@ToolParserManager.register_module("granite-20b-fc")
class Granite20bFCToolParser(ToolParser):
"""
Tool call parser for the granite-20b-functioncalling model intended
for use with the examples/tool_chat_template_granite20b_fc.jinja
template.
Used when --enable-auto-tool-choice --tool-call-parser granite-20-fc
are all set
"""
def __init__(self, tokenizer: AnyTokenizer):
super().__init__(tokenizer)
self.bot_token = "<function_call>"
self.tool_start_token = self.bot_token
self.tool_call_regex = re.compile(r"<function_call>\s*")
def extract_tool_calls(
self, model_output: str, request: ChatCompletionRequest
) -> ExtractedToolCallInformation:
if self.tool_start_token not in model_output:
return ExtractedToolCallInformation(
tools_called=False, tool_calls=[], content=model_output
)
dec = JSONDecoder()
try:
matches = list(self.tool_call_regex.finditer(model_output))
logger.debug("Found %d tool call matches", len(matches))
raw_function_calls = []
for i, match in enumerate(matches):
# position after the <function_call> tag
start_of_json = match.end()
# end_index == the start of the next function call
# (if exists)
next_function_call_start = (
matches[i + 1].start() if i + 1 < len(matches) else None
)
raw_function_calls.append(
dec.raw_decode(
model_output[start_of_json:next_function_call_start]
)[0]
)
logger.debug("Extracted %d tool calls", len(raw_function_calls))
tool_calls = [
ToolCall(
type="function",
function=FunctionCall(
name=function_call["name"],
# function call args are JSON but as a string
arguments=json.dumps(
function_call["arguments"], ensure_ascii=False
),
),
)
for function_call in raw_function_calls
]
content = model_output[: model_output.find(self.bot_token)]
return ExtractedToolCallInformation(
tools_called=True,
tool_calls=tool_calls,
content=content if content else None,
)
except Exception as e:
logger.error("Error in extracting tool call from response %s", e)
return ExtractedToolCallInformation(
tools_called=False, tool_calls=[], content=model_output
)
def extract_tool_calls_streaming(
self,
previous_text: str,
current_text: str,
delta_text: str,
previous_token_ids: Sequence[int],
current_token_ids: Sequence[int],
delta_token_ids: Sequence[int],
request: ChatCompletionRequest,
) -> DeltaMessage | None:
if len(current_text) < len(self.bot_token) and self.bot_token.startswith(
current_text
):
return None
if not current_text.startswith(self.bot_token):
return DeltaMessage(content=delta_text)
# bit mask flags for partial JSON parsing. If the name hasn't been
# sent yet, don't allow sending
# an incomplete string since OpenAI only ever (as far as I have
# seen) allows sending the entire tool/ function name at once.
flags = Allow.ALL if self.current_tool_name_sent else Allow.ALL & ~Allow.STR
try:
tool_call_arr = []
is_complete = []
try:
start_idx = len(self.bot_token)
start_idx = consume_space(start_idx, current_text)
while start_idx < len(current_text):
(obj, end_idx) = partial_json_loads(current_text[start_idx:], flags)
is_complete.append(
is_complete_json(current_text[start_idx : start_idx + end_idx])
)
start_idx += end_idx
start_idx = consume_space(start_idx, current_text)
start_idx += len(self.bot_token)
start_idx = consume_space(start_idx, current_text)
tool_call_arr.append(obj)
except partial_json_parser.core.exceptions.MalformedJSON:
logger.debug("not enough tokens to parse into JSON yet")
return None
# select as the current tool call the one we're on the state at
current_tool_call: dict = (
tool_call_arr[self.current_tool_id] if len(tool_call_arr) > 0 else {}
)
# case -- if no tokens have been streamed for the tool, e.g.
# only the array brackets, stream nothing
if len(tool_call_arr) == 0:
return None
# case: we are starting a new tool in the array
# -> array has > 0 length AND length has moved past cursor
elif (
len(tool_call_arr) > 0 and len(tool_call_arr) > self.current_tool_id + 1
):
# if we're moving on to a new call, first make sure we
# haven't missed anything in the previous one that was
# auto-generated due to JSON completions, but wasn't
# streamed to the client yet.
if self.current_tool_id >= 0:
cur_arguments = current_tool_call.get("arguments")
if cur_arguments:
cur_args_json = json.dumps(cur_arguments, ensure_ascii=False)
sent = len(self.streamed_args_for_tool[self.current_tool_id])
argument_diff = cur_args_json[sent:]
logger.debug("got arguments diff: %s", argument_diff)
delta = DeltaMessage(
tool_calls=[
DeltaToolCall(
index=self.current_tool_id,
function=DeltaFunctionCall(
arguments=argument_diff
).model_dump(exclude_none=True),
)
]
)
self.streamed_args_for_tool[self.current_tool_id] += (
argument_diff
)
else:
delta = None
else:
delta = None
# re-set stuff pertaining to progress in the current tool
self.current_tool_id = len(tool_call_arr) - 1
self.current_tool_name_sent = False
self.streamed_args_for_tool.append("")
logger.debug("starting on new tool %d", self.current_tool_id)
return delta
# if the current tool name hasn't been sent, send if available
# - otherwise send nothing
elif not self.current_tool_name_sent:
function_name = current_tool_call.get("name")
if function_name:
delta = DeltaMessage(
tool_calls=[
DeltaToolCall(
index=self.current_tool_id,
type="function",
id=make_tool_call_id(),
function=DeltaFunctionCall(
name=function_name
).model_dump(exclude_none=True),
)
]
)
self.current_tool_name_sent = True
else:
delta = None
# now we know we're on the same tool call and we're streaming
# arguments
else:
cur_arguments = current_tool_call.get("arguments")
delta = None
if cur_arguments:
sent = len(self.streamed_args_for_tool[self.current_tool_id])
cur_args_json = json.dumps(cur_arguments, ensure_ascii=False)
prev_arguments = self.prev_tool_call_arr[self.current_tool_id].get(
"arguments"
)
argument_diff = None
if is_complete[self.current_tool_id]:
argument_diff = cur_args_json[sent:]
elif prev_arguments:
prev_args_json = json.dumps(prev_arguments, ensure_ascii=False)
if cur_args_json != prev_args_json:
prefix = find_common_prefix(prev_args_json, cur_args_json)
argument_diff = prefix[sent:]
if argument_diff is not None:
delta = DeltaMessage(
tool_calls=[
DeltaToolCall(
index=self.current_tool_id,
function=DeltaFunctionCall(
arguments=argument_diff
).model_dump(exclude_none=True),
)
]
)
self.streamed_args_for_tool[self.current_tool_id] += (
argument_diff
)
self.prev_tool_call_arr = tool_call_arr
return delta
except Exception as e:
logger.error("Error trying to handle streaming tool call: %s", e)
logger.debug(
"Skipping chunk as a result of tool streaming extraction error"
)
return None