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simplify

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Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
This commit is contained in:
Woosuk Kwon 2025-08-25 09:23:05 -07:00
parent b1d52734f7
commit a851aaa0fc
3 changed files with 58 additions and 63 deletions
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117
vllm/v1/worker/gpu_block_table.py
View File

@ -37,10 +37,6 @@ class BlockTables:
self.block_tables: list[torch.Tensor] = []
# [num_kv_cache_groups, max_num_cached_reqs, max_num_blocks]
self.block_table_buffers: list[torch.Tensor] = []
# [num_kv_cache_groups, max_num_reqs]
self.num_blocks: list[torch.Tensor] = []
# [num_kv_cache_groups, max_num_tokens]
self.slot_mappings: list[torch.Tensor] = []
for i in range(self.num_kv_cache_groups):
block_size = self.block_sizes[i]
max_num_blocks = cdiv(self.max_model_len, block_size)
@ -61,44 +57,40 @@ class BlockTables:
)
self.block_table_buffers.append(block_table_buffer)
num_blocks = torch.zeros(self.max_num_reqs,
dtype=torch.int32,
device=self.device)
self.num_blocks.append(num_blocks)
slot_mapping = torch.zeros(self.max_num_batched_tokens,
dtype=torch.int64,
device=self.device)
self.slot_mappings.append(slot_mapping)
self.block_table_ptrs = self._make_ptr_tensor(self.block_tables)
self.buffer_ptrs = self._make_ptr_tensor(self.block_table_buffers)
self.block_table_strides = torch.tensor(
[b.stride(0) for b in self.block_tables],
dtype=torch.int64,
device=self.device)
self.num_blocks_ptrs = self._make_ptr_tensor(self.num_blocks)
self.block_sizes_tensor = torch.tensor(self.block_sizes,
dtype=torch.int32,
device=self.device)
self.slot_mapping_ptrs = self._make_ptr_tensor(self.slot_mappings)
self.num_blocks = torch.zeros(self.num_kv_cache_groups,
self.max_num_reqs,
dtype=torch.int32,
device=self.device)
self.slot_mappings = torch.zeros(self.num_kv_cache_groups,
self.max_num_batched_tokens,
dtype=torch.int64,
device=self.device)
# Misc buffers.
self.req_indices = self._make_buffer(self.max_num_reqs, torch.int32)
self.overwrite = self._make_buffer(self.max_num_reqs, torch.bool)
self.cu_num_new_blocks: list[CpuGpuBuffer] = []
self.new_block_ids: list[CpuGpuBuffer] = []
for i in range(self.num_kv_cache_groups):
self.cu_num_new_blocks.append(
self._make_buffer(self.max_num_reqs + 1, torch.int32))
# NOTE(woosuk): Here, we assume that total number of new blocks
# is ALWAYS less than max_num_batched_tokens.
# TODO(woosuk): Rigorously verify that this assumption is correct.
self.new_block_ids.append(
self._make_buffer(self.max_num_batched_tokens, torch.int32))
self.req_indices = self._make_buffer(self.max_num_reqs,
dtype=torch.int32)
self.overwrite = self._make_buffer(self.max_num_reqs, dtype=torch.bool)
self.cu_num_new_blocks = self._make_buffer(self.num_kv_cache_groups,
self.max_num_reqs + 1,
dtype=torch.int32)
# NOTE(woosuk): Here, we assume that total number of new blocks
# is ALWAYS less than max_num_batched_tokens.
# TODO(woosuk): Rigorously verify that this assumption is correct.
self.new_block_ids = self._make_buffer(self.num_kv_cache_groups,
self.max_num_batched_tokens,
dtype=torch.int32)
def _make_buffer(self, n: int, dtype: torch.dtype) -> CpuGpuBuffer:
return CpuGpuBuffer(n,
def _make_buffer(self, *args, dtype: torch.dtype) -> CpuGpuBuffer:
return CpuGpuBuffer(*args,
dtype=dtype,
pin_memory=self.pin_memory,
device=self.device)
@ -126,24 +118,21 @@ class BlockTables:
self.req_indices.np[:num_reqs] = req_indices
self.overwrite.np[:num_reqs] = overwrite
for i in range(self.num_kv_cache_groups):
self.cu_num_new_blocks[i].np[:num_reqs + 1] = cu_num_new_blocks[i]
self.cu_num_new_blocks.np[i, :num_reqs + 1] = cu_num_new_blocks[i]
n = len(new_block_ids[i])
self.new_block_ids[i].np[:n] = new_block_ids[i]
self.new_block_ids.np[i, :n] = new_block_ids[i]
cu_num_new_blocks_ptrs = self._make_ptr_tensor(
[x.copy_to_gpu(num_reqs + 1) for x in self.cu_num_new_blocks])
new_block_ids_ptrs = self._make_ptr_tensor([
x.copy_to_gpu(len(new_block_ids[i]))
for i, x in enumerate(self.new_block_ids)
])
_append_block_ids_kernel[(num_reqs, self.num_kv_cache_groups)](
self.req_indices.copy_to_gpu(num_reqs),
cu_num_new_blocks_ptrs,
new_block_ids_ptrs,
self.cu_num_new_blocks.copy_to_gpu(),
self.cu_num_new_blocks.gpu.stride(0),
self.new_block_ids.copy_to_gpu(),
self.new_block_ids.gpu.stride(0),
self.overwrite.copy_to_gpu(num_reqs),
self.block_table_strides,
self.buffer_ptrs,
self.num_blocks_ptrs,
self.num_blocks,
self.num_blocks.stride(0),
BLOCK_SIZE=1024,
)
@ -157,7 +146,8 @@ class BlockTables:
self.buffer_ptrs,
self.block_table_ptrs,
self.block_table_strides,
self.num_blocks_ptrs,
self.num_blocks,
self.num_blocks.stride(0),
BLOCK_SIZE=1024,
)
return tuple(b[:batch_size] for b in self.block_tables)
@ -178,7 +168,8 @@ class BlockTables:
self.block_table_ptrs,
self.block_table_strides,
self.block_sizes_tensor,
self.slot_mapping_ptrs,
self.slot_mappings,
self.slot_mappings.stride(0),
PAD_ID=PAD_SLOT_ID,
BLOCK_SIZE=1024,
)
@ -189,13 +180,16 @@ class BlockTables:
def _append_block_ids_kernel(
# Inputs
req_indices, # [num_reqs]
cu_num_new_block_ptrs, # [num_kv_cache_groups, num_reqs + 1]
new_block_id_ptrs, # [num_kv_cache_groups, num_new_blocks]
cu_num_new_blocks_ptr, # [num_kv_cache_groups, num_reqs + 1]
cu_num_new_blocks_stride,
new_block_ids_ptr, # [num_kv_cache_groups, num_new_blocks]
new_block_ids_stride,
overwrite, # [num_reqs]
block_table_strides, # [num_kv_cache_groups]
# Outputs
block_table_buffer_ptrs, # [num_kv_cache_groups]
num_block_ptrs, # [num_kv_cache_groups]
num_blocks_ptr, # [num_kv_cache_groups, max_num_reqs]
num_blocks_stride,
# Constants
BLOCK_SIZE: tl.constexpr,
):
@ -204,19 +198,19 @@ def _append_block_ids_kernel(
req_idx = tl.load(req_indices + batch_idx)
do_overwrite = tl.load(overwrite + batch_idx)
cu_num_new_blocks_ptr = _load_ptr(cu_num_new_block_ptrs + group_id,
tl.int32)
start_idx = tl.load(cu_num_new_blocks_ptr + batch_idx)
end_idx = tl.load(cu_num_new_blocks_ptr + batch_idx + 1)
group_new_blocks_ptr = (cu_num_new_blocks_ptr +
group_id * cu_num_new_blocks_stride)
start_idx = tl.load(group_new_blocks_ptr + batch_idx)
end_idx = tl.load(group_new_blocks_ptr + batch_idx + 1)
num_new_blocks = end_idx - start_idx
num_blocks_ptr = _load_ptr(num_block_ptrs + group_id, tl.int32)
group_num_blocks_ptr = num_blocks_ptr + group_id * num_blocks_stride
if do_overwrite:
dst_start_idx = 0
else:
dst_start_idx = tl.load(num_blocks_ptr + req_idx)
dst_start_idx = tl.load(group_num_blocks_ptr + req_idx)
dst_end_idx = dst_start_idx + num_new_blocks
tl.store(num_blocks_ptr + req_idx, dst_end_idx)
tl.store(group_num_blocks_ptr + req_idx, dst_end_idx)
# Destination
block_table_buffer_ptr = _load_ptr(block_table_buffer_ptrs + group_id,
@ -224,10 +218,11 @@ def _append_block_ids_kernel(
block_table_stride = tl.load(block_table_strides + group_id)
buffer_row_ptr = block_table_buffer_ptr + req_idx * block_table_stride
new_block_ids_ptr = _load_ptr(new_block_id_ptrs + group_id, tl.int32)
group_new_block_ids_ptr = (new_block_ids_ptr +
group_id * new_block_ids_stride)
for i in tl.range(0, num_new_blocks, BLOCK_SIZE):
offset = i + tl.arange(0, BLOCK_SIZE)
block_ids = tl.load(new_block_ids_ptr + start_idx + offset,
block_ids = tl.load(group_new_block_ids_ptr + start_idx + offset,
mask=offset < num_new_blocks)
tl.store(buffer_row_ptr + dst_start_idx + offset,
block_ids,
@ -240,7 +235,8 @@ def _compute_block_tables_kernel(
src_block_table_ptrs, # [num_kv_cache_groups]
dst_block_table_ptrs, # [num_kv_cache_groups]
block_table_strides, # [num_kv_cache_groups]
num_blocks_ptrs, # [num_kv_cache_groups]
num_blocks_ptr, # [num_kv_cache_groups, max_num_reqs]
num_blocks_stride,
BLOCK_SIZE: tl.constexpr,
):
batch_idx = tl.program_id(0)
@ -248,8 +244,8 @@ def _compute_block_tables_kernel(
group_id = tl.program_id(1)
req_idx = tl.load(batch_idx_to_req_idx + batch_idx)
num_blocks_ptr = _load_ptr(num_blocks_ptrs + group_id, tl.int32)
num_blocks = tl.load(num_blocks_ptr + req_idx)
group_num_blocks_ptr = num_blocks_ptr + group_id * num_blocks_stride
num_blocks = tl.load(group_num_blocks_ptr + req_idx)
stride = tl.load(block_table_strides + group_id)
src_block_table_ptr = _load_ptr(src_block_table_ptrs + group_id, tl.int32)
@ -272,14 +268,15 @@ def _compute_slot_mappings_kernel(
block_table_ptrs, # [num_kv_cache_groups]
block_table_strides, # [num_kv_cache_groups]
page_sizes, # [num_kv_cache_groups]
slot_mapping_ptrs, # [num_kv_cache_groups]
slot_mappings_ptr, # [num_kv_cache_groups, max_num_tokens]
slot_mappings_stride,
PAD_ID: tl.constexpr,
BLOCK_SIZE: tl.constexpr,
):
req_idx = tl.program_id(0)
# kv cache group id
group_id = tl.program_id(1)
slot_mapping_ptr = _load_ptr(slot_mapping_ptrs + group_id, tl.int64)
slot_mapping_ptr = slot_mappings_ptr + group_id * slot_mappings_stride
if req_idx == tl.num_programs(0) - 1:
# Pad remaining slots to -1. This is needed for CUDA graphs.

2
vllm/v1/worker/gpu_model_runner.py
View File

@ -659,7 +659,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
)
slot_mappings = self.block_tables.compute_slot_mappings(
query_start_loc, self.positions[:total_num_scheduled_tokens])
query_start_loc, self.positions.gpu[:total_num_scheduled_tokens])
# Used in the below loop.
query_start_loc_cpu = self.query_start_loc.cpu[:num_reqs + 1]

2
vllm/v1/worker/gpu_worker_states.py
View File

@ -109,8 +109,6 @@ class RequestState:
self.req_id_to_index: dict[str, int] = {}
self.index_to_req_id: dict[int, str] = {}
self.free_indices = list(range(max_num_cached_reqs))
# Used to construct the input batch.
self._add_scalar_attr("idx_mapping", torch.int32)
# Request states.
self.req_data: dict[int, RequestData] = {}