update

src/diffusers/commands/daggr_app.py

@@ -0,0 +1,447 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import ast
+import re
+from argparse import ArgumentParser, Namespace
+from collections import OrderedDict
+from dataclasses import dataclass, field
+
+from ..utils import logging
+from . import BaseDiffusersCLICommand
+
+
+logger = logging.get_logger("diffusers-cli/daggr")
+
+INTERNAL_TYPE_NAMES = {
+    "Tensor",
+    "Generator",
+}
+
+INTERNAL_TYPE_FULL_NAMES = {
+    "torch.Tensor",
+    "torch.Generator",
+    "torch.dtype",
+}
+
+SLIDER_PARAMS = {
+    "height": {"minimum": 256, "maximum": 2048, "step": 64},
+    "width": {"minimum": 256, "maximum": 2048, "step": 64},
+    "num_inference_steps": {"minimum": 1, "maximum": 100, "step": 1},
+    "guidance_scale": {"minimum": 0, "maximum": 30, "step": 0.5},
+    "strength": {"minimum": 0, "maximum": 1, "step": 0.05},
+    "control_guidance_start": {"minimum": 0, "maximum": 1, "step": 0.05},
+    "control_guidance_end": {"minimum": 0, "maximum": 1, "step": 0.05},
+    "controlnet_conditioning_scale": {"minimum": 0, "maximum": 2, "step": 0.1},
+}
+
+
+@dataclass
+class BlockInfo:
+    name: str
+    class_name: str
+    description: str
+    inputs: list
+    outputs: list
+    user_inputs: list = field(default_factory=list)
+    port_connections: list = field(default_factory=list)
+    fixed_inputs: list = field(default_factory=list)
+
+
+def daggr_command_factory(args: Namespace):
+    return DaggrCommand(
+        repo_id=args.repo_id,
+        output=args.output or "daggr_app.py",
+        workflow=getattr(args, "workflow", None),
+        trigger_inputs=getattr(args, "trigger_inputs", None),
+    )
+
+
+class DaggrCommand(BaseDiffusersCLICommand):
+    @staticmethod
+    def register_subcommand(parser: ArgumentParser):
+        daggr_parser = parser.add_parser("daggr", help="Generate a daggr app from a modular pipeline repo.")
+        daggr_parser.add_argument(
+            "repo_id",
+            type=str,
+            help="HuggingFace Hub repo ID containing a modular pipeline (with modular_model_index.json).",
+        )
+        daggr_parser.add_argument(
+            "--output",
+            type=str,
+            default="daggr_app.py",
+            help="Output file path for the generated daggr app. Default: daggr_app.py",
+        )
+        daggr_parser.add_argument(
+            "--workflow",
+            type=str,
+            default=None,
+            help="Named workflow to resolve conditional blocks (e.g. 'text2image', 'image2image').",
+        )
+        daggr_parser.add_argument(
+            "--trigger-inputs",
+            nargs="*",
+            default=None,
+            help="Trigger input names for manual conditional resolution.",
+        )
+        daggr_parser.set_defaults(func=daggr_command_factory)
+
+    def __init__(
+        self,
+        repo_id: str,
+        output: str = "daggr_app.py",
+        workflow: str | None = None,
+        trigger_inputs: list | None = None,
+    ):
+        self.repo_id = repo_id
+        self.output = output
+        self.workflow = workflow
+        self.trigger_inputs = trigger_inputs
+
+    def run(self):
+        from ..modular_pipelines.modular_pipeline import ModularPipelineBlocks
+
+        logger.info(f"Loading blocks from {self.repo_id}...")
+        blocks = ModularPipelineBlocks.from_pretrained(self.repo_id, trust_remote_code=True)
+        blocks_class_name = blocks.__class__.__name__
+
+        if self.workflow:
+            logger.info(f"Resolving workflow: {self.workflow}")
+            exec_blocks = blocks.get_workflow(self.workflow)
+        elif self.trigger_inputs:
+            trigger_kwargs = {name: True for name in self.trigger_inputs}
+            logger.info(f"Resolving with trigger inputs: {self.trigger_inputs}")
+            exec_blocks = blocks.get_execution_blocks(**trigger_kwargs)
+        else:
+            logger.info("Resolving default execution blocks...")
+            exec_blocks = blocks.get_execution_blocks()
+
+        block_infos = _analyze_blocks(exec_blocks)
+        _classify_inputs(block_infos)
+
+        workflow_label = self.workflow or "default"
+        workflow_resolve_code = self._get_workflow_resolve_code()
+        code = _generate_code(block_infos, self.repo_id, blocks_class_name, workflow_label, workflow_resolve_code)
+
+        try:
+            ast.parse(code)
+        except SyntaxError as e:
+            logger.warning(f"Generated code has syntax error: {e}")
+
+        with open(self.output, "w") as f:
+            f.write(code)
+
+        logger.info(f"Daggr app written to {self.output}")
+        print(f"Generated daggr app: {self.output}")
+        print(f"  Pipeline: {blocks_class_name}")
+        print(f"  Workflow: {workflow_label}")
+        print(f"  Blocks: {len(block_infos)}")
+        print(f"\nRun with: python {self.output}")
+
+    def _get_workflow_resolve_code(self):
+        if self.workflow:
+            return f"_pipeline._blocks.get_workflow({self.workflow!r})"
+        elif self.trigger_inputs:
+            kwargs_str = ", ".join(f"{name!r}: True" for name in self.trigger_inputs)
+            return f"_pipeline._blocks.get_execution_blocks(**{{{kwargs_str}}})"
+        else:
+            return "_pipeline._blocks.get_execution_blocks()"
+
+
+def _analyze_blocks(exec_blocks):
+    block_infos = []
+    for name, block in exec_blocks.sub_blocks.items():
+        info = BlockInfo(
+            name=name,
+            class_name=block.__class__.__name__,
+            description=getattr(block, "description", "") or "",
+            inputs=list(block.inputs) if hasattr(block, "inputs") else [],
+            outputs=list(block.intermediate_outputs) if hasattr(block, "intermediate_outputs") else [],
+        )
+        block_infos.append(info)
+    return block_infos
+
+
+def _get_type_name(type_hint):
+    if type_hint is None:
+        return None
+    if hasattr(type_hint, "__name__"):
+        return type_hint.__name__
+    if hasattr(type_hint, "__module__") and hasattr(type_hint, "__qualname__"):
+        return f"{type_hint.__module__}.{type_hint.__qualname__}"
+    return str(type_hint)
+
+
+def _is_internal_type(type_hint):
+    if type_hint is None:
+        return True
+    type_name = _get_type_name(type_hint)
+    if type_name is None:
+        return True
+    if type_name in INTERNAL_TYPE_NAMES or type_name in INTERNAL_TYPE_FULL_NAMES:
+        return True
+    type_str = str(type_hint)
+    for full_name in INTERNAL_TYPE_FULL_NAMES:
+        if full_name in type_str:
+            return True
+    if type_str.startswith("dict[") or type_str == "dict":
+        return True
+    return False
+
+
+def _type_hint_to_gradio(type_hint, param_name, default=None):
+    if _is_internal_type(type_hint):
+        return None
+
+    if param_name in SLIDER_PARAMS:
+        slider_opts = SLIDER_PARAMS[param_name]
+        val = default if default is not None else slider_opts.get("minimum", 0)
+        return (
+            f'gr.Slider(label="{param_name}", value={val!r}, '
+            f"minimum={slider_opts['minimum']}, maximum={slider_opts['maximum']}, "
+            f"step={slider_opts['step']})"
+        )
+
+    type_name = _get_type_name(type_hint)
+    type_str = str(type_hint)
+
+    if type_name == "str" or type_hint is str:
+        lines = 3 if "prompt" in param_name else 1
+        default_repr = f", value={default!r}" if default is not None else ""
+        return f'gr.Textbox(label="{param_name}", lines={lines}{default_repr})'
+
+    if type_name == "int" or type_hint is int:
+        val = f", value={default!r}" if default is not None else ""
+        return f'gr.Number(label="{param_name}", precision=0{val})'
+
+    if type_name == "float" or type_hint is float:
+        val = f", value={default!r}" if default is not None else ""
+        return f'gr.Number(label="{param_name}"{val})'
+
+    if type_name == "bool" or type_hint is bool:
+        val = default if default is not None else False
+        return f'gr.Checkbox(label="{param_name}", value={val!r})'
+
+    if "Image" in type_str:
+        if "list" in type_str.lower():
+            return f'gr.Gallery(label="{param_name}")'
+        return f'gr.Image(label="{param_name}")'
+
+    if default is not None:
+        return f'gr.Textbox(label="{param_name}", value={default!r})'
+
+    return f'gr.Textbox(label="{param_name}")'
+
+
+def _output_type_to_gradio(type_hint, param_name):
+    if _is_internal_type(type_hint):
+        return None
+    type_str = str(type_hint)
+    if "Image" in type_str:
+        if "list" in type_str.lower():
+            return f'gr.Gallery(label="{param_name}")'
+        return f'gr.Image(label="{param_name}")'
+    if type_hint is str:
+        return f'gr.Textbox(label="{param_name}")'
+    if type_hint is int or type_hint is float:
+        return f'gr.Number(label="{param_name}")'
+    return None
+
+
+def _classify_inputs(block_infos):
+    all_prior_outputs = {}
+
+    for info in block_infos:
+        user_inputs = []
+        port_connections = []
+        fixed_inputs = []
+
+        for inp in info.inputs:
+            if inp.name is None:
+                continue
+            if inp.name in all_prior_outputs:
+                port_connections.append((inp.name, all_prior_outputs[inp.name]))
+            elif _is_internal_type(inp.type_hint):
+                fixed_inputs.append(inp)
+            else:
+                user_inputs.append(inp)
+
+        info.user_inputs = user_inputs
+        info.port_connections = port_connections
+        info.fixed_inputs = fixed_inputs
+
+        for out in info.outputs:
+            if out.name and out.name not in all_prior_outputs:
+                all_prior_outputs[out.name] = info.name
+
+
+def _sanitize_name(name):
+    sanitized = re.sub(r"[^a-zA-Z0-9_]", "_", name)
+    if sanitized and sanitized[0].isdigit():
+        sanitized = f"_{sanitized}"
+    return sanitized
+
+
+def _generate_code(block_infos, repo_id, blocks_class_name, workflow_label, workflow_resolve_code):
+    lines = []
+
+    lines.append(f'"""Daggr app for {blocks_class_name} ({workflow_label} workflow)')
+    lines.append("Generated by: diffusers-cli daggr")
+    lines.append('"""')
+    lines.append("")
+    lines.append("import gradio as gr")
+    lines.append("from daggr import FnNode, InputNode, Graph")
+    lines.append("")
+    lines.append("")
+
+    # Pipeline and resolved blocks loader
+    lines.append("_pipeline = None")
+    lines.append("_exec_blocks = None")
+    lines.append("")
+    lines.append("")
+    lines.append("def _get_pipeline():")
+    lines.append("    global _pipeline, _exec_blocks")
+    lines.append("    if _pipeline is None:")
+    lines.append("        from diffusers import ModularPipeline")
+    lines.append(f"        _pipeline = ModularPipeline.from_pretrained({repo_id!r}, trust_remote_code=True)")
+    lines.append("        _pipeline.load_components()")
+    lines.append(f"        _exec_blocks = {workflow_resolve_code}")
+    lines.append("    return _pipeline, _exec_blocks")
+    lines.append("")
+    lines.append("")
+
+    # Wrapper functions
+    for info in block_infos:
+        fn_name = f"run_{_sanitize_name(info.name)}"
+        all_input_names = []
+        for inp in info.inputs:
+            if inp.name is not None:
+                all_input_names.append(inp.name)
+
+        params = ", ".join(all_input_names)
+        lines.append(f"def {fn_name}({params}):")
+        lines.append("    from diffusers.modular_pipelines.modular_pipeline import PipelineState")
+        lines.append("")
+        lines.append("    pipe, exec_blocks = _get_pipeline()")
+        lines.append("    state = PipelineState()")
+        for inp_name in all_input_names:
+            lines.append(f'    state.set("{inp_name}", {inp_name})')
+        lines.append(f'    block = exec_blocks.sub_blocks["{info.name}"]')
+        lines.append("    _, state = block(pipe, state)")
+
+        if len(info.outputs) == 0:
+            lines.append("    return None")
+        elif len(info.outputs) == 1:
+            out = info.outputs[0]
+            lines.append(f'    return state.get("{out.name}")')
+        else:
+            out_names = [out.name for out in info.outputs]
+            out_dict = ", ".join(f'"{n}": state.get("{n}")' for n in out_names)
+            lines.append(f"    return {{{out_dict}}}")
+        lines.append("")
+        lines.append("")
+
+    # Collect all user-facing inputs across blocks
+    all_user_inputs = OrderedDict()
+    for info in block_infos:
+        for inp in info.user_inputs:
+            if inp.name not in all_user_inputs:
+                all_user_inputs[inp.name] = inp
+
+    # InputNode
+    if all_user_inputs:
+        lines.append("# -- User Inputs --")
+        lines.append('user_inputs = InputNode("User Inputs", ports={')
+        for inp_name, inp in all_user_inputs.items():
+            gradio_comp = _type_hint_to_gradio(inp.type_hint, inp_name, inp.default)
+            if gradio_comp:
+                lines.append(f'    "{inp_name}": {gradio_comp},')
+        lines.append("})")
+        lines.append("")
+        lines.append("")
+
+    # FnNode definitions
+    lines.append("# -- Pipeline Blocks --")
+    node_var_names = {}
+
+    for info in block_infos:
+        var_name = f"{_sanitize_name(info.name)}_node"
+        node_var_names[info.name] = var_name
+        fn_name = f"run_{_sanitize_name(info.name)}"
+
+        display_name = info.name.replace("_", " ").replace(".", " > ").title()
+
+        # Build inputs dict
+        input_entries = []
+        for inp in info.inputs:
+            if inp.name is None:
+                continue
+
+            connected = False
+            for conn_name, source_block in info.port_connections:
+                if conn_name == inp.name:
+                    source_var = node_var_names[source_block]
+                    input_entries.append(f'    "{inp.name}": {source_var}.{inp.name},')
+                    connected = True
+                    break
+
+            if not connected:
+                if inp.name in all_user_inputs:
+                    input_entries.append(f'    "{inp.name}": user_inputs.{inp.name},')
+                elif inp.default is not None:
+                    input_entries.append(f'    "{inp.name}": {inp.default!r},')
+                else:
+                    input_entries.append(f'    "{inp.name}": None,')
+
+        # Build outputs dict
+        output_entries = []
+        for out in info.outputs:
+            gradio_out = _output_type_to_gradio(out.type_hint, out.name)
+            if gradio_out:
+                output_entries.append(f'    "{out.name}": {gradio_out},')
+            else:
+                output_entries.append(f'    "{out.name}": None,')
+
+        lines.append(f"{var_name} = FnNode(")
+        lines.append(f"    fn={fn_name},")
+        lines.append(f'    name="{display_name}",')
+
+        if input_entries:
+            lines.append("    inputs={")
+            lines.extend(input_entries)
+            lines.append("    },")
+
+        if output_entries:
+            lines.append("    outputs={")
+            lines.extend(output_entries)
+            lines.append("    },")
+
+        lines.append(")")
+        lines.append("")
+
+    # Graph
+    lines.append("")
+    lines.append("# -- Graph --")
+    all_node_vars = []
+    if all_user_inputs:
+        all_node_vars.append("user_inputs")
+    all_node_vars.extend(node_var_names[info.name] for info in block_infos)
+
+    graph_name = f"{blocks_class_name} - {workflow_label}"
+    nodes_str = ", ".join(all_node_vars)
+    lines.append(f'graph = Graph("{graph_name}", nodes=[{nodes_str}])')
+    lines.append("graph.launch()")
+    lines.append("")
+
+    return "\n".join(lines)

src/diffusers/commands/diffusers_cli.py

@@ -16,6 +16,7 @@
 from argparse import ArgumentParser
 
 from .custom_blocks import CustomBlocksCommand
+from .daggr_app import DaggrCommand
 from .env import EnvironmentCommand
 from .fp16_safetensors import FP16SafetensorsCommand
 
@@ -28,6 +29,7 @@ def main():
     EnvironmentCommand.register_subcommand(commands_parser)
     FP16SafetensorsCommand.register_subcommand(commands_parser)
     CustomBlocksCommand.register_subcommand(commands_parser)
+    DaggrCommand.register_subcommand(commands_parser)
 
     # Let's go
     args = parser.parse_args()

src/diffusers/models/_modeling_parallel.py

@@ -60,16 +60,6 @@ class ContextParallelConfig:
         rotate_method (`str`, *optional*, defaults to `"allgather"`):
             Method to use for rotating key/value states across devices in ring attention. Currently, only `"allgather"`
             is supported.
-        ulysses_anything (`bool`, *optional*, defaults to `False`):
-            Whether to enable "Ulysses Anything" mode, which supports arbitrary sequence lengths and head counts that
-            are not evenly divisible by `ulysses_degree`. When enabled, `ulysses_degree` must be greater than 1 and
-            `ring_degree` must be 1.
-        mesh (`torch.distributed.device_mesh.DeviceMesh`, *optional*):
-            A custom device mesh to use for context parallelism. If provided, this mesh will be used instead of
-            creating a new one. This is useful when combining context parallelism with other parallelism strategies
-            (e.g., FSDP, tensor parallelism) that share the same device mesh. The mesh must have both "ring" and
-            "ulysses" dimensions. Use size 1 for dimensions not being used (e.g., `mesh_shape=(2, 1, 4)` with
-            `mesh_dim_names=("ring", "ulysses", "fsdp")` for ring attention only with FSDP).
 
     """
 
@@ -78,7 +68,6 @@ class ContextParallelConfig:
     convert_to_fp32: bool = True
     # TODO: support alltoall
     rotate_method: Literal["allgather", "alltoall"] = "allgather"
-    mesh: torch.distributed.device_mesh.DeviceMesh | None = None
     # Whether to enable ulysses anything attention to support
     # any sequence lengths and any head numbers.
     ulysses_anything: bool = False
@@ -135,7 +124,7 @@ class ContextParallelConfig:
                 f"The product of `ring_degree` ({self.ring_degree}) and `ulysses_degree` ({self.ulysses_degree}) must not exceed the world size ({world_size})."
             )
 
-        self._flattened_mesh = self._mesh["ring", "ulysses"]._flatten()
+        self._flattened_mesh = self._mesh._flatten()
         self._ring_mesh = self._mesh["ring"]
         self._ulysses_mesh = self._mesh["ulysses"]
         self._ring_local_rank = self._ring_mesh.get_local_rank()

src/diffusers/models/modeling_utils.py

@@ -1567,7 +1567,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         mesh = None
         if config.context_parallel_config is not None:
             cp_config = config.context_parallel_config
-            mesh = cp_config.mesh or torch.distributed.device_mesh.init_device_mesh(
+            mesh = torch.distributed.device_mesh.init_device_mesh(
                 device_type=device_type,
                 mesh_shape=cp_config.mesh_shape,
                 mesh_dim_names=cp_config.mesh_dim_names,

src/diffusers/modular_pipelines/modular_pipeline.py

@@ -14,7 +14,6 @@
 import importlib
 import inspect
 import os
-import shutil
 import sys
 import traceback
 import warnings
@@ -1884,36 +1883,6 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
         )
         return pipeline
 
-    def _maybe_save_custom_code(self, save_directory: str | os.PathLike):
-        """Save custom code files (blocks config and Python modules) to the save directory."""
-        if self._blocks is None:
-            return
-
-        blocks_module = type(self._blocks).__module__
-        is_custom_code = not blocks_module.startswith("diffusers.") and blocks_module != "diffusers"
-        if not is_custom_code:
-            return
-
-        os.makedirs(save_directory, exist_ok=True)
-
-        self._blocks.save_pretrained(save_directory)
-
-        source_file = inspect.getfile(type(self._blocks))
-        module_file = os.path.basename(source_file)
-        dest_file = os.path.join(save_directory, module_file)
-
-        if os.path.abspath(source_file) != os.path.abspath(dest_file):
-            shutil.copyfile(source_file, dest_file)
-
-        from ..utils.dynamic_modules_utils import get_relative_import_files
-
-        for rel_file in get_relative_import_files(source_file):
-            rel_name = os.path.relpath(rel_file, os.path.dirname(source_file))
-            rel_dest = os.path.join(save_directory, rel_name)
-            if os.path.abspath(rel_file) != os.path.abspath(rel_dest):
-                os.makedirs(os.path.dirname(rel_dest), exist_ok=True)
-                shutil.copyfile(rel_file, rel_dest)
-
     def save_pretrained(
         self,
         save_directory: str | os.PathLike,
@@ -2029,8 +1998,6 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
                 component_spec_dict["subfolder"] = component_name
                 self.register_to_config(**{component_name: (library, class_name, component_spec_dict)})
 
-        self._maybe_save_custom_code(save_directory)
-
         self.save_config(save_directory=save_directory)
 
         if push_to_hub:

tests/models/testing_utils/parallelism.py

@@ -60,7 +60,12 @@ def _context_parallel_worker(rank, world_size, master_port, model_class, init_di
         model.eval()
 
         # 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()}
+        inputs_on_device = {}
+        for key, value in inputs_dict.items():
+            if isinstance(value, torch.Tensor):
+                inputs_on_device[key] = value.to(device)
+            else:
+                inputs_on_device[key] = value
 
         # Enable context parallelism
         cp_config = ContextParallelConfig(**cp_dict)
@@ -84,59 +89,6 @@ def _context_parallel_worker(rank, world_size, master_port, model_class, init_di
             dist.destroy_process_group()
 
 
-def _custom_mesh_worker(
-    rank,
-    world_size,
-    master_port,
-    model_class,
-    init_dict,
-    cp_dict,
-    mesh_shape,
-    mesh_dim_names,
-    inputs_dict,
-    return_dict,
-):
-    """Worker function for context parallel testing with a user-provided custom DeviceMesh."""
-    try:
-        os.environ["MASTER_ADDR"] = "localhost"
-        os.environ["MASTER_PORT"] = str(master_port)
-        os.environ["RANK"] = str(rank)
-        os.environ["WORLD_SIZE"] = str(world_size)
-
-        dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)
-
-        torch.cuda.set_device(rank)
-        device = torch.device(f"cuda:{rank}")
-
-        model = model_class(**init_dict)
-        model.to(device)
-        model.eval()
-
-        inputs_on_device = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in inputs_dict.items()}
-
-        # DeviceMesh must be created after init_process_group, inside each worker process.
-        mesh = torch.distributed.device_mesh.init_device_mesh(
-            "cuda", mesh_shape=mesh_shape, mesh_dim_names=mesh_dim_names
-        )
-        cp_config = ContextParallelConfig(**cp_dict, mesh=mesh)
-        model.enable_parallelism(config=cp_config)
-
-        with torch.no_grad():
-            output = model(**inputs_on_device, return_dict=False)[0]
-
-        if rank == 0:
-            return_dict["status"] = "success"
-            return_dict["output_shape"] = list(output.shape)
-
-    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()
-
-
 @is_context_parallel
 @require_torch_multi_accelerator
 class ContextParallelTesterMixin:
@@ -174,48 +126,3 @@ class ContextParallelTesterMixin:
         assert return_dict.get("status") == "success", (
             f"Context parallel inference failed: {return_dict.get('error', 'Unknown error')}"
         )
-
-    @pytest.mark.parametrize(
-        "cp_type,mesh_shape,mesh_dim_names",
-        [
-            ("ring_degree", (2, 1, 1), ("ring", "ulysses", "fsdp")),
-            ("ulysses_degree", (1, 2, 1), ("ring", "ulysses", "fsdp")),
-        ],
-        ids=["ring-3d-fsdp", "ulysses-3d-fsdp"],
-    )
-    def test_context_parallel_custom_mesh(self, cp_type, mesh_shape, mesh_dim_names):
-        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.")
-
-        world_size = 2
-        init_dict = self.get_init_dict()
-        inputs_dict = {k: v.cpu() if isinstance(v, torch.Tensor) else v for k, v in self.get_dummy_inputs().items()}
-        cp_dict = {cp_type: world_size}
-
-        master_port = _find_free_port()
-        manager = mp.Manager()
-        return_dict = manager.dict()
-
-        mp.spawn(
-            _custom_mesh_worker,
-            args=(
-                world_size,
-                master_port,
-                self.model_class,
-                init_dict,
-                cp_dict,
-                mesh_shape,
-                mesh_dim_names,
-                inputs_dict,
-                return_dict,
-            ),
-            nprocs=world_size,
-            join=True,
-        )
-
-        assert return_dict.get("status") == "success", (
-            f"Custom mesh context parallel inference failed: {return_dict.get('error', 'Unknown error')}"
-        )