mirror of
https://github.com/NVIDIA/Model-Optimizer.git
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146d1d9d40
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com> Signed-off-by: Chenjie Luo <108829653+cjluo-nv@users.noreply.github.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Co-authored-by: Chenjie Luo <chenjie@omniml.ai> Co-authored-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
114 lines
3.5 KiB
Python
114 lines
3.5 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 importlib.metadata as metadata
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import subprocess
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from dataclasses import asdict, dataclass
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from pathlib import Path
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import pytest
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import torch
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PTQ_EXAMPLE_DIR = Path(__file__).parents[2] / "examples" / "llm_ptq"
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@dataclass
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class PTQCommand:
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quant: str
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tasks: str = "quant"
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calib: int = 16
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sparsity: str | None = None
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kv_cache_quant: str | None = None
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trust_remote_code: bool = False
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calib_batch_size: int | None = None
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auto_quantize_bits: float | None = None
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tp: int | None = None
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pp: int | None = None
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min_sm: int | None = None
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max_sm: int | None = None
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min_gpu: int | None = None
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batch: int | None = None
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def run(self, model_path: str):
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if self.min_sm and torch.cuda.get_device_capability() < (
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self.min_sm // 10,
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self.min_sm % 10,
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):
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pytest.skip(reason=f"Requires sm{self.min_sm} or higher")
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return
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if self.max_sm and torch.cuda.get_device_capability() > (
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self.max_sm // 10,
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self.max_sm % 10,
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):
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pytest.skip(reason=f"Requires sm{self.max_sm} or lower")
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return
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if self.min_gpu and torch.cuda.device_count() < self.min_gpu:
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pytest.skip(reason=f"Requires at least {self.min_gpu} GPUs")
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return
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param_dict = asdict(self)
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param_dict.pop("min_sm", None)
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param_dict.pop("min_gpu", None)
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trust_remote_code = param_dict.pop("trust_remote_code", False)
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args = ["--model", model_path]
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for key, value in param_dict.items():
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if value is not None:
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args.append(f"--{key}")
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args.append(f"{value}")
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if trust_remote_code:
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args.append("--trust_remote_code")
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self.command = ["scripts/huggingface_example.sh", "--no-verbose", *args]
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subprocess.run(self.command, cwd=PTQ_EXAMPLE_DIR, check=True)
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def param_str(self):
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param_dict = asdict(self)
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param_dict.pop("trust_remote_code", False)
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return "_".join(str(value) for value in param_dict.values() if value is not None).replace(
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",", "_"
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)
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class WithRequirements:
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requirements = []
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@pytest.fixture(scope="class", autouse=True)
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def install(self):
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save_deps = []
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for mod, ver in self.requirements:
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try:
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save_ver = metadata.version(mod)
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except metadata.PackageNotFoundError:
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save_ver = None
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save_deps.append((mod, save_ver))
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spec = f"{mod}=={ver}" if ver else mod
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subprocess.run(["pip", "install", spec], check=True)
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yield
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for mod, ver in save_deps:
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if ver:
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subprocess.run(["pip", "install", f"{mod}=={ver}"], check=True)
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else:
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subprocess.run(["pip", "uninstall", "--yes", mod], check=True)
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