from __future__ import annotations
import json
import warnings
from typing import (
TYPE_CHECKING,
Any,
Dict,
Generic,
List,
Optional,
Protocol,
Tuple,
Type,
Union,
)
import torch
from torch.nn.modules import Module
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoTokenizer,
BatchEncoding,
PretrainedConfig,
PreTrainedModel,
PreTrainedTokenizer,
)
from transformers.models.auto import modeling_auto
from transformers.models.llama.configuration_llama import LlamaConfig
from typing_extensions import Self
from ..intervention import Envoy
from ..intervention.contexts import InterleavingTracer
from ..intervention.graph import InterventionNodeType, InterventionProxyType
from ..util import WrapperModule
from .mixins import RemoteableMixin
class LanguageModel(RemoteableMixin):
"""LanguageModels are NNsight wrappers around transformers language models.
Inputs can be in the form of:
Prompt: (str)
Prompts: (List[str])
Batched prompts: (List[List[str]])
Tokenized prompt: (Union[List[int], torch.Tensor])
Tokenized prompts: (Union[List[List[int]], torch.Tensor])
Direct input: (Dict[str,Any])
If using a custom model, you also need to provide the tokenizer like ``LanguageModel(custom_model, tokenizer=tokenizer)``
Calls to generate pass arguments downstream to :func:`GenerationMixin.generate`
Attributes:
config (PretrainedConfig): Huggingface config file loaded from repository or checkpoint.
tokenizer (PreTrainedTokenizer): Tokenizer for LMs.
automodel (Type): AutoModel type from transformer auto models.
model (PreTrainedModel): Meta version of underlying auto model.
"""
__methods__ = {"generate": "_generate"}
tokenizer: PreTrainedTokenizer
[docs]
class Generator(WrapperModule):
[docs]
class Streamer(WrapperModule):
def put(self, *args):
return self(*args)
def end(self):
pass
def __init__(self) -> None:
super().__init__()
self.streamer = LanguageModel.Generator.Streamer()
def __init__(
self,
*args,
config: Optional[PretrainedConfig] = None,
tokenizer: Optional[PreTrainedTokenizer] = None,
automodel: Type[AutoModel] = AutoModelForCausalLM,
**kwargs,
) -> None:
self.automodel = (
automodel
if not isinstance(automodel, str)
else getattr(modeling_auto, automodel)
)
self.config = config
self.tokenizer = tokenizer
self.repo_id: str = None
super().__init__(*args, **kwargs)
self.generator: Envoy[InterventionProxyType, InterventionNodeType] = (
LanguageModel.Generator()
)
def _load_config(self, repo_id: str, **kwargs):
if self.config is None:
self.config = AutoConfig.from_pretrained(repo_id, **kwargs)
def _load_tokenizer(self, repo_id: str, **kwargs):
if self.tokenizer is None:
if "padding_side" not in kwargs:
kwargs["padding_side"] = "left"
self.tokenizer = AutoTokenizer.from_pretrained(
repo_id, config=self.config, **kwargs
)
if getattr(self.tokenizer, "pad_token", None) is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
def _load_meta(
self,
repo_id: str,
tokenizer_kwargs: Optional[Dict[str, Any]] = {},
patch_llama_scan: bool = True,
**kwargs,
) -> Module:
self.repo_id = repo_id
self._load_config(repo_id, **kwargs)
self._load_tokenizer(repo_id, **tokenizer_kwargs)
if (
patch_llama_scan
and isinstance(self.config, LlamaConfig)
and isinstance(self.config.rope_scaling, dict)
and "rope_type" in self.config.rope_scaling
):
self.config.rope_scaling["rope_type"] = "default"
model = self.automodel.from_config(self.config, trust_remote_code=True)
return model
def _load(
self,
repo_id: str,
tokenizer_kwargs: Optional[Dict[str, Any]] = {},
patch_llama_scan: bool = True,
**kwargs,
) -> PreTrainedModel:
self._load_config(repo_id, **kwargs)
self._load_tokenizer(repo_id, **tokenizer_kwargs)
if (
patch_llama_scan
and isinstance(self.config, LlamaConfig)
and isinstance(self.config.rope_scaling, dict)
and "rope_type" in self.config.rope_scaling
):
self.config.rope_scaling["rope_type"] = "llama3"
model = self.automodel.from_pretrained(repo_id, config=self.config, **kwargs)
return model
def _tokenize(
self,
inputs: Union[
str,
List[str],
List[List[str]],
List[int],
List[List[int]],
torch.Tensor,
Dict[str, Any],
],
**kwargs,
):
if isinstance(inputs, str) or (
isinstance(inputs, list) and isinstance(inputs[0], int)
):
inputs = [inputs]
if isinstance(inputs, torch.Tensor) and inputs.ndim == 1:
inputs = inputs.unsqueeze(0)
if not isinstance(inputs[0], str):
inputs = [{"input_ids": ids} for ids in inputs]
return self.tokenizer.pad(inputs, return_tensors="pt", **kwargs)
return self.tokenizer(inputs, return_tensors="pt", padding=True, **kwargs)
def _prepare_input(
self,
*inputs: Tuple[
Union[
str,
List[str],
List[List[str]],
List[int],
List[List[int]],
torch.Tensor,
List[torch.Tensor],
Dict[str, Any],
BatchEncoding,
]
],
input_ids: Union[
List[int], List[List[int]], torch.Tensor, List[torch.Tensor]
] = None,
labels: Any = None,
**kwargs,
) -> Tuple[BatchEncoding, int]:
if input_ids is not None:
assert len(inputs) == 0
inputs = (input_ids,)
assert len(inputs) == 1
inputs = inputs[0]
if isinstance(inputs, dict):
inputs = BatchEncoding(inputs)
elif isinstance(inputs, BatchEncoding):
pass
else:
inputs = self._tokenize(inputs, **kwargs)
if labels is not None:
labels = self._tokenize(labels, **kwargs)["input_ids"]
return ((inputs,), {"labels": labels}), len(inputs["input_ids"])
def _batch(
self,
batched_inputs: Optional[Tuple[Tuple[BatchEncoding], Dict[str, Any]]],
input: BatchEncoding,
labels: Optional[torch.Tensor] = None,
) -> Tuple[Dict[str, Any]]:
if batched_inputs is None:
return ((input,), {"labels": labels})
batched_labels = batched_inputs[1]["labels"]
batched_inputs = batched_inputs[0][0]
attention_mask = batched_inputs["attention_mask"]
batched_inputs = [
{"input_ids": ids}
for ids in [
*batched_inputs["input_ids"].tolist(),
*input["input_ids"].tolist(),
]
]
batched_inputs = self.tokenizer.pad(batched_inputs, return_tensors="pt")
if labels is not None:
batched_labels = torch.cat((batched_labels, labels))
if self.tokenizer.padding_side == "left":
batched_inputs["attention_mask"][
: attention_mask.shape[0], -attention_mask.shape[1] :
] = attention_mask
else:
batched_inputs["attention_mask"][
: attention_mask.shape[0], : attention_mask.shape[1]
] = attention_mask
return ((batched_inputs,), {"labels": batched_labels})
def _execute(self, inputs: BatchEncoding, **kwargs) -> Any:
inputs = inputs.to(self.device)
return self._model(
**inputs,
**kwargs,
)
def _generate(
self,
inputs: BatchEncoding,
max_new_tokens=1,
streamer: Any = None,
**kwargs,
):
if streamer is None:
streamer = self.generator.streamer
inputs = inputs.to(self.device)
output = self._model.generate(
**inputs,
**kwargs,
streamer=streamer,
max_new_tokens=max_new_tokens,
)
self.generator(output)
return output
def _remoteable_model_key(self) -> str:
return json.dumps(
{"repo_id": self.repo_id} # , "torch_dtype": str(self._model.dtype)}
)
@classmethod
def _remoteable_from_model_key(cls, model_key: str, **kwargs) -> Self:
kwargs = {**json.loads(model_key), **kwargs}
repo_id = kwargs.pop("repo_id")
return LanguageModel(repo_id, **kwargs)
if TYPE_CHECKING:
[docs]
class LanguageModel(LanguageModel, PreTrainedModel):
def generate(self, *args, **kwargs) -> InterleavingTracer:
pass
