[VLM] split multimodal scheduling from mm_utils (#32415)

test/registered/chunked_prefill/test_mm_chunked_embedding_unit.py

@@ -1,18 +1,18 @@
 """Unit tests for per-item DataEmbeddingFunc results in the chunked mm path.
 
 A DataEmbeddingFunc may return either one combined [tokens, hidden] tensor or
-one tensor per item (see mm_utils.DataEmbeddingFunc). These tests assert the
+one tensor per item (see mm_schedule.DataEmbeddingFunc). These tests assert the
 two forms produce bitwise-identical chunked-prefill embeddings, and that the
 per-item form yields cache entries that own their storage (a torch.split view
 of the combined tensor pins the whole concatenated buffer).
 
-CPU-only: exercises mm_utils internals directly, no engine or GPU.
+CPU-only: exercises mm_schedule internals directly, no engine or GPU.
 """
 
 import pytest
 import torch
 
-from sglang.srt.managers import mm_utils
+from sglang.srt.managers import mm_schedule
 from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
 from sglang.srt.runtime_context import get_context, get_parallel
 from sglang.test.ci.ci_register import register_cpu_ci
@@ -66,6 +66,14 @@ CHUNKS = [(0, 8), (8, 8), (16, 8), (24, 6)]
 _CPU = torch.device("cpu")
 
 
+@pytest.fixture(autouse=True)
+def _skip_cuda_ipc_acknowledgement(monkeypatch):
+    """Keep CPU embedding tests independent of tensor-parallel runtime state."""
+    monkeypatch.setattr(
+        mm_schedule, "_acknowledge_deferred_cuda_ipc_cache_hits", lambda _items: None
+    )
+
+
 def _num_tokens(item: MultimodalDataItem) -> int:
     start, end = item.offsets[0]
     return end - start + 1
@@ -97,10 +105,10 @@ def _make_items():
 
 
 def _run_by_item_chunks(encoder):
-    mm_utils.init_mm_embedding_cache(1 << 30)
+    mm_schedule.init_mm_embedding_cache(1 << 30)
     items = _make_items()
     return [
-        mm_utils._get_chunked_embedding_by_item(
+        mm_schedule._get_chunked_embedding_by_item(
             encoder, items, ITEM_OFFSETS, prefix_len, extend_len, _CPU
         )
         for prefix_len, extend_len in CHUNKS
@@ -108,12 +116,12 @@ def _run_by_item_chunks(encoder):
 
 
 def _run_full_chunks(encoder):
-    mm_utils.init_mm_embedding_cache(1 << 30)
+    mm_schedule.init_mm_embedding_cache(1 << 30)
     items = _make_items()
     input_ids = torch.zeros(TOTAL_LEN, dtype=torch.long)
     outs = []
     for prefix_len, extend_len in CHUNKS:
-        chunk, _ = mm_utils._get_chunked_embedding_full(
+        chunk, _ = mm_schedule._get_chunked_embedding_full(
             encoder, items, ITEM_OFFSETS, prefix_len, extend_len, input_ids, _CPU
         )
         outs.append(chunk)
@@ -150,13 +158,13 @@ def test_full_matches_by_item():
 
 
 def test_list_cache_entries_own_storage():
-    mm_utils.init_mm_embedding_cache(1 << 30)
+    mm_schedule.init_mm_embedding_cache(1 << 30)
     items = _make_items()
-    mm_utils._get_chunked_embedding_by_item(
+    mm_schedule._get_chunked_embedding_by_item(
         _encoder_list, items, ITEM_OFFSETS, 0, TOTAL_LEN, _CPU
     )
     for item in items:
-        emb = mm_utils.embedding_cache.get_single(item.hash).embedding
+        emb = mm_schedule.embedding_cache.get_single(item.hash).embedding
         own_bytes = emb.numel() * emb.element_size()
         assert emb.untyped_storage().nbytes() == own_bytes
 
@@ -164,14 +172,14 @@ def test_list_cache_entries_own_storage():
 def test_tensor_cache_entries_share_storage():
     # Documents the motivation for the per-item form: split views of the
     # combined tensor keep the whole concatenated buffer alive.
-    mm_utils.init_mm_embedding_cache(1 << 30)
+    mm_schedule.init_mm_embedding_cache(1 << 30)
     items = _make_items()
-    mm_utils._get_chunked_embedding_by_item(
+    mm_schedule._get_chunked_embedding_by_item(
         _encoder_tensor, items, ITEM_OFFSETS, 0, TOTAL_LEN, _CPU
     )
     total_tokens = sum(_num_tokens(item) for item in items)
     for item in items:
-        emb = mm_utils.embedding_cache.get_single(item.hash).embedding
+        emb = mm_schedule.embedding_cache.get_single(item.hash).embedding
         assert (
             emb.untyped_storage().nbytes() == total_tokens * HIDDEN * emb.element_size()
         )