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init v0.23.0

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Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>
This commit is contained in:
Sun Ruoxi
2026-08-27 15:11:51 +08:00
parent b582a8e7d1
commit 7f8a1b1f7a
2849 changed files with 712887 additions and 22001 deletions
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451
vllm_ascend/worker/block_table.py
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@@ -1,27 +1,61 @@
from typing import Optional, Union
import numpy as np
import torch
from vllm.distributed import get_dcp_group
from vllm.utils import cdiv
from vllm.distributed import get_dcp_group, get_pcp_group
from vllm.utils.math_utils import cdiv
from vllm.v1.attention.backends.utils import PAD_SLOT_ID
from vllm.v1.kv_cache_interface import KVCacheGroupSpec, MambaSpec, UniformTypeKVCacheSpecs
from vllm.v1.utils import CpuGpuBuffer
from vllm.v1.worker.block_table import _compute_slot_mapping_kernel
from vllm.v1.worker.cp_utils import get_total_cp_world_size
class BlockTable:
def __init__(self,
block_size: int,
max_num_reqs: int,
max_num_blocks_per_req: int,
max_num_batched_tokens: int,
pin_memory: bool,
device: torch.device,
kernel_sizes: Union[list[int], None] = None):
def __init__(
self,
block_size: int,
max_num_reqs: int,
max_num_blocks_per_req: int,
max_num_batched_tokens: int,
pin_memory: bool,
device: torch.device,
kernel_sizes: list[int] | None = None,
cp_kv_cache_interleave_size: int = 1,
num_speculative_tokens: int = 0,
kv_cache_group: KVCacheGroupSpec = None,
):
self.max_num_reqs = max_num_reqs
self.pcp_world_size = get_pcp_group().world_size
self.pcp_rank = get_pcp_group().rank_in_group if self.pcp_world_size > 1 else 0
self.dcp_world_size = get_dcp_group().world_size
self.dcp_rank = get_dcp_group().rank_in_group
compress_ratio = 1
if (
kv_cache_group is not None
and hasattr(kv_cache_group, "kv_cache_spec")
and isinstance(kv_cache_group.kv_cache_spec, UniformTypeKVCacheSpecs)
):
kv_cache_spec = next(iter(kv_cache_group.kv_cache_spec.kv_cache_specs.values()), None)
if kv_cache_spec is not None and hasattr(kv_cache_spec, "compress_ratio"):
compress_ratio = kv_cache_spec.compress_ratio
if (
kv_cache_group is not None
and hasattr(kv_cache_group, "kv_cache_spec")
and (self.pcp_world_size * self.dcp_world_size > 1)
and isinstance(kv_cache_group.kv_cache_spec, MambaSpec)
):
max_num_blocks_per_req = max_num_blocks_per_req * self.pcp_world_size * self.dcp_world_size
max_num_blocks_per_req = max(cdiv(max_num_blocks_per_req, compress_ratio), 1)
self.max_num_blocks_per_req = max_num_blocks_per_req
self.max_num_batched_tokens = max_num_batched_tokens
self.pin_memory = pin_memory
self.device = device
self.physical_block_size = block_size
self.is_mamba_group = (
kv_cache_group is not None
and hasattr(kv_cache_group, "kv_cache_spec")
and isinstance(kv_cache_group.kv_cache_spec, MambaSpec)
)
# If kernel_sizes is None or [0], use physical block size (no splitting)
if kernel_sizes is None or kernel_sizes == [0]:
self.block_size = block_size
@@ -32,61 +66,44 @@ class BlockTable:
# Find the first kernel size that divides physical_block_size evenly
selected_kernel_size = None
for kernel_size in kernel_sizes:
if kernel_size > 0 \
and self.physical_block_size % kernel_size == 0:
if kernel_size > 0 and self.physical_block_size % kernel_size == 0:
selected_kernel_size = kernel_size
break
if selected_kernel_size is None:
raise ValueError(
f"None of the kernel sizes {kernel_sizes} can divide "
f"physical block size {self.physical_block_size} evenly")
f"physical block size {self.physical_block_size} evenly"
)
self.block_size = selected_kernel_size
self.logical_block_size = selected_kernel_size
self.blocks_per_phys_block = (self.physical_block_size //
self.logical_block_size)
self.blocks_per_phys_block = self.physical_block_size // self.logical_block_size
if self.blocks_per_phys_block > 1:
self.use_hybrid_blocks = True
else:
self.use_hybrid_blocks = False
if self.use_hybrid_blocks:
logical_table_size = (max_num_blocks_per_req *
self.blocks_per_phys_block)
logical_table_size = max_num_blocks_per_req * self.blocks_per_phys_block
else:
logical_table_size = max_num_blocks_per_req
self.block_table = torch.zeros(
(max_num_reqs, logical_table_size),
device=self.device,
dtype=torch.int32,
)
self.block_table_cpu = torch.zeros(
(max_num_reqs, logical_table_size),
device="cpu",
dtype=torch.int32,
pin_memory=pin_memory,
)
self.block_table_np = self.block_table_cpu.numpy()
duplicate_size = 1
if self.pcp_world_size * self.dcp_world_size > 1:
duplicate_size += num_speculative_tokens
self.block_table = self._make_buffer(max_num_reqs * duplicate_size, logical_table_size, dtype=torch.int32)
self.num_blocks_per_row = np.zeros(max_num_reqs, dtype=np.int32)
# MTP slot preparation appends up to num_speculative_tokens - 1
# draft positions for every request in addition to graph padding.
num_mtp_draft_slots = max(num_speculative_tokens - 1, 0) * self.max_num_reqs
self.slot_mapping = self._make_buffer(
self.max_num_batched_tokens + 2 * self.pcp_world_size * self.max_num_reqs + num_mtp_draft_slots,
dtype=torch.int32,
)
self.slot_mapping_cpu = torch.zeros(self.max_num_batched_tokens,
dtype=torch.int64,
device="cpu",
pin_memory=self.pin_memory)
self.slot_mapping_np = self.slot_mapping_cpu.numpy()
self.slot_mapping = torch.zeros(self.max_num_batched_tokens,
dtype=torch.int64,
device=self.device)
try:
self.dcp_world_size = get_dcp_group().world_size
self.dcp_rank = get_dcp_group().rank_in_group
except AssertionError:
# DCP might not be initialized in testing
self.dcp_world_size = 1
self.dcp_rank = 0
self.kernel_sizes = kernel_sizes
self.cp_kv_cache_interleave_size = cp_kv_cache_interleave_size
def append_row(
self,
@@ -102,17 +119,22 @@ class BlockTable:
num_blocks = len(block_ids)
start = self.num_blocks_per_row[row_idx]
self.block_table_np[row_idx, start:start + num_blocks] = block_ids
self.block_table.np[row_idx, start : start + num_blocks] = block_ids
self.num_blocks_per_row[row_idx] += num_blocks
def add_row(self, block_ids: list[int], row_idx: int) -> None:
self.num_blocks_per_row[row_idx] = 0
self.append_row(block_ids, row_idx)
def clear_row(self, row_idx: int) -> None:
num_blocks = self.num_blocks_per_row[row_idx]
if num_blocks > 0:
self.block_table.np[row_idx, :num_blocks] = 0
self.num_blocks_per_row[row_idx] = 0
def move_row(self, src: int, tgt: int) -> None:
num_blocks = self.num_blocks_per_row[src]
self.block_table_np[tgt, :num_blocks] = self.block_table_np[
src, :num_blocks]
self.block_table.np[tgt, :num_blocks] = self.block_table.np[src, :num_blocks]
self.num_blocks_per_row[tgt] = num_blocks
def swap_row(self, src: int, tgt: int) -> None:
@@ -121,10 +143,46 @@ class BlockTable:
self.num_blocks_per_row[src] = num_blocks_tgt
self.num_blocks_per_row[tgt] = num_blocks_src
self.block_table_np[[src, tgt]] = self.block_table_np[[tgt, src]]
self.block_table.np[[src, tgt]] = self.block_table.np[[tgt, src]]
def compute_slot_mapping(self, req_indices: np.ndarray,
positions: np.ndarray) -> None:
def compute_slot_mapping(
self,
num_reqs: int,
query_start_loc: torch.Tensor,
positions: torch.Tensor,
) -> None:
num_tokens = positions.shape[0]
total_cp_world_size = self.pcp_world_size * self.dcp_world_size
total_cp_rank = self.pcp_rank * self.dcp_world_size + self.dcp_rank
if self.dcp_world_size * self.pcp_world_size > 1:
req_indices = torch.repeat_interleave(
torch.arange(num_reqs, dtype=torch.int32, device=query_start_loc.device),
query_start_loc[1:] - query_start_loc[:-1],
output_size=num_tokens,
)
self._compute_pcp_dcp_slot_mapping(req_indices, positions)
else:
_compute_slot_mapping_kernel[(num_reqs + 1,)](
num_tokens,
self.max_num_batched_tokens,
query_start_loc,
positions,
self.block_table.gpu,
self.block_table.gpu.stride(0),
self.block_size,
self.slot_mapping.gpu,
TOTAL_CP_WORLD_SIZE=total_cp_world_size,
TOTAL_CP_RANK=total_cp_rank,
CP_KV_CACHE_INTERLEAVE_SIZE=self.cp_kv_cache_interleave_size,
PAD_ID=PAD_SLOT_ID,
BLOCK_SIZE=1024,
)
def compute_slot_mapping_draft(
self,
req_indices: np.ndarray | torch.Tensor,
positions: np.ndarray | torch.Tensor,
) -> None:
# E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
# -> [0, 0, K, K, K + 1, K + 1, K + 2, 2 * K, 2 * K, 2 * K + 1]
# where K is the max_num_blocks_per_req and the block size is 2.
@@ -132,75 +190,102 @@ class BlockTable:
# here because M (max_model_len) is not necessarily divisible by
# block_size.
if self.dcp_world_size > 1:
# Note(hc): The DCP implement store kvcache with an interleave
# style, the kvcache for the token whose token_idx is i is
# always stored on the GPU whose dcp_rank equals i % cp_world_size:
# Use a "virtual block" which equals to world_size * block_size
# for block_table_indices calculation.
virtual_block_size = self.block_size * self.dcp_world_size
if self.dcp_world_size * self.pcp_world_size > 1:
if not isinstance(req_indices, torch.Tensor):
req_indices = torch.from_numpy(req_indices)
if not isinstance(positions, torch.Tensor):
positions = torch.from_numpy(positions)
self._compute_pcp_dcp_slot_mapping(req_indices, positions)
else:
if isinstance(req_indices, torch.Tensor):
if req_indices.device.type != "cpu":
raise ValueError("Device tensor inputs are only supported for CP draft slot mapping.")
req_indices = req_indices.numpy()
if isinstance(positions, torch.Tensor):
if positions.device.type != "cpu":
raise ValueError("Device tensor inputs are only supported for CP draft slot mapping.")
positions = positions.numpy()
assert self.kernel_sizes is not None
assert self.block_size == self.kernel_sizes[0]
# IMPORTANT: In hybrid mode, positions are in logical block space,
# but we need to map them to the correct logical block table indices
logical_block_idx = positions // virtual_block_size
logical_block_idx = positions // self.block_size
# Account for the expanded logical table
# (always needed with unified tensor)
# Each physical block is split into multiple logical blocks
# The logical table has been expanded to accommodate this
block_table_indices = (req_indices * self.max_num_blocks_per_req *
self.blocks_per_phys_block +
logical_block_idx)
block_table_indices = (
req_indices * self.max_num_blocks_per_req * self.blocks_per_phys_block + logical_block_idx
)
block_numbers = self.block_table_np.ravel()[block_table_indices]
# Use virtual_block_size for mask calculation, which marks local
# tokens.
virtual_block_offsets = positions % virtual_block_size
mask = virtual_block_offsets % self.dcp_world_size == self.dcp_rank
# Calculate local block_offsets
block_offsets = virtual_block_offsets // self.dcp_world_size
# Calculate slot_mapping
block_offsets = positions % self.block_size
block_numbers = self.block_table.np.ravel()[block_table_indices]
np.add(
block_numbers * self.block_size,
block_offsets,
out=self.slot_mapping.np[: req_indices.shape[0]],
)
self.slot_mapping.copy_to_gpu(req_indices.shape[0])
def _compute_pcp_dcp_slot_mapping(
self,
req_indices: torch.Tensor,
positions: torch.Tensor,
) -> None:
# Note(hc): The DCP implement store kvcache with an interleave
# style, the kvcache for the token whose token_idx is i is
# always stored on the GPU whose dcp_rank equals i % pcp_world_size:
# Use a "virtual block" which equals to world_size * block_size
# for block_table_indices calculation.
# virtual_block_size = self.block_size * self.dcp_world_size * self.pcp_world_size
# IMPORTANT: In hybrid mode, positions are in logical block space,
# but we need to map them to the correct logical block table indices
# logical_block_idx = positions // virtual_block_size
total_cp_world_size = self.dcp_world_size * self.pcp_world_size
virtual_physical_block_size = self.physical_block_size * total_cp_world_size
physical_block_idx = positions // virtual_physical_block_size
virtual_block_offsets = positions % virtual_physical_block_size
self.current_rank = self.dcp_world_size * self.pcp_rank + self.dcp_rank
mask = virtual_block_offsets // self.cp_kv_cache_interleave_size % total_cp_world_size == self.current_rank
local_physical_offsets = (
virtual_block_offsets
// (total_cp_world_size * self.cp_kv_cache_interleave_size)
* self.cp_kv_cache_interleave_size
+ virtual_block_offsets % self.cp_kv_cache_interleave_size
)
logical_block_idx = physical_block_idx * self.blocks_per_phys_block + (
local_physical_offsets // self.block_size
)
block_table_indices = req_indices * self.max_num_blocks_per_req * self.blocks_per_phys_block + logical_block_idx
block_offsets = local_physical_offsets % self.block_size
if block_table_indices.device.type != "cpu":
block_numbers = self.block_table.gpu.flatten()[block_table_indices]
slot_mapping = block_numbers * self.block_size + block_offsets
# Write final slots, use -1 for not-local
self.slot_mapping_np[:req_indices.shape[0]] = np.where(
mask, slot_mapping, -1)
self.slot_mapping.gpu[: req_indices.shape[0]] = torch.where(mask, slot_mapping, -1)
else:
assert self.kernel_sizes is not None
if self.block_size == self.kernel_sizes[0]:
# IMPORTANT: In hybrid mode, positions are in logical block space,
# but we need to map them to the correct logical block table indices
logical_block_idx = positions // self.block_size
# Account for the expanded logical table
# (always needed with unified tensor)
# Each physical block is split into multiple logical blocks
# The logical table has been expanded to accommodate this
block_table_indices = (
req_indices * self.max_num_blocks_per_req *
self.blocks_per_phys_block + logical_block_idx)
block_numbers = self.block_table_np.ravel(
)[block_table_indices]
block_offsets = positions % self.block_size
np.add(block_numbers * self.block_size,
block_offsets,
out=self.slot_mapping_np[:req_indices.shape[0]])
block_numbers = self.block_table.cpu.flatten()[block_table_indices]
slot_mapping = block_numbers * self.block_size + block_offsets
self.slot_mapping.cpu[: req_indices.shape[0]] = torch.where(mask, slot_mapping, -1)
def commit_block_table(self, num_reqs: int) -> None:
self.block_table[:num_reqs].copy_(self.block_table_cpu[:num_reqs],
non_blocking=True)
def commit_slot_mapping(self, num_tokens: int) -> None:
self.slot_mapping[:num_tokens].copy_(
self.slot_mapping_cpu[:num_tokens], non_blocking=True)
self.block_table.gpu[:num_reqs].copy_(
self.block_table.cpu[:num_reqs].clone().pin_memory(),
non_blocking=True,
)
def clear(self) -> None:
self.block_table.fill_(0)
self.block_table_cpu.fill_(0)
self.block_table.cpu.fill_(0)
def _convert_physical_to_logical_blocks(
self, physical_blocks: np.ndarray) -> np.ndarray:
def _convert_physical_to_logical_blocks(self, physical_blocks: np.ndarray) -> np.ndarray:
"""Convert physical block IDs to logical block IDs."""
if not self.use_hybrid_blocks:
return physical_blocks
@@ -213,46 +298,45 @@ class BlockTable:
# [1*split_ratio, 1*split_ratio+1, ...]
# But we need to account for the fact that block 0 is special
base_logical = phys_block * self.blocks_per_phys_block
logical_blocks.extend(
range(base_logical, base_logical + self.blocks_per_phys_block))
logical_blocks.extend(range(base_logical, base_logical + self.blocks_per_phys_block))
return np.array(logical_blocks, dtype=np.int32)
def get_device_tensor(self) -> torch.Tensor:
def get_device_tensor(self, num_reqs: int | None = None) -> torch.Tensor:
"""Returns the device tensor of the block table."""
return self.block_table
if num_reqs is not None:
return self.block_table.gpu[:num_reqs]
return self.block_table.gpu
def get_cpu_tensor(self) -> torch.Tensor:
"""Returns the CPU tensor of the block table."""
return self.block_table_cpu
return self.block_table.cpu
def get_numpy_array(self) -> np.ndarray:
"""Returns the numpy array of the block table."""
return self.block_table_np
return self.block_table.np
def _make_buffer(self, *size: int | torch.SymInt, dtype: torch.dtype) -> CpuGpuBuffer:
return CpuGpuBuffer(*size, dtype=dtype, device=self.device, pin_memory=self.pin_memory)
class MultiGroupBlockTable:
"""The BlockTables for each KV cache group."""
def __init__(self,
max_num_reqs: int,
max_model_len: int,
max_num_batched_tokens: int,
pin_memory: bool,
device: torch.device,
block_sizes: list[int],
num_speculative_tokens: int = 0,
kernel_sizes: Optional[list[list[int]]] = None) -> None:
# Note(hc): each dcp rank only store
# (max_model_len//dcp_world_size) tokens in kvcache,
# so the block_size which used for calc max_num_blocks_per_req
# must be multiplied by dcp_world_size.
try:
dcp_world_size = get_dcp_group().world_size
except AssertionError:
# DCP might not be initialized in testing
dcp_world_size = 1
def __init__(
self,
max_num_reqs: int,
max_model_len: int,
max_num_batched_tokens: int,
pin_memory: bool,
device: torch.device,
block_sizes: list[int],
num_speculative_tokens: int = 0,
max_num_blocks: list[int] | None = None,
kernel_sizes: list[list[int]] | None = None,
cp_kv_cache_interleave_size: int = 1,
kv_cache_groups: KVCacheGroupSpec = None,
) -> None:
if kernel_sizes is None:
kernel_sizes = [[0]] * len(block_sizes)
# Ensure kernel_sizes matches block_sizes length
@@ -260,21 +344,60 @@ class MultiGroupBlockTable:
kernel_sizes = kernel_sizes * len(block_sizes)
elif len(kernel_sizes) != len(block_sizes):
raise ValueError(
f"kernel_sizes length ({len(kernel_sizes)}) must match "
f"block_sizes length ({len(block_sizes)})")
f"kernel_sizes length ({len(kernel_sizes)}) must match block_sizes length ({len(block_sizes)})"
)
if max_num_blocks is None:
# Note(hc): each dcp rank only store
# (max_model_len//dcp_world_size) tokens in kvcache,
# so the block_size which used for calc max_num_blocks_per_req
# must be multiplied by dcp_world_size.
total_cp_world_size = get_total_cp_world_size()
max_num_blocks = [cdiv(max_model_len, block_size * total_cp_world_size) for block_size in block_sizes]
if len(max_num_blocks) != len(block_sizes):
raise ValueError(
f"max_num_blocks length ({len(max_num_blocks)}) must match block_sizes length ({len(block_sizes)})"
)
# Use zip to pair block_sizes with kernel_sizes one-to-one
self.block_tables = [
BlockTable(
block_size, max_num_reqs,
max(cdiv(max_model_len, block_size * dcp_world_size),
1 + num_speculative_tokens), max_num_batched_tokens,
pin_memory, device, kernel_size_list)
for block_size, kernel_size_list in zip(block_sizes, kernel_sizes)
]
if kv_cache_groups is not None:
self.block_tables = [
BlockTable(
block_size,
max_num_reqs,
max_num_blocks_per_req,
max_num_batched_tokens,
pin_memory,
device,
kernel_size_list,
cp_kv_cache_interleave_size,
num_speculative_tokens,
kv_cache_group,
)
for block_size, kernel_size_list, max_num_blocks_per_req, kv_cache_group in zip(
block_sizes, kernel_sizes, max_num_blocks, kv_cache_groups
)
]
else:
self.block_tables = [
BlockTable(
block_size,
max_num_reqs,
max_num_blocks_per_req,
max_num_batched_tokens,
pin_memory,
device,
kernel_size_list,
cp_kv_cache_interleave_size,
num_speculative_tokens,
)
for block_size, kernel_size_list, max_num_blocks_per_req in zip(
block_sizes, kernel_sizes, max_num_blocks
)
]
def append_row(self, block_ids: tuple[list[int], ...],
row_idx: int) -> None:
def append_row(self, block_ids: tuple[list[int], ...], row_idx: int) -> None:
for i, block_table in enumerate(self.block_tables):
block_table.append_row(block_ids[i], row_idx)
@@ -282,6 +405,10 @@ class MultiGroupBlockTable:
for i, block_table in enumerate(self.block_tables):
block_table.add_row(block_ids[i], row_idx)
def clear_row(self, row_idx: int) -> None:
for block_table in self.block_tables:
block_table.clear_row(row_idx)
def move_row(self, src: int, tgt: int) -> None:
for block_table in self.block_tables:
block_table.move_row(src, tgt)
@@ -290,19 +417,41 @@ class MultiGroupBlockTable:
for block_table in self.block_tables:
block_table.swap_row(src, tgt)
def compute_slot_mapping(self, req_indices: np.ndarray,
positions: np.ndarray) -> None:
for block_table in self.block_tables:
block_table.compute_slot_mapping(req_indices, positions)
def compute_slot_mapping(
self,
num_reqs: int,
query_start_loc: torch.Tensor,
positions: torch.Tensor,
positions_compressed_list: list[np.ndarray] | None = None,
req_indices_compressed_list: list[np.ndarray] | None = None,
) -> None:
for i, block_table in enumerate(self.block_tables):
if block_table.is_mamba_group:
continue
if positions_compressed_list and req_indices_compressed_list:
block_table.compute_slot_mapping_draft(req_indices_compressed_list[i], positions_compressed_list[i])
else:
block_table.compute_slot_mapping(num_reqs, query_start_loc, positions)
def compute_slot_mapping_draft(
self,
req_indices: np.ndarray | torch.Tensor,
positions: np.ndarray | torch.Tensor,
positions_compressed_list: list[np.ndarray] | None = None,
req_indices_compressed_list: list[np.ndarray] | None = None,
) -> None:
for i, block_table in enumerate(self.block_tables):
if block_table.is_mamba_group:
continue
if positions_compressed_list and req_indices_compressed_list:
block_table.compute_slot_mapping_draft(req_indices_compressed_list[i], positions_compressed_list[i])
else:
block_table.compute_slot_mapping_draft(req_indices, positions)
def commit_block_table(self, num_reqs: int) -> None:
for block_table in self.block_tables:
block_table.commit_block_table(num_reqs)
def commit_slot_mapping(self, num_tokens: int) -> None:
for block_table in self.block_tables:
block_table.commit_slot_mapping(num_tokens)
def clear(self) -> None:
for block_table in self.block_tables:
block_table.clear()