[V1] Move more control of kv cache initialization from model_executor to EngineCore

[heheda12345]

This pr changes the workflow of EngineCore._initialize_kv_caches to enable more flexible control of kv cache format in the future.
It is splitted from #11938 and is a preparation for #11382
Original workflow:

num_gpu_blocks, _ = self.model_executor.determine_num_available_blocks()
self.model_executor.initialize(num_gpu_blocks)

New workflow:

# Get all kv cache tensor needed by the model
kv_cache_spec = self.model_executor.get_kv_cache_spec()

# Profiles the peak memory usage of the model to determine how much
# memory can be allocated for kv cache.
availble_gpu_memory = self.model_executor.get_available_memory()

# Get the kv cache tensor size
kv_cache_config, num_gpu_blocks = get_kv_cache_config(
    vllm_config, kv_cache_spec, availble_gpu_memory)


# Initialize kv cache and warmup the execution
self.model_executor.initialize(kv_cache_config)

This pr introduces 2 new concepts:

1. KVCacheSpec, a data structure to represent the kv cache needed by each attention layer, which is constructed by asking the model runner to analyze all Attention modules. Will add more types of Spec in the future, e.g., SlidingWindowSpec, MLASpec
2. KVCacheConfig, a class to represent how to allocate the kv cache Tensor. It is quite simple now, i.e., tensors with the same size. But it may be extended to the following cases:
   1. tensors with different sizes, to support MLA & spec decode
   2. allocate a global buffer, and make the kv_cache tensors to point to different offsets, to support multiple types of layer sharing the same memory pool.

[github-actions]

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