wehub-resource-sync
1198 lines
46 KiB
Python
1198 lines
46 KiB
Python
# Copyright (c) 2022 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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convert_uint16_to_float,
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get_device_place,
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is_custom_device,
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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.autograd.ir_backward import grad
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from paddle.base import Program, core, program_guard
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from paddle.base.backward import append_backward
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class TestWhereOp(OpTest):
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def setUp(self):
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self.op_type = 'where'
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self.prim_op_type = 'prim'
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self.python_api = paddle.where
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self.public_python_api = paddle.where
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self.check_cinn = True
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self.init_config()
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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 test_check_output(self):
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self.check_output(check_cinn=self.check_cinn, check_pir=True)
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def test_check_grad(self):
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self.check_grad(
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['X', 'Y'],
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'Out',
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check_cinn=self.check_cinn,
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check_pir=True,
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check_prim_pir=True,
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)
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def init_config(self):
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self.x = np.random.uniform((-3), 5, 100).astype('float64')
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self.y = np.random.uniform((-3), 5, 100).astype('float64')
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self.cond = np.zeros(100).astype('bool')
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class TestWhereOp2(TestWhereOp):
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def init_config(self):
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self.x = np.random.uniform((-5), 5, (60, 2)).astype('float64')
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self.y = np.random.uniform((-5), 5, (60, 2)).astype('float64')
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self.cond = np.ones((60, 2)).astype('bool')
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class TestWhereFP16OP(TestWhereOp):
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def init_config(self):
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self.dtype = np.float16
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self.x = np.random.uniform((-5), 5, (60, 2)).astype(self.dtype)
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self.y = np.random.uniform((-5), 5, (60, 2)).astype(self.dtype)
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self.cond = np.ones((60, 2)).astype('bool')
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip complex due to lack of mean support",
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)
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class TestWhereOpComplex64(TestWhereOp):
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def init_config(self):
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self.dtype = np.complex64
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self.x = np.random.uniform((-5), 5, (60, 2)).astype(self.dtype)
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self.y = np.random.uniform((-5), 5, (60, 2)).astype(self.dtype)
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self.cond = np.ones((60, 2)).astype('bool')
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip complex due to lack of mean support",
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)
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class TestWhereOpComplex128(TestWhereOp):
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def init_config(self):
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self.dtype = np.complex128
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self.x = np.random.uniform((-5), 5, (60, 2)).astype(self.dtype)
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self.y = np.random.uniform((-5), 5, (60, 2)).astype(self.dtype)
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self.cond = np.ones((60, 2)).astype('bool')
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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 TestWhereBF16OP(OpTest):
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def setUp(self):
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self.op_type = 'where'
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self.prim_op_type = 'prim'
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self.dtype = np.uint16
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self.python_api = paddle.where
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self.public_python_api = paddle.where
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self.check_cinn = True
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self.init_config()
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self.inputs = {
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'Condition': self.cond,
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'X': convert_float_to_uint16(self.x),
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'Y': convert_float_to_uint16(self.y),
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}
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self.outputs = {
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'Out': convert_float_to_uint16(np.where(self.cond, self.x, self.y))
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}
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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_cinn=self.check_cinn, check_pir=True
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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(
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place,
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['X', 'Y'],
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'Out',
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numeric_grad_delta=0.05,
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check_cinn=self.check_cinn,
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check_pir=True,
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check_prim_pir=True,
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)
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def init_config(self):
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self.x = np.random.uniform((-5), 5, (60, 2)).astype(np.float32)
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self.y = np.random.uniform((-5), 5, (60, 2)).astype(np.float32)
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self.cond = np.random.randint(2, size=(60, 2)).astype('bool')
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class TestWhereOp3(TestWhereOp):
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def init_config(self):
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self.x = np.random.uniform((-3), 5, (20, 2, 4)).astype('float64')
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self.y = np.random.uniform((-3), 5, (20, 2, 4)).astype('float64')
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self.cond = np.array(np.random.randint(2, size=(20, 2, 4)), dtype=bool)
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class TestWhereOp_ZeroSize(TestWhereOp):
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def init_config(self):
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self.x = np.random.uniform((-5), 5, (60, 0)).astype('float64')
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self.y = np.random.uniform((-5), 5, (60, 0)).astype('float64')
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self.cond = np.ones((60, 0)).astype('bool')
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class TestWhereAPI(unittest.TestCase):
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def setUp(self):
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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, use_cuda=False):
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paddle.enable_static()
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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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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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cond = paddle.static.data(
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name='cond', shape=[-1, *self.shape], dtype='bool'
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)
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if not paddle.framework.use_pir_api():
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cond.desc.set_need_check_feed(False)
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x = paddle.static.data(
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name='x', shape=[-1, *self.shape], dtype='float32'
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)
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if not paddle.framework.use_pir_api():
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x.desc.set_need_check_feed(False)
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y = paddle.static.data(
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name='y', shape=[-1, *self.shape], dtype='float32'
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)
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if not paddle.framework.use_pir_api():
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y.desc.set_need_check_feed(False)
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x.stop_gradient = x_stop_gradient
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if not paddle.framework.use_pir_api():
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x.desc.set_need_check_feed(False)
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y.stop_gradient = y_stop_gradient
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if not paddle.framework.use_pir_api():
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y.desc.set_need_check_feed(False)
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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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for use_cuda in [False, True]:
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if use_cuda and (
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not (
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base.core.is_compiled_with_cuda()
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or is_custom_device()
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)
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):
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break
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place = (
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get_device_place() if use_cuda else base.CPUPlace()
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)
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exe = base.Executor(place)
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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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paddle.static.default_main_program(),
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feed={
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'cond': self.cond,
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'x': self.x,
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'y': self.y,
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},
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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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paddle.static.default_main_program(),
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feed={
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'cond': self.cond,
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'x': self.x,
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'y': self.y,
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},
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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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paddle.disable_static()
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def test_pir_api(self, use_cuda=False):
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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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with (
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paddle.pir_utils.IrGuard(),
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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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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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loss = paddle.mean(result)
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[x_grad, y_grad] = grad(loss, (x, y))
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default_main_program = paddle.static.default_main_program()
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fetch_list = [result]
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if x_stop_gradient is False:
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fetch_list.append(x_grad)
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if y_stop_gradient is False:
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fetch_list.append(y_grad)
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for use_cuda in [False, True]:
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if use_cuda and (
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not (
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base.core.is_compiled_with_cuda()
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or is_custom_device()
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)
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):
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break
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place = (
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get_device_place() if use_cuda else base.CPUPlace()
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)
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exe = base.Executor(place)
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out = exe.run(
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default_main_program,
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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[1], 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[2], self.ref_y_backward(out[2])
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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[1], 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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paddle.enable_static()
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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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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for use_cuda in [False, True]:
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if use_cuda and (
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not (
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base.core.is_compiled_with_cuda() or is_custom_device()
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)
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):
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return
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place = get_device_place() if use_cuda else base.CPUPlace()
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exe = base.Executor(place)
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out = exe.run(
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paddle.static.default_main_program(),
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feed={'x': x_i, 'y': y_i},
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fetch_list=[result],
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)
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np.testing.assert_array_equal(
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out[0], np.where((x_i > 1), x_i, y_i)
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)
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paddle.disable_static()
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def test_scalar(self):
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paddle.enable_static()
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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cond_shape = [4]
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cond = paddle.static.data(
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name='cond', shape=cond_shape, dtype='bool'
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)
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x_data = 1.0
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y_data = 2.0
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cond_data = np.array([False, False, True, True]).astype('bool')
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result = paddle.where(condition=cond, x=x_data, y=y_data)
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for use_cuda in [False, True]:
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if use_cuda and (
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not (
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base.core.is_compiled_with_cuda() or is_custom_device()
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)
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):
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return
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place = get_device_place() if use_cuda else base.CPUPlace()
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exe = base.Executor(place)
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out = exe.run(
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paddle.static.default_main_program(),
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feed={'cond': cond_data},
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fetch_list=[result],
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)
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expect = np.where(cond_data, x_data, y_data)
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np.testing.assert_array_equal(out[0], expect)
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paddle.disable_static()
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def __test_where_with_broadcast_static(self, cond_shape, x_shape, y_shape):
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paddle.enable_static()
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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cond = paddle.static.data(
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name='cond', shape=cond_shape, dtype='bool'
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)
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x = paddle.static.data(name='x', shape=x_shape, dtype='float32')
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y = paddle.static.data(name='y', shape=y_shape, dtype='float32')
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cond_data_tmp = np.random.random(size=cond_shape).astype('float32')
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cond_data = cond_data_tmp < 0.3
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x_data = np.random.random(size=x_shape).astype('float32')
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y_data = np.random.random(size=y_shape).astype('float32')
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result = paddle.where(condition=cond, x=x, y=y)
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for use_cuda in [False, True]:
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if use_cuda and (
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not (
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base.core.is_compiled_with_cuda() or is_custom_device()
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)
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):
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return
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place = get_device_place() if use_cuda else base.CPUPlace()
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exe = base.Executor(place)
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out = exe.run(
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paddle.static.default_main_program(),
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feed={'cond': cond_data, 'x': x_data, 'y': y_data},
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fetch_list=[result],
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)
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expect = np.where(cond_data, x_data, y_data)
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np.testing.assert_array_equal(out[0], expect)
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def __test_where_with_type_promotion(
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self, x_dtype, y_dtype, expected_dtype=None
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):
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paddle.enable_static()
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main_program = paddle.static.Program()
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shape = [3, 10]
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with paddle.static.program_guard(main_program):
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cond = paddle.static.data(name='cond', shape=[3, 10], dtype='bool')
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x = paddle.static.data(name='x', shape=shape, dtype=x_dtype)
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y = paddle.static.data(name='y', shape=shape, dtype=y_dtype)
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cond_data_tmp = np.random.random(size=shape).astype('float32')
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cond_data = cond_data_tmp < 0.3
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if x_dtype != 'bfloat16':
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x_data = np.random.random(size=shape).astype(x_dtype)
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else:
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x_data = convert_float_to_uint16(
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np.random.random(size=shape).astype('float32')
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)
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if y_dtype != 'bfloat16':
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y_data = np.random.random(size=shape).astype(y_dtype)
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else:
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y_data = convert_float_to_uint16(
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np.random.random(size=shape).astype('float32')
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)
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result = paddle.where(condition=cond, x=x, y=y)
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for use_cuda in [False, True]:
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if use_cuda and (
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not (
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base.core.is_compiled_with_cuda() or is_custom_device()
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)
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):
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return
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place = get_device_place() if use_cuda else base.CPUPlace()
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exe = base.Executor(place)
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out = exe.run(
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paddle.static.default_main_program(),
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feed={'cond': cond_data, 'x': x_data, 'y': y_data},
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fetch_list=[result],
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)
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if x_dtype == 'bfloat16' or y_dtype == 'bfloat16':
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x_data_convert = (
|
|
convert_uint16_to_float(x_data)
|
|
if x_dtype == 'bfloat16'
|
|
else x_data
|
|
)
|
|
y_data_convert = (
|
|
convert_uint16_to_float(y_data)
|
|
if y_dtype == 'bfloat16'
|
|
else y_data
|
|
)
|
|
expect = np.where(cond_data, x_data_convert, y_data_convert)
|
|
np.testing.assert_array_equal(out[0], expect)
|
|
self.assertEqual(out[0].dtype.__str__(), expected_dtype)
|
|
else:
|
|
expect = np.where(cond_data, x_data, y_data)
|
|
np.testing.assert_array_equal(out[0], expect)
|
|
self.assertEqual(out[0].dtype, expect.dtype)
|
|
|
|
def test_static_api_broadcast_1(self):
|
|
cond_shape = [2, 4]
|
|
a_shape = [2, 2, 4]
|
|
b_shape = [2, 2, 4]
|
|
self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)
|
|
|
|
def test_static_api_broadcast_2(self):
|
|
cond_shape = [2, 1]
|
|
a_shape = [2, 2, 4]
|
|
b_shape = [2, 2, 4]
|
|
self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)
|
|
|
|
def test_static_api_broadcast_3(self):
|
|
cond_shape = [2, 2, 1]
|
|
a_shape = [2, 2, 4]
|
|
b_shape = [2, 2, 4]
|
|
self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)
|
|
|
|
def test_static_api_broadcast_4(self):
|
|
cond_shape = [2, 1, 4]
|
|
a_shape = [2, 2, 4]
|
|
b_shape = [2, 2, 4]
|
|
self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)
|
|
|
|
def test_static_api_broadcast_5(self):
|
|
cond_shape = [3, 2, 2, 4]
|
|
a_shape = [2, 2, 4]
|
|
b_shape = [2, 2, 4]
|
|
self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)
|
|
|
|
def test_static_api_broadcast_6(self):
|
|
cond_shape = [2, 2, 4]
|
|
a_shape = [2, 2, 1]
|
|
b_shape = [2, 2, 1]
|
|
self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)
|
|
|
|
def test_static_api_broadcast_7(self):
|
|
cond_shape = [2, 2, 4]
|
|
a_shape = [2, 1, 4]
|
|
b_shape = [2, 1, 4]
|
|
self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)
|
|
|
|
def test_static_api_broadcast_8(self):
|
|
cond_shape = [3, 2, 2, 4]
|
|
a_shape = [2, 2, 1]
|
|
b_shape = [2, 2, 1]
|
|
self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)
|
|
|
|
def test_static_api_type_promotion_fp16_fp32(self):
|
|
x_dtype = 'float16'
|
|
y_dtype = 'float32'
|
|
self.__test_where_with_type_promotion(x_dtype, y_dtype)
|
|
self.__test_where_with_type_promotion(y_dtype, x_dtype)
|
|
|
|
def test_static_api_type_promotion_fp16_fp64(self):
|
|
x_dtype = 'float16'
|
|
y_dtype = 'float64'
|
|
self.__test_where_with_type_promotion(x_dtype, y_dtype)
|
|
self.__test_where_with_type_promotion(y_dtype, x_dtype)
|
|
|
|
def test_static_api_type_promotion_fp32_fp64(self):
|
|
x_dtype = 'float32'
|
|
y_dtype = 'float64'
|
|
self.__test_where_with_type_promotion(x_dtype, y_dtype)
|
|
self.__test_where_with_type_promotion(y_dtype, x_dtype)
|
|
|
|
@unittest.skipIf(
|
|
not (
|
|
(paddle.is_compiled_with_cuda() or is_custom_device())
|
|
and paddle.base.core.is_bfloat16_supported(get_device_place())
|
|
),
|
|
"bf16 is not supported in current device",
|
|
)
|
|
def test_static_api_type_promotion_bf16_fp16(self):
|
|
x_dtype = 'bfloat16'
|
|
y_dtype = 'float16'
|
|
self.__test_where_with_type_promotion(x_dtype, y_dtype, 'float32')
|
|
self.__test_where_with_type_promotion(y_dtype, x_dtype, 'float32')
|
|
|
|
@unittest.skipIf(
|
|
not (
|
|
(paddle.is_compiled_with_cuda() or is_custom_device())
|
|
and paddle.base.core.is_bfloat16_supported(get_device_place())
|
|
),
|
|
"bf16 is not supported in current device",
|
|
)
|
|
def test_static_api_type_promotion_bf16_fp32(self):
|
|
x_dtype = 'bfloat16'
|
|
y_dtype = 'float32'
|
|
self.__test_where_with_type_promotion(x_dtype, y_dtype, 'float32')
|
|
self.__test_where_with_type_promotion(y_dtype, x_dtype, 'float32')
|
|
|
|
@unittest.skipIf(
|
|
not (
|
|
(paddle.is_compiled_with_cuda() or is_custom_device())
|
|
and paddle.base.core.is_bfloat16_supported(get_device_place())
|
|
),
|
|
"bf16 is not supported in current device",
|
|
)
|
|
def test_static_api_type_promotion_bf16_fp64(self):
|
|
x_dtype = 'bfloat16'
|
|
y_dtype = 'float64'
|
|
self.__test_where_with_type_promotion(x_dtype, y_dtype, 'float64')
|
|
self.__test_where_with_type_promotion(y_dtype, x_dtype, 'float64')
|
|
|
|
|
|
class TestWhereDygraphAPI(unittest.TestCase):
|
|
def test_api(self):
|
|
with base.dygraph.guard():
|
|
x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype('float64')
|
|
y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype('float64')
|
|
cond_i = np.array([False, False, True, True]).astype('bool')
|
|
x = paddle.to_tensor(x_i)
|
|
y = paddle.to_tensor(y_i)
|
|
cond = paddle.to_tensor(cond_i)
|
|
out = paddle.where(cond, x, y)
|
|
np.testing.assert_array_equal(
|
|
out.numpy(), np.where(cond_i, x_i, y_i)
|
|
)
|
|
|
|
def test_scalar(self):
|
|
with base.dygraph.guard():
|
|
cond_i = np.array([False, False, True, True]).astype('bool')
|
|
x = 1.0
|
|
y = 2.0
|
|
cond = paddle.to_tensor(cond_i)
|
|
out = paddle.where(cond, x, y)
|
|
np.testing.assert_array_equal(out.numpy(), np.where(cond_i, x, y))
|
|
|
|
def __test_where_with_broadcast_dygraph(self, cond_shape, a_shape, b_shape):
|
|
with base.dygraph.guard():
|
|
cond_tmp = paddle.rand(cond_shape)
|
|
cond = cond_tmp < 0.3
|
|
a = paddle.rand(a_shape)
|
|
b = paddle.rand(b_shape)
|
|
result = paddle.where(cond, a, b)
|
|
result = result.numpy()
|
|
expect = np.where(cond, a, b)
|
|
np.testing.assert_array_equal(expect, result)
|
|
|
|
def test_dygraph_api_broadcast_1(self):
|
|
cond_shape = [2, 4]
|
|
a_shape = [2, 2, 4]
|
|
b_shape = [2, 2, 4]
|
|
self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)
|
|
|
|
def test_dygraph_api_broadcast_2(self):
|
|
cond_shape = [2, 1]
|
|
a_shape = [2, 2, 4]
|
|
b_shape = [2, 2, 4]
|
|
self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)
|
|
|
|
def test_dygraph_api_broadcast_3(self):
|
|
cond_shape = [2, 2, 1]
|
|
a_shape = [2, 2, 4]
|
|
b_shape = [2, 2, 4]
|
|
self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)
|
|
|
|
def test_dygraph_api_broadcast_4(self):
|
|
cond_shape = [2, 1, 4]
|
|
a_shape = [2, 2, 4]
|
|
b_shape = [2, 2, 4]
|
|
self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)
|
|
|
|
def test_dygraph_api_broadcast_5(self):
|
|
cond_shape = [3, 2, 2, 4]
|
|
a_shape = [2, 2, 4]
|
|
b_shape = [2, 2, 4]
|
|
self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)
|
|
|
|
def test_dygraph_api_broadcast_6(self):
|
|
cond_shape = [2, 2, 4]
|
|
a_shape = [2, 2, 1]
|
|
b_shape = [2, 2, 1]
|
|
self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)
|
|
|
|
def test_dygraph_api_broadcast_7(self):
|
|
cond_shape = [2, 2, 4]
|
|
a_shape = [2, 1, 4]
|
|
b_shape = [2, 1, 4]
|
|
self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)
|
|
|
|
def test_dygraph_api_broadcast_8(self):
|
|
cond_shape = [3, 2, 2, 4]
|
|
a_shape = [2, 2, 1]
|
|
b_shape = [2, 2, 1]
|
|
self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)
|
|
|
|
def test_where_type_promotion_f2_f4(self):
|
|
with base.dygraph.guard():
|
|
cond = np.array([False, False, True, True]).astype('bool')
|
|
x = np.array([0.9383, 0.1983, 3.2, 1.2]).astype('float16')
|
|
y = np.array([1.0, 1.0, 1.0, 1.0]).astype('float32')
|
|
np.testing.assert_equal(
|
|
paddle.float32,
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(x),
|
|
paddle.to_tensor(y),
|
|
).dtype,
|
|
)
|
|
np.testing.assert_array_equal(
|
|
np.where(cond, x, y),
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(x),
|
|
paddle.to_tensor(y),
|
|
).numpy(),
|
|
)
|
|
np.testing.assert_array_equal(
|
|
np.where(cond, y, x),
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(y),
|
|
paddle.to_tensor(x),
|
|
).numpy(),
|
|
)
|
|
|
|
def test_where_type_promotion_f2_f8(self):
|
|
with base.dygraph.guard():
|
|
cond = np.array([False, False, True, True]).astype('bool')
|
|
x = np.array([0.9383, 0.1983, 3.2, 1.2]).astype('float16')
|
|
y = np.array([1.0, 1.0, 1.0, 1.0]).astype('float64')
|
|
np.testing.assert_equal(
|
|
paddle.float64,
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(x),
|
|
paddle.to_tensor(y),
|
|
).dtype,
|
|
)
|
|
np.testing.assert_array_equal(
|
|
np.where(cond, x, y),
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(x),
|
|
paddle.to_tensor(y),
|
|
).numpy(),
|
|
)
|
|
np.testing.assert_array_equal(
|
|
np.where(cond, y, x),
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(y),
|
|
paddle.to_tensor(x),
|
|
).numpy(),
|
|
)
|
|
|
|
def test_where_type_promotion_f4_f8(self):
|
|
with base.dygraph.guard():
|
|
cond = np.array([False, False, True, True]).astype('bool')
|
|
x = np.array([0.9383, 0.1983, 3.2, 1.2]).astype('float32')
|
|
y = np.array([1.0, 1.0, 1.0, 1.0]).astype('float64')
|
|
np.testing.assert_equal(
|
|
paddle.float64,
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(x),
|
|
paddle.to_tensor(y),
|
|
).dtype,
|
|
)
|
|
np.testing.assert_array_equal(
|
|
np.where(cond, x, y),
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(x),
|
|
paddle.to_tensor(y),
|
|
).numpy(),
|
|
)
|
|
np.testing.assert_array_equal(
|
|
np.where(cond, y, x),
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(y),
|
|
paddle.to_tensor(x),
|
|
).numpy(),
|
|
)
|
|
|
|
def test_where_type_promotion_f2_bf(self):
|
|
with base.dygraph.guard():
|
|
cond = np.array([False, False, True, True]).astype('bool')
|
|
x = np.array([0.9383, 0.1983, 3.2, 1.2]).astype('float16')
|
|
y = np.array([1.0, 1.0, 1.0, 1.0]).astype('float32')
|
|
y = convert_uint16_to_float(convert_float_to_uint16(y))
|
|
np.testing.assert_equal(
|
|
paddle.float32,
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(x),
|
|
paddle.to_tensor(y),
|
|
).dtype,
|
|
)
|
|
np.testing.assert_array_equal(
|
|
np.where(cond, x, y),
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(x),
|
|
paddle.to_tensor(y),
|
|
).numpy(),
|
|
)
|
|
np.testing.assert_array_equal(
|
|
np.where(cond, y, x),
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(y),
|
|
paddle.to_tensor(x),
|
|
).numpy(),
|
|
)
|
|
|
|
def test_where_type_promotion_f4_bf(self):
|
|
with base.dygraph.guard():
|
|
cond = np.array([False, False, True, True]).astype('bool')
|
|
x = np.array([0.9383, 0.1983, 3.2, 1.2]).astype('float32')
|
|
y = np.array([1.0, 1.0, 1.0, 1.0]).astype('float32')
|
|
y = convert_uint16_to_float(convert_float_to_uint16(y))
|
|
np.testing.assert_equal(
|
|
paddle.float32,
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(x),
|
|
paddle.to_tensor(y),
|
|
).dtype,
|
|
)
|
|
np.testing.assert_array_equal(
|
|
np.where(cond, x, y),
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(x),
|
|
paddle.to_tensor(y),
|
|
).numpy(),
|
|
)
|
|
np.testing.assert_array_equal(
|
|
np.where(cond, y, x),
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(y),
|
|
paddle.to_tensor(x),
|
|
),
|
|
)
|
|
|
|
def test_where_type_promotion_f8_bf(self):
|
|
with base.dygraph.guard():
|
|
cond = np.array([False, False, True, True]).astype('bool')
|
|
x = np.array([0.9383, 0.1983, 3.2, 1.2]).astype('float64')
|
|
y = np.array([1.0, 1.0, 1.0, 1.0]).astype('float32')
|
|
y = convert_uint16_to_float(convert_float_to_uint16(y))
|
|
np.testing.assert_equal(
|
|
paddle.float64,
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(x),
|
|
paddle.to_tensor(y),
|
|
).dtype,
|
|
)
|
|
np.testing.assert_array_equal(
|
|
np.where(cond, x, y),
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(x),
|
|
paddle.to_tensor(y),
|
|
).numpy(),
|
|
)
|
|
np.testing.assert_array_equal(
|
|
np.where(cond, y, x),
|
|
paddle.where(
|
|
paddle.to_tensor(cond),
|
|
paddle.to_tensor(y),
|
|
paddle.to_tensor(x),
|
|
).numpy(),
|
|
)
|
|
|
|
def test_where_condition(self):
|
|
data = np.array([[True, False], [False, True]])
|
|
with program_guard(Program(), Program()):
|
|
x = paddle.static.data(name='x', shape=[(-1), 2], dtype='bool')
|
|
if not paddle.framework.use_pir_api():
|
|
x.desc.set_need_check_feed(False)
|
|
y = paddle.where(x)
|
|
self.assertEqual(type(y), tuple)
|
|
self.assertEqual(len(y), 2)
|
|
z = paddle.concat(list(y), axis=0)
|
|
exe = base.Executor(base.CPUPlace())
|
|
(res,) = exe.run(
|
|
feed={'x': data}, fetch_list=[z], return_numpy=False
|
|
)
|
|
expect_out = np.array([0, 1, 0, 1])
|
|
np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
|
|
data = np.array([True, True, False])
|
|
with program_guard(Program(), Program()):
|
|
x = paddle.static.data(name='x', shape=[-1], dtype='bool')
|
|
if not paddle.framework.use_pir_api():
|
|
x.desc.set_need_check_feed(False)
|
|
y = paddle.where(x)
|
|
self.assertEqual(type(y), tuple)
|
|
self.assertEqual(len(y), 1)
|
|
z = paddle.concat(list(y), axis=0)
|
|
exe = base.Executor(base.CPUPlace())
|
|
(res,) = exe.run(
|
|
feed={'x': data}, fetch_list=[z], return_numpy=False
|
|
)
|
|
expect_out = np.array([0, 1])
|
|
np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
|
|
|
|
|
|
class TestWhereDygraphAPIBroadcast(unittest.TestCase):
|
|
def test_broadcast_scalar(self):
|
|
with base.dygraph.guard():
|
|
x_i = np.random.randn(4, 5, 6).astype('float64')
|
|
y_i = -1.0
|
|
cond_i = np.random.randn(1, 1, 6).astype('bool')
|
|
x = paddle.to_tensor(x_i)
|
|
y = paddle.to_tensor(y_i)
|
|
cond = paddle.to_tensor(cond_i)
|
|
out = paddle.where(cond, x, y)
|
|
np.testing.assert_array_equal(
|
|
out.numpy(), np.where(cond_i, x_i, y_i)
|
|
)
|
|
|
|
def test_broadcast_to_x(self):
|
|
with base.dygraph.guard():
|
|
x_i = np.random.randn(4, 5, 6).astype('float64')
|
|
y_i = np.random.randn(1, 5, 6).astype('float64')
|
|
cond_i = np.random.randn(1, 1, 6).astype('bool')
|
|
x = paddle.to_tensor(x_i)
|
|
y = paddle.to_tensor(y_i)
|
|
cond = paddle.to_tensor(cond_i)
|
|
out = paddle.where(cond, x, y)
|
|
np.testing.assert_array_equal(
|
|
out.numpy(), np.where(cond_i, x_i, y_i)
|
|
)
|
|
|
|
def test_broadcast_to_y(self):
|
|
with base.dygraph.guard():
|
|
x_i = np.random.randn(1, 5, 6).astype('float64')
|
|
y_i = np.random.randn(4, 5, 6).astype('float64')
|
|
cond_i = np.random.randn(1, 1, 6).astype('bool')
|
|
x = paddle.to_tensor(x_i)
|
|
y = paddle.to_tensor(y_i)
|
|
cond = paddle.to_tensor(cond_i)
|
|
out = paddle.where(cond, x, y)
|
|
np.testing.assert_array_equal(
|
|
out.numpy(), np.where(cond_i, x_i, y_i)
|
|
)
|
|
|
|
def test_broadcast_to_cond(self):
|
|
with base.dygraph.guard():
|
|
x_i = np.random.randn(1, 1, 6).astype('float64')
|
|
y_i = np.random.randn(1, 5, 1).astype('float64')
|
|
cond_i = np.random.randn(4, 5, 6).astype('bool')
|
|
x = paddle.to_tensor(x_i)
|
|
y = paddle.to_tensor(y_i)
|
|
cond = paddle.to_tensor(cond_i)
|
|
out = paddle.where(cond, x, y)
|
|
np.testing.assert_array_equal(
|
|
out.numpy(), np.where(cond_i, x_i, y_i)
|
|
)
|
|
|
|
def test_can_not_broadcast(self):
|
|
with base.dygraph.guard():
|
|
x_i = np.random.randn(1, 1, 6).astype('float64')
|
|
y_i = np.random.randn(1, 5, 3).astype('float64')
|
|
cond_i = np.random.randn(4, 5, 6).astype('bool')
|
|
x = paddle.to_tensor(x_i)
|
|
y = paddle.to_tensor(y_i)
|
|
cond = paddle.to_tensor(cond_i)
|
|
|
|
with self.assertRaises(ValueError):
|
|
_ = paddle.where(cond, x, y)
|
|
|
|
|
|
class TestWhereDygraphAPIDtypePromotion(unittest.TestCase):
|
|
def test_dtype_auto_promotion_float(self):
|
|
with base.dygraph.guard():
|
|
x_i = np.random.randn(4, 5, 6).astype('float32')
|
|
y_i = np.random.randn(4, 5, 6).astype('float64')
|
|
cond_i = np.random.randn(4, 5, 6).astype('bool')
|
|
x = paddle.to_tensor(x_i)
|
|
y = paddle.to_tensor(y_i)
|
|
cond = paddle.to_tensor(cond_i)
|
|
out = paddle.where(cond, x, y)
|
|
self.assertEqual(out.dtype, y.dtype)
|
|
np.testing.assert_array_equal(
|
|
out.numpy(), np.where(cond_i, x_i, y_i)
|
|
)
|
|
|
|
|
|
class TestWhereOpError(unittest.TestCase):
|
|
def test_errors(self):
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype('float64')
|
|
y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype('float64')
|
|
cond_i = np.array([False, False, True, True]).astype('bool')
|
|
|
|
def test_Variable():
|
|
paddle.where(cond_i, x_i, y_i)
|
|
|
|
self.assertRaises(TypeError, test_Variable)
|
|
|
|
def test_Value():
|
|
with paddle.pir_utils.IrGuard():
|
|
paddle.where(cond_i, x_i, y_i)
|
|
|
|
self.assertRaises(TypeError, test_Value)
|
|
|
|
def test_type():
|
|
with paddle.pir_utils.OldIrGuard():
|
|
x = paddle.static.data(
|
|
name='x', shape=[-1, 4], dtype='bool'
|
|
)
|
|
if not paddle.framework.use_pir_api():
|
|
x.desc.set_need_check_feed(False)
|
|
y = paddle.static.data(
|
|
name='y', shape=[-1, 4], dtype='float16'
|
|
)
|
|
if not paddle.framework.use_pir_api():
|
|
y.desc.set_need_check_feed(False)
|
|
cond = paddle.static.data(
|
|
name='cond', shape=[-1, 4], dtype='int32'
|
|
)
|
|
if not paddle.framework.use_pir_api():
|
|
cond.desc.set_need_check_feed(False)
|
|
paddle.where(cond, x, y)
|
|
|
|
self.assertRaises(TypeError, test_type)
|
|
|
|
def test_value_error(self):
|
|
with base.dygraph.guard():
|
|
cond_shape = [2, 2, 4]
|
|
cond_tmp = paddle.rand(cond_shape)
|
|
cond = cond_tmp < 0.3
|
|
a = paddle.rand(cond_shape)
|
|
self.assertRaises(ValueError, paddle.where, cond, a)
|
|
|
|
|
|
class TestWhereDygraphAPINonBoolCondition(unittest.TestCase):
|
|
def test_condition_with_wrong_dtype(self):
|
|
with base.dygraph.guard():
|
|
cond = paddle.to_tensor([True, False])
|
|
|
|
for dtype in [
|
|
paddle.int64,
|
|
paddle.int32,
|
|
paddle.float32,
|
|
paddle.float64,
|
|
]:
|
|
cond_wrong_dtype = cond.to(dtype)
|
|
with self.assertRaises(ValueError):
|
|
paddle.where(cond_wrong_dtype, 1, 0)
|
|
|
|
def test_condition_inplace_with_wrong_dtype(self):
|
|
with base.dygraph.guard():
|
|
cond = paddle.to_tensor([True, False])
|
|
|
|
x = paddle.zeros_like(cond).astype("float32")
|
|
for dtype in [
|
|
paddle.int64,
|
|
paddle.int32,
|
|
paddle.float32,
|
|
paddle.float64,
|
|
]:
|
|
cond_wrong_dtype = cond.to(dtype)
|
|
with self.assertRaises(ValueError):
|
|
x = x.where_(cond_wrong_dtype, x, x)
|
|
|
|
|
|
@unittest.skipIf(
|
|
core.is_compiled_with_xpu(),
|
|
"Skip XPU for zero size tensor is not fully supported",
|
|
)
|
|
class TestWhereZeroSizeTensor(unittest.TestCase):
|
|
def init_inputs_outputs(self, shapes):
|
|
cond = np.random.randint(0, 2, size=shapes[0]).astype('bool')
|
|
x = np.random.random(shapes[1]).astype('float64')
|
|
y = np.random.random(shapes[2]).astype('float64')
|
|
out_ref = np.where(cond, x, y)
|
|
return (cond, x, y), out_ref
|
|
|
|
def _test_with_shapes(self, shapes):
|
|
inputs, out_ref = self.init_inputs_outputs(shapes)
|
|
|
|
with dygraph_guard():
|
|
tensors = [paddle.to_tensor(inp) for inp in inputs]
|
|
result = paddle.where(tensors[0], tensors[1], tensors[2])
|
|
np.testing.assert_allclose(result, out_ref, rtol=1e-05)
|
|
|
|
with (
|
|
static_guard(),
|
|
paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
),
|
|
):
|
|
cond_t = paddle.static.data(
|
|
name='cond', shape=[2, 3, 5], dtype='bool'
|
|
)
|
|
x_t = paddle.static.data(name='x', shape=[2, 3, 5], dtype='float64')
|
|
y_t = paddle.static.data(name='y', shape=[2, 3, 5], dtype='float64')
|
|
result = paddle.where(cond_t, x_t, y_t)
|
|
|
|
exe = base.Executor(base.CPUPlace())
|
|
out = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={'cond': inputs[0], 'x': inputs[1], 'y': inputs[2]},
|
|
fetch_list=[result],
|
|
)
|
|
np.testing.assert_allclose(out[0], out_ref, rtol=1e-05)
|
|
|
|
def test_api_with_zero_size_input(self):
|
|
self._test_with_shapes([(0, 0), (0, 0), (0, 0)])
|
|
self._test_with_shapes([(0, 1), (0, 1), (0, 1)])
|
|
self._test_with_shapes([(0, 2, 1), (0, 2, 1), (0, 2, 1)])
|
|
self._test_with_shapes([(5, 17, 0, 6), (5, 17, 0, 6), (5, 17, 0, 6)])
|
|
self._test_with_shapes([(0, 5, 17, 6), (0, 5, 17, 6), (0, 5, 17, 6)])
|
|
|
|
|
|
@unittest.skipIf(
|
|
core.is_compiled_with_xpu(),
|
|
"Skip XPU for bool dtype is not fully supported",
|
|
)
|
|
class TestWhereBoolInput(unittest.TestCase):
|
|
def test_api_with_dygraph(self):
|
|
cond = np.random.randint(0, 2, size=[2, 3, 5]).astype('bool')
|
|
x = np.random.random([2, 3, 5]).astype('bool')
|
|
y = np.random.random([2, 3, 5]).astype('bool')
|
|
out_ref = np.where(cond, x, y)
|
|
|
|
with dygraph_guard():
|
|
cond_t = paddle.to_tensor(cond)
|
|
x_t = paddle.to_tensor(x)
|
|
y_t = paddle.to_tensor(y)
|
|
result = paddle.where(cond_t, x_t, y_t)
|
|
np.testing.assert_allclose(result, out_ref, rtol=1e-05)
|
|
|
|
def test_api_with_static(self):
|
|
cond = np.random.randint(0, 2, size=[2, 3, 5]).astype('bool')
|
|
x = np.random.random([2, 3, 5]).astype('bool')
|
|
y = np.random.random([2, 3, 5]).astype('bool')
|
|
out_ref = np.where(cond, x, y)
|
|
|
|
with (
|
|
static_guard(),
|
|
paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
),
|
|
):
|
|
cond_t = paddle.static.data(
|
|
name='cond', shape=[2, 3, 5], dtype='bool'
|
|
)
|
|
x_t = paddle.static.data(name='x', shape=[2, 3, 5], dtype='bool')
|
|
y_t = paddle.static.data(name='y', shape=[2, 3, 5], dtype='bool')
|
|
result = paddle.where(cond_t, x_t, y_t)
|
|
|
|
exe = base.Executor(base.CPUPlace())
|
|
out = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={'cond': cond, 'x': x, 'y': y},
|
|
fetch_list=[result],
|
|
)
|
|
np.testing.assert_allclose(out[0], out_ref, rtol=1e-05)
|
|
|
|
|
|
class TestWhereAlias(unittest.TestCase):
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
|
|
def test_where_alias(self):
|
|
"""
|
|
Test the alias of where function.
|
|
``where(condition=cond, input=x, other=y)`` is equivalent to
|
|
``where(condition=cond, x=x, y=y)``
|
|
"""
|
|
shape = [2, 4]
|
|
cond = paddle.randint(0, 2, shape).astype("bool")
|
|
x = paddle.rand(shape).astype("float32")
|
|
y = paddle.rand(shape).astype("float32")
|
|
|
|
# Test all alias combinations
|
|
combinations = [
|
|
{"condition": cond, "x": x, "y": y},
|
|
{"condition": cond, "input": x, "y": y},
|
|
{"condition": cond, "x": x, "other": y},
|
|
{"condition": cond, "input": x, "other": y},
|
|
]
|
|
|
|
# Get baseline result
|
|
expected = np.where(cond.numpy(), x.numpy(), y.numpy())
|
|
|
|
for params in combinations:
|
|
out = paddle.where(**params)
|
|
np.testing.assert_allclose(out.numpy(), expected, rtol=1e-05)
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestWhereOut(unittest.TestCase):
|
|
def setUp(self):
|
|
self.cond_np = np.random.randint(0, 2, size=[2, 3, 5]).astype('bool')
|
|
self.x_np = np.random.random([2, 3, 5]).astype('float32')
|
|
self.y_np = np.random.random([2, 3, 5]).astype('float32')
|
|
|
|
def test_api_with_dygraph(self):
|
|
paddle.disable_static()
|
|
cond = paddle.to_tensor(self.cond_np)
|
|
x = paddle.to_tensor(self.x_np)
|
|
y = paddle.to_tensor(self.y_np)
|
|
out_holder = paddle.zeros_like(cond)
|
|
out_ref = paddle.where(cond, x, y)
|
|
|
|
paddle.where(cond, x, y, out=out_holder)
|
|
np.testing.assert_allclose(out_holder, out_ref, rtol=1e-20)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
paddle.enable_static()
|
|
unittest.main()
|