wehub-resource-sync
378 lines
13 KiB
Python
378 lines
13 KiB
Python
# Copyright (c) 2018 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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paddle_static_guard,
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)
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from utils import dygraph_guard, static_guard
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import paddle
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from paddle import base
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from paddle.base import core
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class TestLinspaceOpCommonCase(OpTest):
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def setUp(self):
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self.op_type = "linspace"
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self.python_api = paddle.linspace
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self._set_dtype()
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self._set_data()
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self.attrs = {'dtype': self.attr_dtype}
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def _set_dtype(self):
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self.dtype = "float32"
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self.attr_dtype = paddle.float32
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def _set_data(self):
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self.outputs = {'Out': np.arange(0, 11).astype(self.dtype)}
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self.inputs = {
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'Start': np.array([0]).astype(self.dtype),
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'Stop': np.array([10]).astype(self.dtype),
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'Num': np.array([11]).astype('int32'),
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}
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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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class TestLinspaceOpReverseCase(TestLinspaceOpCommonCase):
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def _set_data(self):
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self.inputs = {
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'Start': np.array([10]).astype(self.dtype),
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'Stop': np.array([0]).astype(self.dtype),
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'Num': np.array([11]).astype('int32'),
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}
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self.outputs = {'Out': np.arange(10, -1, -1).astype(self.dtype)}
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class TestLinspaceOpNumOneCase(TestLinspaceOpCommonCase):
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def _set_data(self):
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self.inputs = {
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'Start': np.array([10]).astype(self.dtype),
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'Stop': np.array([0]).astype(self.dtype),
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'Num': np.array([1]).astype('int32'),
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}
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self.outputs = {'Out': np.array([10], dtype=self.dtype)}
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class TestLinspaceOpCommonCaseFP16(TestLinspaceOpCommonCase):
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def _set_dtype(self):
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self.dtype = np.float16
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self.attr_dtype = paddle.float16
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class TestLinspaceOpReverseCaseFP16(TestLinspaceOpReverseCase):
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def _set_dtype(self):
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self.dtype = np.float16
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self.attr_dtype = paddle.float16
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class TestLinspaceOpNumOneCaseFP16(TestLinspaceOpNumOneCase):
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def _set_dtype(self):
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self.dtype = np.float16
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self.attr_dtype = paddle.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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'not supported bf16',
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)
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class TestLinspaceOpCommonCaseBF16(TestLinspaceOpCommonCaseFP16):
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def _set_dtype(self):
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self.dtype = np.uint16
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self.attr_dtype = paddle.bfloat16
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def _set_data(self):
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self.outputs = {
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'Out': convert_float_to_uint16(np.arange(0, 11).astype("float32"))
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}
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self.inputs = {
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'Start': convert_float_to_uint16(np.array([0]).astype("float32")),
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'Stop': convert_float_to_uint16(np.array([10]).astype("float32")),
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'Num': np.array([11]).astype('int32'),
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}
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def test_check_output(self):
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return self.check_output_with_place(
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get_device_place(), check_pir=True, check_symbol_infer=False
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)
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class TestLinspaceOpReverseCaseBF16(TestLinspaceOpCommonCaseBF16):
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def _set_data(self):
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self.inputs = {
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'Start': convert_float_to_uint16(np.array([10]).astype("float32")),
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'Stop': convert_float_to_uint16(np.array([0]).astype("float32")),
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'Num': np.array([11]).astype('int32'),
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}
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self.outputs = {
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'Out': convert_float_to_uint16(
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np.arange(10, -1, -1).astype("float32")
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)
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}
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class TestLinspaceOpNumOneCaseBF16(TestLinspaceOpCommonCaseBF16):
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def _set_data(self):
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self.inputs = {
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'Start': convert_float_to_uint16(np.array([10]).astype("float32")),
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'Stop': convert_float_to_uint16(np.array([0]).astype("float32")),
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'Num': np.array([1]).astype('int32'),
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}
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self.outputs = {
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'Out': convert_float_to_uint16(np.array([10], dtype="float32"))
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}
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class TestLinspaceAPI(unittest.TestCase):
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def test_variable_input1(self):
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with paddle_static_guard():
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start = paddle.full(shape=[1], fill_value=0, dtype='float32')
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stop = paddle.full(shape=[1], fill_value=10, dtype='float32')
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num = paddle.full(shape=[1], fill_value=5, dtype='int32')
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out = paddle.linspace(start, stop, num, dtype='float32')
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exe = base.Executor(place=base.CPUPlace())
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res = exe.run(base.default_main_program(), fetch_list=[out])
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np_res = np.linspace(0, 10, 5, dtype='float32')
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self.assertEqual((res == np_res).all(), True)
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def test_variable_input2(self):
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start = paddle.full(shape=[1], fill_value=0, dtype='float32')
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stop = paddle.full(shape=[1], fill_value=10, dtype='float32')
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num = paddle.full(shape=[1], fill_value=5, dtype='int32')
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out = paddle.linspace(start, stop, num, dtype='float32')
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np_res = np.linspace(0, 10, 5, dtype='float32')
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self.assertEqual((out.numpy() == np_res).all(), True)
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def test_dtype(self):
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with paddle_static_guard():
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out_1 = paddle.linspace(0, 10, 5, dtype='float32')
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out_2 = paddle.linspace(0, 10, 5, dtype=np.float32)
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out_3 = paddle.linspace(0, 10, 5, dtype=core.VarDesc.VarType.FP32)
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exe = base.Executor(place=base.CPUPlace())
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res_1, res_2, res_3 = exe.run(
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base.default_main_program(), fetch_list=[out_1, out_2, out_3]
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)
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np.testing.assert_array_equal(res_1, res_2)
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def test_name(self):
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if paddle.framework.use_pir_api():
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return
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with (
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paddle_static_guard(),
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paddle.static.program_guard(paddle.static.Program()),
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):
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out = paddle.linspace(
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0, 10, 5, dtype='float32', name='linspace_res'
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)
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assert 'linspace_res' in out.name
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class TestLinspaceAPINewParams(unittest.TestCase):
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def test_out_parameter(self):
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with dygraph_guard():
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for dtype in ['float32', 'float64']:
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out_tensor = paddle.empty([5], dtype=dtype)
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original_ptr = out_tensor.data_ptr()
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result = paddle.linspace(0, 10, 5, dtype=dtype, out=out_tensor)
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self.assertEqual(result.data_ptr(), original_ptr)
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self.assertEqual(result.data_ptr(), out_tensor.data_ptr())
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np_expected = np.linspace(0, 10, 5).astype(dtype)
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np.testing.assert_allclose(
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result.numpy(), np_expected, rtol=1e-5
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)
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def test_device_cpu(self):
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with dygraph_guard():
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result = paddle.linspace(0, 10, 5, dtype='float32', device='cpu')
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self.assertTrue(result.place.is_cpu_place())
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np_expected = np.linspace(0, 10, 5, dtype='float32')
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np.testing.assert_allclose(result.numpy(), np_expected, rtol=1e-5)
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def test_requires_grad_true(self):
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with dygraph_guard():
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result = paddle.linspace(
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0, 10, 5, dtype='float32', requires_grad=True
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)
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self.assertFalse(result.stop_gradient)
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np_expected = np.linspace(0, 10, 5, dtype='float32')
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np.testing.assert_allclose(result.numpy(), np_expected, rtol=1e-5)
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def test_all_new_params_combination(self):
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with dygraph_guard():
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paddle.device.set_device('cpu')
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out_tensor = paddle.empty([5], dtype='float32')
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result = paddle.linspace(
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0,
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10,
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5,
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dtype='float32',
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out=out_tensor,
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device='cpu',
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requires_grad=True,
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)
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self.assertEqual(result.data_ptr(), out_tensor.data_ptr())
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self.assertTrue(result.place.is_cpu_place())
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self.assertFalse(result.stop_gradient)
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np_expected = np.linspace(0, 10, 5, dtype='float32')
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np.testing.assert_allclose(result.numpy(), np_expected, rtol=1e-5)
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class TestLinspaceAPIAliases(unittest.TestCase):
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def test_alias_end(self):
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with dygraph_guard():
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result1 = paddle.linspace(0, stop=10, num=5, dtype='float32')
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result2 = paddle.linspace(0, end=10, num=5, dtype='float32')
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np.testing.assert_array_equal(result1.numpy(), result2.numpy())
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def test_alias_steps(self):
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with dygraph_guard():
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result1 = paddle.linspace(0, 10, num=5, dtype='float32')
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result2 = paddle.linspace(0, 10, steps=5, dtype='float32')
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np.testing.assert_array_equal(result1.numpy(), result2.numpy())
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def test_both_aliases(self):
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with dygraph_guard():
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result1 = paddle.linspace(0, stop=10, num=5, dtype='float32')
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result2 = paddle.linspace(0, end=10, steps=5, dtype='float32')
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np.testing.assert_array_equal(result1.numpy(), result2.numpy())
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np_expected = np.linspace(0, 10, 5, dtype='float32')
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np.testing.assert_allclose(result2.numpy(), np_expected, rtol=1e-5)
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def test_imperative(self):
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out1 = paddle.linspace(0, 10, 5, dtype='float32')
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np_out1 = np.linspace(0, 10, 5, dtype='float32')
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out2 = paddle.linspace(0, 10, 5, dtype='int32')
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np_out2 = np.linspace(0, 10, 5, dtype='int32')
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out3 = paddle.linspace(0, 10, 200, dtype='int32')
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np_out3 = np.linspace(0, 10, 200, dtype='int32')
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self.assertEqual((out1.numpy() == np_out1).all(), True)
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self.assertEqual((out2.numpy() == np_out2).all(), True)
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self.assertEqual((out3.numpy() == np_out3).all(), True)
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class TestLinspaceOpError(unittest.TestCase):
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def test_errors(self):
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with (
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paddle_static_guard(),
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paddle.base.program_guard(
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paddle.base.Program(), paddle.base.Program()
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),
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):
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def test_dtype():
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paddle.linspace(0, 10, 1, dtype="int8")
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self.assertRaises(TypeError, test_dtype)
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def test_dtype1():
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paddle.linspace(0, 10, 1.33, dtype="int32")
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self.assertRaises(TypeError, test_dtype1)
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def test_start_type():
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paddle.linspace([0], 10, 1, dtype="float32")
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self.assertRaises(TypeError, test_start_type)
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def test_end_type():
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paddle.linspace(0, [10], 1, dtype="float32")
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self.assertRaises(TypeError, test_end_type)
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def test_step_dtype():
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paddle.linspace(0, 10, [0], dtype="float32")
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self.assertRaises(TypeError, test_step_dtype)
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def test_start_dtype():
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start = paddle.static.data(
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shape=[1], dtype="float64", name="start"
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)
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paddle.linspace(start, 10, 1, dtype="float32")
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self.assertRaises(ValueError, test_start_dtype)
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def test_end_dtype():
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end = paddle.static.data(shape=[1], dtype="float64", name="end")
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paddle.linspace(0, end, 1, dtype="float32")
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self.assertRaises(ValueError, test_end_dtype)
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def test_num_dtype():
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num = paddle.static.data(shape=[1], dtype="int32", name="step")
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paddle.linspace(0, 10, num, dtype="float32")
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self.assertRaises(TypeError, test_step_dtype)
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class TestLinspaceOpEmptyTensor(unittest.TestCase):
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def _get_places(self):
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places = [base.CPUPlace()]
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if paddle.is_compiled_with_cuda() or is_custom_device():
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places.append(get_device_place())
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return places
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def _test_linspace_empty_static(self, place):
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with (
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static_guard(),
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paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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),
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):
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out = paddle.linspace(0, 10, 0, dtype='float32')
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exe = paddle.static.Executor(place)
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res = exe.run(fetch_list=[out])
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self.assertEqual(res[0].shape, (0,))
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self.assertEqual(len(res[0]), 0)
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def _test_linspace_empty_dynamic(self):
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with dygraph_guard():
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out = paddle.linspace(0, 10, 0, dtype='float32')
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self.assertEqual(out.shape, [0])
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self.assertEqual(len(out.numpy()), 0)
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def test_empty_tensor(self):
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places = self._get_places()
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for place in places:
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self._test_linspace_empty_static(place)
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self._test_linspace_empty_dynamic()
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if __name__ == "__main__":
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unittest.main()
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