wehub-resource-sync
487 lines
17 KiB
Python
487 lines
17 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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from op_test import get_device, is_custom_device
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import paddle
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from paddle.framework import core
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def contiguous_strides(shape):
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strides = [0] * len(shape)
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running = 1
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for i in range(len(shape) - 1, -1, -1):
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strides[i] = running
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running *= shape[i]
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return strides
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class TestZeroSizeParameter(unittest.TestCase):
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def setUp(self):
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self.places = [
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"cpu",
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]
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if (
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paddle.device.is_compiled_with_cuda() or is_custom_device()
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) and paddle.device.cuda.device_count() > 0:
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self.places.append(get_device())
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self.parameter_dtypes = [
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'float16',
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'float32',
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'float64',
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]
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self.zero_size_shapes = [
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[0, 4],
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[0, 0],
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[4, 0],
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[0, 5, 6],
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[6, 5, 0, 0],
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[0, 0, 0, 12],
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]
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def test_create_parameter(self):
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for place in self.places:
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paddle.device.set_device(place)
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for parameter_dtype in self.parameter_dtypes:
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for zero_size_shape in self.zero_size_shapes:
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class Model(paddle.nn.Layer):
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def __init__(self) -> None:
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super().__init__()
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self.dummy_linear = paddle.nn.Linear(3, 4)
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self.w = self.create_parameter(
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shape=zero_size_shape, dtype=parameter_dtype
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)
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model = Model()
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model = model
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self.assertEqual(
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model.w.shape,
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zero_size_shape,
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msg=f"Check failed at: {parameter_dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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model.w.data_ptr(),
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0,
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msg=f"Check failed at: {parameter_dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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str(model.w.place),
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str(model.dummy_linear.weight.place),
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msg=f"Check failed at: {parameter_dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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model.w.strides,
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contiguous_strides(zero_size_shape),
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msg=f"Check failed at: {parameter_dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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model.w.is_contiguous(),
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True,
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msg=f"Check failed at: {parameter_dtype}, {zero_size_shape}",
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)
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class TestZeroSizeForward(unittest.TestCase):
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def setUp(self):
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self.places = [
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"cpu",
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]
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if (
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paddle.device.is_compiled_with_cuda() or is_custom_device()
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) and paddle.device.cuda.device_count() > 0:
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self.places.append(get_device())
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self.dtypes = [
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'bool',
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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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'complex64',
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'complex128',
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]
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self.zero_size_shapes = [
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[0, 4],
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[0, 0],
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[4, 0],
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[0, 5, 6],
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[6, 5, 0, 0],
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[0, 0, 0, 12],
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]
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def test_forward_eager(self):
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"""Test for simple API call"""
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for place in self.places:
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paddle.device.set_device(place)
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for dtype in self.dtypes:
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for zero_size_shape in self.zero_size_shapes:
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x = paddle.ones(zero_size_shape, dtype=dtype)
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self.assertEqual(x.data_ptr(), 0)
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if x.dtype == paddle.bool:
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y = ~x
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else:
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y = x + 1
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self.assertEqual(
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y.shape,
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zero_size_shape,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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y.data_ptr(),
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0,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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y.strides,
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contiguous_strides(zero_size_shape),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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str(y.place),
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str(x.place),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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y.dtype,
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x.dtype,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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y.is_contiguous(),
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x.is_contiguous(),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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def test_forward_static(self):
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"""Test for simple API call"""
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def forward_func(x):
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if x.dtype == paddle.bool:
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y = ~x
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else:
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y = x + 1
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return y
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for place in self.places:
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paddle.device.set_device(place)
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static_forward_func = paddle.jit.to_static(
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forward_func, full_graph=True, backend=None
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)
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for dtype in self.dtypes:
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for zero_size_shape in self.zero_size_shapes:
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x = paddle.ones(zero_size_shape, dtype=dtype)
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self.assertEqual(x.data_ptr(), 0)
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y = static_forward_func(x)
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self.assertEqual(
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y.shape,
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zero_size_shape,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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y.data_ptr(),
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0,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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y.strides,
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contiguous_strides(zero_size_shape),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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str(y.place),
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str(x.place),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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y.dtype,
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x.dtype,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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y.is_contiguous(),
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x.is_contiguous(),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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@unittest.skipIf(core.is_compiled_with_xpu(), "Skip XPU for xpu place issue")
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class TestZeroSizeBackward(unittest.TestCase):
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def setUp(self):
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self.places = [
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"cpu",
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]
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if (
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paddle.device.is_compiled_with_cuda() or is_custom_device()
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) and paddle.device.cuda.device_count() > 0:
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self.places.append(get_device())
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# Only floating and complex needs gradient
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self.dtypes = [
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'float16',
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'float32',
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'float64',
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'complex64',
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'complex128',
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]
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self.zero_size_shapes = [
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[0, 4],
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[0, 0],
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[4, 0],
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[0, 5, 6],
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[6, 5, 0, 0],
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[0, 0, 0, 12],
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]
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def test_backward_eager(self):
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"""Test for simple API call"""
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for place in self.places:
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paddle.device.set_device(place)
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for dtype in self.dtypes:
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for zero_size_shape in self.zero_size_shapes:
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x = paddle.ones(zero_size_shape, dtype=dtype)
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x.stop_gradient = False
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self.assertEqual(x.data_ptr(), 0)
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y = x * 2 + 1
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(x_grad,) = paddle.grad(y, x, create_graph=True)
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self.assertEqual(
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x_grad.shape,
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zero_size_shape,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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x_grad.data_ptr(),
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0,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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x_grad.strides,
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contiguous_strides(zero_size_shape),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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str(x_grad.place),
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str(x.place),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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x_grad.dtype,
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x.dtype,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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x_grad.is_contiguous(),
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x.is_contiguous(),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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def test_backward_static(self):
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"""Test for simple API call"""
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def gradient_func(x):
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y = x * 2 + 1
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return paddle.grad(y, x)
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for place in self.places:
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paddle.device.set_device(place)
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for dtype in self.dtypes:
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for zero_size_shape in self.zero_size_shapes:
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x = paddle.ones(zero_size_shape, dtype=dtype)
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x.stop_gradient = False
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self.assertEqual(x.data_ptr(), 0)
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static_gradient_func = paddle.jit.to_static(
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gradient_func, full_graph=True, backend=None
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)
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(x_grad,) = static_gradient_func(x)
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self.assertEqual(
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x_grad.shape,
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zero_size_shape,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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x_grad.data_ptr(),
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0,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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x_grad.strides,
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contiguous_strides(zero_size_shape),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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str(x_grad.place),
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str(x.place),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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x_grad.dtype,
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x.dtype,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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x_grad.is_contiguous(),
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x.is_contiguous(),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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@unittest.skipIf(core.is_compiled_with_xpu(), "Skip XPU for xpu place issue")
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class TestZeroSizeBackwardWithGradientAccumulation(unittest.TestCase):
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def setUp(self):
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self.places = [
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"cpu",
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]
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if (
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paddle.device.is_compiled_with_cuda() or is_custom_device()
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) and paddle.device.cuda.device_count() > 0:
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self.places.append(get_device())
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# Only floating and complex needs gradient
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self.dtypes = [
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# 'float16',
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'float32',
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'float64',
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'complex64',
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'complex128',
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]
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self.zero_size_shapes = [
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[0, 4],
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[4, 0],
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[0, 5, 6],
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[6, 12, 0, 0],
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[0, 0, 0, 12],
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]
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def test_backward_eager(self):
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"""Test for simple API call"""
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for place in self.places:
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paddle.device.set_device(place)
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for dtype in self.dtypes:
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for zero_size_shape in self.zero_size_shapes:
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x = paddle.ones(zero_size_shape, dtype=dtype)
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x.stop_gradient = False
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self.assertEqual(x.data_ptr(), 0)
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def forward_func(x):
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y1 = x / 2
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y2 = x + 1
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return y1 + y2
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(x_grad,) = paddle.grad(
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forward_func(x), x, create_graph=True
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)
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self.assertEqual(
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x_grad.shape,
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zero_size_shape,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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x_grad.data_ptr(),
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0,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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x_grad.strides,
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contiguous_strides(zero_size_shape),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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str(x_grad.place),
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str(x.place),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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x_grad.dtype,
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x.dtype,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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x_grad.is_contiguous(),
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x.is_contiguous(),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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def test_backward_static(self):
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"""Test for simple API call"""
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def gradient_func(x):
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y1 = x / 2
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y2 = x + 1
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out = y1 + y2
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return paddle.grad(out, x)
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for place in self.places:
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paddle.device.set_device(place)
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for dtype in self.dtypes:
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for zero_size_shape in self.zero_size_shapes:
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x = paddle.ones(zero_size_shape, dtype=dtype)
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x.stop_gradient = False
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self.assertEqual(x.data_ptr(), 0)
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static_gradient_func = paddle.jit.to_static(
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gradient_func, full_graph=True, backend=None
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)
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(x_grad,) = static_gradient_func(x)
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self.assertEqual(
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x_grad.shape,
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zero_size_shape,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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x_grad.data_ptr(),
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0,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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x_grad.strides,
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contiguous_strides(zero_size_shape),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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str(x_grad.place),
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str(x.place),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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x_grad.dtype,
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x.dtype,
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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self.assertEqual(
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x_grad.is_contiguous(),
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x.is_contiguous(),
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msg=f"Check failed at: {dtype}, {zero_size_shape}",
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)
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if __name__ == '__main__':
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unittest.main()
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