mirror of
https://github.com/NVIDIA/Model-Optimizer.git
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66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2024 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 _test_utils.examples.run_command import run_diffusers_cmd
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# Use the tiny models for faster testing
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# NOTE: For tiny sdxl and sd3, we have to use Float dtype instead of Half from transformers v4.49.0
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SD3_ARGS = ["--model", "sd3-medium", "--override-model-path", "hf-internal-testing/tiny-sd3-pipe"]
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SD3_DTYPE = "Float"
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# fmt: off
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def test_sd3_int8_level3(int8_args, tmp_path):
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torch_ckpt_path = tmp_path / "sd3-medium.int8.level3.pt"
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onnx_dir = tmp_path / "sd3-medium.int8.level3.onnx"
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restore_onnx_dir = tmp_path / "sd3-medium.int8.level3.restore.onnx"
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# INT8 Level 3
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run_diffusers_cmd(
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[
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"python", "quantize.py",
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*SD3_ARGS, *int8_args,
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"--model-dtype", SD3_DTYPE,
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"--trt-high-precision-dtype", SD3_DTYPE,
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"--batch-size", "2",
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"--quantized-torch-ckpt-save-path", torch_ckpt_path,
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"--onnx-dir", onnx_dir,
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],
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)
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# INT8 Level 3 Restore
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run_diffusers_cmd(
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[
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"python", "quantize.py",
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*SD3_ARGS, *int8_args,
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"--model-dtype", SD3_DTYPE,
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"--trt-high-precision-dtype", SD3_DTYPE,
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"--restore-from", torch_ckpt_path,
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"--onnx-dir", restore_onnx_dir,
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],
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)
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# Inference - DQ only
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run_diffusers_cmd(
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[
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"python", "diffusion_trt.py",
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*SD3_ARGS,
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"--model-dtype", SD3_DTYPE,
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"--onnx-load-path", onnx_dir / "model.onnx",
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"--dq-only",
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],
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)
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