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558 | class IpexLLM(CustomLLM):
r"""IPEX-LLM.
Example:
.. code-block:: python
from llama_index.llms.ipex_llm import IpexLLM
llm = IpexLLM(model_path="/path/to/llama/model")
"""
model_name: str = Field(
default=DEFAULT_HUGGINGFACE_MODEL,
description=(
"The model name to use from HuggingFace. "
"Unused if `model` is passed in directly."
),
)
load_in_4bit: bool = Field(
default=True,
description=(
"Whether to load model in 4bit." "Unused if `load_in_low_bit` is not None."
),
)
load_in_low_bit: str = Field(
default=None,
description=(
"Which low bit precisions to use when loading model. "
"Example values: 'sym_int4', 'asym_int4', 'fp4', 'nf4', 'fp8', etc."
"Will override `load_in_4bit` if this is specified."
),
)
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,
)
tokenizer_name: str = Field(
default=DEFAULT_HUGGINGFACE_MODEL,
description=(
"The name of the tokenizer to use from HuggingFace. "
"Unused if `tokenizer` is passed in directly."
),
)
device_map: str = Field(
default="auto", description="The device_map to use. Defaults to 'auto'."
)
stopping_ids: List[int] = Field(
default_factory=list,
description=(
"The stopping ids to use. "
"Generation stops when these token IDs are predicted."
),
)
tokenizer_outputs_to_remove: list = Field(
default_factory=list,
description=(
"The outputs to remove from the tokenizer. "
"Sometimes huggingface tokenizers return extra inputs that cause errors."
),
)
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.",
)
is_chat_model: bool = Field(
default=False,
description=(
LLMMetadata.__fields__["is_chat_model"].field_info.description
+ " Be sure to verify that you either pass an appropriate tokenizer "
"that can convert prompts to properly formatted chat messages or a "
"`messages_to_prompt` that does so."
),
)
_model: Any = PrivateAttr()
_tokenizer: Any = PrivateAttr()
_stopping_criteria: Any = PrivateAttr()
def __init__(
self,
context_window: int = DEFAULT_CONTEXT_WINDOW,
max_new_tokens: int = DEFAULT_NUM_OUTPUTS,
tokenizer_name: str = DEFAULT_HUGGINGFACE_MODEL,
model_name: str = DEFAULT_HUGGINGFACE_MODEL,
load_in_4bit: Optional[bool] = True,
load_in_low_bit: Optional[str] = None,
model: Optional[Any] = None,
tokenizer: Optional[Any] = None,
device_map: Optional[str] = "auto",
stopping_ids: Optional[List[int]] = None,
tokenizer_kwargs: Optional[dict] = None,
tokenizer_outputs_to_remove: Optional[list] = None,
model_kwargs: Optional[dict] = None,
generate_kwargs: Optional[dict] = None,
is_chat_model: Optional[bool] = False,
callback_manager: Optional[CallbackManager] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
low_bit_model: Optional[bool] = False,
) -> None:
"""
Construct IpexLLM.
Args:
context_window: The maximum number of tokens available for input.
max_new_tokens: The maximum number of tokens to generate.
tokenizer_name: The name of the tokenizer to use from HuggingFace.
Unused if `tokenizer` is passed in directly.
model_name: The model name to use from HuggingFace.
Unused if `model` is passed in directly.
model: The HuggingFace model.
tokenizer: The tokenizer.
device_map: The device_map to use. Defaults to 'auto'.
stopping_ids: The stopping ids to use.
Generation stops when these token IDs are predicted.
tokenizer_kwargs: The kwargs to pass to the tokenizer.
tokenizer_outputs_to_remove: The outputs to remove from the tokenizer.
Sometimes huggingface tokenizers return extra inputs that cause errors.
model_kwargs: The kwargs to pass to the model during initialization.
generate_kwargs: The kwargs to pass to the model during generation.
is_chat_model: Whether the model is `chat`
callback_manager: Callback manager.
messages_to_prompt: Function to convert messages to prompt.
completion_to_prompt: Function to convert messages to prompt.
pydantic_program_mode: DEFAULT.
output_parser: BaseOutputParser.
Returns:
None.
"""
model_kwargs = model_kwargs or {}
if model:
self._model = model
else:
self._model = self._load_model(
low_bit_model, load_in_4bit, load_in_low_bit, model_name, model_kwargs
)
if "xpu" in device_map:
self._model = self._model.to(device_map)
# check context_window
config_dict = self._model.config.to_dict()
model_context_window = int(
config_dict.get("max_position_embeddings", context_window)
)
if model_context_window and model_context_window < context_window:
logger.warning(
f"Supplied context_window {context_window} is greater "
f"than the model's max input size {model_context_window}. "
"Disable this warning by setting a lower context_window."
)
context_window = model_context_window
tokenizer_kwargs = tokenizer_kwargs or {}
if "max_length" not in tokenizer_kwargs:
tokenizer_kwargs["max_length"] = context_window
if tokenizer:
self._tokenizer = tokenizer
else:
try:
self._tokenizer = AutoTokenizer.from_pretrained(
tokenizer_name, **tokenizer_kwargs
)
except Exception:
self._tokenizer = LlamaTokenizer.from_pretrained(
tokenizer_name, trust_remote_code=True
)
if tokenizer_name != model_name:
logger.warning(
f"The model `{model_name}` and tokenizer `{tokenizer_name}` "
f"are from different paths, please ensure that they are compatible."
)
# setup stopping criteria
stopping_ids_list = stopping_ids or []
if self._tokenizer.pad_token is None:
self._tokenizer.pad_token = self._tokenizer.eos_token
class StopOnTokens(StoppingCriteria):
def __call__(
self,
input_ids: torch.LongTensor,
scores: torch.FloatTensor,
**kwargs: Any,
) -> bool:
for stop_id in stopping_ids_list:
if input_ids[0][-1] == stop_id:
return True
return False
self._stopping_criteria = StoppingCriteriaList([StopOnTokens()])
messages_to_prompt = messages_to_prompt or self._tokenizer_messages_to_prompt
super().__init__(
context_window=context_window,
max_new_tokens=max_new_tokens,
tokenizer_name=tokenizer_name,
model_name=model_name,
device_map=device_map,
stopping_ids=stopping_ids or [],
tokenizer_kwargs=tokenizer_kwargs or {},
tokenizer_outputs_to_remove=tokenizer_outputs_to_remove or [],
model_kwargs=model_kwargs or {},
generate_kwargs=generate_kwargs or {},
is_chat_model=is_chat_model,
callback_manager=callback_manager,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def from_model_id(
cls,
context_window: int = DEFAULT_CONTEXT_WINDOW,
max_new_tokens: int = DEFAULT_NUM_OUTPUTS,
tokenizer_name: str = DEFAULT_HUGGINGFACE_MODEL,
model_name: str = DEFAULT_HUGGINGFACE_MODEL,
load_in_4bit: Optional[bool] = True,
load_in_low_bit: Optional[str] = None,
model: Optional[Any] = None,
tokenizer: Optional[Any] = None,
device_map: Optional[str] = "auto",
stopping_ids: Optional[List[int]] = None,
tokenizer_kwargs: Optional[dict] = None,
tokenizer_outputs_to_remove: Optional[list] = None,
model_kwargs: Optional[dict] = None,
generate_kwargs: Optional[dict] = None,
is_chat_model: Optional[bool] = False,
callback_manager: Optional[CallbackManager] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
):
return cls(
context_window=context_window,
max_new_tokens=max_new_tokens,
tokenizer_name=tokenizer_name,
model_name=model_name,
load_in_4bit=load_in_4bit,
load_in_low_bit=load_in_low_bit,
model=model,
tokenizer=tokenizer,
device_map=device_map,
stopping_ids=stopping_ids,
tokenizer_kwargs=tokenizer_kwargs,
tokenizer_outputs_to_remove=tokenizer_outputs_to_remove,
model_kwargs=model_kwargs,
generate_kwargs=generate_kwargs,
is_chat_model=is_chat_model,
callback_manager=callback_manager,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
low_bit_model=False,
)
@classmethod
def from_model_id_low_bit(
cls,
context_window: int = DEFAULT_CONTEXT_WINDOW,
max_new_tokens: int = DEFAULT_NUM_OUTPUTS,
tokenizer_name: str = DEFAULT_HUGGINGFACE_MODEL,
model_name: str = DEFAULT_HUGGINGFACE_MODEL,
model: Optional[Any] = None,
tokenizer: Optional[Any] = None,
device_map: Optional[str] = "auto",
stopping_ids: Optional[List[int]] = None,
tokenizer_kwargs: Optional[dict] = None,
tokenizer_outputs_to_remove: Optional[list] = None,
model_kwargs: Optional[dict] = None,
generate_kwargs: Optional[dict] = None,
is_chat_model: Optional[bool] = False,
callback_manager: Optional[CallbackManager] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
):
return cls(
context_window=context_window,
max_new_tokens=max_new_tokens,
tokenizer_name=tokenizer_name,
model_name=model_name,
model=model,
tokenizer=tokenizer,
device_map=device_map,
stopping_ids=stopping_ids,
tokenizer_kwargs=tokenizer_kwargs,
tokenizer_outputs_to_remove=tokenizer_outputs_to_remove,
model_kwargs=model_kwargs,
generate_kwargs=generate_kwargs,
is_chat_model=is_chat_model,
callback_manager=callback_manager,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
low_bit_model=True,
)
@classmethod
def class_name(cls) -> str:
return "IpexLLM"
@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=self.is_chat_model,
)
def _load_model(
self,
low_bit_model: bool,
load_in_4bit: bool,
load_in_low_bit: str,
model_name: str,
model_kwargs: Any,
) -> Any:
"""Attempts to load a model with AutoModelForCausalLM and falls back to AutoModel on failure."""
from ipex_llm.transformers import AutoModelForCausalLM, AutoModel
load_kwargs = {"use_cache": True, "trust_remote_code": True}
if not low_bit_model:
if load_in_low_bit is not None:
load_function_name = "from_pretrained"
load_kwargs["load_in_low_bit"] = load_in_low_bit
else:
load_function_name = "from_pretrained"
load_kwargs["load_in_4bit"] = load_in_4bit
else:
load_function_name = "load_low_bit"
try:
# Attempt to load with AutoModelForCausalLM
return self._load_model_general(
AutoModelForCausalLM,
load_function_name,
model_name,
load_kwargs,
model_kwargs,
)
except Exception:
# Fallback to AutoModel if there's an exception
return self._load_model_general(
AutoModel, load_function_name, model_name, load_kwargs, model_kwargs
)
def _load_model_general(
self,
model_class: Any,
load_function_name: str,
model_name: str,
load_kwargs,
model_kwargs: dict,
) -> Any:
"""General function to attempt to load a model."""
try:
load_function = getattr(model_class, load_function_name)
return load_function(model_name, **{**load_kwargs, **model_kwargs})
except Exception as e:
logger.error(
f"Failed to load model using {model_class.__name__}.{load_function_name}: {e}"
)
def _tokenizer_messages_to_prompt(self, messages: Sequence[ChatMessage]) -> str:
"""
Use the tokenizer to convert messages to prompt. Fallback to generic.
Args:
messages: Sequence of ChatMessage.
Returns:
Str of response.
"""
if hasattr(self._tokenizer, "apply_chat_template"):
messages_dict = [
{"role": message.role.value, "content": message.content}
for message in messages
]
tokens = self._tokenizer.apply_chat_template(messages_dict)
return self._tokenizer.decode(tokens)
return generic_messages_to_prompt(messages)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, formatted=True, **kwargs)
return stream_completion_response_to_chat_response(completion_response)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""
Complete by LLM.
Args:
prompt: Prompt for completion.
formatted: Whether the prompt is formatted by wrapper.
kwargs: Other kwargs for complete.
Returns:
CompletionReponse after generation.
"""
if not formatted:
prompt = self.completion_to_prompt(prompt)
input_ids = self._tokenizer(prompt, return_tensors="pt")
input_ids = input_ids.to(self._model.device)
# remove keys from the tokenizer if needed, to avoid HF errors
for key in self.tokenizer_outputs_to_remove:
if key in input_ids:
input_ids.pop(key, None)
tokens = self._model.generate(
**input_ids,
max_new_tokens=self.max_new_tokens,
stopping_criteria=self._stopping_criteria,
pad_token_id=self._tokenizer.pad_token_id,
**self.generate_kwargs,
)
completion_tokens = tokens[0][input_ids["input_ids"].size(1) :]
completion = self._tokenizer.decode(completion_tokens, skip_special_tokens=True)
return CompletionResponse(text=completion, raw={"model_output": tokens})
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
"""
Complete by LLM in stream.
Args:
prompt: Prompt for completion.
formatted: Whether the prompt is formatted by wrapper.
kwargs: Other kwargs for complete.
Returns:
CompletionReponse after generation.
"""
from transformers import TextIteratorStreamer
if not formatted:
prompt = self.completion_to_prompt(prompt)
input_ids = self._tokenizer.encode(prompt, return_tensors="pt")
input_ids = input_ids.to(self._model.device)
for key in self.tokenizer_outputs_to_remove:
if key in input_ids:
input_ids.pop(key, None)
streamer = TextIteratorStreamer(
self._tokenizer, skip_prompt=True, skip_special_tokens=True
)
generation_kwargs = dict(
input_ids=input_ids,
streamer=streamer,
max_new_tokens=self.max_new_tokens,
stopping_criteria=self._stopping_criteria,
pad_token_id=self._tokenizer.pad_token_id,
**self.generate_kwargs,
)
thread = Thread(target=self._model.generate, kwargs=generation_kwargs)
thread.start()
# create generator based off of streamer
def gen() -> CompletionResponseGen:
text = ""
for x in streamer:
text += x
yield CompletionResponse(text=text, delta=x)
return gen()
|