[Misc] Remove cache stream and cache events (#3461)

tests/worker/test_swap.py

+import torch
+
+from vllm.engine.arg_utils import EngineArgs
+from vllm.worker.worker import Worker
+from vllm.utils import get_distributed_init_method, get_ip, get_open_port
+
+
+def test_swap() -> None:
+    # Configure the engine.
+    engine_args = EngineArgs(model="facebook/opt-125m",
+                             dtype="half",
+                             load_format="dummy")
+    (model_config, cache_config, parallel_config, scheduler_config,
+     device_config, _) = engine_args.create_engine_configs()
+    cache_config.num_gpu_blocks = 100
+    cache_config.num_cpu_blocks = 100
+
+    # Create the worker.
+    distributed_init_method = get_distributed_init_method(
+        get_ip(), get_open_port())
+    worker = Worker(
+        model_config=model_config,
+        parallel_config=parallel_config,
+        scheduler_config=scheduler_config,
+        device_config=device_config,
+        local_rank=0,
+        rank=0,
+        distributed_init_method=distributed_init_method,
+        is_driver_worker=True,
+    )
+
+    # Initialize the worker.
+    worker.init_model()
+    worker.load_model()
+    worker.init_cache_engine(cache_config)
+    worker.warm_up_model()
+
+    # Randomly initialize the cache.
+    gpu_cache = worker.cache_engine.gpu_cache
+    cpu_cache = worker.cache_engine.cpu_cache
+    num_layers = len(gpu_cache)
+    for i in range(num_layers):
+        gpu_key_cache, gpu_value_cache = gpu_cache[i]
+        gpu_key_cache.random_()
+        gpu_value_cache.random_()
+        cpu_key_cache, cpu_value_cache = cpu_cache[i]
+        cpu_key_cache.random_()
+        cpu_value_cache.random_()
+
+    allclose = lambda a, b: torch.allclose(
+        a.cuda(), b.cuda(), rtol=0.0, atol=0.0)
+
+    # Test swap out.
+    blocks_to_swap_out = {3: 72, 56: 35, 84: 34}
+    worker.execute_model(seq_group_metadata_list=[],
+                         blocks_to_swap_in={},
+                         blocks_to_swap_out=blocks_to_swap_out,
+                         blocks_to_copy={})
+    for i in range(num_layers):
+        gpu_key_cache, gpu_value_cache = gpu_cache[i]
+        cpu_key_cache, cpu_value_cache = cpu_cache[i]
+        for src, dst in blocks_to_swap_out.items():
+            assert allclose(gpu_key_cache[src], cpu_key_cache[dst])
+            assert allclose(gpu_value_cache[src], cpu_value_cache[dst])
+
+    # Test swap in.
+    blocks_to_swap_in = {19: 45, 67: 23, 12: 78, 40: 99, 1: 71}
+    worker.execute_model(seq_group_metadata_list=[],
+                         blocks_to_swap_in=blocks_to_swap_in,
+                         blocks_to_swap_out={},
+                         blocks_to_copy={})
+    for i in range(num_layers):
+        gpu_key_cache, gpu_value_cache = gpu_cache[i]
+        cpu_key_cache, cpu_value_cache = cpu_cache[i]
+        for src, dst in blocks_to_swap_in.items():
+            assert allclose(gpu_key_cache[dst], cpu_key_cache[src])
+            assert allclose(gpu_value_cache[dst], cpu_value_cache[src])

vllm/worker/cache_engine.py

@@ -38,7 +38,7 @@ class CacheEngine:
         self.num_gpu_blocks = cache_config.num_gpu_blocks
         self.num_cpu_blocks = cache_config.num_cpu_blocks
 
-        # Skip initializing CUDA stream and buffer for Neuron backend.
+        # Skip initializing KV cache for Neuron backend.
         if is_neuron():
             return
 
@@ -51,12 +51,6 @@ class CacheEngine:
         self.gpu_cache = self.allocate_gpu_cache()
         self.cpu_cache = self.allocate_cpu_cache()
 
-        # Initialize the stream for caching operations.
-        self.cache_stream = torch.cuda.Stream()
-        assert self.cache_stream != torch.cuda.current_stream()
-        # Initialize the events for stream synchronization.
-        self.events = [torch.cuda.Event() for _ in range(self.num_layers)]
-
     def get_key_block_shape(self) -> Tuple[int, int, int, int]:
         element_size = torch.tensor([], dtype=self.dtype).element_size()
         x = 16 // element_size
@@ -126,17 +120,13 @@ class CacheEngine:
     ) -> None:
         from vllm._C import cache_ops
 
-        with torch.cuda.stream(self.cache_stream):
-            for i in range(self.num_layers):
-                src_key_cache, src_value_cache = src[i]
-                dst_key_cache, dst_value_cache = dst[i]
-                # Copy the key blocks.
-                cache_ops.swap_blocks(src_key_cache, dst_key_cache, src_to_dst)
-                # Copy the value blocks.
-                cache_ops.swap_blocks(src_value_cache, dst_value_cache,
-                                      src_to_dst)
-                event = self.events[i]
-                event.record(stream=self.cache_stream)
+        for i in range(self.num_layers):
+            src_key_cache, src_value_cache = src[i]
+            dst_key_cache, dst_value_cache = dst[i]
+            # Copy the key blocks.
+            cache_ops.swap_blocks(src_key_cache, dst_key_cache, src_to_dst)
+            # Copy the value blocks.
+            cache_ops.swap_blocks(src_value_cache, dst_value_cache, src_to_dst)
 
     def swap_in(self, src_to_dst: Dict[int, int]) -> None:
         self._swap(self.cpu_cache, self.gpu_cache, src_to_dst)

vllm/worker/worker.py

@@ -65,7 +65,6 @@ class Worker:
         # self.init_cache_engine().
         self.cache_config = None
         self.cache_engine = None
-        self.cache_events = None
         self.gpu_cache = None
 
     def init_model(self, cupy_port: Optional[int] = None) -> None:
@@ -148,7 +147,6 @@ class Worker:
         self.cache_config = cache_config
         self.cache_engine = CacheEngine(self.cache_config, self.model_config,
                                         self.parallel_config)
-        self.cache_events = self.cache_engine.events
         self.gpu_cache = self.cache_engine.gpu_cache
         self.model_runner.set_block_size(self.cache_engine.block_size)
 
@@ -166,24 +164,13 @@ class Worker:
         blocks_to_copy: Dict[int, List[int]],
     ) -> None:
         # Issue cache operations.
-        issued_cache_op = False
+        # TODO(woosuk): Profile swapping overhead and optimize if needed.
         if blocks_to_swap_in:
             self.cache_engine.swap_in(blocks_to_swap_in)
-            issued_cache_op = True
         if blocks_to_swap_out:
             self.cache_engine.swap_out(blocks_to_swap_out)
-            issued_cache_op = True
         if blocks_to_copy:
             self.cache_engine.copy(blocks_to_copy)
-            issued_cache_op = True
-
-        cache_events = self.cache_events if issued_cache_op else None
-
-        # Wait for cache operations to finish.
-        # TODO(woosuk): Profile swapping overhead and optimize if needed.
-        if cache_events is not None:
-            for event in cache_events:
-                event.wait()
 
     @torch.inference_mode()
     def execute_model(