[perf] inference: regional fullgraph torch.compile of the DiT blocks (port of #1718 to the inference path)

Extend the training-side regional-compile port (internal 863e87342, GO
verdict vsa_gate/compile_ab/VERDICT.md) to inference so every H3 run can
compile the 52 transformer blocks. Opt-in and off by default:

- New FastVideoArgs.inference_torch_compile (CLI --inference-torch-compile),
  env FASTVIDEO_INFERENCE_TORCH_COMPILE=1 folded in __post_init__ (the
  attention_backend parse-once pattern), reachable through
  PipelineSelection.experimental {"inference_torch_compile": true} exactly
  like the VSA_sparsity / VSA_tile_size knobs.
- maybe_load_fsdp_model applies the compile right after the transformer
  loads: per-_compile_conditions block, fullgraph=True + inductor
  options.emulate_precision_casts injected, no user kwargs needed (the
  compile-A/B verdict recipe). prepare_for_compile still runs first, so the
  H3 fusion-inertness warning of #1735 is preserved.
- _regional_compile_unsupported_reason ported from the training loader:
  VSA / VSA-H3 backends and the FASTVIDEO_DISABLE_ATTENTION_COMPILE=1
  escape hatch degrade the transformer to eager with one warning; FLASH_ATTN
  on flash-attn 3 is rejected with an actionable message. FA4/FA2/SDPA
  compile through their existing custom-op boundaries (forward-only is
  enough at inference; the training port's backward custom ops are not
  needed under no_grad).
- FASTVIDEO_DISABLE_ATTENTION_COMPILE default flipped to 0 (attention is
  traced by default), matching the training port and upstream #1718 —
  eager runs are unaffected (torch.compiler.disable is inert outside
  dynamo).
- enable_torch_compile + inference_torch_compile together skip the
  pipeline-level DiT compile (the loader already owns those forwards).
- examples: --inference-torch-compile on basic_minimax_h3_t2v.py (dense
  target) and basic_fasth3.py (exercises the VSA degrade path).
- CPU-safe contract tests for the guard and the kwargs injection.

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

examples/inference/basic/basic_fasth3.py

@@ -85,6 +85,12 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--compile-mode",
                         default=None,
                         help='torch.compile mode, e.g. "reduce-overhead" for CUDA graphs')
+    parser.add_argument("--inference-torch-compile",
+                        action="store_true",
+                        help="regional fullgraph torch.compile of each DiT block after load. NOTE: this "
+                        "script always runs the VSA-H3 backend, which is not fullgraph-traceable — the "
+                        "loader logs one warning and keeps the transformer eager. The flag is exposed "
+                        "here to exercise exactly that guard")
     parser.add_argument("--repeats",
                         type=int,
                         default=1,
@@ -119,6 +125,8 @@ def main() -> None:
     }
     if args.vsa_sparsity > 0.0:
         experimental["VSA_sparsity"] = args.vsa_sparsity
+    if args.inference_torch_compile:
+        experimental["inference_torch_compile"] = True
 
     generator = VideoGenerator.from_config(
         GeneratorConfig(

examples/inference/basic/basic_minimax_h3_t2v.py

@@ -15,6 +15,7 @@ from fastvideo.api import (
     OffloadConfig,
     OutputConfig,
     ParallelismConfig,
+    PipelineSelection,
     SamplingConfig,
 )
 
@@ -41,6 +42,12 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--compile-mode",
                         default=None,
                         help='torch.compile mode, e.g. "reduce-overhead" for CUDA graphs')
+    parser.add_argument("--inference-torch-compile",
+                        action="store_true",
+                        help="regional fullgraph torch.compile of each DiT block after load (the #1718 "
+                        "training-port semantics: no kwargs; fullgraph + emulate_precision_casts injected). "
+                        "First generation pays the inductor JIT (~1-2 min); use --repeats >= 2 and time "
+                        "the last repeat. FASTVIDEO_INFERENCE_TORCH_COMPILE=1 is equivalent")
     parser.add_argument("--repeats",
                         type=int,
                         default=1,
@@ -54,9 +61,16 @@ def main() -> None:
     output_dir = Path(args.output)
     output_dir.mkdir(parents=True, exist_ok=True)
 
+    # Boot-time run configuration folded into FastVideoArgs (the same
+    # experimental-dict route basic_fasth3.py uses for the VSA knobs).
+    experimental: dict[str, object] = {}
+    if args.inference_torch_compile:
+        experimental["inference_torch_compile"] = True
+
     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,