Merge branch 'main' into codex-vae-spatial-decode-auto

tests/diffusion/distributed/test_expert_parallel_layout.py

@@ -64,6 +64,7 @@ def test_moe_ep_maps_diffusion_sp_cfg_dp_to_vllm_groups(monkeypatch):
         local_rank,
         backend,
         group_name,
+        use_all2all=False,
     ):
         del backend
         group = _FakeGroup(
@@ -73,6 +74,7 @@ def test_moe_ep_maps_diffusion_sp_cfg_dp_to_vllm_groups(monkeypatch):
             device_communicator=object(),
             reduce_scatter=lambda tensor, **kwargs: tensor,
         )
+        group.use_all2all = use_all2all
         created_groups.append(group)
         return group
 
@@ -121,6 +123,9 @@ def test_moe_ep_maps_diffusion_sp_cfg_dp_to_vllm_groups(monkeypatch):
     assert parallel_state.vllm_parallel_state._PCP.device_communicator is not None
     assert parallel_state.vllm_parallel_state._DP.device_communicator is not None
     assert parallel_state.vllm_parallel_state._EP.device_communicator is not None
+    # vLLM 0.27's MoE oracle asserts the EP communicator has an all2all
+    # manager, which vLLM only builds for groups created with use_all2all.
+    assert parallel_state.vllm_parallel_state._EP.use_all2all is True
     assert hasattr(parallel_state.vllm_parallel_state._PCP, "reduce_scatter")
     assert hasattr(parallel_state.vllm_parallel_state._DP, "reduce_scatter")
     assert hasattr(parallel_state.vllm_parallel_state._EP, "reduce_scatter")
@@ -313,3 +318,35 @@ def test_non_moe_ep_fails_before_vllm_ep_remap(monkeypatch):
     assert parallel_state.vllm_parallel_state._PCP is None
     assert parallel_state.vllm_parallel_state._EP is None
     assert parallel_state._EXPERT_PARALLEL_GROUP_RANKS is None
+
+
+@pytest.mark.cpu
+@pytest.mark.core_model
+def test_init_vllm_group_forwards_use_all2all_only_when_supported(monkeypatch):
+    """use_all2all reaches vLLM only on versions whose API accepts it.
+
+    vLLM 0.27 builds the EP all2all manager only for groups created with
+    use_all2all=True (its MoE oracle then asserts the manager exists —
+    build 2953, HunyuanImage3-DIT accuracy). Older vLLM has no such kwarg,
+    so forwarding it unconditionally would TypeError at init.
+    """
+    received = {}
+
+    def modern(group_ranks, local_rank, backend, group_name=None, use_device_communicator=True, use_all2all=False):
+        received["modern"] = use_all2all
+        return object()
+
+    monkeypatch.setattr(parallel_state.vllm_parallel_state, "init_model_parallel_group", modern)
+    parallel_state.init_vllm_model_parallel_group([[0]], 0, "gloo", "ep", use_all2all=True)
+    assert received["modern"] is True
+
+    parallel_state.init_vllm_model_parallel_group([[0]], 0, "gloo", "tp")
+    assert received["modern"] is False
+
+    def legacy(group_ranks, local_rank, backend, group_name=None, use_device_communicator=True):
+        received["legacy"] = True
+        return object()
+
+    monkeypatch.setattr(parallel_state.vllm_parallel_state, "init_model_parallel_group", legacy)
+    parallel_state.init_vllm_model_parallel_group([[0]], 0, "gloo", "ep", use_all2all=True)
+    assert received["legacy"] is True

tests/diffusion/distributed/test_wan_spatial_shard.py

@@ -251,6 +251,128 @@ def test_reshard_from_trimmed_width_pads_invalid_columns(monkeypatch: pytest.Mon
     assert torch.equal(out.flatten(), torch.tensor([4.0, 0.0]))
 
 
+@pytest.mark.core_model
+@pytest.mark.cpu
+@pytest.mark.parametrize("split_dim", ["height", "width"])
+@pytest.mark.parametrize(
+    ("full_extent", "world_size"),
+    [
+        pytest.param(5, 4, id="four-ranks-empty-tail"),
+        pytest.param(20, 8, id="production-eight-ranks-empty-tail"),
+        pytest.param(39, 2, id="two-ranks-partial-tail"),
+    ],
+)
+def test_reshard_from_trimmed_extent_clamps_and_reconstructs(
+    monkeypatch: pytest.MonkeyPatch,
+    split_dim: str,
+    full_extent: int,
+    world_size: int,
+):
+    local_extent = (full_extent + world_size - 1) // world_size
+    shape = (1, 1, 1, full_extent, 1) if split_dim == "height" else (1, 1, 1, 1, full_extent)
+    x = torch.arange(full_extent, dtype=torch.float32).reshape(shape)
+    outputs = []
+
+    for rank in range(world_size):
+        monkeypatch.setattr(wan_spatial_shard, "_rank_world", lambda group, rank=rank: (rank, world_size))
+        token = wan_spatial_shard._SPATIAL_SHARD_CONTEXT.set(
+            wan_spatial_shard.SpatialShardContext(
+                input_extent=full_extent,
+                local_input_extent=local_extent,
+                split_dim=split_dim,
+                rank=rank,
+                world_size=world_size,
+            )
+        )
+        try:
+            out = wan_spatial_shard.reshard_from_trimmed_extent(
+                x,
+                local_extent=local_extent,
+                split_dim=split_dim,
+                group=object(),
+            )
+        finally:
+            wan_spatial_shard._SPATIAL_SHARD_CONTEXT.reset(token)
+
+        start = rank * local_extent
+        valid_extent = min(local_extent, max(0, full_extent - start))
+        expected = torch.zeros(local_extent, dtype=torch.float32)
+        if valid_extent:
+            expected[:valid_extent] = torch.arange(start, start + valid_extent, dtype=torch.float32)
+        assert out.shape[_spatial_dim_for_test(split_dim)] == local_extent
+        assert torch.equal(out.flatten(), expected)
+        outputs.append(out)
+
+    reconstructed = torch.cat(outputs, dim=_spatial_dim_for_test(split_dim))
+    reconstructed = reconstructed.narrow(_spatial_dim_for_test(split_dim), 0, full_extent)
+    assert torch.equal(reconstructed, x)
+
+
+def _spatial_dim_for_test(split_dim: str) -> int:
+    return -2 if split_dim == "height" else -1
+
+
+@pytest.mark.core_model
+@pytest.mark.cpu
+def test_reshard_from_trimmed_extent_rejects_rank_mismatch(monkeypatch: pytest.MonkeyPatch):
+    monkeypatch.setattr(wan_spatial_shard, "_rank_world", lambda group: (1, 2))
+    x = torch.zeros((1, 1, 1, 1, 4), dtype=torch.float32)
+    token = wan_spatial_shard._SPATIAL_SHARD_CONTEXT.set(
+        wan_spatial_shard.SpatialShardContext(
+            input_extent=4,
+            local_input_extent=2,
+            split_dim="width",
+            rank=0,
+            world_size=2,
+        )
+    )
+    try:
+        with pytest.raises(RuntimeError, match="group_rank=1, context_rank=0"):
+            wan_spatial_shard.reshard_from_trimmed_extent(
+                x,
+                local_extent=2,
+                split_dim="width",
+                group=object(),
+            )
+    finally:
+        wan_spatial_shard._SPATIAL_SHARD_CONTEXT.reset(token)
+
+
+@pytest.mark.core_model
+@pytest.mark.cpu
+def test_attention_wrapper_rejects_spatial_extent_change(monkeypatch: pytest.MonkeyPatch):
+    class ShrinkingWanAttentionBlock(torch.nn.Module):
+        def forward(self, x):
+            return x[..., :-1]
+
+    monkeypatch.setattr(wan_spatial_shard, "_rank_world", lambda group: (0, 2))
+    monkeypatch.setattr(
+        wan_spatial_shard,
+        "all_gather_along_dim",
+        lambda x, *, group, dim, dst=None: torch.cat([x, x], dim=dim),
+    )
+    module = ShrinkingWanAttentionBlock()
+    wan_spatial_shard._patch_attention_block(module, group=object(), split_dim="width")
+    x = torch.zeros((1, 1, 1, 1, 2), dtype=torch.float32)
+    token = wan_spatial_shard._SPATIAL_SHARD_CONTEXT.set(
+        wan_spatial_shard.SpatialShardContext(
+            input_extent=4,
+            local_input_extent=2,
+            split_dim="width",
+            rank=0,
+            world_size=2,
+        )
+    )
+    try:
+        with pytest.raises(
+            RuntimeError,
+            match=r"local_extent=2, gathered_extent=4, trimmed_extent=4, output_extent=3",
+        ):
+            module(x)
+    finally:
+        wan_spatial_shard._SPATIAL_SHARD_CONTEXT.reset(token)
+
+
 @pytest.mark.core_model
 @pytest.mark.cpu
 def test_halo_exchange_single_rank_noop(monkeypatch: pytest.MonkeyPatch):

tests/diffusion/layers/test_fused_moe.py

@@ -110,7 +110,7 @@ class TestFusedMoEFactory:
         monkeypatch.setitem(
             sys.modules,
             "vllm.model_executor.layers.fused_moe",
-            SimpleNamespace(FusedMoE=mock_vllm_fused_moe),
+            SimpleNamespace(FusedMoEFactory=mock_vllm_fused_moe),
         )
 
         result = fused_moe.FusedMoE(prefix="test", a=1, reduce_results=True)
@@ -139,7 +139,7 @@ class TestFusedMoEFactory:
         monkeypatch.setitem(
             sys.modules,
             "vllm.model_executor.layers.fused_moe",
-            SimpleNamespace(FusedMoE=mocker.MagicMock(return_value=mock_runner)),
+            SimpleNamespace(FusedMoEFactory=mocker.MagicMock(return_value=mock_runner)),
         )
         mocker.patch.object(fused_moe, "current_omni_platform", mocker.MagicMock())
         mock_set_num_tokens = mocker.patch.object(fused_moe, "_set_forward_context_num_tokens")
@@ -169,7 +169,7 @@ class TestFusedMoEFactory:
         monkeypatch.setitem(
             sys.modules,
             "vllm.model_executor.layers.fused_moe",
-            SimpleNamespace(FusedMoE=mocker.MagicMock(return_value=mock_runner)),
+            SimpleNamespace(FusedMoEFactory=mocker.MagicMock(return_value=mock_runner)),
         )
         mocker.patch.object(fused_moe, "current_omni_platform", mocker.MagicMock())
         mock_set_num_tokens = mocker.patch.object(fused_moe, "_set_forward_context_num_tokens")

tests/diffusion/lora/test_loader.py

@@ -0,0 +1,438 @@
+from collections import defaultdict
+
+import pytest
+import torch
+import torch.nn as nn
+from diffusers.loaders.lora_conversion_utils import (
+    _convert_non_diffusers_qwen_lora_to_diffusers,
+)
+from pytest_mock import MockerFixture
+from torch.testing import assert_close
+
+from vllm_omni.diffusion.lora.loader import (
+    LoraLoaderMixin,
+    QwenImageLoraLoaderMixin,
+    WanLoraLoaderMixin,
+    _prepare_lora_delta,
+    _remap_state_dict_keys,
+)
+
+HEAD_DIM = 24
+RANK = 2
+NUM_LAYERS = 10
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+class DummyTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        d_model: int,
+        bias: bool = False,
+        stacked_params: bool = False,
+    ):
+        super().__init__()
+        self.attn = nn.Module()
+        if stacked_params:
+            self.attn.to_qkv = nn.Linear(d_model * 3, d_model, bias=bias)
+        else:
+            self.attn.to_q = nn.Linear(d_model, d_model, bias=bias)
+            self.attn.to_k = nn.Linear(d_model, d_model, bias=bias)
+            self.attn.to_v = nn.Linear(d_model, d_model, bias=bias)
+        self.attn.to_out = nn.Linear(d_model, d_model, bias=bias)
+
+
+class DummyTransformer(nn.Module):
+    def __init__(self, num_layers: int, d_model: int, bias: bool = False, stacked_params: bool = False):
+        super().__init__()
+
+        self.blocks = nn.ModuleList(
+            [DummyTransformerBlock(d_model, bias=bias, stacked_params=stacked_params) for _ in range(num_layers)]
+        )
+        if stacked_params:
+            self.stacked_params_mapping = [
+                (".attn.to_qkv", ".attn.to_q", "q"),
+                (".attn.to_qkv", ".attn.to_k", "k"),
+                (".attn.to_qkv", ".attn.to_v", "v"),
+            ]
+
+
+class DummyPipeline(nn.Module, LoraLoaderMixin):
+    def __init__(self, num_layers: int, d_model: int, bias: bool = False, stacked_params: bool = False):
+        super().__init__()
+        self.transformer = DummyTransformer(num_layers, d_model, bias=bias, stacked_params=stacked_params)
+
+
+# ======================================================
+# helper functions for test
+# ======================================================
+def _filter_parameters(named_params, mode: str = "all"):
+    if mode == "all":
+        return named_params
+
+    filtered = []
+    for name, param in named_params:
+        if name.endswith(mode):
+            filtered.append((name, param))
+
+    return filtered
+
+
+def assert_params_equal(module, original_params, mode: str = "all"):
+    for name, param in _filter_parameters(module.named_parameters(), mode):
+        assert_close(param, original_params[name])
+
+
+def assert_params_not_equal(module, original_params, mode: str = "all"):
+    for name, param in _filter_parameters(module.named_parameters(), mode):
+        assert not torch.allclose(param, original_params[name])
+
+
+def make_dummy_lora_state_dict(
+    base_key,
+    weight_a: torch.Tensor | None = None,
+    weight_b: torch.Tensor | None = None,
+    weight_bias: torch.Tensor | None = None,
+    bias: bool = False,
+    lora_a_suffix: str = "lora_A.weight",
+    lora_b_suffix: str = "lora_B.weight",
+    lora_bias_suffix: str = "bias",
+):
+    """
+    Create a dummy LoRA state dict.
+
+    Returns:
+        tuple: (state_dict, expected_delta)
+    """
+    key_a = f"{base_key}.{lora_a_suffix}"
+    key_b = f"{base_key}.{lora_b_suffix}"
+    key_bias = f"{base_key}.{lora_bias_suffix}" if bias else None
+
+    if weight_a is None:
+        weight_a = torch.randn(RANK, HEAD_DIM)
+    if weight_b is None:
+        weight_b = torch.randn(HEAD_DIM, RANK)
+
+    state_dict = {}
+    expected_delta = None
+
+    if bias:
+        state_dict[key_a] = weight_a
+        state_dict[key_b] = weight_b
+        weight_bias = torch.randn(HEAD_DIM)
+        state_dict[key_bias] = weight_bias
+        expected_delta = weight_bias
+    else:
+        state_dict[key_a] = weight_a
+        state_dict[key_b] = weight_b
+        expected_delta = weight_b @ weight_a
+
+    return state_dict, expected_delta
+
+
+def make_lora_state_dict_for_module(
+    module,
+    lora_rank=RANK,
+    bias=False,
+    lora_a_suffix: str = "lora_A.weight",
+    lora_b_suffix: str = "lora_B.weight",
+    lora_bias_suffix: str = "bias",
+    prefix: str = "transformer",
+    remap_proj_out: bool = True,
+):
+    param_to_weight_names = defaultdict(list)
+    if hasattr(module, "stacked_params_mapping"):
+        for param_name, weight_name, _ in module.stacked_params_mapping:
+            param_to_weight_names[param_name].append(weight_name)
+
+    state_dict = {}
+    for name, param in module.named_parameters(prefix=prefix):
+        if name.endswith(".weight"):
+            base_key = name[: -len(".weight")]
+            if remap_proj_out and base_key.endswith(".to_out"):
+                base_key = base_key.replace(".to_out", ".to_out.0")
+
+            d_in, d_out = param.shape
+            is_stacked_param = False
+            for param_name, weight_names in param_to_weight_names.items():
+                if param_name not in name:
+                    continue
+                is_stacked_param = True
+                target_in = d_in // len(weight_names)
+                for weight_name in weight_names:
+                    lora_key = base_key.replace(param_name, weight_name)
+                    weight_a = torch.randn(lora_rank, d_out)
+                    weight_b = torch.randn(target_in, lora_rank)
+                    state_dict[f"{lora_key}.{lora_a_suffix}"] = weight_a
+                    state_dict[f"{lora_key}.{lora_b_suffix}"] = weight_b
+
+            if is_stacked_param:
+                continue
+
+            weight_a = torch.randn(lora_rank, d_out)
+            weight_b = torch.randn(d_in, lora_rank)
+            key_a = f"{base_key}.{lora_a_suffix}"
+            key_b = f"{base_key}.{lora_b_suffix}"
+            state_dict[key_a] = weight_a
+            state_dict[key_b] = weight_b
+        elif name.endswith(".bias") and bias:
+            base_key = name[: -len(".bias")]
+            if remap_proj_out and base_key.endswith(".to_out"):
+                base_key = base_key.replace(".to_out", ".to_out.0")
+
+            d_bias = param.shape[0]
+            is_stacked_param = False
+            for param_name, weight_names in param_to_weight_names.items():
+                if param_name not in name:
+                    continue
+                is_stacked_param = True
+                target_bias = d_bias // len(weight_names)
+                for weight_name in weight_names:
+                    lora_key = base_key.replace(param_name, weight_name)
+                    weight_bias = torch.randn(target_bias)
+                    state_dict[f"{lora_key}.{lora_bias_suffix}"] = weight_bias
+
+            if is_stacked_param:
+                continue
+
+            key_bias = f"{base_key}.{lora_bias_suffix}"
+            state_dict[key_bias] = torch.randn_like(param)
+
+    return state_dict
+
+
+# ======================================================
+# Test loader helper functions
+# ======================================================
+def test_prepare_lora_delta_with_stacked_params():
+    base_key = "transformer.attn.to_qkv"
+
+    sd0, delta0 = make_dummy_lora_state_dict("transformer.attn.to_q")
+    sd1, delta1 = make_dummy_lora_state_dict("transformer.attn.to_k")
+    sd2, delta2 = make_dummy_lora_state_dict("transformer.attn.to_v")
+
+    lora_state_dict = {}
+    lora_state_dict.update(sd0)
+    lora_state_dict.update(sd1)
+    lora_state_dict.update(sd2)
+    expected_delta = torch.concat([delta0, delta1, delta2])
+
+    stacked_mapping = {".attn.to_qkv": [".attn.to_q", ".attn.to_k", ".attn.to_v"]}
+
+    actual_delta, used_keys = _prepare_lora_delta(
+        lora_state_dict,
+        base_key,
+        stacked_mapping,
+    )
+
+    # validate the delta
+    assert actual_delta is not None
+    assert_close(actual_delta, expected_delta)
+
+    # validate the used keys
+    key_q_a = "transformer.attn.to_q.lora_A.weight"
+    key_q_b = "transformer.attn.to_q.lora_B.weight"
+    key_k_a = "transformer.attn.to_k.lora_A.weight"
+    key_k_b = "transformer.attn.to_k.lora_B.weight"
+    key_v_a = "transformer.attn.to_v.lora_A.weight"
+    key_v_b = "transformer.attn.to_v.lora_B.weight"
+
+    for key in [key_q_a, key_q_b, key_k_a, key_k_b, key_v_a, key_v_b]:
+        assert key in used_keys
+
+
+def test_remap_state_dict_keys_matched_two():
+    lora_state_dict, _ = make_dummy_lora_state_dict(
+        "transformer.attn.to_out.0", bias=True, lora_bias_suffix="lora_B.bias"
+    )
+
+    lora_state_dict = _remap_state_dict_keys(
+        lora_state_dict,
+        [(".to_out.0.", ".to_out."), (".lora_B.bias", ".bias")],
+    )
+
+    assert len(lora_state_dict) == 3
+    assert "transformer.attn.to_out.bias" in lora_state_dict
+
+
+# ======================================================
+# Test LoraLoaderMixin
+# ======================================================
+class TestLoraLoaderMixin:
+    @pytest.mark.parametrize(
+        "bias, stacked_params",
+        [
+            (False, False),
+            (True, False),
+            (False, True),
+            (True, True),
+        ],
+        ids=[
+            "basic",
+            "bias",
+            "stacked_params",
+            "bias_stacked_params",
+        ],
+    )
+    def test_load_lora_into_module(self, bias: bool, stacked_params: bool):
+        pipeline = DummyPipeline(NUM_LAYERS, HEAD_DIM, bias=bias, stacked_params=stacked_params)
+        original_params = {name: param.clone() for name, param in pipeline.transformer.named_parameters()}
+
+        lora_state_dict = make_lora_state_dict_for_module(pipeline.transformer, bias=bias)
+        assert len(lora_state_dict) > 0
+
+        # remap the keys to vllm-omni format
+        lora_state_dict = _remap_state_dict_keys(lora_state_dict, [(".to_out.0.", ".to_out.")])
+
+        used_keys = pipeline.load_lora_into_module(lora_state_dict, pipeline.transformer)
+        # validate the parameters are updated
+        assert_params_not_equal(pipeline.transformer, original_params)
+
+        # validate all keys are used
+        missing_keys = set(lora_state_dict.keys()) - used_keys
+        assert len(missing_keys) == 0
+
+    def test_unload_module_lora(self):
+        pipeline = DummyPipeline(NUM_LAYERS, HEAD_DIM)
+        original_params = {name: param.clone() for name, param in pipeline.transformer.named_parameters()}
+
+        lora_state_dict = make_lora_state_dict_for_module(pipeline.transformer)
+        # validate the state dict is valid
+        assert len(lora_state_dict) > 0
+
+        # remap the keys to vllm-omni format
+        lora_state_dict = _remap_state_dict_keys(lora_state_dict, [(".to_out.0.", ".to_out.")])
+
+        used_keys = pipeline.load_lora_into_module(lora_state_dict, pipeline.transformer)
+
+        # validate the weights are updated
+        assert_params_not_equal(pipeline.transformer, original_params)
+
+        # validate all keys are used
+        missing_keys = set(lora_state_dict.keys()) - used_keys
+        assert len(missing_keys) == 0
+
+        unload_used_keys = pipeline.unload_module_lora(lora_state_dict, pipeline.transformer)
+        missing_keys = set(lora_state_dict.keys()) - unload_used_keys
+        assert len(missing_keys) == 0
+        assert len(unload_used_keys - used_keys) == 0
+
+        # validate the weights are restored
+        assert_params_equal(pipeline.transformer, original_params)
+
+
+# ======================================================
+# Test QwenImageLoraLoaderMixin
+# ======================================================
+class DummyQwenImagePipeline(nn.Module, QwenImageLoraLoaderMixin):
+    def __init__(self, num_layers: int, d_model: int, bias: bool = False):
+        super().__init__()
+        self.transformer = DummyTransformer(num_layers, d_model, bias=bias, stacked_params=True)
+
+
+class TestQwenImageLoraLoaderMixin:
+    def test_load_lora_weights(self, mocker: MockerFixture):
+        pipeline = DummyQwenImagePipeline(NUM_LAYERS, HEAD_DIM)
+        original_params = {name: param.clone() for name, param in pipeline.transformer.named_parameters()}
+
+        lora_state_dict = make_lora_state_dict_for_module(pipeline.transformer)
+        assert len(lora_state_dict) > 0
+
+        mocker.patch(
+            "vllm_omni.diffusion.lora.loader.get_converter_by_pipeline",
+            return_value=_convert_non_diffusers_qwen_lora_to_diffusers,
+        )
+        pipeline.load_lora_weights(lora_state_dict, "adapter0")
+
+        # validate the weights are updated after lora loaded
+        assert_params_not_equal(pipeline.transformer, original_params)
+
+        # validate lora_loaded map is updated after lora loaded
+        assert "adapter0" in pipeline.lora_loaded
+
+    def test_unload_lora_weights(self, mocker: MockerFixture):
+        pipeline = DummyQwenImagePipeline(NUM_LAYERS, HEAD_DIM)
+        original_params = {name: param.clone() for name, param in pipeline.transformer.named_parameters()}
+
+        lora_state_dict = make_lora_state_dict_for_module(pipeline.transformer)
+        assert len(lora_state_dict) > 0
+
+        mocker.patch(
+            "vllm_omni.diffusion.lora.loader.get_converter_by_pipeline",
+            return_value=_convert_non_diffusers_qwen_lora_to_diffusers,
+        )
+        pipeline.load_lora_weights(lora_state_dict, "adapter0")
+        # validate the weights are updated after lora loaded
+        assert_params_not_equal(pipeline.transformer, original_params)
+
+        # validate lora_loaded map is updated after lora loaded
+        assert "adapter0" in pipeline.lora_loaded
+
+        pipeline.unload_lora_weights("adapter0")
+        # validate the weights are restored after lora unloaded
+        assert_params_equal(pipeline.transformer, original_params)
+
+        # validate lora_loaded map is updated
+        assert "adapter0" not in pipeline.lora_loaded
+
+    def test_load_diffusers_lora_applies_non_unit_alpha(self, mocker: MockerFixture):
+        pipeline = DummyQwenImagePipeline(1, HEAD_DIM)
+        to_out = pipeline.transformer.blocks[0].attn.to_out
+        original_weight = to_out.weight.detach().clone()
+        base_key = "blocks.0.attn.to_out.0"
+        lora_a = torch.randn(RANK, HEAD_DIM)
+        lora_b = torch.randn(HEAD_DIM, RANK)
+        alpha = torch.tensor(6.0)
+        state_dict = {
+            f"{base_key}.lora_A.weight": lora_a,
+            f"{base_key}.lora_B.weight": lora_b,
+            f"{base_key}.alpha": alpha,
+        }
+        mocker.patch(
+            "vllm_omni.diffusion.lora.loader.get_converter_by_pipeline",
+            return_value=_convert_non_diffusers_qwen_lora_to_diffusers,
+        )
+
+        pipeline.load_lora_weights(state_dict, "adapter0")
+
+        expected_weight = original_weight + (lora_b @ lora_a) * (alpha.item() / RANK)
+        assert_close(to_out.weight, expected_weight)
+
+        pipeline.unload_lora_weights("adapter0")
+        assert_close(to_out.weight, original_weight)
+
+
+# ======================================================
+# Test WanLoraLoaderMixin
+# ======================================================
+class DummyWanPipeline(nn.Module, WanLoraLoaderMixin):
+    def __init__(self, num_layers: int, d_model: int):
+        super().__init__()
+        self.has_transformer_2 = True
+        self.transformer = DummyTransformer(num_layers, d_model, stacked_params=True)
+        self.transformer_2 = DummyTransformer(num_layers, d_model, stacked_params=True)
+
+
+def test_wan_load_lora_weights_targets_both_transformers(mocker: MockerFixture):
+    pipeline = DummyWanPipeline(NUM_LAYERS, HEAD_DIM)
+    original_high = {name: param.clone() for name, param in pipeline.transformer.named_parameters()}
+    original_low = {name: param.clone() for name, param in pipeline.transformer_2.named_parameters()}
+    high_state_dict = make_lora_state_dict_for_module(pipeline.transformer)
+    low_state_dict = make_lora_state_dict_for_module(pipeline.transformer_2)
+
+    mocker.patch(
+        "vllm_omni.diffusion.lora.loader._load_lora_state_dict",
+        side_effect=[high_state_dict, low_state_dict],
+    )
+
+    pipeline.load_lora_weights(["high.safetensors", "low.safetensors"], "adapter0")
+
+    assert_params_not_equal(pipeline.transformer, original_high)
+    assert_params_not_equal(pipeline.transformer_2, original_low)
+    assert set(pipeline.lora_loaded["adapter0"]) == {"transformer", "transformer_2"}
+
+    pipeline.unload_lora_weights("adapter0")
+
+    assert_params_equal(pipeline.transformer, original_high)
+    assert_params_equal(pipeline.transformer_2, original_low)
+    assert "adapter0" not in pipeline.lora_loaded

tests/diffusion/models/lingbot_video/test_request_utils.py

@@ -348,3 +348,53 @@ def test_normalize_lingbot_request_model_size_overrides_sampling_dimensions():
         num_frames=81,
     )
     assert (config.height, config.width) == (480, 480)
+
+
+def test_normalize_lingbot_request_reads_size_from_extra_args():
+    """Regression: /v1/videos delivers `extra_params` via sampling.extra_args.
+
+    The nightly TI2V case sends `extra_params={"size": "320x192"}` with no
+    height/width form fields; the size used to be dropped and the video came
+    out at the 480x480 default (build 2952, LingBot Video MoE Function Test).
+    """
+    config = _normalize(
+        {
+            "prompt": "motion",
+            "modalities": ["video"],
+            "multi_modal_data": {"image": Image.new("RGB", (320, 192))},
+        },
+        num_frames=9,
+        extra_args={"size": "320x192"},
+    )
+    assert config.mode is LingBotGenerationMode.TI2V
+    assert (config.height, config.width) == (192, 320)
+
+
+def test_normalize_lingbot_request_extra_args_size_overrides_sampling_dimensions():
+    config = _normalize(
+        {"prompt": "motion", "modalities": ["video"]},
+        width=480,
+        height=480,
+        num_frames=81,
+        extra_args={"size": "320x192"},
+    )
+    assert (config.height, config.width) == (192, 320)
+
+
+def test_normalize_lingbot_request_reads_width_height_from_extra_args():
+    # Parity with the pre-#5311 pipeline, which honored extra_args height/width.
+    config = _normalize(
+        {"prompt": "motion", "modalities": ["video"]},
+        num_frames=81,
+        extra_args={"width": 320, "height": 192},
+    )
+    assert (config.height, config.width) == (192, 320)
+
+
+def test_resolve_lingbot_output_dimensions_reads_extra_fields():
+    assert resolve_lingbot_output_dimensions(extra_fields={"size": "320x192"}) == (320, 192)
+    assert resolve_lingbot_output_dimensions(
+        sampling_width=480,
+        sampling_height=480,
+        extra_fields={"resolution": "720p", "ratio": "16:9"},
+    ) == (736, 1280)

tests/diffusion/quantization/test_quantization_quality.py

@@ -515,8 +515,14 @@ def test_quantization_quality(config: QualityTestConfig):
 
     generate_fn = _generate_video if config.task == "t2v" else _generate_image
 
+    # Run both arms eager: LPIPS must measure quantization only. With compile
+    # on, a compiler failure in one arm (e.g. inductor's CantSplit on fp8 FLUX)
+    # silently drops that arm to eager while the other stays compiled, and the
+    # metric then includes compile-state differences (fp8_ltx2 rose from ~0.09
+    # to 0.1291 that way in build 2954). Mirrors
+    # vllm_omni/quantization/tools/compare_diffusion_trajectory_similarity.py.
     # --- BF16 baseline ---
-    bl_kwargs: dict = {"model": config.baseline_ref()}
+    bl_kwargs: dict = {"model": config.baseline_ref(), "enforce_eager": True}
     if config.enable_cpu_offload:
         bl_kwargs["enable_cpu_offload"] = True
     omni_bl = Omni(**bl_kwargs)
@@ -528,7 +534,7 @@ def test_quantization_quality(config: QualityTestConfig):
 
     # --- Quantized ---
     quantization = config.quantization_ref()
-    qt_kwargs: dict = {"model": config.quantized_ref()}
+    qt_kwargs: dict = {"model": config.quantized_ref(), "enforce_eager": True}
     if config.enable_cpu_offload:
         qt_kwargs["enable_cpu_offload"] = True
     if quantization is None:

tests/diffusion/test_compile.py

@@ -74,3 +74,37 @@ def test_regionally_compile_does_not_partially_mutate_on_setup_failure(monkeypat
         regionally_compile(model, dynamic=True)
 
     assert [block.forward.__func__ for block in model.transformer_blocks] == original_forwards
+
+
+def test_compiled_block_preserves_forward_signature_for_inspection(monkeypatch):
+    """cache-dit matches blocks via inspect.signature(block.forward).
+
+    Whatever regionally_compile installs as the block forward must stay
+    signature-transparent: parameter names and the return annotation drive
+    cache-dit's ForwardPattern match (build 2954, Multi-GPU Layered job
+    failed when a bare *args/**kwargs wrapper hid them; torch.compile's own
+    wrapper preserves the signature).
+    """
+    import inspect
+
+    import torch
+
+    class _SignatureBlock(nn.Module):
+        def forward(self, hidden_states, encoder_hidden_states=None) -> "torch.Tensor":
+            return hidden_states
+
+    class _Model(nn.Module):
+        _repeated_blocks = ["_SignatureBlock"]
+
+        def __init__(self) -> None:
+            super().__init__()
+            self.blocks = nn.ModuleList([_SignatureBlock()])
+
+    model = _Model()
+    monkeypatch.setattr(compile_module.torch, "compile", lambda fn, *a, **k: fn)
+    regionally_compile(model)
+
+    sig = inspect.signature(model.blocks[0].forward)
+    assert set(sig.parameters.keys()) == {"hidden_states", "encoder_hidden_states"}
+    assert "torch.Tensor" in str(sig.return_annotation)
+    assert model.blocks[0].forward("x") == "x"

tests/diffusion/test_inductor_divisibility_patch.py

@@ -0,0 +1,83 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Regression tests for the inductor factorable-divisibility patch.
+
+torch 2.13 proves symbolic divisibility with torch's own unevaluated Mod,
+so ``15360*s31 + 15360*s87`` is no longer provably divisible by
+``s31 + s87`` and inductor raises CantSplit on FLUX's fp8 graph (nightly
+builds 2953/2954). The patch adds an exact-polynomial-division proof that
+is sound for every symbol assignment; on torch versions whose original
+check already proves the case (<= 2.11, via sympy.Mod), the patch
+self-extinguishes.
+"""
+
+import pytest
+import sympy
+from torch._inductor.sizevars import SizeVarAllocator
+
+import vllm_omni.patch as patch_module
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
+
+_S1, _S2 = sympy.symbols("s31 s87", positive=True, integer=True)
+
+
+@pytest.mark.parametrize(
+    ("numerator", "denominator", "expected"),
+    [
+        (15360 * _S1 + 15360 * _S2, _S1 + _S2, True),  # the CantSplit expression
+        (7 * _S1 + 7 * _S2, _S1 + _S2, True),
+        (_S1 * _S2, _S2, True),
+        (_S1 * _S2 + _S2, _S2, True),  # s2*(s1 + 1)
+        (3 * _S1, 2 * _S1, False),
+        (_S1 + 1, _S1, False),
+        (15360 * _S1 + 15359 * _S2, _S1 + _S2, False),
+        (sympy.Integer(12), sympy.Integer(4), True),
+        (sympy.Integer(12), sympy.Integer(5), False),
+    ],
+)
+def test_provably_factorable_multiple(numerator, denominator, expected):
+    assert patch_module._provably_factorable_multiple(numerator, denominator) is expected
+
+
+def test_non_polynomial_atoms_never_reach_cancel():
+    from torch.utils._sympy.functions import FloorDiv, ModularIndexing
+
+    assert patch_module._provably_factorable_multiple(FloorDiv(_S1, 2) * 4, _S1) is False
+    assert patch_module._provably_factorable_multiple(ModularIndexing(_S1, 1, 8) * _S2, _S2) is False
+
+
+def test_divisibility_holds_after_patch_regardless_of_torch_version():
+    """The end contract on any torch: the CantSplit expression proves divisible.
+
+    On torch <= 2.11 the original method proves it (patch self-extinguished);
+    on torch >= 2.13 the installed wrapper proves it via exact division.
+    """
+    sv = SizeVarAllocator()
+    method = SizeVarAllocator.statically_known_multiple_of
+    patched = getattr(method, "_vllm_omni_factorable_divisibility", False)
+    assert method(sv, 15360 * _S1 + 15360 * _S2, _S1 + _S2) is True
+    # Negative control must stay non-divisible either way.
+    assert method(sv, 3 * _S1, 2 * _S1) is False
+    if patched:
+        # Wrapper installed: the original alone must have failed the probe.
+        assert patch_module._provably_factorable_multiple(15360 * _S1 + 15360 * _S2, _S1 + _S2) is True
+
+
+def test_patch_installs_when_original_cannot_prove(monkeypatch):
+    """Simulate torch 2.13's unevaluated-Mod prover: the wrapper must install."""
+
+    def weak(self, numerator, denominator):
+        return False
+
+    monkeypatch.setattr(SizeVarAllocator, "statically_known_multiple_of", weak)
+    patch_module._patch_inductor_factorable_divisibility()
+    method = SizeVarAllocator.statically_known_multiple_of
+    assert getattr(method, "_vllm_omni_factorable_divisibility", False) is True
+
+    sv = SizeVarAllocator()
+    assert method(sv, 15360 * _S1 + 15360 * _S2, _S1 + _S2) is True
+    assert method(sv, 3 * _S1, 2 * _S1) is False
+    # Idempotency: a second install call must not wrap the wrapper.
+    patch_module._patch_inductor_factorable_divisibility()
+    assert SizeVarAllocator.statically_known_multiple_of is method