[Misc] Replace cuda hard code with current_platform (#16983)

Signed-off-by: shen-shanshan <467638484@qq.com>

vllm/distributed/parallel_state.py

@@ -1221,8 +1221,9 @@ def cleanup_dist_env_and_memory(shutdown_ray: bool = False):
         ray.shutdown()
     gc.collect()
     from vllm.platforms import current_platform
-    if not current_platform.is_cpu():
-        torch.cuda.empty_cache()
+    empty_cache = current_platform.empty_cache
+    if empty_cache is not None:
+        empty_cache()
     try:
         torch._C._host_emptyCache()
     except AttributeError:

vllm/forward_context.py

@@ -120,7 +120,10 @@ def set_forward_context(attn_metadata: Any,
             # we use synchronous scheduling right now,
             # adding a sync point here should not affect
             # scheduling of the next batch
-            torch.cuda.synchronize()
+            from vllm.platforms import current_platform
+            synchronize = current_platform.synchronize
+            if synchronize is not None:
+                synchronize()
             now = time.perf_counter()
             # time measurement is in milliseconds
             batchsize_forward_time[batchsize].append(

vllm/spec_decode/metrics.py

@@ -126,12 +126,12 @@ class AsyncMetricsCollector:
         """Copy rejection/typical-acceptance sampling metrics
         (number of accepted tokens, etc) to CPU asynchronously.
 
-        Returns a CUDA event recording when the copy is complete.
+        Returns a device event recording when the copy is complete.
         """
         assert self._copy_stream is not None
-        self._copy_stream.wait_stream(torch.cuda.current_stream())
+        self._copy_stream.wait_stream(current_platform.current_stream())
 
-        with torch.cuda.stream(self._copy_stream):
+        with current_platform.stream(self._copy_stream):
             self._aggregate_num_accepted_tokens.copy_(
                 self.spec_decode_sampler.num_accepted_tokens,
                 non_blocking=True)
@@ -142,7 +142,7 @@ class AsyncMetricsCollector:
             self._aggregate_num_draft_tokens = (
                 self.spec_decode_sampler.num_draft_tokens)
 
-        aggregate_metrics_ready = torch.cuda.Event()
+        aggregate_metrics_ready = current_platform.Event()
         aggregate_metrics_ready.record(self._copy_stream)
 
         return aggregate_metrics_ready