forked from mindspore-Ecosystem/mindspore
362 lines
12 KiB
Python
362 lines
12 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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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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# ============================================================================
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import numpy as np
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import pytest
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from mindspore import ops, nn, ParameterTuple, context, set_seed
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from mindspore.train import DatasetHelper, connect_network_with_dataset
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import mindspore.dataset as ds
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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set_seed(2)
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def _exec_preprocess(network, is_train, dataset, dataset_sink_mode, epoch_num, sink_size):
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if dataset_sink_mode and not is_train:
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dataset.__loop_size__ = 1
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dataset_helper = DatasetHelper(
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dataset, dataset_sink_mode, sink_size, epoch_num)
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if dataset_sink_mode:
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network = connect_network_with_dataset(network, dataset_helper)
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return dataset_helper, network
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def dynamic_shape_sink_process(network, dataset, is_train=True):
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dataset_sink_mode = True
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sink_size = 1
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epoch_num = 1
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dataset_helper, network = _exec_preprocess(
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network, is_train, dataset, dataset_sink_mode, epoch_num, sink_size)
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network.set_train(is_train)
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for inputs in dataset_helper:
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outputs = network(*inputs)
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return outputs
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def fixed_shape_process(network, dataset, is_train=True):
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network.set_train(is_train)
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for inputs in dataset.create_tuple_iterator():
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outputs = network(*inputs)
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return outputs
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def dataset_generator(data_list):
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for data in data_list:
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yield data
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def get_columns(tensor_num):
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columns = []
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for i in range(tensor_num):
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columns.append("data" + str(i))
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return columns
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def compare(output, expect):
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if isinstance(output, (tuple, list)):
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assert isinstance(expect, (tuple, list))
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for output_, expect_ in zip(output, expect):
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if not compare(output_, expect_):
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return False
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else:
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if not np.allclose(output.asnumpy(), expect.asnumpy(), rtol=1.0e-4, atol=1.0e-4):
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return False
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return True
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class GradNetWrtX(nn.Cell):
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def __init__(self, net):
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super(GradNetWrtX, self).__init__()
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self.net = net
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self.grad_op = ops.GradOperation(
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get_all=True, get_by_list=True, sens_param=True)
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self.params = ParameterTuple(net.trainable_params())
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def construct(self, *inputs):
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gradient_function = self.grad_op(self.net, self.params)
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return gradient_function(*inputs)
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class ConcatNet(nn.Cell):
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def __init__(self, axis):
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super(ConcatNet, self).__init__()
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self.op = ops.Concat(axis)
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def construct(self, x1, x2):
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return self.op((x1, x2))
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def dynamic_concat_run(is_grad):
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axis = 1
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dtype = np.float32
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data_list = []
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for i in [2, 64]:
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data = []
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data.append(np.random.rand(16, i).astype(dtype))
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data.append(np.random.rand(16, i).astype(dtype))
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if is_grad:
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data.append(np.random.rand(16, i*2).astype(dtype))
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data_list.append(tuple(data))
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column_names = get_columns(len(data_list[0]))
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dataset = ds.GeneratorDataset(data_list, column_names, shuffle=False)
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dynamic_columns = {column_names[0]: [
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16, None], column_names[1]: [16, None]}
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if is_grad:
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dynamic_columns[column_names[-1]] = [16, None]
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dataset.set_dynamic_columns(columns=dynamic_columns)
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net = ConcatNet(axis)
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if is_grad:
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net = GradNetWrtX(net)
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output = dynamic_shape_sink_process(net, dataset)
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output_cmp = fixed_shape_process(net, dataset)
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assert compare(output, output_cmp)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_dynamic_concat_forward():
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"""
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Feature: Test Concat.
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Description: The shape of inputs is dynamic.
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Expectation: Assert that results are consistent with fixed shape.
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"""
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dynamic_concat_run(False)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_dynamic_concat_backward():
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"""
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Feature: Test backward of Concat.
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Description: The shape of inputs is dynamic.
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Expectation: Assert that results are consistent with fixed shape.
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"""
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dynamic_concat_run(True)
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class BatchNormNet(nn.Cell):
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def __init__(self, c):
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super(BatchNormNet, self).__init__()
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self.bn = nn.BatchNorm1d(c)
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def construct(self, input_data):
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x = self.bn(input_data)
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return x
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_dynamic_bachnorm():
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"""
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Feature: Test BatchNorm and its backward.
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Description: The shape of inputs is dynamic.
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Expectation: Assert that results are consistent with fixed shape.
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"""
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c = 256
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dtype = np.float32
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data_list = []
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for i in [2, 64]:
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data = []
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data.append(np.random.rand(i, c).astype(dtype))
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data.append(np.random.rand(i, c).astype(dtype))
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data_list.append(tuple(data))
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column_names = get_columns(len(data_list[0]))
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dataset = ds.GeneratorDataset(data_list, column_names, shuffle=False)
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dynamic_columns = {column_names[0]: [None, c], column_names[1]: [None, c]}
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dataset.set_dynamic_columns(columns=dynamic_columns)
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net = GradNetWrtX(BatchNormNet(c))
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gradients = dynamic_shape_sink_process(net, dataset)
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gradients_cmp = fixed_shape_process(net, dataset)
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assert compare(gradients, gradients_cmp)
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class ReshapeNet(nn.Cell):
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def construct(self, x, y):
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shape_of_y = ops.TensorShape()(y)
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return ops.Reshape()(x, shape_of_y)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_dynamic_reshape():
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"""
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Feature: Test Reshape.
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Description: The shape of inputs is dynamic.
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Expectation: Assert that results are consistent with fixed shape.
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"""
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dtype = np.float32
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data_list = []
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for i in [2, 96]:
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data = []
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data.append(np.random.rand(i, 64, 1).astype(dtype))
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data.append(np.random.rand(i, 64).astype(dtype))
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data_list.append(tuple(data))
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column_names = get_columns(len(data_list[0]))
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dataset = ds.GeneratorDataset(data_list, column_names, shuffle=False)
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dynamic_columns = {column_names[0]: [
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None, 64, 1], column_names[1]: [None, 64]}
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dataset.set_dynamic_columns(columns=dynamic_columns)
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net = ReshapeNet()
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output = dynamic_shape_sink_process(net, dataset)
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output_cmp = fixed_shape_process(net, dataset)
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assert compare(output, output_cmp)
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class ReduceSumInputAxisNet(nn.Cell):
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def __init__(self):
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super(ReduceSumInputAxisNet, self).__init__()
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self.reduce = ops.ReduceSum()
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def construct(self, x, y):
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return self.reduce(x, y)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_dynamic_reduce_sum_input_axis():
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"""
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Feature: Test ReduceSum with axis is input.
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Description: The shape of inputs is dynamic.
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Expectation: Assert that results are consistent with result of the numpy compute
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"""
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dtype = np.float32
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data_list = []
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for i in [2, 96]:
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data = []
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data.append(np.random.rand(i, 256).astype(dtype))
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data.append(np.array([1], dtype=np.int64))
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data_list.append(tuple(data))
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column_names = get_columns(len(data_list[0]))
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dataset = ds.GeneratorDataset(data_list, column_names, shuffle=False)
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dynamic_columns = {column_names[0]: [None, 256]}
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dataset.set_dynamic_columns(columns=dynamic_columns)
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net = ReduceSumInputAxisNet()
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output = dynamic_shape_sink_process(net, dataset)
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# Currently, the parameter axis of ReduceSum operator is dynamic(tensor) is
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# not supported under the fixed shape, so numpy is used for comparison
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inputs = data_list[0]
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output_cmp = np.sum(inputs[0], inputs[1][0])
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assert np.allclose(output.asnumpy(), output_cmp, rtol=1.0e-4, atol=1.0e-4)
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class NopNet(nn.Cell):
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def construct(self, x):
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x1 = ops.squeeze(x)
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y1 = ops.expand_dims(x1, 1)
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return ops.sub(y1, x1)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_dynamic_nop():
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"""
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Feature: Test Nop.
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Description: The shape of inputs is dynamic.
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Expectation: Assert that results are consistent with fixed shape.
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"""
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dtype = np.float32
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data_list = []
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for i in [2, 64]:
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data = []
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data.append(np.random.rand(i, 1).astype(dtype))
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data_list.append(tuple(data))
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column_names = get_columns(len(data_list[0]))
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dataset = ds.GeneratorDataset(data_list, column_names, shuffle=False)
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dynamic_columns = {column_names[0]: [None, 1]}
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dataset.set_dynamic_columns(columns=dynamic_columns)
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net = NopNet()
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output = dynamic_shape_sink_process(net, dataset)
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output_cmp = fixed_shape_process(net, dataset)
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assert compare(output, output_cmp)
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class ReduceSumNet(nn.Cell):
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def __init__(self, axis=()):
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super(ReduceSumNet, self).__init__()
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self.reduce = ops.ReduceSum()
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self.axis = axis
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def construct(self, x):
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return self.reduce(x, self.axis)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_dynamic_reduce_sum():
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"""
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Feature: Test ReduceSum and its backward.
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Description: The shape of inputs is dynamic.
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Expectation: Assert that results are consistent with result of with fixed shape.
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"""
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dtype = np.float32
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data_list = []
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for i in [2, 96]:
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data = []
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data.append(np.random.rand(i, 256).astype(dtype))
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data.append(np.array(1).astype(np.float32))
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data_list.append(tuple(data))
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column_names = get_columns(len(data_list[0]))
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dataset = ds.GeneratorDataset(data_list, column_names, shuffle=False)
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dynamic_columns = {column_names[0]: [None, 256]}
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dataset.set_dynamic_columns(columns=dynamic_columns)
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net = GradNetWrtX(ReduceSumNet())
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output = dynamic_shape_sink_process(net, dataset)
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output_cmp = fixed_shape_process(net, dataset)
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assert compare(output, output_cmp)
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class AddNet(nn.Cell):
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def construct(self, x, y):
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return ops.add(x, y)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_dynamic_add():
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"""
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Feature: Test add and its backward.
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Description: The shape of inputs is dynamic.
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Expectation: Assert that results are consistent with result of with fixed shape.
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"""
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dtype = np.float32
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data_list = []
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for i in [2, 96]:
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data = []
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data.append(np.random.rand(i, 256).astype(dtype))
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data.append(np.random.rand(i, 256).astype(dtype))
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data.append(np.random.rand(i, 256).astype(dtype))
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data_list.append(tuple(data))
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column_names = get_columns(len(data_list[0]))
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dataset = ds.GeneratorDataset(data_list, column_names, shuffle=False)
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dynamic_columns = {column_names[0]: [None, 256], column_names[1]: [
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None, 256], column_names[2]: [None, 256]}
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dataset.set_dynamic_columns(columns=dynamic_columns)
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net = GradNetWrtX(AddNet())
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output = dynamic_shape_sink_process(net, dataset)
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output_cmp = fixed_shape_process(net, dataset)
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assert compare(output, output_cmp)
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