mirror of
https://github.com/NVIDIA/Model-Optimizer.git
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86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2023-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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 copy 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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from pathlib import Path
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import pytest
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import torch
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pytest.importorskip("diffusers")
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from diffusers import UNet2DConditionModel
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import modelopt.torch.opt as mto
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def get_tiny_unet(**config_kwargs) -> UNet2DConditionModel:
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"""Create a tiny UNet2DConditionModel for testing."""
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kwargs = {
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"sample_size": 8,
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"in_channels": 2,
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"out_channels": 2,
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"down_block_types": ("DownBlock2D",),
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"up_block_types": ("UpBlock2D",),
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"block_out_channels": (2,),
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"layers_per_block": 1,
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"cross_attention_dim": 2,
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"attention_head_dim": 1,
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"norm_num_groups": 1,
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"mid_block_type": None,
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}
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kwargs.update(**config_kwargs)
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tiny_unet = UNet2DConditionModel(**kwargs)
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return tiny_unet
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def create_tiny_unet_dir(tmp_path: Path, **config_kwargs) -> Path:
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"""Create and save a tiny UNet model to a directory."""
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tiny_unet = get_tiny_unet(**config_kwargs)
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tiny_unet.save_pretrained(tmp_path / "tiny_unet")
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return tmp_path / "tiny_unet"
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def get_unet_dummy_inputs(model: UNet2DConditionModel, batch_size: int = 1):
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"""Generate dummy inputs for testing UNet models."""
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latents = torch.randn(
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batch_size, model.config.in_channels, model.config.sample_size, model.config.sample_size
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)
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timestep = torch.tensor([0])
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encoder_hidden_states = torch.randn(batch_size, 1, model.config.cross_attention_dim)
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return {"sample": latents, "timestep": timestep, "encoder_hidden_states": encoder_hidden_states}
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def df_output_tester(model_ref, model_test):
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"""Test if two diffusers models produce the same output."""
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inputs = get_unet_dummy_inputs(model_ref)
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model_ref.eval()
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model_test.eval()
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with torch.no_grad():
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output_ref = model_ref(**inputs).sample
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output_test = model_test(**inputs).sample
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assert torch.allclose(output_ref, output_test)
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def df_modelopt_state_and_output_tester(model_ref, model_test):
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"""Test if two diffusers models have the same modelopt state and outputs."""
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model_ref_state = mto.modelopt_state(model_ref)
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model_test_state = mto.modelopt_state(model_test)
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assert model_ref_state == model_test_state
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df_output_tester(model_ref, model_test)
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