model: prune comments and redundant tests in dsv41 vision CP

python/sglang/srt/models/deepseek_v4.py

@@ -4922,8 +4922,6 @@ class DeepseekV4ForCausalLM(nn.Module):
             self.image_start = nn.Parameter(torch.empty(config.hidden_size))
             self.image_end = nn.Parameter(torch.empty(config.hidden_size))
             self.image_newline = nn.Parameter(torch.empty(config.hidden_size))
-        # Ranks of this group run identical image chunks; one owner encodes each
-        # span and broadcasts it. None keeps the replicated encoder.
         self.mm_owner_group = (
             select_owner_group(get_parallel())
             if self.vision is not None and _is_cuda

test/registered/unit/managers/test_mm_owner_embedding.py

@@ -229,8 +229,6 @@ class _VisionStub(DeepseekV4ForCausalLM):
 
 
 class _TracedGroup:
-    """Records every collective (op, source, shape, group size) around a real coordinator."""
-
     def __init__(self, inner):
         self.inner = inner
         self.trace = []
@@ -441,7 +439,6 @@ def _topology_program(rank, world_size):
     singles = _coordinator([[r] for r in range(8)], rank)
     out = {}
 
-    # TP8, DP1, CP off.
     tp_group, attn_tp, attn_cp = (
         _TracedGroup(tp8),
         _TracedGroup(tp8),
@@ -469,7 +466,6 @@ def _topology_program(rank, world_size):
     assert torch.equal(embeds, _expected_embeds(embed, requests, {x: 0, y: 5, z: 2}))
     out["cp1"] = {"encoded": list(model.encoded), "trace": list(attn_tp.trace)}
 
-    # TP8, DP1, CP8 through the CP runner.
     tp_group, attn_tp, attn_cp = (
         _TracedGroup(tp8),
         _TracedGroup(singles),
@@ -516,7 +512,7 @@ def _topology_program(rank, world_size):
     finally:
         init_cp_strategy(enable_prefill_cp=False, cp_size=1, cp_strategy="interleave")
 
-    # TP8, attention-DP2, CP off: one replica is text-only first.
+    # One attention-DP replica is text-only first.
     tp_group, attn_tp, attn_cp = (
         _TracedGroup(tp8),
         _TracedGroup(replicas),
@@ -804,7 +800,6 @@ def test_owner_actions_and_cache_lifetime_across_asymmetric_ranks():
 
 
 def test_tp8_cp1_and_cp8_dedupe_and_attention_dp_replicas_stay_isolated():
-    """One encode per key on TP8 with CP off and CP8; attention-DP replicas never share a collective."""
     results = _run_ranks(8, _topology_program)
 
     cp1 = [r["cp1"] for r in results]
@@ -839,7 +834,6 @@ def test_tp8_cp1_and_cp8_dedupe_and_attention_dp_replicas_stay_isolated():
 
 
 def test_failures_agree_before_payload_text_embedding_or_body():
-    """Every rank raises the same error and none enters a later collective."""
     results = _run_ranks(4, _failure_program)
     cases = (
         "prepare",

test/registered/unit/models/test_deepseek_v41_vision_cp_inputs.py

@@ -200,21 +200,6 @@ class TestDeepseekV41VisionPrefillCPInputs(CustomTestCase):
                 input_embeds=input_embeds,
             )
 
-    def test_prepare_model_inputs_merges_on_full_layout(self):
-        forward_batch, item = _build_batch()
-        scheduler_ids = forward_batch.input_ids.clone()
-
-        model_ids, embeds = self._prepare(forward_batch)
-
-        self.assertTrue(torch.equal(forward_batch.input_ids, scheduler_ids))
-        self.assertIs(forward_batch.mm_input_embeds, embeds)
-        self.assertTrue(torch.equal(model_ids, _canonical(scheduler_ids)))
-        self.assertTrue(torch.equal(embeds[IMAGE_ROWS], _image_span(item)[1:7]))
-        text_rows = model_ids != IMAGE_TOKEN_ID
-        with torch.no_grad():
-            text_embeds = self.embed(scheduler_ids[text_rows])
-        self.assertTrue(torch.equal(embeds[text_rows], text_embeds))
-
     def test_cp_runner_merges_before_shard(self):
         runner = EagerRunner.__new__(EagerRunner)
         runner.model_runner = SimpleNamespace(model=self.model)

test/registered/unit/server_args/test_server_args.py

@@ -4123,14 +4123,6 @@ class TestDeepseekV41VisionPrefillCPArgs(CustomTestCase):
         ):
             handle_model_specific_adjustments(args)
 
-    def test_interleave_eager_prefill_is_accepted(self):
-        args = self._args()
-        validate_deepseek_v41_features(args)
-        self.assertEqual(
-            resolution_result(args, "cuda_graph_config").prefill.backend,
-            Backend.DISABLED,
-        )
-
     def test_zigzag_is_rejected_only_with_vision(self):
         with self.assertRaisesRegex(ValueError, "requires --cp-strategy interleave"):
             validate_deepseek_v41_features(self._args(cp_strategy="zigzag"))