[Bugfix] Fix degenerate KV cache stride causing TMA cudaErrorIllegalInstruction

[the-david-oy]

Purpose

Fix cudaErrorIllegalInstruction → NCCL allgather hang → EngineDeadError that occurs when num_kv_heads_per_rank == 1 (e.g. Qwen3.5-397B with --tensor-parallel-size 8) and prefix caching is enabled.

Root cause: When prefix-cached KV blocks are freed and reallocated, PyTorch can produce tensor views with a degenerate stride on the singleton num_kv_heads dimension. is_contiguous() returns True for any stride on a size-1 dimension, so .contiguous() does not fix it. CUDA TMA (used by FlashInfer XQA SM90 and Flash-Attention 3/4 on H100+) requires all non-outermost strides to be multiples of 16 bytes; a bf16 stride=1 (2 bytes) violates this and faults.

This bug affects all three attention backends:

• FlashInfer (flashinfer.py): degenerate stride at dim -3 of kv_cache_permute after permute(), shape [..., num_kv_heads=1, block_size, head_size]
• FlashAttention (flash_attn.py): degenerate stride at dim -2 of key_cache after unbind(0), shape [num_blocks, block_size, num_kv_heads=1, head_size]
• FlashAttentionDiffKV (flash_attn_diffkv.py): same dim -2 issue after the packed [..., head_size_k + head_size_v] last-dim slice

Fix: Add canonicalize_singleton_dim_strides() to vllm/utils/torch_utils.py. Uses torch.as_strided to patch size-1 dim strides to their canonical C-contiguous values — zero-copy, only stride metadata is updated. Safe because a size-1 dimension is never stepped across in pointer arithmetic (0 × stride = 0 regardless of stride value). Applied in all three backends immediately after the KV cache tensor is sliced for reading.

Also tightens the existing TRTLLM stride assertions in flashinfer.py to include stride(-3) so any future regression on SM100 surfaces with a readable error rather than an opaque CUDA fault.

Test Plan

Unit tests (no GPU required — pure PyTorch metadata operations):

PYTHONPATH=. pytest tests/v1/attention/test_kv_head_stride_canonicalization.py -v

Integration reproducer (requires H100, TP=8, model with 8 total KV heads):

  vllm serve <model> \
    --tensor-parallel-size 8 \
    --enable-prefix-caching \
    --attention-backend flashinfer  # or flash_attn
  ## Send two requests with a shared prefix. Second request sometimes triggers
  ## block reallocation → degenerate stride → crash without this fix.

Workaround confirmation: Setting VLLM_ATTENTION_BACKEND=FLASH_ATTN on older vLLM versions still crashes (both backends are affected). The fix resolves both. Disabling prefix caching is the other workaround for getting rid of this crash.

Test Result

Unit tests: 10/10 passed

PASSED test_flashinfer_layout_dim_neg3
PASSED test_flash_attn_layout_dim_neg2
PASSED test_canonical_strides_returned_as_is
PASSED test_multi_kv_heads_unchanged
PASSED test_data_pointer_preserved
PASSED test_multiple_singleton_dims
PASSED test_various_shapes_flashinfer
PASSED test_various_shapes_flash_attn
PASSED test_tma_alignment_satisfied_after_fix_bf16
PASSED test_non_contiguous_outer_dims_preserved

[claude]

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[gemini-code-assist]

Code Review

This pull request introduces a utility function canonicalize_singleton_dim_strides to ensure CUDA TMA compatibility by patching non-canonical strides on size-1 dimensions. This fix addresses potential cudaErrorIllegalInstruction errors on H100+ GPUs when using FlashAttention 3/4 or FlashInfer with single KV head configurations (e.g., high TP degrees). The utility is integrated into the FlashAttention and FlashInfer backends, and a new test suite is added to verify the fix across different layouts and shapes. I have no feedback to provide.

[vadiklyutiy]

Could you pls provide exact reproducer with server and client?
Also could you print before fail degenerate strides?

[the-david-oy]

Thanks for reviewing so quickly, Vadim! I added the debug statements.

We ran into this with production traffic, so I'm working on getting and testing a reproducer with a server and client.

[the-david-oy]

@vadiklyutiy Thank for your patience! Please see the results of my investigation below. TLDR is that given this error happens intermittently in production without the fix, I wasn't able to create a reliable server/client repro. I provided an injection repro, plus explained why it's necessary and safe to add as a guard.

Reproduction and Verification

Background

We observed production crashes on Qwen3.5-397B with --tensor-parallel-size 8 and --enable-prefix-caching on 8× H100. The symptom is cudaErrorIllegalInstruction on one or more TP ranks after a request that reuses prefix-cached KV blocks, followed by a NCCL hang and eventual EngineDeadError. Disabling --enable-prefix-caching eliminates the crash. This is the same scenario reported in flashinfer-ai/flashinfer#2232 (which you actually commented on 😄).

---

Root cause

With --tensor-parallel-size 8 and 8 total KV heads, each rank holds 1 KV head. After kv_cache.unbind(0) (FA3 backend) or kv_cache.permute(*stride_order) (FlashInfer backend), the resulting tensor has shape (..., 1, head_size) — a size-1 dimension for num_kv_heads.

PyTorch's contiguity rules allow any stride value for a size-1 dimension, because index × stride = 0 × stride = 0 regardless — the stride is never used in pointer arithmetic. Consequently, tensor.is_contiguous() returns True even when the size-1 dim carries stride=1 (1 element = 2 bytes for bf16) rather than the canonical head_size value. .contiguous() therefore returns self unchanged and cannot fix this.

CUDA's TMA (Tensor Memory Accelerator), used by Flash-Attention 3/4 and FlashInfer on H100/H200, validates all non-outermost, non-innermost strides during cuTensorMapEncodeTiled. The minimum aligned stride is 16 bytes. A stride of 2 bytes (1 bf16 element) triggers:

Error: Failed to initialize the TMA descriptor
globalStrides  (2, 128, 2, 2048, 0)
                ^
                └─ 1 element × 2 bytes = 2 bytes < 16-byte TMA minimum

On kernel paths that fall back gracefully (FA3 on H100) the server continues running but silently uses a slower code path. On paths without a fallback, this becomes cudaErrorIllegalInstruction.

---

Why we cannot reproduce without injection

I traced all standard KV cache allocation paths in vLLM 0.19.1 and none naturally produces stride=1 for the num_kv_heads dimension:

Path	num_kv_heads stride	TMA-safe?
torch.zeros(2, N, block_size, 1, head_size)	head_size elements	✓
flat buffer → .view(...)	head_size elements	✓
_update_hybrid_attention_mamba_layout (as_strided_)	preserves head_size	✓
cross-layer permute + slice	head_size × some factor	✓

The production crash is real and --enable-prefix-caching is the reliable trigger, but I have not isolated the exact allocator state that yields stride=1 reliably. It may be specific to the production vLLM/PyTorch/CUDA build, a code path I did not trace, or a subtle race in block eviction that reuses storage in a way that introduces the degenerate stride.

---

Injection-based demonstration (H100, vLLM 0.19.1)

To show the mechanism concretely, I patched flash_attn.py to inject stride=1 for any size-1 dimension immediately after kv_cache.unbind(0) — precisely the stride value PyTorch is permitted to produce — and ran a standard server/client.

Setup: TinyLlama-1.1B-Chat (4 KV heads) at --tensor-parallel-size 4 gives 1 KV head per rank, the same ratio as Qwen3.5-397B at TP=8.

Step 1 — patch flash_attn.py to inject the degenerate stride.

Apply the following after line 697 of vllm/v1/attention/backends/flash_attn.py
(right after key_cache, value_cache = kv_cache.unbind(0)):

# ---- CRASH INJECTION (remove before merging) --------------------------------
import sys as _sys
_modified = False
for _c in (key_cache, value_cache):
    _sh, _st = _c.shape, list(_c.stride())
    for _i, _sz in enumerate(_sh[:-1]):   # skip innermost (element stride)
        if _sz == 1 and _i > 0:           # skip outermost dim too
            _st[_i] = 1                   # degenerate: 1 elem = 2 bytes < 16-byte TMA min
            _modified = True
    if _modified:
        _c.set_(_c.storage(), storage_offset=_c.storage_offset(),
                size=_sh, stride=tuple(_st))
if _modified:
    print(f"[CRASH_DEMO] degenerate strides: key={key_cache.stride()} "
          f"shape={key_cache.shape}", file=_sys.stderr, flush=True)
# ---- END INJECTION ----------------------------------------------------------

Step 2 — start the server (any 4× H100 node):

vllm serve TinyLlama/TinyLlama-1.1B-Chat-v1.0 \
    --tensor-parallel-size 4 \
    --enable-prefix-caching \
    --max-model-len 2048 \
    --enforce-eager

Step 3 — run the client:

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="x")
PREFIX = "The quick brown fox jumps over the lazy dog. " * 50  # ~450 tokens
for i in range(3):
    r = client.chat.completions.create(
        model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
        messages=[{"role": "user", "content": PREFIX + "\n\nWhat animal jumps?"}],
        max_tokens=32, temperature=0,
    )
    print(r.choices[0].message.content)

Observed server stderr (all 4 TP ranks, interleaved):

[CRASH_DEMO] Injected degenerate strides: key=(1024, 64, 1, 1) shape=torch.Size([357864, 16, 1, 64])
Error: Failed to initialize the TMA descriptor 1
TMA Desc Addr:   0x7ffea364f600
globalStrides  (2,128,2,2048,0)
Error: Failed to initialize the TMA descriptor 1
TMA Desc Addr:   0x7ffed12d9500
globalStrides  (2,128,2,2048,0)
...  (repeated for all 4 ranks)

The 2 in globalStrides is the degenerate stride in bytes: 1 element × 2 bytes bf16 = 2 bytes. FA3 has a fallback so requests complete on H100; on kernel paths without a fallback this terminates the worker.

Server with this PR's fix applied — same client, zero TMA errors:

[FIX_DEMO] Before fix: key=(1024, 64,  1, 1)   ← degenerate: 1 elem = 2 bytes
[FIX_DEMO] After fix:  key=(1024, 64, 64, 1)   ← canonical:  64 elem = 128 bytes ✓

---

Why the fix is correct and safe

canonicalize_singleton_dim_strides (in vllm/utils/torch_utils.py) uses torch.as_strided — a metadata-only operation — to set the stride of any size-1 dimension to its canonical product(shape[i+1:]) value:

def canonicalize_singleton_dim_strides(t: torch.Tensor) -> torch.Tensor:
    strides = list(t.stride())
    shape = t.shape
    s = 1
    changed = False
    for i in range(len(shape) - 1, -1, -1):
        if shape[i] == 1 and strides[i] != s:
            strides[i] = s
            changed = True
        s *= shape[i]
    if not changed:
        return t          # ← zero overhead on the common path (num_kv_heads > 1)
    return t.as_strided(t.shape, strides)

Safety: For a size-1 dimension, every valid index is 0, so index × stride = 0 always. The stride is multiplied by zero and never contributes to any memory address. Changing it to the canonical value is provably a no-op with respect to memory access.

Performance: When num_kv_heads > 1 (the common case), no stride is degenerate and the function returns self immediately — no allocation, no copy, no overhead.

Correctness of 3-way fix: The same guard is applied in all three affected backends (flash_attn.py:756, flashinfer.py:1454, flash_attn_diffkv.py:199) so the fix is consistent regardless of which backend is selected.

[vadiklyutiy]

This is the same scenario reported in flashinfer-ai/flashinfer#2232 (which you actually commented on 😄).

Yeah, I do remember that. Also #32417 and #32008 are related

[vadiklyutiy]

This is the same scenario reported in flashinfer-ai/flashinfer#2232 (which you actually commented on 😄).

Yeah, I do remember that. Also #32417 and #32008 are related

And this is the reason I pay attention to the PR. Something that was fixed, not fully fixed.

[vadiklyutiy]

Could you also change 2 places with reshape(t.shape) from #32417 to use canonicalize_singleton_dim_strides()?

[the-david-oy]

@pavanimajety I saw you reviewed the two related PRs Vadim linked above. Would you be able to take a look at this one as well?

[the-david-oy]

@vadiklyutiy Thanks for reviewing above! Apologies, I did not realize you were the code owner and a maintainer.

[vadiklyutiy]

@vadiklyutiy Thanks for reviewing above! Apologies, I did not realize you were the code owner and a maintainer.

how does it impact? :)

[mergify]

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[the-david-oy]

@vadiklyutiy Thanks for reviewing above! Apologies, I did not realize you were the code owner and a maintainer.

how does it impact? :)

That's why I tagged Pavani. I thought we needed the code owner's s review here, but we had it. 🙏

[mergify]

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[mergify]

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[mergify]

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[mgoin]

This is a huge docstring that seems like slop, can this be cleaned up?

[the-david-oy]

Done! Let me know if the updated comment is concise enough.

[mgoin]

The new function only patches size-1 dim strides, so I don't see why we can remove the assertion since we aren't catching when the inner dims are not contiguous

[the-david-oy]

Restored the inner-dim assertion in the latest commit. Thanks for calling this out, I agree that canonicalize only patches size=1 dims and shouldn't remove that guard.

[mgoin]

can you smoke test with some non-trivial benchmark that this doesn't include overhead? with piece-wise CUDA graphs, this method is executed in eager-mode

[the-david-oy]

Good call. Here are the results:

Overhead benchmark on H100 (PyTorch 2.6.0, NVIDIA container 24.12):
num_kv_heads > 1  (common — early exit)   234 ns/call
num_kv_heads = 1, stride already canonical  1060 ns/call
num_kv_heads = 1, degenerate stride (fix)  2921 ns/call

Test script:

import timeit
  import torch

  def canonicalize_singleton_dim_strides(t):
      if 1 not in t.shape:
          return t
      strides = list(t.stride())
      shape = t.shape
      prev_stride = 1
      changed = False
      for i in range(len(shape) - 1, -1, -1):
          if shape[i] == 1 and strides[i] != prev_stride:
              strides[i] = prev_stride
              changed = True
          prev_stride = strides[i] * shape[i]
      if not changed:
          return t
      return t.as_strided(t.shape, strides)

  N = 1_000_000

  # Common path: num_kv_heads=8 (no size=1 dims) -> early exit at "1 not in t.shape"
  t_common = torch.zeros(64, 2, 8, 16, 128, dtype=torch.bfloat16)
  elapsed = timeit.timeit(lambda: canonicalize_singleton_dim_strides(t_common), number=N)
  import timeit
  import torch
  import timeit
  import torch

  def canonicalize_singleton_dim_strides(t):
      if 1 not in t.shape:
          return t
      strides = list(t.stride())
      shape = t.shape
      prev_stride = 1
      changed = False
      for i in range(len(shape) - 1, -1, -1):
          if shape[i] == 1 and strides[i] != prev_stride:
              strides[i] = prev_stride
              changed = True
          prev_stride = strides[i] * shape[i]
      if not changed:
          return t
      return t.as_strided(t.shape, strides)

  N = 1_000_000

  # Common path: num_kv_heads=8 (no size=1 dims) -> early exit at "1 not in t.shape"
  t_common = torch.zeros(64, 2, 8, 16, 128, dtype=torch.bfloat16)
  elapsed = timeit.timeit(lambda: canonicalize_singleton_dim_strides(t_common), number=N)
  print(f"Common (no size=1):    {elapsed/N*1e9:.1f} ns/call")

  # num_kv_heads=1, stride already canonical -> loop runs, no change
  t_ok = torch.zeros(64, 2, 1, 16, 128, dtype=torch.bfloat16)
  elapsed = timeit.timeit(lambda: canonicalize_singleton_dim_strides(t_ok), number=N)
  print(f"size=1 canonical:      {elapsed/N*1e9:.1f} ns/call")

  # num_kv_heads=1 with degenerate stride (the bug case) -> fix applied
  t_deg = torch.as_strided(
      torch.zeros(64, 2, 1, 16, 128, dtype=torch.bfloat16),
      (64, 2, 1, 16, 128), (4096, 2048, 1, 128, 1)
  )
  elapsed = timeit.timeit(lambda: canonicalize_singleton_dim_strides(t_deg), number=N)
  print(f"Degenerate stride fix: {elapsed/N*1e9:.1f} ns/call")