[docs] H3 parallel VAE: document the compiled-decoder cross-process determinism caveat

With enable_torch_compile_vae (#1734, opt-in) inductor autotunes kernels per
process, so chunk decodes on other ranks differ from the serial rank's decode
the way two serial processes differ (GB200 @124f: max 63/255 on <0.5% of
pixels, mean ~1e-2/255; audio and chunk 0 bit-identical). Eager decoder (the
default) stays bitwise-equal to serial decode_to_pixels - measured, both
strategies, x3, 124f+345f.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

docs/design/inference_schema_parity_inventory.yaml

@@ -76,6 +76,10 @@ surfaces:
       lora_target_modules: "Legacy LoRA configuration surface pending dedicated component API."
       output_type: "Legacy output formatting surface pending GenerationResult cleanup."
       VSA_sparsity: "Model-specific inference optimization not yet represented in the typed public schema."
+      VSA_tile_size: "VSA-H3 tile geometry request; model-specific optimization not yet represented in the typed public schema."
+      vae_parallel_decode: "MiniMax-H3 sequence-parallel VAE decode opt-in; model-specific optimization not yet represented in the typed public schema."
+      vae_parallel_encode: "MiniMax-H3 sequence-parallel reference VAE encode opt-in; model-specific optimization not yet represented in the typed public schema."
+      vae_parallel_decode_strategy: "Chunk-transport collective for vae_parallel_decode; model-specific optimization not yet represented in the typed public schema."
       attention_backend: "Process-wide default attention-backend request applied per component at load time; kernel-selection knob not yet represented in the typed public schema."
       moba_config_path: "Model-specific MoBA optimization surface not yet represented in the typed public schema."
       master_port: "Executor/bootstrap compatibility field; not part of the canonical inference schema."

examples/inference/basic/README.md

@@ -33,6 +33,14 @@ For the typed config/request path added during the inference API refactor:
 python examples/inference/basic/basic_dmd_new_api.py
 ```
 
+For the few-step (4-step, DMD2-distilled) MiniMax-H3 preview, generating synchronized video and audio, optionally with block-sparse VSA attention:
+```
+python examples/inference/basic/basic_fasth3.py --prompt "your prompt" [--vsa-sparsity 0.9]
+```
+The default checkpoint `FastVideo/FastVideo-Minimax-FastH3-Preview-v0.1` is private on the Hub while its license review completes; until it flips public, pass `--model-path` with a local snapshot of the release.
+
+On Blackwell (sm_100) GPUs with a `fastvideo-kernel` build that carries the sm_100a block-sparse extension, `--vsa-kernel sm100a` routes the tile-64 attention forwards through the CUDA kernel instead of Triton (it sets `FASTVIDEO_VSA_SM100A=1` before the pipeline boots); if the extension or the arch is missing, the run warns once and falls back to Triton.
+
 ## Basic Walkthrough
 
 All you need to generate videos using multi-gpus from state-of-the-art diffusion pipelines is the following few lines!

examples/inference/basic/basic_fasth3.py

@@ -0,0 +1,180 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Few-step video+audio generation with the DMD2-distilled MiniMax H3 preview.
+
+FastVideo/FastVideo-Minimax-FastH3-Preview-v0.1 is a 4-step distillation of
+MiniMaxAI/MiniMax-H3 (data-free DMD2): it walks a 4-step grid on the release
+sampler's shift-12 schedule instead of the base model's 50 steps, generating
+synchronized video and audio in one pipeline call.
+
+The student was trained with block-sparse video attention (VSA, 64-token
+tiles) and its checkpoint carries the trained sparse-gate parameters
+(``attn.to_gate_compress``), so this script always runs the VSA-H3 attention
+backend. At the default ``--vsa-sparsity 0.0`` the attention math is exactly
+dense (every tile is selected); raise the sparsity for additional speedup.
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+from pathlib import Path
+
+from fastvideo import VideoGenerator
+from fastvideo.api import (
+    CompileConfig,
+    EngineConfig,
+    GenerationRequest,
+    GeneratorConfig,
+    OffloadConfig,
+    OutputConfig,
+    ParallelismConfig,
+    PipelineSelection,
+    SamplingConfig,
+)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--model-path", default="FastVideo/FastVideo-Minimax-FastH3-Preview-v0.1")
+    # The HF repo is private while the MiniMax H3 Community License review
+    # completes; until it flips public, pass --model-path with a local
+    # snapshot of the release instead (e.g. the team export at
+    # /mnt/lustre/vlm-wlsaidhi/fastvideo/exports/FastVideo-Minimax-FastH3-Preview-v0.1).
+    parser.add_argument("--prompt", required=True)
+    parser.add_argument("--output", default="outputs/fasth3")
+    parser.add_argument("--height", type=int, default=768)
+    parser.add_argument("--width", type=int, default=1344)
+    parser.add_argument("--num-frames", type=int, default=124)
+    # num_inference_steps counts sigma-GRID POINTS, matching the base model's
+    # convention ("50 steps" = a 50-point grid = 49 transformer forwards). The
+    # student's distilled 4-step grid is 4 FORWARDS, i.e. a 5-point grid
+    # (t = 1000, 750, 500, 250 -> 0 on the shift-12 schedule) — so the correct
+    # default here is 5. Other grids are off-distribution.
+    parser.add_argument("--steps",
+                        type=int,
+                        default=5,
+                        help="num_inference_steps = sigma-grid points; N points run N-1 denoising "
+                        "forwards. 5 (default) is the distilled 4-forward grid")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--num-gpus", type=int, default=4)
+    parser.add_argument("--vsa-sparsity",
+                        type=float,
+                        default=0.0,
+                        help="Run-level VSA sparsity in [0, 1). 0.0 (default) selects every tile, which is "
+                        "exactly dense attention; the student was trained at 0.9")
+    # 64 is the trained contract: the student was TRAINED with 64-token
+    # (4,4,4) tiles, and its to_gate_compress gates were learned against
+    # pooling at that granularity — keep 64 unless you are ablating.
+    parser.add_argument("--vsa-tile-size",
+                        type=int,
+                        choices=(64, 256),
+                        default=64,
+                        help="VSA-H3 tile size in tokens; 64 (default) is what the student was trained "
+                        "with and runs the native Triton block-sparse path, 256 is the FA4-CuTe-capable "
+                        "geometry for ablations")
+    parser.add_argument("--vsa-kernel",
+                        choices=("triton", "sm100a"),
+                        default="triton",
+                        help="Block-sparse kernel for the tile-64 attention forward: triton (default, "
+                        "portable fwd+bwd) or sm100a — the opt-in Blackwell CUDA forward "
+                        "(fastvideo_kernel.block_sparse_attn_sm100a). sm100a needs an sm_100 GPU and a "
+                        "fastvideo-kernel build that carries the extension; if a precondition fails at "
+                        "run time the attention layer logs one warning and falls back to Triton. Only "
+                        "meaningful with --vsa-tile-size 64")
+    parser.add_argument("--torch-compile", action="store_true", help="torch.compile the DiT transformer path")
+    parser.add_argument("--compile-mode",
+                        default=None,
+                        help='torch.compile mode, e.g. "reduce-overhead" for CUDA graphs')
+    parser.add_argument("--repeats",
+                        type=int,
+                        default=1,
+                        help="generate N times; with --torch-compile the first run pays "
+                        "compilation, so steady-state is the last repeat")
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    output_dir = Path(args.output)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    if args.vsa_kernel == "sm100a":
+        # The attention backend reads FASTVIDEO_VSA_SM100A per forward; set it
+        # before the pipeline boots so spawned GPU workers inherit it. The
+        # kernel is forward-only and inference runs under no-grad, so every
+        # denoising forward qualifies for the CUDA route.
+        os.environ["FASTVIDEO_VSA_SM100A"] = "1"
+
+    # Boot-time run configuration, folded into FastVideoArgs (the same route
+    # examples/inference/basic/basic_minimax_h3_t2v.py uses for sparsity):
+    # - attention_backend: the checkpoint carries trained to_gate_compress
+    #   gates, which only exist under the VSA-H3 backend — a dense-backend
+    #   load would reject them as unexpected weights. Layers that do not
+    #   support VSA-H3 (e.g. the token refiner) fall back to flash attention.
+    # - VSA_tile_size: forwarded even at sparsity 0.0 because the gate-compress
+    #   branch pools per tile, and the gates were trained at 64 tokens/tile.
+    experimental: dict[str, object] = {
+        "attention_backend": "VIDEO_SPARSE_ATTN_H3",
+        "VSA_tile_size": args.vsa_tile_size,
+    }
+    if args.vsa_sparsity > 0.0:
+        experimental["VSA_sparsity"] = args.vsa_sparsity
+
+    generator = VideoGenerator.from_config(
+        GeneratorConfig(
+            model_path=args.model_path,
+            pipeline=PipelineSelection(experimental=experimental),
+            engine=EngineConfig(
+                num_gpus=args.num_gpus,
+                use_fsdp_inference=args.num_gpus > 1,
+                parallelism=ParallelismConfig(tp_size=1, sp_size=args.num_gpus),
+                offload=OffloadConfig(
+                    dit=False,
+                    dit_layerwise=False,
+                    text_encoder=True,
+                    vae=True,
+                    pin_cpu_memory=False,
+                ),
+                compile=CompileConfig(
+                    enabled=args.torch_compile,
+                    mode=args.compile_mode,
+                ),
+            ),
+        ))
+    try:
+        request = GenerationRequest(
+            prompt=args.prompt,
+            negative_prompt="",
+            sampling=SamplingConfig(
+                height=args.height,
+                width=args.width,
+                num_frames=args.num_frames,
+                fps=24,
+                num_inference_steps=args.steps,
+                # the base model is guidance-distilled; the student inherits it
+                guidance_scale=1.0,
+                batch_cfg=False,
+                seed=args.seed,
+            ),
+            output=OutputConfig(
+                output_path=str(output_dir / "fasth3.mp4"),
+                save_video=True,
+                return_frames=False,
+            ),
+        )
+        result = generator.generate(request)
+        print(f"Output written to: {result.video_path}")
+        if result.generation_time is not None:
+            # machine-readable: benchmark harnesses parse this line to separate
+            # generation from model-load time (last occurrence = steady state)
+            print(f"Generation time: {result.generation_time:.2f}s")
+        for _ in range(args.repeats - 1):
+            result = generator.generate(request)
+            if result.generation_time is not None:
+                print(f"Generation time: {result.generation_time:.2f}s")
+    finally:
+        generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()

fastvideo/attention/backends/flash_attn.py

@@ -19,7 +19,12 @@ from fastvideo.attention.backends.abstract import (
 from fastvideo.logger import init_logger
 
 logger = init_logger(__name__)
-logger.info("Using FlashAttention-%s backend", fa_version)
+# Every worker records the loaded FlashAttention implementation so a
+# distributed profiling log contains one backend receipt per rank.
+logger.info("Worker %s Using FlashAttention-%s backend",
+            os.environ.get("RANK", "0"),
+            fa_version,
+            local_main_process_only=False)
 
 # FP4 FA4 support: quantize Q/K to NVFP4 E2M1 for block-scaled MMA on Blackwell.
 # Requires: flash-attention-fp4, flashinfer, cutlass-dsl. Enable via nvfp4_fa4=True kwarg.

fastvideo/attention/backends/video_sparse_attn_h3.py

@@ -5,13 +5,17 @@ H3 runs one joint bidirectional attention over
 ``[text | condition keyframes | audio | generated video]``, so this
 backend differs from the Wan-tuned ``video_sparse_attn``:
 
-- Tiles are ``[segment-pure prefix chunks] + [3D (4,8,8) video tiles]``;
-  prefix tiles never straddle segment boundaries.
+- Tiles are ``[segment-pure prefix chunks] + [3D video tiles]``; prefix
+  tiles never straddle segment boundaries. The tile size is selectable at
+  metadata build time: 256 tokens ``(4,8,8)`` (default) or 64 tokens
+  ``(4,4,4)`` (see ``VSA_H3_TILE_SHAPES``).
 - Selection is pure Python on pooled tile scores; the block-sparse kernel
   consumes an explicit bool mask, so no kernel changes are needed.
-- The compression branch is gated by ``to_gate_compress``, which the H3
-  checkpoint does not carry: the loader zero-initializes it, so untrained
-  inference is exactly pure sparse and finetuning can learn the gate.
+- The compression branch is gated by ``to_gate_compress``, which the base
+  H3 checkpoint does not carry: the loader zero-initializes it, so
+  untrained inference is exactly pure sparse and finetuning can learn the
+  gate. VSA-distilled students (e.g. FastVideo-Minimax-H3-Preview) ship
+  trained gates, which load and activate the branch.
 - Non-video *queries* are always dense. Non-video *keys* are either
   always-selected for every query ("exempt", default) or compete in
   top-k under a FLOP-matched budget ("compete") — the ablation axis,
@@ -20,22 +24,46 @@ backend differs from the Wan-tuned ``video_sparse_attn``:
   (``vsa_dense_first_n_steps``, ``vsa_dense_layers``) let mixed schedules
   run the diffuse steps/layers dense while pushing the rest harder.
 
-Targets sm10.x through the FA4 CuTe 256-tile path
+At tile 256 this targets sm10.x through the FA4 CuTe 256-tile path
 (``FASTVIDEO_VSA_CUTEDSL=1``); the Triton 256→64 expansion is the
-fallback and keeps identical mask semantics.
+fallback and keeps identical mask semantics. At tile 64 the block map is
+already at the kernels' native 64-token granularity, so both forward and
+backward run the Triton block-sparse kernels directly (no expansion,
+``FASTVIDEO_VSA_CUTEDSL`` does not apply). A third, opt-in route exists
+for the tile-64 FORWARD only: ``FASTVIDEO_VSA_SM100A=1`` sends no-grad
+forwards through the sm_100a CUDA block-sparse kernel
+(``fastvideo_kernel.block_sparse_attn_sm100a``, upstream PR #1719 plus
+our per-q-tile ``q2k_num`` fix) when the extension is built, the device
+is sm_100, and the geometry qualifies; grad-tracking forwards and every
+backward stay on Triton unchanged. If the env is set but a precondition
+fails, the route logs one warning and falls back.
 """
 
 import functools
 import math
+import os
 from dataclasses import dataclass
 from typing import Any
 
 import torch
 
 try:
+    from fastvideo_kernel.block_sparse_attn import block_sparse_attn as block_sparse_attn_64_bhsd
     from fastvideo_kernel.block_sparse_attn_256 import block_sparse_attn_256_bshd
+    from fastvideo_kernel.triton_kernels.index import map_to_index
 except ImportError:
+    block_sparse_attn_64_bhsd = None
     block_sparse_attn_256_bshd = None
+    map_to_index = None
+
+try:
+    # Optional: only present in fastvideo_kernel builds that carry the sm_100a
+    # CUDA block-sparse forward (upstream PR #1719). The module itself imports
+    # fine without the compiled symbols (`_HAS_VSA_SM100A` is then False and
+    # `is_supported` says no), so this only guards *module* availability.
+    from fastvideo_kernel import block_sparse_attn_sm100a as _sm100a
+except ImportError:
+    _sm100a = None
 
 from fastvideo.attention.backends.abstract import (AttentionBackend, AttentionImpl, AttentionMetadata,
                                                    AttentionMetadataBuilder, layer_idx_from_prefix)
@@ -43,51 +71,115 @@ from fastvideo.attention.backends.video_sparse_attn import (compute_topk, constr
                                                             get_non_pad_index, get_tile_partition_indices,
                                                             scatter_into_tile_buf)
 from fastvideo.attention.backends.video_sparse_attn_h3_probe import probe_enabled, record_probe
+from fastvideo.logger import init_logger
 
-VSA_H3_TILE_SIZE = (4, 8, 8)  # 256 elements -> FA4 CuTe fastpath on sm10.x
+logger = init_logger(__name__)
+
+# Opt-in switch for the sm_100a CUDA forward on the tile-64 no-grad path.
+VSA_SM100A_ENV = "FASTVIDEO_VSA_SM100A"
+
+VSA_H3_TILE_SIZE = (4, 8, 8)  # 256 elements -> FA4 CuTe fastpath on sm10.x (default)
 _TILE_ELEMS = math.prod(VSA_H3_TILE_SIZE)
+# Selectable tile geometries, keyed by element count (= the build-time
+# ``tile_size``). 64 runs the native 64-token Triton block-sparse kernels for
+# forward AND backward — the block map is already at kernel granularity, so no
+# 256->64 mask expansion is involved and FASTVIDEO_VSA_CUTEDSL does not apply.
+VSA_H3_TILE_SHAPES: dict[int, tuple[int, int, int]] = {
+    _TILE_ELEMS: VSA_H3_TILE_SIZE,
+    64: (4, 4, 4),
+}
 
 
-def token_tile_and_valid(variable_block_sizes: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+def token_tile_and_valid(variable_block_sizes: torch.Tensor,
+                         tile_elems: int = _TILE_ELEMS) -> tuple[torch.Tensor, torch.Tensor]:
     """Per padded-token tile id and pad-validity mask.
 
     The single encoding of the padding contract, shared by the probe and the
     test oracle so they cannot drift from the backend's tile geometry.
+    ``tile_elems`` must match the metadata the sizes came from
+    (``MiniMaxH3VSAMetadata.tile_elems``).
     """
     device = variable_block_sizes.device
-    token_tile = torch.arange(variable_block_sizes.numel(), device=device).repeat_interleave(_TILE_ELEMS)
-    token_valid = (torch.arange(_TILE_ELEMS, device=device)[None, :] < variable_block_sizes[:, None]).reshape(-1)
+    token_tile = torch.arange(variable_block_sizes.numel(), device=device).repeat_interleave(tile_elems)
+    token_valid = (torch.arange(tile_elems, device=device)[None, :] < variable_block_sizes[:, None]).reshape(-1)
     return token_tile, token_valid
 
 
+def _validate_h3_tile_geometry(
+    prefix_segments: tuple[int, ...],
+    dit_seq_shape: tuple[int, int, int],
+    variable_block_sizes: torch.Tensor,
+    untile_combined_index: torch.Tensor,
+    tile_elems: int = _TILE_ELEMS,
+) -> None:
+    """Fail synchronously on out-of-bounds tile geometry.
+
+    Invariants the block-sparse kernel trusts without checking:
+    every tile's valid size is in (0, tile_elems]; the sizes sum to the
+    packed sequence length; and ``untile_combined_index`` maps each packed
+    row to exactly one non-pad slot of the padded tile buffer. A violation
+    would surface only as an async device fault at some later kernel or
+    collective (e.g. an FSDP all-gather), which is unattributable — so raise
+    here, once per cached geometry, with the numbers in hand.
+    """
+    total = sum(prefix_segments) + math.prod(dit_seq_shape)
+    n_pad = variable_block_sizes.numel() * tile_elems
+    sizes_min = int(variable_block_sizes.min())
+    sizes_max = int(variable_block_sizes.max())
+    sizes_sum = int(variable_block_sizes.sum())
+    if sizes_min < 1 or sizes_max > tile_elems or sizes_sum != total:
+        raise ValueError(f"VSA-H3 tile sizes out of bounds for prefix={prefix_segments}, video={dit_seq_shape}, "
+                         f"tile_elems={tile_elems}: min={sizes_min}, max={sizes_max}, sum={sizes_sum}, "
+                         f"expected sum={total}.")
+    if untile_combined_index.numel() != total:
+        raise ValueError(f"VSA-H3 untile index has {untile_combined_index.numel()} entries for a packed "
+                         f"sequence of {total} rows (prefix={prefix_segments}, video={dit_seq_shape}).")
+    idx_min = int(untile_combined_index.min())
+    idx_max = int(untile_combined_index.max())
+    if idx_min < 0 or idx_max >= n_pad:
+        # Range first: the pad-slot gather below would itself index out of
+        # bounds (the very async fault this guard exists to preempt).
+        raise ValueError(f"VSA-H3 untile index is not an injective map into non-pad slots: range "
+                         f"[{idx_min}, {idx_max}] vs padded length {n_pad} "
+                         f"(prefix={prefix_segments}, video={dit_seq_shape}).")
+    in_tile_offset = untile_combined_index % tile_elems
+    maps_into_pad = bool((in_tile_offset >= variable_block_sizes[untile_combined_index // tile_elems]).any())
+    if maps_into_pad or int(torch.unique(untile_combined_index).numel()) != total:
+        raise ValueError(f"VSA-H3 untile index is not an injective map into non-pad slots: "
+                         f"pad-slot hit={maps_into_pad} "
+                         f"(prefix={prefix_segments}, video={dit_seq_shape}).")
+
+
 @functools.lru_cache(maxsize=10)
 def _h3_tile_geometry(
     prefix_segments: tuple[int, ...],
     dit_seq_shape: tuple[int, int, int],
     device: torch.device,
+    tile_shape: tuple[int, int, int] = VSA_H3_TILE_SIZE,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, int]:
     """Tile the packed sequence: segment-pure prefix chunks, then video tiles.
 
     Returns (tile_partition_indices, variable_block_sizes,
     untile_combined_index, num_prefix_tiles, num_video_tiles).
     """
+    tile_elems = math.prod(tile_shape)
     prefix_len = sum(prefix_segments)
 
     prefix_sizes: list[int] = []
     for segment in prefix_segments:
-        full, rem = divmod(segment, _TILE_ELEMS)
-        prefix_sizes.extend([_TILE_ELEMS] * full)
+        full, rem = divmod(segment, tile_elems)
+        prefix_sizes.extend([tile_elems] * full)
         if rem:
             prefix_sizes.append(rem)
     num_prefix_tiles = len(prefix_sizes)
 
-    ts_t, ts_h, ts_w = VSA_H3_TILE_SIZE
+    ts_t, ts_h, ts_w = tile_shape
     t, h, w = dit_seq_shape
     num_tiles = (math.ceil(t / ts_t), math.ceil(h / ts_h), math.ceil(w / ts_w))
-    video_sizes = construct_variable_block_sizes(dit_seq_shape, num_tiles, device, VSA_H3_TILE_SIZE)
+    video_sizes = construct_variable_block_sizes(dit_seq_shape, num_tiles, device, tile_shape)
     num_video_tiles = int(video_sizes.numel())
 
-    video_indices = get_tile_partition_indices(dit_seq_shape, VSA_H3_TILE_SIZE, device) + prefix_len
+    video_indices = get_tile_partition_indices(dit_seq_shape, tile_shape, device) + prefix_len
     tile_partition_indices = torch.cat([
         torch.arange(prefix_len, device=device, dtype=torch.long),
         video_indices,
@@ -100,9 +192,11 @@ def _h3_tile_geometry(
 
     # get_non_pad_index is lru-cached on tensor identity; variable_block_sizes
     # is itself cached by this function, so the identity stays stable.
-    non_pad_index = get_non_pad_index(variable_block_sizes, _TILE_ELEMS)
+    non_pad_index = get_non_pad_index(variable_block_sizes, tile_elems)
 
     untile_combined_index = non_pad_index[torch.argsort(tile_partition_indices)]
+    # One-time (lru-cached) synchronous bounds check; see _validate_h3_tile_geometry.
+    _validate_h3_tile_geometry(prefix_segments, dit_seq_shape, variable_block_sizes, untile_combined_index, tile_elems)
     return (tile_partition_indices, variable_block_sizes, untile_combined_index, num_prefix_tiles, num_video_tiles)
 
 
@@ -139,6 +233,9 @@ class MiniMaxH3VSAMetadata(AttentionMetadata):
     exempt: bool
     variable_block_sizes: torch.Tensor
     untile_combined_index: torch.Tensor
+    # tokens per tile (256 or 64); selects the tile geometry AND the kernel
+    # route in forward() (256 -> VSA-256 CuTe/Triton, 64 -> native Triton)
+    tile_elems: int = _TILE_ELEMS
     # layers forced dense regardless of sparsity (probe-guided opt-outs)
     dense_layers: tuple[int, ...] = ()
     # Single-slot holder for the padded tile buffer, owned by the BUILDER so
@@ -158,24 +255,28 @@ class MiniMaxH3VSAMetadataBuilder(AttentionMetadataBuilder):
         pass
 
     def build(  # type: ignore
-            self,
-            current_timestep: int,
-            raw_latent_shape: tuple[int, int, int],
-            patch_size: tuple[int, int, int],
-            VSA_sparsity: float,
-            prefix_segments: tuple[int, ...],
-            device: torch.device,
-            exempt: bool = True,
-            dense_layers: tuple[int, ...] = (),
-            **kwargs: dict[str, Any],
+        self,
+        current_timestep: int,
+        raw_latent_shape: tuple[int, int, int],
+        patch_size: tuple[int, int, int],
+        VSA_sparsity: float,
+        prefix_segments: tuple[int, ...],
+        device: torch.device,
+        exempt: bool = True,
+        dense_layers: tuple[int, ...] = (),
+        tile_size: int = _TILE_ELEMS,
+        **kwargs: dict[str, Any],
     ) -> MiniMaxH3VSAMetadata:
+        tile_shape = VSA_H3_TILE_SHAPES.get(int(tile_size))
+        if tile_shape is None:
+            raise ValueError(f"VSA-H3 tile_size must be one of {sorted(VSA_H3_TILE_SHAPES)}, got {tile_size!r}")
         dit_seq_shape = (raw_latent_shape[0] // patch_size[0], raw_latent_shape[1] // patch_size[1],
                          raw_latent_shape[2] // patch_size[2])
         prefix_segments = tuple(int(s) for s in prefix_segments if s > 0)
         total_seq_length = sum(prefix_segments) + math.prod(dit_seq_shape)
 
         (_tile_partition_indices, variable_block_sizes, untile_combined_index, num_prefix_tiles,
-         num_video_tiles) = _h3_tile_geometry(prefix_segments, dit_seq_shape, device)
+         num_video_tiles) = _h3_tile_geometry(prefix_segments, dit_seq_shape, device, tile_shape)
 
         return MiniMaxH3VSAMetadata(
             current_timestep=current_timestep,
@@ -186,13 +287,14 @@ class MiniMaxH3VSAMetadataBuilder(AttentionMetadataBuilder):
             exempt=exempt,
             variable_block_sizes=variable_block_sizes,
             untile_combined_index=untile_combined_index,
+            tile_elems=int(tile_size),
             dense_layers=tuple(int(layer) for layer in dense_layers),
             tile_buf_holder=self._tile_buf_holder,
         )
 
 
-def _pool_tiles(x: torch.Tensor, variable_block_sizes: torch.Tensor) -> torch.Tensor:
-    """fp32 mean over each 256-token tile. x: [B, S_pad, H, D] -> [B, H, n_tiles, D].
+def _pool_tiles(x: torch.Tensor, variable_block_sizes: torch.Tensor, tile_elems: int = _TILE_ELEMS) -> torch.Tensor:
+    """fp32 mean over each tile_elems-token tile. x: [B, S_pad, H, D] -> [B, H, n_tiles, D].
 
     Pad positions in the tile buffer are guaranteed zero (zeros-init, never
     written), so a plain sum with fp32 accumulation needs no validity mask
@@ -200,8 +302,8 @@ def _pool_tiles(x: torch.Tensor, variable_block_sizes: torch.Tensor) -> torch.Te
     the masked mean exactly.
     """
     batch, seq_len, heads, dim = x.shape
-    n_tiles = seq_len // _TILE_ELEMS
-    pooled = x.view(batch, n_tiles, _TILE_ELEMS, heads, dim).sum(dim=2, dtype=torch.float32)
+    n_tiles = seq_len // tile_elems
+    pooled = x.view(batch, n_tiles, tile_elems, heads, dim).sum(dim=2, dtype=torch.float32)
     pooled = pooled / variable_block_sizes.view(1, -1, 1, 1)
     return pooled.permute(0, 2, 1, 3)
 
@@ -232,6 +334,24 @@ def _build_block_mask(
     return mask
 
 
+def _sm100a_unavailable_reason(sm100a_mod: Any, query_bhsd: torch.Tensor, variable_block_sizes: torch.Tensor,
+                               grad_mode: bool) -> str | None:
+    """Why the opt-in sm_100a forward route cannot run here, or None if it can.
+
+    Pure decision logic, split out so the routing is unit-testable without a
+    GPU or the compiled extension (tests substitute ``sm100a_mod``). Order
+    matters only for the message: the cheapest, most actionable reason first.
+    """
+    if sm100a_mod is None:
+        return "fastvideo_kernel.block_sparse_attn_sm100a is not installed"
+    if grad_mode:
+        return "inputs require grad and the sm_100a kernel is forward-only; grad paths keep Triton"
+    if not sm100a_mod.is_supported(query_bhsd, variable_block_sizes):
+        return ("block_sparse_attn_sm100a.is_supported returned False (needs an sm_100 device, a built "
+                "extension, bf16, head_dim 128, an even tile count, and integer tile sizes)")
+    return None
+
+
 class MiniMaxH3VSAImpl(AttentionImpl):
 
     def __init__(
@@ -259,7 +379,7 @@ class MiniMaxH3VSAImpl(AttentionImpl):
                              f"got {x.shape[1]}. A non-packed sequence (e.g. the token refiner) is "
                              "routed to the VSA-H3 backend; exclude it from the supported backends.")
         n_tiles = attn_metadata.variable_block_sizes.numel()
-        target_shape = (x.shape[0], n_tiles * _TILE_ELEMS, x.shape[-2], x.shape[-1])
+        target_shape = (x.shape[0], n_tiles * attn_metadata.tile_elems, x.shape[-2], x.shape[-1])
 
         # single scatter: untile_combined_index maps original row i to its
         # padded slot, so this is exactly the inverse of postprocess_output
@@ -281,7 +401,11 @@ class MiniMaxH3VSAImpl(AttentionImpl):
         gate_compress: torch.Tensor | None,
         attn_metadata: MiniMaxH3VSAMetadata,
     ) -> torch.Tensor:
-        if block_sparse_attn_256_bshd is None:
+        tile_elems = attn_metadata.tile_elems
+        if tile_elems == 64:
+            if block_sparse_attn_64_bhsd is None:
+                raise NotImplementedError("fastvideo_kernel.block_sparse_attn is not installed")
+        elif block_sparse_attn_256_bshd is None:
             raise NotImplementedError("fastvideo_kernel.block_sparse_attn_256 is not installed")
 
         # probe-guided per-layer opt-out: diffuse layers run dense (all-True
@@ -291,8 +415,8 @@ class MiniMaxH3VSAImpl(AttentionImpl):
 
         scores = None
         if layer_sparsity > 0.0 or gate_compress is not None or probe_dir is not None:
-            q_pooled = _pool_tiles(query, attn_metadata.variable_block_sizes)
-            k_pooled = _pool_tiles(key, attn_metadata.variable_block_sizes)
+            q_pooled = _pool_tiles(query, attn_metadata.variable_block_sizes, tile_elems)
+            k_pooled = _pool_tiles(key, attn_metadata.variable_block_sizes, tile_elems)
             scores = torch.matmul(q_pooled, k_pooled.transpose(-2, -1)) / (query.shape[-1]**0.5)
             if probe_dir is not None:
                 record_probe(probe_dir, self.layer_idx, query, key, scores, attn_metadata)
@@ -309,14 +433,66 @@ class MiniMaxH3VSAImpl(AttentionImpl):
                 attn_metadata.exempt,
             )
 
-        out, _ = block_sparse_attn_256_bshd(query, key, value, mask, attn_metadata.variable_block_sizes)
+        if tile_elems == 64:
+            # Native 64-token path: the block map is already at the kernels'
+            # granularity. Both 64-token entries take BHSD ([B, H, S_pad, D]);
+            # mirror block_sparse_attn_256_bshd's Triton branch and transpose
+            # around the call.
+            q_bhsd = query.transpose(1, 2).contiguous()
+            k_bhsd = key.transpose(1, 2).contiguous()
+            v_bhsd = value.transpose(1, 2).contiguous()
+
+            # Opt-in sm_100a CUDA forward (upstream PR #1719 + per-q-tile
+            # q2k_num fix). Forward-only: grad-tracking calls stay on Triton
+            # so autograd keeps the Triton fwd+bwd pairing untouched. The
+            # kernel does return an LSE in Triton's M format, so a future
+            # fwd/bwd pairing is possible, but it is not built here.
+            use_sm100a = False
+            if os.environ.get(VSA_SM100A_ENV, "0") == "1":
+                grad_mode = torch.is_grad_enabled() and (query.requires_grad or key.requires_grad
+                                                         or value.requires_grad)
+                reason = _sm100a_unavailable_reason(_sm100a, q_bhsd, attn_metadata.variable_block_sizes, grad_mode)
+                if reason is None and map_to_index is None:
+                    reason = "fastvideo_kernel.triton_kernels.index (map_to_index) is not importable"
+                if reason is None:
+                    use_sm100a = True
+                elif not torch.compiler.is_compiling():
+                    logger.warning_once(f"{VSA_SM100A_ENV}=1 but falling back to the Triton-64 kernels: {reason}")
+
+            if use_sm100a:
+                # The sm_100a entry is index-native; compact the bool map the
+                # same way the Triton bool entry does internally. Per-row
+                # counts are NON-uniform here (prefix query tiles are dense,
+                # video tiles run prefix+top-k) -- legal for the fixed kernel,
+                # silently wrong on the pre-fix upstream one.
+                q2k_idx, q2k_num = map_to_index(mask)
+                out_bhsd, _ = _sm100a.block_sparse_attn_sm100a(
+                    q_bhsd,
+                    k_bhsd,
+                    v_bhsd,
+                    q2k_idx,
+                    q2k_num,
+                    attn_metadata.variable_block_sizes.to(torch.int32),
+                    need_lse=False,
+                )
+            else:
+                out_bhsd, _ = block_sparse_attn_64_bhsd(
+                    q_bhsd,
+                    k_bhsd,
+                    v_bhsd,
+                    mask,
+                    attn_metadata.variable_block_sizes,
+                )
+            out = out_bhsd.transpose(1, 2).contiguous()
+        else:
+            out, _ = block_sparse_attn_256_bshd(query, key, value, mask, attn_metadata.variable_block_sizes)
 
         if gate_compress is not None:
             # Wan-style compression branch: dense attention over pooled tiles,
             # broadcast to each tile's rows, scaled by the learned gate
             # (zero-initialized for H3 => branch contributes nothing until
             # finetuned; the model layer skips it entirely for all-zero gates).
-            v_pooled = _pool_tiles(value, attn_metadata.variable_block_sizes)
+            v_pooled = _pool_tiles(value, attn_metadata.variable_block_sizes, tile_elems)
             out_c = torch.matmul(torch.softmax(scores, dim=-1), v_pooled)  # [B, H, n_tiles, D]
             out_c = out_c.permute(0, 2, 1, 3).to(out.dtype)  # [B, n_tiles, H, D]
             batch, seq_len, heads, dim = out.shape
@@ -325,7 +501,7 @@ class MiniMaxH3VSAImpl(AttentionImpl):
             # autograd node saved for its backward, so an in-place add here
             # bumps its version counter and backward dies with "one of the
             # variables needed for gradient computation has been modified".
-            out_tiled = out.view(batch, n_tiles, _TILE_ELEMS, heads, dim)
-            gate_tiled = gate_compress.view(batch, n_tiles, _TILE_ELEMS, heads, dim)
+            out_tiled = out.view(batch, n_tiles, tile_elems, heads, dim)
+            gate_tiled = gate_compress.view(batch, n_tiles, tile_elems, heads, dim)
             out = (out_tiled + out_c.unsqueeze(2) * gate_tiled).view(batch, seq_len, heads, dim)
         return out

fastvideo/attention/backends/video_sparse_attn_h3_probe.py

@@ -60,7 +60,7 @@ def record_probe(
     gen = torch.Generator(device="cpu").manual_seed(step * 1000 + layer)
     # sample among video rows in the PADDED/tiled domain that are non-pad
     from fastvideo.attention.backends.video_sparse_attn_h3 import token_tile_and_valid
-    token_tile, token_valid = token_tile_and_valid(attn_metadata.variable_block_sizes)
+    token_tile, token_valid = token_tile_and_valid(attn_metadata.variable_block_sizes, attn_metadata.tile_elems)
     video_rows = torch.nonzero((token_tile >= P) & token_valid, as_tuple=False).flatten()
     idx = video_rows[torch.randint(0, video_rows.numel(), (_TRUE_ROWS, ), generator=gen).to(query.device)]
 

fastvideo/configs/models/encoders/minimax_h3_qwen3_vl.py

@@ -62,14 +62,7 @@ class MiniMaxH3Qwen3VLArchConfig(TextEncoderArchConfig):
     hidden_size: int = 5120
     intermediate_size: int = 25600
     num_hidden_layers: int = 64
-    # H3 conditions on one intermediate hidden state and reads nothing above it,
-    # so the remaining layers are built, weight-loaded and then discarded: 14
-    # layers, 13.7 GB in bf16. Building exactly this many leaves that hidden
-    # state bit-identical, because the tuple records each layer's *input*, so
-    # entry N is the output of layer N-1. Set to None to keep the full stack.
-    # Must equal MINIMAX_H3_TEXT_ENCODER_LAYER in
-    # fastvideo/pipelines/basic/minimax_h3/packing.py; a test pins them together
-    # rather than importing across the models -> pipelines boundary.
+    output_hidden_state_index: int = 50
     num_hidden_layers_override: int | None = 50
     num_attention_heads: int = 64
     num_key_value_heads: int = 8
@@ -116,7 +109,7 @@ class MiniMaxH3Qwen3VLArchConfig(TextEncoderArchConfig):
     vision_initializer_range: float = 0.02
     vision_deepstack_visual_indexes: tuple[int, ...] = (8, 16, 24)
 
-    output_hidden_states: bool = True
+    output_hidden_states: bool = False
     stacked_params_mapping: list[tuple[str, str, str | int]] = field(default_factory=list)
     _fsdp_shard_conditions: list = field(default_factory=lambda: [
         _is_language_transformer_layer,
@@ -127,15 +120,16 @@ class MiniMaxH3Qwen3VLArchConfig(TextEncoderArchConfig):
     ])
 
     def __post_init__(self) -> None:
-        # Runs both at construction and after ``update_model_arch`` merges the
-        # checkpoint's config.json, so it also guards config-file overrides. A
-        # non-positive override would build no decoder layers at all, and a
-        # negative one would additionally make the surplus-key filter drop
-        # every ``language_model.layers.*`` checkpoint key, so the conditioner
-        # would "load" with no transformer stack and only fail at generation.
-        if self.num_hidden_layers_override is not None and self.num_hidden_layers_override < 1:
-            raise ValueError("MiniMax H3 Qwen3-VL num_hidden_layers_override must be a positive layer count "
-                             f"or None for the full stack; got {self.num_hidden_layers_override}.")
+        if self.output_hidden_state_index <= 0 or self.output_hidden_state_index > self.num_hidden_layers:
+            raise ValueError("MiniMax H3 Qwen3-VL output_hidden_state_index must be in "
+                             f"[1, {self.num_hidden_layers}], got {self.output_hidden_state_index}.")
+        if self.num_hidden_layers_override is not None:
+            if self.num_hidden_layers_override <= 0:
+                raise ValueError("MiniMax H3 Qwen3-VL num_hidden_layers_override must be positive or None.")
+            if self.num_hidden_layers_override < self.output_hidden_state_index:
+                raise ValueError("MiniMax H3 Qwen3-VL num_hidden_layers_override must build through "
+                                 f"hidden_states[{self.output_hidden_state_index}], got "
+                                 f"{self.num_hidden_layers_override}.")
 
         rope_scaling = dict(self.rope_scaling or {})
         self.mrope_interleaved = bool(rope_scaling.get("mrope_interleaved", self.mrope_interleaved))

fastvideo/envs.py

@@ -21,12 +21,17 @@ if TYPE_CHECKING:
     FASTVIDEO_TRACE_FUNCTION: int = 0
     FASTVIDEO_ATTENTION_BACKEND: str | None = None
     FASTVIDEO_FA4: bool = False
+    FASTVIDEO_MINIMAX_H3_FUSIONS: str = ""
+    FASTVIDEO_VAE_PARALLEL_DECODE: bool = False
+    FASTVIDEO_VAE_PARALLEL_ENCODE: bool = False
+    FASTVIDEO_VAE_PARALLEL_DECODE_STRATEGY: str | None = None
     FASTVIDEO_WORKER_MULTIPROC_METHOD: str = "spawn"
     FASTVIDEO_TARGET_DEVICE: str = "cuda"
     MAX_JOBS: str | None = None
     NVCC_THREADS: str | None = None
     CMAKE_BUILD_TYPE: str | None = None
     VERBOSE: bool = False
+    FASTVIDEO_NVTX_PROFILE: bool = False
     FASTVIDEO_TORCH_PROFILER_DIR: str | None = None
     FASTVIDEO_TORCH_PROFILER_RECORD_SHAPES: bool = False
     FASTVIDEO_TORCH_PROFILER_WITH_PROFILE_MEMORY: bool = False
@@ -217,10 +222,34 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "FASTVIDEO_FA4":
     lambda: os.getenv("FASTVIDEO_FA4", "0") != "0",
 
+    # If set (=1), MiniMax-H3 VAE decode (and, with the ENCODE variant,
+    # reference-video encode) round-robins its temporal chunks across the
+    # sequence-parallel ranks instead of running serially on the output rank.
+    # Folded into FastVideoArgs.vae_parallel_decode / vae_parallel_encode at
+    # construction (parse-once). The STRATEGY variant picks the chunk
+    # transport collective: "gather" (default) or "all_gather".
+    "FASTVIDEO_VAE_PARALLEL_DECODE":
+    lambda: os.getenv("FASTVIDEO_VAE_PARALLEL_DECODE", "0") != "0",
+    "FASTVIDEO_VAE_PARALLEL_ENCODE":
+    lambda: os.getenv("FASTVIDEO_VAE_PARALLEL_ENCODE", "0") != "0",
+    "FASTVIDEO_VAE_PARALLEL_DECODE_STRATEGY":
+    lambda: os.getenv("FASTVIDEO_VAE_PARALLEL_DECODE_STRATEGY", None),
+
+    # Opt-in MiniMax-H3 inference-only Triton fusions adapted from the
+    # NVlabs/Sana Sol-Engine implementation. Accepts `all`, `1`, or a
+    # comma-separated subset of `modulate,qknorm_rope,swiglu`. An empty value
+    # (the default), `0`, or `none` keeps the eager implementation.
+    "FASTVIDEO_MINIMAX_H3_FUSIONS":
+    lambda: os.getenv("FASTVIDEO_MINIMAX_H3_FUSIONS", ""),
+
     # Use dedicated multiprocess context for workers.
     "FASTVIDEO_WORKER_MULTIPROC_METHOD":
     lambda: os.getenv("FASTVIDEO_WORKER_MULTIPROC_METHOD", "spawn"),
 
+    # Emit lightweight NVTX ranges for external profilers such as Nsight Systems.
+    "FASTVIDEO_NVTX_PROFILE":
+    lambda: os.getenv("FASTVIDEO_NVTX_PROFILE", "0") != "0",
+
     # Enables torch profiler if set. Path to the directory where torch profiler
     # traces are saved. Note that it must be an absolute path.
     "FASTVIDEO_TORCH_PROFILER_DIR":

fastvideo/fastvideo_args.py

@@ -146,6 +146,19 @@ class FastVideoArgs:
     vae_cpu_offload: bool = True
     pin_cpu_memory: bool = True
 
+    # Sequence-parallel MiniMax-H3 VAE (opt-in, default off). With SP > 1 the
+    # video VAE's temporal chunks (decode) and clips (reference encode) are
+    # round-robined across the sequence-parallel ranks and reassembled
+    # bit-exactly on the group's first rank instead of running serially on
+    # one rank while the others idle. ``__post_init__`` folds the
+    # FASTVIDEO_VAE_PARALLEL_DECODE / FASTVIDEO_VAE_PARALLEL_ENCODE env vars
+    # into these fields (parse-once, like attention_backend), and
+    # FASTVIDEO_VAE_PARALLEL_DECODE_STRATEGY overrides the chunk-transport
+    # collective ("gather" or "all_gather").
+    vae_parallel_decode: bool = False
+    vae_parallel_encode: bool = False
+    vae_parallel_decode_strategy: str | None = None
+
     # Compilation
     # ``enable_torch_compile`` covers the DiT path (transformer,
     # transformer_2, and the LTX-2 stage-2 transformer_refine).
@@ -169,6 +182,7 @@ class FastVideoArgs:
 
     # VSA parameters
     VSA_sparsity: float = 0.0  # inference/validation sparsity
+    VSA_tile_size: int = 256  # VSA-H3 tile size (256 or 64); 64 = native Triton path
 
     # V-MoBA parameters
     moba_config_path: str | None = None
@@ -286,8 +300,27 @@ class FastVideoArgs:
             env_backend = envs.FASTVIDEO_ATTENTION_BACKEND
             if env_backend is not None and backend_name_to_enum(env_backend) is not None:
                 self.attention_backend = env_backend
+        self._fold_vae_parallel_env()
         self.check_fastvideo_args()
 
+    def _fold_vae_parallel_env(self) -> None:
+        """Parse-once adapters for the sequence-parallel VAE env vars."""
+        import fastvideo.envs as envs
+
+        # Mirrors fastvideo.models.vaes.minimax_h3_parallel.DECODE_GATHER_STRATEGIES /
+        # DEFAULT_DECODE_GATHER_STRATEGY (kept literal here so constructing args
+        # never imports model modules; a unit test pins the two in sync).
+        strategies = ("gather", "all_gather")
+        if not self.vae_parallel_decode and envs.FASTVIDEO_VAE_PARALLEL_DECODE:
+            self.vae_parallel_decode = True
+        if not self.vae_parallel_encode and envs.FASTVIDEO_VAE_PARALLEL_ENCODE:
+            self.vae_parallel_encode = True
+        if self.vae_parallel_decode_strategy is None:
+            self.vae_parallel_decode_strategy = envs.FASTVIDEO_VAE_PARALLEL_DECODE_STRATEGY or "gather"
+        if self.vae_parallel_decode_strategy not in strategies:
+            raise ValueError(f"vae_parallel_decode_strategy must be one of {strategies}, "
+                             f"got {self.vae_parallel_decode_strategy!r}.")
+
     def _apply_transformer_quant(self) -> None:
         """Pin the typed ``transformer_quant`` instance onto ``dit_config``.
 
@@ -631,6 +664,18 @@ class FastVideoArgs:
             "Pin memory for CPU offload. Only added as a temp workaround if it throws \"CUDA error: invalid argument\". "
             "Should be enabled in almost all cases",
         )
+        parser.add_argument(
+            "--vae-parallel-decode",
+            action=StoreBoolean,
+            help="With sequence parallelism, round-robin MiniMax-H3 VAE decode chunks across the SP ranks "
+            "and reassemble bit-exactly on the output rank (default: serial decode on the output rank)",
+        )
+        parser.add_argument(
+            "--vae-parallel-encode",
+            action=StoreBoolean,
+            help="With sequence parallelism, round-robin MiniMax-H3 reference-video VAE encode clips across "
+            "the SP ranks; every rank keeps the identical full encoding (default: serial encode on every rank)",
+        )
         parser.add_argument(
             "--disable-autocast",
             action=StoreBoolean,
@@ -644,6 +689,12 @@ class FastVideoArgs:
             default=FastVideoArgs.VSA_sparsity,
             help="Validation sparsity for VSA",
         )
+        parser.add_argument(
+            "--VSA-tile-size",
+            type=int,
+            default=FastVideoArgs.VSA_tile_size,
+            help="VSA-H3 tile size in tokens (256 or 64); 64 runs the native Triton block-sparse path",
+        )
 
         # Master port for distributed training/inference
         parser.add_argument(

fastvideo/logger.py

@@ -114,7 +114,10 @@ def _info(logger: Logger,
     is_local_main_process = local_rank == 0
 
     if (main_process_only and is_main_process) or (local_main_process_only and is_local_main_process):
-        logger.log(logging.INFO, msg, *args, stacklevel=2, **kwargs)
+        # Honor an explicit stacklevel (info_once routes through here with
+        # stacklevel already set) instead of passing the keyword twice.
+        stacklevel = kwargs.pop("stacklevel", 2)
+        logger.log(logging.INFO, msg, *args, stacklevel=stacklevel, **kwargs)
 
     global _warned_local_main_process, _warned_main_process
 

fastvideo/models/dits/minimax_h3.py

@@ -10,6 +10,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+from fastvideo import envs
 from fastvideo.attention import DistributedAttention
 from fastvideo.attention.layer import DistributedAttention_VSA
 from fastvideo.attention.selector import get_attn_backend
@@ -23,12 +24,50 @@ from fastvideo.layers.linear import ReplicatedLinear
 from fastvideo.layers.mlp import MLP
 from fastvideo.layers.quantization import QuantizationConfig
 from fastvideo.layers.visual_embedding import Timesteps
+from fastvideo.logger import init_logger
 from fastvideo.models.dits.base import BaseDiT
+from fastvideo.models.dits.minimax_h3_fusions import (
+    HAVE_TRITON,
+    fused_qknorm_rope,
+    fused_residual_gate_rmsnorm_modulate,
+    fused_rmsnorm_modulate,
+    minimax_h3_swiglu,
+)
 from fastvideo.platforms import AttentionBackendEnum
+from fastvideo.profiler import nvtx_range
 from fastvideo.utils import get_compute_dtype
 
+logger = init_logger(__name__)
+
 MINIMAX_H3_MODALITY_NUM = 3
 _CFG = MiniMaxH3Config()
+_MINIMAX_H3_FUSION_NAMES = frozenset({"modulate", "qknorm_rope", "swiglu"})
+
+
+def _enabled_minimax_h3_fusions(value: str | None = None) -> frozenset[str]:
+    """Parse the independently switchable inference fusion set."""
+    raw = envs.FASTVIDEO_MINIMAX_H3_FUSIONS if value is None else value
+    normalized = raw.strip().lower()
+    if normalized in {"", "0", "none"}:
+        return frozenset()
+    if normalized in {"1", "all"}:
+        return _MINIMAX_H3_FUSION_NAMES
+    enabled = frozenset(item.strip() for item in normalized.split(",") if item.strip())
+    unknown = enabled - _MINIMAX_H3_FUSION_NAMES
+    if unknown:
+        supported = ",".join(sorted(_MINIMAX_H3_FUSION_NAMES))
+        raise ValueError(f"Unknown MiniMax H3 fusion(s) {sorted(unknown)}; expected a subset of {supported}.")
+    return enabled
+
+
+def _can_run_minimax_h3_fusion(tensor: torch.Tensor) -> bool:
+    """Triton kernels are inference-only and stay outside Dynamo capture.
+
+    The ``HAVE_TRITON`` check makes the eager fallback exact: on a CUDA build
+    whose Triton failed to import, an enabled fusion falls back instead of
+    hitting the strict wrappers' hard RuntimeError mid-forward.
+    """
+    return (HAVE_TRITON and tensor.is_cuda and not torch.is_grad_enabled() and not torch.compiler.is_compiling())
 
 
 class MiniMaxH3RotaryPosEmbed(nn.Module):
@@ -62,6 +101,7 @@ class MiniMaxH3FeedForward(nn.Module):
         ffn_dim: int,
         quant_config: QuantizationConfig | None = None,
         prefix: str = "",
+        fuse_swiglu: bool = False,
     ) -> None:
         super().__init__()
         self.fc_in = ReplicatedLinear(
@@ -78,11 +118,15 @@ class MiniMaxH3FeedForward(nn.Module):
             quant_config=quant_config,
             prefix=f"{prefix}.fc_out",
         )
+        self.fuse_swiglu = fuse_swiglu
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         hidden_states, _ = self.fc_in(hidden_states)
-        hidden_states, gate = hidden_states.chunk(2, dim=-1)
-        hidden_states = hidden_states * F.silu(gate)
+        if self.fuse_swiglu and _can_run_minimax_h3_fusion(hidden_states):
+            hidden_states = minimax_h3_swiglu(hidden_states)
+        else:
+            hidden_states, gate = hidden_states.chunk(2, dim=-1)
+            hidden_states = hidden_states * F.silu(gate)
         hidden_states, _ = self.fc_out(hidden_states)
         return hidden_states
 
@@ -99,6 +143,7 @@ class MiniMaxH3Attention(nn.Module):
         supported_attention_backends: tuple[AttentionBackendEnum, ...],
         quant_config: QuantizationConfig | None,
         prefix: str,
+        fuse_qknorm_rope: bool = False,
     ) -> None:
         super().__init__()
         self.num_attention_heads = num_attention_heads
@@ -134,6 +179,7 @@ class MiniMaxH3Attention(nn.Module):
             quant_config=quant_config,
             prefix=f"{prefix}.to_out",
         )
+        self.fuse_qknorm_rope = fuse_qknorm_rope
         # VSA carries a learned gate on its pooled-compression branch. The H3
         # checkpoint has no such weight, so the loader zero-initializes it
         # (ALLOWED_NEW_PARAM_PATTERNS) and the branch is exactly disabled
@@ -211,11 +257,18 @@ class MiniMaxH3Attention(nn.Module):
         query = query.unflatten(-1, (self.num_attention_heads, self.attention_head_dim))
         key = key.unflatten(-1, (self.num_attention_heads, self.attention_head_dim))
         value = value.unflatten(-1, (self.num_attention_heads, self.attention_head_dim))
-        query = self.norm_q(query)
-        key = self.norm_k(key)
-        if rotary_emb is not None:
-            query = self._apply_rotary_emb(query, rotary_emb)
-            key = self._apply_rotary_emb(key, rotary_emb)
+        if (self.fuse_qknorm_rope and rotary_emb is not None and _can_run_minimax_h3_fusion(query)):
+            cos, sin = rotary_emb
+            cos = cos.to(query.dtype)
+            sin = sin.to(query.dtype)
+            query = fused_qknorm_rope(query, self.norm_q.weight, cos, sin, self.norm_q.eps)
+            key = fused_qknorm_rope(key, self.norm_k.weight, cos, sin, self.norm_k.eps)
+        else:
+            query = self.norm_q(query)
+            key = self.norm_k(key)
+            if rotary_emb is not None:
+                query = self._apply_rotary_emb(query, rotary_emb)
+                key = self._apply_rotary_emb(key, rotary_emb)
 
         # H3 rotates only 96/128 channels, which the generic `freqs_cis`
         # branch cannot express. Apply it above, then pass no RoPE here.
@@ -397,6 +450,9 @@ class MiniMaxH3TransformerBlock(nn.Module):
         quant_config: QuantizationConfig | None,
         prefix: str,
         adaln_apply_silu: bool = True,
+        fuse_modulate: bool = False,
+        fuse_qknorm_rope: bool = False,
+        fuse_swiglu: bool = False,
     ) -> None:
         super().__init__()
         self.norm1 = nn.RMSNorm(hidden_size, eps=norm_eps)
@@ -408,6 +464,7 @@ class MiniMaxH3TransformerBlock(nn.Module):
             supported_attention_backends,
             quant_config,
             prefix=f"{prefix}.attn",
+            fuse_qknorm_rope=fuse_qknorm_rope,
         )
         self.norm2 = nn.RMSNorm(hidden_size, eps=norm_eps)
         self.ff = MiniMaxH3FeedForward(
@@ -415,6 +472,7 @@ class MiniMaxH3TransformerBlock(nn.Module):
             ffn_dim,
             quant_config=quant_config,
             prefix=f"{prefix}.ff",
+            fuse_swiglu=fuse_swiglu,
         )
         self.adaln_proj = MiniMaxH3AdaLayerNormModulation(
             time_embed_dim,
@@ -423,6 +481,7 @@ class MiniMaxH3TransformerBlock(nn.Module):
             prefix=f"{prefix}.adaln_proj",
             apply_silu=adaln_apply_silu,
         )
+        self.fuse_modulate = fuse_modulate
 
     def forward(
         self,
@@ -435,19 +494,39 @@ class MiniMaxH3TransformerBlock(nn.Module):
         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
             t.to(hidden_states.dtype) for t in self.adaln_proj(temb))
 
-        residual = hidden_states
-        norm_hidden_states = self.norm1(hidden_states)
-        norm_hidden_states = norm_hidden_states * (
-            1.0 + scale_msa.index_select(0, adaln_indices)) + shift_msa.index_select(0, adaln_indices)
+        use_modulate_fusion = self.fuse_modulate and _can_run_minimax_h3_fusion(hidden_states)
+        if use_modulate_fusion:
+            norm_hidden_states = fused_rmsnorm_modulate(
+                hidden_states,
+                self.norm1.weight,
+                scale_msa,
+                shift_msa,
+                adaln_indices,
+                self.norm1.eps,
+            )
+        else:
+            norm_hidden_states = self.norm1(hidden_states)
+            norm_hidden_states = norm_hidden_states * (
+                1.0 + scale_msa.index_select(0, adaln_indices)) + shift_msa.index_select(0, adaln_indices)
         attention_output = self.attn(norm_hidden_states, rotary_emb, original_seq_len)
-        hidden_states = residual + gate_msa.index_select(0, adaln_indices) * attention_output
-
-        residual = hidden_states
-        norm_hidden_states = self.norm2(hidden_states)
-        norm_hidden_states = norm_hidden_states * (
-            1.0 + scale_mlp.index_select(0, adaln_indices)) + shift_mlp.index_select(0, adaln_indices)
+        if use_modulate_fusion:
+            hidden_states, norm_hidden_states = fused_residual_gate_rmsnorm_modulate(
+                hidden_states,
+                attention_output,
+                gate_msa,
+                self.norm2.weight,
+                scale_mlp,
+                shift_mlp,
+                adaln_indices,
+                self.norm2.eps,
+            )
+        else:
+            hidden_states = hidden_states + gate_msa.index_select(0, adaln_indices) * attention_output
+            norm_hidden_states = self.norm2(hidden_states)
+            norm_hidden_states = norm_hidden_states * (
+                1.0 + scale_mlp.index_select(0, adaln_indices)) + shift_mlp.index_select(0, adaln_indices)
         feed_forward_output = self.ff(norm_hidden_states)
-        return residual + gate_mlp.index_select(0, adaln_indices) * feed_forward_output
+        return hidden_states + gate_mlp.index_select(0, adaln_indices) * feed_forward_output
 
 
 class MiniMaxH3Transformer3DModel(BaseDiT):
@@ -493,6 +572,17 @@ class MiniMaxH3Transformer3DModel(BaseDiT):
     def __init__(self, config: MiniMaxH3Config, hf_config: dict[str, Any]) -> None:
         super().__init__(config, hf_config)
         arch = config.arch_config
+        self.enabled_fusions = _enabled_minimax_h3_fusions()
+        if self.enabled_fusions:
+            if HAVE_TRITON:
+                logger.info(
+                    "MiniMax H3 inference fusions enabled: %s (CUDA inference-only; grad-enabled and "
+                    "torch.compile-captured forwards fall back to eager).",
+                    ",".join(sorted(self.enabled_fusions)))
+            else:
+                logger.warning(
+                    "FASTVIDEO_MINIMAX_H3_FUSIONS requested %s but Triton is unavailable; "
+                    "every forward stays on the eager path.", ",".join(sorted(self.enabled_fusions)))
         sp_world_size = get_sp_world_size() if model_parallel_is_initialized() else 1
         if arch.num_attention_heads % sp_world_size:
             raise ValueError(f"MiniMax H3 attention heads ({arch.num_attention_heads}) must be divisible by "
@@ -590,6 +680,9 @@ class MiniMaxH3Transformer3DModel(BaseDiT):
                 config.quant_config,
                 prefix=f"{config.prefix}.transformer_blocks.{index}",
                 adaln_apply_silu=self.adaln_rank is None,
+                fuse_modulate="modulate" in self.enabled_fusions,
+                fuse_qknorm_rope="qknorm_rope" in self.enabled_fusions,
+                fuse_swiglu="swiglu" in self.enabled_fusions,
             ) for index in range(arch.num_layers)
         ])
         self.norm_out = MiniMaxH3AdaLayerNormOut(
@@ -616,6 +709,20 @@ class MiniMaxH3Transformer3DModel(BaseDiT):
         )
         self.__post_init__()
 
+    def prepare_for_compile(self) -> None:
+        """Pipeline hook, called once right before torch.compile wraps the blocks.
+
+        Dynamo capture traces the eager branch of every fusion guard, so an
+        enabled ``FASTVIDEO_MINIMAX_H3_FUSIONS`` set is silently inert inside
+        compiled block forwards (H3 compiles per-block by default). Say so
+        once instead of leaving the flag looking active.
+        """
+        if self.enabled_fusions:
+            logger.warning(
+                "torch.compile is enabled for MiniMax H3, so the requested inference fusions (%s) are "
+                "inert inside compiled block forwards; the compiled eager path runs instead.",
+                ",".join(sorted(self.enabled_fusions)))
+
     def materialize_non_persistent_buffers(
         self,
         device: torch.device,
@@ -734,14 +841,17 @@ class MiniMaxH3Transformer3DModel(BaseDiT):
             local_timestep_indices, _ = sequence_model_parallel_shard(local_timestep_indices, dim=0)
             rotary_emb = (rotary_cos, rotary_sin)
 
-        for block in self.transformer_blocks:
-            packed_hidden_states = block(
-                packed_hidden_states,
-                temb,
-                adaln_indices,
-                rotary_emb,
-                original_seq_len,
-            )
+        # The eager driver owns profiling markers while each block's compiled
+        # forward owns the graph that the marker surrounds.
+        for block_index, block in enumerate(self.transformer_blocks):
+            with nvtx_range(f"minimax_h3.transformer_block.{block_index}"):
+                packed_hidden_states = block(
+                    packed_hidden_states,
+                    temb,
+                    adaln_indices,
+                    rotary_emb,
+                    original_seq_len,
+                )
 
         packed_hidden_states = self.norm_out(
             packed_hidden_states,

fastvideo/models/dits/minimax_h3_fusions/__init__.py

@@ -0,0 +1,20 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Inference-only MiniMax H3 fusions adapted from NVlabs/Sana Sol-Engine.
+
+Source: https://github.com/NVlabs/Sana/tree/sol-engine/models/minimax_h3/GB200
+"""
+
+from .modulation import (
+    fused_residual_gate_rmsnorm_modulate,
+    fused_rmsnorm_modulate,
+)
+from .qknorm_rope import HAVE_TRITON, fused_qknorm_rope
+from .swiglu import minimax_h3_swiglu
+
+__all__ = [
+    "HAVE_TRITON",
+    "fused_qknorm_rope",
+    "fused_residual_gate_rmsnorm_modulate",
+    "fused_rmsnorm_modulate",
+    "minimax_h3_swiglu",
+]

fastvideo/models/dits/minimax_h3_fusions/modulation.py

@@ -0,0 +1,302 @@
+# SPDX-License-Identifier: Apache-2.0
+"""MiniMax H3 RMSNorm and row-indexed modulation fusions."""
+
+from __future__ import annotations
+
+import math
+
+import torch
+
+try:
+    import triton
+    import triton.language as tl
+except ImportError as exc:  # pragma: no cover - depends on the runtime image
+    triton = None
+    tl = None
+    _TRITON_IMPORT_ERROR: ImportError | None = exc
+else:
+    _TRITON_IMPORT_ERROR = None
+
+
+__all__ = [
+    "fused_residual_gate_rmsnorm_modulate",
+    "fused_rmsnorm_modulate",
+]
+
+_SUPPORTED_DTYPES = (torch.float16, torch.bfloat16, torch.float32)
+_rmsnorm_modulate_kernel = None
+_residual_gate_rmsnorm_modulate_kernel = None
+
+
+if triton is not None:
+
+    @triton.jit
+    def _rmsnorm_modulate_kernel(
+        out_ptr,
+        x_ptr,
+        weight_ptr,
+        scale_ptr,
+        shift_ptr,
+        index_ptr,
+        n_cols,
+        n_index,
+        eps,
+        stride_x_row,
+        stride_scale_row,
+        stride_shift_row,
+        BLOCK: tl.constexpr,
+    ):
+        row = tl.program_id(0).to(tl.int64)
+        cols = tl.arange(0, BLOCK)
+        mask = cols < n_cols
+        x_offsets = row * stride_x_row + cols
+        table_row = tl.load(index_ptr + row % n_index).to(tl.int64)
+
+        x = tl.load(x_ptr + x_offsets, mask=mask, other=0.0).to(tl.float32)
+        variance = tl.sum(x * x, axis=0) / n_cols
+        normed = x * tl.math.rsqrt(variance + eps)
+        weight = tl.load(weight_ptr + cols, mask=mask, other=0.0).to(tl.float32)
+        scale = tl.load(
+            scale_ptr + table_row * stride_scale_row + cols,
+            mask=mask,
+            other=0.0,
+        ).to(tl.float32)
+        shift = tl.load(
+            shift_ptr + table_row * stride_shift_row + cols,
+            mask=mask,
+            other=0.0,
+        ).to(tl.float32)
+        output = normed * weight * (1.0 + scale) + shift
+        tl.store(out_ptr + x_offsets, output.to(out_ptr.dtype.element_ty), mask=mask)
+
+    @triton.jit
+    def _residual_gate_rmsnorm_modulate_kernel(
+        hidden_out_ptr,
+        normed_out_ptr,
+        residual_ptr,
+        branch_ptr,
+        gate_ptr,
+        weight_ptr,
+        scale_ptr,
+        shift_ptr,
+        index_ptr,
+        n_cols,
+        n_index,
+        eps,
+        stride_input_row,
+        stride_gate_row,
+        stride_scale_row,
+        stride_shift_row,
+        BLOCK: tl.constexpr,
+    ):
+        row = tl.program_id(0).to(tl.int64)
+        cols = tl.arange(0, BLOCK)
+        mask = cols < n_cols
+        input_offsets = row * stride_input_row + cols
+        table_row = tl.load(index_ptr + row % n_index).to(tl.int64)
+
+        residual = tl.load(residual_ptr + input_offsets, mask=mask, other=0.0).to(tl.float32)
+        branch = tl.load(branch_ptr + input_offsets, mask=mask, other=0.0).to(tl.float32)
+        gate = tl.load(
+            gate_ptr + table_row * stride_gate_row + cols,
+            mask=mask,
+            other=0.0,
+        ).to(tl.float32)
+        hidden = residual + gate * branch
+        tl.store(hidden_out_ptr + input_offsets, hidden.to(hidden_out_ptr.dtype.element_ty), mask=mask)
+
+        variance = tl.sum(hidden * hidden, axis=0) / n_cols
+        normed = hidden * tl.math.rsqrt(variance + eps)
+        weight = tl.load(weight_ptr + cols, mask=mask, other=0.0).to(tl.float32)
+        scale = tl.load(
+            scale_ptr + table_row * stride_scale_row + cols,
+            mask=mask,
+            other=0.0,
+        ).to(tl.float32)
+        shift = tl.load(
+            shift_ptr + table_row * stride_shift_row + cols,
+            mask=mask,
+            other=0.0,
+        ).to(tl.float32)
+        output = normed * weight * (1.0 + scale) + shift
+        tl.store(normed_out_ptr + input_offsets, output.to(normed_out_ptr.dtype.element_ty), mask=mask)
+
+
+def _validate_contract(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    tables: tuple[torch.Tensor, ...],
+    index: torch.Tensor,
+    eps: float,
+) -> None:
+    if x.ndim < 2:
+        raise ValueError(f"x must have shape (..., sequence_length, hidden_size), got {tuple(x.shape)}.")
+    if x.numel() == 0 or x.shape[-1] == 0:
+        raise ValueError("x must not be empty.")
+    if x.dtype not in _SUPPORTED_DTYPES:
+        raise TypeError(f"x must use float16, bfloat16, or float32, got {x.dtype}.")
+    hidden_size = x.shape[-1]
+    sequence_length = x.shape[-2]
+    if weight.shape != (hidden_size, ):
+        raise ValueError(f"weight must have shape ({hidden_size},), got {tuple(weight.shape)}.")
+    if weight.dtype not in _SUPPORTED_DTYPES:
+        raise TypeError(f"weight must use float16, bfloat16, or float32, got {weight.dtype}.")
+    if index.ndim != 1 or index.numel() != sequence_length:
+        raise ValueError(
+            f"index must have shape ({sequence_length},) so it can wrap over batch rows, got {tuple(index.shape)}."
+        )
+    if index.dtype not in (torch.int32, torch.int64):
+        raise TypeError(f"index must use int32 or int64, got {index.dtype}.")
+    if not isinstance(eps, (float, int)) or isinstance(eps, bool) or not math.isfinite(eps) or eps <= 0:
+        raise ValueError(f"eps must be a positive finite number, got {eps!r}.")
+
+    table_rows = tables[0].shape[0] if tables and tables[0].ndim == 2 else None
+    for name, table in zip(("gate", "scale", "shift")[-len(tables):], tables, strict=True):
+        if table.ndim != 2 or table.shape[1] != hidden_size:
+            raise ValueError(f"{name} must have shape (table_rows, {hidden_size}), got {tuple(table.shape)}.")
+        if table.shape[0] == 0 or table.shape[0] != table_rows:
+            raise ValueError("all modulation tables must have the same non-zero row count.")
+        if table.dtype not in _SUPPORTED_DTYPES:
+            raise TypeError(f"{name} must use float16, bfloat16, or float32, got {table.dtype}.")
+
+    tensors = (x, weight, *tables, index)
+    if any(tensor.device != x.device for tensor in tensors[1:]):
+        raise ValueError("x, weight, modulation tables, and index must be on the same device.")
+
+
+def _validate_residual_branch(residual: torch.Tensor, branch: torch.Tensor) -> None:
+    if branch.shape != residual.shape:
+        raise ValueError(f"branch must match residual shape {tuple(residual.shape)}, got {tuple(branch.shape)}.")
+    if branch.dtype != residual.dtype:
+        raise TypeError(f"branch dtype must match residual dtype {residual.dtype}, got {branch.dtype}.")
+    if branch.device != residual.device:
+        raise ValueError("branch and residual must be on the same device.")
+
+
+def _require_triton_cuda(x: torch.Tensor) -> None:
+    if triton is None:
+        detail = f": {_TRITON_IMPORT_ERROR}" if _TRITON_IMPORT_ERROR is not None else ""
+        raise RuntimeError(f"MiniMax H3 modulation fusion requires Triton{detail}.")
+    if x.device.type != "cuda":
+        raise RuntimeError(f"MiniMax H3 modulation fusion requires CUDA tensors, got device {x.device}.")
+
+
+def _require_forward_only(*tensors: torch.Tensor) -> None:
+    if torch.is_grad_enabled() and any(tensor.requires_grad for tensor in tensors):
+        raise RuntimeError("MiniMax H3 modulation fusion is forward-only and does not support autograd.")
+
+
+def _next_power_of_two(value: int) -> int:
+    return 1 << (value - 1).bit_length()
+
+
+def _num_warps(block_size: int) -> int:
+    if block_size >= 8192:
+        return 16
+    if block_size >= 2048:
+        return 8
+    return 4
+
+
+def _row_addressable(table: torch.Tensor) -> torch.Tensor:
+    return table if table.stride(-1) == 1 else table.contiguous()
+
+
+def fused_rmsnorm_modulate(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    scale: torch.Tensor,
+    shift: torch.Tensor,
+    index: torch.Tensor,
+    eps: float,
+) -> torch.Tensor:
+    """Run RMSNorm and row-indexed modulation in one strict Triton kernel.
+
+    ``index`` values must lie in ``[0, table_rows)``. Unlike eager
+    ``index_select``, the kernel does not raise on out-of-range values (a
+    device-side bounds check would synchronize); callers are safe by
+    construction (``timestep_indices * 3 + token_tags``, SP pads with 0).
+    """
+    _validate_contract(x, weight, (scale, shift), index, eps)
+    _require_forward_only(x, weight, scale, shift)
+    _require_triton_cuda(x)
+
+    hidden_size = x.shape[-1]
+    flat_x = x.reshape(-1, hidden_size).contiguous()
+    weight = weight.contiguous()
+    scale = _row_addressable(scale)
+    shift = _row_addressable(shift)
+    index = index.contiguous()
+    output = torch.empty_like(flat_x)
+    block_size = _next_power_of_two(hidden_size)
+    _rmsnorm_modulate_kernel[(flat_x.shape[0], )](
+        output,
+        flat_x,
+        weight,
+        scale,
+        shift,
+        index,
+        hidden_size,
+        index.numel(),
+        eps,
+        flat_x.stride(0),
+        scale.stride(0),
+        shift.stride(0),
+        BLOCK=block_size,
+        num_warps=_num_warps(block_size),
+    )
+    return output.view_as(x)
+
+
+def fused_residual_gate_rmsnorm_modulate(
+    residual: torch.Tensor,
+    branch: torch.Tensor,
+    gate: torch.Tensor,
+    weight: torch.Tensor,
+    scale: torch.Tensor,
+    shift: torch.Tensor,
+    index: torch.Tensor,
+    eps: float,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Fuse residual update, row-indexed gate, RMSNorm, and modulation.
+
+    ``index`` values must lie in ``[0, table_rows)``; see
+    :func:`fused_rmsnorm_modulate` for why the wrapper does not check them.
+    """
+    _validate_residual_branch(residual, branch)
+    _validate_contract(residual, weight, (gate, scale, shift), index, eps)
+    _require_forward_only(residual, branch, gate, weight, scale, shift)
+    _require_triton_cuda(residual)
+
+    hidden_size = residual.shape[-1]
+    flat_residual = residual.reshape(-1, hidden_size).contiguous()
+    flat_branch = branch.reshape(-1, hidden_size).contiguous()
+    weight = weight.contiguous()
+    gate = _row_addressable(gate)
+    scale = _row_addressable(scale)
+    shift = _row_addressable(shift)
+    index = index.contiguous()
+    hidden = torch.empty_like(flat_residual)
+    modulated = torch.empty_like(flat_residual)
+    block_size = _next_power_of_two(hidden_size)
+    _residual_gate_rmsnorm_modulate_kernel[(flat_residual.shape[0], )](
+        hidden,
+        modulated,
+        flat_residual,
+        flat_branch,
+        gate,
+        weight,
+        scale,
+        shift,
+        index,
+        hidden_size,
+        index.numel(),
+        eps,
+        flat_residual.stride(0),
+        gate.stride(0),
+        scale.stride(0),
+        shift.stride(0),
+        BLOCK=block_size,
+        num_warps=_num_warps(block_size),
+    )
+    return hidden.view_as(residual), modulated.view_as(residual)

fastvideo/models/dits/minimax_h3_fusions/qknorm_rope.py

@@ -0,0 +1,174 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Fused per-head RMSNorm and partial rotary embedding for MiniMax H3."""
+
+from __future__ import annotations
+
+import math
+
+import torch
+
+try:
+    import triton
+    import triton.language as tl
+
+    HAVE_TRITON = True
+except ImportError:  # pragma: no cover - exercised only in environments without Triton
+    triton = None
+    tl = None
+    HAVE_TRITON = False
+
+
+if HAVE_TRITON:
+
+    @triton.jit
+    def _qknorm_partial_rope_kernel(
+        out_ptr,
+        x_ptr,
+        weight_ptr,
+        cos_ptr,
+        sin_ptr,
+        head_dim,
+        rotary_dim,
+        half_rotary_dim,
+        num_heads,
+        seq_len,
+        eps,
+        BLOCK_SIZE: tl.constexpr,
+    ):
+        # int64, like the sibling kernels: with int32 program ids,
+        # ``row * head_dim`` wraps once the flattened input reaches 2**31
+        # elements (H3's 56 heads x 128 head_dim crosses that at
+        # batch*seq >= 299_593 tokens per rank) and the loads/stores below
+        # become out-of-bounds. ``seq_index`` inherits int64 from ``row``.
+        row = tl.program_id(0).to(tl.int64)
+        seq_index = (row // num_heads) % seq_len
+        cols = tl.arange(0, BLOCK_SIZE)
+        head_mask = cols < head_dim
+        row_offset = row * head_dim
+
+        x = tl.load(x_ptr + row_offset + cols, mask=head_mask, other=0.0).to(tl.float32)
+        variance = tl.sum(x * x, axis=0) / head_dim
+        inv_rms = tl.math.rsqrt(variance + eps)
+        weight = tl.load(weight_ptr + cols, mask=head_mask, other=0.0).to(tl.float32)
+        normalized = x * inv_rms * weight
+
+        rotary_mask = cols < rotary_dim
+        first_half = cols < half_rotary_dim
+        partner_col = tl.where(first_half, cols + half_rotary_dim, cols - half_rotary_dim)
+        partner_x = tl.load(
+            x_ptr + row_offset + partner_col,
+            mask=rotary_mask,
+            other=0.0,
+        ).to(tl.float32)
+        partner_weight = tl.load(weight_ptr + partner_col, mask=rotary_mask, other=0.0).to(tl.float32)
+        partner_normalized = partner_x * inv_rms * partner_weight
+        rotated = tl.where(first_half, -partner_normalized, partner_normalized)
+
+        table_offset = seq_index * rotary_dim + cols
+        cos = tl.load(cos_ptr + table_offset, mask=rotary_mask, other=1.0).to(tl.float32)
+        sin = tl.load(sin_ptr + table_offset, mask=rotary_mask, other=0.0).to(tl.float32)
+        rotary_output = normalized * cos + rotated * sin
+        output = tl.where(rotary_mask, rotary_output, normalized)
+        tl.store(out_ptr + row_offset + cols, output.to(out_ptr.dtype.element_ty), mask=head_mask)
+
+
+_SUPPORTED_DTYPES = (torch.float16, torch.bfloat16, torch.float32)
+
+
+def _validate_inputs(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    eps: float,
+) -> tuple[int, int, int, int, int]:
+    for name, tensor in (("x", x), ("weight", weight), ("cos", cos), ("sin", sin)):
+        if not isinstance(tensor, torch.Tensor):
+            raise TypeError(f"{name} must be a torch.Tensor, got {type(tensor).__name__}")
+
+    if x.ndim != 4:
+        raise ValueError(f"x must have shape (batch, seq, heads, head_dim), got {tuple(x.shape)}")
+    batch, seq_len, num_heads, head_dim = x.shape
+    if min(batch, seq_len, num_heads, head_dim) <= 0:
+        raise ValueError(f"x dimensions must all be positive, got {tuple(x.shape)}")
+    if weight.shape != (head_dim, ):
+        raise ValueError(f"weight must have shape ({head_dim},), got {tuple(weight.shape)}")
+    if cos.ndim != 2:
+        raise ValueError(f"cos must have shape (seq, rotary_dim), got {tuple(cos.shape)}")
+    if sin.shape != cos.shape:
+        raise ValueError(f"sin must match cos shape {tuple(cos.shape)}, got {tuple(sin.shape)}")
+    if cos.shape[0] != seq_len:
+        raise ValueError(f"cos/sin sequence length must be {seq_len}, got {cos.shape[0]}")
+
+    rotary_dim = cos.shape[1]
+    if rotary_dim <= 0:
+        raise ValueError(f"rotary_dim must be positive, got {rotary_dim}")
+    if rotary_dim > head_dim:
+        raise ValueError(f"rotary_dim must not exceed head_dim, got rotary_dim={rotary_dim}, head_dim={head_dim}")
+    if rotary_dim % 2:
+        raise ValueError(f"rotary_dim must be even, got {rotary_dim}")
+
+    if x.dtype not in _SUPPORTED_DTYPES:
+        raise TypeError(f"x dtype must be float16, bfloat16, or float32, got {x.dtype}")
+    for name, tensor in (("weight", weight), ("cos", cos), ("sin", sin)):
+        if tensor.dtype != x.dtype:
+            raise TypeError(f"{name} dtype must match x dtype {x.dtype}, got {tensor.dtype}")
+        if tensor.device != x.device:
+            raise ValueError(f"{name} device must match x device {x.device}, got {tensor.device}")
+
+    if not isinstance(eps, (float, int)) or not math.isfinite(float(eps)) or eps <= 0:
+        raise ValueError(f"eps must be a positive finite number, got {eps!r}")
+    return batch, seq_len, num_heads, head_dim, rotary_dim
+
+
+def fused_qknorm_rope(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    eps: float,
+) -> torch.Tensor:
+    """Run per-head RMSNorm and partial RoPE in one Sol-Engine-style kernel.
+
+    RMSNorm reduction and RoPE arithmetic stay in FP32 registers until the
+    final store. Triton's reduction order and the absence of eager's BF16
+    intermediate materializations can produce small, expected rounding drift.
+
+    Row offsets are computed in int64, so inputs beyond 2**31 total elements
+    (about 300k tokens per rank at H3's 56 heads x 128 head_dim) address
+    correctly.
+    """
+    batch, seq_len, num_heads, head_dim, rotary_dim = _validate_inputs(x, weight, cos, sin, eps)
+    if not weight.is_contiguous():
+        raise ValueError("weight must be contiguous")
+    if not cos.is_contiguous() or not sin.is_contiguous():
+        raise ValueError("cos and sin must be contiguous (seq, rotary_dim) tables")
+    if not x.is_cuda:
+        raise RuntimeError("fused_qknorm_rope requires CUDA tensors")
+    if not HAVE_TRITON:
+        raise RuntimeError("fused_qknorm_rope requires Triton")
+    if torch.is_grad_enabled() and any(tensor.requires_grad for tensor in (x, weight, cos, sin)):
+        raise RuntimeError("fused_qknorm_rope is inference-only and does not implement autograd")
+
+    flat_x = x.reshape(-1, head_dim).contiguous()
+    flat_out = torch.empty_like(flat_x)
+    block_size = 1 << (head_dim - 1).bit_length()
+    _qknorm_partial_rope_kernel[(flat_x.shape[0], )](
+        flat_out,
+        flat_x,
+        weight,
+        cos,
+        sin,
+        head_dim,
+        rotary_dim,
+        rotary_dim // 2,
+        num_heads,
+        seq_len,
+        eps,
+        BLOCK_SIZE=block_size,
+        num_warps=4,
+    )
+    return flat_out.view(batch, seq_len, num_heads, head_dim)
+
+
+__all__ = ["HAVE_TRITON", "fused_qknorm_rope"]

fastvideo/models/dits/minimax_h3_fusions/swiglu.py

@@ -0,0 +1,104 @@
+# SPDX-License-Identifier: Apache-2.0
+"""MiniMax H3's value-first packed SwiGLU fusion."""
+
+from __future__ import annotations
+
+import torch
+
+try:
+    import triton
+    import triton.language as tl
+
+    HAVE_TRITON = True
+except ImportError:  # pragma: no cover - exercised only in environments without Triton
+    triton = None
+    tl = None
+    HAVE_TRITON = False
+
+
+def _validate_input(x: torch.Tensor) -> int:
+    if x.ndim == 0:
+        raise ValueError("MiniMax H3 SwiGLU expects at least one dimension")
+
+    packed_width = x.shape[-1]
+    if packed_width == 0 or packed_width % 2 != 0:
+        raise ValueError(
+            "MiniMax H3 SwiGLU expects a positive even last dimension containing packed (value, gate) halves, "
+            f"got {packed_width}"
+        )
+    if not x.is_floating_point():
+        raise TypeError(f"MiniMax H3 SwiGLU expects a floating-point tensor, got {x.dtype}")
+    return packed_width // 2
+
+
+if HAVE_TRITON:
+
+    @triton.jit
+    def _minimax_h3_swiglu_kernel(
+        out_ptr,
+        x_ptr,
+        ffn_dim,
+        stride_in_row,
+        stride_out_row,
+        BLOCK_SIZE: tl.constexpr,
+    ):
+        row = tl.program_id(0).to(tl.int64)
+        cols = tl.arange(0, BLOCK_SIZE)
+        mask = cols < ffn_dim
+
+        value = tl.load(x_ptr + row * stride_in_row + cols, mask=mask, other=0.0).to(tl.float32)
+        gate = tl.load(x_ptr + row * stride_in_row + ffn_dim + cols, mask=mask, other=0.0).to(tl.float32)
+        # Match Sol-Engine: keep the complete SwiGLU expression in FP32 and
+        # convert only the final output store.
+        out = value * (gate * tl.sigmoid(gate))
+        tl.store(out_ptr + row * stride_out_row + cols, out.to(out_ptr.dtype.element_ty), mask=mask)
+
+else:
+    _minimax_h3_swiglu_kernel = None
+
+
+def _num_warps(block_size: int) -> int:
+    if block_size >= 8192:
+        return 16
+    if block_size >= 2048:
+        return 8
+    return 4
+
+
+def minimax_h3_swiglu(x: torch.Tensor) -> torch.Tensor:
+    """Run the forward-only Triton fusion over an H3 ``(..., 2 * ffn_dim)`` input.
+
+    This is intentionally a strict kernel wrapper: callers own fallback policy and
+    must only invoke it for a supported CUDA inference path.
+    """
+    ffn_dim = _validate_input(x)
+    if not x.is_cuda:
+        raise ValueError("MiniMax H3 fused SwiGLU requires a CUDA tensor")
+    if x.dtype not in (torch.float16, torch.bfloat16, torch.float32):
+        raise TypeError(f"MiniMax H3 fused SwiGLU supports float16, bfloat16, and float32, got {x.dtype}")
+    if torch.is_grad_enabled() and x.requires_grad:
+        raise RuntimeError("MiniMax H3 fused SwiGLU is forward-only and does not implement autograd")
+    if _minimax_h3_swiglu_kernel is None:
+        raise RuntimeError("MiniMax H3 fused SwiGLU requires Triton")
+
+    packed_width = x.shape[-1]
+    flat = x.reshape(-1, packed_width).contiguous()
+    output_shape = (*x.shape[:-1], ffn_dim)
+    if flat.shape[0] == 0:
+        return torch.empty(output_shape, dtype=x.dtype, device=x.device)
+
+    out = torch.empty((flat.shape[0], ffn_dim), dtype=x.dtype, device=x.device)
+    block_size = triton.next_power_of_2(ffn_dim)
+    _minimax_h3_swiglu_kernel[(flat.shape[0],)](
+        out,
+        flat,
+        ffn_dim,
+        flat.stride(0),
+        out.stride(0),
+        BLOCK_SIZE=block_size,
+        num_warps=_num_warps(block_size),
+    )
+    return out.view(output_shape)
+
+
+__all__ = ["HAVE_TRITON", "minimax_h3_swiglu"]

fastvideo/models/encoders/base.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 from abc import ABC, abstractmethod
 from dataclasses import field
+from typing import Any, Generic, TypeVar
 
 import torch
 from torch import nn
@@ -8,11 +9,16 @@ from torch import nn
 from fastvideo.configs.models.encoders import (BaseEncoderOutput, ImageEncoderConfig, TextEncoderConfig)
 from fastvideo.platforms import AttentionBackendEnum
 
+TextEncoderOutputT = TypeVar("TextEncoderOutputT")
+
+
+class TextEncoder(nn.Module, ABC, Generic[TextEncoderOutputT]):
+    """Base for native encoders with a model-specific forward output contract."""
 
-class TextEncoder(nn.Module, ABC):
     _fsdp_shard_conditions: list = field(default_factory=lambda: [])
     _stacked_params_mapping: list[tuple[str, str, str]] = field(default_factory=list)
     _supported_attention_backends: tuple[AttentionBackendEnum, ...] = TextEncoderConfig()._supported_attention_backends
+    supported_checkpoint_quantization_methods: frozenset[str] = frozenset()
 
     def __init__(self, config: TextEncoderConfig) -> None:
         super().__init__()
@@ -23,13 +29,7 @@ class TextEncoder(nn.Module, ABC):
             raise ValueError(f"Subclass {self.__class__.__name__} must define _supported_attention_backends")
 
     @abstractmethod
-    def forward(self,
-                input_ids: torch.Tensor | None,
-                position_ids: torch.Tensor | None = None,
-                attention_mask: torch.Tensor | None = None,
-                inputs_embeds: torch.Tensor | None = None,
-                output_hidden_states: bool | None = None,
-                **kwargs) -> BaseEncoderOutput:
+    def forward(self, *args: Any, **kwargs: Any) -> TextEncoderOutputT:
         pass
 
     @property

fastvideo/models/encoders/minimax_h3_checkpoint_fp8.py

@@ -0,0 +1,453 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Serialized block-FP8 execution for the MiniMax-H3 Qwen3-VL encoder."""
+
+from typing import Any
+
+import torch
+from torch import nn
+from torch.nn.parameter import Parameter
+
+try:
+    import triton
+    import triton.language as tl
+except ImportError:
+    triton = None
+    tl = None
+
+from fastvideo.distributed import get_tp_world_size
+from fastvideo.layers.linear import LinearBase, LinearMethodBase
+from fastvideo.layers.quantization.base_config import QuantizationConfig
+from fastvideo.layers.quantization.fp8_config import FP8_DTYPE
+from fastvideo.models.utils import set_weight_attrs
+
+
+class MiniMaxH3SerializedFP8Config(QuantizationConfig):
+    """Serialized 128x128 block-FP8 contract for the H3 text encoder."""
+
+    def __init__(self, weight_block_size: tuple[int, int]) -> None:
+        super().__init__()
+        if weight_block_size != (128, 128):
+            raise ValueError("MiniMax-H3 serialized FP8 requires weight_block_size=[128, 128], "
+                             f"got {list(weight_block_size)}")
+        self.weight_block_size = weight_block_size
+        self.is_checkpoint_fp8_serialized = True
+        self.activation_scheme = "dynamic"
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "fp8"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
+        return [torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 100
+
+    @staticmethod
+    def get_config_filenames() -> list[str]:
+        return []
+
+    @classmethod
+    def from_config(cls, config: dict[str, Any]) -> "MiniMaxH3SerializedFP8Config":
+        quant_method = str(config.get("quant_method", "")).lower()
+        if quant_method != "fp8":
+            raise ValueError(f"MiniMax-H3 only supports serialized FP8 text-encoder checkpoints, got {quant_method!r}")
+        if str(config.get("activation_scheme", "")).lower() != "dynamic":
+            raise ValueError("MiniMax-H3 serialized FP8 requires dynamic activation quantization")
+        if str(config.get("fmt", "e4m3")).lower() not in ("e4m3", "float8_e4m3fn"):
+            raise ValueError(f"MiniMax-H3 serialized FP8 requires E4M3 weights, got {config.get('fmt')!r}")
+        block_size = config.get("weight_block_size")
+        if not isinstance(block_size, list | tuple) or len(block_size) != 2:
+            raise ValueError("MiniMax-H3 serialized FP8 requires a two-dimensional weight_block_size")
+        ignored_layers = config.get("modules_to_not_convert", config.get("ignored_layers", []))
+        if not isinstance(ignored_layers, list | tuple):
+            raise ValueError("MiniMax-H3 serialized FP8 modules_to_not_convert must be a sequence")
+        language_exclusions = [
+            name for name in ignored_layers
+            if isinstance(name, str) and (name.startswith("language_model.") or ".language_model." in name)
+        ]
+        if language_exclusions:
+            raise ValueError("MiniMax-H3 does not support partially quantized language stacks; "
+                             f"ignored language layers: {language_exclusions[:3]}")
+        if not any(isinstance(name, str) and "visual" in name for name in ignored_layers):
+            raise ValueError("MiniMax-H3 serialized FP8 requires the vision stack to be listed in "
+                             "modules_to_not_convert")
+        return cls((int(block_size[0]), int(block_size[1])))
+
+    def validate_runtime(self, device: torch.device) -> None:
+        if device.type != "cuda":
+            raise RuntimeError("MiniMax-H3 serialized blockwise FP8 requires a CUDA device; "
+                               f"got {device.type!r}")
+        capability = torch.cuda.get_device_capability(device)
+        capability_number = capability[0] * 10 + capability[1]
+        if capability_number < self.get_min_capability():
+            raise RuntimeError("MiniMax-H3 serialized blockwise FP8 requires GPU capability "
+                               f"sm{self.get_min_capability()} or newer, got sm{capability_number}")
+        if capability[0] not in (10, 12):
+            raise RuntimeError("MiniMax-H3 serialized blockwise FP8 currently adapts SGLang's Blackwell "
+                               f"FlashInfer path; got unsupported sm{capability_number}")
+        _require_sglang_per_token_group_fp8_quantization()
+        _get_flashinfer_groupwise_fp8_gemm()
+
+    def get_quant_method(self, layer: torch.nn.Module, prefix: str):
+        if isinstance(layer, LinearBase) and ".language_model.layers." in prefix:
+            return MiniMaxH3SerializedFP8LinearMethod(self.weight_block_size)
+        return None
+
+
+# Copyright 2024 SGLang Team
+# Licensed under the Apache License, Version 2.0.
+# Adapted from SGLang's per-token-group quantization kernels and Blackwell
+# FlashInfer dispatch at commit f99c62063c7dcfcd06784b885dc08cb52cf23865:
+# https://github.com/sgl-project/sglang/blob/f99c62063c7dcfcd06784b885dc08cb52cf23865/python/sglang/kernels/ops/quantization/fp8_kernel.py
+# https://github.com/sgl-project/sglang/blob/f99c62063c7dcfcd06784b885dc08cb52cf23865/python/sglang/srt/layers/quantization/fp8_utils.py
+if triton is not None:
+
+    @triton.jit
+    def _h3_per_token_group_quant_fp8_row_major(
+        input_ptr,
+        output_ptr,
+        scale_ptr,
+        group_size,
+        eps,
+        fp8_min,
+        fp8_max,
+        BLOCK: tl.constexpr,
+    ):
+        group_id = tl.program_id(0)
+        input_ptr += group_id.to(tl.int64) * group_size
+        output_ptr += group_id.to(tl.int64) * group_size
+        scale_ptr += group_id
+
+        offsets = tl.arange(0, BLOCK)
+        mask = offsets < group_size
+        values = tl.load(input_ptr + offsets, mask=mask, other=0.0).to(tl.float32)
+        absmax = tl.maximum(tl.max(tl.abs(values)), eps)
+        scale = absmax / fp8_max
+        quantized = tl.clamp(values / scale, fp8_min, fp8_max).to(output_ptr.dtype.element_ty)
+
+        tl.store(output_ptr + offsets, quantized, mask=mask)
+        tl.store(scale_ptr, scale)
+
+    @triton.jit
+    def _h3_per_token_group_quant_fp8_column_major(
+        input_ptr,
+        output_ptr,
+        scale_ptr,
+        group_size,
+        input_columns,
+        scale_column_stride,
+        eps,
+        fp8_min,
+        fp8_max,
+        BLOCK: tl.constexpr,
+    ):
+        group_id = tl.program_id(0)
+        input_ptr += group_id.to(tl.int64) * group_size
+        output_ptr += group_id.to(tl.int64) * group_size
+
+        groups_per_row = input_columns // group_size
+        scale_column = group_id % groups_per_row
+        scale_row = group_id // groups_per_row
+        scale_ptr += scale_column * scale_column_stride + scale_row
+
+        offsets = tl.arange(0, BLOCK)
+        mask = offsets < group_size
+        values = tl.load(input_ptr + offsets, mask=mask, other=0.0).to(tl.float32)
+        absmax = tl.maximum(tl.max(tl.abs(values)), eps)
+        scale = absmax / fp8_max
+        quantized = tl.clamp(values / scale, fp8_min, fp8_max).to(output_ptr.dtype.element_ty)
+
+        tl.store(output_ptr + offsets, quantized, mask=mask)
+        tl.store(scale_ptr, scale)
+else:
+    _h3_per_token_group_quant_fp8_row_major = None
+    _h3_per_token_group_quant_fp8_column_major = None
+
+
+def _require_sglang_per_token_group_fp8_quantization() -> None:
+    if (triton is None or _h3_per_token_group_quant_fp8_row_major is None
+            or _h3_per_token_group_quant_fp8_column_major is None):
+        raise RuntimeError(
+            "MiniMax-H3 serialized blockwise FP8 requires Triton for SGLang-compatible "
+            "per-token-group activation quantization")
+
+
+def _sglang_per_token_group_quant_fp8(
+    input_tensor: torch.Tensor,
+    group_size: int,
+    *,
+    column_major_scales: bool,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """SGLang-compatible dynamic FP8 quantization for contiguous 2-D activations."""
+    _require_sglang_per_token_group_fp8_quantization()
+    if input_tensor.ndim != 2:
+        raise ValueError(f"per-token-group FP8 quantization expects 2-D input, got {input_tensor.ndim}-D")
+    if not input_tensor.is_contiguous():
+        raise ValueError("per-token-group FP8 quantization requires contiguous input")
+    if input_tensor.shape[-1] % group_size:
+        raise ValueError(f"activation width {input_tensor.shape[-1]} is not divisible by group_size={group_size}")
+
+    quantized = torch.empty_like(input_tensor, dtype=FP8_DTYPE)
+    rows, columns = input_tensor.shape
+    groups_per_row = columns // group_size
+    if column_major_scales:
+        scales = torch.empty(
+            (groups_per_row, rows),
+            device=input_tensor.device,
+            dtype=torch.float32,
+        ).permute(1, 0)
+    else:
+        scales = torch.empty(
+            (rows, groups_per_row),
+            device=input_tensor.device,
+            dtype=torch.float32,
+        )
+
+    if rows:
+        num_groups = input_tensor.numel() // group_size
+        block = triton.next_power_of_2(group_size)
+        num_warps = min(max(block // 256, 1), 8)
+        if column_major_scales:
+            _h3_per_token_group_quant_fp8_column_major[(num_groups,)](
+                input_tensor,
+                quantized,
+                scales,
+                group_size,
+                columns,
+                scales.stride(1),
+                1e-10,
+                -448.0,
+                448.0,
+                BLOCK=block,
+                num_warps=num_warps,
+                num_stages=1,
+            )
+        else:
+            _h3_per_token_group_quant_fp8_row_major[(num_groups,)](
+                input_tensor,
+                quantized,
+                scales,
+                group_size,
+                1e-10,
+                -448.0,
+                448.0,
+                BLOCK=block,
+                num_warps=num_warps,
+                num_stages=1,
+            )
+    return quantized, scales
+
+
+def _get_flashinfer_groupwise_fp8_gemm():
+    try:
+        from flashinfer.gemm import gemm_fp8_nt_groupwise
+    except (AttributeError, ImportError) as error:
+        raise RuntimeError(
+            "MiniMax-H3 serialized blockwise FP8 requires "
+            "flashinfer.gemm.gemm_fp8_nt_groupwise (validated with flashinfer-python==0.6.8). "
+            "FastVideo will not re-quantize this checkpoint to tensorwise FP8.") from error
+    return gemm_fp8_nt_groupwise
+
+
+def _get_flashinfer_groupwise_backend(device: torch.device) -> str:
+    capability = torch.cuda.get_device_capability(device)
+    if capability[0] >= 12:
+        return "cutlass"
+    if capability[0] == 10:
+        return "trtllm"
+    capability_number = capability[0] * 10 + capability[1]
+    raise RuntimeError(f"FlashInfer groupwise FP8 requires a Blackwell GPU, got sm{capability_number}")
+
+
+def _flashinfer_gemm_w8a8_block_fp8_linear_with_fallback(
+    input_tensor: torch.Tensor,
+    weight: torch.Tensor,
+    block_size: tuple[int, int],
+    weight_scale: torch.Tensor,
+    bias: torch.Tensor | None = None,
+) -> torch.Tensor:
+    input_2d = input_tensor.view(-1, input_tensor.shape[-1])
+    output_shape = [*input_tensor.shape[:-1], weight.shape[0]]
+    backend = _get_flashinfer_groupwise_backend(input_tensor.device)
+    if input_2d.dtype != torch.bfloat16:
+        raise RuntimeError("MiniMax-H3 FlashInfer groupwise FP8 requires BF16 activations; "
+                           f"got {input_2d.dtype}. The SGLang FP16 Triton GEMM fallback is not enabled for H3.")
+    if backend == "trtllm" and input_2d.shape[1] < 256:
+        raise RuntimeError("MiniMax-H3 FlashInfer TRTLLM groupwise FP8 requires K >= 256; "
+                           f"got K={input_2d.shape[1]}. The SGLang Triton GEMM fallback is not enabled for H3.")
+
+    gemm_fp8_nt_groupwise = _get_flashinfer_groupwise_fp8_gemm()
+    block_n, block_k = block_size
+    q_input, x_scale = _sglang_per_token_group_quant_fp8(
+        input_2d,
+        block_k,
+        column_major_scales=(backend == "trtllm"),
+    )
+    if backend == "cutlass":
+        m, k = input_2d.shape
+        n = weight.shape[0]
+        expected_x_scale_shape = (k // block_k, m)
+        expected_weight_scale_shape = (k // block_k, n // block_n)
+        if x_scale.shape == (m, k // block_k):
+            x_scale = x_scale.transpose(-1, -2).contiguous()
+        if weight_scale.shape == (n // block_n, k // block_k):
+            weight_scale = weight_scale.transpose(-1, -2).contiguous()
+        if x_scale.shape != expected_x_scale_shape or weight_scale.shape != expected_weight_scale_shape:
+            raise RuntimeError("FlashInfer CUTLASS block-FP8 scale layout mismatch: "
+                               f"x_scale={tuple(x_scale.shape)}, weight_scale={tuple(weight_scale.shape)}, "
+                               f"expected={expected_x_scale_shape}/{expected_weight_scale_shape}")
+        if x_scale.dtype != torch.float32 or weight_scale.dtype != torch.float32:
+            raise RuntimeError("FlashInfer CUTLASS block-FP8 scales must be float32")
+        output = gemm_fp8_nt_groupwise(
+            q_input,
+            weight,
+            x_scale.contiguous(),
+            weight_scale.contiguous(),
+            out_dtype=input_2d.dtype,
+            backend="cutlass",
+            scale_major_mode="MN",
+        )
+    else:
+        expected_x_scale_shape = (input_2d.shape[0], input_2d.shape[1] // block_k)
+        expected_weight_scale_shape = (weight.shape[0] // block_n, weight.shape[1] // block_k)
+        if x_scale.shape != expected_x_scale_shape or x_scale.stride(0) != 1:
+            raise RuntimeError("FlashInfer TRTLLM block-FP8 activation scale layout mismatch: "
+                               f"shape={tuple(x_scale.shape)}, stride={x_scale.stride()}, "
+                               f"expected column-major {expected_x_scale_shape}")
+        if weight_scale.shape != expected_weight_scale_shape:
+            raise RuntimeError("FlashInfer TRTLLM block-FP8 weight scale layout mismatch: "
+                               f"shape={tuple(weight_scale.shape)}, expected={expected_weight_scale_shape}")
+        output = gemm_fp8_nt_groupwise(
+            q_input,
+            weight,
+            x_scale,
+            weight_scale,
+            out_dtype=input_2d.dtype,
+            backend="trtllm",
+        )
+    if bias is not None:
+        output += bias
+    return output.to(dtype=input_2d.dtype).view(*output_shape)
+
+
+class MiniMaxH3SerializedFP8LinearMethod(LinearMethodBase):
+    """Execute serialized 128x128 block-FP8 weights without re-quantizing them."""
+
+    def __init__(self, weight_block_size: tuple[int, int]) -> None:
+        super().__init__()
+        self.weight_block_size = weight_block_size
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: list[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ) -> None:
+        output_size_per_partition = sum(output_partition_sizes)
+        block_n, block_k = self.weight_block_size
+        tp_size = get_tp_world_size()
+        if tp_size > 1 and input_size // input_size_per_partition == tp_size:
+            if input_size_per_partition % block_k:
+                raise ValueError(f"Weight input_size_per_partition={input_size_per_partition} is not divisible "
+                                 f"by block_k={block_k}")
+        if tp_size > 1 and output_size // output_size_per_partition == tp_size:
+            for output_partition_size in output_partition_sizes:
+                if output_partition_size % block_n:
+                    raise ValueError(f"Weight output_partition_size={output_partition_size} is not divisible "
+                                     f"by block_n={block_n}")
+
+        layer.logical_widths = output_partition_sizes
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_size_per_partition = output_size_per_partition
+        layer.orig_dtype = params_dtype
+
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        weight = Parameter(
+            torch.empty(output_size_per_partition, input_size_per_partition, dtype=FP8_DTYPE),
+            requires_grad=False,
+        )
+        set_weight_attrs(weight, {
+            "input_dim": 1,
+            "output_dim": 0,
+            "weight_loader": weight_loader,
+        })
+        layer.register_parameter("weight", weight)
+
+        scale = Parameter(
+            torch.empty((output_size_per_partition + block_n - 1) // block_n,
+                        (input_size_per_partition + block_k - 1) // block_k,
+                        dtype=torch.float32),
+            requires_grad=False,
+        )
+        set_weight_attrs(scale, {
+            "input_dim": 1,
+            "output_dim": 0,
+            "weight_loader": weight_loader,
+        })
+        scale.data.fill_(torch.finfo(torch.float32).min)
+        layer.register_parameter("weight_scale_inv", scale)
+        layer.register_parameter("input_scale", None)
+
+    def process_weights_after_loading(self, layer: nn.Module) -> None:
+        weight = getattr(layer, "weight", None)
+        block_scales = getattr(layer, "weight_scale_inv", None)
+        if weight is None or block_scales is None:
+            raise ValueError("Serialized MiniMax-H3 FP8 linear is missing weight or weight_scale_inv")
+        if weight.dtype != FP8_DTYPE:
+            raise ValueError(f"Serialized MiniMax-H3 FP8 weight must be {FP8_DTYPE}, got {weight.dtype}")
+        if block_scales.dtype != torch.float32:
+            raise ValueError("Serialized MiniMax-H3 FP8 weight_scale_inv must be float32, "
+                             f"got {block_scales.dtype}")
+
+        block_n, block_k = self.weight_block_size
+        output_size, input_size = weight.shape
+        if output_size % block_n or input_size % block_k:
+            raise ValueError("Serialized MiniMax-H3 FP8 weight dimensions must be divisible by the 128x128 block size; "
+                             f"got {tuple(weight.shape)}")
+        expected_scale_shape = (output_size // block_n, input_size // block_k)
+        if tuple(block_scales.shape) != expected_scale_shape:
+            raise ValueError("Serialized MiniMax-H3 FP8 scale shape mismatch: "
+                             f"expected {expected_scale_shape}, got {tuple(block_scales.shape)}")
+        if not bool(torch.isfinite(block_scales).all()) or bool((block_scales <= 0).any()):
+            raise ValueError("Serialized MiniMax-H3 FP8 weight_scale_inv must contain finite positive values")
+        layer.weight.data = weight.data
+        layer.weight_scale_inv.data = block_scales.data
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        if x.device.type != "cuda":
+            raise RuntimeError("MiniMax-H3 serialized blockwise FP8 execution requires CUDA")
+
+        capability = torch.cuda.get_device_capability(x.device)
+        capability_number = capability[0] * 10 + capability[1]
+        if capability_number < MiniMaxH3SerializedFP8Config.get_min_capability():
+            raise RuntimeError("MiniMax-H3 serialized blockwise FP8 requires GPU capability "
+                               f"sm{MiniMaxH3SerializedFP8Config.get_min_capability()} or newer, "
+                               f"got sm{capability_number}")
+
+        if not x.is_contiguous():
+            x = x.contiguous()
+        return _flashinfer_gemm_w8a8_block_fp8_linear_with_fallback(
+            x,
+            layer.weight,
+            self.weight_block_size,
+            layer.weight_scale_inv,
+            bias,
+        )
+
+
+__all__ = [
+    "MiniMaxH3SerializedFP8Config",
+    "MiniMaxH3SerializedFP8LinearMethod",
+]

fastvideo/models/encoders/minimax_h3_qwen3_vl.py

@@ -8,13 +8,13 @@ import torch
 import torch.nn.functional as F
 from torch import nn
 
-from fastvideo.configs.models.encoders import BaseEncoderOutput
 from fastvideo.configs.models.encoders.minimax_h3_qwen3_vl import MiniMaxH3Qwen3VLConfig
 from fastvideo.distributed import get_tp_world_size
 from fastvideo.layers.layernorm import RMSNorm
 from fastvideo.layers.linear import ColumnParallelLinear, RowParallelLinear
 from fastvideo.layers.vocab_parallel_embedding import VocabParallelEmbedding
 from fastvideo.models.encoders.base import TextEncoder
+from fastvideo.models.encoders.minimax_h3_checkpoint_fp8 import MiniMaxH3SerializedFP8Config
 from fastvideo.models.loader.weight_utils import default_weight_loader
 
 
@@ -227,19 +227,13 @@ class MiniMaxH3Qwen3VLLanguageModel(nn.Module):
             org_num_embeddings=config.vocab_size,
             quant_config=quant_config,
         )
-        # Build only as far as the consumer reads. The hidden-state tuple records
-        # each layer's input, so stopping after N layers still yields entry N,
-        # the output of layer N-1, unchanged. Everything above it exists only to
-        # feed `last_hidden_state`, which nothing consumes.
         override = config.num_hidden_layers_override
         self.num_layers = (config.num_hidden_layers
                            if override is None else min(config.num_hidden_layers, override))
+        self.output_hidden_state_index = config.output_hidden_state_index
         self.layers = nn.ModuleList(
             MiniMaxH3Qwen3VLTextDecoderLayer(config, prefix=f"{config.prefix}.language_model.layers.{index}")
             for index in range(self.num_layers))
-        # The final norm sits above the tapped layer, so a truncated stack drops
-        # it. Keeping it would overwrite the tapped entry with a normalised
-        # tensor and change conditioning without raising anything.
         self.norm = (RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
                      if self.num_layers == config.num_hidden_layers else None)
         self.rotary_emb = MiniMaxH3Qwen3VLTextRotaryEmbedding(config)
@@ -249,18 +243,14 @@ class MiniMaxH3Qwen3VLLanguageModel(nn.Module):
         inputs_embeds: torch.Tensor,
         position_ids: torch.Tensor,
         attention_mask: torch.Tensor | None,
-        output_hidden_states: bool,
         visual_pos_masks: torch.Tensor | None,
         deepstack_visual_embeds: list[torch.Tensor] | None,
-    ) -> BaseEncoderOutput:
+    ) -> torch.Tensor:
         if attention_mask is not None and bool(attention_mask.to(torch.bool).all()):
             attention_mask = None
         position_embeddings = self.rotary_emb(inputs_embeds, position_ids)
         hidden_states = inputs_embeds
-        all_hidden_states: tuple[torch.Tensor, ...] | None = () if output_hidden_states else None
         for layer_index, layer in enumerate(self.layers):
-            if all_hidden_states is not None:
-                all_hidden_states += (hidden_states, )
             hidden_states = layer(hidden_states, position_embeddings, attention_mask)
             if deepstack_visual_embeds is not None and layer_index < len(deepstack_visual_embeds):
                 if visual_pos_masks is None:
@@ -269,13 +259,9 @@ class MiniMaxH3Qwen3VLLanguageModel(nn.Module):
                 visual = deepstack_visual_embeds[layer_index].to(hidden_states.device, hidden_states.dtype)
                 updated = hidden_states[mask].clone() + visual
                 hidden_states[mask] = updated
-        if self.norm is not None:
-            hidden_states = self.norm(hidden_states)
-        # Truncated or not, the last entry is appended here, so the tapped index
-        # lands in the same place either way.
-        if all_hidden_states is not None:
-            all_hidden_states += (hidden_states, )
-        return BaseEncoderOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
+            if layer_index + 1 == self.output_hidden_state_index:
+                return hidden_states
+        raise RuntimeError(f"MiniMax-H3 text stack did not reach hidden_states[{self.output_hidden_state_index}]")
 
 
 class MiniMaxH3Qwen3VLVisionPatchEmbed(nn.Module):
@@ -513,10 +499,18 @@ class MiniMaxH3Qwen3VLVisionModel(nn.Module):
         return self.merger(hidden_states), deepstack_features
 
 
-class MiniMaxH3Qwen3VLConditioner(TextEncoder):
-    """FastVideo-native Qwen3-VL body without the unused language-model head."""
+class MiniMaxH3Qwen3VLConditioner(TextEncoder[torch.Tensor]):
+    """H3 conditioner returning the unnormalized layer-50 hidden tensor."""
 
     supports_hf_from_pretrained = False
+    supported_checkpoint_quantization_methods = frozenset({"fp8"})
+
+    @classmethod
+    def checkpoint_quantization_config_from_metadata(
+        cls,
+        metadata: dict[str, Any],
+    ) -> MiniMaxH3SerializedFP8Config:
+        return MiniMaxH3SerializedFP8Config.from_config(metadata)
 
     def __init__(self, config: MiniMaxH3Qwen3VLConfig) -> None:
         super().__init__(config)
@@ -530,15 +524,12 @@ class MiniMaxH3Qwen3VLConditioner(TextEncoder):
 
     @property
     def num_hidden_layers(self) -> int:
-        """The checkpoint architecture's nominal depth, matching its config.json.
-
-        When ``num_hidden_layers_override`` truncates the stack at the
-        conditioning tap, fewer layers exist; the built count is
-        ``self.language_model.num_layers``, and the hidden-state tuple has
-        ``num_layers + 1`` entries, not ``num_hidden_layers + 1``.
-        """
         return self.config.num_hidden_layers
 
+    @property
+    def num_built_hidden_layers(self) -> int:
+        return self.language_model.num_layers
+
     def _get_rope_index(
         self,
         input_ids: torch.Tensor,
@@ -631,35 +622,39 @@ class MiniMaxH3Qwen3VLConditioner(TextEncoder):
                              f"tokens={int(mask.sum())}, features={features.shape[0]}")
         return mask
 
-    def forward(
+    # no_grad, NOT inference_mode: with text_encoder_cpu_offload=True (the
+    # FastVideoArgs default) the loader FSDP2-shards this conditioner, and
+    # FSDP2's wait_for_unshard reads tensor._version via
+    # _unsafe_preserve_version_counter - inference tensors do not track
+    # version counters, so inference_mode crashes the first encode. no_grad
+    # frees the same activation memory and keeps prompt_embeds ordinary
+    # tensors (safe for any future backward through the conditioning).
+    @torch.no_grad()
+    def encode_ids(
         self,
-        input_ids: torch.Tensor | None,
-        position_ids: torch.Tensor | None = None,
-        attention_mask: torch.Tensor | None = None,
-        inputs_embeds: torch.Tensor | None = None,
-        output_hidden_states: bool | None = None,
+        input_ids: torch.Tensor,
+        *,
         pixel_values: torch.Tensor | None = None,
-        pixel_values_videos: torch.Tensor | None = None,
         image_grid_thw: torch.Tensor | None = None,
+        pixel_values_videos: torch.Tensor | None = None,
         video_grid_thw: torch.Tensor | None = None,
-        mm_token_type_ids: torch.Tensor | None = None,
-        **kwargs: Any,
-    ) -> BaseEncoderOutput:
-        del mm_token_type_ids, kwargs
-        if (input_ids is None) == (inputs_embeds is None):
-            raise ValueError("Exactly one of input_ids or inputs_embeds is required")
-        if inputs_embeds is None:
-            assert input_ids is not None
-            inputs_embeds = self.language_model.embed_tokens(input_ids)
-        if input_ids is None and (pixel_values is not None or pixel_values_videos is not None):
-            raise ValueError("Multimodal Qwen3-VL inputs require input_ids for placeholder matching")
+    ) -> torch.Tensor:
+        if input_ids.ndim != 1:
+            raise ValueError(f"MiniMax-H3 slim forward expects 1-D input_ids, got shape={tuple(input_ids.shape)}")
+        if (pixel_values is None) != (image_grid_thw is None):
+            raise ValueError("pixel_values and image_grid_thw must be provided together")
+        if (pixel_values_videos is None) != (video_grid_thw is None):
+            raise ValueError("pixel_values_videos and video_grid_thw must be provided together")
+
+        input_ids = input_ids.unsqueeze(0)
+        inputs_embeds = self.language_model.embed_tokens(input_ids)
 
         image_mask = None
         video_mask = None
         image_deepstack = None
         video_deepstack = None
         if pixel_values is not None:
-            if input_ids is None or image_grid_thw is None:
+            if image_grid_thw is None:
                 raise ValueError("pixel_values require input_ids and image_grid_thw")
             image_features, image_deepstack = self._visual_features(pixel_values, image_grid_thw)
             image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
@@ -667,7 +662,7 @@ class MiniMaxH3Qwen3VLConditioner(TextEncoder):
                                                 "image")
             inputs_embeds = inputs_embeds.masked_scatter(image_mask.unsqueeze(-1), image_features)
         if pixel_values_videos is not None:
-            if input_ids is None or video_grid_thw is None:
+            if video_grid_thw is None:
                 raise ValueError("pixel_values_videos require input_ids and video_grid_thw")
             video_features, video_deepstack = self._visual_features(pixel_values_videos, video_grid_thw)
             video_features = video_features.to(inputs_embeds.device, inputs_embeds.dtype)
@@ -695,50 +690,34 @@ class MiniMaxH3Qwen3VLConditioner(TextEncoder):
             visual_mask = video_mask
             deepstack_features = video_deepstack
 
-        if position_ids is None:
-            if input_ids is None:
-                sequence_length = inputs_embeds.shape[1]
-                position_ids = torch.arange(sequence_length,
-                                            device=inputs_embeds.device).view(1, 1,
-                                                                              -1).expand(3, inputs_embeds.shape[0], -1)
-            else:
-                position_ids = self._get_rope_index(input_ids, image_grid_thw, video_grid_thw, attention_mask)
-        output_hidden_states = self.config.output_hidden_states if output_hidden_states is None else output_hidden_states
-        outputs = self.language_model(
+        position_ids = self._get_rope_index(input_ids, image_grid_thw, video_grid_thw, None)
+        hidden_states = self.language_model(
             inputs_embeds,
             position_ids,
-            attention_mask,
-            output_hidden_states,
+            None,
             visual_mask,
             deepstack_features,
         )
-        outputs.attention_mask = attention_mask
-        return outputs
+        if hidden_states.ndim != 3 or hidden_states.shape[0] != 1:
+            raise RuntimeError(f"MiniMax-H3 language model returned unexpected shape={tuple(hidden_states.shape)}")
+        return hidden_states[0]
 
-    def _is_above_the_tap(self, name: str) -> bool:
-        """Whether this checkpoint key belongs to a layer we did not build.
-
-        A truncated language stack still ships every layer in the checkpoint, and
-        the unexpected-key check below is strict on purpose, so the surplus keys
-        have to be dropped here rather than by relaxing it.
-        """
-        language_model = self.language_model
-        # The final norm is dropped exactly when the stack is truncated, so its
-        # absence is the signal.
-        if language_model.norm is not None:
-            return False
-        if name == "language_model.norm.weight":
-            return True
-        prefix = "language_model.layers."
-        if not name.startswith(prefix):
-            return False
-        index = name[len(prefix):].split(".", 1)[0]
-        if not index.isdigit():
-            return False
-        # Only drop indexes the full stack would have built. Anything at or
-        # above the checkpoint's own num_hidden_layers is corrupt and must
-        # still raise below, exactly as it does without truncation.
-        return language_model.num_layers <= int(index) < self.config.num_hidden_layers
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        *,
+        pixel_values: torch.Tensor | None = None,
+        image_grid_thw: torch.Tensor | None = None,
+        pixel_values_videos: torch.Tensor | None = None,
+        video_grid_thw: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        return self.encode_ids(
+            input_ids,
+            pixel_values=pixel_values,
+            image_grid_thw=image_grid_thw,
+            pixel_values_videos=pixel_values_videos,
+            video_grid_thw=video_grid_thw,
+        )
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         parameters = dict(self.named_parameters())
@@ -748,7 +727,7 @@ class MiniMaxH3Qwen3VLConditioner(TextEncoder):
             if source_name == "lm_head.weight":
                 continue
             name = source_name[6:] if source_name.startswith("model.") else source_name
-            if self._is_above_the_tap(name):
+            if self._is_omitted_checkpoint_key(name):
                 continue
             if name not in parameters:
                 raise ValueError(f"Unexpected MiniMax-H3 Qwen3-VL checkpoint key: {source_name}")
@@ -758,7 +737,23 @@ class MiniMaxH3Qwen3VLConditioner(TextEncoder):
             loaded.add(name)
         return loaded
 
+    def _is_omitted_checkpoint_key(self, name: str) -> bool:
+        """Return whether a valid checkpoint key belongs to an unbuilt layer."""
+        language_model = self.language_model
+        if language_model.norm is not None:
+            return False
+        if name == "language_model.norm.weight":
+            return True
+        prefix = "language_model.layers."
+        if not name.startswith(prefix):
+            return False
+        index = name[len(prefix):].split(".", 1)[0]
+        return (index.isdigit() and language_model.num_layers <= int(index) < self.config.num_hidden_layers)
+
 
 EntryClass = MiniMaxH3Qwen3VLConditioner
 
-__all__ = ["MiniMaxH3Qwen3VLConditioner"]
+__all__ = [
+    "MiniMaxH3Qwen3VLConditioner",
+    "MiniMaxH3SerializedFP8Config",
+]

fastvideo/models/loader/component_loader.py

@@ -9,7 +9,7 @@ from abc import ABC, abstractmethod
 from collections.abc import Generator, Iterable
 from contextlib import nullcontext
 from copy import deepcopy
-from typing import cast
+from typing import Any, cast
 
 import torch
 import torch.distributed as dist
@@ -30,9 +30,13 @@ from fastvideo.distributed import get_local_torch_device
 from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.layers.quantization import get_quantization_config
 from fastvideo.logger import init_logger
-from fastvideo.models.encoders.base import TextEncoder
 from fastvideo.models.hf_transformer_utils import get_diffusers_config
 from fastvideo.models.loader.fsdp_load import maybe_load_fsdp_model, shard_model
+from fastvideo.models.loader.text_encoder_quantization import (
+    _configure_text_encoder_quantization,
+    _process_quantized_text_encoder_weights,
+    _resolve_text_encoder_checkpoint_path,
+)
 from fastvideo.models.loader.utils import set_default_torch_dtype
 from fastvideo.models.loader.weight_utils import (
     filter_duplicate_safetensors_files,
@@ -347,22 +351,46 @@ class TextEncoderLoader(ComponentLoader):
         if cpu_offload is None:
             cpu_offload = fastvideo_args.text_encoder_cpu_offload
         use_cpu_offload = (cpu_offload and len(getattr(model_config, "_fsdp_shard_conditions", [])) > 0)
+        runtime_device = get_local_torch_device()
 
         from fastvideo.platforms import current_platform
 
         if cpu_offload:
             target_device = (torch.device("mps") if current_platform.is_mps() else torch.device("cpu"))
 
-        # Set quantization config if specified
-        if (use_text_encoder_override and fastvideo_args.override_text_encoder_quant is not None):
-            if fastvideo_args.override_text_encoder_safetensors is None:
-                raise ValueError("override_text_encoder_quant is set but override_text_encoder_safetensors is None")
-            quant_cls = get_quantization_config(fastvideo_args.override_text_encoder_quant)
-            model_config.quant_config = quant_cls()
-
         with set_default_torch_dtype(PRECISION_TO_TYPE[dtype]):
             architectures = getattr(model_config, "architectures", [])
             model_cls, _ = ModelRegistry.resolve_model_cls(architectures)
+            checkpoint_path = _resolve_text_encoder_checkpoint_path(
+                model_path,
+                fastvideo_args,
+                use_text_encoder_override,
+            )
+            checkpoint_quant_config = _configure_text_encoder_quantization(
+                model_config,
+                model_cls,
+                checkpoint_path,
+            )
+            if checkpoint_quant_config is not None:
+                if fastvideo_args.override_text_encoder_quant is not None:
+                    raise ValueError("Serialized checkpoint quantization is selected from checkpoint metadata; "
+                                     "override_text_encoder_quant is an online conversion option and must be unset")
+                requested_dtype = PRECISION_TO_TYPE[dtype]
+                if requested_dtype not in checkpoint_quant_config.get_supported_act_dtypes():
+                    raise ValueError(f"Serialized {checkpoint_quant_config.get_name()} text encoder does not support "
+                                     f"activation dtype {requested_dtype}")
+                checkpoint_quant_config.validate_runtime(runtime_device)
+                logger.info(
+                    "Selected serialized %s text-encoder checkpoint execution from %s",
+                    checkpoint_quant_config.get_name(),
+                    checkpoint_path,
+                )
+            elif use_text_encoder_override and fastvideo_args.override_text_encoder_quant is not None:
+                if fastvideo_args.override_text_encoder_safetensors is None:
+                    raise ValueError("override_text_encoder_quant is set but override_text_encoder_safetensors is None")
+                quant_cls = get_quantization_config(fastvideo_args.override_text_encoder_quant)
+                model_config.quant_config = quant_cls()
+
             if getattr(model_cls, "supports_hf_from_pretrained", False):
                 model = model_cls.from_pretrained_local(  # type: ignore[attr-defined]
                     model_path,
@@ -381,11 +409,20 @@ class TextEncoderLoader(ComponentLoader):
 
             weights_to_load = {name for name, _ in model.named_parameters()}
             if (use_text_encoder_override and fastvideo_args.override_text_encoder_safetensors is not None):
-                loaded_weights: set[str] = model.load_weights(
-                    safetensors_weights_iterator(
-                        [fastvideo_args.override_text_encoder_safetensors],
+                if os.path.isdir(checkpoint_path):
+                    override_weights = self._get_all_weights(
+                        model,
+                        checkpoint_path,
+                        to_cpu=bool(cpu_offload),
+                    )
+                else:
+                    if self.counter_before_loading_weights == 0.0:
+                        self.counter_before_loading_weights = time.perf_counter()
+                    override_weights = safetensors_weights_iterator(
+                        [checkpoint_path],
                         to_cpu=use_cpu_offload,
-                    ))  # type: ignore
+                    )
+                loaded_weights: set[str] = model.load_weights(override_weights)  # type: ignore
             else:
                 loaded_weights: set[str] = model.load_weights(
                     self._get_all_weights(
@@ -400,6 +437,10 @@ class TextEncoderLoader(ComponentLoader):
                 self.counter_after_loading_weights - self.counter_before_loading_weights,
             )
 
+            if checkpoint_quant_config is not None:
+                processed_linears = _process_quantized_text_encoder_weights(model, runtime_device)
+                logger.info("Validated %d serialized blockwise FP8 text-encoder linears", processed_linears)
+
             # Explicitly move model to target device after loading weights
             model = model.to(target_device)
 
@@ -442,7 +483,7 @@ class TextEncoderLoader(ComponentLoader):
             # that have loaded weights tracking currently.
             # if loaded_weights is not None:
             weights_not_loaded = weights_to_load - loaded_weights
-            if weights_not_loaded and model_config.quant_config is None:
+            if weights_not_loaded and (model_config.quant_config is None or checkpoint_quant_config is not None):
                 raise ValueError("Following weights were not initialized from "
                                  f"checkpoint: {weights_not_loaded}")
 
@@ -1057,7 +1098,12 @@ class TransformerLoader(ComponentLoader):
             # so recording here makes the decision readable from the loaded
             # transformer — and records the narrowed one for teacher/critic.
             resolved = record_resolved_attention_backend(dit_config)
-            logger.info("transformer attention backend: %s", resolved.name if resolved else "automatic selection")
+            # Every worker records its resolved backend so distributed profile
+            # snapshots can prove that all ranks use the requested kernels.
+            logger.info("Worker %s transformer attention backend: %s",
+                        os.environ.get("RANK", "0"),
+                        resolved.name if resolved else "automatic selection",
+                        local_main_process_only=False)
             model = maybe_load_fsdp_model(
                 model_cls=model_cls,
                 init_params={

fastvideo/models/loader/text_encoder_quantization.py

@@ -0,0 +1,127 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Checkpoint-serialized quantization lifecycle for native text encoders."""
+
+import json
+import os
+from itertools import chain
+from typing import Any
+
+import torch
+import torch.nn as nn
+from safetensors.torch import safe_open
+
+from fastvideo.configs.models import EncoderConfig
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.layers.linear import LinearBase, UnquantizedLinearMethod
+from fastvideo.layers.quantization.base_config import QuantizationConfig
+from fastvideo.models.encoders.base import TextEncoder
+
+
+def _resolve_text_encoder_checkpoint_path(
+    model_path: str,
+    fastvideo_args: FastVideoArgs,
+    use_text_encoder_override: bool,
+) -> str:
+    override = fastvideo_args.override_text_encoder_safetensors if use_text_encoder_override else None
+    checkpoint_path = override or model_path
+    if not os.path.exists(checkpoint_path):
+        raise FileNotFoundError(f"Text-encoder checkpoint does not exist: {checkpoint_path}")
+    if not os.path.isdir(checkpoint_path) and not os.path.isfile(checkpoint_path):
+        raise ValueError(f"Text-encoder checkpoint must be a file or directory: {checkpoint_path}")
+    return checkpoint_path
+
+
+def _read_text_encoder_checkpoint_quantization_config(checkpoint_path: str) -> dict[str, Any] | None:
+    checkpoint_dir = checkpoint_path if os.path.isdir(checkpoint_path) else os.path.dirname(checkpoint_path)
+    config_path = os.path.join(checkpoint_dir, "config.json")
+    if os.path.isfile(config_path):
+        try:
+            with open(config_path, encoding="utf-8") as config_file:
+                checkpoint_config = json.load(config_file)
+        except json.JSONDecodeError as error:
+            raise ValueError(f"Invalid text-encoder checkpoint config: {config_path}") from error
+        quantization_config = checkpoint_config.get("quantization_config")
+        if quantization_config is not None:
+            if not isinstance(quantization_config, dict):
+                raise ValueError(f"quantization_config in {config_path} must be an object")
+            return quantization_config
+
+    if not os.path.isfile(checkpoint_path) or not checkpoint_path.endswith(".safetensors"):
+        return None
+    with safe_open(checkpoint_path, framework="pt", device="cpu") as checkpoint_file:
+        metadata = checkpoint_file.metadata() or {}
+    for key in ("quantization_config", "_quantization_metadata"):
+        serialized = metadata.get(key)
+        if serialized is None:
+            continue
+        try:
+            quantization_config = json.loads(serialized)
+        except json.JSONDecodeError as error:
+            raise ValueError(f"Invalid {key} metadata in {checkpoint_path}") from error
+        if not isinstance(quantization_config, dict):
+            raise ValueError(f"{key} metadata in {checkpoint_path} must decode to an object")
+        return quantization_config
+    return None
+
+
+def _configure_text_encoder_quantization(
+    model_config: EncoderConfig,
+    model_cls: type[nn.Module],
+    checkpoint_path: str,
+) -> QuantizationConfig | None:
+    if not issubclass(model_cls, TextEncoder):
+        return None
+    checkpoint_quantization = _read_text_encoder_checkpoint_quantization_config(checkpoint_path)
+    if checkpoint_quantization is None:
+        return None
+
+    quant_method = str(checkpoint_quantization.get("quant_method", "")).lower()
+    if not quant_method:
+        raise ValueError(f"Quantized text-encoder checkpoint {checkpoint_path} does not declare quant_method")
+    supported_methods = getattr(model_cls, "supported_checkpoint_quantization_methods", frozenset())
+    if quant_method not in supported_methods:
+        supported = ", ".join(sorted(supported_methods)) or "none"
+        raise ValueError(f"Text encoder {model_cls.__name__} does not support serialized {quant_method!r} "
+                         f"checkpoints (supported: {supported})")
+
+    factory = getattr(model_cls, "checkpoint_quantization_config_from_metadata", None)
+    if not callable(factory):
+        raise ValueError(f"Text encoder {model_cls.__name__} advertises serialized {quant_method!r} support "
+                         "without a checkpoint quantization factory")
+    quant_config = factory(checkpoint_quantization)
+    model_config.quant_config = quant_config
+    return quant_config
+
+
+def _module_tensor_device(module: nn.Module) -> torch.device | None:
+    devices = {
+        tensor.device
+        for tensor in chain(
+            module.parameters(recurse=False),
+            module.buffers(recurse=False),
+        )
+    }
+    if len(devices) > 1:
+        raise ValueError(f"Quantized text-encoder module {type(module).__name__} spans multiple devices: {devices}")
+    return next(iter(devices), None)
+
+
+def _process_quantized_text_encoder_weights(model: nn.Module, process_device: torch.device) -> int:
+    """Run quantized post-load hooks one linear at a time on ``process_device``."""
+    processed = 0
+    for module in model.modules():
+        if not isinstance(module, LinearBase) or isinstance(module.quant_method, UnquantizedLinearMethod):
+            continue
+        if module.quant_method is None:
+            continue
+        original_device = _module_tensor_device(module)
+        try:
+            module.to(process_device)
+            module.quant_method.process_weights_after_loading(module)
+        finally:
+            if original_device is not None:
+                module.to(original_device)
+        processed += 1
+    if processed == 0:
+        raise ValueError("Serialized quantized text-encoder checkpoint selected, but no quantized linear layers exist")
+    return processed

fastvideo/models/vaes/minimax_h3_audio.py

@@ -392,9 +392,16 @@ class MiniMaxH3AudioBigVGANDecoder(nn.Module):
         return torch.clamp(hidden_states, min=-1.0, max=1.0)
 
 
+def _is_minimax_h3_audio_vae_decoder(name: str, submodule: nn.Module) -> bool:
+    """Select the audio decoder that serves the H3 VAE ``decode`` path."""
+    return name == "decoder" and isinstance(submodule, MiniMaxH3AudioBigVGANDecoder)
+
+
 class MiniMaxH3AudioVAE(nn.Module):
     """DAC encoder plus BigVGAN decoder for mono 32 kHz waveforms."""
 
+    _compile_conditions = [_is_minimax_h3_audio_vae_decoder]
+
     def __init__(self, config: MiniMaxH3AudioVAEConfig):
         super().__init__()
         self.config = config

fastvideo/models/vaes/minimax_h3_parallel.py

@@ -0,0 +1,378 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Sequence-parallel chunk scheduling for the MiniMax-H3 video VAE.
+
+The H3 video VAE decodes a video as a series of temporal-chunk decoder
+forwards whose outputs are joined by a short deterministic frame blend
+(``AutoencoderKLMiniMaxH3._decode_chunks``), and encodes videos as fully
+independent ``clip_length``-frame encoder forwards. Neither the chunk decode
+nor the clip encode has any cross-chunk data dependency — only the *joining*
+of decoded chunks (overlap blending, frame trimming) is sequential. This
+module round-robins the chunk/clip forwards across the ranks of a
+sequence-parallel group and replays the serial joining logic on the
+assembling rank, reproducing the serial result bit for bit.
+
+Bit-exactness contract:
+- every rank holds an identical copy of the inputs (the H3 DiT all-gathers
+  its outputs, and reference pixels are prepared identically on all ranks);
+- a chunk decoded on any rank is bitwise the tensor the serial loop would
+  produce (identical weights, inputs, and deterministic kernels on identical
+  GPUs), and NCCL transports it bitwise;
+- every serialization point of the serial algorithm (overlap blending, frame
+  trimming, pixel denormalization, output-buffer copies, moment
+  concatenation and token-drop trimming) runs on the assembling rank in
+  serial order via the same VAE methods the serial path uses.
+
+Collective safety: all group ranks must call these functions together with
+identically shaped inputs. Work proceeds in rounds of one collective each;
+ranks without a chunk in the final round contribute a placeholder tensor, so
+participation is uniform by construction and no rank-dependent branch guards
+a collective.
+
+Caveat — compiled decoders (``enable_torch_compile_vae``): inductor autotunes
+kernel configs per process at first call, so a compiled decoder is only
+deterministic WITHIN a process, not across processes. Chunks decoded on other
+ranks then differ from the serial rank's decode of the same chunk exactly as
+two serial runs in different processes would (measured on GB200 at 124f:
+max 63/255 on <0.5% of pixels, mean ~1e-2/255, first chunk bit-identical).
+With the eager decoder — the pipeline default — parallel output is bitwise
+equal to serial ``decode_to_pixels``.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import torch
+
+from fastvideo.models.vaes.minimax_h3_video import (
+    AutoencoderKLMiniMaxH3,
+    AutoencoderKLOutput,
+    DiagonalGaussianDistribution,
+)
+from fastvideo.profiler import nvtx_range
+
+if TYPE_CHECKING:
+    from fastvideo.distributed.parallel_state import GroupCoordinator
+
+# Collective used to move decoded chunk segments to the assembling rank.
+# "gather" moves each segment once (destination-only); "all_gather" also
+# leaves every rank with every segment. Both are exact; the default is the
+# faster one measured on GB200 NVL72 (see the PR notes).
+DECODE_GATHER_STRATEGIES = ("gather", "all_gather")
+DEFAULT_DECODE_GATHER_STRATEGY = "gather"
+
+
+def parallel_chunk_indices(num_chunks: int, world_size: int, rank_in_group: int) -> list[int]:
+    """Round-robin chunk ownership: chunk ``i`` belongs to rank ``i % world_size``."""
+    if num_chunks < 0:
+        raise ValueError(f"num_chunks must be non-negative, got {num_chunks}.")
+    if world_size < 1:
+        raise ValueError(f"world_size must be positive, got {world_size}.")
+    if not 0 <= rank_in_group < world_size:
+        raise ValueError(f"rank_in_group {rank_in_group} out of range for world_size {world_size}.")
+    return list(range(rank_in_group, num_chunks, world_size))
+
+
+def _num_rounds(num_chunks: int, world_size: int) -> int:
+    return -(-num_chunks // world_size)
+
+
+def _decode_segment(vae: AutoencoderKLMiniMaxH3, z_padded: torch.Tensor, chunk_index: int) -> torch.Tensor:
+    """Decode one temporal chunk's clip and keep the frames the join consumes.
+
+    The serial loop uses two spans of each decoded clip: the chunk body
+    ``clip[:, :, frame_pre_padding:chunk_num_frames]`` and (when
+    ``token_drop > 0``) the blend tail
+    ``clip[:, :, chunk_num_frames + frame_pre_padding:]``. Everything from
+    ``frame_pre_padding`` on covers both, so one contiguous slice per chunk
+    travels over the wire. ``.contiguous()`` also detaches the segment from
+    any decoder-owned storage (e.g. a compiled decoder's reuse pools) before
+    the next chunk decode can overwrite it.
+    """
+    start = chunk_index * vae.tokens_chunk_size
+    with nvtx_range(f"minimax_h3.vae.parallel_chunk.{chunk_index}"):
+        clip = vae._decode_clip(z_padded[:, :, start:start + vae.tokens_chunk_size + vae.token_overlap])
+        return clip[:, :, vae.frame_pre_padding:].contiguous()
+
+
+class _ChunkAssembler:
+    """Replay the serial chunk-joining semantics of ``_decode_chunks`` +
+    ``_decode_to_pixels`` on gathered chunk segments, in chunk order.
+
+    On CUDA the joining kernels and output copies run on a dedicated side
+    stream: they depend only on already-gathered segments, so running them
+    off the main stream keeps the assembling rank's next chunk decode (and
+    therefore every other rank's next collective) off the assembly's tail.
+    Stream placement cannot change values — the ops and their order are
+    identical — so bit-exactness with the serial path is unaffected.
+    """
+
+    def __init__(self, vae: AutoencoderKLMiniMaxH3, output: torch.Tensor, output_num_frames: int,
+                 non_blocking: bool, device: torch.device) -> None:
+        self._vae = vae
+        self._output = output
+        self._output_num_frames = output_num_frames
+        self._non_blocking = non_blocking
+        self._body_frames = vae.tokens_chunk_size * vae.temporal_compression_ratio - vae.frame_pre_padding
+        self._overlap: torch.Tensor | None = None
+        self._frame_start = 0
+        self._stream = torch.cuda.Stream(device) if device.type == "cuda" else None
+
+    def push(self, segment: torch.Tensor) -> None:
+        """Consume the next chunk's segment (``clip[:, :, frame_pre_padding:]``)."""
+        if self._stream is None:
+            self._push(segment)
+            return
+        # The segment is produced on the current (collective) stream; hand it
+        # to the assembly stream and pin its storage until assembly reads it.
+        self._stream.wait_stream(torch.cuda.current_stream(segment.device))
+        segment.record_stream(self._stream)
+        with torch.cuda.stream(self._stream):
+            self._push(segment)
+
+    def _push(self, segment: torch.Tensor) -> None:
+        vae = self._vae
+        chunk = segment[:, :, :self._body_frames]
+        if self._overlap is not None:
+            chunk = vae._blend(self._overlap, chunk, vae.frame_overlap, dim=-3)
+        num_frames = min(chunk.shape[2], self._output_num_frames - self._frame_start)
+        chunk = chunk[:, :, :num_frames]
+        # The tail past the body (and its pre-padding gap) is the next
+        # chunk's blend overlap — the serial loop's ``next_overlap``.
+        self._overlap = segment[:, :, self._body_frames + vae.frame_pre_padding:] if vae.config.token_drop > 0 else None
+        if num_frames > 0:
+            self._emit(chunk)
+
+    def finalize(self) -> None:
+        """Emit the final overlap tail exactly as the serial generator does."""
+        if self._overlap is not None and self._frame_start < self._output_num_frames:
+            tail = self._overlap[:, :, :self._output_num_frames - self._frame_start]
+            if self._stream is None:
+                self._emit(tail)
+            else:
+                with torch.cuda.stream(self._stream):
+                    self._emit(tail)
+        if self._frame_start != self._output.shape[2]:
+            raise RuntimeError(
+                f"MiniMax-H3 decode wrote {self._frame_start} frames into an output buffer expecting "
+                f"{self._output.shape[2]}.")
+
+    def synchronize(self) -> None:
+        """Drain assembly kernels and output copies before the buffer is read."""
+        if self._stream is not None:
+            self._stream.synchronize()
+
+    def _emit(self, chunk: torch.Tensor) -> None:
+        pixels = self._vae.denormalize_pixels(chunk.float()).clamp_(0, 1)
+        self._vae._copy_chunk_pixels(pixels, self._output, self._frame_start, self._non_blocking)
+        self._frame_start += pixels.shape[2]
+
+
+def _broadcast_segment_meta(group: "GroupCoordinator",
+                            segment: torch.Tensor | None) -> tuple[torch.dtype, tuple[int, ...]]:
+    """Share the leader's real segment dtype/shape so placeholder tensors match.
+
+    The decoder's output dtype depends on the surrounding autocast context;
+    deriving it on the leader from an actually decoded segment (instead of
+    predicting it) keeps collective dtypes correct by construction.
+    """
+    meta = (segment.dtype, tuple(segment.shape)) if segment is not None else None
+    meta = group.broadcast_object(meta, src=0)
+    if meta is None:
+        raise RuntimeError("MiniMax-H3 parallel VAE meta broadcast returned no leader metadata.")
+    return meta
+
+
+def decode_to_pixels_parallel(
+    vae: AutoencoderKLMiniMaxH3,
+    z: torch.Tensor,
+    output: torch.Tensor | None,
+    group: "GroupCoordinator",
+    strategy: str = DEFAULT_DECODE_GATHER_STRATEGY,
+) -> torch.Tensor | None:
+    """Chunk-parallel ``decode_to_pixels`` across a sequence-parallel group.
+
+    All group ranks call this together with identical ``z``. Temporal chunks
+    are decoded round-robin across the group and their segments move to the
+    group's first rank, which assembles bitwise the serial
+    ``decode_to_pixels`` result into ``output``. Only the first rank passes
+    ``output`` (validated exactly like the serial API); other ranks pass
+    ``None`` and receive ``None``.
+    """
+    if strategy not in DECODE_GATHER_STRATEGIES:
+        raise ValueError(f"Unknown parallel-decode strategy {strategy!r}; expected one of {DECODE_GATHER_STRATEGIES}.")
+    is_leader = group.rank_in_group == 0
+    if is_leader:
+        if output is None:
+            raise ValueError("The first sequence-parallel rank must provide the CPU output buffer.")
+        expected_shape = vae.decoded_pixel_shape(z.shape)
+        if output.device.type != "cpu" or output.dtype != torch.float32 or tuple(output.shape) != expected_shape:
+            raise ValueError(
+                "`output` must be a CPU float32 tensor with shape "
+                f"{expected_shape}, got device={output.device}, dtype={output.dtype}, shape={tuple(output.shape)}.")
+    elif output is not None:
+        raise ValueError("Only the first sequence-parallel rank may provide an output buffer.")
+    if group.world_size == 1:
+        return vae.decode_to_pixels(z, output)
+
+    try:
+        if vae.use_slicing and z.shape[0] > 1:
+            for batch_index, z_slice in enumerate(z.split(1)):
+                slice_output = output[batch_index:batch_index + 1] if output is not None else None
+                _decode_single_parallel(vae, z_slice, slice_output, group, strategy)
+        else:
+            _decode_single_parallel(vae, z, output, group, strategy)
+    finally:
+        # Drain the leader's async chunk copies before the caller (or an
+        # exception handler) can read or release the pinned buffer.
+        if output is not None and vae._streams_chunk_copies(z, output):
+            torch.cuda.current_stream(z.device).synchronize()
+    return output
+
+
+def _decode_single_parallel(
+    vae: AutoencoderKLMiniMaxH3,
+    z: torch.Tensor,
+    output: torch.Tensor | None,
+    group: "GroupCoordinator",
+    strategy: str,
+) -> None:
+    pad_tokens, num_chunks, output_num_frames = vae._temporal_decode_plan(z.shape[2])
+    if pad_tokens > 0:
+        z = torch.cat([z, z[:, :, -1:].repeat(1, 1, pad_tokens, 1, 1)], dim=2)
+    world_size = group.world_size
+    rank = group.rank_in_group
+
+    # Every rank decodes its round-0 chunk BEFORE the metadata rendezvous so
+    # the first decodes run concurrently (a rank that waited on the broadcast
+    # first would idle a full chunk-decode behind the leader). The leader
+    # owns chunk 0 under round-robin assignment, so its segment supplies real
+    # dtype/shape for placeholder rounds instead of guessing autocast state.
+    first_segment = _decode_segment(vae, z, rank) if rank < num_chunks else None
+    segment_dtype, segment_shape = _broadcast_segment_meta(group, first_segment if rank == 0 else None)
+
+    assembler = None
+    if output is not None:
+        non_blocking = vae._streams_chunk_copies(z, output)
+        assembler = _ChunkAssembler(vae, output, output_num_frames, non_blocking, z.device)
+
+    try:
+        segment_frames = segment_shape[2]
+        for round_index in range(_num_rounds(num_chunks, world_size)):
+            chunk_index = round_index * world_size + rank
+            if chunk_index >= num_chunks:
+                segment = torch.zeros(segment_shape, dtype=segment_dtype, device=z.device)
+            elif round_index == 0 and first_segment is not None:
+                segment = first_segment
+            else:
+                segment = _decode_segment(vae, z, chunk_index)
+            with nvtx_range(f"minimax_h3.vae.parallel_{strategy}.{round_index}"):
+                if strategy == "gather":
+                    gathered = group.gather(segment, dst=0, dim=2)
+                else:
+                    gathered = group.all_gather(segment, dim=2)
+            if assembler is None or gathered is None:
+                continue
+            for slot in range(world_size):
+                if round_index * world_size + slot >= num_chunks:
+                    break
+                assembler.push(gathered.narrow(2, slot * segment_frames, segment_frames))
+        if assembler is not None:
+            assembler.finalize()
+    finally:
+        # Drain assembly-stream copies into ``output`` even on the error path
+        # so an exception cannot leave an in-flight DMA into a buffer the
+        # caller may release.
+        if assembler is not None:
+            assembler.synchronize()
+
+
+def _encode_clip_moments(vae: AutoencoderKLMiniMaxH3, pixels: torch.Tensor, clip_index: int) -> torch.Tensor:
+    """Encode one ``clip_length``-frame clip exactly as ``_encode_pixels`` does."""
+    clip_length = vae.config.clip_length
+    frame_start = clip_index * clip_length
+    with nvtx_range(f"minimax_h3.vae.parallel_encode_clip.{clip_index}"):
+        clip = pixels[:, :, frame_start:frame_start + clip_length].to(
+            device=vae.pixel_mean.device,
+            dtype=torch.float32,
+        )
+        if pixels.dtype == torch.uint8:
+            clip = clip / 255.0
+        if clip.shape[2] < clip_length:
+            pad_frames = clip[:, :, -1:].repeat(1, 1, clip_length - clip.shape[2], 1, 1)
+            clip = torch.cat([clip, pad_frames], dim=2)
+        clip = vae.normalize_pixels(clip)
+        return vae._encode_clip(clip).contiguous()
+
+
+def encode_pixels_parallel(
+    vae: AutoencoderKLMiniMaxH3,
+    pixels: torch.Tensor,
+    group: "GroupCoordinator",
+) -> AutoencoderKLOutput:
+    """Clip-parallel ``encode_pixels`` across a sequence-parallel group.
+
+    Encoder clips have no cross-clip dependency (no overlap, no blending), so
+    ranks encode disjoint clips and all-gather the per-clip moment tensors.
+    Every rank returns the identical full posterior — preserving the serial
+    contract that all ranks hold the same encoded latents — bitwise equal to
+    ``vae.encode_pixels(pixels)``. Moments are latent-sized (a few MB per
+    clip), so the all-gather is negligible next to the clip forwards.
+    """
+    if pixels.ndim != 5 or pixels.shape[1] != vae.config.in_channels or pixels.shape[2] <= 0:
+        raise ValueError(
+            f"`pixels` must have shape [B, {vae.config.in_channels}, T, H, W] with T > 0, "
+            f"got {tuple(pixels.shape)}.")
+    if pixels.device.type != "cpu":
+        raise ValueError(f"`pixels` must remain on CPU, got device={pixels.device}.")
+    if pixels.dtype != torch.uint8 and not pixels.is_floating_point():
+        raise TypeError(f"`pixels` must use uint8 or a floating-point dtype, got {pixels.dtype}.")
+    if group.world_size == 1:
+        return vae.encode_pixels(pixels)
+    if vae.use_slicing and pixels.shape[0] > 1:
+        moments = torch.cat([_encode_single_parallel(vae, pixel_slice, group) for pixel_slice in pixels.split(1)])
+    else:
+        moments = _encode_single_parallel(vae, pixels, group)
+    return AutoencoderKLOutput(latent_dist=DiagonalGaussianDistribution(moments))
+
+
+def _encode_single_parallel(vae: AutoencoderKLMiniMaxH3, pixels: torch.Tensor,
+                            group: "GroupCoordinator") -> torch.Tensor:
+    clip_length = vae.config.clip_length
+    num_clips = -(-pixels.shape[2] // clip_length)
+    world_size = group.world_size
+    rank = group.rank_in_group
+
+    # Same first-work-then-rendezvous ordering as the decode path: encode the
+    # round-0 clip before the metadata broadcast so first encodes overlap.
+    first_moments = _encode_clip_moments(vae, pixels, rank) if rank < num_clips else None
+    moment_dtype, moment_shape = _broadcast_segment_meta(group, first_moments if rank == 0 else None)
+
+    moment_tokens = moment_shape[2]
+    parts: list[torch.Tensor] = []
+    for round_index in range(_num_rounds(num_clips, world_size)):
+        clip_index = round_index * world_size + rank
+        if clip_index >= num_clips:
+            moments = torch.zeros(moment_shape, dtype=moment_dtype, device=vae.pixel_mean.device)
+        elif round_index == 0 and first_moments is not None:
+            moments = first_moments
+        else:
+            moments = _encode_clip_moments(vae, pixels, clip_index)
+        gathered = group.all_gather(moments, dim=2)
+        for slot in range(world_size):
+            if round_index * world_size + slot >= num_clips:
+                break
+            parts.append(gathered.narrow(2, slot * moment_tokens, moment_tokens))
+    encoded = torch.cat(parts, dim=2)
+    if vae.config.token_drop > 0:
+        encoded = encoded[:, :, :-vae.config.token_drop]
+    return encoded
+
+
+__all__ = [
+    "DECODE_GATHER_STRATEGIES",
+    "DEFAULT_DECODE_GATHER_STRATEGY",
+    "decode_to_pixels_parallel",
+    "encode_pixels_parallel",
+    "parallel_chunk_indices",
+]

fastvideo/models/vaes/minimax_h3_video.py

@@ -15,7 +15,10 @@ import torch.nn as nn
 import torch.nn.functional as F
 from torch.utils.checkpoint import checkpoint
 
+from fastvideo.attention import get_attn_backend
 from fastvideo.configs.models.vaes.minimax_h3_video import MiniMaxH3VideoVAEConfig
+from fastvideo.platforms import AttentionBackendEnum
+from fastvideo.profiler import nvtx_range
 
 
 class DiagonalGaussianDistribution:
@@ -292,6 +295,7 @@ class MiniMaxH3VideoRotaryPosEmbed(nn.Module):
 class MiniMaxH3VideoAttention(nn.Module):
 
     def __init__(self, dim: int, heads: int, dim_head: int, eps: float = 1e-5, bias: bool = True) -> None:
+        """Build projections and the selected dense FastVideo attention implementation."""
         super().__init__()
         self.heads = heads
         self.dim_head = dim_head
@@ -303,12 +307,34 @@ class MiniMaxH3VideoAttention(nn.Module):
         self.to_k = nn.Linear(dim, inner_dim, bias=bias)
         self.to_v = nn.Linear(dim, inner_dim, bias=bias)
         self.to_out = nn.ModuleList([nn.Linear(inner_dim, dim, bias=bias), nn.Dropout(0.0)])
+        self.attn_impl = None
+        from fastvideo.platforms import current_platform
+
+        if current_platform.is_cuda_alike():
+            attention_backend = get_attn_backend(
+                dim_head,
+                # FlashAttention executes the FP32 VAE activations in BF16 and
+                # restores FP32 output, so resolve against the kernel dtype.
+                torch.bfloat16,
+                supported_attention_backends=(
+                    AttentionBackendEnum.TORCH_SDPA,
+                    AttentionBackendEnum.FLASH_ATTN,
+                ),
+            )
+            self.attn_impl = attention_backend.get_impl_cls()(
+                num_heads=heads,
+                head_size=dim_head,
+                softmax_scale=dim_head**-0.5,
+                num_kv_heads=heads,
+                causal=False,
+            )
 
     def forward(
         self,
         hidden_states: torch.Tensor,
         rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
     ) -> torch.Tensor:
+        """Apply dense self-attention to one spatial VAE token sequence."""
         query = self.to_q(hidden_states).unflatten(2, (self.heads, -1))
         key = self.to_k(hidden_states).unflatten(2, (self.heads, -1))
         value = self.to_v(hidden_states).unflatten(2, (self.heads, -1))
@@ -329,9 +355,17 @@ class MiniMaxH3VideoAttention(nn.Module):
             query = torch.cat([query_rotary * cos + query_rotated * sin, query_pass], dim=-1)
             key = torch.cat([key_rotary * cos + key_rotated * sin, key_pass], dim=-1)
 
-        query, key, value = (tensor.permute(0, 2, 1, 3) for tensor in (query, key, value))
-        hidden_states = F.scaled_dot_product_attention(query, key, value)
-        hidden_states = hidden_states.permute(0, 2, 1, 3).flatten(2, 3)
+        if self.attn_impl is not None and query.device.type != "cpu":
+            # VAE decoding has no diffusion-step metadata, so call the selected
+            # backend implementation directly with dense BSHD tensors.
+            hidden_states = self.attn_impl.forward(query, key, value, None)
+            hidden_states = hidden_states.flatten(2, 3)
+        else:
+            # Keep CPU construction and execution available without requiring
+            # an accelerator attention backend.
+            query, key, value = (tensor.permute(0, 2, 1, 3) for tensor in (query, key, value))
+            hidden_states = F.scaled_dot_product_attention(query, key, value)
+            hidden_states = hidden_states.permute(0, 2, 1, 3).flatten(2, 3)
         return self.to_out[0](hidden_states)
 
 
@@ -434,6 +468,7 @@ class MiniMaxH3VideoViTDecoder3d(nn.Module):
         self.gradient_checkpointing = False
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        """Decode one latent spatial input through the H3 video transformer."""
         batch_size, num_channels, num_frames, height, width = hidden_states.shape
         hidden_states = hidden_states.permute(0, 2, 3, 4, 1).reshape(
             batch_size,
@@ -483,6 +518,11 @@ class MiniMaxH3VideoViTDecoder3d(nn.Module):
         )
 
 
+def _is_minimax_h3_video_vae_decoder(name: str, submodule: nn.Module) -> bool:
+    """Select the video decoder that serves the H3 VAE ``decode`` path."""
+    return name == "decoder" and isinstance(submodule, MiniMaxH3VideoViTDecoder3d)
+
+
 class AutoencoderKLMiniMaxH3(nn.Module):
     """MiniMax-H3 causal encoder and ViT decoder with exact release geometry."""
 
@@ -490,6 +530,7 @@ class AutoencoderKLMiniMaxH3(nn.Module):
     _no_split_modules = ["MiniMaxH3VideoResnetBlock3d", "MiniMaxH3VideoTransformerBlock"]
     _repeated_blocks = ["MiniMaxH3VideoTransformerBlock"]
     _keep_in_fp32_modules = ["encoder", "decoder", "quant_conv", "post_quant_conv"]
+    _compile_conditions = [_is_minimax_h3_video_vae_decoder]
 
     def __init__(self, config: MiniMaxH3VideoVAEConfig) -> None:
         super().__init__()
@@ -655,12 +696,15 @@ class AutoencoderKLMiniMaxH3(nn.Module):
         slice_rest[dim] = slice(blend_extent, None)
         return torch.cat([blended, b[tuple(slice_rest)]], dim=dim)
 
+    # The fixed spatial tile grid reuses one compiled blend-and-concatenate graph.
+    @torch.compile(backend="inductor", mode="reduce-overhead", dynamic=False)
     def _stitch_tiles(
         self,
         tiles: list[list[torch.Tensor]],
         height_overlaps: list[int],
         width_overlaps: list[int],
     ) -> torch.Tensor:
+        """Blend decoded tile overlaps and concatenate the spatial canvas."""
         result_rows = []
         for row_index, row in enumerate(tiles):
             result_row = []
@@ -677,6 +721,12 @@ class AutoencoderKLMiniMaxH3(nn.Module):
             result_rows.append(torch.cat(result_row, dim=-1))
         return torch.cat(result_rows, dim=-2)
 
+    # Each fixed-shape latent tile reuses one compiled decoder-input projection.
+    @torch.compile(backend="inductor", mode="reduce-overhead", dynamic=False)
+    def _project_decoder_tile(self, tile: torch.Tensor) -> torch.Tensor:
+        """Project one spatial latent tile into the decoder input channels."""
+        return self.post_quant_conv(tile)
+
     def _encode_clip(self, x: torch.Tensor) -> torch.Tensor:
         if not self.use_tiling:
             return self.quant_conv(self.encoder(x))
@@ -700,36 +750,64 @@ class AutoencoderKLMiniMaxH3(nn.Module):
             rows.append(row)
         latent_y_overlaps = [overlap // self.spatial_compression_ratio for overlap in y_overlaps]
         latent_x_overlaps = [overlap // self.spatial_compression_ratio for overlap in x_overlaps]
-        return self._stitch_tiles(rows, latent_y_overlaps, latent_x_overlaps)
+        # Under mode="reduce-overhead" the stitched canvas is a CUDA-graph
+        # static buffer that the next _stitch_tiles replay overwrites. Callers
+        # (_encode/_encode_pixels/encode_keyframe) collect per-clip results
+        # across replays before concatenating, so hand them a caller-owned
+        # tensor instead of cudagraph-pooled storage.
+        return self._stitch_tiles(rows, latent_y_overlaps, latent_x_overlaps).clone()
 
     def _decode_clip(self, z: torch.Tensor) -> torch.Tensor:
-        if not self.use_tiling:
-            return self.decoder(self.post_quant_conv(z))
-        height = z.shape[-2] * self.spatial_compression_ratio
-        width = z.shape[-1] * self.spatial_compression_ratio
-        y_indices, y_lengths, y_overlaps = self._split_tiles(
-            height,
-            self.tile_sample_min_height,
-            self.tile_sample_min_overlap_height,
-        )
-        x_indices, x_lengths, x_overlaps = self._split_tiles(
-            width,
-            self.tile_sample_min_width,
-            self.tile_sample_min_overlap_width,
-        )
-        ratio = self.spatial_compression_ratio
-        rows = []
-        for y_position, y_length in zip(y_indices, y_lengths):
-            row = []
-            for x_position, x_length in zip(x_indices, x_lengths):
-                tile = z[
-                    ...,
-                    y_position // ratio:y_position // ratio + y_length // ratio,
-                    x_position // ratio:x_position // ratio + x_length // ratio,
-                ]
-                row.append(self.decoder(self.post_quant_conv(tile)))
-            rows.append(row)
-        return self._stitch_tiles(rows, y_overlaps, x_overlaps)
+        """Decode one temporal clip, with optional overlapping spatial tiles."""
+        with nvtx_range("minimax_h3.vae.decode_clip"):
+            if not self.use_tiling:
+                with nvtx_range("minimax_h3.vae.decode_clip.no_s_tile.post_quant_conv"):
+                    projected_clip = self.post_quant_conv(z)
+                with nvtx_range("minimax_h3.vae.decode_clip.no_s_tile.decoder_forward"):
+                    return self.decoder(projected_clip)
+
+            height = z.shape[-2] * self.spatial_compression_ratio
+            width = z.shape[-1] * self.spatial_compression_ratio
+            with nvtx_range("minimax_h3.vae.decode_clip.split_tiles"):
+                y_indices, y_lengths, y_overlaps = self._split_tiles(
+                    height,
+                    self.tile_sample_min_height,
+                    self.tile_sample_min_overlap_height,
+                )
+                x_indices, x_lengths, x_overlaps = self._split_tiles(
+                    width,
+                    self.tile_sample_min_width,
+                    self.tile_sample_min_overlap_width,
+                )
+
+            ratio = self.spatial_compression_ratio
+            rows = []
+            # The eager tile driver owns NVTX so each marker remains outside
+            # the compiled decoder graph.
+            with nvtx_range("minimax_h3.vae.decode_clip.decode_tiles"):
+                for row_index, (y_position, y_length) in enumerate(zip(y_indices, y_lengths)):
+                    row = []
+                    for column_index, (x_position, x_length) in enumerate(zip(x_indices, x_lengths)):
+                        with nvtx_range(f"minimax_h3.vae.decode_clip.tile.{row_index}.{column_index}"):
+                            tile = z[
+                                ...,
+                                y_position // ratio:y_position // ratio + y_length // ratio,
+                                x_position // ratio:x_position // ratio + x_length // ratio,
+                            ]
+                            projected_tile = self._project_decoder_tile(tile)
+                            with nvtx_range("minimax_h3.vae.decode_clip.tile.decoder_forward"):
+                                decoded_tile = self.decoder(projected_tile)
+                            row.append(decoded_tile)
+                    rows.append(row)
+
+            with nvtx_range("minimax_h3.vae.decode_clip.stitch_tiles"):
+                # Same CUDA-graph output-ownership contract as _encode_clip:
+                # _decode collects chunks across _stitch_tiles replays before
+                # torch.cat, so the pooled canvas must not escape this driver.
+                # (The streaming _decode_to_pixels path copies each chunk out
+                # before the next decode and never held stale storage; the
+                # clone keeps that path correct too at one D2D copy per chunk.)
+                return self._stitch_tiles(rows, y_overlaps, x_overlaps).clone()
 
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
         clip_length = self.config.clip_length
@@ -809,21 +887,27 @@ class AutoencoderKLMiniMaxH3(nn.Module):
 
         output_frame_start = 0
         overlap = None
-        for index in range(num_chunks):
-            start = index * tokens_chunk_size
-            clip = self._decode_clip(z[:, :, start:start + tokens_chunk_size + self.token_overlap])
-            chunk = clip[:, :, self.frame_pre_padding:chunk_num_frames]
-            next_overlap = None
-            if self.config.token_drop > 0:
-                next_overlap = clip[:, :, chunk_num_frames + self.frame_pre_padding:].clone()
-            if overlap is not None:
-                chunk = self._blend(overlap, chunk, self.frame_overlap, dim=-3)
+        for chunk_index in range(num_chunks):
+            with nvtx_range(f"minimax_h3.vae.temporal_chunk.{chunk_index}"):
+                start = chunk_index * tokens_chunk_size
+                clip = self._decode_clip(z[:, :, start:start + tokens_chunk_size + self.token_overlap])
+                with nvtx_range(f"minimax_h3.vae.temporal_chunk.{chunk_index}.frame_segment.0"):
+                    chunk = clip[:, :, self.frame_pre_padding:chunk_num_frames]
+                    if overlap is not None:
+                        chunk = self._blend(overlap, chunk, self.frame_overlap, dim=-3)
+                    num_frames = min(chunk.shape[2], output_num_frames - output_frame_start)
+                    chunk = chunk[:, :, :num_frames]
 
-            num_frames = min(chunk.shape[2], output_num_frames - output_frame_start)
-            if num_frames > 0:
-                yield chunk[:, :, :num_frames]
-                output_frame_start += num_frames
+                next_overlap = None
+                if self.config.token_drop > 0:
+                    with nvtx_range(f"minimax_h3.vae.temporal_chunk.{chunk_index}.frame_segment.1"):
+                        next_overlap = clip[:, :, chunk_num_frames + self.frame_pre_padding:].clone()
+
+            # Yield after the ranges close so consumer-side CPU copies do not inflate decoder timing.
             overlap = next_overlap
+            if num_frames > 0:
+                output_frame_start += num_frames
+                yield chunk
 
         if overlap is not None and output_frame_start < output_num_frames:
             yield overlap[:, :, :output_num_frames - output_frame_start]
@@ -849,31 +933,42 @@ class AutoencoderKLMiniMaxH3(nn.Module):
         """Whether finalized chunks copy to ``output`` asynchronously on the current CUDA stream."""
         return z.device.type == "cuda" and output.is_pinned()
 
-    def _decode_to_pixels(self, z: torch.Tensor, output: torch.Tensor) -> None:
-        """Decode temporal chunks and immediately copy finalized pixels to CPU.
+    @staticmethod
+    def _copy_chunk_pixels(pixels: torch.Tensor, output: torch.Tensor, frame_start: int, non_blocking: bool) -> None:
+        """Copy one finalized fp32 pixel chunk into the CPU ``output`` buffer.
 
         Device-to-host copies run per (batch, channel) plane: the temporal
         slice of ``output`` is strided across channels, but each plane is
         contiguous on both sides, so every transfer stays a direct memcpy
         instead of staging through a pageable CPU temporary. With a pinned
-        ``output`` the copies are additionally asynchronous and overlap the
-        next chunk's decode; ``decode_to_pixels`` synchronizes once before
-        returning.
+        ``output`` and ``non_blocking=True`` the copies are additionally
+        asynchronous on the current CUDA stream; callers synchronize once
+        before releasing the buffer.
+        """
+        target = output[:, :, frame_start:frame_start + pixels.shape[2]]
+        if pixels.device.type == "cuda":
+            pixels = pixels.contiguous()
+            for batch_index in range(pixels.shape[0]):
+                for channel_index in range(pixels.shape[1]):
+                    target[batch_index, channel_index].copy_(pixels[batch_index, channel_index],
+                                                             non_blocking=non_blocking)
+        else:
+            target.copy_(pixels)
+
+    def _decode_to_pixels(self, z: torch.Tensor, output: torch.Tensor) -> None:
+        """Decode temporal chunks and immediately copy finalized pixels to CPU.
+
+        Each finalized chunk streams through ``_copy_chunk_pixels`` (direct
+        per-plane memcpys; asynchronous with a pinned ``output``) so the
+        copies overlap the next chunk's decode; ``decode_to_pixels``
+        synchronizes once before returning.
         """
         non_blocking = self._streams_chunk_copies(z, output)
         output_frame_start = 0
         for chunk in self._decode_chunks(z):
             num_frames = chunk.shape[2]
             pixels = self.denormalize_pixels(chunk.float()).clamp_(0, 1)
-            target = output[:, :, output_frame_start:output_frame_start + num_frames]
-            if z.device.type == "cuda":
-                pixels = pixels.contiguous()
-                for batch_index in range(pixels.shape[0]):
-                    for channel_index in range(pixels.shape[1]):
-                        target[batch_index, channel_index].copy_(pixels[batch_index, channel_index],
-                                                                 non_blocking=non_blocking)
-            else:
-                target.copy_(pixels)
+            self._copy_chunk_pixels(pixels, output, output_frame_start, non_blocking)
             output_frame_start += num_frames
         if output_frame_start != output.shape[2]:
             raise RuntimeError(

fastvideo/pipelines/basic/minimax_h3/stages/minimax_h3_conditioning.py

@@ -12,9 +12,9 @@ from torch.distributed.tensor import DTensor
 from fastvideo.distributed import get_local_torch_device
 from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.models.encoders.minimax_h3_qwen3_vl import MiniMaxH3Qwen3VLConditioner
+from fastvideo.profiler import nvtx_range
 from fastvideo.pipelines.basic.minimax_h3.packing import (
     MINIMAX_H3_IMAGE_PAD_TOKEN,
-    MINIMAX_H3_TEXT_ENCODER_LAYER,
     MINIMAX_H3_TEXT_TAG,
     MINIMAX_H3_VIDEO_PAD_TOKEN,
     MINIMAX_H3_VIDEO_TAG,
@@ -42,25 +42,6 @@ def _token_ids(tokenized: Any) -> list[int]:
     return [int(token_id) for token_id in input_ids]
 
 
-def _create_mm_token_type_ids(processor: Any, token_ids: list[int]) -> list[list[int]]:
-    """Build Qwen3-VL modality IDs across old and new Transformers releases."""
-    create_ids = getattr(processor, "create_mm_token_type_ids", None)
-    if callable(create_ids):
-        return create_ids([token_ids])
-
-    modality_ids = [0] * len(token_ids)
-    for modality, modality_type in (("image", 1), ("video", 2), ("audio", 3)):
-        special_ids = getattr(processor, f"{modality}_token_ids", None)
-        if special_ids is None:
-            special_id = getattr(processor, f"{modality}_token_id", None)
-            special_ids = [] if special_id is None else [special_id]
-        resolved_ids = {int(special_id) for special_id in special_ids if special_id is not None}
-        for index, token_id in enumerate(token_ids):
-            if token_id in resolved_ids:
-                modality_ids[index] = modality_type
-    return [modality_ids]
-
-
 def build_ref2va_presentation(
     tokenizer: Any,
     prompt: str,
@@ -155,20 +136,10 @@ class MiniMaxH3ConditioningStage(PipelineStage):
         device: torch.device,
         **vision_inputs: torch.Tensor | None,
     ) -> tuple[torch.Tensor, torch.Tensor]:
-        hidden_state_index = MINIMAX_H3_TEXT_ENCODER_LAYER
-        input_ids = torch.tensor([token_ids], dtype=torch.long, device=device)
-        mm_token_type_ids = torch.as_tensor(
-            _create_mm_token_type_ids(self.processor, token_ids),
-            dtype=torch.long,
-            device=device,
-        )
+        input_ids = torch.tensor(token_ids, dtype=torch.long, device=device)
         dtype = self.conditioner.dtype
-        outputs = self.conditioner(
-            input_ids=input_ids,
-            attention_mask=torch.ones_like(input_ids),
-            mm_token_type_ids=mm_token_type_ids,
-            use_cache=False,
-            output_hidden_states=True,
+        prompt_embeds = self.conditioner(
+            input_ids,
             **{
                 name:
                 None if value is None else value.to(
@@ -178,10 +149,10 @@ class MiniMaxH3ConditioningStage(PipelineStage):
                 for name, value in vision_inputs.items()
             },
         )
-        if outputs.hidden_states is None or len(outputs.hidden_states) <= hidden_state_index:
-            raise ValueError(f"Qwen3-VL did not return `hidden_states[{hidden_state_index}]`.")
+        if prompt_embeds.ndim != 2 or prompt_embeds.shape[0] != len(token_ids):
+            raise ValueError(f"MiniMax-H3 slim text encoder returned unexpected shape={tuple(prompt_embeds.shape)}")
         return (
-            outputs.hidden_states[hidden_state_index].to(device=device, dtype=dtype),
+            prompt_embeds.unsqueeze(0).to(device=device, dtype=dtype),
             torch.tensor(token_tags, dtype=torch.long),
         )
 
@@ -286,6 +257,7 @@ class MiniMaxH3ConditioningStage(PipelineStage):
 
     @torch.no_grad()
     def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
+        """Encode one H3 prompt presentation and attach its packed text features."""
         device = get_local_torch_device()
         first_param = next(self.conditioner.parameters(), None)
         moved_for_forward = (fastvideo_args.text_encoder_cpu_offload and first_param is not None
@@ -293,10 +265,13 @@ class MiniMaxH3ConditioningStage(PipelineStage):
         if moved_for_forward:
             self.conditioner.to(device)
         try:
-            if self.ref2va:
-                prompt_embeds, text_token_tags = self._encode_ref2va(batch, device)
-            else:
-                prompt_embeds, text_token_tags = self._encode_fl2va(batch, device)
+            # Keep both H3 prompt-presentation modes under one text-encoding
+            # range so Nsight Systems exposes their complete conditioning cost.
+            with nvtx_range("minimax_h3.text_encoding"):
+                if self.ref2va:
+                    prompt_embeds, text_token_tags = self._encode_ref2va(batch, device)
+                else:
+                    prompt_embeds, text_token_tags = self._encode_fl2va(batch, device)
         finally:
             if moved_for_forward:
                 self.conditioner.to("cpu")

fastvideo/pipelines/basic/minimax_h3/stages/minimax_h3_decoding.py

@@ -7,10 +7,13 @@ from typing import Any
 
 import torch
 
-from fastvideo.distributed import get_local_torch_device, get_world_group, model_parallel_is_initialized
+from fastvideo.distributed import get_local_torch_device, get_sp_group, model_parallel_is_initialized
 from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.logger import init_logger
 from fastvideo.models.vaes.minimax_h3_audio import MiniMaxH3AudioVAE
+from fastvideo.models.vaes.minimax_h3_parallel import DEFAULT_DECODE_GATHER_STRATEGY, decode_to_pixels_parallel
 from fastvideo.models.vaes.minimax_h3_video import AutoencoderKLMiniMaxH3
+from fastvideo.profiler import nvtx_range
 from fastvideo.pipelines.basic.minimax_h3.packing import (
     MiniMaxH3PackedLayout,
     unpack_audio_tokens,
@@ -23,6 +26,8 @@ from fastvideo.pipelines.stages.validators import StageValidators as V
 from fastvideo.pipelines.stages.validators import VerificationResult
 from fastvideo.utils import is_pin_memory_available
 
+logger = init_logger(__name__)
+
 
 def _layout(batch: ForwardBatch) -> MiniMaxH3PackedLayout:
     layout = batch.extra.get(MINIMAX_H3_LAYOUT_KEY)
@@ -31,6 +36,23 @@ def _layout(batch: ForwardBatch) -> MiniMaxH3PackedLayout:
     return layout
 
 
+def _decode_participation(fastvideo_args: FastVideoArgs, want_parallel: bool) -> tuple[Any, bool, bool]:
+    """Resolve (sp_group, is_output_rank, parallel) for the VAE decode stages.
+
+    The executors consume rank 0's ForwardBatch and the training validation
+    callback consumes each sequence-parallel group leader's, so the output
+    rank is the SP group's first rank (identical to world rank 0 in the
+    single-group e2e case). ``parallel`` is only true when every group rank
+    will run the decode body — the collectives inside require uniform
+    participation, so no rank-dependent branch may guard them.
+    """
+    if not model_parallel_is_initialized():
+        return None, True, False
+    sp_group = get_sp_group()
+    parallel = bool(want_parallel) and sp_group.world_size > 1
+    return sp_group, sp_group.is_first_rank, parallel
+
+
 class MiniMaxH3VideoDecodingStage(PipelineStage):
     """Drop visual condition rows, unpatchify, and decode the target video."""
 
@@ -55,11 +77,14 @@ class MiniMaxH3VideoDecodingStage(PipelineStage):
 
     @torch.no_grad()
     def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
-        if model_parallel_is_initialized() and not get_world_group().is_first_rank:
-            # Distributed executors consume rank 0's ForwardBatch. Keep a
+        """Decode H3 video latents into normalized CPU pixels."""
+        placeholder = torch.empty((0, 3, 0, 0, 0), device="cpu", dtype=torch.float32)
+        sp_group, is_output_rank, parallel = _decode_participation(fastvideo_args, fastvideo_args.vae_parallel_decode)
+        if not is_output_rank and not parallel:
+            # Consumers read the output rank's ForwardBatch. Keep a
             # verifier-compatible placeholder on other ranks and avoid
             # duplicating the full VAE decode and CPU output buffer.
-            batch.output = torch.empty((0, 3, 0, 0, 0), device="cpu", dtype=torch.float32)
+            batch.output = placeholder
             return batch
 
         layout = _layout(batch)
@@ -79,19 +104,33 @@ class MiniMaxH3VideoDecodingStage(PipelineStage):
         try:
             latents = self.vae.denormalize_latents(latents.to(device=device, dtype=torch.float32))
             if fastvideo_args.output_type == "latent":
-                batch.output = latents.detach().float().cpu()
+                # No collectives on this path, so uniform participation is
+                # trivial: every rank returns here.
+                batch.output = latents.detach().float().cpu() if is_output_rank else placeholder
                 return batch
 
-            output = torch.empty(
-                self.vae.decoded_pixel_shape(latents.shape),
-                device="cpu",
-                dtype=torch.float32,
-                pin_memory=fastvideo_args.pin_cpu_memory and is_pin_memory_available(),
-            )
-            # The published decode recipe uses FP16 autocast over FP32 weights.
-            with torch.autocast(device_type=device.type, dtype=torch.float16, enabled=device.type == "cuda"):
-                self.vae.decode_to_pixels(latents, output)
-            batch.output = output
+            output = None
+            if is_output_rank:
+                output = torch.empty(
+                    self.vae.decoded_pixel_shape(latents.shape),
+                    device="cpu",
+                    dtype=torch.float32,
+                    pin_memory=fastvideo_args.pin_cpu_memory and is_pin_memory_available(),
+                )
+            # Attribute the streamed decoder computation while retaining
+            # per-chunk device-to-host transfer and pinned-buffer reuse.
+            with (
+                    nvtx_range("minimax_h3.vae"),
+                    torch.autocast(device_type=device.type, dtype=torch.float16, enabled=device.type == "cuda"),
+            ):
+                if parallel:
+                    strategy = fastvideo_args.vae_parallel_decode_strategy or DEFAULT_DECODE_GATHER_STRATEGY
+                    logger.info_once(f"MiniMax-H3 VAE decode: sequence-parallel chunks across "
+                                     f"{sp_group.world_size} ranks ({strategy})")
+                    decode_to_pixels_parallel(self.vae, latents, output, sp_group, strategy=strategy)
+                else:
+                    self.vae.decode_to_pixels(latents, output)
+            batch.output = output if is_output_rank else placeholder
             return batch
         finally:
             if fastvideo_args.vae_cpu_offload:
@@ -121,7 +160,10 @@ class MiniMaxH3AudioDecodingStage(PipelineStage):
 
     @torch.no_grad()
     def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
-        if model_parallel_is_initialized() and not get_world_group().is_first_rank:
+        """Decode H3 audio latents into a stereo CPU waveform."""
+        # Audio decode is sub-second, so it always runs serially on the SP
+        # group's first rank (the rank whose ForwardBatch consumers read).
+        if model_parallel_is_initialized() and not get_sp_group().is_first_rank:
             batch.extra["audio"] = torch.empty((0, 2), device="cpu", dtype=torch.float32)
             batch.extra["audio_sample_rate"] = self.audio_vae.sampling_rate
             self._clear_runtime(batch)
@@ -144,7 +186,10 @@ class MiniMaxH3AudioDecodingStage(PipelineStage):
                 self._clear_runtime(batch)
                 return batch
 
-            decoded = self.audio_vae.decode(latents).sample.float()
+            # The range isolates waveform synthesis from packing and runtime
+            # cleanup so the audio decoder has one stable timeline boundary.
+            with nvtx_range("minimax_h3.audio_vae"):
+                decoded = self.audio_vae.decode(latents).sample.float()
             if decoded.ndim != 3 or decoded.shape[0] != 2 or decoded.shape[1] != 1:
                 raise ValueError("MiniMax-H3 audio VAE must decode stereo channels as two mono batch items; "
                                  f"got {tuple(decoded.shape)}.")

fastvideo/pipelines/basic/minimax_h3/stages/minimax_h3_denoising.py

@@ -11,8 +11,8 @@ from fastvideo.attention.selector import component_attention_backend, get_attn_b
 from fastvideo.distributed import get_local_torch_device
 from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.forward_context import set_forward_context
-from fastvideo.profiler import profiler_region
 from fastvideo.hooks.activation_trace import trace_step
+from fastvideo.profiler import nvtx_range, profiler_region
 from fastvideo.pipelines.basic.minimax_h3.packing import (
     MINIMAX_H3_KEYFRAME_NOISE_AUG,
     MiniMaxH3PackedLayout,
@@ -89,6 +89,7 @@ class MiniMaxH3DenoisingStage(PipelineStage):
 
     @torch.no_grad()
     def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
+        """Denoise the packed H3 video and audio streams over one shared schedule."""
         layout = batch.extra.get(MINIMAX_H3_LAYOUT_KEY)
         if not isinstance(layout, MiniMaxH3PackedLayout):
             raise ValueError("MiniMax-H3 packed layout is missing before denoising.")
@@ -145,9 +146,15 @@ class MiniMaxH3DenoisingStage(PipelineStage):
             vsa_exempt = vsa_mode == "exempt"
             vsa_dense_layers = tuple(batch.extra.get("vsa_dense_layers", ()))
             vsa_dense_first_n = int(batch.extra.get("vsa_dense_first_n_steps", 0))
+            # Run-level tile geometry (256 default, 64 = native Triton path),
+            # plumbed like the run-level sparsity; the builder validates the
+            # value against VSA_H3_TILE_SHAPES.
+            vsa_tile_size = int(fastvideo_args.VSA_tile_size)
 
         try:
-            with profiler_region("inference_denoising"):
+            # The stage range groups the complete denoising loop while the
+            # indexed model ranges retain timing detail for every H3 block.
+            with profiler_region("inference_denoising"), nvtx_range("minimax_h3.dit"):
                 for index, (video_timestep,
                             audio_timestep) in enumerate(zip(video_timesteps, audio_timesteps, strict=True)):
                     unique_timesteps, timestep_indices = row_timestep_plan[index]
@@ -167,6 +174,7 @@ class MiniMaxH3DenoisingStage(PipelineStage):
                             device=device,
                             exempt=vsa_exempt,
                             dense_layers=vsa_dense_layers,
+                            tile_size=vsa_tile_size,
                         )
                     # Under torch.compile(mode="reduce-overhead") each denoising
                     # step must be marked, or cudagraph trees flag cross-step

fastvideo/pipelines/basic/minimax_h3/stages/minimax_h3_latent_preparation.py

@@ -9,8 +9,10 @@ import numpy as np
 import torch
 from diffusers.utils.torch_utils import randn_tensor
 
-from fastvideo.distributed import get_local_torch_device
+from fastvideo.distributed import get_local_torch_device, get_sp_group, model_parallel_is_initialized
 from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.logger import init_logger
+from fastvideo.models.vaes.minimax_h3_parallel import encode_pixels_parallel
 from fastvideo.pipelines.basic.minimax_h3.packing import (
     MINIMAX_H3_AUDIO_CHANNELS,
     MINIMAX_H3_KEYFRAME_ENCODE_SEED,
@@ -36,6 +38,8 @@ from fastvideo.pipelines.stages.base import PipelineStage
 from fastvideo.pipelines.stages.validators import StageValidators as V
 from fastvideo.pipelines.stages.validators import VerificationResult
 
+logger = init_logger(__name__)
+
 MINIMAX_H3_LAYOUT_KEY = "minimax_h3_layout"
 
 
@@ -105,8 +109,20 @@ class MiniMaxH3LatentPreparationStage(PipelineStage):
         self,
         references: list[MiniMaxH3PreparedReference],
         device: torch.device,
+        fastvideo_args: FastVideoArgs,
     ) -> list[torch.Tensor]:
         patch_size = self.transformer.patch_size
+        # Reference encode runs on every rank (all ranks hold identical
+        # prepared references), so clip-parallel encode keeps participation
+        # uniform by construction: each rank encodes a clip subset and the
+        # all-gather leaves the identical full posterior everywhere.
+        parallel_group = None
+        if fastvideo_args.vae_parallel_encode and model_parallel_is_initialized():
+            sp_group = get_sp_group()
+            if sp_group.world_size > 1:
+                parallel_group = sp_group
+                logger.info_once(f"MiniMax-H3 reference VAE encode: sequence-parallel clips across "
+                                 f"{sp_group.world_size} ranks")
         rows: list[torch.Tensor] = []
         for reference in references:
             if reference.media_type == "audio":
@@ -120,7 +136,10 @@ class MiniMaxH3LatentPreparationStage(PipelineStage):
                     raise ValueError("MiniMax-H3 reference video frames are missing.")
                 frames = reference.frames[:trim_reference_num_frames(reference.frames.shape[0])]
                 pixels = torch.from_numpy(np.ascontiguousarray(frames)).permute(3, 0, 1, 2)[None]
-                posterior = self.vae.encode_pixels(pixels).latent_dist
+                if parallel_group is not None:
+                    posterior = encode_pixels_parallel(self.vae, pixels, parallel_group).latent_dist
+                else:
+                    posterior = self.vae.encode_pixels(pixels).latent_dist
                 latents = self.vae.normalize_latents(_sample_visual_posterior(posterior).to(
                     torch.float16).float()).cpu()
             reference.num_latent_frames = int(latents.shape[2])
@@ -201,7 +220,7 @@ class MiniMaxH3LatentPreparationStage(PipelineStage):
         vae_device = get_local_torch_device()
         self.vae.to(vae_device)
         try:
-            video_rows = self._encode_visual_rows(references, vae_device)
+            video_rows = self._encode_visual_rows(references, vae_device, fastvideo_args)
         finally:
             if fastvideo_args.vae_cpu_offload:
                 self.vae.to("cpu")

fastvideo/profiler.py

@@ -38,6 +38,25 @@ logger = init_logger(__name__)
 _GLOBAL_CONTROLLER: TorchProfilerController | None = None
 
 
+@contextlib.contextmanager
+def nvtx_range(name: str):
+    """Emit one optional NVTX range for an external CUDA profiler.
+
+    ``FASTVIDEO_NVTX_PROFILE=1`` enables the marker. The context manager stays
+    a no-op without CUDA so call sites can remain shared with CPU tests.
+    """
+    enabled = envs.FASTVIDEO_NVTX_PROFILE and torch.cuda.is_available()
+    if not enabled:
+        yield
+        return
+
+    torch.cuda.nvtx.range_push(name)
+    try:
+        yield
+    finally:
+        torch.cuda.nvtx.range_pop()
+
+
 @dataclass(frozen=True)
 class ProfilerRegion:
     """Metadata describing a profiler region."""

fastvideo/tests/attention/test_vsa_h3_metadata.py

@@ -5,20 +5,29 @@ reference. The same reference doubles as the GPU kernel parity oracle."""
 
 import math
 
+import pytest
 import torch
 import torch.nn.functional as F
 
 from fastvideo.attention.backends.video_sparse_attn_h3 import (_TILE_ELEMS, MiniMaxH3VSAImpl,
                                                                MiniMaxH3VSAMetadataBuilder, _build_block_mask,
-                                                               _pool_tiles, token_tile_and_valid)
+                                                               _pool_tiles, _validate_h3_tile_geometry,
+                                                               token_tile_and_valid)
 
 _720P = dict(raw_latent_shape=(30, 44, 80), patch_size=(1, 2, 2), prefix_segments=(512, 1760, 400))
 _TINY = dict(raw_latent_shape=(8, 8, 12), patch_size=(1, 2, 2), prefix_segments=(7, 5, 3))
+# (4,4,4) coverage: dit grid (9, 10, 13) is ragged in all three dims
+# (t: 4+4+1, h: 4+4+2, w: 4+4+4+1) and every prefix segment leaves a
+# partial tail tile at 64 (70 -> 64+6, 5 -> 5, 130 -> 64+64+2).
+_TINY64 = dict(raw_latent_shape=(9, 20, 26), patch_size=(1, 2, 2), prefix_segments=(70, 5, 130))
+# production-shape request: 768x1344, 124 frames -> latents (37, 48, 84),
+# patch (1,2,2) -> token grid (37, 24, 42); text 300 + audio 414 rows.
+_PROD = dict(raw_latent_shape=(37, 48, 84), patch_size=(1, 2, 2), prefix_segments=(300, 0, 414))
 
 _CPU = torch.device("cpu")
 
 
-def _build(spec, sparsity=0.0, device=_CPU):
+def _build(spec, sparsity=0.0, device=_CPU, tile_size=_TILE_ELEMS):
     return MiniMaxH3VSAMetadataBuilder().build(
         current_timestep=0,
         raw_latent_shape=spec["raw_latent_shape"],
@@ -26,6 +35,7 @@ def _build(spec, sparsity=0.0, device=_CPU):
         VSA_sparsity=sparsity,
         prefix_segments=spec["prefix_segments"],
         device=device,
+        tile_size=tile_size,
     )
 
 
@@ -36,7 +46,7 @@ def _impl():
 def reference_sparse_attention(query, key, value, mask, meta):
     """Token-level oracle: SDPA over the padded tile buffer with the block
     mask expanded to tokens. query/key/value: tiled [B, S_pad, H, D]."""
-    token_tile, token_valid = token_tile_and_valid(meta.variable_block_sizes)
+    token_tile, token_valid = token_tile_and_valid(meta.variable_block_sizes, meta.tile_elems)
     out = torch.empty_like(query)
     for b in range(query.shape[0]):
         for h in range(query.shape[2]):
@@ -133,9 +143,117 @@ def test_prefix_queries_stay_dense_at_high_sparsity():
             "video rows should actually be sparse at 75%"
 
 
+# ---------------------------------------------------------------------------
+# 64-token (4,4,4) tile geometry
+# ---------------------------------------------------------------------------
+
+
+def test_geometry_tile64_ragged_tails():
+    """Hand-computed (4,4,4) oracle on a grid ragged in all three dims."""
+    meta = _build(_TINY64, tile_size=64)
+    assert meta.tile_elems == 64
+    t, h, w = 9, 10, 13  # raw latents (9, 20, 26) under patch (1, 2, 2)
+    n_t, n_h, n_w = 3, 3, 4
+    prefix_len = sum(_TINY64["prefix_segments"])
+    seq = prefix_len + t * h * w
+    assert meta.total_seq_length == seq
+    assert meta.num_prefix_tiles == 2 + 1 + 3
+    assert meta.num_video_tiles == n_t * n_h * n_w
+    assert int(meta.variable_block_sizes.sum()) == seq
+    assert int(meta.variable_block_sizes.max()) <= 64
+    assert meta.variable_block_sizes[:meta.num_prefix_tiles].tolist() == [64, 6, 5, 64, 64, 2]
+
+    # per-tile valid sizes: product of the per-dim clamped tails
+    expected = torch.tensor([
+        min(4, t - 4 * tt) * min(4, h - 4 * hh) * min(4, w - 4 * ww) for tt in range(n_t) for hh in range(n_h)
+        for ww in range(n_w)
+    ],
+                            dtype=torch.long)
+    assert torch.equal(meta.variable_block_sizes[meta.num_prefix_tiles:], expected)
+    assert int(expected.min()) == 1 * 2 * 1  # the (t,h,w) ragged corner
+
+    # every packed video row lands in the 3D tile its (t,h,w) coordinate says
+    idx = meta.untile_combined_index
+    row = torch.arange(t * h * w)
+    row_t, row_h, row_w = row // (h * w), (row // w) % h, row % w
+    expected_tile = meta.num_prefix_tiles + ((row_t // 4) * n_h + row_h // 4) * n_w + row_w // 4
+    assert torch.equal(idx[prefix_len:] // 64, expected_tile)
+    # and in a non-pad slot of that tile
+    assert bool((idx % 64 < meta.variable_block_sizes[idx // 64]).all())
+
+    # untile(tile(x)) == x on the 64-wide padded buffer
+    x = torch.randn(1, seq, 2, 4)
+    buf = _impl().tile(x, meta)
+    assert buf.shape[1] == meta.variable_block_sizes.numel() * 64
+    assert torch.equal(buf[:, idx], x)
+
+
+def test_geometry_tile64_production_shape():
+    """Production latents (37, 48, 84): ragged t and w tails at (4,4,4)."""
+    meta64 = _build(_PROD, tile_size=64)
+    assert meta64.num_prefix_tiles == 5 + 7  # 300 -> 4x64+44, 414 -> 6x64+30
+    assert meta64.num_video_tiles == 10 * 6 * 11  # (37, 24, 42) / (4, 4, 4)
+    assert meta64.total_seq_length == 300 + 414 + 37 * 24 * 42
+    assert int(meta64.variable_block_sizes.sum()) == meta64.total_seq_length
+    sizes_vid = meta64.variable_block_sizes[meta64.num_prefix_tiles:]
+    assert int(sizes_vid.max()) == 64 and int(sizes_vid.min()) == 1 * 4 * 2  # (t, w) ragged corner
+
+    # same packed sequence under the default 256 geometry, fewer tiles
+    meta256 = _build(_PROD)
+    assert meta256.tile_elems == _TILE_ELEMS
+    assert meta256.num_prefix_tiles == 2 + 2
+    assert meta256.num_video_tiles == 10 * 3 * 6
+    assert meta256.total_seq_length == meta64.total_seq_length
+
+    x = torch.randn(1, meta64.total_seq_length, 2, 4)
+    buf = _impl().tile(x, meta64)
+    assert torch.equal(buf[:, meta64.untile_combined_index], x)
+
+
+def test_sparsity_zero_matches_dense_sdpa_tile64():
+    torch.manual_seed(2)
+    meta = _build(_TINY64, tile_size=64)
+    seq = meta.total_seq_length
+    q, k, v = (torch.randn(1, seq, 2, 8) for _ in range(3))
+    impl = _impl()
+    tq, tk, tv = (impl.tile(t, meta).clone() for t in (q, k, v))
+
+    scores = torch.matmul(_pool_tiles(tq, meta.variable_block_sizes, meta.tile_elems),
+                          _pool_tiles(tk, meta.variable_block_sizes, meta.tile_elems).transpose(-2, -1))
+    mask = _build_block_mask(scores, meta.num_prefix_tiles, meta.num_video_tiles, 0.0, exempt=True)
+    sparse_out = impl.postprocess_output(reference_sparse_attention(tq, tk, tv, mask, meta), meta)
+
+    dense_out = F.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)).transpose(1, 2)
+    assert torch.allclose(sparse_out, dense_out, atol=1e-5), (sparse_out - dense_out).abs().max()
+
+
+def test_geometry_guard_enforces_tile64_bound():
+    """A 65-token tile passes the 256 bound but must fail the 64 one."""
+    meta = _build(_TINY64, tile_size=64)
+    prefix = tuple(s for s in _TINY64["prefix_segments"] if s > 0)
+    dit_shape = (9, 10, 13)
+    sizes = meta.variable_block_sizes.clone()
+    sizes[0] = 65
+    with pytest.raises(ValueError, match="tile sizes out of bounds"):
+        _validate_h3_tile_geometry(prefix, dit_shape, sizes, meta.untile_combined_index, 64)
+    # the untampered tile-64 geometry passes its own bound
+    _validate_h3_tile_geometry(prefix, dit_shape, meta.variable_block_sizes, meta.untile_combined_index, 64)
+
+
+def test_builder_rejects_unknown_tile_size():
+    for bad in (0, 128, 512):
+        with pytest.raises(ValueError, match="tile_size"):
+            _build(_TINY, tile_size=bad)
+
+
 if __name__ == "__main__":
     test_geometry_720p()
     test_mask_policy()
     test_sparsity_zero_matches_dense_sdpa()
     test_prefix_queries_stay_dense_at_high_sparsity()
+    test_geometry_tile64_ragged_tails()
+    test_geometry_tile64_production_shape()
+    test_sparsity_zero_matches_dense_sdpa_tile64()
+    test_geometry_guard_enforces_tile64_bound()
+    test_builder_rejects_unknown_tile_size()
     print("all VSA-H3 CPU checks passed")

fastvideo/tests/attention/test_vsa_h3_sm100a_route.py

@@ -0,0 +1,181 @@
+# SPDX-License-Identifier: Apache-2.0
+"""CPU checks for the VSA-H3 tile-64 sm_100a route selection.
+
+The opt-in third kernel route (``FASTVIDEO_VSA_SM100A=1``) must (a) stay off by
+default, (b) engage only when the extension is present, the device qualifies,
+and the forward carries no grad, and (c) fall back to the Triton-64 entry with
+one warning when the env is set but a precondition fails. All device/extension
+probes are monkeypatched; no GPU or kernel install needed.
+"""
+
+import pytest
+import torch
+
+import fastvideo.attention.backends.video_sparse_attn_h3 as vsa_h3
+from fastvideo.attention.backends.video_sparse_attn_h3 import (VSA_SM100A_ENV, MiniMaxH3VSAImpl,
+                                                               MiniMaxH3VSAMetadataBuilder, _sm100a_unavailable_reason)
+
+# Small tile-64 geometry: 2 prefix segments + a (4,4,8)-token video grid.
+_SPEC = dict(raw_latent_shape=(4, 8, 16), patch_size=(1, 2, 2), prefix_segments=(70, 30))
+_HEADS, _DIM = 2, 128
+
+
+def _build_meta():
+    return MiniMaxH3VSAMetadataBuilder().build(
+        current_timestep=0,
+        raw_latent_shape=_SPEC["raw_latent_shape"],
+        patch_size=_SPEC["patch_size"],
+        VSA_sparsity=0.0,
+        prefix_segments=_SPEC["prefix_segments"],
+        device=torch.device("cpu"),
+        tile_size=64,
+    )
+
+
+def _tiled_qkv(meta, requires_grad=False):
+    # bf16 like the real tiled buffers, so forward()'s dtype-cast warning
+    # stays out of the warning assertions below.
+    s_pad = meta.variable_block_sizes.numel() * 64
+    return tuple(
+        torch.randn(1, s_pad, _HEADS, _DIM, dtype=torch.bfloat16, requires_grad=requires_grad) for _ in range(3))
+
+
+class _FakeSm100a:
+    """Stands in for fastvideo_kernel.block_sparse_attn_sm100a."""
+
+    def __init__(self, supported=True):
+        self.supported = supported
+        self.calls = []
+
+    def is_supported(self, q, variable_block_sizes):
+        return self.supported
+
+    def block_sparse_attn_sm100a(self, q, k, v, q2k_idx, q2k_num, variable_block_sizes, need_lse=True):
+        self.calls.append(dict(q=q, q2k_idx=q2k_idx, q2k_num=q2k_num, vbs=variable_block_sizes,
+                               need_lse=need_lse))
+        return q.clone(), None
+
+
+def _fake_map_to_index(block_map):
+    """Pure-torch stand-in for the Triton map_to_index (same contract)."""
+    b, h, t, n = block_map.shape
+    idx = torch.full((b, h, t, n), -1, dtype=torch.int32)
+    num = block_map.sum(dim=-1, dtype=torch.int32)
+    for bi in range(b):
+        for hi in range(h):
+            for ti in range(t):
+                cols = torch.nonzero(block_map[bi, hi, ti], as_tuple=False).flatten()
+                idx[bi, hi, ti, :cols.numel()] = cols.to(torch.int32)
+    return idx, num
+
+
+class _FakeTriton:
+    def __init__(self):
+        self.calls = 0
+
+    def __call__(self, q, k, v, mask, variable_block_sizes):
+        self.calls += 1
+        return q.clone(), None
+
+
+@pytest.fixture()
+def routed(monkeypatch):
+    """Backend with both kernel entries faked; returns (fakes, run)."""
+    fake_sm = _FakeSm100a()
+    fake_triton = _FakeTriton()
+    monkeypatch.setattr(vsa_h3, "_sm100a", fake_sm)
+    monkeypatch.setattr(vsa_h3, "block_sparse_attn_64_bhsd", fake_triton)
+    monkeypatch.setattr(vsa_h3, "map_to_index", _fake_map_to_index)
+    meta = _build_meta()
+    impl = MiniMaxH3VSAImpl(num_heads=_HEADS, head_size=_DIM, causal=False, softmax_scale=_DIM**-0.5)
+
+    def run(requires_grad=False):
+        q, k, v = _tiled_qkv(meta, requires_grad=requires_grad)
+        return impl.forward(q, k, v, None, meta)
+
+    return fake_sm, fake_triton, run, meta
+
+
+def test_reason_covers_every_precondition():
+    q = torch.randn(1, _HEADS, 128, _DIM)
+    vbs = torch.full((2, ), 64, dtype=torch.long)
+    assert "not installed" in _sm100a_unavailable_reason(None, q, vbs, grad_mode=False)
+    ok = _FakeSm100a(supported=True)
+    assert "forward-only" in _sm100a_unavailable_reason(ok, q, vbs, grad_mode=True)
+    bad = _FakeSm100a(supported=False)
+    assert "is_supported" in _sm100a_unavailable_reason(bad, q, vbs, grad_mode=False)
+    assert _sm100a_unavailable_reason(ok, q, vbs, grad_mode=False) is None
+
+
+def test_default_off_routes_triton(routed, monkeypatch):
+    fake_sm, fake_triton, run, _ = routed
+    monkeypatch.delenv(VSA_SM100A_ENV, raising=False)
+    run()
+    assert fake_triton.calls == 1
+    assert fake_sm.calls == []
+
+
+def test_env_on_routes_sm100a_with_index_metadata(routed, monkeypatch):
+    fake_sm, fake_triton, run, meta = routed
+    monkeypatch.setenv(VSA_SM100A_ENV, "1")
+    out = run()
+    assert fake_triton.calls == 0
+    assert len(fake_sm.calls) == 1
+    call = fake_sm.calls[0]
+    n_tiles = meta.variable_block_sizes.numel()
+    # sparsity 0 -> all-True mask -> every row's count is n_tiles
+    assert call["q2k_num"].dtype == torch.int32 and (call["q2k_num"] == n_tiles).all()
+    assert call["q2k_idx"].shape[-1] == n_tiles and call["q2k_idx"].dtype == torch.int32
+    assert call["vbs"].dtype == torch.int32
+    assert call["need_lse"] is False
+    # BHSD kernel result comes back in the backend's BSHD layout
+    assert out.shape == (1, n_tiles * 64, _HEADS, _DIM)
+
+
+def test_env_on_grad_inputs_fall_back_to_triton(routed, monkeypatch):
+    fake_sm, fake_triton, run, _ = routed
+    monkeypatch.setenv(VSA_SM100A_ENV, "1")
+    run(requires_grad=True)
+    assert fake_triton.calls == 1
+    assert fake_sm.calls == []
+    # ...but the same process still routes no-grad forwards to sm_100a
+    run(requires_grad=False)
+    assert len(fake_sm.calls) == 1
+
+
+def test_env_on_unsupported_warns_once_and_falls_back(routed, monkeypatch):
+    fake_sm, fake_triton, run, _ = routed
+    fake_sm.supported = False
+    monkeypatch.setenv(VSA_SM100A_ENV, "1")
+    warnings = []
+    monkeypatch.setattr(vsa_h3.logger, "warning_once", warnings.append)
+    run()
+    run()
+    assert fake_triton.calls == 2
+    assert fake_sm.calls == []
+    assert len(warnings) == 2  # warning_once dedups by message; both carry the same one line
+    assert warnings[0] == warnings[1]
+    assert VSA_SM100A_ENV in warnings[0] and "is_supported" in warnings[0]
+
+
+def test_env_on_missing_module_warns_and_falls_back(routed, monkeypatch):
+    fake_sm, fake_triton, run, _ = routed
+    monkeypatch.setattr(vsa_h3, "_sm100a", None)
+    monkeypatch.setenv(VSA_SM100A_ENV, "1")
+    warnings = []
+    monkeypatch.setattr(vsa_h3.logger, "warning_once", warnings.append)
+    run()
+    assert fake_triton.calls == 1
+    assert warnings and "not installed" in warnings[0]
+
+
+def test_env_on_no_grad_context_detaches_route_from_leaf_flags(routed, monkeypatch):
+    """A requires_grad leaf under torch.no_grad() is still a no-grad forward."""
+    fake_sm, fake_triton, run, meta = routed
+    monkeypatch.setenv(VSA_SM100A_ENV, "1")
+    impl = MiniMaxH3VSAImpl(num_heads=_HEADS, head_size=_DIM, causal=False, softmax_scale=_DIM**-0.5)
+    q, k, v = _tiled_qkv(meta, requires_grad=True)
+    with torch.no_grad():
+        impl.forward(q, k, v, None, meta)
+    assert len(fake_sm.calls) == 1
+    assert fake_triton.calls == 0

fastvideo/tests/contract/test_profiler_regions.py

@@ -15,6 +15,12 @@ import json
 import os
 import subprocess
 import sys
+from unittest.mock import Mock
+
+import pytest
+import torch
+
+from fastvideo.profiler import nvtx_range
 
 # Five-window child: ops before any region, inside a region, between regions,
 # inside a second (short-named) region, after the last region. Exits without
@@ -105,3 +111,73 @@ def test_noop_without_profiler_dir(tmp_path):
     proc = subprocess.run([sys.executable, "-c", child], env=env,
                           capture_output=True, text=True, timeout=300)
     assert proc.returncode == 0, proc.stderr
+
+
+def test_nvtx_range_disabled_is_noop(monkeypatch):
+    """Keep CUDA NVTX untouched when external profiling is disabled."""
+    range_push = Mock()
+    range_pop = Mock()
+    monkeypatch.setenv("FASTVIDEO_NVTX_PROFILE", "0")
+    monkeypatch.setattr(torch.cuda, "is_available", lambda: True)
+    monkeypatch.setattr(torch.cuda.nvtx, "range_push", range_push)
+    monkeypatch.setattr(torch.cuda.nvtx, "range_pop", range_pop)
+
+    with nvtx_range("disabled"):
+        body_executed = True
+
+    assert body_executed is True
+    range_push.assert_not_called()
+    range_pop.assert_not_called()
+
+
+def test_nvtx_range_without_cuda_is_noop(monkeypatch):
+    """Keep NVTX untouched when profiling is enabled on a CPU-only process."""
+    range_push = Mock()
+    range_pop = Mock()
+    monkeypatch.setenv("FASTVIDEO_NVTX_PROFILE", "1")
+    monkeypatch.setattr(torch.cuda, "is_available", lambda: False)
+    monkeypatch.setattr(torch.cuda.nvtx, "range_push", range_push)
+    monkeypatch.setattr(torch.cuda.nvtx, "range_pop", range_pop)
+
+    with nvtx_range("cpu-only"):
+        body_executed = True
+
+    assert body_executed is True
+    range_push.assert_not_called()
+    range_pop.assert_not_called()
+
+
+def test_nvtx_range_enabled_orders_push_body_pop(monkeypatch):
+    """Place the profiled body between one matching NVTX push and pop."""
+    events = []
+    monkeypatch.setenv("FASTVIDEO_NVTX_PROFILE", "1")
+    monkeypatch.setattr(torch.cuda, "is_available", lambda: True)
+    monkeypatch.setattr(torch.cuda.nvtx, "range_push", lambda name: events.append(("push", name)))
+    monkeypatch.setattr(torch.cuda.nvtx, "range_pop", lambda: events.append(("pop", None)))
+
+    with nvtx_range("minimax_h3.test"):
+        events.append(("body", None))
+
+    assert events == [
+        ("push", "minimax_h3.test"),
+        ("body", None),
+        ("pop", None),
+    ]
+
+
+def test_nvtx_range_body_exception_pops_and_propagates(monkeypatch):
+    """Balance the NVTX stack while preserving a body exception."""
+    events = []
+    monkeypatch.setenv("FASTVIDEO_NVTX_PROFILE", "1")
+    monkeypatch.setattr(torch.cuda, "is_available", lambda: True)
+    monkeypatch.setattr(torch.cuda.nvtx, "range_push", lambda name: events.append(("push", name)))
+    monkeypatch.setattr(torch.cuda.nvtx, "range_pop", lambda: events.append(("pop", None)))
+
+    with pytest.raises(RuntimeError, match="profile body failed"):
+        with nvtx_range("minimax_h3.failure"):
+            raise RuntimeError("profile body failed")
+
+    assert events == [
+        ("push", "minimax_h3.failure"),
+        ("pop", None),
+    ]

fastvideo/tests/encoders/test_minimax_h3_qwen3_vl_checkpoint_fp8.py

@@ -0,0 +1,281 @@
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import json
+import os
+
+import pytest
+import torch
+
+os.environ.setdefault("MASTER_ADDR", "localhost")
+os.environ.setdefault("MASTER_PORT", "29514")
+
+import fastvideo.models.encoders.minimax_h3_checkpoint_fp8 as h3_fp8
+from fastvideo.configs.models.encoders.minimax_h3_qwen3_vl import MiniMaxH3Qwen3VLConfig
+from fastvideo.layers.linear import ColumnParallelLinear, UnquantizedLinearMethod
+from fastvideo.layers.vocab_parallel_embedding import UnquantizedEmbeddingMethod, VocabParallelEmbedding
+from fastvideo.models.encoders.base import TextEncoder
+from fastvideo.models.encoders.minimax_h3_checkpoint_fp8 import (
+    MiniMaxH3SerializedFP8Config,
+    MiniMaxH3SerializedFP8LinearMethod,
+)
+from fastvideo.models.encoders.minimax_h3_qwen3_vl import MiniMaxH3Qwen3VLConditioner
+from fastvideo.models.loader.text_encoder_quantization import (
+    _configure_text_encoder_quantization,
+    _process_quantized_text_encoder_weights,
+    _read_text_encoder_checkpoint_quantization_config,
+)
+
+
+def _checkpoint_quantization_config(**overrides) -> dict:
+    config = {
+        "quant_method": "fp8",
+        "activation_scheme": "dynamic",
+        "fmt": "e4m3",
+        "weight_block_size": [128, 128],
+        "modules_to_not_convert": ["model.visual", "lm_head"],
+    }
+    config.update(overrides)
+    return config
+
+
+def test_h3_accepts_only_the_serialized_blockwise_checkpoint_contract() -> None:
+    config = MiniMaxH3SerializedFP8Config.from_config(_checkpoint_quantization_config())
+    assert config.weight_block_size == (128, 128)
+    assert config.get_supported_act_dtypes() == [torch.bfloat16]
+
+    with pytest.raises(ValueError, match=r"weight_block_size=\[128, 128\]"):
+        MiniMaxH3SerializedFP8Config.from_config(
+            _checkpoint_quantization_config(weight_block_size=[1, 128]))
+    with pytest.raises(ValueError, match="dynamic activation"):
+        MiniMaxH3SerializedFP8Config.from_config(
+            _checkpoint_quantization_config(activation_scheme="static"))
+    with pytest.raises(ValueError, match="vision stack"):
+        MiniMaxH3SerializedFP8Config.from_config(
+            _checkpoint_quantization_config(modules_to_not_convert=["lm_head"]))
+    with pytest.raises(ValueError, match="partially quantized language"):
+        MiniMaxH3SerializedFP8Config.from_config(
+            _checkpoint_quantization_config(modules_to_not_convert=["model.visual", "language_model.layers.3"]))
+
+
+def test_serialized_fp8_allocates_checkpoint_weight_and_scale_without_requantization(distributed_setup) -> None:
+    config = MiniMaxH3SerializedFP8Config.from_config(_checkpoint_quantization_config())
+    layer = ColumnParallelLinear(
+        input_size=128,
+        output_size=256,
+        bias=False,
+        quant_config=config,
+        prefix="minimax_h3_qwen3_vl.language_model.layers.0.self_attn.q_proj",
+    )
+
+    assert isinstance(layer.quant_method, MiniMaxH3SerializedFP8LinearMethod)
+    assert layer.weight.dtype == torch.float8_e4m3fn
+    assert layer.weight.shape == (256, 128)
+    assert layer.weight_scale_inv.dtype == torch.float32
+    assert layer.weight_scale_inv.shape == (2, 1)
+
+    layer.weight.data.zero_()
+    layer.weight_scale_inv.data.fill_(0.25)
+    weight_pointer = layer.weight.data_ptr()
+    scale_pointer = layer.weight_scale_inv.data_ptr()
+    layer.quant_method.process_weights_after_loading(layer)
+
+    assert layer.weight.data_ptr() == weight_pointer
+    assert layer.weight_scale_inv.data_ptr() == scale_pointer
+    assert not hasattr(layer, "_fp8_weight")
+
+
+def test_serialized_fp8_quantizes_only_language_linears(distributed_setup) -> None:
+    config = MiniMaxH3SerializedFP8Config.from_config(_checkpoint_quantization_config())
+    visual_linear = ColumnParallelLinear(
+        input_size=128,
+        output_size=128,
+        bias=False,
+        quant_config=config,
+        prefix="minimax_h3_qwen3_vl.visual.blocks.0.attn.proj",
+    )
+    embedding = VocabParallelEmbedding(
+        num_embeddings=128,
+        embedding_dim=128,
+        org_num_embeddings=128,
+        quant_config=config,
+        prefix="minimax_h3_qwen3_vl.language_model.embed_tokens",
+    )
+
+    assert isinstance(visual_linear.quant_method, UnquantizedLinearMethod)
+    assert visual_linear.weight.dtype == torch.get_default_dtype()
+    assert isinstance(embedding.quant_method, UnquantizedEmbeddingMethod)
+    assert embedding.weight.dtype == torch.get_default_dtype()
+
+
+def test_serialized_fp8_cpu_execution_fails_closed(distributed_setup) -> None:
+    config = MiniMaxH3SerializedFP8Config.from_config(_checkpoint_quantization_config())
+    layer = ColumnParallelLinear(
+        input_size=128,
+        output_size=128,
+        bias=False,
+        quant_config=config,
+        prefix="minimax_h3_qwen3_vl.language_model.layers.0.mlp.up_proj",
+    )
+    layer.weight.data.zero_()
+    layer.weight_scale_inv.data.fill_(1.0)
+    assert isinstance(layer.quant_method, MiniMaxH3SerializedFP8LinearMethod)
+    layer.quant_method.process_weights_after_loading(layer)
+
+    with pytest.raises(RuntimeError, match="requires CUDA"):
+        layer(torch.zeros(2, 128, dtype=torch.bfloat16))
+
+
+def test_runtime_preflight_reports_capability_and_missing_dependencies(monkeypatch) -> None:
+    config = MiniMaxH3SerializedFP8Config.from_config(_checkpoint_quantization_config())
+    monkeypatch.setattr(torch.cuda, "get_device_capability", lambda device: (8, 0))
+    with pytest.raises(RuntimeError, match="sm100 or newer"):
+        config.validate_runtime(torch.device("cuda"))
+
+    monkeypatch.setattr(torch.cuda, "get_device_capability", lambda device: (10, 0))
+
+    def missing_quantizer() -> None:
+        raise RuntimeError("SGLang-compatible Triton quantizer is missing")
+
+    monkeypatch.setattr(h3_fp8, "_require_sglang_per_token_group_fp8_quantization", missing_quantizer)
+    with pytest.raises(RuntimeError, match="Triton quantizer is missing"):
+        config.validate_runtime(torch.device("cuda"))
+
+    monkeypatch.setattr(h3_fp8, "_require_sglang_per_token_group_fp8_quantization", lambda: None)
+
+    def missing_flashinfer():
+        raise RuntimeError("FlashInfer groupwise GEMM is missing")
+
+    monkeypatch.setattr(h3_fp8, "_get_flashinfer_groupwise_fp8_gemm", missing_flashinfer)
+    with pytest.raises(RuntimeError, match="FlashInfer groupwise GEMM is missing"):
+        config.validate_runtime(torch.device("cuda"))
+
+
+def test_loader_detects_and_capability_gates_checkpoint_metadata(tmp_path) -> None:
+    checkpoint_config = _checkpoint_quantization_config()
+    (tmp_path / "config.json").write_text(
+        json.dumps({"quantization_config": checkpoint_config}),
+        encoding="utf-8",
+    )
+
+    assert _read_text_encoder_checkpoint_quantization_config(str(tmp_path)) == checkpoint_config
+    model_config = MiniMaxH3Qwen3VLConfig()
+    quant_config = _configure_text_encoder_quantization(
+        model_config,
+        MiniMaxH3Qwen3VLConditioner,
+        str(tmp_path),
+    )
+    assert isinstance(quant_config, MiniMaxH3SerializedFP8Config)
+    assert model_config.quant_config is quant_config
+
+    unsupported_config = MiniMaxH3Qwen3VLConfig()
+    with pytest.raises(ValueError, match="does not support serialized 'fp8'"):
+        _configure_text_encoder_quantization(
+            unsupported_config,
+            TextEncoder,
+            str(tmp_path),
+        )
+
+
+def test_loader_leaves_bf16_checkpoint_path_unchanged(tmp_path) -> None:
+    (tmp_path / "config.json").write_text(json.dumps({"architectures": ["Qwen3VLModel"]}), encoding="utf-8")
+    model_config = MiniMaxH3Qwen3VLConfig()
+
+    quant_config = _configure_text_encoder_quantization(
+        model_config,
+        MiniMaxH3Qwen3VLConditioner,
+        str(tmp_path),
+    )
+
+    assert quant_config is None
+    assert model_config.quant_config is None
+
+
+def test_post_load_processing_visits_only_serialized_fp8_linears(distributed_setup) -> None:
+    config = MiniMaxH3SerializedFP8Config.from_config(_checkpoint_quantization_config())
+    quantized = ColumnParallelLinear(
+        input_size=128,
+        output_size=128,
+        bias=False,
+        quant_config=config,
+        prefix="minimax_h3_qwen3_vl.language_model.layers.0.self_attn.q_proj",
+    )
+    plain = ColumnParallelLinear(
+        input_size=128,
+        output_size=128,
+        bias=False,
+        prefix="plain",
+    )
+    quantized.weight.data.zero_()
+    quantized.weight_scale_inv.data.fill_(1.0)
+    model = torch.nn.ModuleList([quantized, plain])
+
+    assert _process_quantized_text_encoder_weights(model, torch.device("cpu")) == 1
+    assert quantized.weight.device.type == "cpu"
+    assert plain.weight.device.type == "cpu"
+
+
+def test_flashinfer_groupwise_path_pins_output_dtype_and_trtllm_scale_layout(monkeypatch) -> None:
+    input_tensor = torch.zeros(2, 256, dtype=torch.bfloat16)
+    weight = torch.zeros(128, 256, dtype=torch.float8_e4m3fn)
+    weight_scale = torch.ones(1, 2, dtype=torch.float32)
+    quantized_input = torch.zeros_like(input_tensor, dtype=torch.float8_e4m3fn)
+    input_scale = torch.empty(2, 2, dtype=torch.float32).t()
+    input_scale.fill_(1.0)
+    receipt: dict[str, object] = {}
+
+    def fake_quantize(
+        value: torch.Tensor,
+        group_size: int,
+        *,
+        column_major_scales: bool,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        assert value.data_ptr() == input_tensor.data_ptr()
+        assert value.shape == input_tensor.shape
+        assert group_size == 128
+        assert column_major_scales is True
+        return quantized_input, input_scale
+
+    def fake_gemm(
+        activation: torch.Tensor,
+        checkpoint_weight: torch.Tensor,
+        activation_scale: torch.Tensor,
+        checkpoint_scale: torch.Tensor,
+        *,
+        out_dtype: torch.dtype,
+        backend: str,
+    ) -> torch.Tensor:
+        receipt.update(
+            activation=activation,
+            checkpoint_weight=checkpoint_weight,
+            activation_scale=activation_scale,
+            checkpoint_scale=checkpoint_scale,
+            out_dtype=out_dtype,
+            backend=backend,
+        )
+        return torch.zeros(activation.shape[0], checkpoint_weight.shape[0], dtype=out_dtype)
+
+    monkeypatch.setattr(h3_fp8, "_get_flashinfer_groupwise_backend", lambda device: "trtllm")
+    monkeypatch.setattr(h3_fp8, "_sglang_per_token_group_quant_fp8", fake_quantize)
+    monkeypatch.setattr(h3_fp8, "_get_flashinfer_groupwise_fp8_gemm", lambda: fake_gemm)
+
+    previous_default_dtype = torch.get_default_dtype()
+    torch.set_default_dtype(torch.float32)
+    try:
+        output = h3_fp8._flashinfer_gemm_w8a8_block_fp8_linear_with_fallback(
+            input_tensor,
+            weight,
+            (128, 128),
+            weight_scale,
+        )
+        assert torch.get_default_dtype() == torch.float32
+    finally:
+        torch.set_default_dtype(previous_default_dtype)
+
+    assert output.dtype == torch.bfloat16
+    assert receipt["out_dtype"] == torch.bfloat16
+    assert receipt["backend"] == "trtllm"
+    assert receipt["activation"] is quantized_input
+    assert receipt["checkpoint_weight"] is weight
+    assert receipt["checkpoint_scale"] is weight_scale
+    assert receipt["activation_scale"] is input_scale

fastvideo/tests/encoders/test_minimax_h3_qwen3_vl_truncation.py

@@ -1,27 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
-"""The Qwen3-VL stack is built only as far as MiniMax H3 reads.
-
-H3 conditions on one intermediate hidden state. The layers above it were built,
-weight-loaded and then discarded, which is 13.7 GB in bf16 and the difference
-between fitting and not fitting on a 121 GB unified-memory device.
-
-The dangerous part is not the truncation, it is getting the tuple index wrong.
-`hidden_states` records each layer's *input*, so entry N is the output of layer
-N-1, and the final entry comes from the norm that sits above the whole stack. A
-truncated stack that still applies that norm puts a normalised tensor where the
-raw one belongs: the length check in the conditioning stage still passes, and
-conditioning silently changes. These tests pin the index, the content, and the
-constant the two sides agree on.
-"""
+"""MiniMax-H3 Qwen3-VL layer truncation and slim-forward tests."""
 from __future__ import annotations
 
+import inspect
 import os
 
 import pytest
 import torch
 
-# Matches the other encoder tests: the module registry these build against wants
-# a process group, and a single-rank one needs a rendezvous address.
 os.environ.setdefault("MASTER_ADDR", "localhost")
 os.environ.setdefault("MASTER_PORT", "29513")
 
@@ -34,21 +20,15 @@ from fastvideo.pipelines.basic.minimax_h3.packing import MINIMAX_H3_TEXT_ENCODER
 
 
 def _small_arch(**overrides) -> MiniMaxH3Qwen3VLArchConfig:
-    """A stack small enough to run on CPU but shaped like the real one.
-
-    Everything goes through the constructor so ``__post_init__`` validates the
-    small shape the same way it validates the real one.
-    """
     kwargs: dict = dict(
         vocab_size=64,
         hidden_size=16,
         intermediate_size=32,
         num_hidden_layers=8,
+        output_hidden_state_index=5,
         num_attention_heads=2,
         num_key_value_heads=1,
         head_dim=8,
-        # __post_init__ reads the sections out of rope_scaling, and they must
-        # cover exactly half of each head.
         rope_scaling={
             "mrope_interleaved": True,
             "mrope_section": [2, 1, 1],
@@ -61,25 +41,27 @@ def _small_arch(**overrides) -> MiniMaxH3Qwen3VLArchConfig:
 
 
 def _small_config(**overrides) -> MiniMaxH3Qwen3VLConfig:
-    """The outer config, which is what the modules take.
-
-    ``ModelConfig.__getattr__`` forwards the architecture fields, so the modules
-    read ``prefix`` off this object and everything else off ``arch_config``.
-    """
     config = MiniMaxH3Qwen3VLConfig()
     config.arch_config = _small_arch(**overrides)
     return config
 
 
 def test_default_matches_the_index_the_pipeline_reads() -> None:
-    """The two sides cannot import each other, so pin them here instead.
+    config = MiniMaxH3Qwen3VLArchConfig()
 
-    `fastvideo/models/` must not import from `fastvideo/pipelines/`, so the tap
-    is written down twice. If they drift, conditioning reads a hidden state that
-    was never built and the run dies with an index error at generation time,
-    after a full model load.
-    """
-    assert MiniMaxH3Qwen3VLArchConfig().num_hidden_layers_override == MINIMAX_H3_TEXT_ENCODER_LAYER
+    assert config.output_hidden_state_index == MINIMAX_H3_TEXT_ENCODER_LAYER
+    assert config.num_hidden_layers_override == MINIMAX_H3_TEXT_ENCODER_LAYER
+
+
+def test_rejects_build_depth_that_cannot_reach_the_output() -> None:
+    for override in (0, 4):
+        with pytest.raises(ValueError, match="num_hidden_layers_override"):
+            _small_arch(num_hidden_layers_override=override)
+
+
+def test_rejects_output_index_above_the_checkpoint_depth() -> None:
+    with pytest.raises(ValueError, match="output_hidden_state_index"):
+        _small_arch(output_hidden_state_index=9, num_hidden_layers_override=None)
 
 
 def test_builds_only_up_to_the_override(distributed_setup) -> None:
@@ -87,7 +69,6 @@ def test_builds_only_up_to_the_override(distributed_setup) -> None:
 
     assert model.num_layers == 5
     assert len(model.layers) == 5
-    # The norm sits above the tap, so a truncated stack must not keep it.
     assert model.norm is None
 
 
@@ -98,104 +79,94 @@ def test_override_none_keeps_the_full_stack(distributed_setup) -> None:
     assert model.norm is not None
 
 
+def test_nominal_and_built_depths_remain_distinct(distributed_setup) -> None:
+    from fastvideo.models.encoders.minimax_h3_qwen3_vl import MiniMaxH3Qwen3VLConditioner
+
+    conditioner = MiniMaxH3Qwen3VLConditioner(_small_config(num_hidden_layers_override=5))
+
+    assert conditioner.num_hidden_layers == 8
+    assert conditioner.num_built_hidden_layers == 5
+
+
 def test_override_above_the_stack_does_not_over_build(distributed_setup) -> None:
-    # num_hidden_layers comes from the checkpoint's config.json via
-    # update_model_arch, so a smaller variant must clamp rather than ask for
-    # layers that do not exist.
     model = MiniMaxH3Qwen3VLLanguageModel(_small_config(num_hidden_layers_override=99))
 
     assert model.num_layers == 8
     assert model.norm is not None
 
 
-def test_override_equal_to_the_stack_keeps_the_norm(distributed_setup) -> None:
-    """The exact boundary of the clamp: a stack cut at its own depth is full.
-
-    A checkpoint with exactly ``override`` layers taps its final layer, whose
-    tuple entry sits after the norm in the full model, so the norm must stay
-    and nothing may be filtered from the checkpoint.
-    """
-    model = MiniMaxH3Qwen3VLLanguageModel(_small_config(num_hidden_layers_override=8))
-
-    assert model.num_layers == 8
-    assert model.norm is not None
-
-
-def test_non_positive_override_is_rejected() -> None:
-    """A non-positive override would build no decoder layers at all.
-
-    Worse, a negative one makes ``num_layers`` disagree with the built stack
-    and the surplus-key filter would then drop every layer key, so the
-    conditioner would load "successfully" with no transformer. Reject it at
-    config construction, and again when update_model_arch re-validates.
-    """
-    for override in (0, -1):
-        with pytest.raises(ValueError, match="num_hidden_layers_override"):
-            _small_arch(num_hidden_layers_override=override)
-
-    config = _small_config()
-    with pytest.raises(ValueError, match="num_hidden_layers_override"):
-        config.update_model_arch({"num_hidden_layers_override": 0})
-
-
 def test_tapped_hidden_state_is_unchanged_by_truncation(distributed_setup) -> None:
-    """The whole point: entry `tap` must be bit-identical either way."""
+    """The slim model returns the raw output at the selected layer."""
     tap = 5
     full = MiniMaxH3Qwen3VLLanguageModel(_small_config(num_hidden_layers_override=None))
     cut = MiniMaxH3Qwen3VLLanguageModel(_small_config(num_hidden_layers_override=tap))
 
-    # These modules allocate uninitialised storage and expect a checkpoint, so
-    # give them finite weights before running anything through them.
     torch.manual_seed(0)
     for parameter in full.parameters():
         parameter.data.normal_(std=0.02)
-    # Then make the shared prefix identical, which is the only part the tapped
-    # hidden state depends on.
     for (_, a), (_, b) in zip(full.layers[:tap].named_parameters(),
                               cut.layers[:tap].named_parameters(),
                               strict=True):
         b.data.copy_(a.data)
     torch.manual_seed(1)
     inputs_embeds = torch.randn(1, 6, 16)
-    # mRoPE indexes three axes (t, h, w); text tokens share the same position on
-    # all three.
     position_ids = torch.arange(6).view(1, 1, 6).expand(3, 1, 6)
     with torch.no_grad():
-        full_out = full(inputs_embeds, position_ids, None, True, None, None)
-        cut_out = cut(inputs_embeds, position_ids, None, True, None, None)
+        expected = inputs_embeds
+        position_embeddings = full.rotary_emb(inputs_embeds, position_ids)
+        for layer in full.layers[:tap]:
+            expected = layer(expected, position_embeddings, None)
+        full_out = full(inputs_embeds, position_ids, None, None, None)
+        cut_out = cut(inputs_embeds, position_ids, None, None, None)
 
-    assert torch.equal(full_out.hidden_states[tap], cut_out.hidden_states[tap])
-    # And the truncated model must not offer states it never computed.
-    assert len(cut_out.hidden_states) == tap + 1
-    # The whole shared prefix must match, not just the tap: this is the same
-    # comparison the production-loader parity gate runs against the official
-    # model, and it is what catches a truncated stack that still applied the
-    # final norm to its last entry.
-    for index, (cut_state, full_state) in enumerate(zip(cut_out.hidden_states, full_out.hidden_states,
-                                                        strict=False)):
-        assert torch.equal(cut_state, full_state), f"hidden state {index} changed under truncation"
+    assert torch.equal(expected, full_out)
+    assert torch.equal(expected, cut_out)
+
+
+def test_conditioning_stage_adapts_slim_sequence_output() -> None:
+    from fastvideo.pipelines.basic.minimax_h3.stages.minimax_h3_conditioning import MiniMaxH3ConditioningStage
+
+    class FakeConditioner:
+
+        dtype = torch.float32
+
+        def __call__(self, input_ids: torch.Tensor, **kwargs) -> torch.Tensor:
+            assert input_ids.ndim == 1
+            assert not kwargs
+            return torch.ones(input_ids.shape[0], 4)
+
+    stage = MiniMaxH3ConditioningStage(conditioner=FakeConditioner(), tokenizer=None, processor=None, ref2va=False)
+    embeddings, tags = stage._encode_tokens([1, 2, 3], [0, 0, 0], torch.device("cpu"))
+
+    assert embeddings.shape == (1, 3, 4)
+    assert tags.shape == (3, )
+
+
+def test_conditioner_exposes_only_the_slim_forward_contract() -> None:
+    from fastvideo.models.encoders.minimax_h3_qwen3_vl import MiniMaxH3Qwen3VLConditioner
+
+    assert tuple(inspect.signature(MiniMaxH3Qwen3VLConditioner.forward).parameters) == (
+        "self",
+        "input_ids",
+        "pixel_values",
+        "image_grid_thw",
+        "pixel_values_videos",
+        "video_grid_thw",
+    )
 
 
 def test_truncated_model_drops_the_surplus_checkpoint_keys(distributed_setup) -> None:
-    """The unexpected-key check is strict on purpose, so the surplus keys have
-    to be filtered rather than the check relaxed."""
     from fastvideo.models.encoders.minimax_h3_qwen3_vl import MiniMaxH3Qwen3VLConditioner
 
     conditioner = MiniMaxH3Qwen3VLConditioner(_small_config(num_hidden_layers_override=5))
 
-    assert conditioner._is_above_the_tap("language_model.layers.5.mlp.gate_proj.weight")
-    assert conditioner._is_above_the_tap("language_model.layers.7.self_attn.q_proj.weight")
-    assert conditioner._is_above_the_tap("language_model.norm.weight")
-    # Kept: layers we built, the embeddings, and the vision tower.
-    assert not conditioner._is_above_the_tap("language_model.layers.4.mlp.gate_proj.weight")
-    assert not conditioner._is_above_the_tap("language_model.embed_tokens.weight")
-    assert not conditioner._is_above_the_tap("visual.blocks.0.attn.qkv.weight")
-    # The filter only drops indexes the full stack would have built. A key at
-    # or above the checkpoint's own num_hidden_layers is corrupt, and it must
-    # keep raising as unexpected exactly as it does without truncation.
-    assert not conditioner._is_above_the_tap("language_model.layers.8.mlp.gate_proj.weight")
-    with pytest.raises(ValueError, match="Unexpected"):
-        conditioner.load_weights([("model.language_model.layers.8.mlp.gate_proj.weight", torch.zeros(1))])
+    assert conditioner._is_omitted_checkpoint_key("language_model.layers.5.mlp.gate_proj.weight")
+    assert conditioner._is_omitted_checkpoint_key("language_model.layers.7.self_attn.q_proj.weight")
+    assert conditioner._is_omitted_checkpoint_key("language_model.norm.weight")
+    assert not conditioner._is_omitted_checkpoint_key("language_model.layers.4.mlp.gate_proj.weight")
+    assert not conditioner._is_omitted_checkpoint_key("language_model.embed_tokens.weight")
+    assert not conditioner._is_omitted_checkpoint_key("visual.blocks.0.attn.qkv.weight")
+    assert not conditioner._is_omitted_checkpoint_key("language_model.layers.8.mlp.gate_proj.weight")
 
 
 def test_full_stack_filters_nothing(distributed_setup) -> None:
@@ -203,5 +174,14 @@ def test_full_stack_filters_nothing(distributed_setup) -> None:
 
     conditioner = MiniMaxH3Qwen3VLConditioner(_small_config(num_hidden_layers_override=None))
 
-    assert not conditioner._is_above_the_tap("language_model.layers.7.mlp.gate_proj.weight")
-    assert not conditioner._is_above_the_tap("language_model.norm.weight")
+    assert not conditioner._is_omitted_checkpoint_key("language_model.layers.7.mlp.gate_proj.weight")
+    assert not conditioner._is_omitted_checkpoint_key("language_model.norm.weight")
+
+
+def test_corrupt_layer_above_checkpoint_depth_remains_unexpected(distributed_setup) -> None:
+    from fastvideo.models.encoders.minimax_h3_qwen3_vl import MiniMaxH3Qwen3VLConditioner
+
+    conditioner = MiniMaxH3Qwen3VLConditioner(_small_config(num_hidden_layers_override=5))
+
+    with pytest.raises(ValueError, match="Unexpected"):
+        conditioner.load_weights([("language_model.layers.8.mlp.gate_proj.weight", torch.empty(1))])

fastvideo/tests/stages/test_minimax_h3_vae_streaming.py

@@ -61,7 +61,8 @@ def test_reference_video_encode_keeps_pixels_on_cpu() -> None:
         media_type="video",
         frames=np.zeros((22, 16, 16, 3), dtype=np.uint8),
     )
-    rows = stage._encode_visual_rows([reference], torch.device("cpu"))
+    args = SimpleNamespace(vae_parallel_encode=False)
+    rows = stage._encode_visual_rows([reference], torch.device("cpu"), args)
 
     assert observed["pixels"].dtype == torch.uint8
     assert observed["pixels"].device.type == "cpu"
@@ -96,7 +97,7 @@ def test_decode_stage_uses_cpu_output_buffer(monkeypatch) -> None:
     monkeypatch.setattr(minimax_h3_decoding, "get_local_torch_device", lambda: torch.device("cpu"))
     result = MiniMaxH3VideoDecodingStage(VAE(), SimpleNamespace(patch_size=(1, 1, 1))).forward(
         batch,
-        SimpleNamespace(output_type="pil", pin_cpu_memory=False, vae_cpu_offload=False),
+        SimpleNamespace(output_type="pil", pin_cpu_memory=False, vae_cpu_offload=False, vae_parallel_decode=False),
     )
 
     torch.testing.assert_close(observed["latents"], latents)
@@ -114,8 +115,9 @@ def test_decode_stages_skip_vae_on_non_output_rank(monkeypatch) -> None:
             raise AssertionError("non-output ranks must not execute a VAE")
 
     monkeypatch.setattr(minimax_h3_decoding, "model_parallel_is_initialized", lambda: True)
-    monkeypatch.setattr(minimax_h3_decoding, "get_world_group", lambda: SimpleNamespace(is_first_rank=False))
-    args = SimpleNamespace(output_type="pil", pin_cpu_memory=False, vae_cpu_offload=True)
+    monkeypatch.setattr(minimax_h3_decoding, "get_sp_group",
+                        lambda: SimpleNamespace(is_first_rank=False, world_size=4, rank_in_group=1))
+    args = SimpleNamespace(output_type="pil", pin_cpu_memory=False, vae_cpu_offload=True, vae_parallel_decode=False)
 
     video = MiniMaxH3VideoDecodingStage(VAE(), SimpleNamespace()).forward(ForwardBatch(data_type="video"), args)
     assert video.output.shape == (0, 3, 0, 0, 0)
@@ -128,3 +130,56 @@ def test_decode_stages_skip_vae_on_non_output_rank(monkeypatch) -> None:
     assert audio.latents is None
     assert audio.audio_latents is None
     assert MINIMAX_H3_LAYOUT_KEY not in audio.extra
+
+
+def test_parallel_decode_runs_on_every_rank(monkeypatch) -> None:
+    """With vae_parallel_decode, non-leader ranks must enter the decode body
+    (the collectives inside require uniform participation) and only the
+    leader owns the CPU output buffer."""
+    latent_shape = (1, 4, 2, 4, 4)
+    rows = patchify_video_latents(torch.randn(latent_shape), (1, 1, 1))
+    calls = []
+
+    class VAE:
+
+        def to(self, device):
+            return self
+
+        def denormalize_latents(self, decoded_latents):
+            return decoded_latents
+
+        def decoded_pixel_shape(self, shape):
+            return (1, 3, 5, 16, 16)
+
+    def fake_parallel(vae, latents, output, group, strategy):
+        calls.append((group.rank_in_group, output, strategy))
+        if output is not None:
+            output.fill_(0.5)
+        return output
+
+    monkeypatch.setattr(minimax_h3_decoding, "get_local_torch_device", lambda: torch.device("cpu"))
+    monkeypatch.setattr(minimax_h3_decoding, "model_parallel_is_initialized", lambda: True)
+    monkeypatch.setattr(minimax_h3_decoding, "decode_to_pixels_parallel", fake_parallel)
+    args = SimpleNamespace(output_type="pil",
+                           pin_cpu_memory=False,
+                           vae_cpu_offload=False,
+                           vae_parallel_decode=True,
+                           vae_parallel_decode_strategy="gather")
+
+    for rank, is_first in ((0, True), (2, False)):
+        monkeypatch.setattr(
+            minimax_h3_decoding, "get_sp_group",
+            lambda rank=rank, is_first=is_first: SimpleNamespace(is_first_rank=is_first,
+                                                                 world_size=4,
+                                                                 rank_in_group=rank))
+        batch = ForwardBatch(data_type="video", latents=rows.clone(), raw_latent_shape=latent_shape)
+        batch.extra[MINIMAX_H3_LAYOUT_KEY] = _layout(rows.shape[0], latent_shape)
+        result = MiniMaxH3VideoDecodingStage(VAE(), SimpleNamespace(patch_size=(1, 1, 1))).forward(batch, args)
+        if is_first:
+            assert result.output.shape == (1, 3, 5, 16, 16)
+            assert torch.all(result.output == 0.5)
+        else:
+            assert result.output.shape == (0, 3, 0, 0, 0)
+
+    assert [(rank, output is not None) for rank, output, _ in calls] == [(0, True), (2, False)]
+    assert all(strategy == "gather" for _, _, strategy in calls)

fastvideo/tests/transformers/test_minimax_h3_fusion_routing.py

@@ -0,0 +1,231 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Focused routing and FA4 integration checks for MiniMax-H3 fusions."""
+
+from __future__ import annotations
+
+import pytest
+import torch
+
+from fastvideo.platforms import AttentionBackendEnum
+
+
+@pytest.mark.parametrize(
+    ("raw", "expected"),
+    [
+        ("", frozenset()),
+        ("0", frozenset()),
+        ("none", frozenset()),
+        ("all", frozenset({"modulate", "qknorm_rope", "swiglu"})),
+        ("1", frozenset({"modulate", "qknorm_rope", "swiglu"})),
+        ("swiglu, modulate", frozenset({"swiglu", "modulate"})),
+    ],
+)
+def test_minimax_h3_fusion_selector(raw: str, expected: frozenset[str]) -> None:
+    from fastvideo.models.dits.minimax_h3 import _enabled_minimax_h3_fusions
+
+    assert _enabled_minimax_h3_fusions(raw) == expected
+
+
+def test_minimax_h3_fusion_selector_rejects_unknown_name() -> None:
+    from fastvideo.models.dits.minimax_h3 import _enabled_minimax_h3_fusions
+
+    with pytest.raises(ValueError, match="Unknown MiniMax H3 fusion"):
+        _enabled_minimax_h3_fusions("swiglu,unknown")
+
+
+def test_swiglu_fusion_stays_on_eager_path_with_grad(monkeypatch: pytest.MonkeyPatch) -> None:
+    import fastvideo.models.dits.minimax_h3 as h3
+
+    def unexpected_kernel(_: torch.Tensor) -> torch.Tensor:
+        raise AssertionError("inference-only fusion ran with grad enabled")
+
+    monkeypatch.setattr(h3, "minimax_h3_swiglu", unexpected_kernel)
+    layer = h3.MiniMaxH3FeedForward(8, 16, fuse_swiglu=True)
+    inputs = torch.randn(2, 3, 8, requires_grad=True)
+    layer(inputs).sum().backward()
+
+    assert inputs.grad is not None
+
+
+def test_all_minimax_h3_fusions_match_one_eager_block_under_fa4(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Exercise the real block wiring without loading any H3 checkpoint."""
+    if not torch.cuda.is_available() or not torch.cuda.is_bf16_supported():
+        pytest.skip("BF16 CUDA is required")
+    pytest.importorskip("triton")
+    flash_attn = pytest.importorskip("flash_attn")
+    if "fa4" not in getattr(flash_attn, "__version__", "").lower():
+        pytest.skip("the focused integration test requires the FA4 environment")
+
+    monkeypatch.setenv("FASTVIDEO_ATTENTION_BACKEND", "FLASH_ATTN")
+    monkeypatch.setenv("FASTVIDEO_FA4", "1")
+    monkeypatch.setenv("MASTER_ADDR", "127.0.0.1")
+    monkeypatch.setenv("MASTER_PORT", "29573")
+    monkeypatch.setenv("RANK", "0")
+    monkeypatch.setenv("WORLD_SIZE", "1")
+    monkeypatch.setenv("LOCAL_RANK", "0")
+
+    from fastvideo.distributed import cleanup_dist_env_and_memory, maybe_init_distributed_environment_and_model_parallel
+    from fastvideo.forward_context import set_forward_context
+    from fastvideo.models.dits.minimax_h3 import MiniMaxH3RotaryPosEmbed, MiniMaxH3TransformerBlock
+
+    maybe_init_distributed_environment_and_model_parallel(1, 1)
+    try:
+        kwargs = dict(
+            hidden_size=128,
+            num_attention_heads=1,
+            attention_head_dim=128,
+            ffn_dim=256,
+            time_embed_dim=64,
+            norm_eps=1e-5,
+            qk_norm_eps=1e-5,
+            supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN, ),
+            quant_config=None,
+            prefix="minimax_h3.test_block",
+        )
+        previous_default_dtype = torch.get_default_dtype()
+        torch.set_default_dtype(torch.bfloat16)
+        try:
+            eager = MiniMaxH3TransformerBlock(**kwargs)
+            fused = MiniMaxH3TransformerBlock(
+                **kwargs,
+                fuse_modulate=True,
+                fuse_qknorm_rope=True,
+                fuse_swiglu=True,
+            )
+        finally:
+            torch.set_default_dtype(previous_default_dtype)
+
+        with torch.no_grad():
+            for name, parameter in eager.named_parameters():
+                if "norm" in name and name.endswith("weight"):
+                    parameter.fill_(1.0)
+                elif parameter.ndim > 1:
+                    torch.nn.init.normal_(parameter, mean=0.0, std=0.02)
+                else:
+                    parameter.zero_()
+        fused.load_state_dict(eager.state_dict(), strict=True)
+
+        device = torch.device("cuda")
+        eager = eager.to(device=device, dtype=torch.bfloat16).eval()
+        fused = fused.to(device=device, dtype=torch.bfloat16).eval()
+        generator = torch.Generator(device=device).manual_seed(2026)
+        hidden_states = torch.randn(2, 12, 128, generator=generator, device=device, dtype=torch.bfloat16)
+        temb = torch.randn(2, 64, generator=generator, device=device, dtype=torch.bfloat16)
+        adaln_indices = torch.arange(12, device=device, dtype=torch.long).remainder(6)
+        position_ids = torch.zeros(12, 3, device=device, dtype=torch.float32)
+        position_ids[:, 0] = torch.arange(12, device=device)
+        rotary_emb = MiniMaxH3RotaryPosEmbed(rope_freq_dim=16, rope_theta=10000.0).to(device)(position_ids)
+        inputs = dict(
+            hidden_states=hidden_states,
+            temb=temb,
+            adaln_indices=adaln_indices,
+            rotary_emb=tuple(value.to(torch.bfloat16) for value in rotary_emb),
+            original_seq_len=12,
+        )
+
+        with torch.inference_mode(), set_forward_context(current_timestep=0, attn_metadata=None):
+            eager_output = eager(**inputs)
+            fused_output = fused(**inputs)
+
+        # Sol-Engine keeps fused intermediates in FP32 registers until their
+        # final BF16 stores, so the opt-in path is close but not bit-identical.
+        torch.testing.assert_close(fused_output, eager_output, atol=3e-2, rtol=3e-2)
+    finally:
+        cleanup_dist_env_and_memory()
+
+
+def test_minimax_h3_fusions_engage_on_cuda_inference(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Pin the positive side of the routing guard.
+
+    The parity test above still passes if ``_can_run_minimax_h3_fusion``
+    silently degrades to always-False (both blocks then run the identical
+    eager path), so count the fused-kernel calls: one CUDA inference forward
+    through a fully fused block must hit ``fused_rmsnorm_modulate`` once,
+    ``fused_residual_gate_rmsnorm_modulate`` once, ``fused_qknorm_rope``
+    twice (q and k), and ``minimax_h3_swiglu`` once -- and a grad-enabled
+    forward must leave every counter unchanged.
+    """
+    if not torch.cuda.is_available() or not torch.cuda.is_bf16_supported():
+        pytest.skip("BF16 CUDA is required")
+    pytest.importorskip("triton")
+
+    monkeypatch.setenv("FASTVIDEO_ATTENTION_BACKEND", "TORCH_SDPA")
+    monkeypatch.setenv("MASTER_ADDR", "127.0.0.1")
+    monkeypatch.setenv("MASTER_PORT", "29574")
+    monkeypatch.setenv("RANK", "0")
+    monkeypatch.setenv("WORLD_SIZE", "1")
+    monkeypatch.setenv("LOCAL_RANK", "0")
+
+    import fastvideo.models.dits.minimax_h3 as h3
+    from fastvideo.distributed import cleanup_dist_env_and_memory, maybe_init_distributed_environment_and_model_parallel
+    from fastvideo.forward_context import set_forward_context
+
+    calls = dict.fromkeys(("rmsnorm_modulate", "residual_gate_rmsnorm_modulate", "qknorm_rope", "swiglu"), 0)
+
+    def _counting(name: str, real):
+
+        def wrapper(*args, **kwargs):
+            calls[name] += 1
+            return real(*args, **kwargs)
+
+        return wrapper
+
+    monkeypatch.setattr(h3, "fused_rmsnorm_modulate", _counting("rmsnorm_modulate", h3.fused_rmsnorm_modulate))
+    monkeypatch.setattr(h3, "fused_residual_gate_rmsnorm_modulate",
+                        _counting("residual_gate_rmsnorm_modulate", h3.fused_residual_gate_rmsnorm_modulate))
+    monkeypatch.setattr(h3, "fused_qknorm_rope", _counting("qknorm_rope", h3.fused_qknorm_rope))
+    monkeypatch.setattr(h3, "minimax_h3_swiglu", _counting("swiglu", h3.minimax_h3_swiglu))
+
+    maybe_init_distributed_environment_and_model_parallel(1, 1)
+    try:
+        block = h3.MiniMaxH3TransformerBlock(
+            hidden_size=128,
+            num_attention_heads=1,
+            attention_head_dim=128,
+            ffn_dim=256,
+            time_embed_dim=64,
+            norm_eps=1e-5,
+            qk_norm_eps=1e-5,
+            supported_attention_backends=(AttentionBackendEnum.TORCH_SDPA, ),
+            quant_config=None,
+            prefix="minimax_h3.engagement_block",
+            fuse_modulate=True,
+            fuse_qknorm_rope=True,
+            fuse_swiglu=True,
+        )
+        device = torch.device("cuda")
+        block = block.to(device=device, dtype=torch.bfloat16).eval()
+
+        generator = torch.Generator(device=device).manual_seed(2026)
+        hidden_states = torch.randn(2, 12, 128, generator=generator, device=device, dtype=torch.bfloat16)
+        temb = torch.randn(2, 64, generator=generator, device=device, dtype=torch.bfloat16)
+        adaln_indices = torch.arange(12, device=device, dtype=torch.long).remainder(6)
+        position_ids = torch.zeros(12, 3, device=device, dtype=torch.float32)
+        position_ids[:, 0] = torch.arange(12, device=device)
+        rotary_emb = h3.MiniMaxH3RotaryPosEmbed(rope_freq_dim=16, rope_theta=10000.0).to(device)(position_ids)
+        inputs = dict(
+            hidden_states=hidden_states,
+            temb=temb,
+            adaln_indices=adaln_indices,
+            rotary_emb=tuple(value.to(torch.bfloat16) for value in rotary_emb),
+            original_seq_len=12,
+        )
+
+        with torch.inference_mode(), set_forward_context(current_timestep=0, attn_metadata=None):
+            block(**inputs)
+        engaged = dict(calls)
+        assert engaged == {
+            "rmsnorm_modulate": 1,
+            "residual_gate_rmsnorm_modulate": 1,
+            "qknorm_rope": 2,
+            "swiglu": 1,
+        }, engaged
+
+        grad_inputs = {**inputs, "hidden_states": hidden_states.clone().requires_grad_(True)}
+        with set_forward_context(current_timestep=0, attn_metadata=None):
+            block(**grad_inputs)
+        assert dict(calls) == engaged, f"a fusion ran under grad: {calls} vs {engaged}"
+    finally:
+        cleanup_dist_env_and_memory()

fastvideo/tests/transformers/test_minimax_h3_modulation_fusion.py

@@ -0,0 +1,173 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+from fastvideo.models.dits.minimax_h3_fusions.modulation import (
+    fused_residual_gate_rmsnorm_modulate,
+    fused_rmsnorm_modulate,
+)
+
+
+EPS = 1e-6
+SOL_ENGINE_BF16_TOLERANCE = 3e-2
+
+
+def _chunk_tables(rows: int, hidden_size: int, *, device: torch.device | str = "cpu", dtype=torch.float32):
+    wide = torch.randn(rows, 6 * hidden_size, device=device, dtype=dtype)
+    tables = wide.chunk(6, dim=-1)
+    assert all(table.stride() == (6 * hidden_size, 1) for table in tables)
+    assert all(not table.is_contiguous() for table in tables)
+    return tables
+
+
+def _eager_rmsnorm_modulate(x, weight, scale, shift, index):
+    normed = F.rms_norm(x, (x.shape[-1], ), weight, EPS)
+    return normed * (1.0 + scale.index_select(0, index)) + shift.index_select(0, index)
+
+
+def _eager_residual_gate_rmsnorm_modulate(residual, branch, gate, weight, scale, shift, index):
+    hidden = residual + gate.index_select(0, index) * branch
+    normed = F.rms_norm(hidden, (hidden.shape[-1], ), weight, EPS)
+    modulated = normed * (1.0 + scale.index_select(0, index)) + shift.index_select(0, index)
+    return hidden, modulated
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires a CUDA GPU")
+def test_bf16_sol_engine_fusions_match_production_eager_within_tolerance():
+    """Sol-Engine keeps fused intermediates in FP32 until its output stores."""
+    pytest.importorskip("triton")
+    torch.manual_seed(2)
+    device = torch.device("cuda")
+    batch, sequence_length, hidden_size, table_rows = 2, 9, 5376, 6
+    residual = torch.randn(batch, sequence_length, hidden_size, device=device, dtype=torch.bfloat16)
+    branch = torch.randn_like(residual)
+    weight = torch.randn(hidden_size, device=device, dtype=torch.bfloat16)
+    shift, scale, gate, shift_mlp, scale_mlp, _ = _chunk_tables(
+        table_rows,
+        hidden_size,
+        device=device,
+        dtype=torch.bfloat16,
+    )
+    index = torch.tensor([5, 0, 4, 1, 3, 2, 5, 1, 0], device=device, dtype=torch.int64)
+
+    expected_norm1 = _eager_rmsnorm_modulate(residual, weight, scale, shift, index)
+    actual_norm1 = fused_rmsnorm_modulate(residual, weight, scale, shift, index, EPS)
+    expected_hidden, expected_norm2 = _eager_residual_gate_rmsnorm_modulate(
+        residual,
+        branch,
+        gate,
+        weight,
+        scale_mlp,
+        shift_mlp,
+        index,
+    )
+    actual_hidden, actual_norm2 = fused_residual_gate_rmsnorm_modulate(
+        residual,
+        branch,
+        gate,
+        weight,
+        scale_mlp,
+        shift_mlp,
+        index,
+        EPS,
+    )
+
+    # Production eager materializes BF16 after each PyTorch operator. The
+    # single-kernel Sol-Engine path deliberately removes those round points.
+    torch.testing.assert_close(
+        actual_norm1,
+        expected_norm1,
+        rtol=SOL_ENGINE_BF16_TOLERANCE,
+        atol=SOL_ENGINE_BF16_TOLERANCE,
+    )
+    torch.testing.assert_close(
+        actual_hidden,
+        expected_hidden,
+        rtol=SOL_ENGINE_BF16_TOLERANCE,
+        atol=SOL_ENGINE_BF16_TOLERANCE,
+    )
+    torch.testing.assert_close(
+        actual_norm2,
+        expected_norm2,
+        rtol=SOL_ENGINE_BF16_TOLERANCE,
+        atol=SOL_ENGINE_BF16_TOLERANCE,
+    )
+
+
+@pytest.mark.parametrize(
+    ("mutate", "error", "match"),
+    [
+        (lambda args: args | {"x": args["x"][0, 0]}, ValueError, "shape"),
+        (lambda args: args | {"weight": args["weight"][:-1]}, ValueError, "weight"),
+        (lambda args: args | {"index": args["index"][:-1]}, ValueError, "index"),
+        (lambda args: args | {"index": args["index"].float()}, TypeError, "index"),
+        (lambda args: args | {"eps": 0.0}, ValueError, "eps"),
+    ],
+)
+def test_fusion_rejects_invalid_contracts(mutate, error, match):
+    args = {
+        "x": torch.randn(2, 3, 8),
+        "weight": torch.randn(8),
+        "scale": torch.randn(4, 8),
+        "shift": torch.randn(4, 8),
+        "index": torch.tensor([0, 3, 1]),
+        "eps": EPS,
+    }
+    with pytest.raises(error, match=match):
+        fused_rmsnorm_modulate(**mutate(args))
+
+
+def test_residual_fusion_rejects_mismatched_branch():
+    residual = torch.randn(2, 3, 8)
+    with pytest.raises(ValueError, match="branch"):
+        fused_residual_gate_rmsnorm_modulate(
+            residual,
+            torch.randn(2, 2, 8),
+            torch.randn(4, 8),
+            torch.randn(8),
+            torch.randn(4, 8),
+            torch.randn(4, 8),
+            torch.tensor([0, 1, 2]),
+            EPS,
+        )
+
+
+def test_triton_wrappers_fail_explicitly_on_cpu():
+    x = torch.randn(1, 2, 8)
+    branch = torch.randn_like(x)
+    weight = torch.randn(8)
+    gate = torch.randn(3, 8)
+    scale = torch.randn(3, 8)
+    shift = torch.randn(3, 8)
+    index = torch.tensor([0, 2])
+
+    with pytest.raises(RuntimeError, match="Triton|CUDA"):
+        fused_rmsnorm_modulate(x, weight, scale, shift, index, EPS)
+    with pytest.raises(RuntimeError, match="Triton|CUDA"):
+        fused_residual_gate_rmsnorm_modulate(x, branch, gate, weight, scale, shift, index, EPS)
+
+
+def test_triton_wrappers_reject_autograd_before_backend_check():
+    x = torch.randn(1, 2, 8)
+    branch = torch.randn_like(x, requires_grad=True)
+    weight = torch.randn(8, requires_grad=True)
+    gate = torch.randn(3, 8)
+    scale = torch.randn(3, 8)
+    shift = torch.randn(3, 8)
+    index = torch.tensor([0, 2])
+
+    with pytest.raises(RuntimeError, match="forward-only"):
+        fused_rmsnorm_modulate(x, weight, scale, shift, index, EPS)
+    with pytest.raises(RuntimeError, match="forward-only"):
+        fused_residual_gate_rmsnorm_modulate(
+            x,
+            branch,
+            gate,
+            weight.detach(),
+            scale,
+            shift,
+            index,
+            EPS,
+        )

fastvideo/tests/transformers/test_minimax_h3_qknorm_rope_fusion.py

@@ -0,0 +1,192 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+from fastvideo.models.dits.minimax_h3 import MiniMaxH3Attention
+from fastvideo.models.dits.minimax_h3_fusions.qknorm_rope import (
+    HAVE_TRITON,
+    fused_qknorm_rope,
+)
+
+
+def _rotary_tables(
+    seq_len: int,
+    rotary_dim: int,
+    *,
+    dtype: torch.dtype,
+    device: torch.device | str,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    angles = torch.randn(seq_len, rotary_dim // 2, dtype=torch.float32, device=device)
+    angles = torch.cat((angles, angles), dim=-1)
+    return angles.cos().to(dtype), angles.sin().to(dtype)
+
+
+def _eager_qknorm_rope(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    eps: float,
+) -> torch.Tensor:
+    normalized = F.rms_norm(x, (x.shape[-1], ), weight, eps)
+    return MiniMaxH3Attention._apply_rotary_emb(normalized, (cos, sin))
+
+
+def test_fused_qknorm_rope_rejects_invalid_rotary_dim() -> None:
+    x = torch.randn(2, 3, 4, 128)
+    weight = torch.ones(128)
+
+    cos, sin = _rotary_tables(3, 130, dtype=x.dtype, device=x.device)
+    with pytest.raises(ValueError, match="rotary_dim must not exceed head_dim"):
+        fused_qknorm_rope(x, weight, cos, sin, 1e-6)
+
+    cos = torch.randn(3, 95)
+    sin = torch.randn_like(cos)
+    with pytest.raises(ValueError, match="rotary_dim must be even"):
+        fused_qknorm_rope(x, weight, cos, sin, 1e-6)
+
+
+def test_fused_qknorm_rope_rejects_shape_mismatches() -> None:
+    x = torch.randn(2, 3, 4, 128)
+    weight = torch.ones(128)
+    cos, sin = _rotary_tables(3, 96, dtype=x.dtype, device=x.device)
+
+    with pytest.raises(ValueError, match="x must have shape"):
+        fused_qknorm_rope(x[0], weight, cos, sin, 1e-6)
+    with pytest.raises(ValueError, match="weight must have shape"):
+        fused_qknorm_rope(x, weight[:-1], cos, sin, 1e-6)
+    with pytest.raises(ValueError, match="sequence length"):
+        fused_qknorm_rope(x, weight, cos[:-1], sin[:-1], 1e-6)
+    with pytest.raises(ValueError, match="sin must match cos shape"):
+        fused_qknorm_rope(x, weight, cos, sin[:, :-2], 1e-6)
+
+
+@pytest.mark.parametrize("noncontiguous_input", ["weight", "cos", "sin"])
+def test_fused_qknorm_rope_rejects_noncontiguous_linear_inputs(noncontiguous_input: str) -> None:
+    x = torch.randn(2, 3, 4, 128)
+    weight = torch.ones(256)[::2]
+    cos = torch.randn(3, 192)[:, ::2]
+    sin = torch.randn(3, 192)[:, ::2]
+    assert not weight.is_contiguous()
+    assert not cos.is_contiguous()
+    assert not sin.is_contiguous()
+
+    if noncontiguous_input != "weight":
+        weight = weight.contiguous()
+    if noncontiguous_input != "cos":
+        cos = cos.contiguous()
+    if noncontiguous_input != "sin":
+        sin = sin.contiguous()
+
+    with pytest.raises(ValueError, match="must be contiguous"):
+        fused_qknorm_rope(x, weight, cos, sin, 1e-6)
+
+
+def test_fused_qknorm_rope_requires_matching_precast_dtype() -> None:
+    x = torch.randn(2, 3, 4, 128, dtype=torch.float32)
+    weight = torch.ones(128, dtype=torch.float32)
+    cos, sin = _rotary_tables(3, 96, dtype=torch.bfloat16, device=x.device)
+
+    with pytest.raises(TypeError, match="cos dtype must match x dtype"):
+        fused_qknorm_rope(x, weight, cos, sin, 1e-6)
+
+
+def test_fused_qknorm_rope_does_not_accept_missing_rotary_tables() -> None:
+    x = torch.randn(2, 3, 4, 128)
+    weight = torch.ones(128)
+
+    with pytest.raises(TypeError, match="cos must be a torch.Tensor"):
+        fused_qknorm_rope(x, weight, None, None, 1e-6)  # type: ignore[arg-type]
+
+
+def test_fused_qknorm_rope_requires_cuda() -> None:
+    x = torch.randn(2, 3, 4, 128)
+    weight = torch.ones(128)
+    cos, sin = _rotary_tables(3, 96, dtype=x.dtype, device=x.device)
+
+    with pytest.raises(RuntimeError, match="requires CUDA"):
+        fused_qknorm_rope(x, weight, cos, sin, 1e-6)
+
+
+@pytest.mark.parametrize(
+    "rotary_dim,shape,use_input_view",
+    [
+        pytest.param(96, (2, 11, 5, 128), False, id="partial-96-batch2-seq11-heads5"),
+        pytest.param(128, (2, 7, 3, 128), True, id="full-128-noncontiguous-input-view"),
+    ],
+)
+def test_fused_qknorm_rope_matches_eager_bf16_cuda(
+    rotary_dim: int,
+    shape: tuple[int, ...],
+    use_input_view: bool,
+) -> None:
+    if not torch.cuda.is_available():
+        pytest.skip("CUDA is required for the Triton fusion")
+    if not HAVE_TRITON:
+        pytest.skip("Triton is required for the fusion")
+
+    torch.manual_seed(1)
+    device = torch.device("cuda")
+    if use_input_view:
+        x = torch.randn(*shape[:-1], shape[-1] * 2, dtype=torch.bfloat16, device=device)[..., ::2]
+        assert not x.is_contiguous()
+    else:
+        x = torch.randn(shape, dtype=torch.bfloat16, device=device)
+    weight = (1.0 + 0.05 * torch.randn(shape[-1], dtype=torch.bfloat16, device=device)).contiguous()
+    cos, sin = _rotary_tables(shape[1], rotary_dim, dtype=x.dtype, device=device)
+
+    with torch.inference_mode():
+        actual = fused_qknorm_rope(x, weight, cos, sin, 1e-6)
+        expected = _eager_qknorm_rope(x, weight, cos, sin, 1e-6)
+
+    # The fused kernel keeps RMSNorm and both RoPE products in FP32 registers
+    # until its final BF16 store. Eager materializes BF16 intermediates, and
+    # PyTorch/Triton reductions need not use the same summation order.
+    torch.testing.assert_close(actual, expected, atol=2e-2, rtol=2e-2)
+    assert actual.shape == x.shape
+    assert actual.dtype == x.dtype
+    assert actual.is_contiguous()
+
+
+def test_fused_qknorm_rope_matches_eager_beyond_int32_element_count() -> None:
+    """Regression: kernel row offsets must be int64.
+
+    With int32 offsets, ``row * head_dim`` wraps once the flattened input
+    crosses 2**31 elements and the kernel reads/writes out of bounds (CUDA
+    illegal memory access). ``(1, 8_500_000, 2, 128)`` is 2.176e9 elements,
+    just past the boundary; for H3's 56 heads x 128 head_dim the equivalent
+    is ``batch*seq >= 299_593`` tokens per rank, reachable at SP=1.
+
+    GPU assumption: needs ~16 GiB free CUDA memory (input + output at
+    bf16 plus the fp32 rotary-table construction); skips below 20 GiB.
+    """
+    if not torch.cuda.is_available():
+        pytest.skip("CUDA is required for the Triton fusion")
+    if not HAVE_TRITON:
+        pytest.skip("Triton is required for the fusion")
+    free_bytes, _ = torch.cuda.mem_get_info()
+    if free_bytes < 20 * 1024**3:
+        pytest.skip("needs ~20 GiB free GPU memory for a >2**31-element input")
+
+    heads, head_dim, rotary_dim = 2, 128, 96
+    seq_len = 8_500_000
+    assert seq_len * heads * head_dim > 2**31
+
+    torch.manual_seed(9)
+    device = torch.device("cuda")
+    x = torch.randn(1, seq_len, heads, head_dim, dtype=torch.bfloat16, device=device)
+    weight = (1.0 + 0.05 * torch.randn(head_dim, dtype=torch.bfloat16, device=device)).contiguous()
+    cos, sin = _rotary_tables(seq_len, rotary_dim, dtype=x.dtype, device=device)
+
+    with torch.inference_mode():
+        fused = fused_qknorm_rope(x, weight, cos, sin, 1e-6)
+        torch.cuda.synchronize()
+        # Compare only head/tail slices against eager: a full-tensor eager
+        # reference would double peak memory for no extra coverage, and the
+        # tail rows are exactly the ones an int32 wrap corrupts first.
+        expected_head = _eager_qknorm_rope(x[:, :8], weight, cos[:8], sin[:8], 1e-6)
+        expected_tail = _eager_qknorm_rope(x[:, -8:], weight, cos[-8:], sin[-8:], 1e-6)
+        torch.testing.assert_close(fused[:, :8], expected_head, atol=2e-2, rtol=2e-2)
+        torch.testing.assert_close(fused[:, -8:], expected_tail, atol=2e-2, rtol=2e-2)

fastvideo/tests/transformers/test_minimax_h3_swiglu_fusion.py

@@ -0,0 +1,73 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Focused tests for MiniMax H3's value-first packed SwiGLU fusion."""
+
+from __future__ import annotations
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+from fastvideo.models.dits.minimax_h3_fusions.swiglu import minimax_h3_swiglu
+
+
+def _require_bf16_triton_cuda() -> None:
+    if not torch.cuda.is_available():
+        pytest.skip("MiniMax H3 fused SwiGLU requires CUDA")
+    if not torch.cuda.is_bf16_supported():
+        pytest.skip("MiniMax H3 fused SwiGLU parity requires BF16 support")
+    pytest.importorskip("triton", reason="MiniMax H3 fused SwiGLU requires Triton")
+
+
+def _assert_bf16_parity(x: torch.Tensor) -> None:
+    value, gate = x.chunk(2, dim=-1)
+    expected = value * F.silu(gate)
+    actual = minimax_h3_swiglu(x)
+    assert actual.shape == (*x.shape[:-1], x.shape[-1] // 2)
+    assert actual.dtype == x.dtype
+    assert actual.device == x.device
+    # Sol-Engine keeps the full SwiGLU expression in FP32 until the output
+    # store, while eager F.silu materializes a BF16 intermediate.
+    torch.testing.assert_close(actual, expected, rtol=2e-2, atol=2e-2)
+
+
+@pytest.mark.parametrize("last_dim", [0, 7])
+def test_minimax_h3_swiglu_rejects_nonpositive_or_odd_last_dimension(last_dim: int) -> None:
+    x = torch.empty((2, last_dim), dtype=torch.float32)
+
+    with pytest.raises(ValueError, match="positive even last dimension"):
+        minimax_h3_swiglu(x)
+
+
+def test_minimax_h3_swiglu_strict_wrapper_rejects_cpu() -> None:
+    with pytest.raises(ValueError, match="requires a CUDA tensor"):
+        minimax_h3_swiglu(torch.randn(2, 8))
+
+
+@pytest.mark.gpu
+def test_minimax_h3_swiglu_multidimensional_bf16_gpu_parity() -> None:
+    _require_bf16_triton_cuda()
+    torch.manual_seed(0)
+    x = torch.randn((2, 3, 5, 66), device="cuda", dtype=torch.bfloat16)
+
+    _assert_bf16_parity(x)
+
+
+@pytest.mark.gpu
+def test_minimax_h3_swiglu_noncontiguous_bf16_gpu_parity() -> None:
+    _require_bf16_triton_cuda()
+    torch.manual_seed(1)
+    storage = torch.randn((2, 3, 148), device="cuda", dtype=torch.bfloat16)
+    x = storage[..., ::2]
+    assert not x.is_contiguous()
+
+    _assert_bf16_parity(x)
+
+
+@pytest.mark.gpu
+def test_minimax_h3_swiglu_real_ffn_dim_bf16_gpu() -> None:
+    _require_bf16_triton_cuda()
+    torch.manual_seed(2)
+    ffn_dim = 14336
+    x = torch.randn((1, 2 * ffn_dim), device="cuda", dtype=torch.bfloat16)
+
+    _assert_bf16_parity(x)

fastvideo/tests/vaes/test_minimax_h3_parallel_vae.py

@@ -0,0 +1,314 @@
+# SPDX-License-Identifier: Apache-2.0
+"""CPU tests for sequence-parallel MiniMax-H3 VAE chunk decode / clip encode.
+
+The collective transport is simulated with a threaded fake group (one thread
+per simulated rank, barrier-synchronized slots), so the REAL drivers in
+``fastvideo.models.vaes.minimax_h3_parallel`` — chunk assignment, placeholder
+rounds, metadata broadcast, gathered-segment assembly, halo/blend math — run
+end to end on CPU and are checked bit-exactly against the serial APIs.
+"""
+
+import threading
+
+import pytest
+import torch
+from torch.testing import assert_close
+
+from fastvideo.configs.models.vaes.minimax_h3_video import (
+    MiniMaxH3VideoVAEArchConfig,
+    MiniMaxH3VideoVAEConfig,
+)
+from fastvideo.models.vaes.minimax_h3_parallel import (
+    DECODE_GATHER_STRATEGIES,
+    DEFAULT_DECODE_GATHER_STRATEGY,
+    decode_to_pixels_parallel,
+    encode_pixels_parallel,
+    parallel_chunk_indices,
+)
+from fastvideo.models.vaes.minimax_h3_video import AutoencoderKLMiniMaxH3
+
+
+def _tiny_vae(token_drop: int = 3) -> AutoencoderKLMiniMaxH3:
+    arch = MiniMaxH3VideoVAEArchConfig(
+        latent_channels=4,
+        block_out_channels=(32, 32),
+        layers_per_block=1,
+        spatial_downsample_factors=(2, 2),
+        temporal_downsample_factors=(2, 2),
+        decoder_num_layers=1,
+        decoder_num_attention_heads=1,
+        decoder_attention_head_dim=8,
+        decoder_num_register_tokens=2,
+        decoder_ffn_mult=1,
+        token_drop=token_drop,
+        latents_mean=(0.0, ) * 4,
+        latents_std=(1.0, ) * 4,
+    )
+    return AutoencoderKLMiniMaxH3(
+        MiniMaxH3VideoVAEConfig(
+            arch_config=arch,
+            use_tiling=False,
+            use_temporal_tiling=False,
+            use_parallel_tiling=False,
+        )).eval()
+
+
+class _ThreadedFakeGroup:
+    """Barrier-synchronized in-process stand-in for a GroupCoordinator.
+
+    One thread per simulated rank runs the SPMD driver; ``gather`` /
+    ``all_gather`` / ``broadcast_object`` rendezvous through shared slots
+    with a double barrier (all writes land, everyone reads, then slots are
+    reusable). Matches the GroupCoordinator call signatures the drivers use.
+    """
+
+    def __init__(self, world_size: int) -> None:
+        self.world_size = world_size
+        self._local = threading.local()
+        self._barrier = threading.Barrier(world_size)
+        self._slots: list = [None] * world_size
+        self._object = None
+
+    @property
+    def rank_in_group(self) -> int:
+        return self._local.rank
+
+    def broadcast_object(self, obj=None, src: int = 0):
+        if self.world_size == 1:
+            return obj
+        if self.rank_in_group == src:
+            self._object = obj
+        self._barrier.wait()
+        received = self._object
+        self._barrier.wait()
+        return received
+
+    def all_gather(self, input_: torch.Tensor, dim: int = -1) -> torch.Tensor:
+        if self.world_size == 1:
+            return input_
+        self._slots[self.rank_in_group] = input_
+        self._barrier.wait()
+        gathered = torch.cat([slot for slot in self._slots], dim=dim)
+        self._barrier.wait()
+        return gathered
+
+    def gather(self, input_: torch.Tensor, dst: int = 0, dim: int = -1):
+        if self.world_size == 1:
+            return input_
+        self._slots[self.rank_in_group] = input_
+        self._barrier.wait()
+        gathered = torch.cat([slot for slot in self._slots], dim=dim) if self.rank_in_group == dst else None
+        self._barrier.wait()
+        return gathered
+
+    def run(self, fn) -> list:
+        """Run ``fn(rank)`` on one thread per rank; re-raise the first error."""
+        results: list = [None] * self.world_size
+        errors: list = [None] * self.world_size
+
+        def _target(rank: int) -> None:
+            self._local.rank = rank
+            try:
+                # inference_mode is thread-local; the drivers run inference-only.
+                with torch.inference_mode():
+                    results[rank] = fn(rank)
+            except BaseException as error:  # noqa: BLE001 - propagate to the test
+                errors[rank] = error
+                self._barrier.abort()
+
+        threads = [threading.Thread(target=_target, args=(rank, )) for rank in range(self.world_size)]
+        for thread in threads:
+            thread.start()
+        for thread in threads:
+            thread.join()
+        for error in errors:
+            if error is not None and not isinstance(error, threading.BrokenBarrierError):
+                raise error
+        for error in errors:
+            if error is not None:
+                raise error
+        return results
+
+
+@pytest.mark.parametrize("num_chunks,world_size", ((0, 4), (1, 4), (7, 4), (8, 4), (20, 4), (5, 3), (2, 5)))
+def test_parallel_chunk_indices_partition(num_chunks: int, world_size: int) -> None:
+    """Round-robin ownership covers every chunk exactly once, in order."""
+    owned = [parallel_chunk_indices(num_chunks, world_size, rank) for rank in range(world_size)]
+    flattened = sorted(index for indices in owned for index in indices)
+    assert flattened == list(range(num_chunks))
+    for rank, indices in enumerate(owned):
+        assert indices == sorted(indices)
+        assert all(index % world_size == rank for index in indices)
+        # Round-robin balance: no rank holds more than one extra chunk.
+        assert len(indices) in (num_chunks // world_size, -(-num_chunks // world_size))
+
+
+def test_parallel_chunk_indices_validates() -> None:
+    with pytest.raises(ValueError, match="world_size"):
+        parallel_chunk_indices(4, 0, 0)
+    with pytest.raises(ValueError, match="rank_in_group"):
+        parallel_chunk_indices(4, 2, 2)
+    with pytest.raises(ValueError, match="num_chunks"):
+        parallel_chunk_indices(-1, 2, 0)
+
+
+# Latent frames cover: one padded chunk (3), pad on the intra-clip tail (6),
+# two blended chunks (12), three chunks plus pad trim (13). World sizes cover
+# fewer chunks than ranks, uneven rounds, and the exact-multiple case.
+@pytest.mark.parametrize("world_size", (2, 3, 4, 5))
+@pytest.mark.parametrize("latent_frames", (3, 6, 12, 13))
+@pytest.mark.parametrize("strategy", DECODE_GATHER_STRATEGIES)
+@torch.inference_mode()
+def test_parallel_decode_matches_serial(world_size: int, latent_frames: int, strategy: str) -> None:
+    torch.manual_seed(20260821 + latent_frames)
+    vae = _tiny_vae()
+    latents = torch.randn(1, 4, latent_frames, 4, 4)
+    expected = torch.empty(vae.decoded_pixel_shape(latents.shape), dtype=torch.float32)
+    vae.decode_to_pixels(latents, expected)
+
+    group = _ThreadedFakeGroup(world_size)
+
+    def _rank_main(rank: int):
+        output = torch.empty(vae.decoded_pixel_shape(latents.shape), dtype=torch.float32) if rank == 0 else None
+        return decode_to_pixels_parallel(vae, latents.clone(), output, group, strategy=strategy)
+
+    results = group.run(_rank_main)
+    assert all(result is None for result in results[1:])
+    assert_close(results[0], expected, atol=0.0, rtol=0.0)
+
+
+@torch.inference_mode()
+def test_parallel_decode_without_token_drop() -> None:
+    """token_drop == 0 has no overlap halo; the assembler must skip blending."""
+    torch.manual_seed(20260822)
+    vae = _tiny_vae(token_drop=0)
+    assert vae.frame_overlap == 0
+    latents = torch.randn(1, 4, 10, 4, 4)
+    expected = torch.empty(vae.decoded_pixel_shape(latents.shape), dtype=torch.float32)
+    vae.decode_to_pixels(latents, expected)
+
+    group = _ThreadedFakeGroup(3)
+
+    def _rank_main(rank: int):
+        output = torch.empty(vae.decoded_pixel_shape(latents.shape), dtype=torch.float32) if rank == 0 else None
+        return decode_to_pixels_parallel(vae, latents.clone(), output, group)
+
+    results = group.run(_rank_main)
+    assert_close(results[0], expected, atol=0.0, rtol=0.0)
+
+
+@torch.inference_mode()
+def test_parallel_decode_batched_slicing_matches_serial() -> None:
+    torch.manual_seed(20260823)
+    vae = _tiny_vae()
+    vae.enable_slicing()
+    latents = torch.randn(2, 4, 7, 4, 4)
+    expected = torch.empty(vae.decoded_pixel_shape(latents.shape), dtype=torch.float32)
+    vae.decode_to_pixels(latents, expected)
+
+    group = _ThreadedFakeGroup(2)
+
+    def _rank_main(rank: int):
+        output = torch.empty(vae.decoded_pixel_shape(latents.shape), dtype=torch.float32) if rank == 0 else None
+        return decode_to_pixels_parallel(vae, latents.clone(), output, group)
+
+    results = group.run(_rank_main)
+    assert_close(results[0], expected, atol=0.0, rtol=0.0)
+
+
+@torch.inference_mode()
+def test_parallel_decode_world_size_one_is_serial() -> None:
+    torch.manual_seed(20260824)
+    vae = _tiny_vae()
+    latents = torch.randn(1, 4, 7, 4, 4)
+    expected = torch.empty(vae.decoded_pixel_shape(latents.shape), dtype=torch.float32)
+    vae.decode_to_pixels(latents, expected)
+
+    group = _ThreadedFakeGroup(1)
+
+    def _rank_main(rank: int):
+        output = torch.empty(vae.decoded_pixel_shape(latents.shape), dtype=torch.float32)
+        return decode_to_pixels_parallel(vae, latents, output, group)
+
+    results = group.run(_rank_main)
+    assert_close(results[0], expected, atol=0.0, rtol=0.0)
+
+
+def test_parallel_decode_validates_buffers_and_strategy() -> None:
+    vae = _tiny_vae()
+    latents = torch.randn(1, 4, 7, 4, 4)
+    output = torch.empty(vae.decoded_pixel_shape(latents.shape), dtype=torch.float32)
+    group = _ThreadedFakeGroup(1)
+    group._local.rank = 0
+
+    with pytest.raises(ValueError, match="strategy"):
+        decode_to_pixels_parallel(vae, latents, output, group, strategy="scatter")
+    with pytest.raises(ValueError, match="must provide the CPU output buffer"):
+        decode_to_pixels_parallel(vae, latents, None, group)
+    with pytest.raises(ValueError, match="CPU float32 tensor"):
+        decode_to_pixels_parallel(vae, latents, output[:, :, :-1], group)
+
+    group._local.rank = 1  # simulate a non-leader passing a buffer
+    group.world_size = 2
+    with pytest.raises(ValueError, match="Only the first sequence-parallel rank"):
+        decode_to_pixels_parallel(vae, latents, output, group)
+
+
+@pytest.mark.parametrize("world_size", (2, 4))
+@pytest.mark.parametrize("num_frames", (16, 22, 40))
+@torch.inference_mode()
+def test_parallel_encode_matches_serial(world_size: int, num_frames: int) -> None:
+    """Every rank must hold the full serial moments, bit for bit."""
+    torch.manual_seed(20260825 + num_frames)
+    vae = _tiny_vae()
+    pixels = torch.randint(0, 256, (1, 3, num_frames, 16, 16), dtype=torch.uint8)
+    expected = vae.encode_pixels(pixels).latent_dist.parameters
+
+    group = _ThreadedFakeGroup(world_size)
+    results = group.run(lambda rank: encode_pixels_parallel(vae, pixels, group).latent_dist.parameters)
+    for moments in results:
+        assert_close(moments, expected, atol=0.0, rtol=0.0)
+
+
+@torch.inference_mode()
+def test_parallel_encode_float_and_batched_slicing() -> None:
+    torch.manual_seed(20260826)
+    vae = _tiny_vae()
+    vae.enable_slicing()
+    pixels = torch.rand(2, 3, 22, 16, 16)
+    expected = vae.encode_pixels(pixels).latent_dist.parameters
+
+    group = _ThreadedFakeGroup(3)
+    results = group.run(lambda rank: encode_pixels_parallel(vae, pixels, group).latent_dist.parameters)
+    for moments in results:
+        assert_close(moments, expected, atol=0.0, rtol=0.0)
+
+
+def test_parallel_encode_validates_input() -> None:
+    vae = _tiny_vae()
+    group = _ThreadedFakeGroup(1)
+    group._local.rank = 0
+    with pytest.raises(ValueError, match="must remain on CPU"):
+        encode_pixels_parallel(vae, torch.empty(1, 3, 4, 16, 16, device="meta"), group)
+    with pytest.raises(TypeError, match="uint8 or a floating-point"):
+        encode_pixels_parallel(vae, torch.zeros(1, 3, 4, 16, 16, dtype=torch.int32), group)
+    with pytest.raises(ValueError, match="must have shape"):
+        encode_pixels_parallel(vae, torch.zeros(1, 4, 4, 16, 16), group)
+
+
+def test_fastvideo_args_strategy_literals_match_module() -> None:
+    """fastvideo_args mirrors the strategy literals to avoid importing model
+    modules at args construction; keep the two in sync."""
+    from fastvideo.fastvideo_args import FastVideoArgs
+
+    args = FastVideoArgs(model_path="test/parallel-vae")
+    assert args.vae_parallel_decode is False
+    assert args.vae_parallel_encode is False
+    assert args.vae_parallel_decode_strategy == DEFAULT_DECODE_GATHER_STRATEGY
+    assert args.vae_parallel_decode_strategy in DECODE_GATHER_STRATEGIES
+
+    for strategy in DECODE_GATHER_STRATEGIES:
+        assert FastVideoArgs(model_path="test/parallel-vae",
+                             vae_parallel_decode_strategy=strategy).vae_parallel_decode_strategy == strategy
+    with pytest.raises(ValueError, match="vae_parallel_decode_strategy"):
+        FastVideoArgs(model_path="test/parallel-vae", vae_parallel_decode_strategy="scatter")

fastvideo/tests/vaes/test_minimax_h3_parallel_vae_gpu.py

@@ -0,0 +1,110 @@
+# SPDX-License-Identifier: Apache-2.0
+"""GPU regression test for sequence-parallel MiniMax-H3 VAE decode/encode.
+
+Requires a multi-GPU torchrun launch (real NCCL collectives across an SP
+group); skipped otherwise:
+
+    torchrun --nproc-per-node=4 -m pytest \
+        fastvideo/tests/vaes/test_minimax_h3_parallel_vae_gpu.py -q
+
+Asserts the parallel drivers are bitwise equal to the serial rank-local
+decode/encode under the pipeline's fp16 autocast, for both transport
+strategies, and that repeated parallel runs are deterministic.
+"""
+
+import os
+
+import pytest
+import torch
+from torch.testing import assert_close
+
+from fastvideo.configs.models.vaes.minimax_h3_video import (
+    MiniMaxH3VideoVAEArchConfig,
+    MiniMaxH3VideoVAEConfig,
+)
+from fastvideo.models.vaes.minimax_h3_video import AutoencoderKLMiniMaxH3
+
+_WORLD_SIZE = int(os.environ.get("WORLD_SIZE", "1"))
+
+
+def _tiny_vae() -> AutoencoderKLMiniMaxH3:
+    """Same tiny geometry as test_minimax_h3_parallel_vae (test dirs are not packages)."""
+    arch = MiniMaxH3VideoVAEArchConfig(
+        latent_channels=4,
+        block_out_channels=(32, 32),
+        layers_per_block=1,
+        spatial_downsample_factors=(2, 2),
+        temporal_downsample_factors=(2, 2),
+        decoder_num_layers=1,
+        decoder_num_attention_heads=1,
+        decoder_attention_head_dim=8,
+        decoder_num_register_tokens=2,
+        decoder_ffn_mult=1,
+        latents_mean=(0.0, ) * 4,
+        latents_std=(1.0, ) * 4,
+    )
+    return AutoencoderKLMiniMaxH3(
+        MiniMaxH3VideoVAEConfig(
+            arch_config=arch,
+            use_tiling=False,
+            use_temporal_tiling=False,
+            use_parallel_tiling=False,
+        )).eval()
+
+pytestmark = [
+    pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA"),
+    pytest.mark.skipif(_WORLD_SIZE < 2, reason="requires a torchrun launch with WORLD_SIZE > 1"),
+]
+
+
+@pytest.fixture(scope="module")
+def sp_group():
+    from fastvideo.distributed import get_sp_group, maybe_init_distributed_environment_and_model_parallel
+    maybe_init_distributed_environment_and_model_parallel(1, _WORLD_SIZE)
+    return get_sp_group()
+
+
+@pytest.mark.parametrize("strategy", ("gather", "all_gather"))
+@pytest.mark.parametrize("latent_frames", (3, 13))
+@torch.no_grad()
+def test_parallel_decode_bitwise_matches_serial_on_gpu(sp_group, strategy: str, latent_frames: int) -> None:
+    from fastvideo.models.vaes.minimax_h3_parallel import decode_to_pixels_parallel
+
+    device = torch.device("cuda", torch.cuda.current_device())
+    torch.manual_seed(20260821)  # identical weights on every rank
+    vae = _tiny_vae().to(device)
+    latents = torch.randn(1, 4, latent_frames, 4, 4, generator=torch.Generator().manual_seed(7)).to(device)
+
+    with torch.autocast(device_type="cuda", dtype=torch.float16):
+        expected = torch.empty(vae.decoded_pixel_shape(latents.shape), dtype=torch.float32, pin_memory=True)
+        vae.decode_to_pixels(latents, expected)
+
+        outputs = []
+        for _ in range(3):  # repeat-determinism
+            output = None
+            if sp_group.is_first_rank:
+                output = torch.empty(vae.decoded_pixel_shape(latents.shape), dtype=torch.float32, pin_memory=True)
+            result = decode_to_pixels_parallel(vae, latents, output, sp_group, strategy=strategy)
+            outputs.append(result.clone() if result is not None else None)
+
+    if sp_group.is_first_rank:
+        for output in outputs:
+            assert_close(output, expected, atol=0.0, rtol=0.0)
+    else:
+        assert all(output is None for output in outputs)
+
+
+@torch.no_grad()
+def test_parallel_encode_bitwise_matches_serial_on_gpu(sp_group) -> None:
+    from fastvideo.models.vaes.minimax_h3_parallel import encode_pixels_parallel
+
+    device = torch.device("cuda", torch.cuda.current_device())
+    torch.manual_seed(20260821)
+    vae = _tiny_vae().to(device)
+    pixels = torch.randint(0, 256, (1, 3, 40, 16, 16), dtype=torch.uint8,
+                           generator=torch.Generator().manual_seed(9))
+
+    expected = vae.encode_pixels(pixels).latent_dist.parameters
+    for _ in range(3):
+        moments = encode_pixels_parallel(vae, pixels, sp_group).latent_dist.parameters
+        assert_close(moments, expected, atol=0.0, rtol=0.0)

fastvideo/tests/vaes/test_minimax_h3_vae_compile.py

@@ -0,0 +1,428 @@
+# SPDX-License-Identifier: Apache-2.0
+"""CPU contract tests for MiniMax H3 VAE compilation and profiling ranges.
+
+The final test is a CUDA regression gate for the reduce-overhead tile path
+(real tiled decode/encode with an unmocked ``_stitch_tiles``).
+"""
+
+from contextlib import contextmanager
+from types import MethodType, SimpleNamespace
+from typing import Any
+from unittest.mock import Mock, patch
+
+import pytest
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from fastvideo.models.vaes.minimax_h3_audio import (
+    MiniMaxH3AudioBigVGANDecoder,
+    MiniMaxH3AudioVAE,
+)
+from fastvideo.models.vaes.minimax_h3_video import (
+    AutoencoderKLMiniMaxH3,
+    MiniMaxH3VideoAttention,
+    MiniMaxH3VideoViTDecoder3d,
+)
+from fastvideo.platforms import AttentionBackendEnum
+from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
+
+
+def _empty_typed_module(module_type: type[nn.Module]) -> nn.Module:
+    """Create a weightless instance that retains its production module type."""
+    module = object.__new__(module_type)
+    nn.Module.__init__(module)
+    return module
+
+
+def _assert_dynamic_compile_selects_decoder(
+    vae_type: type[nn.Module],
+    decoder_type: type[nn.Module],
+) -> None:
+    """Verify one H3 VAE compiles only its top-level decoder in place."""
+    vae = _empty_typed_module(vae_type)
+    decoder = _empty_typed_module(decoder_type)
+    same_type_under_another_name = _empty_typed_module(decoder_type)
+    unrelated_submodule = nn.Identity()
+    vae.decoder = decoder
+    vae.same_type_under_another_name = same_type_under_another_name
+    vae.unrelated_submodule = unrelated_submodule
+
+    compiled_forward = Mock(name="compiled_forward")
+    compile_kwargs = {"backend": "inductor", "dynamic": False}
+    with patch(
+        "fastvideo.pipelines.composed_pipeline_base.torch.compile",
+        return_value=compiled_forward,
+    ) as compile_mock:
+        compiled_count = ComposedPipelineBase._compile_with_conditions(vae, compile_kwargs)
+
+    assert compiled_count == 1
+    compile_mock.assert_called_once()
+    selected_forward = compile_mock.call_args.args[0]
+    assert selected_forward.__self__ is decoder
+    assert selected_forward.__func__ is decoder_type.forward
+    assert compile_mock.call_args.kwargs == compile_kwargs
+    assert decoder.forward is compiled_forward
+    assert "forward" not in same_type_under_another_name.__dict__
+    assert "forward" not in unrelated_submodule.__dict__
+
+    wrong_type_vae = _empty_typed_module(vae_type)
+    wrong_type_vae.decoder = nn.Identity()
+    with patch("fastvideo.pipelines.composed_pipeline_base.torch.compile") as wrong_type_compile:
+        wrong_type_count = ComposedPipelineBase._compile_with_conditions(wrong_type_vae, compile_kwargs)
+    assert wrong_type_count == 0
+    wrong_type_compile.assert_not_called()
+
+
+def _assert_reduce_overhead_compile(compiled_function: Any) -> None:
+    """Verify a class-owned compile boundary enables CUDA Graph replay."""
+    assert hasattr(compiled_function, "get_compiler_config")
+    assert compiled_function.get_compiler_config()["triton.cudagraphs"] is True
+
+
+def test_video_attention_uses_selected_fastvideo_backend() -> None:
+    """Pass BSHD tensors to the selected dense backend without forward metadata."""
+    backend_call: dict[str, Any] = {}
+
+    class RecordingAttentionImpl:
+        """Record the backend construction and forward contracts."""
+
+        def __init__(self, **kwargs: Any) -> None:
+            backend_call["init"] = kwargs
+
+        def forward(
+            self,
+            query: torch.Tensor,
+            key: torch.Tensor,
+            value: torch.Tensor,
+            attention_metadata: Any,
+        ) -> torch.Tensor:
+            """Return values unchanged after recording the backend inputs."""
+            backend_call["shapes"] = (query.shape, key.shape, value.shape)
+            backend_call["metadata"] = attention_metadata
+            return value
+
+    class RecordingAttentionBackend:
+        """Supply the recording implementation through the backend API."""
+
+        @staticmethod
+        def get_impl_cls() -> type[RecordingAttentionImpl]:
+            return RecordingAttentionImpl
+
+    with (
+        patch("fastvideo.platforms.current_platform") as current_platform,
+        patch(
+            "fastvideo.models.vaes.minimax_h3_video.get_attn_backend",
+            return_value=RecordingAttentionBackend,
+        ) as get_backend,
+    ):
+        current_platform.is_cuda_alike.return_value = True
+        attention = MiniMaxH3VideoAttention(dim=8, heads=2, dim_head=4)
+
+    attention.to_q = nn.Identity()
+    attention.to_k = nn.Identity()
+    attention.to_v = nn.Identity()
+    attention.norm_q = nn.Identity()
+    attention.norm_k = nn.Identity()
+    attention.to_out[0] = nn.Identity()
+    output = attention(torch.empty((1, 3, 8), device="meta"))
+
+    get_backend.assert_called_once_with(
+        4,
+        torch.bfloat16,
+        supported_attention_backends=(
+            AttentionBackendEnum.TORCH_SDPA,
+            AttentionBackendEnum.FLASH_ATTN,
+        ),
+    )
+    assert backend_call["init"] == {
+        "num_heads": 2,
+        "head_size": 4,
+        "softmax_scale": 0.5,
+        "num_kv_heads": 2,
+        "causal": False,
+    }
+    assert backend_call["shapes"] == ((1, 3, 2, 4), ) * 3
+    assert backend_call["metadata"] is None
+    assert output.shape == (1, 3, 8)
+
+
+def test_video_attention_cpu_uses_torch_sdpa() -> None:
+    """Use PyTorch SDPA when H3 VAE attention receives CPU tensors."""
+    with (
+        patch("fastvideo.platforms.current_platform") as current_platform,
+        patch("fastvideo.models.vaes.minimax_h3_video.get_attn_backend") as get_backend,
+    ):
+        current_platform.is_cuda_alike.return_value = False
+        attention = MiniMaxH3VideoAttention(dim=8, heads=2, dim_head=4)
+
+    get_backend.assert_not_called()
+    assert attention.attn_impl is None
+
+    attention.to_q = nn.Identity()
+    attention.to_k = nn.Identity()
+    attention.to_v = nn.Identity()
+    attention.norm_q = nn.Identity()
+    attention.norm_k = nn.Identity()
+    attention.to_out[0] = nn.Identity()
+    hidden_states = torch.randn(1, 3, 8)
+    query = hidden_states.unflatten(2, (2, 4)).permute(0, 2, 1, 3)
+    expected = F.scaled_dot_product_attention(query, query, query).permute(0, 2, 1, 3).flatten(2, 3)
+
+    torch.testing.assert_close(attention(hidden_states), expected)
+
+
+def test_compile_with_conditions_selects_minimax_h3_video_decoder() -> None:
+    """Compile the registered video decoder with the VAE runtime kwargs."""
+    assert not hasattr(MiniMaxH3VideoViTDecoder3d.forward, "get_compiler_config")
+    _assert_dynamic_compile_selects_decoder(AutoencoderKLMiniMaxH3, MiniMaxH3VideoViTDecoder3d)
+
+
+def test_project_decoder_tile_uses_reduce_overhead_compile() -> None:
+    """Compile the per-tile decoder-input projection with CUDA Graph replay."""
+    _assert_reduce_overhead_compile(AutoencoderKLMiniMaxH3._project_decoder_tile)
+
+
+def test_stitch_tiles_uses_reduce_overhead_compile() -> None:
+    """Compile spatial tile blending and concatenation with CUDA Graph replay."""
+    _assert_reduce_overhead_compile(AutoencoderKLMiniMaxH3._stitch_tiles)
+
+
+def test_compile_with_conditions_selects_minimax_h3_audio_decoder() -> None:
+    """Compile the audio VAE decoder that the H3 waveform decode path calls."""
+    _assert_dynamic_compile_selects_decoder(MiniMaxH3AudioVAE, MiniMaxH3AudioBigVGANDecoder)
+
+
+def test_decode_emits_indexed_temporal_chunk_ranges() -> None:
+    """Nest frame-segment ranges under each temporal decoder chunk range."""
+    vae = _empty_typed_module(AutoencoderKLMiniMaxH3)
+    vae.tokens_chunk_size = 1
+    vae.token_overlap = 1
+    vae.temporal_compression_ratio = 1
+    vae.frame_pre_padding = 0
+    vae.frame_overlap = 1
+    vae.config = SimpleNamespace(token_drop=1)
+    vae._decode_clip = Mock(return_value=torch.zeros((1, 1, 2, 1, 1)))
+    range_events = []
+
+    @contextmanager
+    def record_range(name: str):
+        range_events.append(("enter", name))
+        try:
+            yield
+        finally:
+            range_events.append(("exit", name))
+
+    with patch("fastvideo.models.vaes.minimax_h3_video.nvtx_range", record_range):
+        decoded = vae._decode(torch.zeros((1, 1, 2, 1, 1)))
+
+    assert decoded.shape == (1, 1, 3, 1, 1)
+    assert vae._decode_clip.call_count == 2
+    assert range_events == [
+        ("enter", "minimax_h3.vae.temporal_chunk.0"),
+        ("enter", "minimax_h3.vae.temporal_chunk.0.frame_segment.0"),
+        ("exit", "minimax_h3.vae.temporal_chunk.0.frame_segment.0"),
+        ("enter", "minimax_h3.vae.temporal_chunk.0.frame_segment.1"),
+        ("exit", "minimax_h3.vae.temporal_chunk.0.frame_segment.1"),
+        ("exit", "minimax_h3.vae.temporal_chunk.0"),
+        ("enter", "minimax_h3.vae.temporal_chunk.1"),
+        ("enter", "minimax_h3.vae.temporal_chunk.1.frame_segment.0"),
+        ("exit", "minimax_h3.vae.temporal_chunk.1.frame_segment.0"),
+        ("enter", "minimax_h3.vae.temporal_chunk.1.frame_segment.1"),
+        ("exit", "minimax_h3.vae.temporal_chunk.1.frame_segment.1"),
+        ("exit", "minimax_h3.vae.temporal_chunk.1"),
+    ]
+
+
+def test_decode_clip_no_spatial_tiling_stage_ranges() -> None:
+    """Separate untiled latent projection and decoder ranges."""
+    vae = _empty_typed_module(AutoencoderKLMiniMaxH3)
+    vae.use_tiling = False
+    range_events = []
+    vae.post_quant_conv = nn.Identity()
+    vae.post_quant_conv.register_forward_hook(
+        lambda _module, _args, _output: range_events.append(("call", "post_quant_conv")))
+    vae.decoder = nn.Identity()
+    vae.decoder.register_forward_hook(
+        lambda _module, _args, _output: range_events.append(("call", "decoder_forward")))
+    latent_clip = torch.zeros((1, 1, 1, 2, 2))
+
+    @contextmanager
+    def record_range(name: str):
+        range_events.append(("enter", name))
+        try:
+            yield
+        finally:
+            range_events.append(("exit", name))
+
+    with patch("fastvideo.models.vaes.minimax_h3_video.nvtx_range", record_range):
+        decoded_clip = vae._decode_clip(latent_clip)
+
+    assert decoded_clip is latent_clip
+    assert range_events == [
+        ("enter", "minimax_h3.vae.decode_clip"),
+        ("enter", "minimax_h3.vae.decode_clip.no_s_tile.post_quant_conv"),
+        ("call", "post_quant_conv"),
+        ("exit", "minimax_h3.vae.decode_clip.no_s_tile.post_quant_conv"),
+        ("enter", "minimax_h3.vae.decode_clip.no_s_tile.decoder_forward"),
+        ("call", "decoder_forward"),
+        ("exit", "minimax_h3.vae.decode_clip.no_s_tile.decoder_forward"),
+        ("exit", "minimax_h3.vae.decode_clip"),
+    ]
+
+
+def test_decode_clip_emits_tiled_stage_ranges() -> None:
+    """Nest indexed decoder tiles between tile-splitting and stitching ranges."""
+    vae = _empty_typed_module(AutoencoderKLMiniMaxH3)
+    vae.use_tiling = True
+    vae.spatial_compression_ratio = 1
+    vae.tile_sample_min_height = 1
+    vae.tile_sample_min_width = 1
+    vae.tile_sample_min_overlap_height = 0
+    vae.tile_sample_min_overlap_width = 0
+    vae._split_tiles = Mock(side_effect=[
+        ([0, 1], [1, 1], [0]),
+        ([0, 1], [1, 1], [0]),
+    ])
+    vae.post_quant_conv = nn.Identity()
+    vae._project_decoder_tile = Mock(side_effect=vae.post_quant_conv)
+    vae.decoder = nn.Identity()
+    stitched_clip = torch.zeros((1, 1, 1, 2, 2))
+    vae._stitch_tiles = Mock(return_value=stitched_clip)
+    range_events = []
+
+    @contextmanager
+    def record_range(name: str):
+        range_events.append(("enter", name))
+        try:
+            yield
+        finally:
+            range_events.append(("exit", name))
+
+    with patch("fastvideo.models.vaes.minimax_h3_video.nvtx_range", record_range):
+        decoded_clip = vae._decode_clip(torch.zeros((1, 1, 1, 2, 2)))
+
+    # The tile driver must hand back a caller-owned copy: under
+    # mode="reduce-overhead" the stitched canvas is CUDA-graph pooled storage
+    # that the next replay overwrites, so returning it by identity is a bug.
+    assert decoded_clip is not stitched_clip
+    assert torch.equal(decoded_clip, stitched_clip)
+    assert vae._split_tiles.call_count == 2
+    assert vae._project_decoder_tile.call_count == 4
+    assert vae._stitch_tiles.call_count == 1
+    assert range_events == [
+        ("enter", "minimax_h3.vae.decode_clip"),
+        ("enter", "minimax_h3.vae.decode_clip.split_tiles"),
+        ("exit", "minimax_h3.vae.decode_clip.split_tiles"),
+        ("enter", "minimax_h3.vae.decode_clip.decode_tiles"),
+        ("enter", "minimax_h3.vae.decode_clip.tile.0.0"),
+        ("enter", "minimax_h3.vae.decode_clip.tile.decoder_forward"),
+        ("exit", "minimax_h3.vae.decode_clip.tile.decoder_forward"),
+        ("exit", "minimax_h3.vae.decode_clip.tile.0.0"),
+        ("enter", "minimax_h3.vae.decode_clip.tile.0.1"),
+        ("enter", "minimax_h3.vae.decode_clip.tile.decoder_forward"),
+        ("exit", "minimax_h3.vae.decode_clip.tile.decoder_forward"),
+        ("exit", "minimax_h3.vae.decode_clip.tile.0.1"),
+        ("enter", "minimax_h3.vae.decode_clip.tile.1.0"),
+        ("enter", "minimax_h3.vae.decode_clip.tile.decoder_forward"),
+        ("exit", "minimax_h3.vae.decode_clip.tile.decoder_forward"),
+        ("exit", "minimax_h3.vae.decode_clip.tile.1.0"),
+        ("enter", "minimax_h3.vae.decode_clip.tile.1.1"),
+        ("enter", "minimax_h3.vae.decode_clip.tile.decoder_forward"),
+        ("exit", "minimax_h3.vae.decode_clip.tile.decoder_forward"),
+        ("exit", "minimax_h3.vae.decode_clip.tile.1.1"),
+        ("exit", "minimax_h3.vae.decode_clip.decode_tiles"),
+        ("enter", "minimax_h3.vae.decode_clip.stitch_tiles"),
+        ("exit", "minimax_h3.vae.decode_clip.stitch_tiles"),
+        ("exit", "minimax_h3.vae.decode_clip"),
+    ]
+
+
+def _tiny_real_vae() -> AutoencoderKLMiniMaxH3:
+    """Random-weight VAE small enough for a real tiled decode/encode on GPU."""
+    from fastvideo.configs.models.vaes.minimax_h3_video import (
+        MiniMaxH3VideoVAEArchConfig,
+        MiniMaxH3VideoVAEConfig,
+    )
+
+    arch = MiniMaxH3VideoVAEArchConfig(
+        latent_channels=4,
+        block_out_channels=(32, 32),
+        layers_per_block=1,
+        spatial_downsample_factors=(2, 2),
+        temporal_downsample_factors=(2, 2),
+        decoder_num_layers=1,
+        decoder_num_attention_heads=1,
+        decoder_attention_head_dim=8,
+        decoder_num_register_tokens=2,
+        decoder_ffn_mult=1,
+        latents_mean=(0.0, ) * 4,
+        latents_std=(1.0, ) * 4,
+    )
+    return AutoencoderKLMiniMaxH3(
+        MiniMaxH3VideoVAEConfig(
+            arch_config=arch,
+            use_tiling=False,
+            use_temporal_tiling=False,
+            use_parallel_tiling=False,
+        )).eval()
+
+
+def _dynamo_original(compiled_function: Any) -> Any:
+    """Return the eager callable behind a ``torch.compile``-decorated function."""
+    original = getattr(compiled_function, "_torchdynamo_orig_callable", None)
+    if original is None:
+        original = getattr(compiled_function, "__wrapped__", None)
+    assert original is not None, "cannot recover the eager tile helpers"
+    return original
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="reduce-overhead tile compile requires CUDA graphs")
+@torch.inference_mode()
+def test_tiled_decode_and_encode_survive_cudagraph_buffer_reuse_on_cuda() -> None:
+    """Real tiled decode()/encode() with an unmocked reduce-overhead ``_stitch_tiles``.
+
+    Regression gate for the CUDA-graph output-clobbering bug: the stitched
+    canvas is a cudagraph static buffer, and the collect-then-``torch.cat``
+    consumers (``_decode``/``_encode``/``_encode_pixels``) hold chunk/clip
+    results across subsequent ``_stitch_tiles`` replays. Without the eager
+    ``.clone()`` at the tile-driver returns, the first tiled ``decode()`` with
+    >=2 temporal chunks raises ``accessing tensor output of CUDAGraphs that
+    has been overwritten by a subsequent run``. This test needs >=2 chunks
+    (decode), >=2 clips (encode), and a >=2x2 spatial tile grid.
+    """
+    torch.manual_seed(20260821)
+    vae = _tiny_real_vae().to("cuda")
+    vae.enable_tiling(16, 16, 4, 4)
+
+    # 8 latent tokens = 2 temporal chunks (tokens_chunk_size 5); 8x8 latents =
+    # 32x32 pixels = a 2x2 grid of 16px tiles.
+    z = torch.randn(1, 4, 8, 8, 8, device="cuda")
+    pad_tokens, num_chunks, _ = vae._temporal_decode_plan(z.shape[2])
+    assert num_chunks >= 2, "decode workload must span multiple stitch replays"
+
+    decoded_first = vae.decode(z).sample
+    decoded_second = vae.decode(z).sample
+    assert torch.equal(decoded_first, decoded_second)
+
+    # 34 frames = 2 encode clips of clip_length 17 -> 2 stitch replays.
+    pixels = torch.rand(1, 3, 34, 32, 32, device="cuda")
+    encoded_first = vae.encode(pixels).latent_dist.parameters
+    encoded_second = vae.encode(pixels).latent_dist.parameters
+    assert torch.equal(encoded_first, encoded_second)
+
+    uint8_pixels = torch.randint(0, 256, (1, 3, 34, 32, 32), dtype=torch.uint8)
+    streamed_first = vae.encode_pixels(uint8_pixels).latent_dist.parameters
+    streamed_second = vae.encode_pixels(uint8_pixels).latent_dist.parameters
+    assert torch.equal(streamed_first, streamed_second)
+
+    # Output parity vs the fully eager tile helpers (same weights, same math;
+    # the tolerance absorbs inductor fusion reassociation only).
+    eager_vae = _tiny_real_vae().to("cuda")
+    eager_vae.load_state_dict(vae.state_dict())
+    eager_vae.enable_tiling(16, 16, 4, 4)
+    eager_vae._stitch_tiles = MethodType(_dynamo_original(AutoencoderKLMiniMaxH3._stitch_tiles), eager_vae)
+    eager_vae._project_decoder_tile = MethodType(_dynamo_original(AutoencoderKLMiniMaxH3._project_decoder_tile),
+                                                 eager_vae)
+    torch.testing.assert_close(decoded_first, eager_vae.decode(z).sample, atol=2e-4, rtol=2e-4)
+    torch.testing.assert_close(encoded_first, eager_vae.encode(pixels).latent_dist.parameters, atol=2e-4, rtol=2e-4)

fastvideo/tests/worker/test_gpu_worker.py

@@ -1,11 +1,40 @@
 from types import SimpleNamespace
+from unittest.mock import Mock
 
 import pytest
 import torch
 
 from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.pipelines import ForwardBatch
-from fastvideo.worker.gpu_worker import Worker
+from fastvideo.worker.gpu_worker import Worker, _log_cuda_device_uuid
+
+
+def test_cuda_device_uuid_receipt_is_disabled_without_nvtx_profiling(monkeypatch) -> None:
+    """Avoid NVIDIA property access during ordinary worker initialization."""
+    get_device_properties = Mock()
+    monkeypatch.setenv("FASTVIDEO_NVTX_PROFILE", "0")
+    monkeypatch.setattr(torch.cuda, "get_device_properties", get_device_properties)
+
+    _log_cuda_device_uuid(0, torch.device("cuda:0"))
+
+    get_device_properties.assert_not_called()
+
+
+def test_cuda_device_uuid_receipt_identifies_profiled_worker(monkeypatch) -> None:
+    """Bind one profiled worker rank to its NVIDIA device UUID in logs."""
+    log_info = Mock()
+    monkeypatch.setenv("FASTVIDEO_NVTX_PROFILE", "1")
+    monkeypatch.setattr(torch.cuda, "get_device_properties", lambda device: SimpleNamespace(uuid="device-uuid"))
+    monkeypatch.setattr("fastvideo.worker.gpu_worker.logger.info", log_info)
+
+    _log_cuda_device_uuid(2, torch.device("cuda:0"))
+
+    log_info.assert_called_once_with(
+        "Worker %d CUDA device UUID: GPU-%s",
+        2,
+        "device-uuid",
+        local_main_process_only=False,
+    )
 
 
 def _worker_returning(output_batch: ForwardBatch) -> Worker:

fastvideo/worker/gpu_worker.py

@@ -4,6 +4,7 @@ from typing import Any, cast
 
 import torch
 
+import fastvideo.envs as envs
 from fastvideo.distributed import (cleanup_dist_env_and_memory, maybe_init_distributed_environment_and_model_parallel)
 from fastvideo.distributed.parallel_state import get_local_torch_device
 from fastvideo.fastvideo_args import FastVideoArgs
@@ -13,6 +14,14 @@ from fastvideo.pipelines import ForwardBatch, LoRAPipeline, build_pipeline
 logger = init_logger(__name__)
 
 
+def _log_cuda_device_uuid(rank: int, device: torch.device) -> None:
+    """Record an NVIDIA worker UUID when external NVTX profiling is enabled."""
+    if not envs.FASTVIDEO_NVTX_PROFILE:
+        return
+    device_uuid = torch.cuda.get_device_properties(device).uuid
+    logger.info("Worker %d CUDA device UUID: GPU-%s", rank, device_uuid, local_main_process_only=False)
+
+
 class Worker:
 
     def __init__(self, fastvideo_args: FastVideoArgs, local_rank: int, rank: int, distributed_init_method: str):
@@ -61,6 +70,8 @@ class Worker:
         if current_platform.is_cuda_alike():
             torch.cuda.set_device(self.device)
             self.init_gpu_memory = torch.cuda.mem_get_info(self.device)[0]
+            if current_platform.is_cuda():
+                _log_cuda_device_uuid(self.rank, self.device)
         else:
             # For MPS, we can't get memory info the same way
             self.init_gpu_memory = 0

tests/local_tests/encoders/test_minimax_h3_qwen3_vl_parity.py

@@ -6,11 +6,8 @@ pipeline with FastVideo's production ``TextEncoderLoader`` path.  It covers
 the three numerical branches the H3 pipelines exercise: text-only tokens,
 image features, and video features.
 
-The production encoder is built only as far as the layer-50 conditioning tap
-by default (``num_hidden_layers_override``), so it returns fewer hidden states
-than the official full stack.  Every state it does build is compared
-bit-exactly against the official value at the same index, which pins the tap
-and would catch a truncated stack that still applied the final norm.
+The production encoder returns only the selected layer-50 hidden state, which
+is compared bit-exactly with the same state from the official full stack.
 """
 
 from __future__ import annotations
@@ -152,13 +149,13 @@ def _make_cases(root: Path) -> dict[str, dict[str, torch.Tensor]]:
     return cases
 
 
-def _run_cases(
+def _run_reference_cases(
     model: torch.nn.Module,
     cases: dict[str, dict[str, torch.Tensor]],
     device: torch.device,
-) -> dict[str, tuple[torch.Tensor, ...]]:
+) -> dict[str, torch.Tensor]:
     dtype = next(model.parameters()).dtype
-    outputs: dict[str, tuple[torch.Tensor, ...]] = {}
+    outputs: dict[str, torch.Tensor] = {}
     for name, case in cases.items():
         inputs = {
             key: value.to(device=device, dtype=dtype if key.startswith("pixel_values") else value.dtype)
@@ -172,7 +169,28 @@ def _run_cases(
             )
         assert result.hidden_states is not None
         assert len(result.hidden_states) > MINIMAX_H3_TEXT_ENCODER_LAYER
-        outputs[name] = tuple(hidden_state.detach().cpu() for hidden_state in result.hidden_states)
+        outputs[name] = result.hidden_states[MINIMAX_H3_TEXT_ENCODER_LAYER][0].detach().cpu()
+    return outputs
+
+
+def _run_production_cases(
+    model: torch.nn.Module,
+    cases: dict[str, dict[str, torch.Tensor]],
+    device: torch.device,
+) -> dict[str, torch.Tensor]:
+    dtype = next(model.parameters()).dtype
+    outputs: dict[str, torch.Tensor] = {}
+    for name, case in cases.items():
+        inputs = {
+            key: value.to(device=device, dtype=dtype if key.startswith("pixel_values") else value.dtype)
+            for key, value in case.items()
+            if key not in {"attention_mask", "mm_token_type_ids"}
+        }
+        inputs["input_ids"] = inputs["input_ids"][0]
+        with torch.inference_mode():
+            result = model(**inputs)
+        assert result.ndim == 2
+        outputs[name] = result.detach().cpu()
     return outputs
 
 
@@ -217,32 +235,25 @@ def test_minimax_h3_qwen3_vl_parity() -> None:
     assert not load_errors, f"Official Qwen3-VL checkpoint did not load strictly: {load_errors}"
     official = official_full.model.eval().to(device)
     del official_full
-    expected = _run_cases(official, cases, device)
+    expected = _run_reference_cases(official, cases, device)
     del official
     _reclaim_vram()
 
     production = TextEncoderLoader().load(str(root / "text_encoder"), _production_loader_args())
     assert getattr(production, "_fastvideo_input_device", device) == device
-    actual = _run_cases(production, cases, device)
+    actual = _run_production_cases(production, cases, device)
 
     assert actual.keys() == expected.keys()
-    # The production stack is built only as far as the conditioning tap by
-    # default (``num_hidden_layers_override``), so it yields one hidden state
-    # per built layer plus the embeddings, while the official model always
-    # yields the full tuple. Every state the production model produces must be
-    # bit-identical to the official value at the same index; the shared-prefix
-    # comparison would in particular catch a truncated stack that still
-    # applied the final norm, which is the failure mode that silently changes
-    # conditioning. With the override set to None the lengths are equal and
-    # this remains the original full comparison, final normed state included.
-    built_layers = int(production.language_model.num_layers)
     for name in expected:
-        assert len(actual[name]) == built_layers + 1
-        assert len(actual[name]) <= len(expected[name])
-        for layer, (result, reference) in enumerate(zip(actual[name], expected[name], strict=False)):
-            assert_close(result, reference, atol=0.0, rtol=0.0, msg=lambda message: f"{name} layer {layer}: {message}")
-        result = actual[name][MINIMAX_H3_TEXT_ENCODER_LAYER]
-        reference = expected[name][MINIMAX_H3_TEXT_ENCODER_LAYER]
+        result = actual[name]
+        reference = expected[name]
+        assert_close(
+            result,
+            reference,
+            atol=0.0,
+            rtol=0.0,
+            msg=lambda message: f"{name} layer {MINIMAX_H3_TEXT_ENCODER_LAYER}: {message}",
+        )
         drift = (result.float() - reference.float()).abs()
         print(
             f"{name}: max_abs={drift.max().item():.8f} mean_abs={drift.mean().item():.8f}",

tests/local_tests/minimax_h3/README.md

@@ -57,10 +57,9 @@ pytest \
 ```
 
 With a gate enabled, missing CUDA, source, or weights is a failure. Recorded component evidence is exact for both DiT
-partitions, the video VAE, and all Qwen3-VL hidden states; audio decode has maximum absolute drift `2.4e-7`. The
-production Qwen3-VL stack is now built only to the layer-50 conditioning tap by default
-(`num_hidden_layers_override`), so the encoder gate compares every hidden state the production model builds
-bit-exactly against the official full stack at the same index.
+partitions and the video VAE; audio decode has maximum absolute drift `2.4e-7`. The encoder gate compares the slim
+forward's selected layer-50 hidden state bit-exactly against the same state from the official full stack across text,
+image, and video inputs.
 
 The video VAE test verifies the reference checkout at commit
 `abc5e9bf71fd38f53cd471bc3acaa84bc5ecbfdc` and compares the production CPU `uint8` `encode_pixels()` path against