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# coding=utf-8
# 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 json
import os
from collections import defaultdict
from typing import Any, Dict, Optional, Type
import pytest
import torch
import torch.nn as nn
from accelerate.utils.modeling import _get_proper_dtype, compute_module_sizes, dtype_byte_size
from diffusers.utils import SAFE_WEIGHTS_INDEX_NAME, _add_variant, logging
from diffusers.utils.testing_utils import require_accelerator, require_torch_multi_accelerator
from ...testing_utils import assert_tensors_close, torch_device
def named_persistent_module_tensors(
module: nn.Module,
recurse: bool = False,
):
"""
A helper function that gathers all the tensors (parameters + persistent buffers) of a given module.
Args:
module (`torch.nn.Module`):
The module we want the tensors on.
recurse (`bool`, *optional`, defaults to `False`):
Whether or not to go look in every submodule or just return the direct parameters and buffers.
"""
yield from module.named_parameters(recurse=recurse)
for named_buffer in module.named_buffers(recurse=recurse):
name, _ = named_buffer
# Get parent by splitting on dots and traversing the model
parent = module
if "." in name:
parent_name = name.rsplit(".", 1)[0]
for part in parent_name.split("."):
parent = getattr(parent, part)
name = name.split(".")[-1]
if name not in parent._non_persistent_buffers_set:
yield named_buffer
def compute_module_persistent_sizes(
model: nn.Module,
dtype: str | torch.device | None = None,
special_dtypes: dict[str, str | torch.device] | None = None,
):
"""
Compute the size of each submodule of a given model (parameters + persistent buffers).
"""
if dtype is not None:
dtype = _get_proper_dtype(dtype)
dtype_size = dtype_byte_size(dtype)
if special_dtypes is not None:
special_dtypes = {key: _get_proper_dtype(dtyp) for key, dtyp in special_dtypes.items()}
special_dtypes_size = {key: dtype_byte_size(dtyp) for key, dtyp in special_dtypes.items()}
module_sizes = defaultdict(int)
module_list = []
module_list = named_persistent_module_tensors(model, recurse=True)
for name, tensor in module_list:
if special_dtypes is not None and name in special_dtypes:
size = tensor.numel() * special_dtypes_size[name]
elif dtype is None:
size = tensor.numel() * dtype_byte_size(tensor.dtype)
elif str(tensor.dtype).startswith(("torch.uint", "torch.int", "torch.bool")):
# According to the code in set_module_tensor_to_device, these types won't be converted
# so use their original size here
size = tensor.numel() * dtype_byte_size(tensor.dtype)
else:
size = tensor.numel() * min(dtype_size, dtype_byte_size(tensor.dtype))
name_parts = name.split(".")
for idx in range(len(name_parts) + 1):
module_sizes[".".join(name_parts[:idx])] += size
return module_sizes
def calculate_expected_num_shards(index_map_path):
"""
Calculate expected number of shards from index file.
Args:
index_map_path: Path to the sharded checkpoint index file
Returns:
int: Expected number of shards
"""
with open(index_map_path) as f:
weight_map_dict = json.load(f)["weight_map"]
first_key = list(weight_map_dict.keys())[0]
weight_loc = weight_map_dict[first_key] # e.g., diffusion_pytorch_model-00001-of-00002.safetensors
expected_num_shards = int(weight_loc.split("-")[-1].split(".")[0])
return expected_num_shards
def check_device_map_is_respected(model, device_map):
for param_name, param in model.named_parameters():
# Find device in device_map
while len(param_name) > 0 and param_name not in device_map:
param_name = ".".join(param_name.split(".")[:-1])
if param_name not in device_map:
raise ValueError("device map is incomplete, it does not contain any device for `param_name`.")
param_device = device_map[param_name]
if param_device in ["cpu", "disk"]:
assert param.device == torch.device("meta"), f"Expected device 'meta' for {param_name}, got {param.device}"
else:
assert param.device == torch.device(param_device), (
f"Expected device {param_device} for {param_name}, got {param.device}"
)
def cast_inputs_to_dtype(inputs, current_dtype, target_dtype):
if torch.is_tensor(inputs):
return inputs.to(target_dtype) if inputs.dtype == current_dtype else inputs
if isinstance(inputs, dict):
return {k: cast_inputs_to_dtype(v, current_dtype, target_dtype) for k, v in inputs.items()}
if isinstance(inputs, list):
return [cast_inputs_to_dtype(v, current_dtype, target_dtype) for v in inputs]
return inputs
class BaseModelTesterConfig:
"""
Base class defining the configuration interface for model testing.
This class defines the contract that all model test classes must implement.
It provides a consistent interface for accessing model configuration, initialization
parameters, and test inputs across all testing mixins.
Required properties (must be implemented by subclasses):
- model_class: The model class to test
Optional properties (can be overridden, have sensible defaults):
- pretrained_model_name_or_path: Hub repository ID for pretrained model (default: None)
- pretrained_model_kwargs: Additional kwargs for from_pretrained (default: {})
- output_shape: Expected output shape for output validation tests (default: None)
- model_split_percents: Percentages for model parallelism tests (default: [0.5, 0.7])
Required methods (must be implemented by subclasses):
- get_init_dict(): Returns dict of arguments to initialize the model
- get_dummy_inputs(): Returns dict of inputs to pass to the model forward pass
Example usage:
class MyModelTestConfig(BaseModelTesterConfig):
@property
def model_class(self):
return MyModel
@property
def pretrained_model_name_or_path(self):
return "org/my-model"
@property
def output_shape(self):
return (1, 3, 32, 32)
def get_init_dict(self):
return {"in_channels": 3, "out_channels": 3}
def get_dummy_inputs(self):
return {"sample": torch.randn(1, 3, 32, 32, device=torch_device)}
class TestMyModel(MyModelTestConfig, ModelTesterMixin, QuantizationTesterMixin):
pass
"""
# ==================== Required Properties ====================
@property
def model_class(self) -> Type[nn.Module]:
"""The model class to test. Must be implemented by subclasses."""
raise NotImplementedError("Subclasses must implement the `model_class` property.")
# ==================== Optional Properties ====================
@property
def pretrained_model_name_or_path(self) -> Optional[str]:
"""Hub repository ID for the pretrained model (used for quantization and hub tests)."""
return None
@property
def pretrained_model_kwargs(self) -> Dict[str, Any]:
"""Additional kwargs to pass to from_pretrained (e.g., subfolder, variant)."""
return {}
@property
def output_shape(self) -> Optional[tuple]:
"""Expected output shape for output validation tests."""
return None
@property
def model_split_percents(self) -> list:
"""Percentages for model parallelism tests."""
return [0.9]
# ==================== Required Methods ====================
def get_init_dict(self) -> Dict[str, Any]:
"""
Returns dict of arguments to initialize the model.
Returns:
Dict[str, Any]: Initialization arguments for the model constructor.
Example:
return {
"in_channels": 3,
"out_channels": 3,
"sample_size": 32,
}
"""
raise NotImplementedError("Subclasses must implement `get_init_dict()`.")
def get_dummy_inputs(self) -> Dict[str, Any]:
"""
Returns dict of inputs to pass to the model forward pass.
Returns:
Dict[str, Any]: Input tensors/values for model.forward().
Example:
return {
"sample": torch.randn(1, 3, 32, 32, device=torch_device),
"timestep": torch.tensor([1], device=torch_device),
}
"""
raise NotImplementedError("Subclasses must implement `get_dummy_inputs()`.")
class ModelTesterMixin:
"""
Base mixin class for model testing with common test methods.
This mixin expects the test class to also inherit from BaseModelTesterConfig
(or implement its interface) which provides:
- model_class: The model class to test
- get_init_dict(): Returns dict of arguments to initialize the model
- get_dummy_inputs(): Returns dict of inputs to pass to the model forward pass
Example:
class MyModelTestConfig(BaseModelTesterConfig):
model_class = MyModel
def get_init_dict(self): ...
def get_dummy_inputs(self): ...
class TestMyModel(MyModelTestConfig, ModelTesterMixin):
pass
"""
@torch.no_grad()
def test_from_save_pretrained(self, tmp_path, atol=5e-5, rtol=5e-5):
torch.manual_seed(0)
model = self.model_class(**self.get_init_dict())
model.to(torch_device)
model.eval()
model.save_pretrained(tmp_path)
new_model = self.model_class.from_pretrained(tmp_path)
new_model.to(torch_device)
for param_name in model.state_dict().keys():
param_1 = model.state_dict()[param_name]
param_2 = new_model.state_dict()[param_name]
assert param_1.shape == param_2.shape, (
f"Parameter shape mismatch for {param_name}. Original: {param_1.shape}, loaded: {param_2.shape}"
)
image = model(**self.get_dummy_inputs(), return_dict=False)[0]
new_image = new_model(**self.get_dummy_inputs(), return_dict=False)[0]
assert_tensors_close(image, new_image, atol=atol, rtol=rtol, msg="Models give different forward passes.")
@torch.no_grad()
def test_from_save_pretrained_variant(self, tmp_path, atol=5e-5, rtol=0):
model = self.model_class(**self.get_init_dict())
model.to(torch_device)
model.eval()
model.save_pretrained(tmp_path, variant="fp16")
new_model = self.model_class.from_pretrained(tmp_path, variant="fp16")
with pytest.raises(OSError) as exc_info:
self.model_class.from_pretrained(tmp_path)
assert "Error no file named diffusion_pytorch_model.bin found in directory" in str(exc_info.value)
new_model.to(torch_device)
image = model(**self.get_dummy_inputs(), return_dict=False)[0]
new_image = new_model(**self.get_dummy_inputs(), return_dict=False)[0]
assert_tensors_close(image, new_image, atol=atol, rtol=rtol, msg="Models give different forward passes.")
@pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.bfloat16], ids=["fp32", "fp16", "bf16"])
def test_from_save_pretrained_dtype(self, tmp_path, dtype):
model = self.model_class(**self.get_init_dict())
model.to(torch_device)
model.eval()
if torch_device == "mps" and dtype == torch.bfloat16:
pytest.skip(reason=f"{dtype} is not supported on {torch_device}")
model.to(dtype)
model.save_pretrained(tmp_path)
new_model = self.model_class.from_pretrained(tmp_path, low_cpu_mem_usage=True, torch_dtype=dtype)
assert new_model.dtype == dtype
if hasattr(self.model_class, "_keep_in_fp32_modules") and self.model_class._keep_in_fp32_modules is None:
# When loading without accelerate dtype == torch.float32 if _keep_in_fp32_modules is not None
new_model = self.model_class.from_pretrained(tmp_path, low_cpu_mem_usage=False, torch_dtype=dtype)
assert new_model.dtype == dtype
@torch.no_grad()
def test_determinism(self, atol=1e-5, rtol=0):
model = self.model_class(**self.get_init_dict())
model.to(torch_device)
model.eval()
first = model(**self.get_dummy_inputs(), return_dict=False)[0]
second = model(**self.get_dummy_inputs(), return_dict=False)[0]
first_flat = first.flatten()
second_flat = second.flatten()
mask = ~(torch.isnan(first_flat) | torch.isnan(second_flat))
first_filtered = first_flat[mask]
second_filtered = second_flat[mask]
assert_tensors_close(
first_filtered, second_filtered, atol=atol, rtol=rtol, msg="Model outputs are not deterministic"
)
@torch.no_grad()
def test_output(self, expected_output_shape=None):
model = self.model_class(**self.get_init_dict())
model.to(torch_device)
model.eval()
inputs_dict = self.get_dummy_inputs()
output = model(**inputs_dict, return_dict=False)[0]
assert output is not None, "Model output is None"
assert output[0].shape == expected_output_shape or self.output_shape, (
f"Output shape does not match expected. Expected {expected_output_shape}, got {output.shape}"
)
@torch.no_grad()
def test_outputs_equivalence(self, atol=1e-5, rtol=0):
def set_nan_tensor_to_zero(t):
device = t.device
if device.type == "mps":
t = t.to("cpu")
t[t != t] = 0
return t.to(device)
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (list, tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif isinstance(tuple_object, dict):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
assert_tensors_close(
set_nan_tensor_to_zero(tuple_object),
set_nan_tensor_to_zero(dict_object),
atol=atol,
rtol=rtol,
msg="Tuple and dict output are not equal",
)
model = self.model_class(**self.get_init_dict())
model.to(torch_device)
model.eval()
outputs_dict = model(**self.get_dummy_inputs())
outputs_tuple = model(**self.get_dummy_inputs(), return_dict=False)
recursive_check(outputs_tuple, outputs_dict)
def test_getattr_is_correct(self, caplog):
init_dict = self.get_init_dict()
model = self.model_class(**init_dict)
# save some things to test
model.dummy_attribute = 5
model.register_to_config(test_attribute=5)
logger_name = "diffusers.models.modeling_utils"
with caplog.at_level(logging.WARNING, logger=logger_name):
caplog.clear()
assert hasattr(model, "dummy_attribute")
assert getattr(model, "dummy_attribute") == 5
assert model.dummy_attribute == 5
# no warning should be thrown
assert caplog.text == ""
with caplog.at_level(logging.WARNING, logger=logger_name):
caplog.clear()
assert hasattr(model, "save_pretrained")
fn = model.save_pretrained
fn_1 = getattr(model, "save_pretrained")
assert fn == fn_1
# no warning should be thrown
assert caplog.text == ""
# warning should be thrown for config attributes accessed directly
with pytest.warns(FutureWarning):
assert model.test_attribute == 5
with pytest.warns(FutureWarning):
assert getattr(model, "test_attribute") == 5
with pytest.raises(AttributeError) as error:
model.does_not_exist
assert str(error.value) == f"'{type(model).__name__}' object has no attribute 'does_not_exist'"
@require_accelerator
@pytest.mark.skipif(
torch_device not in ["cuda", "xpu"],
reason="float16 and bfloat16 can only be used with an accelerator",
)
def test_keep_in_fp32_modules(self, tmp_path):
model = self.model_class(**self.get_init_dict())
fp32_modules = model._keep_in_fp32_modules
if fp32_modules is None or len(fp32_modules) == 0:
pytest.skip("Model does not have _keep_in_fp32_modules defined.")
# Save the model and reload with float16 dtype
# _keep_in_fp32_modules is only enforced during from_pretrained loading
model.save_pretrained(tmp_path)
model = self.model_class.from_pretrained(tmp_path, torch_dtype=torch.float16).to(torch_device)
for name, param in model.named_parameters():
if any(module_to_keep_in_fp32 in name.split(".") for module_to_keep_in_fp32 in fp32_modules):
assert param.dtype == torch.float32, f"Parameter {name} should be float32 but got {param.dtype}"
else:
assert param.dtype == torch.float16, f"Parameter {name} should be float16 but got {param.dtype}"
@require_accelerator
@pytest.mark.skipif(
torch_device not in ["cuda", "xpu"],
reason="float16 and bfloat16 can only be use for inference with an accelerator",
)
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16], ids=["fp16", "bf16"])
@torch.no_grad()
def test_from_save_pretrained_dtype_inference(self, tmp_path, dtype, atol=1e-4, rtol=0):
model = self.model_class(**self.get_init_dict())
model.to(torch_device)
fp32_modules = model._keep_in_fp32_modules or []
model.to(dtype).save_pretrained(tmp_path)
model_loaded = self.model_class.from_pretrained(tmp_path, torch_dtype=dtype).to(torch_device)
for name, param in model_loaded.named_parameters():
if fp32_modules and any(
module_to_keep_in_fp32 in name.split(".") for module_to_keep_in_fp32 in fp32_modules
):
assert param.data.dtype == torch.float32
else:
assert param.data.dtype == dtype
inputs = cast_inputs_to_dtype(self.get_dummy_inputs(), torch.float32, dtype)
output = model(**inputs, return_dict=False)[0]
output_loaded = model_loaded(**inputs, return_dict=False)[0]
assert_tensors_close(
output, output_loaded, atol=atol, rtol=rtol, msg=f"Loaded model output differs for {dtype}"
)
@require_accelerator
@torch.no_grad()
def test_sharded_checkpoints(self, tmp_path, atol=1e-5, rtol=0):
torch.manual_seed(0)
config = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**config).eval()
model = model.to(torch_device)
base_output = model(**inputs_dict, return_dict=False)[0]
model_size = compute_module_persistent_sizes(model)[""]
max_shard_size = int((model_size * 0.75) / (2**10)) # Convert to KB as these test models are small
model.cpu().save_pretrained(tmp_path, max_shard_size=f"{max_shard_size}KB")
assert os.path.exists(os.path.join(tmp_path, SAFE_WEIGHTS_INDEX_NAME)), "Index file should exist"
# Check if the right number of shards exists
expected_num_shards = calculate_expected_num_shards(os.path.join(tmp_path, SAFE_WEIGHTS_INDEX_NAME))
actual_num_shards = len([file for file in os.listdir(tmp_path) if file.endswith(".safetensors")])
assert actual_num_shards == expected_num_shards, (
f"Expected {expected_num_shards} shards, got {actual_num_shards}"
)
new_model = self.model_class.from_pretrained(tmp_path).eval()
new_model = new_model.to(torch_device)
torch.manual_seed(0)
inputs_dict_new = self.get_dummy_inputs()
new_output = new_model(**inputs_dict_new, return_dict=False)[0]
assert_tensors_close(
base_output, new_output, atol=atol, rtol=rtol, msg="Output should match after sharded save/load"
)
@require_accelerator
@torch.no_grad()
def test_sharded_checkpoints_with_variant(self, tmp_path, atol=1e-5, rtol=0):
torch.manual_seed(0)
config = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**config).eval()
model = model.to(torch_device)
base_output = model(**inputs_dict, return_dict=False)[0]
model_size = compute_module_persistent_sizes(model)[""]
max_shard_size = int((model_size * 0.75) / (2**10)) # Convert to KB as these test models are small
variant = "fp16"
model.cpu().save_pretrained(tmp_path, max_shard_size=f"{max_shard_size}KB", variant=variant)
index_filename = _add_variant(SAFE_WEIGHTS_INDEX_NAME, variant)
assert os.path.exists(os.path.join(tmp_path, index_filename)), (
f"Variant index file {index_filename} should exist"
)
# Check if the right number of shards exists
expected_num_shards = calculate_expected_num_shards(os.path.join(tmp_path, index_filename))
actual_num_shards = len([file for file in os.listdir(tmp_path) if file.endswith(".safetensors")])
assert actual_num_shards == expected_num_shards, (
f"Expected {expected_num_shards} shards, got {actual_num_shards}"
)
new_model = self.model_class.from_pretrained(tmp_path, variant=variant).eval()
new_model = new_model.to(torch_device)
torch.manual_seed(0)
inputs_dict_new = self.get_dummy_inputs()
new_output = new_model(**inputs_dict_new, return_dict=False)[0]
assert_tensors_close(
base_output, new_output, atol=atol, rtol=rtol, msg="Output should match after variant sharded save/load"
)
@torch.no_grad()
def test_sharded_checkpoints_with_parallel_loading(self, tmp_path, atol=1e-5, rtol=0):
from diffusers.utils import constants
torch.manual_seed(0)
config = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**config).eval()
model = model.to(torch_device)
base_output = model(**inputs_dict, return_dict=False)[0]
model_size = compute_module_persistent_sizes(model)[""]
max_shard_size = int((model_size * 0.75) / (2**10)) # Convert to KB as these test models are small
# Save original values to restore after test
original_parallel_loading = constants.HF_ENABLE_PARALLEL_LOADING
original_parallel_workers = getattr(constants, "HF_PARALLEL_WORKERS", None)
try:
model.cpu().save_pretrained(tmp_path, max_shard_size=f"{max_shard_size}KB")
assert os.path.exists(os.path.join(tmp_path, SAFE_WEIGHTS_INDEX_NAME)), "Index file should exist"
# Check if the right number of shards exists
expected_num_shards = calculate_expected_num_shards(os.path.join(tmp_path, SAFE_WEIGHTS_INDEX_NAME))
actual_num_shards = len([file for file in os.listdir(tmp_path) if file.endswith(".safetensors")])
assert actual_num_shards == expected_num_shards, (
f"Expected {expected_num_shards} shards, got {actual_num_shards}"
)
# Load without parallel loading
constants.HF_ENABLE_PARALLEL_LOADING = False
model_sequential = self.model_class.from_pretrained(tmp_path).eval()
model_sequential = model_sequential.to(torch_device)
# Load with parallel loading
constants.HF_ENABLE_PARALLEL_LOADING = True
constants.DEFAULT_HF_PARALLEL_LOADING_WORKERS = 2
torch.manual_seed(0)
model_parallel = self.model_class.from_pretrained(tmp_path).eval()
model_parallel = model_parallel.to(torch_device)
torch.manual_seed(0)
inputs_dict_parallel = self.get_dummy_inputs()
output_parallel = model_parallel(**inputs_dict_parallel, return_dict=False)[0]
assert_tensors_close(
base_output, output_parallel, atol=atol, rtol=rtol, msg="Output should match with parallel loading"
)
finally:
# Restore original values
constants.HF_ENABLE_PARALLEL_LOADING = original_parallel_loading
if original_parallel_workers is not None:
constants.HF_PARALLEL_WORKERS = original_parallel_workers
@require_torch_multi_accelerator
@torch.no_grad()
def test_model_parallelism(self, tmp_path, atol=1e-5, rtol=0):
if self.model_class._no_split_modules is None:
pytest.skip("Test not supported for this model as `_no_split_modules` is not set.")
config = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**config).eval()
model = model.to(torch_device)
torch.manual_seed(0)
base_output = model(**inputs_dict, return_dict=False)[0]
model_size = compute_module_sizes(model)[""]
max_gpu_sizes = [int(p * model_size) for p in self.model_split_percents]
model.cpu().save_pretrained(tmp_path)
for max_size in max_gpu_sizes:
max_memory = {0: max_size, 1: model_size * 2, "cpu": model_size * 2}
new_model = self.model_class.from_pretrained(tmp_path, device_map="auto", max_memory=max_memory)
# Making sure part of the model will be on GPU 0 and GPU 1
assert set(new_model.hf_device_map.values()) == {0, 1}, "Model should be split across GPUs"
check_device_map_is_respected(new_model, new_model.hf_device_map)
torch.manual_seed(0)
new_output = new_model(**inputs_dict, return_dict=False)[0]
assert_tensors_close(
base_output, new_output, atol=atol, rtol=rtol, msg="Output should match with model parallelism"
)