254 lines
9.5 KiB
Python
254 lines
9.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Iterable
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import torch
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from vllm.triton_utils import tl, triton
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from vllm.utils.math_utils import cdiv
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from vllm.v1.attention.backends.utils import PAD_SLOT_ID
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from vllm.v1.worker.gpu.buffer_utils import StagedWriteTensor, UvaBackedTensor
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class BlockTables:
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def __init__(
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self,
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block_sizes: list[int],
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max_num_reqs: int,
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max_num_batched_tokens: int,
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max_model_len: int,
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device: torch.device,
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cp_size: int = 1,
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cp_rank: int = 0,
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cp_interleave: int = 1,
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):
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self.block_sizes = block_sizes
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self.max_num_reqs = max_num_reqs
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self.max_num_batched_tokens = max_num_batched_tokens
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self.max_model_len = max_model_len
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self.device = device
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self.cp_size = cp_size
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self.cp_rank = cp_rank
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self.cp_interleave = cp_interleave
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self.num_kv_cache_groups = len(self.block_sizes)
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# num_kv_cache_groups x [max_num_reqs, max_num_blocks]
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self.block_tables: list[StagedWriteTensor] = []
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for i in range(self.num_kv_cache_groups):
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block_size = self.block_sizes[i]
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# When using DCP, each request's KV cache is sharded among different ranks.
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# As a result, one block on the current rank covers `block_size * cp_size`
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# tokens in the full, global (unsharded) sequence.
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max_num_blocks = cdiv(self.max_model_len, block_size * self.cp_size)
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block_table = StagedWriteTensor(
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(self.max_num_reqs, max_num_blocks),
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dtype=torch.int32,
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device=device,
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)
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self.block_tables.append(block_table)
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self.block_table_ptrs = self._make_ptr_tensor(
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[b.gpu for b in self.block_tables]
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)
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self.block_table_strides = torch.tensor(
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[b.gpu.stride(0) for b in self.block_tables],
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dtype=torch.int64,
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device=self.device,
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)
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self.block_sizes_tensor = torch.tensor(
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self.block_sizes, dtype=torch.int32, device=self.device
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)
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self.num_blocks = UvaBackedTensor(
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(self.num_kv_cache_groups, self.max_num_reqs),
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dtype=torch.int32,
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)
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# Block tables used for model's forward pass.
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# num_kv_cache_groups x [max_num_reqs, max_num_blocks]
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self.input_block_tables: list[torch.Tensor] = [
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torch.zeros_like(b.gpu) for b in self.block_tables
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]
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self.input_block_table_ptrs = self._make_ptr_tensor(self.input_block_tables)
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self.slot_mappings = torch.zeros(
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self.num_kv_cache_groups,
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self.max_num_batched_tokens,
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dtype=torch.int64,
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device=self.device,
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)
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def _make_ptr_tensor(self, x: Iterable[torch.Tensor]) -> torch.Tensor:
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# NOTE(woosuk): Use uint64 instead of int64 to cover all possible addresses.
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return torch.tensor(
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[t.data_ptr() for t in x], dtype=torch.uint64, device=self.device
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)
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def append_block_ids(
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self,
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req_index: int,
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new_block_ids: tuple[list[int], ...],
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overwrite: bool,
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) -> None:
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for i in range(self.num_kv_cache_groups):
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start = self.num_blocks.np[i, req_index] if not overwrite else 0
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block_ids = new_block_ids[i]
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self.block_tables[i].stage_write(req_index, start, block_ids)
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self.num_blocks.np[i, req_index] = start + len(block_ids)
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def apply_staged_writes(self) -> None:
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# TODO(woosuk): This can be inefficient since it launches one kernel per
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# block table. Implement a kernel to handle all block tables at once.
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for block_table in self.block_tables:
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block_table.apply_write()
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self.num_blocks.copy_to_uva()
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def gather_block_tables(
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self, idx_mapping: torch.Tensor
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) -> tuple[torch.Tensor, ...]:
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num_reqs = idx_mapping.shape[0]
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_gather_block_tables_kernel[(self.num_kv_cache_groups, num_reqs)](
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idx_mapping,
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self.block_table_ptrs,
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self.input_block_table_ptrs,
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self.block_table_strides,
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self.num_blocks.gpu,
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self.num_blocks.gpu.stride(0),
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BLOCK_SIZE=1024, # type: ignore
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)
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return tuple(block_table[:num_reqs] for block_table in self.input_block_tables)
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def get_dummy_block_tables(self, num_reqs: int) -> tuple[torch.Tensor, ...]:
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return tuple(block_table[:num_reqs] for block_table in self.input_block_tables)
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def compute_slot_mappings(
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self,
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idx_mapping: torch.Tensor,
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query_start_loc: torch.Tensor,
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positions: torch.Tensor,
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) -> torch.Tensor:
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num_reqs = idx_mapping.shape[0]
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num_tokens = positions.shape[0]
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num_groups = self.num_kv_cache_groups
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_compute_slot_mappings_kernel[(num_groups, num_reqs + 1)](
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num_tokens,
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self.max_num_batched_tokens,
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idx_mapping,
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query_start_loc,
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positions,
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self.block_table_ptrs,
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self.block_table_strides,
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self.block_sizes_tensor,
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self.slot_mappings,
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self.slot_mappings.stride(0),
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self.cp_rank,
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CP_SIZE=self.cp_size,
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CP_INTERLEAVE=self.cp_interleave,
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PAD_ID=PAD_SLOT_ID,
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TRITON_BLOCK_SIZE=1024, # type: ignore
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)
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return self.slot_mappings[:, :num_tokens]
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def get_dummy_slot_mappings(self, num_tokens: int) -> torch.Tensor:
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self.slot_mappings.fill_(PAD_SLOT_ID)
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return self.slot_mappings[:, :num_tokens]
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@triton.jit
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def _gather_block_tables_kernel(
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batch_idx_to_req_idx, # [batch_size]
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src_block_table_ptrs, # [num_kv_cache_groups]
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dst_block_table_ptrs, # [num_kv_cache_groups]
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block_table_strides, # [num_kv_cache_groups]
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num_blocks_ptr, # [num_kv_cache_groups, max_num_reqs]
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num_blocks_stride,
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BLOCK_SIZE: tl.constexpr,
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):
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# kv cache group id
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group_id = tl.program_id(0)
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batch_idx = tl.program_id(1)
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req_idx = tl.load(batch_idx_to_req_idx + batch_idx)
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group_num_blocks_ptr = num_blocks_ptr + group_id * num_blocks_stride
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num_blocks = tl.load(group_num_blocks_ptr + req_idx)
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stride = tl.load(block_table_strides + group_id)
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src_block_table_ptr = _load_ptr(src_block_table_ptrs + group_id, tl.int32)
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src_row_ptr = src_block_table_ptr + req_idx * stride
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dst_block_table_ptr = _load_ptr(dst_block_table_ptrs + group_id, tl.int32)
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dst_row_ptr = dst_block_table_ptr + batch_idx * stride
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for i in tl.range(0, num_blocks, BLOCK_SIZE):
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offset = i + tl.arange(0, BLOCK_SIZE)
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block_ids = tl.load(src_row_ptr + offset, mask=offset < num_blocks)
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tl.store(dst_row_ptr + offset, block_ids, mask=offset < num_blocks)
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@triton.jit
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def _compute_slot_mappings_kernel(
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num_tokens,
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max_num_tokens,
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idx_mapping, # [num_reqs]
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query_start_loc, # [num_reqs + 1]
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pos, # [num_tokens]
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block_table_ptrs, # [num_kv_cache_groups]
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block_table_strides, # [num_kv_cache_groups]
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block_sizes, # [num_kv_cache_groups]
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slot_mappings_ptr, # [num_kv_cache_groups, max_num_tokens]
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slot_mappings_stride,
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cp_rank,
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CP_SIZE: tl.constexpr,
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CP_INTERLEAVE: tl.constexpr,
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PAD_ID: tl.constexpr,
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TRITON_BLOCK_SIZE: tl.constexpr,
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):
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# kv cache group id
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group_id = tl.program_id(0)
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batch_idx = tl.program_id(1)
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slot_mapping_ptr = slot_mappings_ptr + group_id * slot_mappings_stride
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if batch_idx == tl.num_programs(1) - 1:
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# Pad remaining slots to -1. This is needed for CUDA graphs.
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for i in range(num_tokens, max_num_tokens, TRITON_BLOCK_SIZE):
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offset = i + tl.arange(0, TRITON_BLOCK_SIZE)
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tl.store(slot_mapping_ptr + offset, PAD_ID, mask=offset < max_num_tokens)
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return
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block_table_ptr = _load_ptr(block_table_ptrs + group_id, tl.int32)
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block_table_stride = tl.load(block_table_strides + group_id)
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block_size = tl.load(block_sizes + group_id)
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req_state_idx = tl.load(idx_mapping + batch_idx)
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start_idx = tl.load(query_start_loc + batch_idx)
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end_idx = tl.load(query_start_loc + batch_idx + 1)
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for i in range(start_idx, end_idx, TRITON_BLOCK_SIZE):
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offset = i + tl.arange(0, TRITON_BLOCK_SIZE)
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positions = tl.load(pos + offset, mask=offset < end_idx, other=0)
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block_indices = positions // (block_size * CP_SIZE)
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block_offsets = positions % (block_size * CP_SIZE)
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block_numbers = tl.load(
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block_table_ptr + req_state_idx * block_table_stride + block_indices
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)
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if CP_SIZE == 1:
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# Common case: Context parallelism is not used.
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slot_ids = block_numbers * block_size + block_offsets
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else:
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# Context parallelism is used.
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is_local = block_offsets // CP_INTERLEAVE % CP_SIZE == cp_rank
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rounds = block_offsets // (CP_INTERLEAVE * CP_SIZE)
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remainder = block_offsets % CP_INTERLEAVE
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local_offsets = rounds * CP_INTERLEAVE + remainder
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slot_ids = block_numbers * block_size + local_offsets
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slot_ids = tl.where(is_local, slot_ids, PAD_ID)
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tl.store(slot_mapping_ptr + offset, slot_ids, mask=offset < end_idx)
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@triton.jit
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def _load_ptr(ptr_to_ptr, elem_dtype):
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ptr = tl.load(ptr_to_ptr)
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ptr = tl.cast(ptr, tl.pointer_type(elem_dtype))
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return tl.multiple_of(ptr, 16)
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