wehub-resource-sync
151 lines
5.0 KiB
Python
151 lines
5.0 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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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 unittest
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import numpy as np
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import paddle
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from paddle import _legacy_C_ops
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class TestQuantizeLinerAPI(unittest.TestCase):
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"""
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test for quantize_linear and dequantize_linear
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"""
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def setUp(self):
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np.random.seed(2025)
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paddle.disable_static()
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def run_case(self, function_name, xshape, axis, bit_length, qmin, qmax):
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func = getattr(_legacy_C_ops, function_name, None)
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if func is None:
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raise ValueError(
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f"No function named '{function_name}' found in _legacy_C_ops."
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)
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x_np = np.random.uniform(-0.1, 0.1, xshape).astype("float32")
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x_paddle = paddle.to_tensor(
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x_np, dtype="float32", place=paddle.XPUPlace(0)
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)
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x_paddle_cpu = paddle.to_tensor(
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x_np, dtype="float32", place=paddle.CPUPlace()
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)
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zero_paddle = paddle.to_tensor(
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[0], dtype="float32", place=paddle.XPUPlace(0)
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)
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zero_paddle_cpu = paddle.to_tensor(
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[0], dtype="float32", place=paddle.CPUPlace()
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)
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if axis == -1:
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scale_paddle = paddle.to_tensor(
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[0.5], dtype="float32", place=paddle.XPUPlace(0)
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)
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scale_paddle_cpu = paddle.to_tensor(
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[0.5], dtype="float32", place=paddle.CPUPlace()
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)
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elif axis == 0:
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scale_np = np.random.uniform(-0.1, 0.1, xshape[0]).astype("float32")
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scale_paddle = paddle.to_tensor(
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scale_np, dtype="float32", place=paddle.XPUPlace(0)
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)
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scale_paddle_cpu = paddle.to_tensor(
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scale_np, dtype="float32", place=paddle.CPUPlace()
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)
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elif axis == 1:
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scale_np = np.random.uniform(-0.1, 0.1, xshape[1]).astype("float32")
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scale_paddle = paddle.to_tensor(
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scale_np, dtype="float32", place=paddle.XPUPlace(0)
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)
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scale_paddle_cpu = paddle.to_tensor(
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scale_np, dtype="float32", place=paddle.CPUPlace()
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)
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else:
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raise AssertionError(
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"quant axis other than -1, 0, 1 is not supported in XPU"
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)
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paddle.set_device("xpu")
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y_xpu = func(
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x_paddle,
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scale_paddle,
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zero_paddle,
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"quant_axis",
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axis,
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"bit_length",
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bit_length,
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"qmin",
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qmin,
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"qmax",
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qmax,
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)
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paddle.set_device("cpu")
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y_cpu = func(
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x_paddle_cpu,
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scale_paddle_cpu,
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zero_paddle_cpu,
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"quant_axis",
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axis,
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"bit_length",
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bit_length,
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"qmin",
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qmin,
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"qmax",
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qmax,
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)
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np.testing.assert_allclose(y_xpu.numpy(), y_cpu.numpy(), atol=0, rtol=0)
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def test_quantize(self):
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for axis in [-1, 0, 1]:
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self.run_case("quantize_linear", [3, 5], axis, 4, -8, 7)
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self.run_case("quantize_linear", [10, 12], axis, 4, -8, 7)
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self.run_case("quantize_linear", [10, 12], axis, 8, -128, 127)
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self.run_case("quantize_linear", [10, 12, 15], axis, 4, -8, 7)
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self.run_case("quantize_linear", [10, 12, 15], axis, 8, -128, 127)
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def test_dequantize(self):
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for axis in [-1, 0, 1]:
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self.run_case("dequantize_linear", [3, 5], axis, 4, -8, 7)
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self.run_case("dequantize_linear", [10, 12], axis, 4, -8, 7)
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self.run_case("dequantize_linear", [10, 12], axis, 8, -128, 127)
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self.run_case("dequantize_linear", [10, 12, 15], axis, 4, -8, 7)
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self.run_case("dequantize_linear", [10, 12, 15], axis, 8, -128, 127)
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def test_weight_only_linear_empty_batch_xpu(self):
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paddle.disable_static()
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paddle.set_device("xpu")
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x = paddle.empty([0, 1, 512], dtype="float16")
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weight_int32 = paddle.randint(low=-128, high=127, shape=[1024, 512])
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weight = paddle.cast(weight_int32, "int8")
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bias = paddle.zeros([1024], dtype="float16")
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weight_scale = paddle.ones([1024], dtype="float16")
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out = paddle.nn.quant.weight_only_linear(
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x,
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weight=weight,
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bias=bias,
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weight_scale=weight_scale,
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weight_dtype="int8",
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)
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self.assertEqual(list(out.shape), [0, 1024])
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if __name__ == "__main__":
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unittest.main()
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