Roll back more variable renamings

Signed-off-by: John St. John <jstjohn@nvidia.com>

Author: jstjohn

bionemo-recipes/recipes/evo2_megatron/src/bionemo/evo2/models/megatron/hyena/hyena_mixer.py

@@ -119,9 +119,6 @@ def __init__(
         self.fast_conv_mixer = self.hyena_config.fast_conv_mixer
 
         self.use_subquadratic_ops = self.transformer_config.use_subquadratic_ops
-        # TODO: Re-enable B2BCausalConv1dModule for short/medium Hyena layers once
-        # subquadratic-ops updates it to support causal_conv1d 1.6+ semantics.
-        self.use_fused_b2b_causal_conv1d = False
 
         # Per attention head and per partition values.
         assert torch.distributed.is_initialized()
@@ -200,9 +197,9 @@ def __init__(
                 use_conv_bias=self.transformer_config.use_short_conv_bias,
             )
 
-            if self.use_fused_b2b_causal_conv1d:
-                # Create a wrapper module that doesn't register parameters
-                # Use the existing weights from the original model
+            if self.use_subquadratic_ops:
+                # The B2B kernel is guarded in hyena_utils and fails early if the local CUDA stack
+                # cannot run subquadratic_ops_torch correctly.
                 self.b2b_kernel = B2BCausalConv1dModule(
                     self.hyena_proj_conv,
                     self.mixer,
@@ -231,9 +228,9 @@ def __init__(
                 max_sequence_length,
             )
 
-            if self.use_fused_b2b_causal_conv1d and self.operator_type == "hyena_medium_conv":
-                # Create a wrapper module that doesn't register parameters
-                # Use the existing weights from the original model
+            if self.use_subquadratic_ops and self.operator_type == "hyena_medium_conv":
+                # The B2B kernel is guarded in hyena_utils and fails early if the local CUDA stack
+                # cannot run subquadratic_ops_torch correctly.
                 self.b2b_kernel = B2BCausalConv1dModule(
                     self.hyena_proj_conv,
                     self.mixer,
@@ -311,12 +308,12 @@ def forward(self, x, layer_past=None, inference_context=None, _hyena_use_cp=True
         else:
             features = rearrange(features, "l b d -> b d l").contiguous()
 
-        is_b2b_eligible = self.use_fused_b2b_causal_conv1d and self.operator_type in [
+        is_b2b_eligible = self.use_subquadratic_ops and self.operator_type in [
             "hyena_short_conv",
             "hyena_medium_conv",
         ]
-        # b2b runs during training (no inference_context) or during prefill (no FIR cache yet).
-        # During decode (cache populated, L=1) we fall back to the regular per-token step path.
+        # B2B runs during training (no inference_context) or during prefill (no FIR cache yet).
+        # During decode, fall back to the regular per-token step path.
         is_prefill = inference_context is not None and id(self.hyena_proj_conv) not in getattr(
             inference_context, "fir_filter_state_dict", {}
         )