SGLang対vLLM:高スループットLLM推論、RadixAttentionと構造化デコーディング

# vLLM PagedAttention conceptual illustration (simplified)
import torch

class PagedAttentionKVManager:
    def __init__(self, block_size, gpu_memory_limit_gb):
        self.block_size = block_size
        self.gpu_memory_limit_bytes = gpu_memory_limit_gb * 1024**3
        self.physical_blocks = {} # {block_id: torch.Tensor}
        self.free_block_ids = set()
        self.next_block_id = 0

    def _allocate_physical_block(self):
        if self.next_block_id in self.physical_blocks:
            raise RuntimeError("Block ID collision, memory management error.")
        # Simulate actual GPU memory allocation
        block_tensor = torch.empty((self.block_size, self.block_size, 2, 128), # K/V, head_dim
                                   dtype=torch.float16, device='cuda')
        self.physical_blocks[self.next_block_id] = block_tensor
        block_id = self.next_block_id
        self.next_block_id += 1
        return block_id

    def get_sequence_blocks(self, sequence_id, num_blocks_needed):
        # In a real system, this would involve a page table lookup
        # and allocation of new blocks if existing ones are full.
        allocated_blocks = []
        for _ in range(num_blocks_needed):
            if not self.free_block_ids:
                # Allocate new physical block if no free ones
                block_id = self._allocate_physical_block()
            else:
                block_id = self.free_block_ids.pop()
            allocated_blocks.append(block_id)
        return allocated_blocks

    def free_sequence_blocks(self, block_ids):
        self.free_block_ids.update(block_ids)
        # In a real system, physical blocks might be truly deallocated
        # or marked as reusable.

# Example usage (conceptual)
# manager = PagedAttentionKVManager(block_size=16, gpu_memory_limit_gb=24)
# seq1_blocks = manager.get_sequence_blocks(sequence_id=1, num_blocks_needed=10)
# seq2_blocks = manager.get_sequence_blocks(sequence_id=2, num_blocks_needed=15)
# manager.free_sequence_blocks(seq1_blocks)

# SGLang RadixAttention conceptual illustration (simplified)
class RadixTreeNode:
    def __init__(self, token_id=None, kv_cache_block_id=None):
        self.token_id = token_id # Token represented by this node
        self.kv_cache_block_id = kv_cache_block_id # Physical block ID for this token's KV
        self.children = {} # {token_id: RadixTreeNode}
        self.sequences = set() # Set of sequence_ids passing through this node

class RadixAttentionKVManager:
    def __init__(self, block_size):
        self.root = RadixTreeNode()
        self.block_size = block_size
        self.physical_blocks = {} # {block_id: torch.Tensor}
        self.next_block_id = 0

    def _allocate_physical_block(self):
        block_id = self.next_block_id
        self.physical_blocks[block_id] = torch.empty((self.block_size, self.block_size, 2, 128),
                                                     dtype=torch.float16, device='cuda')
        self.next_block_id += 1
        return block_id

    def get_or_create_path(self, sequence_id, tokens):
        current_node = self.root
        path_blocks = []
        for token in tokens:
            if token not in current_node.children:
                # Create new node and allocate KV block
                new_block_id = self._allocate_physical_block()
                current_node.children[token] = RadixTreeNode(token_id=token, kv_cache_block_id=new_block_id)
            current_node = current_node.children[token]
            current_node.sequences.add(sequence_id)
            path_blocks.append(current_node.kv_cache_block_id)
        return path_blocks

    def remove_sequence(self, sequence_id, tokens):
        # Traverse and remove sequence_id from nodes.
        # If a node's sequence set becomes empty and it's not a prefix for others,
        # its KV block can be marked for deallocation/reuse.
        pass # Complex logic for actual tree pruning and block freeing

# Example usage (conceptual)
# manager = RadixAttentionKVManager(block_size=16)
# seq1_tokens = [1, 5, 2, 8]
# seq1_blocks = manager.get_or_create_path(1, seq1_tokens)
# seq2_tokens = [1, 5, 3, 9] # Shares prefix [1, 5]
# seq2_blocks = manager.get_or_create_path(2, seq2_tokens)
# print(f"Seq1 blocks: {seq1_blocks}") # First two blocks might be shared
# print(f"Seq2 blocks: {seq2_blocks}") # First two blocks might be shared

# SGLang XGrammar example for JSON output
import sglang as sg

@sg.function
def generate_json_object(s, user_query):
    s += sg.user(user_query)
    s += sg.assistant(
        r'```json' +
        r'{' +
        r'  "name": "' + sg.gen("name", max_tokens=16, stop='"') + r'",' +
        r'  "age": ' + sg.gen("age", max_tokens=4, stop=',') + r',' +
        r'  "city": "' + sg.gen("city", max_tokens=16, stop='"') + r'"' +
        r'}' +
        r'```'
    )

# Example usage (assuming sglang server is running)
# state = sg.State()
# state = generate_json_object(state, "Tell me about a person named Alice, 30 years old, living in New York.")
# print(state["name"])
# print(state["age"])
# print(state["city"])

# vLLM with Outlines example for JSON output
import outlines
from vllm import LLM, SamplingParams

# 1. Define the JSON schema
json_schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer"},
        "city": {"type": "string"}
    },
    "required": ["name", "age", "city"]
}

# 2. Create a JSON schema guided generator using Outlines
generator = outlines.generate.json(LLM, json_schema) # LLM here is a placeholder for vLLM's model instance

# 3. Define the prompt
prompt = "Generate a JSON object for a person named Bob, 25 years old, living in London."

# 4. Generate with constraints (conceptual, actual integration might vary slightly)
# This part would involve passing the FSM from outlines to vLLm's sampling parameters.
# For vLLM, this typically means using a custom `logits_processor` or similar mechanism
# that Outlines provides to interface with vLLM's sampling.
#
# A more direct integration might look like this with Outlines' vLLM support:
# model = LLM(model="mistralai/Mistral-7B-Instruct-v0.2", trust_remote_code=True)
# generator = outlines.generate.json(model, json_schema)
# result = generator(prompt)
# print(result)

# For a more direct vLLM-only approach (without Outlines, less flexible):
# This would require manually implementing a logits processor.
# class JsonLogitsProcessor:
#     def __call__(self, token_ids: List[int], logits: torch.Tensor) -> torch.Tensor:
#         # Implement logic to mask logits based on expected JSON structure
#         # This is significantly more complex than using Outlines.
#         return logits
#
# sampling_params = SamplingParams(temperature=0.0, top_p=1.0, max_tokens=100,
#                                  logits_processor=[JsonLogitsProcessor()])
# outputs = model.generate(prompt, sampling_params)