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Jac-Zac commited on May 12

Commit

99c28ab

1 Parent(s): 4df7d97

Adding per layer clustering

Browse files

Files changed (9) hide show

app.py +0 -6
pyproject.toml +1 -1
tabs/chat.py +13 -10
tabs/chat_ui.py +8 -2
tabs/compare.py +77 -12
utils/chat.py +13 -3
utils/chat_export.py +2 -2
utils/runtime.py +3 -0
uv.lock +55 -55

app.py CHANGED Viewed

@@ -157,12 +157,6 @@ def main() -> None:
     install_catppuccin_theme(st.get_option("theme.base"))
-    # Deferred: importing torch is slow; keep it after dotenv load (done at
-    # module level above) so the Streamlit page config renders immediately.
-    import torch
-    torch.set_grad_enabled(False)
     sidebar = _sidebar_controls()
     if sidebar.active_tab == "Extract":

     install_catppuccin_theme(st.get_option("theme.base"))
     sidebar = _sidebar_controls()
     if sidebar.active_tab == "Extract":

pyproject.toml CHANGED Viewed

@@ -5,7 +5,7 @@ description = "Streamlit UI for persona-vectors"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
-    "persona-vectors>=0.7.2",
     "persona-data>=0.4.2",
     "datasets>=4.8.5",
     "huggingface-hub>=1.14.0",

 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
+    "persona-vectors>=0.7.3",
     "persona-data>=0.4.2",
     "datasets>=4.8.5",
     "huggingface-hub>=1.14.0",

tabs/chat.py CHANGED Viewed

@@ -9,8 +9,6 @@ from tabs.chat_ui import (
     render_persona_prompt_controls,
     render_system_prompt,
 )
-from tabs.compare_chat import render_compare_mode
-from tabs.probe_ui import render_probe_inspector
 from utils.chat import (
     ChatReply,
     build_chat_messages,
@@ -155,6 +153,8 @@ def render_chat_tab(remote: bool, model_name: str, dataset_source: str) -> None:
         last_token_contrast_key=_LAST_TOKEN_CONTRAST_KEY,
     )
     if tools.compare_mode:
         render_compare_mode(
             remote,
             model_name,
@@ -225,14 +225,17 @@ def render_chat_tab(remote: bool, model_name: str, dataset_source: str) -> None:
             ),
         )
-    render_probe_inspector(
-        context_key=context_key,
-        model_name=model_name,
-        remote=remote,
-        active_system_prompt=active_system_prompt,
-        chat_state=chat_state,
-        enabled=tools.probe_enabled,
-    )
     render_chat_window(
         chat_log=chat_log,

     render_persona_prompt_controls,
     render_system_prompt,
 )
 from utils.chat import (
     ChatReply,
     build_chat_messages,
         last_token_contrast_key=_LAST_TOKEN_CONTRAST_KEY,
     )
     if tools.compare_mode:
+        from tabs.compare_chat import render_compare_mode
         render_compare_mode(
             remote,
             model_name,
             ),
         )
+    if tools.probe_enabled:
+        from tabs.probe_ui import render_probe_inspector
+        render_probe_inspector(
+            context_key=context_key,
+            model_name=model_name,
+            remote=remote,
+            active_system_prompt=active_system_prompt,
+            chat_state=chat_state,
+            enabled=True,
+        )
     render_chat_window(
         chat_log=chat_log,

tabs/chat_ui.py CHANGED Viewed

@@ -1,11 +1,12 @@
 from collections.abc import Callable
 from dataclasses import asdict, dataclass
-from typing import Any
 import streamlit as st
 from persona_data.synth_persona import PersonaData
-from utils.contrast import TokenContrast, render_contrast_html
 from utils.helpers import (
     CHAT_PROMPT_MODE_LABEL_TO_KEY,
     CHAT_PROMPT_MODE_LABELS,
@@ -14,6 +15,9 @@ from utils.helpers import (
     widget_key,
 )
 GENERATION_DEFAULTS = {
     "max_new_tokens": 256,
     "temperature": 1.0,
@@ -326,6 +330,8 @@ def render_chat_message(
     contrast: TokenContrast | None = message.get("_contrast") if show_contrast else None
     with st.chat_message(message["role"]):
         if contrast is not None:
             st.html(render_contrast_html(contrast))
         else:
             st.markdown(message["content"])

+from __future__ import annotations
 from collections.abc import Callable
 from dataclasses import asdict, dataclass
+from typing import TYPE_CHECKING, Any
 import streamlit as st
 from persona_data.synth_persona import PersonaData
 from utils.helpers import (
     CHAT_PROMPT_MODE_LABEL_TO_KEY,
     CHAT_PROMPT_MODE_LABELS,
     widget_key,
 )
+if TYPE_CHECKING:
+    from utils.contrast import TokenContrast
 GENERATION_DEFAULTS = {
     "max_new_tokens": 256,
     "temperature": 1.0,
     contrast: TokenContrast | None = message.get("_contrast") if show_contrast else None
     with st.chat_message(message["role"]):
         if contrast is not None:
+            from utils.contrast import render_contrast_html
             st.html(render_contrast_html(contrast))
         else:
             st.markdown(message["content"])

tabs/compare.py CHANGED Viewed

@@ -72,6 +72,17 @@ _DEFAULT_PERSONA_LIMITS = {
 }
 _MAX_SIMILARITY_CELLS = 4_000_000
 _MAX_PAIR_TRAJECTORY_TRACES = 500
 def _is_assistant_persona(persona_id: str, persona_name: str | None = None) -> bool:
@@ -728,21 +739,65 @@ def _render_layered_figure_analysis(
             return
     n_clusters = None
     if figure_kind in {"pca", "umap"}:
-        use_kmeans = st.toggle(
-            "Color by K-means clusters",
             value=False,
-            key=widget_key("load", "kmeans_enabled", scope, store_id(store)),
-            help="Run K-means on persona vectors and color each persona by cluster.",
         )
-        if use_kmeans:
-            n_clusters = st.slider(
-                "K (clusters)",
-                min_value=2,
-                max_value=min(10, len(persona_ids)),
-                value=min(3, len(persona_ids)),
-                key=widget_key("load", "kmeans_k", scope, store_id(store)),
             )
     fig_key = widget_key(
         "load",
@@ -753,6 +808,10 @@ def _render_layered_figure_analysis(
         figure_kind,
         str(n_components),
         str(n_clusters),
         variant,
         "persona_vector",
         persona_key,
@@ -781,8 +840,14 @@ def _render_layered_figure_analysis(
             build_kwargs = {}
             if figure_kind in {"umap", "pca"}:
                 build_kwargs["n_components"] = n_components
-                if n_clusters is not None:
                     build_kwargs["n_clusters"] = n_clusters
             if figure_kind == "similarity" and pair_trajectories:
                 main_fig, extra_fig = build_similarity_figures(
                     samples,

 }
 _MAX_SIMILARITY_CELLS = 4_000_000
 _MAX_PAIR_TRAJECTORY_TRACES = 500
+_CLUSTER_METHODS = {
+    "K-means": "kmeans",
+    "Agglomerative": "agglomerative",
+    "HDBSCAN": "hdbscan",
+}
+_CLUSTER_MODES = {
+    "Mean across layers": "mean_across_layers",
+    "First selected layer": "first_layer",
+    "Per layer": "per_layer",
+}
+_CLUSTER_LINKAGES = ["ward", "complete", "average", "single"]
 def _is_assistant_persona(persona_id: str, persona_name: str | None = None) -> bool:
             return
     n_clusters = None
+    cluster_mode = None
+    cluster_method = None
+    cluster_linkage = None
+    min_cluster_size = None
     if figure_kind in {"pca", "umap"}:
+        use_clusters = st.toggle(
+            "Color by clusters",
             value=False,
+            key=widget_key("load", "clusters_enabled", scope, store_id(store)),
+            help="Cluster persona vectors and color points by cluster.",
         )
+        if use_clusters:
+            method_label = st.selectbox(
+                "Cluster algorithm",
+                options=list(_CLUSTER_METHODS),
+                index=0,
+                key=widget_key("load", "cluster_method", scope, store_id(store)),
+            )
+            cluster_method = _CLUSTER_METHODS[method_label]
+            if cluster_method in {"kmeans", "agglomerative"}:
+                n_clusters = st.slider(
+                    "K (clusters)",
+                    min_value=2,
+                    max_value=min(10, len(persona_ids)),
+                    value=min(3, len(persona_ids)),
+                    key=widget_key("load", "cluster_k", scope, store_id(store)),
+                )
+            if cluster_method == "agglomerative":
+                cluster_linkage = st.selectbox(
+                    "Linkage",
+                    options=_CLUSTER_LINKAGES,
+                    index=0,
+                    key=widget_key("load", "cluster_linkage", scope, store_id(store)),
+                )
+            if cluster_method == "hdbscan":
+                min_cluster_size = st.slider(
+                    "Minimum cluster size",
+                    min_value=2,
+                    max_value=len(persona_ids),
+                    value=min(5, len(persona_ids)),
+                    key=widget_key(
+                        "load",
+                        "cluster_min_cluster_size",
+                        scope,
+                        store_id(store),
+                    ),
+                )
+            mode_label = st.selectbox(
+                "Cluster fit",
+                options=list(_CLUSTER_MODES),
+                index=0,
+                key=widget_key("load", "cluster_mode", scope, store_id(store)),
+                help=(
+                    "Mean across layers is the previous behavior. First selected "
+                    "layer keeps one fixed clustering from the first frame. Per layer "
+                    "recomputes clustering for each animation frame."
+                ),
             )
+            cluster_mode = _CLUSTER_MODES[mode_label]
     fig_key = widget_key(
         "load",
         figure_kind,
         str(n_components),
         str(n_clusters),
+        str(cluster_mode),
+        str(cluster_method),
+        str(cluster_linkage),
+        str(min_cluster_size),
         variant,
         "persona_vector",
         persona_key,
             build_kwargs = {}
             if figure_kind in {"umap", "pca"}:
                 build_kwargs["n_components"] = n_components
+                if cluster_method is not None:
+                    build_kwargs["cluster_method"] = cluster_method
                     build_kwargs["n_clusters"] = n_clusters
+                    build_kwargs["cluster_mode"] = cluster_mode
+                    if cluster_linkage is not None:
+                        build_kwargs["cluster_linkage"] = cluster_linkage
+                    if min_cluster_size is not None:
+                        build_kwargs["min_cluster_size"] = min_cluster_size
             if figure_kind == "similarity" and pair_trajectories:
                 main_fig, extra_fig = build_similarity_figures(
                     samples,

utils/chat.py CHANGED Viewed

@@ -1,13 +1,17 @@
 import logging
 from contextlib import contextmanager, nullcontext
 from dataclasses import dataclass
-from typing import Literal
-import torch
-from nnterp import StandardizedTransformer
 from persona_data.prompts import format_messages, format_prompt, normalize_messages
 from persona_data.synth_persona import PersonaData
 logger = logging.getLogger(__name__)
 SystemPromptMode = Literal["empty", "templated", "biography", "custom"]
@@ -129,6 +133,8 @@ def format_generation_prompt(
 def resolve_saved_tensor(value: object) -> torch.Tensor:
     """Resolve an nnsight ``.save()`` proxy (or raw tensor) to a CPU tensor."""
     resolved = value.value if getattr(value, "value", None) is not None else value
     if not isinstance(resolved, torch.Tensor):
         raise TypeError(f"Trace result did not resolve to a tensor: {type(resolved)!r}")
@@ -154,6 +160,8 @@ def _seeded_rng(seed: int | None):
         yield
         return
     cuda_ctx = torch.random.fork_rng(devices=range(torch.cuda.device_count()))
     mps_ctx = (
         torch.random.fork_rng(devices=range(1), device_type="mps")
@@ -199,6 +207,8 @@ def generate_chat_reply(
         ChatReply with generated text and token ids.
     """
     tokenizer = model.tokenizer
     prompt, prompt_token_count = format_generation_prompt(messages, tokenizer)

+from __future__ import annotations
 import logging
 from contextlib import contextmanager, nullcontext
 from dataclasses import dataclass
+from typing import TYPE_CHECKING, Literal
 from persona_data.prompts import format_messages, format_prompt, normalize_messages
 from persona_data.synth_persona import PersonaData
+if TYPE_CHECKING:
+    import torch
+    from nnterp import StandardizedTransformer
 logger = logging.getLogger(__name__)
 SystemPromptMode = Literal["empty", "templated", "biography", "custom"]
 def resolve_saved_tensor(value: object) -> torch.Tensor:
     """Resolve an nnsight ``.save()`` proxy (or raw tensor) to a CPU tensor."""
+    import torch
     resolved = value.value if getattr(value, "value", None) is not None else value
     if not isinstance(resolved, torch.Tensor):
         raise TypeError(f"Trace result did not resolve to a tensor: {type(resolved)!r}")
         yield
         return
+    import torch
     cuda_ctx = torch.random.fork_rng(devices=range(torch.cuda.device_count()))
     mps_ctx = (
         torch.random.fork_rng(devices=range(1), device_type="mps")
         ChatReply with generated text and token ids.
     """
+    import torch
     tokenizer = model.tokenizer
     prompt, prompt_token_count = format_generation_prompt(messages, tokenizer)

utils/chat_export.py CHANGED Viewed

@@ -3,8 +3,6 @@ from dataclasses import asdict, is_dataclass
 from datetime import datetime, timezone
 from pathlib import Path
-from persona_data.environment import get_artifacts_dir
 from utils.helpers import slugify
@@ -48,6 +46,8 @@ def save_chat_export(
         The path the export was written to.
     """
     payload = {
         "model_name": model_name,
         "dataset_source": dataset_source,

 from datetime import datetime, timezone
 from pathlib import Path
 from utils.helpers import slugify
         The path the export was written to.
     """
+    from persona_data.environment import get_artifacts_dir
     payload = {
         "model_name": model_name,
         "dataset_source": dataset_source,

utils/runtime.py CHANGED Viewed

@@ -103,6 +103,9 @@ def cached_model(model_name: str):
     model objects when toggling NDIF.
     """
     from nnterp import StandardizedTransformer
     return StandardizedTransformer(model_name)

     model objects when toggling NDIF.
     """
+    import torch
     from nnterp import StandardizedTransformer
+    torch.set_grad_enabled(False)
     return StandardizedTransformer(model_name)

uv.lock CHANGED Viewed

@@ -748,11 +748,11 @@ wheels = [
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