1164 lines
45 KiB
Python
1164 lines
45 KiB
Python
from __future__ import annotations
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import logging
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import threading
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from typing import Optional
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import torch
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from sglang.kernels.ops.kvcache.hicache import (
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can_use_write_back_jit_kernel,
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)
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from sglang.kernels.ops.kvcache.hicache import (
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transfer_hicache_all_layer_mla_staged_lf_pf as jit_transfer_hicache_all_layer_mla_staged_lf_pf,
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)
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from sglang.kernels.ops.kvcache.hisparse import transfer_cache_dsv4_mla
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from sglang.srt.utils import is_cuda, is_hip, is_mps, is_npu, is_xpu
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_is_cuda = is_cuda()
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_is_hip = is_hip()
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_is_npu = is_npu()
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_is_xpu = is_xpu()
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_is_mps = is_mps()
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if _is_cuda or _is_hip:
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from sgl_kernel.kvcacheio import (
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transfer_kv_all_layer_direct_lf_pf,
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transfer_kv_all_layer_mla,
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transfer_kv_all_layer_mla_lf_pf,
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transfer_kv_direct,
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transfer_kv_per_layer_direct_pf_lf,
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transfer_kv_per_layer_mla,
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transfer_kv_per_layer_mla_pf_lf,
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)
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if _is_npu:
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from sgl_kernel_npu.kvcacheio import TransferDirection, transfer_kv_dim_exchange
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logger = logging.getLogger(__name__)
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from sglang.srt.mem_cache.pool_host import HostKVCache
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from sglang.srt.mem_cache.pool_host.base import (
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_WRITE_BACK_STAGING_PAGE_CHUNK,
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host_memory_budget_bytes,
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synchronized,
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)
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from sglang.srt.mem_cache.pool_host.common import (
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ALLOC_MEMORY_FUNCS,
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get_allocator_from_storage,
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make_kernel_ptr_table,
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)
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from sglang.srt.mem_cache.pool_host.hisparse import HiSparseHostPoolMixin
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# ---- V4 Compressed KV Host Pools ----
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class LogicalHostPool:
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"""Pure-logical anchor pool for V4 HiCache.
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The pool manages page-aligned token slots but holds no KV tensor. V4
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compressed side pools use these logical FULL indices as stable page anchors.
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"""
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def __init__(self, size: int, page_size: int, layout: str = "layer_first"):
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if size % page_size != 0:
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raise ValueError(
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"LogicalHostPool size must be page-aligned, "
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f"got size={size}, page_size={page_size}"
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)
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self.size = size
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# Stands in for a host pool (and group anchor); DCP never widens it.
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self.logical_size = size
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self.page_size = page_size
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self.device = "cpu"
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self.layout = layout
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self.dtype = torch.uint8
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self.layer_num = 0
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self.start_layer = 0
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self.end_layer = 0
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self.kv_buffer = None
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self.size_per_token = 0
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self.allocator = None
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self.can_use_write_back_jit = True
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self.lock = threading.RLock()
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self.clear()
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@synchronized
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def clear(self):
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self.free_slots = torch.arange(self.size, dtype=torch.int64)
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# Match HostKVCache's lazy release path: defer large free-list merges
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# until an allocation needs the released slots.
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self.release_slots = []
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self.num_release_slots = 0
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def destroy(self) -> None:
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"""Logical anchors own no backing buffers or registrations to release."""
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return None
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def available_size(self):
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return len(self.free_slots) + self.num_release_slots
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def _merge_release_slots(self):
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if self.num_release_slots == 0:
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return
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if len(self.free_slots) == 0 and len(self.release_slots) == 1:
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self.free_slots = self.release_slots[0]
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else:
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self.free_slots = torch.cat([self.free_slots, *self.release_slots])
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self.release_slots = []
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self.num_release_slots = 0
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@synchronized
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def alloc(self, need_size: int) -> Optional[torch.Tensor]:
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if need_size % self.page_size != 0:
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raise ValueError(
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"LogicalHostPool allocation must be page-aligned, "
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f"got need_size={need_size}, page_size={self.page_size}"
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)
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if need_size > self.available_size():
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return None
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if need_size > len(self.free_slots):
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self._merge_release_slots()
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select_index = self.free_slots[:need_size]
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self.free_slots = self.free_slots[need_size:]
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return select_index
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@synchronized
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def free(self, indices: torch.Tensor) -> int:
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if len(indices) % self.page_size != 0:
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raise ValueError(
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"LogicalHostPool free must be page-aligned, "
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f"got len(indices)={len(indices)}, page_size={self.page_size}"
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)
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indices_cpu = indices.to(dtype=torch.int64, device="cpu").flatten()
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if indices_cpu.numel() == 0:
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return 0
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self.release_slots.append(indices_cpu)
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self.num_release_slots += len(indices_cpu)
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return len(indices)
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def backup_from_device_all_layer(
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self, device_pool, host_indices, device_indices, io_backend
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):
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pass
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def load_to_device_per_layer(
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self,
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device_pool,
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host_indices,
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device_indices,
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layer_id,
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io_backend,
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*,
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is_draft: bool = False,
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):
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pass
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def get_data_page(self, index, flat=True):
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return torch.empty(0, dtype=torch.uint8)
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def get_dummy_flat_data_page(self):
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return torch.empty(0, dtype=torch.uint8)
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def set_from_flat_data_page(self, index, data_page):
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pass
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def get_page_buffer_meta(self, indices):
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return None
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def get_ksize_per_token(self):
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return 0
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class DeepSeekV4PagedHostPool(HiSparseHostPoolMixin, HostKVCache):
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"""Host mirror for a DeepSeek V4 paged KV/indexer sub-pool."""
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def __init__(
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self,
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pool_name: str,
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device_buffers: list[torch.Tensor],
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item_bytes: int,
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num_host_pages: int,
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slot_page_size: int,
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layout: str = "layer_first",
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device: str = "cpu",
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pin_memory: bool = True,
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allocator_type: str = "default",
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page_aligned_only: bool = False,
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):
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self.pool_name = pool_name
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self.layer_num = len(device_buffers)
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self.item_bytes = item_bytes
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# A page row of the FP4 indexer buffers is a grouped slot layout rather
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# than a flat token array, so the token-granular copy used for fused
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# DSv4 C4 rows does not apply and only whole pages may move.
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self.page_aligned_only = page_aligned_only
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self.num_host_pages = num_host_pages
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self.slot_page_size = slot_page_size
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self.dtype = torch.uint8
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self.device = device
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self.pin_memory = pin_memory
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self.allocator = get_allocator_from_storage(allocator_type)
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self.page_size = slot_page_size
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self.size = num_host_pages * slot_page_size
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self.layout = layout
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self.size_per_token = item_bytes
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self.start_layer = 0
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self.end_layer = self.layer_num
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self.lock = threading.RLock()
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self.device_buffers = device_buffers
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self.gpu_device = device_buffers[0].device if device_buffers else device
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requested_bytes = self.layer_num * num_host_pages * self.item_bytes
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available_bytes = host_memory_budget_bytes()
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if requested_bytes > available_bytes:
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raise ValueError(
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f"Not enough host memory for V4 paged pool {pool_name}. "
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f"Requesting {requested_bytes / 1e9:.2f} GB but only have "
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f"{available_bytes / 1e9:.2f} GB free."
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)
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# ALLOC_MEMORY_FUNCS is keyed by device *type* string ("npu"/"musa"/...),
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# not torch.device objects; a torch.device key silently falls back to
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# cudaHostRegister, which fails on NPU. Resolve the alloc func by type str.
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_alloc_key = (
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self.gpu_device.type
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if isinstance(self.gpu_device, torch.device)
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else str(self.gpu_device)
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)
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alloc_func = ALLOC_MEMORY_FUNCS[_alloc_key]
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self.data_refs = []
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if self.layout == "layer_first":
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self.kv_buffer = [
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alloc_func(
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(num_host_pages, self.item_bytes),
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dtype=self.dtype,
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device=self.device,
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pin_memory=self.pin_memory,
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allocator=self.allocator,
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)
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for _ in range(self.layer_num)
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]
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self.data_refs = [self.kv_buffer[i] for i in range(self.layer_num)]
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elif self.layout == "page_first":
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self.kv_buffer = alloc_func(
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(num_host_pages, self.layer_num, self.item_bytes),
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dtype=self.dtype,
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device=self.device,
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pin_memory=self.pin_memory,
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allocator=self.allocator,
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registration_granularity_bytes=self.layer_num * self.item_bytes,
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)
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elif self.layout == "page_first_direct":
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self.kv_buffer = alloc_func(
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(num_host_pages, self.layer_num, 1, self.item_bytes),
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dtype=self.dtype,
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device=self.device,
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pin_memory=self.pin_memory,
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allocator=self.allocator,
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registration_granularity_bytes=self.layer_num * self.item_bytes,
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)
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else:
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raise ValueError(f"Unsupported layout: {self.layout}")
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logger.info(
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"Allocating %.2f GB host memory for V4 paged pool '%s' "
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"(layers=%d, pages=%d, item_bytes=%d, layout=%s).",
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requested_bytes / 1e9,
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self.pool_name,
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self.layer_num,
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num_host_pages,
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self.item_bytes,
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self.layout,
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)
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self.device_ptrs = torch.tensor(
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[x.data_ptr() for x in self.device_buffers],
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dtype=torch.uint64,
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device=self.gpu_device,
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)
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self.data_ptrs = (
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make_kernel_ptr_table(
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self.data_refs,
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self.gpu_device,
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host_memory_registered=self.pin_memory,
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)
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if self.data_refs
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else None
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)
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self.can_use_jit = False
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self.can_use_write_back_jit = False
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self._init_write_back_staging_buffers()
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self.clear()
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def _init_write_back_staging_buffers(self):
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self.staging_buffer = None
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if self.layout != "page_first" or (_is_npu or _is_xpu or _is_mps):
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return
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self.can_use_write_back_jit = _is_cuda and can_use_write_back_jit_kernel(
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element_size=self.item_bytes * self.dtype.itemsize,
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)
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staging_page_capacity = min(self.num_host_pages, _WRITE_BACK_STAGING_PAGE_CHUNK)
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self.staging_buffer = torch.empty(
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(staging_page_capacity, self.layer_num, self.item_bytes),
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dtype=self.dtype,
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device=self.gpu_device,
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)
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def _host_page_view(self, l: int) -> torch.Tensor:
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"""View a host layer as ``[pages, 1, P, 1, dim]``."""
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if self.layout == "layer_first":
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layer_buffer = self.kv_buffer[l]
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elif self.layout == "page_first":
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layer_buffer = self.kv_buffer[:, l, :]
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elif self.layout == "page_first_direct":
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layer_buffer = self.kv_buffer[:, l, 0, :]
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else:
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raise ValueError(
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f"{self.pool_name} _host_page_view: kernel_ascend requires "
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"layer_first/page_first/page_first_direct layout, "
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f"got {self.layout!r}"
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)
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device_buffer = self.device_buffers[l]
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return layer_buffer.view(device_buffer.dtype).view(
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self.num_host_pages,
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1,
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device_buffer.shape[1],
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1,
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device_buffer.shape[-1],
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)
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def get_contiguous_buf_infos(self):
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"""Return per-layer page-row buffers for PD direct-to-host transfer."""
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data_ptrs = [tensor.data_ptr() for tensor in self.data_refs]
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data_lens = [self.kv_buffer[i].nbytes for i in range(self.layer_num)]
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item_lens = [self.item_bytes * self.dtype.itemsize] * self.layer_num
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return data_ptrs, data_lens, item_lens
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def _to_page_indices(self, indices: torch.Tensor) -> torch.Tensor:
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return indices.reshape(-1, self.slot_page_size)[:, 0] // self.slot_page_size
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def _to_native_page_indices(
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self, indices: torch.Tensor, native_page_size: int
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) -> torch.Tensor:
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"""Expand logical page indices into a device buffer's native slots."""
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rows = self._to_page_indices(indices)
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offsets = torch.arange(native_page_size, dtype=rows.dtype, device=rows.device)
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return (rows[:, None] * native_page_size + offsets).reshape(-1)
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def _unaligned_transfer_error(
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self, host_indices: torch.Tensor, device_indices: torch.Tensor
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) -> ValueError:
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return ValueError(
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f"{self.pool_name} expects page-aligned indices: got "
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f"{host_indices.numel()} host and {device_indices.numel()} device "
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f"indices for page size {self.slot_page_size}."
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)
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def _has_transfer_indices(
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self, host_indices: torch.Tensor | None, device_indices: torch.Tensor | None
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) -> bool:
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if host_indices is None or device_indices is None:
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return False
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if host_indices.numel() != device_indices.numel():
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raise ValueError(
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f"{self.pool_name} transfer index size mismatch: "
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f"host={host_indices.numel()}, device={device_indices.numel()}"
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)
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return host_indices.numel() > 0
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def get_size_per_token(self):
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return self.item_bytes
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def get_ksize_per_token(self):
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return self.item_bytes
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def init_kv_buffer(self):
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return self.kv_buffer
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def get_hybrid_pool_buffer(self):
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return self.kv_buffer if isinstance(self.kv_buffer, list) else [self.kv_buffer]
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def clear(self):
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self.free_slots = torch.arange(self.size, dtype=torch.int64)
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self.release_slots = []
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self.num_release_slots = 0
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def available_size(self):
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return len(self.free_slots) + self.num_release_slots
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|
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@synchronized
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def alloc(self, need_size: int) -> Optional[torch.Tensor]:
|
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need_size = (
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(need_size + self.slot_page_size - 1) // self.slot_page_size
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) * self.slot_page_size
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if need_size > self.available_size():
|
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return None
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|
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if need_size > len(self.free_slots):
|
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self._merge_release_slots()
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select_index = self.free_slots[:need_size]
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self.free_slots = self.free_slots[need_size:]
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return select_index
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|
|
@synchronized
|
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def free(self, indices: torch.Tensor) -> int:
|
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indices_cpu = indices.cpu()
|
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if indices_cpu.numel() == 0:
|
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return 0
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|
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self.release_slots.append(indices_cpu)
|
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self.num_release_slots += len(indices_cpu)
|
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return len(indices)
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|
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def backup_from_device_all_layer(
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self, device_pool, host_indices, device_indices, io_backend
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):
|
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if not self._has_transfer_indices(host_indices, device_indices):
|
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return
|
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if (
|
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host_indices.numel() % self.slot_page_size != 0
|
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or device_indices.numel() % self.slot_page_size != 0
|
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):
|
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# Whole C4 pages can use the normal HiCache page-row copy below.
|
|
# Token-granular DSV4 C4 copy needs this helper because a token is
|
|
# not one contiguous byte range in the paged row:
|
|
# [value0..value63][scale0..scale63].
|
|
if self.page_aligned_only:
|
|
raise self._unaligned_transfer_error(host_indices, device_indices)
|
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transfer_cache_dsv4_mla(
|
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src_ptrs=self.device_ptrs,
|
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dst_ptrs=self.data_ptrs,
|
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src_indices=device_indices.to(dtype=torch.int64),
|
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dst_indices=host_indices.to(dtype=torch.int64),
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)
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return
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host_rows = self._to_page_indices(host_indices)
|
|
device_rows = self._to_page_indices(device_indices)
|
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if io_backend == "kernel" and self.layout == "layer_first":
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transfer_kv_all_layer_mla(
|
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src_layers=self.device_ptrs,
|
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dst_layers=self.data_ptrs,
|
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src_indices=device_rows,
|
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dst_indices=host_rows,
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item_size=self.item_bytes,
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num_layers=self.layer_num,
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)
|
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elif io_backend == "kernel" and self.layout == "page_first":
|
|
if self.can_use_write_back_jit:
|
|
jit_transfer_hicache_all_layer_mla_staged_lf_pf(
|
|
ptr_src=self.device_ptrs,
|
|
src_indices=device_rows,
|
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dst_indices=host_rows,
|
|
staging=self.staging_buffer,
|
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dst=self.kv_buffer,
|
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page_size=1,
|
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element_size=self.item_bytes,
|
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)
|
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else:
|
|
transfer_kv_all_layer_mla_lf_pf(
|
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src_layers=self.device_ptrs,
|
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dst=self.kv_buffer,
|
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src_indices=device_rows,
|
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dst_indices=host_rows,
|
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item_size=self.item_bytes,
|
|
dst_layout_dim=self.layer_num * self.item_bytes,
|
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num_layers=self.layer_num,
|
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)
|
|
elif io_backend == "direct" and self.layout == "layer_first":
|
|
transfer_kv_direct(
|
|
src_layers=self.device_buffers,
|
|
dst_layers=self.data_refs,
|
|
src_indices=device_rows,
|
|
dst_indices=host_rows,
|
|
page_size=1,
|
|
)
|
|
elif io_backend == "direct" and self.layout == "page_first_direct":
|
|
transfer_kv_all_layer_direct_lf_pf(
|
|
src_ptrs=self.device_buffers,
|
|
dst_ptrs=[self.kv_buffer],
|
|
src_indices=device_rows,
|
|
dst_indices=host_rows,
|
|
page_size=1,
|
|
)
|
|
elif io_backend == "kernel_ascend":
|
|
# HiCache keeps KV-derived pools in the FULL logical coordinate
|
|
# space. Ascend consumes the native slots of the shaped device
|
|
# buffer, so translate the already resolved page rows only at this
|
|
# backend boundary.
|
|
native_page_size = int(self.device_buffers[0].shape[1])
|
|
native_device_indices = self._to_native_page_indices(
|
|
device_indices, native_page_size
|
|
)
|
|
native_host_indices = self._to_native_page_indices(
|
|
host_indices, native_page_size
|
|
)
|
|
for l in range(self.layer_num):
|
|
dev_view = self.device_buffers[l].unsqueeze(0)
|
|
# dev_view: [1, dev_pages, native_page_size, 1, kv_dim]
|
|
host_view = self._host_page_view(l)
|
|
# host_view: [num_host_pages, 1, native_page_size, 1, kv_dim]
|
|
transfer_kv_dim_exchange(
|
|
device_k=dev_view,
|
|
host_k=host_view,
|
|
device_v=torch.empty(0, device=dev_view.device),
|
|
host_v=torch.empty(0, device="cpu"),
|
|
device_indices=native_device_indices,
|
|
host_indices=native_host_indices,
|
|
page_size=native_page_size,
|
|
direction=TransferDirection.D2H,
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
f"Unsupported V4 paged host layout/backend: {self.layout}/{io_backend}"
|
|
)
|
|
|
|
def load_to_device_per_layer(
|
|
self,
|
|
device_pool,
|
|
host_indices,
|
|
device_indices,
|
|
layer_id,
|
|
io_backend,
|
|
*,
|
|
is_draft: bool = False,
|
|
):
|
|
if not self._has_transfer_indices(host_indices, device_indices):
|
|
return
|
|
if (
|
|
host_indices.numel() % self.slot_page_size != 0
|
|
or device_indices.numel() % self.slot_page_size != 0
|
|
):
|
|
# Same DSV4 C4 layout issue as backup: this is token-granular
|
|
# preload, so it cannot use the normal HiCache page-row copy.
|
|
if self.page_aligned_only:
|
|
raise self._unaligned_transfer_error(host_indices, device_indices)
|
|
transfer_cache_dsv4_mla(
|
|
src_ptrs=self.data_ptrs[layer_id : layer_id + 1],
|
|
dst_ptrs=self.device_ptrs[layer_id : layer_id + 1],
|
|
src_indices=host_indices.to(dtype=torch.int64),
|
|
dst_indices=device_indices.to(dtype=torch.int64),
|
|
)
|
|
return
|
|
host_rows = self._to_page_indices(host_indices)
|
|
device_rows = self._to_page_indices(device_indices)
|
|
|
|
if io_backend == "kernel" and self.layout == "layer_first":
|
|
transfer_kv_per_layer_mla(
|
|
src=self.data_refs[layer_id],
|
|
dst=self.device_buffers[layer_id],
|
|
src_indices=host_rows,
|
|
dst_indices=device_rows,
|
|
item_size=self.item_bytes,
|
|
)
|
|
elif io_backend == "kernel" and self.layout == "page_first":
|
|
transfer_kv_per_layer_mla_pf_lf(
|
|
src=self.kv_buffer,
|
|
dst=self.device_buffers[layer_id],
|
|
src_indices=host_rows,
|
|
dst_indices=device_rows,
|
|
layer_id=layer_id,
|
|
item_size=self.item_bytes,
|
|
src_layout_dim=self.layer_num * self.item_bytes,
|
|
)
|
|
elif io_backend == "direct" and self.layout == "layer_first":
|
|
transfer_kv_direct(
|
|
src_layers=[self.data_refs[layer_id]],
|
|
dst_layers=[self.device_buffers[layer_id]],
|
|
src_indices=host_rows,
|
|
dst_indices=device_rows,
|
|
page_size=1,
|
|
)
|
|
elif io_backend == "direct" and self.layout == "page_first_direct":
|
|
transfer_kv_per_layer_direct_pf_lf(
|
|
src_ptrs=[self.kv_buffer],
|
|
dst_ptrs=[self.device_buffers[layer_id]],
|
|
src_indices=host_rows,
|
|
dst_indices=device_rows,
|
|
layer_id=layer_id,
|
|
page_size=1,
|
|
)
|
|
elif io_backend == "kernel_ascend":
|
|
# NPU whole-page H2D via Ascend dim-exchange op, for layer_id only.
|
|
native_page_size = int(self.device_buffers[layer_id].shape[1])
|
|
native_device_indices = self._to_native_page_indices(
|
|
device_indices, native_page_size
|
|
)
|
|
native_host_indices = self._to_native_page_indices(
|
|
host_indices, native_page_size
|
|
)
|
|
dev_view = self.device_buffers[layer_id].unsqueeze(0)
|
|
# dev_view: [1, dev_pages, native_page_size, 1, kv_dim]
|
|
host_view = self._host_page_view(layer_id)
|
|
# host_view: [num_host_pages, 1, native_page_size, 1, kv_dim]
|
|
transfer_kv_dim_exchange(
|
|
device_k=dev_view,
|
|
host_k=host_view,
|
|
device_v=torch.empty(0, device=dev_view.device),
|
|
host_v=torch.empty(0, device="cpu"),
|
|
device_indices=native_device_indices,
|
|
host_indices=native_host_indices,
|
|
page_size=native_page_size,
|
|
direction=TransferDirection.H2D,
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
f"Unsupported V4 paged host layout/backend: {self.layout}/{io_backend}"
|
|
)
|
|
|
|
def get_data_page(self, index, flat=True):
|
|
index = int(index) // self.slot_page_size
|
|
if self.layout == "layer_first":
|
|
data_page = torch.stack(
|
|
[self.kv_buffer[i][index] for i in range(self.layer_num)]
|
|
)
|
|
elif self.layout in ["page_first", "page_first_direct"]:
|
|
data_page = self.kv_buffer[index]
|
|
else:
|
|
raise ValueError(f"Unsupported layout: {self.layout}")
|
|
return data_page.flatten() if flat else data_page
|
|
|
|
def get_dummy_flat_data_page(self):
|
|
return torch.zeros(
|
|
(self.layer_num, self.item_bytes),
|
|
dtype=self.dtype,
|
|
device=self.device,
|
|
pin_memory=self.pin_memory,
|
|
).flatten()
|
|
|
|
def set_from_flat_data_page(self, index, data_page):
|
|
index = int(index) // self.slot_page_size
|
|
if self.layout == "layer_first":
|
|
data = data_page.view(self.dtype).reshape(self.layer_num, self.item_bytes)
|
|
for i in range(self.layer_num):
|
|
self.kv_buffer[i][index].copy_(data[i])
|
|
elif self.layout == "page_first":
|
|
self.kv_buffer[index].copy_(
|
|
data_page.view(self.dtype).reshape(self.layer_num, self.item_bytes)
|
|
)
|
|
elif self.layout == "page_first_direct":
|
|
self.kv_buffer[index].copy_(
|
|
data_page.view(self.dtype).reshape(self.layer_num, 1, self.item_bytes)
|
|
)
|
|
else:
|
|
raise ValueError(f"Unsupported layout: {self.layout}")
|
|
|
|
def get_page_buffer_meta(self, indices):
|
|
ptr_list = []
|
|
rows = self._to_page_indices(indices).tolist()
|
|
if self.layout == "layer_first":
|
|
for row in rows:
|
|
page_index = int(row)
|
|
for layer_id in range(self.layer_num):
|
|
ptr = (
|
|
self.kv_buffer[layer_id].data_ptr()
|
|
+ page_index * self.item_bytes * self.dtype.itemsize
|
|
)
|
|
ptr_list.append(ptr)
|
|
element_size = self.item_bytes * self.dtype.itemsize
|
|
return ptr_list, [element_size] * len(ptr_list)
|
|
if self.layout in ["page_first", "page_first_direct"]:
|
|
page_bytes = self.layer_num * self.item_bytes * self.dtype.itemsize
|
|
for row in rows:
|
|
ptr_list.append(self.kv_buffer[int(row)].data_ptr())
|
|
return ptr_list, [page_bytes] * len(ptr_list)
|
|
raise ValueError(f"Unsupported layout: {self.layout}")
|
|
|
|
def is_stride_page_aligned(self, page_size_bytes: int = 4096) -> bool:
|
|
if self.layout not in ["page_first", "page_first_direct"]:
|
|
return False
|
|
page_bytes = self.layer_num * self.item_bytes * self.dtype.itemsize
|
|
return (
|
|
self.kv_buffer.data_ptr() % page_size_bytes == 0
|
|
and page_bytes % page_size_bytes == 0
|
|
)
|
|
|
|
|
|
class DeepSeekV4StateHostPool(HostKVCache):
|
|
"""Host pool for V4 CompressStatePool page rows."""
|
|
|
|
def __init__(
|
|
self,
|
|
pool_name: str,
|
|
state_pools: list,
|
|
num_host_pages: int,
|
|
swa_page_size: int,
|
|
layout: str = "layer_first",
|
|
device: str = "cpu",
|
|
pin_memory: bool = True,
|
|
allocator_type: str = "default",
|
|
):
|
|
if any(pool is None for pool in state_pools):
|
|
raise ValueError(f"{pool_name} state_pools must not contain None")
|
|
|
|
self.pool_name = pool_name
|
|
self.state_pools = state_pools
|
|
self.layer_num = len(state_pools)
|
|
self.num_host_pages = num_host_pages
|
|
self.swa_page_size = swa_page_size
|
|
self.dtype = torch.uint8
|
|
self.device = device
|
|
self.pin_memory = pin_memory
|
|
self.allocator = get_allocator_from_storage(allocator_type)
|
|
self.page_size = swa_page_size
|
|
self.size = num_host_pages * swa_page_size
|
|
self.layout = layout
|
|
self.start_layer = 0
|
|
self.end_layer = self.layer_num
|
|
self.lock = threading.RLock()
|
|
|
|
self.ring_size = 0
|
|
self.state_page_bytes = 0
|
|
self.device_page_views = []
|
|
self.gpu_device = device
|
|
self._init_device_page_views()
|
|
self.size_per_token = self.state_page_bytes
|
|
|
|
requested_bytes = self.layer_num * num_host_pages * self.state_page_bytes
|
|
available_bytes = host_memory_budget_bytes()
|
|
if requested_bytes > available_bytes:
|
|
raise ValueError(
|
|
f"Not enough host memory for V4 state pool {pool_name}. "
|
|
f"Requesting {requested_bytes / 1e9:.2f} GB but only have "
|
|
f"{available_bytes / 1e9:.2f} GB free."
|
|
)
|
|
|
|
# ALLOC_MEMORY_FUNCS is keyed by device *type* string ("npu"/"musa"/...),
|
|
# not torch.device objects; resolve the key the same way PagedHostPool does.
|
|
_state_alloc_key = (
|
|
self.gpu_device.type
|
|
if isinstance(self.gpu_device, torch.device)
|
|
else str(self.gpu_device)
|
|
)
|
|
alloc_func = ALLOC_MEMORY_FUNCS[_state_alloc_key]
|
|
self.data_refs = []
|
|
if self.layout == "layer_first":
|
|
self.kv_buffer = [
|
|
alloc_func(
|
|
(num_host_pages, self.state_page_bytes),
|
|
dtype=self.dtype,
|
|
device=self.device,
|
|
pin_memory=self.pin_memory,
|
|
allocator=self.allocator,
|
|
)
|
|
for _ in range(self.layer_num)
|
|
]
|
|
self.data_refs = [self.kv_buffer[i] for i in range(self.layer_num)]
|
|
elif self.layout == "page_first":
|
|
self.kv_buffer = alloc_func(
|
|
(num_host_pages, self.layer_num, self.state_page_bytes),
|
|
dtype=self.dtype,
|
|
device=self.device,
|
|
pin_memory=self.pin_memory,
|
|
allocator=self.allocator,
|
|
registration_granularity_bytes=(self.layer_num * self.state_page_bytes),
|
|
)
|
|
elif self.layout == "page_first_direct":
|
|
self.kv_buffer = alloc_func(
|
|
(num_host_pages, self.layer_num, 1, self.state_page_bytes),
|
|
dtype=self.dtype,
|
|
device=self.device,
|
|
pin_memory=self.pin_memory,
|
|
allocator=self.allocator,
|
|
registration_granularity_bytes=(self.layer_num * self.state_page_bytes),
|
|
)
|
|
else:
|
|
raise ValueError(f"Unsupported layout: {self.layout}")
|
|
logger.info(
|
|
"Allocating %.2f GB host memory for V4 state pool '%s' "
|
|
"(layers=%d, pages=%d, state_page_bytes=%d, layout=%s).",
|
|
requested_bytes / 1e9,
|
|
self.pool_name,
|
|
self.layer_num,
|
|
num_host_pages,
|
|
self.state_page_bytes,
|
|
self.layout,
|
|
)
|
|
self.device_ptrs = torch.tensor(
|
|
[x.data_ptr() for x in self.device_page_views],
|
|
dtype=torch.uint64,
|
|
device=self.gpu_device,
|
|
)
|
|
self.data_ptrs = (
|
|
make_kernel_ptr_table(
|
|
self.data_refs,
|
|
self.gpu_device,
|
|
host_memory_registered=self.pin_memory,
|
|
)
|
|
if self.data_refs
|
|
else None
|
|
)
|
|
self.can_use_jit = False
|
|
self.can_use_write_back_jit = False
|
|
self._init_write_back_staging_buffers()
|
|
|
|
def _init_device_page_views(self) -> None:
|
|
expected_ring_size = None
|
|
expected_state_page_bytes = None
|
|
for pool in self.state_pools:
|
|
state_tensor = pool.kv_score_buffer.kv_score
|
|
if not state_tensor.is_contiguous():
|
|
raise ValueError(f"{self.pool_name} state tensor must be contiguous")
|
|
ring_size = pool.ring_size
|
|
slot_bytes = state_tensor[0].nbytes
|
|
state_page_bytes = ring_size * slot_bytes
|
|
if expected_ring_size is None:
|
|
expected_ring_size = ring_size
|
|
expected_state_page_bytes = state_page_bytes
|
|
self.gpu_device = state_tensor.device
|
|
elif (
|
|
expected_ring_size != ring_size
|
|
or expected_state_page_bytes != state_page_bytes
|
|
):
|
|
raise ValueError(
|
|
f"{self.pool_name} state pools must share ring size and slot bytes"
|
|
)
|
|
|
|
state_bytes = state_tensor.view(torch.uint8).reshape(
|
|
state_tensor.shape[0], -1
|
|
)
|
|
usable_slots = (state_tensor.shape[0] // ring_size) * ring_size
|
|
self.device_page_views.append(
|
|
state_bytes[:usable_slots].reshape(-1, state_page_bytes)
|
|
)
|
|
|
|
self.ring_size = expected_ring_size or 0
|
|
self.state_page_bytes = expected_state_page_bytes or 0
|
|
|
|
def _init_write_back_staging_buffers(self):
|
|
self.staging_buffer = None
|
|
if self.layout != "page_first" or (_is_npu or _is_xpu or _is_mps):
|
|
return
|
|
|
|
self.can_use_write_back_jit = _is_cuda and can_use_write_back_jit_kernel(
|
|
element_size=self.state_page_bytes * self.dtype.itemsize,
|
|
)
|
|
staging_page_capacity = min(self.num_host_pages, _WRITE_BACK_STAGING_PAGE_CHUNK)
|
|
self.staging_buffer = torch.empty(
|
|
(staging_page_capacity, self.layer_num, self.state_page_bytes),
|
|
dtype=self.dtype,
|
|
device=self.gpu_device,
|
|
)
|
|
|
|
def _to_page_indices(self, indices: torch.Tensor) -> torch.Tensor:
|
|
if indices.numel() % self.swa_page_size != 0:
|
|
raise ValueError(
|
|
f"{self.pool_name} transfer indices must be SWA-page-aligned, "
|
|
f"got numel={indices.numel()}, swa_page_size={self.swa_page_size}"
|
|
)
|
|
return indices.reshape(-1, self.swa_page_size)[:, 0] // self.swa_page_size
|
|
|
|
def _ring_op_indices(self, rows: torch.Tensor) -> torch.Tensor:
|
|
"""Expand each SWA page row into ``ring_size`` operator indices.
|
|
|
|
For example, row ``r`` maps to ``r * ring_size + arange(ring_size)``."""
|
|
ar = torch.arange(self.ring_size, device=rows.device, dtype=rows.dtype)
|
|
return (rows.reshape(-1, 1) * self.ring_size + ar).reshape(-1)
|
|
|
|
def _state_host_page_view(self, l: int) -> torch.Tensor:
|
|
"""View host state layer ``l`` as ``[pages, 1, R, 1, last_dim]``.
|
|
|
|
``last_dim = state_page_bytes // R // state_dtype.itemsize``."""
|
|
state_dtype = self.state_pools[l].kv_score_buffer.kv_score.dtype
|
|
last_dim = self.state_page_bytes // self.ring_size // state_dtype.itemsize
|
|
if self.layout == "layer_first":
|
|
layer_buffer = self.kv_buffer[l]
|
|
elif self.layout == "page_first":
|
|
layer_buffer = self.kv_buffer[:, l, :]
|
|
elif self.layout == "page_first_direct":
|
|
layer_buffer = self.kv_buffer[:, l, 0, :]
|
|
else:
|
|
raise ValueError(
|
|
f"{self.pool_name} _state_host_page_view: kernel_ascend requires "
|
|
"layer_first/page_first/page_first_direct layout, "
|
|
f"got {self.layout!r}"
|
|
)
|
|
return layer_buffer.view(state_dtype).view(
|
|
self.num_host_pages, 1, self.ring_size, 1, last_dim
|
|
)
|
|
|
|
def get_size_per_token(self):
|
|
return self.state_page_bytes
|
|
|
|
def get_ksize_per_token(self):
|
|
return self.state_page_bytes
|
|
|
|
def init_kv_buffer(self):
|
|
return self.kv_buffer
|
|
|
|
def get_hybrid_pool_buffer(self):
|
|
return self.kv_buffer if isinstance(self.kv_buffer, list) else [self.kv_buffer]
|
|
|
|
def clear(self):
|
|
pass
|
|
|
|
def available_size(self):
|
|
raise NotImplementedError(
|
|
f"{self.pool_name} reuses SWA transfer indices and has no allocator"
|
|
)
|
|
|
|
@synchronized
|
|
def alloc(self, need_size: int) -> Optional[torch.Tensor]:
|
|
raise NotImplementedError(
|
|
f"{self.pool_name} reuses SWA transfer indices and has no allocator"
|
|
)
|
|
|
|
@synchronized
|
|
def free(self, indices: torch.Tensor) -> int:
|
|
raise NotImplementedError(
|
|
f"{self.pool_name} reuses SWA transfer indices and has no free list"
|
|
)
|
|
|
|
def backup_from_device_all_layer(
|
|
self, device_pool, host_indices, device_indices, io_backend
|
|
):
|
|
if host_indices is None or device_indices is None:
|
|
return
|
|
host_rows = self._to_page_indices(host_indices)
|
|
device_rows = self._to_page_indices(device_indices)
|
|
if io_backend == "kernel" and self.layout == "layer_first":
|
|
assert self.data_ptrs is not None
|
|
transfer_kv_all_layer_mla(
|
|
src_layers=self.device_ptrs,
|
|
dst_layers=self.data_ptrs,
|
|
src_indices=device_rows,
|
|
dst_indices=host_rows,
|
|
item_size=self.state_page_bytes,
|
|
num_layers=self.layer_num,
|
|
)
|
|
elif io_backend == "kernel" and self.layout == "page_first":
|
|
if self.can_use_write_back_jit:
|
|
jit_transfer_hicache_all_layer_mla_staged_lf_pf(
|
|
ptr_src=self.device_ptrs,
|
|
src_indices=device_rows,
|
|
dst_indices=host_rows,
|
|
staging=self.staging_buffer,
|
|
dst=self.kv_buffer,
|
|
page_size=1,
|
|
element_size=self.state_page_bytes,
|
|
)
|
|
else:
|
|
transfer_kv_all_layer_mla_lf_pf(
|
|
src_layers=self.device_ptrs,
|
|
dst=self.kv_buffer,
|
|
src_indices=device_rows,
|
|
dst_indices=host_rows,
|
|
item_size=self.state_page_bytes,
|
|
dst_layout_dim=self.layer_num * self.state_page_bytes,
|
|
num_layers=self.layer_num,
|
|
)
|
|
elif io_backend == "direct" and self.layout == "layer_first":
|
|
transfer_kv_direct(
|
|
src_layers=self.device_page_views,
|
|
dst_layers=self.data_refs,
|
|
src_indices=device_rows,
|
|
dst_indices=host_rows,
|
|
page_size=1,
|
|
)
|
|
elif io_backend == "direct" and self.layout == "page_first_direct":
|
|
transfer_kv_all_layer_direct_lf_pf(
|
|
src_ptrs=self.device_page_views,
|
|
dst_ptrs=[self.kv_buffer],
|
|
src_indices=device_rows,
|
|
dst_indices=host_rows,
|
|
page_size=1,
|
|
)
|
|
elif io_backend == "kernel_ascend":
|
|
# Ascend copies ring_size state slots for each page-aligned SWA row.
|
|
# _ring_op_indices expands device and host rows into operator indices.
|
|
dev_op_indices = self._ring_op_indices(device_rows)
|
|
host_op_indices = self._ring_op_indices(host_rows)
|
|
for l in range(self.layer_num):
|
|
state_dtype = self.state_pools[l].kv_score_buffer.kv_score.dtype
|
|
last_dim = (
|
|
self.state_page_bytes // self.ring_size // state_dtype.itemsize
|
|
)
|
|
num_dev_pages = self.device_page_views[l].shape[0]
|
|
dev_view = (
|
|
self.device_page_views[l]
|
|
.view(state_dtype)
|
|
.view(num_dev_pages, self.ring_size, 1, last_dim)
|
|
.unsqueeze(0)
|
|
)
|
|
# dev_view: [1, num_dev_pages, R, 1, last_dim]
|
|
host_view = self._state_host_page_view(l)
|
|
# host_view: [num_host_pages, 1, R, 1, last_dim]
|
|
transfer_kv_dim_exchange(
|
|
device_k=dev_view,
|
|
host_k=host_view,
|
|
device_v=torch.empty(0, device=dev_view.device),
|
|
host_v=torch.empty(0, device="cpu"),
|
|
device_indices=dev_op_indices,
|
|
host_indices=host_op_indices,
|
|
page_size=self.ring_size,
|
|
direction=TransferDirection.D2H,
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
f"Unsupported V4 state host layout/backend: {self.layout}/{io_backend}"
|
|
)
|
|
|
|
def load_to_device_per_layer(
|
|
self,
|
|
device_pool,
|
|
host_indices,
|
|
device_indices,
|
|
layer_id,
|
|
io_backend,
|
|
*,
|
|
is_draft: bool = False,
|
|
):
|
|
if host_indices is None or device_indices is None:
|
|
return
|
|
host_rows = self._to_page_indices(host_indices)
|
|
device_rows = self._to_page_indices(device_indices)
|
|
if io_backend == "kernel" and self.layout == "layer_first":
|
|
transfer_kv_per_layer_mla(
|
|
src=self.data_refs[layer_id],
|
|
dst=self.device_page_views[layer_id],
|
|
src_indices=host_rows,
|
|
dst_indices=device_rows,
|
|
item_size=self.state_page_bytes,
|
|
)
|
|
elif io_backend == "kernel" and self.layout == "page_first":
|
|
transfer_kv_per_layer_mla_pf_lf(
|
|
src=self.kv_buffer,
|
|
dst=self.device_page_views[layer_id],
|
|
src_indices=host_rows,
|
|
dst_indices=device_rows,
|
|
layer_id=layer_id,
|
|
item_size=self.state_page_bytes,
|
|
src_layout_dim=self.layer_num * self.state_page_bytes,
|
|
)
|
|
elif io_backend == "direct" and self.layout == "layer_first":
|
|
transfer_kv_direct(
|
|
src_layers=[self.data_refs[layer_id]],
|
|
dst_layers=[self.device_page_views[layer_id]],
|
|
src_indices=host_rows,
|
|
dst_indices=device_rows,
|
|
page_size=1,
|
|
)
|
|
elif io_backend == "direct" and self.layout == "page_first_direct":
|
|
transfer_kv_per_layer_direct_pf_lf(
|
|
src_ptrs=[self.kv_buffer],
|
|
dst_ptrs=[self.device_page_views[layer_id]],
|
|
src_indices=host_rows,
|
|
dst_indices=device_rows,
|
|
layer_id=layer_id,
|
|
page_size=1,
|
|
)
|
|
elif io_backend == "kernel_ascend":
|
|
# NPU whole-page H2D via Ascend dim-exchange op, for layer_id only.
|
|
# See backup_from_device_all_layer: indices must be ring-row indices
|
|
# (ring_size entries per SWA page), not raw SWA locs.
|
|
R = self.ring_size
|
|
dev_op_indices = self._ring_op_indices(device_rows)
|
|
host_op_indices = self._ring_op_indices(host_rows)
|
|
state_dtype = self.state_pools[layer_id].kv_score_buffer.kv_score.dtype
|
|
last_dim = self.state_page_bytes // R // state_dtype.itemsize
|
|
num_dev_pages = self.device_page_views[layer_id].shape[0]
|
|
dev_view = (
|
|
self.device_page_views[layer_id]
|
|
.view(state_dtype)
|
|
.view(num_dev_pages, R, 1, last_dim)
|
|
.unsqueeze(0)
|
|
)
|
|
# dev_view: [1, num_dev_pages, R, 1, last_dim]
|
|
host_view = self._state_host_page_view(layer_id)
|
|
# host_view: [num_host_pages, 1, R, 1, last_dim]
|
|
transfer_kv_dim_exchange(
|
|
device_k=dev_view,
|
|
host_k=host_view,
|
|
device_v=torch.empty(0, device=dev_view.device),
|
|
host_v=torch.empty(0, device="cpu"),
|
|
device_indices=dev_op_indices,
|
|
host_indices=host_op_indices,
|
|
page_size=R,
|
|
direction=TransferDirection.H2D,
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
f"Unsupported V4 state host layout/backend: {self.layout}/{io_backend}"
|
|
)
|
|
|
|
def get_data_page(self, index, flat=True):
|
|
index = int(index) // self.swa_page_size
|
|
if self.layout == "layer_first":
|
|
data_page = torch.stack(
|
|
[self.kv_buffer[i][index] for i in range(self.layer_num)]
|
|
)
|
|
elif self.layout in ["page_first", "page_first_direct"]:
|
|
data_page = self.kv_buffer[index]
|
|
else:
|
|
raise ValueError(f"Unsupported layout: {self.layout}")
|
|
return data_page.flatten() if flat else data_page
|
|
|
|
def get_dummy_flat_data_page(self):
|
|
return torch.zeros(
|
|
(self.layer_num, self.state_page_bytes),
|
|
dtype=self.dtype,
|
|
device=self.device,
|
|
pin_memory=self.pin_memory,
|
|
).flatten()
|
|
|
|
def set_from_flat_data_page(self, index, data_page):
|
|
index = int(index) // self.swa_page_size
|
|
if self.layout == "layer_first":
|
|
data = data_page.view(self.dtype).reshape(
|
|
self.layer_num, self.state_page_bytes
|
|
)
|
|
for i in range(self.layer_num):
|
|
self.kv_buffer[i][index].copy_(data[i])
|
|
elif self.layout == "page_first":
|
|
self.kv_buffer[index].copy_(
|
|
data_page.view(self.dtype).reshape(
|
|
self.layer_num, self.state_page_bytes
|
|
)
|
|
)
|
|
elif self.layout == "page_first_direct":
|
|
self.kv_buffer[index].copy_(
|
|
data_page.view(self.dtype).reshape(
|
|
self.layer_num, 1, self.state_page_bytes
|
|
)
|
|
)
|
|
else:
|
|
raise ValueError(f"Unsupported layout: {self.layout}")
|
|
|
|
def get_page_buffer_meta(self, indices):
|
|
ptr_list = []
|
|
rows = self._to_page_indices(indices).tolist()
|
|
if self.layout == "layer_first":
|
|
for row in rows:
|
|
page_index = int(row)
|
|
for layer_id in range(self.layer_num):
|
|
ptr = (
|
|
self.kv_buffer[layer_id].data_ptr()
|
|
+ page_index * self.state_page_bytes * self.dtype.itemsize
|
|
)
|
|
ptr_list.append(ptr)
|
|
element_size = self.state_page_bytes * self.dtype.itemsize
|
|
return ptr_list, [element_size] * len(ptr_list)
|
|
if self.layout in ["page_first", "page_first_direct"]:
|
|
page_bytes = self.layer_num * self.state_page_bytes * self.dtype.itemsize
|
|
for row in rows:
|
|
ptr_list.append(self.kv_buffer[int(row)].data_ptr())
|
|
return ptr_list, [page_bytes] * len(ptr_list)
|
|
raise ValueError(f"Unsupported layout: {self.layout}")
|
|
|
|
def is_stride_page_aligned(self, page_size_bytes: int = 4096) -> bool:
|
|
if self.layout not in ["page_first", "page_first_direct"]:
|
|
return False
|
|
page_bytes = self.layer_num * self.state_page_bytes * self.dtype.itemsize
|
|
return (
|
|
self.kv_buffer.data_ptr() % page_size_bytes == 0
|
|
and page_bytes % page_size_bytes == 0
|
|
)
|