update

.ai/AGENTS.md

@@ -10,34 +10,24 @@ Strive to write code as simple and explicit as possible.
 
 ---
 
-## Code formatting
+### Dependencies
+- No new mandatory dependency without discussion (e.g. `einops`)
+- Optional deps guarded with `is_X_available()` and a dummy in `utils/dummy_*.py`
 
+## Code formatting
 - `make style` and `make fix-copies` should be run as the final step before opening a PR
 
 ### Copied Code
-
 - Many classes are kept in sync with a source via a `# Copied from ...` header comment
 - Do not edit a `# Copied from` block directly — run `make fix-copies` to propagate changes from the source
 - Remove the header to intentionally break the link
 
 ### Models
-
-- See [models.md](models.md) for model conventions, attention pattern, implementation rules, dependencies, and gotchas.
-- See the [model-integration](./skills/model-integration/SKILL.md) skill for the full integration workflow, file structure, test setup, and other details.
-
-### Pipelines & Schedulers
-
-- Pipelines inherit from `DiffusionPipeline`
-- Schedulers use `SchedulerMixin` with `ConfigMixin`
-- Use `@torch.no_grad()` on pipeline `__call__`
-- Support `output_type="latent"` for skipping VAE decode
-- Support `generator` parameter for reproducibility
-- Use `self.progress_bar(timesteps)` for progress tracking
-- Don't subclass an existing pipeline for a variant — DO NOT use an existing pipeline class (e.g., `FluxPipeline`) to override another pipeline (e.g., `FluxImg2ImgPipeline`) which will be a part of the core codebase (`src`)
+- All layer calls should be visible directly in `forward` — avoid helper functions that hide `nn.Module` calls.
+- Avoid graph breaks for `torch.compile` compatibility — do not insert NumPy operations in forward implementations and any other patterns that can break `torch.compile` compatibility with `fullgraph=True`.
+- See the **model-integration** skill for the attention pattern, pipeline rules, test setup instructions, and other important details.
 
 ## Skills
 
-Task-specific guides live in `.ai/skills/` and are loaded on demand by AI agents. Available skills include:
-
-- [model-integration](./skills/model-integration/SKILL.md) (adding/converting pipelines)
-- [parity-testing](./skills/parity-testing/SKILL.md) (debugging numerical parity).
+Task-specific guides live in `.ai/skills/` and are loaded on demand by AI agents.
+Available skills: **model-integration** (adding/converting pipelines), **parity-testing** (debugging numerical parity).

.ai/models.md

@@ -1,76 +0,0 @@
-# Model conventions and rules
-
-Shared reference for model-related conventions, patterns, and gotchas.
-Linked from `AGENTS.md`, `skills/model-integration/SKILL.md`, and `review-rules.md`.
-
-## Coding style
-
-- All layer calls should be visible directly in `forward` — avoid helper functions that hide `nn.Module` calls.
-- Avoid graph breaks for `torch.compile` compatibility — do not insert NumPy operations in forward implementations and any other patterns that can break `torch.compile` compatibility with `fullgraph=True`.
-- No new mandatory dependency without discussion (e.g. `einops`). Optional deps guarded with `is_X_available()` and a dummy in `utils/dummy_*.py`.
-
-## Common model conventions
-
-- Models use `ModelMixin` with `register_to_config` for config serialization
-
-## Attention pattern
-
-Attention must follow the diffusers pattern: both the `Attention` class and its processor are defined in the model file. The processor's `__call__` handles the actual compute and must use `dispatch_attention_fn` rather than calling `F.scaled_dot_product_attention` directly. The attention class inherits `AttentionModuleMixin` and declares `_default_processor_cls` and `_available_processors`.
-
-```python
-# transformer_mymodel.py
-
-class MyModelAttnProcessor:
-    _attention_backend = None
-    _parallel_config = None
-
-    def __call__(self, attn, hidden_states, attention_mask=None, ...):
-        query = attn.to_q(hidden_states)
-        key = attn.to_k(hidden_states)
-        value = attn.to_v(hidden_states)
-        # reshape, apply rope, etc.
-        hidden_states = dispatch_attention_fn(
-            query, key, value,
-            attn_mask=attention_mask,
-            backend=self._attention_backend,
-            parallel_config=self._parallel_config,
-        )
-        hidden_states = hidden_states.flatten(2, 3)
-        return attn.to_out[0](hidden_states)
-
-
-class MyModelAttention(nn.Module, AttentionModuleMixin):
-    _default_processor_cls = MyModelAttnProcessor
-    _available_processors = [MyModelAttnProcessor]
-
-    def __init__(self, query_dim, heads=8, dim_head=64, ...):
-        super().__init__()
-        self.to_q = nn.Linear(query_dim, heads * dim_head, bias=False)
-        self.to_k = nn.Linear(query_dim, heads * dim_head, bias=False)
-        self.to_v = nn.Linear(query_dim, heads * dim_head, bias=False)
-        self.to_out = nn.ModuleList([nn.Linear(heads * dim_head, query_dim), nn.Dropout(0.0)])
-        self.set_processor(MyModelAttnProcessor())
-
-    def forward(self, hidden_states, attention_mask=None, **kwargs):
-        return self.processor(self, hidden_states, attention_mask, **kwargs)
-```
-
-Consult the implementations in `src/diffusers/models/transformers/` if you need further references.
-
-## Gotchas
-
-1. **Forgetting `__init__.py` lazy imports.** Every new class must be registered in the appropriate `__init__.py` with lazy imports. Missing this causes `ImportError` that only shows up when users try `from diffusers import YourNewClass`.
-
-2. **Using `einops` or other non-PyTorch deps.** Reference implementations often use `einops.rearrange`. Always rewrite with native PyTorch (`reshape`, `permute`, `unflatten`). Don't add the dependency. If a dependency is truly unavoidable, guard its import: `if is_my_dependency_available(): import my_dependency`.
-
-3. **Missing `make fix-copies` after `# Copied from`.** If you add `# Copied from` annotations, you must run `make fix-copies` to propagate them. CI will fail otherwise.
-
-4. **Wrong `_supports_cache_class` / `_no_split_modules`.** These class attributes control KV cache and device placement. Copy from a similar model and verify -- wrong values cause silent correctness bugs or OOM errors.
-
-5. **Missing `@torch.no_grad()` on pipeline `__call__`.** Forgetting this causes GPU OOM from gradient accumulation during inference.
-
-6. **Config serialization gaps.** Every `__init__` parameter in a `ModelMixin` subclass must be captured by `register_to_config`. If you add a new param but forget to register it, `from_pretrained` will silently use the default instead of the saved value.
-
-7. **Forgetting to update `_import_structure` and `_lazy_modules`.** The top-level `src/diffusers/__init__.py` has both -- missing either one causes partial import failures.
-
-8. **Hardcoded dtype in model forward.** Don't hardcode `torch.float32` or `torch.bfloat16` in the model's forward pass. Use the dtype of the input tensors or `self.dtype` so the model works with any precision.

.ai/review-rules.md

@@ -3,8 +3,8 @@
 Review-specific rules for Claude. Focus on correctness — style is handled by ruff.
 
 Before reviewing, read and apply the guidelines in:
-- [AGENTS.md](AGENTS.md) — coding style, copied code
-- [models.md](models.md) — model conventions, attention pattern, implementation rules, dependencies, gotchas
+- [AGENTS.md](AGENTS.md) — coding style, dependencies, copied code, model conventions
+- [skills/model-integration/SKILL.md](skills/model-integration/SKILL.md) — attention pattern, pipeline rules, implementation checklist, gotchas
 - [skills/parity-testing/SKILL.md](skills/parity-testing/SKILL.md) — testing rules, comparison utilities
 - [skills/parity-testing/pitfalls.md](skills/parity-testing/pitfalls.md) — known pitfalls (dtype mismatches, config assumptions, etc.)
 

.ai/skills/model-integration/SKILL.md

@@ -65,19 +65,89 @@ docs/source/en/api/
 - [ ] Run `make style` and `make quality`
 - [ ] Test parity with reference implementation (see `parity-testing` skill)
 
-### Model conventions, attention pattern, and implementation rules
+### Attention pattern
 
-See [../../models.md](../../models.md) for the attention pattern, implementation rules, common conventions, dependencies, and gotchas. These apply to all model work.
+Attention must follow the diffusers pattern: both the `Attention` class and its processor are defined in the model file. The processor's `__call__` handles the actual compute and must use `dispatch_attention_fn` rather than calling `F.scaled_dot_product_attention` directly. The attention class inherits `AttentionModuleMixin` and declares `_default_processor_cls` and `_available_processors`.
 
-### Model integration specific rules
+```python
+# transformer_mymodel.py
 
-**Don't combine structural changes with behavioral changes.** Restructuring code to fit diffusers APIs (ModelMixin, ConfigMixin, etc.) is unavoidable. But don't also "improve" the algorithm, refactor computation order, or rename internal variables for aesthetics. Keep numerical logic as close to the reference as possible, even if it looks unclean. For standard → modular, this is stricter: copy loop logic verbatim and only restructure into blocks. Clean up in a separate commit after parity is confirmed.
+class MyModelAttnProcessor:
+    _attention_backend = None
+    _parallel_config = None
+
+    def __call__(self, attn, hidden_states, attention_mask=None, ...):
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(hidden_states)
+        # reshape, apply rope, etc.
+        hidden_states = dispatch_attention_fn(
+            query, key, value,
+            attn_mask=attention_mask,
+            backend=self._attention_backend,
+            parallel_config=self._parallel_config,
+        )
+        hidden_states = hidden_states.flatten(2, 3)
+        return attn.to_out[0](hidden_states)
+
+
+class MyModelAttention(nn.Module, AttentionModuleMixin):
+    _default_processor_cls = MyModelAttnProcessor
+    _available_processors = [MyModelAttnProcessor]
+
+    def __init__(self, query_dim, heads=8, dim_head=64, ...):
+        super().__init__()
+        self.to_q = nn.Linear(query_dim, heads * dim_head, bias=False)
+        self.to_k = nn.Linear(query_dim, heads * dim_head, bias=False)
+        self.to_v = nn.Linear(query_dim, heads * dim_head, bias=False)
+        self.to_out = nn.ModuleList([nn.Linear(heads * dim_head, query_dim), nn.Dropout(0.0)])
+        self.set_processor(MyModelAttnProcessor())
+
+    def forward(self, hidden_states, attention_mask=None, **kwargs):
+        return self.processor(self, hidden_states, attention_mask, **kwargs)
+```
+
+Consult the implementations in `src/diffusers/models/transformers/` if you need further references.
+
+### Implementation rules
+
+1. **Don't combine structural changes with behavioral changes.** Restructuring code to fit diffusers APIs (ModelMixin, ConfigMixin, etc.) is unavoidable. But don't also "improve" the algorithm, refactor computation order, or rename internal variables for aesthetics. Keep numerical logic as close to the reference as possible, even if it looks unclean. For standard → modular, this is stricter: copy loop logic verbatim and only restructure into blocks. Clean up in a separate commit after parity is confirmed.
+2. **Pipelines must inherit from `DiffusionPipeline`.** Consult implementations in `src/diffusers/pipelines` in case you need references.
+3. **Don't subclass an existing pipeline for a variant.** DO NOT use an existing pipeline class (e.g., `FluxPipeline`) to override another pipeline (e.g., `FluxImg2ImgPipeline`) which will be a part of the core codebase (`src`).
 
 ### Test setup
 
 - Slow tests gated with `@slow` and `RUN_SLOW=1`
 - All model-level tests must use the `BaseModelTesterConfig`, `ModelTesterMixin`, `MemoryTesterMixin`, `AttentionTesterMixin`, `LoraTesterMixin`, and `TrainingTesterMixin` classes initially to write the tests. Any additional tests should be added after discussions with the maintainers. Use `tests/models/transformers/test_models_transformer_flux.py` as a reference.
 
+### Common diffusers conventions
+
+- Pipelines inherit from `DiffusionPipeline`
+- Models use `ModelMixin` with `register_to_config` for config serialization
+- Schedulers use `SchedulerMixin` with `ConfigMixin`
+- Use `@torch.no_grad()` on pipeline `__call__`
+- Support `output_type="latent"` for skipping VAE decode
+- Support `generator` parameter for reproducibility
+- Use `self.progress_bar(timesteps)` for progress tracking
+
+## Gotchas
+
+1. **Forgetting `__init__.py` lazy imports.** Every new class must be registered in the appropriate `__init__.py` with lazy imports. Missing this causes `ImportError` that only shows up when users try `from diffusers import YourNewClass`.
+
+2. **Using `einops` or other non-PyTorch deps.** Reference implementations often use `einops.rearrange`. Always rewrite with native PyTorch (`reshape`, `permute`, `unflatten`). Don't add the dependency. If a dependency is truly unavoidable, guard its import: `if is_my_dependency_available(): import my_dependency`.
+
+3. **Missing `make fix-copies` after `# Copied from`.** If you add `# Copied from` annotations, you must run `make fix-copies` to propagate them. CI will fail otherwise.
+
+4. **Wrong `_supports_cache_class` / `_no_split_modules`.** These class attributes control KV cache and device placement. Copy from a similar model and verify -- wrong values cause silent correctness bugs or OOM errors.
+
+5. **Missing `@torch.no_grad()` on pipeline `__call__`.** Forgetting this causes GPU OOM from gradient accumulation during inference.
+
+6. **Config serialization gaps.** Every `__init__` parameter in a `ModelMixin` subclass must be captured by `register_to_config`. If you add a new param but forget to register it, `from_pretrained` will silently use the default instead of the saved value.
+
+7. **Forgetting to update `_import_structure` and `_lazy_modules`.** The top-level `src/diffusers/__init__.py` has both -- missing either one causes partial import failures.
+
+8. **Hardcoded dtype in model forward.** Don't hardcode `torch.float32` or `torch.bfloat16` in the model's forward pass. Use the dtype of the input tensors or `self.dtype` so the model works with any precision.
+
 ---
 
 ## Modular Pipeline Conversion

src/diffusers/models/attention_dispatch.py

@@ -862,23 +862,23 @@ def _native_attention_backward_op(
     key.requires_grad_(True)
     value.requires_grad_(True)
 
-    with torch.enable_grad():
-        query_t, key_t, value_t = (x.permute(0, 2, 1, 3) for x in (query, key, value))
-        out = torch.nn.functional.scaled_dot_product_attention(
-            query=query_t,
-            key=key_t,
-            value=value_t,
-            attn_mask=ctx.attn_mask,
-            dropout_p=ctx.dropout_p,
-            is_causal=ctx.is_causal,
-            scale=ctx.scale,
-            enable_gqa=ctx.enable_gqa,
-        )
-        out = out.permute(0, 2, 1, 3)
+    query_t, key_t, value_t = (x.permute(0, 2, 1, 3) for x in (query, key, value))
+    out = torch.nn.functional.scaled_dot_product_attention(
+        query=query_t,
+        key=key_t,
+        value=value_t,
+        attn_mask=ctx.attn_mask,
+        dropout_p=ctx.dropout_p,
+        is_causal=ctx.is_causal,
+        scale=ctx.scale,
+        enable_gqa=ctx.enable_gqa,
+    )
+    out = out.permute(0, 2, 1, 3)
 
-        grad_query_t, grad_key_t, grad_value_t = torch.autograd.grad(
-            outputs=out, inputs=[query_t, key_t, value_t], grad_outputs=grad_out, retain_graph=False
-        )
+    grad_out_t = grad_out.permute(0, 2, 1, 3)
+    grad_query_t, grad_key_t, grad_value_t = torch.autograd.grad(
+        outputs=out, inputs=[query_t, key_t, value_t], grad_outputs=grad_out_t, retain_graph=False
+    )
 
     grad_query = grad_query_t.permute(0, 2, 1, 3)
     grad_key = grad_key_t.permute(0, 2, 1, 3)

src/diffusers/pipelines/pipeline_utils.py

@@ -764,7 +764,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
         token = kwargs.pop("token", None)
         revision = kwargs.pop("revision", None)
         from_flax = kwargs.pop("from_flax", False)
-        torch_dtype = kwargs.pop("torch_dtype", None)
+        torch_dtype = kwargs.pop("torch_dtype", torch.float32)
         custom_pipeline = kwargs.pop("custom_pipeline", None)
         custom_revision = kwargs.pop("custom_revision", None)
         provider = kwargs.pop("provider", None)

tests/models/autoencoders/test_models_autoencoder_dc.py

@@ -13,29 +13,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import pytest
-import torch
+import unittest
 
 from diffusers import AutoencoderDC
 
-from ...testing_utils import IS_GITHUB_ACTIONS, enable_full_determinism, torch_device
-from ..testing_utils import BaseModelTesterConfig, MemoryTesterMixin, ModelTesterMixin, TrainingTesterMixin
+from ...testing_utils import IS_GITHUB_ACTIONS, enable_full_determinism, floats_tensor, torch_device
+from ..test_modeling_common import ModelTesterMixin
 from .testing_utils import AutoencoderTesterMixin
 
 
 enable_full_determinism()
 
 
-class AutoencoderDCTesterConfig(BaseModelTesterConfig):
-    @property
-    def model_class(self):
-        return AutoencoderDC
+class AutoencoderDCTests(ModelTesterMixin, AutoencoderTesterMixin, unittest.TestCase):
+    model_class = AutoencoderDC
+    main_input_name = "sample"
+    base_precision = 1e-2
 
-    @property
-    def output_shape(self):
-        return (3, 32, 32)
-
-    def get_init_dict(self):
+    def get_autoencoder_dc_config(self):
         return {
             "in_channels": 3,
             "latent_channels": 4,
@@ -61,34 +56,33 @@ class AutoencoderDCTesterConfig(BaseModelTesterConfig):
             "scaling_factor": 0.41407,
         }
 
-    def get_dummy_inputs(self, seed=0):
-        torch.manual_seed(seed)
+    @property
+    def dummy_input(self):
         batch_size = 4
         num_channels = 3
         sizes = (32, 32)
-        image = torch.randn(batch_size, num_channels, *sizes).to(torch_device)
+
+        image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
+
         return {"sample": image}
 
-    # Bridge for AutoencoderTesterMixin which still uses the old interface
+    @property
+    def input_shape(self):
+        return (3, 32, 32)
+
+    @property
+    def output_shape(self):
+        return (3, 32, 32)
+
     def prepare_init_args_and_inputs_for_common(self):
-        return self.get_init_dict(), self.get_dummy_inputs()
+        init_dict = self.get_autoencoder_dc_config()
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
 
+    @unittest.skipIf(IS_GITHUB_ACTIONS, reason="Skipping test inside GitHub Actions environment")
+    def test_layerwise_casting_inference(self):
+        super().test_layerwise_casting_inference()
 
-class TestAutoencoderDC(AutoencoderDCTesterConfig, ModelTesterMixin):
-    base_precision = 1e-2
-
-
-class TestAutoencoderDCTraining(AutoencoderDCTesterConfig, TrainingTesterMixin):
-    """Training tests for AutoencoderDC."""
-
-
-class TestAutoencoderDCMemory(AutoencoderDCTesterConfig, MemoryTesterMixin):
-    """Memory optimization tests for AutoencoderDC."""
-
-    @pytest.mark.skipif(IS_GITHUB_ACTIONS, reason="Skipping test inside GitHub Actions environment")
+    @unittest.skipIf(IS_GITHUB_ACTIONS, reason="Skipping test inside GitHub Actions environment")
     def test_layerwise_casting_memory(self):
         super().test_layerwise_casting_memory()
-
-
-class TestAutoencoderDCSlicingTiling(AutoencoderDCTesterConfig, AutoencoderTesterMixin):
-    """Slicing and tiling tests for AutoencoderDC."""

tests/models/autoencoders/testing_utils.py

@@ -44,9 +44,9 @@ class AutoencoderTesterMixin:
             if isinstance(output, dict):
                 output = output.to_tuple()[0]
 
-        assert output is not None
+        self.assertIsNotNone(output)
         expected_shape = inputs_dict["sample"].shape
-        assert output.shape == expected_shape, "Input and output shapes do not match"
+        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
 
     def test_enable_disable_tiling(self):
         if not hasattr(self.model_class, "enable_tiling"):

tests/models/testing_utils/parallelism.py

@@ -98,64 +98,6 @@ def _context_parallel_worker(rank, world_size, master_port, model_class, init_di
             dist.destroy_process_group()
 
 
-def _context_parallel_backward_worker(
-    rank, world_size, master_port, model_class, init_dict, cp_dict, inputs_dict, return_dict
-):
-    """Worker function for context parallel backward pass testing."""
-    try:
-        # Set up distributed environment
-        os.environ["MASTER_ADDR"] = "localhost"
-        os.environ["MASTER_PORT"] = str(master_port)
-        os.environ["RANK"] = str(rank)
-        os.environ["WORLD_SIZE"] = str(world_size)
-
-        # Get device configuration
-        device_config = DEVICE_CONFIG.get(torch_device, DEVICE_CONFIG["cuda"])
-        backend = device_config["backend"]
-        device_module = device_config["module"]
-
-        # Initialize process group
-        dist.init_process_group(backend=backend, rank=rank, world_size=world_size)
-
-        # Set device for this process
-        device_module.set_device(rank)
-        device = torch.device(f"{torch_device}:{rank}")
-
-        # Create model in training mode
-        model = model_class(**init_dict)
-        model.to(device)
-        model.train()
-
-        # Move inputs to device
-        inputs_on_device = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in inputs_dict.items()}
-
-        # Enable context parallelism
-        cp_config = ContextParallelConfig(**cp_dict)
-        model.enable_parallelism(config=cp_config)
-
-        # Run forward and backward pass
-        output = model(**inputs_on_device, return_dict=False)[0]
-        loss = output.sum()
-        loss.backward()
-
-        # Check that backward actually produced at least one valid gradient
-        grads = [p.grad for p in model.parameters() if p.requires_grad and p.grad is not None]
-        has_valid_grads = len(grads) > 0 and all(torch.isfinite(g).all() for g in grads)
-
-        # Only rank 0 reports results
-        if rank == 0:
-            return_dict["status"] = "success"
-            return_dict["has_valid_grads"] = bool(has_valid_grads)
-
-    except Exception as e:
-        if rank == 0:
-            return_dict["status"] = "error"
-            return_dict["error"] = str(e)
-    finally:
-        if dist.is_initialized():
-            dist.destroy_process_group()
-
-
 def _custom_mesh_worker(
     rank,
     world_size,
@@ -262,51 +204,6 @@ class ContextParallelTesterMixin:
     def test_context_parallel_batch_inputs(self, cp_type):
         self.test_context_parallel_inference(cp_type, batch_size=2)
 
-    @pytest.mark.parametrize("cp_type", ["ulysses_degree", "ring_degree"], ids=["ulysses", "ring"])
-    def test_context_parallel_backward(self, cp_type, batch_size: int = 1):
-        if not torch.distributed.is_available():
-            pytest.skip("torch.distributed is not available.")
-
-        if not hasattr(self.model_class, "_cp_plan") or self.model_class._cp_plan is None:
-            pytest.skip("Model does not have a _cp_plan defined for context parallel inference.")
-
-        if cp_type == "ring_degree":
-            active_backend, _ = _AttentionBackendRegistry.get_active_backend()
-            if active_backend == AttentionBackendName.NATIVE:
-                pytest.skip("Ring attention is not supported with the native attention backend.")
-
-        world_size = 2
-        init_dict = self.get_init_dict()
-        inputs_dict = self.get_dummy_inputs(batch_size=batch_size)
-
-        # Move all tensors to CPU for multiprocessing
-        inputs_dict = {k: v.cpu() if isinstance(v, torch.Tensor) else v for k, v in inputs_dict.items()}
-        cp_dict = {cp_type: world_size}
-
-        # Find a free port for distributed communication
-        master_port = _find_free_port()
-
-        # Use multiprocessing manager for cross-process communication
-        manager = mp.Manager()
-        return_dict = manager.dict()
-
-        # Spawn worker processes
-        mp.spawn(
-            _context_parallel_backward_worker,
-            args=(world_size, master_port, self.model_class, init_dict, cp_dict, inputs_dict, return_dict),
-            nprocs=world_size,
-            join=True,
-        )
-
-        assert return_dict.get("status") == "success", (
-            f"Context parallel backward pass failed: {return_dict.get('error', 'Unknown error')}"
-        )
-        assert return_dict.get("has_valid_grads"), "Context parallel backward pass did not produce valid gradients."
-
-    @pytest.mark.parametrize("cp_type", ["ulysses_degree", "ring_degree"], ids=["ulysses", "ring"])
-    def test_context_parallel_backward_batch_inputs(self, cp_type):
-        self.test_context_parallel_backward(cp_type, batch_size=2)
-
     @pytest.mark.parametrize(
         "cp_type,mesh_shape,mesh_dim_names",
         [