multimodalart/minimax-h3-aoti
MiniMax-H3 AoTI packages
AOT-Inductor packages for one MiniMaxH3TransformerBlock, keyed <width>/torch<X.Y>/sm<cc>/<shape>. One package serves all 50 blocks of a transformer: it carries no weights, and spaces.aoti_patch binds each block's own live state_dict() on its first forward. Built and consumed by h3_aoti.py in the MiniMax-H3 Spaces; built off-Space by job_bf16_aoti.py on an rtx-pro-6000 Job.
What is here
bf16/torch2.11/sm120/dynamic is the one that matters: a dynamic sequence dimension, so it serves every canvas, every duration and every prompt length. The <HxWxF> keys hold byte-identical copies of it, for a Space configured to look artifacts up per canvas. _inputs/ holds cached conditioner output (prompt_embeds + text_token_tags) so a build job never has to load the 62 GiB Qwen3-VL. _reports/ holds each build's full JSON report.
A dynamic package is not a nicety. build_packed_sequence pads nothing, so S = num_text_tokens + condition_rows + audio_rows + video_rows moves with the prompt: the same canvas is S = 37726 for a 16-token prompt and S = 37728 for an 18-token one. A static package serves exactly one prompt length.
Measured, unquantized bfloat16, 124 frames, everything resident on one RTX PRO 6000 Blackwell
The absolute saving is near-constant at ~0.5 s/step, which is what AoTI can remove: 50 blocks of kernel-launch overhead plus the norm / rotary / AdaLN-gather epilogues. It cannot touch the matmuls, and at S = 37726 one block is ~70 TFLOP of GEMM and attention, so the released 768x1344 canvas is compute bound. The smaller the canvas, the more this pays.
Proven end to end on the pool: the MiniMax-H3 Space loads bf16/torch2.11/sm120/dynamic at startup, patches all 50 blocks and generates.
The trap, recorded because the symptom is a segfault with no message
spaces.zero.torch.aoti.LazyAOTIModel binds a package's constants by name, intersecting the block's state_dict() with get_constant_fqns(), and silently keeps whatever it cannot match. A shallow clone (_shallow_clone_module, which aoti_patch uses and which the export side was mirroring) exported in torch.export's default non-strict mode lifts every weight twice — once as a named PARAMETER, once as an anonymous lifted_tensor_<N> CONSTANT_TENSOR sharing the same data_ptr(). The anonymous half binds to nothing and the compiled kernel reads pointers nobody set. Measured on the real block, at full size, in plain bfloat16 with no tensor subclass anywhere:
So it is neither accelerate's offload hooks nor torchao's tensor subclasses, which were both blamed first. Export the live block when it has nothing to flatten; use strict=True when a subclass forces the clone.
temb is padded to a fixed 4 rows
temb is (num_distinct_timesteps, time_embed_dim) — one row at step 0, where video and audio still share a noise level, two from step 1 where their sigma schedules diverge — and the block gathers from a 3 * rows AdaLN table, so the row count is part of the compiled shape. Padding to a fixed 4 rows makes it constant; rows past the live ones are never gathered, so the output is unchanged. Validated rather than assumed: the build replays a real 1-row and a real 2-row call through the compiled block and diffs both against eager.