nodes/hao-ai-lab-FastVideo
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

[bugfix] set FASTVIDEO_H3_VAE_TILE_BATCH in tests through envs overrides

Browse Source 
The env-var policy test flagged the monkeypatch.setenv/delenv writes in the
H3 VAE tile-batch tests. Use envs.FASTVIDEO_H3_VAE_TILE_BATCH.override via
the env_overrides fixture, and regenerate the env table in
docs/contributing/env_vars.md for the new default and description.
This commit is contained in:
Aryan Kumar
2026-11-01 15:17:55 -08:00
parent 78a83c8276
commit 10e2e03c0f
3 changed files with 16 additions and 14 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/contributing/env_vars.md
+2 -2
View File
@@ -278,9 +278,9 @@ longer exists also fails the test, so the fixing pull request deletes its entry.
| `FASTVIDEO_H3_SPLICE_FROM_STEP` | int | `4` | eval | First denoising step run by FASTVIDEO_H3_SPLICE_TRANSFORMER. |
| `FASTVIDEO_H3_ENCODER_LAYERWISE` | bool | `0` | performance | Stream MiniMax-H3 text-encoder language layers through exact-size pinned host memory (text-only prompts). |
| `FASTVIDEO_H3_ENCODER_FUSED_DEQUANT` | bool | `0` | performance | Expand the serialized NVFP4 MiniMax-H3 text encoder with one fused Triton pass on GPUs without FP4 GEMM. |
| `FASTVIDEO_H3_VAE_TILE_BATCH` | int | `1` | performance | Spatial tiles per MiniMax-H3 video VAE decoder call; 1 decodes per tile. |
| `FASTVIDEO_H3_VAE_TILE_BATCH` | int | `8` | performance | Spatial tiles per MiniMax-H3 video VAE decoder call. The ViT decoder treats batch entries independently, so batching tiles matches per-tile decoding, except for NVFP4 decoders with dynamic activation scales, which always decode per tile. On GB200 at 768x1344, 8 tiles per call decodes 107 frames in 3.7 s instead of 5.4 s and 362 frames in 12.8 s instead of 18.3 s (+0.7 GiB peak). 1 decodes per tile. |
| `FASTVIDEO_H3_REF2VA_MEMO_ENTRIES` | int | `0` | performance | Entries in each content-keyed MiniMax-H3 memo of reference encodes (Qwen3-VL Ref2VA presentation, VAE keyframe latents); repeat references are reused exactly. 0 disables. |
| `FASTVIDEO_H3_VAE_TILE_PARALLEL` | bool | `0` | performance | Split MiniMax-H3 VAE spatial tiles (not only temporal chunks) across the sequence-parallel ranks in the parallel decode and keyframe encode; bitwise equal to the serial path. |
| `FASTVIDEO_H3_VAE_TILE_PARALLEL` | bool | `0` | performance | Split MiniMax-H3 VAE spatial tiles (not only temporal chunks) across the sequence-parallel ranks in the parallel decode and keyframe encode; bitwise equal to the serial path. Requires FASTVIDEO_H3_VAE_TILE_BATCH=1 (the split replays the per-tile path). |
| `FASTVIDEO_H3_VAE_NVFP4_FUSED` | bool | `1` | performance | Run NVFP4 MiniMax-H3 VAE decoder blocks through the bit-exact fused Triton kernels at inference. |
| `FASTVIDEO_H3_VAE_NVFP4_CUDA_GRAPH` | bool | `1` | performance | Replay small fused NVFP4 MiniMax-H3 VAE decoder calls (at most four 1797-token tiles) as per-shape CUDA graphs. |
| `FASTVIDEO_H3_VAE_NVFP4_DECODER` | path | unset | performance | Exported NVFP4 MiniMax-H3 video decoder (.pt, or .safetensors with bf16 masters or pre-packed NVFP4 weights) that replaces the VAE decoder at load; skips the INT8 ConvRot overlay. |
fastvideo/tests/vaes/test_minimax_h3_vae_compile.py
+3 -2
View File
@@ -15,6 +15,7 @@ import torch
import torch.nn as nn
import torch.nn.functional as F
import fastvideo.envs as envs
from fastvideo.models.vaes.minimax_h3_audio import (
MiniMaxH3AudioBigVGANDecoder,
MiniMaxH3AudioVAE,
@@ -350,9 +351,9 @@ def test_decode_clip_no_spatial_tiling_stage_ranges() -> None:
]
def test_decode_clip_emits_tiled_stage_ranges(monkeypatch) -> None:
def test_decode_clip_emits_tiled_stage_ranges(env_overrides) -> None:
"""Nest indexed decoder tiles between tile-splitting and stitching ranges."""
monkeypatch.setenv("FASTVIDEO_H3_VAE_TILE_BATCH", "1")
env_overrides.enter_context(envs.FASTVIDEO_H3_VAE_TILE_BATCH.override(1))
vae = _empty_typed_module(AutoencoderKLMiniMaxH3)
vae.use_tiling = True
vae.spatial_compression_ratio = 1
fastvideo/tests/vaes/test_minimax_h3_video_vae_streaming.py
+11 -10
View File
@@ -3,6 +3,7 @@ import pytest
import torch
from torch.testing import assert_close
import fastvideo.envs as envs
from fastvideo.configs.models.vaes.minimax_h3_video import (
MiniMaxH3VideoVAEArchConfig,
MiniMaxH3VideoVAEConfig,
@@ -184,24 +185,24 @@ def test_decode_to_pixels_rejects_incomplete_output(monkeypatch) -> None:
vae.decode_to_pixels(latents, output)
def test_tile_batch_defaults_to_eight(monkeypatch) -> None:
def test_tile_batch_defaults_to_eight(env_overrides) -> None:
from fastvideo.models.vaes.minimax_h3_video import _tile_batch_size
monkeypatch.delenv("FASTVIDEO_H3_VAE_TILE_BATCH", raising=False)
env_overrides.enter_context(envs.FASTVIDEO_H3_VAE_TILE_BATCH.override(None))
assert _tile_batch_size() == 8
monkeypatch.setenv("FASTVIDEO_H3_VAE_TILE_BATCH", "3")
env_overrides.enter_context(envs.FASTVIDEO_H3_VAE_TILE_BATCH.override(3))
assert _tile_batch_size() == 3
monkeypatch.setenv("FASTVIDEO_H3_VAE_TILE_BATCH", "0")
env_overrides.enter_context(envs.FASTVIDEO_H3_VAE_TILE_BATCH.override(0))
assert _tile_batch_size() == 1
def test_dynamic_nvfp4_act_scale_decodes_per_tile(monkeypatch) -> None:
def test_dynamic_nvfp4_act_scale_decodes_per_tile(env_overrides) -> None:
from fastvideo.models.vaes.minimax_h3_nvfp4_decoder import NVFP4DecoderLinear
from fastvideo.models.vaes.minimax_h3_video import _tile_batch_size
decoder = torch.nn.Module()
decoder.proj = NVFP4DecoderLinear(torch.zeros(16, 16), None, None, torch.float32, act_scale="dynamic")
monkeypatch.setenv("FASTVIDEO_H3_VAE_TILE_BATCH", "8")
env_overrides.enter_context(envs.FASTVIDEO_H3_VAE_TILE_BATCH.override(8))
assert _tile_batch_size(decoder) == 1
assert _tile_batch_size() == 8
@@ -213,7 +214,7 @@ def test_dynamic_nvfp4_act_scale_decodes_per_tile(monkeypatch) -> None:
@torch.inference_mode()
def test_dynamic_nvfp4_act_scale_decodes_one_tile_per_call(monkeypatch) -> None:
def test_dynamic_nvfp4_act_scale_decodes_one_tile_per_call(env_overrides) -> None:
from fastvideo.models.vaes.minimax_h3_nvfp4_decoder import NVFP4DecoderLinear
torch.manual_seed(20260814)
@@ -223,7 +224,7 @@ def test_dynamic_nvfp4_act_scale_decodes_one_tile_per_call(monkeypatch) -> None:
tile_sample_min_overlap_height=2,
tile_sample_min_overlap_width=2)
latents = torch.randn(1, 4, 12, 4, 4)
monkeypatch.setenv("FASTVIDEO_H3_VAE_TILE_BATCH", "8")
env_overrides.enter_context(envs.FASTVIDEO_H3_VAE_TILE_BATCH.override(8))
calls = []
decoder_forward = vae.decoder.forward
@@ -247,7 +248,7 @@ def test_dynamic_nvfp4_act_scale_decodes_one_tile_per_call(monkeypatch) -> None:
@torch.inference_mode()
def test_batched_tile_decode_matches_per_tile(monkeypatch) -> None:
def test_batched_tile_decode_matches_per_tile(env_overrides) -> None:
torch.manual_seed(20260814)
vae = _tiny_vae()
vae.enable_tiling(tile_sample_min_height=8,
@@ -257,7 +258,7 @@ def test_batched_tile_decode_matches_per_tile(monkeypatch) -> None:
latents = torch.randn(1, 4, 12, 4, 4)
outputs = {}
for tile_batch in (1, 8):
monkeypatch.setenv("FASTVIDEO_H3_VAE_TILE_BATCH", str(tile_batch))
env_overrides.enter_context(envs.FASTVIDEO_H3_VAE_TILE_BATCH.override(tile_batch))
output = torch.empty(vae.decoded_pixel_shape(latents.shape), dtype=torch.float32)
vae.decode_to_pixels(latents, output)
outputs[tile_batch] = output