6575099a06
Removed ~460 lines of unused/redundant code from offload_engine.py: - CUDA gather methods (gathered_h2d_*, update_gather_indices) - Legacy async transfer methods (prefetch_block_async, offload_block_async) - Legacy sync/wait methods (wait_for_block, wait_all_transfers, sync_indices) - Legacy compatibility methods (load_to_compute_layer, wait_compute_layer) - Unused gather_indices tensors and memory calculations Updated class docstring to reflect current architecture. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
872 lines
34 KiB
Python
872 lines
34 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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# Import for type hints only (avoid circular import)
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from nanovllm.kvcache.sparse import SparsePolicy
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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_gpu_blocks, block_size, kv_heads, head_dim] (no layer dimension)
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- CPU cache: [num_layers, num_cpu_blocks, block_size, kv_heads, head_dim] (pinned)
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Features:
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- Unified ring buffer for chunked prefill/decode
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- Per-layer prefill buffer for async offload
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- Cross-layer pipeline for decode with double-buffering
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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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sparse_policy: "SparsePolicy" = None,
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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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# ========== Per-layer decode buffer ==========
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# During decode, all layers share decode_slot (no layer dimension in GPU cache).
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# This causes accumulated tokens to be overwritten by each layer.
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# Solution: Maintain separate per-layer buffers for decode tokens.
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# Shape: [num_layers, block_size, kv_heads, head_dim]
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# Memory: num_layers * block_size * kv_heads * head_dim * dtype_size
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# e.g., 28 * 1024 * 8 * 128 * 2 = 58.7 MB (acceptable)
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self.decode_k_buffer = torch.zeros(
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num_layers, block_size, num_kv_heads, head_dim,
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dtype=dtype, device="cuda"
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)
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self.decode_v_buffer = torch.zeros(
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num_layers, block_size, num_kv_heads, head_dim,
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dtype=dtype, device="cuda"
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)
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decode_buf_mb = 2 * num_layers * block_size * num_kv_heads * head_dim * dtype.itemsize / (1024 * 1024)
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logger.info(f" Per-layer decode buffer: {decode_buf_mb:.1f} MB")
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# ========== Cross-layer pipeline buffers for decode ==========
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# Double-buffered layer cache for pipelined decode:
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# - Buffer A: Current layer's prefilled KV being computed
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# - Buffer B: Next layer's prefilled KV being loaded
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# Shape: [max_prefill_blocks, block_size, kv_heads, head_dim]
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# Memory: 2 * max_prefill_blocks * block_size * kv_heads * head_dim * dtype_size
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max_prefill_blocks = num_cpu_blocks # Can hold all prefill blocks
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self.layer_k_buffer_a = torch.zeros(
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max_prefill_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.layer_v_buffer_a = torch.zeros(
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max_prefill_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.layer_k_buffer_b = torch.zeros(
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max_prefill_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.layer_v_buffer_b = torch.zeros(
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max_prefill_blocks, block_size, num_kv_heads, head_dim,
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dtype=dtype, device="cuda"
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)
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layer_buf_mb = 4 * max_prefill_blocks * block_size * num_kv_heads * head_dim * dtype.itemsize / (1024 * 1024)
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logger.info(f" Cross-layer pipeline buffers: {layer_buf_mb:.1f} MB ({max_prefill_blocks} blocks × 2)")
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# Pipeline state tracking
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self._pipeline_active = False
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self._pipeline_current_buffer = 0 # 0 = buffer A, 1 = buffer B
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self._pipeline_next_layer_event = torch.cuda.Event()
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self._pipeline_cpu_blocks: list = [] # CPU block IDs to load
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self._pipeline_num_blocks = 0
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self._pipeline_layer_stream = torch.cuda.Stream() # Dedicated stream for layer loading
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# ========== Per-layer prefill buffer for async offload ==========
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# During chunked prefill, all layers share the same GPU slot. This means
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# each layer must wait for offload to complete before the next layer can
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# write to the same slot. This serializes offloads and hurts performance.
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# Solution: Maintain separate per-layer buffers for prefill.
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# Each layer writes to its own buffer, enabling fully async offloads.
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# Shape: [num_layers, block_size, kv_heads, head_dim]
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self.prefill_k_buffer = torch.zeros(
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num_layers, block_size, num_kv_heads, head_dim,
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dtype=dtype, device="cuda"
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)
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self.prefill_v_buffer = torch.zeros(
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num_layers, block_size, num_kv_heads, head_dim,
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dtype=dtype, device="cuda"
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)
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prefill_buf_mb = 2 * num_layers * block_size * num_kv_heads * head_dim * dtype.itemsize / (1024 * 1024)
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logger.info(f" Per-layer prefill buffer: {prefill_buf_mb:.1f} MB")
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# Per-layer offload events for async prefill offload
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# Each layer has its own event to track offload completion
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self.prefill_offload_events = [torch.cuda.Event() for _ in range(num_layers)]
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# Per-layer transfer streams for parallel offloads
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self.prefill_offload_streams = [torch.cuda.Stream() for _ in range(num_layers)]
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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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# 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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# ========== Sparse attention policy (set at construction time) ==========
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self.sparse_policy = sparse_policy
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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 attention layer.
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NOTE: GPU cache has no layer dimension - all layers share the same slots.
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The layer_id parameter is kept for API compatibility but not used.
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Returns:
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(k_cache, v_cache) tensors
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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, self.v_cache_gpu
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# ========== Memory info ==========
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def gpu_memory_bytes(self) -> int:
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"""Total GPU memory used by KV caches."""
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return (
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self.k_cache_gpu.numel() * self.k_cache_gpu.element_size() +
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self.v_cache_gpu.numel() * self.v_cache_gpu.element_size()
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)
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def cpu_memory_bytes(self) -> int:
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"""Total CPU memory used by KV caches."""
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return (
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self.k_cache_cpu.numel() * self.k_cache_cpu.element_size() +
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self.v_cache_cpu.numel() * self.v_cache_cpu.element_size()
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)
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def __repr__(self) -> str:
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return (
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f"OffloadEngine(\n"
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f" num_layers={self.num_layers},\n"
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f" num_gpu_blocks={self.num_gpu_blocks},\n"
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f" num_cpu_blocks={self.num_cpu_blocks},\n"
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f" block_size={self.block_size},\n"
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f" kv_heads={self.num_kv_heads},\n"
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f" head_dim={self.head_dim},\n"
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f" dtype={self.dtype},\n"
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f" ring_buffer: {self.num_ring_slots} slots, decode_slot={self.decode_slot}, decode_load_slots={self.decode_load_slots},\n"
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f" gpu_memory={self.gpu_memory_bytes() / 1024**2:.1f}MB,\n"
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f" cpu_memory={self.cpu_memory_bytes() / 1024**2:.1f}MB\n"
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f")"
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)
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def wait_all_offload_done(self) -> None:
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"""Wait for all offload operations to complete."""
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self.transfer_stream_main.synchronize()
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# ========== Unified Ring Buffer methods ==========
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# ----- Prefill: Ring Buffer slot management -----
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def get_write_slot_for_prefill(self, chunk_idx: int) -> int:
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"""
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Get ring buffer slot for writing prefill chunk.
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For prefill, ALL slots are used as ring buffer, cycling through.
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Args:
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chunk_idx: Current chunk index (0, 1, 2, ...)
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Returns:
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GPU slot index for writing
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"""
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return chunk_idx % self.num_ring_slots
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def get_load_slots_for_prefill(self, write_slot_idx: int) -> List[int]:
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"""
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Get available slots for loading previous chunks during prefill.
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Excludes the current write slot to avoid conflict.
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Args:
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write_slot_idx: Current write slot index
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Returns:
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List of slot indices available for loading (N-1 slots)
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"""
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return [i for i in range(self.num_ring_slots) if i != write_slot_idx]
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# ----- Decode: slot management -----
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def get_load_slots_for_decode(self) -> List[int]:
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"""
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Get slots available for loading during decode.
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Excludes decode_slot (slot[0]) since it's used for writing new token's KV.
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Returns:
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List of slot indices for loading (slots[1:])
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"""
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return self.decode_load_slots
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# ----- Per-slot Per-layer loading methods -----
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def record_slot_compute_done(self, slot_idx: int) -> None:
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"""
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Record that computation using this slot's data is done.
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This event is used by load_to_slot_layer to ensure we don't overwrite
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data before it's been read by attention computation.
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Args:
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slot_idx: GPU slot index that was just used for computation
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"""
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self.ring_slot_compute_done[slot_idx].record()
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def load_to_slot_layer(self, slot_idx: int, layer_id: int, cpu_block_id: int) -> None:
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"""
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Async load a single CPU block to a ring buffer slot for one layer.
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This is the core building block for ring buffer pipelining.
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GPU cache has no layer dimension - slots are shared across all layers.
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CPU cache still has layer dimension for persistent storage.
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Before starting the transfer, waits for:
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1. Any previous compute on this slot to complete
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Args:
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slot_idx: Target GPU slot index
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layer_id: Layer index to load (for CPU cache indexing)
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cpu_block_id: Source CPU block ID
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"""
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logger.debug(f"Ring load: layer={layer_id}, CPU[{cpu_block_id}] -> GPU slot[{slot_idx}]")
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# Use per-slot stream for parallel transfers across different slots
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stream = self.slot_transfer_streams[slot_idx]
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torch.cuda.nvtx.range_push(f"H2D: L{layer_id} CPU[{cpu_block_id}]->Slot[{slot_idx}]")
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with torch.cuda.stream(stream):
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# Wait for previous compute on this slot to complete before overwriting
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# This prevents data race: transfer must not start until attention finishes reading
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stream.wait_event(self.ring_slot_compute_done[slot_idx])
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# Also wait for any pending offload of this slot to complete
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# This prevents race: load must not write GPU slot while offload is reading from it
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stream.wait_event(self.ring_slot_offload_done[slot_idx])
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# GPU: no layer dimension, CPU: has layer dimension
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self.k_cache_gpu[slot_idx].copy_(
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self.k_cache_cpu[layer_id, cpu_block_id], non_blocking=True
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)
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self.v_cache_gpu[slot_idx].copy_(
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self.v_cache_cpu[layer_id, cpu_block_id], non_blocking=True
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)
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self.ring_slot_ready[slot_idx].record(stream)
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torch.cuda.nvtx.range_pop()
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def wait_slot_layer(self, slot_idx: int) -> None:
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"""
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Wait for a slot's loading to complete.
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Args:
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slot_idx: GPU slot index to wait for
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"""
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self.compute_stream.wait_event(self.ring_slot_ready[slot_idx])
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|
||
# 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,
|
||
num_valid_tokens: int = -1,
|
||
is_prefill: bool = True,
|
||
) -> 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
|
||
num_valid_tokens: Number of valid tokens in this block (-1 = use block_size)
|
||
is_prefill: True if in prefill phase, False if in decode phase
|
||
"""
|
||
logger.debug(f"Ring offload: GPU slot[{slot_idx}] -> CPU[layer={layer_id}, block={cpu_block_id}]")
|
||
|
||
# Collect metadata BEFORE offload (while k_cache is still on GPU)
|
||
valid_tokens = num_valid_tokens if num_valid_tokens > 0 else self.block_size
|
||
k_cache = self.k_cache_gpu[slot_idx]
|
||
|
||
if self.sparse_policy is not None:
|
||
if is_prefill:
|
||
self.sparse_policy.on_prefill_offload(cpu_block_id, layer_id, k_cache, valid_tokens)
|
||
else:
|
||
self.sparse_policy.on_decode_offload(cpu_block_id, layer_id, k_cache, valid_tokens)
|
||
|
||
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
|
||
|
||
# ========== 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
|
||
|
||
# Use get_kv_for_slot for consistency with attention.py
|
||
k, v = self.get_kv_for_slot(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}")
|
||
|
||
# ========== Cross-layer Pipeline Methods for Decode ==========
|
||
|
||
def start_decode_pipeline(self, cpu_block_ids: List[int]) -> None:
|
||
"""
|
||
Start cross-layer pipeline for decode.
|
||
|
||
Called at the beginning of a decode step to initialize the pipeline.
|
||
Preloads Layer 0's data into buffer A.
|
||
|
||
Args:
|
||
cpu_block_ids: List of CPU block IDs for prefilled blocks
|
||
"""
|
||
if not cpu_block_ids:
|
||
self._pipeline_active = False
|
||
return
|
||
|
||
self._pipeline_active = True
|
||
self._pipeline_cpu_blocks = cpu_block_ids
|
||
self._pipeline_num_blocks = len(cpu_block_ids)
|
||
self._pipeline_current_buffer = 0
|
||
|
||
# Preload Layer 0 into buffer A
|
||
self._load_layer_to_buffer(0, 0) # layer_id=0, buffer_idx=0 (A)
|
||
|
||
def get_decode_layer_kv(self, layer_id: int, num_blocks: int) -> Tuple[Tensor, Tensor]:
|
||
"""
|
||
Get KV cache for a layer during decode.
|
||
|
||
If pipeline is active, returns data from the current buffer.
|
||
Also triggers preloading of the next layer (if not last layer).
|
||
|
||
Args:
|
||
layer_id: Current layer ID
|
||
num_blocks: Number of blocks to return
|
||
|
||
Returns:
|
||
(k_cache, v_cache) tensors, shape: [num_blocks, block_size, kv_heads, head_dim]
|
||
"""
|
||
if not self._pipeline_active:
|
||
raise RuntimeError("Decode pipeline not active. Call start_decode_pipeline first.")
|
||
|
||
# Wait for current layer's data to be ready
|
||
self.compute_stream.wait_event(self._pipeline_next_layer_event)
|
||
|
||
# Get current buffer
|
||
if self._pipeline_current_buffer == 0:
|
||
k = self.layer_k_buffer_a[:num_blocks]
|
||
v = self.layer_v_buffer_a[:num_blocks]
|
||
else:
|
||
k = self.layer_k_buffer_b[:num_blocks]
|
||
v = self.layer_v_buffer_b[:num_blocks]
|
||
|
||
# Trigger preloading of next layer (if not last layer)
|
||
next_layer_id = layer_id + 1
|
||
if next_layer_id < self.num_layers:
|
||
# Use the other buffer for next layer
|
||
next_buffer_idx = 1 - self._pipeline_current_buffer
|
||
self._load_layer_to_buffer(next_layer_id, next_buffer_idx)
|
||
# Switch to next buffer for next layer
|
||
self._pipeline_current_buffer = next_buffer_idx
|
||
|
||
return k, v
|
||
|
||
def _load_layer_to_buffer(self, layer_id: int, buffer_idx: int) -> None:
|
||
"""
|
||
Async load a layer's prefilled blocks to the specified buffer.
|
||
|
||
Uses sgDMA for efficient strided transfer from CPU cache.
|
||
|
||
Args:
|
||
layer_id: Layer index to load
|
||
buffer_idx: 0 for buffer A, 1 for buffer B
|
||
"""
|
||
num_blocks = self._pipeline_num_blocks
|
||
cpu_block_ids = self._pipeline_cpu_blocks
|
||
|
||
# Select target buffer
|
||
if buffer_idx == 0:
|
||
k_buffer = self.layer_k_buffer_a
|
||
v_buffer = self.layer_v_buffer_a
|
||
else:
|
||
k_buffer = self.layer_k_buffer_b
|
||
v_buffer = self.layer_v_buffer_b
|
||
|
||
# Load all blocks for this layer using dedicated stream
|
||
with torch.cuda.stream(self._pipeline_layer_stream):
|
||
for i, cpu_block_id in enumerate(cpu_block_ids):
|
||
# Copy from CPU cache (has layer dimension) to GPU buffer
|
||
k_buffer[i].copy_(
|
||
self.k_cache_cpu[layer_id, cpu_block_id],
|
||
non_blocking=True
|
||
)
|
||
v_buffer[i].copy_(
|
||
self.v_cache_cpu[layer_id, cpu_block_id],
|
||
non_blocking=True
|
||
)
|
||
# Record event when all transfers complete
|
||
self._pipeline_next_layer_event.record(self._pipeline_layer_stream)
|
||
|
||
def end_decode_pipeline(self) -> None:
|
||
"""
|
||
End the cross-layer pipeline.
|
||
|
||
Called at the end of a decode step to clean up pipeline state.
|
||
"""
|
||
if self._pipeline_active:
|
||
# Ensure all transfers complete before ending
|
||
self._pipeline_layer_stream.synchronize()
|
||
self._pipeline_active = False
|
||
self._pipeline_cpu_blocks = []
|
||
self._pipeline_num_blocks = 0
|
||
|
||
def is_pipeline_active(self) -> bool:
|
||
"""Check if decode pipeline is currently active."""
|
||
return self._pipeline_active
|
||
|
||
# ========== Per-layer Prefill Buffer Methods ==========
|
||
# These methods enable async offload during chunked prefill by using
|
||
# per-layer buffers instead of shared GPU slots.
|
||
|
||
def get_prefill_buffer(self, layer_id: int) -> Tuple[Tensor, Tensor]:
|
||
"""
|
||
Get prefill buffer for a layer.
|
||
|
||
Args:
|
||
layer_id: Layer index
|
||
|
||
Returns:
|
||
(k_buffer, v_buffer), shape: [block_size, kv_heads, head_dim]
|
||
"""
|
||
return self.prefill_k_buffer[layer_id], self.prefill_v_buffer[layer_id]
|
||
|
||
def get_prefill_buffer_slice(
|
||
self,
|
||
layer_id: int,
|
||
num_tokens: int,
|
||
) -> Tuple[Tensor, Tensor]:
|
||
"""
|
||
Get a slice of prefill buffer for attention computation.
|
||
|
||
Args:
|
||
layer_id: Layer index
|
||
num_tokens: Number of valid tokens in current chunk
|
||
|
||
Returns:
|
||
(k, v) with shape [1, num_tokens, kv_heads, head_dim]
|
||
"""
|
||
k = self.prefill_k_buffer[layer_id, :num_tokens].unsqueeze(0)
|
||
v = self.prefill_v_buffer[layer_id, :num_tokens].unsqueeze(0)
|
||
return k, v
|
||
|
||
def offload_prefill_buffer_async(
|
||
self,
|
||
layer_id: int,
|
||
cpu_block_id: int,
|
||
num_valid_tokens: int = -1,
|
||
) -> None:
|
||
"""
|
||
Async offload prefill buffer to CPU (no waiting required).
|
||
|
||
This uses per-layer streams and events to enable fully async offloads.
|
||
Each layer can offload independently without blocking other layers.
|
||
|
||
Args:
|
||
layer_id: Layer index
|
||
cpu_block_id: Target CPU block ID
|
||
num_valid_tokens: Number of valid tokens (-1 = use block_size)
|
||
"""
|
||
valid_tokens = num_valid_tokens if num_valid_tokens > 0 else self.block_size
|
||
|
||
# Collect sparse policy metadata before offload
|
||
if self.sparse_policy is not None:
|
||
k_cache = self.prefill_k_buffer[layer_id]
|
||
self.sparse_policy.on_prefill_offload(cpu_block_id, layer_id, k_cache, valid_tokens)
|
||
|
||
# Use per-layer stream for parallel offloads
|
||
stream = self.prefill_offload_streams[layer_id]
|
||
|
||
torch.cuda.nvtx.range_push(f"AsyncPrefillOffload: L{layer_id}->CPU[{cpu_block_id}]")
|
||
with torch.cuda.stream(stream):
|
||
# Wait for compute to finish writing to prefill buffer
|
||
stream.wait_stream(self.compute_stream)
|
||
|
||
# Copy from prefill buffer to CPU
|
||
self.k_cache_cpu[layer_id, cpu_block_id].copy_(
|
||
self.prefill_k_buffer[layer_id], non_blocking=True
|
||
)
|
||
self.v_cache_cpu[layer_id, cpu_block_id].copy_(
|
||
self.prefill_v_buffer[layer_id], non_blocking=True
|
||
)
|
||
|
||
# Record completion event
|
||
self.prefill_offload_events[layer_id].record(stream)
|
||
torch.cuda.nvtx.range_pop()
|
||
|
||
def wait_all_prefill_offloads(self) -> None:
|
||
"""Wait for all prefill buffer offloads to complete."""
|
||
for stream in self.prefill_offload_streams:
|
||
stream.synchronize()
|
||
|
||
def wait_prefill_offload(self, layer_id: int) -> None:
|
||
"""Wait for a specific layer's prefill offload to complete."""
|
||
self.prefill_offload_events[layer_id].synchronize()
|