Source code for langchain_community.llms.deepsparse

# flake8: noqa
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union
from langchain_core.pydantic_v1 import root_validator
from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.language_models.llms import LLM
from langchain_community.llms.utils import enforce_stop_tokens
from langchain_core.outputs import GenerationChunk


[docs]class DeepSparse(LLM): """Neural Magic DeepSparse LLM interface. To use, you should have the ``deepsparse`` or ``deepsparse-nightly`` python package installed. See https://github.com/neuralmagic/deepsparse This interface let's you deploy optimized LLMs straight from the [SparseZoo](https://sparsezoo.neuralmagic.com/?useCase=text_generation) Example: .. code-block:: python from langchain_community.llms import DeepSparse llm = DeepSparse(model="zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base_quant-none") """ # noqa: E501 pipeline: Any #: :meta private: model: str """The path to a model file or directory or the name of a SparseZoo model stub.""" model_config: Optional[Dict[str, Any]] = None """Keyword arguments passed to the pipeline construction. Common parameters are sequence_length, prompt_sequence_length""" generation_config: Union[None, str, Dict] = None """GenerationConfig dictionary consisting of parameters used to control sequences generated for each prompt. Common parameters are: max_length, max_new_tokens, num_return_sequences, output_scores, top_p, top_k, repetition_penalty.""" streaming: bool = False """Whether to stream the results, token by token.""" @property def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return { "model": self.model, "model_config": self.model_config, "generation_config": self.generation_config, "streaming": self.streaming, } @property def _llm_type(self) -> str: """Return type of llm.""" return "deepsparse" @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that ``deepsparse`` package is installed.""" try: from deepsparse import Pipeline except ImportError: raise ImportError( "Could not import `deepsparse` package. " "Please install it with `pip install deepsparse[llm]`" ) model_config = values["model_config"] or {} values["pipeline"] = Pipeline.create( task="text_generation", model_path=values["model"], **model_config, ) return values def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Generate text from a prompt. Args: prompt: The prompt to generate text from. stop: A list of strings to stop generation when encountered. Returns: The generated text. Example: .. code-block:: python from langchain_community.llms import DeepSparse llm = DeepSparse(model="zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base_quant-none") llm.invoke("Tell me a joke.") """ if self.streaming: combined_output = "" for chunk in self._stream( prompt=prompt, stop=stop, run_manager=run_manager, **kwargs ): combined_output += chunk.text text = combined_output else: text = ( self.pipeline(sequences=prompt, **self.generation_config) .generations[0] .text ) if stop is not None: text = enforce_stop_tokens(text, stop) return text async def _acall( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Generate text from a prompt. Args: prompt: The prompt to generate text from. stop: A list of strings to stop generation when encountered. Returns: The generated text. Example: .. code-block:: python from langchain_community.llms import DeepSparse llm = DeepSparse(model="zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base_quant-none") llm.invoke("Tell me a joke.") """ if self.streaming: combined_output = "" async for chunk in self._astream( prompt=prompt, stop=stop, run_manager=run_manager, **kwargs ): combined_output += chunk.text text = combined_output else: text = ( self.pipeline(sequences=prompt, **self.generation_config) .generations[0] .text ) if stop is not None: text = enforce_stop_tokens(text, stop) return text def _stream( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[GenerationChunk]: """Yields results objects as they are generated in real time. It also calls the callback manager's on_llm_new_token event with similar parameters to the OpenAI LLM class method of the same name. Args: prompt: The prompt to pass into the model. stop: Optional list of stop words to use when generating. Returns: A generator representing the stream of tokens being generated. Yields: A dictionary like object containing a string token. Example: .. code-block:: python from langchain_community.llms import DeepSparse llm = DeepSparse( model="zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base_quant-none", streaming=True ) for chunk in llm.stream("Tell me a joke", stop=["'","\n"]): print(chunk, end='', flush=True) # noqa: T201 """ inference = self.pipeline( sequences=prompt, streaming=True, **self.generation_config ) for token in inference: chunk = GenerationChunk(text=token.generations[0].text) yield chunk if run_manager: run_manager.on_llm_new_token(token=chunk.text) async def _astream( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[GenerationChunk]: """Yields results objects as they are generated in real time. It also calls the callback manager's on_llm_new_token event with similar parameters to the OpenAI LLM class method of the same name. Args: prompt: The prompt to pass into the model. stop: Optional list of stop words to use when generating. Returns: A generator representing the stream of tokens being generated. Yields: A dictionary like object containing a string token. Example: .. code-block:: python from langchain_community.llms import DeepSparse llm = DeepSparse( model="zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base_quant-none", streaming=True ) for chunk in llm.stream("Tell me a joke", stop=["'","\n"]): print(chunk, end='', flush=True) # noqa: T201 """ inference = self.pipeline( sequences=prompt, streaming=True, **self.generation_config ) for token in inference: chunk = GenerationChunk(text=token.generations[0].text) yield chunk if run_manager: await run_manager.on_llm_new_token(token=chunk.text)