fix reviews

Signed-off-by: Gaohan123 <gaohan19@huawei.com>

README.md

@@ -110,11 +110,6 @@ bash run.sh
 ```
 The output audio is saved in ./output_audio
 
-## To-do list
-- [x] Offline inference example for Qwen2.5-omni with single request
-- [x] Adaptation from current vllm branch to stable vllm v0.11.0
-- [ ] Offline inference example for Qwen2.5-omni with streaming multiple requests
-- [ ] Online inference support
-- [ ] Support for other models
+## Further details
 
-For detailed model management, see [vllm_omni_design.md](docs/architecture/vllm_omni_design.md) and [high_level_arch_design.md](docs/architecture/high_level_arch_design.md).
+For detailed architecture design, see [vllm_omni_design.md](docs/architecture/vllm_omni_design.md) and [high_level_arch_design.md](docs/architecture/high_level_arch_design.md).

examples/offline_inference/qwen_2_5_omni/README.md

@@ -73,10 +73,3 @@ Modify PYTHONPATH in run.sh as your path of vllm_omni. Then run.
 bash run.sh
 ```
 The output audio is saved in ./output_audio
-
-## To-do list
-- [x] Offline inference example for Qwen2.5-omni with single request
-- [x] Adaptation from current vllm branch to stable vllm v0.11.0
-- [ ] Offline inference example for Qwen2.5-omni with streaming multiple requests
-- [ ] Online inference support
-- [ ] Support for other models

vllm_omni/core/sched/diffusion_scheduler.py

@@ -2,7 +2,6 @@ import time
 from collections import defaultdict
 from typing import Optional
 
-from vllm.distributed.kv_events import KVEventBatch
 from vllm.v1.core.kv_cache_manager import KVCacheBlocks
 from vllm.v1.core.sched.request_queue import create_request_queue
 from vllm.v1.core.sched.scheduler import (
@@ -145,23 +144,6 @@ class DiffusionScheduler(OmniScheduler):
             meta = self.connector.build_connector_meta(scheduler_output)
             scheduler_output.kv_connector_metadata = meta
 
-        # Publish KV events (aligned with v1)
-        events = self.kv_cache_manager.take_events()
-
-        # collect KV cache events from connector
-        if self.connector is not None:
-            connector_events = self.connector.take_events()
-            if connector_events:
-                if events is None:
-                    events = list(connector_events)
-                else:
-                    events.extend(connector_events)
-
-        # publish collected KV cache events
-        if events:
-            batch = KVEventBatch(ts=time.time(), events=events)
-            self.kv_event_publisher.publish(batch)
-
         # Update internal state (advance num_computed_tokens, free encoder inputs,
         # etc.)
         self._update_after_schedule(scheduler_output)

vllm_omni/worker/gpu_diffusion_model_runner.py

@@ -26,7 +26,6 @@ from vllm.v1.worker.gpu_model_runner import (
     get_pp_group,
     set_forward_context,
 )
-from vllm.v1.worker.ubatch_splitting import ubatch_split
 from vllm.v1.worker.ubatch_utils import UBatchSlices
 from vllm.v1.worker.utils import sanity_check_mm_encoder_outputs
 from vllm_omni.outputs import OmniModelRunnerOutput
@@ -400,28 +399,9 @@ class GPUDiffusionModelRunner(OmniGPUModelRunner):
         assert len(num_scheduled_tokens_list) == num_reqs
         num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
 
-        total_num_scheduled_tokens = int(num_scheduled_tokens.sum())
-
         ubatch_slices = None
         num_tokens_after_padding = None
 
-        # We currently only microbatch if the number of tokens is
-        # over a certain threshold.
-        if self.parallel_config.enable_dbo and allow_microbatching:
-            ubatch_slices, ubatch_num_tokens_after_padding = ubatch_split(
-                num_scheduled_tokens,
-                total_num_scheduled_tokens,
-                total_num_scheduled_tokens,
-                uniform_decode=uniform_decode,
-                vllm_config=self.vllm_config,
-            )
-            # Currently when DBO is enabled `ubatch_split` returns
-            # the num_tokens_after_padding for a single ubatch, but we have 2
-            # TODO(sage,lucas): this is cruft that should be addressed in the
-            # padding refactor.
-            if ubatch_num_tokens_after_padding is not None:
-                num_tokens_after_padding = ubatch_num_tokens_after_padding * 2
-
         # If we failed to microbatch, currently need to resynchronize
         # TODO(lucas,sage): we should be able to avoid this second sync by
         #  refactoring `get_dp_padding_ubatch` and `get_dp_padding` into