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https://github.com/NVIDIA/Model-Optimizer.git
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76 lines
2.7 KiB
Python
76 lines
2.7 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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from _test_utils.torch_quantization.quantize_common import INT4_AWQ_CLIP_CFG
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from accelerate import init_empty_weights, load_checkpoint_and_dispatch
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from transformers import AutoConfig, AutoModelForCausalLM
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import modelopt.torch.quantization as mtq
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from modelopt.torch.quantization.utils import (
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enable_weight_access_and_writeback,
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is_quantized_linear,
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)
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@pytest.mark.parametrize(
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"quant_cfg",
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[
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mtq.INT4_AWQ_CFG,
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mtq.INT8_SMOOTHQUANT_CFG,
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INT4_AWQ_CLIP_CFG,
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mtq.NVFP4_SVDQUANT_DEFAULT_CFG,
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mtq.INT8_DEFAULT_CFG,
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],
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)
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def test_cpu_offloaded_tinyllama(tmp_path, quant_cfg):
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tiny_llama_dir = create_tiny_llama_dir(tmp_path, num_hidden_layers=2)
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config = AutoConfig.from_pretrained(tiny_llama_dir)
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model_ref = AutoModelForCausalLM.from_pretrained(
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tiny_llama_dir, torch_dtype=config.torch_dtype
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).cuda()
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inputs = torch.randint(0, model_ref.config.vocab_size, (1, 4)).cuda()
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mtq.quantize(model_ref, quant_cfg, lambda model: model(inputs))
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output_ref = model_ref(inputs)
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with init_empty_weights():
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model = AutoModelForCausalLM.from_config(config)
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device_map = {
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n: 0
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for n, m in model.named_modules()
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if "layers" not in n or n.split("layers.")[-1].isdigit()
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}
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device_map["model.layers.0"] = "cpu"
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model = load_checkpoint_and_dispatch(model, tiny_llama_dir, device_map=device_map)
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assert all(p.device == torch.device("meta") for p in model.model.layers[0].parameters())
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mtq.quantize(model, quant_cfg, lambda model: model(inputs))
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output_test = model(inputs)
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for name, module in model.named_modules():
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if is_quantized_linear(module):
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with enable_weight_access_and_writeback(module, model):
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assert torch.allclose(module.weight, model_ref.get_submodule(name).weight)
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assert torch.allclose(output_ref.logits, output_test.logits)
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