[Bugfix] Apply attention sinks in the Transformers backend - #52156
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Transformers models with learnable attention sinks (GPT-OSS, GraniteSWA, GraniteMoeSWA, DeepSeek V4, MiMo-V2-Flash) pass the per-head sink to the attention interface as `s_aux`. Only the attention impl can fold a sink into the softmax denominator, so it must reach `Attention` at construction time, but `vllm_attention_forward` swallowed it in `**kwargs`, making every softmax in the model wrong. Locate the sink parameter, materialize this rank's slice of the heads with a sharded weight loader and pass it as `Attention(sinks=...)`. Raise instead of silently ignoring `s_aux` when a model applies sinks we failed to find. Attention instances were also invisible to `named_modules()` because they are only kept in a plain dict, so `process_weights_after_loading` never ran for them - which is where FlashInfer upcasts sinks to the fp32 tensor its kernel requires. Register them in an `nn.ModuleList`, replacing the narrower `_vllm_mla_attn` workaround. For ibm-granite/granite-swash-2b in bf16, prompt logprobs against HF eager improve from a mean absolute delta of 72.4925 to 6.8942, and greedy output goes from degenerate to correct. Co-authored-by: Claude <noreply@anthropic.com> Signed-off-by: Thomas Parnell <tpa@zurich.ibm.com>
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logits_scaling in the Transformers backend
| "modeling backend could not find the parameter holding them, so the " | ||
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s_aux isn't actually used here so this just checks that the Transformers modelling code calls forward as expected. One way this could become load bearing would be if it validates that s_aux == self_attn.sinks in the first forward
Discover the sink from the graph instead of guessing the parameter name. `_as_leaf_call` collapses the untraceable attention interface into one opaque node; record the keyword arguments that name a graph value or a parameter of the traced module, so `s_aux` becomes an edge instead of being dropped. The new `SinkFuser` reads it off that node and takes the parameter name from the resulting `get_attr` node, so nothing depends on what the model calls it, and materializes this rank's slice of the heads in `fuse` (`Attention` holds a reference to the tensor the checkpoint loads into, so it cannot wait for `init_parameters`). A sink is one attribute of an attention module, so `Fusers` now holds a tuple of fusers per module, grouped so that at most one forward-rewriting fuser and one sink fuser apply: sinks compose with the module's projection fusion, and an MLA attention with sinks gets both. Sinks stay per instance - the cache key includes the module's own parameter names and `SinkFuser.validate` re-checks the parameter is set - so a class whose layers only sometimes have sinks (MiMo-V2-Flash) is handled correctly. Apply the `s_aux` guard to the MLA interface too: no vLLM MLA backend supports sinks, so DeepSeek V4 now fails loudly rather than serving wrong output. Co-authored-by: Claude <noreply@anthropic.com> Signed-off-by: Thomas Parnell <tpa@zurich.ibm.com>
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We should be able bolt this onto the new It contains a very similar pattern where we want to read |
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
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WalkthroughChangesThe change propagates attention sink parameters through Transformers attention fusion and instance creation. Standard and MLA paths validate sink support. Tests verify attention dispatch and sink values. Attention sink propagation
Estimated code review effort: 3 (Moderate) | ~25 minutes Merge Risk: ⚪ Minimal · up to This change propagates learnable attention sinks through the Transformers backend and adds coverage for sink loading and attention registration. The available evidence indicates the intended paths are wired and validated, with no remaining merge-blocking risk identified. Suggested reviewers: Sequence Diagram(s)sequenceDiagram
participant HFModel
participant AttentionFuser
participant TransformersAttention
participant vLLMAttention
HFModel->>AttentionFuser: provide s_aux sink expression
AttentionFuser->>TransformersAttention: install sharded sink parameter
TransformersAttention->>vLLMAttention: pass s_aux during forward
vLLMAttention-->>TransformersAttention: validate sink support
TransformersAttention-->>HFModel: produce attention output
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✅ Passed checks (4 passed)
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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
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Inline comments:
In `@tests/models/transformers/test_backend.py`:
- Line 157: Update the sink-loading expression in test_sinks to call
AttentionFuser.sinks instead of the undefined singular sink method, preserving
the existing model submodule, float conversion, and CPU transfer behavior.
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I can confirm that when running the BF16 GPT-OSS model ( curl -s http://localhost:8000/v1/completions -H "Content-Type: application/json" -d '{"model":"lmsys/gpt-oss-20b-bf16","prompt":"The boiling point of water at sea level is","max_tokens":13,"temperature":0}'
cc @tdoublep |
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in Transformers modeling backend
…ect#52156) Signed-off-by: Thomas Parnell <tpa@zurich.ibm.com> Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Signed-off-by: Jyotirmoy Roy <jyotirmoyroy649@gmail.com>
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Purpose
vLLM PR #48270 adds native
GraniteSWAForCausalLM/GraniteMoeSWAForCausalLMbecause "the transformers backend does NOT handle these models correctly. Sink tokens get dropped silently and so the model gives wrong output." That is accurate, and it is a bug in the Transformers modeling backend rather than something inherent to those models, so this PR fixes it there and every current and future sink model works on the backend.Transformers models with learnable attention sinks (GPT-OSS, GraniteSWA, GraniteMoeSWA, DeepSeek V4, MiMo-V2-Flash, ...) hand the per-head sink to the attention interface as
s_aux. Only the attention impl can fold a sink into the softmax denominator, so vLLM has to pass it toAttentionat construction time.vllm_attention_forwardaccepted it in**kwargsand dropped it, so every softmax in the model was wrong.Reproduced with
ibm-granite/granite-swash-2b(greedy, transformers backend):The capital of France is''' Paris.\nThe capital of France is the largest city...'def fibonacci(n):'::::::::::::::::::::::::'The boiling point of water at sea level is'\n\n\n at at at at at'' 100 degrees Celsius (212 degrees Fahrenheit).'Two defects were involved:
Base.find_sinksnow locates the parameter Transformers keeps the sinks in,Base.init_sinkmaterializes it as this rank's slice of the heads with a sharded weight loader, andcreate_attention_instancespasses it asAttention(sinks=...).vllm_attention_forwardnow raises instead of silently ignorings_auxif a model applies sinks that we failed to find.Attentioninstances were invisible tonamed_modules(). They are only stored in a plain dict, soprocess_weights_after_loadingnever ran for them, which is where FlashInfer upcasts sinks to the fp32 tensor its kernel requires. They are now registered in annn.ModuleList, which also replaces the narrower_vllm_mla_attnworkaround that existed for MLA layers.Test plan
New tests in
tests/models/transformers/test_backend.py:test_sinksloadstiny-random/gpt-oss-bf16on the backend and asserts every attention layer has a sink and that the tensor it was handed matches the checkpoint. Skipped below SM90, where no backend supports sinks.check_implementationnow also asserts that attention instances are registered submodules, covering defect 2 for every model it is used with.Commands and results (1x GB200, transformers 5.15.0):
Model evaluation
Prompt logprobs for a 55-token paragraph,
ibm-granite/granite-swash-2b, bf16, against HF eager as the reference (mean logprob -1.5943):Greedy continuations become token-identical to HF with this PR. The residual logprob gap is a second, unrelated bug in the backend - Granite's
logits_scalingdivisor is ignored - fixed separately in #52158, which takes the same measurement to a mean absolute delta of 0.0165.Notes
is:pr is:openfor "transformers backend" and "sinks"). [Model] Add GraniteSWA and GraniteMoeSWA #48270 adds native implementations for two Granite variants; this fixes the backend for all sink models. The two changes are compatible.sharded_weight_loader(0)pattern (as ingpt_oss.py) but was not exercised, since only one GPU was available. Worth runningtest_distributed-style coverage before merge.