654 lines
21 KiB
Python
654 lines
21 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, Tensor
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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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import mindspore as ms
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from mindspore.common.initializer import One
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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 np_type_to_ms(data_type):
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if data_type == np.float32:
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return ms.float32
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if data_type == np.float64:
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return ms.float64
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if data_type == np.int32:
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return ms.int32
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if data_type == np.int64:
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return ms.int64
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raise ValueError("Unsupportted datatype: {}".format(data_type))
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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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def comm_func(dyn_range, input_shp, data_type, op_net, num=None, output_compare_idx=None):
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list_data = []
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for i in dyn_range:
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tmp_data = []
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for data_shp in input_shp:
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if num is None:
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cur_shp = [dim if dim is not None else i for dim in data_shp]
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else:
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cur_shp = []
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k = 0
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for dim in data_shp:
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if dim is not None:
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cur_shp.append(dim)
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elif k == 1:
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cur_shp.append(num)
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else:
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cur_shp.append(i)
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k = k + 1
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tmp_data.append(np.random.random(cur_shp).astype(data_type))
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list_data.append(tuple(tmp_data))
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data_map = {}
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dyn_tensors = []
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for i, val in enumerate(input_shp):
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data_map["data" + str(i + 1)] = val
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if None in val:
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dyn_tensors.append(Tensor(dtype=np_type_to_ms(data_type), shape=val))
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else:
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dyn_tensors.append(Tensor(dtype=np_type_to_ms(data_type), shape=val, init=One()))
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dataset = ds.GeneratorDataset(list_data, list(data_map.keys()))
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op_net.set_inputs(*dyn_tensors)
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gradient = dynamic_shape_sink_process(op_net, dataset)
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gradient_cmp = fixed_shape_process(op_net, dataset)
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if output_compare_idx is None:
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assert compare(gradient, gradient_cmp)
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else:
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assert compare(gradient[output_compare_idx], gradient_cmp[output_compare_idx])
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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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dyn_tensors = []
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for val in dynamic_columns.values():
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dyn_tensors.append(Tensor(dtype=ms.float32, shape=val))
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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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net.set_inputs(*dyn_tensors)
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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.level1
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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.level1
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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.level1
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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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t0 = Tensor(dtype=ms.float32, shape=[None, c])
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t1 = Tensor(dtype=ms.float32, shape=[None, c])
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net = GradNetWrtX(BatchNormNet(c))
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net.set_inputs(t0, t1)
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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.level1
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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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net = ReshapeNet()
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t0 = Tensor(dtype=ms.float32, shape=[None, 64, 1])
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t1 = Tensor(dtype=ms.float32, shape=[None, 64])
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net.set_inputs(t0, t1)
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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.level1
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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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net = ReduceSumInputAxisNet()
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t0 = Tensor(dtype=ms.float32, shape=[None, 256])
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t1 = Tensor(dtype=ms.int64, shape=[1], init=One())
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net.set_inputs(t0, t1)
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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.level1
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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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net = NopNet()
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t0 = Tensor(dtype=ms.float32, shape=[None, 1])
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net.set_inputs(t0)
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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.level1
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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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net = GradNetWrtX(ReduceSumNet())
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t0 = Tensor(dtype=ms.float32, shape=[None, 256])
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t1 = Tensor(dtype=ms.float32, shape=[], init=One())
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net.set_inputs(t0, t1)
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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.level1
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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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net = GradNetWrtX(AddNet())
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t0 = Tensor(dtype=ms.float32, shape=[None, 256])
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t1 = Tensor(dtype=ms.float32, shape=[None, 256])
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t2 = Tensor(dtype=ms.float32, shape=[None, 256])
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net.set_inputs(t0, t1, t2)
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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 BatchNorm(nn.Cell):
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def __init__(self):
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super(BatchNorm, self).__init__()
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self.batch_norm = ops.BatchNorm()
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def construct(self, input_x, scale, bias, mean, variance):
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out = self.batch_norm(input_x, scale, bias, mean, variance)
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return out
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class MaxPool(nn.Cell):
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def __init__(self):
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super(MaxPool, self).__init__()
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self.maxpool = ops.MaxPool(pad_mode="VALID", kernel_size=2, strides=1)
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def construct(self, x):
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out = self.maxpool(x)
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return out
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class SigmoidCrossEntropyWithLogits(nn.Cell):
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def __init__(self):
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super(SigmoidCrossEntropyWithLogits, self).__init__()
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self.op = ops.SigmoidCrossEntropyWithLogits()
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def construct(self, x, y):
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out = self.op(x, y)
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return out
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class Sigmoid(nn.Cell):
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def __init__(self):
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super(Sigmoid, self).__init__()
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self.op = ops.Sigmoid()
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def construct(self, x):
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out = self.op(x)
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return out
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class ResizeNearestNeighbor(nn.Cell):
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def __init__(self):
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super(ResizeNearestNeighbor, self).__init__()
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self.op = ops.ResizeNearestNeighbor((2, 2))
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def construct(self, x):
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out = self.op(x)
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return out
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@pytest.mark.level1
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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_batchnorm():
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"""
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Feature: Test Dynamic batchnorm 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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dynamic_range = range(2, 64)
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data_type = np.float32
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input_shape = [(None, 64), (64,), (64,), (64,), (64,)]
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net = BatchNorm()
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comm_func(dynamic_range, input_shape, data_type, net, output_compare_idx=0)
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@pytest.mark.level1
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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_batchnorm2():
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"""
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Feature: Test Dynamic batchnorm 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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dynamic_range = range(2, 64)
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data_type = np.float32
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input_shape = [(64, None), (None,), (None,), (None,), (None,)]
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net = BatchNorm()
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comm_func(dynamic_range, input_shape, data_type, net, output_compare_idx=0)
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@pytest.mark.level1
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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_maxpool1():
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"""
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Feature: Test Dynamic maxpool 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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dynamic_range = range(2, 64)
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data_type = np.float32
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input_shape = [(32, 16, 32, None)]
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net = MaxPool()
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comm_func(dynamic_range, input_shape, data_type, net)
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@pytest.mark.level1
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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_maxpool2():
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"""
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Feature: Test Dynamic maxpool 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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dynamic_range = range(2, 64)
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data_type = np.float32
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input_shape = [(32, 16, None, 8)]
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net = MaxPool()
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comm_func(dynamic_range, input_shape, data_type, net)
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@pytest.mark.level1
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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_maxpool3():
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"""
|
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Feature: Test Dynamic maxpool 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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dynamic_range = range(2, 64)
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data_type = np.float32
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input_shape = [(32, None, 32, 8)]
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net = MaxPool()
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comm_func(dynamic_range, input_shape, data_type, net)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
|
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@pytest.mark.env_onecard
|
|
def test_dynamic_maxpool4():
|
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"""
|
|
Feature: Test Dynamic maxpool and its backward. The input shape is dynamic.
|
|
Description: The input shape is dynamic.
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Expectation: Assert that results are consistent with fixed shape.
|
|
"""
|
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dynamic_range = range(2, 64)
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data_type = np.float32
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input_shape = [(None, 16, 32, 8)]
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net = MaxPool()
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comm_func(dynamic_range, input_shape, data_type, net)
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@pytest.mark.level1
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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_sigmoid_cross_entropy_with_logits():
|
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"""
|
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Feature: Test Dynamic SigmoidCrossEntropyWithLogits and its backward. The input shape is dynamic.
|
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Description: The input shape is dynamic.
|
|
Expectation: Assert that results are consistent with fixed shape.
|
|
"""
|
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dynamic_range = range(2, 64)
|
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data_type = np.float32
|
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input_shape = [(None, 16, 32, 8), (None, 16, 32, 8)]
|
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net = SigmoidCrossEntropyWithLogits()
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comm_func(dynamic_range, input_shape, data_type, net)
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|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_dynamic_sigmoid_cross_entropy_with_logits_grad():
|
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"""
|
|
Feature: Test Dynamic SigmoidCrossEntropyWithLogitsGrad and its backward. The input shape is dynamic.
|
|
Description: The input shape is dynamic.
|
|
Expectation: Assert that results are consistent with fixed shape.
|
|
"""
|
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dynamic_range = range(2, 64)
|
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data_type = np.float32
|
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input_shape = [(4, 16, None, 8), (4, 16, None, 8), (4, 16, None, 8)]
|
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net = GradNetWrtX(SigmoidCrossEntropyWithLogits())
|
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comm_func(dynamic_range, input_shape, data_type, net)
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|
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@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
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@pytest.mark.env_onecard
|
|
def test_dynamic_sigmoid_grad():
|
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"""
|
|
Feature: Test Dynamic SigmoidGrad and its backward. The input shape is dynamic.
|
|
Description: The input shape is dynamic.
|
|
Expectation: Assert that results are consistent with fixed shape.
|
|
"""
|
|
dynamic_range = range(2, 64)
|
|
data_type = np.float32
|
|
input_shape = [(4, 16, None, 8), (4, 16, None, 8)]
|
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net = GradNetWrtX(Sigmoid())
|
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comm_func(dynamic_range, input_shape, data_type, net)
|
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|
|
|
|
@pytest.mark.level1
|
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@pytest.mark.platform_x86_gpu_training
|
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@pytest.mark.env_onecard
|
|
def test_dynamic_resize_nearest_neighbor():
|
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"""
|
|
Feature: Test Dynamic ResizeNearestNeighbor and its backward. The input shape is dynamic.
|
|
Description: The input shape is dynamic.
|
|
Expectation: Assert that results are consistent with fixed shape.
|
|
"""
|
|
dynamic_range = range(2, 64)
|
|
data_type = np.float32
|
|
input_shape = [(4, 16, None, 8)]
|
|
net = ResizeNearestNeighbor()
|
|
comm_func(dynamic_range, input_shape, data_type, net)
|
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|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_dynamic_resize_nearest_neighbor_grad():
|
|
"""
|
|
Feature: Test Dynamic ResizeNearestNeighborGrad and its backward. The input shape is dynamic.
|
|
Description: The input shape is dynamic.
|
|
Expectation: Assert that results are consistent with fixed shape.
|
|
"""
|
|
dynamic_range = range(2, 64)
|
|
data_type = np.float32
|
|
input_shape = [(4, 16, None, 8), (4, 16, 2, 2)]
|
|
net = GradNetWrtX(ResizeNearestNeighbor())
|
|
comm_func(dynamic_range, input_shape, data_type, net)
|