Split bypass prerequisites

modelopt/torch/puzzletron/anymodel/model_descriptor/base.py

@@ -169,6 +169,19 @@ def uses_autocast() -> bool:
         """
         return True
 
+    @staticmethod
+    def pruning_mixins() -> Dict[str, Any]:
+        """Return available pruning mixins for bypass distillation.
+
+        Override in subclasses to provide model-specific pruning mixins, e.g.
+        ``{"kv_heads": KVHeadsPruningMixIn(...), "experts_removal": ExpertRemovalPruningMixIn(...)}``.
+
+        Returns an empty dict by default so that descriptors that do not need
+        model-specific weight-slicing (e.g. Llama with standard FFN truncation)
+        can rely on the generic ``create_child_state_dict`` fallback path.
+        """
+        return {}
+
     @staticmethod
     def get_language_model_config(config):
         """Get the language model config from a PretrainedConfig.

modelopt/torch/puzzletron/anymodel/models/gpt_oss/gpt_oss_model_descriptor.py

@@ -28,6 +28,7 @@
     ExpertRemovalLayerDescriptor,
     ExpertRemovalPruningMixIn,
 )
+from ....pruning.kv_heads_pruning_mixin import KVHeadsLayerDescriptor, KVHeadsPruningMixIn
 
 # Expert removal is supported for unquantized models (test models).
 # Production models use MXFP4 quantized MoE with combined tensors
@@ -37,7 +38,11 @@
 from ...model_descriptor import ModelDescriptor, ModelDescriptorFactory
 from ...puzzformer.no_op import MatchingZeros, Same, return_tuple_of_size
 
-__all__ = ["GptOssModelDescriptor", "GptOssExpertRemovalLayerDescriptor"]
+__all__ = [
+    "GptOssExpertRemovalLayerDescriptor",
+    "GptOssKVHeadsLayerDescriptor",
+    "GptOssModelDescriptor",
+]
 
 
 @ModelDescriptorFactory.register_decorator("gpt_oss")
@@ -173,7 +178,29 @@ def pruning_mixins() -> Dict[str, PruningMixIn]:
         Note: Expert removal works for unquantized models (test models).
         Production models use MXFP4 quantization which is not yet supported.
         """
-        return {"expert_removal": ExpertRemovalPruningMixIn(GptOssExpertRemovalLayerDescriptor())}
+        # Single instance shared between the canonical key and the legacy alias
+        # so resolve_pruning_mixin returns the same object regardless of which
+        # name a caller uses.
+        expert_mixin = ExpertRemovalPruningMixIn(GptOssExpertRemovalLayerDescriptor())
+        return {
+            "experts_removal": expert_mixin,
+            # Backward-compat alias: this key was "expert_removal" before the
+            # bypass branch standardised on "experts_removal" (matching the
+            # NemotronH descriptor). Kept so external scripts that still call
+            # `resolve_pruning_mixin("expert_removal", GptOssModelDescriptor)`
+            # continue to work. Remove after a deprecation cycle.
+            "expert_removal": expert_mixin,
+            "kv_heads": KVHeadsPruningMixIn(GptOssKVHeadsLayerDescriptor()),
+        }
+
+
+@dataclass
+class GptOssKVHeadsLayerDescriptor(KVHeadsLayerDescriptor):
+    o_proj_name: str = "self_attn.o_proj"
+    attn_prefix_name: str = "model.layers.{layer_idx}.self_attn"
+    qkvo_weight_names: List[str] = field(
+        default_factory=lambda: ["q_proj", "k_proj", "v_proj", "o_proj"]
+    )
 
 
 @dataclass

modelopt/torch/puzzletron/anymodel/models/nemotron_h/nemotron_h_model_descriptor.py

@@ -29,11 +29,16 @@
     ExpertRemovalLayerDescriptor,
     ExpertRemovalPruningMixIn,
 )
+from ....pruning.kv_heads_pruning_mixin import KVHeadsLayerDescriptor, KVHeadsPruningMixIn
 from ....pruning.pruning_mixin import PruningMixIn
 from ...model_descriptor import ModelDescriptor, ModelDescriptorFactory
 from ...puzzformer.no_op import MatchingZeros, Same
 
-__all__ = ["NemotronHExpertRemovalLayerDescriptor", "NemotronHModelDescriptor"]
+__all__ = [
+    "NemotronHExpertRemovalLayerDescriptor",
+    "NemotronHKVHeadsLayerDescriptor",
+    "NemotronHModelDescriptor",
+]
 
 
 def get_dynamic_modules(module_cls_str: str) -> List[Type[nn.Module]]:
@@ -51,6 +56,15 @@ def get_dynamic_modules(module_cls_str: str) -> List[Type[nn.Module]]:
     return matches
 
 
+@dataclass
+class NemotronHKVHeadsLayerDescriptor(KVHeadsLayerDescriptor):
+    o_proj_name: str = "mixer.o_proj"
+    attn_prefix_name: str = "backbone.layers.{layer_idx}.mixer"
+    qkvo_weight_names: List[str] = field(
+        default_factory=lambda: ["q_proj", "k_proj", "v_proj", "o_proj"]
+    )
+
+
 @dataclass
 class NemotronHExpertRemovalLayerDescriptor(ExpertRemovalLayerDescriptor):
     target_name: str = "mixer.gate"
@@ -251,4 +265,5 @@ def build_attention_predicates() -> Dict[str, re.Pattern]:
     def pruning_mixins() -> Dict[str, PruningMixIn]:
         return {
             "experts_removal": ExpertRemovalPruningMixIn(NemotronHExpertRemovalLayerDescriptor()),
+            "kv_heads": KVHeadsPruningMixIn(NemotronHKVHeadsLayerDescriptor()),
         }

modelopt/torch/puzzletron/anymodel/models/nemotron_h_v2/nemotron_h_v2_model_descriptor.py

@@ -29,11 +29,16 @@
     FFNIntermediateLayerDescriptor,
     FFNIntermediatePruningMixIn,
 )
+from ....pruning.kv_heads_pruning_mixin import KVHeadsLayerDescriptor, KVHeadsPruningMixIn
 from ....pruning.pruning_mixin import PruningMixIn
 from ...model_descriptor import ModelDescriptor, ModelDescriptorFactory
 from ...puzzformer.no_op import MatchingZeros, Same
 
-__all__ = ["NemotronHV2FFNIntermediateLayerDescriptor", "NemotronHV2ModelDescriptor"]
+__all__ = [
+    "NemotronHV2FFNIntermediateLayerDescriptor",
+    "NemotronHV2KVHeadsLayerDescriptor",
+    "NemotronHV2ModelDescriptor",
+]
 
 
 def get_dynamic_modules(module_cls_str: str) -> List[Type[nn.Module]]:
@@ -69,6 +74,15 @@ class NemotronHV2FFNIntermediateLayerDescriptor(FFNIntermediateLayerDescriptor):
     linear_weight_names: List[str] = field(default_factory=lambda: ["down_proj", "up_proj"])
 
 
+@dataclass
+class NemotronHV2KVHeadsLayerDescriptor(KVHeadsLayerDescriptor):
+    o_proj_name: str = "mixer.o_proj"
+    attn_prefix_name: str = "backbone.layers.{layer_idx}.mixer"
+    qkvo_weight_names: List[str] = field(
+        default_factory=lambda: ["q_proj", "k_proj", "v_proj", "o_proj"]
+    )
+
+
 @ModelDescriptorFactory.register_decorator("nemotron_h_v2")
 class NemotronHV2ModelDescriptor(ModelDescriptor):
     _DECODER_LAYER_CLS: Type[nn.Module] = None
@@ -251,5 +265,6 @@ def pruning_mixins() -> Dict[str, PruningMixIn]:
             "ffn_intermediate": FFNIntermediatePruningMixIn(
                 NemotronHV2FFNIntermediateLayerDescriptor()
             ),
+            "kv_heads": KVHeadsPruningMixIn(NemotronHV2KVHeadsLayerDescriptor()),
             # TODO: Add expert removal support when ExpertRemovalPruningMixIn is migrated
         }

modelopt/torch/puzzletron/anymodel/models/qwen3_vl/qwen3_vl_model_descriptor.py

@@ -26,9 +26,13 @@
 )
 
 from ....block_config import BlockConfig
-from ....pruning.expert_removal_pruning_mixin import ExpertRemovalLayerDescriptor
+from ....pruning.expert_removal_pruning_mixin import (
+    ExpertRemovalLayerDescriptor,
+    ExpertRemovalPruningMixIn,
+)
 from ....pruning.ffn_intermediate_pruning_mixin import FFNIntermediateLayerDescriptor
-from ....pruning.kv_heads_pruning_mixin import KVHeadsLayerDescriptor
+from ....pruning.kv_heads_pruning_mixin import KVHeadsLayerDescriptor, KVHeadsPruningMixIn
+from ....pruning.pruning_mixin import PruningMixIn
 from ...model_descriptor import ModelDescriptor, ModelDescriptorFactory
 from ...puzzformer.no_op import MatchingZeros, Same, return_tuple_of_size
 
@@ -56,6 +60,13 @@ def get_language_model_config(config):
         """Qwen3-VL has nested text_config for language model parameters."""
         return config.text_config if hasattr(config, "text_config") else config
 
+    @staticmethod
+    def pruning_mixins() -> Dict[str, PruningMixIn]:
+        return {
+            "experts_removal": ExpertRemovalPruningMixIn(Qwen3VLExpertRemovalLayerDescriptor()),
+            "kv_heads": KVHeadsPruningMixIn(Qwen3VLKVHeadsLayerDescriptor()),
+        }
+
     @staticmethod
     def decoder_layer_cls():
         return Qwen3VLMoeTextDecoderLayer

modelopt/torch/puzzletron/pruning/kv_heads_pruning_mixin.py

@@ -24,7 +24,12 @@
 )
 
 from .pruning_mixin import LayerDescriptor, PruningMixIn
-from .pruning_utils import GQAInitMode, _init_attention_biases, _init_attention_weights
+from .pruning_utils import (
+    GQAInitMode,
+    _init_attention_biases,
+    _init_attention_weights,
+    _lm_head_dim,
+)
 
 __all__ = [
     "KVHeadsLayerDescriptor",
@@ -74,7 +79,7 @@ def prune_single_layer(
             f"{attn_prefix}.{proj_name}" for proj_name in self.layer_descriptor.qkvo_weight_names
         ]
 
-        head_size = new_config.head_dim
+        head_size = _lm_head_dim(new_config)
         for part in ["weight", "bias"]:
             attn_keys = [f"{name}.{part}" for name in [q_name, k_name, v_name, o_name]]
             q_key, k_key, v_key, o_key = attn_keys

modelopt/torch/puzzletron/pruning/pruning_utils.py

@@ -52,6 +52,7 @@ class MlpInitMode(Enum):
     PruneByActivationsLog = "PruneByActivationsLog"
     ExpertRemoval = "ExpertRemoval"
     ConcatExpertsIntoDenseFFN = "ConcatExpertsIntoDenseFFN"
+    MoEChannelPruning = "MoEChannelPruning"
 
 
 class LinearInitMode(Enum):
@@ -66,6 +67,30 @@ class HiddenSizeInitMode(Enum):
     CopyAsIs = "CopyAsIs"
 
 
+def _lm_attrs(config):
+    """Return the language-model sub-config for VL configs, else the config itself.
+
+    VL configs nest language-model fields like ``num_attention_heads``, ``head_dim``,
+    and ``hidden_size`` under a sub-config. The attribute name varies by family —
+    ``text_config`` (Qwen3-VL, Llava, Idefics), ``language_config`` (Llama-4 and a
+    handful of others), and ``llm_config`` (InternVL and friends) are all common.
+    Probe each before falling back to the raw config.
+    """
+    for attr in ("text_config", "language_config", "llm_config"):
+        sub = getattr(config, attr, None)
+        if sub is not None:
+            return sub
+    return config
+
+
+def _lm_head_dim(config) -> int:
+    lm_config = _lm_attrs(config)
+    head_dim = getattr(lm_config, "head_dim", None)
+    if head_dim is not None:
+        return head_dim
+    return lm_config.hidden_size // lm_config.num_attention_heads
+
+
 def resolve_pruning_mixin(
     pruning_mixin, descriptor: Type[ModelDescriptor]
 ) -> PruningMixIn | List[PruningMixIn]:
@@ -224,10 +249,13 @@ def _init_attention_weights(
     head_size,
     mlp_init_config,
 ):
-    assert new_config.num_attention_heads == original_config.num_attention_heads, (
-        f"({new_config.num_attention_heads=}) != ({original_config.num_attention_heads=})"
+    new_lm = _lm_attrs(new_config)
+    orig_lm = _lm_attrs(original_config)
+    assert new_lm.num_attention_heads == orig_lm.num_attention_heads, (
+        f"({new_lm.num_attention_heads=}) != ({orig_lm.num_attention_heads=})"
     )
-    num_q_heads = new_config.num_attention_heads
+    num_q_heads = new_lm.num_attention_heads
+    # block_configs lives on the outer puzzletron-converted config, not on text_config.
     num_kv_heads = new_config.block_configs[layer_idx].attention.num_key_value_heads
     orig_num_kv_heads = original_config.block_configs[layer_idx].attention.num_key_value_heads
 
@@ -372,17 +400,29 @@ def _init_attention_biases(
     head_size,
     mlp_init_config,
 ):
-    assert new_config.num_attention_heads == original_config.num_attention_heads, (
-        f"({new_config.num_attention_heads=}) != ({original_config.num_attention_heads=})"
+    new_lm = _lm_attrs(new_config)
+    orig_lm = _lm_attrs(original_config)
+    assert new_lm.num_attention_heads == orig_lm.num_attention_heads, (
+        f"({new_lm.num_attention_heads=}) != ({orig_lm.num_attention_heads=})"
     )
-    num_q_heads = new_config.num_attention_heads
+    num_q_heads = new_lm.num_attention_heads
+    # block_configs lives on the outer puzzletron-converted config, not on text_config.
     num_kv_heads = new_config.block_configs[layer_idx].attention.num_key_value_heads
     orig_num_kv_heads = original_config.block_configs[layer_idx].attention.num_key_value_heads
     n_heads_in_group = num_q_heads // num_kv_heads
     orig_n_heads_in_group = num_q_heads // orig_num_kv_heads
 
-    o_proj_bias = new_config.o_proj_bias
-    attention_bias = new_config.attention_bias
+    # Some HF native configs (e.g. GptOssConfig) don't expose o_proj_bias / attention_bias as
+    # top-level attributes the way puzzletron's DeciLM-style configs do. Fall back to probing
+    # the new state dict for the actual bias keys when the attribute is missing.
+    # KVHeadsPruningMixIn only calls this helper after filtering to keys present in
+    # new_state_dict, so the probe mirrors the caller's already-selected bias tensors.
+    o_proj_bias = getattr(new_config, "o_proj_bias", None)
+    if o_proj_bias is None:
+        o_proj_bias = o_key in new_state_dict
+    attention_bias = getattr(new_config, "attention_bias", None)
+    if attention_bias is None:
+        attention_bias = q_key in new_state_dict
 
     # If no biases
     if not (o_proj_bias or attention_bias):
@@ -438,8 +478,8 @@ def _init_attention_biases(
         assert not is_original_mha, (
             "Degrouping can only be done on original models that are GQA themselves."
         )
-        n_groups = new_config.num_attention_heads // n_heads_in_group
-        orig_n_groups = original_config.num_attention_heads // orig_n_heads_in_group
+        n_groups = new_lm.num_attention_heads // n_heads_in_group
+        orig_n_groups = orig_lm.num_attention_heads // orig_n_heads_in_group
         assert n_groups % orig_n_groups == 0, f"{n_groups=} must be a divisor of {orig_n_groups=}"
         n_repeats = n_groups // orig_n_groups
         if n_repeats > 1:

modelopt/torch/puzzletron/sewing_kit/passage.py

@@ -45,6 +45,7 @@
     "PassageOutput",
     "Predicate",
     "always_false_predicate",
+    "always_true_predicate",
     "Passage",
     "patch_module",
 ]