wehub-resource-sync
387 lines
14 KiB
Python
387 lines
14 KiB
Python
# Copyright (c) 2024 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 random
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import unittest
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from itertools import product
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import numpy as np
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from op_test import get_device_place, get_devices, is_custom_device
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import paddle
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from paddle.base import core
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paddle.enable_static()
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class TestCartesianProdAPIBase(unittest.TestCase):
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def setUp(self):
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self.init_setting()
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self.a_shape = [random.randint(1, 5)]
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self.b_shape = [random.randint(1, 5)]
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self.c_shape = [random.randint(1, 5)]
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self.d_shape = [0]
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self.a_np = np.random.random(self.a_shape).astype(self.dtype_np)
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self.b_np = np.random.random(self.b_shape).astype(self.dtype_np)
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self.c_np = np.random.random(self.c_shape).astype(self.dtype_np)
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self.d_np = np.empty(0, self.dtype_np)
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self.place = get_devices()
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def init_setting(self):
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self.dtype_np = 'float32'
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def test_static_graph(self):
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paddle.enable_static()
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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for place in self.place:
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with paddle.static.program_guard(main_program, startup_program):
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a = paddle.static.data(
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name="a", shape=self.a_shape, dtype=self.dtype_np
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)
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b = paddle.static.data(
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name="b", shape=self.b_shape, dtype=self.dtype_np
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)
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c = paddle.static.data(
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name="c", shape=self.c_shape, dtype=self.dtype_np
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)
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d = paddle.static.data(
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name="d", shape=self.d_shape, dtype=self.dtype_np
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)
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out1 = paddle.cartesian_prod([a, b, c])
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out2 = paddle.cartesian_prod([a, b, c, d])
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out3 = paddle.cartesian_prod([a])
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exe = paddle.static.Executor(place=place)
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feed_list = {
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"a": self.a_np,
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"b": self.b_np,
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"c": self.c_np,
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"d": self.d_np,
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}
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pd_res = exe.run(
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main_program,
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feed=feed_list,
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fetch_list=[out1, out2, out3],
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)
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ref_res = np.array(
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list(product(self.a_np, self.b_np, self.c_np))
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)
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np.testing.assert_allclose(ref_res, pd_res[0])
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# test empty
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ref_res = np.array(
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list(product(self.a_np, self.b_np, self.c_np, self.d_np))
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).reshape([0, 4])
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np.testing.assert_allclose(ref_res, pd_res[1])
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ref_res = np.array(list(product(self.a_np)))
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np.testing.assert_allclose(ref_res.flatten(), pd_res[2])
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def test_dygraph(self):
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paddle.disable_static()
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for place in self.place:
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paddle.device.set_device(place)
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a = paddle.to_tensor(self.a_np)
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b = paddle.to_tensor(self.b_np)
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c = paddle.to_tensor(self.c_np)
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d = paddle.to_tensor(self.d_np)
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pd_res1 = paddle.cartesian_prod([a, b, c])
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ref_res = np.array(list(product(self.a_np, self.b_np, self.c_np)))
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np.testing.assert_allclose(ref_res, pd_res1)
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# test empty
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pd_res2 = paddle.cartesian_prod([a, b, c, d])
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ref_res = np.array(
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list(product(self.a_np, self.b_np, self.c_np, self.d_np))
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).reshape([0, 4])
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np.testing.assert_allclose(ref_res, pd_res2)
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pd_res3 = paddle.cartesian_prod([a])
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ref_res = np.array(list(product(self.a_np)))
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np.testing.assert_allclose(ref_res.flatten(), pd_res3)
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class TestCartesianProd_ZeroSize(unittest.TestCase):
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def setUp(self):
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self.init_setting()
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self.a_shape = [random.randint(1, 5)]
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self.b_shape = [0]
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self.a_np = np.random.random(self.a_shape).astype(self.dtype_np)
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self.b_np = np.empty(0, self.dtype_np)
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self.place = get_devices()
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def init_setting(self):
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self.dtype_np = 'float32'
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def test_static_graph(self):
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paddle.enable_static()
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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for place in self.place:
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with paddle.static.program_guard(main_program, startup_program):
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a = paddle.static.data(
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name="a", shape=self.a_shape, dtype=self.dtype_np
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)
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b = paddle.static.data(
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name="b", shape=self.b_shape, dtype=self.dtype_np
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)
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out1 = paddle.cartesian_prod([a, b])
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exe = paddle.static.Executor(place=place)
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feed_list = {
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"a": self.a_np,
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"b": self.b_np,
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}
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pd_res = exe.run(
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main_program,
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feed=feed_list,
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fetch_list=[out1],
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)
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ref_res = np.array(list(product(self.a_np, self.b_np))).reshape(
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[0, 2]
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)
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np.testing.assert_allclose(ref_res, pd_res[0])
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def test_dygraph(self):
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paddle.disable_static()
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for place in self.place:
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paddle.device.set_device(place)
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a = paddle.to_tensor(self.a_np)
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b = paddle.to_tensor(self.b_np)
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pd_res = paddle.cartesian_prod([a, b])
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ref_res = np.array(list(product(self.a_np, self.b_np))).reshape(
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[0, 2]
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)
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np.testing.assert_allclose(ref_res, pd_res)
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def test_grad(self):
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paddle.disable_static()
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for place in self.place:
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paddle.device.set_device(place)
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a = paddle.to_tensor(self.a_np)
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a.stop_gradient = False
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b = paddle.to_tensor(self.b_np)
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b.stop_gradient = False
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out = paddle.cartesian_prod([a, b])
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loss = paddle.sum(out)
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loss.backward()
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np.testing.assert_allclose(a.grad.shape, a.shape)
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class TestCartesianProdErrors(unittest.TestCase):
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def test_errors(self):
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def test_input_not_1D():
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data_np = np.random.random((10, 10)).astype(np.float32)
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data_tensor = [paddle.to_tensor(data_np)]
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res = paddle.cartesian_prod(data_tensor)
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self.assertRaises(ValueError, test_input_not_1D)
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class TestCartesianProdAPI1(TestCartesianProdAPIBase):
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def init_setting(self):
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self.dtype_np = 'int32'
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class TestCartesianProdAPI2(TestCartesianProdAPIBase):
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def init_setting(self):
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self.dtype_np = 'int64'
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class TestCartesianProdAPI3(TestCartesianProdAPIBase):
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def init_setting(self):
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self.dtype_np = 'float64'
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class TestCartesianProdAPI4(TestCartesianProdAPIBase):
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def init_setting(self):
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self.dtype_np = 'complex64'
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class TestCartesianProdAPI5(TestCartesianProdAPIBase):
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def init_setting(self):
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self.dtype_np = 'complex128'
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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_float16_supported(get_device_place()),
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"core is not compiled with CUDA or not support the float16",
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)
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class TestCartesianProdAPIFP16(unittest.TestCase):
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def setUp(self):
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self.dtype_np = 'float16'
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self.a_shape = [random.randint(1, 5)]
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self.b_shape = [random.randint(1, 5)]
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self.c_shape = [random.randint(1, 5)]
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self.d_shape = [0]
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self.a_np = np.random.random(self.a_shape).astype(self.dtype_np)
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self.b_np = np.random.random(self.b_shape).astype(self.dtype_np)
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self.c_np = np.random.random(self.c_shape).astype(self.dtype_np)
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self.d_np = np.empty(0, self.dtype_np)
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self.place = get_device_place()
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def test_static_graph(self):
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paddle.enable_static()
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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a = paddle.static.data(
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name="a", shape=self.a_shape, dtype=self.dtype_np
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)
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b = paddle.static.data(
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name="b", shape=self.b_shape, dtype=self.dtype_np
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)
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c = paddle.static.data(
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name="c", shape=self.c_shape, dtype=self.dtype_np
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)
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d = paddle.static.data(
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name="d", shape=self.d_shape, dtype=self.dtype_np
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)
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out1 = paddle.cartesian_prod([a, b, c])
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out2 = paddle.cartesian_prod([a, b, c, d])
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out3 = paddle.cartesian_prod([a])
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exe = paddle.static.Executor(place=self.place)
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feed_list = {
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"a": self.a_np,
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"b": self.b_np,
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"c": self.c_np,
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"d": self.d_np,
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}
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pd_res = exe.run(
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main_program,
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feed=feed_list,
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fetch_list=[out1, out2, out3],
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)
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ref_res = np.array(list(product(self.a_np, self.b_np, self.c_np)))
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np.testing.assert_allclose(ref_res, pd_res[0])
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# test empty
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ref_res = np.array(
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list(product(self.a_np, self.b_np, self.c_np, self.d_np))
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).reshape([0, 4])
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np.testing.assert_allclose(ref_res, pd_res[1])
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ref_res = np.array(list(product(self.a_np)))
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np.testing.assert_allclose(ref_res.flatten(), pd_res[2])
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def test_dygraph(self):
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paddle.disable_static(self.place)
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a = paddle.to_tensor(self.a_np)
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b = paddle.to_tensor(self.b_np)
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c = paddle.to_tensor(self.c_np)
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d = paddle.to_tensor(self.d_np)
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pd_res1 = paddle.cartesian_prod([a, b, c])
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ref_res = np.array(list(product(self.a_np, self.b_np, self.c_np)))
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np.testing.assert_allclose(ref_res, pd_res1)
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# test empty
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pd_res2 = paddle.cartesian_prod([a, b, c, d])
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ref_res = np.array(
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list(product(self.a_np, self.b_np, self.c_np, self.d_np))
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).reshape([0, 4])
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np.testing.assert_allclose(ref_res, pd_res2)
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pd_res3 = paddle.cartesian_prod([a])
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ref_res = np.array(list(product(self.a_np)))
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np.testing.assert_allclose(ref_res.flatten(), pd_res3)
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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 the bfloat16",
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)
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class TestCartesianProdAPIBF16(unittest.TestCase):
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def setUp(self):
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self.dtype_np = 'uint16'
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self.a_shape = [random.randint(1, 5)]
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self.b_shape = [random.randint(1, 5)]
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self.c_shape = [random.randint(1, 5)]
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self.d_shape = [0]
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self.a_np = np.random.random(self.a_shape).astype(self.dtype_np)
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self.b_np = np.random.random(self.b_shape).astype(self.dtype_np)
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self.c_np = np.random.random(self.c_shape).astype(self.dtype_np)
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self.d_np = np.empty(0, self.dtype_np)
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self.place = get_device_place()
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def test_static_graph(self):
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paddle.enable_static()
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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a = paddle.static.data(
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name="a", shape=self.a_shape, dtype=self.dtype_np
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)
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b = paddle.static.data(
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name="b", shape=self.b_shape, dtype=self.dtype_np
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)
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c = paddle.static.data(
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name="c", shape=self.c_shape, dtype=self.dtype_np
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)
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d = paddle.static.data(
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name="d", shape=self.d_shape, dtype=self.dtype_np
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)
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out1 = paddle.cartesian_prod([a, b, c])
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out2 = paddle.cartesian_prod([a, b, c, d])
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out3 = paddle.cartesian_prod([a])
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exe = paddle.static.Executor(place=self.place)
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feed_list = {
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"a": self.a_np,
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"b": self.b_np,
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"c": self.c_np,
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"d": self.d_np,
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}
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pd_res = exe.run(
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main_program,
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feed=feed_list,
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fetch_list=[out1, out2, out3],
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)
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ref_res = np.array(list(product(self.a_np, self.b_np, self.c_np)))
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np.testing.assert_allclose(ref_res, pd_res[0])
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# test empty
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ref_res = np.array(
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list(product(self.a_np, self.b_np, self.c_np, self.d_np))
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).reshape([0, 4])
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np.testing.assert_allclose(ref_res, pd_res[1])
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ref_res = np.array(list(product(self.a_np)))
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np.testing.assert_allclose(ref_res.flatten(), pd_res[2])
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def test_dygraph(self):
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paddle.disable_static(self.place)
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a = paddle.to_tensor(self.a_np)
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b = paddle.to_tensor(self.b_np)
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c = paddle.to_tensor(self.c_np)
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d = paddle.to_tensor(self.d_np)
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pd_res1 = paddle.cartesian_prod([a, b, c])
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ref_res = np.array(list(product(self.a_np, self.b_np, self.c_np)))
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np.testing.assert_allclose(ref_res, pd_res1)
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# test empty
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pd_res2 = paddle.cartesian_prod([a, b, c, d])
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ref_res = np.array(
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list(product(self.a_np, self.b_np, self.c_np, self.d_np))
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).reshape([0, 4])
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np.testing.assert_allclose(ref_res, pd_res2)
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pd_res3 = paddle.cartesian_prod([a])
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ref_res = np.array(list(product(self.a_np)))
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np.testing.assert_allclose(ref_res.flatten(), pd_res3)
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if __name__ == '__main__':
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unittest.main()
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