wehub-resource-sync
84 lines
3.4 KiB
Python
84 lines
3.4 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2023 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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#
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"""Tests of Quant Module Replacement"""
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import pytest
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import numpy as np
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import torch
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from pytorch_quantization import nn as quant_nn
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from pytorch_quantization import quant_modules
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from pytorch_quantization.quant_modules import QuantModuleReplacementHelper
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import tests.utils as test_utils
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from tests.fixtures import verbose
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# pylint:disable=missing-docstring, no-self-use
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class TestQuantModuleReplace():
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def test_simple_default_args(self):
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replacement_helper = QuantModuleReplacementHelper()
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replacement_helper.prepare_state()
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replacement_helper.apply_quant_modules()
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# Linear module should not be replaced with its quantized version
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assert(type(quant_nn.QuantLinear(16, 256, 3)) == type(torch.nn.Linear(16, 256, 3)))
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assert(type(quant_nn.QuantConv2d(16, 256, 3)) == type(torch.nn.Conv2d(16, 256, 3)))
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replacement_helper.restore_float_modules()
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def test_with_no_replace_list(self):
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no_replace_list = ["Linear"]
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custom_quant_modules = None
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replacement_helper = QuantModuleReplacementHelper()
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replacement_helper.prepare_state(no_replace_list, custom_quant_modules)
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replacement_helper.apply_quant_modules()
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# Linear module should not be replaced with its quantized version
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assert(type(quant_nn.QuantLinear(16, 256, 3)) != type(torch.nn.Linear(16, 256, 3)))
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assert(type(quant_nn.QuantConv2d(16, 256, 3)) == type(torch.nn.Conv2d(16, 256, 3)))
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replacement_helper.restore_float_modules()
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def test_with_custom_quant_modules(self):
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no_replace_list = ["Linear"]
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custom_quant_modules = [(torch.nn, "Linear", quant_nn.QuantLinear)]
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replacement_helper = QuantModuleReplacementHelper()
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replacement_helper.prepare_state(no_replace_list, custom_quant_modules)
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replacement_helper.apply_quant_modules()
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# Although no replace list indicates Linear module should not be replaced with its
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# quantized version, since the custom_quant_modules still contains the Linear module's
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# mapping, it will replaced.
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assert(type(quant_nn.QuantLinear(16, 256, 3)) == type(torch.nn.Linear(16, 256, 3)))
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assert(type(quant_nn.QuantConv2d(16, 256, 3)) == type(torch.nn.Conv2d(16, 256, 3)))
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replacement_helper.restore_float_modules()
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def test_initialize_deactivate(self):
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no_replace_list = ["Linear"]
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custom_quant_modules = [(torch.nn, "Linear", quant_nn.QuantLinear)]
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quant_modules.initialize(no_replace_list, custom_quant_modules)
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assert(type(quant_nn.QuantLinear(16, 256, 3)) == type(torch.nn.Linear(16, 256, 3)))
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assert(type(quant_nn.QuantConv2d(16, 256, 3)) == type(torch.nn.Conv2d(16, 256, 3)))
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quant_modules.deactivate()
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