mirror of
https://github.com/huggingface/diffusers.git
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239 lines
10 KiB
Python
239 lines
10 KiB
Python
# coding=utf-8
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# Copyright 2026 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 functools
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import gc
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import huggingface_hub
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import pytest
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import torch
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from huggingface_hub import HfApi
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from safetensors.torch import _getdtype
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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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backend_empty_cache,
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is_single_file,
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nightly,
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require_hf_token,
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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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# Config entries that legitimately differ between a checkpoint that's loaded with `from_pretrained()` and the one loaded with `from_single_file()`.
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PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
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@functools.lru_cache(maxsize=None)
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def fetch_checkpoint_metadata(ckpt_path):
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"""
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Fetch a single file checkpoint's keys, shapes and dtypes without downloading its weights.
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Only the safetensors header crosses the network, and the result is cached for the session because every test
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in a class asks for the same one. Returns `None` for checkpoints that are not safetensors.
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"""
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pretrained_model_name_or_path, weight_name = _extract_repo_id_and_weights_name(ckpt_path)
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if not weight_name.endswith(".safetensors"):
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return None
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return HfApi().parse_safetensors_file_metadata(pretrained_model_name_or_path, weight_name)
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# The checkpoints these tests resolve against include gated repos, so they only run where an
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# authenticated token is available. In CI that is everywhere except fork PRs, whose
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# `pull_request` runs resolve the token secret to an empty value.
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@require_hf_token
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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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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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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 get_dummy_single_file_state_dict(self):
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"""
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A stand-in for the single file checkpoint's state dict.
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Keys, shapes and dtypes match the real checkpoint exactly, while the tensors themselves are on meta and
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hold no data. Pass it to `from_single_file` with `device="meta"` to exercise the loading path without
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materializing a weight. The conversion functions consume the dict they are handed, so build a fresh one
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for every load.
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"""
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metadata = fetch_checkpoint_metadata(self.ckpt_path)
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if metadata is None:
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pytest.skip(f"{self.ckpt_path} is not a safetensors checkpoint")
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return {
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name: torch.empty(tuple(tensor.shape), dtype=_getdtype(tensor.dtype), device="meta")
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for name, tensor in metadata.tensors.items()
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}
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def test_single_file_model_config(self):
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# An empty model supplies the registered defaults (`out_channels` and friends) that a bare config dict
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# would be missing.
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with torch.device("meta"):
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model = self.model_class.from_config(self.pretrained_model_name_or_path, **self.pretrained_model_kwargs)
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config = model.config
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model_single_file = self.model_class.from_single_file(self.get_dummy_single_file_state_dict(), device="meta")
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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 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={config[param_name]}, single_file={param_value}"
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)
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def test_single_file_loading_local_files_only(self, monkeypatch):
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state_dict = self.get_dummy_single_file_state_dict()
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# The checkpoint is already in memory, so the config is the only thing left to resolve. Fetch it into the
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# cache the way a previous run would have, and keep it as the reference to compare against.
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config = self.model_class.load_config(self.pretrained_model_name_or_path, **self.pretrained_model_kwargs)
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# Cut the Hub off, so anything the load does not find locally raises instead of quietly downloading.
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monkeypatch.setattr(huggingface_hub.constants, "HF_HUB_OFFLINE", True)
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model_single_file = self.model_class.from_single_file(state_dict, local_files_only=True, device="meta")
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# Resolving from the cache has to land on the same config as resolving from the Hub.
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for param_name, param_value in config.items():
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if param_name in PARAMS_TO_IGNORE:
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continue
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assert model_single_file.config[param_name] == param_value, (
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f"{param_name} differs when loading with local_files_only=True: "
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f"pretrained={param_value}, single_file={model_single_file.config[param_name]}"
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)
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def test_single_file_loading_with_diffusers_config(self):
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model_single_file = self.model_class.from_single_file(
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self.get_dummy_single_file_state_dict(),
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config=self.pretrained_model_name_or_path,
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device="meta",
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**self.pretrained_model_kwargs,
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)
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# An empty model supplies the registered defaults (`out_channels` and friends) that a bare config dict
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# would be missing.
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with torch.device("meta"):
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model = self.model_class.from_config(self.pretrained_model_name_or_path, **self.pretrained_model_kwargs)
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config = model.config
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# Compare configs
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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 config[param_name] == param_value, (
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f"{param_name} differs: pretrained={config[param_name]}, single_file={param_value}"
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)
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@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16], ids=["bf16", "fp16"])
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def test_single_file_loading_dtype(self, dtype):
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model_single_file = self.model_class.from_single_file(
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self.get_dummy_single_file_state_dict(), device="meta", dtype=dtype
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)
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for name, param in model_single_file.named_parameters():
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assert param.dtype == dtype, f"Parameter {name} should be {dtype} but got {param.dtype}"
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@nightly
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def test_single_file_model_parameters(self):
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pretrained_kwargs = {**self.pretrained_model_kwargs}
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single_file_kwargs = {}
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if self.torch_dtype:
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pretrained_kwargs["dtype"] = self.torch_dtype
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single_file_kwargs["dtype"] = self.torch_dtype
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# Both models load onto the CPU, so keep only the state dicts and drop the models themselves.
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model = self.model_class.from_pretrained(self.pretrained_model_name_or_path, **pretrained_kwargs)
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state_dict = model.state_dict()
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del model
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gc.collect()
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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_single_file = model_single_file.state_dict()
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del model_single_file
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gc.collect()
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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 torch.equal(param, param_single_file), f"Parameter values differ for {key}"
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@nightly
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def test_single_file_loading_with_device_map(self):
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single_file_kwargs = {"device_map": "auto"}
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if self.torch_dtype:
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single_file_kwargs["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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