Fix multi-gpu inference and duplicate BOS issue for decoder-only

CHANGELOG.md

@@ -4,7 +4,9 @@
 
 ## 🚀 Features
 
-- Added new models `DbrxForCausalLM`, `OlmoForCausalLM`, `Phi3ForCausalLM`, `Qwen2MoeForCausalLM` to model config.
+- Added new models `DbrxForCausalLM`, `OlmoForCausalLM`, `Phi3ForCausalLM`, `Qwen2MoeForCausalLM`, `Gemma2ForCausalLM` to model config.
+
+- Add `rescale_attributions` to Inseq CLI commands for `rescale=True` ([#280](https://github.com/inseq-team/inseq/pull/280)).
 
 - Rows and columns in the visualization now have indices alongside tokens to facilitate index-based slicing, aggregation and alignment [#282](https://github.com/inseq-team/inseq/pull/282)
 
@@ -23,7 +25,7 @@ out.save("output.json")
 out.save("output_fp16.json", scores_precision="float16") # or "float8"
 
 # Automatic conversion to float32
-out_loaded = inseq.FeatureAttributionOutput.load("output_fp16.json") 
+out_loaded = inseq.FeatureAttributionOutput.load("output_fp16.json")
 ```
 
 - - A new `SliceAggregator` (`"slices"`) is added to allow for slicing source (in encoder-decoder) or target (in decoder-only) tokens from a `FeatureAttributionSequenceOutput` object, using the same syntax of `ContiguousSpanAggregator`. The `__getitem__` method of the `FeatureAttributionSequenceOutput` is a shortcut for this, allowing slicing with `[start:stop]` syntax. [#282](https://github.com/inseq-team/inseq/pull/282)
@@ -71,18 +73,19 @@ out_female = attrib_model.attribute(
 ## 🔧 Fixes and Refactoring
 
 - Fix the issue in the attention implementation from [#268](https://github.com/inseq-team/inseq/issues/268) where non-terminal position in the tensor were set to nan if they were 0s ([#269](https://github.com/inseq-team/inseq/pull/269)).
-  
+
 - Fix the pad token in cases where it is not specified by default in the loaded model (e.g. for Qwen models) ([#269](https://github.com/inseq-team/inseq/pull/269)).
 
 - Fix bug reported in [#266](https://github.com/inseq-team/inseq/issues/266) making `value_zeroing` unusable for SDPA attention. This enables using the method on models using SDPA attention as default (e.g. `GemmaForCausalLM`) without passing `model_kwargs={'attn_implementation': 'eager'}` ([#267](https://github.com/inseq-team/inseq/pull/267)).
 
-- The directions of generated/attributed tokens were clarified in the visualization using arrows instead of x/y [#282](https://github.com/inseq-team/inseq/pull/282)
+- Fix multi-device support and duplicate BOS for chat template models ([#280](https://github.com/inseq-team/inseq/pull/280)).
 
+- The directions of generated/attributed tokens were clarified in the visualization using arrows instead of x/y [#282](https://github.com/inseq-team/inseq/pull/282)
 
 ## 📝 Documentation and Tutorials
 
 *No changes*
 
 ## 💥 Breaking Changes
 
-*No changes*
+*No changes*

README.md

@@ -280,7 +280,7 @@ Our vision for Inseq is to create a centralized, comprehensive and robust set of
 
 ## Citing Inseq
 
-If you use Inseq in your research we suggest to include a mention to the specific release (e.g. v0.4.0) and we kindly ask you to cite our reference paper as:
+If you use Inseq in your research we suggest to include a mention to the specific release (e.g. v0.6.0) and we kindly ask you to cite our reference paper as:
 
 ```bibtex
 @inproceedings{sarti-etal-2023-inseq,
@@ -308,7 +308,7 @@ If you use Inseq in your research we suggest to include a mention to the specifi
 Inseq has been used in various research projects. A list of known publications that use Inseq to conduct interpretability analyses of generative models is shown below.
 
 > [!TIP]
-> Last update: May 2024. Please open a pull request to add your publication to the list.
+> Last update: June 2024. Please open a pull request to add your publication to the list.
 
 <details>
   <summary><b>2023</b></summary>
@@ -331,6 +331,9 @@ Inseq has been used in various research projects. A list of known publications t
     <li><a href="https://arxiv.org/abs/2402.00794">ReAGent: A Model-agnostic Feature Attribution Method for Generative Language Models</a> (Zhao et al., 2024)</li>
     <li><a href="https://arxiv.org/abs/2404.02421">Revisiting subword tokenization: A case study on affixal negation in large language models</a> (Truong et al., 2024)</li>
     <li><a href="https://hal.science/hal-04581586">Exploring NMT Explainability for Translators Using NMT Visualising Tools</a> (Gonzalez-Saez et al., 2024)</li>
+    <li><a href="https://arxiv.org/abs/2405.14899">DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning</a> (Zhou et al., 2024)</li>
+    <li><a href="https://arxiv.org/abs/2406.06399">Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for Dialogue</a> (Alghisi et al., 2024)</li>
+    <li><a href="https://arxiv.org/abs/2406.13663">Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation</a> (Qi, Sarti et al., 2024)</li>
   </ol>
 
 </details>

inseq/commands/attribute/attribute.py

@@ -11,12 +11,14 @@ def aggregate_attribution_scores(
     selectors: Optional[list[int]] = None,
     aggregators: Optional[list[str]] = None,
     normalize_attributions: bool = False,
+    rescale_attributions: bool = False,
 ) -> FeatureAttributionOutput:
     if selectors is not None and aggregators is not None:
         for select_idx, aggregator_fn in zip(selectors, aggregators):
             out = out.aggregate(
                 aggregator=aggregator_fn,
                 normalize=normalize_attributions,
+                rescale=rescale_attributions,
                 select_idx=select_idx,
                 do_post_aggregation_checks=False,
             )
@@ -79,6 +81,7 @@ def attribute(input_texts, generated_texts, args: AttributeExtendedArgs):
                 selectors=args.attribution_selectors,
                 aggregators=args.attribution_aggregators,
                 normalize_attributions=args.normalize_attributions,
+                rescale_attributions=args.rescale_attributions,
             )
         print(f"Saving {'aggregated ' if args.aggregate_output else ''}attributions to {args.save_path}")
         out.save(args.save_path, overwrite=True)

inseq/commands/attribute/attribute_args.py

@@ -61,6 +61,14 @@ class AttributeBaseArgs:
             "for each context are normalized to sum up to 1, providing a relative notion of input salience."
         ),
     )
+    rescale_attributions: bool = cli_arg(
+        default=False,
+        help=(
+            "Whether to rescale the attribution scores for each context. If ``True``, the attribution scores "
+            "for each context are rescaled to sum up to the number of tokens in the input, providing an absolute"
+            " notion of input salience."
+        ),
+    )
     model_kwargs: dict = cli_arg(
         default_factory=dict,
         help="Additional keyword arguments passed to the model constructor in JSON format.",

inseq/commands/attribute_context/attribute_context.py

@@ -211,6 +211,7 @@ def attribute_context_with_model(args: AttributeContextArgs, model: HuggingfaceM
             selectors=args.attribution_selectors,
             aggregators=args.attribution_aggregators,
             normalize_attributions=args.normalize_attributions,
+            rescale_attributions=args.rescale_attributions,
         )[0]
         if args.show_intermediate_outputs:
             cci_attrib_out.show(do_aggregation=False)

inseq/models/attribution_model.py

@@ -411,7 +411,9 @@ def attribute(
                     "Step scores are not supported for final step methods since they do not iterate over the full"
                     " sequence. Please remove the step scores and compute them separatly passing method='dummy'."
                 )
-        input_texts, generated_texts = format_input_texts(input_texts, generated_texts)
+        input_texts, generated_texts = format_input_texts(
+            input_texts, generated_texts, skip_special_tokens, self.special_tokens
+        )
         has_generated_texts = generated_texts is not None
         if not self.is_encoder_decoder:
             for i in range(len(input_texts)):

inseq/models/huggingface_model.py

@@ -456,6 +456,12 @@ def get_attentions_dict(
     ) -> dict[str, MultiLayerMultiUnitScoreTensor]:
         if output.encoder_attentions is None or output.decoder_attentions is None:
             raise ValueError("Model does not support attribution relying on attention outputs.")
+        if output.encoder_attentions is not None:
+            output.encoder_attentions = tuple(att.to("cpu") for att in output.encoder_attentions)
+        if output.decoder_attentions is not None:
+            output.decoder_attentions = tuple(att.to("cpu") for att in output.decoder_attentions)
+        if output.cross_attentions is not None:
+            output.cross_attentions = tuple(att.to("cpu") for att in output.cross_attentions)
         return {
             "encoder_self_attentions": torch.stack(output.encoder_attentions, dim=1),
             "decoder_self_attentions": torch.stack(output.decoder_attentions, dim=1),
@@ -506,6 +512,8 @@ def configure_embeddings_scale(self):
     def get_attentions_dict(output: CausalLMOutput) -> dict[str, MultiLayerMultiUnitScoreTensor]:
         if output.attentions is None:
             raise ValueError("Model does not support attribution relying on attention outputs.")
+        else:
+            output.attentions = tuple(att.to("cpu") for att in output.attentions)
         return {
             "decoder_self_attentions": torch.stack(output.attentions, dim=1),
         }

inseq/models/model_config.yaml

@@ -20,6 +20,9 @@ FalconForCausalLM:
 GemmaForCausalLM:
     self_attention_module: "self_attn"
     value_vector: "value_states"
+Gemma2ForCausalLM:
+    self_attention_module: "self_attn"
+    value_vector: "value_states"
 GPTBigCodeForCausalLM:
     self_attention_module: "attn"
     value_vector: "value"

inseq/utils/misc.py

@@ -202,6 +202,8 @@ def isnotebook():
 def format_input_texts(
     texts: TextInput,
     ref_texts: Optional[TextInput] = None,
+    skip_special_tokens: bool = False,
+    special_tokens: list[str] = [],
 ) -> tuple[list[str], list[str]]:
     texts = [texts] if isinstance(texts, str) else texts
     reference_texts = [ref_texts] if isinstance(ref_texts, str) else ref_texts
@@ -211,6 +213,11 @@ def format_input_texts(
                 len(texts), len(reference_texts)
             )
         )
+    if skip_special_tokens:
+        for special_token in special_tokens:
+            texts = [text.replace(special_token, "") for text in texts]
+            if reference_texts is not None:
+                reference_texts = [text.replace(special_token, "") for text in reference_texts]
     return texts, reference_texts