Revision 5&6

.gitignore

@@ -0,0 +1,2 @@
+.venv
+.venv/**

app.py

@@ -14,43 +14,80 @@ TOKENIZER = "microsoft/Phi-3-mini-4k-instruct"
 
 dataset = load_dataset("kisate-team/feature-explanations", split="train")
 
-layers = dataset.unique("layer")
-
-features = {layer:{item["feature"]:item for item in dataset if item["layer"] == layer} for layer in layers}
+def find_revions():
+    revisions = set()
+    for parquet in parquets:
+        if parquet.endswith(".parquet"):
+            parts = parquet.split("-")
+            if len(parts) > 2:
+                revisions.add(int(parts[2][1:]))
+    return sorted(revisions)
+
+def find_layers(revision):
+    layers = set()
+    for parquet in parquets:
+        if parquet.endswith(".parquet"):
+            parts = parquet.split("-")
+            if len(parts) > 2 and int(parts[2][1:]) == revision:
+                layers.add(int(parts[1][1:]))
+    return sorted(layers)
+
+revisions = find_revions()
+layers = {
+    revision: find_layers(revision) for revision in revisions
+}
+
+features = {
+    revision: {
+        layer: {
+            item["feature"]:item for item in dataset if item["layer"] == layer and item["version"] == revision
+        } for layer in layers[revision]
+    } for revision in revisions
+}
+
+# layers = dataset.unique("layer")
 
 nearby = 8
 stride = 0.25
 n_bins = 10
 
-def make_cache_name(layer):
-    return f"{cache_path}/phi-l{layer}-r4-st0.25x128-activations.parquet"
+def make_cache_name(layer, revision):
+    return f"{cache_path}/phi-l{layer}-r{revision}-st0.25x128-activations.parquet"
 
 with gr.Blocks() as demo:
     feature_table = gr.State(None)
 
-    tokenizer_name = gr.Textbox(TOKENIZER)
-    layer_dropdown = gr.Dropdown(layers)
-    feature_dropdown = gr.Dropdown()
+    tokenizer_name = gr.Textbox(TOKENIZER, label="Tokenizer")
+    revision_dropdown = gr.Dropdown(revisions, label="Revision")
+
+    layer_dropdown = gr.Dropdown(layers[4], label="Layer")
 
     def update_features(layer):
         feature_dropdown = gr.Dropdown(features[layer].keys())
         return feature_dropdown
-
-    layer_dropdown.input(update_features, layer_dropdown, feature_dropdown)
-
+    
+    def update_layers(revision):
+        layer_dropdown = gr.Dropdown(layers[revision])
+        return layer_dropdown
 
     frequency = gr.Number(0, label="Total frequency (%)")
+
+    # layer_dropdown.input(update_features, layer_dropdown, feature_dropdown)
     # histogram = gr.LinePlot(x="activation", y="freq")
 
-    autoi_expl = gr.Textbox()
-    selfe_expl = gr.Textbox()
+    revision_dropdown.input(update_layers, revision_dropdown, layer_dropdown)
+
+    feature_input = gr.Number(0, label="Feature")
+
+    autoi_expl = gr.Textbox(label="AutoInterp Explanation")
+    selfe_expl = gr.Textbox(label="SelfExplain Explanation")
 
     cm = gr.HighlightedText()
     frame = gr.Highlightedtext()
 
-    def update(layer, feature, tokenizer_name):
+    def update(revision, layer, feature, tokenizer_name):
         tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
-        table = pq.read_table(make_cache_name(layer))
+        table = pq.read_table(make_cache_name(layer, revision))
         table_feat = table.filter(pc.field("feature") == feature).to_pandas()
 
         # freq_t = table_feat[["activation", "freq"]]
@@ -85,19 +122,22 @@ with gr.Blocks() as demo:
             flat_data = []
             color_map_data = []
 
-        autoi_expl = features[layer][feature]["explanation"]
-        selfe_expl = features[layer][feature]["gen_explanations"]
-
-        if selfe_expl is not None:
-            selfe_expl = "\n".join(
-                f"{i+1}. \"{x}\"" for i, x in enumerate(selfe_expl)
-            )
+        if feature in features[revision][layer]:
+            autoi_expl = features[revision][layer][feature]["explanation"]
+            selfe_expl = features[revision][layer][feature]["gen_explanations"]
+            if selfe_expl is not None:
+                selfe_expl = "\n".join(
+                    f"{i+1}. \"{x}\"" for i, x in enumerate(selfe_expl)
+                )
 
+        else:
+            autoi_expl = "No explanation found"
+            selfe_expl = "No explanation found"
         return flat_data, color_map_data, total_freq, autoi_expl, selfe_expl
         
 
-    feature_dropdown.change(update, [layer_dropdown, feature_dropdown, tokenizer_name], [frame, cm, frequency, autoi_expl, selfe_expl])
-    # feature_input.change(update, [dropdown, feature_input, tokenizer_name, token_range], [frame, cm, histogram, frequency])
+    # feature_dropdown.change(update, [layer_dropdown, feature_dropdown, tokenizer_name], [frame, cm, frequency, autoi_expl, selfe_expl])
+    feature_input.change(update, [revision_dropdown, layer_dropdown, feature_input, tokenizer_name], [frame, cm, frequency, autoi_expl, selfe_expl])
 
 
 if __name__ == "__main__":

requirements.txt

@@ -2,4 +2,5 @@ pyarrow
 transformers[cpu]
 numpy
 pandas
-datasets
+datasets
+gradio

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