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https://github.com/huggingface/diffusers.git
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0b76728e27
* update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update --------- Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
129 lines
4.2 KiB
Python
129 lines
4.2 KiB
Python
# coding=utf-8
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# Copyright 2025 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import socket
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import pytest
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import torch
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import torch.distributed as dist
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import torch.multiprocessing as mp
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from diffusers.models._modeling_parallel import ContextParallelConfig
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from ...testing_utils import (
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is_context_parallel,
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require_torch_multi_accelerator,
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)
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def _find_free_port():
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"""Find a free port on localhost."""
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with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
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s.bind(("", 0))
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s.listen(1)
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port = s.getsockname()[1]
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return port
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def _context_parallel_worker(rank, world_size, master_port, model_class, init_dict, cp_dict, inputs_dict, return_dict):
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"""Worker function for context parallel testing."""
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try:
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# Set up distributed environment
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os.environ["MASTER_ADDR"] = "localhost"
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os.environ["MASTER_PORT"] = str(master_port)
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os.environ["RANK"] = str(rank)
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os.environ["WORLD_SIZE"] = str(world_size)
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# Initialize process group
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dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)
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# Set device for this process
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torch.cuda.set_device(rank)
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device = torch.device(f"cuda:{rank}")
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# Create model
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model = model_class(**init_dict)
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model.to(device)
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model.eval()
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# Move inputs to device
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inputs_on_device = {}
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for key, value in inputs_dict.items():
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if isinstance(value, torch.Tensor):
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inputs_on_device[key] = value.to(device)
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else:
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inputs_on_device[key] = value
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# Enable context parallelism
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cp_config = ContextParallelConfig(**cp_dict)
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model.enable_parallelism(config=cp_config)
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# Run forward pass
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with torch.no_grad():
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output = model(**inputs_on_device, return_dict=False)[0]
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# Only rank 0 reports results
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if rank == 0:
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return_dict["status"] = "success"
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return_dict["output_shape"] = list(output.shape)
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except Exception as e:
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if rank == 0:
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return_dict["status"] = "error"
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return_dict["error"] = str(e)
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finally:
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if dist.is_initialized():
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dist.destroy_process_group()
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@is_context_parallel
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@require_torch_multi_accelerator
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class ContextParallelTesterMixin:
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@pytest.mark.parametrize("cp_type", ["ulysses_degree", "ring_degree"], ids=["ulysses", "ring"])
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def test_context_parallel_inference(self, cp_type):
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if not torch.distributed.is_available():
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pytest.skip("torch.distributed is not available.")
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if not hasattr(self.model_class, "_cp_plan") or self.model_class._cp_plan is None:
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pytest.skip("Model does not have a _cp_plan defined for context parallel inference.")
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world_size = 2
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init_dict = self.get_init_dict()
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inputs_dict = self.get_dummy_inputs()
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# Move all tensors to CPU for multiprocessing
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inputs_dict = {k: v.cpu() if isinstance(v, torch.Tensor) else v for k, v in inputs_dict.items()}
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cp_dict = {cp_type: world_size}
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# Find a free port for distributed communication
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master_port = _find_free_port()
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# Use multiprocessing manager for cross-process communication
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manager = mp.Manager()
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return_dict = manager.dict()
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# Spawn worker processes
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mp.spawn(
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_context_parallel_worker,
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args=(world_size, master_port, self.model_class, init_dict, cp_dict, inputs_dict, return_dict),
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nprocs=world_size,
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join=True,
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)
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assert return_dict.get("status") == "success", (
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f"Context parallel inference failed: {return_dict.get('error', 'Unknown error')}"
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)
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