from __future__ import annotations
import json
import warnings
from typing import Any, Dict, List, Optional, Tuple, Type, Union
import torch
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoTokenizer,
BatchEncoding,
PreTrainedModel,
PreTrainedTokenizer,
)
from transformers.models.auto import modeling_auto
from transformers.models.llama.configuration_llama import LlamaConfig
from typing_extensions import Self
from nnsight.envoy import Envoy
from ..intervention import InterventionProxy
from ..util import WrapperModule
from . import NNsight
from .mixins import GenerationMixin, RemoteableMixin
[docs]
class TokenIndexer:
"""Helper class to directly access token indices of hidden states.
Directly indexes the second dimension of tensors.
Makes positive indices negative as tokens are padded on the left.
Args:
proxy (InterventionProxy): Proxy to aid in token indexing.
"""
def __init__(self, proxy: InterventionProxy) -> None:
self.proxy = proxy
def convert_idx(self, idx: int):
if idx >= 0:
n_tokens = self.proxy.node.proxy_value.shape[1]
idx = -(n_tokens - idx)
return idx
def __getitem__(self, key: int) -> LanguageModelProxy:
key = self.convert_idx(key)
return self.proxy[:, key]
def __setitem__(self, key: int, value: Union[LanguageModelProxy, Any]) -> None:
key = self.convert_idx(key)
self.proxy[:, key] = value
[docs]
class LanguageModelProxy(InterventionProxy):
"""
Indexing by token of hidden states can easily done using ``.token[<idx>]`` or ``.t[<idx>]``
.. code-block:: python
with runner.invoke('The Eiffel Tower is in the city of') as invoker:
logits = model.lm_head.output.t[0].save()
print(logits.value)
This would save only the first token of the output for this module.
This should be used when using multiple invokes as the batching and padding of multiple inputs could mean the indices for tokens shifts around and this take care of that.
Args:
InterventionProxy (_type_): _description_
Returns:
_type_: _description_
"""
@property
def token(self) -> TokenIndexer:
"""Property used to do token based indexing on a proxy.
Directly indexes the second dimension of tensors.
Makes positive indices negative as tokens are padded on the left.
Example:
.. code-block:: python
model.transformer.h[0].mlp.output.token[0]
Is equivalent to:
.. code-block:: python
model.transformer.h[0].mlp.output.token[:,-3]
For a proxy tensor with 3 tokens.
Returns:
TokenIndexer: Object to do token based indexing.
"""
return TokenIndexer(self)
@property
def t(self) -> TokenIndexer:
"""Property as alias for InterventionProxy.token"""
return self.token
[docs]
class LanguageModel(GenerationMixin, RemoteableMixin, NNsight):
"""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.
"""
proxy_class = LanguageModelProxy
def __new__(cls, *args, **kwargs) -> Self | Envoy:
return object.__new__(cls)
def __init__(
self,
model_key: Union[str, torch.nn.Module],
*args,
tokenizer: Optional[PreTrainedTokenizer] = None,
automodel: Type[AutoModel] = AutoModelForCausalLM,
**kwargs,
) -> None:
self.tokenizer: PreTrainedTokenizer = tokenizer
self._model: PreTrainedModel = None
self.automodel = (
automodel
if not isinstance(automodel, str)
else getattr(modeling_auto, automodel)
)
if isinstance(model_key, torch.nn.Module):
setattr(model_key, "generator", WrapperModule())
super().__init__(model_key, *args, **kwargs)
def _load(
self,
repo_id: str,
tokenizer_kwargs: Optional[Dict[str, Any]] = None,
patch_llama_scan: bool = True,
**kwargs,
) -> PreTrainedModel:
config = kwargs.pop("config", None) or AutoConfig.from_pretrained(
repo_id, **kwargs
)
if self.tokenizer is None:
if tokenizer_kwargs is None:
tokenizer_kwargs = {}
if "padding_side" not in tokenizer_kwargs:
tokenizer_kwargs["padding_side"] = "left"
self.tokenizer = AutoTokenizer.from_pretrained(
repo_id, config=config, **tokenizer_kwargs
)
if not hasattr(self.tokenizer.pad_token, "pad_token"):
self.tokenizer.pad_token = self.tokenizer.eos_token
if (
patch_llama_scan
and isinstance(config, LlamaConfig)
and isinstance(config.rope_scaling, dict)
and "rope_type" in config.rope_scaling
):
config.rope_scaling["rope_type"] = "default"
model = self.automodel.from_config(config, trust_remote_code=True)
setattr(model, "generator", WrapperModule())
return model
if (
patch_llama_scan
and isinstance(config, LlamaConfig)
and isinstance(config.rope_scaling, dict)
and "rope_type" in config.rope_scaling
):
config.rope_scaling["rope_type"] = "llama3"
model = self.automodel.from_pretrained(repo_id, config=config, **kwargs)
setattr(model, "generator", WrapperModule())
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, BatchEncoding):
return inputs
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_inputs(
self,
inputs: Union[
str,
List[str],
List[List[str]],
List[int],
List[List[int]],
torch.Tensor,
Dict[str, Any],
BatchEncoding,
],
labels: Any = None,
**kwargs,
) -> Tuple[BatchEncoding, int]:
if isinstance(inputs, dict):
new_inputs = dict()
tokenized_inputs = self._tokenize(inputs["input_ids"], **kwargs)
new_inputs["input_ids"] = tokenized_inputs["input_ids"]
if "attention_mask" in inputs:
for ai, attn_mask in enumerate(inputs["attention_mask"]):
tokenized_inputs["attention_mask"][
ai, -len(attn_mask) :
] = attn_mask
new_inputs["attention_mask"] = tokenized_inputs["attention_mask"]
if "labels" in inputs:
labels = self._tokenize(inputs["labels"], **kwargs)
new_inputs["labels"] = labels["input_ids"]
return (BatchEncoding(new_inputs),), len(new_inputs["input_ids"])
inputs = self._tokenize(inputs, **kwargs)
if labels is not None:
labels = self._tokenize(labels, **kwargs)
inputs["labels"] = labels["input_ids"]
return (inputs,), len(inputs["input_ids"])
def _batch_inputs(
self,
batched_inputs: Optional[Dict[str, Any]],
prepared_inputs: BatchEncoding,
) -> Tuple[Dict[str, Any]]:
if batched_inputs is None:
batched_inputs = {"input_ids": []}
if "labels" in prepared_inputs:
batched_inputs["labels"] = []
if "attention_mask" in prepared_inputs:
batched_inputs["attention_mask"] = []
else:
batched_inputs = batched_inputs[0]
batched_inputs["input_ids"].extend(prepared_inputs["input_ids"])
if "labels" in prepared_inputs:
batched_inputs["labels"].extend(prepared_inputs["labels"])
if "attention_mask" in prepared_inputs:
batched_inputs["attention_mask"].extend(prepared_inputs["attention_mask"])
return (batched_inputs,)
def _execute_forward(self, prepared_inputs: Any, *args, **kwargs):
device = next(self._model.parameters()).device
return self._model(
*args,
**prepared_inputs.to(device),
**kwargs,
)
def _execute_generate(
self, prepared_inputs: Any, *args, max_new_tokens=1, **kwargs
):
device = next(self._model.parameters()).device
output = self._model.generate(
*args,
**prepared_inputs.to(device),
max_new_tokens=max_new_tokens,
**kwargs,
)
self._model.generator(output)
return output
def _remoteable_model_key(self) -> str:
return json.dumps(
{"repo_id": self._model_key} # , "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)
