wehub-resource-sync
274 lines
8.7 KiB
Python
274 lines
8.7 KiB
Python
# Copyright (c) 2020 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 sys
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import unittest
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import numpy as np
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import paddle
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sys.path.append("..")
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from numpy.random import random as rand
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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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get_places,
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is_custom_device,
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)
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import paddle.base.dygraph as dg
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from paddle import static
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from paddle.base import core
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paddle.enable_static()
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class TestConjOp(OpTest):
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def setUp(self):
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self.op_type = "conj"
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self.python_api = paddle.tensor.conj
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self.init_dtype_type()
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self.init_input_output()
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def init_dtype_type(self):
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self.dtype = np.complex64
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def init_input_output(self):
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x = (
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np.random.random((12, 14)) + 1j * np.random.random((12, 14))
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).astype(self.dtype)
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out = np.conj(x)
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self.inputs = {'X': OpTest.np_dtype_to_base_dtype(x)}
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self.outputs = {'Out': out}
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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_normal(self):
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self.check_grad(
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['X'],
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'Out',
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check_pir=True,
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)
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class TestConjOpZeroSize1(TestConjOp):
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def init_input_output(self):
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x = (np.random.random((0, 14)) + 1j * np.random.random((0, 14))).astype(
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self.dtype
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)
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out = np.conj(x)
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self.inputs = {'X': OpTest.np_dtype_to_base_dtype(x)}
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self.outputs = {'Out': out}
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class TestConjOpZeroSize2(TestConjOp):
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def init_input_output(self):
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x = (
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np.random.random((2, 0, 14)) + 1j * np.random.random((2, 0, 14))
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).astype(self.dtype)
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out = np.conj(x)
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self.inputs = {'X': OpTest.np_dtype_to_base_dtype(x)}
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self.outputs = {'Out': out}
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class TestConjOpZeroSize3(TestConjOp):
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def init_input_output(self):
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x = (np.random.random(0) + 1j * np.random.random(0)).astype(self.dtype)
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out = np.conj(x)
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self.inputs = {'X': OpTest.np_dtype_to_base_dtype(x)}
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self.outputs = {'Out': out}
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class TestComplexConjOp(unittest.TestCase):
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def setUp(self):
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self._dtypes = ["float32", "float64"]
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self._places = get_places()
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def test_conj_api(self):
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for dtype in self._dtypes:
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input = rand([2, 20, 2, 3]).astype(dtype) + 1j * rand(
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[2, 20, 2, 3]
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).astype(dtype)
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for place in self._places:
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with dg.guard(place):
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var_x = paddle.to_tensor(input)
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result = paddle.conj(var_x).numpy()
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target = np.conj(input)
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np.testing.assert_array_equal(result, target)
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def test_conj_operator(self):
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for dtype in self._dtypes:
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input = rand([2, 20, 2, 3]).astype(dtype) + 1j * rand(
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[2, 20, 2, 3]
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).astype(dtype)
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for place in self._places:
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with dg.guard(place):
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var_x = paddle.to_tensor(input)
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result = var_x.conj().numpy()
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target = np.conj(input)
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np.testing.assert_array_equal(result, target)
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def test_conj_static_mode(self):
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def init_input_output(dtype):
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input = rand([2, 20, 2, 3]).astype(dtype) + 1j * rand(
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[2, 20, 2, 3]
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).astype(dtype)
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return {'x': input}, np.conj(input)
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for dtype in self._dtypes:
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input_dict, np_res = init_input_output(dtype)
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for place in self._places:
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with static.program_guard(static.Program()):
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x_dtype = (
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np.complex64 if dtype == "float32" else np.complex128
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)
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x = static.data(
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name="x", shape=[2, 20, 2, 3], dtype=x_dtype
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)
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out = paddle.conj(x)
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exe = static.Executor(place)
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out_value = exe.run(feed=input_dict, fetch_list=[out])
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np.testing.assert_array_equal(np_res, out_value[0])
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def test_conj_api_real_number(self):
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for dtype in self._dtypes:
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input = rand([2, 20, 2, 3]).astype(dtype)
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for place in self._places:
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with dg.guard(place):
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var_x = paddle.to_tensor(input)
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result = paddle.conj(var_x).numpy()
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target = np.conj(input)
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np.testing.assert_array_equal(result, target)
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class Testfp16ConjOp(unittest.TestCase):
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def testfp16(self):
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if paddle.is_compiled_with_cuda() or is_custom_device():
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input_x = (
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np.random.random((12, 14)) + 1j * np.random.random((12, 14))
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).astype('float16')
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with static.program_guard(static.Program()):
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x = static.data(name="x", shape=[12, 14], dtype='float16')
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out = paddle.conj(x)
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if paddle.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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exe = paddle.static.Executor(place)
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exe.run(paddle.static.default_startup_program())
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out = exe.run(feed={'x': input_x}, fetch_list=[out])
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class TestConjFP16OP(TestConjOp):
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def init_dtype_type(self):
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self.dtype = np.float16
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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 and not support the bfloat16",
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)
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class TestConjBF16(OpTest):
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def setUp(self):
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self.op_type = "conj"
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self.python_api = paddle.tensor.conj
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self.init_dtype_type()
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self.init_input_output()
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def init_dtype_type(self):
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self.dtype = np.uint16
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def init_input_output(self):
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x = (
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np.random.random((12, 14)) + 1j * np.random.random((12, 14))
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).astype(np.float32)
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out = np.conj(x)
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self.inputs = {'X': convert_float_to_uint16(x)}
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self.outputs = {'Out': convert_float_to_uint16(out)}
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place(
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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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place = get_device_place()
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self.check_grad_with_place(place, ['X'], 'Out', check_pir=True)
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class TestConjAPI_Compatibility(unittest.TestCase):
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def setUp(self):
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self.x = np.random.random([2, 20, 2, 3]) + 1j * np.random.random(
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[2, 20, 2, 3]
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)
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self.out = np.conj(self.x)
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self.dtype = np.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.conj(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.conj(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.conj(input=x)
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paddle_dygraph_out.append(out3)
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ref_out = np.conj(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(name="x", shape=[2, 20, 2, 3], dtype=self.dtype)
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# Position args (args)
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out1 = paddle.conj(x)
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# Key words args (kwargs) for paddle
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out2 = paddle.conj(x=x)
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# Key words args for torch
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out3 = paddle.conj(input=x)
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# Tensor method args
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out4 = x.conj()
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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.conj(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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