wehub-resource-sync
392 lines
14 KiB
Python
392 lines
14 KiB
Python
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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import unittest
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from contextlib import contextmanager
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import numpy as np
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from op_test import OpTest, get_device_place, get_places, is_custom_device
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import paddle
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from paddle import static
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from paddle.base import core
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class TestElementwiseModOp(OpTest):
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def init_kernel_type(self):
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self.use_onednn = False
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def setUp(self):
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self.op_type = "elementwise_floordiv"
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self.prim_op_type = "comp"
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self.python_api = paddle.floor_divide
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self.public_python_api = paddle.floor_divide
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self.dtype = np.int32
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self.axis = -1
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self.init_dtype()
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self.init_input_output()
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self.init_kernel_type()
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self.init_axis()
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self.inputs = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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'Y': OpTest.np_dtype_to_base_dtype(self.y),
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}
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self.attrs = {'axis': self.axis, 'use_onednn': self.use_onednn}
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self.outputs = {'Out': self.out}
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def test_check_output(self):
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self.check_output(check_pir=True)
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def init_input_output(self):
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self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype)
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self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype)
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self.out = np.floor_divide(self.x, self.y)
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def init_dtype(self):
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pass
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def init_axis(self):
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pass
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class TestElementwiseFloorDivOp_ZeroDim1(TestElementwiseModOp):
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def init_input_output(self):
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self.x = np.random.uniform(0, 10000, []).astype(self.dtype)
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self.y = np.random.uniform(0, 1000, []).astype(self.dtype)
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self.out = np.floor_divide(self.x, self.y)
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class TestElementwiseFloorDivOp_ZeroDim2(TestElementwiseModOp):
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def init_input_output(self):
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self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype)
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self.y = np.random.uniform(0, 1000, []).astype(self.dtype)
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self.out = np.floor_divide(self.x, self.y)
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class TestElementwiseFloorDivOp_ZeroDim3(TestElementwiseModOp):
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def init_input_output(self):
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self.x = np.random.uniform(0, 10000, []).astype(self.dtype)
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self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype)
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self.out = np.floor_divide(self.x, self.y)
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class TestElementwiseModOp_scalar(TestElementwiseModOp):
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def init_input_output(self):
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scale_x = random.randint(0, 100000000)
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scale_y = random.randint(1, 100000000)
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self.x = (np.random.rand(2, 3, 4) * scale_x).astype(self.dtype)
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self.y = (np.random.rand(1) * scale_y + 1).astype(self.dtype)
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self.out = np.floor_divide(self.x, self.y)
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class TestElementwiseModOpInverse(TestElementwiseModOp):
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def init_input_output(self):
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self.x = np.random.uniform(0, 10000, [10]).astype(self.dtype)
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self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype)
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self.out = np.floor_divide(self.x, self.y)
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class TestElementwiseFloorDivOp_OneDim(TestElementwiseModOp):
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def init_input_output(self):
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self.x = np.random.uniform(0, 10000, [10]).astype(self.dtype)
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self.y = np.random.uniform(0, 1000, [10]).astype(self.dtype)
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self.out = np.floor_divide(self.x, self.y)
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@contextmanager
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def device_guard(device=None):
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old = paddle.get_device()
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yield paddle.set_device(device)
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paddle.set_device(old)
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class TestFloorDivideOp(unittest.TestCase):
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def test_static(self):
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paddle.enable_static()
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for p in get_places():
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for dtype in (
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'int32',
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'int64',
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'float16',
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'float32',
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'float64',
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):
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np_x = np.array([2, 3, 8, 7]).astype(dtype)
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np_y = np.array([1, 5, 3, 3]).astype(dtype)
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mp, sp = static.Program(), static.Program()
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with static.program_guard(mp, sp):
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x = static.data("x", shape=[4], dtype=dtype)
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y = static.data("y", shape=[4], dtype=dtype)
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z = paddle.floor_divide(x, y)
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exe = static.Executor(p)
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exe.run(sp)
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[np_z] = exe.run(
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mp, feed={"x": np_x, "y": np_y}, fetch_list=[z]
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)
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z_expected = np.floor_divide(np_x, np_y)
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self.assertEqual((np_z == z_expected).all(), True)
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np_x = np.array([2, 3, 8, 7]).astype("uint16")
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np_y = np.array([1, 5, 3, 3]).astype("uint16")
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mp, sp = static.Program(), static.Program()
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with static.program_guard(mp, sp):
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x = static.data("x", shape=[4], dtype="uint16")
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y = static.data("y", shape=[4], dtype="uint16")
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z = paddle.floor_divide(x, y)
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exe = static.Executor(p)
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exe.run(sp)
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[np_z] = exe.run(mp, feed={"x": np_x, "y": np_y}, fetch_list=[z])
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z_expected = np.array([16384, 0, 16384, 16384], dtype='uint16')
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self.assertEqual((np_z == z_expected).all(), True)
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def test_dygraph(self):
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paddle.disable_static()
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for p in get_places():
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for dtype in (
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'uint8',
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'int8',
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'int16',
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'int32',
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'int64',
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'float16',
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'float32',
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'float64',
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):
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np_x = np.array([2, 3, 8, 7]).astype(dtype)
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np_y = np.array([1, 5, 3, 3]).astype(dtype)
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x = paddle.to_tensor(np_x)
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y = paddle.to_tensor(np_y)
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z = paddle.floor_divide(x, y)
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np_z = z.numpy()
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z_expected = np.floor_divide(np_x, np_y)
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self.assertEqual((np_z == z_expected).all(), True)
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np_x = np.array([2, 3, 8, 7])
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np_y = np.array([1, 5, 3, 3])
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x = paddle.to_tensor(np_x, dtype='bfloat16')
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y = paddle.to_tensor(np_y, dtype="bfloat16")
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z = paddle.floor_divide(x, y)
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np_z = z.numpy()
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z_expected = np.array([16384, 0, 16384, 16384], dtype='uint16')
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self.assertEqual((np_z == z_expected).all(), True)
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for dtype in (
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'int8',
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'int16',
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'int32',
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'int64',
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'float16',
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'float32',
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'float64',
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):
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np_x = -np.array([2, 3, 8, 7]).astype(dtype)
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np_y = np.array([1, 5, 3, 3]).astype(dtype)
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x = paddle.to_tensor(np_x)
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y = paddle.to_tensor(np_y)
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z = paddle.floor_divide(x, y)
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np_z = z.numpy()
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z_expected = np.floor_divide(np_x, np_y)
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self.assertEqual((np_z == z_expected).all(), True)
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np_x = -np.array([2, 3, 8, 7])
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np_y = np.array([1, 5, 3, 3])
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x = paddle.to_tensor(np_x, dtype='bfloat16')
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y = paddle.to_tensor(np_y, dtype="bfloat16")
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z = paddle.floor_divide(x, y)
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np_z = z.numpy()
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z_expected = np.array([49152, 49024, 49216, 49216], dtype='uint16')
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self.assertEqual((np_z == z_expected).all(), True)
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for dtype in ('float32', 'float64', 'float16'):
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try:
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# divide by zero
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np_x = np.array([2])
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np_y = np.array([0, 0, 0])
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x = paddle.to_tensor(np_x, dtype=dtype)
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y = paddle.to_tensor(np_y, dtype=dtype)
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z = paddle.floor_divide(x, y)
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np_z = z.numpy()
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# [np.inf, np.inf, np.inf]
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z_expected = np.floor_divide(np_x, np_y)
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self.assertEqual((np_z == z_expected).all(), True)
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except Exception as e:
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pass
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# divide by zero
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np_x = np.array([2])
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np_y = np.array([0, 0, 0])
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x = paddle.to_tensor(np_x, dtype='bfloat16')
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y = paddle.to_tensor(np_y, dtype="bfloat16")
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z = paddle.floor_divide(x, y)
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np_z = z.numpy()
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z_expected = np.array([32640, 32640, 32640], dtype='uint16')
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self.assertEqual((np_z == z_expected).all(), True)
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with device_guard('cpu'):
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# divide by zero
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np_x = np.array([2, 3, 4])
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np_y = np.array([0])
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x = paddle.to_tensor(np_x)
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y = paddle.to_tensor(np_y)
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try:
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z = x // y
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except Exception as e:
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pass
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# divide by zero
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for dtype in ("uint8", 'int8', 'int16', 'int32', 'int64'):
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np_x = np.array([2])
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np_y = np.array([0, 0, 0])
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x = paddle.to_tensor(np_x, dtype=dtype)
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y = paddle.to_tensor(np_y, dtype=dtype)
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try:
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z = x // y
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except Exception as e:
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pass
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paddle.enable_static()
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestElementwiseFloorDivOp_Stride(OpTest):
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no_need_check_grad = True
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def setUp(self):
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self.op_type = "elementwise_floordiv"
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self.python_api = paddle.floor_divide
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self.public_python_api = paddle.floor_divide
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self.transpose_api = paddle.transpose
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self.as_stride_api = paddle.as_strided
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self.init_dtype()
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self.init_input_output()
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self.inputs_stride = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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'Y': OpTest.np_dtype_to_base_dtype(self.y_trans),
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}
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self.inputs = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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'Y': OpTest.np_dtype_to_base_dtype(self.y),
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}
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self.outputs = {'Out': self.out}
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def init_dtype(self):
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self.dtype = np.float64
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self.val_dtype = np.float64
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def test_check_output(self):
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place = get_device_place()
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self.check_strided_forward = True
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self.check_output(
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place,
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)
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.out = np.floor_divide(self.x, self.y)
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self.perm = [1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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def test_check_gradient(self):
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pass
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class TestElementwiseFloorDivOp_Stride1(TestElementwiseFloorDivOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.out = np.floor_divide(self.x, self.y)
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self.perm = [0, 1, 3, 2]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseFloorDivOp_Stride2(TestElementwiseFloorDivOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.out = np.floor_divide(self.x, self.y)
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self.perm = [0, 2, 1, 3]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseFloorDivOp_Stride3(TestElementwiseFloorDivOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
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self.out = np.floor_divide(self.x, self.y)
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self.perm = [0, 1, 3, 2]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseFloorDivOp_Stride4(TestElementwiseFloorDivOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [1, 2, 13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
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self.out = np.floor_divide(self.x, self.y)
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self.perm = [1, 0, 2, 3]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseFloorDivOp_Stride5(TestElementwiseFloorDivOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "as_stride"
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self.x = np.random.uniform(0.1, 1, [23, 10, 1, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [23, 2, 13, 20]).astype(self.dtype)
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self.y_trans = self.y
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self.y = self.y[:, 0:1, :, 0:1]
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self.out = np.floor_divide(self.x, self.y)
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self.shape_param = [23, 1, 13, 1]
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self.stride_param = [520, 260, 20, 1]
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class TestElementwiseFloorDivOp_Stride_ZeroDim1(
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TestElementwiseFloorDivOp_Stride
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):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, []).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.out = np.floor_divide(self.x, self.y)
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self.perm = [1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseFloorDivOp_Stride_ZeroSize1(
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TestElementwiseFloorDivOp_Stride
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):
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def init_data(self):
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self.strided_input_type = "transpose"
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self.x = np.random.rand(1, 0, 2).astype('float32')
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self.y = np.random.rand(3, 0, 1).astype('float32')
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self.out = np.floor_divide(self.x, self.y)
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self.perm = [2, 1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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if __name__ == '__main__':
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unittest.main()
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