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| 1 | +# SPDX-License-Identifier: Apache-2.0 |
| 2 | +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project |
| 3 | + |
| 4 | +import json |
| 5 | +from unittest.mock import MagicMock |
| 6 | + |
| 7 | +import pytest |
| 8 | + |
| 9 | +from tests.entrypoints.openai.tool_parsers.utils import ( |
| 10 | + run_tool_extraction, run_tool_extraction_streaming) |
| 11 | +from vllm.entrypoints.openai.protocol import FunctionCall, ToolCall |
| 12 | +from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager |
| 13 | + |
| 14 | + |
| 15 | +def make_tool_call(name, arguments): |
| 16 | + return ToolCall(type="function", |
| 17 | + function=FunctionCall(name=name, |
| 18 | + arguments=json.dumps(arguments))) |
| 19 | + |
| 20 | + |
| 21 | +# TODO: add reason prefix and suffix. |
| 22 | + |
| 23 | + |
| 24 | +@pytest.mark.parametrize( |
| 25 | + "model_output,expected_tool_calls,expected_content", |
| 26 | + [ |
| 27 | + # No tool call |
| 28 | + ("How can I help you today?", [], "How can I help you today?"), |
| 29 | + # Single tool call, no content |
| 30 | + ( |
| 31 | + "<tool_calls>[{\"name\": \"get_weather\", \"arguments\": {\"city\": \"San Francisco\", \"metric\": \"celsius\"}}]</tool_calls>", #noqa: E501 |
| 32 | + [ |
| 33 | + make_tool_call("get_weather", { |
| 34 | + "city": "San Francisco", |
| 35 | + "metric": "celsius" |
| 36 | + }) |
| 37 | + ], |
| 38 | + None), |
| 39 | + # Multiple tool calls |
| 40 | + ( |
| 41 | + "<tool_calls>[{\"name\": \"get_weather\", \"arguments\": {\"city\": \"San Francisco\", \"metric\": \"celsius\"}}, {\"name\": \"register_user\", \"arguments\": {\"name\": \"John Doe\", \"age\": 37, \"address\": {\"city\": \"San Francisco\", \"state\": \"CA\"}, \"role\": null, \"passed_test\": true, \"aliases\": [\"John\", \"Johnny\"]}}]</tool_calls>", #noqa: E501 |
| 42 | + [ |
| 43 | + make_tool_call("get_weather", { |
| 44 | + "city": "San Francisco", |
| 45 | + "metric": "celsius" |
| 46 | + }), |
| 47 | + make_tool_call( |
| 48 | + "register_user", { |
| 49 | + "name": "John Doe", |
| 50 | + "age": 37, |
| 51 | + "address": { |
| 52 | + "city": "San Francisco", |
| 53 | + "state": "CA" |
| 54 | + }, |
| 55 | + "role": None, |
| 56 | + "passed_test": True, |
| 57 | + "aliases": ["John", "Johnny"] |
| 58 | + }) |
| 59 | + ], |
| 60 | + None), |
| 61 | + # Content before tool call |
| 62 | + ( |
| 63 | + "I will call the tool now. <tool_calls>[{\"name\": \"get_weather\", \"arguments\": {\"city\": \"Boston\"}}]</tool_calls>", #noqa: E501 |
| 64 | + [make_tool_call("get_weather", {"city": "Boston"})], |
| 65 | + "I will call the tool now. "), |
| 66 | + # Content after tool call (should be stripped) |
| 67 | + ( |
| 68 | + "<tool_calls>[{\"name\": \"get_weather\", \"arguments\": {\"city\": \"Seattle\"}}]</tool_calls>\nThank you!", #noqa: E501 |
| 69 | + [make_tool_call("get_weather", {"city": "Seattle"})], |
| 70 | + None), |
| 71 | + ]) |
| 72 | +def test_hunyuan_a13b_tool_parser_extract(model_output, expected_tool_calls, |
| 73 | + expected_content): |
| 74 | + mock_tokenizer = MagicMock() |
| 75 | + tool_parser: ToolParser = ToolParserManager.get_tool_parser( |
| 76 | + "hunyuan_a13b")(mock_tokenizer) |
| 77 | + content, tool_calls = run_tool_extraction(tool_parser, |
| 78 | + model_output, |
| 79 | + streaming=False) |
| 80 | + |
| 81 | + # align the random id. |
| 82 | + for idx in range(len(tool_calls)): |
| 83 | + tool_calls[idx].id = expected_tool_calls[idx].id |
| 84 | + assert tool_calls == expected_tool_calls |
| 85 | + assert content == expected_content |
| 86 | + |
| 87 | + |
| 88 | +# Streaming test: simulate incremental output |
| 89 | +@pytest.mark.parametrize("model_deltas,expected_tool_calls", [ |
| 90 | + ([ |
| 91 | + "<tool_calls>[{\"name\": \"get_weather\", ", |
| 92 | + "\"arguments\": {\"city\": \"San Francisco\", ", |
| 93 | + "\"metric\": \"celsius\"}}]", "</tool_calls>" |
| 94 | + ], [ |
| 95 | + make_tool_call("get_weather", { |
| 96 | + "city": "San Francisco", |
| 97 | + "metric": "celsius" |
| 98 | + }) |
| 99 | + ]), |
| 100 | + ([ |
| 101 | + "<tool_calls>[{\"name\":", " \"get_weather\",", " \"arguments\":", |
| 102 | + " {\"city\": \"Boston\"}", "}]", "</tool_calls>" |
| 103 | + ], [make_tool_call("get_weather", {"city": "Boston"})]), |
| 104 | + ([ |
| 105 | + "", "<tool_calls>[{\"name\":", " \"get_weather\",", " \"arguments\":", |
| 106 | + " {\"city\": \"Boston\"}", "}]", "</tool_calls>", "\n</answer>" |
| 107 | + ], [make_tool_call("get_weather", {"city": "Boston"})]), |
| 108 | +]) |
| 109 | +def test_hunyuan_a13b_tool_parser_streaming(model_deltas, expected_tool_calls): |
| 110 | + mock_tokenizer = MagicMock() |
| 111 | + |
| 112 | + tool_parser: ToolParser = ToolParserManager.get_tool_parser( |
| 113 | + "hunyuan_a13b")(mock_tokenizer) |
| 114 | + reconstructor = run_tool_extraction_streaming( |
| 115 | + tool_parser, model_deltas, assert_one_tool_per_delta=False) |
| 116 | + |
| 117 | + # align the random id. |
| 118 | + for idx in range(len(reconstructor.tool_calls)): |
| 119 | + reconstructor.tool_calls[idx].id = expected_tool_calls[idx].id |
| 120 | + |
| 121 | + assert reconstructor.tool_calls == expected_tool_calls |
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