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xc-llm-ascend/tests/ut/worker/test_block_table.py
Qiu e07938047e [UT][PCP&DCP] UT for block_table.py (#5032) 
## Purpose
This PR add unit test for `compute_slot_mapping` function in
`block_table.py` with various `pcp_size` & `dcp_size` &
`cp_kv_cache_interleave_size`.

## Test Plan
```
pytest tests/ut/worker/test_block_table.py
```
## Test Result
```
==== 3 passed, 2 warnings in 0.20s ====
```
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>
2026-01-06 11:19:25 +08:00

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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
#
import unittest
from unittest.mock import MagicMock, patch
import numpy as np
import torch
from vllm.distributed.parallel_state import GroupCoordinator
from tests.ut.base import TestBase
class TestBlockTableComputeSlotMapping(TestBase):
"""Test suite for BlockTable.compute_slot_mapping() method
This test suite covers different configurations of DCP (Decode Context Parallelism),
PCP (Prefill Context Parallelism), and cp_kv_cache_interleave_size to ensure
correct slot_mapping calculation on different ranks.
"""
def setUp(self):
"""Set up common test fixtures"""
self.block_size = 128
self.max_num_reqs = 4
self.max_num_blocks_per_req = 128
self.max_num_batched_tokens = 512
self.pin_memory = False
self.device = torch.device("cpu")
self.kernel_sizes = [128]
def create_block_table(self, dcp_world_size, dcp_rank, pcp_world_size,
pcp_rank, cp_kv_cache_interleave_size):
"""Helper method to create BlockTable with mocked distributed groups"""
with patch('vllm_ascend.worker.block_table.get_dcp_group') as mock_get_dcp_group, \
patch('vllm_ascend.worker.block_table.get_pcp_group') as mock_get_pcp_group:
# Mock DCP group
mock_dcp_group = MagicMock(spec=GroupCoordinator)
mock_dcp_group.world_size = dcp_world_size
mock_dcp_group.rank_in_group = dcp_rank
mock_get_dcp_group.return_value = mock_dcp_group
# Mock PCP group
mock_pcp_group = MagicMock(spec=GroupCoordinator)
mock_pcp_group.world_size = pcp_world_size
mock_pcp_group.rank_in_group = pcp_rank
mock_get_pcp_group.return_value = mock_pcp_group
from vllm_ascend.worker.block_table import BlockTable
block_table = BlockTable(
block_size=self.block_size,
max_num_reqs=self.max_num_reqs,
max_num_blocks_per_req=self.max_num_blocks_per_req,
max_num_batched_tokens=self.max_num_batched_tokens,
pin_memory=self.pin_memory,
device=self.device,
kernel_sizes=self.kernel_sizes,
cp_kv_cache_interleave_size=cp_kv_cache_interleave_size,
num_speculative_tokens=0)
return block_table
def setup_block_table_data(self, block_table, num_reqs=2):
"""Helper method to populate block table with test data"""
# Add block IDs for each request
for i in range(num_reqs):
block_ids = list(range(i * 4,
(i + 1) * 4)) # [0,1,2,3], [4,5,6,7], etc.
block_table.add_row(block_ids, i)
def _test_slot_mapping_for_ranks(self, dcp_world_size, pcp_world_size,
cp_kv_cache_interleave_size,
test_configs):
"""Helper method to test slot_mapping across multiple ranks
Args:
dcp_world_size: Number of DCP ranks
pcp_world_size: Number of PCP ranks
cp_kv_cache_interleave_size: Interleave size for KV cache
test_configs: List of tuples (dcp_rank, pcp_rank, req_indices, positions, expected_result)
"""
for dcp_rank, pcp_rank, req_indices, positions, expected_result in test_configs:
with self.subTest(dcp_rank=dcp_rank, pcp_rank=pcp_rank):
block_table = self.create_block_table(
dcp_world_size, dcp_rank, pcp_world_size, pcp_rank,
cp_kv_cache_interleave_size)
num_reqs = max(req_indices) + 1 if len(req_indices) > 0 else 1
self.setup_block_table_data(block_table, num_reqs=num_reqs)
block_table.compute_slot_mapping(req_indices, positions)
actual_result = block_table.slot_mapping.np[:len(positions)]
np.testing.assert_array_equal(
actual_result, expected_result,
f"DCP={dcp_world_size}, PCP={pcp_world_size}, "
f"interleave={cp_kv_cache_interleave_size}, "
f"dcp_rank={dcp_rank}, pcp_rank={pcp_rank}")
def test_compute_slot_mapping_dcp1_pcp1_interleave1(self):
"""Test compute_slot_mapping with DCP=1, PCP=1, interleave_size=1
With no parallelism (DCP=1, PCP=1), all tokens are local to the single rank.
Setup:
- Block size: 16
- Request 0 has blocks: [0, 1, 2, 3]
- Request 1 has blocks: [4, 5, 6, 7]
Test positions for each request:
- Request 0, position 0: block_id=0, offset=0 → slot = 0*128+0 = 0
- Request 0, position 1: block_id=0, offset=1 → slot = 0*128+1 = 1
- Request 1, position 0: block_id=4, offset=0 → slot = 4*128+0 = 512
- Request 1, position 1: block_id=4, offset=1 → slot = 4*128+1 = 513
"""
req_indices = np.array([0, 0, 1, 1], dtype=np.int32)
positions = np.array([0, 1, 0, 1], dtype=np.int32)
expected_result = np.array([0, 1, 512, 513], dtype=np.int32)
test_configs = [
(0, 0, req_indices, positions, expected_result),
]
self._test_slot_mapping_for_ranks(dcp_world_size=1,
pcp_world_size=1,
cp_kv_cache_interleave_size=1,
test_configs=test_configs)
def test_compute_slot_mapping_dcp4_pcp2_interleave1(self):
"""Test compute_slot_mapping with DCP=4, PCP=2, interleave_size=1
With interleave_size=1, tokens are distributed round-robin across all 8 ranks:
- Position 0 → Rank 0
- Position 1 → Rank 1
- Position 2 → Rank 2
- ...
- Position 7 → Rank 7
- Position 8 → Rank 0 (wraps around)
"""
req_indices = np.array([0] * 16, dtype=np.int32)
positions = np.array(list(range(16)), dtype=np.int32)
# Manually computed expected values for each rank
# Rank assignment: current_rank = 4 * pcp_rank + dcp_rank
test_configs = []
# For each rank, specify which positions it owns and their local slot mapping
rank_expectations = {
# Rank 0 (pcp=0, dcp=0): positions 0, 8
0: [0, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1],
# Rank 1 (pcp=0, dcp=1): positions 1, 9
1: [-1, 0, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1],
# Rank 2 (pcp=0, dcp=2): positions 2, 10
2: [-1, -1, 0, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1],
# Rank 3 (pcp=0, dcp=3): positions 3, 11
3: [-1, -1, -1, 0, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1],
# Rank 4 (pcp=1, dcp=0): positions 4, 12
4: [-1, -1, -1, -1, 0, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1],
# Rank 5 (pcp=1, dcp=1): positions 5, 13
5: [-1, -1, -1, -1, -1, 0, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1],
# Rank 6 (pcp=1, dcp=2): positions 6, 14
6: [-1, -1, -1, -1, -1, -1, 0, -1, -1, -1, -1, -1, -1, -1, 1, -1],
# Rank 7 (pcp=1, dcp=3): positions 7, 15
7: [-1, -1, -1, -1, -1, -1, -1, 0, -1, -1, -1, -1, -1, -1, -1, 1],
}
for pcp_rank in range(2):
for dcp_rank in range(4):
current_rank = 4 * pcp_rank + dcp_rank
expected_result = np.array(rank_expectations[current_rank],
dtype=np.int32)
test_configs.append((dcp_rank, pcp_rank, req_indices,
positions, expected_result))
self._test_slot_mapping_for_ranks(dcp_world_size=4,
pcp_world_size=2,
cp_kv_cache_interleave_size=1,
test_configs=test_configs)
def test_compute_slot_mapping_dcp4_pcp2_interleave128(self):
"""Test compute_slot_mapping with DCP=4, PCP=2, interleave_size=128
With interleave_size=128, tokens are distributed in chunks of 128 across ranks.
Virtual block size = 16 * 4 * 2 = 128
Token distribution with interleave_size=128:
- Positions 0-127 belong to rank 0 (first chunk of 128)
- Positions 128-255 belong to rank 1 (second chunk of 128)
- Positions 256-383 belong to rank 2 (third chunk of 128)
- And so on...
Using 130 positions ensures we test both rank 0 (positions 0-127) and rank 1 (positions 128-129).
"""
num_positions = 130
req_indices = np.array([0] * num_positions, dtype=np.int32)
positions = np.array(list(range(num_positions)), dtype=np.int32)
# With interleave_size=128 and virtual_block_size=128:
# Positions 0-127 belong to rank 0
# Positions 128-129 belong to rank 1
test_configs = []
# Build expected results for each rank
for pcp_rank in range(2):
for dcp_rank in range(4):
current_rank = 4 * pcp_rank + dcp_rank
expected_result = []
if current_rank == 0:
# Rank 0 gets positions 0-127
# Each maps to its local slot: 0, 1, 2, ..., 127
for pos in range(130):
if pos < 128:
expected_result.append(pos)
else:
expected_result.append(-1)
elif current_rank == 1:
# Rank 1 gets positions 128-129
# Position 128 maps to local slot 0, position 129 to local slot 1
for pos in range(130):
if pos == 128:
expected_result.append(0)
elif pos == 129:
expected_result.append(1)
else:
expected_result.append(-1)
else:
# All other ranks get no positions
expected_result = [-1] * 130
test_configs.append(
(dcp_rank, pcp_rank, req_indices, positions,
np.array(expected_result, dtype=np.int32)))
self._test_slot_mapping_for_ranks(dcp_world_size=4,
pcp_world_size=2,
cp_kv_cache_interleave_size=128,
test_configs=test_configs)
if __name__ == '__main__':
unittest.main()
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