Fit reporters on input embeddings as a sanity check (#209)

* Start saving and fitting a reporter to the input embeddings

* Rename layer 0 to 'input' to make it more clear

* Actually rename layer 0 correctly

* Handle layer_stride correctly

elk/extraction/extraction.py

@@ -79,7 +79,9 @@ def __post_init__(self, layer_stride: int):
  config = assert_type(
  PretrainedConfig, AutoConfig.from_pretrained(self.model)
  )
- self.layers = tuple(range(0, config.num_hidden_layers, layer_stride))
+ # Note that we always include 0 which is the embedding layer
+ layer_range = range(1, config.num_hidden_layers + 1, layer_stride)
+ self.layers = (0,) + tuple(layer_range)
 
  def explode(self) -> list["Extract"]:
  """Explode this config into a list of configs, one for each layer."""
@@ -141,8 +143,8 @@ def extract_hiddens(
  world_size=world_size,
  )
 
- # Iterating over questions
- layer_indices = cfg.layers or tuple(range(model.config.num_hidden_layers))
+ # Add one to the number of layers to account for the embedding layer
+ layer_indices = cfg.layers or tuple(range(model.config.num_hidden_layers + 1))
 
  global_max_examples = p_cfg.max_examples[0 if split_type == "train" else 1]
  # break `max_examples` among the processes roughly equally
@@ -234,9 +236,6 @@ def extract_hiddens(
  hiddens = (
  outputs.get("decoder_hidden_states") or outputs["hidden_states"]
  )
- # First element of list is the input embeddings
- hiddens = hiddens[1:]
-
  # Throw out layers we don't care about
  hiddens = [hiddens[i] for i in layer_indices]
 
@@ -329,7 +328,8 @@ def get_splits() -> SplitDict:
  dtype="int16",
  shape=(num_variants, num_classes, model_cfg.hidden_size),
  )
- for layer in cfg.layers or range(model_cfg.num_hidden_layers)
+ # Add 1 to include the embedding layer
+ for layer in cfg.layers or range(model_cfg.num_hidden_layers + 1)
  }
  other_cols = {
  "variant_ids": Sequence(

elk/run.py

@@ -172,6 +172,9 @@ def apply_to_layers(
  # Make sure the CSV is written even if we crash or get interrupted
  if df_buf:
  df = pd.concat(df_buf).sort_values(by="layer")
+
+ # Rename layer 0 to "input" to make it more clear
+ df["layer"].replace(0, "input", inplace=True)
  df.round(4).to_csv(f, index=False)
  if self.debug:
  save_debug_log(self.datasets, self.out_dir)