Fix lora specific conditions in profile-run (#319)

HabanaAI#256 breaks LoRA specific flow
which was handled through `is_profile_run` flag to distinguish warmup
and profile-run phase.

Introduces a new flag `is_lora_profile_run` to handle this LoRA specific
flow in profile-run.

Author: vivekgoe

vllm/worker/habana_model_runner.py

@@ -1292,15 +1292,17 @@ def profile_run(self) -> None:
         max_seq_len = min(self.prompt_seq_bucket_cfg[-1],
                           self.max_num_batched_tokens // max_batch_size)
 
-        self.warmup_scenario(max_batch_size, max_seq_len, True, kv_caches)
+        self.warmup_scenario(max_batch_size, max_seq_len, True, kv_caches,
+                             False, True)
         return
 
     def warmup_scenario(self,
                         batch_size,
                         seq_len,
                         is_prompt,
                         kv_caches,
-                        is_profile_run=False) -> None:
+                        is_pt_profiler_run=False,
+                        is_lora_profile_run=False) -> None:
         use_graphs = self._use_graphs(batch_size, seq_len, is_prompt)
         scenario_name = ("warmup_"
                          f"{'prompt' if is_prompt else 'decode'}_"
@@ -1314,7 +1316,7 @@ def warmup_scenario(self,
         # passed in, which contains a lora from the lora warmup path.
         dummy_lora_requests: List[LoRARequest] = []
         dummy_lora_requests_per_seq: List[LoRARequest] = []
-        if self.lora_config and is_profile_run:
+        if self.lora_config and is_lora_profile_run:
             assert self.lora_manager is not None
             with self.lora_manager.dummy_lora_cache():
                 for idx in range(self.lora_config.max_loras):
@@ -1332,8 +1334,8 @@ def warmup_scenario(self,
                     for idx in range(max_num_seqs)
                 ]
         self.profiler.start('internal', scenario_name)
-        times = 3 if use_graphs or is_profile_run else 1
-        if self.lora_config and not is_profile_run:
+        times = 3 if use_graphs or is_pt_profiler_run else 1
+        if self.lora_config and not is_lora_profile_run:
             lora_mapping = LoRAMapping(
                 [0] * batch_size * seq_len,
                 [0] * batch_size * seq_len,
@@ -1364,7 +1366,7 @@ def warmup_scenario(self,
             ]
         torch.hpu.synchronize()
         profiler = None
-        if is_profile_run and self.is_driver_worker:
+        if is_pt_profiler_run and self.is_driver_worker:
             profiler = setup_profiler()
             profiler.start()
         for _ in range(times):