chore: import upstream snapshot with attribution

2026-07-13 13:36:55 +08:00
commit c8a779b1bb
1887 changed files with 3245738 additions and 0 deletions
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+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+"""Tests of Quant Module Replacement"""
+import pytest
+import numpy as np
+
+import torch
+
+from pytorch_quantization import nn as quant_nn
+from pytorch_quantization import quant_modules
+from pytorch_quantization.quant_modules import QuantModuleReplacementHelper
+import tests.utils as test_utils
+from tests.fixtures import verbose
+
+# pylint:disable=missing-docstring, no-self-use
+
+class TestQuantModuleReplace():
+
+    def test_simple_default_args(self):
+        replacement_helper = QuantModuleReplacementHelper()
+        replacement_helper.prepare_state()
+        replacement_helper.apply_quant_modules()
+
+        # Linear module should not be replaced with its quantized version
+        assert(type(quant_nn.QuantLinear(16, 256, 3)) == type(torch.nn.Linear(16, 256, 3)))
+        assert(type(quant_nn.QuantConv2d(16, 256, 3)) == type(torch.nn.Conv2d(16, 256, 3)))
+
+        replacement_helper.restore_float_modules()
+
+    def test_with_no_replace_list(self):
+        no_replace_list = ["Linear"]
+        custom_quant_modules = None
+        replacement_helper = QuantModuleReplacementHelper()
+        replacement_helper.prepare_state(no_replace_list, custom_quant_modules)
+        replacement_helper.apply_quant_modules()
+
+        # Linear module should not be replaced with its quantized version
+        assert(type(quant_nn.QuantLinear(16, 256, 3)) != type(torch.nn.Linear(16, 256, 3)))
+        assert(type(quant_nn.QuantConv2d(16, 256, 3)) == type(torch.nn.Conv2d(16, 256, 3)))
+
+        replacement_helper.restore_float_modules()
+
+    def test_with_custom_quant_modules(self):
+        no_replace_list = ["Linear"]
+        custom_quant_modules = [(torch.nn, "Linear", quant_nn.QuantLinear)]
+        replacement_helper = QuantModuleReplacementHelper()
+        replacement_helper.prepare_state(no_replace_list, custom_quant_modules)
+        replacement_helper.apply_quant_modules()
+
+        # Although no replace list indicates Linear module should not be replaced with its
+        # quantized version, since the custom_quant_modules still contains the Linear module's
+        # mapping, it will replaced.
+        assert(type(quant_nn.QuantLinear(16, 256, 3)) == type(torch.nn.Linear(16, 256, 3)))
+        assert(type(quant_nn.QuantConv2d(16, 256, 3)) == type(torch.nn.Conv2d(16, 256, 3)))
+
+        replacement_helper.restore_float_modules()
+
+    def test_initialize_deactivate(self):
+        no_replace_list = ["Linear"]
+        custom_quant_modules = [(torch.nn, "Linear", quant_nn.QuantLinear)]
+
+        quant_modules.initialize(no_replace_list, custom_quant_modules)
+
+        assert(type(quant_nn.QuantLinear(16, 256, 3)) == type(torch.nn.Linear(16, 256, 3)))
+        assert(type(quant_nn.QuantConv2d(16, 256, 3)) == type(torch.nn.Conv2d(16, 256, 3)))
+
+        quant_modules.deactivate()