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https://github.com/huggingface/diffusers.git
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b6f7933044
* start adding compilation tests for quantization. * fixes * make common utility. * modularize. * add group offloading+compile * xfail * update * Update tests/quantization/test_torch_compile_utils.py Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com> * fixes --------- Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
88 lines
3.3 KiB
Python
88 lines
3.3 KiB
Python
# coding=utf-8
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# Copyright 2024 The HuggingFace Team 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 clone 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 gc
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import unittest
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import torch
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from diffusers import DiffusionPipeline
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from diffusers.utils.testing_utils import backend_empty_cache, require_torch_gpu, slow, torch_device
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@require_torch_gpu
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@slow
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class QuantCompileTests(unittest.TestCase):
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quantization_config = None
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def setUp(self):
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super().setUp()
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gc.collect()
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backend_empty_cache(torch_device)
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torch.compiler.reset()
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def tearDown(self):
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super().tearDown()
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gc.collect()
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backend_empty_cache(torch_device)
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torch.compiler.reset()
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def _init_pipeline(self, quantization_config, torch_dtype):
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pipe = DiffusionPipeline.from_pretrained(
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"stabilityai/stable-diffusion-3-medium-diffusers",
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quantization_config=quantization_config,
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torch_dtype=torch_dtype,
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)
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return pipe
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def _test_torch_compile(self, quantization_config, torch_dtype=torch.bfloat16):
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pipe = self._init_pipeline(quantization_config, torch_dtype).to("cuda")
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# import to ensure fullgraph True
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pipe.transformer.compile(fullgraph=True)
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for _ in range(2):
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# small resolutions to ensure speedy execution.
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pipe("a dog", num_inference_steps=3, max_sequence_length=16, height=256, width=256)
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def _test_torch_compile_with_cpu_offload(self, quantization_config, torch_dtype=torch.bfloat16):
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pipe = self._init_pipeline(quantization_config, torch_dtype)
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pipe.enable_model_cpu_offload()
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pipe.transformer.compile()
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for _ in range(2):
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# small resolutions to ensure speedy execution.
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pipe("a dog", num_inference_steps=3, max_sequence_length=16, height=256, width=256)
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def _test_torch_compile_with_group_offload(self, quantization_config, torch_dtype=torch.bfloat16):
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torch._dynamo.config.cache_size_limit = 10000
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pipe = self._init_pipeline(quantization_config, torch_dtype)
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group_offload_kwargs = {
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"onload_device": torch.device("cuda"),
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"offload_device": torch.device("cpu"),
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"offload_type": "leaf_level",
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"use_stream": True,
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"non_blocking": True,
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}
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pipe.transformer.enable_group_offload(**group_offload_kwargs)
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pipe.transformer.compile()
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for name, component in pipe.components.items():
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if name != "transformer" and isinstance(component, torch.nn.Module):
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if torch.device(component.device).type == "cpu":
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component.to("cuda")
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for _ in range(2):
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# small resolutions to ensure speedy execution.
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pipe("a dog", num_inference_steps=3, max_sequence_length=16, height=256, width=256)
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