[V1] Remove _get_cache_block_size (vllm-project#12214)

Signed-off-by: Chen Zhang <[email protected]>

vllm/v1/worker/gpu_worker.py

@@ -8,14 +8,13 @@
 import torch.nn as nn
 
 import vllm.envs as envs
-from vllm.config import CacheConfig, ModelConfig, ParallelConfig, VllmConfig
+from vllm.config import ParallelConfig, VllmConfig
 from vllm.distributed import (ensure_model_parallel_initialized,
                               init_distributed_environment,
                               set_custom_all_reduce)
 from vllm.logger import init_logger
 from vllm.model_executor import set_random_seed
 from vllm.platforms import current_platform
-from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, LayerBlockType, get_dtype_size
 from vllm.v1.core.scheduler import SchedulerOutput
 from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
 from vllm.v1.outputs import ModelRunnerOutput
@@ -235,24 +234,3 @@ def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype):
                 f"of at least 8.0. Your {gpu_name} GPU {compute_str}. "
                 "You can use float16 instead by explicitly setting the"
                 "`dtype` flag in CLI, for example: --dtype=half.")
-
-
-def _get_cache_block_size(
-    cache_config: CacheConfig,
-    model_config: ModelConfig,
-    parallel_config: ParallelConfig,
-) -> int:
-    head_size = model_config.get_head_size()
-    num_heads = model_config.get_num_kv_heads(parallel_config)
-    num_attention_layers = model_config.get_num_layers_by_block_type(
-        parallel_config, LayerBlockType.attention)
-
-    key_cache_block = cache_config.block_size * num_heads * head_size
-    value_cache_block = key_cache_block
-    total = num_attention_layers * (key_cache_block + value_cache_block)
-    if cache_config.cache_dtype == "auto":
-        dtype = model_config.dtype
-    else:
-        dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
-    dtype_size = get_dtype_size(dtype)
-    return dtype_size * total