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Qwen3.5-0.8B and Qwen3.6-35B-A3B (and their multimodal variants) use QwenGatedDeltaNetAttention, which inherits mamba_type=GDN_ATTN from the GatedDeltaNetAttention base class. When VLLM_BATCH_INVARIANT=1 the selector called GDNAttentionBackend.supports_batch_invariance(), which defaulted to False, raising RuntimeError for every Qwen3.5/3.6 request. Fixes: 1. GDNAttentionBackend.supports_batch_invariance() → True, so the selector allows GDN layers to run under VLLM_BATCH_INVARIANT=1. 2. _forward_core: when VLLM_BATCH_INVARIANT=1, process each prefill sequence independently through chunk_gated_delta_rule (one kernel launch per sequence with its own cu_seqlens=[0,seq_len] and fresh chunk_indices/chunk_offsets). The FLA/Triton kernel's internal chunking depends on batch geometry, so the same sequence produces different logprobs when co-batched with other sequences; per-sequence dispatch guarantees bit-identical results regardless of batch size. 3. _forward_core: decode paths (split_non_spec and decode-only) also loop per-sequence under VLLM_BATCH_INVARIANT=1 for the same reason. 4. Test utils: detect Qwen3.5 (model_type="qwen3_5") and Qwen3-Next/3.6 (dual_chunk_attention_config present) and restrict BACKENDS to ["GDN_ATTN"]; add get_attention_config() helper that returns an empty dict for GDN_ATTN (auto-selected by model arch, not via attention_config["backend"]). 5. Test: pass enforce_eager=True for GDN_ATTN (no CUDA-graph support in batch-invariant mode); skip flex_attn block params for GDN_ATTN. Tested on H100 NVL: Qwen3-30B-A3B 5/5 ✅, Qwen3.5-0.8B and Qwen3.6-35B-A3B now pass with VLLM_BATCH_INVARIANT=1. Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: Yuval Luria <yuvalluria@users.noreply.github.com> Signed-off-by: Yuval Luria <yluria@redhat.com>
rearrange_mixed_qkv returns [1, seq_len, heads, dim] (leading batch=1). The decode per-sequence loops were slicing query/key/value with [ss:se] (first dim), so for sequence i>0 the slice was empty — causing fused_sigmoid_gating_delta_rule_update to raise: ValueError: batch size expected 1 rather than 0 when using cu_seqlens Fix: use [:, ss:se] to slice along the sequence dimension in both the split-case decode loop and the decode-only loop. The prefill loop (chunk_gated_delta_rule path) already used [:, s:e]. Tested on H100 NVL: Qwen3.5-0.8B 5/5 ✅, Qwen3.6-35B-A3B retesting. Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: Yuval Luria <yuvalluria@users.noreply.github.com> Signed-off-by: Yuval Luria <yluria@redhat.com>
non_spec_query_start_loc and non_spec_state_indices_tensor are typed as Tensor | None; assert-not-None before indexing them in the three VLLM_BATCH_INVARIANT per-sequence loops so mypy is satisfied. Similarly assert prefill_query_start_loc is not None before .tolist(). Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: Yuval Luria <yuvalluria@users.noreply.github.com> Signed-off-by: Yuval Luria <yluria@redhat.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: Yuval Luria <yuvalluria@users.noreply.github.com> Signed-off-by: Yuval Luria <yluria@redhat.com>
The batched causal_conv1d_fn Triton kernel is not reduction-order invariant: internal tile geometry depends on total sequence length, causing NaN outputs in specific GDN layers at large batch sizes (e.g. np=29 prefill). This was the remaining divergence source after the per-sequence chunk_gated_delta_rule and decode-path fixes. When VLLM_BATCH_INVARIANT=1, process each prefill sequence through causal_conv1d_fn independently with a sliced conv_state view, then concatenate. The non-BATCH_INVARIANT path is unchanged. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: Yuval Luria <yuvalluria@users.noreply.github.com> Signed-off-by: Yuval Luria <yluria@redhat.com>
… invariance Remove the per-seq causal_conv1d_fn loop (hunk 3.5): the metadata=None dispatch path in causal_conv1d_fn launches the Triton kernel with different tiling than the metadata path, producing numerically different results and breaking the needle test. Add use_cp=False to fi_chunk_gated_delta_rule under VLLM_BATCH_INVARIANT: the FlashInfer kernel's use_cp="auto" selects different kernel variants based on batch composition, causing ~0.002 logprob divergence between BS=1 and BS=N (exact match of finetunej's diagnosis in vllm-project#49827). Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: Yuval Luria <yuvalluria@users.noreply.github.com> Signed-off-by: Yuval Luria <yluria@redhat.com>
Replace .item()-based slicing and ssm_state[si:si+1] initial_state with tensor-index slices (_si_dec = state_indices[i:i+1]) passed as ssm_state_indices directly, and pass the full ssm_state pool as initial_state. This avoids Python-level graph breaks during CUDA graph capture and is consistent with how QwenGDNAttentionBackend already handles the mixed-batch decode path. Signed-off-by: Yuval Luria <yuvalluria@users.noreply.github.com> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: Yuval Luria <yuvalluria@users.noreply.github.com> Signed-off-by: Yuval Luria <yluria@redhat.com>
…le fused path When VLLM_BATCH_INVARIANT=True and in decode-only mode, GEMM (N sequences) and GEMV (1 sequence) use different CUDA kernel variants with different FP accumulation order. The ~1e-7 difference propagates through in_proj_qkvz and in_proj_ba, then gets amplified through the SSM recurrence (b_h = gate*b_h + beta*v*k^T) to ~4e-5 per decode step. Fix: project each decode token independently (N separate GEMV calls) so the projections match BS=1 behavior exactly. Forward context is used to detect the decode-only batch invariant case with minimal overhead. Also add `not VLLM_BATCH_INVARIANT` guard on use_fused_gdn_decode: the fused norm-packed kernel processes all decode tokens jointly, which is not safe under batch invariance mode. Signed-off-by: Yuval Luria <yluria@redhat.com>
When VLLM_BATCH_INVARIANT=True: - Skip fused packed-decode path (enable_packed_recurrent_decode) so the per-sequence decode loop is always used, ensuring BS=1 == BS=N. - Run causal_conv1d_fn once per prefill sequence instead of batched, so conv states are identical regardless of batch composition. Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: Yuval Luria <yluria@redhat.com>
non_spec_query_start_loc covers ALL non-spec sequences (both prefill and decode when chunked-prefill mixes them). Previous fix iterated only over num_prefills, causing a size mismatch crash when decode tokens were in the same batch as prefill tokens. Fix: iterate numel()-1 of the cu_seqlens tensor instead of num_prefills. Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: Yuval Luria <yluria@redhat.com>
Different GEMM M dimensions (BS=1: M=prompt_len vs BS=N: M=total_tokens) cause cublas to select different algorithms with different FP accumulation order, producing ~1e-3 logprob drift amplified by SSM recurrence. Project each prefill sequence independently so M matches the BS=1 case. Only activates for pure-prefill batches (num_decodes==0) to keep the mixed prefill+decode path unchanged. Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: Yuval Luria <yluria@redhat.com>
rmsnorm_fn from layernorm_guard.py uses calc_rows_per_block() which selects ROWS_PER_BLOCK as a Triton constexpr based on M (total rows). Different M values (e.g. BS=1 prefill vs BS=N prefill) compile separate Triton kernel binaries with different FP reduction orders for the row variance sum, producing different per-row results for the same input. When VLLM_BATCH_INVARIANT=True, fall back to the native PyTorch path (forward_native) which uses torch.mean(dim=-1) — a per-row reduction that is independent of total batch size. Fixes 24/32 prompt failures in test_logprobs_bitwise_batch_invariance_bs1_vs_bsN for Qwen3.5-0.8B (GDN_ATTN backend). Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: Yuval Luria <yluria@redhat.com>
[P1] Unify per-request projection for all batch types via non_spec_query_start_loc. The previous code only handled pure-decode and pure-prefill; mixed batches fell through to a single batched GEMM, breaking batch invariance. The unified loop covers decode (1-token slices), prefill (seq-len slices), and mixed batches uniformly. Speculative decoding explicitly raises RuntimeError. [P2] Restrict supports_batch_invariance() to NVIDIA CUDA only. The ROCm AITER and XPU forward paths are unmodified and not batch-invariant. [P2] Remove GDN_ATTN from the default CUDA backend list in test utils. GDN_ATTN is now only added when the test model actually contains GDN layers (model_type="qwen3_5" or dual_chunk_attention_config present). The model-type check is now unconditional, not gated on VLLM_TEST_MODEL. Signed-off-by: Yuval Luria <yluria@redhat.com>
VLLM_BATCH_INVARIANT=1 required --enforce-eager on GDN models: capture aborted with "Cannot copy between CPU and CUDA tensors during CUDA graph capture unless the CPU tensor is pinned". Two host/device syncs on the decode path caused it. 1. The input projection called non_spec_query_start_loc.tolist() (D2H) to drive the per-sequence loop. For a pure-decode batch each non-spec sequence contributes exactly one token, so the boundaries are [0, 1, ... num_tokens] and follow from the token count with no device read; the trip count has to be static for capture anyway. Prefill and mixed batches keep the existing path, since they are not captured and their lengths genuinely vary. 2. Both per-sequence decode loops rebuilt cu_seqlens with torch.tensor([0, 1], device=...) on every iteration, an H2D copy from pageable memory and also num_decodes * num_layers redundant copies per step. It is now a cached buffer allocated during warmup. The decode loops were already written capture-safe; this removes the two syncs in front of them. Measured on H100 with Qwen/Qwen3.6-35B-A3B-FP8: invariance still holds bitwise (12/12) under capture, throughput goes from 84.97 to 638.09 tok/s, and the cost over a non-invariant baseline drops from 16.1x to 1.45x. Signed-off-by: bfoing <40759640+bfoing@users.noreply.github.com>
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Follow-up to vllm-project#45819, opened against that branch so it can be merged or
cherry-picked directly.
VLLM_BATCH_INVARIANT=1currently requires--enforce-eageron GDN models.Without it, capture aborts with:
Two host/device syncs run on the decode path and capture rejects both:
non_spec_query_start_loc.tolist()(D2H) todrive the per-sequence loop. For a pure-decode batch each non-spec
sequence contributes exactly one token, so the boundaries are
[0, 1, ... num_tokens]and follow from the token count with no deviceread. The trip count has to be static for capture anyway. Prefill and
mixed batches keep the existing
.tolist()path, since they are notcaptured and their lengths genuinely vary.
cu_seqlenswithtorch.tensor([0, 1], device=...)every iteration. That is an H2D copyfrom pageable memory, and also
num_decodes * num_layersredundantcopies per step. Now a cached buffer allocated during warmup.
The decode loops were already written capture-safe (tensor-index slices,
range(attn_metadata.num_decodes)); this removes the two syncs in front ofthem.
Measurements
H100,
Qwen/Qwen3.6-35B-A3B-FP8, serving path. 4 prompts sent solo thenco-batched at BS 2/8/32, comparing token ids and full precision logprobs.
All rows same host:
Invariance still holds bitwise under capture, and the cost against the
non-invariant baseline drops from 16.1x to 1.45x. Capture was verified to
actually occur (
FULL_AND_PIECEWISE, real capture sizes) rather thansilently falling back to eager.
tests/v1/determinism/test_batch_invariance.pywith graphs enabled goesfrom 4 passed / 3 failed to 6 passed / 1 failed on
Qwen/Qwen3.5-0.8B.The remaining failure,
test_decode_logprobs_match_prefill_logprobs, isunrelated to this change: exactly 60 mismatches with or without the patch,
under eager or graphs, with or without vllm-project#43317, and it passes on the non-GDN
Qwen/Qwen3-1.7B.