forked from mindspore-Ecosystem/mindspore
135 lines
4.6 KiB
Python
135 lines
4.6 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 pytest
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import numpy as np
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import mindspore.nn as nn
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from mindspore.ops import operations as P
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from mindspore import Tensor
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from mindspore import context
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from mindspore.ops.functional import vmap
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from mindspore.common import dtype as ms_type
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class SeluOpNet(nn.Cell):
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def __init__(self):
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super(SeluOpNet, self).__init__()
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self.selu = P.SeLU()
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def construct(self, input_x):
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output = self.selu(input_x)
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return output
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class SeluVMapNet(nn.Cell):
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def __init__(self, forward_net, in_axes, out_axes):
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super(SeluVMapNet, self).__init__()
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self.net = forward_net
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self.in_axes = in_axes
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self.out_axes = out_axes
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def construct(self, input_x):
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return vmap(self.net, self.in_axes, self.out_axes)(input_x)
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def selu_op_np_bencmark(input_x):
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"""
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Feature: generate a selu numpy benchmark.
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Description: The input shape need to match to output shape.
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Expectation: match to np mindspore SeLU.
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"""
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alpha = 1.67326324
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scale = 1.05070098
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alpha_dot_scale = scale * alpha
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result = np.zeros_like(input_x, dtype=input_x.dtype)
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for index, _ in np.ndenumerate(input_x):
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if input_x[index] >= 0.0:
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result[index] = scale * input_x[index]
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else:
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result[index] = alpha_dot_scale * np.expm1(input_x[index])
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return result
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.parametrize("data_type", [np.int8, np.int32, np.float32, np.float16])
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@pytest.mark.parametrize("data_shape", [(4,), (3, 4), (4, 5, 7)])
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def test_selu_op(data_type, data_shape):
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"""
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Feature: Test Selu.
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Description: The input shape need to match to output shape.
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Expectation: match to np benchmark.
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"""
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error = 1e-6
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if data_type == np.float16:
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error = 1e-3
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input_data = np.random.random(data_shape).astype(data_type)
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benchmark_output = selu_op_np_bencmark(input_data)
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context.set_context(mode=context.GRAPH_MODE)
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selu = SeluOpNet()
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output = selu(Tensor(input_data))
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np.testing.assert_allclose(output.asnumpy(), benchmark_output, rtol=error)
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context.set_context(mode=context.PYNATIVE_MODE)
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output = selu(Tensor(input_data))
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np.testing.assert_allclose(output.asnumpy(), benchmark_output, rtol=error)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_gpu_training
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def test_selu_vmap_gpu():
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"""
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Feature: test SeLU vmap on CPU.
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Description: inputs(input_x) with batch.
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Expectation: the result match with expect
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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data_type = np.float32
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data_shape = (10, 5, 7)
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input_data = np.random.random(data_shape).astype(data_type)
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in_axes = 0
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out_axes = 0
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loss = 1e-6
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benchmark_output = selu_op_np_bencmark(input_data)
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selu = SeluOpNet()
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output = SeluVMapNet(selu, in_axes, out_axes)(Tensor(input_data))
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assert np.allclose(output.asnumpy(), benchmark_output, rtol=loss, atol=loss)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_gpu_training
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def test_selu_dy_shape():
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"""
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Feature: Test SeLU DynamicShape.
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Description: The input data type only float16 and float32.
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Expectation: match to np benchmark.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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data_type = np.float32
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ms_data_type = ms_type.float32
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data_shape = (10, 5, 7)
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input_x_np = np.random.random(data_shape).astype(data_type)
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loss = 1e-6
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benchmark_output = selu_op_np_bencmark(input_x_np)
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selu_net = SeluOpNet()
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input_dyn = Tensor(shape=[10, 5, None], dtype=ms_data_type)
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selu_net.set_inputs(input_dyn)
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ms_result = selu_net(Tensor(input_x_np))
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np.testing.assert_allclose(benchmark_output, ms_result.asnumpy(), rtol=loss, atol=loss)
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context.set_context(mode=context.PYNATIVE_MODE)
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ms_result = selu_net(Tensor(input_x_np))
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np.testing.assert_allclose(benchmark_output, ms_result.asnumpy(), rtol=loss, atol=loss)
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