wehub-resource-sync
262 lines
8.0 KiB
Python
262 lines
8.0 KiB
Python
# Copyright (c) 2021 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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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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import paddle
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from paddle import static
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from paddle.base import core, dygraph
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paddle.enable_static()
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def angle_grad(x, dout):
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if np.iscomplexobj(x):
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def angle_grad_element(xi, douti):
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if xi == 0:
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return 0
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rsquare = np.abs(xi) ** 2
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return -douti * xi.imag / rsquare + 1j * douti * xi.real / rsquare
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return np.vectorize(angle_grad_element)(x, dout)
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else:
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return np.zeros_like(x).astype(x.dtype)
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class TestAngleOpFloat(OpTest):
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def setUp(self):
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self.op_type = "angle"
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self.python_api = paddle.angle
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self.prim_op_type = "prim"
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self.public_python_api = paddle.angle
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self.dtype = "float64"
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self.x = np.linspace(-5, 5, 101).astype(self.dtype)
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out_ref = np.angle(self.x)
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self.inputs = {'X': self.x}
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self.outputs = {'Out': out_ref}
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad(self):
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self.check_grad(
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['X'],
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'Out',
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user_defined_grads=[
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angle_grad(self.x, np.ones_like(self.x) / self.x.size)
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],
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check_pir=True,
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check_prim_pir=True,
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)
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class TestAngleFP16Op(TestAngleOpFloat):
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def setUp(self):
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self.op_type = "angle"
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self.python_api = paddle.angle
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self.prim_op_type = "prim"
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self.public_python_api = paddle.angle
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self.dtype = "float16"
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self.x = np.linspace(-5, 5, 101).astype(self.dtype)
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out_ref = np.angle(self.x)
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self.inputs = {'X': self.x}
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self.outputs = {'Out': out_ref}
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support bfloat16",
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)
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class TestAngleBF16Op(OpTest):
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def setUp(self):
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self.op_type = "angle"
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self.python_api = paddle.angle
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self.prim_op_type = "prim"
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self.public_python_api = paddle.angle
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self.dtype = np.uint16
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self.np_dtype = np.float32
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self.x = np.linspace(-5, 5, 101).astype(self.np_dtype)
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out_ref = np.angle(self.x)
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self.inputs = {'X': self.x}
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self.outputs = {'Out': out_ref}
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self.inputs['X'] = convert_float_to_uint16(self.inputs['X'])
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self.outputs['Out'] = convert_float_to_uint16(self.outputs['Out'])
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self.place = get_device_place()
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def test_check_output(self):
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self.check_output_with_place(
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self.place, check_pir=True, check_symbol_infer=False
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)
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def test_check_grad(self):
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self.check_grad_with_place(
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self.place,
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['X'],
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'Out',
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user_defined_grads=[
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angle_grad(self.x, np.ones_like(self.x) / self.x.size)
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],
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check_pir=True,
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check_prim_pir=True,
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)
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class TestAngleOpComplex(OpTest):
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def setUp(self):
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self.op_type = "angle"
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self.python_api = paddle.angle
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self.dtype = "complex128"
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real = np.expand_dims(np.linspace(-2, 2, 11), -1).astype("float64")
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imag = np.linspace(-2, 2, 11).astype("float64")
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self.x = real + 1j * imag
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out_ref = np.angle(self.x)
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self.inputs = {'X': self.x}
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self.outputs = {'Out': out_ref}
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad(self):
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self.check_grad(
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['X'],
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'Out',
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user_defined_grads=[
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angle_grad(self.x, np.ones_like(self.x) / self.x.size)
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],
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check_pir=True,
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)
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class TestAngleAPI(unittest.TestCase):
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def setUp(self):
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self.x = np.random.randn(2, 3) + 1j * np.random.randn(2, 3)
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self.out = np.angle(self.x)
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self.dtype = "complex128"
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def test_dygraph(self):
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with dygraph.guard():
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x = paddle.to_tensor(self.x)
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out_np = paddle.angle(x).numpy()
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np.testing.assert_allclose(self.out, out_np, rtol=1e-05)
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def test_static(self):
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mp, sp = static.Program(), static.Program()
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with static.program_guard(mp, sp):
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x = static.data("x", shape=[2, 3], dtype=self.dtype)
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out = paddle.angle(x)
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exe = static.Executor()
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exe.run(sp)
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[out_np] = exe.run(mp, feed={"x": self.x}, fetch_list=[out])
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np.testing.assert_allclose(self.out, out_np, rtol=1e-05)
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class TestAngleAPIWithNan(TestAngleAPI):
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def setUp(self):
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self.x = np.array([np.nan, -1, 1], dtype=np.float64)
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self.out = np.angle(self.x)
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self.dtype = "float64"
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class TestZeroSize(unittest.TestCase):
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def setUp(self):
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self.x = np.random.randn(2, 0) + 1j * np.random.randn(2, 0)
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self.out = np.angle(self.x)
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def test_0size(self):
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with dygraph.guard():
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x = paddle.to_tensor(self.x)
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out_np = paddle.angle(x).numpy()
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np.testing.assert_allclose(self.out, out_np, rtol=1e-05)
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class TestAngleAPI_Compatibility(unittest.TestCase):
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def setUp(self):
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self.x = np.random.randn(2, 3) + 1j * np.random.randn(2, 3)
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self.out = np.angle(self.x)
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self.dtype = "complex128"
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self.place = get_device_place()
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def test_dygraph_Compatibility(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.x)
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paddle_dygraph_out = []
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# Position args (args)
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out1 = paddle.angle(x)
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paddle_dygraph_out.append(out1)
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# Key words args (kwargs) for paddle
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out2 = paddle.angle(x=x)
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paddle_dygraph_out.append(out2)
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# Key words args for torch
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out3 = paddle.angle(input=x)
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paddle_dygraph_out.append(out3)
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# Tensor method args
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out4 = paddle.empty([])
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out5 = x.angle(x, out=out4)
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paddle_dygraph_out.append(out4)
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paddle_dygraph_out.append(out5)
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# Tensor method kwargs
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out6 = x.angle()
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paddle_dygraph_out.append(out6)
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# Test out
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out7 = paddle.empty([])
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paddle.angle(x, out=out7)
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paddle_dygraph_out.append(out7)
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# Numpy reference out
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ref_out = np.angle(self.x)
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# Check
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for out in paddle_dygraph_out:
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np.testing.assert_allclose(ref_out, out.numpy())
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paddle.enable_static()
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def test_static_Compatibility(self):
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main = paddle.static.Program()
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startup = paddle.static.Program()
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with paddle.static.program_guard(main, startup):
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x = static.data("x", shape=[2, 3], dtype=self.dtype)
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# Position args (args)
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out1 = paddle.angle(x)
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# Key words args (kwargs) for paddle
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out2 = paddle.angle(x=x)
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# Key words args for torch
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out3 = paddle.angle(input=x)
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# Tensor method args
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out4 = x.angle()
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exe = paddle.static.Executor(self.place)
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fetches = exe.run(
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main,
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feed={"x": self.x},
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fetch_list=[out1, out2, out3, out4],
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)
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ref_out = np.angle(self.x)
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for out in fetches:
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np.testing.assert_allclose(out, ref_out)
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if __name__ == "__main__":
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unittest.main()
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