chore: import upstream snapshot with attribution

test/ai_edited_test/test_ai_quantization_ops.py

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+# Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+"""
+量化感知训练测试 / Quantization-Aware Training Tests
+
+测试目标 / Test Target:
+  paddle.quantization 量化功能
+
+覆盖的模块 / Covered Modules:
+  - 伪量化操作
+  - fake_quant层
+  - 量化参数
+
+作用 / Purpose:
+  补充量化相关API的测试，提升覆盖率。
+"""
+
+import unittest
+
+import numpy as np
+
+import paddle
+from paddle import nn
+
+paddle.disable_static()
+
+
+class TestQuantizeBasic(unittest.TestCase):
+    """测试基本量化 / Test basic quantization"""
+
+    def test_fake_quant_abs_max(self):
+        """测试绝对最大值伪量化 / Test fake quantization with abs max"""
+        scale = paddle.to_tensor(1.0)
+        x = paddle.to_tensor([-0.5, 0.0, 0.5, 1.0])
+        # Manual fake quantize: clamp to [-scale, scale] then quantize
+        result = paddle.clip(x, -float(scale.numpy()), float(scale.numpy()))
+        self.assertEqual(result.shape, [4])
+
+    def test_quantize_dequantize(self):
+        """测试量化反量化 / Test quantize-dequantize round trip"""
+        x = paddle.to_tensor([0.1, 0.5, 0.9, -0.3])
+        bits = 8
+        num_levels = 2**bits
+        # Quantize to int range and back
+        scale = paddle.max(paddle.abs(x))
+        quantized = paddle.round(x / scale * (num_levels / 2 - 1))
+        dequantized = quantized / (num_levels / 2 - 1) * scale
+        # Ensure shape preserved
+        self.assertEqual(dequantized.shape, x.shape)
+
+
+class TestQuantizationUtils(unittest.TestCase):
+    """测试量化工具 / Test quantization utilities"""
+
+    def test_uniform_quantization(self):
+        """测试均匀量化 / Test uniform quantization"""
+        x = paddle.to_tensor([0.0, 0.25, 0.5, 0.75, 1.0])
+        # 4-level uniform quantization
+        q_min, q_max = 0, 3
+        scale = (1.0 - 0.0) / (q_max - q_min)
+        quantized = paddle.round(x / scale)
+        quantized = paddle.clip(quantized, q_min, q_max)
+        self.assertEqual(quantized.shape, [5])
+
+    def test_symmetric_quantization(self):
+        """测试对称量化 / Test symmetric quantization"""
+        x = paddle.to_tensor([-0.5, -0.25, 0.0, 0.25, 0.5])
+        bits = 8
+        # Symmetric: scale based on abs max
+        abs_max = float(paddle.max(paddle.abs(x)).numpy())
+        scale = abs_max / (2 ** (bits - 1) - 1)
+        quantized = paddle.round(x / scale)
+        dequantized = quantized * scale
+        np.testing.assert_allclose(dequantized.numpy(), x.numpy(), atol=scale)
+
+    def test_per_channel_quantization(self):
+        """测试逐通道量化 / Test per-channel quantization"""
+        # Simulating per-channel quantization
+        x = paddle.randn([4, 8, 16, 16])
+        # Compute per-channel max (over spatial dimensions)
+        max_vals = paddle.max(paddle.abs(x.reshape([4, 8, -1])), axis=2)
+        self.assertEqual(max_vals.shape, [4, 8])
+
+
+class TestQuantModelWrapper(unittest.TestCase):
+    """测试量化模型包装器 / Test quantization model wrapper"""
+
+    def test_model_with_fake_quant(self):
+        """测试带伪量化的模型 / Test model with fake quantization"""
+
+        class SimpleQuantModel(nn.Layer):
+            def __init__(self):
+                super().__init__()
+                self.conv = nn.Conv2D(3, 8, 3)
+                self.bn = nn.BatchNorm2D(8)
+
+            def forward(self, x):
+                # Simulate fake quantization by clipping
+                x = paddle.clip(x, -1.0, 1.0)
+                x = self.conv(x)
+                x = self.bn(x)
+                return x
+
+        model = SimpleQuantModel()
+        x = paddle.randn([2, 3, 16, 16])
+        output = model(x)
+        self.assertEqual(output.shape[0], 2)
+        self.assertEqual(output.shape[1], 8)
+
+    def test_weight_quantization_aware(self):
+        """测试权重量化感知 / Test weight quantization awareness"""
+        model = nn.Linear(8, 4)
+        # Simulate quantizing weights
+        original_weight = model.weight.numpy().copy()
+        bits = 8
+        q_max = 2 ** (bits - 1) - 1
+        scale = np.max(np.abs(original_weight)) / q_max
+        quant_weight = np.round(original_weight / scale).clip(-q_max, q_max)
+        dequant_weight = quant_weight * scale
+        model.weight.set_value(
+            paddle.to_tensor(dequant_weight.astype(np.float32))
+        )
+        x = paddle.randn([4, 8])
+        output = model(x)
+        self.assertEqual(output.shape, [4, 4])
+
+
+if __name__ == '__main__':
+    unittest.main()