49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183 | class ModelScopeLLM(CustomLLM):
"""ModelScope LLM."""
model_name: str = Field(
default=DEFAULT_MODELSCOPE_MODEL,
description=(
"The model name to use from ModelScope. "
"Unused if `model` is passed in directly."
),
)
model_revision: str = Field(
default=DEFAULT_MODELSCOPE_MODEL_REVISION,
description=(
"The model revision to use from ModelScope. "
"Unused if `model` is passed in directly."
),
)
task_name: str = Field(
default=DEFAULT_MODELSCOPE_TASK,
description=("The ModelScope task type, for llm use default chat."),
)
dtype: str = Field(
default=DEFAULT_MODELSCOPE_DTYPE,
description=("The ModelScope task type, for llm use default chat."),
)
context_window: int = Field(
default=DEFAULT_CONTEXT_WINDOW,
description="The maximum number of tokens available for input.",
gt=0,
)
max_new_tokens: int = Field(
default=DEFAULT_NUM_OUTPUTS,
description="The maximum number of tokens to generate.",
gt=0,
)
system_prompt: str = Field(
default="",
description=(
"The system prompt, containing any extra instructions or context. "
"The model card on ModelScope should specify if this is needed."
),
)
query_wrapper_prompt: PromptTemplate = Field(
default=PromptTemplate("{query_str}"),
description=(
"The query wrapper prompt, containing the query placeholder. "
"The model card on ModelScope should specify if this is needed. "
"Should contain a `{query_str}` placeholder."
),
)
device_map: str = Field(
default="auto", description="The device_map to use. Defaults to 'auto'."
)
tokenizer_kwargs: dict = Field(
default_factory=dict, description="The kwargs to pass to the tokenizer."
)
model_kwargs: dict = Field(
default_factory=dict,
description="The kwargs to pass to the model during initialization.",
)
generate_kwargs: dict = Field(
default_factory=dict,
description="The kwargs to pass to the model during generation.",
)
_pipeline: Any = PrivateAttr()
def __init__(
self,
model_name: str = DEFAULT_MODELSCOPE_MODEL,
model_revision: str = DEFAULT_MODELSCOPE_MODEL_REVISION,
task_name: str = DEFAULT_MODELSCOPE_TASK,
dtype: str = DEFAULT_MODELSCOPE_DTYPE,
model: Optional[Any] = None,
device_map: Optional[str] = "auto",
model_kwargs: Optional[dict] = None,
generate_kwargs: Optional[dict] = None,
callback_manager: Optional[CallbackManager] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
) -> None:
"""Initialize params."""
model_kwargs = model_kwargs or {}
if model:
pipeline = model
else:
pipeline = pipeline_builder(
task=task_name,
model=model_name,
model_revision=model_revision,
llm_first=True,
torch_dtype=_STR_DTYPE_TO_TORCH_DTYPE[dtype],
device_map=device_map,
)
super().__init__(
model_kwargs=model_kwargs or {},
generate_kwargs=generate_kwargs or {},
callback_manager=callback_manager,
pydantic_program_mode=pydantic_program_mode,
)
self._pipeline = pipeline
@classmethod
def class_name(cls) -> str:
return "ModelScope_LLM"
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
context_window=self.context_window,
num_output=self.max_new_tokens,
model_name=self.model_name,
is_chat_model=False,
)
@llm_completion_callback()
def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
return text_to_completion_response(self._pipeline(prompt, **kwargs))
@llm_completion_callback()
def stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen:
yield self.complete(prompt, **kwargs)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
return modelscope_message_to_chat_response(
self._pipeline(chat_message_to_modelscope_messages(messages), **kwargs)
)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
yield self.chat(messages, **kwargs)
|
