745 lines
25 KiB
Python
745 lines
25 KiB
Python
"""
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High-performance CPU-GPU KV cache transfer engine.
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Key design principles for CUDA Graph compatibility:
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1. All tensor addresses are fixed at initialization
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2. Only index tensor contents change between graph replays
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3. Supports both async transfer (for prefill) and graph-based transfer (for decode)
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"""
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import torch
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from torch import Tensor
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from typing import Dict, List, Tuple, Optional
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from dataclasses import dataclass
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from nanovllm.kvcache.kernels import gathered_copy_kv
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from nanovllm.utils.logger import get_logger
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logger = get_logger("offload_engine")
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@dataclass
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class TransferEvent:
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"""Tracks a pending async transfer."""
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event: torch.cuda.Event
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layer_id: int
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src_block_id: int
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dst_block_id: int
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direction: str # "h2d" or "d2h"
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class OffloadEngine:
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"""
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High-performance CPU-GPU async transfer engine for KV cache offloading.
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Memory layout:
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- GPU cache: [num_layers, num_gpu_blocks, block_size, kv_heads, head_dim]
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- CPU cache: [num_layers, num_cpu_blocks, block_size, kv_heads, head_dim] (pinned)
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- Gather indices: [num_layers, num_gpu_blocks] (fixed address, variable content)
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CUDA Graph compatibility:
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- gathered_h2d_layer() can be captured into CUDA graphs
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- update_gather_indices() is called outside graphs to prepare indices
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- All tensor addresses remain fixed across graph replays
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"""
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def __init__(
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self,
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num_layers: int,
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num_gpu_blocks: int,
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num_cpu_blocks: int,
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block_size: int,
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num_kv_heads: int,
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head_dim: int,
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dtype: torch.dtype = torch.float16,
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num_streams: int = 4,
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):
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self.num_layers = num_layers
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self.num_gpu_blocks = num_gpu_blocks
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self.num_cpu_blocks = num_cpu_blocks
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self.block_size = block_size
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self.num_kv_heads = num_kv_heads
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self.head_dim = head_dim
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self.dtype = dtype
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self.kv_dim = num_kv_heads * head_dim
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self.block_numel = block_size * self.kv_dim
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# ========== Ping-Pong 双缓冲配置 ==========
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assert num_gpu_blocks >= 2, "Ping-Pong需要至少2个GPU blocks"
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self.ping_size = num_gpu_blocks // 2
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self.pong_size = num_gpu_blocks - self.ping_size
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self.ping_slots = list(range(self.ping_size)) # [0, 1, 2, ...]
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self.pong_slots = list(range(self.ping_size, num_gpu_blocks)) # [ping_size, ping_size+1, ...]
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self.num_gpu_slots = num_gpu_blocks # alias
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# ========== Fixed-address GPU KV cache ==========
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# Shape: [num_layers, num_gpu_blocks, block_size, kv_heads, head_dim]
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self.k_cache_gpu = torch.empty(
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num_layers, num_gpu_blocks, block_size, num_kv_heads, head_dim,
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dtype=dtype, device="cuda"
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)
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self.v_cache_gpu = torch.empty(
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num_layers, num_gpu_blocks, block_size, num_kv_heads, head_dim,
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dtype=dtype, device="cuda"
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)
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# ========== Fixed-address CPU KV cache (pinned memory) ==========
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self.k_cache_cpu = torch.empty(
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num_layers, num_cpu_blocks, block_size, num_kv_heads, head_dim,
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dtype=dtype, device="cpu", pin_memory=True
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)
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self.v_cache_cpu = torch.empty(
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num_layers, num_cpu_blocks, block_size, num_kv_heads, head_dim,
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dtype=dtype, device="cpu", pin_memory=True
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)
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# ========== Fixed-address gather indices (content is variable) ==========
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# gather_indices[layer][i] = CPU block id to copy to GPU slot i
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# -1 means no-op (skip this slot)
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self.gather_indices_cpu = torch.empty(
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num_layers, num_gpu_blocks,
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dtype=torch.int64, device="cpu", pin_memory=True
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)
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self.gather_indices_cpu.fill_(-1)
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self.gather_indices_gpu = torch.full(
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(num_layers, num_gpu_blocks), -1,
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dtype=torch.int64, device="cuda"
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)
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# ========== Transfer streams for async operations ==========
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self.transfer_streams = [torch.cuda.Stream() for _ in range(num_streams)]
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self.compute_stream = torch.cuda.current_stream()
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self._stream_idx = 0
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# ========== Ping-Pong 专用 stream 和事件 ==========
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self.pingpong_stream = torch.cuda.Stream() # 专用于Ping-Pong传输
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# 同步事件 - 加载完成
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self.ping_ready = torch.cuda.Event()
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self.pong_ready = torch.cuda.Event()
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# 同步事件 - offload完成
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self.ping_offload_done = torch.cuda.Event()
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self.pong_offload_done = torch.cuda.Event()
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# ========== Event tracking for async transfers ==========
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self.pending_events: Dict[Tuple[int, int], torch.cuda.Event] = {}
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def _get_next_stream(self) -> torch.cuda.Stream:
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"""Round-robin stream selection for parallel transfers."""
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stream = self.transfer_streams[self._stream_idx]
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self._stream_idx = (self._stream_idx + 1) % len(self.transfer_streams)
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return stream
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# ========== CUDA Graph compatible methods ==========
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def gathered_h2d_layer(self, layer_id: int) -> None:
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"""
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Execute gathered H2D copy for a single layer.
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This method is CUDA Graph compatible - can be captured into a graph.
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Before calling, update_gather_indices() must be called to set up
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which CPU blocks to copy to which GPU slots.
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Args:
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layer_id: Layer index to transfer
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"""
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gathered_copy_kv(
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k_src=self.k_cache_cpu[layer_id],
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v_src=self.v_cache_cpu[layer_id],
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k_dst=self.k_cache_gpu[layer_id],
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v_dst=self.v_cache_gpu[layer_id],
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indices=self.gather_indices_gpu[layer_id],
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)
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def gathered_h2d_all_layers(self) -> None:
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"""
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Execute gathered H2D copy for all layers.
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CUDA Graph compatible - can be captured into a single graph.
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"""
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for layer_id in range(self.num_layers):
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self.gathered_h2d_layer(layer_id)
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def update_gather_indices(
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self,
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layer_id: int,
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mappings: List[Tuple[int, int]],
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) -> None:
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"""
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Update gather indices for a layer (call OUTSIDE CUDA graph).
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Args:
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layer_id: Layer index
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mappings: List of (cpu_block_id, gpu_slot) tuples
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Only these slots will be updated; others keep their values
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"""
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for cpu_block_id, gpu_slot in mappings:
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self.gather_indices_cpu[layer_id, gpu_slot] = cpu_block_id
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# Async copy to GPU
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self.gather_indices_gpu[layer_id].copy_(
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self.gather_indices_cpu[layer_id],
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non_blocking=True
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)
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def update_gather_indices_all_layers(
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self,
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mappings_per_layer: List[List[Tuple[int, int]]],
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) -> None:
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"""
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Update gather indices for all layers.
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Args:
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mappings_per_layer: mappings_per_layer[layer_id] = [(cpu_block_id, gpu_slot), ...]
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"""
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for layer_id, mappings in enumerate(mappings_per_layer):
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for cpu_block_id, gpu_slot in mappings:
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self.gather_indices_cpu[layer_id, gpu_slot] = cpu_block_id
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# Batch copy all layers
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self.gather_indices_gpu.copy_(self.gather_indices_cpu, non_blocking=True)
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def clear_gather_indices(self, layer_id: Optional[int] = None) -> None:
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"""
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Clear gather indices (set all to -1, meaning no-op).
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Args:
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layer_id: If provided, clear only this layer; otherwise clear all
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"""
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if layer_id is not None:
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self.gather_indices_cpu[layer_id].fill_(-1)
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self.gather_indices_gpu[layer_id].fill_(-1)
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else:
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self.gather_indices_cpu.fill_(-1)
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self.gather_indices_gpu.fill_(-1)
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# ========== Async transfer methods (for prefill, outside CUDA graph) ==========
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def prefetch_block_async(
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self,
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layer_id: int,
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cpu_block_id: int,
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gpu_block_id: int,
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) -> torch.cuda.Event:
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"""
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Async prefetch a single block from CPU to GPU.
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For use in prefill phase where CUDA graphs are not used.
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Args:
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layer_id: Layer index
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cpu_block_id: Source block in CPU cache
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gpu_block_id: Destination slot in GPU cache
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Returns:
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CUDA event that signals completion
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"""
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stream = self._get_next_stream()
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event = torch.cuda.Event()
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logger.debug(f"H2D prefetch: layer={layer_id}, CPU[{cpu_block_id}] -> GPU[{gpu_block_id}]")
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with torch.cuda.stream(stream):
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# K cache
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self.k_cache_gpu[layer_id, gpu_block_id].copy_(
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self.k_cache_cpu[layer_id, cpu_block_id],
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non_blocking=True
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)
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# V cache
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self.v_cache_gpu[layer_id, gpu_block_id].copy_(
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self.v_cache_cpu[layer_id, cpu_block_id],
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non_blocking=True
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)
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event.record()
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self.pending_events[(layer_id, gpu_block_id)] = event
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return event
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def prefetch_blocks_batch_async(
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self,
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transfers: List[Tuple[int, int, int]], # [(layer_id, cpu_block_id, gpu_block_id), ...]
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) -> List[torch.cuda.Event]:
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"""
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Batch async prefetch multiple blocks.
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Args:
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transfers: List of (layer_id, cpu_block_id, gpu_block_id) tuples
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Returns:
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List of CUDA events for each transfer
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"""
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events = []
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for layer_id, cpu_block_id, gpu_block_id in transfers:
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event = self.prefetch_block_async(layer_id, cpu_block_id, gpu_block_id)
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events.append(event)
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return events
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def offload_block_async(
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self,
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layer_id: int,
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gpu_block_id: int,
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cpu_block_id: int,
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) -> torch.cuda.Event:
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"""
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Async offload a block from GPU to CPU.
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Args:
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layer_id: Layer index
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gpu_block_id: Source slot in GPU cache
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cpu_block_id: Destination block in CPU cache
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Returns:
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CUDA event that signals completion
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"""
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stream = self._get_next_stream()
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event = torch.cuda.Event()
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logger.debug(f"D2H offload: layer={layer_id}, GPU[{gpu_block_id}] -> CPU[{cpu_block_id}]")
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with torch.cuda.stream(stream):
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# Wait for any compute using this block
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stream.wait_stream(self.compute_stream)
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# K cache
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self.k_cache_cpu[layer_id, cpu_block_id].copy_(
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self.k_cache_gpu[layer_id, gpu_block_id],
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non_blocking=True
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)
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# V cache
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self.v_cache_cpu[layer_id, cpu_block_id].copy_(
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self.v_cache_gpu[layer_id, gpu_block_id],
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non_blocking=True
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)
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event.record()
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return event
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def offload_blocks_batch_async(
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self,
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transfers: List[Tuple[int, int, int]], # [(layer_id, gpu_block_id, cpu_block_id), ...]
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) -> List[torch.cuda.Event]:
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"""
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Batch async offload multiple blocks.
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Args:
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transfers: List of (layer_id, gpu_block_id, cpu_block_id) tuples
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Returns:
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List of CUDA events
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"""
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events = []
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for layer_id, gpu_block_id, cpu_block_id in transfers:
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event = self.offload_block_async(layer_id, gpu_block_id, cpu_block_id)
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events.append(event)
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return events
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# ========== Chunked Decode: Load CPU blocks to GPU slots ==========
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def load_cpu_blocks_to_gpu_slots(
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self,
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layer_id: int,
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cpu_block_ids: List[int],
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gpu_slot_ids: List[int],
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) -> None:
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"""
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Load CPU blocks to specific GPU slots for chunked decode.
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Uses the main GPU KV cache slots, not a separate temp buffer.
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This is the same mechanism as chunked prefill uses.
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Args:
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layer_id: Layer index
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cpu_block_ids: List of CPU block IDs to load
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gpu_slot_ids: List of GPU slot IDs to load into (must be same length)
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"""
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assert len(cpu_block_ids) == len(gpu_slot_ids)
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if cpu_block_ids:
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logger.debug(f"H2D chunked load: layer={layer_id}, CPU{cpu_block_ids} -> GPU{gpu_slot_ids}")
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stream = self._get_next_stream()
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with torch.cuda.stream(stream):
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for cpu_block_id, gpu_slot in zip(cpu_block_ids, gpu_slot_ids):
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# Copy from pinned CPU memory to GPU KV cache slot
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self.k_cache_gpu[layer_id, gpu_slot].copy_(
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self.k_cache_cpu[layer_id, cpu_block_id],
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non_blocking=True
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)
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self.v_cache_gpu[layer_id, gpu_slot].copy_(
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self.v_cache_cpu[layer_id, cpu_block_id],
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non_blocking=True
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)
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# Wait for transfer to complete
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stream.synchronize()
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def load_cpu_blocks_to_gpu_slots_async(
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self,
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layer_id: int,
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cpu_block_ids: List[int],
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gpu_slot_ids: List[int],
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) -> torch.cuda.Event:
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"""
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Async version: Load CPU blocks to GPU slots.
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Args:
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layer_id: Layer index
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cpu_block_ids: List of CPU block IDs to load
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gpu_slot_ids: List of GPU slot IDs to load into
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Returns:
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CUDA event to wait on
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"""
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assert len(cpu_block_ids) == len(gpu_slot_ids)
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if cpu_block_ids:
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logger.debug(f"H2D chunked load async: layer={layer_id}, CPU{cpu_block_ids} -> GPU{gpu_slot_ids}")
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stream = self._get_next_stream()
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event = torch.cuda.Event()
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with torch.cuda.stream(stream):
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for cpu_block_id, gpu_slot in zip(cpu_block_ids, gpu_slot_ids):
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self.k_cache_gpu[layer_id, gpu_slot].copy_(
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self.k_cache_cpu[layer_id, cpu_block_id],
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non_blocking=True
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)
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self.v_cache_gpu[layer_id, gpu_slot].copy_(
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self.v_cache_cpu[layer_id, cpu_block_id],
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non_blocking=True
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)
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event.record()
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return event
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def load_cpu_blocks_to_gpu_slots_all_layers(
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self,
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cpu_block_ids: List[int],
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gpu_slot_ids: List[int],
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) -> None:
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"""
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Load CPU blocks to GPU slots for ALL layers at once.
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More efficient than per-layer loading when we know the mapping upfront.
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Args:
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cpu_block_ids: List of CPU block IDs to load
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gpu_slot_ids: List of GPU slot IDs to load into
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"""
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assert len(cpu_block_ids) == len(gpu_slot_ids)
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if cpu_block_ids:
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logger.debug(f"H2D all layers: CPU{cpu_block_ids} -> GPU{gpu_slot_ids}")
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stream = self._get_next_stream()
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with torch.cuda.stream(stream):
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for cpu_block_id, gpu_slot in zip(cpu_block_ids, gpu_slot_ids):
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# Copy all layers at once
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self.k_cache_gpu[:, gpu_slot].copy_(
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self.k_cache_cpu[:, cpu_block_id],
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non_blocking=True
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)
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self.v_cache_gpu[:, gpu_slot].copy_(
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self.v_cache_cpu[:, cpu_block_id],
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non_blocking=True
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)
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stream.synchronize()
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# ========== Synchronization methods ==========
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def wait_for_block(self, layer_id: int, gpu_block_id: int) -> None:
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"""Wait for a specific block's transfer to complete."""
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key = (layer_id, gpu_block_id)
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if key in self.pending_events:
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self.pending_events[key].synchronize()
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del self.pending_events[key]
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def wait_all_transfers(self) -> None:
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"""Wait for all pending transfers to complete."""
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for stream in self.transfer_streams:
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stream.synchronize()
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self.pending_events.clear()
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def sync_indices(self) -> None:
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"""Synchronize to ensure all index updates are complete."""
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torch.cuda.current_stream().synchronize()
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# ========== Cache access methods ==========
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def get_layer_cache(self, layer_id: int) -> Tuple[Tensor, Tensor]:
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"""
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Get GPU K/V cache tensors for a specific layer.
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Returns:
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(k_cache, v_cache) tensors for the layer
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Shape: [num_gpu_blocks, block_size, kv_heads, head_dim]
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"""
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return self.k_cache_gpu[layer_id], self.v_cache_gpu[layer_id]
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def get_all_gpu_cache(self) -> Tuple[Tensor, Tensor]:
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"""
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Get full GPU K/V cache tensors.
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Returns:
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(k_cache, v_cache) tensors
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Shape: [num_layers, num_gpu_blocks, block_size, kv_heads, head_dim]
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"""
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return self.k_cache_gpu, self.v_cache_gpu
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def get_cpu_block(
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self,
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layer_id: int,
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cpu_block_id: int,
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) -> Tuple[Tensor, Tensor]:
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"""
|
||
Get a specific CPU block's K/V cache.
|
||
|
||
Returns:
|
||
(k_cache, v_cache) for the block
|
||
Shape: [block_size, kv_heads, head_dim]
|
||
"""
|
||
return (
|
||
self.k_cache_cpu[layer_id, cpu_block_id],
|
||
self.v_cache_cpu[layer_id, cpu_block_id],
|
||
)
|
||
|
||
# ========== Memory info ==========
|
||
|
||
def gpu_memory_bytes(self) -> int:
|
||
"""Total GPU memory used by KV caches."""
|
||
return (
|
||
self.k_cache_gpu.numel() * self.k_cache_gpu.element_size() +
|
||
self.v_cache_gpu.numel() * self.v_cache_gpu.element_size() +
|
||
self.gather_indices_gpu.numel() * self.gather_indices_gpu.element_size()
|
||
)
|
||
|
||
def cpu_memory_bytes(self) -> int:
|
||
"""Total CPU memory used by KV caches."""
|
||
return (
|
||
self.k_cache_cpu.numel() * self.k_cache_cpu.element_size() +
|
||
self.v_cache_cpu.numel() * self.v_cache_cpu.element_size() +
|
||
self.gather_indices_cpu.numel() * self.gather_indices_cpu.element_size()
|
||
)
|
||
|
||
def __repr__(self) -> str:
|
||
return (
|
||
f"OffloadEngine(\n"
|
||
f" num_layers={self.num_layers},\n"
|
||
f" num_gpu_blocks={self.num_gpu_blocks},\n"
|
||
f" num_cpu_blocks={self.num_cpu_blocks},\n"
|
||
f" block_size={self.block_size},\n"
|
||
f" kv_heads={self.num_kv_heads},\n"
|
||
f" head_dim={self.head_dim},\n"
|
||
f" dtype={self.dtype},\n"
|
||
f" ping_size={self.ping_size}, pong_size={self.pong_size},\n"
|
||
f" gpu_memory={self.gpu_memory_bytes() / 1024**2:.1f}MB,\n"
|
||
f" cpu_memory={self.cpu_memory_bytes() / 1024**2:.1f}MB\n"
|
||
f")"
|
||
)
|
||
|
||
# ========== Ping-Pong 双缓冲方法 ==========
|
||
|
||
def load_to_ping(self, cpu_block_ids: List[int]) -> None:
|
||
"""
|
||
异步加载CPU blocks到Ping buffer。
|
||
|
||
Args:
|
||
cpu_block_ids: 要加载的CPU block IDs列表
|
||
"""
|
||
if not cpu_block_ids:
|
||
self.ping_ready.record(self.pingpong_stream)
|
||
return
|
||
|
||
num_to_load = min(len(cpu_block_ids), self.ping_size)
|
||
logger.debug(f"Ping load: CPU{cpu_block_ids[:num_to_load]} -> GPU ping slots {self.ping_slots[:num_to_load]}")
|
||
|
||
with torch.cuda.stream(self.pingpong_stream):
|
||
for i in range(num_to_load):
|
||
cpu_id = cpu_block_ids[i]
|
||
gpu_slot = self.ping_slots[i]
|
||
# 所有层一起复制
|
||
self.k_cache_gpu[:, gpu_slot].copy_(
|
||
self.k_cache_cpu[:, cpu_id], non_blocking=True
|
||
)
|
||
self.v_cache_gpu[:, gpu_slot].copy_(
|
||
self.v_cache_cpu[:, cpu_id], non_blocking=True
|
||
)
|
||
self.ping_ready.record(self.pingpong_stream)
|
||
|
||
def load_to_pong(self, cpu_block_ids: List[int]) -> None:
|
||
"""
|
||
异步加载CPU blocks到Pong buffer。
|
||
|
||
Args:
|
||
cpu_block_ids: 要加载的CPU block IDs列表
|
||
"""
|
||
if not cpu_block_ids:
|
||
self.pong_ready.record(self.pingpong_stream)
|
||
return
|
||
|
||
num_to_load = min(len(cpu_block_ids), self.pong_size)
|
||
logger.debug(f"Pong load: CPU{cpu_block_ids[:num_to_load]} -> GPU pong slots {self.pong_slots[:num_to_load]}")
|
||
|
||
with torch.cuda.stream(self.pingpong_stream):
|
||
for i in range(num_to_load):
|
||
cpu_id = cpu_block_ids[i]
|
||
gpu_slot = self.pong_slots[i]
|
||
self.k_cache_gpu[:, gpu_slot].copy_(
|
||
self.k_cache_cpu[:, cpu_id], non_blocking=True
|
||
)
|
||
self.v_cache_gpu[:, gpu_slot].copy_(
|
||
self.v_cache_cpu[:, cpu_id], non_blocking=True
|
||
)
|
||
self.pong_ready.record(self.pingpong_stream)
|
||
|
||
def wait_ping(self) -> None:
|
||
"""等待Ping buffer加载完成。"""
|
||
self.compute_stream.wait_event(self.ping_ready)
|
||
|
||
def wait_pong(self) -> None:
|
||
"""等待Pong buffer加载完成。"""
|
||
self.compute_stream.wait_event(self.pong_ready)
|
||
|
||
def offload_buffer_to_cpu(
|
||
self,
|
||
buffer: str,
|
||
cpu_block_ids: List[int],
|
||
) -> None:
|
||
"""
|
||
异步将buffer中的KV offload到CPU。
|
||
|
||
Args:
|
||
buffer: "ping" 或 "pong"
|
||
cpu_block_ids: 目标CPU block IDs列表
|
||
"""
|
||
slots = self.ping_slots if buffer == "ping" else self.pong_slots
|
||
event = self.ping_offload_done if buffer == "ping" else self.pong_offload_done
|
||
|
||
if not cpu_block_ids:
|
||
event.record(self.pingpong_stream)
|
||
return
|
||
|
||
num_to_offload = min(len(cpu_block_ids), len(slots))
|
||
logger.debug(f"{buffer.capitalize()} offload: GPU {slots[:num_to_offload]} -> CPU{cpu_block_ids[:num_to_offload]}")
|
||
|
||
with torch.cuda.stream(self.pingpong_stream):
|
||
# 等待计算完成
|
||
self.pingpong_stream.wait_stream(self.compute_stream)
|
||
|
||
for i in range(num_to_offload):
|
||
gpu_slot = slots[i]
|
||
cpu_id = cpu_block_ids[i]
|
||
self.k_cache_cpu[:, cpu_id].copy_(
|
||
self.k_cache_gpu[:, gpu_slot], non_blocking=True
|
||
)
|
||
self.v_cache_cpu[:, cpu_id].copy_(
|
||
self.v_cache_gpu[:, gpu_slot], non_blocking=True
|
||
)
|
||
event.record(self.pingpong_stream)
|
||
|
||
def offload_slot_to_cpu(
|
||
self,
|
||
gpu_slot: int,
|
||
cpu_block_id: int,
|
||
) -> None:
|
||
"""
|
||
异步将单个GPU slot的KV offload到CPU。
|
||
|
||
Args:
|
||
gpu_slot: GPU slot ID
|
||
cpu_block_id: 目标CPU block ID
|
||
"""
|
||
logger.debug(f"Slot offload: GPU[{gpu_slot}] -> CPU[{cpu_block_id}]")
|
||
|
||
with torch.cuda.stream(self.pingpong_stream):
|
||
self.pingpong_stream.wait_stream(self.compute_stream)
|
||
self.k_cache_cpu[:, cpu_block_id].copy_(
|
||
self.k_cache_gpu[:, gpu_slot], non_blocking=True
|
||
)
|
||
self.v_cache_cpu[:, cpu_block_id].copy_(
|
||
self.v_cache_gpu[:, gpu_slot], non_blocking=True
|
||
)
|
||
|
||
def wait_ping_offload_done(self) -> None:
|
||
"""等待Ping buffer offload完成。"""
|
||
self.compute_stream.wait_event(self.ping_offload_done)
|
||
|
||
def wait_pong_offload_done(self) -> None:
|
||
"""等待Pong buffer offload完成。"""
|
||
self.compute_stream.wait_event(self.pong_offload_done)
|
||
|
||
def wait_all_offload_done(self) -> None:
|
||
"""等待所有offload完成。"""
|
||
self.pingpong_stream.synchronize()
|
||
|
||
def get_kv_for_ping_slots(
|
||
self,
|
||
layer_id: int,
|
||
num_slots: int,
|
||
) -> Tuple[Tensor, Tensor]:
|
||
"""
|
||
获取Ping buffer中指定数量slots的KV。
|
||
|
||
Args:
|
||
layer_id: 层ID
|
||
num_slots: 需要的slot数量
|
||
|
||
Returns:
|
||
(k_cache, v_cache)，shape: [1, num_slots * block_size, kv_heads, head_dim]
|
||
"""
|
||
slots = self.ping_slots[:num_slots]
|
||
k = self.k_cache_gpu[layer_id, slots] # [num_slots, block_size, heads, dim]
|
||
v = self.v_cache_gpu[layer_id, slots]
|
||
# Reshape: [num_slots, block_size, heads, dim] -> [1, num_slots*block_size, heads, dim]
|
||
k = k.reshape(1, -1, self.num_kv_heads, self.head_dim)
|
||
v = v.reshape(1, -1, self.num_kv_heads, self.head_dim)
|
||
return k, v
|
||
|
||
def get_kv_for_pong_slots(
|
||
self,
|
||
layer_id: int,
|
||
num_slots: int,
|
||
) -> Tuple[Tensor, Tensor]:
|
||
"""
|
||
获取Pong buffer中指定数量slots的KV。
|
||
|
||
Args:
|
||
layer_id: 层ID
|
||
num_slots: 需要的slot数量
|
||
|
||
Returns:
|
||
(k_cache, v_cache)，shape: [1, num_slots * block_size, kv_heads, head_dim]
|
||
"""
|
||
slots = self.pong_slots[:num_slots]
|
||
k = self.k_cache_gpu[layer_id, slots]
|
||
v = self.v_cache_gpu[layer_id, slots]
|
||
k = k.reshape(1, -1, self.num_kv_heads, self.head_dim)
|
||
v = v.reshape(1, -1, self.num_kv_heads, self.head_dim)
|
||
return k, v
|
||
|
||
def get_kv_for_slots(
|
||
self,
|
||
layer_id: int,
|
||
gpu_slots: List[int],
|
||
) -> Tuple[Tensor, Tensor]:
|
||
"""
|
||
获取指定GPU slots的KV。
|
||
|
||
Args:
|
||
layer_id: 层ID
|
||
gpu_slots: GPU slot IDs列表
|
||
|
||
Returns:
|
||
(k_cache, v_cache)，shape: [1, len(slots) * block_size, kv_heads, head_dim]
|
||
"""
|
||
if not gpu_slots:
|
||
return None, None
|
||
k = self.k_cache_gpu[layer_id, gpu_slots]
|
||
v = self.v_cache_gpu[layer_id, gpu_slots]
|
||
k = k.reshape(1, -1, self.num_kv_heads, self.head_dim)
|
||
v = v.reshape(1, -1, self.num_kv_heads, self.head_dim)
|
||
return k, v |