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398 | class OpenVINOGenAILLM(CustomLLM):
r"""OpenVINO GenAI LLM.
Examples:
`pip install llama-index-llms-openvino-genai`
```python
from llama_index.llms.openvino_genai import OpenVINOgenAILLM
llm = OpenVINOGenAILLM(
model_path=model_path,
device="CPU",
)
response = llm.complete("What is the meaning of life?")
print(str(response))
```
"""
model_path: str = Field(
default=None,
description=("The model path to use from local. "),
)
system_prompt: str = Field(
default="",
description=(
"The system prompt, containing any extra instructions or context. "
"The model card on HuggingFace 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 HuggingFace should specify if this is needed. "
"Should contain a `{query_str}` placeholder."
),
)
device: str = Field(
default="auto", description="The device to use. Defaults to 'auto'."
)
is_chat_model: bool = Field(
default=False,
description=(
LLMMetadata.model_fields["is_chat_model"].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."
),
)
config: str = Field(
default=None,
description=("The LLM generation configurations."),
)
_pip: Any = PrivateAttr()
_tokenizer: Any = PrivateAttr()
_streamer: Any = PrivateAttr()
def __init__(
self,
model_path: str,
config: Optional[dict] = None,
tokenizer: Optional[Any] = None,
device: Optional[str] = "CPU",
query_wrapper_prompt: Union[str, PromptTemplate] = "{query_str}",
is_chat_model: Optional[bool] = False,
callback_manager: Optional[CallbackManager] = None,
system_prompt: str = "",
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
**kwargs: Any,
) -> None:
class IterableStreamer(openvino_genai.StreamerBase):
"""
A custom streamer class for handling token streaming
and detokenization with buffering.
Attributes:
tokenizer (Tokenizer): The tokenizer used for encoding
and decoding tokens.
tokens_cache (list): A buffer to accumulate tokens
for detokenization.
text_queue (Queue): A synchronized queue
for storing decoded text chunks.
print_len (int): The length of the printed text
to manage incremental decoding.
"""
def __init__(self, tokenizer: Any) -> None:
"""
Initializes the IterableStreamer with the given tokenizer.
Args:
tokenizer (Tokenizer): The tokenizer to use for encoding
and decoding tokens.
"""
super().__init__()
self.tokenizer = tokenizer
self.tokens_cache: list[int] = []
self.text_queue: Any = queue.Queue()
self.print_len = 0
def __iter__(self) -> self:
"""
Returns the iterator object itself.
"""
return self
def __next__(self) -> str:
"""
Returns the next value from the text queue.
Returns:
str: The next decoded text chunk.
Raises:
StopIteration: If there are no more elements in the queue.
"""
value = (
self.text_queue.get()
) # get() will be blocked until a token is available.
if value is None:
raise StopIteration
return value
def get_stop_flag(self) -> bool:
"""
Checks whether the generation process should be stopped.
Returns:
bool: Always returns False in this implementation.
"""
return False
def put_word(self, word: Any) -> None:
"""
Puts a word into the text queue.
Args:
word (str): The word to put into the queue.
"""
self.text_queue.put(word)
def put(self, token_id: int) -> bool:
"""
Processes a token and manages the decoding buffer.
Adds decoded text to the queue.
Args:
token_id (int): The token_id to process.
Returns:
bool: True if generation should be stopped, False otherwise.
"""
self.tokens_cache.append(token_id)
text = self.tokenizer.decode(
self.tokens_cache, skip_special_tokens=True
)
word = ""
if len(text) > self.print_len and text[-1] == "\n":
word = text[self.print_len :]
self.tokens_cache = []
self.print_len = 0
elif len(text) >= 3 and text[-3:] == chr(65533):
pass
elif len(text) > self.print_len:
word = text[self.print_len :]
self.print_len = len(text)
self.put_word(word)
if self.get_stop_flag():
self.end()
return True
else:
return False
def end(self) -> None:
"""
Flushes residual tokens from the buffer
and puts a None value in the queue to signal the end.
"""
text = self.tokenizer.decode(
self.tokens_cache, skip_special_tokens=True
)
if len(text) > self.print_len:
word = text[self.print_len :]
self.put_word(word)
self.tokens_cache = []
self.print_len = 0
self.put_word(None)
def reset(self) -> None:
"""
Resets the state.
"""
self.tokens_cache = []
self.text_queue = queue.Queue()
self.print_len = 0
class ChunkStreamer(IterableStreamer):
def __init__(self, tokenizer: Any, tokens_len: int = 4) -> None:
super().__init__(tokenizer)
self.tokens_len = tokens_len
def put(self, token_id: int) -> bool:
if (len(self.tokens_cache) + 1) % self.tokens_len != 0:
self.tokens_cache.append(token_id)
return False
return super().put(token_id)
"""Initialize params."""
pipe = openvino_genai.LLMPipeline(model_path, device, config, **kwargs)
config = pipe.get_generation_config()
tokenizer = tokenizer or pipe.get_tokenizer()
streamer = ChunkStreamer(tokenizer)
if isinstance(query_wrapper_prompt, str):
query_wrapper_prompt = PromptTemplate(query_wrapper_prompt)
messages_to_prompt = messages_to_prompt or self._tokenizer_messages_to_prompt
super().__init__(
tokenizer=tokenizer,
model_name=model_path,
device=device,
query_wrapper_prompt=query_wrapper_prompt,
is_chat_model=is_chat_model,
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
)
self._pipe = pipe
self._tokenizer = tokenizer
self._streamer = streamer
self.config = config
@classmethod
def class_name(cls) -> str:
return "OpenVINO_GenAI_LLM"
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
model_name=self.model_path,
is_chat_model=self.is_chat_model,
)
def _tokenizer_messages_to_prompt(self, messages: Sequence[ChatMessage]) -> str:
"""Use the tokenizer to convert messages to prompt. Fallback to generic."""
if hasattr(self._tokenizer, "apply_chat_template"):
messages_dict = [
{"role": message.role.value, "content": message.content}
for message in messages
]
return (
self._tokenizer.apply_chat_template(
messages_dict, add_generation_prompt=True
)
if isinstance(self._tokenizer, openvino_genai.Tokenizer)
else self._tokenizer.apply_chat_template(
messages_dict, tokenize=False, add_generation_prompt=True
)
)
return generic_messages_to_prompt(messages)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""Completion endpoint."""
full_prompt = prompt
if not formatted:
if self.query_wrapper_prompt:
full_prompt = self.query_wrapper_prompt.format(query_str=prompt)
if self.completion_to_prompt:
full_prompt = self.completion_to_prompt(full_prompt)
elif self.system_prompt:
full_prompt = f"{self.system_prompt} {full_prompt}"
if not isinstance(self._tokenizer, openvino_genai.Tokenizer):
inputs = self._tokenizer(
full_prompt, add_special_tokens=False, return_tensors="np"
)
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
full_prompt = openvino_genai.TokenizedInputs(
ov.Tensor(input_ids), ov.Tensor(attention_mask)
)
tokens = self._pipe.generate(full_prompt, self.config, **kwargs)
if not isinstance(self._tokenizer, openvino_genai.Tokenizer):
completion_tokens = tokens[0][inputs["input_ids"].size(1) :]
completion = self._tokenizer.decode(
completion_tokens, skip_special_tokens=True
)
else:
completion = tokens
return CompletionResponse(text=completion, raw={"model_output": tokens})
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
"""Streaming completion endpoint."""
full_prompt = prompt
if not formatted:
if self.query_wrapper_prompt:
full_prompt = self.query_wrapper_prompt.format(query_str=prompt)
if self.system_prompt:
full_prompt = f"{self.system_prompt} {full_prompt}"
if not isinstance(self._tokenizer, openvino_genai.Tokenizer):
inputs = self._tokenizer(
full_prompt, add_special_tokens=False, return_tensors="np"
)
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
full_prompt = openvino_genai.TokenizedInputs(
ov.Tensor(input_ids), ov.Tensor(attention_mask)
)
stream_complete = Event()
def generate_and_signal_complete() -> None:
"""
generation function for single thread.
"""
self._streamer.reset()
self._pipe.generate(full_prompt, self.config, self._streamer, **kwargs)
stream_complete.set()
self._streamer.end()
t1 = Thread(target=generate_and_signal_complete)
t1.start()
# create generator based off of streamer
def gen() -> CompletionResponseGen:
text = ""
for x in self._streamer:
text += x
yield CompletionResponse(text=text, delta=x)
return gen()
@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)
|
