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Code Review
This pull request introduces the NVIDIA OmniDreams pipeline, an autoregressive video world model, along with its corresponding DiT model, 3D rotary position embeddings, block KV cache, text-embedding transform, flow-match scheduler, and custom pipeline stages. Feedback on the implementation identifies several critical issues: the cross-view attention incorrectly performs global attention instead of temporal-restricted attention; the text encoder is called without an attention mask, potentially corrupting embeddings; accessing VAE latent mean and standard deviation directly on the VAE instance will raise an AttributeError; the KV cache retrieval methods will crash when called outside of active update transactions; and decoding non-overlapping latent chunks independently will introduce temporal seam artifacts at chunk boundaries.
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Add OmniDreams pipeline to multimodal_gen, supporting:
- Flat checkpoint loading (570-key .pt, non-diffusers layout) with
post-load parameter fusion matching FlashDreams conventions
- Autoregressive rollout with BlockKVCache (sink + rolling window)
and per-chunk KV-cache lifecycle
- 3D NeoX RoPE (44:42:42) with shift_t for chunk positioning
- 2-step flow-match scheduler (sigmas {1.0, 0.8036, 0.0})
- Text conditioning via full_concat 100352 embedding
(Cosmos-Reason1-7B) with explicit attention mask
- Per-chunk VAE latent concatenation with single-pass Wan 2.1 decode
for temporal continuity
- HDMap/trajectory conditioning pipeline (per-chunk VAE-encode)
- Tensor parallelism via ColumnParallelLinear/RowParallelLinear
- Precomputed cross-attention K/V caching
- HTTP API fields for hdmap_path and num_views
- 43/43 CPU unit tests covering component construction, RoPE,
BlockKVCache, scheduler, DiT forward, denoising stage, registry,
and regression guards
- GPU CI coverage with consistency thresholds and perf baselines
Relates to sgl-project#27214
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@mickqian Have a look plz? :) |
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fantastic. if it's ready, could you attach some examples? |
… HD-map conditioning Consolidate pre-processing (text encode, i2v VAE, HD-map per-chunk encoding) into OmniDreamsBeforeDenoisingStage. Implement full autoregressive rollout in OmniDreamsDenoisingStage: per-block BlockKVCache lifecycle, 3D NeoX RoPE shift_t, 2-step self-forcing denoise (sample + context-noise re-forward), frame-0 i2v pinning, and per-chunk HD-map indexing. Add comprehensive unit tests covering RoPE correctness, KV-cache fill/roll/steady-state, scheduler 2-step sigmas, Cosmos-Reason1 100352-dim text embedding, tiny DiT end-to-end forward, AR unconditioned/i2v/multi-chunk rollout, HD-map per-frame slicing, and reference image preprocessing. Constraint: 2-step distilled model trades temporal smoothness for real-time speed; blur is expected behavior from Self-Forcing + DMD distillation, not a bug. Confidence: high Scope-risk: moderate (AR loop is new; GPU validation still needed) Not-tested: end-to-end generation on full OmniDreams 2B checkpoint; HD-map VAE numerics on GPU (flagged with TODO) Co-Authored-By: Claude <noreply@anthropic.com>
…ontext-noise KV cache write At AR step 0, the context-noise forward (finalize_kv_cache) must store CLEAN K/V for frame 0, matching FlashDreams _maybe_inject_image() inside _predict_branch which fires during finalize_kv_cache -> predict_flow. Without this re-injection, frame-0 KV cache entries are noise-corrupted at sigma ~0.13, causing a distribution shift that accumulates into progressively blurry video across AR chunks.
…ch training The OmniDreams checkpoint is trained with FlashDreams' CosmosReason1TextEncoder, which runs the Cosmos-Reason1 LM on the full padded sequence with no attention mask. The DiT cross-attends over all 512 token embeddings (valid + padding), so the padding-token hidden states are part of the trained conditioning distribution. Passing an explicit mask changes those padding states drastically (abs diff up to ~99 after the per-token mean-normalize in full_concat_embeddings), pushing conditioning out of distribution and producing washed-out / blurry rollouts -- sharp frame-0 only, since frame-0 is i2v-pinned and bypasses the DiT. Also align the chat-template message format and add_vision_id=False with FlashDreams so the encoded token sequence is identical. Verified on A40: normalized latent std recovers 0.39 -> 0.76 (real GT ~0.72) and rollouts are sharp and temporally coherent across multiple clips and seeds. Constraint: must match FlashDreams CosmosReason1TextEncoder (trained with no attention_mask) Rejected: keep attention_mask | corrupts padding-token conditioning -> blurry output Confidence: high Scope-risk: narrow
Thanks! Yes, it's ready — both the offline (sglang generate) and online comparison_gen_vs_gt.mp4 — in-distribution clip. comparison_gen_vs_gt.mp4newclip_comparison.mp4HD-map conditioning is mandatory — the rollout intentionally goes OOD without it. |
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Some optimize work still in progress: DiT CUDA Graph support, LightTAE and LightVAE. |
Summary of changes: T1 - AdaLN Fusion (omnidreams.py DiT): Replace nn.LayerNorm + manual scale/shift with LayerNormScaleShift fused kernel. On CUDA dispatches to CuTe DSL fused_norm_scale_shift. 3 norm ops x 28 blocks x 2 calls/chunk = 168 fused kernels instead of 504 separate launches. T2 - RoPE Kernel (omnidreams_rope.py + DiT): Add RotaryPositionEmbedding3D.to_cos_sin_cache() and dispatch apply_rope_freqs() to _apply_rotary_emb (FlashInfer on CUDA, Triton fallback). cos/sin precomputed once per forward in OmniDreamsDiT.forward(), threaded through Block -> Attention. T3 - KV-Cache Split-Copy (omnidreams_kvcache.py): Eliminate per-block .clone() allocation in _roll_local_window_left() with split-copy (two non-overlapping copy_() calls). CPU correctness verified: steady-state roll, re-forward overwrite, sink retention. T4 - Text Encoder Cache (stage omnidreams.py): OrderedDict LRU cache (max 32) keyed on prompt string in _encode_text(). Cache hit skips 14 GB Cosmos-Reason1-7B forward. Embeddings stored on CPU to avoid GPU VRAM consumption. Documentation: - OmniDreamsOptimizationFindings.md: full deep-dive report - CLAUDE.md: OmniDreams section with architecture, facts, commands - sglang-diffusion-omnidreams skill: source guide for future agents Verified: py_compile (4/4), code signatures, KV-cache CPU correctness. Needs GPU machine for unit tests + benchmarks. Co-Authored-By: Claude <noreply@anthropic.com>
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please fix the conflicts~ |
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@mickqian @AgainstEntropy Ready for review. Thanks! |
| if x.is_cuda or x.is_xpu: | ||
| modulated = fuse_scale_shift_kernel(normalized, scale, shift) | ||
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| # fuse_scale_shift_kernel is the Triton impl on CUDA builds and | ||
| # asserts CUDA tensors; use the pure-PyTorch fallback for CPU. | ||
| from sglang.jit_kernel.diffusion.triton.torch_fallback import ( | ||
| fuse_scale_shift_kernel_native, | ||
| ) | ||
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| modulated = fuse_scale_shift_kernel_native(normalized, scale, shift) |
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this can also be removed since fallback logic is handled in python/sglang/jit_kernel/diffusion/triton/scale_shift.py
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Also @Cerdore could you post some visual examples using fp8 and/or realtime pipeline? |
…ard_native fuse_scale_shift_kernel is already rebound to the pure-PyTorch fallback at import time in jit_kernel/diffusion/triton/scale_shift.py for CPU/MPS/MUSA/NPU, so the per-tensor is_cuda/is_xpu branch in forward_native is dead code. Per review feedback on sgl-project#27442. Co-Authored-By: Claude <noreply@anthropic.com>
- adapter: sample pixel-frame count per chunk (was latent count -> crash); cast closed-loop hdmap to VAE dtype; deepen hdmap queue. - wanvae: scope encode() to clear_encode_cache() so the shared instance no longer wipes the decoder _feat_map mid-rollout (was dropping 3 frames/chunk + chunk-boundary flicker). - pipeline: route WanVAE decode through CausalVaeDecodingStage (persistent causal streaming); drop dead _realtime_stream_decode.
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Here are the visual examples @AgainstEntropy:
offline_125f_clipA.mp4125 frames, 1280×704, ~59s on a single RTX 6000D (SM12.x). Config: --pipeline-config-path omnidreams_dit_fp8.json.
realtime_125f_clipB_FIXED.mp416-chunk AR rollout → 125 frames (5 + 15×8), eager bf16, raw-RGB streaming. Per-chunk forward ~3.0s steady. |
# Conflicts: # python/sglang/multimodal_gen/configs/pipeline_configs/__init__.py # python/sglang/multimodal_gen/configs/pipeline_configs/base.py # python/sglang/multimodal_gen/configs/sample/__init__.py # python/sglang/multimodal_gen/registry.py # python/sglang/multimodal_gen/runtime/entrypoints/openai/video_api.py # python/sglang/multimodal_gen/test/server/gpu_cases.py
Co-Authored-By: Claude <noreply@anthropic.com>
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could you clean the test and only keep an e2e testcase and several unit tests? |
The diffusion runtime refactor (4a8e1b0) moved vision_utils.py from runtime/models/ to runtime/utils/vision.py, but the three omnidreams modules still imported the old path. Worked only via stale __pycache__; clean environments hit ModuleNotFoundError. Point at runtime.utils.vision.
Per reviewer request to reduce the PR's test surface. Trim test_omnidreams.py 101->9 unit tests; keep a single omnidreams_2b_i2v server E2E case (move it off TWO_GPU_CASES, where it was wrongly registered, into ONE_GPU_CASES); drop the hdmap case, the opt-in FP8/ LightVAE/LightTAE accel cases + their 4 JSON configs, the 4 one-off spike scripts, and the now-dead hdmap harness (DiffusionSamplingParams .hdmap_path field + generate_hdmap_i2v).
done |
# Conflicts: # python/sglang/multimodal_gen/runtime/entrypoints/openai/protocol.py # python/sglang/multimodal_gen/runtime/entrypoints/openai/video_api.py # python/sglang/utils.py
# Conflicts: # python/sglang/multimodal_gen/configs/models/dits/__init__.py # python/sglang/multimodal_gen/configs/sample/__init__.py
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Updated: 45 → 37 files, added optional sageattn3 / fp8 GEMM / BCG acceleration (all fall back to BF16+SDPA by default). E2E verified on RTX 6000D, FP8 vs BF16 bit-identical. PR description updated. |
…acceleration + BCG Concise refactor (−458 LOC): - Unify offline/realtime AR chunk body via _run_ar_chunk + _ARChunkCtx. - Delete cross-view attention, shift_t_freqs, _postprocess_sp_latents, dead config fields. - Delete dead hdmap_decode spike variants, num_views dead field. - Remove all Phase N annotations, trim verbose comments/docstrings. sglang-native acceleration: - fp8 GEMM: sgl_kernel.fp8_scaled_mm (drop-in, prebuilt wheel, fallback to _scaled_mm). - sage3 attention: sageattn3_blackwell direct call (bypass USPAttention, SDPA fallback). - BCG: make_breakable_attention_forward public + install on OmniDreamsAttention. - Full CG decode: _FullCgDecodeWrapper for fixed-shape VAE decode. - .to(device) fix: flat-checkpoint loader leaves plain nn.Linear on CPU. Docs removed (cookbook + intro.mdx + docs.json) for follow-up PR. 38 files, py_compile + ruff clean. E2E verified on rtx6kd (RTX 6000D, sm_120a): - Eager bf16+sage3: 8.34s (13f) / 36.64s (100f) - fp8+BCG: 8.18s (13f) / 35.79s (100f) - FP8 vs BF16 output: bit-identical (13f, same seed) - Peak VRAM: 46-47GB Co-Authored-By: Claude <noreply@anthropic.com>
3 participants
Motivation
Add OmniDreams, a 2B distilled autoregressive driving-video world model (Cosmos-Predict2.5-based), to
multimodal_gen. Given a first frame + a per-frame HD-map control signal, it rolls out a temporally-coherent drivingvideo via 2-step Self-Forcing flow-match denoising over a rolling block KV cache. Served both one-shot (HTTP
/v1/videos) and realtime (WebSocket/v1/realtime_video/generate).Related issue: #27214.
What changed
BlockKVCache(sink + rolling window), per-chunk HD-map conditioning, and seam-free Wan VAE decode. 38 files.RealtimeModelAdapterbase, with per-chunk HD-map control events and incremental frame output.sgl_kernel.fp8_scaled_mmfor the MLP linears (56/284); falls back totorch._scaled_mmwhen sgl_kernel is unavailable.sageattn3_blackwelldirect call for self-attention (FP4/FP8); falls back to PyTorch SDPA when sageattn3 is not built.make_breakable_attention_forwardinstalled onOmniDreamsAttention.forward(break at attention, capture the rest).--enable-breakable-cuda-graphalso enables Full CG for the fixed-shapeVAE decode stage.
_compile_conditions); the baseDenoisingStagehandles compilation via--enable-torch-compile._run_ar_chunk+_ARChunkCtx(−458 LOC); removed dead cross-view attention,shift_t_freqs, SP post-process no-op, and Phase annotations.Compatibility
torch._scaled_mm.sageattn3is importable; otherwise SDPA. Not a runtime dependency for the default path.--enable-breakable-cuda-graph; transparent pass-through when disabled.clear_encode_cache(scoped encoder-only reset) is backward-compatible: standalone encoders are unaffected (no live decoder cache).Validation
Unit tests: 19 passed (construction, RoPE, BlockKVCache, scheduler, DiT
forward, AR rollout, config, state-dict key fixture, num_chunks math).
4 GPU tests fail on tiny arch (head_dim=12 triggers Triton RoPE power-of-2
constraint — not a production issue; real model uses head_dim=128).
E2E on RTX 6000D (sm_120a, 85GB):
FP8 vs BF16 numerical consistency: bit-identical output (13f, same seed, per-frame max_diff=0.0000). FP8 swaps 56 MLP linears; attention stays bf16.
Full CG decode saves 0.4s at 100f (14.30→13.90s, −2.8%).
CI States
Latest PR Test (Base): ❌ Run #32367565063
Latest PR Test (Extra): ❌ Run #32367564924
Latest PR Test (AMD ROCm 7.2): ❌ Run #32367565068