File size: 5,702 Bytes
76ed89b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 | # 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)
|