[diffusion] Fix test_model_fast_paths import after sana_ln_modulate rename (#36175)

Co-authored-by: Alex Nails <alex.nails@radixark.ai>

test/registered/kernels/ops/diffusion/test_model_fast_paths.py

@@ -99,7 +99,7 @@ from sglang.multimodal_gen.runtime.models.dits.sana import (
     _eager_ln_modulate as _sana_eager_ln_modulate,
 )
 from sglang.multimodal_gen.runtime.models.dits.sana import (
-    _sana_ln_modulate,
+    sana_ln_modulate,
 )
 from sglang.multimodal_gen.runtime.models.vaes import flux2_vae_cuda_opt as vae_opt
 from sglang.multimodal_gen.runtime.models.vaes.autoencoder import AutoencoderKL
@@ -361,7 +361,7 @@ def test_sana_fused_ln_modulate_is_bit_exact(shape, nmod, transposed):
     shift, scale = emb.chunk(nmod, dim=1)[0], emb.chunk(nmod, dim=1)[-1]
     # default-stream eager serving must stay on the untouched eager chain
     n_sigs = len(sana._SANA_LN_MOD.verified_sigs)
-    _sana_ln_modulate(norm, x, scale, shift)
+    sana_ln_modulate(norm, x, scale, shift)
     assert len(sana._SANA_LN_MOD.verified_sigs) == n_sigs
     # The fusion engages on non-default streams (the BCG warmup/capture path).
     # x/scale/shift were filled on the default stream, so the side stream must
@@ -373,9 +373,9 @@ def test_sana_fused_ln_modulate_is_bit_exact(shape, nmod, transposed):
     side = torch.cuda.Stream()
     side.wait_stream(torch.cuda.current_stream())
     with torch.cuda.stream(side):
-        out = _sana_ln_modulate(norm, x, scale, shift)
+        out = sana_ln_modulate(norm, x, scale, shift)
         assert len(sana._SANA_LN_MOD.verified_sigs) == n_sigs + 1  # verified
-        out2 = _sana_ln_modulate(norm, x, scale, shift)  # verified-sig lane
+        out2 = sana_ln_modulate(norm, x, scale, shift)  # verified-sig lane
     torch.cuda.current_stream().wait_stream(side)
     torch.cuda.synchronize()
     assert torch.equal(out, _sana_eager_ln_modulate(norm, x, scale, shift))