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[Bugfix][Platform] Fix GLM47 tool-call finish backfill (#7710)

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### What this PR does / why we need it?
This rebases the GLM47 tool-call parser fix onto `releases/v0.18.0`
after the MiniMax usage-accounting patch merged upstream on March 27,
2026.

It fixes OpenAI chat tool-call streaming for GLM47 by:
- draining terminal parser chunks that contain both the final argument
text and the closing `</tool_call>` suffix
- computing finish backfill from the tool argument bytes actually
emitted to the client, instead of trusting parser-internal buffered
state
- adding focused regression tests for finish backfill and terminal chunk
handling

### Does this PR introduce _any_ user-facing change?
Yes. GLM47 OpenAI-compatible streaming tool-call responses now emit
correct final chunks and argument payloads on `releases/v0.18.0`.

### How was this patch tested?
- `pytest -q tests/ut/patch/platform/test_patch_glm_tool_call_parser.py
tests/ut/patch/platform/test_patch_minimax_usage_accounting.py`
- `python -m pre_commit run --files
vllm_ascend/patch/platform/patch_glm_tool_call_parser.py
tests/ut/patch/platform/test_patch_glm_tool_call_parser.py
vllm_ascend/patch/platform/__init__.py vllm_ascend/patch/__init__.py`

---------

Signed-off-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com>
Co-authored-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com>
This commit is contained in:
jack
2026-03-28 09:15:04 +08:00
committed by GitHub
parent 6fbd0049df
commit f83cb0e6dc
4 changed files with 1403 additions and 0 deletions
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tests/ut/patch/platform/test_patch_glm_tool_call_parser.py Normal file
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# SPDX-License-Identifier: Apache-2.0
from vllm.entrypoints.openai.chat_completion.protocol import (
ChatCompletionRequest,
ChatCompletionResponseStreamChoice,
ChatCompletionStreamResponse,
)
from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat
from vllm.entrypoints.openai.engine.protocol import (
DeltaFunctionCall,
DeltaMessage,
DeltaToolCall,
)
from vllm.tool_parsers.glm4_moe_tool_parser import Glm4MoeModelToolParser
from vllm.tool_parsers.glm47_moe_tool_parser import Glm47MoeModelToolParser
class FakeTokenizer:
def get_vocab(self):
return {
"<tool_call>": 1,
"</tool_call>": 2,
"<arg_key>": 3,
"</arg_key>": 4,
"<arg_value>": 5,
"</arg_value>": 6,
}
def _reset_streaming_state(parser):
parser._buffer = ""
parser._in_tool_call = False
parser.current_tool_name_sent = False
parser._current_tool_name = None
parser._pending_key = None
parser._streaming_string_value = False
parser.prev_tool_call_arr = []
parser.current_tool_id = -1
parser.streamed_args_for_tool = []
parser._tool_call_ids = []
parser._args_started = []
parser._args_closed = []
parser._seen_keys = []
def test_create_remaining_args_delta_uses_fallback_metadata_for_args_only_delta():
original_delta = DeltaMessage(
tool_calls=[
DeltaToolCall(
index=0,
function=DeltaFunctionCall(arguments='{"files":['),
)
]
)
result = OpenAIServingChat._create_remaining_args_delta(
original_delta,
'{"files":[{"filepath":"HumanEval-X/README.md"}]}',
0,
fallback_tool_call_id="call_files",
fallback_tool_call_type="function",
fallback_tool_call_name="builtin_read_many_files",
)
tc = result.tool_calls[0]
assert tc.index == 0
assert tc.id == "call_files"
assert tc.type == "function"
assert tc.function.name == "builtin_read_many_files"
assert tc.function.arguments == ('{"files":[{"filepath":"HumanEval-X/README.md"}]}')
def test_create_remaining_args_delta_prefers_current_metadata_over_fallback():
original_delta = DeltaMessage(
tool_calls=[
DeltaToolCall(
index=0,
id="call_current",
type="function",
function=DeltaFunctionCall(
name="current_name",
arguments='{"files":[',
),
)
]
)
result = OpenAIServingChat._create_remaining_args_delta(
original_delta,
"]}",
0,
fallback_tool_call_id="call_fallback",
fallback_tool_call_type="function",
fallback_tool_call_name="fallback_name",
)
tc = result.tool_calls[0]
assert tc.id == "call_current"
assert tc.type == "function"
assert tc.function.name == "current_name"
assert tc.function.arguments == "]}"
def test_record_streamed_tool_args_tracks_emitted_bytes():
streamed_tool_args = {0: '{"files":['}
delta_message = DeltaMessage(
tool_calls=[
DeltaToolCall(
index=0,
function=DeltaFunctionCall(arguments='{"filepath":"HumanEval-X/README.md"}]}'),
)
]
)
OpenAIServingChat._record_streamed_tool_args(delta_message, streamed_tool_args)
assert streamed_tool_args[0] == ('{"files":[{"filepath":"HumanEval-X/README.md"}]}')
def test_compute_remaining_tool_args_handles_compact_prefix():
remaining = OpenAIServingChat._compute_remaining_tool_args(
expected_args={"a": 1},
streamed_args='{"a":1',
)
assert remaining == "}"
def test_compute_remaining_tool_args_handles_stringified_expected_args():
remaining = OpenAIServingChat._compute_remaining_tool_args(
expected_args='{"a":1}',
streamed_args='{"a":1',
)
assert remaining == "}"
def test_compute_remaining_tool_args_handles_glm_mixed_whitespace_prefix():
expected_args = {
"todos": [
{
"content": "A",
"activeForm": "B",
"status": "in_progress",
}
]
}
remaining = OpenAIServingChat._compute_remaining_tool_args(
expected_args=expected_args,
streamed_args=('{"todos":[{"content": "A", "activeForm": "B", "status": "in_progress"}]'),
)
assert remaining == "}"
def test_compute_remaining_tool_args_backfills_missing_suffix_for_glm_partial_prefix():
expected_args = {
"todos": [
{
"content": "A",
"activeForm": "B",
"status": "in_progress",
}
]
}
remaining = OpenAIServingChat._compute_remaining_tool_args(
expected_args=expected_args,
streamed_args='{"todos":[{"content": "A"',
)
assert remaining == ',"activeForm":"B","status":"in_progress"}]}'
def test_compute_remaining_tool_args_returns_empty_for_non_matching_prefix():
remaining = OpenAIServingChat._compute_remaining_tool_args(
expected_args={"a": 1},
streamed_args="not-json",
)
assert remaining == ""
def test_compute_remaining_tool_args_returns_full_call_when_no_args_were_sent():
remaining = OpenAIServingChat._compute_remaining_tool_args(
expected_args={
"todos": "- [x] 分析项目结构和代码\n- [ ] 添加单元测试框架",
},
streamed_args="",
)
assert remaining == ('{"todos": "- [x] 分析项目结构和代码\\n- [ ] 添加单元测试框架"}')
def test_glm_streaming_final_chunk_emits_inline_string_value():
parser = Glm4MoeModelToolParser(FakeTokenizer())
_reset_streaming_state(parser)
request = ChatCompletionRequest(
model="zai-org/GLM-4.7",
messages=[],
tools=[
{
"type": "function",
"function": {
"name": "builtin_get_problems",
"parameters": {
"type": "object",
"properties": {
"filepath": {"type": "string"},
},
},
},
}
],
)
chunks = [
"<tool_call>",
"builtin_get_problems\n",
"<arg_key>filepath</arg_key>",
"<arg_value>pong.py</arg_value></tool_call>",
]
last_tool_delta = None
for chunk in chunks:
result = parser.extract_tool_calls_streaming(
previous_text="",
current_text="",
delta_text=chunk,
previous_token_ids=[],
current_token_ids=[],
delta_token_ids=[],
request=request,
)
if result is not None and result.tool_calls:
last_tool_delta = result
assert last_tool_delta is not None
assert last_tool_delta.tool_calls[0].function.arguments == '{"filepath":"pong.py"}'
assert parser.streamed_args_for_tool == ['{"filepath":"pong.py"}']
assert parser.prev_tool_call_arr == [
{
"name": "builtin_get_problems",
"arguments": {"filepath": "pong.py"},
}
]
def test_glm47_streaming_delta_serializes_tool_call_fields():
parser = Glm47MoeModelToolParser(FakeTokenizer())
_reset_streaming_state(parser)
request = ChatCompletionRequest(
model="GLM-5",
messages=[],
tools=[
{
"type": "function",
"function": {
"name": "builtin_get_problems",
"parameters": {
"type": "object",
"properties": {
"filepath": {"type": "string"},
},
},
},
}
],
)
chunks = [
"<tool_call>",
"builtin_get_problems\n",
"<arg_key>filepath</arg_key>",
"<arg_value>pong.py</arg_value></tool_call>",
]
serialized_deltas = []
for chunk in chunks:
result = parser.extract_tool_calls_streaming(
previous_text="",
current_text="",
delta_text=chunk,
previous_token_ids=[],
current_token_ids=[],
delta_token_ids=[],
request=request,
)
if result is None:
continue
choice = ChatCompletionResponseStreamChoice(
index=0,
delta=result,
logprobs=None,
finish_reason=None,
)
response = ChatCompletionStreamResponse(
id="chatcmpl-test",
created=0,
model="GLM-5",
choices=[choice],
)
serialized_deltas.append(response.model_dump(exclude_unset=True)["choices"][0]["delta"])
assert len(serialized_deltas) == 2
assert serialized_deltas[0]["tool_calls"][0]["type"] == "function"
assert serialized_deltas[0]["tool_calls"][0]["function"]["name"] == "builtin_get_problems"
assert serialized_deltas[-1] != {}
assert serialized_deltas[-1]["tool_calls"][0]["index"] == 0
assert serialized_deltas[-1]["tool_calls"][0]["function"]["arguments"] == '{"filepath":"pong.py"}'