211 lines
8.3 KiB
Python
211 lines
8.3 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 typing import Union
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import numpy as np
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import torch
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from vllm.distributed import get_dcp_group
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from vllm.logger import init_logger
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from vllm.utils import cdiv
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from vllm.v1.utils import CpuGpuBuffer
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logger = init_logger(__name__)
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class BlockTable:
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def __init__(
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self,
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block_size: int,
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max_num_reqs: int,
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max_num_blocks_per_req: int,
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max_num_batched_tokens: int,
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pin_memory: bool,
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device: torch.device,
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):
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self.block_size = block_size
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self.max_num_reqs = max_num_reqs
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self.max_num_blocks_per_req = max_num_blocks_per_req
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self.max_num_batched_tokens = max_num_batched_tokens
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self.pin_memory = pin_memory
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self.device = device
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self.block_table = self._make_buffer(max_num_reqs,
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max_num_blocks_per_req,
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dtype=torch.int32)
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self.num_blocks_per_row = np.zeros(max_num_reqs, dtype=np.int32)
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self.slot_mapping = self._make_buffer(self.max_num_batched_tokens,
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dtype=torch.int64)
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try:
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self.dcp_world_size = get_dcp_group().world_size
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self.dcp_rank = get_dcp_group().rank_in_group
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except AssertionError:
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# DCP might not be initialized in testing
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self.dcp_world_size = 1
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self.dcp_rank = 0
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def append_row(
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self,
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block_ids: list[int],
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row_idx: int,
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) -> None:
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if not block_ids:
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return
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num_blocks = len(block_ids)
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start = self.num_blocks_per_row[row_idx]
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self.num_blocks_per_row[row_idx] += num_blocks
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self.block_table.np[row_idx, start:start + num_blocks] = block_ids
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def add_row(self, block_ids: list[int], row_idx: int) -> None:
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self.num_blocks_per_row[row_idx] = 0
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self.append_row(block_ids, row_idx)
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def move_row(self, src: int, tgt: int) -> None:
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num_blocks = self.num_blocks_per_row[src]
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block_table_np = self.block_table.np
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block_table_np[tgt, :num_blocks] = block_table_np[src, :num_blocks]
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self.num_blocks_per_row[tgt] = num_blocks
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def swap_row(self, src: int, tgt: int) -> None:
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src_tgt, tgt_src = [src, tgt], [tgt, src]
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self.num_blocks_per_row[src_tgt] = self.num_blocks_per_row[tgt_src]
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self.block_table.np[src_tgt] = self.block_table.np[tgt_src]
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def compute_slot_mapping(self, req_indices: np.ndarray,
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positions: np.ndarray) -> None:
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# E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
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# -> [0, 0, K, K, K + 1, K + 1, K + 2, 2 * K, 2 * K, 2 * K + 1]
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# where K is the max_num_blocks_per_req and the block size is 2.
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# NOTE(woosuk): We can't simply use `token_indices // block_size`
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# here because M (max_model_len) is not necessarily divisible by
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# block_size.
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if self.dcp_world_size > 1:
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# Note(hc): The DCP implement store kvcache with an interleave
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# style, the kvcache for the token whose token_idx is i is
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# always stored on the GPU whose dcp_rank equals i % cp_world_size:
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# Use a "virtual block" which equals to world_size * block_size
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# for block_table_indices calculation.
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virtual_block_size = self.block_size * self.dcp_world_size
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block_table_indices = (req_indices * self.max_num_blocks_per_req +
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positions // virtual_block_size)
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block_numbers = self.block_table.np.ravel()[block_table_indices]
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# Use virtual_block_size for mask calculation, which marks local
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# tokens.
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virtual_block_offsets = positions % virtual_block_size
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mask = virtual_block_offsets % self.dcp_world_size == self.dcp_rank
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# Calculate local block_offsets
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block_offsets = virtual_block_offsets // self.dcp_world_size
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# Calculate slot_mapping
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slot_mapping = block_numbers * self.block_size + block_offsets
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# Write final slots, use -1 for not-local
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self.slot_mapping.np[:req_indices.shape[0]] = np.where(
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mask, slot_mapping, -1)
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else:
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block_table_indices = (req_indices * self.max_num_blocks_per_req +
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positions // self.block_size)
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block_numbers = self.block_table.np.ravel()[block_table_indices]
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block_offsets = positions % self.block_size
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np.add(block_numbers * self.block_size,
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block_offsets,
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out=self.slot_mapping.np[:req_indices.shape[0]])
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def commit_block_table(self, num_reqs: int) -> None:
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self.block_table.copy_to_gpu(num_reqs)
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def commit_slot_mapping(self, num_tokens: int) -> None:
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self.slot_mapping.copy_to_gpu(num_tokens)
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def clear(self) -> None:
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self.block_table.gpu.fill_(0)
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self.block_table.cpu.fill_(0)
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def get_device_tensor(self, num_reqs: int) -> torch.Tensor:
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"""Returns the device tensor of the block table."""
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return self.block_table.gpu[:num_reqs]
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def get_cpu_tensor(self) -> torch.Tensor:
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"""Returns the CPU tensor of the block table."""
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return self.block_table.cpu
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def get_numpy_array(self) -> np.ndarray:
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"""Returns the numpy array of the block table."""
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return self.block_table.np
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def _make_buffer(self, *size: Union[int, torch.SymInt],
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dtype: torch.dtype) -> CpuGpuBuffer:
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return CpuGpuBuffer(*size,
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dtype=dtype,
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device=self.device,
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pin_memory=self.pin_memory)
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class MultiGroupBlockTable:
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"""The BlockTables for each KV cache group."""
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def __init__(self,
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max_num_reqs: int,
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max_model_len: int,
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max_num_batched_tokens: int,
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pin_memory: bool,
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device: torch.device,
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block_sizes: list[int],
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num_speculative_tokens: int = 0) -> None:
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# Note(hc): each dcp rank only store
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# (max_model_len//dcp_world_size) tokens in kvcache,
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# so the block_size which used for calc max_num_blocks_per_req
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# must be multiplied by dcp_world_size.
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try:
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dcp_world_size = get_dcp_group().world_size
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except AssertionError:
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# DCP might not be initialized in testing
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dcp_world_size = 1
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self.block_tables = [
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BlockTable(
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block_size, max_num_reqs,
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max(cdiv(max_model_len, block_size * dcp_world_size),
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1 + num_speculative_tokens), max_num_batched_tokens,
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pin_memory, device) for block_size in block_sizes
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]
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def append_row(self, block_ids: tuple[list[int], ...],
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row_idx: int) -> None:
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for i, block_table in enumerate(self.block_tables):
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block_table.append_row(block_ids[i], row_idx)
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def add_row(self, block_ids: tuple[list[int], ...], row_idx: int) -> None:
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for i, block_table in enumerate(self.block_tables):
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block_table.add_row(block_ids[i], row_idx)
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def move_row(self, src: int, tgt: int) -> None:
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for block_table in self.block_tables:
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block_table.move_row(src, tgt)
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def swap_row(self, src: int, tgt: int) -> None:
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for block_table in self.block_tables:
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block_table.swap_row(src, tgt)
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def compute_slot_mapping(self, req_indices: np.ndarray,
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positions: np.ndarray) -> None:
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for block_table in self.block_tables:
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block_table.compute_slot_mapping(req_indices, positions)
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def commit_block_table(self, num_reqs: int) -> None:
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for block_table in self.block_tables:
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block_table.commit_block_table(num_reqs)
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def commit_slot_mapping(self, num_tokens: int) -> None:
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for block_table in self.block_tables:
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block_table.commit_slot_mapping(num_tokens)
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def clear(self) -> None:
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for block_table in self.block_tables:
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block_table.clear()
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def __getitem__(self, idx: int) -> "BlockTable":
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"""Returns the BlockTable for the i-th KV cache group."""
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return self.block_tables[idx]
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