Initial upload
Browse files- .gitattributes +1 -0
- chat_template.jinja +159 -0
- config.json +2863 -0
- configuration_minimax_m2.py +200 -0
- generation_config.json +9 -0
- model-00001-of-00031.safetensors +3 -0
- model-00002-of-00031.safetensors +3 -0
- model-00003-of-00031.safetensors +3 -0
- model-00004-of-00031.safetensors +3 -0
- model-00005-of-00031.safetensors +3 -0
- model-00006-of-00031.safetensors +3 -0
- model-00007-of-00031.safetensors +3 -0
- model-00008-of-00031.safetensors +3 -0
- model-00009-of-00031.safetensors +3 -0
- model-00010-of-00031.safetensors +3 -0
- model-00011-of-00031.safetensors +3 -0
- model-00012-of-00031.safetensors +3 -0
- model-00013-of-00031.safetensors +3 -0
- model-00014-of-00031.safetensors +3 -0
- model-00015-of-00031.safetensors +3 -0
- model-00016-of-00031.safetensors +3 -0
- model-00017-of-00031.safetensors +3 -0
- model-00018-of-00031.safetensors +3 -0
- model-00019-of-00031.safetensors +3 -0
- model-00020-of-00031.safetensors +3 -0
- model-00021-of-00031.safetensors +3 -0
- model-00022-of-00031.safetensors +3 -0
- model-00023-of-00031.safetensors +3 -0
- model-00024-of-00031.safetensors +3 -0
- model-00025-of-00031.safetensors +3 -0
- model-00026-of-00031.safetensors +3 -0
- model-00027-of-00031.safetensors +3 -0
- model-00028-of-00031.safetensors +3 -0
- model-00029-of-00031.safetensors +3 -0
- model-00030-of-00031.safetensors +3 -0
- model-00031-of-00031.safetensors +3 -0
- model.safetensors.index.json +0 -0
- modeling_minimax_m2.py +706 -0
- tokenizer.json +3 -0
- tokenizer_config.json +66 -0
.gitattributes
CHANGED
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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chat_template.jinja
ADDED
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@@ -0,0 +1,159 @@
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| 1 |
+
{# ----------‑‑‑ special token variables ‑‑‑---------- #}
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| 2 |
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{%- set toolcall_begin_token = '<minimax:tool_call>' -%}
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| 3 |
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{%- set toolcall_end_token = '</minimax:tool_call>' -%}
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| 4 |
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{#- Tool Rendering Functions ============================================== -#}
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| 5 |
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{%- macro render_tool_namespace(namespace_name, tool_list) -%}
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{%- for tool in tool_list -%}
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<tool>{{ tool.function | tojson(ensure_ascii=False) }}</tool>
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{% endfor -%}
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| 9 |
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{%- endmacro -%}
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{%- macro visible_text(content) -%}
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{%- if content is string -%}
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{{ content }}
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{%- elif content is iterable and content is not mapping -%}
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{%- for item in content -%}
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{%- if item is mapping and item.type == 'text' -%}
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{{- item.text }}
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{%- elif item is string -%}
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{{- item }}
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{%- endif -%}
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{%- endfor -%}
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{%- else -%}
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{{- content }}
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{%- endif -%}
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{%- endmacro -%}
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{#- System Message Construction ============================================ -#}
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{%- macro build_system_message(system_message) -%}
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{%- if system_message and system_message.content -%}
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{{- visible_text(system_message.content) }}
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| 29 |
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{%- else -%}
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{%- if model_identity is not defined -%}
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{%- set model_identity = "You are a helpful assistant. Your name is MiniMax-M2.7 and is built by MiniMax." -%}
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{%- endif -%}
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{{- model_identity }}
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{%- endif -%}
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{#- Handle current_date -#}
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{%- if system_message and system_message.current_date -%}
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{{- '\n' ~ 'Current date: ' + system_message.current_date }}
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{%- endif -%}
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{#- Handle current_location -#}
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{%- if system_message and system_message.current_location -%}
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{{- '\n' ~ 'Current location: ' + system_message.current_location }}
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{%- endif -%}
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{%- endmacro -%}
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{#- Main Template Logic ================================================= -#}
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{#- Extract system message (only first message if it's system) -#}
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{%- set system_message = none -%}
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{%- set conversation_messages = messages -%}
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{%- if messages and messages[0].role == "system" -%}
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{%- set system_message = messages[0] -%}
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{%- set conversation_messages = messages[1:] -%}
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{%- endif -%}
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{#- Get the last user message turn, for interleved thinking -#}
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{%- set ns = namespace(last_user_index=-1) %}
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{% for m in conversation_messages %}
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{%- if m.role == 'user' %}
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{% set ns.last_user_index = loop.index0 -%}
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{%- endif %}
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{%- endfor %}
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{#- Render system message -#}
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{{- ']~!b[' ~ ']~b]system' ~ '\n' }}
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{{- build_system_message(system_message) }}
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{#- Render tools if available -#}
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{%- if tools -%}
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{{- '\n\n' ~ '# Tools' ~ '\n' ~ 'You may call one or more tools to assist with the user query.\nHere are the tools available in JSONSchema format:' ~ '\n' }}
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{{- '\n' ~ '<tools>' ~ '\n' }}
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{{- render_tool_namespace("functions", tools) }}
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{{- '</tools>' ~ '\n\n' }}
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{{- 'When making tool calls, use XML format to invoke tools and pass parameters:' ~ '\n' }}
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{{- '\n' ~ toolcall_begin_token }}
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<invoke name="tool-name-1">
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<parameter name="param-key-1">param-value-1</parameter>
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<parameter name="param-key-2">param-value-2</parameter>
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...
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</invoke>
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{{- '\n' ~ toolcall_end_token }}
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{%- endif -%}
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{{- '[e~[\n' }}
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{#- Render messages -#}
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{%- set last_tool_call = namespace(name=none) -%}
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{%- for message in conversation_messages -%}
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{%- if message.role == 'assistant' -%}
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{#- Only render reasoning_content if no user message follows -#}
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{{- ']~b]ai' ~ '\n' }}
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{%- set reasoning_content = '' %}
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{%- set content = visible_text(message.content) %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].strip('\n').split('<think>')[-1].strip('\n') %}
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{%- set content = content.split('</think>')[-1].strip('\n') %}
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{%- endif %}
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| 96 |
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{%- endif %}
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{%- if reasoning_content and loop.index0 > ns.last_user_index -%}
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{{- '<think>' ~ '\n' ~ reasoning_content ~ '\n' ~ '</think>' ~ '\n\n' }}
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{%- endif -%}
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{%- if content -%}
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{{- content }}
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{%- endif -%}
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| 103 |
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{%- if message.tool_calls -%}
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| 104 |
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{{- '\n' ~ toolcall_begin_token ~ '\n' }}
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| 105 |
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{%- for tool_call in message.tool_calls -%}
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| 107 |
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{%- if tool_call.function %}
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| 108 |
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{%- set tool_call = tool_call.function %}
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| 109 |
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{%- endif %}
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| 110 |
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{{- '<invoke name="' + tool_call.name + '">' }}
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| 111 |
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{% set _args = tool_call.arguments %}
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| 112 |
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{%- for k, v in _args.items() %}
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| 113 |
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{{- '<parameter name="' + k + '">' }}
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| 114 |
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{{- v | tojson(ensure_ascii=False) if v is not string else v }}
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| 115 |
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{{- '</parameter>' }}
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| 116 |
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{% endfor %}
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| 117 |
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{{- '</invoke>' ~ '\n' }}
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| 118 |
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{%- endfor -%}
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| 119 |
+
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| 120 |
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{{- toolcall_end_token}}
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| 121 |
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{%- set last_tool_call.name = message.tool_calls[-1].name -%}
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| 122 |
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{%- else -%}
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| 123 |
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{%- set last_tool_call.name = none -%}
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| 124 |
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{%- endif -%}
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| 125 |
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{{- '[e~[' ~ '\n' }}
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| 126 |
+
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| 127 |
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{%- elif message.role == 'tool' -%}
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| 128 |
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{%- if last_tool_call.name is none -%}
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| 129 |
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{{- raise_exception("Message has tool role, but there was no previous assistant message with a tool call!") }}
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| 130 |
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{%- endif -%}
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| 131 |
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{%- if loop.first or (conversation_messages[loop.index0 - 1].role != 'tool') -%}
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| 132 |
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{{- ']~b]tool' }}
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| 133 |
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{%- endif -%}
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| 134 |
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{%- if message.content is string -%}
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| 135 |
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{{- '\n<response>' }}
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| 136 |
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{{- message.content }}
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| 137 |
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{{- '</response>' }}
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| 138 |
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{%- else -%}
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| 139 |
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{%- for tr in message.content -%}
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| 140 |
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{{- '\n<response>' }}
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| 141 |
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{{- tr.output if tr.output is defined else (tr.text if tr.type == 'text' and tr.text is defined else tr) }}
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| 142 |
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{{- '\n</response>' }}
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| 143 |
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{%- endfor -%}
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| 144 |
+
{%- endif -%}
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| 145 |
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{%- if loop.last or (conversation_messages[loop.index0 + 1].role != 'tool') -%}
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| 146 |
+
{{- '[e~[\n' -}}
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| 147 |
+
{%- endif -%}
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| 148 |
+
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| 149 |
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{%- elif message.role == 'user' -%}
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| 150 |
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{{- ']~b]user' ~ '\n' }}
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| 151 |
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{{- visible_text(message.content) }}
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| 152 |
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{{- '[e~[' ~ '\n' }}
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| 153 |
+
{%- endif -%}
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| 154 |
+
{%- endfor -%}
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| 155 |
+
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| 156 |
+
{#- Generation prompt -#}
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| 157 |
+
{%- if add_generation_prompt -%}
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| 158 |
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{{- ']~b]ai' ~ '\n' ~ '<think>' ~ '\n' }}
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| 159 |
+
{%- endif -%}
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config.json
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|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"MiniMaxM2ForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attn_type_list": [
|
| 6 |
+
1,
|
| 7 |
+
1,
|
| 8 |
+
1,
|
| 9 |
+
1,
|
| 10 |
+
1,
|
| 11 |
+
1,
|
| 12 |
+
1,
|
| 13 |
+
1,
|
| 14 |
+
1,
|
| 15 |
+
1,
|
| 16 |
+
1,
|
| 17 |
+
1,
|
| 18 |
+
1,
|
| 19 |
+
1,
|
| 20 |
+
1,
|
| 21 |
+
1,
|
| 22 |
+
1,
|
| 23 |
+
1,
|
| 24 |
+
1,
|
| 25 |
+
1,
|
| 26 |
+
1,
|
| 27 |
+
1,
|
| 28 |
+
1,
|
| 29 |
+
1,
|
| 30 |
+
1,
|
| 31 |
+
1,
|
| 32 |
+
1,
|
| 33 |
+
1,
|
| 34 |
+
1,
|
| 35 |
+
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| 2651 |
+
"group_size": 64,
|
| 2652 |
+
"bits": 8
|
| 2653 |
+
},
|
| 2654 |
+
"model.layers.45.block_sparse_moe.switch_mlp.down_proj": {
|
| 2655 |
+
"group_size": 64,
|
| 2656 |
+
"bits": 8
|
| 2657 |
+
},
|
| 2658 |
+
"model.layers.46.block_sparse_moe.switch_mlp.gate_proj": {
|
| 2659 |
+
"group_size": 64,
|
| 2660 |
+
"bits": 8
|
| 2661 |
+
},
|
| 2662 |
+
"model.layers.46.block_sparse_moe.switch_mlp.up_proj": {
|
| 2663 |
+
"group_size": 64,
|
| 2664 |
+
"bits": 8
|
| 2665 |
+
},
|
| 2666 |
+
"model.layers.46.block_sparse_moe.switch_mlp.down_proj": {
|
| 2667 |
+
"group_size": 64,
|
| 2668 |
+
"bits": 8
|
| 2669 |
+
},
|
| 2670 |
+
"model.layers.47.block_sparse_moe.switch_mlp.gate_proj": {
|
| 2671 |
+
"group_size": 64,
|
| 2672 |
+
"bits": 8
|
| 2673 |
+
},
|
| 2674 |
+
"model.layers.47.block_sparse_moe.switch_mlp.up_proj": {
|
| 2675 |
+
"group_size": 64,
|
| 2676 |
+
"bits": 8
|
| 2677 |
+
},
|
| 2678 |
+
"model.layers.47.block_sparse_moe.switch_mlp.down_proj": {
|
| 2679 |
+
"group_size": 64,
|
| 2680 |
+
"bits": 8
|
| 2681 |
+
},
|
| 2682 |
+
"model.layers.48.block_sparse_moe.switch_mlp.gate_proj": {
|
| 2683 |
+
"group_size": 64,
|
| 2684 |
+
"bits": 8
|
| 2685 |
+
},
|
| 2686 |
+
"model.layers.48.block_sparse_moe.switch_mlp.up_proj": {
|
| 2687 |
+
"group_size": 64,
|
| 2688 |
+
"bits": 8
|
| 2689 |
+
},
|
| 2690 |
+
"model.layers.48.block_sparse_moe.switch_mlp.down_proj": {
|
| 2691 |
+
"group_size": 64,
|
| 2692 |
+
"bits": 8
|
| 2693 |
+
},
|
| 2694 |
+
"model.layers.49.block_sparse_moe.switch_mlp.gate_proj": {
|
| 2695 |
+
"group_size": 64,
|
| 2696 |
+
"bits": 8
|
| 2697 |
+
},
|
| 2698 |
+
"model.layers.49.block_sparse_moe.switch_mlp.up_proj": {
|
| 2699 |
+
"group_size": 64,
|
| 2700 |
+
"bits": 8
|
| 2701 |
+
},
|
| 2702 |
+
"model.layers.49.block_sparse_moe.switch_mlp.down_proj": {
|
| 2703 |
+
"group_size": 64,
|
| 2704 |
+
"bits": 8
|
| 2705 |
+
},
|
| 2706 |
+
"model.layers.50.block_sparse_moe.switch_mlp.gate_proj": {
|
| 2707 |
+
"group_size": 64,
|
| 2708 |
+
"bits": 8
|
| 2709 |
+
},
|
| 2710 |
+
"model.layers.50.block_sparse_moe.switch_mlp.up_proj": {
|
| 2711 |
+
"group_size": 64,
|
| 2712 |
+
"bits": 8
|
| 2713 |
+
},
|
| 2714 |
+
"model.layers.50.block_sparse_moe.switch_mlp.down_proj": {
|
| 2715 |
+
"group_size": 64,
|
| 2716 |
+
"bits": 8
|
| 2717 |
+
},
|
| 2718 |
+
"model.layers.51.block_sparse_moe.switch_mlp.gate_proj": {
|
| 2719 |
+
"group_size": 64,
|
| 2720 |
+
"bits": 8
|
| 2721 |
+
},
|
| 2722 |
+
"model.layers.51.block_sparse_moe.switch_mlp.up_proj": {
|
| 2723 |
+
"group_size": 64,
|
| 2724 |
+
"bits": 8
|
| 2725 |
+
},
|
| 2726 |
+
"model.layers.51.block_sparse_moe.switch_mlp.down_proj": {
|
| 2727 |
+
"group_size": 64,
|
| 2728 |
+
"bits": 8
|
| 2729 |
+
},
|
| 2730 |
+
"model.layers.52.block_sparse_moe.switch_mlp.gate_proj": {
|
| 2731 |
+
"group_size": 64,
|
| 2732 |
+
"bits": 8
|
| 2733 |
+
},
|
| 2734 |
+
"model.layers.52.block_sparse_moe.switch_mlp.up_proj": {
|
| 2735 |
+
"group_size": 64,
|
| 2736 |
+
"bits": 8
|
| 2737 |
+
},
|
| 2738 |
+
"model.layers.52.block_sparse_moe.switch_mlp.down_proj": {
|
| 2739 |
+
"group_size": 64,
|
| 2740 |
+
"bits": 8
|
| 2741 |
+
},
|
| 2742 |
+
"model.layers.53.block_sparse_moe.switch_mlp.gate_proj": {
|
| 2743 |
+
"group_size": 64,
|
| 2744 |
+
"bits": 8
|
| 2745 |
+
},
|
| 2746 |
+
"model.layers.53.block_sparse_moe.switch_mlp.up_proj": {
|
| 2747 |
+
"group_size": 64,
|
| 2748 |
+
"bits": 8
|
| 2749 |
+
},
|
| 2750 |
+
"model.layers.53.block_sparse_moe.switch_mlp.down_proj": {
|
| 2751 |
+
"group_size": 64,
|
| 2752 |
+
"bits": 8
|
| 2753 |
+
},
|
| 2754 |
+
"model.layers.54.block_sparse_moe.switch_mlp.gate_proj": {
|
| 2755 |
+
"group_size": 64,
|
| 2756 |
+
"bits": 8
|
| 2757 |
+
},
|
| 2758 |
+
"model.layers.54.block_sparse_moe.switch_mlp.up_proj": {
|
| 2759 |
+
"group_size": 64,
|
| 2760 |
+
"bits": 8
|
| 2761 |
+
},
|
| 2762 |
+
"model.layers.54.block_sparse_moe.switch_mlp.down_proj": {
|
| 2763 |
+
"group_size": 64,
|
| 2764 |
+
"bits": 8
|
| 2765 |
+
},
|
| 2766 |
+
"model.layers.55.block_sparse_moe.switch_mlp.gate_proj": {
|
| 2767 |
+
"group_size": 64,
|
| 2768 |
+
"bits": 8
|
| 2769 |
+
},
|
| 2770 |
+
"model.layers.55.block_sparse_moe.switch_mlp.up_proj": {
|
| 2771 |
+
"group_size": 64,
|
| 2772 |
+
"bits": 8
|
| 2773 |
+
},
|
| 2774 |
+
"model.layers.55.block_sparse_moe.switch_mlp.down_proj": {
|
| 2775 |
+
"group_size": 64,
|
| 2776 |
+
"bits": 8
|
| 2777 |
+
},
|
| 2778 |
+
"model.layers.56.block_sparse_moe.switch_mlp.gate_proj": {
|
| 2779 |
+
"group_size": 64,
|
| 2780 |
+
"bits": 8
|
| 2781 |
+
},
|
| 2782 |
+
"model.layers.56.block_sparse_moe.switch_mlp.up_proj": {
|
| 2783 |
+
"group_size": 64,
|
| 2784 |
+
"bits": 8
|
| 2785 |
+
},
|
| 2786 |
+
"model.layers.56.block_sparse_moe.switch_mlp.down_proj": {
|
| 2787 |
+
"group_size": 64,
|
| 2788 |
+
"bits": 8
|
| 2789 |
+
},
|
| 2790 |
+
"model.layers.57.block_sparse_moe.switch_mlp.gate_proj": {
|
| 2791 |
+
"group_size": 64,
|
| 2792 |
+
"bits": 8
|
| 2793 |
+
},
|
| 2794 |
+
"model.layers.57.block_sparse_moe.switch_mlp.up_proj": {
|
| 2795 |
+
"group_size": 64,
|
| 2796 |
+
"bits": 8
|
| 2797 |
+
},
|
| 2798 |
+
"model.layers.57.block_sparse_moe.switch_mlp.down_proj": {
|
| 2799 |
+
"group_size": 64,
|
| 2800 |
+
"bits": 8
|
| 2801 |
+
},
|
| 2802 |
+
"model.layers.58.block_sparse_moe.switch_mlp.gate_proj": {
|
| 2803 |
+
"group_size": 64,
|
| 2804 |
+
"bits": 8
|
| 2805 |
+
},
|
| 2806 |
+
"model.layers.58.block_sparse_moe.switch_mlp.up_proj": {
|
| 2807 |
+
"group_size": 64,
|
| 2808 |
+
"bits": 8
|
| 2809 |
+
},
|
| 2810 |
+
"model.layers.58.block_sparse_moe.switch_mlp.down_proj": {
|
| 2811 |
+
"group_size": 64,
|
| 2812 |
+
"bits": 8
|
| 2813 |
+
},
|
| 2814 |
+
"model.layers.59.block_sparse_moe.switch_mlp.gate_proj": {
|
| 2815 |
+
"group_size": 64,
|
| 2816 |
+
"bits": 8
|
| 2817 |
+
},
|
| 2818 |
+
"model.layers.59.block_sparse_moe.switch_mlp.up_proj": {
|
| 2819 |
+
"group_size": 64,
|
| 2820 |
+
"bits": 8
|
| 2821 |
+
},
|
| 2822 |
+
"model.layers.59.block_sparse_moe.switch_mlp.down_proj": {
|
| 2823 |
+
"group_size": 64,
|
| 2824 |
+
"bits": 8
|
| 2825 |
+
},
|
| 2826 |
+
"model.layers.60.block_sparse_moe.switch_mlp.gate_proj": {
|
| 2827 |
+
"group_size": 64,
|
| 2828 |
+
"bits": 8
|
| 2829 |
+
},
|
| 2830 |
+
"model.layers.60.block_sparse_moe.switch_mlp.up_proj": {
|
| 2831 |
+
"group_size": 64,
|
| 2832 |
+
"bits": 8
|
| 2833 |
+
},
|
| 2834 |
+
"model.layers.60.block_sparse_moe.switch_mlp.down_proj": {
|
| 2835 |
+
"group_size": 64,
|
| 2836 |
+
"bits": 8
|
| 2837 |
+
},
|
| 2838 |
+
"model.layers.61.block_sparse_moe.switch_mlp.gate_proj": {
|
| 2839 |
+
"group_size": 64,
|
| 2840 |
+
"bits": 8
|
| 2841 |
+
},
|
| 2842 |
+
"model.layers.61.block_sparse_moe.switch_mlp.up_proj": {
|
| 2843 |
+
"group_size": 64,
|
| 2844 |
+
"bits": 8
|
| 2845 |
+
},
|
| 2846 |
+
"lm_head": {
|
| 2847 |
+
"group_size": 64,
|
| 2848 |
+
"bits": 8
|
| 2849 |
+
}
|
| 2850 |
+
},
|
| 2851 |
+
"rms_norm_eps": 1e-06,
|
| 2852 |
+
"rope_theta": 5000000,
|
| 2853 |
+
"rotary_dim": 64,
|
| 2854 |
+
"scoring_func": "sigmoid",
|
| 2855 |
+
"shared_intermediate_size": 0,
|
| 2856 |
+
"tie_word_embeddings": false,
|
| 2857 |
+
"transformers_version": "4.46.1",
|
| 2858 |
+
"use_cache": true,
|
| 2859 |
+
"use_mtp": true,
|
| 2860 |
+
"use_qk_norm": true,
|
| 2861 |
+
"use_routing_bias": true,
|
| 2862 |
+
"vocab_size": 200064
|
| 2863 |
+
}
|
configuration_minimax_m2.py
ADDED
|
@@ -0,0 +1,200 @@
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|
| 1 |
+
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
|
| 2 |
+
# This file was automatically generated from src/transformers/models/minimax_m2/modular_minimax_m2.py.
|
| 3 |
+
# Do NOT edit this file manually as any edits will be overwritten by the generation of
|
| 4 |
+
# the file from the modular. If any change should be done, please apply the change to the
|
| 5 |
+
# modular_minimax_m2.py file directly. One of our CI enforces this.
|
| 6 |
+
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
|
| 7 |
+
# coding=utf-8
|
| 8 |
+
# Copyright 2025 the HuggingFace Team. All rights reserved.
|
| 9 |
+
#
|
| 10 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 11 |
+
# you may not use this file except in compliance with the License.
|
| 12 |
+
# You may obtain a copy of the License at
|
| 13 |
+
#
|
| 14 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 15 |
+
#
|
| 16 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 17 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 18 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 19 |
+
# See the License for the specific language governing permissions and
|
| 20 |
+
# limitations under the License.
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
from transformers.configuration_utils import PretrainedConfig
|
| 24 |
+
|
| 25 |
+
|
| 26 |
+
class MiniMaxM2Config(PretrainedConfig):
|
| 27 |
+
r"""
|
| 28 |
+
This is the configuration class to store the configuration of a [`MiniMaxM2Model`]. It is used to instantiate an
|
| 29 |
+
MiniMaxM2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
|
| 30 |
+
with the defaults will yield a similar configuration to that of the MiniMaxM2-7B-v0.1 or MiniMaxM2-7B-Instruct-v0.1.
|
| 31 |
+
|
| 32 |
+
[minimax_m2ai/MiniMaxM2-8x7B](https://huggingface.co/minimax_m2ai/MiniMaxM2-8x7B)
|
| 33 |
+
[minimax_m2ai/MiniMaxM2-7B-Instruct-v0.1](https://huggingface.co/minimax_m2ai/MiniMaxM2-7B-Instruct-v0.1)
|
| 34 |
+
|
| 35 |
+
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
| 36 |
+
documentation from [`PretrainedConfig`] for more information.
|
| 37 |
+
|
| 38 |
+
|
| 39 |
+
Args:
|
| 40 |
+
vocab_size (`int`, *optional*, defaults to 32000):
|
| 41 |
+
Vocabulary size of the MiniMaxM2 model. Defines the number of different tokens that can be represented by the
|
| 42 |
+
`inputs_ids` passed when calling [`MiniMaxM2Model`]
|
| 43 |
+
hidden_size (`int`, *optional*, defaults to 4096):
|
| 44 |
+
Dimension of the hidden representations.
|
| 45 |
+
intermediate_size (`int`, *optional*, defaults to 14336):
|
| 46 |
+
Dimension of the MLP representations.
|
| 47 |
+
num_hidden_layers (`int`, *optional*, defaults to 32):
|
| 48 |
+
Number of hidden layers in the Transformer encoder.
|
| 49 |
+
num_attention_heads (`int`, *optional*, defaults to 32):
|
| 50 |
+
Number of attention heads for each attention layer in the Transformer encoder.
|
| 51 |
+
num_key_value_heads (`int`, *optional*, defaults to 8):
|
| 52 |
+
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
| 53 |
+
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
| 54 |
+
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
| 55 |
+
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
| 56 |
+
by meanpooling all the original heads within that group. For more details, check out [this
|
| 57 |
+
paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `8`.
|
| 58 |
+
head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
|
| 59 |
+
The attention head dimension.
|
| 60 |
+
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
| 61 |
+
The non-linear activation function (function or string) in the decoder.
|
| 62 |
+
max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
|
| 63 |
+
The maximum sequence length that this model might ever be used with. MiniMaxM2's sliding window attention
|
| 64 |
+
allows sequence of up to 4096*32 tokens.
|
| 65 |
+
initializer_range (`float`, *optional*, defaults to 0.02):
|
| 66 |
+
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
| 67 |
+
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
|
| 68 |
+
The epsilon used by the rms normalization layers.
|
| 69 |
+
use_cache (`bool`, *optional*, defaults to `True`):
|
| 70 |
+
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
| 71 |
+
relevant if `config.is_decoder=True`.
|
| 72 |
+
pad_token_id (`int`, *optional*):
|
| 73 |
+
The id of the padding token.
|
| 74 |
+
bos_token_id (`int`, *optional*, defaults to 1):
|
| 75 |
+
The id of the "beginning-of-sequence" token.
|
| 76 |
+
eos_token_id (`int`, *optional*, defaults to 2):
|
| 77 |
+
The id of the "end-of-sequence" token.
|
| 78 |
+
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
| 79 |
+
Whether the model's input and output word embeddings should be tied.
|
| 80 |
+
rope_theta (`float`, *optional*, defaults to 1000000.0):
|
| 81 |
+
The base period of the RoPE embeddings.
|
| 82 |
+
sliding_window (`int`, *optional*):
|
| 83 |
+
Sliding window attention window size. If not specified, will default to `4096`.
|
| 84 |
+
attention_dropout (`float`, *optional*, defaults to 0.0):
|
| 85 |
+
The dropout ratio for the attention probabilities.
|
| 86 |
+
num_experts_per_tok (`int`, *optional*, defaults to 2):
|
| 87 |
+
The number of experts to route per-token, can be also interpreted as the `top-k` routing
|
| 88 |
+
parameter
|
| 89 |
+
num_local_experts (`int`, *optional*, defaults to 8):
|
| 90 |
+
Number of experts per Sparse MLP layer.
|
| 91 |
+
output_router_logits (`bool`, *optional*, defaults to `False`):
|
| 92 |
+
Whether or not the router logits should be returned by the model. Enabling this will also
|
| 93 |
+
allow the model to output the auxiliary loss. See [here]() for more details
|
| 94 |
+
router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
|
| 95 |
+
The aux loss factor for the total loss.
|
| 96 |
+
router_jitter_noise (`float`, *optional*, defaults to 0.0):
|
| 97 |
+
Amount of noise to add to the router.
|
| 98 |
+
|
| 99 |
+
```python
|
| 100 |
+
>>> from transformers import MiniMaxM2Model, MiniMaxM2Config
|
| 101 |
+
|
| 102 |
+
>>> # Initializing a MiniMaxM2 7B style configuration
|
| 103 |
+
>>> configuration = MiniMaxM2Config()
|
| 104 |
+
|
| 105 |
+
>>> # Initializing a model from the MiniMaxM2 7B style configuration
|
| 106 |
+
>>> model = MiniMaxM2Model(configuration)
|
| 107 |
+
|
| 108 |
+
>>> # Accessing the model configuration
|
| 109 |
+
>>> configuration = model.config
|
| 110 |
+
```"""
|
| 111 |
+
|
| 112 |
+
model_type = "minimax_m2"
|
| 113 |
+
keys_to_ignore_at_inference = ["past_key_values"]
|
| 114 |
+
base_model_tp_plan = {
|
| 115 |
+
"layers.*.self_attn.q_proj": "colwise",
|
| 116 |
+
"layers.*.self_attn.k_proj": "colwise",
|
| 117 |
+
"layers.*.self_attn.v_proj": "colwise",
|
| 118 |
+
"layers.*.self_attn.o_proj": "rowwise",
|
| 119 |
+
"layers.*.block_sparse_moe.gate": "colwise_rep", # we need to replicate here to correctly route experts
|
| 120 |
+
"layers.*.block_sparse_moe.experts.*.w1": "colwise",
|
| 121 |
+
"layers.*.block_sparse_moe.experts.*.w2": "rowwise",
|
| 122 |
+
"layers.*.block_sparse_moe.experts.*.w3": "colwise",
|
| 123 |
+
}
|
| 124 |
+
base_model_pp_plan = {
|
| 125 |
+
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
|
| 126 |
+
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
|
| 127 |
+
"norm": (["hidden_states"], ["hidden_states"]),
|
| 128 |
+
}
|
| 129 |
+
|
| 130 |
+
def __init__(
|
| 131 |
+
self,
|
| 132 |
+
vocab_size=32000,
|
| 133 |
+
hidden_size=4096,
|
| 134 |
+
intermediate_size=14336,
|
| 135 |
+
num_hidden_layers=32,
|
| 136 |
+
num_attention_heads=32,
|
| 137 |
+
num_key_value_heads=8,
|
| 138 |
+
head_dim=None,
|
| 139 |
+
hidden_act="silu",
|
| 140 |
+
max_position_embeddings=4096 * 32,
|
| 141 |
+
initializer_range=0.02,
|
| 142 |
+
rms_norm_eps=1e-5,
|
| 143 |
+
use_cache=True,
|
| 144 |
+
pad_token_id=None,
|
| 145 |
+
bos_token_id=1,
|
| 146 |
+
eos_token_id=2,
|
| 147 |
+
tie_word_embeddings=False,
|
| 148 |
+
rope_theta=1e6,
|
| 149 |
+
sliding_window=None,
|
| 150 |
+
attention_dropout=0.0,
|
| 151 |
+
num_experts_per_tok=2,
|
| 152 |
+
num_local_experts=8,
|
| 153 |
+
output_router_logits=False,
|
| 154 |
+
router_aux_loss_coef=0.001,
|
| 155 |
+
router_jitter_noise=0.0,
|
| 156 |
+
**kwargs,
|
| 157 |
+
):
|
| 158 |
+
self.vocab_size = vocab_size
|
| 159 |
+
self.max_position_embeddings = max_position_embeddings
|
| 160 |
+
self.hidden_size = hidden_size
|
| 161 |
+
self.intermediate_size = intermediate_size
|
| 162 |
+
self.num_hidden_layers = num_hidden_layers
|
| 163 |
+
self.num_attention_heads = num_attention_heads
|
| 164 |
+
self.sliding_window = sliding_window
|
| 165 |
+
|
| 166 |
+
# for backward compatibility
|
| 167 |
+
if num_key_value_heads is None:
|
| 168 |
+
num_key_value_heads = num_attention_heads
|
| 169 |
+
|
| 170 |
+
self.num_key_value_heads = num_key_value_heads
|
| 171 |
+
self.hidden_act = hidden_act
|
| 172 |
+
self.initializer_range = initializer_range
|
| 173 |
+
self.rms_norm_eps = rms_norm_eps
|
| 174 |
+
self.use_cache = use_cache
|
| 175 |
+
self.rope_theta = rope_theta
|
| 176 |
+
self.attention_dropout = attention_dropout
|
| 177 |
+
self.head_dim = head_dim
|
| 178 |
+
|
| 179 |
+
self.num_experts_per_tok = num_experts_per_tok
|
| 180 |
+
self.num_local_experts = num_local_experts
|
| 181 |
+
self.output_router_logits = output_router_logits
|
| 182 |
+
self.router_aux_loss_coef = router_aux_loss_coef
|
| 183 |
+
self.router_jitter_noise = router_jitter_noise
|
| 184 |
+
|
| 185 |
+
self.use_qk_norm = kwargs.pop("use_qk_norm", False)
|
| 186 |
+
self.rotary_dim = kwargs.pop("rotary_dim", self.head_dim)
|
| 187 |
+
self.partial_rotary_factor = kwargs.pop("partial_rotary_factor", 1)
|
| 188 |
+
if self.head_dim is not None:
|
| 189 |
+
self.partial_rotary_factor = self.rotary_dim / self.head_dim
|
| 190 |
+
|
| 191 |
+
super().__init__(
|
| 192 |
+
pad_token_id=pad_token_id,
|
| 193 |
+
bos_token_id=bos_token_id,
|
| 194 |
+
eos_token_id=eos_token_id,
|
| 195 |
+
tie_word_embeddings=tie_word_embeddings,
|
| 196 |
+
**kwargs,
|
| 197 |
+
)
|
| 198 |
+
|
| 199 |
+
|
| 200 |
+
__all__ = ["MiniMaxM2Config"]
|
generation_config.json
ADDED
|
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token_id": 200019,
|
| 3 |
+
"do_sample": true,
|
| 4 |
+
"eos_token_id": 200020,
|
| 5 |
+
"temperature": 1.0,
|
| 6 |
+
"top_p": 0.95,
|
| 7 |
+
"top_k": 40,
|
| 8 |
+
"transformers_version": "4.46.1"
|
| 9 |
+
}
|
model-00001-of-00031.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
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|
|
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|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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|
| 3 |
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|
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ADDED
|
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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ADDED
|
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version https://git-lfs.github.com/spec/v1
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ADDED
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ADDED
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ADDED
|
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version https://git-lfs.github.com/spec/v1
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ADDED
|
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version https://git-lfs.github.com/spec/v1
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ADDED
|
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version https://git-lfs.github.com/spec/v1
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ADDED
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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model-00020-of-00031.safetensors
ADDED
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|
|
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|
|
|
|
|
|
|
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version https://git-lfs.github.com/spec/v1
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model-00021-of-00031.safetensors
ADDED
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|
|
|
|
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version https://git-lfs.github.com/spec/v1
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|
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ADDED
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
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version https://git-lfs.github.com/spec/v1
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|
model-00023-of-00031.safetensors
ADDED
|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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model.safetensors.index.json
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modeling_minimax_m2.py
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|
| 1 |
+
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
|
| 2 |
+
# This file was automatically generated from src/transformers/models/minimax_m2/modular_minimax_m2.py.
|
| 3 |
+
# Do NOT edit this file manually as any edits will be overwritten by the generation of
|
| 4 |
+
# the file from the modular. If any change should be done, please apply the change to the
|
| 5 |
+
# modular_minimax_m2.py file directly. One of our CI enforces this.
|
| 6 |
+
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
|
| 7 |
+
# coding=utf-8
|
| 8 |
+
# Copyright 2025 the HuggingFace Team. All rights reserved.
|
| 9 |
+
#
|
| 10 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 11 |
+
# you may not use this file except in compliance with the License.
|
| 12 |
+
# You may obtain a copy of the License at
|
| 13 |
+
#
|
| 14 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 15 |
+
#
|
| 16 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 17 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 18 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 19 |
+
# See the License for the specific language governing permissions and
|
| 20 |
+
# limitations under the License.
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
from collections.abc import Callable
|
| 24 |
+
from typing import Optional, Union, Unpack
|
| 25 |
+
|
| 26 |
+
import torch
|
| 27 |
+
from torch import nn
|
| 28 |
+
|
| 29 |
+
from transformers.activations import ACT2FN
|
| 30 |
+
from transformers.cache_utils import Cache, DynamicCache
|
| 31 |
+
from transformers.generation import GenerationMixin
|
| 32 |
+
from transformers.integrations import use_kernel_forward_from_hub
|
| 33 |
+
from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
|
| 34 |
+
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
|
| 35 |
+
from transformers.modeling_layers import (
|
| 36 |
+
GenericForQuestionAnswering,
|
| 37 |
+
GenericForSequenceClassification,
|
| 38 |
+
GenericForTokenClassification,
|
| 39 |
+
GradientCheckpointingLayer,
|
| 40 |
+
)
|
| 41 |
+
from transformers.modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
|
| 42 |
+
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
|
| 43 |
+
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
|
| 44 |
+
from transformers.utils import TransformersKwargs, auto_docstring, can_return_tuple
|
| 45 |
+
from transformers.utils.deprecation import deprecate_kwarg
|
| 46 |
+
from transformers.utils.generic import OutputRecorder, check_model_inputs
|
| 47 |
+
from .configuration_minimax_m2 import MiniMaxM2Config
|
| 48 |
+
|
| 49 |
+
|
| 50 |
+
class MiniMaxM2MLP(nn.Module):
|
| 51 |
+
def __init__(self, config: MiniMaxM2Config):
|
| 52 |
+
super().__init__()
|
| 53 |
+
self.ffn_dim = config.intermediate_size
|
| 54 |
+
self.hidden_dim = config.hidden_size
|
| 55 |
+
|
| 56 |
+
self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
|
| 57 |
+
self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
|
| 58 |
+
self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
|
| 59 |
+
|
| 60 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
| 61 |
+
|
| 62 |
+
def forward(self, hidden_states):
|
| 63 |
+
current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
|
| 64 |
+
current_hidden_states = self.w2(current_hidden_states)
|
| 65 |
+
return current_hidden_states
|
| 66 |
+
|
| 67 |
+
|
| 68 |
+
class MiniMaxM2Experts(nn.ModuleList):
|
| 69 |
+
"""
|
| 70 |
+
ModuleList of experts.
|
| 71 |
+
"""
|
| 72 |
+
|
| 73 |
+
def __init__(self, config: MiniMaxM2Config):
|
| 74 |
+
super().__init__()
|
| 75 |
+
self.top_k = config.num_experts_per_tok
|
| 76 |
+
self.num_experts = config.num_local_experts
|
| 77 |
+
for _ in range(self.num_experts):
|
| 78 |
+
self.append(MiniMaxM2MLP(config))
|
| 79 |
+
|
| 80 |
+
def forward(
|
| 81 |
+
self, hidden_states: torch.Tensor, top_k_index: torch.Tensor, top_k_weights: torch.Tensor
|
| 82 |
+
) -> torch.Tensor:
|
| 83 |
+
"""
|
| 84 |
+
Args:
|
| 85 |
+
hidden_states: (batch_size * sequence_length, hidden_dim)
|
| 86 |
+
selected_experts: (batch_size * sequence_length, top_k)
|
| 87 |
+
routing_weights: (batch_size * sequence_length, top_k)
|
| 88 |
+
Returns:
|
| 89 |
+
(batch_size * sequence_length, hidden_dim)
|
| 90 |
+
"""
|
| 91 |
+
final_hidden_states = torch.zeros_like(hidden_states)
|
| 92 |
+
expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts).permute(2, 1, 0)
|
| 93 |
+
|
| 94 |
+
expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
|
| 95 |
+
for expert_idx in expert_hit:
|
| 96 |
+
idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
|
| 97 |
+
current_state = hidden_states[None, top_x].reshape(-1, hidden_states.shape[-1])
|
| 98 |
+
current_hidden_states = self[expert_idx](current_state) * top_k_weights[top_x, idx, None]
|
| 99 |
+
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
|
| 100 |
+
return final_hidden_states
|
| 101 |
+
|
| 102 |
+
|
| 103 |
+
class MiniMaxM2SparseMoeBlock(nn.Module):
|
| 104 |
+
def __init__(self, config):
|
| 105 |
+
super().__init__()
|
| 106 |
+
self.top_k = config.num_experts_per_tok
|
| 107 |
+
self.jitter_noise = config.router_jitter_noise
|
| 108 |
+
self.gate = nn.Linear(config.hidden_size, config.num_local_experts, bias=False)
|
| 109 |
+
self.experts = MiniMaxM2Experts(config)
|
| 110 |
+
self.register_buffer("e_score_correction_bias", torch.zeros(config.num_local_experts))
|
| 111 |
+
|
| 112 |
+
def route_tokens_to_experts(self, router_logits):
|
| 113 |
+
routing_weights = torch.nn.functional.sigmoid(router_logits.float())
|
| 114 |
+
scores_for_choice = routing_weights + self.e_score_correction_bias
|
| 115 |
+
_, top_k_index = torch.topk(scores_for_choice, self.top_k, dim=-1, sorted=False)
|
| 116 |
+
top_k_weights = routing_weights.gather(1, top_k_index)
|
| 117 |
+
top_k_weights /= top_k_weights.sum(dim=-1, keepdim=True)
|
| 118 |
+
return top_k_index, top_k_weights.to(router_logits.dtype)
|
| 119 |
+
|
| 120 |
+
def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
|
| 121 |
+
batch_size, sequence_length, hidden_dim = hidden_states.shape
|
| 122 |
+
if self.training and self.jitter_noise > 0:
|
| 123 |
+
hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
|
| 124 |
+
hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
|
| 125 |
+
router_logits = self.gate(hidden_states)
|
| 126 |
+
top_k_index, top_k_weights = self.route_tokens_to_experts(router_logits)
|
| 127 |
+
hidden_states = self.experts(hidden_states, top_k_index, top_k_weights.to(hidden_states.dtype))
|
| 128 |
+
hidden_states = hidden_states.reshape(batch_size, sequence_length, hidden_dim)
|
| 129 |
+
return hidden_states, router_logits
|
| 130 |
+
|
| 131 |
+
|
| 132 |
+
@use_kernel_forward_from_hub("RMSNorm")
|
| 133 |
+
class MiniMaxM2RMSNorm(nn.Module):
|
| 134 |
+
def __init__(self, hidden_size, eps=1e-6):
|
| 135 |
+
"""
|
| 136 |
+
MiniMaxM2RMSNorm is equivalent to T5LayerNorm
|
| 137 |
+
"""
|
| 138 |
+
super().__init__()
|
| 139 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
| 140 |
+
self.variance_epsilon = eps
|
| 141 |
+
|
| 142 |
+
def forward(self, hidden_states):
|
| 143 |
+
input_dtype = hidden_states.dtype
|
| 144 |
+
hidden_states = hidden_states.to(torch.float32)
|
| 145 |
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
| 146 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
| 147 |
+
return self.weight * hidden_states.to(input_dtype)
|
| 148 |
+
|
| 149 |
+
def extra_repr(self):
|
| 150 |
+
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
|
| 151 |
+
|
| 152 |
+
|
| 153 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
| 154 |
+
"""
|
| 155 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
| 156 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
| 157 |
+
"""
|
| 158 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
| 159 |
+
if n_rep == 1:
|
| 160 |
+
return hidden_states
|
| 161 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
| 162 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
| 163 |
+
|
| 164 |
+
|
| 165 |
+
def eager_attention_forward(
|
| 166 |
+
module: nn.Module,
|
| 167 |
+
query: torch.Tensor,
|
| 168 |
+
key: torch.Tensor,
|
| 169 |
+
value: torch.Tensor,
|
| 170 |
+
attention_mask: Optional[torch.Tensor],
|
| 171 |
+
scaling: float,
|
| 172 |
+
dropout: float = 0.0,
|
| 173 |
+
**kwargs: Unpack[TransformersKwargs],
|
| 174 |
+
):
|
| 175 |
+
key_states = repeat_kv(key, module.num_key_value_groups)
|
| 176 |
+
value_states = repeat_kv(value, module.num_key_value_groups)
|
| 177 |
+
|
| 178 |
+
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
|
| 179 |
+
if attention_mask is not None:
|
| 180 |
+
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
| 181 |
+
attn_weights = attn_weights + causal_mask
|
| 182 |
+
|
| 183 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
|
| 184 |
+
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
|
| 185 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
| 186 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
| 187 |
+
|
| 188 |
+
return attn_output, attn_weights
|
| 189 |
+
|
| 190 |
+
|
| 191 |
+
def rotate_half(x):
|
| 192 |
+
"""Rotates half the hidden dims of the input."""
|
| 193 |
+
x1 = x[..., : x.shape[-1] // 2]
|
| 194 |
+
x2 = x[..., x.shape[-1] // 2 :]
|
| 195 |
+
return torch.cat((-x2, x1), dim=-1)
|
| 196 |
+
|
| 197 |
+
|
| 198 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
|
| 199 |
+
"""Applies Rotary Position Embedding to the query and key tensors.
|
| 200 |
+
|
| 201 |
+
Args:
|
| 202 |
+
q (`torch.Tensor`): The query tensor.
|
| 203 |
+
k (`torch.Tensor`): The key tensor.
|
| 204 |
+
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
| 205 |
+
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
| 206 |
+
position_ids (`torch.Tensor`, *optional*):
|
| 207 |
+
Deprecated and unused.
|
| 208 |
+
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
| 209 |
+
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
| 210 |
+
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
| 211 |
+
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
| 212 |
+
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
| 213 |
+
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
| 214 |
+
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
| 215 |
+
Returns:
|
| 216 |
+
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
| 217 |
+
"""
|
| 218 |
+
cos = cos.unsqueeze(unsqueeze_dim)
|
| 219 |
+
sin = sin.unsqueeze(unsqueeze_dim)
|
| 220 |
+
|
| 221 |
+
# Keep half or full tensor for later concatenation
|
| 222 |
+
rotary_dim = cos.shape[-1]
|
| 223 |
+
q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
|
| 224 |
+
k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]
|
| 225 |
+
|
| 226 |
+
# Apply rotary embeddings on the first half or full tensor
|
| 227 |
+
q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin)
|
| 228 |
+
k_embed = (k_rot * cos) + (rotate_half(k_rot) * sin)
|
| 229 |
+
|
| 230 |
+
# Concatenate back to full shape
|
| 231 |
+
q_embed = torch.cat([q_embed, q_pass], dim=-1)
|
| 232 |
+
k_embed = torch.cat([k_embed, k_pass], dim=-1)
|
| 233 |
+
return q_embed, k_embed
|
| 234 |
+
|
| 235 |
+
|
| 236 |
+
class MiniMaxM2Attention(nn.Module):
|
| 237 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
| 238 |
+
|
| 239 |
+
def __init__(self, config: MiniMaxM2Config, layer_idx: int):
|
| 240 |
+
super().__init__()
|
| 241 |
+
self.config = config
|
| 242 |
+
self.layer_idx = layer_idx
|
| 243 |
+
self.head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
|
| 244 |
+
self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
|
| 245 |
+
self.scaling = self.head_dim**-0.5
|
| 246 |
+
self.attention_dropout = config.attention_dropout
|
| 247 |
+
self.is_causal = True
|
| 248 |
+
self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
|
| 249 |
+
self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
|
| 250 |
+
self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
|
| 251 |
+
self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
|
| 252 |
+
|
| 253 |
+
self.use_qk_norm = config.use_qk_norm
|
| 254 |
+
if self.use_qk_norm:
|
| 255 |
+
self.q_norm = MiniMaxM2RMSNorm(self.head_dim * config.num_attention_heads, eps=config.rms_norm_eps)
|
| 256 |
+
self.k_norm = MiniMaxM2RMSNorm(self.head_dim * config.num_key_value_heads, eps=config.rms_norm_eps)
|
| 257 |
+
|
| 258 |
+
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
|
| 259 |
+
def forward(
|
| 260 |
+
self,
|
| 261 |
+
hidden_states: torch.Tensor,
|
| 262 |
+
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
| 263 |
+
attention_mask: Optional[torch.Tensor],
|
| 264 |
+
past_key_values: Optional[Cache] = None,
|
| 265 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 266 |
+
**kwargs: Unpack[FlashAttentionKwargs],
|
| 267 |
+
) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
|
| 268 |
+
input_shape = hidden_states.shape[:-1]
|
| 269 |
+
hidden_shape = (*input_shape, -1, self.head_dim)
|
| 270 |
+
|
| 271 |
+
query_states = self.q_proj(hidden_states)
|
| 272 |
+
key_states = self.k_proj(hidden_states)
|
| 273 |
+
value_states = self.v_proj(hidden_states)
|
| 274 |
+
|
| 275 |
+
if self.use_qk_norm: # main diff from Llama
|
| 276 |
+
query_states = self.q_norm(query_states)
|
| 277 |
+
key_states = self.k_norm(key_states)
|
| 278 |
+
|
| 279 |
+
key_states = key_states.view(hidden_shape)
|
| 280 |
+
query_states = query_states.view(hidden_shape)
|
| 281 |
+
value_states = value_states.view(hidden_shape)
|
| 282 |
+
|
| 283 |
+
query_states = query_states.transpose(1, 2)
|
| 284 |
+
key_states = key_states.transpose(1, 2)
|
| 285 |
+
value_states = value_states.transpose(1, 2)
|
| 286 |
+
|
| 287 |
+
cos, sin = position_embeddings
|
| 288 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
| 289 |
+
|
| 290 |
+
if past_key_values is not None:
|
| 291 |
+
# sin and cos are specific to RoPE models; position_ids needed for the static cache
|
| 292 |
+
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
| 293 |
+
key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
| 294 |
+
|
| 295 |
+
attention_interface: Callable = eager_attention_forward
|
| 296 |
+
if self.config._attn_implementation != "eager":
|
| 297 |
+
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
|
| 298 |
+
|
| 299 |
+
attn_output, attn_weights = attention_interface(
|
| 300 |
+
self,
|
| 301 |
+
query_states,
|
| 302 |
+
key_states,
|
| 303 |
+
value_states,
|
| 304 |
+
attention_mask,
|
| 305 |
+
dropout=0.0 if not self.training else self.attention_dropout,
|
| 306 |
+
scaling=self.scaling,
|
| 307 |
+
**kwargs,
|
| 308 |
+
)
|
| 309 |
+
|
| 310 |
+
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
| 311 |
+
attn_output = self.o_proj(attn_output)
|
| 312 |
+
return attn_output, attn_weights
|
| 313 |
+
|
| 314 |
+
|
| 315 |
+
class MiniMaxM2DecoderLayer(GradientCheckpointingLayer):
|
| 316 |
+
def __init__(self, config: MiniMaxM2Config, layer_idx: int):
|
| 317 |
+
super().__init__()
|
| 318 |
+
self.hidden_size = config.hidden_size
|
| 319 |
+
|
| 320 |
+
self.self_attn = MiniMaxM2Attention(config, layer_idx)
|
| 321 |
+
|
| 322 |
+
self.block_sparse_moe = MiniMaxM2SparseMoeBlock(config)
|
| 323 |
+
self.input_layernorm = MiniMaxM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 324 |
+
self.post_attention_layernorm = MiniMaxM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 325 |
+
|
| 326 |
+
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
|
| 327 |
+
def forward(
|
| 328 |
+
self,
|
| 329 |
+
hidden_states: torch.Tensor,
|
| 330 |
+
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
| 331 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 332 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 333 |
+
past_key_values: Optional[Cache] = None,
|
| 334 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 335 |
+
**kwargs: Unpack[TransformersKwargs],
|
| 336 |
+
) -> torch.FloatTensor:
|
| 337 |
+
residual = hidden_states
|
| 338 |
+
|
| 339 |
+
hidden_states = self.input_layernorm(hidden_states)
|
| 340 |
+
|
| 341 |
+
# Self Attention
|
| 342 |
+
hidden_states, _ = self.self_attn(
|
| 343 |
+
hidden_states=hidden_states,
|
| 344 |
+
position_embeddings=position_embeddings,
|
| 345 |
+
attention_mask=attention_mask,
|
| 346 |
+
position_ids=position_ids,
|
| 347 |
+
past_key_values=past_key_values,
|
| 348 |
+
cache_position=cache_position,
|
| 349 |
+
**kwargs,
|
| 350 |
+
)
|
| 351 |
+
hidden_states = residual + hidden_states
|
| 352 |
+
|
| 353 |
+
# Fully Connected
|
| 354 |
+
residual = hidden_states
|
| 355 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
| 356 |
+
hidden_states, _ = self.block_sparse_moe(hidden_states)
|
| 357 |
+
hidden_states = residual + hidden_states
|
| 358 |
+
|
| 359 |
+
return hidden_states
|
| 360 |
+
|
| 361 |
+
|
| 362 |
+
class MiniMaxM2RotaryEmbedding(nn.Module):
|
| 363 |
+
inv_freq: torch.Tensor # fix linting for `register_buffer`
|
| 364 |
+
|
| 365 |
+
def __init__(self, config: MiniMaxM2Config, device=None):
|
| 366 |
+
super().__init__()
|
| 367 |
+
# BC: "rope_type" was originally "type"
|
| 368 |
+
if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
|
| 369 |
+
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
|
| 370 |
+
else:
|
| 371 |
+
self.rope_type = "default"
|
| 372 |
+
self.max_seq_len_cached = config.max_position_embeddings
|
| 373 |
+
self.original_max_seq_len = config.max_position_embeddings
|
| 374 |
+
|
| 375 |
+
self.config = config
|
| 376 |
+
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
|
| 377 |
+
|
| 378 |
+
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
|
| 379 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
| 380 |
+
self.original_inv_freq = self.inv_freq
|
| 381 |
+
|
| 382 |
+
@torch.no_grad()
|
| 383 |
+
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
|
| 384 |
+
def forward(self, x, position_ids):
|
| 385 |
+
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
|
| 386 |
+
position_ids_expanded = position_ids[:, None, :].float()
|
| 387 |
+
|
| 388 |
+
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
|
| 389 |
+
with torch.autocast(device_type=device_type, enabled=False): # Force float32
|
| 390 |
+
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
| 391 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
| 392 |
+
cos = emb.cos() * self.attention_scaling
|
| 393 |
+
sin = emb.sin() * self.attention_scaling
|
| 394 |
+
|
| 395 |
+
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
| 396 |
+
|
| 397 |
+
|
| 398 |
+
@auto_docstring
|
| 399 |
+
class MiniMaxM2PreTrainedModel(PreTrainedModel):
|
| 400 |
+
config: MiniMaxM2Config
|
| 401 |
+
base_model_prefix = "model"
|
| 402 |
+
supports_gradient_checkpointing = True
|
| 403 |
+
_no_split_modules = ["MiniMaxM2DecoderLayer"]
|
| 404 |
+
_skip_keys_device_placement = ["past_key_values"]
|
| 405 |
+
_supports_flash_attn = True
|
| 406 |
+
_supports_sdpa = True
|
| 407 |
+
_supports_flex_attn = True
|
| 408 |
+
_can_compile_fullgraph = False # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
|
| 409 |
+
_supports_attention_backend = True
|
| 410 |
+
_can_record_outputs = {
|
| 411 |
+
"router_logits": OutputRecorder(MiniMaxM2SparseMoeBlock, index=1),
|
| 412 |
+
"hidden_states": MiniMaxM2DecoderLayer,
|
| 413 |
+
"attentions": MiniMaxM2Attention,
|
| 414 |
+
}
|
| 415 |
+
|
| 416 |
+
|
| 417 |
+
@auto_docstring
|
| 418 |
+
class MiniMaxM2Model(MiniMaxM2PreTrainedModel):
|
| 419 |
+
def __init__(self, config: MiniMaxM2Config):
|
| 420 |
+
super().__init__(config)
|
| 421 |
+
self.padding_idx = config.pad_token_id
|
| 422 |
+
self.vocab_size = config.vocab_size
|
| 423 |
+
|
| 424 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
| 425 |
+
self.layers = nn.ModuleList(
|
| 426 |
+
[MiniMaxM2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
| 427 |
+
)
|
| 428 |
+
self.norm = MiniMaxM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
| 429 |
+
self.rotary_emb = MiniMaxM2RotaryEmbedding(config=config)
|
| 430 |
+
self.gradient_checkpointing = False
|
| 431 |
+
|
| 432 |
+
# Initialize weights and apply final processing
|
| 433 |
+
self.post_init()
|
| 434 |
+
|
| 435 |
+
@check_model_inputs
|
| 436 |
+
@auto_docstring
|
| 437 |
+
def forward(
|
| 438 |
+
self,
|
| 439 |
+
input_ids: Optional[torch.LongTensor] = None,
|
| 440 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 441 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 442 |
+
past_key_values: Optional[Cache] = None,
|
| 443 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 444 |
+
use_cache: Optional[bool] = None,
|
| 445 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 446 |
+
**kwargs: Unpack[TransformersKwargs],
|
| 447 |
+
) -> MoeModelOutputWithPast:
|
| 448 |
+
if (input_ids is None) ^ (inputs_embeds is not None):
|
| 449 |
+
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
| 450 |
+
|
| 451 |
+
if use_cache and past_key_values is None:
|
| 452 |
+
past_key_values = DynamicCache(config=self.config)
|
| 453 |
+
|
| 454 |
+
if inputs_embeds is None:
|
| 455 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
| 456 |
+
|
| 457 |
+
if cache_position is None:
|
| 458 |
+
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
| 459 |
+
cache_position = torch.arange(
|
| 460 |
+
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
| 461 |
+
)
|
| 462 |
+
if position_ids is None:
|
| 463 |
+
position_ids = cache_position.unsqueeze(0)
|
| 464 |
+
|
| 465 |
+
mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask
|
| 466 |
+
causal_mask = mask_function(
|
| 467 |
+
config=self.config,
|
| 468 |
+
input_embeds=inputs_embeds,
|
| 469 |
+
attention_mask=attention_mask,
|
| 470 |
+
cache_position=cache_position,
|
| 471 |
+
past_key_values=past_key_values,
|
| 472 |
+
position_ids=position_ids,
|
| 473 |
+
)
|
| 474 |
+
|
| 475 |
+
hidden_states = inputs_embeds
|
| 476 |
+
|
| 477 |
+
# create position embeddings to be shared across the decoder layers
|
| 478 |
+
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
| 479 |
+
|
| 480 |
+
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
| 481 |
+
hidden_states = decoder_layer(
|
| 482 |
+
hidden_states,
|
| 483 |
+
position_embeddings=position_embeddings,
|
| 484 |
+
attention_mask=causal_mask,
|
| 485 |
+
position_ids=position_ids,
|
| 486 |
+
past_key_values=past_key_values,
|
| 487 |
+
use_cache=use_cache,
|
| 488 |
+
cache_position=cache_position,
|
| 489 |
+
**kwargs,
|
| 490 |
+
)
|
| 491 |
+
|
| 492 |
+
hidden_states = self.norm(hidden_states)
|
| 493 |
+
|
| 494 |
+
return MoeModelOutputWithPast( # only diff with Mistral is the output type, we need MoE
|
| 495 |
+
last_hidden_state=hidden_states,
|
| 496 |
+
past_key_values=past_key_values,
|
| 497 |
+
)
|
| 498 |
+
|
| 499 |
+
|
| 500 |
+
def load_balancing_loss_func(
|
| 501 |
+
gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
|
| 502 |
+
num_experts: Optional[int] = None,
|
| 503 |
+
top_k=2,
|
| 504 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 505 |
+
) -> Union[torch.Tensor, int]:
|
| 506 |
+
r"""
|
| 507 |
+
Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
|
| 508 |
+
|
| 509 |
+
See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
|
| 510 |
+
function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
|
| 511 |
+
experts is too unbalanced.
|
| 512 |
+
|
| 513 |
+
Args:
|
| 514 |
+
gate_logits:
|
| 515 |
+
Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
|
| 516 |
+
shape [batch_size X sequence_length, num_experts].
|
| 517 |
+
num_experts:
|
| 518 |
+
Number of experts
|
| 519 |
+
top_k:
|
| 520 |
+
The number of experts to route per-token, can be also interpreted as the `top-k` routing
|
| 521 |
+
parameter.
|
| 522 |
+
attention_mask (`torch.Tensor`, *optional*):
|
| 523 |
+
The attention_mask used in forward function
|
| 524 |
+
shape [batch_size X sequence_length] if not None.
|
| 525 |
+
|
| 526 |
+
Returns:
|
| 527 |
+
The auxiliary loss.
|
| 528 |
+
"""
|
| 529 |
+
if gate_logits is None or not isinstance(gate_logits, tuple):
|
| 530 |
+
return 0
|
| 531 |
+
|
| 532 |
+
if isinstance(gate_logits, tuple):
|
| 533 |
+
compute_device = gate_logits[0].device
|
| 534 |
+
concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
|
| 535 |
+
|
| 536 |
+
routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
|
| 537 |
+
|
| 538 |
+
_, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
|
| 539 |
+
|
| 540 |
+
expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
|
| 541 |
+
|
| 542 |
+
if attention_mask is None:
|
| 543 |
+
# Compute the percentage of tokens routed to each experts
|
| 544 |
+
tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
|
| 545 |
+
|
| 546 |
+
# Compute the average probability of routing to these experts
|
| 547 |
+
router_prob_per_expert = torch.mean(routing_weights, dim=0)
|
| 548 |
+
else:
|
| 549 |
+
batch_size, sequence_length = attention_mask.shape
|
| 550 |
+
num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
|
| 551 |
+
|
| 552 |
+
# Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
|
| 553 |
+
expert_attention_mask = (
|
| 554 |
+
attention_mask[None, :, :, None, None]
|
| 555 |
+
.expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
|
| 556 |
+
.reshape(-1, top_k, num_experts)
|
| 557 |
+
.to(compute_device)
|
| 558 |
+
)
|
| 559 |
+
|
| 560 |
+
# Compute the percentage of tokens routed to each experts
|
| 561 |
+
tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
|
| 562 |
+
expert_attention_mask, dim=0
|
| 563 |
+
)
|
| 564 |
+
|
| 565 |
+
# Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
|
| 566 |
+
router_per_expert_attention_mask = (
|
| 567 |
+
attention_mask[None, :, :, None]
|
| 568 |
+
.expand((num_hidden_layers, batch_size, sequence_length, num_experts))
|
| 569 |
+
.reshape(-1, num_experts)
|
| 570 |
+
.to(compute_device)
|
| 571 |
+
)
|
| 572 |
+
|
| 573 |
+
# Compute the average probability of routing to these experts
|
| 574 |
+
router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
|
| 575 |
+
router_per_expert_attention_mask, dim=0
|
| 576 |
+
)
|
| 577 |
+
|
| 578 |
+
overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
|
| 579 |
+
return overall_loss * num_experts
|
| 580 |
+
|
| 581 |
+
|
| 582 |
+
@auto_docstring
|
| 583 |
+
class MiniMaxM2ForCausalLM(MiniMaxM2PreTrainedModel, GenerationMixin):
|
| 584 |
+
_tied_weights_keys = ["lm_head.weight"]
|
| 585 |
+
_tp_plan = {"lm_head": "colwise_rep"}
|
| 586 |
+
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
| 587 |
+
|
| 588 |
+
def __init__(self, config):
|
| 589 |
+
super().__init__(config)
|
| 590 |
+
self.model = MiniMaxM2Model(config)
|
| 591 |
+
self.vocab_size = config.vocab_size
|
| 592 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
| 593 |
+
self.router_aux_loss_coef = config.router_aux_loss_coef
|
| 594 |
+
self.num_experts = config.num_local_experts
|
| 595 |
+
self.num_experts_per_tok = config.num_experts_per_tok
|
| 596 |
+
|
| 597 |
+
# Initialize weights and apply final processing
|
| 598 |
+
self.post_init()
|
| 599 |
+
|
| 600 |
+
@can_return_tuple
|
| 601 |
+
@auto_docstring
|
| 602 |
+
def forward(
|
| 603 |
+
self,
|
| 604 |
+
input_ids: Optional[torch.LongTensor] = None,
|
| 605 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 606 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 607 |
+
past_key_values: Optional[Cache] = None,
|
| 608 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 609 |
+
labels: Optional[torch.LongTensor] = None,
|
| 610 |
+
use_cache: Optional[bool] = None,
|
| 611 |
+
output_router_logits: Optional[bool] = None,
|
| 612 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 613 |
+
logits_to_keep: Union[int, torch.Tensor] = 0,
|
| 614 |
+
**kwargs: Unpack[TransformersKwargs],
|
| 615 |
+
) -> MoeCausalLMOutputWithPast:
|
| 616 |
+
r"""
|
| 617 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 618 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
| 619 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
| 620 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
| 621 |
+
|
| 622 |
+
Example:
|
| 623 |
+
|
| 624 |
+
```python
|
| 625 |
+
>>> from transformers import AutoTokenizer, MiniMaxM2ForCausalLM
|
| 626 |
+
|
| 627 |
+
>>> model = MiniMaxM2ForCausalLM.from_pretrained("mistralai/MiniMaxM2-8x7B-v0.1")
|
| 628 |
+
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/MiniMaxM2-8x7B-v0.1")
|
| 629 |
+
|
| 630 |
+
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
| 631 |
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
| 632 |
+
|
| 633 |
+
>>> # Generate
|
| 634 |
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
| 635 |
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
| 636 |
+
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
| 637 |
+
```"""
|
| 638 |
+
|
| 639 |
+
output_router_logits = (
|
| 640 |
+
output_router_logits if output_router_logits is not None else self.config.output_router_logits
|
| 641 |
+
)
|
| 642 |
+
|
| 643 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
| 644 |
+
outputs: MoeModelOutputWithPast = self.model(
|
| 645 |
+
input_ids=input_ids,
|
| 646 |
+
attention_mask=attention_mask,
|
| 647 |
+
position_ids=position_ids,
|
| 648 |
+
past_key_values=past_key_values,
|
| 649 |
+
inputs_embeds=inputs_embeds,
|
| 650 |
+
use_cache=use_cache,
|
| 651 |
+
output_router_logits=output_router_logits,
|
| 652 |
+
cache_position=cache_position,
|
| 653 |
+
**kwargs,
|
| 654 |
+
)
|
| 655 |
+
|
| 656 |
+
hidden_states = outputs.last_hidden_state
|
| 657 |
+
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
| 658 |
+
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
| 659 |
+
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
| 660 |
+
|
| 661 |
+
loss = None
|
| 662 |
+
if labels is not None:
|
| 663 |
+
loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
|
| 664 |
+
|
| 665 |
+
aux_loss = None
|
| 666 |
+
if output_router_logits:
|
| 667 |
+
aux_loss = load_balancing_loss_func(
|
| 668 |
+
outputs.router_logits,
|
| 669 |
+
self.num_experts,
|
| 670 |
+
self.num_experts_per_tok,
|
| 671 |
+
attention_mask,
|
| 672 |
+
)
|
| 673 |
+
if labels is not None:
|
| 674 |
+
loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
|
| 675 |
+
|
| 676 |
+
return MoeCausalLMOutputWithPast(
|
| 677 |
+
loss=loss,
|
| 678 |
+
aux_loss=aux_loss,
|
| 679 |
+
logits=logits,
|
| 680 |
+
past_key_values=outputs.past_key_values,
|
| 681 |
+
hidden_states=outputs.hidden_states,
|
| 682 |
+
attentions=outputs.attentions,
|
| 683 |
+
router_logits=outputs.router_logits,
|
| 684 |
+
)
|
| 685 |
+
|
| 686 |
+
|
| 687 |
+
class MiniMaxM2ForSequenceClassification(GenericForSequenceClassification, MiniMaxM2PreTrainedModel):
|
| 688 |
+
pass
|
| 689 |
+
|
| 690 |
+
|
| 691 |
+
class MiniMaxM2ForTokenClassification(GenericForTokenClassification, MiniMaxM2PreTrainedModel):
|
| 692 |
+
pass
|
| 693 |
+
|
| 694 |
+
|
| 695 |
+
class MiniMaxM2ForQuestionAnswering(GenericForQuestionAnswering, MiniMaxM2PreTrainedModel):
|
| 696 |
+
pass
|
| 697 |
+
|
| 698 |
+
|
| 699 |
+
__all__ = [
|
| 700 |
+
"MiniMaxM2ForCausalLM",
|
| 701 |
+
"MiniMaxM2ForQuestionAnswering",
|
| 702 |
+
"MiniMaxM2Model",
|
| 703 |
+
"MiniMaxM2PreTrainedModel",
|
| 704 |
+
"MiniMaxM2ForSequenceClassification",
|
| 705 |
+
"MiniMaxM2ForTokenClassification",
|
| 706 |
+
]
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:2b0639a138c0ccae63bce932b102cd4e1a344a9b7374cc3a9d42512592ddb52f
|
| 3 |
+
size 15522577
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,66 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"backend": "tokenizers",
|
| 4 |
+
"bos_token": "]~!b[",
|
| 5 |
+
"clean_up_tokenization_spaces": false,
|
| 6 |
+
"eos_token": "[e~[",
|
| 7 |
+
"errors": "replace",
|
| 8 |
+
"extra_special_tokens": [
|
| 9 |
+
"<code_interpreter>",
|
| 10 |
+
"<commit_after>",
|
| 11 |
+
"<commit_before>",
|
| 12 |
+
"<commit_msg>",
|
| 13 |
+
"<empty_output>",
|
| 14 |
+
"<filename>",
|
| 15 |
+
"<fim_middle>",
|
| 16 |
+
"<fim_pad>",
|
| 17 |
+
"<fim_prefix>",
|
| 18 |
+
"<fim_suffix>",
|
| 19 |
+
"<function_call>",
|
| 20 |
+
"<gh_stars>",
|
| 21 |
+
"]<]speech[>[",
|
| 22 |
+
"]<]image[>[",
|
| 23 |
+
"]<]video[>[",
|
| 24 |
+
"]<]start of speech[>[",
|
| 25 |
+
"]<]end of speech[>[",
|
| 26 |
+
"]<]start of image[>[",
|
| 27 |
+
"]<]end of image[>[",
|
| 28 |
+
"]<]start of video[>[",
|
| 29 |
+
"]<]end of video[>[",
|
| 30 |
+
"]<]vision pad[>[",
|
| 31 |
+
"]~!b[",
|
| 32 |
+
"<issue_closed>",
|
| 33 |
+
"<issue_comment>",
|
| 34 |
+
"<issue_start>",
|
| 35 |
+
"<jupyter_code>",
|
| 36 |
+
"<jupyter_output>",
|
| 37 |
+
"<jupyter_start>",
|
| 38 |
+
"<jupyter_text>",
|
| 39 |
+
"<reponame>",
|
| 40 |
+
"[e~[",
|
| 41 |
+
"]!d~[",
|
| 42 |
+
"]!p~[",
|
| 43 |
+
"]~b]",
|
| 44 |
+
"<jupyter_error>",
|
| 45 |
+
"<add_file>",
|
| 46 |
+
"<delete_file>",
|
| 47 |
+
"<rename_file>",
|
| 48 |
+
"<edit_file>",
|
| 49 |
+
"<commit_message>",
|
| 50 |
+
"<empty_source_file>",
|
| 51 |
+
"<repo_struct>",
|
| 52 |
+
"<code_context>",
|
| 53 |
+
"<file_content>",
|
| 54 |
+
"<source_files>",
|
| 55 |
+
"<pr_start>",
|
| 56 |
+
"<review_comment>",
|
| 57 |
+
"<filepath>",
|
| 58 |
+
"<file_sep>"
|
| 59 |
+
],
|
| 60 |
+
"is_local": true,
|
| 61 |
+
"model_max_length": 40960000,
|
| 62 |
+
"pad_token": null,
|
| 63 |
+
"tokenizer_class": "GPT2Tokenizer",
|
| 64 |
+
"tool_parser_type": "minimax_m2",
|
| 65 |
+
"unk_token": "]!d~["
|
| 66 |
+
}
|