wehub-resource-sync
213 lines
7.8 KiB
Python
213 lines
7.8 KiB
Python
# Copyright (c) 2023 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 get_test_cover_info import (
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XPUOpTestWrapper,
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create_test_class,
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get_xpu_op_support_types,
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)
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from op_test import convert_float_to_uint16
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from op_test_xpu import XPUOpTest
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import paddle
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from paddle import base
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from paddle.base.backward import append_backward
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paddle.enable_static()
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class XPUTestWhereOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'where'
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class TestXPUWhereOp(XPUOpTest):
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def setUp(self):
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self.init_config()
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self.init_data()
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self.convert_data_if_bf16()
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self.inputs = {'Condition': self.cond, 'X': self.x, 'Y': self.y}
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self.outputs = {'Out': np.where(self.cond, self.x, self.y)}
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def init_data(self):
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self.x = np.random.uniform(-3, 5, (100))
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self.y = np.random.uniform(-3, 5, (100))
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self.cond = np.zeros(100).astype("bool")
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def convert_data_if_bf16(self):
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if self.dtype == np.uint16:
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self.x = convert_float_to_uint16(self.x)
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self.y = convert_float_to_uint16(self.y)
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else:
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self.x = self.x.astype(self.dtype)
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self.y = self.y.astype(self.dtype)
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def init_config(self):
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self.op_type = "where"
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self.dtype = self.in_type
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self.place = paddle.XPUPlace(0)
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self.__class__.no_need_check_grad = True
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def test_check_output(self):
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self.check_output_with_place(self.place)
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def test_check_grad(self):
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self.check_grad_with_place(self.place, ['X', 'Y'], 'Out')
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class TestXPUWhereOp2(TestXPUWhereOp):
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def init_data(self):
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self.x = np.random.uniform(-5, 5, (60, 2))
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self.y = np.random.uniform(-5, 5, (60, 2))
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self.cond = np.ones((60, 2)).astype("bool")
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class TestXPUWhereOp3(TestXPUWhereOp):
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def init_data(self):
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self.x = np.random.uniform(-3, 5, (20, 2, 4))
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self.y = np.random.uniform(-3, 5, (20, 2, 4))
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self.cond = np.array(
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np.random.randint(2, size=(20, 2, 4)), dtype=bool
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)
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support_types = get_xpu_op_support_types('where')
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for stype in support_types:
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create_test_class(globals(), XPUTestWhereOp, stype)
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class TestXPUWhereAPI(unittest.TestCase):
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def setUp(self):
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self.__class__.use_xpu = True
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self.place = paddle.XPUPlace(0)
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self.init_data()
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def init_data(self):
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self.shape = [10, 15]
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self.cond = np.array(np.random.randint(2, size=self.shape), dtype=bool)
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self.x = np.random.uniform(-2, 3, self.shape).astype(np.float32)
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self.y = np.random.uniform(-2, 3, self.shape).astype(np.float32)
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self.out = np.where(self.cond, self.x, self.y)
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def ref_x_backward(self, dout):
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return np.where(self.cond, dout, 0)
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def ref_y_backward(self, dout):
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return np.where(~self.cond, dout, 0)
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def test_api(self):
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for x_stop_gradient in [False, True]:
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for y_stop_gradient in [False, True]:
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train_prog = base.Program()
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startup = base.Program()
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with base.program_guard(train_prog, startup):
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cond = paddle.static.data(
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name='cond', shape=self.shape, dtype='bool'
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)
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x = paddle.static.data(
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name='x', shape=self.shape, dtype='float32'
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)
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y = paddle.static.data(
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name='y', shape=self.shape, dtype='float32'
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)
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x.stop_gradient = x_stop_gradient
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y.stop_gradient = y_stop_gradient
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result = paddle.where(cond, x, y)
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result.stop_gradient = False
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append_backward(paddle.mean(result))
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exe = base.Executor(self.place)
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exe.run(startup)
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if paddle.framework.use_pir_api():
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fetch_list = [result]
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out = exe.run(
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train_prog,
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feed={'cond': self.cond, 'x': self.x, 'y': self.y},
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fetch_list=fetch_list,
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)
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np.testing.assert_array_equal(out[0], self.out)
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else:
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fetch_list = [result, result.grad_name]
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if x_stop_gradient is False:
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fetch_list.append(x.grad_name)
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if y_stop_gradient is False:
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fetch_list.append(y.grad_name)
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out = exe.run(
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train_prog,
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feed={'cond': self.cond, 'x': self.x, 'y': self.y},
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fetch_list=fetch_list,
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)
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np.testing.assert_array_equal(out[0], self.out)
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if x_stop_gradient is False:
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np.testing.assert_array_equal(
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out[2], self.ref_x_backward(out[1])
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)
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if y.stop_gradient is False:
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np.testing.assert_array_equal(
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out[3], self.ref_y_backward(out[1])
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)
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elif y.stop_gradient is False:
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np.testing.assert_array_equal(
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out[2], self.ref_y_backward(out[1])
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)
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def test_api_broadcast(self, use_cuda=False):
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train_prog = base.Program()
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startup = base.Program()
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with base.program_guard(train_prog, startup):
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x = paddle.static.data(name='x', shape=[-1, 4, 1], dtype='float32')
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y = paddle.static.data(name='y', shape=[-1, 4, 2], dtype='float32')
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x_i = (
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np.array([[0.9383, 0.1983, 3.2, 1.2]])
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.astype("float32")
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.reshape([1, 4, 1])
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)
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y_i = (
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np.array([[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]])
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.astype("float32")
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.reshape([1, 4, 2])
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)
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result = paddle.where(x > 1, x=x, y=y)
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exe = base.Executor(self.place)
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exe.run(startup)
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out = exe.run(
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train_prog, feed={'x': x_i, 'y': y_i}, fetch_list=[result]
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)
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np.testing.assert_array_equal(out[0], np.where(x_i > 1, x_i, y_i))
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class TestWhereDygraphAPI(unittest.TestCase):
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def test_api(self):
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with base.dygraph.guard(paddle.XPUPlace(0)):
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x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype("float32")
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y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype("float32")
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cond_i = np.array([False, False, True, True]).astype("bool")
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x = paddle.to_tensor(x_i)
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y = paddle.to_tensor(y_i)
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cond = paddle.to_tensor(cond_i)
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out = paddle.where(cond, x, y)
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np.testing.assert_array_equal(
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out.numpy(), np.where(cond_i, x_i, y_i)
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)
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if __name__ == '__main__':
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unittest.main()
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