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Expand prefill CP KV capacity by sharding persistent NSA KV

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Prefill CP previously replicated NSA/MLA persistent KV on every CP rank, so CP8 consumed eight copies of KV memory while exposing only one rank of logical cache capacity. This change splits logical KV locs from per-rank physical storage, shards MLA latent KV and NSA index K/scale by deterministic page ownership, and keeps existing NSA attention kernels working through a full-view runtime materialization layer.

Mooncake PD transfer now sends each prefill CP rank's owned physical pages with explicit logical page positions so non-CP decode can reconstruct full-layout KV. The implementation is guarded by an explicit server flag and startup checks, and the design documentation records the implemented scope, debug environment, and Phase 3 boundary.

Constraint: Phase 2 must preserve existing NSA attention/index kernels via runtime full-view materialization
Constraint: Decode side remains non-CP and receives full KV through Mooncake
Rejected: Shard-aware NSA attention in this change | belongs to Phase 3 because it requires distributed topk/softmax/output contracts
Rejected: Request-contiguous CP ownership | unstable under chunked prefill and tied to attention split mode
Confidence: medium
Scope-risk: broad
Directive: Do not enable round-robin CP shared KV without wiring runtime materialization/PD transfer contracts for that split mode
Directive: Keep SGLANG_DEBUG_CP_SHARED_KV disabled for perf measurements; it intentionally enables CUDA-syncing diagnostics
Tested: Remote py_compile for shared-KV touched Python files in g0034 container
Tested: Remote pytest selected cp_shared/shared_kv/nsa suite: 37 passed, 34 deselected
Not-tested: Full GLM5 multi-node throughput/regression run after final doc update
Not-tested: Phase 3 shard-aware runtime, round-robin CP mode, and non-Mooncake PD backends
This commit is contained in:
laoyao0822
2026-04-26 04:11:17 +08:00
parent 76c830dda8
commit f8fca72635
22 changed files with 2489 additions and 90 deletions
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test/registered/unit/mem_cache/test_cp_shared_kv_layout.py
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import unittest
import numpy as np
import torch
from sglang.srt.mem_cache.cp_shared_kv_layout import CpSharedKVLayout
from sglang.test.ci.ci_register import register_cpu_ci
register_cpu_ci(est_time=1, suite="stage-a-test-cpu")
class TestCpSharedKVLayout(unittest.TestCase):
def test_page_owner_skips_dummy_page(self):
layout = CpSharedKVLayout(page_size=64, cp_size=4, cp_rank=0)
pages = torch.tensor([-2, -1, 0, 1, 2, 3, 4, 5, 8, 9], dtype=torch.int64)
owners = layout.owner_for_logical_pages(pages)
self.assertEqual(owners.tolist(), [-1, -1, -1, 0, 1, 2, 3, 0, 3, 0])
def test_logical_to_physical_pages_keeps_dummy_zero(self):
layout = CpSharedKVLayout(page_size=64, cp_size=4, cp_rank=0)
pages = torch.tensor([0, 1, 2, 3, 4, 5, 8, 9], dtype=torch.int64)
physical = layout.logical_pages_to_physical(pages)
self.assertEqual(physical.tolist(), [0, 1, 1, 1, 1, 2, 2, 3])
def test_owned_mask_and_loc_translation(self):
layout = CpSharedKVLayout(page_size=64, cp_size=4, cp_rank=2)
locs = torch.tensor([0, 64, 128, 192, 256, 320, 384], dtype=torch.int64)
mask = layout.owned_by_this_rank(locs)
self.assertEqual(
mask.tolist(), [False, False, False, True, False, False, False]
)
physical = layout.logical_locs_to_physical(locs[mask])
self.assertEqual(physical.tolist(), [64])
def test_negative_and_dummy_locs_are_never_owned(self):
layout = CpSharedKVLayout(page_size=64, cp_size=4, cp_rank=2)
locs = torch.tensor([-1, 0, 1, 63, 64, 128, 192], dtype=torch.int64)
mask = layout.owned_by_this_rank(locs)
self.assertEqual(
mask.tolist(), [False, False, False, False, False, False, True]
)
def test_numpy_filter_returns_request_absolute_positions(self):
layout = CpSharedKVLayout(page_size=64, cp_size=4, cp_rank=1)
logical_pages = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.int32)
request_positions = np.arange(10, 19, dtype=np.int32)
src_physical, positions = layout.filter_owned_pages_np(
logical_pages, request_positions
)
self.assertEqual(src_physical.tolist(), [1, 2])
self.assertEqual(positions.tolist(), [11, 15])
class TestCPSharedPagedAllocator(unittest.TestCase):
def test_shared_allocator_exposes_logical_capacity(self):
from sglang.srt.mem_cache.allocator import CPSharedPagedTokenToKVPoolAllocator
allocator = CPSharedPagedTokenToKVPoolAllocator(
logical_size=64 * 8,
physical_size=64 * 2,
page_size=64,
dtype=torch.bfloat16,
device="cpu",
kvcache=None,
need_sort=False,
cp_size=4,
cp_rank=0,
)
self.assertEqual(allocator.available_size(), 64 * 8)
locs = allocator.alloc(64 * 2)
self.assertEqual(locs.numel(), 64 * 2)
self.assertEqual(allocator.available_size(), 64 * 6)
allocator.free(locs)
self.assertEqual(allocator.available_size(), 64 * 8)
if __name__ == "__main__":
unittest.main()
test/registered/unit/mem_cache/test_cp_shared_kv_runtime.py
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import unittest
from types import SimpleNamespace
from unittest.mock import patch
import torch
from sglang.test.ci.ci_register import register_cpu_ci
register_cpu_ci(est_time=1, suite="stage-a-test-cpu")
class TestCpSharedKVRuntimeHelpers(unittest.TestCase):
def test_all_reduce_uses_group_fast_path_for_float_buffers(self):
from sglang.srt.layers.attention.nsa import cp_shared_kv_runtime as runtime
class DummyGroup:
device_group = object()
def __init__(self):
self.called = False
def all_reduce(self, tensor):
self.called = True
dummy_group = DummyGroup()
buffer = torch.ones((2, 3), dtype=torch.float32)
with patch.object(
runtime, "get_attention_cp_group", return_value=dummy_group
), patch("torch.distributed.all_reduce") as dist_all_reduce:
out = runtime._all_reduce_materialized_buffer(buffer, cp_size=2)
self.assertIs(out, buffer)
self.assertTrue(dummy_group.called)
dist_all_reduce.assert_not_called()
def test_all_reduce_copies_out_of_place_group_result_for_float_buffers(self):
from sglang.srt.layers.attention.nsa import cp_shared_kv_runtime as runtime
class DummyGroup:
device_group = object()
def all_reduce(self, tensor):
return tensor + 7
dummy_group = DummyGroup()
buffer = torch.ones((2, 3), dtype=torch.float32)
with patch.object(
runtime, "get_attention_cp_group", return_value=dummy_group
), patch("torch.distributed.all_reduce") as dist_all_reduce:
out = runtime._all_reduce_materialized_buffer(buffer, cp_size=2)
self.assertIs(out, buffer)
self.assertTrue(torch.equal(buffer, torch.full((2, 3), 8.0)))
dist_all_reduce.assert_not_called()
def test_all_reduce_bypasses_custom_group_for_byte_buffers(self):
from sglang.srt.layers.attention.nsa import cp_shared_kv_runtime as runtime
class DummyGroup:
device_group = object()
def all_reduce(self, tensor):
raise AssertionError("byte buffers must bypass custom all-reduce")
dummy_group = DummyGroup()
buffer = torch.ones((2, 3), dtype=torch.uint8)
with patch.object(
runtime, "get_attention_cp_group", return_value=dummy_group
), patch("torch.distributed.all_reduce") as dist_all_reduce:
out = runtime._all_reduce_materialized_buffer(buffer, cp_size=2)
self.assertIs(out, buffer)
dist_all_reduce.assert_called_once()
self.assertIs(dist_all_reduce.call_args.args[0], buffer)
self.assertIs(dist_all_reduce.call_args.kwargs["group"], dummy_group.device_group)
def test_build_dense_page_remap_preserves_sentinels(self):
from sglang.srt.layers.attention.nsa.cp_shared_kv_runtime import (
build_dense_page_remap,
)
logical_pages = torch.tensor([0, 5, 2, -1, 5, 9, 2], dtype=torch.int32)
unique_pages, dense_pages = build_dense_page_remap(logical_pages)
self.assertEqual(unique_pages.tolist(), [2, 5, 9])
self.assertEqual(dense_pages.tolist(), [0, 2, 1, -1, 2, 3, 1])
def test_remap_logical_locs_to_dense_locs(self):
from sglang.srt.layers.attention.nsa.cp_shared_kv_runtime import (
remap_logical_locs_to_dense_locs,
)
logical_locs = torch.tensor([0, 1, 8, 9, -1, 20], dtype=torch.int32)
unique_pages = torch.tensor([2, 5], dtype=torch.int32)
dense_locs = remap_logical_locs_to_dense_locs(
logical_locs,
unique_logical_pages=unique_pages,
page_size=4,
)
self.assertEqual(dense_locs.tolist(), [0, 1, 4, 5, -1, 8])
def test_materialize_local_token_kv_pages(self):
from sglang.srt.layers.attention.nsa.cp_shared_kv_runtime import (
build_dense_page_remap,
materialize_local_token_kv_pages,
)
from sglang.srt.mem_cache.cp_shared_kv_layout import CpSharedKVLayout
layout = CpSharedKVLayout(page_size=4, cp_size=4, cp_rank=1)
kv_cache = torch.arange(0, 16 * 2, dtype=torch.float32).view(16, 1, 2)
logical_pages = torch.tensor([1, 2, 5, 7, 10], dtype=torch.int32)
unique_pages, _ = build_dense_page_remap(logical_pages)
dense_kv = materialize_local_token_kv_pages(
kv_cache=kv_cache,
unique_logical_pages=unique_pages,
layout=layout,
page_size=4,
)
self.assertEqual(list(dense_kv.shape), [24, 1, 2])
self.assertTrue(torch.equal(dense_kv[8:12], kv_cache[4:8]))
self.assertTrue(torch.equal(dense_kv[20:24], kv_cache[12:16]))
self.assertEqual(float(dense_kv[4:8].abs().sum().item()), 0.0)
def test_materialize_local_paged_index_buffer(self):
from sglang.srt.layers.attention.nsa.cp_shared_kv_runtime import (
build_dense_page_remap,
materialize_local_paged_buffer,
)
from sglang.srt.mem_cache.cp_shared_kv_layout import CpSharedKVLayout
layout = CpSharedKVLayout(page_size=4, cp_size=4, cp_rank=1)
page_buffer = torch.arange(0, 5 * 3, dtype=torch.uint8).view(5, 3)
logical_pages = torch.tensor([1, 2, 5, 7, 10], dtype=torch.int32)
unique_pages, dense_pages = build_dense_page_remap(logical_pages)
dense_page_buffer = materialize_local_paged_buffer(
page_buffer=page_buffer,
unique_logical_pages=unique_pages,
layout=layout,
)
self.assertEqual(dense_pages.tolist(), [1, 2, 3, 4, 5])
self.assertEqual(list(dense_page_buffer.shape), [6, 3])
self.assertTrue(torch.equal(dense_page_buffer[2], page_buffer[1]))
self.assertTrue(torch.equal(dense_page_buffer[5], page_buffer[3]))
self.assertEqual(int(dense_page_buffer[1].sum().item()), 0)
def test_filter_owned_logical_locs_for_persistent_writes(self):
from sglang.srt.layers.attention.nsa.cp_shared_kv_runtime import (
filter_owned_logical_locs,
)
from sglang.srt.mem_cache.cp_shared_kv_layout import CpSharedKVLayout
layout = CpSharedKVLayout(page_size=64, cp_size=4, cp_rank=2)
logical_locs = torch.tensor(
[0, 64, 128, 192, 256, 320, 384, 448, 449],
dtype=torch.int64,
)
owned_mask, physical_locs = filter_owned_logical_locs(logical_locs, layout)
self.assertEqual(
owned_mask.tolist(),
[False, False, False, True, False, False, False, True, True],
)
self.assertEqual(physical_locs.tolist(), [64, 128, 129])
def test_materialize_token_kv_raises_on_locs_outside_physical_pool(self):
from sglang.srt.layers.attention.nsa import cp_shared_kv_runtime as runtime
from sglang.srt.mem_cache.cp_shared_kv_layout import CpSharedKVLayout
layout = CpSharedKVLayout(page_size=4, cp_size=4, cp_rank=1)
kv_cache = torch.arange(0, 12 * 2, dtype=torch.float32).view(12, 1, 2)
logical_locs = torch.tensor([4, 8, 20, 40, -1], dtype=torch.int64)
with patch.object(
runtime, "cp_shared_kv_debug_enabled", return_value=True
), patch.object(runtime, "_all_reduce_materialized_buffer", lambda x, _: x):
with self.assertRaisesRegex(
RuntimeError,
"CP shared KV materialize got logical token locs outside the physical pool",
):
runtime.materialize_shared_token_kv_buffer(
kv_cache=kv_cache,
logical_locs=logical_locs,
layout=layout,
page_size=4,
)
def test_materialize_token_kv_skips_physical_pool_validation_when_debug_disabled(self):
from sglang.srt.layers.attention.nsa import cp_shared_kv_runtime as runtime
from sglang.srt.mem_cache.cp_shared_kv_layout import CpSharedKVLayout
layout = CpSharedKVLayout(page_size=4, cp_size=4, cp_rank=1)
kv_cache = torch.arange(0, 12 * 2, dtype=torch.float32).view(12, 1, 2)
# Logical page 9 maps outside the local physical pool, but is owned by
# rank 0. With debug disabled, this source-finding validation should
# not synchronize or raise on rank 1.
logical_locs = torch.tensor([4, 36, -1], dtype=torch.int64)
with patch.object(
runtime, "cp_shared_kv_debug_enabled", return_value=False
), patch.object(runtime, "_all_reduce_materialized_buffer", lambda x, _: x):
_, dense_locs = runtime.materialize_shared_token_kv_buffer(
kv_cache=kv_cache,
logical_locs=logical_locs,
layout=layout,
page_size=4,
)
self.assertEqual(dense_locs.tolist(), [4, 8, -1])
def test_debug_materialize_token_kv_allows_minus_one_sentinel(self):
from sglang.srt.layers.attention.nsa import cp_shared_kv_runtime as runtime
from sglang.srt.mem_cache.cp_shared_kv_layout import CpSharedKVLayout
layout = CpSharedKVLayout(page_size=4, cp_size=1, cp_rank=0)
kv_cache = torch.arange(0, 16, dtype=torch.float32).view(16, 1, 1)
logical_locs = torch.tensor([4, -1, 8], dtype=torch.int64)
with patch.object(runtime, "cp_shared_kv_debug_enabled", return_value=True):
with patch.object(runtime, "_all_reduce_materialized_buffer", lambda x, _: x):
_, dense_locs = runtime.materialize_shared_token_kv_buffer(
kv_cache=kv_cache,
logical_locs=logical_locs,
layout=layout,
page_size=4,
)
self.assertEqual(dense_locs.tolist(), [4, -1, 8])
def test_debug_materialize_token_kv_rejects_below_minus_one_locs(self):
from sglang.srt.layers.attention.nsa import cp_shared_kv_runtime as runtime
from sglang.srt.mem_cache.cp_shared_kv_layout import CpSharedKVLayout
layout = CpSharedKVLayout(page_size=4, cp_size=1, cp_rank=0)
kv_cache = torch.arange(0, 16, dtype=torch.float32).view(16, 1, 1)
logical_locs = torch.tensor([4, -2, 8], dtype=torch.int64)
with patch.object(runtime, "cp_shared_kv_debug_enabled", return_value=True):
with self.assertRaisesRegex(
RuntimeError,
"CP shared KV token materialize got logical_locs below -1",
):
runtime.materialize_shared_token_kv_buffer(
kv_cache=kv_cache,
logical_locs=logical_locs,
layout=layout,
page_size=4,
)
def test_debug_materialize_paged_buffer_rejects_negative_pages(self):
from sglang.srt.layers.attention.nsa import cp_shared_kv_runtime as runtime
from sglang.srt.mem_cache.cp_shared_kv_layout import CpSharedKVLayout
layout = CpSharedKVLayout(page_size=4, cp_size=1, cp_rank=0)
page_buffer = torch.arange(0, 4 * 3, dtype=torch.uint8).view(4, 3)
logical_pages = torch.tensor([1, -1, 2], dtype=torch.int32)
with patch.object(runtime, "cp_shared_kv_debug_enabled", return_value=True):
with self.assertRaisesRegex(
RuntimeError,
"CP shared KV paged materialize got negative logical_pages",
):
runtime.materialize_shared_paged_buffer(
page_buffer=page_buffer,
logical_pages=logical_pages,
layout=layout,
)
def test_debug_filter_owned_logical_locs_rejects_negative_write_locs(self):
from sglang.srt.layers.attention.nsa import cp_shared_kv_runtime as runtime
from sglang.srt.mem_cache.cp_shared_kv_layout import CpSharedKVLayout
layout = CpSharedKVLayout(page_size=64, cp_size=4, cp_rank=2)
logical_locs = torch.tensor([192, -1], dtype=torch.int64)
with patch.object(runtime, "cp_shared_kv_debug_enabled", return_value=True):
with self.assertRaisesRegex(
RuntimeError,
"CP shared KV persistent write got negative logical_locs",
):
runtime.filter_owned_logical_locs(logical_locs, layout)
def test_materialize_token_kv_uses_remap_source_for_target_subset(self):
from sglang.srt.layers.attention.nsa import cp_shared_kv_runtime as runtime
from sglang.srt.mem_cache.cp_shared_kv_layout import CpSharedKVLayout
layout = CpSharedKVLayout(page_size=4, cp_size=1, cp_rank=0)
kv_cache = torch.arange(0, 32, dtype=torch.float32).view(32, 1, 1)
logical_locs = torch.tensor([8, 20, -1], dtype=torch.int64)
remap_source_locs = torch.tensor([4, 8, 12, 16, 20], dtype=torch.int64)
with patch.object(runtime, "_all_reduce_materialized_buffer", lambda x, _: x):
dense_kv, dense_locs = runtime.materialize_shared_token_kv_buffer(
kv_cache=kv_cache,
logical_locs=logical_locs,
remap_logical_locs=remap_source_locs,
layout=layout,
page_size=4,
)
self.assertEqual(dense_locs.tolist(), [8, 20, -1])
self.assertEqual(list(dense_kv.shape), [24, 1, 1])
self.assertTrue(torch.equal(dense_kv[8:12], kv_cache[8:12]))
self.assertTrue(torch.equal(dense_kv[20:24], kv_cache[20:24]))
def test_materialize_token_kv_keeps_dense_shape_for_shared_remap_source(self):
from sglang.srt.layers.attention.nsa import cp_shared_kv_runtime as runtime
from sglang.srt.mem_cache.cp_shared_kv_layout import CpSharedKVLayout
layout = CpSharedKVLayout(page_size=4, cp_size=1, cp_rank=0)
kv_cache = torch.arange(0, 32, dtype=torch.float32).view(32, 1, 1)
remap_source_locs = torch.tensor([4, 8, 12, 16, 20], dtype=torch.int64)
with patch.object(runtime, "_all_reduce_materialized_buffer", lambda x, _: x):
dense_kv_a, dense_locs_a = runtime.materialize_shared_token_kv_buffer(
kv_cache=kv_cache,
logical_locs=torch.tensor([4, 20], dtype=torch.int64),
remap_logical_locs=remap_source_locs,
layout=layout,
page_size=4,
)
dense_kv_b, dense_locs_b = runtime.materialize_shared_token_kv_buffer(
kv_cache=kv_cache,
logical_locs=torch.tensor([8, 16], dtype=torch.int64),
remap_logical_locs=remap_source_locs,
layout=layout,
page_size=4,
)
self.assertEqual(list(dense_kv_a.shape), [24, 1, 1])
self.assertEqual(list(dense_kv_b.shape), [24, 1, 1])
self.assertEqual(dense_locs_a.tolist(), [4, 20])
self.assertEqual(dense_locs_b.tolist(), [8, 16])
def test_materialize_paged_buffer_raises_on_pages_outside_physical_pool(self):
from sglang.srt.layers.attention.nsa import cp_shared_kv_runtime as runtime
from sglang.srt.mem_cache.cp_shared_kv_layout import CpSharedKVLayout
layout = CpSharedKVLayout(page_size=4, cp_size=4, cp_rank=1)
page_buffer = torch.arange(0, 3 * 4, dtype=torch.uint8).view(3, 4)
logical_pages = torch.tensor([1, 2, 6, 10], dtype=torch.int32)
with patch.object(
runtime, "cp_shared_kv_debug_enabled", return_value=True
), patch.object(runtime, "_all_reduce_materialized_buffer", lambda x, _: x):
with self.assertRaisesRegex(
RuntimeError,
"CP shared KV materialize got logical pages outside the physical page buffer",
):
runtime.materialize_shared_paged_buffer(
page_buffer=page_buffer,
logical_pages=logical_pages,
layout=layout,
)
class TestCpSharedKVLazyDebugLogging(unittest.TestCase):
def test_mla_write_filter_does_not_build_debug_summaries_when_debug_disabled(self):
from sglang.srt.layers.attention import nsa_backend
from sglang.srt.mem_cache.cp_shared_kv_layout import CpSharedKVLayout
layout = CpSharedKVLayout(page_size=4, cp_size=2, cp_rank=1)
forward_batch = SimpleNamespace(
uses_cp_shared_kv=True,
cp_shared_kv_layout=layout,
forward_mode="test",
)
cache_loc = torch.tensor([4, 8, 12, 16], dtype=torch.int64)
k = torch.arange(0, 8, dtype=torch.float32).view(4, 1, 2)
k_rope = torch.arange(0, 4, dtype=torch.float32).view(4, 1, 1)
with patch.object(
nsa_backend, "tensor_debug_summary", side_effect=AssertionError
), patch.object(
nsa_backend, "tensor_debug_checksum", side_effect=AssertionError
), patch(
"sglang.srt.environ.envs.SGLANG_DEBUG_CP_SHARED_KV.get",
return_value=False,
):
physical_locs, k_to_write, k_rope_to_write = (
nsa_backend.NativeSparseAttnBackend._maybe_filter_shared_mla_kv_write(
None,
forward_batch,
cache_loc,
k,
k_rope,
)
)
self.assertEqual(physical_locs.tolist(), [4, 8])
self.assertEqual(k_to_write.shape[0], 2)
self.assertEqual(k_rope_to_write.shape[0], 2)
def test_index_write_filter_does_not_build_debug_summaries_when_debug_disabled(self):
from sglang.srt.layers.attention.nsa import nsa_indexer
from sglang.srt.mem_cache.cp_shared_kv_layout import CpSharedKVLayout
layout = CpSharedKVLayout(page_size=4, cp_size=2, cp_rank=1)
forward_batch = SimpleNamespace(
uses_cp_shared_kv=True,
cp_shared_kv_layout=layout,
forward_mode="test",
out_cache_loc=torch.tensor([4, 8, 12, 16], dtype=torch.int64),
)
key = torch.arange(0, 8, dtype=torch.float32).view(4, 2)
with patch.object(
nsa_indexer, "tensor_debug_summary", side_effect=AssertionError
), patch.object(
nsa_indexer, "tensor_debug_checksum", side_effect=AssertionError
), patch(
"sglang.srt.environ.envs.SGLANG_DEBUG_CP_SHARED_KV.get",
return_value=False,
):
physical_locs, key_to_write = nsa_indexer.Indexer._filter_shared_index_write(
None,
forward_batch,
key,
)
self.assertEqual(physical_locs.tolist(), [4, 8])
self.assertEqual(key_to_write.shape[0], 2)
if __name__ == "__main__":
unittest.main()