Turned back on the Marlin tests (vllm-project#121)

SUMMARY:
Turns back on the marlin tests. Issue was that vllm was not properly
tearing itself down. Calling the gc explicitly seems to have resolved
this in the short term.

In general, we should get to the bottom of why vllm does not shut down
cleanly.

TEST PLAN:
Automation

tests/models/test_marlin.py

@@ -17,6 +17,7 @@
 
 import pytest
 import torch
+import gc
 from compare_utils import check_logprobs_close
 from dataclasses import dataclass
 from vllm.model_executor.layers.quantization import _QUANTIZATION_CONFIG_REGISTRY
@@ -45,7 +46,6 @@ class ModelPair:
 ]
 
 
-@pytest.mark.skip(reason="out of memory")
 @pytest.mark.flaky(reruns=2)
 @pytest.mark.skipif(marlin_not_supported,
                     reason="Marlin is not supported on this GPU type.")
@@ -67,24 +67,27 @@ def test_models(
     marlin_outputs = marlin_model.generate_greedy_logprobs(
         example_prompts, max_tokens, num_logprobs)
 
-    # Note: not sure why, but deleting just the model on Ada Lovelace
-    #   does not free the GPU memory. On Ampere, deleting the just model
-    #   frees the memory.
+    # vllm memory cleanup is poor. This seems to fix things.
+    # NOTE: upstream sync should use downstream version.
     del marlin_model.model.llm_engine.driver_worker
     del marlin_model
 
+    gc.collect()
+    torch.cuda.empty_cache()
+
     gptq_model = vllm_runner_nm(model_pair.model_gptq,
                                 dtype=dtype,
                                 max_model_len=MAX_MODEL_LEN)
     gptq_outputs = gptq_model.generate_greedy_logprobs(example_prompts,
                                                        max_tokens,
                                                        num_logprobs)
 
-    # Note: not sure why, but deleting just the model on Ada Lovelace
-    #   does not free the GPU memory. On Ampere, deleting the just model
-    #   frees the memory.
+    # vllm memory cleanup is poor. This seems to fix things.
+    # NOTE: upstream sync should use downstream version.
     del gptq_model.model.llm_engine.driver_worker
     del gptq_model
+    gc.collect()
+    torch.cuda.empty_cache()
 
     # loop through the prompts
     # use logprobs or else this will consistently run out of memory