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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 gc
import os
import pytest
import torch
from ...testing_utils import (
backend_empty_cache,
is_torch_compile,
require_accelerator,
require_torch_version_greater,
torch_device,
)
@is_torch_compile
@require_accelerator
@require_torch_version_greater("2.7.1")
class TorchCompileTesterMixin:
"""
Mixin class for testing torch.compile functionality on models.
Expected from config mixin:
- model_class: The model class to test
Optional properties:
- different_shapes_for_compilation: List of (height, width) tuples for dynamic shape testing (default: None)
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: compile
Use `pytest -m "not compile"` to skip these tests
"""
@property
def different_shapes_for_compilation(self) -> list[tuple[int, int]] | None:
"""Optional list of (height, width) tuples for dynamic shape testing."""
return None
def setup_method(self):
torch.compiler.reset()
gc.collect()
backend_empty_cache(torch_device)
def teardown_method(self):
torch.compiler.reset()
gc.collect()
backend_empty_cache(torch_device)
@torch.no_grad()
def test_torch_compile_recompilation_and_graph_break(self):
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**init_dict).to(torch_device)
model.eval()
model = torch.compile(model, fullgraph=True)
with (
torch._inductor.utils.fresh_inductor_cache(),
torch._dynamo.config.patch(error_on_recompile=True),
):
_ = model(**inputs_dict)
_ = model(**inputs_dict)
@torch.no_grad()
def test_torch_compile_repeated_blocks(self, recompile_limit=1):
if self.model_class._repeated_blocks is None:
pytest.skip("Skipping test as the model class doesn't have `_repeated_blocks` set.")
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**init_dict).to(torch_device)
model.eval()
model.compile_repeated_blocks(fullgraph=True)
if self.model_class.__name__ == "UNet2DConditionModel":
recompile_limit = 2
with (
torch._inductor.utils.fresh_inductor_cache(),
torch._dynamo.config.patch(recompile_limit=recompile_limit),
):
_ = model(**inputs_dict)
_ = model(**inputs_dict)
@torch.no_grad()
def test_compile_with_group_offloading(self):
if not self.model_class._supports_group_offloading:
pytest.skip("Model does not support group offloading.")
torch._dynamo.config.cache_size_limit = 10000
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**init_dict)
model.eval()
group_offload_kwargs = {
"onload_device": torch_device,
"offload_device": "cpu",
"offload_type": "block_level",
"num_blocks_per_group": 1,
"use_stream": True,
"non_blocking": True,
}
model.enable_group_offload(**group_offload_kwargs)
model.compile()
_ = model(**inputs_dict)
_ = model(**inputs_dict)
@torch.no_grad()
def test_compile_on_different_shapes(self):
if self.different_shapes_for_compilation is None:
pytest.skip(f"Skipping as `different_shapes_for_compilation` is not set for {self.__class__.__name__}.")
torch.fx.experimental._config.use_duck_shape = False
init_dict = self.get_init_dict()
model = self.model_class(**init_dict).to(torch_device)
model.eval()
model = torch.compile(model, fullgraph=True, dynamic=True)
for height, width in self.different_shapes_for_compilation:
with torch._dynamo.config.patch(error_on_recompile=True):
inputs_dict = self.get_dummy_inputs(height=height, width=width)
_ = model(**inputs_dict)
@torch.no_grad()
def test_compile_works_with_aot(self, tmp_path):
from torch._inductor.package import load_package
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
model = self.model_class(**init_dict).to(torch_device)
exported_model = torch.export.export(model, args=(), kwargs=inputs_dict)
package_path = os.path.join(str(tmp_path), f"{self.model_class.__name__}.pt2")
_ = torch._inductor.aoti_compile_and_package(exported_model, package_path=package_path)
assert os.path.exists(package_path), f"Package file not created at {package_path}"
loaded_binary = load_package(package_path, run_single_threaded=True)
model.forward = loaded_binary
_ = model(**inputs_dict)
_ = model(**inputs_dict)