Use NCCL instead of ray for control-plane communication to remove serialization overhead

docs/source/models/adding_model.rst

@@ -58,11 +58,10 @@ Next, you need to rewrite the :code:`forward` methods of your model by following
     +    positions: torch.Tensor,
     +    kv_caches: List[KVCache],
     +    input_metadata: InputMetadata,
-    +    cache_events: Optional[List[torch.cuda.Event]],
-    +) -> SamplerOutput:
+    +) -> Optional[SamplerOutput]:
 
-3. Update the code by considering that :code:`input_ids` and :code:`positions` are now flattened tensors.
-4. Replace the attention operation with either :code:`PagedAttention`, :code:`PagedAttentionWithRoPE`, or :code:`PagedAttentionWithALiBi` depending on the model's architecture.
+1. Update the code by considering that :code:`input_ids` and :code:`positions` are now flattened tensors.
+2. Replace the attention operation with either :code:`PagedAttention`, :code:`PagedAttentionWithRoPE`, or :code:`PagedAttentionWithALiBi` depending on the model's architecture.
 
 .. note::
     Currently, vLLM supports the basic multi-head attention mechanism and its variant with rotary positional embeddings.

requirements-rocm.txt

@@ -3,8 +3,6 @@ typing-extensions>=4.8.0
 starlette
 psutil
 ray >= 2.5.1
-pandas  # Required for Ray data.
-pyarrow  # Required for Ray data.
 sentencepiece  # Required for LLaMA tokenizer.
 numpy
 tokenizers>=0.15.0

requirements.txt

@@ -1,8 +1,6 @@
 ninja  # For faster builds.
 psutil
 ray >= 2.5.1
-pandas  # Required for Ray data.
-pyarrow  # Required for Ray data.
 sentencepiece  # Required for LLaMA tokenizer.
 numpy
 torch == 2.1.2

tests/async_engine/test_api_server.py

@@ -8,18 +8,22 @@
 import requests
 
 
-def _query_server(prompt: str) -> dict:
+def _query_server(prompt: str, max_tokens: int = 5) -> dict:
     response = requests.post("http://localhost:8000/generate",
                              json={
                                  "prompt": prompt,
-                                 "max_tokens": 100,
+                                 "max_tokens": max_tokens,
                                  "temperature": 0,
                                  "ignore_eos": True
                              })
     response.raise_for_status()
     return response.json()
 
 
+def _query_server_long(prompt: str) -> dict:
+    return _query_server(prompt, max_tokens=500)
+
+
 @pytest.fixture
 def api_server():
     script_path = Path(__file__).parent.joinpath(
@@ -68,10 +72,11 @@ def test_api_server(api_server):
         for result in pool.map(_query_server, prompts):
             assert result
 
+    with Pool(32) as pool:
         # Cancel requests
         prompts = ["canceled requests"] * 100
-        pool.map_async(_query_server, prompts)
-        time.sleep(0.001)
+        pool.map_async(_query_server_long, prompts)
+        time.sleep(0.01)
         pool.terminate()
         pool.join()
 

tests/distributed/test_comm_ops.py

@@ -8,7 +8,7 @@
 import torch
 
 from vllm.config import ParallelConfig
-from vllm.engine.ray_utils import get_open_port
+from vllm.utils import get_open_port
 from vllm.model_executor.parallel_utils.communication_op import (
     tensor_model_parallel_all_reduce,
     tensor_model_parallel_all_gather,

tests/kernels/test_cache.py

@@ -46,12 +46,13 @@ def test_copy_blocks(
     src_blocks = random.sample(range(num_blocks), num_mappings)
     remainig_blocks = list(set(range(num_blocks)) - set(src_blocks))
     dst_blocks = random.sample(remainig_blocks, 2 * num_mappings)
-    block_mapping = {}
+    copy_src = []
+    copy_dst = []
     for i in range(num_mappings):
-        src = src_blocks[i]
-        dst1 = dst_blocks[2 * i]
-        dst2 = dst_blocks[2 * i + 1]
-        block_mapping[src] = [dst1, dst2]
+        copy_src.append(src_blocks[i])
+        copy_dst.append(dst_blocks[2 * i])
+        copy_src.append(src_blocks[i])
+        copy_dst.append(dst_blocks[2 * i + 1])
 
     # Create the KV caches.
     key_caches, value_caches = kv_cache_factory(num_blocks, block_size,
@@ -63,15 +64,14 @@ def test_copy_blocks(
     cloned_value_caches = [value_cache.clone() for value_cache in value_caches]
 
     # Call the copy blocks kernel.
-    cache_ops.copy_blocks(key_caches, value_caches, block_mapping)
+    cache_ops.copy_blocks(key_caches, value_caches, copy_src, copy_dst)
 
     # Run the reference implementation.
-    for src, dsts in block_mapping.items():
-        for dst in dsts:
-            for cloned_key_cache in cloned_key_caches:
-                cloned_key_cache[dst].copy_(cloned_key_cache[src])
-            for cloned_value_cache in cloned_value_caches:
-                cloned_value_cache[dst].copy_(cloned_value_cache[src])
+    for src, dst in zip(copy_src, copy_dst):
+        for cloned_key_cache in cloned_key_caches:
+            cloned_key_cache[dst].copy_(cloned_key_cache[src])
+        for cloned_value_cache in cloned_value_caches:
+            cloned_value_cache[dst].copy_(cloned_value_cache[src])
 
     # Compare the results.
     for key_cache, cloned_key_cache in zip(key_caches, cloned_key_caches):

tests/worker/test_model_runner.py

@@ -33,8 +33,9 @@ def test_prepare_prompt():
         expected_selected_token_indices.append(selected_token_start_idx +
                                                prompt_len - 1)
         selected_token_start_idx += max_seq_len
-    input_tokens, input_positions, _ = model_runner._prepare_prompt(
-        seq_group_metadata_list)
+    input_tokens, input_positions, _, return_prompt_lens = (
+        model_runner._prepare_prompt(seq_group_metadata_list))
+    assert return_prompt_lens == prompt_lens
     sampling_metadata = model_runner._prepare_sample(seq_group_metadata_list,
                                                      prompt_lens)
     assert input_tokens.shape == (batch_size, max_seq_len)

vllm/engine/async_llm_engine.py

@@ -185,45 +185,51 @@ async def step_async(self) -> List[RequestOutput]:
         """
         seq_group_metadata_list, scheduler_outputs = self.scheduler.schedule()
 
-        # Execute the model.
-        output = (await self._run_workers_async(
-            "execute_model",
-            seq_group_metadata_list=seq_group_metadata_list,
-            blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in,
-            blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out,
-            blocks_to_copy=scheduler_outputs.blocks_to_copy,
-        )) if not scheduler_outputs.is_empty() else []
+        if not scheduler_outputs.is_empty():
+            # Execute the model.
+            all_outputs = await self._run_workers_async(
+                "execute_model",
+                driver_kwargs={
+                    "seq_group_metadata_list": seq_group_metadata_list,
+                    "blocks_to_swap_in": scheduler_outputs.blocks_to_swap_in,
+                    "blocks_to_swap_out": scheduler_outputs.blocks_to_swap_out,
+                    "blocks_to_copy": scheduler_outputs.blocks_to_copy,
+                })
+
+            # Only the driver worker returns the sampling results.
+            output = all_outputs[0]
+        else:
+            output = []
 
         return self._process_model_outputs(output, scheduler_outputs)
 
     async def _run_workers_async(
         self,
         method: str,
         *args,
-        get_all_outputs: bool = False,
+        driver_args: Optional[List[Any]] = None,
+        driver_kwargs: Optional[Dict[str, Any]] = None,
         **kwargs,
     ) -> Any:
         """Runs the given method on all workers."""
         coros = []
-        for worker in self.workers:
-            if self.parallel_config.worker_use_ray:
-                coros.append(
-                    worker.execute_method.remote(method, *args, **kwargs))
-            else:
-                executor = getattr(worker, method)
-                coros.append(asyncio.get_event_loop().run_in_executor(
-                    None, partial(executor, *args, **kwargs)))
 
-        all_outputs = await asyncio.gather(*coros)
+        if driver_args is None:
+            driver_args = args
+        if driver_kwargs is None:
+            driver_kwargs = kwargs
 
-        if get_all_outputs:
-            return all_outputs
+        # Run the driver worker asynchronously.
+        driver_executor = getattr(self.driver_worker, method)
+        coros.append(asyncio.get_event_loop().run_in_executor(
+            None, partial(driver_executor, *driver_args, **driver_kwargs)))
 
-        # Make sure all workers have the same results.
-        output = all_outputs[0]
-        for other_output in all_outputs[1:]:
-            assert output == other_output
-        return output
+        # Run the ray workers asynchronously.
+        for worker in self.workers:
+            coros.append(worker.execute_method.remote(method, *args, **kwargs))
+
+        all_outputs = await asyncio.gather(*coros)
+        return all_outputs
 
 
 class AsyncLLMEngine:
@@ -488,13 +494,12 @@ def from_engine_args(cls,
         engine_configs = engine_args.create_engine_configs()
         parallel_config = engine_configs[2]
         # Initialize the cluster.
-        distributed_init_method, placement_group = initialize_cluster(
-            parallel_config, engine_args.engine_use_ray)
+        placement_group = initialize_cluster(parallel_config,
+                                             engine_args.engine_use_ray)
         # Create the async LLM engine.
         engine = cls(parallel_config.worker_use_ray,
                      engine_args.engine_use_ray,
                      *engine_configs,
-                     distributed_init_method,
                      placement_group,
                      log_requests=not engine_args.disable_log_requests,
                      log_stats=not engine_args.disable_log_stats,