mirror of
https://github.com/handsomezhuzhu/vllm-npu-plugin.git
synced 2026-02-20 19:50:15 +00:00
313 lines
13 KiB
Python
313 lines
13 KiB
Python
from typing import Optional, 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.utils import cdiv
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class BlockTable:
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def __init__(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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kernel_sizes: Union[list[int], None] = None):
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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.physical_block_size = block_size
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# If kernel_sizes is None or [0], use physical block size (no splitting)
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if kernel_sizes is None or kernel_sizes == [0]:
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self.block_size = block_size
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self.logical_block_size = block_size
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self.blocks_per_phys_block = 1
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self.use_hybrid_blocks = False
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else:
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# Find the first kernel size that divides physical_block_size evenly
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selected_kernel_size = None
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for kernel_size in kernel_sizes:
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if kernel_size > 0 \
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and self.physical_block_size % kernel_size == 0:
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selected_kernel_size = kernel_size
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break
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if selected_kernel_size is None:
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raise ValueError(
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f"None of the kernel sizes {kernel_sizes} can divide "
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f"physical block size {self.physical_block_size} evenly")
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self.block_size = selected_kernel_size
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self.logical_block_size = selected_kernel_size
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self.blocks_per_phys_block = (self.physical_block_size //
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self.logical_block_size)
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if self.blocks_per_phys_block > 1:
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self.use_hybrid_blocks = True
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else:
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self.use_hybrid_blocks = False
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if self.use_hybrid_blocks:
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logical_table_size = (max_num_blocks_per_req *
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self.blocks_per_phys_block)
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else:
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logical_table_size = max_num_blocks_per_req
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self.block_table = torch.zeros(
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(max_num_reqs, logical_table_size),
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device=self.device,
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dtype=torch.int32,
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)
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self.block_table_cpu = torch.zeros(
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(max_num_reqs, logical_table_size),
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device="cpu",
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dtype=torch.int32,
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pin_memory=pin_memory,
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)
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self.block_table_np = self.block_table_cpu.numpy()
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self.num_blocks_per_row = np.zeros(max_num_reqs, dtype=np.int32)
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self.slot_mapping_cpu = torch.zeros(self.max_num_batched_tokens,
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dtype=torch.int64,
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device="cpu",
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pin_memory=self.pin_memory)
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self.slot_mapping_np = self.slot_mapping_cpu.numpy()
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self.slot_mapping = torch.zeros(self.max_num_batched_tokens,
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dtype=torch.int64,
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device=self.device)
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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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self.kernel_sizes = kernel_sizes
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def append_row(
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self,
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block_ids,
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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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block_ids = np.array(block_ids)
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if self.use_hybrid_blocks:
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block_ids = self._convert_physical_to_logical_blocks(block_ids)
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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.block_table_np[row_idx, start:start + num_blocks] = block_ids
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self.num_blocks_per_row[row_idx] += num_blocks
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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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self.block_table_np[tgt, :num_blocks] = self.block_table_np[
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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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num_blocks_src = self.num_blocks_per_row[src]
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num_blocks_tgt = self.num_blocks_per_row[tgt]
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self.num_blocks_per_row[src] = num_blocks_tgt
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self.num_blocks_per_row[tgt] = num_blocks_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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# IMPORTANT: In hybrid mode, positions are in logical block space,
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# but we need to map them to the correct logical block table indices
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logical_block_idx = positions // virtual_block_size
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# Account for the expanded logical table
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# (always needed with unified tensor)
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# Each physical block is split into multiple logical blocks
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# The logical table has been expanded to accommodate this
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block_table_indices = (req_indices * self.max_num_blocks_per_req *
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self.blocks_per_phys_block +
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logical_block_idx)
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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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assert self.kernel_sizes is not None
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if self.block_size == self.kernel_sizes[0]:
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# IMPORTANT: In hybrid mode, positions are in logical block space,
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# but we need to map them to the correct logical block table indices
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logical_block_idx = positions // self.block_size
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# Account for the expanded logical table
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# (always needed with unified tensor)
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# Each physical block is split into multiple logical blocks
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# The logical table has been expanded to accommodate this
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block_table_indices = (
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req_indices * self.max_num_blocks_per_req *
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self.blocks_per_phys_block + logical_block_idx)
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block_numbers = self.block_table_np.ravel(
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)[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[:num_reqs].copy_(self.block_table_cpu[:num_reqs],
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non_blocking=True)
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def commit_slot_mapping(self, num_tokens: int) -> None:
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self.slot_mapping[:num_tokens].copy_(
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self.slot_mapping_cpu[:num_tokens], non_blocking=True)
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def clear(self) -> None:
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self.block_table.fill_(0)
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self.block_table_cpu.fill_(0)
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def _convert_physical_to_logical_blocks(
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self, physical_blocks: np.ndarray) -> np.ndarray:
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"""Convert physical block IDs to logical block IDs."""
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if not self.use_hybrid_blocks:
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return physical_blocks
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# Create logical block IDs by splitting each physical block
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logical_blocks: list[int] = []
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for phys_block in physical_blocks:
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# Convert physical block to multiple logical blocks
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# Physical block 1 becomes logical blocks
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# [1*split_ratio, 1*split_ratio+1, ...]
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# But we need to account for the fact that block 0 is special
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base_logical = phys_block * self.blocks_per_phys_block
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logical_blocks.extend(
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range(base_logical, base_logical + self.blocks_per_phys_block))
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return np.array(logical_blocks, dtype=np.int32)
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def get_device_tensor(self) -> torch.Tensor:
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"""Returns the device tensor of the block table."""
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return self.block_table
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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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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,
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kernel_sizes: Optional[list[list[int]]] = None) -> 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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if kernel_sizes is None:
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kernel_sizes = [[0]] * len(block_sizes)
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# Ensure kernel_sizes matches block_sizes length
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elif len(kernel_sizes) == 1 and len(block_sizes) > 1:
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kernel_sizes = kernel_sizes * len(block_sizes)
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elif len(kernel_sizes) != len(block_sizes):
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raise ValueError(
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f"kernel_sizes length ({len(kernel_sizes)}) must match "
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f"block_sizes length ({len(block_sizes)})")
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# Use zip to pair block_sizes with kernel_sizes one-to-one
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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, kernel_size_list)
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for block_size, kernel_size_list in zip(block_sizes, kernel_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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