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[Feature] add tool parser (#3518)

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* [Feature] Pass through the `chat_template_kwargs` to the data processing module (#3421)

* fix chat_template_args

* fix args

* add offline

* add offline

* fix

* fix

* fix default enable_thinking value

* fix default enable_thinking value

* modify condition

* Revert "modify condition"

This reverts commit 26430bdeb1.

* fix unit test

* add Tool Parser (#3272)

* add tool-parser

* add tool-parser

* add tool parser

* add tool parser

* fix

* add offline

* add offline

* fix

* parsers:tool&reasoning

* 修改tool parser名称·

* update

* fix reasoning-parser

* add requirements

* fix finish reason

* fix

* fix reasoning-parser

* fix

* fix

* fix

* fix

* fix

---------

Co-authored-by: zhuzixuan <zhuzixuan@baidu.com>

* [Feature] add tool parser (#3483)

* add tool parser

* add x1 enable_thinking

* restart ci

* fix vl reasoning parser

* modify call style

* modify call style

* add offline enablethinking

* fix completion

* fix

* fix unit test

* fix unit test

* fix unit test

* fix vl reasoning parser

* fix vl reasoning parser

* fix unit test

---------

Co-authored-by: zhuzixuan <zhuzixuan@baidu.com>
This commit is contained in:
luukunn
2025-08-22 11:14:35 +08:00
committed by GitHub
parent d97aab25bc
commit 4a9c04a746
31 changed files with 1289 additions and 70 deletions
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48
fastdeploy/entrypoints/openai/serving_completion.py
View File

@@ -240,9 +240,9 @@ class OpenAIServingCompletion:
dealer.close()
self.engine_client.semaphore.release()
def calc_finish_reason(self, max_tokens, token_num, output):
def calc_finish_reason(self, max_tokens, token_num, output, tool_called):
if max_tokens is None or token_num != max_tokens:
if self.engine_client.reasoning_parser == "ernie_x1" and output.get("finish_reason", "") == "tool_calls":
if tool_called or output.get("tool_call"):
return "tool_calls"
else:
return "stop"
@@ -271,6 +271,7 @@ class OpenAIServingCompletion:
output_tokens = [0] * num_choices
inference_start_time = [0] * num_choices
first_iteration = [True] * num_choices
tool_called = [False] * num_choices
max_streaming_response_tokens = (
request.max_streaming_response_tokens
if request.max_streaming_response_tokens is not None
@@ -343,25 +344,34 @@ class OpenAIServingCompletion:
if request.logprobs and output_top_logprobs is not None:
logprobs_res = self._create_completion_logprobs(output_top_logprobs, request.logprobs, 0)
choices.append(
CompletionResponseStreamChoice(
index=idx,
text=output["text"],
prompt_token_ids=None,
completion_token_ids=output.get("token_ids") if request.return_token_ids else None,
raw_prediction=output.get("raw_prediction") if request.return_token_ids else None,
completion_tokens=output.get("raw_prediction") if request.return_token_ids else None,
tool_calls=output.get("tool_call_content"),
reasoning_content=output.get("reasoning_content"),
arrival_time=arrival_time,
logprobs=logprobs_res,
)
)
output_tokens[idx] += 1
delta_message = CompletionResponseStreamChoice(
index=idx,
text=output["text"],
prompt_token_ids=None,
completion_token_ids=output.get("token_ids") if request.return_token_ids else None,
tool_calls=None,
raw_prediction=output.get("raw_prediction") if request.return_token_ids else None,
completion_tokens=output.get("raw_prediction") if request.return_token_ids else None,
reasoning_content="",
arrival_time=arrival_time,
logprobs=logprobs_res,
)
if not res["finished"] and "delta_message" in output:
delta_message_output = output["delta_message"]
if delta_message_output is None:
continue
delta_message.text = delta_message_output.content or ""
delta_message.reasoning_content = delta_message_output.reasoning_content or ""
if delta_message_output.tool_calls:
delta_message.tool_calls = delta_message_output.tool_calls
tool_called[idx] = True
choices.append(delta_message)
if res["finished"]:
choices[-1].finish_reason = self.calc_finish_reason(
request.max_tokens, output_tokens[idx], output
request.max_tokens, output_tokens[idx], output, tool_called[idx]
)
send_idx = output.get("send_idx")
# 只有当 send_idx 明确为 0 时才记录日志
@@ -460,7 +470,7 @@ class OpenAIServingCompletion:
token_ids = output["token_ids"]
output_text = output["text"]
finish_reason = self.calc_finish_reason(request.max_tokens, final_res["output_token_ids"], output)
finish_reason = self.calc_finish_reason(request.max_tokens, final_res["output_token_ids"], output, False)
choice_data = CompletionResponseChoice(
token_ids=token_ids,
@@ -473,7 +483,7 @@ class OpenAIServingCompletion:
text_after_process=text_after_process_list[idx] if request.return_token_ids else None,
prompt_tokens=text_after_process_list[idx] if request.return_token_ids else None,
reasoning_content=output.get("reasoning_content"),
tool_calls=output.get("tool_call_content"),
tool_calls=output.get("tool_call"),
logprobs=aggregated_logprobs,
finish_reason=finish_reason,
)