678 lines
30 KiB
Python
678 lines
30 KiB
Python
import pickle
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import torch
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import torch.distributed as dist
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from multiprocessing.synchronize import Event
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from multiprocessing.shared_memory import SharedMemory
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from nanovllm.config import Config
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from nanovllm.engine.sequence import Sequence
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from nanovllm.models.qwen3 import Qwen3ForCausalLM
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from nanovllm.layers.sampler import GreedySampler
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from nanovllm.utils.context import set_context, get_context, reset_context
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from nanovllm.utils.loader import load_model
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from nanovllm.utils.logger import get_logger
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from nanovllm.kvcache import create_kvcache_manager, KVCacheManager
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logger = get_logger("model_runner")
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class ModelRunner:
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def __init__(self, config: Config, rank: int, event: Event | list[Event]):
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self.config = config
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hf_config = config.hf_config
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self.block_size = config.kvcache_block_size
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self.enforce_eager = config.enforce_eager
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self.world_size = config.tensor_parallel_size
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self.rank = rank
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self.event = event
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dist.init_process_group("nccl", "tcp://localhost:2333", world_size=self.world_size, rank=rank)
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torch.cuda.set_device(rank)
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default_dtype = torch.get_default_dtype()
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torch.set_default_dtype(hf_config.torch_dtype)
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torch.set_default_device("cuda")
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self.model = Qwen3ForCausalLM(hf_config)
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load_model(self.model, config.model)
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self.sampler = GreedySampler()
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self.warmup_model()
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self.allocate_kv_cache()
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if not self.enforce_eager:
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self.capture_cudagraph()
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torch.set_default_device("cpu")
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torch.set_default_dtype(default_dtype)
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if self.world_size > 1:
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if rank == 0:
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self.shm = SharedMemory(name="nanovllm", create=True, size=2**20)
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dist.barrier()
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else:
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dist.barrier()
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self.shm = SharedMemory(name="nanovllm")
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self.loop()
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def exit(self):
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if self.world_size > 1:
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self.shm.close()
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dist.barrier()
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if self.rank == 0:
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self.shm.unlink()
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if not self.enforce_eager:
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del self.graphs, self.graph_pool
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torch.cuda.synchronize()
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dist.destroy_process_group()
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def loop(self):
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while True:
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method_name, args = self.read_shm()
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self.call(method_name, *args)
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if method_name == "exit":
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break
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def read_shm(self):
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assert self.world_size > 1 and self.rank > 0
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self.event.wait()
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n = int.from_bytes(self.shm.buf[0:4], "little")
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method_name, *args = pickle.loads(self.shm.buf[4:n+4])
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self.event.clear()
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return method_name, args
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def write_shm(self, method_name, *args):
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assert self.world_size > 1 and self.rank == 0
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data = pickle.dumps([method_name, *args])
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n = len(data)
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self.shm.buf[0:4] = n.to_bytes(4, "little")
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self.shm.buf[4:n+4] = data
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for event in self.event:
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event.set()
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def call(self, method_name, *args):
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if self.world_size > 1 and self.rank == 0:
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self.write_shm(method_name, *args)
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method = getattr(self, method_name, None)
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return method(*args)
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def warmup_model(self):
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torch.cuda.empty_cache()
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torch.cuda.reset_peak_memory_stats()
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# Use a reasonable warmup length instead of max_model_len
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# Warmup only needs to trigger CUDA kernel JIT compilation
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# Using 2 blocks is sufficient and avoids huge memory allocation
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warmup_len = min(self.block_size * 2, self.config.max_model_len)
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warmup_len = max(warmup_len, 128) # At least 128 tokens
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num_seqs = min(self.config.max_num_batched_tokens // warmup_len, self.config.max_num_seqs, 4)
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num_seqs = max(num_seqs, 1)
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seqs = [Sequence([0] * warmup_len) for _ in range(num_seqs)]
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self.run(seqs, True)
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torch.cuda.empty_cache()
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def allocate_kv_cache(self):
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config = self.config
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hf_config = config.hf_config
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free, total = torch.cuda.mem_get_info()
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used = total - free
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peak = torch.cuda.memory_stats()["allocated_bytes.all.peak"]
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current = torch.cuda.memory_stats()["allocated_bytes.all.current"]
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num_kv_heads = hf_config.num_key_value_heads // self.world_size
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head_dim = getattr(hf_config, "head_dim", hf_config.hidden_size // hf_config.num_attention_heads)
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block_bytes = 2 * hf_config.num_hidden_layers * self.block_size * num_kv_heads * head_dim * hf_config.torch_dtype.itemsize
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# Calculate max GPU blocks based on available memory
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max_gpu_blocks = int(total * config.gpu_memory_utilization - used - peak + current) // block_bytes
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assert max_gpu_blocks > 0
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# Determine final GPU blocks: user-specified or auto (max available)
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if config.num_gpu_blocks > 0:
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num_gpu_blocks = min(config.num_gpu_blocks, max_gpu_blocks)
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else:
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num_gpu_blocks = max_gpu_blocks
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if config.enable_cpu_offload:
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# Three-region design: CPU is primary storage, GPU is working buffer
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# CPU blocks = all blocks needed to support max_model_len (stores complete KV for one max sequence)
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# GPU blocks = three-region working buffer (user-specified or auto)
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num_cpu_blocks = (config.max_model_len + self.block_size - 1) // self.block_size
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config.num_gpu_kvcache_blocks = num_gpu_blocks
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config.num_cpu_kvcache_blocks = num_cpu_blocks
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# For backward compatibility
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config.num_kvcache_blocks = num_gpu_blocks + num_cpu_blocks
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else:
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config.num_kvcache_blocks = num_gpu_blocks
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config.num_gpu_kvcache_blocks = num_gpu_blocks
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config.num_cpu_kvcache_blocks = 0
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# Create KV cache manager using factory
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self.kvcache_manager: KVCacheManager = create_kvcache_manager(config)
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# Allocate cache through manager
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self.kvcache_manager.allocate_cache(
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num_layers=hf_config.num_hidden_layers,
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num_kv_heads=num_kv_heads,
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head_dim=head_dim,
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dtype=hf_config.torch_dtype,
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)
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# Log KV cache allocation info with detailed per-token breakdown
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gpu_memory_mb = config.num_gpu_kvcache_blocks * block_bytes / (1024 ** 2)
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cpu_memory_mb = config.num_cpu_kvcache_blocks * block_bytes / (1024 ** 2)
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total_memory_mb = gpu_memory_mb + cpu_memory_mb
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# Calculate per-token KV cache usage
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# KV per token = 2 (K+V) * num_layers * kv_heads * head_dim * dtype_size
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dtype_size = 2 if hf_config.torch_dtype in [torch.float16, torch.bfloat16] else 4
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per_token_kv_bytes = 2 * hf_config.num_hidden_layers * num_kv_heads * head_dim * dtype_size
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per_token_kv_kb = per_token_kv_bytes / 1024
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logger.info(
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f"KV Cache per-token: {per_token_kv_kb:.2f}KB "
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f"(2 * {hf_config.num_hidden_layers}layers * {num_kv_heads}kv_heads * {head_dim}head_dim * {dtype_size}bytes)"
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)
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logger.info(
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f"KV Cache per-block: {block_bytes / (1024**2):.2f}MB "
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f"({per_token_kv_kb:.2f}KB * {self.block_size}tokens)"
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)
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if config.enable_cpu_offload:
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compute_size = config.num_gpu_kvcache_blocks // 2
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tokens_per_chunk = compute_size * self.block_size
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logger.info(
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f"KV Cache allocated (Chunked Offload mode): "
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f"GPU={config.num_gpu_kvcache_blocks} blocks ({gpu_memory_mb:.1f}MB), "
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f"CPU={config.num_cpu_kvcache_blocks} blocks ({cpu_memory_mb:.1f}MB), "
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f"Total={total_memory_mb:.1f}MB"
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)
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logger.info(
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f"Chunked Offload config: compute_size={compute_size} blocks, "
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f"tokens_per_chunk={tokens_per_chunk}, "
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f"block_size={self.block_size}"
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)
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else:
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logger.info(
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f"KV Cache allocated: "
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f"GPU={config.num_gpu_kvcache_blocks} blocks ({gpu_memory_mb:.1f}MB), "
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f"block_size={self.block_size}"
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)
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# Bind layer caches to attention modules and set layer_id
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layer_id = 0
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for module in self.model.modules():
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if hasattr(module, "k_cache") and hasattr(module, "v_cache"):
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k_cache, v_cache = self.kvcache_manager.get_layer_cache(layer_id)
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module.k_cache = k_cache
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module.v_cache = v_cache
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# Set layer_id for chunked prefill support
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if hasattr(module, "layer_id"):
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module.layer_id = layer_id
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layer_id += 1
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def prepare_block_tables(self, seqs: list[Sequence]):
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max_len = max(len(seq.block_table) for seq in seqs)
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block_tables = [seq.block_table + [-1] * (max_len - len(seq.block_table)) for seq in seqs]
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block_tables = torch.tensor(block_tables, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
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return block_tables
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def prepare_prefill(self, seqs: list[Sequence], chunk_info: list[tuple] = None):
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"""
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Prepare inputs for prefill.
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Args:
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seqs: List of sequences to prefill
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chunk_info: Optional chunked prefill info from get_gpu_block_tables_partial().
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If provided, only process blocks in the chunk.
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Format: [(gpu_block_ids, start_block_idx, end_block_idx), ...]
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"""
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# Check if any sequence has blocks (not warmup)
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has_blocks = any(seq.block_table for seq in seqs)
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gpu_block_tables = None
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if has_blocks and hasattr(self, 'kvcache_manager'):
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if chunk_info is None:
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# Standard prefill - try to get all blocks
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# This may fail if GPU doesn't have enough capacity
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self.kvcache_manager.prepare_for_attention(seqs, is_prefill=True)
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gpu_block_tables = self.kvcache_manager.get_gpu_block_tables(seqs)
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else:
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# Chunked prefill - use provided chunk info
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gpu_block_tables = [info[0] for info in chunk_info]
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input_ids = []
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positions = []
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cu_seqlens_q = [0]
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cu_seqlens_k = [0]
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max_seqlen_q = 0
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max_seqlen_k = 0
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slot_mapping = []
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block_tables = None
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for seq_idx, seq in enumerate(seqs):
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if chunk_info is not None:
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# Chunked prefill: only process blocks in the chunk
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gpu_blocks, start_block_idx, end_block_idx = chunk_info[seq_idx]
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if not gpu_blocks:
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continue
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# Calculate token range for this chunk
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start_token = start_block_idx * self.block_size
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end_token = min(end_block_idx * self.block_size, len(seq))
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if end_block_idx == seq.num_blocks:
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# Last chunk includes partial last block
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end_token = len(seq)
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# Input tokens for this chunk
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chunk_tokens = seq[start_token:end_token]
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input_ids.extend(chunk_tokens)
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positions.extend(list(range(start_token, end_token)))
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seqlen_q = end_token - start_token
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seqlen_k = end_token # Context includes all tokens up to this point
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cu_seqlens_q.append(cu_seqlens_q[-1] + seqlen_q)
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cu_seqlens_k.append(cu_seqlens_k[-1] + seqlen_k)
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max_seqlen_q = max(seqlen_q, max_seqlen_q)
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max_seqlen_k = max(seqlen_k, max_seqlen_k)
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# Slot mapping for blocks in this chunk
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for i, gpu_block_id in enumerate(gpu_blocks):
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block_idx = start_block_idx + i
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start = gpu_block_id * self.block_size
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if block_idx != seq.num_blocks - 1:
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end = start + self.block_size
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else:
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end = start + seq.last_block_num_tokens
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slot_mapping.extend(list(range(start, end)))
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else:
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# Standard prefill
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seqlen = len(seq)
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input_ids.extend(seq[seq.num_cached_tokens:])
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positions.extend(list(range(seq.num_cached_tokens, seqlen)))
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seqlen_q = seqlen - seq.num_cached_tokens
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seqlen_k = seqlen
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cu_seqlens_q.append(cu_seqlens_q[-1] + seqlen_q)
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cu_seqlens_k.append(cu_seqlens_k[-1] + seqlen_k)
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max_seqlen_q = max(seqlen_q, max_seqlen_q)
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max_seqlen_k = max(seqlen_k, max_seqlen_k)
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if not seq.block_table: # warmup
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continue
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# Use GPU physical block IDs for slot mapping
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gpu_blocks = gpu_block_tables[seq_idx]
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for i in range(seq.num_cached_blocks, seq.num_blocks):
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start = gpu_blocks[i] * self.block_size
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if i != seq.num_blocks - 1:
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end = start + self.block_size
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else:
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end = start + seq.last_block_num_tokens
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slot_mapping.extend(list(range(start, end)))
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if cu_seqlens_k[-1] > cu_seqlens_q[-1] and gpu_block_tables: # prefix cache
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block_tables = self._prepare_gpu_block_tables(gpu_block_tables)
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input_ids = torch.tensor(input_ids, dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
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positions = torch.tensor(positions, dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
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cu_seqlens_q = torch.tensor(cu_seqlens_q, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
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cu_seqlens_k = torch.tensor(cu_seqlens_k, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
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slot_mapping = torch.tensor(slot_mapping, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
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set_context(True, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, slot_mapping, None, block_tables)
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return input_ids, positions
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def prepare_decode(self, seqs: list[Sequence]):
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# Prepare KV cache (updates gather_indices for hybrid manager)
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if hasattr(self, 'kvcache_manager'):
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self.kvcache_manager.prepare_for_attention(seqs, is_prefill=False)
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# Get GPU physical block tables
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gpu_block_tables = self.kvcache_manager.get_gpu_block_tables(seqs)
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else:
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gpu_block_tables = [list(seq.block_table) for seq in seqs]
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input_ids = []
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positions = []
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slot_mapping = []
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context_lens = []
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for seq_idx, seq in enumerate(seqs):
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input_ids.append(seq.last_token)
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positions.append(len(seq) - 1)
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context_lens.append(len(seq))
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# Use GPU physical block ID for slot mapping
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gpu_blocks = gpu_block_tables[seq_idx]
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slot_mapping.append(gpu_blocks[-1] * self.block_size + seq.last_block_num_tokens - 1)
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input_ids = torch.tensor(input_ids, dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
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positions = torch.tensor(positions, dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
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slot_mapping = torch.tensor(slot_mapping, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
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context_lens = torch.tensor(context_lens, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
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# Use GPU physical block tables for attention
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block_tables = self._prepare_gpu_block_tables(gpu_block_tables)
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set_context(False, slot_mapping=slot_mapping, context_lens=context_lens, block_tables=block_tables)
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return input_ids, positions
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def _prepare_gpu_block_tables(self, gpu_block_tables: list[list[int]]):
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"""Prepare block tables tensor from GPU physical block IDs."""
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max_len = max(len(bt) for bt in gpu_block_tables)
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padded = [bt + [-1] * (max_len - len(bt)) for bt in gpu_block_tables]
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return torch.tensor(padded, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
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def prepare_sample(self, seqs: list[Sequence]):
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temperatures = []
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for seq in seqs:
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temperatures.append(seq.temperature)
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temperatures = torch.tensor(temperatures, dtype=torch.float32, pin_memory=True).cuda(non_blocking=True)
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return temperatures
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@torch.inference_mode()
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def run_model(self, input_ids: torch.Tensor, positions: torch.Tensor, is_prefill: bool):
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context = get_context()
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# Use eager mode for: prefill, enforce_eager, large batch, or chunked attention
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# Chunked attention requires dynamic KV loading that can't be captured in CUDA Graph
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use_eager = is_prefill or self.enforce_eager or input_ids.size(0) > 512 or context.is_chunked_prefill
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if use_eager:
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return self.model.compute_logits(self.model(input_ids, positions))
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else:
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bs = input_ids.size(0)
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context = get_context()
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graph = self.graphs[next(x for x in self.graph_bs if x >= bs)]
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graph_vars = self.graph_vars
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graph_vars["input_ids"][:bs] = input_ids
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graph_vars["positions"][:bs] = positions
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graph_vars["slot_mapping"].fill_(-1)
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graph_vars["slot_mapping"][:bs] = context.slot_mapping
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graph_vars["context_lens"].zero_()
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graph_vars["context_lens"][:bs] = context.context_lens
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graph_vars["block_tables"][:bs, :context.block_tables.size(1)] = context.block_tables
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graph.replay()
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return self.model.compute_logits(graph_vars["outputs"][:bs])
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def run(self, seqs: list[Sequence], is_prefill: bool) -> list[int]:
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# Check if Chunked Offload mode should be used (all blocks on CPU)
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if hasattr(self, 'kvcache_manager') and hasattr(self.kvcache_manager, 'get_all_cpu_blocks'):
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use_chunked_offload = self._should_use_chunked_offload(seqs, is_prefill)
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if use_chunked_offload:
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if is_prefill:
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return self.run_chunked_offload_prefill(seqs)
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else:
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return self.run_chunked_offload_decode(seqs)
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input_ids, positions = self.prepare_prefill(seqs) if is_prefill else self.prepare_decode(seqs)
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temperatures = self.prepare_sample(seqs) if self.rank == 0 else None
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logits = self.run_model(input_ids, positions, is_prefill)
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token_ids = self.sampler(logits, temperatures).tolist() if self.rank == 0 else None
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reset_context()
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return token_ids
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def _should_use_chunked_offload(self, seqs: list[Sequence], is_prefill: bool) -> bool:
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"""
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Check if three-region mode should be used.
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|
Use three-region when:
|
|
- CPU offload is enabled
|
|
- There are blocks on CPU (either allocated there or offloaded)
|
|
- Sequence exceeds GPU Compute region capacity
|
|
"""
|
|
if not hasattr(self.kvcache_manager, 'offload_engine'):
|
|
return False
|
|
|
|
for seq in seqs:
|
|
if not seq.block_table:
|
|
continue # Skip warmup sequences
|
|
|
|
# Check if any blocks are on CPU
|
|
cpu_blocks, _ = self.kvcache_manager.get_all_cpu_blocks(seq)
|
|
if cpu_blocks:
|
|
# Has CPU blocks - use three-region
|
|
return True
|
|
|
|
# Check if sequence needs more blocks than GPU Compute region can hold
|
|
compute_size = self.kvcache_manager.offload_engine.num_compute_blocks
|
|
if seq.num_blocks > compute_size:
|
|
# Needs chunked processing
|
|
return True
|
|
|
|
return False
|
|
|
|
def run_chunked_offload_prefill(self, seqs: list[Sequence]) -> list[int]:
|
|
"""
|
|
Run prefill with unified ring buffer (CPU is primary storage).
|
|
|
|
Flow:
|
|
1. All blocks are allocated to CPU (primary storage)
|
|
2. Each chunk writes KV to ring buffer slot[chunk_idx % N]
|
|
3. After each chunk, offload from ring buffer slot to CPU
|
|
4. All N-1 other slots are used to load previous chunks for attention
|
|
"""
|
|
import sys
|
|
|
|
assert len(seqs) == 1, "Ring buffer prefill only supports single sequence"
|
|
seq = seqs[0]
|
|
|
|
offload_engine = self.kvcache_manager.offload_engine
|
|
# Each chunk uses 1 ring buffer slot = 1 block
|
|
tokens_per_chunk = self.block_size
|
|
|
|
total_tokens = len(seq)
|
|
num_chunks = (total_tokens + tokens_per_chunk - 1) // tokens_per_chunk
|
|
print(f"[Ring Buffer Prefill] Starting: {total_tokens} tokens, "
|
|
f"ring_slots={offload_engine.num_ring_slots}, chunk={tokens_per_chunk} tokens, "
|
|
f"total_chunks={num_chunks}",
|
|
file=sys.stderr)
|
|
|
|
chunk_idx = 0
|
|
logits = None
|
|
processed_tokens = 0
|
|
|
|
# Get CPU block table for offload targets
|
|
cpu_block_ids, logical_ids = self.kvcache_manager.get_all_cpu_blocks(seq)
|
|
|
|
while processed_tokens < total_tokens:
|
|
chunk_start = processed_tokens
|
|
chunk_end = min(processed_tokens + tokens_per_chunk, total_tokens)
|
|
|
|
# Get ring buffer slot for this chunk
|
|
write_slot = offload_engine.get_write_slot_for_prefill(chunk_idx)
|
|
|
|
# CPU block index for this chunk
|
|
block_idx = chunk_idx
|
|
|
|
print(f"[Ring Buffer Prefill] Chunk {chunk_idx}: tokens {chunk_start}-{chunk_end}, "
|
|
f"write_slot={write_slot}",
|
|
file=sys.stderr)
|
|
|
|
# Prepare inputs
|
|
input_ids, positions = self._prepare_chunked_offload_chunk(
|
|
seq, chunk_start, chunk_end, write_slot, block_idx, chunk_idx
|
|
)
|
|
|
|
if input_ids.numel() == 0:
|
|
break
|
|
|
|
# Run model forward
|
|
logits = self.run_model(input_ids, positions, is_prefill=True)
|
|
reset_context()
|
|
|
|
# Mark block as prefilled
|
|
if block_idx < len(seq.block_table):
|
|
logical_id = seq.block_table[block_idx]
|
|
self.kvcache_manager.prefilled_blocks.add(logical_id)
|
|
|
|
# Offload this chunk's ring buffer slot to CPU (async)
|
|
if block_idx < len(cpu_block_ids):
|
|
cpu_block_id = cpu_block_ids[block_idx]
|
|
|
|
# Call sparse policy hook before offload (to capture metadata)
|
|
sparse_policy = self.kvcache_manager.sparse_policy
|
|
if sparse_policy is not None:
|
|
num_tokens = chunk_end - chunk_start
|
|
for layer_id in range(offload_engine.num_layers):
|
|
k_cache = offload_engine.k_cache_gpu[layer_id, write_slot, :num_tokens]
|
|
sparse_policy.on_block_offloaded(
|
|
cpu_block_id=cpu_block_id,
|
|
layer_id=layer_id,
|
|
k_cache=k_cache,
|
|
num_valid_tokens=num_tokens,
|
|
)
|
|
|
|
offload_engine.offload_slot_to_cpu(write_slot, cpu_block_id)
|
|
|
|
# Wait for offload to complete before next chunk
|
|
# (slot will be reused after N chunks)
|
|
offload_engine.wait_slot_offload(write_slot)
|
|
|
|
processed_tokens = chunk_end
|
|
chunk_idx += 1
|
|
|
|
# Wait for all offloads to complete
|
|
offload_engine.wait_all_offload_done()
|
|
|
|
print(f"[Ring Buffer Prefill] Complete: {chunk_idx} chunks", file=sys.stderr)
|
|
|
|
# Sample from last logits
|
|
# For chunked prefill, ParallelLMHead automatically selects last position's logits
|
|
temperatures = self.prepare_sample(seqs) if self.rank == 0 else None
|
|
if logits is not None:
|
|
token_ids = self.sampler(logits, temperatures).tolist() if self.rank == 0 else None
|
|
else:
|
|
token_ids = [0] if self.rank == 0 else None
|
|
|
|
return token_ids
|
|
|
|
def _prepare_chunked_offload_chunk(
|
|
self,
|
|
seq: Sequence,
|
|
chunk_start: int,
|
|
chunk_end: int,
|
|
write_slot: int,
|
|
block_idx: int,
|
|
chunk_idx: int,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
"""Prepare inputs for a chunked offload prefill chunk (ring buffer design)."""
|
|
# Input tokens for this chunk
|
|
input_ids = seq[chunk_start:chunk_end]
|
|
positions = list(range(chunk_start, chunk_end))
|
|
|
|
# Create slot mapping pointing to the single write_slot
|
|
slot_mapping = []
|
|
for pos in range(chunk_start, chunk_end):
|
|
pos_in_block = pos % self.block_size
|
|
slot = write_slot * self.block_size + pos_in_block
|
|
slot_mapping.append(slot)
|
|
|
|
# Convert to tensors
|
|
num_tokens = chunk_end - chunk_start
|
|
input_ids = torch.tensor(input_ids, dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
|
|
positions = torch.tensor(positions, dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
|
|
slot_mapping = torch.tensor(slot_mapping, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
|
|
|
|
# Set up context for chunked prefill
|
|
seqlen = num_tokens
|
|
cu_seqlens_q = torch.tensor([0, seqlen], dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
|
|
cu_seqlens_k = torch.tensor([0, seqlen], dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
|
|
|
|
set_context(
|
|
is_prefill=True,
|
|
cu_seqlens_q=cu_seqlens_q,
|
|
cu_seqlens_k=cu_seqlens_k,
|
|
max_seqlen_q=seqlen,
|
|
max_seqlen_k=seqlen,
|
|
slot_mapping=slot_mapping,
|
|
is_chunked_prefill=True,
|
|
kvcache_manager=self.kvcache_manager,
|
|
chunked_seq=seq,
|
|
current_chunk_idx=chunk_idx, # Pass chunk index for ring buffer pipeline
|
|
)
|
|
|
|
return input_ids, positions
|
|
|
|
def run_chunked_offload_decode(self, seqs: list[Sequence]) -> list[int]:
|
|
"""
|
|
Run decode with ring buffer (CPU is primary storage).
|
|
|
|
All KV is on CPU. Uses decode_slot (slot[0]) to write new KV.
|
|
Other slots (slots[1:]) are used to load previous KV chunks via pipeline.
|
|
New token's KV is written to decode_slot then offloaded to CPU only when block is full.
|
|
|
|
Key: decode_slot is dedicated to writing new KV, never used for loading.
|
|
Optimization: Batch offloads - only offload when block is full, attend to all accumulated tokens.
|
|
"""
|
|
assert len(seqs) == 1, "Ring buffer decode only supports single sequence"
|
|
seq = seqs[0]
|
|
|
|
offload_engine = self.kvcache_manager.offload_engine
|
|
|
|
# Prepare inputs
|
|
input_ids = torch.tensor([seq.last_token], dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
|
|
positions = torch.tensor([len(seq) - 1], dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
|
|
|
|
# Use Decode region (slot 0) to write new KV
|
|
decode_slot = offload_engine.decode_slot # = 0
|
|
pos_in_block = (len(seq) - 1) % self.block_size
|
|
slot = decode_slot * self.block_size + pos_in_block
|
|
slot_mapping = torch.tensor([slot], dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
|
|
context_len = torch.tensor([len(seq)], dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
|
|
|
|
# Get decode start position for accumulated token tracking
|
|
decode_start_pos = self.kvcache_manager.get_decode_start_pos(seq)
|
|
|
|
# Set up context for chunked decode
|
|
set_context(
|
|
is_prefill=False,
|
|
slot_mapping=slot_mapping,
|
|
context_lens=context_len,
|
|
is_chunked_prefill=True, # Use chunked attention path
|
|
kvcache_manager=self.kvcache_manager,
|
|
chunked_seq=seq,
|
|
decode_pos_in_block=pos_in_block,
|
|
decode_start_pos_in_block=decode_start_pos,
|
|
)
|
|
|
|
# Run model forward pass
|
|
logits = self.run_model(input_ids, positions, is_prefill=False)
|
|
reset_context()
|
|
|
|
# Only offload when block is full (pos_in_block == block_size - 1)
|
|
# This avoids unnecessary offloading on every decode step
|
|
if pos_in_block == self.block_size - 1:
|
|
last_cpu_block = self.kvcache_manager.get_last_cpu_block(seq)
|
|
if last_cpu_block >= 0:
|
|
offload_engine.offload_decode_slot(last_cpu_block)
|
|
offload_engine.wait_all_offload_done()
|
|
# Reset decode start position for next block
|
|
self.kvcache_manager.reset_decode_start_pos(seq)
|
|
|
|
# Sample
|
|
temperatures = self.prepare_sample(seqs) if self.rank == 0 else None
|
|
token_ids = self.sampler(logits, temperatures).tolist() if self.rank == 0 else None
|
|
|
|
return token_ids
|
|
|
|
@torch.inference_mode()
|
|
def capture_cudagraph(self):
|
|
config = self.config
|
|
hf_config = config.hf_config
|
|
max_bs = min(self.config.max_num_seqs, 512)
|
|
max_num_blocks = (config.max_model_len + self.block_size - 1) // self.block_size
|
|
input_ids = torch.zeros(max_bs, dtype=torch.int64)
|
|
positions = torch.zeros(max_bs, dtype=torch.int64)
|
|
slot_mapping = torch.zeros(max_bs, dtype=torch.int32)
|
|
context_lens = torch.zeros(max_bs, dtype=torch.int32)
|
|
block_tables = torch.zeros(max_bs, max_num_blocks, dtype=torch.int32)
|
|
outputs = torch.zeros(max_bs, hf_config.hidden_size)
|
|
self.graph_bs = [1, 2, 4, 8] + list(range(16, max_bs + 1, 16))
|
|
self.graphs = {}
|
|
self.graph_pool = None
|
|
|
|
for bs in reversed(self.graph_bs):
|
|
graph = torch.cuda.CUDAGraph()
|
|
set_context(False, slot_mapping=slot_mapping[:bs], context_lens=context_lens[:bs], block_tables=block_tables[:bs])
|
|
outputs[:bs] = self.model(input_ids[:bs], positions[:bs]) # warmup
|
|
with torch.cuda.graph(graph, self.graph_pool):
|
|
outputs[:bs] = self.model(input_ids[:bs], positions[:bs]) # capture
|
|
if self.graph_pool is None:
|
|
self.graph_pool = graph.pool()
|
|
self.graphs[bs] = graph
|
|
torch.cuda.synchronize()
|
|
reset_context()
|
|
|
|
self.graph_vars = dict(
|
|
input_ids=input_ids,
|
|
positions=positions,
|
|
slot_mapping=slot_mapping,
|
|
context_lens=context_lens,
|
|
block_tables=block_tables,
|
|
outputs=outputs,
|
|
)
|