1021 lines
38 KiB
Python
1021 lines
38 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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import torch.cuda.nvtx
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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.comm import memcpy_2d_async
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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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# ========== sgDMA pitch parameters for strided transfers ==========
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# CPU cache: [num_layers, num_cpu_blocks, block_size, kv_heads, head_dim]
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# GPU cache: [num_gpu_blocks, block_size, kv_heads, head_dim] (no layer dim)
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# For CPU-to-GPU transfer (H2D): copy single layer, single block at a time
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# For all-layer CPU operations (D2H offload to all layers): use sgDMA
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self.dtype_size = dtype.itemsize
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# CPU pitch: stride between layers in CPU cache (for all-layer operations)
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self.cpu_pitch = num_cpu_blocks * self.block_numel * self.dtype_size
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# GPU has no layer dimension, so single block transfer is contiguous
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self.gpu_block_bytes = self.block_numel * self.dtype_size
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self.height = num_layers # For CPU all-layer operations
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logger.info(f"sgDMA parameters: cpu_pitch={self.cpu_pitch}, "
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f"gpu_block_bytes={self.gpu_block_bytes}, height={self.height}")
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# ========== Unified Ring Buffer configuration ==========
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# Constraint checks
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assert num_gpu_blocks >= 2, \
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f"Need at least 2 GPU blocks for ring buffer, got {num_gpu_blocks}"
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# Unified Ring Buffer: all slots cycle for prefill
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# Prefill: use ALL slots as ring buffer (slot[chunk_idx % N])
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# Decode: slot[0] as decode_slot, slots[1:] for loading previous chunks
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self.num_ring_slots = num_gpu_blocks
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self.ring_slots = list(range(num_gpu_blocks))
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# Decode phase uses slot[0] for writing new token's KV
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self.decode_slot = 0
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# Decode phase uses slots[1:] for loading previous chunks from CPU
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self.decode_load_slots = list(range(1, num_gpu_blocks))
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self.num_decode_load_slots = len(self.decode_load_slots)
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self.num_gpu_slots = num_gpu_blocks # alias
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logger.info(f"Unified Ring Buffer: {self.num_ring_slots} slots total")
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logger.info(f" Prefill: all slots as ring buffer [0..{num_gpu_blocks-1}]")
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logger.info(f" Decode: slot[0] as decode_slot, slots[1..{num_gpu_blocks-1}] for loading")
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# ========== Fixed-address GPU KV cache ==========
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# Shape: [num_gpu_blocks, block_size, kv_heads, head_dim]
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# NOTE: No num_layers dimension! GPU slots are shared across layers.
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# Each layer reuses the same slots (layers execute sequentially).
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# This saves 28x GPU memory compared to per-layer allocation.
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self.k_cache_gpu = torch.zeros(
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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.zeros(
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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.zeros(
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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.zeros(
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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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# Log memory allocation
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gpu_mem_mb = self.gpu_memory_bytes() / (1024 * 1024)
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cpu_mem_mb = self.cpu_memory_bytes() / (1024 * 1024)
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logger.info(f" GPU memory: {gpu_mem_mb:.1f} MB, CPU memory: {cpu_mem_mb:.1f} MB")
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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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# IMPORTANT: Create a dedicated compute stream (not default stream!)
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# Default stream has implicit synchronization with other streams,
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# which prevents overlap between transfer and compute.
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self.compute_stream = torch.cuda.Stream()
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self._stream_idx = 0
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# ========== Per-slot transfer streams for parallel H2D ==========
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# Each slot has its own stream to enable parallel transfers
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# This allows multiple slots to load simultaneously
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self.slot_transfer_streams = [torch.cuda.Stream() for _ in range(self.num_ring_slots)]
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logger.info(f" Created {self.num_ring_slots} per-slot transfer streams")
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# ========== Ring Buffer dedicated stream and events ==========
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self.transfer_stream_main = torch.cuda.Stream() # Main transfer stream (for legacy/batch ops)
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# Decode offload event
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self.decode_offload_done = torch.cuda.Event()
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# ========== Per-slot events for ring buffer ==========
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# Since GPU cache has no layer dimension and layers execute sequentially,
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# we only need per-slot events (not per-slot per-layer).
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# ring_slot_ready[slot_idx] = CUDA Event for H2D completion
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# ring_slot_offload_done[slot_idx] = CUDA Event for D2H completion
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self.ring_slot_ready = [torch.cuda.Event() for _ in range(self.num_ring_slots)]
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self.ring_slot_offload_done = [torch.cuda.Event() for _ in range(self.num_ring_slots)]
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# ========== Per-slot compute_done events for async pipeline ==========
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# ring_slot_compute_done[slot_idx] = CUDA Event for compute completion
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# This ensures we don't overwrite data before it's been read by attention
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self.ring_slot_compute_done = [torch.cuda.Event() for _ in range(self.num_ring_slots)]
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# Initialize all compute_done events (record them once)
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# This prevents undefined behavior on first load_to_slot_layer call
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for slot_idx in range(self.num_ring_slots):
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self.ring_slot_compute_done[slot_idx].record()
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torch.cuda.synchronize() # Ensure all events are recorded
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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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# ========== Debug hook mode ==========
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self._debug_mode = False
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self._debug_hooks: List = [] # External hooks for debug events
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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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# NOTE: These methods need to be updated for the new GPU cache architecture.
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# GPU cache no longer has layer dimension, so gathered copy semantics change.
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# For now, these are kept for reference but should not be used without updating.
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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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WARNING: This method needs updating for new GPU cache architecture.
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GPU cache no longer has layer dimension.
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"""
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# GPU cache has no layer dimension - use flat indexing
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# Source is CPU[layer_id], dest is GPU (shared across layers)
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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, # No layer indexing
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v_dst=self.v_cache_gpu, # No layer indexing
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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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WARNING: In new architecture, GPU slots are shared across layers.
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This method would overwrite slots multiple times. Not recommended.
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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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GPU cache has no layer dimension - layer_id is for CPU cache indexing.
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Args:
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layer_id: Layer index (for CPU cache)
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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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# GPU: no layer dimension, CPU: has layer dimension
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self.k_cache_gpu[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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self.v_cache_gpu[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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GPU cache has no layer dimension - layer_id is for CPU cache indexing.
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Args:
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layer_id: Layer index (for CPU cache)
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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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# GPU: no layer dimension, CPU: has layer dimension
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self.k_cache_cpu[layer_id, cpu_block_id].copy_(
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self.k_cache_gpu[gpu_block_id],
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non_blocking=True
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)
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self.v_cache_cpu[layer_id, cpu_block_id].copy_(
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self.v_cache_gpu[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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GPU cache has no layer dimension - layer_id is for CPU cache indexing.
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Args:
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layer_id: Layer index (for CPU cache)
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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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# GPU: no layer dimension, CPU: has layer dimension
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self.k_cache_gpu[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[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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GPU cache has no layer dimension - layer_id is for CPU cache indexing.
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Args:
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layer_id: Layer index (for CPU cache)
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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):
|
|
for cpu_block_id, gpu_slot in zip(cpu_block_ids, gpu_slot_ids):
|
|
# GPU: no layer dimension, CPU: has layer dimension
|
|
self.k_cache_gpu[gpu_slot].copy_(
|
|
self.k_cache_cpu[layer_id, cpu_block_id],
|
|
non_blocking=True
|
|
)
|
|
self.v_cache_gpu[gpu_slot].copy_(
|
|
self.v_cache_cpu[layer_id, cpu_block_id],
|
|
non_blocking=True
|
|
)
|
|
event.record()
|
|
|
|
return event
|
|
|
|
# NOTE: load_cpu_blocks_to_gpu_slots_all_layers removed - GPU cache no longer has
|
|
# layer dimension. Each GPU slot holds data for ONE layer at a time.
|
|
|
|
# ========== Synchronization methods ==========
|
|
|
|
def wait_for_block(self, layer_id: int, gpu_block_id: int) -> None:
|
|
"""Wait for a specific block's transfer to complete."""
|
|
key = (layer_id, gpu_block_id)
|
|
if key in self.pending_events:
|
|
self.pending_events[key].synchronize()
|
|
del self.pending_events[key]
|
|
|
|
def wait_all_transfers(self) -> None:
|
|
"""Wait for all pending transfers to complete."""
|
|
for stream in self.transfer_streams:
|
|
stream.synchronize()
|
|
self.pending_events.clear()
|
|
|
|
def sync_indices(self) -> None:
|
|
"""Synchronize to ensure all index updates are complete."""
|
|
torch.cuda.default_stream().synchronize()
|
|
|
|
# ========== Cache access methods ==========
|
|
|
|
def get_layer_cache(self, layer_id: int) -> Tuple[Tensor, Tensor]:
|
|
"""
|
|
Get GPU K/V cache tensors for attention layer.
|
|
|
|
NOTE: GPU cache has no layer dimension - all layers share the same slots.
|
|
The layer_id parameter is kept for API compatibility but not used.
|
|
|
|
Returns:
|
|
(k_cache, v_cache) tensors
|
|
Shape: [num_gpu_blocks, block_size, kv_heads, head_dim]
|
|
"""
|
|
# GPU cache is shared across all layers (no layer dimension)
|
|
return self.k_cache_gpu, self.v_cache_gpu
|
|
|
|
def get_all_gpu_cache(self) -> Tuple[Tensor, Tensor]:
|
|
"""
|
|
Get full GPU K/V cache tensors.
|
|
|
|
NOTE: GPU cache has no layer dimension in the new architecture.
|
|
|
|
Returns:
|
|
(k_cache, v_cache) tensors
|
|
Shape: [num_gpu_blocks, block_size, kv_heads, head_dim]
|
|
"""
|
|
return self.k_cache_gpu, self.v_cache_gpu
|
|
|
|
def get_cpu_block(
|
|
self,
|
|
layer_id: int,
|
|
cpu_block_id: int,
|
|
) -> Tuple[Tensor, Tensor]:
|
|
"""
|
|
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" ring_buffer: {self.num_ring_slots} slots, decode_slot={self.decode_slot}, decode_load_slots={self.decode_load_slots},\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")"
|
|
)
|
|
|
|
def wait_all_offload_done(self) -> None:
|
|
"""Wait for all offload operations to complete."""
|
|
self.transfer_stream_main.synchronize()
|
|
|
|
# ========== Unified Ring Buffer methods ==========
|
|
|
|
# ----- Prefill: Ring Buffer slot management -----
|
|
|
|
def get_write_slot_for_prefill(self, chunk_idx: int) -> int:
|
|
"""
|
|
Get ring buffer slot for writing prefill chunk.
|
|
|
|
For prefill, ALL slots are used as ring buffer, cycling through.
|
|
|
|
Args:
|
|
chunk_idx: Current chunk index (0, 1, 2, ...)
|
|
|
|
Returns:
|
|
GPU slot index for writing
|
|
"""
|
|
return chunk_idx % self.num_ring_slots
|
|
|
|
def get_load_slots_for_prefill(self, write_slot_idx: int) -> List[int]:
|
|
"""
|
|
Get available slots for loading previous chunks during prefill.
|
|
|
|
Excludes the current write slot to avoid conflict.
|
|
|
|
Args:
|
|
write_slot_idx: Current write slot index
|
|
|
|
Returns:
|
|
List of slot indices available for loading (N-1 slots)
|
|
"""
|
|
return [i for i in range(self.num_ring_slots) if i != write_slot_idx]
|
|
|
|
# ----- Decode: slot management -----
|
|
|
|
def get_load_slots_for_decode(self) -> List[int]:
|
|
"""
|
|
Get slots available for loading during decode.
|
|
|
|
Excludes decode_slot (slot[0]) since it's used for writing new token's KV.
|
|
|
|
Returns:
|
|
List of slot indices for loading (slots[1:])
|
|
"""
|
|
return self.decode_load_slots
|
|
|
|
# ----- Per-slot Per-layer loading methods -----
|
|
|
|
def record_slot_compute_done(self, slot_idx: int) -> None:
|
|
"""
|
|
Record that computation using this slot's data is done.
|
|
|
|
This event is used by load_to_slot_layer to ensure we don't overwrite
|
|
data before it's been read by attention computation.
|
|
|
|
Args:
|
|
slot_idx: GPU slot index that was just used for computation
|
|
"""
|
|
self.ring_slot_compute_done[slot_idx].record()
|
|
|
|
def load_to_slot_layer(self, slot_idx: int, layer_id: int, cpu_block_id: int) -> None:
|
|
"""
|
|
Async load a single CPU block to a ring buffer slot for one layer.
|
|
|
|
This is the core building block for ring buffer pipelining.
|
|
GPU cache has no layer dimension - slots are shared across all layers.
|
|
CPU cache still has layer dimension for persistent storage.
|
|
|
|
Before starting the transfer, waits for:
|
|
1. Any previous compute on this slot to complete
|
|
|
|
Args:
|
|
slot_idx: Target GPU slot index
|
|
layer_id: Layer index to load (for CPU cache indexing)
|
|
cpu_block_id: Source CPU block ID
|
|
"""
|
|
logger.debug(f"Ring load: layer={layer_id}, CPU[{cpu_block_id}] -> GPU slot[{slot_idx}]")
|
|
|
|
# Use per-slot stream for parallel transfers across different slots
|
|
stream = self.slot_transfer_streams[slot_idx]
|
|
|
|
torch.cuda.nvtx.range_push(f"H2D: L{layer_id} CPU[{cpu_block_id}]->Slot[{slot_idx}]")
|
|
with torch.cuda.stream(stream):
|
|
# Wait for previous compute on this slot to complete before overwriting
|
|
# This prevents data race: transfer must not start until attention finishes reading
|
|
stream.wait_event(self.ring_slot_compute_done[slot_idx])
|
|
|
|
# Also wait for any pending offload of this slot to complete
|
|
# This prevents race: load must not write GPU slot while offload is reading from it
|
|
stream.wait_event(self.ring_slot_offload_done[slot_idx])
|
|
|
|
# GPU: no layer dimension, CPU: has layer dimension
|
|
self.k_cache_gpu[slot_idx].copy_(
|
|
self.k_cache_cpu[layer_id, cpu_block_id], non_blocking=True
|
|
)
|
|
self.v_cache_gpu[slot_idx].copy_(
|
|
self.v_cache_cpu[layer_id, cpu_block_id], non_blocking=True
|
|
)
|
|
self.ring_slot_ready[slot_idx].record(stream)
|
|
torch.cuda.nvtx.range_pop()
|
|
|
|
def wait_slot_layer(self, slot_idx: int) -> None:
|
|
"""
|
|
Wait for a slot's loading to complete.
|
|
|
|
Args:
|
|
slot_idx: GPU slot index to wait for
|
|
"""
|
|
self.compute_stream.wait_event(self.ring_slot_ready[slot_idx])
|
|
|
|
# NOTE: load_to_slot_all_layers removed - GPU cache no longer has layer dimension.
|
|
# Each GPU slot holds data for ONE layer at a time. Layers execute sequentially,
|
|
# reusing the same GPU slots.
|
|
|
|
# ----- Slot offload methods -----
|
|
|
|
# NOTE: offload_slot_to_cpu (all-layers) removed - GPU cache no longer has layer dimension.
|
|
# Use offload_slot_layer_to_cpu for per-layer offloading.
|
|
|
|
def wait_slot_offload(self, slot_idx: int) -> None:
|
|
"""Wait for slot offload to complete."""
|
|
self.compute_stream.wait_event(self.ring_slot_offload_done[slot_idx])
|
|
|
|
def offload_slot_layer_to_cpu(self, slot_idx: int, layer_id: int, cpu_block_id: int) -> None:
|
|
"""
|
|
Async offload a ring buffer slot to CPU for one layer.
|
|
|
|
GPU cache has no layer dimension, so we copy from GPU slot to the
|
|
specific layer in CPU cache.
|
|
|
|
Args:
|
|
slot_idx: Source GPU slot index
|
|
layer_id: Target layer in CPU cache
|
|
cpu_block_id: Target CPU block ID
|
|
"""
|
|
logger.debug(f"Ring offload: GPU slot[{slot_idx}] -> CPU[layer={layer_id}, block={cpu_block_id}]")
|
|
|
|
torch.cuda.nvtx.range_push(f"D2H: Slot[{slot_idx}]->CPU[L{layer_id},B{cpu_block_id}]")
|
|
with torch.cuda.stream(self.transfer_stream_main):
|
|
# Wait for both compute_stream and default stream
|
|
# - compute_stream: for flash attention operations
|
|
# - default_stream: for store_kvcache which runs on default stream
|
|
self.transfer_stream_main.wait_stream(self.compute_stream)
|
|
self.transfer_stream_main.wait_stream(torch.cuda.default_stream())
|
|
|
|
# GPU: no layer dimension, CPU: has layer dimension
|
|
self.k_cache_cpu[layer_id, cpu_block_id].copy_(
|
|
self.k_cache_gpu[slot_idx], non_blocking=True
|
|
)
|
|
self.v_cache_cpu[layer_id, cpu_block_id].copy_(
|
|
self.v_cache_gpu[slot_idx], non_blocking=True
|
|
)
|
|
self.ring_slot_offload_done[slot_idx].record(self.transfer_stream_main)
|
|
torch.cuda.nvtx.range_pop()
|
|
|
|
# ----- KV access methods for ring buffer -----
|
|
|
|
def get_kv_for_slot(self, slot_idx: int) -> Tuple[Tensor, Tensor]:
|
|
"""
|
|
Get KV for a single ring buffer slot.
|
|
|
|
GPU cache has no layer dimension - slots contain data for whatever
|
|
layer was most recently loaded.
|
|
|
|
Args:
|
|
slot_idx: GPU slot index
|
|
|
|
Returns:
|
|
(k_cache, v_cache), shape: [1, block_size, kv_heads, head_dim]
|
|
"""
|
|
k = self.k_cache_gpu[slot_idx].unsqueeze(0) # [1, block_size, heads, dim]
|
|
v = self.v_cache_gpu[slot_idx].unsqueeze(0)
|
|
return k, v
|
|
|
|
def get_kv_for_slots(
|
|
self,
|
|
slot_indices: List[int],
|
|
) -> Tuple[Tensor, Tensor]:
|
|
"""
|
|
Get KV for multiple ring buffer slots.
|
|
|
|
GPU cache has no layer dimension - returns data from specified slots.
|
|
|
|
Args:
|
|
slot_indices: List of GPU slot indices
|
|
|
|
Returns:
|
|
(k_cache, v_cache), shape: [1, len(slots) * block_size, kv_heads, head_dim]
|
|
"""
|
|
if not slot_indices:
|
|
return None, None
|
|
k = self.k_cache_gpu[slot_indices]
|
|
v = self.v_cache_gpu[slot_indices]
|
|
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
|
|
|
|
# ----- Decode slot methods (kept for decode phase) -----
|
|
# NOTE: For decode with CPU offload, the flow is per-layer:
|
|
# 1. Each layer stores to decode_slot (same GPU memory, reused)
|
|
# 2. Each layer offloads its data to CPU[layer_id, block_id]
|
|
# 3. Each layer loads prev blocks from CPU[layer_id] when needed
|
|
|
|
def offload_decode_slot_layer(self, layer_id: int, cpu_block_id: int) -> None:
|
|
"""
|
|
Offload KV from decode slot (slot[0]) to CPU for one layer.
|
|
|
|
Args:
|
|
layer_id: Layer ID
|
|
cpu_block_id: Target CPU block ID
|
|
"""
|
|
# Reuse the existing per-layer offload method
|
|
self.offload_slot_layer_to_cpu(self.decode_slot, layer_id, cpu_block_id)
|
|
|
|
def wait_decode_offload(self) -> None:
|
|
"""Wait for decode slot offload to complete."""
|
|
self.wait_slot_offload(self.decode_slot)
|
|
|
|
def get_kv_for_decode_slot(
|
|
self,
|
|
pos_in_block: int,
|
|
) -> Tuple[Tensor, Tensor]:
|
|
"""
|
|
Get KV at specified position in decode slot.
|
|
|
|
GPU cache has no layer dimension - decode slot contains data for
|
|
whatever layer was most recently stored.
|
|
|
|
Args:
|
|
pos_in_block: Token position within block (0 to block_size-1)
|
|
|
|
Returns:
|
|
(k_cache, v_cache), shape: [1, 1, kv_heads, head_dim]
|
|
"""
|
|
k = self.k_cache_gpu[self.decode_slot, pos_in_block:pos_in_block+1]
|
|
v = self.v_cache_gpu[self.decode_slot, pos_in_block:pos_in_block+1]
|
|
k = k.unsqueeze(0)
|
|
v = v.unsqueeze(0)
|
|
return k, v
|
|
|
|
def get_kv_for_decode_slot_accumulated(
|
|
self,
|
|
num_tokens: int,
|
|
) -> Tuple[Tensor, Tensor]:
|
|
"""
|
|
Get accumulated KV in decode slot (positions 0 to num_tokens-1).
|
|
|
|
GPU cache has no layer dimension - decode slot contains data for
|
|
whatever layer was most recently stored.
|
|
|
|
Args:
|
|
num_tokens: Number of accumulated tokens (1 to block_size)
|
|
|
|
Returns:
|
|
(k_cache, v_cache), shape: [1, num_tokens, kv_heads, head_dim]
|
|
"""
|
|
k = self.k_cache_gpu[self.decode_slot, :num_tokens]
|
|
v = self.v_cache_gpu[self.decode_slot, :num_tokens]
|
|
k = k.unsqueeze(0)
|
|
v = v.unsqueeze(0)
|
|
return k, v
|
|
|
|
# ----- Legacy compatibility methods (for decode double-buffering) -----
|
|
# NOTE: GPU cache has no layer dimension. Layer ID is used for CPU cache indexing only.
|
|
|
|
def load_to_compute_layer(self, layer_id: int, cpu_block_ids: List[int]) -> None:
|
|
"""
|
|
Legacy: Load CPU blocks to decode_load_slots for decode double-buffering.
|
|
|
|
Uses first half of decode_load_slots as 'compute' region.
|
|
GPU cache has no layer dimension - layer_id is for CPU cache indexing.
|
|
"""
|
|
if not cpu_block_ids:
|
|
return
|
|
|
|
half = max(1, len(self.decode_load_slots) // 2)
|
|
slots = self.decode_load_slots[:half]
|
|
num_to_load = min(len(cpu_block_ids), len(slots))
|
|
|
|
with torch.cuda.stream(self.transfer_stream_main):
|
|
for i in range(num_to_load):
|
|
cpu_id = cpu_block_ids[i]
|
|
gpu_slot = slots[i]
|
|
# GPU: no layer dimension, CPU: has layer dimension
|
|
self.k_cache_gpu[gpu_slot].copy_(
|
|
self.k_cache_cpu[layer_id, cpu_id], non_blocking=True
|
|
)
|
|
self.v_cache_gpu[gpu_slot].copy_(
|
|
self.v_cache_cpu[layer_id, cpu_id], non_blocking=True
|
|
)
|
|
if num_to_load > 0:
|
|
self.ring_slot_ready[slots[0]].record(self.transfer_stream_main)
|
|
|
|
def wait_compute_layer(self) -> None:
|
|
"""Legacy: Wait for 'compute' region loading."""
|
|
if self.decode_load_slots:
|
|
self.wait_slot_layer(self.decode_load_slots[0])
|
|
|
|
def load_to_prefetch_layer(self, layer_id: int, cpu_block_ids: List[int]) -> None:
|
|
"""
|
|
Legacy: Load CPU blocks to decode_load_slots for decode double-buffering.
|
|
|
|
Uses second half of decode_load_slots as 'prefetch' region.
|
|
GPU cache has no layer dimension - layer_id is for CPU cache indexing.
|
|
"""
|
|
if not cpu_block_ids:
|
|
return
|
|
|
|
half = max(1, len(self.decode_load_slots) // 2)
|
|
slots = self.decode_load_slots[half:]
|
|
if not slots:
|
|
slots = self.decode_load_slots # Fallback if only 1-2 slots
|
|
num_to_load = min(len(cpu_block_ids), len(slots))
|
|
|
|
with torch.cuda.stream(self.transfer_stream_main):
|
|
for i in range(num_to_load):
|
|
cpu_id = cpu_block_ids[i]
|
|
gpu_slot = slots[i]
|
|
# GPU: no layer dimension, CPU: has layer dimension
|
|
self.k_cache_gpu[gpu_slot].copy_(
|
|
self.k_cache_cpu[layer_id, cpu_id], non_blocking=True
|
|
)
|
|
self.v_cache_gpu[gpu_slot].copy_(
|
|
self.v_cache_cpu[layer_id, cpu_id], non_blocking=True
|
|
)
|
|
if num_to_load > 0:
|
|
self.ring_slot_ready[slots[0]].record(self.transfer_stream_main)
|
|
|
|
def wait_prefetch_layer(self) -> None:
|
|
"""Legacy: Wait for 'prefetch' region loading."""
|
|
half = max(1, len(self.decode_load_slots) // 2)
|
|
slots = self.decode_load_slots[half:]
|
|
if slots:
|
|
self.wait_slot_layer(slots[0])
|
|
elif self.decode_load_slots:
|
|
self.wait_slot_layer(self.decode_load_slots[0])
|
|
|
|
def get_kv_for_compute(
|
|
self,
|
|
num_blocks: int,
|
|
) -> Tuple[Tensor, Tensor]:
|
|
"""Legacy: Get KV from 'compute' region (first half of decode_load_slots)."""
|
|
half = max(1, len(self.decode_load_slots) // 2)
|
|
slots = self.decode_load_slots[:half][:num_blocks]
|
|
return self.get_kv_for_slots(slots)
|
|
|
|
def get_kv_for_prefetch(
|
|
self,
|
|
num_blocks: int,
|
|
) -> Tuple[Tensor, Tensor]:
|
|
"""Legacy: Get KV from 'prefetch' region (second half of decode_load_slots)."""
|
|
half = max(1, len(self.decode_load_slots) // 2)
|
|
slots = self.decode_load_slots[half:]
|
|
if not slots:
|
|
slots = self.decode_load_slots
|
|
slots = slots[:num_blocks]
|
|
return self.get_kv_for_slots(slots)
|
|
|
|
# ========== Debug Hook Interface ==========
|
|
#
|
|
# Minimal generic hook system for debugging.
|
|
# Framework only provides hook registration and tensor access.
|
|
# All verification logic is external.
|
|
|
|
def enable_debug_mode(self) -> None:
|
|
"""Enable debug mode."""
|
|
self._debug_mode = True
|
|
logger.info("OffloadEngine debug mode ENABLED")
|
|
|
|
def disable_debug_mode(self) -> None:
|
|
"""Disable debug mode and clear all hooks."""
|
|
self._debug_mode = False
|
|
self._debug_hooks.clear()
|
|
logger.info("OffloadEngine debug mode DISABLED")
|
|
|
|
@property
|
|
def debug_mode(self) -> bool:
|
|
"""Check if debug mode is enabled."""
|
|
return self._debug_mode
|
|
|
|
def register_debug_hook(self, hook_fn) -> None:
|
|
"""
|
|
Register a debug hook.
|
|
|
|
The hook is called after H2D load completes (after wait_slot_layer),
|
|
receiving the loaded tensor for inspection.
|
|
|
|
Args:
|
|
hook_fn: Callable with signature:
|
|
(slot_idx: int, layer_id: int, cpu_block_id: int, k: Tensor, v: Tensor) -> None
|
|
- k, v: GPU tensor views for the loaded slot
|
|
|
|
Example:
|
|
def my_hook(slot_idx, layer_id, cpu_block_id, k, v):
|
|
if layer_id == 0:
|
|
k_val = k.float().mean().item()
|
|
print(f"Loaded block {cpu_block_id}, K mean = {k_val}")
|
|
|
|
offload_engine.register_debug_hook(my_hook)
|
|
"""
|
|
self._debug_hooks.append(hook_fn)
|
|
|
|
def remove_debug_hook(self, hook_fn) -> None:
|
|
"""Remove a registered debug hook."""
|
|
if hook_fn in self._debug_hooks:
|
|
self._debug_hooks.remove(hook_fn)
|
|
|
|
def _call_debug_hooks(self, slot_idx: int, layer_id: int, cpu_block_id: int) -> None:
|
|
"""
|
|
Call all registered debug hooks with loaded tensor (internal use).
|
|
|
|
Called by attention.py after wait_slot_layer completes.
|
|
GPU cache has no layer dimension - slot contains data for the layer
|
|
that was just loaded.
|
|
"""
|
|
if not self._debug_mode or not self._debug_hooks:
|
|
return
|
|
|
|
# GPU cache has no layer dimension
|
|
k = self.k_cache_gpu[slot_idx]
|
|
v = self.v_cache_gpu[slot_idx]
|
|
|
|
for hook in self._debug_hooks:
|
|
try:
|
|
hook(slot_idx, layer_id, cpu_block_id, k, v)
|
|
except Exception as e:
|
|
# Allow pdb quit to propagate
|
|
if e.__class__.__name__ == 'BdbQuit':
|
|
raise
|
|
logger.warning(f"Debug hook error: {e}") |