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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 copy
import gc
import pytest
import torch
from diffusers.training_utils import EMAModel
from ...testing_utils import (
backend_empty_cache,
is_training,
require_torch_accelerator_with_training,
torch_all_close,
torch_device,
)
@is_training
@require_torch_accelerator_with_training
class TrainingTesterMixin:
"""
Mixin class for testing training functionality on models.
Expected from config mixin:
- model_class: The model class to test
- output_shape: Tuple defining the expected output shape
Expected methods from config mixin:
- 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
Pytest mark: training
Use `pytest -m "not training"` to skip these tests
"""
def setup_method(self):
gc.collect()
backend_empty_cache(torch_device)
def teardown_method(self):
gc.collect()
backend_empty_cache(torch_device)
def test_training(self):
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**init_dict)
model.to(torch_device)
model.train()
output = model(**inputs_dict, return_dict=False)[0]
noise = torch.randn((output.shape[0],) + self.output_shape).to(torch_device)
loss = torch.nn.functional.mse_loss(output, noise)
loss.backward()
def test_training_with_ema(self):
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**init_dict)
model.to(torch_device)
model.train()
ema_model = EMAModel(model.parameters())
output = model(**inputs_dict, return_dict=False)[0]
noise = torch.randn((output.shape[0],) + self.output_shape).to(torch_device)
loss = torch.nn.functional.mse_loss(output, noise)
loss.backward()
ema_model.step(model.parameters())
def test_gradient_checkpointing(self):
if not self.model_class._supports_gradient_checkpointing:
pytest.skip("Gradient checkpointing is not supported.")
init_dict = self.get_init_dict()
# at init model should have gradient checkpointing disabled
model = self.model_class(**init_dict)
assert not model.is_gradient_checkpointing, "Gradient checkpointing should be disabled at init"
# check enable works
model.enable_gradient_checkpointing()
assert model.is_gradient_checkpointing, "Gradient checkpointing should be enabled"
# check disable works
model.disable_gradient_checkpointing()
assert not model.is_gradient_checkpointing, "Gradient checkpointing should be disabled"
def test_gradient_checkpointing_is_applied(self, expected_set=None):
if not self.model_class._supports_gradient_checkpointing:
pytest.skip("Gradient checkpointing is not supported.")
if expected_set is None:
pytest.skip("expected_set must be provided to verify gradient checkpointing is applied.")
init_dict = self.get_init_dict()
model_class_copy = copy.copy(self.model_class)
model = model_class_copy(**init_dict)
model.enable_gradient_checkpointing()
modules_with_gc_enabled = {}
for submodule in model.modules():
if hasattr(submodule, "gradient_checkpointing"):
assert submodule.gradient_checkpointing, f"{submodule.__class__.__name__} should have GC enabled"
modules_with_gc_enabled[submodule.__class__.__name__] = True
assert set(modules_with_gc_enabled.keys()) == expected_set, (
f"Modules with GC enabled {set(modules_with_gc_enabled.keys())} do not match expected set {expected_set}"
)
assert all(modules_with_gc_enabled.values()), "All modules should have GC enabled"
def test_gradient_checkpointing_equivalence(self, loss_tolerance=1e-5, param_grad_tol=5e-5, skip=None):
if not self.model_class._supports_gradient_checkpointing:
pytest.skip("Gradient checkpointing is not supported.")
if skip is None:
skip = set()
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
inputs_dict_copy = copy.deepcopy(inputs_dict)
torch.manual_seed(0)
model = self.model_class(**init_dict)
model.to(torch_device)
assert not model.is_gradient_checkpointing and model.training
out = model(**inputs_dict, return_dict=False)[0]
# run the backwards pass on the model
model.zero_grad()
labels = torch.randn_like(out)
loss = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
torch.manual_seed(0)
model_2 = self.model_class(**init_dict)
# clone model
model_2.load_state_dict(model.state_dict())
model_2.to(torch_device)
model_2.enable_gradient_checkpointing()
assert model_2.is_gradient_checkpointing and model_2.training
out_2 = model_2(**inputs_dict_copy, return_dict=False)[0]
# run the backwards pass on the model
model_2.zero_grad()
loss_2 = (out_2 - labels).mean()
loss_2.backward()
# compare the output and parameters gradients
assert (loss - loss_2).abs() < loss_tolerance, (
f"Loss difference {(loss - loss_2).abs()} exceeds tolerance {loss_tolerance}"
)
named_params = dict(model.named_parameters())
named_params_2 = dict(model_2.named_parameters())
for name, param in named_params.items():
if "post_quant_conv" in name:
continue
if name in skip:
continue
if param.grad is None:
continue
assert torch_all_close(param.grad.data, named_params_2[name].grad.data, atol=param_grad_tol), (
f"Gradient mismatch for {name}"
)
def test_mixed_precision_training(self):
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**init_dict)
model.to(torch_device)
model.train()
# Test with float16
if torch.device(torch_device).type != "cpu":
with torch.amp.autocast(device_type=torch.device(torch_device).type, dtype=torch.float16):
output = model(**inputs_dict, return_dict=False)[0]
noise = torch.randn((output.shape[0],) + self.output_shape).to(torch_device)
loss = torch.nn.functional.mse_loss(output, noise)
loss.backward()
# Test with bfloat16
if torch.device(torch_device).type != "cpu":
model.zero_grad()
with torch.amp.autocast(device_type=torch.device(torch_device).type, dtype=torch.bfloat16):
output = model(**inputs_dict, return_dict=False)[0]
noise = torch.randn((output.shape[0],) + self.output_shape).to(torch_device)
loss = torch.nn.functional.mse_loss(output, noise)
loss.backward()