forked from mindspore-Ecosystem/mindspore
135 lines
4.8 KiB
Python
135 lines
4.8 KiB
Python
# Copyright 2023 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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import mindspore.ops.operations._rl_inner_ops as rl_ops
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import mindspore.ops.operations._grad_ops as grad_ops
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from mindspore import context, Tensor
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from mindspore.common.parameter import ParameterTuple
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore.ops import composite as c
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize("mode", [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_gru_grad(mode):
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"""
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Feature: test gru_grad cpu operation.
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Description: test gru_grad cpu operation.
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Expectation: no exception.
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"""
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input_size = 10
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hidden_size = 2
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num_layers = 1
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max_seq_len = 5
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batch_size = 2
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context.set_context(mode=mode)
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net = rl_ops.GRUV2(input_size, hidden_size, num_layers, True, False, 0.0)
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input_tensor = Tensor(
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np.ones([max_seq_len, batch_size, input_size]).astype(np.float32))
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h0 = Tensor(
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np.ones([num_layers, batch_size, hidden_size]).astype(np.float32))
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w = Tensor(np.ones([84, 1, 1]).astype(np.float32))
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seq_lengths = Tensor(np.array([4, 3]).astype(np.int32))
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output, hn, out1, _ = net(input_tensor, h0, w, seq_lengths)
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grad_net = grad_ops.GRUV2Grad(
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input_size, hidden_size, num_layers, True, False, 0.0)
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dx, dh, dw = grad_net(input_tensor, h0, w, seq_lengths,
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output, hn, output, hn, out1)
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print("dx:", dx)
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print("dh:", dh)
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print("dw:", dw)
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class GradOfAllInputsAndParams(nn.Cell):
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def __init__(self, network, sens_param):
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super().__init__()
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self.grad = c.GradOperation(
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get_all=True, get_by_list=True, sens_param=sens_param)
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self.network = network
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self.params = ParameterTuple(self.network.trainable_params())
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def construct(self, *inputs):
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gout = self.grad(self.network, self.params)(*inputs)
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return gout
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class NetGruV2(nn.Cell):
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def __init__(self, input_size, hidden_size, num_layers, has_bias, weights, is_train):
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super(NetGruV2, self).__init__()
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self.gruv2 = rl_ops.GRUV2(
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input_size, hidden_size, num_layers, has_bias, False, 0.0, is_train)
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self.weights = weights
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def construct(self, x, h_0, seq_len):
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return self.gruv2(
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x, h_0, self.weights.astype(x.dtype), seq_len)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize("has_bias", [True, False])
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@pytest.mark.parametrize("is_train", [True, False])
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def test_gru_backward(has_bias, is_train):
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"""
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Feature: test GRUV2 backward.
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Description: test gru_grad cpu operation.
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Expectation: no exception.
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"""
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batch_size = 3
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max_seq_length = 5
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input_size = 10
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hidden_size = 3
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num_layers = 1
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num_directions = 1
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seq_lengths = Tensor([5, 3, 2], ms.int32)
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dtype = ms.float32
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x = Tensor(np.random.normal(
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0.0, 1.0, (max_seq_length, batch_size, input_size)), dtype)
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h0 = Tensor(np.random.normal(
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0.0, 1.0, (num_layers * num_directions, batch_size, hidden_size)), dtype)
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weight_size = 135 if has_bias else 117
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weights = Tensor(np.ones([weight_size, 1, 1]).astype(np.float32))
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# graph mode
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context.set_context(mode=context.GRAPH_MODE)
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gru_v2_net = NetGruV2(input_size, hidden_size,
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num_layers, has_bias, weights, is_train)
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grad_net_inp = GradOfAllInputsAndParams(gru_v2_net, sens_param=False)
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grad_net_inp.set_train()
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out_grad, _ = grad_net_inp(x, h0, seq_lengths)
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# pynative mode
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context.set_context(mode=context.PYNATIVE_MODE)
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pynative_gru_v2_net = NetGruV2(input_size, hidden_size,
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num_layers, has_bias, weights, is_train)
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pynative_grad_net_inp = GradOfAllInputsAndParams(
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pynative_gru_v2_net, sens_param=False)
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pynative_grad_net_inp.set_train()
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py_native_out_grad, _ = pynative_grad_net_inp(x, h0, seq_lengths)
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assert np.allclose(out_grad[0].asnumpy(),
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py_native_out_grad[0].asnumpy(), 0.001, 0.001)
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assert np.allclose(out_grad[1].asnumpy(),
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py_native_out_grad[1].asnumpy(), 0.001, 0.001)
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