Test coverage/cleaning up xfails

demos/Othello_GPT.ipynb

@@ -148,6 +148,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
     "# Import stuff\n",
     "import torch\n",
     "import torch.nn as nn\n",
@@ -175,10 +176,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
     "import transformer_lens\n",
     "import transformer_lens.utilities as utils\n",
     "from transformer_lens.hook_points import HookPoint\n",

demos/Santa_Coder.ipynb

@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "da9f5a40",
    "metadata": {
     "execution": {
@@ -63,6 +63,7 @@
    },
    "outputs": [],
    "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
     "# Import stuff\n",
     "import torch\n",
     "import torch.nn as nn\n",

docs/source/content/migrating_to_v3.md

@@ -136,6 +136,10 @@ These work identically on `TransformerBridge` and need no migration:
 
 If your code only touches these APIs, the migration is genuinely just the loading call and (optionally) `enable_compatibility_mode`.
 
+### BERT Next Sentence Prediction
+
+`BertNextSentencePrediction` is not ported to `TransformerBridge`. Keep using `HookedEncoder` + `BertNextSentencePrediction` for NSP workflows. The bridge's BERT adapter does load NSP HuggingFace checkpoints (it rewires the unembed to `cls.seq_relationship`), but the high-level NSP API – sentence-pair tokenization, `[CLS]` pooling, "sequential"/"not sequential" decoding — is not exposed. If this is feature is something you'd like added to TransformerBridge, please file an issue.
+
 ### New in 3.x: streaming generation
 
 Both `HookedTransformer` and `TransformerBridge` now expose `generate_stream`, which yields tokens progressively instead of returning the full completion at once:

tests/integration/model_bridge/compatibility/test_bridge_hook_behavior.py

@@ -84,7 +84,6 @@ def hook_fn(tensor, hook):
         )
         assert fired == {"resid_pre_0", "resid_post_0"}
 
-    @pytest.mark.xfail(reason="add_perma_hook not yet implemented on TransformerBridge")
     def test_perma_hook_persists_across_calls(self, bridge):
         """A permanent hook fires on every forward pass until removed."""
         count = 0

tests/integration/test_centralized_weight_processing.py

@@ -42,53 +42,6 @@ def bridge_and_adapter(self, model_name, device):
         bridge = TransformerBridge.boot_transformers(model_name, device=device)
         return bridge, bridge.adapter, bridge.cfg
 
-    @pytest.mark.skip(
-        reason="API not implemented - ProcessWeights doesn't convert to TL format keys"
-    )
-    def test_processing_with_architecture_adapter(
-        self, raw_hf_model_and_state_dict, bridge_and_adapter
-    ):
-        """Test ProcessWeights.process_weights with architecture adapter."""
-        raw_hf_model, raw_state_dict = raw_hf_model_and_state_dict
-        bridge, adapter, cfg = bridge_and_adapter
-
-        # Preprocess weights first (this converts to TL format with split Q/K/V)
-        preprocessed_state_dict = adapter.preprocess_weights(raw_state_dict)
-
-        # Process with architecture adapter
-        processed_with_adapter = ProcessWeights.process_weights(
-            state_dict=preprocessed_state_dict,
-            cfg=cfg,
-            adapter=adapter,
-            fold_ln=False,
-            center_writing_weights=False,
-            center_unembed=False,
-            fold_value_biases=False,
-        )
-
-        # Verify processing occurred
-        assert len(processed_with_adapter) > 0, "Should process weights with adapter"
-
-        # Check for TransformerLens-style keys (after preprocessing)
-        # These should be in format like: blocks.0.attn.W_Q, blocks.0.attn.W_K, etc.
-        tl_keys = [
-            k
-            for k in processed_with_adapter.keys()
-            if any(
-                pattern in k for pattern in [".W_Q", ".W_K", ".W_V", ".W_O", ".b_Q", ".b_K", ".b_V"]
-            )
-        ]
-        assert (
-            len(tl_keys) > 0
-        ), "Should have TransformerLens-style attention keys after preprocessing"
-
-        # Check that expected TL-style keys exist
-        expected_patterns = ["blocks.0.attn.W_Q", "blocks.0.attn.W_K", "blocks.0.attn.W_V"]
-        for pattern in expected_patterns:
-            assert any(
-                pattern in k for k in processed_with_adapter.keys()
-            ), f"Should have {pattern} in processed weights"
-
     def test_processing_without_architecture_adapter(
         self, raw_hf_model_and_state_dict, bridge_and_adapter
     ):
@@ -116,125 +69,6 @@ def test_processing_without_architecture_adapter(
         ]
         assert len(hf_keys) > 0, "Should have HF-style keys without adapter"
 
-    @pytest.mark.skip(reason="API not implemented - adapter.preprocess_weights doesn't split Q/K/V")
-    def test_processing_with_different_flags(self, raw_hf_model_and_state_dict, bridge_and_adapter):
-        """Test processing with different flag combinations."""
-        raw_hf_model, raw_state_dict = raw_hf_model_and_state_dict
-        bridge, adapter, cfg = bridge_and_adapter
-
-        # Preprocess weights first
-        preprocessed_state_dict = adapter.preprocess_weights(raw_state_dict)
-
-        # Test processing with all flags enabled
-        processed_with_flags = ProcessWeights.process_weights(
-            state_dict=preprocessed_state_dict.copy(),
-            cfg=cfg,
-            adapter=adapter,
-            fold_ln=True,
-            center_writing_weights=True,
-            center_unembed=True,
-            fold_value_biases=True,
-        )
-
-        # Test processing with all flags disabled
-        processed_without_flags = ProcessWeights.process_weights(
-            state_dict=preprocessed_state_dict.copy(),
-            cfg=cfg,
-            adapter=adapter,
-            fold_ln=False,
-            center_writing_weights=False,
-            center_unembed=False,
-            fold_value_biases=False,
-        )
-
-        # Both should process successfully
-        assert len(processed_with_flags) > 0, "Should process weights with flags"
-        assert len(processed_without_flags) > 0, "Should process weights without flags"
-
-    @pytest.mark.skip(reason="API not implemented - adapter.preprocess_weights doesn't split Q/K/V")
-    def test_architecture_divergence_handling(
-        self, raw_hf_model_and_state_dict, bridge_and_adapter
-    ):
-        """Test that adapter preprocessing changes the state dict format."""
-        raw_hf_model, raw_state_dict = raw_hf_model_and_state_dict
-        bridge, adapter, cfg = bridge_and_adapter
-
-        # Preprocess with adapter (splits c_attn into Q/K/V)
-        preprocessed_with_adapter = adapter.preprocess_weights(raw_state_dict)
-
-        # Process with adapter after preprocessing
-        processed_with_adapter = ProcessWeights.process_weights(
-            state_dict=preprocessed_with_adapter,
-            cfg=cfg,
-            adapter=adapter,
-            fold_ln=True,
-            center_writing_weights=True,
-            center_unembed=True,
-            fold_value_biases=True,
-        )
-
-        # Process without adapter (no preprocessing)
-        processed_without_adapter = ProcessWeights.process_weights(
-            state_dict=raw_state_dict.copy(),
-            cfg=cfg,
-            adapter=None,
-            fold_ln=True,
-            center_writing_weights=True,
-            center_unembed=True,
-            fold_value_biases=True,
-        )
-
-        # Results should be different (different processing paths)
-        with_adapter_keys = set(processed_with_adapter.keys())
-        without_adapter_keys = set(processed_without_adapter.keys())
-
-        # Should have some different keys due to different processing
-        assert (
-            with_adapter_keys != without_adapter_keys
-        ), "With and without adapter should produce different key sets"
-
-        # With adapter should have split Q/K/V keys
-        tl_attn_keys = [
-            k for k in with_adapter_keys if any(p in k for p in [".W_Q", ".W_K", ".W_V"])
-        ]
-        assert len(tl_attn_keys) > 0, "With adapter should have split Q/K/V keys"
-
-    @pytest.mark.skip(reason="API not implemented - adapter.preprocess_weights doesn't split Q/K/V")
-    def test_custom_component_processing_integration(
-        self, raw_hf_model_and_state_dict, bridge_and_adapter
-    ):
-        """Test that adapter preprocessing splits QKV weights correctly."""
-        raw_hf_model, raw_state_dict = raw_hf_model_and_state_dict
-        bridge, adapter, cfg = bridge_and_adapter
-
-        # Preprocess weights first - this is what splits Q/K/V
-        preprocessed_weights = adapter.preprocess_weights(raw_state_dict)
-
-        # Process with adapter after preprocessing
-        processed_weights = ProcessWeights.process_weights(
-            state_dict=preprocessed_weights,
-            cfg=cfg,
-            adapter=adapter,
-            fold_ln=False,
-            center_writing_weights=False,
-            center_unembed=False,
-            fold_value_biases=False,
-        )
-
-        # Check for split Q/K/V weights (created by preprocessing)
-        custom_qkv_found = any(".W_Q" in k for k in processed_weights.keys())
-
-        assert custom_qkv_found, "Should have split QKV weights after preprocessing"
-
-        # Verify that QKV splitting occurred for each layer
-        q_keys = [k for k in processed_weights.keys() if ".W_Q" in k]
-        k_keys = [k for k in processed_weights.keys() if ".W_K" in k]
-        v_keys = [k for k in processed_weights.keys() if ".W_V" in k]
-
-        assert len(q_keys) > 0, "Should have Q weight keys"
-        assert len(k_keys) > 0, "Should have K weight keys"
-        assert len(v_keys) > 0, "Should have V weight keys"
-
     def test_computational_correctness_with_existing_pipeline(self, model_name, device):
         """Test that centralized processing maintains computational correctness."""
         test_tokens = torch.tensor([[1, 2, 3, 4, 5]], dtype=torch.long)