Separate allocation logic from scheduler (#11313)
This commit is contained in:
cctry
479
python/sglang/srt/mem_cache/common.py
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479
python/sglang/srt/mem_cache/common.py
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@@ -0,0 +1,479 @@
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from __future__ import annotations
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import logging
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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.mem_cache.allocator import SWATokenToKVPoolAllocator
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from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
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from sglang.srt.mem_cache.chunk_cache import ChunkCache, SWAChunkCache
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from sglang.srt.mem_cache.memory_pool import HybridReqToTokenPool, ReqToTokenPool
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from sglang.srt.server_args import ServerArgs
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from sglang.srt.utils import support_triton
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if TYPE_CHECKING:
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from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
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logger = logging.getLogger(__name__)
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GLOBAL_SERVER_ARGS_KEYS = ["attention_backend"]
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global_server_args_dict = {k: getattr(ServerArgs, k) for k in GLOBAL_SERVER_ARGS_KEYS}
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@triton.jit
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def write_req_to_token_pool_triton(
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req_to_token_ptr, # [max_batch, max_context_len]
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req_pool_indices,
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prefix_tensors,
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pre_lens,
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seq_lens,
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extend_lens,
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out_cache_loc,
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req_to_token_ptr_stride: tl.constexpr,
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):
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BLOCK_SIZE: tl.constexpr = 512
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pid = tl.program_id(0)
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req_pool_index = tl.load(req_pool_indices + pid)
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pre_len = tl.load(pre_lens + pid)
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seq_len = tl.load(seq_lens + pid)
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prefix_tensor = tl.load(prefix_tensors + pid).to(tl.pointer_type(tl.int64))
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# write prefix
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num_loop = tl.cdiv(pre_len, BLOCK_SIZE)
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for i in range(num_loop):
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offset = tl.arange(0, BLOCK_SIZE) + i * BLOCK_SIZE
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mask = offset < pre_len
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value = tl.load(prefix_tensor + offset, mask=mask)
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tl.store(
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req_to_token_ptr + req_pool_index * req_to_token_ptr_stride + offset,
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value,
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mask=mask,
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)
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# NOTE: This can be slow for large bs
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cumsum_start = tl.cast(0, tl.int64)
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for i in range(pid):
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cumsum_start += tl.load(extend_lens + i)
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num_loop = tl.cdiv(seq_len - pre_len, BLOCK_SIZE)
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for i in range(num_loop):
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offset = tl.arange(0, BLOCK_SIZE) + i * BLOCK_SIZE
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mask = offset < (seq_len - pre_len)
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value = tl.load(out_cache_loc + cumsum_start + offset, mask=mask)
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tl.store(
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req_to_token_ptr
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+ req_pool_index * req_to_token_ptr_stride
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+ offset
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+ pre_len,
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value,
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mask=mask,
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)
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def write_cache_indices(
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out_cache_loc: torch.Tensor,
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req_pool_indices_tensor: torch.Tensor,
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req_pool_indices_cpu: torch.Tensor,
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prefix_lens_tensor: torch.Tensor,
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prefix_lens_cpu: torch.Tensor,
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seq_lens_tensor: torch.Tensor,
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seq_lens_cpu: torch.Tensor,
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extend_lens_tensor: torch.Tensor,
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extend_lens_cpu: torch.Tensor,
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prefix_tensors: list[torch.Tensor],
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req_to_token_pool: ReqToTokenPool,
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):
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if support_triton(global_server_args_dict.get("attention_backend")):
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prefix_pointers = torch.tensor(
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[t.data_ptr() for t in prefix_tensors],
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device=req_to_token_pool.device,
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)
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# TODO: some tensors can be reused for ForwardBatchInfo (e.g., extend_lens, cumsum_start)
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write_req_to_token_pool_triton[(req_pool_indices_tensor.shape[0],)](
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req_to_token_pool.req_to_token,
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req_pool_indices_tensor,
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prefix_pointers,
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prefix_lens_tensor,
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seq_lens_tensor,
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extend_lens_tensor,
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out_cache_loc,
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req_to_token_pool.req_to_token.shape[1],
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)
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else:
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pt = 0
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for i in range(req_pool_indices_cpu.shape[0]):
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req_idx = req_pool_indices_cpu[i].item()
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prefix_len = prefix_lens_cpu[i].item()
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seq_len = seq_lens_cpu[i].item()
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extend_len = extend_lens_cpu[i].item()
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req_to_token_pool.write(
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(req_idx, slice(0, prefix_len)),
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prefix_tensors[i],
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)
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req_to_token_pool.write(
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(req_idx, slice(prefix_len, seq_len)),
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out_cache_loc[pt : pt + extend_len],
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)
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pt += extend_len
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def get_last_loc(
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req_to_token: torch.Tensor,
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req_pool_indices_tensor: torch.Tensor,
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prefix_lens_tensor: torch.Tensor,
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) -> torch.Tensor:
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if (
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global_server_args_dict["attention_backend"] != "ascend"
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and global_server_args_dict["attention_backend"] != "torch_native"
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):
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impl = get_last_loc_triton
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else:
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impl = get_last_loc_torch
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return impl(req_to_token, req_pool_indices_tensor, prefix_lens_tensor)
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def get_last_loc_torch(
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req_to_token: torch.Tensor,
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req_pool_indices_tensor: torch.Tensor,
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prefix_lens_tensor: torch.Tensor,
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) -> torch.Tensor:
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return torch.where(
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prefix_lens_tensor > 0,
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req_to_token[req_pool_indices_tensor, prefix_lens_tensor - 1],
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torch.full_like(prefix_lens_tensor, -1),
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)
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@triton.jit
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def get_last_loc_kernel(
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req_to_token,
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req_pool_indices_tensor,
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prefix_lens_tensor,
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result,
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num_tokens,
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req_to_token_stride,
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BLOCK_SIZE: tl.constexpr,
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):
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pid = tl.program_id(0)
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offset = tl.arange(0, BLOCK_SIZE) + pid * BLOCK_SIZE
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mask = offset < num_tokens
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prefix_lens = tl.load(prefix_lens_tensor + offset, mask=mask, other=0)
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req_pool_indices = tl.load(req_pool_indices_tensor + offset, mask=mask, other=0)
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token_mask = prefix_lens > 0
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token_index = req_pool_indices * req_to_token_stride + (prefix_lens - 1)
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tokens = tl.load(req_to_token + token_index, mask=token_mask, other=-1)
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tl.store(result + offset, tokens, mask=mask)
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def get_last_loc_triton(
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req_to_token: torch.Tensor,
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req_pool_indices_tensor: torch.Tensor,
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prefix_lens_tensor: torch.Tensor,
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) -> torch.Tensor:
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BLOCK_SIZE = 256
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num_tokens = prefix_lens_tensor.shape[0]
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result = torch.empty_like(prefix_lens_tensor)
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grid = (triton.cdiv(num_tokens, BLOCK_SIZE),)
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get_last_loc_kernel[grid](
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req_to_token,
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req_pool_indices_tensor,
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prefix_lens_tensor,
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result,
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num_tokens,
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req_to_token.stride(0),
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BLOCK_SIZE,
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)
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return result
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def alloc_token_slots(
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tree_cache: BasePrefixCache,
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num_tokens: int,
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backup_state: bool = False,
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):
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allocator = tree_cache.token_to_kv_pool_allocator
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evict_from_tree_cache(tree_cache, num_tokens)
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state = None
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if backup_state:
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state = allocator.backup_state()
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out_cache_loc = allocator.alloc(num_tokens)
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if out_cache_loc is None:
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error_msg = (
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f"Out of memory. Try to lower your batch size.\n"
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f"Try to allocate {num_tokens} tokens.\n"
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f"{available_and_evictable_str(tree_cache)}"
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)
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logger.error(error_msg)
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if tree_cache is not None:
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tree_cache.pretty_print()
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raise RuntimeError(error_msg)
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return (out_cache_loc, state) if backup_state else out_cache_loc
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def evict_from_tree_cache(tree_cache: BasePrefixCache | None, num_tokens: int):
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if tree_cache is None:
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return
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if isinstance(tree_cache, (SWAChunkCache, ChunkCache)):
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return
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allocator = tree_cache.token_to_kv_pool_allocator
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# Check if this is a hybrid allocator
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if hasattr(allocator, "full_available_size"):
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# Hybrid allocator
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full_available_size = allocator.full_available_size()
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swa_available_size = allocator.swa_available_size()
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if full_available_size < num_tokens or swa_available_size < num_tokens:
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full_num_tokens = max(0, num_tokens - full_available_size)
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swa_num_tokens = max(0, num_tokens - swa_available_size)
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tree_cache.evict(full_num_tokens, swa_num_tokens)
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else:
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# Standard allocator
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if allocator.available_size() < num_tokens:
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tree_cache.evict(num_tokens)
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def alloc_paged_token_slots_extend(
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tree_cache: BasePrefixCache,
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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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backup_state: bool = False,
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):
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# Over estimate the number of tokens: assume each request needs a new page.
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allocator = tree_cache.token_to_kv_pool_allocator
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num_tokens = extend_num_tokens + len(seq_lens_cpu) * allocator.page_size
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evict_from_tree_cache(tree_cache, num_tokens)
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state = None
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if backup_state:
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state = allocator.backup_state()
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out_cache_loc = allocator.alloc_extend(
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prefix_lens,
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prefix_lens_cpu,
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seq_lens,
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seq_lens_cpu,
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last_loc,
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extend_num_tokens,
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)
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if out_cache_loc is None:
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error_msg = (
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f"Prefill out of memory. Try to lower your batch size.\n"
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f"Try to allocate {extend_num_tokens} tokens.\n"
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f"{available_and_evictable_str(tree_cache)}"
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)
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logger.error(error_msg)
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if tree_cache is not None:
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tree_cache.pretty_print()
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raise RuntimeError(error_msg)
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return (out_cache_loc, state) if backup_state else out_cache_loc
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def alloc_req_slots(
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req_to_token_pool: ReqToTokenPool,
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num_reqs: int,
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reqs: list[Req] | None,
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) -> list[int]:
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"""Allocate request slots from the pool."""
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if isinstance(req_to_token_pool, HybridReqToTokenPool):
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req_pool_indices = req_to_token_pool.alloc(num_reqs, reqs)
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else:
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req_pool_indices = req_to_token_pool.alloc(num_reqs)
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if req_pool_indices is None:
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raise RuntimeError(
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"alloc_req_slots runs out of memory. "
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"Please set a smaller number for `--max-running-requests`. "
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f"{req_to_token_pool.available_size()=}, "
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f"{num_reqs=}, "
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)
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return req_pool_indices
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def alloc_for_extend(
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batch: ScheduleBatch,
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) -> tuple[torch.Tensor, torch.Tensor, list[int]]:
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"""
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Allocate KV cache for extend batch and write to req_to_token_pool.
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Returns:
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out_cache_loc: allocated cache locations
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req_pool_indices_device: request pool indices at a device tensor
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req_pool_indices: request pool indices as list
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"""
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# free out-of-window swa tokens
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if isinstance(batch.tree_cache, SWAChunkCache):
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for req, pre_len in zip(batch.reqs, batch.prefix_lens):
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batch.tree_cache.evict_swa(
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req, pre_len, batch.model_config.attention_chunk_size
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)
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bs = len(batch.reqs)
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prefix_tensors = [r.prefix_indices for r in batch.reqs]
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# Create tensors for allocation
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prefix_lens_cpu = torch.tensor(batch.prefix_lens, dtype=torch.int64)
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extend_lens_cpu = torch.tensor(batch.extend_lens, dtype=torch.int64)
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prefix_lens_device = prefix_lens_cpu.to(batch.device, non_blocking=True)
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extend_lens_device = extend_lens_cpu.to(batch.device, non_blocking=True)
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# Allocate req slots
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req_pool_indices = alloc_req_slots(batch.req_to_token_pool, bs, batch.reqs)
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req_pool_indices_cpu = torch.tensor(req_pool_indices, dtype=torch.int64)
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req_pool_indices_device = req_pool_indices_cpu.to(batch.device, non_blocking=True)
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# Allocate KV cache (throws exception on failure)
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if batch.tree_cache.page_size == 1:
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out_cache_loc = alloc_token_slots(batch.tree_cache, batch.extend_num_tokens)
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else:
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# Paged allocation - build last_loc
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last_loc = [
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(
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t[-1:]
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if len(t) > 0
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else torch.tensor([-1], device=batch.tree_cache.device)
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)
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for t in prefix_tensors
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]
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out_cache_loc = alloc_paged_token_slots_extend(
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tree_cache=batch.tree_cache,
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prefix_lens=prefix_lens_device,
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prefix_lens_cpu=prefix_lens_cpu,
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seq_lens=batch.seq_lens,
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seq_lens_cpu=batch.seq_lens_cpu,
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last_loc=torch.cat(last_loc),
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extend_num_tokens=batch.extend_num_tokens,
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)
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# Write to req_to_token_pool
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write_cache_indices(
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out_cache_loc,
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req_pool_indices_device,
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req_pool_indices_cpu,
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prefix_lens_device,
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prefix_lens_cpu,
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batch.seq_lens,
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batch.seq_lens_cpu,
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extend_lens_device,
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extend_lens_cpu,
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prefix_tensors,
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batch.req_to_token_pool,
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)
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return out_cache_loc, req_pool_indices_device, req_pool_indices
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def alloc_paged_token_slots_decode(
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tree_cache: BasePrefixCache,
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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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token_per_req: int = 1,
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) -> torch.Tensor:
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"""Allocate paged KV cache for decode batch."""
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allocator = tree_cache.token_to_kv_pool_allocator
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# Over estimate the number of tokens: assume each request needs a new page.
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num_tokens = len(seq_lens) * allocator.page_size
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evict_from_tree_cache(tree_cache, num_tokens)
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out_cache_loc = allocator.alloc_decode(seq_lens, seq_lens_cpu, last_loc)
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if out_cache_loc is None:
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error_msg = (
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f"Decode out of memory. Try to lower your batch size.\n"
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f"Try to allocate {len(seq_lens) * token_per_req} tokens.\n"
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f"{available_and_evictable_str(tree_cache)}"
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)
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logger.error(error_msg)
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if tree_cache is not None:
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tree_cache.pretty_print()
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raise RuntimeError(error_msg)
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return out_cache_loc
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def alloc_for_decode(batch: ScheduleBatch, token_per_req: int) -> torch.Tensor:
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"""
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Allocate KV cache for decode batch and write to req_to_token_pool.
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Returns:
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out_cache_loc: allocated cache locations
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"""
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if isinstance(batch.tree_cache, SWAChunkCache):
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for req in batch.reqs:
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batch.tree_cache.evict_swa(
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req, req.seqlen - 1, batch.model_config.attention_chunk_size
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)
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bs = batch.seq_lens.shape[0]
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if batch.tree_cache.page_size == 1:
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# Non-paged allocation
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out_cache_loc = alloc_token_slots(batch.tree_cache, bs * token_per_req)
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else:
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# Paged allocation
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last_loc = batch.req_to_token_pool.req_to_token[
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batch.req_pool_indices, batch.seq_lens - 1
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]
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seq_lens_next = batch.seq_lens + token_per_req
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out_cache_loc = alloc_paged_token_slots_decode(
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tree_cache=batch.tree_cache,
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seq_lens=seq_lens_next,
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seq_lens_cpu=batch.seq_lens_cpu + token_per_req,
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last_loc=last_loc,
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token_per_req=token_per_req,
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)
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# Write to req_to_token_pool
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if batch.model_config.is_encoder_decoder:
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locs = batch.encoder_lens + batch.seq_lens
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else:
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locs = batch.seq_lens.clone()
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batch.req_to_token_pool.write(
|
||||
(batch.req_pool_indices, locs), out_cache_loc.to(torch.int32)
|
||||
)
|
||||
|
||||
return out_cache_loc
|
||||
|
||||
|
||||
def available_and_evictable_str(tree_cache) -> str:
|
||||
token_to_kv_pool_allocator = tree_cache.token_to_kv_pool_allocator
|
||||
if isinstance(token_to_kv_pool_allocator, SWATokenToKVPoolAllocator):
|
||||
full_available_size = token_to_kv_pool_allocator.full_available_size()
|
||||
swa_available_size = token_to_kv_pool_allocator.swa_available_size()
|
||||
full_evictable_size = tree_cache.full_evictable_size()
|
||||
swa_evictable_size = tree_cache.swa_evictable_size()
|
||||
return (
|
||||
f"Available full tokens: {full_available_size + full_evictable_size} ({full_available_size=} + {full_evictable_size=})\n"
|
||||
f"Available swa tokens: {swa_available_size + swa_evictable_size} ({swa_available_size=} + {swa_evictable_size=})\n"
|
||||
f"Full LRU list evictable size: {tree_cache.full_lru_list_evictable_size()}\n"
|
||||
f"SWA LRU list evictable size: {tree_cache.swa_lru_list_evictable_size()}\n"
|
||||
)
|
||||
else:
|
||||
available_size = token_to_kv_pool_allocator.available_size()
|
||||
evictable_size = tree_cache.evictable_size()
|
||||
return f"Available tokens: {available_size + evictable_size} ({available_size=} + {evictable_size=})\n"
|
||||
Reference in New Issue
Block a user