Fixed lora manager tests (#315)

Added the hpu related changes along with gpu to conftest.py file and
test_lora_manager_hpu.py

tests/lora/conftest.py

@@ -24,6 +24,7 @@
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.model_loader import get_model
+from vllm.platforms import current_platform
 
 
 class ContextIDInfo(TypedDict):
@@ -48,18 +49,13 @@ class ContextInfo(TypedDict):
 }]
 
 
-def is_hpu():
-    from importlib import util
-    return util.find_spec('habana_frameworks') is not None
-
-
 def cleanup():
     destroy_model_parallel()
     destroy_distributed_environment()
     with contextlib.suppress(AssertionError):
         torch.distributed.destroy_process_group()
     gc.collect()
-    if not is_hpu():
+    if not current_platform.is_hpu():
         torch.cuda.empty_cache()
     ray.shutdown()
 
@@ -84,12 +80,13 @@ def cleanup_fixture(should_do_global_cleanup_after_test: bool):
 @pytest.fixture
 def dist_init():
     temp_file = tempfile.mkstemp()[1]
+    backend_type = "hccl" if current_platform.is_hpu() else "nccl"
     init_distributed_environment(
         world_size=1,
         rank=0,
         distributed_init_method=f"file://{temp_file}",
         local_rank=0,
-        backend="nccl",
+        backend=backend_type,
     )
     initialize_model_parallel(1, 1)
     yield
@@ -259,8 +256,14 @@ def get_model_patched(*, model_config, device_config, **kwargs):
                              device_config=device_config,
                              **kwargs)
 
-    with patch("vllm.worker.model_runner.get_model", get_model_patched):
-        engine = vllm.LLM("meta-llama/Llama-2-7b-hf", enable_lora=False)
+    if current_platform.is_hpu():
+        with patch("vllm.worker.habana_model_runner.get_model",
+                   get_model_patched):
+            engine = vllm.LLM("meta-llama/Llama-2-7b-hf", enable_lora=False)
+    else:
+        with patch("vllm.worker.model_runner.get_model", get_model_patched):
+            engine = vllm.LLM("meta-llama/Llama-2-7b-hf", enable_lora=False)
+
     yield engine.llm_engine
     del engine
     cleanup()