[Diffusion] MAGI-2: regional compile coverage (#7174)

Signed-off-by: cr-gao <gaochenrui@sjtu.edu.cn>

tests/diffusion/models/magi2/test_native_compile_cuda.py

@@ -0,0 +1,77 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+from __future__ import annotations
+
+import pytest
+import torch
+
+from tests.diffusion.models.magi2.test_native_packing import (
+    _longer_text_tensors,
+    _sampler_tensors,
+    _tiny_model,
+    _tiny_sampler,
+)
+from tests.helpers.mark import hardware_marks
+from vllm_omni.diffusion.models.magi2.sampler_magi2 import CFGConfig
+
+pytestmark = [
+    pytest.mark.diffusion,
+    pytest.mark.core_model,
+    *hardware_marks(res={"cuda": "L4"}, num_cards=1),
+    pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA"),
+]
+
+
+def _cuda_steps(sampler, tensors=_sampler_tensors):
+    timesteps = (torch.tensor([900.0]), torch.tensor([600.0]), torch.tensor([300.0]))
+    return [
+        sampler.prepare_model_input(
+            **{name: value.cuda() for name, value in tensors(seed).items()},
+            t=t,
+            cfg_config=CFGConfig(),
+        )
+        for seed, t in enumerate(timesteps)
+    ]
+
+
+def test_inductor_regions_match_eager_in_deterministic_mode(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("MAGI2_DETERMINISTIC", "1")
+    model = _tiny_model(params_dtype=torch.bfloat16).cuda()
+    sampler = _tiny_sampler(model)
+    steps = _cuda_steps(sampler)
+    expected = [sampler.forward(step) for step in steps]
+    new_request_step = _cuda_steps(sampler)[0]
+    longer_text_step = _cuda_steps(sampler, _longer_text_tensors)[0]
+    expected_new_request = sampler.forward(new_request_step)
+    expected_longer_text = sampler.forward(longer_text_step)
+
+    torch._dynamo.reset()
+    graphs_before = torch._dynamo.utils.counters["stats"]["unique_graphs"]
+    # The pipeline compiles with inductor emulating eager's intermediate
+    # low-precision casts, so fused bf16 chains keep eager's rounding.
+    model.compile_regions(fullgraph=True, dynamic=True, options={"emulate_precision_casts": True})
+    first = sampler.forward(steps[0])
+    with torch._dynamo.config.patch(error_on_recompile=True):
+        compiled = [first, sampler.forward(steps[1]), sampler.forward(steps[2])]
+        repeated = [sampler.forward(step) for step in steps]
+        # A second request repacks the same shapes; a third changes the
+        # text length and therefore every token count.
+        compiled_new_request = sampler.forward(new_request_step)
+        compiled_longer_text = sampler.forward(longer_text_step)
+
+    # Emulated precision casts restore eager's rounding boundaries; what is
+    # left is one-ulp transcendental noise that some Triton backends carry.
+    for (video, audio), (video_ref, audio_ref) in zip(compiled, expected, strict=True):
+        torch.testing.assert_close(video, video_ref, atol=1e-6, rtol=0)
+        torch.testing.assert_close(audio, audio_ref, atol=1e-6, rtol=0)
+    for (video, audio), (video_again, audio_again) in zip(compiled, repeated, strict=True):
+        assert torch.equal(video, video_again)
+        assert torch.equal(audio, audio_again)
+    for (video, audio), (video_ref, audio_ref) in (
+        (compiled_new_request, expected_new_request),
+        (compiled_longer_text, expected_longer_text),
+    ):
+        torch.testing.assert_close(video, video_ref, atol=1e-6, rtol=0)
+        torch.testing.assert_close(audio, audio_ref, atol=1e-6, rtol=0)
+    assert torch._dynamo.utils.counters["stats"]["unique_graphs"] - graphs_before == 7