wehub-resource-sync
578 lines
18 KiB
Python
578 lines
18 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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import paddle
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from paddle import base
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from paddle.base import Program, core, program_guard
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class TestAddMMOp(OpTest):
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# test basic
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def setUp(self):
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self.op_type = "addmm"
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self.prim_op_type = "comp"
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self.python_api = paddle.addmm
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self.public_python_api = paddle.addmm
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self.init_dtype_type()
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self.inputs = {
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'Input': np.random.random((100, 1)).astype(self.dtype),
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'X': np.random.random((100, 10)).astype(self.dtype),
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'Y': np.random.random((10, 20)).astype(self.dtype),
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}
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self.outputs = {
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'Out': self.inputs['Input']
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+ np.dot(self.inputs['X'], self.inputs['Y'])
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}
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def init_dtype_type(self):
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self.dtype = np.float64
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def test_check_output(self):
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self.check_output(check_pir=True, check_prim_pir=True)
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def test_check_grad_normal(self):
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self.check_grad(
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['Input', 'X', 'Y'],
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'Out',
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check_pir=True,
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check_prim_pir=True,
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)
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def test_check_grad_x(self):
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self.check_grad(
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['X'],
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'Out',
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no_grad_set=None,
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check_pir=True,
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check_prim_pir=True,
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)
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def test_check_grad_y(self):
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self.check_grad(
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['Y'],
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'Out',
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no_grad_set=None,
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check_pir=True,
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check_prim_pir=True,
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)
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def test_check_grad_input(self):
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self.check_grad(
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['Input'],
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'Out',
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no_grad_set=None,
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check_pir=True,
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check_prim_pir=True,
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)
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class TestAddMMFP16Op(TestAddMMOp):
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def init_dtype_type(self):
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self.dtype = np.float16
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def test_check_output(self):
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self.check_output(atol=1e-2)
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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 bfloat16",
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)
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class TestAddMMBF16Op(OpTest):
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def setUp(self):
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self.op_type = "addmm"
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self.python_api = paddle.addmm
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self.init_dtype_type()
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self.inputs = {
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'Input': np.random.random((100, 1)).astype(self.np_dtype),
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'X': np.random.random((100, 10)).astype(self.np_dtype),
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'Y': np.random.random((10, 20)).astype(self.np_dtype),
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}
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self.outputs = {
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'Out': self.inputs['Input']
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+ np.dot(self.inputs['X'], self.inputs['Y'])
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}
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self.inputs['Input'] = convert_float_to_uint16(self.inputs['Input'])
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self.inputs['X'] = convert_float_to_uint16(self.inputs['X'])
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self.inputs['Y'] = convert_float_to_uint16(self.inputs['Y'])
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self.outputs['Out'] = convert_float_to_uint16(self.outputs['Out'])
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self.place = get_device_place()
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def init_dtype_type(self):
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self.dtype = np.uint16
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self.np_dtype = np.float32
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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_normal(self):
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self.check_grad_with_place(self.place, ['Input', 'X', 'Y'], 'Out')
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def test_check_grad_x(self):
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self.check_grad_with_place(self.place, ['X'], 'Out', no_grad_set=None)
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def test_check_grad_y(self):
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self.check_grad_with_place(self.place, ['Y'], 'Out', no_grad_set=None)
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def test_check_grad_input(self):
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self.check_grad_with_place(
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self.place, ['Input'], 'Out', no_grad_set=None
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)
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class TestAddMMOpError(unittest.TestCase):
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# test error
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def test_errors(self):
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with program_guard(Program(), Program()):
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# The input type of addmm_op must be Variable.
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input = base.create_lod_tensor(
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np.array([[-1, -1], [-1, -1]]), [[2]], base.CPUPlace()
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)
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x1 = base.create_lod_tensor(
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np.array([[-1, -1], [-1, -1]]), [[2]], base.CPUPlace()
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)
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x2 = base.create_lod_tensor(
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np.array([[-1, -1], [-1, -1]]), [[2]], base.CPUPlace()
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)
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self.assertRaises(TypeError, paddle.addmm, input, x1, x2)
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# The input dtype of mul_op must be float32 or float64.
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input = paddle.static.data(
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name='input',
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shape=[4, 4],
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dtype="int32",
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)
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x3 = paddle.static.data(name='x3', shape=[4, 4], dtype="int32")
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x4 = paddle.static.data(name='x4', shape=[4, 4], dtype="int32")
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self.assertRaises(TypeError, paddle.addmm, input, x3, x4)
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# x and y dimension mismatch
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x5 = paddle.static.data(
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name='x5',
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shape=[4, 5],
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dtype="float32",
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)
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x6 = paddle.static.data(
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name='x6',
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shape=[4, 4],
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dtype="float32",
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)
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self.assertRaises(ValueError, paddle.addmm, input, x5, x6)
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# input and x are not broadcastable
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x7 = paddle.static.data(
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name='x7',
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shape=[4, 4],
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dtype="float32",
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)
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x8 = paddle.static.data(
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name='x8',
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shape=[4, 4],
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dtype="float32",
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)
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input1 = paddle.static.data(
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name='input1',
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shape=[2, 4],
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dtype="float32",
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)
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self.assertRaises(ValueError, paddle.addmm, input1, x7, x8)
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# input and x are not broadcastable
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x9 = paddle.static.data(
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name='x9',
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shape=[4, 4],
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dtype="float32",
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)
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x10 = paddle.static.data(
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name='x10',
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shape=[4, 4],
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dtype="float32",
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)
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input2 = paddle.static.data(
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name='input2',
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shape=[1, 2],
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dtype="float32",
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)
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self.assertRaises(ValueError, paddle.addmm, input2, x9, x10)
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x11 = paddle.static.data(
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name='x11',
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shape=[4, 4],
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dtype="float32",
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)
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x12 = paddle.static.data(name='x12', shape=[4, 4], dtype="float32")
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input3 = paddle.static.data(
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name='input3',
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shape=[4, 2],
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dtype="float32",
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)
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self.assertRaises(ValueError, paddle.addmm, input3, x11, x12)
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x13 = paddle.static.data(
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name='x13',
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shape=[4, 4],
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dtype="float32",
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)
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x14 = paddle.static.data(
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name='x14',
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shape=[4, 4],
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dtype="float32",
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)
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input4 = paddle.static.data(
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name='input4',
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shape=[3, 1],
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dtype="float32",
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)
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self.assertRaises(ValueError, paddle.addmm, input4, x13, x14)
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class TestAddMMOp2(TestAddMMOp):
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# test alpha and beta
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def setUp(self):
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self.op_type = "addmm"
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self.prim_op_type = "comp"
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self.python_api = paddle.addmm
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self.public_python_api = paddle.addmm
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self.dtype = np.float64
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self.init_dtype_type()
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self.inputs = {
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'Input': np.random.random((20, 30)).astype(self.dtype),
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'X': np.random.random((20, 6)).astype(self.dtype),
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'Y': np.random.random((6, 30)).astype(self.dtype),
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}
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self.attrs = {
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'Alpha': 0.1,
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'Beta': 1.0,
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}
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self.outputs = {
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'Out': self.attrs['Beta'] * self.inputs['Input']
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+ self.attrs['Alpha'] * np.dot(self.inputs['X'], self.inputs['Y'])
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}
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class TestAddMMOp3(OpTest):
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# test broadcast
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def setUp(self):
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self.op_type = "addmm"
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self.prim_op_type = "comp"
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self.python_api = paddle.addmm
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self.public_python_api = paddle.addmm
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self.dtype = np.float64
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self.init_dtype_type()
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self.inputs = {
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'Input': np.random.random((1, 100)).astype(self.dtype),
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'X': np.random.random((20, 10)).astype(self.dtype),
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'Y': np.random.random((10, 100)).astype(self.dtype),
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}
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self.attrs = {
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'Alpha': 0.5,
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'Beta': 2.0,
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}
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self.outputs = {
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'Out': self.attrs['Beta'] * self.inputs['Input']
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+ self.attrs['Alpha'] * np.dot(self.inputs['X'], self.inputs['Y'])
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}
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def init_dtype_type(self):
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pass
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def test_check_output(self):
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self.check_output(check_pir=True, check_prim_pir=True)
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def test_check_grad_normal(self):
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self.check_grad(
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['Input', 'X', 'Y'], 'Out', check_pir=True, check_prim_pir=True
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)
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def test_check_grad_x(self):
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self.check_grad(
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['X'], 'Out', no_grad_set=None, check_pir=True, check_prim_pir=True
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)
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def test_check_grad_y(self):
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self.check_grad(
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['Y'], 'Out', no_grad_set=None, check_pir=True, check_prim_pir=True
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)
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def test_check_grad_input(self):
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self.check_grad(
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['Input'],
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'Out',
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no_grad_set=None,
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check_pir=True,
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check_prim_pir=True,
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)
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class TestAddMMOp4(OpTest):
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# test broadcast
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def setUp(self):
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self.op_type = "addmm"
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self.prim_op_type = "comp"
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self.python_api = paddle.addmm
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self.public_python_api = paddle.addmm
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self.dtype = np.float64
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self.init_dtype_type()
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self.inputs = {
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'Input': np.random.random(100).astype(self.dtype),
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'X': np.random.random((20, 10)).astype(self.dtype),
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'Y': np.random.random((10, 100)).astype(self.dtype),
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}
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self.attrs = {
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'Alpha': 0.5,
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'Beta': 2.0,
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}
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self.outputs = {
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'Out': self.attrs['Beta'] * self.inputs['Input']
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+ self.attrs['Alpha'] * np.dot(self.inputs['X'], self.inputs['Y'])
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}
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def init_dtype_type(self):
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pass
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def test_check_output(self):
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self.check_output(check_pir=True, check_prim_pir=True)
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def test_check_grad_normal(self):
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self.check_grad(
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['Input', 'X', 'Y'], 'Out', check_pir=True, check_prim_pir=True
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)
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def test_check_grad_x(self):
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self.check_grad(['X'], 'Out', no_grad_set=None, check_pir=True)
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def test_check_grad_y(self):
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self.check_grad(['Y'], 'Out', no_grad_set=None, check_pir=True)
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def test_check_grad_input(self):
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self.check_grad(
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['Input'],
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'Out',
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no_grad_set=None,
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check_pir=True,
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)
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class TestAddMMOp5(unittest.TestCase):
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def test_api_with_dygraph(self):
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np_input = np.random.random((20, 30)).astype(np.float32)
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np_x = np.random.random((20, 6)).astype(np.float32)
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np_y = np.random.random((6, 30)).astype(np.float32)
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with base.dygraph.guard():
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input = paddle.to_tensor(np_input)
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x = paddle.to_tensor(np_x)
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y = paddle.to_tensor(np_y)
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out = paddle.tensor.addmm(input, x, y)
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np.testing.assert_allclose(
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np_input + np.dot(np_x, np_y), out.numpy(), rtol=1e-5, atol=1e-8
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)
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class TestAddMMAPI(unittest.TestCase):
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def test_api_error(self):
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data_x = np.ones((2, 2)).astype(np.float32)
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data_y = np.ones((2, 2)).astype(np.float32)
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data_input = np.ones((2, 2)).astype(np.float32)
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paddle.disable_static()
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def test_error1():
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data_x_wrong = np.ones((2, 3)).astype(np.float32)
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x = paddle.to_tensor(data_x_wrong)
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y = paddle.to_tensor(data_y)
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input = paddle.to_tensor(data_input)
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out = paddle.tensor.addmm(
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input=input, x=x, y=y, beta=0.5, alpha=5.0
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)
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self.assertRaises(ValueError, test_error1)
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def test_error2():
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data_x_wrong = np.ones(2).astype(np.float32)
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x = paddle.to_tensor(data_x_wrong)
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y = paddle.to_tensor(data_y)
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input = paddle.to_tensor(data_input)
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out = paddle.tensor.addmm(
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input=input, x=x, y=y, beta=0.5, alpha=5.0
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)
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self.assertRaises(ValueError, test_error2)
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def test_error3():
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data_input_wrong = np.ones((2, 2, 2)).astype(np.float32)
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x = paddle.to_tensor(data_x)
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y = paddle.to_tensor(data_y)
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input = paddle.to_tensor(data_input_wrong)
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out = paddle.tensor.addmm(
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input=input, x=x, y=y, beta=0.5, alpha=5.0
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)
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self.assertRaises(ValueError, test_error3)
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def test_error4():
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data_input_wrong = np.ones(5).astype(np.float32)
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x = paddle.to_tensor(data_x)
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y = paddle.to_tensor(data_y)
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input = paddle.to_tensor(data_input_wrong)
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out = paddle.tensor.addmm(
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input=input, x=x, y=y, beta=0.5, alpha=5.0
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)
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self.assertRaises(ValueError, test_error4)
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paddle.enable_static()
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def test_api_normal_1(self):
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data_x = np.ones((2, 2)).astype(np.float32)
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data_y = np.ones((2, 2)).astype(np.float32)
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data_input = np.ones((2, 2)).astype(np.float32)
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data_alpha = 0.1
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data_beta = 1.0
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paddle.disable_static()
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x = paddle.to_tensor(data_x)
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y = paddle.to_tensor(data_y)
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input = paddle.to_tensor(data_input)
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paddle_output = paddle.tensor.addmm(
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input=input, x=x, y=y, beta=data_beta, alpha=data_alpha
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)
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numpy_output = data_beta * data_input + data_alpha * np.dot(
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data_x, data_y
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)
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np.testing.assert_allclose(
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numpy_output, paddle_output.numpy(), rtol=1e-05
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)
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paddle.enable_static()
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def test_api_normal_2(self):
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data_x = np.ones((3, 10)).astype(np.float32)
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data_y = np.ones((10, 3)).astype(np.float32)
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data_input = np.ones(3).astype(np.float32)
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data_alpha = 0.1
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data_beta = 1.0
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paddle.disable_static()
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x = paddle.to_tensor(data_x)
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y = paddle.to_tensor(data_y)
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input = paddle.to_tensor(data_input)
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paddle_output = paddle.tensor.addmm(
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input=input, x=x, y=y, beta=data_beta, alpha=data_alpha
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)
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numpy_output = data_beta * data_input + data_alpha * np.dot(
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data_x, data_y
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)
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np.testing.assert_allclose(
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numpy_output, paddle_output.numpy(), rtol=1e-05
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)
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paddle.enable_static()
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def test_api_normal_3(self):
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data_x = np.ones((3, 10)).astype(np.float32)
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data_y = np.ones((10, 3)).astype(np.float32)
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data_input = np.ones(1).astype(np.float32)
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data_alpha = 0.1
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|
data_beta = 1.0
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|
|
|
paddle.disable_static()
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|
|
|
x = paddle.to_tensor(data_x)
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|
y = paddle.to_tensor(data_y)
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|
input = paddle.to_tensor(data_input)
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|
paddle_output = paddle.tensor.addmm(
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|
input=input, x=x, y=y, beta=data_beta, alpha=data_alpha
|
|
)
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|
numpy_output = data_beta * data_input + data_alpha * np.dot(
|
|
data_x, data_y
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
numpy_output, paddle_output.numpy(), rtol=1e-05
|
|
)
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestAddmmOp_ZeroSize(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "addmm"
|
|
self.python_api = paddle.addmm
|
|
self.public_python_api = paddle.addmm
|
|
self.init_dtype_type()
|
|
self.init_input()
|
|
self.attrs = {
|
|
'Alpha': 0.5,
|
|
'Beta': 2.0,
|
|
}
|
|
self.outputs = {
|
|
'Out': self.attrs['Beta'] * self.inputs['Input']
|
|
+ self.attrs['Alpha'] * np.dot(self.inputs['X'], self.inputs['Y'])
|
|
}
|
|
|
|
def init_input(self):
|
|
# result shape: [20, 100]
|
|
self.inputs = {
|
|
'Input': np.random.random(100).astype(self.dtype),
|
|
'X': np.random.random((20, 0)).astype(self.dtype),
|
|
'Y': np.random.random((0, 100)).astype(self.dtype),
|
|
}
|
|
|
|
def init_dtype_type(self):
|
|
self.dtype = np.float64
|
|
|
|
def test_check_output(self):
|
|
self.check_output(check_pir=True)
|
|
|
|
def test_check_grad_normal(self):
|
|
self.check_grad(['Input', 'X', 'Y'], 'Out', check_pir=True)
|
|
|
|
|
|
class TestAddmmOp_ZeroSize2(TestAddmmOp_ZeroSize):
|
|
def init_input(self):
|
|
# result shape: [20, 0]
|
|
self.inputs = {
|
|
'Input': np.random.random(0).astype(self.dtype),
|
|
'X': np.random.random((20, 100)).astype(self.dtype),
|
|
'Y': np.random.random((100, 0)).astype(self.dtype),
|
|
}
|
|
|
|
|
|
class TestAddmmOp_ZeroSize3(TestAddmmOp_ZeroSize):
|
|
def init_input(self):
|
|
# result shape: [0, 0]
|
|
self.inputs = {
|
|
'Input': np.random.random(0).astype(self.dtype),
|
|
'X': np.random.random((0, 100)).astype(self.dtype),
|
|
'Y': np.random.random((100, 0)).astype(self.dtype),
|
|
}
|
|
|
|
|
|
if __name__ == "__main__":
|
|
paddle.enable_static()
|
|
unittest.main()
|