vllm-omni/docs/user_guide/diffusion/attention_backends/trtllm.md

TRTLLM Attention

TRTLLM_ATTN runs FlashInfer's trtllm-gen FMHA kernels on datacenter Blackwell GPUs. Selected on its own it computes dense BF16 attention, and its dense performance is on par with FlashAttention-4. It also provides two opt-in, lossy acceleration modes that can be enabled independently or together:

Mode	Config key	What it changes
Skip-Softmax	skip_softmax	Skips the Softmax and PV work of KV tiles whose scores are too low to matter
SAGE quantization	quant	Runs QK^T in INT8 or FP8 and PV in FP8 instead of BF16

Both keys are set through --diffusion-attention-config, as JSON or as vLLM-style dotted flags; the attention backend overview covers both syntaxes and per-role resolution. The examples on this page use JSON.

Attention computes scores S = QK^T, probabilities P = softmax(S), and the output O = PV. SAGE lowers the precision of both matrix multiplications. Skip-Softmax keeps QK^T dense and removes the Softmax and PV work for tiles that QK^T shows to be unimportant. The two modes therefore compose: SAGE makes every tile cheaper, and Skip-Softmax reduces the number of tiles that reach the second half of the kernel.

Requirements

TRTLLM_ATTN requires all of the following:

• an sm100a or sm103a GPU (B200, B300, GB200, GB300); workstation Blackwell (sm120/sm121) is not supported;
• head_dim=128;
• a FlashInfer build that exposes the trtllm-gen kernels (0.6.16rc1 or newer for SAGE);
• an attention path that is mask-free or provides packed-padding metadata. Structural suffix padding is expressed through that metadata rather than an attn_mask tensor.

An explicit selection that violates these requirements raises at startup instead of silently falling back to another backend.

Sequence parallelism

TRTLLM_ATTN runs under no sequence parallelism or under pure Ulysses. Ulysses redistributes the sequence and attention heads around the attention call, but the local computation still goes through the configured backend, so both optional modes work unchanged. Ring and AllGather-KV do not:

• Ring runs its own distributed attention and bypasses the backend. Combining Ring with a skip_softmax key raises; a quant key would be silently ignored, so do not combine them either.
• AllGather-KV changes the Q/KV distribution and is rejected when TRTLLM_ATTN is selected.

Configure eight-way Ulysses alone as:

--usp 8 --ring 1 --allgather-degree 1

A degree of 1 disables that sequence-parallel mode. It does not limit the server to one GPU or affect tensor, pipeline, or VAE parallelism.

Quick start

On datacenter Blackwell the platform selects TRTLLM_ATTN by default when the model declares a compatible path. To select it explicitly, for dense BF16:

vllm serve <model> --omni \
  --diffusion-attention-backend TRTLLM_ATTN

To enable both optimizations with a conservative Skip-Softmax setting and FP8 SAGE:

vllm serve <model> --omni \
  --diffusion-attention-config '{
    "default": {
      "backend": "TRTLLM_ATTN",
      "quant": {"dtype_qk": "fp8_e4m3", "q_block_size": 1, "k_block_size": 16},
      "skip_softmax": {"threshold": 0.05, "disabled_until_timestep": 0.97}
    }
  }'

The startup log reports the selection; look for one of:

Defaulting to diffusion attention backend TRTLLM_ATTN (datacenter Blackwell ..., head_dim 128)
Resolved diffusion attention backend 'TRTLLM_ATTN' for role='self' via attention_config.default

Skip-Softmax

Skip-Softmax (BLASST) skips the Softmax and PV work of KV tiles whose scores fall far below the running row maximum. QK^T always runs, and how many tiles qualify depends on the input. The feature design gives the skip test.

Configuration keys

Key	Range	Meaning
threshold	>= 0; useful values in (0, 1)	Skip threshold, independent of sequence length. Calibration-free.
target_sparsity	[0, 1]	Requested operating point on the checkpoint's calibrated curve. Requires calibration metadata.
disabled_until_timestep	[0, 1]; default 0	Keeps attention dense while the normalized timestep t > D; 0 disables the gate.

threshold and target_sparsity are two ways to set the same kernel threshold; setting both is a configuration error. Exactly one of them enables Skip-Softmax.

Direct threshold

Set threshold when the checkpoint carries no calibration. threshold=0 skips nothing; larger values skip more tiles and lower output fidelity. Values around 0.05 are a reasonable first try for video DiTs; tune against dense output on the same prompt and seed. The value is independent of sequence length; see the feature design for how it reaches the kernel.

vllm serve <model> --omni \
  --diffusion-attention-config \
  '{"default":{"backend":"TRTLLM_ATTN","skip_softmax":{
    "threshold":0.05,"disabled_until_timestep":0.97}}}'

Calibrated target sparsity

A fixed threshold does not produce a fixed fraction of skipped tiles because the score distribution changes with the model, prompt, and shape. NVIDIA ModelOpt can calibrate a per-model curve from sparsity to threshold and store it in the checkpoint's transformer config.json under sparse_attention_config. target_sparsity selects a point on that curve. The achieved sparsity still varies per prompt, layer, and denoising step; the calibration makes the requested value a meaningful target, not a guarantee.

What vLLM-Omni takes from the checkpoint:

• The curve coefficients. Only the a * exp(b * target_sparsity) form ModelOpt writes is supported; another formula is rejected at startup.
• The ignore list: attention layers matching those patterns stay dense.
• For multi-expert Diffusers checkpoints, transformer_2/config.json is read separately; if it is missing, transformer_2 stays dense with a warning.

Checkpoint-level target_sparsity and disabled_until_timestep defaults are not consumed; set them in the vLLM-Omni configuration. The feature design documents the checkpoint format and how the coefficients reach the kernel.

To calibrate a checkpoint yourself, follow the ModelOpt Skip-Softmax example, which exports a copy of the checkpoint with the coefficients embedded. The fitted curve depends on the attention statistics of the calibration shape, so calibrate at the resolution and frame count you will serve.

Requesting target_sparsity for a checkpoint without calibration is a startup error that names the threshold alternative. The ModelOpt Wan2.2 FP8 checkpoint is a calibrated example:

vllm serve nvidia/Wan2.2-T2V-A14B-Diffusers-FP8 --omni \
  --diffusion-attention-config \
  '{"default":{"backend":"TRTLLM_ATTN","skip_softmax":{
    "target_sparsity":0.75,"disabled_until_timestep":0.86}}}'

Timestep gating

The early, high-noise denoising steps fix the global layout of the output, and their errors propagate through every later step. disabled_until_timestep keeps those steps dense and enables Skip-Softmax once the normalized timestep t reaches the configured cutoff. The default 0 is a sentinel that turns the gate off: Skip-Softmax runs on every step and no timestep needs to be published. 1.0 also leaves no step dense, but goes through the gate and therefore requires the pipeline to publish t.

t is the scheduler's own timestep normalized to [0, 1], published by the pipeline for each denoising step; for rectified-flow models it is the current sigma. It runs from near 1.0 down to 0.0, but not linearly in the step index: flow-shifted schedules spend many of their steps at high t. The number of dense steps a given cutoff produces therefore depends on the model's schedule and step count, and follows from the actual timestep sequence:

dense_steps = count(t[i] > disabled_until_timestep)

Derive the cutoff from the schedule of the model you are serving rather than reusing another model's value; the feature design works through an example.

A pipeline that does not publish t stays dense whenever disabled_until_timestep > 0 is set, and logs a warning once. Pipelines publish it through DenoiseProgressMixin.record_denoise_step.

SAGE quantization

SAGE quantization runs both attention matrix multiplications in low precision: Q and K are quantized to INT8 or FP8 E4M3 for QK^T, and P and V use FP8 E4M3 for PV. P is quantized inside the FMHA kernel; V is quantized per channel before the kernel call. vLLM-Omni exposes the Q/K dtype and the Q/K scale granularity. The P and V formats are fixed by the kernel, so the quant key has no V dtype for this backend. This mode is distinct from the standalone SageAttention backends, which use their own kernels.

FP8 Q/K kernels exist on sm100a and sm103a; INT8 Q/K kernels exist on sm100a only.

Key	Values	Meaning
dtype_qk	int8, fp8_e4m3	Q/K quantization dtype. Setting it enables SAGE.
q_block_size	1, 4, 16	Consecutive query tokens sharing one Q scale; default 1
k_block_size	1, 4, 16	Consecutive key tokens sharing one K scale; default 16

Start with the defaults, q_block_size=1 and k_block_size=16, and try smaller K blocks only if output quality does not meet expectations; smaller blocks give finer scales at some cost in speed. When a KV sequence in a call is shorter than k_block_size, that call falls back to dense attention and a warning is logged once.

vllm serve <model> --omni \
  --diffusion-attention-config \
  '{"default":{"backend":"TRTLLM_ATTN","quant":{
    "dtype_qk":"fp8_e4m3","q_block_size":1,"k_block_size":16}}}'

The quant key is shared with FLASHINFER_ATTN, but each backend validates its own fields: float16/bfloat16 Q/K dtypes and dtype_vo are FLASHINFER_ATTN options and are rejected here.

Composing both modes

skip_softmax and quant may appear in the same AttentionSpec. Their quality effects compound, so establish a dense baseline, enable one mode at a time, and then evaluate the combination on the same prompts and seeds.

Modes configured in default apply to every attention role that has no per_role entry. A per_role spec replaces the whole spec for that role and does not inherit quant or skip_softmax from default, so {"backend":"TRTLLM_ATTN"} is the way to keep a short or sensitive attention site dense while the long DiT sequence uses both modes:

vllm serve MiniMaxAI/MiniMax-H3 --omni \
  --diffusion-attention-config '{
    "default": {
      "backend": "TRTLLM_ATTN",
      "quant": {"dtype_qk": "fp8_e4m3", "q_block_size": 1, "k_block_size": 16},
      "skip_softmax": {"threshold": 0.05, "disabled_until_timestep": 0.97}
    },
    "per_role": {
      "minimax_h3.token_refiner": {"backend": "TRTLLM_ATTN"}
    }
  }'

Role names are declared by each model; the attention backend overview covers the resolution order and the equivalent Python API.

End-to-end speedup depends on the share of step time spent in attention, the sequence length, the chosen Q/K precision, and the tile sparsity the input actually yields. Benchmark the exact workload rather than extrapolating from another model.