forked from mindspore-Ecosystem/mindspore
556 lines
19 KiB
Python
556 lines
19 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 context, nn
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from mindspore import Tensor, ParameterTuple
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from mindspore.ops.composite import GradOperation
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from mindspore.nn import Cell
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import mindspore.common.dtype as mstype
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class _Grad(Cell):
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def __init__(self, grad, network, wrt_params=False, real_inputs_count=None):
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super().__init__()
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self.network = network
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self.grad = grad
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self.sens_param = self.grad.sens_param
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self.wrt_params = wrt_params
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self.real_inputs_count = real_inputs_count
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if self.wrt_params:
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self.params = ParameterTuple(self.network.trainable_params())
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def construct(self, *inputs):
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if self.real_inputs_count is None or self.sens_param is False:
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if self.wrt_params:
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return self.grad(self.network, self.params)(*inputs)
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return self.grad(self.network)(*inputs)
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real_inputs = inputs[:self.real_inputs_count]
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sense_param_inputs = inputs[self.real_inputs_count:]
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if self.wrt_params:
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return self.grad(self.network, self.params)(*real_inputs, sense_param_inputs)
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return self.grad(self.network)(*real_inputs, sense_param_inputs)
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class GradOfAllInputs(_Grad):
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"""
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get grad of all inputs
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"""
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def __init__(self, network, sens_param=True, real_inputs_count=None):
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super().__init__(grad=GradOperation(get_all=True, sens_param=sens_param),
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network=network, real_inputs_count=real_inputs_count)
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class CommonFunc():
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def __init__(self, ms_net, np_net):
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super(CommonFunc, self).__init__()
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self.ms_net = ms_net
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self.ms_net.set_grad()
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self.np_net = np_net
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input_dyn0 = Tensor(shape=[8, None, 3], dtype=mstype.float32)
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input_dyn1 = Tensor(shape=[None, 32, 3], dtype=mstype.float32)
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ms_net.set_inputs(input_dyn0, input_dyn1)
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self.input_np0 = np.arange(
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8 * 16 * 3).reshape(8, 16, 3).astype(np.float32)
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self.input_np1 = np.arange(
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16 * 32 * 3).reshape(16, 32, 3).astype(np.float32)
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self.input_np0_t = Tensor(self.input_np0)
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self.input_np1_t = Tensor(self.input_np1)
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self.out_np0 = np.array(1).astype(self.input_np0.dtype)
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self.out_np1 = np.array(1).astype(self.input_np1.dtype)
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def forward_cmp(self):
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out_ms0, out_ms1 = self.ms_net(
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self.input_np0_t, self.input_np1_t)
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self.out_np0, self.out_np1 = self. np_net(
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self.input_np0, self.input_np1)
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assert np.all(out_ms0.asnumpy() == self.out_np0)
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assert np.all(out_ms1.asnumpy() == self.out_np1)
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def grad_impl(self):
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grad_net = GradOfAllInputs(self.ms_net)
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grad_net.set_train()
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grad_net(self.input_np0_t, self.input_np1_t,
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(Tensor(self.out_np0), Tensor(self.out_np1)))
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class NumpySetItem():
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def __init__(self, index, value):
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super(NumpySetItem, self).__init__()
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self.index = index
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self.value = value
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def __call__(self, tensor1, tensor2):
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tensor1[self.index] = self.value
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tensor2[self.index] = self.value
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return tensor1, tensor2
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class TensorSetItem(nn.Cell):
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def __init__(self, index, value):
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super(TensorSetItem, self).__init__()
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self.index = index
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self.value = value
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def construct(self, tensor1, tensor2):
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tensor1[self.index] = self.value
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tensor2[self.index] = self.value
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return tensor1, tensor2
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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_setitem_int_number():
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"""
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Feature: Test index value assignment for dynamic shape Tensor in feed mode.
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Description: The input shape is dynamic, the tensor index is int, value is a number.
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Expectation: Assert the result is equal the numpy result.
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"""
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index = 2
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value = 88.0
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ms_net = TensorSetItem(index, value)
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np_net = NumpySetItem(index, value)
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context.set_context(mode=context.PYNATIVE_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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_setitem_int_tensor():
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"""
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Feature: Test index value assignment for dynamic shape Tensor in feed mode.
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Description: The input shape is dynamic, the tensor index is int, value is a tensor.
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Expectation: Assert the result is equal the numpy result.
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"""
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index = 2
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value = Tensor(np.arange(3).reshape(
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(1 * 3)).astype(np.float32), mstype.float32)
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ms_net = TensorSetItem(index, value)
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np_net = NumpySetItem(index, value.asnumpy())
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context.set_context(mode=context.PYNATIVE_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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_setitem_int_sequence():
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"""
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Feature: Test index value assignment for dynamic shape Tensor in feed mode.
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Description: The input shape is dynamic, the tensor index is int, value is a sequence.
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Expectation: Assert the result is equal the numpy result.
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"""
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index = 2
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value = (1.0, Tensor(5, mstype.float32), 8.0)
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ms_net = TensorSetItem(index, value)
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np_net = NumpySetItem(index, value)
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context.set_context(mode=context.PYNATIVE_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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_setitem_tensor_number():
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"""
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Feature: Test index value assignment for dynamic shape Tensor in feed mode.
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Description: The input shape is dynamic, the tensor index is tensor, value is a number.
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Expectation: Assert the result is equal the numpy result.
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"""
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index = Tensor(
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np.array([[2, 0, 2], [0, 2, 0], [0, 2, 0]], np.int32), mstype.int32)
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value = 88.0
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ms_net = TensorSetItem(index, value)
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np_net = NumpySetItem(index.asnumpy(), value)
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context.set_context(mode=context.PYNATIVE_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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_setitem_tensor_tensor():
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"""
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Feature: Test index value assignment for dynamic shape Tensor in feed mode.
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Description: The input shape is dynamic, the tensor index is tensor, value is a tensor.
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Expectation: Assert the result is equal the numpy result.
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"""
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index = Tensor(
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np.array([[2, 0, 2], [0, 2, 0], [0, 2, 0]], np.int32), mstype.int32)
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value = Tensor(np.arange(3).reshape(
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(1 * 3)).astype(np.float32), mstype.float32)
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ms_net = TensorSetItem(index, value)
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np_net = NumpySetItem(index.asnumpy(), value.asnumpy())
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context.set_context(mode=context.PYNATIVE_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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_setitem_tensor_sequence():
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"""
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Feature: Test index value assignment for dynamic shape Tensor in feed mode.
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Description: The input shape is dynamic, the tensor index is tensor, value is a sequence.
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Expectation: Assert the result is equal the numpy result.
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"""
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index = Tensor(
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np.array([[2, 0, 2], [0, 2, 0], [0, 2, 0]], np.int32), mstype.int32)
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value = (1.0, Tensor(5, mstype.float32), 8.0)
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ms_net = TensorSetItem(index, value)
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np_net = NumpySetItem(index.asnumpy(), value)
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context.set_context(mode=context.PYNATIVE_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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_setitem_none_number():
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"""
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Feature: Test index value assignment for dynamic shape Tensor in feed mode.
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Description: The input shape is dynamic, the tensor index is None, value is a number.
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Expectation: Assert the result is equal the numpy result.
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"""
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index = None
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value = 88.0
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ms_net = TensorSetItem(index, value)
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np_net = NumpySetItem(index, value)
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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_setitem_none_tensor():
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"""
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Feature: Test index value assignment for dynamic shape Tensor in feed mode.
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Description: The input shape is dynamic, the tensor index is None, value is a tensor.
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Expectation: Assert the result is equal the numpy result.
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"""
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index = None
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value = Tensor(np.arange(3).reshape(
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(1 * 3)).astype(np.float32), mstype.float32)
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ms_net = TensorSetItem(index, value)
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np_net = NumpySetItem(index, value.asnumpy())
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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_setitem_none_sequence():
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"""
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Feature: Test index value assignment for dynamic shape Tensor in feed mode.
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Description: The input shape is dynamic, the tensor index is None, value is a sequence.
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Expectation: Assert the result is equal the numpy result.
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"""
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index = None
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value = (1.0, Tensor(5, mstype.float32), 8.0)
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ms_net = TensorSetItem(index, value)
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np_net = NumpySetItem(index, value)
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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_setitem_ellipsis_number():
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"""
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Feature: Test index value assignment for dynamic shape Tensor in feed mode.
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Description: The input shape is dynamic, the tensor index is ..., value is a number.
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Expectation: Assert the result is equal the numpy result.
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"""
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index = ...
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value = 88.0
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ms_net = TensorSetItem(index, value)
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np_net = NumpySetItem(index, value)
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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_setitem_ellipsis_tensor():
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"""
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Feature: Test index value assignment for dynamic shape Tensor in feed mode.
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Description: The input shape is dynamic, the tensor index is ..., value is a tensor.
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Expectation: Assert the result is equal the numpy result.
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"""
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index = ...
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value = Tensor(np.arange(3).reshape(
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(1 * 3)).astype(np.float32), mstype.float32)
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ms_net = TensorSetItem(index, value)
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np_net = NumpySetItem(index, value.asnumpy())
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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_setitem_ellipsis_sequence():
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"""
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Feature: Test index value assignment for dynamic shape Tensor in feed mode.
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Description: The input shape is dynamic, the tensor index is ..., value is a sequence.
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Expectation: Assert the result is equal the numpy result.
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"""
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index = ...
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value = (1.0, Tensor(5, mstype.float32), 8.0)
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ms_net = TensorSetItem(index, value)
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np_net = NumpySetItem(index, value)
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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_setitem_bool_number():
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"""
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Feature: Test index value assignment for dynamic shape Tensor in feed mode.
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Description: The input shape is dynamic, the tensor index is bool(True), value is a number.
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Expectation: Assert the result is equal the numpy result.
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"""
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index = True
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value = 88.0
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ms_net = TensorSetItem(index, value)
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np_net = NumpySetItem(index, value)
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(ms_net, np_net)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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_setitem_bool_tensor():
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"""
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Feature: Test index value assignment for dynamic shape Tensor in feed mode.
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Description: The input shape is dynamic, the tensor index is bool(True), value is a tensor.
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Expectation: Assert the result is equal the numpy result.
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"""
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index = True
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value = Tensor(np.arange(3).reshape(
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(1 * 3)).astype(np.float32), mstype.float32)
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ms_net = TensorSetItem(index, value)
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np_net = NumpySetItem(index, value.asnumpy())
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context.set_context(mode=context.GRAPH_MODE)
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|
fact = CommonFunc(ms_net, np_net)
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|
fact.forward_cmp()
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|
fact.grad_impl()
|
|
|
|
|
|
@pytest.mark.level1
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|
@pytest.mark.platform_x86_cpu
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|
@pytest.mark.platform_x86_gpu_training
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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_setitem_bool_sequence():
|
|
"""
|
|
Feature: Test index value assignment for dynamic shape Tensor in feed mode.
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|
Description: The input shape is dynamic, the tensor index is bool(True), value is a sequence.
|
|
Expectation: Assert the result is equal the numpy result.
|
|
"""
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|
index = True
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|
value = (1.0, Tensor(5, mstype.float32), 8.0)
|
|
ms_net = TensorSetItem(index, value)
|
|
np_net = NumpySetItem(index, value)
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
fact = CommonFunc(ms_net, np_net)
|
|
fact.forward_cmp()
|
|
fact.grad_impl()
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_dynamic_setitem_list_number():
|
|
"""
|
|
Feature: Test index value assignment for dynamic shape Tensor in feed mode.
|
|
Description: The input shape is dynamic, the tensor index is a list of int, value is a number.
|
|
Expectation: Assert the result is equal the numpy result.
|
|
"""
|
|
index = [0, 1]
|
|
value = 88.0
|
|
ms_net = TensorSetItem(index, value)
|
|
np_net = NumpySetItem(index, value)
|
|
context.set_context(mode=context.PYNATIVE_MODE)
|
|
fact = CommonFunc(ms_net, np_net)
|
|
fact.forward_cmp()
|
|
fact.grad_impl()
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
fact = CommonFunc(ms_net, np_net)
|
|
fact.forward_cmp()
|
|
fact.grad_impl()
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_dynamic_setitem_list_tensor():
|
|
"""
|
|
Feature: Test index value assignment for dynamic shape Tensor in feed mode.
|
|
Description: The input shape is dynamic, the tensor index is al ist of bool and int, value is a tensor.
|
|
Expectation: Assert the result is equal the numpy result.
|
|
"""
|
|
index = [True, 5]
|
|
value = Tensor(np.arange(3).reshape(
|
|
(1 * 3)).astype(np.float32), mstype.float32)
|
|
ms_net = TensorSetItem(index, value)
|
|
np_net = NumpySetItem(index, value.asnumpy())
|
|
context.set_context(mode=context.PYNATIVE_MODE)
|
|
fact = CommonFunc(ms_net, np_net)
|
|
fact.forward_cmp()
|
|
fact.grad_impl()
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
fact = CommonFunc(ms_net, np_net)
|
|
fact.forward_cmp()
|
|
fact.grad_impl()
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_dynamic_setitem_list_sequence():
|
|
"""
|
|
Feature: Test index value assignment for dynamic shape Tensor in feed mode.
|
|
Description: The input shape is dynamic, the tensor index is a list of int, value is a sequence.
|
|
Expectation: Assert the result is equal the numpy result.
|
|
"""
|
|
index = [0, 1]
|
|
value = (1.0, Tensor(5, mstype.float32), 8.0)
|
|
ms_net = TensorSetItem(index, value)
|
|
np_net = NumpySetItem(index, value)
|
|
context.set_context(mode=context.PYNATIVE_MODE)
|
|
fact = CommonFunc(ms_net, np_net)
|
|
fact.forward_cmp()
|
|
fact.grad_impl()
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
fact = CommonFunc(ms_net, np_net)
|
|
fact.forward_cmp()
|
|
fact.grad_impl()
|