forked from mindspore-Ecosystem/mindspore
399 lines
13 KiB
Python
399 lines
13 KiB
Python
# Copyright 2021-2022 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="Ascend")
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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(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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# epoch_num=1 sink_size=1: exec one step
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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(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 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(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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def common_func(dynamic_range, input_shape, data_type, op_net):
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data_list = []
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for i in dynamic_range:
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cur_data = []
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for data_shape in input_shape:
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cur_shape = [dim if dim is not None else i for dim in data_shape]
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cur_data.append(np.random.random(cur_shape).astype(data_type))
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data_list.append(tuple(cur_data))
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dynamic_data_map = {}
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for i, val in enumerate(input_shape):
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dynamic_data_map["data" + str(i + 1)] = val
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dataset = ds.GeneratorDataset(data_list, list(dynamic_data_map.keys()))
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dataset.set_dynamic_columns(columns=dynamic_data_map)
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net = GradNetWrtX(op_net)
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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 LayerNormNet(nn.Cell):
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def __init__(self, last_dim):
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super(LayerNormNet, self).__init__()
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self.layernorm = nn.LayerNorm([last_dim])
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def construct(self, x):
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return self.layernorm(x)
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class Conv2dNet(nn.Cell):
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def __init__(self):
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super(Conv2dNet, self).__init__()
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self.conv = nn.Conv2d(3, 10, 4, pad_mode="valid", has_bias=False, weight_init='normal')
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def construct(self, x):
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return self.conv(x)
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class DropoutNet(nn.Cell):
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def __init__(self):
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super(DropoutNet, self).__init__()
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self.drop = nn.Dropout(0.5)
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self.relu = ops.ReLU()
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def construct(self, x):
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x = self.relu(x)
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return self.relu(self.drop(x))
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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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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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class SoftmaxNet(nn.Cell):
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def construct(self, x):
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return ops.Softmax(axis=-1)(x)
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class BatchNormNet(nn.Cell):
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def __init__(self, channels):
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super(BatchNormNet, self).__init__()
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self.bn = nn.BatchNorm2d(channels)
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def construct(self, x):
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return self.bn(x)
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class SquareSumAllNet(nn.Cell):
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def __init__(self):
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super(SquareSumAllNet, self).__init__()
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self.square_sum_all = ops.SquareSumAll()
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def construct(self, x, y):
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return self.square_sum_all(x, y)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_layernorm():
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"""
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Feature: Test LayerNorm and its backward. The input shape is dynamic.
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Description: The second dim of input is unknown.
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Expectation: Assert that results are consistent with fixed shape.
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"""
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last_dim = 32
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batch_size = 16
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dynamic_range = range(20, 23)
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data_type = np.float32
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input_shape = [(batch_size, None, last_dim), (batch_size, None, last_dim)]
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net = LayerNormNet(last_dim)
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common_func(dynamic_range, input_shape, data_type, net)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_conv2d():
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"""
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Feature: Test Conv2d and its backward. The input shape is dynamic.
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Description: Input dim of `H `or `W` is unknown. Conv2d's attr[pad] set to "valid".
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Expectation: Assert that results are consistent with fixed shape.
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"""
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batch_size = 16
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dynamic_range = range(220, 224)
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data_type = np.float32
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input_shape = [(batch_size, 3, None, 112), (batch_size, 10, 219, 109)]
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net = Conv2dNet()
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common_func(dynamic_range, input_shape, data_type, net)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_dropout():
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"""
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Feature: Test Dropout and its backward.
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Description: The input shape is dynamic.
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Expectation: Dropout result is random, assert gradient shape.
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"""
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batch_size = 16
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data_list = []
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for i in range(48, 50):
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data_list.append((np.random.rand(batch_size, i, 256).astype(np.float32),
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np.random.rand(batch_size, i, 256).astype(np.float32)))
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dataset = ds.GeneratorDataset(data_list, ["data1", "data2"])
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dataset.set_dynamic_columns(columns={"data1": [batch_size, None, 256], "data2": [batch_size, None, 256]})
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net = GradNetWrtX(DropoutNet())
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net.set_train()
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gradients = dynamic_shape_sink_process(net, dataset)
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assert gradients[0][0].shape == (batch_size, 49, 256)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_reducesum1():
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"""
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Feature: Test ReduceSum and its backward. The input shape is dynamic.
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Description: axis=(), result of reduce sum is a scalar, gradient shape is the same as input, value is all one.
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Expectation: Assert gradient shape.
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"""
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batch_size = 16
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data_list = []
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for i in range(48, 50):
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data_list.append((np.random.rand(batch_size, i, i + 2).astype(np.float32),
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np.array(1).astype(np.float32)))
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dataset = ds.GeneratorDataset(data_list, ["data1", "data2"])
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dataset.set_dynamic_columns(columns={"data1": [batch_size, None, None], "data2": []})
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net = GradNetWrtX(ReduceSumNet())
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gradients = dynamic_shape_sink_process(net, dataset)
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assert gradients[0][0].shape == (batch_size, 49, 51)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_reducesum2():
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"""
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Feature: Test ReduceSum and its backward. The input shape is dynamic.
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Description: axis is a scalar, not tuple.
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Expectation: Assert that results are consistent with fixed shape.
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"""
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batch_size = 16
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data_list = []
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for i in range(48, 50):
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data_list.append((np.random.rand(batch_size, i, i + 2).astype(np.float32),
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np.random.rand(batch_size, i + 2).astype(np.float32)))
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dataset = ds.GeneratorDataset(data_list, ["data1", "data2"])
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dataset.set_dynamic_columns(columns={"data1": [batch_size, None, None], "data2": [batch_size, None]})
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net = GradNetWrtX(ReduceSumNet(1))
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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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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_add1():
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"""
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Feature: Test Add and its backward. The input shape is dynamic.
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Description: Second input is a scalar. Shape of forward result is the same as first input.
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Expectation: Assert that results are consistent with fixed shape.
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"""
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batch_size = 16
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data_list = []
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for i in range(48, 50):
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data_list.append((np.random.rand(batch_size, i).astype(np.float32),
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np.array(1).astype(np.float32),
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np.random.rand(batch_size, i).astype(np.float32)))
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dataset = ds.GeneratorDataset(data_list, ["data1", "data2", "data3"])
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dataset.set_dynamic_columns(columns={"data1": [batch_size, None], "data2": [], "data3": [batch_size, None]})
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net = GradNetWrtX(AddNet())
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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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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_add2():
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"""
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Feature: Test Add and its backward. The input shape is dynamic.
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Description: Shape of forward result is the same as first input. The axis of reduce_sum in add's bprop will be a
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empty Tensor.
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Expectation: Assert that results are consistent with fixed shape.
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"""
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batch_size = 16
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data_list = []
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for i in range(48, 50):
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data_list.append((np.random.rand(batch_size, 2, i).astype(np.float32),
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np.random.rand(2, i).astype(np.float32),
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np.random.rand(batch_size, 2, i).astype(np.float32)))
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dataset = ds.GeneratorDataset(data_list, ["data1", "data2", "data3"])
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dataset.set_dynamic_columns(columns=
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{"data1": [batch_size, 2, None], "data2": [2, None], "data3": [batch_size, 2, None]})
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net = GradNetWrtX(AddNet())
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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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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_softmax():
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"""
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Feature: Test Softmax and its backward. The input shape is dynamic.
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Description: The input shape is dynamic.
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Expectation: Assert that results are consistent with fixed shape.
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"""
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batch_size = 16
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dynamic_range = range(48, 50)
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data_type = np.float32
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input_shape = [(batch_size, 2, None), (batch_size, 2, None)]
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net = SoftmaxNet()
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common_func(dynamic_range, input_shape, data_type, net)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_batchnorm():
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"""
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Feature: Test Batchnorm2D and its backward. The input shape is dynamic.
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Description: The input shape is dynamic.
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Expectation: Assert that results are consistent with fixed shape.
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"""
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batch_size = 1
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dynamic_range = range(2, 64)
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data_type = np.float32
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input_shape = [(batch_size, 256, None, 12), (batch_size, 256, None, 12)]
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net = BatchNormNet(256)
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common_func(dynamic_range, input_shape, data_type, net)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_square_sum_all():
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"""
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Feature: Test SquareSumAll. The input shape is dynamic.
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Description: The input shape is dynamic.
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Expectation: Assert that results are consistent with fixed shape.
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"""
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batch_size = 16
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data_list = []
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for i in range(1, 4):
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data_list.append((np.random.rand(batch_size, i).astype(np.float32),
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np.random.rand(batch_size, i).astype(np.float32)))
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dataset = ds.GeneratorDataset(data_list, ["data1", "data2"])
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dataset.set_dynamic_columns(columns={"data1": [batch_size, None], "data2": [batch_size, None]})
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net = SquareSumAllNet()
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out = dynamic_shape_sink_process(net, dataset)
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out_expect = fixed_shape_process(net, dataset)
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assert compare(out, out_expect)
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