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Unverified Commit 0e39202c authored by Flora Feng's avatar Flora Feng Committed by GitHub
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[Bugfix] Fix GLM tool parser streaming with MTP or stream interval (#39253) 


Signed-off-by: default avatarsfeng33 <4florafeng@gmail.com>
parent 9dd5ee01
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Showing with 788 additions and 416 deletions
tests/tool_parsers/test_glm47_moe_tool_parser.py
View file @ 0e39202c
......@@ -117,28 +117,24 @@ class TestGlm47ExtractToolCalls:
def _reset(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 = []
parser._sent_content_idx = 0
class TestGlm47Streaming:
def test_no_args(self, glm47_tool_parser, mock_request):
_reset(glm47_tool_parser)
for chunk in ["<tool_call>", "get_current_date", "</tool_call>"]:
chunks = ["<tool_call>", "get_current_date", "</tool_call>"]
current_text = ""
for chunk in chunks:
current_text += chunk
glm47_tool_parser.extract_tool_calls_streaming(
previous_text="",
current_text="",
current_text=current_text,
delta_text=chunk,
previous_token_ids=[],
current_token_ids=[],
......@@ -149,10 +145,7 @@ class TestGlm47Streaming:
def test_with_args(self, glm47_tool_parser, mock_request):
_reset(glm47_tool_parser)
# Split chunks so that the incremental string streaming path
# processes the value, its closing tag, and the tool-call closing
# tag in separate calls.
for chunk in [
chunks = [
"<tool_call>",
"get_weather\n",
"<arg_key>city</arg_key>",
......@@ -160,14 +153,18 @@ class TestGlm47Streaming:
"Beijing",
"</arg_value>",
"</tool_call>",
]:
]
current_text = ""
for chunk in chunks:
current_text += chunk
glm47_tool_parser.extract_tool_calls_streaming(
previous_text="",
current_text="",
current_text=current_text,
delta_text=chunk,
previous_token_ids=[],
current_token_ids=[],
delta_token_ids=[],
request=mock_request,
)
assert glm47_tool_parser.prev_tool_call_arr[0]["arguments"]["city"] == "Beijing"
args = json.loads(glm47_tool_parser.prev_tool_call_arr[0]["arguments"])
assert args["city"] == "Beijing"
tests/tool_parsers/test_glm4_moe_tool_parser.py
View file @ 0e39202c
......@@ -357,81 +357,69 @@ meaningwhile, I will also check the weather in Shanghai.
def test_streaming_basic_functionality(glm4_moe_tool_parser, mock_request):
"""Test basic streaming functionality."""
# Reset streaming state
glm4_moe_tool_parser.current_tool_name_sent = False
glm4_moe_tool_parser.prev_tool_call_arr = []
glm4_moe_tool_parser.current_tool_id = -1
glm4_moe_tool_parser.streamed_args_for_tool = []
_reset_streaming_state(glm4_moe_tool_parser)
# Test with a simple tool call
current_text = """<tool_call>get_weather
<arg_key>city</arg_key>
<arg_value>Beijing</arg_value>
</tool_call>"""
# Mock token IDs for testing
tool_call_start_id = glm4_moe_tool_parser.tool_call_start_token_id or 12345
tool_call_end_id = glm4_moe_tool_parser.tool_call_end_token_id or 12346
result = glm4_moe_tool_parser.extract_tool_calls_streaming(
previous_text="",
current_text=current_text,
delta_text="</tool_call>",
delta_text=current_text,
previous_token_ids=[],
current_token_ids=[tool_call_start_id, tool_call_end_id],
delta_token_ids=[tool_call_end_id],
current_token_ids=[],
delta_token_ids=[],
request=mock_request,
)
# The result behavior depends on the streaming state
# This test mainly ensures no exceptions are thrown
assert result is None or hasattr(result, "tool_calls") or hasattr(result, "content")
# Should return tool call with name and arguments in one shot
assert result is not None
assert result.tool_calls is not None
assert len(result.tool_calls) >= 1
def test_streaming_no_tool_calls(glm4_moe_tool_parser, mock_request):
"""Test streaming when there are no tool calls."""
_reset_streaming_state(glm4_moe_tool_parser)
current_text = "This is just regular text without any tool calls."
result = glm4_moe_tool_parser.extract_tool_calls_streaming(
previous_text="This is just regular text",
previous_text="",
current_text=current_text,
delta_text=" without any tool calls.",
delta_text=current_text,
previous_token_ids=[],
current_token_ids=[],
delta_token_ids=[],
request=mock_request,
)
# Should return the delta text as content
# Should return content
assert result is not None
assert hasattr(result, "content")
assert result.content == " without any tool calls."
assert result.content == current_text
def test_streaming_with_content_before_tool_calls(glm4_moe_tool_parser, mock_request):
"""Test streaming when there's content before tool calls."""
# Reset streaming state
glm4_moe_tool_parser.current_tool_name_sent = False
glm4_moe_tool_parser.prev_tool_call_arr = []
glm4_moe_tool_parser.current_tool_id = -1
glm4_moe_tool_parser.streamed_args_for_tool = []
_reset_streaming_state(glm4_moe_tool_parser)
current_text = "I will help you get the weather<tool_call>"
current_text = "I will help you get the weather.<tool_call>"
result = glm4_moe_tool_parser.extract_tool_calls_streaming(
previous_text="I will help you",
previous_text="",
current_text=current_text,
delta_text="get the weather.<tool_call>",
delta_text=current_text,
previous_token_ids=[],
current_token_ids=[],
delta_token_ids=[],
request=mock_request,
)
# Should return content when no tool call tokens are detected
# Should return content before the <tool_call> tag
assert result is not None
assert hasattr(result, "content")
assert result.content == "get the weather."
assert result.content == "I will help you get the weather."
def test_extract_tool_calls_special_characters(glm4_moe_tool_parser, mock_request):
......@@ -479,26 +467,19 @@ def test_extract_tool_calls_incomplete_tool_call(glm4_moe_tool_parser, mock_requ
def _reset_streaming_state(parser):
"""Helper to reset parser streaming state."""
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 = []
parser._sent_content_idx = 0
def test_streaming_incremental_string_value(glm4_moe_tool_parser, mock_request):
"""Test incremental streaming of string argument values."""
_reset_streaming_state(glm4_moe_tool_parser)
# Simulate streaming a tool call character by character
# Simulate streaming a tool call chunk by chunk
chunks = [
"<tool_call>",
"get_weather\n",
......@@ -511,30 +492,31 @@ def test_streaming_incremental_string_value(glm4_moe_tool_parser, mock_request):
]
collected_fragments = []
current_text = ""
for chunk in chunks:
current_text += chunk
result = glm4_moe_tool_parser.extract_tool_calls_streaming(
previous_text="",
current_text="",
current_text=current_text,
delta_text=chunk,
previous_token_ids=[],
current_token_ids=[],
delta_token_ids=[],
request=mock_request,
)
if result is not None and hasattr(result, "tool_calls") and result.tool_calls:
if result is not None and result.tool_calls:
for tc in result.tool_calls:
if hasattr(tc, "function") and tc.function:
func = tc.function
if isinstance(func, dict):
if func.get("arguments"):
collected_fragments.append(func["arguments"])
if func.get("name"):
collected_fragments.append(f"name:{func['name']}")
else:
if func.arguments:
collected_fragments.append(func.arguments)
if func.name:
collected_fragments.append(f"name:{func.name}")
func = tc.function
if isinstance(func, dict):
if func.get("arguments"):
collected_fragments.append(func["arguments"])
if func.get("name"):
collected_fragments.append(f"name:{func['name']}")
else:
if func.arguments:
collected_fragments.append(func.arguments)
if func.name:
collected_fragments.append(f"name:{func.name}")
# Verify we got incremental streaming of the argument value
assert len(collected_fragments) > 0
......@@ -547,11 +529,11 @@ def test_streaming_empty_tool_call(glm4_moe_tool_parser, mock_request):
"""Test that empty tool calls don't cause infinite loops."""
_reset_streaming_state(glm4_moe_tool_parser)
# Empty tool call should be handled gracefully
current_text = "<tool_call></tool_call>"
result = glm4_moe_tool_parser.extract_tool_calls_streaming(
previous_text="",
current_text="",
delta_text="<tool_call></tool_call>",
current_text=current_text,
delta_text=current_text,
previous_token_ids=[],
current_token_ids=[],
delta_token_ids=[],
......@@ -561,60 +543,52 @@ def test_streaming_empty_tool_call(glm4_moe_tool_parser, mock_request):
# Should not hang and should return something (None or content)
# The key is that this completes without hanging
assert result is None or hasattr(result, "content") or hasattr(result, "tool_calls")
# State should be properly reset
assert glm4_moe_tool_parser.current_tool_id == -1
def test_streaming_prev_tool_call_arr_updates(glm4_moe_tool_parser, mock_request):
"""Test that prev_tool_call_arr contains parsed dict after tool call."""
"""Test that prev_tool_call_arr is populated incrementally."""
_reset_streaming_state(glm4_moe_tool_parser)
# Stream a complete tool call
name_only = {"name": "get_weather", "arguments": {}}
name_and_args = {"name": "get_weather", "arguments": {"city": "Beijing"}}
chunks = [
# Delta, expected streamed_args_for_tool, expected prev_tool_call_arr
("<tool_call>get_weather\n", "", name_only),
("<arg_key>city</arg_key>", "", name_only),
("<arg_value>Beijing</arg_value>", '{"city": "Beijing"', name_only),
# Note: arguments are only updated when the tool call is complete.
("</tool_call>", '{"city": "Beijing"}', name_and_args),
"<tool_call>get_weather\n",
"<arg_key>city</arg_key>",
"<arg_value>Beijing</arg_value>",
"</tool_call>",
]
for chunk, exp_streamed, exp_prev_tc in chunks:
current_text = ""
for chunk in chunks:
current_text += chunk
glm4_moe_tool_parser.extract_tool_calls_streaming(
previous_text="",
current_text="",
current_text=current_text,
delta_text=chunk,
previous_token_ids=[],
current_token_ids=[],
delta_token_ids=[],
request=mock_request,
)
assert glm4_moe_tool_parser.streamed_args_for_tool[0] == exp_streamed
assert glm4_moe_tool_parser.prev_tool_call_arr[0] == exp_prev_tc
# After the tool call completes, prev_tool_call_arr should have parsed dict
# After the tool call completes, prev_tool_call_arr should be populated
assert len(glm4_moe_tool_parser.prev_tool_call_arr) == 1
tool_entry = glm4_moe_tool_parser.prev_tool_call_arr[0]
assert tool_entry.get("name") == "get_weather"
# arguments should be a dict, not a string
args = tool_entry.get("arguments")
assert isinstance(args, dict), f"Expected dict, got {type(args)}"
assert args.get("city") == "Beijing"
# Test equivalence of prev_tool_call_arr and streamed_args_for_tool
# Simulates logic in chat_completion/serving.py:chat_completion_stream_generator
tool_call_json = json.dumps(tool_entry.get("arguments", {}))
streamed_content = glm4_moe_tool_parser.streamed_args_for_tool[0]
assert tool_call_json.startswith(streamed_content)
# arguments is a JSON string in the re-parse approach
args_str = tool_entry.get("arguments")
assert isinstance(args_str, str), f"Expected str, got {type(args_str)}"
parsed = json.loads(args_str)
assert parsed["city"] == "Beijing"
# streamed_args_for_tool should match prev_tool_call_arr arguments
streamed = glm4_moe_tool_parser.streamed_args_for_tool[0]
assert streamed == args_str
def test_streaming_multiple_tool_calls_sequential(glm4_moe_tool_parser, mock_request):
"""Test streaming multiple sequential tool calls."""
_reset_streaming_state(glm4_moe_tool_parser)
# Stream two tool calls
chunks = [
"<tool_call>get_weather\n",
"<arg_key>city</arg_key>",
......@@ -626,10 +600,12 @@ def test_streaming_multiple_tool_calls_sequential(glm4_moe_tool_parser, mock_req
"</tool_call>",
]
current_text = ""
for chunk in chunks:
current_text += chunk
glm4_moe_tool_parser.extract_tool_calls_streaming(
previous_text="",
current_text="",
current_text=current_text,
delta_text=chunk,
previous_token_ids=[],
current_token_ids=[],
......@@ -639,15 +615,16 @@ def test_streaming_multiple_tool_calls_sequential(glm4_moe_tool_parser, mock_req
# Should have two tool calls in prev_tool_call_arr
assert len(glm4_moe_tool_parser.prev_tool_call_arr) == 2
assert glm4_moe_tool_parser.prev_tool_call_arr[0]["arguments"]["city"] == "Beijing"
assert glm4_moe_tool_parser.prev_tool_call_arr[1]["arguments"]["city"] == "Shanghai"
args0 = json.loads(glm4_moe_tool_parser.prev_tool_call_arr[0]["arguments"])
args1 = json.loads(glm4_moe_tool_parser.prev_tool_call_arr[1]["arguments"])
assert args0["city"] == "Beijing"
assert args1["city"] == "Shanghai"
def test_streaming_json_escape_in_string(glm4_moe_tool_parser, mock_request):
"""Test that special characters in string values are properly escaped."""
_reset_streaming_state(glm4_moe_tool_parser)
# String with characters that need JSON escaping
chunks = [
"<tool_call>send_message\n",
"<arg_key>message</arg_key>",
......@@ -655,10 +632,12 @@ def test_streaming_json_escape_in_string(glm4_moe_tool_parser, mock_request):
"</tool_call>",
]
current_text = ""
for chunk in chunks:
current_text += chunk
glm4_moe_tool_parser.extract_tool_calls_streaming(
previous_text="",
current_text="",
current_text=current_text,
delta_text=chunk,
previous_token_ids=[],
current_token_ids=[],
......@@ -669,10 +648,8 @@ def test_streaming_json_escape_in_string(glm4_moe_tool_parser, mock_request):
# The streamed_args_for_tool should contain valid JSON
assert len(glm4_moe_tool_parser.streamed_args_for_tool) == 1
args_json = glm4_moe_tool_parser.streamed_args_for_tool[0]
# Should be parseable as JSON
parsed = json.loads(args_json)
assert "message" in parsed
# The value should preserve the special characters
assert '"' in parsed["message"] or "world" in parsed["message"]
......@@ -749,27 +726,27 @@ if __name__ == "__main__":
# Count argument fragments
fragment_count = 0
current_text = ""
for chunk in chunks:
current_text += chunk
result = glm4_moe_tool_parser.extract_tool_calls_streaming(
previous_text="",
current_text="",
current_text=current_text,
delta_text=chunk,
previous_token_ids=[],
current_token_ids=[],
delta_token_ids=[],
request=request,
)
if result is not None and hasattr(result, "tool_calls") and result.tool_calls:
if result is not None and result.tool_calls:
for tc in result.tool_calls:
if hasattr(tc, "function") and tc.function:
func = tc.function
args = (
func.get("arguments")
if isinstance(func, dict)
else getattr(func, "arguments", None)
)
if args:
fragment_count += 1
func = tc.function
if isinstance(func, dict):
args = func.get("arguments")
else:
args = getattr(func, "arguments", None)
if args:
fragment_count += 1
# For true incremental streaming, we expect many fragments (10+)
# Old buffered implementation would give only 1-3 fragments
......@@ -927,3 +904,432 @@ def test_unicode_characters_preserved(glm4_moe_tool_parser, mock_request):
parsed_args = json.loads(raw_args)
assert parsed_args["greeting"] == "你好世界"
assert parsed_args["emoji"] == "🎉"
def test_streaming_multi_token_chunks(glm4_moe_tool_parser, mock_request):
"""Test that multi-token chunks (stream_interval > 1) are handled correctly.
With stream_interval > 1 or MTP, multiple XML tags arrive in one delta.
The old buffer-based parser could only return one delta per call, losing
data on the final output. The re-parse approach handles this correctly.
"""
_reset_streaming_state(glm4_moe_tool_parser)
# Simulate stream_interval=3: chunks contain multiple XML tags
chunks = [
"<tool_call>get_weather\n<arg_key>city</arg_key><arg_value>Bei",
"jing</arg_value>",
"</tool_call>",
]
current_text = ""
for chunk in chunks:
current_text += chunk
glm4_moe_tool_parser.extract_tool_calls_streaming(
previous_text="",
current_text=current_text,
delta_text=chunk,
previous_token_ids=[],
current_token_ids=[],
delta_token_ids=[],
request=mock_request,
)
# All data should be captured despite multi-token chunks
assert len(glm4_moe_tool_parser.prev_tool_call_arr) == 1
args = json.loads(glm4_moe_tool_parser.streamed_args_for_tool[0])
assert args["city"] == "Beijing"
def test_streaming_entire_tool_call_at_once(glm4_moe_tool_parser, mock_request):
"""Test that a complete tool call arriving in one delta works.
This simulates the extreme MTP case where all tokens arrive at once.
"""
_reset_streaming_state(glm4_moe_tool_parser)
full_text = (
"<tool_call>get_weather\n"
"<arg_key>city</arg_key>"
"<arg_value>Beijing</arg_value>"
"</tool_call>"
)
result = glm4_moe_tool_parser.extract_tool_calls_streaming(
previous_text="",
current_text=full_text,
delta_text=full_text,
previous_token_ids=[],
current_token_ids=[],
delta_token_ids=[],
request=mock_request,
)
# Should emit tool call with complete arguments in one shot
assert result is not None
assert result.tool_calls is not None
# Verify final state
assert len(glm4_moe_tool_parser.prev_tool_call_arr) == 1
args = json.loads(glm4_moe_tool_parser.streamed_args_for_tool[0])
assert args["city"] == "Beijing"
def test_streaming_content_between_tool_calls_multi_token(
glm4_moe_tool_parser, mock_request
):
"""Test content between tool calls with multi-token chunks."""
_reset_streaming_state(glm4_moe_tool_parser)
# Deliver everything at once — worst case for the old buffer parser
full_text = (
"I will check.\n"
"<tool_call>get_weather\n"
"<arg_key>city</arg_key>"
"<arg_value>Beijing</arg_value>"
"</tool_call>"
"\nAlso Shanghai.\n"
"<tool_call>get_weather\n"
"<arg_key>city</arg_key>"
"<arg_value>Shanghai</arg_value>"
"</tool_call>"
)
# First call with partial text (content only)
partial = "I will check.\n"
result1 = glm4_moe_tool_parser.extract_tool_calls_streaming(
previous_text="",
current_text=partial,
delta_text=partial,
previous_token_ids=[],
current_token_ids=[],
delta_token_ids=[],
request=mock_request,
)
assert result1 is not None
assert result1.content == "I will check.\n"
# Second call with everything
glm4_moe_tool_parser.extract_tool_calls_streaming(
previous_text="",
current_text=full_text,
delta_text=full_text[len(partial) :],
previous_token_ids=[],
current_token_ids=[],
delta_token_ids=[],
request=mock_request,
)
# Should have both tool calls
assert len(glm4_moe_tool_parser.prev_tool_call_arr) == 2
args0 = json.loads(glm4_moe_tool_parser.prev_tool_call_arr[0]["arguments"])
args1 = json.loads(glm4_moe_tool_parser.prev_tool_call_arr[1]["arguments"])
assert args0["city"] == "Beijing"
assert args1["city"] == "Shanghai"
def test_streaming_multi_token_with_multiple_args(glm4_moe_tokenizer):
"""Test multi-token streaming with multiple arguments of mixed types."""
tools = [
ChatCompletionToolsParam(
function=FunctionDefinition(
name="calculate",
parameters={
"type": "object",
"properties": {
"operation": {"type": "string"},
"a": {"type": "number"},
"b": {"type": "number"},
},
},
),
),
]
parser = Glm4MoeModelToolParser(glm4_moe_tokenizer, tools=tools)
request = ChatCompletionRequest(
model=MODEL,
messages=[],
tools=tools,
)
# All arguments arrive in two big chunks (simulates stream_interval=5)
chunks = [
"<tool_call>calculate\n<arg_key>operation</arg_key><arg_value>add</arg_value><arg_key>a</arg_key>",
"<arg_value>42</arg_value><arg_key>b</arg_key><arg_value>3.14</arg_value></tool_call>",
]
current_text = ""
for chunk in chunks:
current_text += chunk
parser.extract_tool_calls_streaming(
previous_text="",
current_text=current_text,
delta_text=chunk,
previous_token_ids=[],
current_token_ids=[],
delta_token_ids=[],
request=request,
)
args = json.loads(parser.streamed_args_for_tool[0])
assert args["operation"] == "add"
assert args["a"] == 42
assert args["b"] == 3.14
def _simulate_streaming(tokenizer, parser, request, text, stream_interval=1):
"""Simulate streaming with a given stream_interval.
Tokens are batched into chunks of ``stream_interval`` tokens,
mimicking how the output processor delivers them.
Returns a list of non-None DeltaMessages.
"""
tokens = tokenizer.encode(text)
previous_text = ""
deltas = []
for i in range(0, len(tokens), stream_interval):
chunk_ids = tokens[i : i + stream_interval]
delta_text = tokenizer.decode(chunk_ids)
current_text = previous_text + delta_text
delta = parser.extract_tool_calls_streaming(
previous_text=previous_text,
current_text=current_text,
delta_text=delta_text,
previous_token_ids=[],
current_token_ids=[],
delta_token_ids=chunk_ids,
request=request,
)
previous_text = current_text
if delta is not None:
deltas.append(delta)
return deltas
def _collect_from_deltas(deltas):
"""Reconstruct tool call names/args and content from a delta stream."""
tools: dict[int, dict] = {}
content_parts: list[str] = []
for d in deltas:
if d.content:
content_parts.append(d.content)
if d.tool_calls:
for tc in d.tool_calls:
func = tc.function
if isinstance(func, dict):
name = func.get("name")
args = func.get("arguments")
else:
name = getattr(func, "name", None)
args = getattr(func, "arguments", None)
idx = tc.index
if idx not in tools:
tools[idx] = {"name": None, "args_fragments": []}
if name:
tools[idx]["name"] = name
if args:
tools[idx]["args_fragments"].append(args)
return content_parts, tools
@pytest.mark.parametrize("stream_interval", [1, 2, 3, 5, 8])
def test_stream_interval_single_tool_call(glm4_moe_tokenizer, stream_interval):
"""Tool call streaming produces correct name + args at any interval."""
tools = [
ChatCompletionToolsParam(
function=FunctionDefinition(
name="get_weather",
parameters={
"type": "object",
"properties": {"city": {"type": "string"}},
},
),
),
]
parser = Glm4MoeModelToolParser(glm4_moe_tokenizer, tools=tools)
request = ChatCompletionRequest(model=MODEL, messages=[], tools=tools)
text = (
"<tool_call>get_weather\n"
"<arg_key>city</arg_key>"
"<arg_value>Beijing</arg_value>"
"</tool_call>"
)
deltas = _simulate_streaming(
glm4_moe_tokenizer, parser, request, text, stream_interval
)
_, tools_found = _collect_from_deltas(deltas)
assert 0 in tools_found
assert tools_found[0]["name"] == "get_weather"
args_json = "".join(tools_found[0]["args_fragments"])
parsed = json.loads(args_json)
assert parsed == {"city": "Beijing"}
@pytest.mark.parametrize("stream_interval", [1, 2, 3, 5, 8])
def test_stream_interval_multiple_tool_calls(glm4_moe_tokenizer, stream_interval):
"""Multiple sequential tool calls with correct indices at any interval."""
tools = [
ChatCompletionToolsParam(
function=FunctionDefinition(
name="get_weather",
parameters={
"type": "object",
"properties": {"city": {"type": "string"}},
},
),
),
]
parser = Glm4MoeModelToolParser(glm4_moe_tokenizer, tools=tools)
request = ChatCompletionRequest(model=MODEL, messages=[], tools=tools)
text = (
"<tool_call>get_weather\n"
"<arg_key>city</arg_key>"
"<arg_value>Beijing</arg_value>"
"</tool_call>"
"<tool_call>get_weather\n"
"<arg_key>city</arg_key>"
"<arg_value>Shanghai</arg_value>"
"</tool_call>"
)
deltas = _simulate_streaming(
glm4_moe_tokenizer, parser, request, text, stream_interval
)
_, tools_found = _collect_from_deltas(deltas)
assert 0 in tools_found and 1 in tools_found
args0 = json.loads("".join(tools_found[0]["args_fragments"]))
args1 = json.loads("".join(tools_found[1]["args_fragments"]))
assert args0 == {"city": "Beijing"}
assert args1 == {"city": "Shanghai"}
@pytest.mark.parametrize("stream_interval", [1, 2, 3, 5, 8])
def test_stream_interval_content_then_tool_call(glm4_moe_tokenizer, stream_interval):
"""Content before a tool call is fully emitted before tool deltas."""
tools = [
ChatCompletionToolsParam(
function=FunctionDefinition(
name="get_weather",
parameters={
"type": "object",
"properties": {"city": {"type": "string"}},
},
),
),
]
parser = Glm4MoeModelToolParser(glm4_moe_tokenizer, tools=tools)
request = ChatCompletionRequest(model=MODEL, messages=[], tools=tools)
text = (
"I will check the weather for you.\n"
"<tool_call>get_weather\n"
"<arg_key>city</arg_key>"
"<arg_value>Beijing</arg_value>"
"</tool_call>"
)
deltas = _simulate_streaming(
glm4_moe_tokenizer, parser, request, text, stream_interval
)
content_parts, tools_found = _collect_from_deltas(deltas)
# Content must be present and precede tool calls
full_content = "".join(content_parts)
assert "I will check the weather" in full_content
# Tool call must be correct
assert 0 in tools_found
assert tools_found[0]["name"] == "get_weather"
args = json.loads("".join(tools_found[0]["args_fragments"]))
assert args == {"city": "Beijing"}
def test_stream_interval_extreme_single_chunk(glm4_moe_tokenizer):
"""Extreme MTP: entire output arrives in one chunk (interval=9999)."""
tools = [
ChatCompletionToolsParam(
function=FunctionDefinition(
name="get_weather",
parameters={
"type": "object",
"properties": {"city": {"type": "string"}},
},
),
),
]
parser = Glm4MoeModelToolParser(glm4_moe_tokenizer, tools=tools)
request = ChatCompletionRequest(model=MODEL, messages=[], tools=tools)
text = (
"Here is the weather.\n"
"<tool_call>get_weather\n"
"<arg_key>city</arg_key>"
"<arg_value>Beijing</arg_value>"
"</tool_call>"
)
deltas = _simulate_streaming(
glm4_moe_tokenizer, parser, request, text, stream_interval=9999
)
content_parts, tools_found = _collect_from_deltas(deltas)
assert "Here is the weather" in "".join(content_parts)
assert 0 in tools_found
assert tools_found[0]["name"] == "get_weather"
args = json.loads("".join(tools_found[0]["args_fragments"]))
assert args == {"city": "Beijing"}
@pytest.mark.parametrize("stream_interval", [1, 2, 5])
def test_stream_interval_content_between_tool_calls(
glm4_moe_tokenizer, stream_interval
):
"""Content between tool calls must be emitted, not silently dropped."""
tools = [
ChatCompletionToolsParam(
function=FunctionDefinition(
name="get_weather",
parameters={
"type": "object",
"properties": {"city": {"type": "string"}},
},
),
),
]
parser = Glm4MoeModelToolParser(glm4_moe_tokenizer, tools=tools)
request = ChatCompletionRequest(model=MODEL, messages=[], tools=tools)
text = (
"Checking Beijing.\n"
"<tool_call>get_weather\n"
"<arg_key>city</arg_key>"
"<arg_value>Beijing</arg_value>"
"</tool_call>"
"\nAlso Shanghai.\n"
"<tool_call>get_weather\n"
"<arg_key>city</arg_key>"
"<arg_value>Shanghai</arg_value>"
"</tool_call>"
)
deltas = _simulate_streaming(
glm4_moe_tokenizer, parser, request, text, stream_interval
)
content_parts, tools_found = _collect_from_deltas(deltas)
full_content = "".join(content_parts)
# Both prefix and inter-tool-call content must appear
assert "Checking Beijing" in full_content
assert "Also Shanghai" in full_content
# Both tool calls must be correct
assert 0 in tools_found and 1 in tools_found
args0 = json.loads("".join(tools_found[0]["args_fragments"]))
args1 = json.loads("".join(tools_found[1]["args_fragments"]))
assert args0 == {"city": "Beijing"}
assert args1 == {"city": "Shanghai"}
vllm/tool_parsers/glm4_moe_tool_parser.py
View file @ 0e39202c
......@@ -37,6 +37,7 @@ from vllm.tool_parsers.abstract_tool_parser import (
Tool,
ToolParser,
)
from vllm.tool_parsers.utils import partial_tag_overlap
logger = init_logger(__name__)
......@@ -44,9 +45,9 @@ logger = init_logger(__name__)
class Glm4MoeModelToolParser(ToolParser):
"""Tool parser for GLM-4 models with incremental string streaming.
This parser emits tool-call deltas incrementally as arguments arrive.
For string-type parameters, content is streamed character-by-character
rather than waiting for the complete </arg_value> tag.
On every streaming call the parser re-parses ``current_text`` to find
``<tool_call>`` regions, builds the JSON arguments string for each tool
call, and diffs against what was previously sent to emit only new content.
"""
def __init__(self, tokenizer: TokenizerLike, tools: list[Tool] | None = None):
......@@ -82,17 +83,17 @@ class Glm4MoeModelToolParser(ToolParser):
self.tool_call_start_token_id = self.vocab.get(self.tool_call_start_token)
self.tool_call_end_token_id = self.vocab.get(self.tool_call_end_token)
self._buffer: str = ""
# Streaming state for incremental tool-call streaming
self._in_tool_call: bool = False
self._current_tool_name: str | None = None
self._pending_key: str | None = None
self._streaming_string_value: bool = False
# Pre-compiled pattern for finding the last <arg_key>...</arg_key>
# before a partial <arg_value> (used in _build_args_json_so_far).
self._arg_key_pattern = re.compile(
re.escape(self.arg_key_start) + r"(.*?)" + re.escape(self.arg_key_end),
re.DOTALL,
)
# Streaming state for re-parse-and-diff approach
self._sent_content_idx: int = 0
self._tool_call_ids: list[str] = []
self._args_started: list[bool] = []
self._args_closed: list[bool] = []
self._seen_keys: list[set[str]] = []
@staticmethod
def _deserialize(value: str) -> Any:
......@@ -222,6 +223,199 @@ class Glm4MoeModelToolParser(ToolParser):
tools_called=False, tool_calls=[], content=model_output
)
def _extract_content(self, current_text: str) -> str | None:
"""Return unsent non-tool-call text, or None.
Collects all text outside ``<tool_call>...</tool_call>`` regions,
including text between consecutive tool calls. Holds back any
suffix that could be a partial ``<tool_call>`` tag.
"""
# Build the "sendable index" — the furthest point we can send
# content up to. We scan through the text collecting segments
# that are outside tool-call regions.
content_segments: list[str] = []
pos = self._sent_content_idx
while pos < len(current_text):
start = current_text.find(self.tool_call_start_token, pos)
if start == -1:
# No more tool calls — send up to (len - partial-tag overlap)
tail = current_text[pos:]
overlap = partial_tag_overlap(tail, self.tool_call_start_token)
sendable = tail[: len(tail) - overlap] if overlap else tail
if sendable:
content_segments.append(sendable)
pos = len(current_text) - overlap
break
# Text before this <tool_call>
if start > pos:
content_segments.append(current_text[pos:start])
# Skip past the </tool_call> (or to end if incomplete)
end = current_text.find(self.tool_call_end_token, start)
if end != -1:
pos = end + len(self.tool_call_end_token)
else:
# Incomplete tool call — nothing more to send
pos = start
break
if content_segments:
self._sent_content_idx = pos
return "".join(content_segments)
# Even if no content, advance past completed tool-call regions
if pos > self._sent_content_idx:
self._sent_content_idx = pos
return None
def _extract_tool_call_regions(self, text: str) -> list[tuple[str, bool]]:
"""Extract ``(inner_text, is_complete)`` for each ``<tool_call>`` region."""
results: list[tuple[str, bool]] = []
pos = 0
while True:
start = text.find(self.tool_call_start_token, pos)
if start == -1:
break
inner_start = start + len(self.tool_call_start_token)
end = text.find(self.tool_call_end_token, inner_start)
if end != -1:
results.append((text[inner_start:end], True))
pos = end + len(self.tool_call_end_token)
else:
# Incomplete tool call — strip partial </tool_call> suffix
raw = text[inner_start:]
overlap = partial_tag_overlap(raw, self.tool_call_end_token)
if overlap:
raw = raw[:-overlap]
results.append((raw, False))
break
return results
def _extract_tool_name_from_region(self, inner_text: str) -> str | None:
"""Extract the tool name from the beginning of a tool-call region.
The name is everything before the first ``\\n`` or ``<arg_key>``.
Returns ``None`` if the name hasn't fully arrived yet.
"""
nl = inner_text.find("\n")
ak = inner_text.find(self.arg_key_start)
candidates = [i for i in [nl, ak] if i != -1]
if not candidates:
return None
cut = min(candidates)
name = inner_text[:cut].strip()
return name if name else None
def _build_args_json_so_far(
self,
tool_name: str,
inner_text: str,
is_complete: bool,
) -> str:
"""Build the JSON arguments string from the XML pairs seen so far.
For complete ``<arg_key>/<arg_value>`` pairs the value is fully
formatted. For the last argument whose ``<arg_value>`` has been
opened but not closed, the partial string content is included
(JSON-escaped, with an opening ``"`` but no closing ``"``).
The closing ``}`` is only appended when ``is_complete`` is True
(i.e. the ``</tool_call>`` tag has arrived).
"""
# Find all complete arg pairs
pairs = self.func_arg_regex.findall(inner_text)
parts: list[str] = []
for key, value in pairs:
key = key.strip()
key_json = json.dumps(key, ensure_ascii=False)
if self._is_string_type(tool_name, key, self.tools):
# Don't strip string values — whitespace is significant
# and must match the partial-value path for diffing.
val_json = json.dumps(value, ensure_ascii=False)
else:
val_json = json.dumps(
self._deserialize(value.strip()), ensure_ascii=False
)
parts.append(f"{key_json}: {val_json}")
# Check for a partial (incomplete) arg value
# Find the last <arg_value> that isn't closed
last_val_start = inner_text.rfind(self.arg_val_start)
last_val_end = inner_text.rfind(self.arg_val_end)
has_partial_value = last_val_start != -1 and (
last_val_end == -1 or last_val_end < last_val_start
)
if has_partial_value:
# Find the key for this partial value
# Look for the last <arg_key>...</arg_key> before this <arg_value>
last_key_match = None
for m in self._arg_key_pattern.finditer(inner_text[:last_val_start]):
last_key_match = m
if last_key_match:
partial_key = last_key_match.group(1).strip()
partial_content_start = last_val_start + len(self.arg_val_start)
partial_content = inner_text[partial_content_start:]
# Hold back any partial </arg_value> suffix
overlap = partial_tag_overlap(partial_content, self.arg_val_end)
if overlap:
partial_content = partial_content[:-overlap]
key_json = json.dumps(partial_key, ensure_ascii=False)
if is_complete:
# Tool call finished but </arg_value> is missing
# (malformed output). Treat partial as complete value
# so the diff naturally closes any open quotes.
if self._is_string_type(tool_name, partial_key, self.tools):
val_json = json.dumps(partial_content, ensure_ascii=False)
else:
val_json = json.dumps(
self._deserialize(partial_content.strip()),
ensure_ascii=False,
)
parts.append(f"{key_json}: {val_json}")
elif self._is_string_type(tool_name, partial_key, self.tools):
escaped = self._json_escape_string_content(partial_content)
# Open quote but no close — more content may arrive
parts.append(f'{key_json}: "{escaped}')
else:
# Non-string partial: include raw content, no wrapping
parts.append(f"{key_json}: {partial_content}")
if not parts:
return "{}" if is_complete else ""
joined = "{" + ", ".join(parts)
if is_complete:
joined += "}"
return joined
def _compute_args_diff(self, index: int, args_so_far: str) -> str | None:
"""Return new argument text not yet sent for tool *index*, or None."""
if not args_so_far or len(args_so_far) <= len(
self.streamed_args_for_tool[index]
):
return None
diff = args_so_far[len(self.streamed_args_for_tool[index]) :]
self.streamed_args_for_tool[index] = args_so_far
self.prev_tool_call_arr[index]["arguments"] = args_so_far
return diff
def _ensure_tool_state_for(self, index: int) -> None:
"""Grow state arrays so that *index* is valid."""
while len(self._tool_call_ids) <= index:
self._tool_call_ids.append(
make_tool_call_id(id_type="random", func_name=None, idx=None)
)
while len(self.streamed_args_for_tool) <= index:
self.streamed_args_for_tool.append("")
while len(self.prev_tool_call_arr) <= index:
self.prev_tool_call_arr.append({})
def extract_tool_calls_streaming(
self,
previous_text: str,
......@@ -235,293 +429,55 @@ class Glm4MoeModelToolParser(ToolParser):
if not self._tools_enabled(request):
return DeltaMessage(content=delta_text) if delta_text else None
self._buffer += delta_text
while True:
if not self._in_tool_call:
start_idx = self._buffer.find(self.tool_call_start_token)
if start_idx == -1:
# Check for partial start token at end of buffer
for i in range(1, len(self.tool_call_start_token)):
if self._buffer.endswith(self.tool_call_start_token[:i]):
out = self._buffer[:-i]
self._buffer = self._buffer[-i:]
return DeltaMessage(content=out) if out else None
out = self._buffer
self._buffer = ""
return DeltaMessage(content=out) if out else None
if start_idx > 0:
out = self._buffer[:start_idx]
self._buffer = self._buffer[start_idx:]
return DeltaMessage(content=out) if out else None
self._buffer = self._buffer[len(self.tool_call_start_token) :]
self._begin_tool_call()
continue
# Parse tool name first
if not self.current_tool_name_sent:
nl = self._buffer.find("\n")
ak = self._buffer.find(self.arg_key_start)
end = self._buffer.find(self.tool_call_end_token)
candidates = [i for i in [nl, ak, end] if i != -1]
if not candidates:
return None
cut = min(candidates)
tool_name = self._buffer[:cut].strip()
if tool_name == "" and cut == end:
# Handle empty tool call like `<tool_call></tool_call>`.
# Consume the tokens and reset state to avoid infinite loop.
self._buffer = self._buffer[end + len(self.tool_call_end_token) :]
self._finish_tool_call()
self._revert_last_tool_call_state()
continue
if cut == nl:
self._buffer = self._buffer[nl + 1 :]
else:
self._buffer = self._buffer[cut:]
self._current_tool_name = tool_name
self.current_tool_name_sent = True
return self._emit_tool_name_delta(tool_name)
assert self._current_tool_name is not None
# Handle incremental string value streaming
if self._streaming_string_value:
val_end = self._buffer.find(self.arg_val_end)
if val_end != -1:
raw_content = self._buffer[:val_end]
self._buffer = self._buffer[val_end + len(self.arg_val_end) :]
self._streaming_string_value = False
self._pending_key = None
escaped = self._json_escape_string_content(raw_content)
frag = escaped + '"'
self.streamed_args_for_tool[self.current_tool_id] += frag
return self._emit_tool_args_delta(frag)
else:
# Check for partial </arg_value> at end
safe_len = len(self._buffer)
for i in range(1, len(self.arg_val_end)):
if self._buffer.endswith(self.arg_val_end[:i]):
safe_len = len(self._buffer) - i
break
if safe_len > 0:
to_emit = self._buffer[:safe_len]
self._buffer = self._buffer[safe_len:]
escaped = self._json_escape_string_content(to_emit)
if escaped:
self.streamed_args_for_tool[self.current_tool_id] += escaped
return self._emit_tool_args_delta(escaped)
return None
# If we have a pending key, parse its value
if self._pending_key is not None:
val_pos = self._buffer.find(self.arg_val_start)
if val_pos == -1:
return None
if val_pos > 0:
self._buffer = self._buffer[val_pos:]
key = (self._pending_key or "").strip()
is_string = self._is_string_type(
self._current_tool_name, key, self.tools
)
if is_string:
# String type: stream incrementally
self._buffer = self._buffer[len(self.arg_val_start) :]
if key in self._seen_keys[self.current_tool_id]:
self._pending_key = None
continue
self._seen_keys[self.current_tool_id].add(key)
key_json = json.dumps(key, ensure_ascii=False)
if not self._args_started[self.current_tool_id]:
frag = "{" + key_json + ': "'
self._args_started[self.current_tool_id] = True
else:
frag = ", " + key_json + ': "'
self.streamed_args_for_tool[self.current_tool_id] += frag
self._streaming_string_value = True
return self._emit_tool_args_delta(frag)
else:
# Non-string type: wait for complete value
val_end = self._buffer.find(self.arg_val_end)
if val_end == -1:
return None
raw_val = self._buffer[len(self.arg_val_start) : val_end].strip()
self._buffer = self._buffer[val_end + len(self.arg_val_end) :]
self._pending_key = None
frag_or_none = self._append_arg_fragment(key=key, raw_val=raw_val)
if frag_or_none:
return self._emit_tool_args_delta(frag_or_none)
continue
# Parse next arg or close
end_pos = self._buffer.find(self.tool_call_end_token)
key_pos = self._buffer.find(self.arg_key_start)
if end_pos != -1 and (key_pos == -1 or end_pos < key_pos):
self._buffer = self._buffer[end_pos + len(self.tool_call_end_token) :]
frag_or_none = self._close_args_if_needed()
# Finalize prev_tool_call_arr with complete parsed arguments
if self._current_tool_name:
try:
full_args_str = self.streamed_args_for_tool[
self.current_tool_id
]
args_dict = json.loads(full_args_str)
self.prev_tool_call_arr[self.current_tool_id] = {
"name": self._current_tool_name,
"arguments": args_dict,
}
except (json.JSONDecodeError, IndexError) as e:
logger.warning(
"Failed to finalize tool call state for tool %d: %s",
self.current_tool_id,
e,
)
self._finish_tool_call()
return (
self._emit_tool_args_delta(frag_or_none) if frag_or_none else None
content = self._extract_content(current_text)
regions = self._extract_tool_call_regions(current_text)
tool_call_deltas: list[DeltaToolCall] = []
for i, (inner_text, is_complete) in enumerate(regions):
self._ensure_tool_state_for(i)
# Extract tool name
tool_name = self._extract_tool_name_from_region(inner_text)
if not tool_name:
break
# Emit tool name (once per tool call)
if "name" not in self.prev_tool_call_arr[i]:
self.prev_tool_call_arr[i]["name"] = tool_name
tool_call_deltas.append(
DeltaToolCall(
index=i,
id=self._tool_call_ids[i],
type="function",
function=DeltaFunctionCall(
name=tool_name,
arguments="",
).model_dump(exclude_none=True),
)
)
if key_pos == -1:
return None
if key_pos > 0:
self._buffer = self._buffer[key_pos:]
key_end = self._buffer.find(self.arg_key_end)
if key_end == -1:
return None
key = self._buffer[len(self.arg_key_start) : key_end]
self._buffer = self._buffer[key_end + len(self.arg_key_end) :]
self._pending_key = key
continue
def _ensure_tool_state(self) -> None:
while len(self._tool_call_ids) <= self.current_tool_id:
self._tool_call_ids.append(
make_tool_call_id(id_type="random", func_name=None, idx=None)
# Build args JSON so far, diff, emit
args_so_far = self._build_args_json_so_far(
tool_name, inner_text, is_complete
)
while len(self.streamed_args_for_tool) <= self.current_tool_id:
self.streamed_args_for_tool.append("")
while len(self.prev_tool_call_arr) <= self.current_tool_id:
self.prev_tool_call_arr.append({})
while len(self._args_started) <= self.current_tool_id:
self._args_started.append(False)
while len(self._args_closed) <= self.current_tool_id:
self._args_closed.append(False)
while len(self._seen_keys) <= self.current_tool_id:
self._seen_keys.append(set())
def _begin_tool_call(self) -> None:
if self.current_tool_id == -1:
self.current_tool_id = 0
else:
self.current_tool_id += 1
self._ensure_tool_state()
self.current_tool_name_sent = False
self._current_tool_name = None
self._pending_key = None
self._streaming_string_value = False
self._in_tool_call = True
def _finish_tool_call(self) -> None:
self._in_tool_call = False
self._current_tool_name = None
self._pending_key = None
self._streaming_string_value = False
def _revert_last_tool_call_state(self) -> None:
"""Revert the state allocation for the last tool call."""
if self.current_tool_id < 0:
return
self._tool_call_ids.pop()
self.streamed_args_for_tool.pop()
self.prev_tool_call_arr.pop()
self._args_started.pop()
self._args_closed.pop()
self._seen_keys.pop()
self.current_tool_id -= 1
def _emit_tool_name_delta(self, tool_name: str) -> DeltaMessage:
self.prev_tool_call_arr[self.current_tool_id] = {
"name": self._current_tool_name,
"arguments": {},
}
return DeltaMessage(
tool_calls=[
DeltaToolCall(
index=self.current_tool_id,
id=self._tool_call_ids[self.current_tool_id],
type="function",
function=DeltaFunctionCall(
name=tool_name,
arguments="",
).model_dump(exclude_none=True),
)
]
)
def _emit_tool_args_delta(self, fragment: str) -> DeltaMessage:
return DeltaMessage(
tool_calls=[
DeltaToolCall(
index=self.current_tool_id,
function=DeltaFunctionCall(arguments=fragment).model_dump(
exclude_none=True
),
diff = self._compute_args_diff(i, args_so_far)
if diff:
tool_call_deltas.append(
DeltaToolCall(
index=i,
function=DeltaFunctionCall(arguments=diff).model_dump(
exclude_none=True
),
)
)
]
)
def _append_arg_fragment(
self,
*,
key: str,
raw_val: str,
) -> str | None:
key = key.strip()
if not key:
return None
if key in self._seen_keys[self.current_tool_id]:
return None
# This function is only called for non-string types (already checked
# by _is_string_type in the caller), so we always deserialize.
val_obj: Any = self._deserialize(raw_val)
key_json = json.dumps(key, ensure_ascii=False)
val_json = json.dumps(val_obj, ensure_ascii=False)
if not self._args_started[self.current_tool_id]:
fragment = "{" + key_json + ": " + val_json
self._args_started[self.current_tool_id] = True
else:
fragment = "," + key_json + ": " + val_json
self._seen_keys[self.current_tool_id].add(key)
self.streamed_args_for_tool[self.current_tool_id] += fragment
return fragment
# Update current_tool_id for serving layer compatibility
if regions:
self.current_tool_id = len(regions) - 1
def _close_args_if_needed(self) -> str | None:
if self._args_closed[self.current_tool_id]:
return None
self._args_closed[self.current_tool_id] = True
if not self._args_started[self.current_tool_id]:
fragment = "{}"
self.streamed_args_for_tool[self.current_tool_id] = fragment
else:
fragment = "}"
self.streamed_args_for_tool[self.current_tool_id] += fragment
return fragment
if content or tool_call_deltas:
return DeltaMessage(
content=content,
tool_calls=tool_call_deltas,
)
return None
vllm/tool_parsers/utils.py
View file @ 0e39202c
......@@ -31,6 +31,19 @@ Tool: TypeAlias = ChatCompletionToolsParam | ResponsesTool
logger = init_logger(__name__)
def partial_tag_overlap(text: str, tag: str) -> int:
"""Length of the longest prefix of *tag* that matches a suffix of *text*.
E.g. text ending in ``"<tool_"`` returns 6 when tag is ``"<tool_call>"``.
Returns 0 when there is no overlap.
"""
max_check = min(len(tag) - 1, len(text))
for k in range(max_check, 0, -1):
if text.endswith(tag[:k]):
return k
return 0
def find_common_prefix(s1: str, s2: str) -> str:
"""
Finds a common prefix that is shared between two strings, if there is one.
......
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