Latest changes to aadpt to upstream master

Signed-off-by: Christian Pinto <christian.pinto@ibm.com>

Author: christian-pinto

examples/offline_inference/prithvi_geospatial_mae.py

@@ -143,7 +143,8 @@ def __init__(self):
         self.model = LLM(
             model=os.path.join(os.path.dirname(__file__), "./model"),
             skip_tokenizer_init=True,
-            dtype="float32",
+            dtype="float16",
+            enforce_eager=True
         )
 
     def run(self, input_data, location_coords):

vllm/v1/engine/core.py

@@ -134,8 +134,17 @@ def _initialize_kv_caches(
             self, vllm_config: VllmConfig) -> tuple[int, int, KVCacheConfig]:
         start = time.time()
 
-        # Get all kv cache needed by the model
-        kv_cache_specs = self.model_executor.get_kv_cache_specs()
+        #TODO: CP start from here
+        if vllm_config.model_config.is_attention_free:
+            # No need for initializing anything related to KV cache if the model
+            # is attention free.
+            kv_cache_specs = []
+            kv_cache_configs = [
+                    KVCacheConfig(num_blocks=0, kv_cache_tensors={}, kv_cache_groups=[])
+                ]
+        else:
+            # Get all kv cache needed by the model
+            kv_cache_specs = self.model_executor.get_kv_cache_specs()
 
         # Profiles the peak memory usage of the model to determine how much
         # memory can be allocated for kv cache.