Mllama kv scale fix (#335)

* Using tensors in the explicit cache function calls from mllama implementation

* Properly creating the tensor

Author: gshtras

vllm/model_executor/models/mllama.py

@@ -829,14 +829,15 @@ def _attention_with_mask(
     ) -> torch.Tensor:
         # Skip writing kv-cache for the initial profiling run.
         if len(kv_cache.shape) > 1:
+            i = torch.ones(1, dtype=torch.float32)
             if current_platform.is_rocm():
                 key_cache, value_cache = PagedAttention.split_kv_cache(
                     kv_cache, self.num_local_key_value_heads, self.head_dim)
                 cached_k = torch.cat([k[s:e] for s, e in kv_range_for_decode])
                 cached_v = torch.cat([v[s:e] for s, e in kv_range_for_decode])
                 PagedAttention.write_to_paged_cache(
                     cached_k, cached_v, key_cache, value_cache,
-                    attn_metadata.cross_slot_mapping, "auto", 1.0, 1.0)
+                    attn_metadata.cross_slot_mapping, "auto", i, i)
             else:
                 if self.attn.backend in (_Backend.FLASH_ATTN,
                                          _Backend.FLASH_ATTN_VLLM_V1):
@@ -852,8 +853,8 @@ def _attention_with_mask(
                         attn_metadata.
                         cross_slot_mapping,  # type: ignore[union-attr]
                         "auto",
-                        1.0,
-                        1.0,
+                        i,
+                        i,
                     )
                 elif self.attn.backend in (_Backend.XFORMERS,
                                            _Backend.TORCH_SDPA):
@@ -866,7 +867,7 @@ def _attention_with_mask(
                         [v[s:e] for s, e in kv_range_for_decode])
                     PagedAttention.write_to_paged_cache(
                         cached_k, cached_v, key_cache, value_cache,
-                        attn_metadata.cross_slot_mapping, "auto", 1.0, 1.0)
+                        attn_metadata.cross_slot_mapping, "auto", i, i)
                 else:
                     raise ValueError(
                         f"Unsupported Attention backend {self.attn.backend} "