mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
481d3855dc
- `llm(prompt)` -> `llm.invoke(prompt)`
- `llm(prompt=prompt` -> `llm.invoke(prompt)` (same with `messages=`)
- `llm(prompt, callbacks=callbacks)` -> `llm.invoke(prompt,
config={"callbacks": callbacks})`
- `llm(prompt, **kwargs)` -> `llm.invoke(prompt, **kwargs)`
229 lines
8.5 KiB
Python
229 lines
8.5 KiB
Python
# 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
|
|
|
|
|
|
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)
|