# flake8: noqa
from langchain_core.utils import pre_init
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union
from langchain_core.utils import pre_init
from pydantic import root_validator
from langchain_core.utils import pre_init
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.utils import pre_init
from langchain_core.language_models.llms import LLM
from langchain_core.utils import pre_init
from langchain_community.llms.utils import enforce_stop_tokens
from langchain_core.utils import pre_init
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_configuration: 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_configuration,
"generation_config": self.generation_config,
"streaming": self.streaming,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "deepsparse"
[docs]
@pre_init
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_configuration"] 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)
if run_manager:
run_manager.on_llm_new_token(token=chunk.text)
yield chunk
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)
if run_manager:
await run_manager.on_llm_new_token(token=chunk.text)
yield chunk
