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https://github.com/NVIDIA/Model-Optimizer.git
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54 lines
1.9 KiB
Python
54 lines
1.9 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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import torch.nn as nn
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from _test_utils.torch_quantization.models import SimpleConv
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import modelopt.torch.quantization as mtq
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from modelopt.torch.quantization import QuantModuleRegistry
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from modelopt.torch.quantization.config import QuantizerAttributeConfig
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from modelopt.torch.quantization.nn import TensorQuantizer
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class QuantReLU(nn.ReLU):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self._setup()
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def _setup(self):
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self.input_quantizer = TensorQuantizer(QuantizerAttributeConfig())
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self.output_quantizer = TensorQuantizer(QuantizerAttributeConfig(enable=False))
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# NOTE: If needed, quantized weights by adding weight quantizer
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def forward(self, x):
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return self.output_quantizer(super().forward(self.input_quantizer(x)))
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def test_register_custom_module():
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model = SimpleConv()
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num_old_modules = sum(type(m) is nn.ReLU for m in model.modules())
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mtq.register(original_cls=nn.ReLU, quantized_cls=QuantReLU)
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mtq.replace_quant_module(model)
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model(model.get_input())
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assert sum(isinstance(m, QuantReLU) for m in model.modules()) == num_old_modules
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mtq.unregister(nn.ReLU)
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assert QuantModuleRegistry.get(nn.ReLU) is None
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