[Core] Subclass ModelRunner to support cross-attention & encoder sequences (towards eventual encoder/decoder model support) (#4942)

Co-authored-by: Andrew Feldman <afeld2012@gmail.com>
Co-authored-by: Nick Hill <nickhill@us.ibm.com>

tests/kernels/utils.py

@@ -8,24 +8,10 @@ from typing import Any, List, NamedTuple, Optional, Tuple, Union
 import pytest
 import torch
 
-from vllm.attention.backends.abstract import (AttentionBackend,
-                                              AttentionMetadata, AttentionType)
+from vllm.attention import AttentionBackend, AttentionMetadata, AttentionType
 from vllm.attention.backends.xformers import XFormersBackend
-from vllm.utils import make_tensor_with_pad
-
-# String name of register which may be set in order to
-# force auto-selection of attention backend by Attention
-# wrapper
-STR_BACKEND_ENV_VAR: str = "VLLM_ATTENTION_BACKEND"
-
-# Possible string values of STR_BACKEND_ENV_VAR
-# register, corresponding to possible backends
-STR_FLASHINFER_ATTN_VAL: str = "FLASHINFER"
-STR_TORCH_SDPA_ATTN_VAL: str = "TORCH_SDPA"
-STR_ROCM_FLASH_ATTN_VAL: str = "ROCM_FLASH"
-STR_XFORMERS_ATTN_VAL: str = "XFORMERS"
-STR_FLASH_ATTN_VAL: str = "FLASH_ATTN"
-STR_INVALID_VAL: str = "INVALID"
+from vllm.utils import (STR_BACKEND_ENV_VAR, STR_XFORMERS_ATTN_VAL,
+                        make_tensor_with_pad)
 
 
 class QKVInputs(NamedTuple):