i messed up and now i've fixed it

vllm/worker/habana_model_runner.py

@@ -1334,8 +1334,8 @@ def warmup_scenario(self,
                         seq_len,
                         is_prompt,
                         kv_caches,
-                        is_profile_run=False,
-                        override_n_runs=None) -> 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'}_"
@@ -1367,10 +1367,8 @@ 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 override_n_runs is not None:
-            times = override_n_runs
-        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,
@@ -1401,27 +1399,19 @@ def warmup_scenario(self,
             ]
         torch.hpu.synchronize()
         profiler = None
-        fwd_times = []
-        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):
-            torch.hpu.synchronize()
-            start = time.perf_counter()
             inputs = self.prepare_model_input(seqs)
-            self.execute_model(inputs, kv_caches, warmup_mode=False)
+            self.execute_model(inputs, kv_caches, warmup_mode=True)
             torch.hpu.synchronize()
-            end = time.perf_counter()
-            elapsed = end - start
-            fwd_times.append(elapsed)
-            print(f'[{batch_size}x{seq_len}x{use_graphs}] tput: {batch_size/elapsed:.3f} tps, time: {elapsed*1000:.3f} ms')
             if profiler:
                 profiler.step()
         if profiler:
             profiler.stop()
         self.profiler.end()
         gc.collect()
-        return fwd_times, use_graphs
 
     def remove_all_loras(self):
         if not self.lora_manager:
@@ -1466,13 +1456,11 @@ def log_warmup(self, phase, i, max_i, batch_size, seq_len):
                f"free_mem:{free_mem}")
         logger.info(msg)
 
-    def warmup_all_buckets(self, buckets, is_prompt, kv_caches, override_n_runs=None):
-        bucket_times = {}
+    def warmup_all_buckets(self, buckets, is_prompt, kv_caches):
         for i, (batch_size, seq_len) in enumerate(reversed(buckets)):
             self.log_warmup('Prompt' if is_prompt else 'Decode', i,
                             len(buckets), batch_size, seq_len)
-            bucket_times[(batch_size, seq_len)] = self.warmup_scenario(batch_size, seq_len, is_prompt, kv_caches, override_n_runs=override_n_runs)
-        return bucket_times 
+            self.warmup_scenario(batch_size, seq_len, is_prompt, kv_caches)
 
     def warmup_graphs(self,
                       strategy,
@@ -1676,14 +1664,6 @@ def warmup_model(self, kv_caches: List[torch.Tensor]) -> None:
         logger.info(msg)
         self.profiler.end()
 
-        if os.environ.get('VLLM_PROFILE_SERVER_CHARACTERISTICS', 'false').lower() == 'true':
-            from vllm.hpu.utils import process_run_characteristics
-            n_runs = int(os.environ.get('VLLM_PROFILE_SERVER_CHARACTERISTICS_N', '5'))
-            decode_times = self.warmup_all_buckets(self.decode_buckets, False, kv_caches, override_n_runs=n_runs)
-            process_run_characteristics(decode_times, block_size=self.cache_config.block_size, prefill=False)
-            prefill_times = self.warmup_all_buckets(self.prompt_buckets, True, kv_caches, override_n_runs=n_runs)
-            process_run_characteristics(prefill_times, block_size=self.cache_config.block_size, prefill=True)
-
     @property
     def vocab_size(self) -> int:
         return self.model_config.get_vocab_size()