk, add_special_tokens=False)) > 1
|
|
|
|
tool_states = {}
|
|
for delta_message in stream_delta_message_generator_from_chunks(
|
|
step3p5_tool_parser, step3p5_tokenizer, delta_text_chunks, request
|
|
):
|
|
print(delta_message)
|
|
if delta_message.tool_calls:
|
|
for tool_call in delta_message.tool_calls:
|
|
idx = tool_call.index
|
|
if idx not in tool_states:
|
|
tool_states[idx] = {
|
|
"name": None,
|
|
"arguments": "",
|
|
}
|
|
if tool_call.function:
|
|
if tool_call.function.name:
|
|
tool_states[idx]["name"] = tool_call.function.name
|
|
if tool_call.function.arguments is not None:
|
|
tool_states[idx]["arguments"] += tool_call.function.arguments
|
|
|
|
assert len(tool_states) == 2
|
|
assert all(state["name"] for state in tool_states.values())
|
|
assert tool_states[0]["name"] == "get_current_weather"
|
|
assert tool_states[1]["name"] == "calculate_area"
|
|
|
|
|
|
def test_extract_tool_calls_non_streaming_multiple_tool_calls_no_content_between(
|
|
step3p5_tool_parser, sample_tools
|
|
):
|
|
"""Test non-streaming extraction with tool calls and no content between them.
|
|
|
|
Scenario: Model outputs "hello" + tool call + tool call.
|
|
Expected: "hello" as content, first tool call parsed (index=0),
|
|
second tool call parsed (index=1).
|
|
No content should appear between the two tool calls.
|
|
"""
|
|
# Model output: hello + tool call + tool call (no content between tool calls)
|
|
model_output = """hello<tool_call>
|
|
<function=get_current_weather>
|
|
<parameter=city>
|
|
Dallas
|
|
</parameter>
|
|
<parameter=state>
|
|
TX
|
|
</parameter>
|
|
</function>
|
|
</tool_call><tool_call>
|
|
<function=calculate_area>
|
|
<parameter=shape>
|
|
rectangle
|
|
</parameter>
|
|
<parameter=dimensions>
|
|
{"width": 10, "height": 5}
|
|
</parameter>
|
|
</function>
|
|
</tool_call>"""
|
|
|
|
request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
|
|
|
|
extracted_tool_calls = step3p5_tool_parser.extract_tool_calls(
|
|
model_output, request=request
|
|
)
|
|
|
|
# Should have exactly two complete tool calls
|
|
assert extracted_tool_calls.tools_called
|
|
assert len(extracted_tool_calls.tool_calls) == 2, (
|
|
"Should have exactly two complete tool calls"
|
|
)
|
|
|
|
# Verify the first tool call (index=0)
|
|
assert extracted_tool_calls.tool_calls[0].function.name == "get_current_weather"
|
|
args_dict_0 = json.loads(extracted_tool_calls.tool_calls[0].function.arguments)
|
|
assert args_dict_0["city"] == "Dallas"
|
|
assert args_dict_0["state"] == "TX"
|
|
|
|
# Verify the second tool call (index=1)
|
|
assert extracted_tool_calls.tool_calls[1].function.name == "calculate_area"
|
|
args_dict_1 = json.loads(extracted_tool_calls.tool_calls[1].function.arguments)
|
|
assert args_dict_1["shape"] == "rectangle"
|
|
assert isinstance(args_dict_1["dimensions"], dict), "dimensions should be a dict"
|
|
assert args_dict_1["dimensions"]["width"] == 10
|
|
assert args_dict_1["dimensions"]["height"] == 5
|
|
|
|
# Verify content: should contain "hello"
|
|
assert extracted_tool_calls.content is not None
|
|
assert "hello" in extracted_tool_calls.content, "Should contain 'hello' as content"
|
|
|
|
# Verify that tool call tags are NOT in the content
|
|
assert "<function=get_current_weather>" not in extracted_tool_calls.content, (
|
|
"First tool call should not be in content"
|
|
)
|
|
assert "<function=calculate_area>" not in extracted_tool_calls.content, (
|
|
"Second tool call should not be in content"
|
|
)
|
|
|
|
|
|
def _accumulate_tool_states(delta_messages):
|
|
"""Accumulate tool call state from a stream of DeltaMessage objects."""
|
|
content = ""
|
|
tool_states = {}
|
|
for delta_message in delta_messages:
|
|
if delta_message.content:
|
|
content += delta_message.content
|
|
if delta_message.tool_calls:
|
|
for tool_call in delta_message.tool_calls:
|
|
idx = tool_call.index
|
|
if idx not in tool_states:
|
|
tool_states[idx] = {
|
|
"id": None,
|
|
"name": None,
|
|
"arguments": "",
|
|
"type": None,
|
|
}
|
|
if tool_call.id:
|
|
tool_states[idx]["id"] = tool_call.id
|
|
if tool_call.type:
|
|
tool_states[idx]["type"] = tool_call.type
|
|
if tool_call.function:
|
|
if tool_call.function.name:
|
|
tool_states[idx]["name"] = tool_call.function.name
|
|
if tool_call.function.arguments is not None:
|
|
tool_states[idx]["arguments"] += tool_call.function.arguments
|
|
return content, tool_states
|
|
|
|
|
|
def test_streaming_mtp_variable_chunks(
|
|
step3p5_tool_parser, step3p5_tokenizer, sample_tools
|
|
):
|
|
"""Regression: MTP variable-size chunks spanning param boundaries (PR #33690)."""
|
|
request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
|
|
|
|
delta_text_chunks = [
|
|
"<tool_call>\n<function=get_current_weather>\n<parameter=city>\n",
|
|
"Dallas\n</parameter>\n<parameter=state>\nTX",
|
|
"\n</parameter>\n<parameter=unit>\nfahrenheit\n</parameter>",
|
|
"\n</function>\n</tool_call>",
|
|
]
|
|
|
|
_, tool_states = _accumulate_tool_states(
|
|
stream_delta_message_generator_from_chunks(
|
|
step3p5_tool_parser, step3p5_tokenizer, delta_text_chunks, request
|
|
)
|
|
)
|
|
|
|
assert len(tool_states) == 1
|
|
|
|
state = tool_states[0]
|
|
assert state["id"] is not None
|
|
assert state["type"] == "function"
|
|
assert state["name"] == "get_current_weather"
|
|
|
|
args = json.loads(state["arguments"])
|
|
assert args["city"] == "Dallas"
|
|
assert args["state"] == "TX"
|
|
assert args["unit"] == "fahrenheit"
|
|
|
|
|
|
def test_streaming_multi_token_per_step(
|
|
step3p5_tool_parser, step3p5_tokenizer, sample_tools
|
|
):
|
|
"""Regression: MTP large chunks spanning multiple tool calls (PR #33690)."""
|
|
model_output = """<tool_call>
|
|
<function=get_current_weather>
|
|
<parameter=city>
|
|
Dallas
|
|
</parameter>
|
|
<parameter=state>
|
|
TX
|
|
</parameter>
|
|
<parameter=unit>
|
|
fahrenheit
|
|
</parameter>
|
|
</function>
|
|
</tool_call>
|
|
<tool_call>
|
|
<function=get_current_weather>
|
|
<parameter=city>
|
|
Orlando
|
|
</parameter>
|
|
<parameter=state>
|
|
FL
|
|
</parameter>
|
|
<parameter=unit>
|
|
celsius
|
|
</parameter>
|
|
</function>
|
|
</tool_call>"""
|
|
|
|
request = ChatCompletionRequest(model=MODEL, messages=[], tools=sample_tools)
|
|
|
|
# MTP-style large chunks
|
|
mtp_chunks = [
|
|
(
|
|
"<tool_call>\n<function=get_current_weather>\n"
|
|
"<parameter=city>\nDallas\n</parameter>\n"
|
|
"<parameter=state>\nTX"
|
|
),
|
|
(
|
|
"\n</parameter>\n<parameter=unit>\nfahrenheit\n</parameter>\n"
|
|
"</function>\n</tool_call>\n"
|
|
"<tool_call>\n<function=get_current_weather>\n"
|
|
"<parameter=city>\nOrlando\n</parameter>\n"
|
|
"<parameter=state>\nFL\n</parameter>\n"
|
|
"<parameter=unit>\ncelsius\n</parameter>\n"
|
|
"</function>\n</tool_call>"
|
|
),
|
|
]
|
|
|
|
_, mtp_tool_states = _accumulate_tool_states(
|
|
stream_delta_message_generator_from_chunks(
|
|
step3p5_tool_parser, step3p5_tokenizer, mtp_chunks, request
|
|
)
|
|
)
|
|
|
|
# Token-by-token streaming (reference)
|
|
step3p5_tool_parser_ref = Step3p5ToolParser(step3p5_tokenizer)
|
|
_, ref_tool_states = _accumulate_tool_states(
|
|
stream_delta_message_generator(
|
|
step3p5_tool_parser_ref, step3p5_tokenizer, model_output, request
|
|
)
|
|
)
|
|
|
|
assert len(mtp_tool_states) == 2
|
|
assert len(ref_tool_states) == 2
|
|
|
|
# MTP results must match reference
|
|
for idx in range(2):
|
|
assert mtp_tool_states[idx]["name"] == ref_tool_states[idx]["name"]
|
|
mtp_args = json.loads(mtp_tool_states[idx]["arguments"])
|
|
ref_args = json.loads(ref_tool_states[idx]["arguments"])
|
|
assert mtp_args == ref_args
|