forked from vllm-project/vllm
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[Frontend][Feature] Add jamba tool parser (vllm-project#9154)
Signed-off-by: Sumit Dubey <[email protected]>
- Loading branch information
Showing
6 changed files
with
595 additions
and
9 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,275 @@ | ||
import json | ||
from typing import Generator, List, Optional | ||
|
||
import partial_json_parser | ||
import pytest | ||
from partial_json_parser.core.options import Allow | ||
|
||
from vllm.entrypoints.openai.protocol import (DeltaMessage, FunctionCall, | ||
ToolCall) | ||
from vllm.entrypoints.openai.tool_parsers import JambaToolParser | ||
from vllm.transformers_utils.detokenizer import detokenize_incrementally | ||
from vllm.transformers_utils.tokenizer import AnyTokenizer, get_tokenizer | ||
|
||
MODEL = "ai21labs/Jamba-tiny-dev" | ||
|
||
|
||
@pytest.fixture(scope="module") | ||
def jamba_tokenizer(): | ||
return get_tokenizer(tokenizer_name=MODEL) | ||
|
||
|
||
@pytest.fixture | ||
def jamba_tool_parser(jamba_tokenizer): | ||
return JambaToolParser(jamba_tokenizer) | ||
|
||
|
||
def assert_tool_calls(actual_tool_calls: List[ToolCall], | ||
expected_tool_calls: List[ToolCall]): | ||
assert len(actual_tool_calls) == len(expected_tool_calls) | ||
|
||
for actual_tool_call, expected_tool_call in zip(actual_tool_calls, | ||
expected_tool_calls): | ||
assert isinstance(actual_tool_call.id, str) | ||
assert len(actual_tool_call.id) > 16 | ||
|
||
assert actual_tool_call.type == "function" | ||
assert actual_tool_call.function == expected_tool_call.function | ||
|
||
|
||
def stream_delta_message_generator( | ||
jamba_tool_parser: JambaToolParser, jamba_tokenizer: AnyTokenizer, | ||
model_output: str) -> Generator[DeltaMessage, None, None]: | ||
all_token_ids = jamba_tokenizer.encode(model_output, | ||
add_special_tokens=False) | ||
|
||
previous_text = "" | ||
previous_tokens = None | ||
prefix_offset = 0 | ||
read_offset = 0 | ||
for i, delta_token in enumerate(all_token_ids): | ||
delta_token_ids = [delta_token] | ||
previous_token_ids = all_token_ids[:i] | ||
current_token_ids = all_token_ids[:i + 1] | ||
|
||
(new_tokens, delta_text, new_prefix_offset, | ||
new_read_offset) = detokenize_incrementally( | ||
tokenizer=jamba_tokenizer, | ||
all_input_ids=current_token_ids, | ||
prev_tokens=previous_tokens, | ||
prefix_offset=prefix_offset, | ||
read_offset=read_offset, | ||
skip_special_tokens=False, | ||
spaces_between_special_tokens=True, | ||
) | ||
|
||
current_text = previous_text + delta_text | ||
|
||
delta_message = jamba_tool_parser.extract_tool_calls_streaming( | ||
previous_text, | ||
current_text, | ||
delta_text, | ||
previous_token_ids, | ||
current_token_ids, | ||
delta_token_ids, | ||
request=None, # type: ignore[arg-type] | ||
) | ||
if delta_message: | ||
yield delta_message | ||
|
||
previous_text = current_text | ||
previous_tokens = previous_tokens + new_tokens if previous_tokens\ | ||
else new_tokens | ||
prefix_offset = new_prefix_offset | ||
read_offset = new_read_offset | ||
|
||
|
||
def test_extract_tool_calls_no_tools(jamba_tool_parser): | ||
model_output = "This is a test" | ||
extracted_tool_calls = jamba_tool_parser.extract_tool_calls( | ||
model_output, request=None) # type: ignore[arg-type] | ||
assert not extracted_tool_calls.tools_called | ||
assert extracted_tool_calls.tool_calls == [] | ||
assert extracted_tool_calls.content == model_output | ||
|
||
|
||
@pytest.mark.parametrize( | ||
ids=[ | ||
"single_tool", | ||
"single_tool_with_content", | ||
"parallel_tools", | ||
], | ||
argnames=["model_output", "expected_tool_calls", "expected_content"], | ||
argvalues=[ | ||
( | ||
''' <tool_calls>[\n {"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}\n]</tool_calls>''', # noqa: E501 | ||
[ | ||
ToolCall(function=FunctionCall(name="get_current_weather", | ||
arguments=json.dumps( | ||
{ | ||
"city": "Dallas", | ||
"state": "TX", | ||
"unit": "fahrenheit" | ||
}))) | ||
], | ||
None), | ||
( | ||
''' 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 | ||
[ | ||
ToolCall(function=FunctionCall(name="get_current_weather", | ||
arguments=json.dumps( | ||
{ | ||
"city": "Dallas", | ||
"state": "TX", | ||
"unit": "fahrenheit" | ||
}))) | ||
], | ||
" Sure! let me call the tool for you."), | ||
( | ||
''' <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 | ||
[ | ||
ToolCall(function=FunctionCall(name="get_current_weather", | ||
arguments=json.dumps( | ||
{ | ||
"city": "Dallas", | ||
"state": "TX", | ||
"unit": "fahrenheit" | ||
}))), | ||
ToolCall(function=FunctionCall(name="get_current_weather", | ||
arguments=json.dumps( | ||
{ | ||
"city": "Orlando", | ||
"state": "FL", | ||
"unit": "fahrenheit" | ||
}))) | ||
], | ||
None) | ||
], | ||
) | ||
def test_extract_tool_calls(jamba_tool_parser, model_output, | ||
expected_tool_calls, expected_content): | ||
extracted_tool_calls = jamba_tool_parser.extract_tool_calls( | ||
model_output, request=None) # type: ignore[arg-type] | ||
assert extracted_tool_calls.tools_called | ||
|
||
assert_tool_calls(extracted_tool_calls.tool_calls, expected_tool_calls) | ||
|
||
assert extracted_tool_calls.content == expected_content | ||
|
||
|
||
@pytest.mark.parametrize( | ||
ids=[ | ||
"no_tools", | ||
"single_tool", | ||
"single_tool_with_content", | ||
"parallel_tools", | ||
], | ||
argnames=["model_output", "expected_tool_calls", "expected_content"], | ||
argvalues=[ | ||
('''This is a test''', [], '''This is a test'''), | ||
( | ||
''' <tool_calls>[\n {"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}\n]</tool_calls>''', # noqa: E501 | ||
[ | ||
ToolCall(function=FunctionCall(name="get_current_weather", | ||
arguments=json.dumps( | ||
{ | ||
"city": "Dallas", | ||
"state": "TX", | ||
"unit": "fahrenheit" | ||
}))) | ||
], | ||
" "), | ||
( | ||
''' 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 | ||
[ | ||
ToolCall(function=FunctionCall(name="get_current_weather", | ||
arguments=json.dumps( | ||
{ | ||
"city": "Dallas", | ||
"state": "TX", | ||
"unit": "fahrenheit" | ||
}))) | ||
], | ||
" Sure! let me call the tool for you."), | ||
( | ||
''' <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 | ||
[ | ||
ToolCall(function=FunctionCall(name="get_current_weather", | ||
arguments=json.dumps( | ||
{ | ||
"city": "Dallas", | ||
"state": "TX", | ||
"unit": "fahrenheit" | ||
}))), | ||
ToolCall(function=FunctionCall(name="get_current_weather", | ||
arguments=json.dumps( | ||
{ | ||
"city": "Orlando", | ||
"state": "FL", | ||
"unit": "fahrenheit" | ||
}))) | ||
], | ||
" ") | ||
], | ||
) | ||
def test_extract_tool_calls_streaming(jamba_tool_parser, jamba_tokenizer, | ||
model_output, expected_tool_calls, | ||
expected_content): | ||
other_content: str = '' | ||
function_names: List[str] = [] | ||
function_args_strs: List[str] = [] | ||
tool_call_idx: int = -1 | ||
tool_call_ids: List[Optional[str]] = [] | ||
|
||
for delta_message in stream_delta_message_generator( | ||
jamba_tool_parser, jamba_tokenizer, model_output): | ||
# role should never be streamed from tool parser | ||
assert not delta_message.role | ||
|
||
if delta_message.content: | ||
other_content += delta_message.content | ||
|
||
streamed_tool_calls = delta_message.tool_calls | ||
|
||
if streamed_tool_calls and len(streamed_tool_calls) > 0: | ||
# make sure only one diff is present - correct even for parallel | ||
assert len(streamed_tool_calls) == 1 | ||
tool_call = streamed_tool_calls[0] | ||
|
||
# if a new tool is being called, set up empty arguments | ||
if tool_call.index != tool_call_idx: | ||
tool_call_idx = tool_call.index | ||
function_args_strs.append("") | ||
tool_call_ids.append(None) | ||
|
||
# if a tool call ID is streamed, make sure one hasn't been already | ||
if tool_call.id and not tool_call_ids[tool_call.index]: | ||
tool_call_ids[tool_call.index] = tool_call.id | ||
|
||
# if parts of the function start being streamed | ||
if tool_call.function: | ||
# if the function name is defined, set it. it should be streamed | ||
# IN ENTIRETY, exactly one time. | ||
if tool_call.function.name: | ||
assert isinstance(tool_call.function.name, str) | ||
function_names.append(tool_call.function.name) | ||
|
||
if tool_call.function.arguments: | ||
# make sure they're a string and then add them to the list | ||
assert isinstance(tool_call.function.arguments, str) | ||
|
||
function_args_strs[ | ||
tool_call.index] += tool_call.function.arguments | ||
|
||
assert other_content == expected_content | ||
|
||
actual_tool_calls = [ | ||
ToolCall(id=tool_call_id, | ||
function=FunctionCall( | ||
name=function_name, | ||
arguments=partial_json_parser.ensure_json( | ||
function_args_str, Allow.OBJ | Allow.STR))) | ||
for tool_call_id, function_name, function_args_str in zip( | ||
tool_call_ids, function_names, function_args_strs) | ||
] | ||
assert_tool_calls(actual_tool_calls, expected_tool_calls) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1,10 +1,12 @@ | ||
from .abstract_tool_parser import ToolParser, ToolParserManager | ||
from .hermes_tool_parser import Hermes2ProToolParser | ||
from .internlm2_tool_parser import Internlm2ToolParser | ||
from .jamba_tool_parser import JambaToolParser | ||
from .llama_tool_parser import Llama3JsonToolParser | ||
from .mistral_tool_parser import MistralToolParser | ||
|
||
__all__ = [ | ||
"ToolParser", "ToolParserManager", "Hermes2ProToolParser", | ||
"MistralToolParser", "Internlm2ToolParser", "Llama3JsonToolParser" | ||
"MistralToolParser", "Internlm2ToolParser", "Llama3JsonToolParser", | ||
"JambaToolParser" | ||
] |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.