chore: import upstream snapshot with attribution

test/quantization/test_apply_per_channel_scale.py

@@ -0,0 +1,127 @@
+# Copyright (c) 2024 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.
+
+import struct
+import unittest
+
+import numpy as np
+
+import paddle
+import paddle.nn.quant as Q
+from paddle.base import core
+
+
+def convert_uint16_to_float(in_list):
+    in_list = np.asarray(in_list)
+    out = np.vectorize(
+        lambda x: struct.unpack(
+            '<f', struct.pack('<I', np.uint32(x) << np.uint32(16))
+        )[0],
+        otypes=[np.float32],
+    )(in_list.flat)
+    return np.reshape(out, in_list.shape)
+
+
+class ApplyPerChannelScaleTest(unittest.TestCase):
+    def config(self):
+        self.rows = 32
+        self.cols = 128
+        self.rtol = 1e-5
+        self.atol = 1e-8
+        self.dtype = 'float16'
+        self.static = False
+
+    def setUp(self):
+        self.config()
+        paddle.set_default_dtype(self.dtype)
+        self.x = paddle.to_tensor(
+            np.random.random(size=(self.rows, self.cols)), self.dtype
+        )
+
+        self.scales = paddle.to_tensor(
+            np.random.uniform(0, 1, size=(self.cols)), self.dtype
+        )
+        self.out_expected = paddle.multiply(self.x, self.scales)
+
+    def get_out_static(self):
+        paddle.enable_static()
+        main = paddle.static.Program()
+        start = paddle.static.Program()
+        with paddle.static.program_guard(main, start):
+            x = paddle.static.data("x", self.x.shape, dtype=self.dtype)
+            scales = paddle.static.data(
+                "scales", self.scales.shape, dtype=self.dtype
+            )
+            x.stop_gradient = True
+            scales.stop_gradient = True
+            out = Q.apply_per_channel_scale(x, scales)
+            feed_dict = {
+                'x': self.x.numpy(),
+                'scales': self.scales.numpy(),
+            }
+
+            exe = paddle.static.Executor(paddle.CUDAPlace(0))
+            exe.run(start)
+            (out,) = exe.run(main, feed=feed_dict, fetch_list=[out])
+        paddle.disable_static()
+        return out
+
+    def test_apply_per_channel_scale(self):
+        if self.static:
+            self.out_real = self.get_out_static()
+        else:
+            paddle.disable_static()
+            self.out_real = Q.apply_per_channel_scale(
+                x=self.x,
+                scales=self.scales,
+            )
+        out_expected = self.out_expected
+        if self.dtype == 'bfloat16' and isinstance(
+            self.out_real, paddle.Tensor
+        ):
+            self.out_real = convert_uint16_to_float(self.out_real)
+            out_expected = convert_uint16_to_float(self.out_expected)
+
+        np.testing.assert_allclose(
+            out_expected, self.out_real, rtol=self.rtol, atol=self.atol
+        )
+
+
+@unittest.skipIf(
+    not core.is_compiled_with_cuda()
+    or paddle.device.cuda.get_device_capability()[0] < 8
+    or not core.is_bfloat16_supported(core.CUDAPlace(0)),
+    "quantized_matmul requires CUDA >= 11.2 and CUDA_ARCH >= 8 or core is not support bfloat16",
+)
+class ApplyPerChannelScaleTestCase1(ApplyPerChannelScaleTest):
+    def config(self):
+        super().config()
+        self.dtype = 'bfloat16'
+
+
+class ApplyPerChannelScaleTestCase2(ApplyPerChannelScaleTest):
+    def config(self):
+        super().config()
+        self.rows = 1024
+        self.cols = 128
+
+
+class ApplyPerChannelScaleStaticTest(ApplyPerChannelScaleTest):
+    def config(self):
+        super().config()
+        self.static = True
+
+
+if __name__ == '__main__':
+    unittest.main()