520 lines
16 KiB
Python
520 lines
16 KiB
Python
from __future__ import annotations
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"""
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Copyright 2025 SGLang Team
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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"""
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Page-aligned memory pool.
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"""
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import abc
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from typing import TYPE_CHECKING
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import torch
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import triton
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import triton.language as tl
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from sglang.srt.utils import get_bool_env_var, get_num_new_pages, next_power_of_2
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if TYPE_CHECKING:
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from sglang.srt.mem_cache.memory_pool import KVCache
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class BaseTokenToKVPoolAllocator(abc.ABC):
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@abc.abstractmethod
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def __init__(
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self,
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size: int,
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page_size: int,
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dtype: torch.dtype,
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device: str,
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kvcache: KVCache,
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need_sort: bool,
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):
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self.size = size
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self.page_size = page_size
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self.dtype = dtype
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self.device = device
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self._kvcache = kvcache
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self.need_sort = need_sort
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self.free_pages = None
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self.release_pages = None
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self.is_not_in_free_group = True
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self.free_group = []
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def debug_print(self) -> str:
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return ""
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def available_size(self):
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return (len(self.free_pages) + len(self.release_pages)) * self.page_size
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def get_kvcache(self):
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return self._kvcache
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def restore_state(self, state):
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self.free_pages, self.release_pages = state
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def backup_state(self):
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return (self.free_pages, self.release_pages)
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def free_group_begin(self):
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self.is_not_in_free_group = False
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self.free_group = []
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def free_group_end(self):
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self.is_not_in_free_group = True
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if self.free_group:
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self.free(torch.cat(self.free_group))
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def merge_and_sort_free(self):
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if len(self.release_pages) > 0:
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self.free_pages = torch.cat((self.free_pages, self.release_pages))
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self.free_pages, _ = torch.sort(self.free_pages)
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self.release_pages = torch.empty(
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(0,), dtype=self.release_pages.dtype, device=self.device
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)
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def get_cpu_copy(self, *args, **kwargs):
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# FIXME: reuse the get_cpu_copy after paged allocator is implemented
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raise NotImplementedError()
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def load_cpu_copy(self, *args, **kwargs):
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# FIXME: reuse the load_cpu_copy after paged allocator is implemented
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raise NotImplementedError()
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def alloc_extend(self, *args, **kwargs):
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raise NotImplementedError("alloc_extend is only for paged allocator")
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def alloc_decode(self, *args, **kwargs):
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raise NotImplementedError("alloc_decode is only for paged allocator")
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@abc.abstractmethod
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def clear(self):
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raise NotImplementedError()
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@abc.abstractmethod
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def alloc(self, need_size: int):
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raise NotImplementedError()
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@abc.abstractmethod
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def free(self, free_index: torch.Tensor):
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raise NotImplementedError()
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class TokenToKVPoolAllocator(BaseTokenToKVPoolAllocator):
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"""An allocator managing the indices to kv cache data."""
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def __init__(
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self,
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size: int,
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dtype: torch.dtype,
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device: str,
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kvcache: KVCache,
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need_sort: bool,
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):
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super().__init__(size, 1, dtype, device, kvcache, need_sort)
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self.clear()
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def clear(self):
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# The padded slot 0 is used for writing dummy outputs from padded tokens.
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self.free_pages = torch.arange(
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1, self.size + 1, dtype=torch.int64, device=self.device
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)
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self.is_not_in_free_group = True
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self.free_group = []
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self.release_pages = torch.empty((0,), dtype=torch.int64, device=self.device)
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def available_size(self):
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# To avoid minor "len(free_pages) * 1" overhead
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return len(self.free_pages) + len(self.release_pages)
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def alloc(self, need_size: int):
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if self.need_sort and need_size > len(self.free_pages):
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self.merge_and_sort_free()
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if need_size > len(self.free_pages):
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return None
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select_index = self.free_pages[:need_size]
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self.free_pages = self.free_pages[need_size:]
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return select_index
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def free(self, free_index: torch.Tensor):
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if free_index.numel() == 0:
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return
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if self.is_not_in_free_group:
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if self.need_sort:
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self.release_pages = torch.cat((self.release_pages, free_index))
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else:
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self.free_pages = torch.cat((self.free_pages, free_index))
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else:
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self.free_group.append(free_index)
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def get_cpu_copy(self, indices):
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return self._kvcache.get_cpu_copy(indices)
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def load_cpu_copy(self, kv_cache_cpu, indices):
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return self._kvcache.load_cpu_copy(kv_cache_cpu, indices)
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def alloc_extend_naive(
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prefix_lens,
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seq_lens,
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last_loc,
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free_pages,
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out_indices,
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page_size,
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device,
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):
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extend_lens = seq_lens - prefix_lens
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end_pos = torch.cumsum(extend_lens, 0)
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start_pos = end_pos - extend_lens
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num_new_pages = (seq_lens + page_size - 1) // page_size - (
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prefix_lens + page_size - 1
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) // page_size
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num_full_new_pages = (seq_lens) // page_size - (
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prefix_lens + page_size - 1
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) // page_size
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need_page = num_new_pages - num_full_new_pages
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end_new_pages = torch.cumsum(num_new_pages, 0)
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start_new_pages = end_new_pages - num_new_pages
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pos_in_page = torch.arange(page_size, device=device, dtype=torch.int32)
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for i in range(len(prefix_lens)):
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num1 = (
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min(
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seq_lens[i],
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(prefix_lens[i] + page_size - 1) // page_size * page_size,
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)
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- prefix_lens[i]
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)
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if num1:
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out_indices[start_pos[i] : start_pos[i] + num1] = (
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last_loc[i] + 1 + pos_in_page[:num1].view(-1)
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)
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if prefix_lens[i] + num1 == seq_lens[i]:
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continue
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num2 = (
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seq_lens[i] // page_size - (prefix_lens[i] + page_size - 1) // page_size
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) * page_size
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if num2:
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pages = (
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free_pages[start_new_pages[i] : end_new_pages[i] - need_page[i]]
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* page_size
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)
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out_indices[start_pos[i] + num1 : start_pos[i] + num1 + num2] = (
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pages.view(-1, 1) + pos_in_page.view(1, -1)
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).view(-1)
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if prefix_lens[i] + num1 + num2 == seq_lens[i]:
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continue
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num3 = seq_lens[i] - seq_lens[i] // page_size * page_size
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if num3:
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out_indices[end_pos[i] - num3 : end_pos[i]] = (
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free_pages[end_new_pages[i] - 1] * page_size + pos_in_page[:num3]
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).view(-1)
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@triton.jit
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def alloc_extend_kernel(
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pre_lens_ptr,
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seq_lens_ptr,
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last_loc_ptr,
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free_page_ptr,
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out_indices,
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bs_upper: tl.constexpr,
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page_size: tl.constexpr,
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max_num_extend_tokens: tl.constexpr,
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):
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pid = tl.program_id(0)
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load_offset = tl.arange(0, bs_upper)
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seq_lens = tl.load(seq_lens_ptr + load_offset, mask=load_offset <= pid)
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pre_lens = tl.load(pre_lens_ptr + load_offset, mask=load_offset <= pid)
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extend_lens = seq_lens - pre_lens
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seq_len = tl.load(seq_lens_ptr + pid)
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pre_len = tl.load(pre_lens_ptr + pid)
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extend_len = seq_len - pre_len
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sum_extend_lens = tl.sum(extend_lens)
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output_start_loc = sum_extend_lens - extend_len
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num_pages_after = (seq_lens + page_size - 1) // page_size
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num_pages_before = (pre_lens + page_size - 1) // page_size
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num_new_pages = num_pages_after - num_pages_before
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num_page_start_loc_self = (seq_len + page_size - 1) // page_size - (
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pre_len + page_size - 1
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) // page_size
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sum_num_new_pages = tl.sum(num_new_pages)
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new_page_start_loc = sum_num_new_pages - num_page_start_loc_self
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# Part 1: fill the old partial page
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last_loc = tl.load(last_loc_ptr + pid)
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num_part1 = (
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min(seq_len, (pre_len + page_size - 1) // page_size * page_size) - pre_len
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)
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offset_one_page = tl.arange(0, page_size)
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tl.store(
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out_indices + output_start_loc + offset_one_page,
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last_loc + 1 + offset_one_page,
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mask=offset_one_page < num_part1,
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)
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if pre_len + num_part1 == seq_len:
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return
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# Part 2: fill the new full pages
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num_part2 = (
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seq_len // page_size * page_size
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- (pre_len + page_size - 1) // page_size * page_size
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)
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offset_many_page = tl.arange(0, max_num_extend_tokens)
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page_start = tl.load(
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free_page_ptr + new_page_start_loc + offset_many_page // page_size,
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mask=offset_many_page < num_part2,
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)
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tl.store(
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out_indices + output_start_loc + num_part1 + offset_many_page,
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page_start * page_size + offset_many_page % page_size,
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mask=offset_many_page < num_part2,
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)
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if pre_len + num_part1 + num_part2 == seq_len:
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return
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# Part 3: fill the new partial page
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num_part3 = seq_len - seq_len // page_size * page_size
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start_loc = tl.load(
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free_page_ptr + new_page_start_loc + num_page_start_loc_self - 1
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)
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tl.store(
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out_indices + output_start_loc + num_part1 + num_part2 + offset_one_page,
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start_loc * page_size + offset_one_page,
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mask=offset_one_page < num_part3,
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)
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@triton.jit
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def alloc_decode_kernel(
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seq_lens_ptr,
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last_loc_ptr,
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free_page_ptr,
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out_indices,
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bs_upper: tl.constexpr,
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page_size: tl.constexpr,
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):
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pid = tl.program_id(0)
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load_offset = tl.arange(0, bs_upper)
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seq_lens = tl.load(seq_lens_ptr + load_offset, mask=load_offset <= pid)
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pre_lens = tl.where(load_offset <= pid, seq_lens - 1, seq_lens)
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seq_len = tl.load(seq_lens_ptr + pid)
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pre_len = seq_len - 1
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num_pages_after = (seq_lens + page_size - 1) // page_size
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num_pages_before = (pre_lens + page_size - 1) // page_size
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num_new_pages = num_pages_after - num_pages_before
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num_page_start_loc_self = (seq_len + page_size - 1) // page_size - (
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pre_len + page_size - 1
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) // page_size
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sum_num_new_pages = tl.sum(num_new_pages)
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new_page_start_loc = sum_num_new_pages - num_page_start_loc_self
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if num_page_start_loc_self == 0:
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last_loc = tl.load(last_loc_ptr + pid)
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tl.store(out_indices + pid, last_loc + 1)
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else:
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page = tl.load(free_page_ptr + new_page_start_loc)
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tl.store(out_indices + pid, page * page_size)
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class PagedTokenToKVPoolAllocator(BaseTokenToKVPoolAllocator):
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"""
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An allocator managing the indices to kv cache data.
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This class has the same interface as `TokenToKVPoolAllocator` but the output
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of one request is always page-aligned.
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TODO: fuse last_loc into the kernel.
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"""
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def __init__(
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self,
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size: int,
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page_size: int,
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dtype: torch.dtype,
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device: str,
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kvcache: KVCache,
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need_sort: bool,
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):
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super().__init__(size, page_size, dtype, device, kvcache, need_sort)
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self.num_pages = size // page_size
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self.debug_mode = get_bool_env_var("SGLANG_DEBUG_MEMORY_POOL")
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self.seen_max_num_extend_tokens_next_power_of_2 = 1
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self.clear()
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def alloc(self, need_size: int):
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# page-aligned allocation, returning contiguous indices of pages
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if self.debug_mode:
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assert (
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need_size % self.page_size == 0
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), "The allocation size should be page-aligned"
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num_pages = need_size // self.page_size
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if self.need_sort and num_pages > len(self.free_pages):
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self.merge_and_sort_free()
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if num_pages > len(self.free_pages):
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return None
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out_pages = self.free_pages[:num_pages]
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self.free_pages = self.free_pages[num_pages:]
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out_indices = (
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out_pages[:, None] * self.page_size
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+ torch.arange(self.page_size, device=self.device)
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).reshape(-1)
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return out_indices
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def alloc_extend(
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self,
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prefix_lens: torch.Tensor,
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prefix_lens_cpu: torch.Tensor,
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seq_lens: torch.Tensor,
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seq_lens_cpu: torch.Tensor,
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last_loc: torch.Tensor,
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extend_num_tokens: int,
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):
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if self.debug_mode:
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assert torch.all(
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(last_loc + 1) % self.page_size == prefix_lens % self.page_size
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)
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self.seen_max_num_extend_tokens_next_power_of_2 = max(
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self.seen_max_num_extend_tokens_next_power_of_2,
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next_power_of_2(extend_num_tokens),
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)
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bs = len(prefix_lens)
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if self.need_sort and extend_num_tokens // self.page_size + bs + 1 > len(
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self.free_pages
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):
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self.merge_and_sort_free()
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out_indices = torch.empty(
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(extend_num_tokens,), dtype=torch.int64, device=self.device
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)
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alloc_extend_kernel[(bs,)](
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prefix_lens,
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seq_lens,
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last_loc,
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self.free_pages,
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out_indices,
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next_power_of_2(bs),
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self.page_size,
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self.seen_max_num_extend_tokens_next_power_of_2,
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)
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if self.debug_mode:
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assert len(torch.unique(out_indices)) == len(out_indices)
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num_new_pages = get_num_new_pages(
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seq_lens=seq_lens_cpu,
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page_size=self.page_size,
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prefix_lens=prefix_lens_cpu,
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)
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if num_new_pages > len(self.free_pages):
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return None
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self.free_pages = self.free_pages[num_new_pages:]
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return out_indices
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def alloc_decode(
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self,
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seq_lens: torch.Tensor,
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seq_lens_cpu: torch.Tensor,
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last_loc: torch.Tensor,
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):
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if self.debug_mode:
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assert torch.all(
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(last_loc + 2) % self.page_size == seq_lens % self.page_size
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)
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bs = len(seq_lens)
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if self.need_sort and bs > len(self.free_pages):
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self.merge_and_sort_free()
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out_indices = torch.empty((bs,), dtype=torch.int64, device=self.device)
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alloc_decode_kernel[(bs,)](
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seq_lens,
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last_loc,
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self.free_pages,
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out_indices,
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next_power_of_2(bs),
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self.page_size,
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)
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if self.debug_mode:
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assert len(torch.unique(out_indices)) == len(out_indices)
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num_new_pages = get_num_new_pages(
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seq_lens=seq_lens_cpu,
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page_size=self.page_size,
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decode=True,
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)
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if num_new_pages > len(self.free_pages):
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return None
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self.free_pages = self.free_pages[num_new_pages:]
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return out_indices
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def free(self, free_index: torch.Tensor):
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if free_index.numel() == 0:
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return
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if self.is_not_in_free_group:
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free_page_indices = torch.unique(free_index // self.page_size)
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if self.need_sort:
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self.release_pages = torch.cat((free_page_indices, self.release_pages))
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else:
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self.free_pages = torch.cat((free_page_indices, self.free_pages))
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else:
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self.free_group.append(free_index)
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if self.debug_mode:
|
|
assert len(torch.unique(self.free_pages)) == len(self.free_pages)
|
|
|
|
def clear(self):
|
|
# The padded slot 0 is used for writing dummy outputs from padded tokens.
|
|
self.free_pages = torch.arange(
|
|
1, self.num_pages + 1, dtype=torch.int64, device=self.device
|
|
)
|
|
self.is_not_in_free_group = True
|
|
self.free_group = []
|
|
self.release_pages = torch.empty((0,), dtype=torch.int64, device=self.device)
|
|
|
|
def get_cpu_copy(self, indices):
|
|
return self._kvcache.get_cpu_copy(indices)
|
|
|
|
def load_cpu_copy(self, kv_cache_cpu, indices):
|
|
return self._kvcache.load_cpu_copy(kv_cache_cpu, indices)
|