use block_size as max_seq_len during profile_run

vllm/worker/habana_model_runner.py

@@ -1034,7 +1034,7 @@ def profile_run(self) -> None:
         num_layers = self.model_config.get_num_layers(self.parallel_config)
         kv_caches = [None] * num_layers
         max_batch_size = self.prompt_bs_bucket_cfg[-1]
-        max_seq_len = self.prompt_seq_bucket_cfg[-1]
+        max_seq_len = self.block_size
 
         self.warmup_scenario(max_batch_size, max_seq_len, True, kv_caches)
 
@@ -1123,8 +1123,8 @@ def warmup_model(self, kv_caches: List[torch.Tensor]) -> None:
         self.profiler.start('internal', 'warmup')
         start_mem = HabanaMemoryProfiler.current_device_memory_usage()
         start_time = time.perf_counter()
-        self.warmup_all_buckets(self.prompt_buckets, True, kv_caches)
-        self.warmup_all_buckets(self.decode_buckets, False, kv_caches)
+        # self.warmup_all_buckets(self.prompt_buckets, True, kv_caches)
+        # self.warmup_all_buckets(self.decode_buckets, False, kv_caches)
 
         if not self.enforce_eager:
             mem_margin = 1.0 - float(
@@ -1160,7 +1160,7 @@ def vocab_size(self) -> int:
 
 def _maybe_wrap_in_hpu_graph(model):
     return htorch.hpu.wrap_in_hpu_graph(HpuModelAdapter(
-        model)) if htorch.utils.internal.is_lazy() else HpuModelAdapter(model)
+        model), disable_tensor_cache=True) if htorch.utils.internal.is_lazy() else HpuModelAdapter(model)
 
 
 class HabanaProfilerCounterHelper():