Revert "[Hardware] Replace torch.cuda.empty_cache with torch.accelerator.empty_cache" (#30681)

.pre-commit-config.yaml

@@ -127,13 +127,6 @@ repos:
     language: python
     types: [python]
     additional_dependencies: [regex]
-  # prevent use torch.cuda APIs
-  - id: check-torch-cuda-call
-    name: "Prevent new 'torch.cuda' APIs call"
-    entry: python tools/pre_commit/check_torch_cuda.py
-    language: python
-    types: [python]
-    additional_dependencies: [regex]
   - id: validate-config
     name: Validate configuration has default values and that each field has a docstring
     entry: python tools/pre_commit/validate_config.py

benchmarks/benchmark_topk_topp.py

@@ -102,7 +102,7 @@ def reset_memory_stats():
     """Reset peak memory statistics."""
     reset_buffer_cache()
     torch.cuda.reset_peak_memory_stats()
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
     gc.collect()
 
 

benchmarks/kernels/benchmark_moe.py

@@ -54,7 +54,7 @@ def clear_triton_cache():
 
     # Clear CUDA memory cache
     if torch.cuda.is_available():
-        torch.accelerator.empty_cache()
+        torch.cuda.empty_cache()
 
     # Try to clear Triton's runtime cache
     try:

benchmarks/kernels/benchmark_reshape_and_cache.py

@@ -104,7 +104,7 @@ def run_cuda_benchmark(n_iters: int) -> float:
 
     # free tensors to mitigate OOM when sweeping
     del key, value, key_cache, value_cache, slot_mapping
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
 
     return lat
 

benchmarks/kernels/benchmark_reshape_and_cache_flash.py

@@ -129,7 +129,7 @@ def run_cuda_benchmark(n_iters: int) -> float:
 
     # free tensors to mitigate OOM when sweeping
     del key, value, key_cache, value_cache, slot_mapping
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
 
     return lat
 

examples/offline_inference/lora_with_quantization_inference.py

@@ -120,7 +120,7 @@ def main():
         # Clean up the GPU memory for the next test
         del engine
         gc.collect()
-        torch.accelerator.empty_cache()
+        torch.cuda.empty_cache()
 
 
 if __name__ == "__main__":

examples/offline_inference/rlhf_colocate.py

@@ -159,7 +159,7 @@ def get_size(p: torch.Tensor) -> int:
         s.close()
         del buffer
         gc.collect()
-        torch.accelerator.empty_cache()
+        torch.cuda.empty_cache()
 
 
 # Ray manages four GPUs.

examples/offline_inference/rlhf_utils.py

@@ -150,7 +150,7 @@ def update_weights_from_ipc(self, zmq_handles: dict[str, str]):
         socket.close()
         del buffer
         gc.collect()
-        torch.accelerator.empty_cache()
+        torch.cuda.empty_cache()
 
     def report_device_id(self) -> str:
         from vllm.platforms import current_platform

tests/compile/test_dynamic_shapes_compilation.py

@@ -99,7 +99,7 @@ def test_dynamic_shapes_compilation(
     # Clean up GPU memory
     del model
     gc.collect()
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
     torch.cuda.synchronize()
     print("GPU memory cleared")
 

tests/conftest.py

@@ -1542,7 +1542,7 @@ def clean_gpu_memory_between_tests():
 
     # Clean up GPU memory after the test
     if torch.cuda.is_available():
-        torch.accelerator.empty_cache()
+        torch.cuda.empty_cache()
         gc.collect()
 
 

tests/entrypoints/openai/test_tensorizer_entrypoint.py

@@ -24,7 +24,7 @@
 
 def _cleanup():
     gc.collect()
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
 
 
 @pytest.fixture(autouse=True)

tests/kernels/mamba/test_causal_conv1d.py

@@ -273,7 +273,7 @@ def test_causal_conv1d_varlen(
     batch, with_padding, dim, seqlen, width, has_bias, silu_activation, itype
 ):
     device = "cuda"
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
     rtol, atol = (3e-4, 1e-3) if itype == torch.float32 else (3e-3, 5e-3)
     if itype == torch.bfloat16:
         rtol, atol = 1e-2, 5e-2

tests/kernels/moe/test_moe.py

@@ -769,7 +769,7 @@ def test_mixtral_moe(
                 requires_grad=False,
             )
             torch.cuda.synchronize()
-            torch.accelerator.empty_cache()
+            torch.cuda.empty_cache()
 
         # FIXME (zyongye) fix this after we move self.kernel
         # assignment in FusedMoE.__init__

tests/model_executor/model_loader/tensorizer_loader/test_tensorizer.py

@@ -178,7 +178,7 @@ def test_load_without_tensorizer_load_format(vllm_runner, capfd, model_ref):
     finally:
         del model
         gc.collect()
-        torch.accelerator.empty_cache()
+        torch.cuda.empty_cache()
 
 
 def test_raise_value_error_on_invalid_load_format(vllm_runner, capfd, model_ref):
@@ -200,7 +200,7 @@ def test_raise_value_error_on_invalid_load_format(vllm_runner, capfd, model_ref)
     finally:
         del model
         gc.collect()
-        torch.accelerator.empty_cache()
+        torch.cuda.empty_cache()
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires 2 GPUs")
@@ -283,7 +283,7 @@ def test_vllm_tensorized_model_has_same_outputs(
     model_ref, vllm_runner, tmp_path, model_path
 ):
     gc.collect()
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
     config = TensorizerConfig(tensorizer_uri=str(model_path))
     args = EngineArgs(model=model_ref)
 

tests/test_regression.py

@@ -49,7 +49,7 @@ def test_gc():
     del llm
 
     gc.collect()
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
 
     # The memory allocated for model and KV cache should be released.
     # The memory allocated for PyTorch and others should be less than 50MB.

tests/v1/e2e/test_async_spec_decode.py

@@ -125,7 +125,7 @@ def test_no_sync_with_spec_decode(
     assert len(outputs[0].outputs[0].text) > 0
 
     del llm
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
     cleanup_dist_env_and_memory()
 
     sync_tracker.assert_no_sync()

tests/v1/e2e/test_lora_with_spec_decode.py

@@ -95,7 +95,7 @@ def test_batch_inference_correctness(
             prompts, sampling_params, lora_request=lora_request
         )
         del ref_llm
-        torch.accelerator.empty_cache()
+        torch.cuda.empty_cache()
         cleanup_dist_env_and_memory()
 
         lora_spec_llm = LLM(
@@ -135,5 +135,5 @@ def test_batch_inference_correctness(
         print(f"match ratio: {matches}/{len(ref_outputs)}")
         assert matches > int(0.90 * len(ref_outputs))
         del lora_spec_llm
-        torch.accelerator.empty_cache()
+        torch.cuda.empty_cache()
         cleanup_dist_env_and_memory()

tests/v1/e2e/test_mamba_prefix_cache.py

@@ -440,7 +440,7 @@ def _run_ref_mamba_state_worker():
         torch.save(cpu_state_ref, "mamba_kv_cache_dict_ref.pth")
         mamba_kv_cache_dict.clear()
         del engine
-        torch.accelerator.empty_cache()
+        torch.cuda.empty_cache()
         cleanup_dist_env_and_memory()
     except Exception:
         traceback.print_exc()
@@ -805,5 +805,5 @@ def test_mamba_prefix_cache(monkeypatch: pytest.MonkeyPatch):
         check_mamba_state_equal(mamba_state_ref, mamba_kv_cache_dict, keys_to_check)
         mamba_kv_cache_dict.clear()
     del engine
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
     cleanup_dist_env_and_memory()

tests/v1/e2e/test_spec_decode.py

@@ -179,7 +179,7 @@ def test_ngram_and_suffix_correctness(
     )
     evaluate_llm_for_gsm8k(spec_llm)
     del spec_llm
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
     cleanup_dist_env_and_memory()
 
 
@@ -240,7 +240,7 @@ def test_suffix_decoding_acceptance(
     assert last_accept_rate > 0.80
 
     del spec_llm
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
     cleanup_dist_env_and_memory()
 
 
@@ -307,14 +307,14 @@ def test_speculators_model_integration(
     verifier_model = spec_llm.llm_engine.vllm_config.model_config.model
 
     del spec_llm
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
     cleanup_dist_env_and_memory()
 
     # Second run: Reference without speculative decoding
     ref_llm = LLM(model=verifier_model, max_model_len=4096)
     ref_outputs = ref_llm.chat(test_prompts, sampling_config)
     del ref_llm
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
     cleanup_dist_env_and_memory()
 
     # Compare outputs
@@ -410,7 +410,7 @@ def _run_eagle_correctness(
         )
         ref_outputs = ref_llm.chat(test_prompts, sampling_config)
         del ref_llm
-        torch.accelerator.empty_cache()
+        torch.cuda.empty_cache()
         cleanup_dist_env_and_memory()
 
         spec_llm = LLM(
@@ -445,7 +445,7 @@ def _run_eagle_correctness(
 
         assert matches > int(0.6 * len(ref_outputs))
         del spec_llm
-        torch.accelerator.empty_cache()
+        torch.cuda.empty_cache()
         cleanup_dist_env_and_memory()
 
 
@@ -715,7 +715,7 @@ def test_mtp_correctness(
             ref_llm, expected_accuracy_threshold=expected_accuracy_threshold
         )
         del ref_llm
-        torch.accelerator.empty_cache()
+        torch.cuda.empty_cache()
         cleanup_dist_env_and_memory()
 
         spec_llm = LLM(
@@ -747,7 +747,7 @@ def test_mtp_correctness(
         # Upon failure, inspect the outputs to check for inaccuracy.
         assert matches > int(MTP_SIMILARITY_RATE * len(ref_outputs))
         del spec_llm
-        torch.accelerator.empty_cache()
+        torch.cuda.empty_cache()
         cleanup_dist_env_and_memory()
 
 
@@ -952,7 +952,7 @@ def assert_draft_model_correctness(args: ArgsTest):
     )
 
     del spec_llm  # CLEANUP
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
     cleanup_dist_env_and_memory()
 
     print(

tests/v1/entrypoints/llm/test_struct_output_generate.py

@@ -857,7 +857,7 @@ def test_structured_output_batched_with_non_structured_outputs_requests(
     # Free memory as soon as possible as failed assertions
     # will short circuit and not free up memory
     del llm
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
     cleanup_dist_env_and_memory()
 
     for index, output in enumerate(outputs):

tests/v1/sample/test_logprobs.py

@@ -530,7 +530,7 @@ def test_logprobs_mode(logprobs_mode: LogprobsMode):
             assert positive_values > 0
     finally:
         del llm
-        torch.accelerator.empty_cache()
+        torch.cuda.empty_cache()
         cleanup_dist_env_and_memory()
 
 
@@ -1065,7 +1065,7 @@ def test_spec_decode_logprobs(
             for logprobs in output.logprobs:
                 ref_logprobs.extend(logprobs.values())
     del ref_llm
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
     cleanup_dist_env_and_memory()
 
     # Run spec decode LLM.
@@ -1095,7 +1095,7 @@ def test_spec_decode_logprobs(
             for logprobs in output.logprobs:
                 spec_logprobs.extend(logprobs.values())
     del spec_llm
-    torch.accelerator.empty_cache()
+    torch.cuda.empty_cache()
     cleanup_dist_env_and_memory()
 
     # Per-token logprobs are expected to be the same.

vllm/compilation/cuda_graph.py

@@ -260,9 +260,7 @@ def __call__(self, *args: Any, **kwargs: Any) -> Any | None:
                     # therefore, we only run gc for the first graph,
                     # and disable gc for the rest of the graphs.
                     stack.enter_context(patch("gc.collect", lambda: None))
-                    stack.enter_context(
-                        patch("torch.accelerator.empty_cache", lambda: None)
-                    )
+                    stack.enter_context(patch("torch.cuda.empty_cache", lambda: None))
 
                 if self.graph_pool is not None:
                     set_graph_pool_id(self.graph_pool)

vllm/distributed/elastic_ep/elastic_execute.py

@@ -408,7 +408,7 @@ def switch_and_prepare(self) -> None:
 
         gc.collect()
         torch.cuda.synchronize()
-        torch.accelerator.empty_cache()
+        torch.cuda.empty_cache()
         unlock_workspace()
         self.worker.compile_or_warm_up_model()
         lock_workspace()