mirror of
https://github.com/huggingface/diffusers.git
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266 lines
10 KiB
Python
266 lines
10 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 gc
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import torch
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from huggingface_hub import hf_hub_download, snapshot_download
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from diffusers.loaders.single_file_utils import _extract_repo_id_and_weights_name
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from ...testing_utils import (
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assert_tensors_close,
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backend_empty_cache,
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is_single_file,
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nightly,
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require_torch_accelerator,
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torch_device,
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)
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from .common import check_device_map_is_respected
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def download_single_file_checkpoint(pretrained_model_name_or_path, filename, tmpdir):
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"""Download a single file checkpoint from the Hub to a temporary directory."""
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path = hf_hub_download(pretrained_model_name_or_path, filename=filename, local_dir=tmpdir)
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return path
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def download_diffusers_config(pretrained_model_name_or_path, tmpdir):
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"""Download diffusers config files (excluding weights) from a repository."""
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path = snapshot_download(
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pretrained_model_name_or_path,
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ignore_patterns=[
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"**/*.ckpt",
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"*.ckpt",
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"**/*.bin",
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"*.bin",
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"**/*.pt",
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"*.pt",
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"**/*.safetensors",
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"*.safetensors",
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],
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allow_patterns=["**/*.json", "*.json", "*.txt", "**/*.txt"],
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local_dir=tmpdir,
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)
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return path
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@nightly
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@require_torch_accelerator
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@is_single_file
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class SingleFileTesterMixin:
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"""
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Mixin class for testing single file loading for models.
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Required properties (must be implemented by subclasses):
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- ckpt_path: Path or Hub path to the single file checkpoint
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Optional properties:
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- torch_dtype: torch dtype to use for testing (default: None)
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- alternate_ckpt_paths: List of alternate checkpoint paths for variant testing (default: None)
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Expected from config mixin:
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- model_class: The model class to test
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- pretrained_model_name_or_path: Hub repository ID for the pretrained model
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- pretrained_model_kwargs: Additional kwargs for from_pretrained (e.g., subfolder)
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Pytest mark: single_file
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Use `pytest -m "not single_file"` to skip these tests
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"""
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# ==================== Required Properties ====================
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@property
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def ckpt_path(self) -> str:
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"""Path or Hub path to the single file checkpoint. Must be implemented by subclasses."""
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raise NotImplementedError("Subclasses must implement the `ckpt_path` property.")
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# ==================== Optional Properties ====================
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@property
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def torch_dtype(self) -> torch.dtype | None:
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"""torch dtype to use for single file testing."""
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return None
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@property
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def alternate_ckpt_paths(self) -> list[str] | None:
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"""List of alternate checkpoint paths for variant testing."""
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return None
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def setup_method(self):
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gc.collect()
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backend_empty_cache(torch_device)
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def teardown_method(self):
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gc.collect()
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backend_empty_cache(torch_device)
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def test_single_file_model_config(self):
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pretrained_kwargs = {"device": torch_device, **self.pretrained_model_kwargs}
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single_file_kwargs = {"device": torch_device}
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if self.torch_dtype:
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pretrained_kwargs["torch_dtype"] = self.torch_dtype
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single_file_kwargs["torch_dtype"] = self.torch_dtype
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model = self.model_class.from_pretrained(self.pretrained_model_name_or_path, **pretrained_kwargs)
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model_single_file = self.model_class.from_single_file(self.ckpt_path, **single_file_kwargs)
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PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
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for param_name, param_value in model_single_file.config.items():
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if param_name in PARAMS_TO_IGNORE:
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continue
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assert model.config[param_name] == param_value, (
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f"{param_name} differs between pretrained loading and single file loading: "
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f"pretrained={model.config[param_name]}, single_file={param_value}"
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)
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def test_single_file_model_parameters(self, atol=1e-5, rtol=1e-5):
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pretrained_kwargs = {"device": torch_device, **self.pretrained_model_kwargs}
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single_file_kwargs = {"device": torch_device}
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if self.torch_dtype:
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pretrained_kwargs["torch_dtype"] = self.torch_dtype
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single_file_kwargs["torch_dtype"] = self.torch_dtype
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model = self.model_class.from_pretrained(self.pretrained_model_name_or_path, **pretrained_kwargs)
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model_single_file = self.model_class.from_single_file(self.ckpt_path, **single_file_kwargs)
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state_dict = model.state_dict()
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state_dict_single_file = model_single_file.state_dict()
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assert set(state_dict.keys()) == set(state_dict_single_file.keys()), (
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"Model parameters keys differ between pretrained and single file loading. "
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f"Missing in single file: {set(state_dict.keys()) - set(state_dict_single_file.keys())}. "
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f"Extra in single file: {set(state_dict_single_file.keys()) - set(state_dict.keys())}"
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)
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for key in state_dict.keys():
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param = state_dict[key]
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param_single_file = state_dict_single_file[key]
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assert param.shape == param_single_file.shape, (
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f"Parameter shape mismatch for {key}: "
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f"pretrained {param.shape} vs single file {param_single_file.shape}"
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)
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assert_tensors_close(
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param, param_single_file, atol=atol, rtol=rtol, msg=f"Parameter values differ for {key}"
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)
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def test_single_file_loading_local_files_only(self, tmp_path):
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single_file_kwargs = {}
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if self.torch_dtype:
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single_file_kwargs["torch_dtype"] = self.torch_dtype
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pretrained_model_name_or_path, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path)
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local_ckpt_path = download_single_file_checkpoint(pretrained_model_name_or_path, weight_name, str(tmp_path))
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model_single_file = self.model_class.from_single_file(
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local_ckpt_path, local_files_only=True, **single_file_kwargs
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)
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assert model_single_file is not None, "Failed to load model with local_files_only=True"
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def test_single_file_loading_with_diffusers_config(self):
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single_file_kwargs = {}
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if self.torch_dtype:
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single_file_kwargs["torch_dtype"] = self.torch_dtype
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# Load with config parameter
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model_single_file = self.model_class.from_single_file(
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self.ckpt_path, config=self.pretrained_model_name_or_path, **single_file_kwargs
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)
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# Load pretrained for comparison
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pretrained_kwargs = {**self.pretrained_model_kwargs}
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if self.torch_dtype:
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pretrained_kwargs["torch_dtype"] = self.torch_dtype
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model = self.model_class.from_pretrained(self.pretrained_model_name_or_path, **pretrained_kwargs)
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# Compare configs
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PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
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for param_name, param_value in model_single_file.config.items():
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if param_name in PARAMS_TO_IGNORE:
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continue
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assert model.config[param_name] == param_value, (
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f"{param_name} differs: pretrained={model.config[param_name]}, single_file={param_value}"
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)
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def test_single_file_loading_with_diffusers_config_local_files_only(self, tmp_path):
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single_file_kwargs = {}
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if self.torch_dtype:
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single_file_kwargs["torch_dtype"] = self.torch_dtype
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pretrained_model_name_or_path, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path)
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local_ckpt_path = download_single_file_checkpoint(pretrained_model_name_or_path, weight_name, str(tmp_path))
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local_diffusers_config = download_diffusers_config(self.pretrained_model_name_or_path, str(tmp_path))
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model_single_file = self.model_class.from_single_file(
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local_ckpt_path, config=local_diffusers_config, local_files_only=True, **single_file_kwargs
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)
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assert model_single_file is not None, "Failed to load model with config and local_files_only=True"
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def test_single_file_loading_dtype(self):
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for dtype in [torch.float32, torch.float16]:
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if torch_device == "mps" and dtype == torch.bfloat16:
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continue
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model_single_file = self.model_class.from_single_file(self.ckpt_path, torch_dtype=dtype)
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assert model_single_file.dtype == dtype, f"Expected dtype {dtype}, got {model_single_file.dtype}"
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# Cleanup
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del model_single_file
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gc.collect()
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backend_empty_cache(torch_device)
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def test_checkpoint_variant_loading(self):
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if not self.alternate_ckpt_paths:
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return
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for ckpt_path in self.alternate_ckpt_paths:
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backend_empty_cache(torch_device)
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single_file_kwargs = {}
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if self.torch_dtype:
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single_file_kwargs["torch_dtype"] = self.torch_dtype
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model = self.model_class.from_single_file(ckpt_path, **single_file_kwargs)
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assert model is not None, f"Failed to load checkpoint from {ckpt_path}"
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del model
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gc.collect()
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backend_empty_cache(torch_device)
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def test_single_file_loading_with_device_map(self):
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single_file_kwargs = {"device_map": torch_device}
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if self.torch_dtype:
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single_file_kwargs["torch_dtype"] = self.torch_dtype
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model = self.model_class.from_single_file(self.ckpt_path, **single_file_kwargs)
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assert model is not None, "Failed to load model with device_map"
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assert hasattr(model, "hf_device_map"), "Model should have hf_device_map attribute when loaded with device_map"
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assert model.hf_device_map is not None, "hf_device_map should not be None when loaded with device_map"
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check_device_map_is_respected(model, model.hf_device_map)
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