Files
AstrAI/astrai/inference/network/tool_parser.py
T
ViperEkura 45cc048fe9 fix: resolve audited training, import, and serving bugs
- shard the Muon Newton-Schulz orthogonalization over the FSDP mesh instead of partial local slices
- import HF checkpoints faithfully: per-head RoPE permutation for q/k projections and qk-norm, qwen3, shared experts, and qk-norm before RoPE (changes numerics for existing use_qk_norm checkpoints)
- make preprocessing and resume self-contained: backfill realigned bucket keys by semantics (masks ones, rest zeros) and snapshot tokenizer files into every checkpoint
- keep RL consistent: sync the offline GRPO old_model each optimizer step and validate online strategies through a public one-off-rollout hook that leaves the replay cache untouched
- fix streaming serving: withhold partial tool-call prefixes with a stream-end flush, stream tool-call arguments from the raw source span, and terminate SSE frames with a blank line
- fix sampling semantics: capture logprobs before top-k/top-p mutate logits in place and detect greedy pipelines polymorphically instead of isinstance bookkeeping
2026-09-03 20:27:41 +08:00

419 lines
13 KiB
Python

"""Tool call parsers for extracting structured tool calls from model output.
Patterned after vLLM's ToolParser abstraction. Each parser knows how to
detect and incrementally extract tool calls from raw generated text.
Subclasses may optionally consume ``token_ids`` for token-level parsing
(e.g. Harmony / VLM-style parsers).
"""
import json
import re
import uuid
from abc import ABC, abstractmethod
from typing import Dict, List, Optional
from astrai.factory import BaseFactory
class BaseToolParser(ABC):
"""Abstract tool call parser — one instance per request.
Maintains streaming state internally so that each call to :meth:`feed`
can diff against previously emitted content.
Args:
tools (list of dict, optional): Tool definitions from the request.
tool_choice (str): ``"auto"`` / ``"required"`` / ``"none"`` or a named
tool choice dict.
"""
def __init__(self, tools: Optional[List[Dict]] = None, tool_choice: str = "auto"):
self.tools = tools or []
self.tool_choice = tool_choice
@abstractmethod
def feed(
self,
body: str,
current_token_ids: Optional[List[int]] = None,
delta_token_ids: Optional[List[int]] = None,
) -> List[Dict]:
"""Feed the *full* accumulated text each step.
Returns a list of delta dicts to emit. Each delta is one of:
- ``{"content": "text"}`` — plain text delta
- ``{"tool_calls": [...]}`` — tool-call delta (OpenAI format)
Returns an empty list when nothing new should be emitted.
Args:
body (str): The complete accumulated generated text so far.
current_token_ids (list of int, optional): All token IDs decoded
into *body* (cumulative).
delta_token_ids (list of int, optional): Only the token IDs for
this chunk.
"""
@abstractmethod
def parse_complete(self, body: str) -> Optional[Dict]:
"""Parse the *complete* generated text after generation ends.
Returns ``None`` when no tool calls were found, otherwise a dict
with ``content`` (str or None) and ``tool_calls`` (list of dicts).
"""
@property
@abstractmethod
def has_tool_calls(self) -> bool:
"""True if the parser detected at least one tool call in the stream."""
def finalize(self, body: str) -> List[Dict]:
"""Flush parser state once generation ended. Default: nothing."""
return []
class ToolParserFactory(BaseFactory["BaseToolParser"]):
pass
_TOOL_CALL_HEAD_RE = re.compile(r'\{\s*"name"\s*:')
def _scan_json(text: str, start: int = 0):
"""Scan for a complete JSON object starting at *start*.
Returns ``(end, complete)`` where *end* is one-past the closing
brace (or ``len(text)`` if unclosed), and *complete* is a bool.
"""
depth = 0
in_string = False
escape = False
for i in range(start, len(text)):
c = text[i]
if escape:
escape = False
continue
if c == "\\":
escape = True
continue
if c == '"':
in_string = not in_string
continue
if in_string:
continue
if c == "{":
depth += 1
elif c == "}":
depth -= 1
if depth == 0:
return i + 1, True
return len(text), False
def _raw_arguments_span(json_str: str) -> Optional[str]:
"""Extract the raw text span of the ``arguments`` value, if possible.
Streaming diffs emit the *source* text of ``arguments``; using the
same span here keeps the completed arguments a prefix-extension of
what was already streamed (``json.dumps`` would re-quote and
re-space the value and corrupt the concatenated result).
"""
m = re.search(r'"arguments"\s*:\s*', json_str)
if not m:
return None
rest = json_str[m.end() :]
if rest[:1] in ("{", "["):
end, ok = _scan_json(rest, 0)
if ok:
return rest[1 : end - 1]
return None
str_match = re.match(r'"(?:[^"\\]|\\.)*"', rest)
if str_match:
return str_match.group(0)
bare = re.match(r"[^,}\s][^,}]*", rest)
return bare.group(0).rstrip() if bare else None
def _parse_tool_call_json(json_str: str, complete: bool):
"""Extract *name* and *arguments* from a tool-call JSON string.
Returns ``(name, args, valid)``.
"""
if complete:
try:
obj = json.loads(json_str)
except json.JSONDecodeError:
return None, "", False
name = obj.get("name")
if not isinstance(name, str) or not name:
return None, "", False
args = obj.get("arguments")
raw = _raw_arguments_span(json_str)
if isinstance(args, dict):
if not args:
args = ""
else:
# Prefer the source span: it matches what streaming
# already emitted (prefix-consistent completion).
args = (
raw
if raw is not None
else json.dumps(args, ensure_ascii=False)[1:-1].rstrip()
)
elif isinstance(args, list):
if not args:
args = ""
else:
args = raw if raw is not None else json.dumps(args, ensure_ascii=False)
elif isinstance(args, str):
pass
else:
args = str(args) if args is not None else ""
return name, args, True
name_match = re.search(r'"name"\s*:\s*"([^"]*)"', json_str)
if not name_match:
return None, "", False
name = name_match.group(1)
args_match = re.search(r'"arguments"\s*:\s*(.*)', json_str, re.DOTALL)
if not args_match:
return name, "", True
raw = args_match.group(1).rstrip()
if raw.startswith("{"):
inner = raw[1:].rstrip()
if inner.endswith("}"):
inner = inner[:-1].rstrip()
raw = inner
return name, raw, True
def _find_tool_calls(text: str, start_pos: int = 0):
"""Find all complete ``{...}`` tool-call objects in *text*.
Returns a list of dicts with keys *start*, *end*, *name*, *args*,
*complete*.
"""
results = []
pos = start_pos
while True:
brace = text.find("{", pos)
if brace == -1:
break
end, complete = _scan_json(text, brace)
if not complete:
break
json_str = text[brace:end]
name, args, valid = _parse_tool_call_json(json_str, complete=True)
if not valid or name is None:
pos = end
continue
results.append(
{
"start": brace,
"end": end,
"name": name,
"args": args,
"complete": True,
}
)
pos = end
return results
def _find_partial_tool_call(text: str, start_pos: int = 0):
"""Find one incomplete (still-generating) tool-call JSON object."""
brace = text.find("{", start_pos)
if brace == -1:
return None
json_str = text[brace:]
if '"name"' not in json_str:
return None
name, args, valid = _parse_tool_call_json(json_str, complete=False)
if not valid or name is None:
return None
return {
"start": brace,
"name": name,
"args": args,
"complete": False,
}
@ToolParserFactory.register("simple_json")
class SimpleJsonToolParser(BaseToolParser):
"""Parser for models that output tool calls as plain JSON objects.
Detects ``{"name": "<func>", "arguments": {...}}`` anywhere in the
generated text. Handles single and (non-overlapping) multiple tool
calls. Text preceding the first tool call is emitted as plain
``content`` deltas.
"""
def __init__(self, tools=None, tool_choice="auto"):
super().__init__(tools, tool_choice)
self._emitted_content_len = 0
self._tc_state: List[Dict] = []
self._has_tool_calls = False
# -------------------------------------------------------------- feed
def feed(
self,
body: str,
current_token_ids: Optional[List[int]] = None,
delta_token_ids: Optional[List[int]] = None,
) -> List[Dict]:
deltas: List[Dict] = []
completed = _find_tool_calls(body)
if not completed:
partial = _find_partial_tool_call(body)
if not partial:
return self._emit_plain_content(body, deltas)
all_tcs = [partial]
else:
all_tcs = completed
partial = _find_partial_tool_call(body, completed[-1]["end"])
if partial:
all_tcs = completed + [partial]
first_start = all_tcs[0]["start"]
if first_start > self._emitted_content_len:
content = body[self._emitted_content_len : first_start]
self._emitted_content_len = first_start
if content:
deltas.append({"content": content})
for i, tc in enumerate(all_tcs):
if i >= len(self._tc_state):
self._tc_state.append(
{
"id": f"call_{uuid.uuid4().hex[:12]}",
"name_emitted": False,
"args_emitted_len": 0,
}
)
self._has_tool_calls = True
st = self._tc_state[i]
if not st["name_emitted"]:
st["name_emitted"] = True
deltas.append(
{
"tool_calls": [
{
"index": i,
"id": st["id"],
"type": "function",
"function": {"name": tc["name"], "arguments": ""},
}
]
}
)
new_args = tc["args"]
if len(new_args) > st["args_emitted_len"]:
diff = new_args[st["args_emitted_len"] :]
st["args_emitted_len"] = len(new_args)
deltas.append(
{
"tool_calls": [
{
"index": i,
"function": {"arguments": diff},
}
]
}
)
return deltas
def _emit_plain_content(self, body: str, deltas: List[Dict]) -> List[Dict]:
safe_end = self._safe_content_end(body)
if safe_end > self._emitted_content_len:
deltas.append({"content": body[self._emitted_content_len : safe_end]})
self._emitted_content_len = safe_end
return deltas
_POSSIBLE_NAME_PREFIX_RE = re.compile(r'^\s*"?(?:n|na|nam|name)?"?\s*:?\s*$')
@classmethod
def _safe_content_end(cls, body: str) -> int:
"""End of *body* that is safe to emit as plain content.
A trailing unclosed ``{`` whose remainder could still grow into
``{"name": ...`` (the name-prefix itself, or any further ``{``
opened after it) is withheld, otherwise a partial ``{"na`` prefix
would leak into user-visible content and never be retracted.
Anything withheld is flushed by :meth:`finalize` when generation
ends without a tool call, so plain text is never lost.
"""
pos = 0
while True:
brace = body.find("{", pos)
if brace == -1:
return len(body)
end, complete = _scan_json(body, brace)
if complete:
pos = end
continue
tail = body[brace + 1 :]
if cls._POSSIBLE_NAME_PREFIX_RE.match(tail) or "{" in tail:
return brace
return len(body)
def finalize(self, body: str) -> List[Dict]:
"""Flush content withheld as a possible tool-call prefix.
Called once generation completed: if no tool call materialised,
emit the withheld remainder so streamed content matches the
non-streaming response.
"""
if self._has_tool_calls:
return []
deltas: List[Dict] = []
if len(body) > self._emitted_content_len:
deltas.append({"content": body[self._emitted_content_len :]})
self._emitted_content_len = len(body)
return deltas
# -------------------------------------------------------- complete
def parse_complete(self, body: str) -> Optional[Dict]:
completed = _find_tool_calls(body)
if not completed:
return None
content = body[: completed[0]["start"]].strip() or None
tool_calls = []
for i, tc in enumerate(completed):
tool_calls.append(
{
"id": f"call_{uuid.uuid4().hex[:12]}",
"type": "function",
"function": {
"name": tc["name"],
"arguments": tc["args"],
},
}
)
return {"content": content, "tool_calls": tool_calls}
@property
def has_tool_calls(self) -> bool:
return self._has_tool_calls