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# Copyright 2025 HuggingFace Inc.
#
# 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 os
import socket
import pytest
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from diffusers.models._modeling_parallel import ContextParallelConfig
from diffusers.models.attention_dispatch import AttentionBackendName, _AttentionBackendRegistry
from ...testing_utils import (
is_attention,
is_context_parallel,
is_kernels_available,
require_torch_multi_accelerator,
torch_device,
)
from .utils import _maybe_cast_to_bf16
# Device configuration mapping
DEVICE_CONFIG = {
"cuda": {"backend": "nccl", "module": torch.cuda},
"xpu": {"backend": "xccl", "module": torch.xpu},
}
def _find_free_port():
"""Find a free port on localhost."""
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.bind(("", 0))
s.listen(1)
port = s.getsockname()[1]
return port
def _context_parallel_worker(
rank, world_size, master_port, model_class, init_dict, cp_dict, inputs_dict, return_dict, attention_backend=None
):
"""Worker function for context parallel 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
model = model_class(**init_dict)
model.to(device)
model.eval()
# Cast as needed.
model, inputs_dict = _maybe_cast_to_bf16(attention_backend, model, inputs_dict)
# 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 attention backend
if attention_backend:
model.set_attention_backend(attention_backend)
# Enable context parallelism
cp_config = ContextParallelConfig(**cp_dict)
model.enable_parallelism(config=cp_config)
# Run forward pass
with torch.no_grad():
output = model(**inputs_on_device, return_dict=False)[0]
# Only rank 0 reports results
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()
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,
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)
# Get device configuration
device_config = DEVICE_CONFIG.get(torch_device, DEVICE_CONFIG["cuda"])
backend = device_config["backend"]
device_module = device_config["module"]
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}")
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(
torch_device, 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:
@pytest.mark.parametrize("cp_type", ["ulysses_degree", "ring_degree"], ids=["ulysses", "ring"])
def test_context_parallel_inference(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_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 inference failed: {return_dict.get('error', 'Unknown error')}"
)
@pytest.mark.parametrize("cp_type", ["ulysses_degree", "ring_degree"], ids=["ulysses", "ring"])
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",
[
("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.")
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 = {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')}"
)
@is_attention
@is_context_parallel
@require_torch_multi_accelerator
class ContextParallelAttentionBackendsTesterMixin:
unsupported_attn_backends: list[str] = []
@pytest.mark.parametrize("cp_type", ["ulysses_degree", "ring_degree"])
@pytest.mark.parametrize(
"attention_backend",
[
"native",
pytest.param(
"flash_hub",
marks=pytest.mark.skipif(not is_kernels_available(), reason="`kernels` is not available."),
),
pytest.param(
"flash_varlen_hub",
marks=pytest.mark.skipif(not is_kernels_available(), reason="`kernels` is not available."),
),
pytest.param(
"_flash_3_hub",
marks=pytest.mark.skipif(not is_kernels_available(), reason="`kernels` is not available."),
),
],
)
@pytest.mark.parametrize("ulysses_anything", [True, False])
@torch.no_grad()
def test_context_parallel_attn_backend_inference(self, cp_type, attention_backend, ulysses_anything):
if not torch.distributed.is_available():
pytest.skip("torch.distributed is not available.")
if getattr(self.model_class, "_cp_plan", None) is None:
pytest.skip("Model does not have a _cp_plan defined for context parallel inference.")
if attention_backend in self.unsupported_attn_backends:
pytest.skip(f"{attention_backend} is not supported for this model.")
if cp_type == "ring_degree":
if attention_backend == AttentionBackendName.NATIVE:
pytest.skip("Skipping test because ring isn't supported with native attention backend.")
elif attention_backend in ("flash_varlen_hub"):
pytest.skip("`ring_degree` is not yet supported for varlen attention hub kernels.")
if ulysses_anything and "ulysses" not in cp_type:
pytest.skip("Skipping test as ulysses anything needs the ulysses degree set.")
world_size = 2
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
# 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}
if ulysses_anything:
cp_dict.update({"ulysses_anything": ulysses_anything})
# 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_worker,
args=(
world_size,
master_port,
self.model_class,
init_dict,
cp_dict,
inputs_dict,
return_dict,
attention_backend,
),
nprocs=world_size,
join=True,
)
assert return_dict.get("status") == "success", (
f"Context parallel inference failed: {return_dict.get('error', 'Unknown error')}"
)
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