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chore: import upstream snapshot with attribution
2026-07-13 12:38:16 +08:00

123 lines
4.1 KiB
Python

# mapping on device memory, host memory and memory allocator
import logging
from typing import Optional
import torch
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.mem_cache.memory_pool import DSATokenToKVPool
from sglang.srt.utils import is_cuda, is_hip
logger = logging.getLogger(__name__)
# sgl_kernel.kvcacheio is only available in CUDA/ROCm sgl-kernel builds (not XPU/MPS/NPU/CPU).
_is_cuda = is_cuda()
_is_hip = is_hip()
if _is_cuda or _is_hip:
from sgl_kernel.kvcacheio import transfer_kv_all_layer_mla
else:
def transfer_kv_all_layer_mla(*args, **kwargs):
raise RuntimeError(
"HiSparse device KV transfer requires sgl_kernel.kvcacheio (CUDA/ROCm). "
"It is not available on this backend."
)
class HiSparseDSATokenToKVPool(DSATokenToKVPool):
def __init__(
self,
size: int,
page_size: int,
kv_lora_rank: int,
dtype: torch.dtype,
qk_rope_head_dim: int,
layer_num: int,
device: str,
index_head_dim: int,
enable_memory_saver: bool,
kv_cache_dim: int,
start_layer: Optional[int] = None,
end_layer: Optional[int] = None,
host_to_device_ratio: int = 2,
):
super().__init__(
size=size,
page_size=page_size,
kv_lora_rank=kv_lora_rank,
dtype=dtype,
qk_rope_head_dim=qk_rope_head_dim,
layer_num=layer_num,
device=device,
index_head_dim=index_head_dim,
enable_memory_saver=enable_memory_saver,
kv_cache_dim=kv_cache_dim,
start_layer=start_layer,
end_layer=end_layer,
index_buf_size=size * host_to_device_ratio,
)
self.bytes_per_token = self.kv_cache_dim * self.dtype.itemsize
def register_mapping(self, full_to_hisparse_device_index_mapping: torch.Tensor):
self.full_to_hisparse_device_index_mapping = (
full_to_hisparse_device_index_mapping
)
def translate_loc_to_hisparse_device(self, compressed_indices: torch.Tensor):
return self.full_to_hisparse_device_index_mapping[compressed_indices]
def _translate_loc_to_hisparse_device(self, compressed_indices: torch.Tensor):
return self.full_to_hisparse_device_index_mapping[compressed_indices]
def translate_loc_from_full_to_hisparse_device(self, full_indices: torch.Tensor):
return self._translate_loc_to_hisparse_device(full_indices)
def translate_loc_from_full_to_compressed(self, full_indices: torch.Tensor):
return full_indices
def set_kv_buffer(
self,
layer: RadixAttention,
loc: torch.Tensor,
cache_k: torch.Tensor,
cache_v: torch.Tensor,
):
loc = self.translate_loc_to_hisparse_device(loc)
super().set_kv_buffer(layer, loc, cache_k, cache_v)
def set_mla_kv_buffer(
self,
layer: RadixAttention,
loc: torch.Tensor,
cache_k_nope: torch.Tensor,
cache_k_rope: torch.Tensor,
):
loc = self.translate_loc_to_hisparse_device(loc)
super().set_mla_kv_buffer(layer, loc, cache_k_nope, cache_k_rope)
def get_mla_kv_buffer(
self,
layer: RadixAttention,
loc: torch.Tensor,
dst_dtype: Optional[torch.dtype] = None,
):
loc = self.translate_loc_to_hisparse_device(loc)
return super().get_mla_kv_buffer(layer, loc, dst_dtype)
def transfer_values_on_device(self, dst_indices, src_indices):
transfer_kv_all_layer_mla(
src_layers=self.data_ptrs,
dst_layers=self.data_ptrs,
src_indices=src_indices,
dst_indices=dst_indices,
item_size=self.bytes_per_token,
num_layers=self.layer_num,
)
def get_cpu_copy(self, indices, mamba_indices=None):
raise NotImplementedError("HiSparseDevicePool does not support get_cpu_copy")
def load_cpu_copy(self, kv_cache_cpu, indices, mamba_indices=None):
raise NotImplementedError("HiSparseDevicePool does not support load_cpu_copy")