[WIP] support MiniMax M2 model (#12129)
Signed-off-by: Xinyuan Tong <xinyuantong.cs@gmail.com> Signed-off-by: xuebi <xuebi@minimaxi.com> Co-authored-by: Xinyuan Tong <xinyuantong.cs@gmail.com> Co-authored-by: Xinyuan Tong <115166877+JustinTong0323@users.noreply.github.com> Co-authored-by: Roger Young <42564206+rogeryoungh@users.noreply.github.com> Co-authored-by: xuebi <xuebi@minimaxi.com>
This commit is contained in:
赵晨阳
@@ -16,6 +16,7 @@ from sglang.srt.function_call.glm4_moe_detector import Glm4MoeDetector
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from sglang.srt.function_call.gpt_oss_detector import GptOssDetector
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from sglang.srt.function_call.kimik2_detector import KimiK2Detector
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from sglang.srt.function_call.llama32_detector import Llama32Detector
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from sglang.srt.function_call.minimax_m2 import MinimaxM2Detector
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from sglang.srt.function_call.mistral_detector import MistralDetector
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from sglang.srt.function_call.pythonic_detector import PythonicDetector
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from sglang.srt.function_call.qwen3_coder_detector import Qwen3CoderDetector
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@@ -49,6 +50,7 @@ class FunctionCallParser:
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"qwen25": Qwen25Detector,
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"qwen3_coder": Qwen3CoderDetector,
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"step3": Step3Detector,
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"minimax-m2": MinimaxM2Detector,
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}
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def __init__(self, tools: List[Tool], tool_call_parser: str):
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367
python/sglang/srt/function_call/minimax_m2.py
Normal file
367
python/sglang/srt/function_call/minimax_m2.py
Normal file
@@ -0,0 +1,367 @@
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import ast
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import html
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import json
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import logging
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import re
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from typing import Any, Dict, List, Tuple
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from sglang.srt.entrypoints.openai.protocol import Tool
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from sglang.srt.function_call.base_format_detector import BaseFormatDetector
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from sglang.srt.function_call.core_types import (
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StreamingParseResult,
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ToolCallItem,
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_GetInfoFunc,
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)
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from sglang.srt.function_call.ebnf_composer import EBNFComposer
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logger = logging.getLogger(__name__)
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def _safe_val(raw: str) -> Any:
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raw = html.unescape(raw.strip())
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try:
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return json.loads(raw)
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except Exception:
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try:
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return ast.literal_eval(raw)
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except Exception:
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return raw
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class MinimaxM2Detector(BaseFormatDetector):
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"""
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Detector for MiniMax M2 models.
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Assumes function call format:
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<minimax:tool_call>
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<invoke name="func1">
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<parameter name="param1">value1</parameter>
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<parameter name="param2">value2</parameter>
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</invoke>
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</minimax:tool_call>
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"""
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def __init__(self):
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super().__init__()
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self.tool_call_start_token: str = "<minimax:tool_call>"
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self.tool_call_end_token: str = "</minimax:tool_call>"
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self.tool_call_prefix: str = '<invoke name="'
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self.tool_call_function_end_token: str = "</invoke>"
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self.tool_call_regex = re.compile(
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r"<minimax:tool_call>(.*?)</minimax:tool_call>|<minimax:tool_call>(.*?)$",
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re.DOTALL,
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)
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self.tool_call_function_regex = re.compile(
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r"<invoke name=\"(.*?)</invoke>|<invoke name=\"(.*)$", re.DOTALL
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)
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self.tool_call_parameter_regex = re.compile(
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r"<parameter name=\"(.*?)</parameter>|<parameter name=\"(.*?)$", re.DOTALL
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)
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self._buf: str = ""
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# Streaming state variables
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self._current_function_name: str = ""
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self._current_parameters: Dict[str, Any] = {}
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self._streamed_parameters: Dict[str, str] = (
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{}
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) # Track what parameter content we've streamed
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self._in_tool_call: bool = False
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self._function_name_sent: bool = False
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def has_tool_call(self, text: str) -> bool:
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return self.tool_call_start_token in text
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def detect_and_parse(self, text: str, tools: List[Tool]) -> StreamingParseResult:
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normal, calls = self._extract(text, tools)
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return StreamingParseResult(normal_text=normal, calls=calls)
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def parse_streaming_increment(
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self, new_text: str, tools: List[Tool]
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) -> StreamingParseResult:
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self._buf += new_text
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normal = ""
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calls: List[ToolCallItem] = []
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# Build tool indices for validation
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if not hasattr(self, "_tool_indices"):
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self._tool_indices = self._get_tool_indices(tools)
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while True:
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# If we're not in a tool call and don't see a start token, return normal text
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if not self._in_tool_call and self.tool_call_start_token not in self._buf:
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normal += self._buf
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self._buf = ""
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break
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# Look for tool call start
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if not self._in_tool_call:
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s = self._buf.find(self.tool_call_start_token)
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if s == -1:
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normal += self._buf
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self._buf = ""
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break
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normal += self._buf[:s]
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self._buf = self._buf[s:]
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self._in_tool_call = True
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self._function_name_sent = False
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self._current_function_name = ""
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self._current_parameters = {}
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self._streamed_parameters = {}
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# Remove the start token
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self._buf = self._buf[len(self.tool_call_start_token) :]
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continue
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# We're in a tool call, try to parse function name if not sent yet
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if not self._function_name_sent:
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# Look for function name pattern: <invoke name=name>
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function_match = re.search(r"<invoke name=\"([^>]+)\">", self._buf)
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if function_match:
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function_name = function_match.group(1).strip()
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# Validate function name
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if function_name in self._tool_indices:
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self._current_function_name = function_name
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self._function_name_sent = True
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# Initialize tool call tracking
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if self.current_tool_id == -1:
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self.current_tool_id = 0
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# Ensure tracking arrays are large enough
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while len(self.prev_tool_call_arr) <= self.current_tool_id:
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self.prev_tool_call_arr.append({})
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while len(self.streamed_args_for_tool) <= self.current_tool_id:
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self.streamed_args_for_tool.append("")
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# Store tool call info
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self.prev_tool_call_arr[self.current_tool_id] = {
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"name": function_name,
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"arguments": {},
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}
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# Send tool name with empty parameters
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calls.append(
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ToolCallItem(
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tool_index=self.current_tool_id,
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name=function_name,
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parameters="",
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)
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)
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# Remove the processed function declaration
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self._buf = self._buf[function_match.end() :]
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continue
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else:
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# Invalid function name, reset state
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logger.warning(f"Invalid function name: {function_name}")
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self._reset_streaming_state()
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normal += self._buf
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self._buf = ""
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break
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else:
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# Function name not complete yet, wait for more text
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break
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# Parse parameters incrementally
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if self._function_name_sent:
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# Process parameters and get any calls to emit
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parameter_calls = self._parse_and_stream_parameters(self._buf)
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calls.extend(parameter_calls)
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# Check if tool call is complete
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if self.tool_call_function_end_token in self._buf:
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end_pos = self._buf.find(self.tool_call_function_end_token)
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# Add closing brace to complete the JSON object
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current_streamed = self.streamed_args_for_tool[self.current_tool_id]
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if current_streamed:
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# Count opening and closing braces to check if JSON is complete
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open_braces = current_streamed.count("{")
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close_braces = current_streamed.count("}")
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if open_braces > close_braces:
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calls.append(
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ToolCallItem(
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tool_index=self.current_tool_id,
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name=None,
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parameters="}",
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)
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)
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self.streamed_args_for_tool[self.current_tool_id] = (
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current_streamed + "}"
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)
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# Complete the tool call
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self._buf = self._buf[
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end_pos + len(self.tool_call_function_end_token) :
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]
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self._reset_streaming_state(True)
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self.current_tool_id += 1
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continue
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else:
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# Tool call not complete yet, wait for more text
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break
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return StreamingParseResult(normal_text=normal, calls=calls)
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def _parse_and_stream_parameters(self, text_to_parse: str) -> List[ToolCallItem]:
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"""
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Parse complete parameter blocks from text and return any tool call items to emit.
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This method:
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1. Finds all complete <parameter> blocks
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2. Parses them into a dictionary
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3. Compares with current parameters and generates diff if needed
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4. Updates internal state
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Args:
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text_to_parse: The text to search for parameter blocks
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Returns:
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List of ToolCallItem objects to emit (may be empty)
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"""
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calls: List[ToolCallItem] = []
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# Find all complete parameter patterns
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param_matches = list(
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re.finditer(
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r"<parameter name=\"([^>]+)\">(.*?)</parameter>",
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text_to_parse,
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re.DOTALL,
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)
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)
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# Build new parameters dictionary
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new_params = {}
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for match in param_matches:
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param_name = match.group(1).strip()
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param_value = match.group(2)
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new_params[param_name] = _safe_val(param_value)
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# Calculate parameter diff to stream with proper incremental JSON building
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if new_params != self._current_parameters:
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previous_args_json = self.streamed_args_for_tool[self.current_tool_id]
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# Build incremental JSON properly
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if not self._current_parameters:
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# First parameter(s) - start JSON object but don't close it yet
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items = []
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for key, value in new_params.items():
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items.append(
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f"{json.dumps(key, ensure_ascii=False)}: {json.dumps(value, ensure_ascii=False)}"
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)
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json_fragment = "{" + ", ".join(items)
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calls.append(
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ToolCallItem(
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tool_index=self.current_tool_id,
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name=None,
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parameters=json_fragment,
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)
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)
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self.streamed_args_for_tool[self.current_tool_id] = json_fragment
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else:
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# Additional parameters - add them incrementally
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new_keys = set(new_params.keys()) - set(self._current_parameters.keys())
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if new_keys:
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# Build the continuation part (no closing brace yet)
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continuation_parts = []
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for key in new_keys:
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value = new_params[key]
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continuation_parts.append(
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f"{json.dumps(key, ensure_ascii=False)}: {json.dumps(value, ensure_ascii=False)}"
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)
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json_fragment = ", " + ", ".join(continuation_parts)
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calls.append(
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ToolCallItem(
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tool_index=self.current_tool_id,
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name=None,
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parameters=json_fragment,
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)
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)
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self.streamed_args_for_tool[self.current_tool_id] = (
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previous_args_json + json_fragment
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)
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# Update current state
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self._current_parameters = new_params
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self.prev_tool_call_arr[self.current_tool_id]["arguments"] = new_params
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return calls
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def _reset_streaming_state(self, still_in_tool_call: bool = False):
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"""Reset streaming state for the next tool call"""
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self._in_tool_call = still_in_tool_call
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self._function_name_sent = False
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self._current_function_name = ""
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self._current_parameters = {}
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self._streamed_parameters = {}
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self.current_tool_name_sent = False
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def _extract(self, text: str, tools: List[Tool]) -> Tuple[str, List[ToolCallItem]]:
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normal_parts: List[str] = []
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calls: List[ToolCallItem] = []
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cursor = 0
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while True:
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s = text.find(self.tool_call_start_token, cursor)
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if s == -1:
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normal_parts.append(text[cursor:])
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break
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normal_parts.append(text[cursor:s])
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e = text.find(self.tool_call_end_token, s)
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if e == -1:
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normal_parts.append(text[s:])
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break
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block = text[s : e + len(self.tool_call_end_token)]
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cursor = e + len(self.tool_call_end_token)
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calls.extend(self._parse_block(block, tools))
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return "".join(normal_parts), calls
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def _parse_block(self, block: str, tools: List[Tool]) -> List[ToolCallItem]:
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res: List[ToolCallItem] = []
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for m in self.tool_call_function_regex.findall(block):
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txt = m[0] if m[0] else m[1]
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if '">' not in txt:
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continue
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idx = txt.index('">')
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fname = txt[:idx].strip()
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body = txt[idx + 2 :]
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params: Dict[str, Any] = {}
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for pm in self.tool_call_parameter_regex.findall(body):
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ptxt = pm[0] if pm[0] else pm[1]
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if '">' not in ptxt:
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continue
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pidx = ptxt.index('">')
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pname = ptxt[:pidx].strip()
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pval = ptxt[pidx + 2 :].lstrip("\n").rstrip("\n")
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params[pname] = _safe_val(pval)
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raw = {"name": fname, "arguments": params}
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try:
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# TODO: fix idx in function call, the index for a function
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# call will always be -1 in parse_base_json
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res.extend(self.parse_base_json(raw, tools))
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except Exception:
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logger.warning("invalid tool call for %s dropped", fname)
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return res
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def supports_structural_tag(self) -> bool:
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return False
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def structure_info(self) -> _GetInfoFunc:
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raise NotImplementedError
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def build_ebnf(self, tools: List[Tool]):
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return EBNFComposer.build_ebnf(
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tools,
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individual_call_start_token=self.tool_call_start_token.replace("\n", "\\n"),
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individual_call_end_token=self.tool_call_end_token.replace("\n", "\\n"),
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tool_call_separator="\\n",
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function_format="xml",
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call_rule_fmt='"<invoke name=\\"{name}\\">\\n" {arguments_rule} "\\n</invoke>"',
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key_value_rule_fmt='"<parameter name=\\"{key}\\">\\n" {valrule} "\\n</parameter>"',
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key_value_separator='"\\n"',
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)
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922
python/sglang/srt/models/minimax_m2.py
Normal file
922
python/sglang/srt/models/minimax_m2.py
Normal file
@@ -0,0 +1,922 @@
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# Copyright 2023-2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
|
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
|
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# distributed under the License is distributed on an "AS IS" BASIS,
|
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
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# ==============================================================================
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# Adapted from DeepSeek and Mixtral implementation
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"""Inference-only MiniMax M2 model compatible with HuggingFace weights."""
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import logging
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from typing import Iterable, Optional, Set, Tuple, Union
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from sglang.srt.distributed import (
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get_moe_expert_parallel_world_size,
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get_pp_group,
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce,
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)
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from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
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from sglang.srt.eplb.expert_location_dispatch import ExpertLocationDispatchInfo
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from sglang.srt.layers.activation import SiluAndMul
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from sglang.srt.layers.communicator import (
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LayerCommunicator,
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LayerScatterModes,
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ScatterMode,
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)
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from sglang.srt.layers.layernorm import RMSNorm
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from sglang.srt.layers.linear import (
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MergedColumnParallelLinear,
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class
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from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
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from sglang.srt.layers.moe.topk import TopK
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from sglang.srt.layers.moe.utils import get_moe_a2a_backend
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
|
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.layers.rotary_embedding import get_rope
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from sglang.srt.layers.utils import PPMissingLayer
|
||||
from sglang.srt.layers.vocab_parallel_embedding import (
|
||||
ParallelLMHead,
|
||||
VocabParallelEmbedding,
|
||||
)
|
||||
from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
|
||||
from sglang.srt.model_loader.weight_utils import (
|
||||
default_weight_loader,
|
||||
maybe_remap_kv_scale_name,
|
||||
)
|
||||
from sglang.srt.server_args import get_global_server_args
|
||||
from sglang.srt.two_batch_overlap import model_forward_maybe_tbo
|
||||
from sglang.srt.utils import (
|
||||
BumpAllocator,
|
||||
add_prefix,
|
||||
get_compiler_backend,
|
||||
is_non_idle_and_non_empty,
|
||||
make_layers,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class MiniMaxM2RMSNormTP(nn.Module):
|
||||
"""RMSNorm with Tensor Parallel support for QK normalization."""
|
||||
|
||||
def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
|
||||
super().__init__()
|
||||
self.tp_world = get_tensor_model_parallel_world_size()
|
||||
self.tp_rank = get_tensor_model_parallel_rank()
|
||||
|
||||
# Weight parameter is sharded across TP ranks
|
||||
self.weight = nn.Parameter(torch.ones(int(hidden_size / self.tp_world)))
|
||||
self.weight.weight_loader = self.weight_loader
|
||||
self.variance_epsilon = eps
|
||||
|
||||
@staticmethod
|
||||
def weight_loader(
|
||||
param: nn.Parameter,
|
||||
loaded_weight: torch.Tensor,
|
||||
) -> None:
|
||||
"""Custom weight loader that handles TP sharding."""
|
||||
tp_world = get_tensor_model_parallel_world_size()
|
||||
tp_rank = get_tensor_model_parallel_rank()
|
||||
|
||||
shard_size = loaded_weight.shape[0] // tp_world
|
||||
shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
|
||||
param.data.copy_(loaded_weight[shard])
|
||||
|
||||
@torch.compile(dynamic=True, backend=get_compiler_backend())
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
residual: Optional[torch.Tensor] = None,
|
||||
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
|
||||
"""Forward pass with TP-aware variance computation."""
|
||||
assert residual is None, "RMSNormTP does not support residual connection."
|
||||
|
||||
orig_dtype = x.dtype
|
||||
x = x.to(torch.float32)
|
||||
|
||||
# Compute variance across the full dimension (not just local shard)
|
||||
variance = x.pow(2).mean(dim=-1, keepdim=True, dtype=torch.float32)
|
||||
|
||||
if self.tp_world > 1:
|
||||
# All-reduce variance across TP ranks to get global variance
|
||||
variance = tensor_model_parallel_all_reduce(variance) / self.tp_world
|
||||
|
||||
# Normalize and apply local weight shard
|
||||
x = x * torch.rsqrt(variance + self.variance_epsilon)
|
||||
x = x.to(orig_dtype) * self.weight
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class MiniMaxM2MLP(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
hidden_size: int,
|
||||
intermediate_size: int,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "mlp",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.gate_up_proj = MergedColumnParallelLinear(
|
||||
hidden_size,
|
||||
[intermediate_size] * 2,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
prefix=add_prefix("gate_up_proj", prefix),
|
||||
)
|
||||
self.down_proj = RowParallelLinear(
|
||||
intermediate_size,
|
||||
hidden_size,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
prefix=add_prefix("down_proj", prefix),
|
||||
)
|
||||
self.act_fn = SiluAndMul()
|
||||
return
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
gate_up, _ = self.gate_up_proj(x)
|
||||
x = self.act_fn(gate_up)
|
||||
x, _ = self.down_proj(x)
|
||||
return x
|
||||
|
||||
|
||||
class MiniMaxM2MoE(nn.Module):
|
||||
"""MiniMax MoE implementation using DeepEP for Expert Parallel support."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
layer_id: int,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
):
|
||||
super().__init__()
|
||||
self.tp_size = get_tensor_model_parallel_world_size()
|
||||
if self.tp_size > config.num_local_experts:
|
||||
raise ValueError(
|
||||
f"Tensor parallel size {self.tp_size} is greater than "
|
||||
f"the number of experts {config.num_local_experts}."
|
||||
)
|
||||
self.use_routing_bias = getattr(config, "use_routing_bias", False)
|
||||
if self.use_routing_bias:
|
||||
self.e_score_correction_bias = nn.Parameter(
|
||||
torch.empty(config.num_local_experts, dtype=torch.float32)
|
||||
)
|
||||
self.e_score_correction_bias.weight_loader = (
|
||||
MiniMaxM2MoE.ebias_weight_loader
|
||||
)
|
||||
else:
|
||||
self.e_score_correction_bias = None
|
||||
|
||||
self.experts = get_moe_impl_class(quant_config)(
|
||||
num_experts=config.num_local_experts
|
||||
+ get_global_server_args().ep_num_redundant_experts,
|
||||
top_k=config.num_experts_per_tok,
|
||||
hidden_size=config.hidden_size,
|
||||
intermediate_size=config.intermediate_size,
|
||||
layer_id=layer_id,
|
||||
quant_config=quant_config,
|
||||
prefix=add_prefix("experts", prefix),
|
||||
)
|
||||
self.topk = TopK(
|
||||
top_k=config.num_experts_per_tok,
|
||||
renormalize=True,
|
||||
scoring_func=config.scoring_func,
|
||||
use_grouped_topk=True, # TODO: Use "grouped top-k" flag only for hardcoded sigmoid scoring
|
||||
num_expert_group=1,
|
||||
topk_group=1,
|
||||
correction_bias=self.e_score_correction_bias,
|
||||
routed_scaling_factor=1.0,
|
||||
)
|
||||
|
||||
self.gate = ReplicatedLinear(
|
||||
config.hidden_size,
|
||||
config.num_local_experts,
|
||||
bias=False,
|
||||
params_dtype=torch.float32,
|
||||
quant_config=None,
|
||||
prefix=add_prefix("gate", prefix),
|
||||
)
|
||||
|
||||
self.layer_id = layer_id
|
||||
|
||||
if get_moe_a2a_backend().is_deepep():
|
||||
self.ep_size = get_moe_expert_parallel_world_size()
|
||||
self.top_k = config.num_experts_per_tok
|
||||
|
||||
@staticmethod
|
||||
def ebias_weight_loader(param: nn.Parameter, loaded_weight: torch.Tensor) -> None:
|
||||
assert param.size() == loaded_weight.size()
|
||||
param.data.copy_(loaded_weight.to(torch.float32))
|
||||
|
||||
def forward(
|
||||
self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
|
||||
) -> torch.Tensor:
|
||||
if get_moe_a2a_backend().is_deepep():
|
||||
return self.forward_deepep(hidden_states, forward_batch)
|
||||
else:
|
||||
return self.forward_normal(hidden_states)
|
||||
|
||||
def forward_normal(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
num_tokens, hidden_dim = hidden_states.shape
|
||||
hidden_states = hidden_states.view(-1, hidden_dim)
|
||||
|
||||
# router_logits: (num_tokens, n_experts)
|
||||
router_logits, _ = self.gate(hidden_states.to(torch.float32))
|
||||
topk_output = self.topk(hidden_states, router_logits)
|
||||
|
||||
final_hidden_states = self.experts(hidden_states, topk_output)
|
||||
if self.tp_size > 1:
|
||||
final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
|
||||
|
||||
return final_hidden_states.view(num_tokens, hidden_dim)
|
||||
|
||||
def forward_deepep(
|
||||
self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
|
||||
) -> torch.Tensor:
|
||||
if hidden_states.shape[0] > 0:
|
||||
# router_logits: (num_tokens, n_experts)
|
||||
router_logits, _ = self.gate(hidden_states.to(torch.float32))
|
||||
topk_weights, topk_idx, _ = self.topk(
|
||||
hidden_states,
|
||||
router_logits,
|
||||
num_token_non_padded=forward_batch.num_token_non_padded,
|
||||
expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
|
||||
layer_id=self.layer_id,
|
||||
),
|
||||
)
|
||||
else:
|
||||
topk_weights, topk_idx, _ = self.topk.empty_topk_output(
|
||||
hidden_states.shape[0], self.top_k
|
||||
)
|
||||
final_hidden_states = self.experts(
|
||||
hidden_states=hidden_states,
|
||||
topk_idx=topk_idx,
|
||||
topk_weights=topk_weights,
|
||||
forward_batch=forward_batch,
|
||||
)
|
||||
|
||||
return final_hidden_states
|
||||
|
||||
# TBO Operations for MiniMax MoE
|
||||
def op_gate(self, state):
|
||||
"""Gate operation for TBO - compute router logits"""
|
||||
if is_non_idle_and_non_empty(
|
||||
state.forward_batch.forward_mode, state.hidden_states_mlp_input
|
||||
): # router_logits: (num_tokens, num_experts)
|
||||
state.router_logits, _ = self.gate(state.hidden_states_mlp_input)
|
||||
else:
|
||||
state.router_logits = None
|
||||
|
||||
def op_select_experts(self, state):
|
||||
"""Expert selection operation for TBO"""
|
||||
router_logits = state.pop("router_logits")
|
||||
hidden_states = state.hidden_states_mlp_input
|
||||
|
||||
if router_logits is not None:
|
||||
with get_global_expert_distribution_recorder().with_current_layer(
|
||||
self.layer_id
|
||||
):
|
||||
state.topk_weights_local, state.topk_idx_local, _ = self.topk(
|
||||
hidden_states=hidden_states,
|
||||
router_logits=router_logits,
|
||||
num_token_non_padded=state.forward_batch.num_token_non_padded,
|
||||
expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
|
||||
layer_id=self.layer_id,
|
||||
),
|
||||
)
|
||||
else:
|
||||
state.topk_idx_local = torch.full(
|
||||
(0, self.top_k), -1, dtype=torch.int, device=hidden_states.device
|
||||
)
|
||||
state.topk_weights_local = torch.empty(
|
||||
(0, self.top_k), dtype=torch.float32, device=hidden_states.device
|
||||
)
|
||||
|
||||
def op_dispatch_a(self, state):
|
||||
"""Dispatch A operation for TBO - start async dispatch"""
|
||||
if self.ep_size > 1:
|
||||
self.experts.deepep_dispatcher.dispatch_a(
|
||||
hidden_states=state.pop("hidden_states_mlp_input"),
|
||||
topk_idx=state.pop("topk_idx_local"),
|
||||
topk_weights=state.pop("topk_weights_local"),
|
||||
forward_batch=state.forward_batch,
|
||||
tbo_subbatch_index=state.get("tbo_subbatch_index"),
|
||||
)
|
||||
|
||||
def op_dispatch_b(self, state):
|
||||
"""Dispatch B operation for TBO - complete async dispatch"""
|
||||
if self.ep_size > 1:
|
||||
with get_global_expert_distribution_recorder().with_current_layer(
|
||||
self.layer_id
|
||||
):
|
||||
state.dispatch_output = self.experts.deepep_dispatcher.dispatch_b(
|
||||
tbo_subbatch_index=state.get("tbo_subbatch_index"),
|
||||
)
|
||||
|
||||
def op_experts(self, state):
|
||||
"""Expert computation for TBO"""
|
||||
state.hidden_states_experts_output = self.experts.moe_impl(
|
||||
dispatch_output=state.dispatch_output,
|
||||
)
|
||||
|
||||
def op_combine_a(self, state):
|
||||
"""Combine A operation for TBO - start async combine"""
|
||||
if self.ep_size > 1:
|
||||
self.experts.deepep_dispatcher.combine_a(
|
||||
hidden_states=state.pop("hidden_states_experts_output"),
|
||||
topk_idx=state.dispatch_output.topk_idx,
|
||||
topk_weights=state.dispatch_output.topk_weights,
|
||||
forward_batch=state.forward_batch,
|
||||
tbo_subbatch_index=state.get("tbo_subbatch_index"),
|
||||
)
|
||||
state.pop("dispatch_output")
|
||||
|
||||
def op_combine_b(self, state):
|
||||
"""Combine B operation for TBO - complete async combine"""
|
||||
if self.ep_size > 1:
|
||||
state.hidden_states_after_combine = (
|
||||
self.experts.deepep_dispatcher.combine_b(
|
||||
tbo_subbatch_index=state.get("tbo_subbatch_index"),
|
||||
)
|
||||
)
|
||||
|
||||
def op_output(self, state):
|
||||
"""Output operation for TBO - final MLP output"""
|
||||
final_hidden_states = state.pop("hidden_states_after_combine")
|
||||
# MiniMax doesn't have shared experts like DeepSeek, so no need to add them
|
||||
state.hidden_states_mlp_output = final_hidden_states
|
||||
|
||||
|
||||
class MiniMaxM2Attention(nn.Module):
|
||||
"""MiniMax Attention implementation with QK normalization and partial RoPE."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
layer_id: int = 0,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
tp_size = get_tensor_model_parallel_world_size()
|
||||
|
||||
# Get dimensions from config
|
||||
self.total_num_heads = config.num_attention_heads
|
||||
assert self.total_num_heads % tp_size == 0
|
||||
self.num_heads = self.total_num_heads // tp_size
|
||||
self.total_num_kv_heads = config.num_key_value_heads
|
||||
|
||||
if self.total_num_kv_heads >= tp_size:
|
||||
# Number of KV heads is greater than TP size, so we partition
|
||||
# the KV heads across multiple tensor parallel GPUs.
|
||||
assert self.total_num_kv_heads % tp_size == 0
|
||||
else:
|
||||
# Number of KV heads is less than TP size, so we replicate
|
||||
# the KV heads across multiple tensor parallel GPUs.
|
||||
assert tp_size % self.total_num_kv_heads == 0
|
||||
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
|
||||
|
||||
# Use head_dim from config if available, otherwise calculate
|
||||
self.head_dim = getattr(
|
||||
config, "head_dim", self.hidden_size // self.total_num_heads
|
||||
)
|
||||
self.q_size = self.num_heads * self.head_dim
|
||||
self.kv_size = self.num_kv_heads * self.head_dim
|
||||
self.scaling = self.head_dim**-0.5
|
||||
|
||||
# RoPE settings - support partial RoPE
|
||||
self.rope_theta = getattr(config, "rope_theta", 10000)
|
||||
self.max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
|
||||
self.rotary_dim = getattr(
|
||||
config, "rotary_dim", self.head_dim
|
||||
) # MiniMax uses rotary_dim=64
|
||||
|
||||
# QK Normalization settings
|
||||
self.use_qk_norm = getattr(config, "use_qk_norm", False)
|
||||
self.qk_norm_type = getattr(config, "qk_norm_type", "per_layer")
|
||||
|
||||
self.qkv_proj = QKVParallelLinear(
|
||||
self.hidden_size,
|
||||
self.head_dim,
|
||||
self.total_num_heads,
|
||||
self.total_num_kv_heads,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
prefix=add_prefix("qkv_proj", prefix),
|
||||
)
|
||||
|
||||
self.o_proj = RowParallelLinear(
|
||||
self.total_num_heads * self.head_dim,
|
||||
self.hidden_size,
|
||||
bias=False,
|
||||
reduce_results=False,
|
||||
quant_config=quant_config,
|
||||
prefix=add_prefix("o_proj", prefix),
|
||||
)
|
||||
|
||||
# Setup RoPE with partial rotary dimension
|
||||
rope_scaling = getattr(config, "rope_scaling", None)
|
||||
self.rotary_emb = get_rope(
|
||||
self.head_dim,
|
||||
rotary_dim=self.rotary_dim, # Use partial rotary dimension
|
||||
max_position=self.max_position_embeddings,
|
||||
base=self.rope_theta,
|
||||
rope_scaling=rope_scaling,
|
||||
)
|
||||
|
||||
# QK Normalization layers
|
||||
if self.use_qk_norm:
|
||||
if self.qk_norm_type == "per_layer":
|
||||
# Use RMSNormTP for proper tensor parallel support
|
||||
# Use total dimensions (before TP sharding) for correct normalization
|
||||
self.q_norm = MiniMaxM2RMSNormTP(
|
||||
self.total_num_heads * self.head_dim, eps=config.rms_norm_eps
|
||||
)
|
||||
self.k_norm = MiniMaxM2RMSNormTP(
|
||||
self.total_num_kv_heads * self.head_dim, eps=config.rms_norm_eps
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Unsupported qk_norm_type: {self.qk_norm_type}")
|
||||
|
||||
self.attn = RadixAttention(
|
||||
self.num_heads,
|
||||
self.head_dim,
|
||||
self.scaling,
|
||||
num_kv_heads=self.num_kv_heads,
|
||||
layer_id=layer_id,
|
||||
quant_config=quant_config,
|
||||
prefix=add_prefix("attn", prefix),
|
||||
)
|
||||
|
||||
def forward_prepare(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
):
|
||||
qkv, _ = self.qkv_proj(hidden_states)
|
||||
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
||||
if self.use_qk_norm:
|
||||
q = self.q_norm(q.contiguous())
|
||||
k = self.k_norm(k.contiguous())
|
||||
else:
|
||||
q, k = q.contiguous(), k.contiguous()
|
||||
q, k = self.rotary_emb(positions, q, k)
|
||||
inner_state = q, k, v, forward_batch
|
||||
return None, forward_batch, inner_state
|
||||
|
||||
def forward_core(self, intermediate_state):
|
||||
_, _, inner_state = intermediate_state
|
||||
attn_output = self.attn(*inner_state)
|
||||
output, _ = self.o_proj(attn_output)
|
||||
return output
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
) -> torch.Tensor:
|
||||
s = self.forward_prepare(
|
||||
positions=positions,
|
||||
hidden_states=hidden_states,
|
||||
forward_batch=forward_batch,
|
||||
)
|
||||
return self.forward_core(s)
|
||||
|
||||
def op_prepare(self, state):
|
||||
state.attn_intermediate_state = self.forward_prepare(
|
||||
positions=state.positions,
|
||||
hidden_states=state.pop("hidden_states_after_comm_pre_attn"),
|
||||
forward_batch=state.forward_batch,
|
||||
)
|
||||
|
||||
def op_core(self, state):
|
||||
state.hidden_states_after_attn = self.forward_core(
|
||||
state.pop("attn_intermediate_state")
|
||||
)
|
||||
|
||||
|
||||
class MiniMaxM2DecoderLayer(nn.Module):
|
||||
"""MiniMax Decoder Layer implementation with MoE support."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
layer_id: int,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
self.layer_id = layer_id
|
||||
|
||||
# TBO support: All MiniMax layers are sparse (MoE)
|
||||
self.is_layer_sparse = True
|
||||
|
||||
self.self_attn = MiniMaxM2Attention(
|
||||
config=config,
|
||||
layer_id=layer_id,
|
||||
quant_config=quant_config,
|
||||
prefix=add_prefix("self_attn", prefix),
|
||||
)
|
||||
|
||||
self.block_sparse_moe = MiniMaxM2MoE(
|
||||
config=config,
|
||||
layer_id=layer_id,
|
||||
quant_config=quant_config,
|
||||
prefix=add_prefix("mlp", prefix),
|
||||
)
|
||||
|
||||
self.input_layernorm = RMSNorm(
|
||||
config.hidden_size, eps=getattr(config, "rms_norm_eps", 1e-6)
|
||||
)
|
||||
self.post_attention_layernorm = RMSNorm(
|
||||
config.hidden_size, eps=getattr(config, "rms_norm_eps", 1e-6)
|
||||
)
|
||||
|
||||
is_previous_layer_sparse = True
|
||||
self.layer_scatter_modes = LayerScatterModes.init_new(
|
||||
layer_id=layer_id,
|
||||
num_layers=config.num_hidden_layers,
|
||||
is_layer_sparse=self.is_layer_sparse,
|
||||
is_previous_layer_sparse=is_previous_layer_sparse,
|
||||
)
|
||||
|
||||
self.layer_communicator = LayerCommunicator(
|
||||
layer_scatter_modes=self.layer_scatter_modes,
|
||||
input_layernorm=self.input_layernorm,
|
||||
post_attention_layernorm=self.post_attention_layernorm,
|
||||
allow_reduce_scatter=True,
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
residual: Optional[torch.Tensor],
|
||||
) -> torch.Tensor:
|
||||
# Self Attention
|
||||
hidden_states, residual = self.layer_communicator.prepare_attn(
|
||||
hidden_states, residual, forward_batch
|
||||
)
|
||||
|
||||
hidden_states = self.self_attn(
|
||||
positions=positions,
|
||||
hidden_states=hidden_states,
|
||||
forward_batch=forward_batch,
|
||||
)
|
||||
|
||||
# Fully Connected (MLP or MoE)
|
||||
|
||||
hidden_states, residual = self.layer_communicator.prepare_mlp(
|
||||
hidden_states, residual, forward_batch
|
||||
)
|
||||
|
||||
hidden_states = self.block_sparse_moe(hidden_states, forward_batch)
|
||||
|
||||
hidden_states, residual = self.layer_communicator.postprocess_layer(
|
||||
hidden_states, residual, forward_batch
|
||||
)
|
||||
|
||||
return hidden_states, residual
|
||||
|
||||
# TBO Operations for MiniMax Decoder Layer
|
||||
def op_comm_prepare_attn(
|
||||
self,
|
||||
state,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
residual: Optional[torch.Tensor],
|
||||
zero_allocator: BumpAllocator,
|
||||
tbo_subbatch_index: Optional[int] = None,
|
||||
):
|
||||
"""Communication prepare for attention - TBO operation"""
|
||||
state.hidden_states_after_comm_pre_attn, state.residual_after_input_ln = (
|
||||
self.layer_communicator.prepare_attn(hidden_states, residual, forward_batch)
|
||||
)
|
||||
state.update(
|
||||
dict(
|
||||
forward_batch=forward_batch,
|
||||
positions=positions,
|
||||
zero_allocator=zero_allocator,
|
||||
tbo_subbatch_index=tbo_subbatch_index,
|
||||
)
|
||||
)
|
||||
|
||||
def op_comm_prepare_mlp(self, state):
|
||||
"""Communication prepare for MLP - TBO operation"""
|
||||
state.hidden_states_mlp_input, state.residual_after_comm_pre_mlp = (
|
||||
self.layer_communicator.prepare_mlp(
|
||||
state.pop("hidden_states_after_attn"),
|
||||
state.pop("residual_after_input_ln"),
|
||||
state.forward_batch,
|
||||
)
|
||||
)
|
||||
|
||||
def op_mlp(self, state):
|
||||
hidden_states = state.pop("hidden_states_mlp_input")
|
||||
state.hidden_states_mlp_output = self.block_sparse_moe(
|
||||
hidden_states, state.forward_batch
|
||||
)
|
||||
|
||||
def op_comm_postprocess_layer(self, state):
|
||||
"""Communication postprocess for layer - TBO operation"""
|
||||
hidden_states, residual = self.layer_communicator.postprocess_layer(
|
||||
state.pop("hidden_states_mlp_output"),
|
||||
state.pop("residual_after_comm_pre_mlp"),
|
||||
state.forward_batch,
|
||||
)
|
||||
|
||||
output = dict(
|
||||
positions=state.positions,
|
||||
hidden_states=hidden_states,
|
||||
residual=residual,
|
||||
forward_batch=state.forward_batch,
|
||||
zero_allocator=state.zero_allocator,
|
||||
tbo_subbatch_index=state.tbo_subbatch_index,
|
||||
)
|
||||
return output
|
||||
|
||||
|
||||
class MiniMaxM2Model(nn.Module):
|
||||
"""MiniMax Model implementation."""
|
||||
|
||||
fall_back_to_pt_during_load = False
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.padding_idx = getattr(config, "pad_token_id", 0)
|
||||
self.vocab_size = config.vocab_size
|
||||
self.pp_group = get_pp_group()
|
||||
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
)
|
||||
|
||||
def layer_fn(idx, prefix: str) -> nn.Module:
|
||||
return MiniMaxM2DecoderLayer(
|
||||
config=config,
|
||||
layer_id=idx,
|
||||
quant_config=quant_config,
|
||||
prefix=prefix,
|
||||
)
|
||||
|
||||
self.layers, self.start_layer, self.end_layer = make_layers(
|
||||
config.num_hidden_layers,
|
||||
layer_fn,
|
||||
pp_rank=self.pp_group.rank_in_group,
|
||||
pp_size=self.pp_group.world_size,
|
||||
prefix=add_prefix("layers", prefix),
|
||||
)
|
||||
if self.pp_group.is_last_rank:
|
||||
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
else:
|
||||
self.norm = PPMissingLayer(return_tuple=True)
|
||||
|
||||
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.embed_tokens(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
input_embeds: torch.Tensor = None,
|
||||
pp_proxy_tensors: Optional[PPProxyTensors] = None,
|
||||
) -> Union[torch.Tensor, PPProxyTensors]:
|
||||
if self.pp_group.is_first_rank:
|
||||
if input_embeds is None:
|
||||
hidden_states = self.get_input_embeddings(input_ids)
|
||||
else:
|
||||
hidden_states = input_embeds
|
||||
residual = None
|
||||
else:
|
||||
assert pp_proxy_tensors is not None
|
||||
hidden_states = pp_proxy_tensors["hidden_states"]
|
||||
residual = pp_proxy_tensors["residual"]
|
||||
|
||||
if forward_batch.can_run_tbo:
|
||||
hidden_states, residual = model_forward_maybe_tbo(
|
||||
layers=self.layers,
|
||||
enable_tbo=True,
|
||||
input_data_scatter_mode=ScatterMode.model_input_output(),
|
||||
positions=positions,
|
||||
forward_batch=forward_batch,
|
||||
hidden_states=hidden_states,
|
||||
residual=residual,
|
||||
)
|
||||
else:
|
||||
for i in range(self.start_layer, self.end_layer):
|
||||
with get_global_expert_distribution_recorder().with_current_layer(i):
|
||||
layer = self.layers[i]
|
||||
hidden_states, residual = layer(
|
||||
positions=positions,
|
||||
forward_batch=forward_batch,
|
||||
hidden_states=hidden_states,
|
||||
residual=residual,
|
||||
)
|
||||
|
||||
if not self.pp_group.is_last_rank:
|
||||
return PPProxyTensors(
|
||||
{"hidden_states": hidden_states, "residual": residual}
|
||||
)
|
||||
|
||||
if residual is not None:
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
else:
|
||||
hidden_states = self.norm(hidden_states)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class MiniMaxM2ForCausalLM(nn.Module):
|
||||
"""MiniMax M2 model for causal language modeling."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.config = config
|
||||
self.quant_config = quant_config
|
||||
|
||||
self.model = MiniMaxM2Model(
|
||||
config, quant_config, prefix=add_prefix("model", prefix)
|
||||
)
|
||||
|
||||
if get_pp_group().is_last_rank:
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
quant_config=None,
|
||||
prefix=add_prefix("lm_head", prefix),
|
||||
)
|
||||
else:
|
||||
self.lm_head = PPMissingLayer()
|
||||
|
||||
self.logits_processor = LogitsProcessor(config)
|
||||
|
||||
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.model.get_input_embeddings(input_ids)
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
input_embeds: torch.Tensor = None,
|
||||
) -> torch.Tensor:
|
||||
# _print_tensor_info(input_ids, "input_ids")
|
||||
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
|
||||
return self.logits_processor(
|
||||
input_ids, hidden_states, self.lm_head, forward_batch
|
||||
)
|
||||
|
||||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
||||
"""Load model weights with proper mapping for MiniMax architecture."""
|
||||
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
("gate_up_proj", "gate_proj", 0),
|
||||
("gate_up_proj", "up_proj", 1),
|
||||
]
|
||||
|
||||
# Params for weights, fp8 weight scales, fp8 activation scales
|
||||
# (param_name, weight_name, expert_id, shard_id)
|
||||
expert_params_mapping = FusedMoE.make_expert_params_mapping(
|
||||
ckpt_gate_proj_name="w1",
|
||||
ckpt_down_proj_name="w2",
|
||||
ckpt_up_proj_name="w3",
|
||||
num_experts=self.config.num_local_experts,
|
||||
)
|
||||
|
||||
params_dict = dict(self.named_parameters())
|
||||
loaded_params: Set[str] = set()
|
||||
for name, loaded_weight in weights:
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
|
||||
spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
|
||||
if spec_layer is not None:
|
||||
continue # skip spec decode layers for main model
|
||||
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
# Skip non-stacked layers and experts (experts handled below).
|
||||
if weight_name not in name:
|
||||
continue
|
||||
# We have mlp.experts[0].gate_proj in the checkpoint.
|
||||
# Since we handle the experts below in expert_params_mapping,
|
||||
# we need to skip here BEFORE we update the name, otherwise
|
||||
# name will be updated to mlp.experts[0].gate_up_proj, which
|
||||
# will then be updated below in expert_params_mapping
|
||||
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
|
||||
if ("mlp.experts." in name) and name not in params_dict:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
for mapping in expert_params_mapping:
|
||||
param_name, weight_name, expert_id, shard_id = mapping
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(
|
||||
param,
|
||||
loaded_weight,
|
||||
name,
|
||||
shard_id=shard_id,
|
||||
expert_id=expert_id,
|
||||
)
|
||||
break
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
|
||||
# Remapping the name of FP8 kv-scale.
|
||||
name = maybe_remap_kv_scale_name(name, params_dict)
|
||||
if name is None:
|
||||
continue
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(
|
||||
param, "weight_loader", default_weight_loader
|
||||
)
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(name)
|
||||
return loaded_params
|
||||
|
||||
@classmethod
|
||||
def get_model_config_for_expert_location(cls, config):
|
||||
from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation
|
||||
|
||||
return ModelConfigForExpertLocation(
|
||||
num_layers=config.num_hidden_layers,
|
||||
num_logical_experts=config.num_local_experts,
|
||||
num_groups=None,
|
||||
)
|
||||
|
||||
|
||||
def get_spec_layer_idx_from_weight_name(
|
||||
config: PretrainedConfig, weight_name: str
|
||||
) -> Optional[int]:
|
||||
if hasattr(config, "num_mtp_modules") and (config.num_mtp_modules > 0):
|
||||
layer_idx = config.num_hidden_layers
|
||||
for i in range(config.num_mtp_modules):
|
||||
if weight_name.startswith(f"model.layers.{layer_idx + i}."):
|
||||
return layer_idx + i
|
||||
return None
|
||||
|
||||
|
||||
# Entry class for model registration
|
||||
EntryClass = MiniMaxM2ForCausalLM
|
||||
@@ -249,6 +249,31 @@ class GptOssDetector(BaseReasoningFormatDetector):
|
||||
)
|
||||
|
||||
|
||||
class MiniMaxAppendThinkDetector(BaseReasoningFormatDetector):
|
||||
"""
|
||||
Append `<think>` token to the beginning of the text.
|
||||
"""
|
||||
|
||||
def __init__(self, stream_reasoning: bool = True, force_reasoning: bool = False):
|
||||
# scheduler.py need `reasoning_parser.detector.think_end_token`
|
||||
super().__init__(
|
||||
"<think>",
|
||||
"</think>",
|
||||
force_reasoning=force_reasoning,
|
||||
stream_reasoning=stream_reasoning,
|
||||
)
|
||||
self.is_first_chunk = False
|
||||
|
||||
def parse_streaming_increment(self, new_text: str) -> StreamingParseResult:
|
||||
if not self.is_first_chunk:
|
||||
self.is_first_chunk = True
|
||||
new_text = self.think_start_token + new_text
|
||||
return StreamingParseResult(normal_text=new_text)
|
||||
|
||||
def detect_and_parse(self, text: str) -> StreamingParseResult:
|
||||
return StreamingParseResult(normal_text=self.think_start_token + text)
|
||||
|
||||
|
||||
class ReasoningParser:
|
||||
"""
|
||||
Parser that handles both streaming and non-streaming scenarios for extracting
|
||||
@@ -268,6 +293,8 @@ class ReasoningParser:
|
||||
"kimi": KimiDetector,
|
||||
"qwen3": Qwen3Detector,
|
||||
"qwen3-thinking": Qwen3Detector,
|
||||
"minimax": Qwen3Detector,
|
||||
"minimax-append-think": MiniMaxAppendThinkDetector,
|
||||
"step3": DeepSeekR1Detector,
|
||||
}
|
||||
|
||||
@@ -285,7 +312,7 @@ class ReasoningParser:
|
||||
raise ValueError(f"Unsupported model type: {model_type}")
|
||||
|
||||
# Special cases where we override force_reasoning
|
||||
if model_type.lower() in {"qwen3-thinking", "gpt-oss"}:
|
||||
if model_type.lower() in {"qwen3-thinking", "gpt-oss", "minimax"}:
|
||||
force_reasoning = True
|
||||
|
||||
# Only pass force_reasoning if explicitly set, let detectors use their defaults
|
||||
|
||||
Reference in New Issue
Block a user