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vLLM-4x-RTX-3090/patches/0036-kv-restore-PP-fix-54793-no-KV-tensors-for-another-st.patch
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MOVZX 777e910dd0 Initial commit 
Signed-off-by: MOVZX <movzx@yahoo.com>
2026-10-15 01:01:52 +07:00

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From 2179328624d5718a369f599920d285040c572a9d Mon Sep 17 00:00:00 2001
From: halt95 <halt95@users.noreply.github.com>
Date: Wed, 23 Sep 2026 07:33:48 -0400
Subject: [PATCH 036/103] kv: restore PP fix #54793 -- no KV tensors for
another stage's layers
A KV cache group with no layer on a pipeline stage was still given tensors
there, and allocate_kv_cache raised StopIteration on the second stage. The
fix was lost in the v0.30.0 rebase. Test drives the real get_kv_cache_configs
with the production layout.
---
tests/v1/test_pp_kv_projection.py | 231 ++++++++++++++++++++++++++++++
vllm/v1/core/kv_cache_utils.py | 9 +-
2 files changed, 239 insertions(+), 1 deletion(-)
create mode 100644 tests/v1/test_pp_kv_projection.py
diff --git a/tests/v1/test_pp_kv_projection.py b/tests/v1/test_pp_kv_projection.py
new file mode 100644
index 0000000..11cb4ef
--- /dev/null
+++ b/tests/v1/test_pp_kv_projection.py
@@ -0,0 +1,231 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Observed on a PP=2 boot: PP1 workers died in ``allocate_kv_cache``
+(``vllm/v1/worker/utils.py:452``) with StopIteration.
+
+Root cause: ``_project_kv_cache_groups_to_worker`` keeps the GLOBAL
+``UniformTypeKVCacheSpecs`` for a group that has no layer on a PP stage, and
+v0.30.0's ``get_kv_cache_config_from_groups`` builds tensors from
+``group_spec.kv_cache_specs`` without checking ``group.layer_names``. On
+Flash-Next the layer-2 PLE short-conv state is a one-layer group of its own
+(SHORT_CONV MambaSpec, PP0 only), so PP1 received a tensor for
+``language_model.model.layers.2.ple`` that is in none of its groups. The E2
+base carried the fix (#54793); the v0.30.0 rebase dropped it.
+
+CPU-only. Specs are built from the model's shipped config (48 layers, QSA every
+4th, TP2, PP 25/23, MTP K=3, fp8 KV, block 3200). FAILING CONTROL: on
+an earlier commit (filter absent) ``test_flash_next_pp1_every_tensor_is_grouped`` and
+``test_empty_projected_uniform_group_gets_no_tensor`` fail; the no-PLE control
+shows the PLE group is the trigger.
+"""
+
+from types import SimpleNamespace
+
+import pytest
+import torch
+
+from vllm.model_executor.layers.mamba.mamba_utils import MambaStateShapeCalculator
+from vllm.v1.attention.backends.registry import MambaAttentionBackendEnum
+from vllm.v1.core import kv_cache_utils as ku
+from vllm.v1.kv_cache_interface import (
+ CircularBufferSpec,
+ FullAttentionSpec,
+ KVCacheGroupSpec,
+ KVCacheLayout,
+ MambaSpec,
+ MLAAttentionSpec,
+ UniformTypeKVCacheSpecs,
+ get_kv_quant_mode,
+)
+
+TP = 2
+BLOCK = 3200 # "Setting attention block size to 3200 tokens" (both stages)
+K = 3 # MTP num_speculative_tokens
+ATTN_PAGE = BLOCK * 1 * 256 * 2 # fp8 K+V, 1 KV head per TP rank, head_dim 256
+
+_MAIN = FullAttentionSpec(
+ block_size=BLOCK,
+ num_kv_heads=2 // TP,
+ head_size=256,
+ head_size_v=256,
+ dtype=torch.uint8,
+ kv_quant_mode=get_kv_quant_mode("fp8_e4m3"),
+)
+# QSAKeyStateCache: ring = 4 * cdiv(4 + K, 4) = 8, head 128 + MRoPE tail 12.
+_RING = CircularBufferSpec(
+ block_size=8, num_kv_heads=1, head_size=140, head_size_v=0, dtype=torch.bfloat16
+)
+# QSACompressedKeyCache: bf16 indexer, compress ratio 4.
+_COMP = MLAAttentionSpec(
+ block_size=BLOCK,
+ num_kv_heads=1,
+ head_size=128,
+ dtype=torch.bfloat16,
+ tokens_per_state=4,
+)
+_GDN = MambaSpec(
+ shapes=tuple(
+ MambaStateShapeCalculator.gated_delta_net_state_shape(
+ TP, 16, 48, 128, 128, 4, K
+ )
+ ),
+ dtypes=(torch.bfloat16, torch.float32),
+ block_size=BLOCK,
+ page_size_padded=ATTN_PAGE,
+ mamba_type=MambaAttentionBackendEnum.GDN_ATTN,
+ num_speculative_blocks=K,
+)
+# Qwen4ExpPLELayer: hc_hidden 2560*4, conv_state_len (4-1)*3, TP-replicated.
+_PLE = MambaSpec(
+ shapes=tuple(
+ MambaStateShapeCalculator.short_conv_state_shape(
+ tp_world_size=1, intermediate_size=10240, conv_kernel=10, num_spec=K
+ )
+ ),
+ dtypes=(torch.bfloat16,),
+ block_size=BLOCK,
+ page_size_padded=ATTN_PAGE,
+ mamba_type=MambaAttentionBackendEnum.SHORT_CONV,
+ tp_replicated=True,
+ num_speculative_blocks=K,
+)
+
+
+def _qsa(prefix: str, specs: dict) -> None:
+ # Registration order in Qwen4ExpQSAAttention.__init__: indexer caches first.
+ specs[f"{prefix}.self_attn.indexer.raw_key_cache"] = _RING
+ specs[f"{prefix}.self_attn.indexer.compressed_key_cache"] = _COMP
+ specs[f"{prefix}.self_attn.attn"] = _MAIN
+
+
+def _stage(lo: int, hi: int, *, mtp: bool, ple: bool = True) -> dict:
+ specs: dict = {}
+ for i in range(lo, hi):
+ prefix = f"language_model.model.layers.{i}"
+ if i == 2 and ple: # ple_layer_ids=[2]; built before the mixer
+ specs[f"{prefix}.ple"] = _PLE
+ if i % 4 == 3: # full_attention_interval=4 -> QSA
+ _qsa(prefix, specs)
+ else:
+ specs[f"{prefix}.linear_attn"] = _GDN
+ if mtp:
+ _qsa("mtp.layers.48", specs)
+ return specs
+
+
+def _config():
+ cache = SimpleNamespace(
+ kv_cache_layout="BLNHC", # reference host: "Using BLNHC KV cache layout."
+ num_gpu_blocks_override=None,
+ prefix_cache_retention_interval=0,
+ mamba_cache_mode="none",
+ get_resolved_kv_cache_layout=lambda: KVCacheLayout.BLNHC,
+ )
+ return SimpleNamespace(
+ cache_config=cache,
+ speculative_config=SimpleNamespace(
+ num_speculative_tokens=K,
+ method="mtp",
+ use_multi_module_mtp=lambda: False,
+ use_eagle=lambda: True,
+ use_eagle_block_drop=lambda: True,
+ ),
+ scheduler_config=SimpleNamespace(
+ disable_hybrid_kv_cache_manager=False, max_num_encoder_input_tokens=0
+ ),
+ attention_config=SimpleNamespace(hisparse_config=None),
+ model_config=SimpleNamespace(
+ original_max_model_len=262144,
+ max_model_len=262144,
+ hf_config=SimpleNamespace(model_type="qwen4_exp"),
+ get_total_num_hidden_layers=lambda: 48,
+ ),
+ device_config=SimpleNamespace(device_type="cuda"),
+ parallel_config=SimpleNamespace(
+ pipeline_parallel_size=2, decode_context_parallel_size=1
+ ),
+ )
+
+
+def _worker_configs(*, ple: bool = True):
+ pp0 = _stage(0, 25, mtp=False, ple=ple) # VLLM_PP_LAYER_PARTITION=25,23
+ pp1 = _stage(25, 48, mtp=True, ple=ple)
+ specs = [pp0, pp0, pp1, pp1] # TP2 x PP2
+ configs = ku.get_kv_cache_configs(_config(), specs, [4_100_000_000] * 4)
+ return specs, configs
+
+
+def _allocate_lookup(config) -> None:
+ """Verbatim group lookup of allocate_kv_cache (vllm/v1/worker/utils.py:452).
+
+ The module itself needs triton at import, so the lookup is replayed here.
+ """
+ for tensor in config.kv_cache_tensors:
+ layer_name = tensor.layers[0]
+ next(
+ (group_id, group)
+ for group_id, group in enumerate(config.kv_cache_groups)
+ if layer_name in group.layer_names
+ )
+
+
+def test_flash_next_pp1_has_an_empty_uniform_group():
+ """Guard that the shape really exercises the path (else the test below
+ could not fail): PP1 projects the PLE group to zero layers while keeping
+ the global UniformTypeKVCacheSpecs."""
+ _, configs = _worker_configs()
+ for rank in (2, 3):
+ empty = [g for g in configs[rank].kv_cache_groups if not g.layer_names]
+ assert len(empty) == 1
+ assert isinstance(empty[0].kv_cache_spec, UniformTypeKVCacheSpecs)
+ assert list(empty[0].kv_cache_spec.kv_cache_specs) == [
+ "language_model.model.layers.2.ple"
+ ]
+ for rank in (0, 1):
+ assert all(g.layer_names for g in configs[rank].kv_cache_groups)
+
+
+def test_flash_next_pp1_every_tensor_is_grouped():
+ specs, configs = _worker_configs()
+ for rank, config in enumerate(configs):
+ tensor_layers = [n for t in config.kv_cache_tensors for n in t.layers]
+ assert sorted(tensor_layers) == sorted(specs[rank]), f"rank {rank}"
+ _allocate_lookup(config) # StopIteration on PP1 before the fix
+
+
+def test_no_ple_control_has_no_empty_group():
+ """Control: remove the PLE state and no group projects empty, so the
+ old code would also have passed -- the PLE group is the trigger."""
+ _, configs = _worker_configs(ple=False)
+ for config in configs:
+ assert all(g.layer_names for g in config.kv_cache_groups)
+ _allocate_lookup(config)
+
+
+def test_empty_projected_uniform_group_gets_no_tensor():
+ """Minimal form (the #54793 unit test, config stand-in for offline runs)."""
+ spec = _MAIN
+ groups = [
+ KVCacheGroupSpec(
+ [],
+ UniformTypeKVCacheSpecs(
+ block_size=spec.block_size, kv_cache_specs={"remote.layer": spec}
+ ),
+ ),
+ KVCacheGroupSpec(["local.layer"], spec),
+ ]
+ config = ku.get_kv_cache_config_from_groups(
+ _config(), groups, available_memory=spec.page_size_bytes * 2
+ )
+ assert [t.layers for t in config.kv_cache_tensors] == [["local.layer"]]
+
+
+@pytest.mark.parametrize("rank", [0, 1])
+def test_pp0_layout_unchanged_by_filter(rank):
+ """Groups that are non-empty on a worker keep every layer: the filter only
+ drops layers the worker does not own."""
+ specs, configs = _worker_configs()
+ grouped = {n for g in configs[rank].kv_cache_groups for n in g.layer_names}
+ assert grouped == set(specs[rank])
+ tensor_layers = [n for t in configs[rank].kv_cache_tensors for n in t.layers]
+ assert sorted(tensor_layers) == sorted(grouped)
diff --git a/vllm/v1/core/kv_cache_utils.py b/vllm/v1/core/kv_cache_utils.py
index 28478b6..d1f827d 100644
--- a/vllm/v1/core/kv_cache_utils.py
+++ b/vllm/v1/core/kv_cache_utils.py
@@ -1768,7 +1768,14 @@ def get_kv_cache_config_from_groups(
layers_by_spec: defaultdict[KVCacheSpec, list[str]] = defaultdict(list)
if isinstance(group_spec, UniformTypeKVCacheSpecs):
for layer_name, spec in group_spec.kv_cache_specs.items():
- layers_by_spec[spec].append(layer_name)
+ # Fork (E2 PP fix #54793, dropped in the v0.30.0 rebase, restored
+ # the vLLM 0.30 rebase): _project_kv_cache_groups_to_worker keeps the GLOBAL
+ # UniformTypeKVCacheSpecs for a group with no layer on this PP
+ # stage, so without this filter the worker gets a tensor for
+ # another stage's layer and allocate_kv_cache's group lookup
+ # raises StopIteration (Flash-Next PP1: the layer-2 PLE state).
+ if layer_name in group.layer_names:
+ layers_by_spec[spec].append(layer_name)
elif group.layer_names:
layers_by_spec[group_spec].extend(group.layer_names)
--
2.43.0
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