1338 lines
61 KiB
Python
1338 lines
61 KiB
Python
import os
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import pickle
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import socket
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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, SparsePolicyType
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from nanovllm.engine.sequence import Sequence
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from nanovllm.models import get_model_class
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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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def _find_free_port() -> int:
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"""Find a free port for distributed communication.
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Uses socket binding with port 0 to let the OS assign an available port.
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"""
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with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
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s.bind(('', 0))
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s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
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return s.getsockname()[1]
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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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# Dynamic port allocation: use env var if set, otherwise find a free port
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env_port = os.environ.get("NANOVLLM_DIST_PORT")
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if env_port is not None:
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port = int(env_port)
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else:
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port = _find_free_port()
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logger.info(f"Auto-assigned distributed port: {port}")
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dist.init_process_group("nccl", f"tcp://localhost:{port}", 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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model_class = get_model_class(hf_config)
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self.model = model_class(hf_config)
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load_model(self.model, config.model)
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self.sampler = GreedySampler()
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# Initialize attention_policy before warmup (will be configured in allocate_kv_cache)
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self.attention_policy = None
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#> Disable warmup for debugging
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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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if config.enable_cpu_offload:
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self.capture_offload_cudagraph()
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else:
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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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if hasattr(self, 'graphs'):
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del self.graphs, self.graph_pool
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if hasattr(self, 'offload_graphs'):
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del self.offload_graphs, self.offload_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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# Create attention policy (always, including FULL)
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# In layerwise offload mode, all attention goes through the policy
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from nanovllm.kvcache.sparse import create_attention_policy
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# Get policy-specific parameters based on type
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if config.sparse_policy == SparsePolicyType.XATTN:
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policy_kwargs = {
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"stride": config.xattn_stride,
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"threshold": config.xattn_threshold,
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"chunk_size": config.xattn_chunk_size,
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"use_triton": config.xattn_use_triton,
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"keep_sink": config.xattn_keep_sink,
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"keep_recent": config.xattn_keep_recent,
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"norm": config.xattn_norm,
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"use_bsa": config.xattn_use_bsa,
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}
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elif config.sparse_policy == SparsePolicyType.MINFERENCE:
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policy_kwargs = {
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"vertical_size": config.minference_vertical_size,
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"slash_size": config.minference_slash_size,
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"adaptive_budget": config.minference_adaptive_budget,
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"num_sink_tokens": config.minference_num_sink_tokens,
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"num_recent_diags": config.minference_num_recent_diags,
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}
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else: # FULL or QUEST
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policy_kwargs = {}
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self.attention_policy = create_attention_policy(config.sparse_policy, **policy_kwargs)
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logger.info(f"Attention policy: {self.attention_policy}")
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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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# Initialize sparse policy if manager has one (CPU offload mode)
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if hasattr(self.kvcache_manager, 'sparse_policy') and self.kvcache_manager.sparse_policy is not None:
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self.kvcache_manager.sparse_policy.initialize(
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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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num_cpu_blocks=config.num_cpu_kvcache_blocks,
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dtype=hf_config.torch_dtype,
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device=torch.device("cuda"),
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)
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logger.info(
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f"Sparse policy initialized: {config.sparse_policy.name} "
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f"(topk={config.sparse_topk_blocks}, threshold={config.sparse_threshold_blocks})"
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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,
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slot_mapping, None, block_tables,
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attention_policy=self.attention_policy)
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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)
|
|
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)
|
|
context_lens = torch.tensor(context_lens, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
|
|
# Use GPU physical block tables for attention
|
|
block_tables = self._prepare_gpu_block_tables(gpu_block_tables)
|
|
set_context(False, slot_mapping=slot_mapping, context_lens=context_lens, block_tables=block_tables)
|
|
return input_ids, positions
|
|
|
|
def _prepare_gpu_block_tables(self, gpu_block_tables: list[list[int]]):
|
|
"""Prepare block tables tensor from GPU physical block IDs."""
|
|
max_len = max(len(bt) for bt in gpu_block_tables)
|
|
padded = [bt + [-1] * (max_len - len(bt)) for bt in gpu_block_tables]
|
|
return torch.tensor(padded, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
|
|
|
|
def prepare_sample(self, seqs: list[Sequence]):
|
|
temperatures = []
|
|
for seq in seqs:
|
|
temperatures.append(seq.temperature)
|
|
temperatures = torch.tensor(temperatures, dtype=torch.float32, pin_memory=True).cuda(non_blocking=True)
|
|
return temperatures
|
|
|
|
@torch.inference_mode()
|
|
def run_model(self, input_ids: torch.Tensor, positions: torch.Tensor, is_prefill: bool):
|
|
# Use eager mode for: prefill, enforce_eager, large batch
|
|
use_eager = is_prefill or self.enforce_eager or input_ids.size(0) > 512
|
|
if use_eager:
|
|
return self.model.compute_logits(self.model(input_ids, positions))
|
|
else:
|
|
bs = input_ids.size(0)
|
|
context = get_context()
|
|
graph = self.graphs[next(x for x in self.graph_bs if x >= bs)]
|
|
graph_vars = self.graph_vars
|
|
graph_vars["input_ids"][:bs] = input_ids
|
|
graph_vars["positions"][:bs] = positions
|
|
graph_vars["slot_mapping"].fill_(-1)
|
|
graph_vars["slot_mapping"][:bs] = context.slot_mapping
|
|
graph_vars["context_lens"].zero_()
|
|
graph_vars["context_lens"][:bs] = context.context_lens
|
|
graph_vars["block_tables"][:bs, :context.block_tables.size(1)] = context.block_tables
|
|
graph.replay()
|
|
return self.model.compute_logits(graph_vars["outputs"][:bs])
|
|
|
|
def run(self, seqs: list[Sequence], is_prefill: bool) -> list[int]:
|
|
#> Check if Layer-wise Offload mode should be used (CPU offload enabled)
|
|
if hasattr(self, 'kvcache_manager') and hasattr(self.kvcache_manager, 'offload_engine'):
|
|
use_layerwise_offload = self._should_use_layerwise_offload(seqs, is_prefill)
|
|
if use_layerwise_offload:
|
|
if is_prefill:
|
|
return self.run_layerwise_offload_prefill(seqs)
|
|
else:
|
|
return self.run_layerwise_offload_decode(seqs)
|
|
|
|
#> Check if contiguous GPU mode should be used (single-seq optimization)
|
|
if self._should_use_contiguous_gpu_mode(seqs, is_prefill):
|
|
if is_prefill:
|
|
return self.run_gpu_only_prefill(seqs)
|
|
else:
|
|
return self.run_gpu_only_decode(seqs)
|
|
|
|
#> Following Code uses standard PagedAttention path
|
|
input_ids, positions = self.prepare_prefill(seqs) if is_prefill else self.prepare_decode(seqs)
|
|
temperatures = self.prepare_sample(seqs) if self.rank == 0 else None
|
|
logits = self.run_model(input_ids, positions, is_prefill)
|
|
token_ids = self.sampler(logits, temperatures).tolist() if self.rank == 0 else None
|
|
reset_context()
|
|
return token_ids
|
|
|
|
def _should_use_contiguous_gpu_mode(self, seqs: list[Sequence], is_prefill: bool) -> bool:
|
|
"""
|
|
Check if contiguous GPU mode should be used for single-seq optimization.
|
|
|
|
Conditions:
|
|
1. Has kvcache_manager with contiguous cache allocated
|
|
2. Not using CPU offload (no offload_engine)
|
|
3. Single sequence (batch_size == 1)
|
|
4. Has blocks allocated (not warmup)
|
|
"""
|
|
# Must have kvcache_manager
|
|
if not hasattr(self, 'kvcache_manager') or self.kvcache_manager is None:
|
|
return False
|
|
|
|
# Must have contiguous cache
|
|
if not hasattr(self.kvcache_manager, 'contiguous_k_cache'):
|
|
return False
|
|
if self.kvcache_manager.contiguous_k_cache is None:
|
|
return False
|
|
|
|
# Must NOT be offload mode
|
|
if hasattr(self.kvcache_manager, 'offload_engine'):
|
|
return False
|
|
|
|
# Single sequence only
|
|
if len(seqs) != 1:
|
|
return False
|
|
|
|
# Has blocks allocated (not warmup)
|
|
if not seqs[0].block_table:
|
|
return False
|
|
|
|
return True
|
|
|
|
# ========== Contiguous GPU-only Methods ==========
|
|
|
|
@torch.inference_mode()
|
|
def run_gpu_only_prefill(self, seqs: list[Sequence]) -> list[int]:
|
|
"""
|
|
GPU-only prefill with contiguous KV cache layout.
|
|
|
|
Mirrors run_layerwise_offload_prefill() but stores to GPU instead of CPU.
|
|
No scatter operations - just contiguous slice assignment.
|
|
|
|
Key design:
|
|
- Process layer-by-layer (not via Attention.forward())
|
|
- Store K,V to contiguous GPU cache (same layout as computed K,V)
|
|
- Use sparse prefill attention if enabled
|
|
"""
|
|
assert len(seqs) == 1, "GPU-only layer-wise prefill only supports single sequence"
|
|
seq = seqs[0]
|
|
|
|
num_layers = len(self.model.model.layers)
|
|
total_tokens = len(seq)
|
|
|
|
logger.debug(f"[GPU-only Prefill] Starting: {total_tokens} tokens, {num_layers} layers")
|
|
|
|
# Get contiguous GPU cache
|
|
k_cache = self.kvcache_manager.contiguous_k_cache
|
|
v_cache = self.kvcache_manager.contiguous_v_cache
|
|
|
|
# Prepare inputs
|
|
input_ids = torch.tensor(seq[:], dtype=torch.int64, device="cuda")
|
|
positions = torch.arange(total_tokens, dtype=torch.int64, device="cuda")
|
|
|
|
# Import FlashAttention
|
|
from flash_attn.flash_attn_interface import flash_attn_varlen_func
|
|
cu_seqlens = torch.tensor([0, total_tokens], dtype=torch.int32, device="cuda")
|
|
|
|
# Embedding
|
|
hidden_states = self.model.model.embed_tokens(input_ids)
|
|
residual = None
|
|
|
|
# Layer-by-layer processing
|
|
for layer_id in range(num_layers):
|
|
layer = self.model.model.layers[layer_id]
|
|
|
|
# Input LayerNorm
|
|
if residual is None:
|
|
hidden_ln, residual = layer.input_layernorm(hidden_states), hidden_states
|
|
else:
|
|
hidden_ln, residual = layer.input_layernorm(hidden_states, residual)
|
|
|
|
# QKV projection
|
|
qkv = layer.self_attn.qkv_proj(hidden_ln)
|
|
q, k, v = qkv.split([
|
|
layer.self_attn.q_size,
|
|
layer.self_attn.kv_size,
|
|
layer.self_attn.kv_size
|
|
], dim=-1)
|
|
|
|
q = q.view(total_tokens, layer.self_attn.num_heads, layer.self_attn.head_dim)
|
|
k = k.view(total_tokens, layer.self_attn.num_kv_heads, layer.self_attn.head_dim)
|
|
v = v.view(total_tokens, layer.self_attn.num_kv_heads, layer.self_attn.head_dim)
|
|
|
|
# Q/K norms (Qwen3 specific - only when qkv_bias=False)
|
|
if not getattr(layer.self_attn, 'qkv_bias', True):
|
|
num_tokens = q.shape[0]
|
|
q = layer.self_attn.q_norm(q.reshape(-1, layer.self_attn.head_dim))
|
|
q = q.view(num_tokens, layer.self_attn.num_heads, layer.self_attn.head_dim)
|
|
k = layer.self_attn.k_norm(k.reshape(-1, layer.self_attn.head_dim))
|
|
k = k.view(num_tokens, layer.self_attn.num_kv_heads, layer.self_attn.head_dim)
|
|
|
|
# RoPE
|
|
q, k = layer.self_attn.rotary_emb(positions, q, k)
|
|
|
|
# Compute attention using policy (uses k, v directly - before store!)
|
|
attn_output = self.attention_policy.compute_prefill(
|
|
q, k, v, layer_id,
|
|
softmax_scale=layer.self_attn.attn.scale,
|
|
)
|
|
|
|
# O projection
|
|
attn_output = attn_output.view(total_tokens, -1)
|
|
hidden_states = layer.self_attn.o_proj(attn_output)
|
|
|
|
# Store K,V to contiguous GPU cache AFTER attention (same as offload pattern)
|
|
k_cache[layer_id, :total_tokens] = k
|
|
v_cache[layer_id, :total_tokens] = v
|
|
|
|
# Post-attention LayerNorm + MLP
|
|
hidden_states, residual = layer.post_attention_layernorm(hidden_states, residual)
|
|
hidden_states = layer.mlp(hidden_states)
|
|
|
|
# Final norm
|
|
hidden_states, _ = self.model.model.norm(hidden_states, residual)
|
|
|
|
# Compute logits for last token
|
|
logits = self.model.compute_logits(hidden_states[-1:])
|
|
|
|
# Record prefill length for decode
|
|
self.kvcache_manager.contiguous_seq_len = total_tokens
|
|
|
|
logger.debug(f"[GPU-only Prefill] Complete: {num_layers} layers processed")
|
|
|
|
# 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 run_gpu_only_decode(self, seqs: list[Sequence]) -> list[int]:
|
|
"""
|
|
Decode using contiguous GPU KV cache.
|
|
|
|
Similar to offload decode but simpler - all KV already on GPU.
|
|
"""
|
|
assert len(seqs) == 1, "GPU-only decode only supports single sequence"
|
|
seq = seqs[0]
|
|
|
|
num_layers = len(self.model.model.layers)
|
|
k_cache = self.kvcache_manager.contiguous_k_cache
|
|
v_cache = self.kvcache_manager.contiguous_v_cache
|
|
context_len = self.kvcache_manager.contiguous_seq_len
|
|
|
|
# Prepare inputs
|
|
input_ids = torch.tensor([seq.last_token], dtype=torch.int64, device="cuda")
|
|
positions = torch.tensor([len(seq) - 1], dtype=torch.int64, device="cuda")
|
|
|
|
from flash_attn.flash_attn_interface import flash_attn_varlen_func
|
|
cu_seqlens_q = torch.tensor([0, 1], dtype=torch.int32, device="cuda")
|
|
|
|
# Embedding
|
|
hidden_states = self.model.model.embed_tokens(input_ids)
|
|
residual = None
|
|
|
|
for layer_id in range(num_layers):
|
|
layer = self.model.model.layers[layer_id]
|
|
|
|
# Input LayerNorm
|
|
if residual is None:
|
|
hidden_ln, residual = layer.input_layernorm(hidden_states), hidden_states
|
|
else:
|
|
hidden_ln, residual = layer.input_layernorm(hidden_states, residual)
|
|
|
|
# QKV projection
|
|
qkv = layer.self_attn.qkv_proj(hidden_ln)
|
|
q, k_new, v_new = qkv.split([
|
|
layer.self_attn.q_size,
|
|
layer.self_attn.kv_size,
|
|
layer.self_attn.kv_size
|
|
], dim=-1)
|
|
|
|
q = q.view(1, layer.self_attn.num_heads, layer.self_attn.head_dim)
|
|
k_new = k_new.view(1, layer.self_attn.num_kv_heads, layer.self_attn.head_dim)
|
|
v_new = v_new.view(1, layer.self_attn.num_kv_heads, layer.self_attn.head_dim)
|
|
|
|
# Q/K norms (Qwen3 specific - only when qkv_bias=False)
|
|
if not getattr(layer.self_attn, 'qkv_bias', True):
|
|
q = layer.self_attn.q_norm(q.reshape(-1, layer.self_attn.head_dim))
|
|
q = q.view(1, layer.self_attn.num_heads, layer.self_attn.head_dim)
|
|
k_new = layer.self_attn.k_norm(k_new.reshape(-1, layer.self_attn.head_dim))
|
|
k_new = k_new.view(1, layer.self_attn.num_kv_heads, layer.self_attn.head_dim)
|
|
|
|
# RoPE
|
|
q, k_new = layer.self_attn.rotary_emb(positions, q, k_new)
|
|
|
|
# Store new K,V to cache
|
|
k_cache[layer_id, context_len] = k_new.squeeze(0)
|
|
v_cache[layer_id, context_len] = v_new.squeeze(0)
|
|
|
|
# Full K,V for attention (including new token)
|
|
k_full = k_cache[layer_id, :context_len + 1]
|
|
v_full = v_cache[layer_id, :context_len + 1]
|
|
|
|
# Attention
|
|
cu_seqlens_k = torch.tensor([0, context_len + 1], dtype=torch.int32, device="cuda")
|
|
attn_output = flash_attn_varlen_func(
|
|
q, k_full, v_full,
|
|
cu_seqlens_q=cu_seqlens_q,
|
|
cu_seqlens_k=cu_seqlens_k,
|
|
max_seqlen_q=1,
|
|
max_seqlen_k=context_len + 1,
|
|
softmax_scale=layer.self_attn.attn.scale,
|
|
causal=False, # Single query, no causal needed
|
|
)
|
|
|
|
# O projection
|
|
attn_output = attn_output.view(1, -1)
|
|
hidden_states = layer.self_attn.o_proj(attn_output)
|
|
|
|
# Post-attention LayerNorm + MLP
|
|
hidden_states, residual = layer.post_attention_layernorm(hidden_states, residual)
|
|
hidden_states = layer.mlp(hidden_states)
|
|
|
|
# Update context length
|
|
self.kvcache_manager.contiguous_seq_len = context_len + 1
|
|
|
|
# Final norm
|
|
hidden_states, _ = self.model.model.norm(hidden_states, residual)
|
|
|
|
# Compute logits
|
|
logits = self.model.compute_logits(hidden_states)
|
|
|
|
# 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
|
|
|
|
def _should_use_layerwise_offload(self, seqs: list[Sequence], is_prefill: bool) -> bool:
|
|
"""
|
|
Check if layer-wise offload mode should be used.
|
|
|
|
Use layer-wise offload when:
|
|
- CPU offload is enabled (offload_engine exists)
|
|
- Sequence has blocks allocated (not warmup)
|
|
"""
|
|
if not hasattr(self.kvcache_manager, 'offload_engine'):
|
|
return False
|
|
|
|
for seq in seqs:
|
|
if seq.block_table:
|
|
# Has blocks - use layer-wise offload
|
|
return True
|
|
|
|
return False
|
|
|
|
# ========== Layer-wise Offload Methods ==========
|
|
|
|
@torch.inference_mode()
|
|
def run_layerwise_offload_prefill(self, seqs: list[Sequence]) -> list[int]:
|
|
"""
|
|
Run prefill with layer-wise processing and async CPU offload.
|
|
|
|
Key design:
|
|
- Process one layer at a time (not one chunk at a time)
|
|
- Each layer: compute → async offload KV to CPU
|
|
- Offload of layer N overlaps with compute of layer N+1
|
|
- Uses OffloadEngine's async API with stream events
|
|
|
|
This enables future sparse attention methods (like MInference)
|
|
that need full KV context per layer for pattern estimation.
|
|
"""
|
|
assert len(seqs) == 1, "Layer-wise offload only supports single sequence"
|
|
seq = seqs[0]
|
|
|
|
offload_engine = self.kvcache_manager.offload_engine
|
|
compute_stream = offload_engine.compute_stream
|
|
num_layers = len(self.model.model.layers)
|
|
total_tokens = len(seq)
|
|
|
|
logger.debug(f"[Layer-wise Prefill] Starting: {total_tokens} tokens, {num_layers} layers")
|
|
|
|
# Get CPU block IDs for offload targets
|
|
cpu_block_ids, logical_ids = self.kvcache_manager.get_all_cpu_blocks(seq)
|
|
|
|
# Prepare inputs
|
|
input_ids = torch.tensor(seq[:], dtype=torch.int64, device="cuda")
|
|
positions = torch.arange(total_tokens, dtype=torch.int64, device="cuda")
|
|
|
|
# Import FlashAttention once
|
|
from flash_attn.flash_attn_interface import flash_attn_varlen_func
|
|
cu_seqlens = torch.tensor([0, total_tokens], dtype=torch.int32, device="cuda")
|
|
|
|
# Step 1: Embedding (on compute stream)
|
|
with torch.cuda.stream(compute_stream):
|
|
hidden_states = self.model.model.embed_tokens(input_ids)
|
|
residual = None
|
|
|
|
# Step 2: Layer-by-layer processing
|
|
for layer_id in range(num_layers):
|
|
layer = self.model.model.layers[layer_id]
|
|
|
|
# 2a. Input LayerNorm (chunked for long sequences)
|
|
# LayerNorm creates float32 temporaries: seq_len * hidden_size * 4 bytes
|
|
# For 64k: 65536 * 4096 * 4 = ~1 GB per operation
|
|
# Using chunk_size=4096 reduces peak to ~125 MB
|
|
layernorm_chunk_size = 128
|
|
if total_tokens > layernorm_chunk_size:
|
|
if residual is None:
|
|
# Chunked input_layernorm
|
|
hs_chunks = hidden_states.split(layernorm_chunk_size, dim=0)
|
|
ln_chunks = []
|
|
res_chunks = []
|
|
for chunk in hs_chunks:
|
|
ln, res = layer.input_layernorm(chunk), chunk
|
|
ln_chunks.append(ln)
|
|
res_chunks.append(res)
|
|
hidden_ln = torch.cat(ln_chunks, dim=0)
|
|
residual = torch.cat(res_chunks, dim=0)
|
|
else:
|
|
# Chunked input_layernorm with residual
|
|
hs_chunks = hidden_states.split(layernorm_chunk_size, dim=0)
|
|
res_chunks_in = residual.split(layernorm_chunk_size, dim=0)
|
|
ln_chunks = []
|
|
res_chunks_out = []
|
|
for hs_chunk, res_chunk in zip(hs_chunks, res_chunks_in):
|
|
ln, res = layer.input_layernorm(hs_chunk, res_chunk)
|
|
ln_chunks.append(ln)
|
|
res_chunks_out.append(res)
|
|
hidden_ln = torch.cat(ln_chunks, dim=0)
|
|
residual = torch.cat(res_chunks_out, dim=0)
|
|
else:
|
|
if residual is None:
|
|
hidden_ln, residual = layer.input_layernorm(hidden_states), hidden_states
|
|
else:
|
|
hidden_ln, residual = layer.input_layernorm(hidden_states, residual)
|
|
|
|
# 2b. Self-attention (full sequence)
|
|
# Chunked QKV projection to reduce activation memory for long sequences
|
|
# QKV activation = seq_len * (q_size + 2*kv_size) * 2 bytes
|
|
# For 64k: 65536 * (4096 + 2*1024) * 2 = ~805 MB
|
|
# Using chunk_size=2048 reduces peak to ~25 MB
|
|
qkv_chunk_size = 128
|
|
if total_tokens > qkv_chunk_size:
|
|
chunks = hidden_ln.split(qkv_chunk_size, dim=0)
|
|
qkv_chunks = []
|
|
for chunk in chunks:
|
|
qkv_chunks.append(layer.self_attn.qkv_proj(chunk))
|
|
qkv = torch.cat(qkv_chunks, dim=0)
|
|
else:
|
|
qkv = layer.self_attn.qkv_proj(hidden_ln)
|
|
|
|
q, k, v = qkv.split([
|
|
layer.self_attn.q_size,
|
|
layer.self_attn.kv_size,
|
|
layer.self_attn.kv_size
|
|
], dim=-1)
|
|
|
|
q = q.view(total_tokens, layer.self_attn.num_heads, layer.self_attn.head_dim)
|
|
k = k.view(total_tokens, layer.self_attn.num_kv_heads, layer.self_attn.head_dim)
|
|
v = v.view(total_tokens, layer.self_attn.num_kv_heads, layer.self_attn.head_dim)
|
|
|
|
# Q/K norms (Qwen3 specific - only when qkv_bias=False)
|
|
if not getattr(layer.self_attn, 'qkv_bias', True):
|
|
num_tokens = q.shape[0]
|
|
q = layer.self_attn.q_norm(q.reshape(-1, layer.self_attn.head_dim))
|
|
q = q.view(num_tokens, layer.self_attn.num_heads, layer.self_attn.head_dim)
|
|
k = layer.self_attn.k_norm(k.reshape(-1, layer.self_attn.head_dim))
|
|
k = k.view(num_tokens, layer.self_attn.num_kv_heads, layer.self_attn.head_dim)
|
|
|
|
# RoPE
|
|
q, k = layer.self_attn.rotary_emb(positions, q, k)
|
|
|
|
# Compute attention using policy
|
|
attn_output = self.attention_policy.compute_prefill(
|
|
q, k, v, layer_id,
|
|
softmax_scale=layer.self_attn.attn.scale,
|
|
)
|
|
|
|
# O projection
|
|
attn_output = attn_output.view(total_tokens, -1)
|
|
hidden_states = layer.self_attn.o_proj(attn_output)
|
|
|
|
# 2c. Post-attention LayerNorm (chunked for long sequences)
|
|
layernorm_chunk_size = 128
|
|
if total_tokens > layernorm_chunk_size:
|
|
# Chunked post_attention_layernorm
|
|
hs_chunks = hidden_states.split(layernorm_chunk_size, dim=0)
|
|
res_chunks_in = residual.split(layernorm_chunk_size, dim=0)
|
|
ln_chunks = []
|
|
res_chunks_out = []
|
|
for hs_chunk, res_chunk in zip(hs_chunks, res_chunks_in):
|
|
ln, res = layer.post_attention_layernorm(hs_chunk, res_chunk)
|
|
ln_chunks.append(ln)
|
|
res_chunks_out.append(res)
|
|
hidden_states = torch.cat(ln_chunks, dim=0)
|
|
residual = torch.cat(res_chunks_out, dim=0)
|
|
else:
|
|
hidden_states, residual = layer.post_attention_layernorm(hidden_states, residual)
|
|
|
|
# Chunked MLP processing to reduce activation memory for long sequences
|
|
# MLP activation = seq_len * intermediate_size * 2 bytes
|
|
# For 64k: 65536 * 14336 * 2 = ~1.75 GB (down_proj input)
|
|
# Using chunk_size=2048 reduces peak to ~55 MB
|
|
mlp_chunk_size = 128
|
|
if total_tokens > mlp_chunk_size:
|
|
chunks = hidden_states.split(mlp_chunk_size, dim=0)
|
|
outputs = []
|
|
for i, chunk in enumerate(chunks):
|
|
outputs.append(layer.mlp(chunk))
|
|
del chunk
|
|
torch.cuda.empty_cache() # Clean after every chunk
|
|
hidden_states = torch.cat(outputs, dim=0)
|
|
del outputs
|
|
torch.cuda.empty_cache()
|
|
else:
|
|
hidden_states = layer.mlp(hidden_states)
|
|
|
|
# 2d. Offload KV to CPU (encapsulated with sparse policy hooks)
|
|
offload_engine.offload_layer_kv_sync(layer_id, k, v, cpu_block_ids, total_tokens)
|
|
|
|
# Step 3: Final norm
|
|
hidden_states, _ = self.model.model.norm(hidden_states, residual)
|
|
|
|
# Step 4: Compute logits for last token
|
|
logits = self.model.compute_logits(hidden_states[-1:])
|
|
|
|
# DEBUG: Check hidden_states and logits at end of prefill
|
|
hs_last = hidden_states[-1, :4].tolist()
|
|
top5_logits, top5_indices = torch.topk(logits[0], 5)
|
|
logger.debug(
|
|
f"[DEBUG] PREFILL END: hidden_states[-1, :4]={hs_last}, "
|
|
f"top5_tokens={top5_indices.tolist()}, top5_logits={top5_logits.tolist()}"
|
|
)
|
|
|
|
# Note: Using sync offload, no wait needed
|
|
|
|
# Mark all blocks as prefilled
|
|
for logical_id in logical_ids:
|
|
self.kvcache_manager.prefilled_blocks.add(logical_id)
|
|
|
|
# DEBUG: Verify CPU cache content after prefill
|
|
first_cpu_block = cpu_block_ids[0]
|
|
last_cpu_block = cpu_block_ids[-1]
|
|
last_block_valid = total_tokens % self.block_size or self.block_size
|
|
k_first = offload_engine.k_cache_cpu[0, first_cpu_block, 0, 0, :4].tolist()
|
|
k_last = offload_engine.k_cache_cpu[0, last_cpu_block, 0, 0, :4].tolist()
|
|
logger.debug(
|
|
f"[DEBUG] AFTER PREFILL: first_cpu_block={first_cpu_block}, last_cpu_block={last_cpu_block}, "
|
|
f"last_block_valid={last_block_valid}, "
|
|
f"k_cache_cpu[0, {first_cpu_block}, 0, 0, :4]={k_first}, "
|
|
f"k_cache_cpu[0, {last_cpu_block}, 0, 0, :4]={k_last}"
|
|
)
|
|
|
|
# Step 5: 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
|
|
|
|
logger.debug(f"[Layer-wise Prefill] Complete: {num_layers} layers processed")
|
|
|
|
return token_ids
|
|
|
|
@torch.inference_mode()
|
|
def run_layerwise_offload_decode(self, seqs: list[Sequence]) -> list[int]:
|
|
"""
|
|
Run decode with ring-buffered layer-wise KV loading from CPU.
|
|
|
|
Key design:
|
|
- Ring buffer pipeline: load layer N+k while computing layer N
|
|
- Uses standard Attention.forward() path (not bypassing)
|
|
- Per-layer decode buffer for accumulating new tokens
|
|
- Async block offload when decode buffer is full
|
|
- Uses CUDA graphs when available (not enforce_eager)
|
|
"""
|
|
assert len(seqs) == 1, "Layer-wise offload only supports single sequence"
|
|
seq = seqs[0]
|
|
|
|
offload_engine = self.kvcache_manager.offload_engine
|
|
compute_stream = offload_engine.compute_stream
|
|
num_layers = len(self.model.model.layers)
|
|
num_buffers = offload_engine.num_kv_buffers
|
|
|
|
# Check if using CUDA graphs
|
|
use_cuda_graph = not self.enforce_eager and hasattr(self, 'offload_graphs')
|
|
|
|
# Prepare inputs
|
|
if use_cuda_graph:
|
|
# Use fixed-address tensors for graph replay
|
|
graph_vars = self.offload_graph_vars
|
|
graph_vars["input_ids"][0] = seq.last_token
|
|
graph_vars["positions"][0] = len(seq) - 1
|
|
input_ids = graph_vars["input_ids"]
|
|
positions = graph_vars["positions"]
|
|
else:
|
|
input_ids = torch.tensor([seq.last_token], dtype=torch.int64, device="cuda")
|
|
positions = torch.tensor([len(seq) - 1], dtype=torch.int64, device="cuda")
|
|
|
|
# Get prefilled CPU blocks and compute valid tokens per block
|
|
cpu_block_table = self.kvcache_manager.get_prefilled_cpu_blocks(seq)
|
|
num_prefill_blocks = len(cpu_block_table)
|
|
total_prefill_tokens = self.kvcache_manager.get_prefill_len(seq)
|
|
|
|
# Calculate valid tokens per block
|
|
valid_tokens_per_block = []
|
|
for block_idx in range(num_prefill_blocks):
|
|
if block_idx == num_prefill_blocks - 1:
|
|
# Last block may be partial
|
|
last_block_tokens = total_prefill_tokens % self.block_size
|
|
if last_block_tokens == 0 and total_prefill_tokens > 0:
|
|
last_block_tokens = self.block_size
|
|
valid_tokens_per_block.append(last_block_tokens)
|
|
else:
|
|
valid_tokens_per_block.append(self.block_size)
|
|
|
|
# Current decode position info
|
|
pos_in_block = (len(seq) - 1) % self.block_size
|
|
decode_start_pos = self.kvcache_manager.get_decode_start_pos(seq)
|
|
num_prev_decode_tokens = pos_in_block - decode_start_pos # Previous decode tokens (not including current)
|
|
|
|
# Total context length (prefill + previous decode tokens)
|
|
# New token will be stored at this position
|
|
context_len = total_prefill_tokens + num_prev_decode_tokens
|
|
|
|
# DEBUG: Log key values for first decode step
|
|
if num_prev_decode_tokens == 0:
|
|
first_cpu_block = cpu_block_table[0] if cpu_block_table else -1
|
|
last_cpu_block = cpu_block_table[-1] if cpu_block_table else -1
|
|
k_first = offload_engine.k_cache_cpu[0, first_cpu_block, 0, 0, :4].tolist() if first_cpu_block >= 0 else []
|
|
k_last = offload_engine.k_cache_cpu[0, last_cpu_block, 0, 0, :4].tolist() if last_cpu_block >= 0 else []
|
|
logger.debug(
|
|
f"[DEBUG] FIRST DECODE STEP: len(seq)={len(seq)}, "
|
|
f"total_prefill_tokens={total_prefill_tokens}, "
|
|
f"num_prefill_blocks={num_prefill_blocks}, "
|
|
f"valid_tokens_per_block[-1]={valid_tokens_per_block[-1] if valid_tokens_per_block else 'N/A'}, "
|
|
f"pos_in_block={pos_in_block}, decode_start_pos={decode_start_pos}, "
|
|
f"context_len={context_len}, "
|
|
f"first_cpu_block={first_cpu_block}, last_cpu_block={last_cpu_block}, "
|
|
f"k_cache_cpu[0, {first_cpu_block}, 0, ...]={k_first}, "
|
|
f"k_cache_cpu[0, {last_cpu_block}, 0, ...]={k_last}"
|
|
)
|
|
|
|
# Context setup for Attention.forward() - contiguous mode (no block tables)
|
|
if use_cuda_graph:
|
|
graph_vars["slot_mapping"][0] = context_len
|
|
graph_vars["context_lens"][0] = context_len + 1
|
|
slot_mapping = graph_vars["slot_mapping"]
|
|
context_lens = graph_vars["context_lens"]
|
|
else:
|
|
slot_mapping = torch.tensor([context_len], dtype=torch.int32, device="cuda")
|
|
context_lens = torch.tensor([context_len + 1], dtype=torch.int32, device="cuda")
|
|
|
|
# Phase 1: Preload first N layers to ring buffer (fill pipeline)
|
|
num_preload = min(num_buffers, num_layers)
|
|
for i in range(num_preload):
|
|
offload_engine.load_layer_kv_to_buffer(
|
|
i, i, cpu_block_table, valid_tokens_per_block
|
|
)
|
|
|
|
# DEBUG: Check ring buffer content after preload (first decode step only)
|
|
if num_prev_decode_tokens == 0:
|
|
# Wait for all load streams to complete
|
|
torch.cuda.synchronize()
|
|
ring_k_0 = offload_engine.layer_k_cache[0, 0, 0, :4].tolist()
|
|
# Check the actual last valid position based on valid_tokens_per_block
|
|
sum_valid = sum(valid_tokens_per_block)
|
|
ring_k_last_valid = offload_engine.layer_k_cache[0, sum_valid - 1, 0, :4].tolist()
|
|
logger.debug(
|
|
f"[DEBUG] AFTER PRELOAD L0: sum_valid={sum_valid}, "
|
|
f"ring_k[0, 0, 0, :4]={ring_k_0}, "
|
|
f"ring_k[0, {sum_valid-1}, 0, :4]={ring_k_last_valid}"
|
|
)
|
|
|
|
# Step 1: Embedding (on compute stream)
|
|
with torch.cuda.stream(compute_stream):
|
|
# DEBUG: Log input token for first decode step
|
|
if num_prev_decode_tokens == 0:
|
|
embed_weight_sample = self.model.model.embed_tokens.weight[input_ids[0], :4].tolist()
|
|
logger.debug(f"[DEBUG] EMBEDDING INPUT: input_ids={input_ids.tolist()}, positions={positions.tolist()}, weight[{input_ids[0]},:4]={embed_weight_sample}")
|
|
|
|
if use_cuda_graph:
|
|
# Copy embedding output to graph's hidden_states
|
|
embedded = self.model.model.embed_tokens(input_ids)
|
|
# DEBUG: Log embedding output for first decode step
|
|
if num_prev_decode_tokens == 0:
|
|
logger.debug(f"[DEBUG] EMBEDDING OUTPUT: embedded[0, :4]={embedded[0, :4].tolist()}")
|
|
graph_vars["hidden_states"].copy_(embedded)
|
|
graph_vars["residual"].zero_() # Reset residual for first layer
|
|
else:
|
|
hidden_states = self.model.model.embed_tokens(input_ids)
|
|
# DEBUG: Log embedding output for first decode step
|
|
if num_prev_decode_tokens == 0:
|
|
logger.debug(f"[DEBUG] EMBEDDING OUTPUT: hidden_states[0, :4]={hidden_states[0, :4].tolist()}")
|
|
residual = None
|
|
|
|
# Phase 2: Layer-by-layer processing with ring buffer pipeline
|
|
for layer_id in range(num_layers):
|
|
layer = self.model.model.layers[layer_id]
|
|
attn_module = layer.self_attn.attn # The Attention module
|
|
current_buffer = layer_id % num_buffers
|
|
|
|
# 2a. Wait for current buffer's load to complete
|
|
offload_engine.wait_buffer_load(current_buffer)
|
|
|
|
# DEBUG: Layer outputs (first decode step, layer 0 and last layer)
|
|
if num_prev_decode_tokens == 0 and (layer_id == 0 or layer_id == num_layers - 1):
|
|
if not use_cuda_graph:
|
|
hs_pre = hidden_states[0, :4].tolist()
|
|
else:
|
|
hs_pre = graph_vars["hidden_states"][0, :4].tolist()
|
|
logger.debug(f"[DEBUG] L{layer_id} BEFORE: hidden_states[0, :4]={hs_pre}")
|
|
|
|
# 2b. Copy previous decode KV from decode buffer to ring buffer
|
|
# Ring buffer already has prefill KV at [0:total_prefill_tokens]
|
|
# We need to add decode KV at [total_prefill_tokens:]
|
|
if num_prev_decode_tokens > 0:
|
|
k_decode_prev, v_decode_prev = offload_engine.get_decode_kv(
|
|
layer_id, decode_start_pos, pos_in_block
|
|
)
|
|
ring_k = offload_engine.layer_k_cache[current_buffer]
|
|
ring_v = offload_engine.layer_v_cache[current_buffer]
|
|
ring_k[total_prefill_tokens:total_prefill_tokens + num_prev_decode_tokens].copy_(k_decode_prev)
|
|
ring_v[total_prefill_tokens:total_prefill_tokens + num_prev_decode_tokens].copy_(v_decode_prev)
|
|
|
|
# 2c. Set Attention module's cache to ring buffer (contiguous format)
|
|
# Shape: [max_seq_len, kv_heads, head_dim] -> [1, max_seq_len, kv_heads, head_dim]
|
|
attn_module.k_cache = offload_engine.layer_k_cache[current_buffer:current_buffer+1]
|
|
attn_module.v_cache = offload_engine.layer_v_cache[current_buffer:current_buffer+1]
|
|
|
|
# 2d. Set context for Attention.forward() - contiguous mode
|
|
set_context(
|
|
is_prefill=False,
|
|
slot_mapping=slot_mapping,
|
|
context_lens=context_lens,
|
|
block_tables=None, # Contiguous mode, no block tables
|
|
)
|
|
|
|
if use_cuda_graph:
|
|
# 2e. Replay CUDA graph for this layer
|
|
self.offload_graphs[layer_id].replay()
|
|
# Synchronize to ensure graph completes before next operation
|
|
torch.cuda.current_stream().synchronize()
|
|
# Copy outputs to inputs for next layer
|
|
if layer_id < num_layers - 1:
|
|
graph_vars["hidden_states"].copy_(graph_vars["layer_outputs"])
|
|
graph_vars["residual"].copy_(graph_vars["layer_residual"])
|
|
else:
|
|
# 2e. Forward through layer using standard path (eager mode)
|
|
# This calls Qwen3Attention.forward() -> Attention.forward()
|
|
# Attention.forward() will:
|
|
# - Store new K,V to ring buffer via store_kvcache
|
|
# - Compute attention via flash_attn_with_kvcache
|
|
hidden_states, residual = layer(positions, hidden_states, residual)
|
|
|
|
# DEBUG: Layer outputs (first decode step, layer 0 and last layer)
|
|
if num_prev_decode_tokens == 0 and (layer_id == 0 or layer_id == num_layers - 1):
|
|
if not use_cuda_graph:
|
|
hs_post = hidden_states[0, :4].tolist()
|
|
else:
|
|
hs_post = graph_vars["layer_outputs"][0, :4].tolist()
|
|
logger.debug(f"[DEBUG] L{layer_id} AFTER: hidden_states[0, :4]={hs_post}")
|
|
|
|
# 2f. Copy new token's KV from ring buffer to decode buffer (for persistence)
|
|
# The new token was stored at position context_len in ring buffer
|
|
ring_k = offload_engine.layer_k_cache[current_buffer]
|
|
ring_v = offload_engine.layer_v_cache[current_buffer]
|
|
offload_engine.decode_k_buffer[layer_id, pos_in_block].copy_(ring_k[context_len])
|
|
offload_engine.decode_v_buffer[layer_id, pos_in_block].copy_(ring_v[context_len])
|
|
|
|
# 2g. Mark buffer compute done (allows next load to reuse this buffer)
|
|
offload_engine.record_buffer_compute_done(current_buffer)
|
|
|
|
# 2h. Start loading next layer to same buffer (after compute done)
|
|
next_layer_to_load = layer_id + num_buffers
|
|
if next_layer_to_load < num_layers:
|
|
offload_engine.load_layer_kv_to_buffer(
|
|
current_buffer, next_layer_to_load, cpu_block_table, valid_tokens_per_block
|
|
)
|
|
|
|
# Step 3: Final norm
|
|
if use_cuda_graph:
|
|
hidden_states, _ = self.model.model.norm(
|
|
graph_vars["layer_outputs"], graph_vars["layer_residual"]
|
|
)
|
|
else:
|
|
hidden_states, _ = self.model.model.norm(hidden_states, residual)
|
|
|
|
# Step 4: Compute logits
|
|
logits = self.model.compute_logits(hidden_states)
|
|
|
|
# Reset context
|
|
reset_context()
|
|
|
|
# Step 5: Handle block-full offload (async)
|
|
if pos_in_block == self.block_size - 1:
|
|
last_cpu_block = self.kvcache_manager.get_last_cpu_block(seq)
|
|
if last_cpu_block >= 0:
|
|
# Async offload decode buffer to CPU
|
|
offload_engine.offload_decode_buffer_async(last_cpu_block)
|
|
|
|
# Mark as prefilled for future decode steps
|
|
logical_id = seq.block_table[-1]
|
|
self.kvcache_manager.prefilled_blocks.add(logical_id)
|
|
|
|
# Reset decode start position
|
|
self.kvcache_manager.reset_decode_start_pos(seq)
|
|
|
|
# Step 6: 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
|
|
|
|
# DEBUG: Log first decode token
|
|
if num_prev_decode_tokens == 0 and token_ids:
|
|
# Get top-5 logits for debugging
|
|
top_logits, top_indices = torch.topk(logits[0], 5)
|
|
logger.debug(
|
|
f"[DEBUG] FIRST DECODE TOKEN: token_id={token_ids[0]}, "
|
|
f"top5_indices={top_indices.tolist()}, "
|
|
f"top5_logits={top_logits.tolist()}"
|
|
)
|
|
|
|
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:
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self.graph_pool = graph.pool()
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self.graphs[bs] = graph
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torch.cuda.synchronize()
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|
reset_context()
|
|
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|
self.graph_vars = dict(
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input_ids=input_ids,
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|
positions=positions,
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slot_mapping=slot_mapping,
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context_lens=context_lens,
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block_tables=block_tables,
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|
outputs=outputs,
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|
)
|
|
|
|
@torch.inference_mode()
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|
def capture_offload_cudagraph(self):
|
|
"""
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|
Capture CUDA graphs for offload decode using ring buffer.
|
|
|
|
Key design:
|
|
- Captures per-layer graphs (not full decode)
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|
- Each layer's graph uses its corresponding ring buffer slot
|
|
- H2D transfers happen outside the graph
|
|
- Graph replays single layer forward pass
|
|
|
|
Ring buffer mapping: buffer_idx = layer_id % num_buffers
|
|
"""
|
|
offload_engine = self.kvcache_manager.offload_engine
|
|
num_layers = len(self.model.model.layers)
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|
num_buffers = offload_engine.num_kv_buffers
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|
hf_config = self.config.hf_config
|
|
|
|
logger.info(f"Capturing offload CUDA graphs: {num_layers} layers, {num_buffers} buffers")
|
|
|
|
# Fixed-address tensors for graph capture (batch_size=1 for offload)
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|
input_ids = torch.zeros(1, dtype=torch.int64, device="cuda")
|
|
positions = torch.zeros(1, dtype=torch.int64, device="cuda")
|
|
slot_mapping = torch.zeros(1, dtype=torch.int32, device="cuda")
|
|
context_lens = torch.ones(1, dtype=torch.int32, device="cuda") # At least 1 for valid attention
|
|
hidden_states = torch.randn(1, hf_config.hidden_size, dtype=hf_config.torch_dtype, device="cuda")
|
|
residual = torch.randn(1, hf_config.hidden_size, dtype=hf_config.torch_dtype, device="cuda")
|
|
|
|
# Per-layer outputs (hidden_states after each layer)
|
|
layer_outputs = torch.zeros(1, hf_config.hidden_size, dtype=hf_config.torch_dtype, device="cuda")
|
|
layer_residual = torch.zeros(1, hf_config.hidden_size, dtype=hf_config.torch_dtype, device="cuda")
|
|
|
|
self.offload_graphs = {}
|
|
self.offload_graph_pool = None
|
|
|
|
# Capture per-layer graphs
|
|
for layer_id in range(num_layers):
|
|
buffer_idx = layer_id % num_buffers
|
|
layer = self.model.model.layers[layer_id]
|
|
attn_module = layer.self_attn.attn
|
|
|
|
# Set Attention cache to ring buffer (fixed address for this layer)
|
|
attn_module.k_cache = offload_engine.layer_k_cache[buffer_idx:buffer_idx+1]
|
|
attn_module.v_cache = offload_engine.layer_v_cache[buffer_idx:buffer_idx+1]
|
|
|
|
# Set context for contiguous mode (no block tables)
|
|
set_context(
|
|
is_prefill=False,
|
|
slot_mapping=slot_mapping,
|
|
context_lens=context_lens,
|
|
block_tables=None,
|
|
)
|
|
|
|
# Warmup run - execute layer and propagate state
|
|
out_h, out_r = layer(positions, hidden_states, residual)
|
|
layer_outputs.copy_(out_h)
|
|
layer_residual.copy_(out_r)
|
|
torch.cuda.synchronize()
|
|
|
|
# Capture graph - use same input/output tensors
|
|
graph = torch.cuda.CUDAGraph()
|
|
with torch.cuda.graph(graph, self.offload_graph_pool):
|
|
out_h, out_r = layer(positions, hidden_states, residual)
|
|
layer_outputs.copy_(out_h)
|
|
layer_residual.copy_(out_r)
|
|
|
|
if self.offload_graph_pool is None:
|
|
self.offload_graph_pool = graph.pool()
|
|
|
|
self.offload_graphs[layer_id] = graph
|
|
reset_context()
|
|
|
|
# Update hidden_states and residual for next layer's capture
|
|
# This ensures subsequent layers see realistic input distributions
|
|
hidden_states.copy_(layer_outputs)
|
|
residual.copy_(layer_residual)
|
|
|
|
# Store graph variables for replay
|
|
self.offload_graph_vars = dict(
|
|
input_ids=input_ids,
|
|
positions=positions,
|
|
slot_mapping=slot_mapping,
|
|
context_lens=context_lens,
|
|
hidden_states=hidden_states,
|
|
residual=residual,
|
|
layer_outputs=layer_outputs,
|
|
layer_residual=layer_residual,
|
|
)
|
|
|
|
logger.info(f"Captured {num_layers} offload CUDA graphs")
|