[Hardware] Replace memory related torch.cuda APIs

benchmarks/attention_benchmarks/runner.py

@@ -418,8 +418,8 @@ def _run_single_benchmark(
     mem_stats = {}
     if config.profile_memory:
         mem_stats = {
-            "allocated_mb": torch.cuda.memory_allocated(device) / 1024**2,
-            "reserved_mb": torch.cuda.memory_reserved(device) / 1024**2,
+            "allocated_mb": torch.accelerator.memory_allocated(device) / 1024**2,
+            "reserved_mb": torch.accelerator.memory_reserved(device) / 1024**2,
         }
 
     return times, mem_stats

benchmarks/benchmark_topk_topp.py

@@ -95,13 +95,16 @@ def create_logits(
 def measure_memory() -> tuple[int, int]:
     """Return (allocated, reserved) memory in bytes."""
     torch.accelerator.synchronize()
-    return torch.cuda.memory_allocated(), torch.cuda.max_memory_allocated()
+    return (
+        torch.accelerator.memory_allocated(),
+        torch.accelerator.max_memory_allocated(),
+    )
 
 
 def reset_memory_stats():
     """Reset peak memory statistics."""
     reset_buffer_cache()
-    torch.cuda.reset_peak_memory_stats()
+    torch.accelerator.reset_peak_memory_stats()
     torch.accelerator.empty_cache()
     gc.collect()
 

tests/test_regression.py

@@ -55,7 +55,7 @@ def test_gc():
     # The memory allocated for model and KV cache should be released.
     # The memory allocated for PyTorch and others should be less than 50MB.
     # Usually, it's around 10MB.
-    allocated = torch.cuda.memory_allocated()
+    allocated = torch.accelerator.memory_allocated()
     assert allocated < 50 * 1024 * 1024
 
 

tests/utils_/test_mem_utils.py

@@ -29,7 +29,7 @@ def test_memory_profiling():
     def measure_current_non_torch():
         free, total = torch.cuda.mem_get_info()
         current_used = total - free
-        current_torch = torch.cuda.memory_reserved()
+        current_torch = torch.accelerator.memory_reserved()
         current_non_torch = current_used - current_torch
         return current_non_torch
 

tools/pre_commit/check_torch_cuda.py

@@ -8,7 +8,7 @@
 # Regex: match `torch.cuda.xxx` but allow `torch.accelerator.xxx`
 # --------------------------------------------------------------------------- #
 _TORCH_CUDA_PATTERNS = [
-    r"\btorch\.cuda\.(empty_cache|synchronize|device_count|current_device|set_device|device\()\b",
+    r"\btorch\.cuda\.(empty_cache|synchronize|device_count|current_device|memory_reserved|memory_allocated|max_memory_allocated|max_memory_reserved|reset_peak_memory_stats|memory_stats|set_device|device\()\b",
     r"\bwith\storch\.cuda\.device\b",
 ]
 

vllm/model_executor/model_loader/base_loader.py

@@ -64,7 +64,7 @@ def load_model(
             # Log peak GPU memory after loading weights. This is needed
             # to have test coverage on peak memory for online quantization.
             if current_platform.is_cuda():
-                peak_memory = torch.cuda.max_memory_allocated()
+                peak_memory = torch.accelerator.max_memory_allocated()
                 logger.debug_once(
                     "Peak GPU memory after loading weights: %s GiB",
                     format_gib(peak_memory),

vllm/utils/mem_utils.py

@@ -93,11 +93,11 @@ def measure(self) -> None:
         device = self.device_
 
         # we measure the torch peak memory usage via allocated_bytes,
-        # rather than `torch.cuda.memory_reserved()` .
-        # After `torch.cuda.reset_peak_memory_stats()`,
-        # `torch.cuda.memory_reserved()` will keep growing, and only shrink
+        # rather than `torch.accelerator.memory_reserved()` .
+        # After `torch.accelerator.reset_peak_memory_stats()`,
+        # `torch.accelerator.memory_reserved()` will keep growing, and only shrink
         # when we call `torch.accelerator.empty_cache()` or OOM happens.
-        self.torch_peak = current_platform.memory_stats(device).get(
+        self.torch_peak = torch.accelerator.memory_stats(device).get(
             "allocated_bytes.all.peak", 0
         )
 
@@ -123,10 +123,10 @@ def measure(self) -> None:
 
         self.cuda_memory = self.total_memory - self.free_memory
 
-        # torch.cuda.memory_reserved() is how many bytes
+        # torch.accelerator.memory_reserved() is how many bytes
         # PyTorch gets from cuda (by calling cudaMalloc, etc.)
         # this is used to measure the non-torch memory usage
-        self.torch_memory = current_platform.memory_reserved(device)
+        self.torch_memory = torch.accelerator.memory_reserved(device)
 
         self.non_torch_memory = self.cuda_memory - self.torch_memory
         self.timestamp = time.time()
@@ -243,15 +243,15 @@ def memory_profiling(
     The memory used for loading weights (a.) is directly given from the
     argument `weights_memory`.
 
-    The increase of `torch.cuda.memory_stats()["allocated_bytes.all.peak"]`
+    The increase of `torch.accelerator.memory_stats()["allocated_bytes.all.peak"]`
     during profiling gives (b.).
 
     The increase of `non_torch_memory` from creating the current vLLM instance
     until after profiling to get (c.).
     """
     gc.collect()
     torch.accelerator.empty_cache()
-    current_platform.reset_peak_memory_stats(baseline_snapshot.device_)
+    torch.accelerator.reset_peak_memory_stats(baseline_snapshot.device_)
 
     result = MemoryProfilingResult(
         before_create=baseline_snapshot,

vllm/v1/worker/gpu_worker.py

@@ -387,7 +387,7 @@ def determine_available_memory(self) -> int:
         ) as profile_result:
             self.model_runner.profile_run()
 
-            profile_torch_peak = current_platform.memory_stats(self.device).get(
+            profile_torch_peak = torch.accelerator.memory_stats(self.device).get(
                 "allocated_bytes.all.peak", 0
             )