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https://github.com/NVIDIA/Model-Optimizer.git
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63 lines
1.8 KiB
Python
63 lines
1.8 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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import pytest
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import torch
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from _test_utils.torch_model.transformers_models import create_tiny_llama_dir
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@pytest.fixture(scope="session")
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def num_gpus():
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return torch.cuda.device_count()
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@pytest.fixture(scope="session")
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def require_2_gpus(num_gpus):
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if num_gpus < 2:
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pytest.skip("At least 2 GPUs required")
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@pytest.fixture(scope="session")
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def cuda_capability():
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return torch.cuda.get_device_capability()
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@pytest.fixture(scope="session")
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def require_sm89(cuda_capability):
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if not cuda_capability >= (8, 9):
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pytest.skip("CUDA capability>=8.9 required")
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@pytest.fixture(scope="session")
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def tiny_llama_path(tmp_path_factory):
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yield str(
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create_tiny_llama_dir(
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tmp_path_factory.mktemp("tiny_llama"),
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with_tokenizer=True,
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hidden_size=512,
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intermediate_size=512,
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)
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)
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# AutoDeploy is only avaialble after TRT-LLM 0.19
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@pytest.fixture(scope="session")
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def require_autodeploy():
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try:
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import tensorrt_llm._torch.auto_deploy # noqa: F401
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except ImportError:
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pytest.skip("TRT-LLM 0.19+ required for AutoDeploy examples.")
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