forked from mindspore-Ecosystem/mindspore
248 lines
9.5 KiB
Python
248 lines
9.5 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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"""test function linearize in graph mode"""
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import numpy as np
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import pytest
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from mindspore import nn
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from mindspore import context
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from mindspore import Tensor
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from mindspore import jit
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from mindspore.ops.functional import linearize, jvp
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context.set_context(mode=context.GRAPH_MODE)
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class SingleInputSingleOutputNet(nn.Cell):
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def construct(self, x):
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return x**3
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class SingleInputMultipleOutputNet(nn.Cell):
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def construct(self, x):
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return x**3, 2 * x
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class MultipleInputSingleOutputNet(nn.Cell):
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def construct(self, x, y):
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return 2 * x + 3 * y
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class MultipleInputMultipleOutputNet(nn.Cell):
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def construct(self, x, y):
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return 2 * x, y**3
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_linearize_single_input_single_output_diverse_v_graph():
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"""
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Features: Function linearize
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Description: Test linearize with single input, single output and linearize v in graph mode.
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Expectation: No exception.
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"""
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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v_0 = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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v_1 = Tensor(np.array([[0, 1], [2, 3]]).astype(np.float32))
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net = SingleInputSingleOutputNet()
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expect_primal, expect_grad_0 = jvp(net, x, v_0)
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expect_primal, expect_grad_1 = jvp(net, x, v_1)
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primal, jvp_fn = linearize(net, x)
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grad_0 = jvp_fn(v_0)
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grad_1 = jvp_fn(v_1)
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assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
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assert np.allclose(grad_0.asnumpy(), expect_grad_0.asnumpy())
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assert np.allclose(grad_1.asnumpy(), expect_grad_1.asnumpy())
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_linearize_single_input_multiple_outputs_diverse_v_graph():
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"""
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Features: Function linearize
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Description: Test linearize with single input, multiple outputs and linearize v in graph mode.
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Expectation: No exception.
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"""
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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v_0 = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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v_1 = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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net = SingleInputMultipleOutputNet()
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expect_primal, expect_grad_0 = jvp(net, x, v_0)
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expect_primal, expect_grad_1 = jvp(net, x, v_1)
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primal, jvp_fn = linearize(net, x)
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grad_0 = jvp_fn(v_0)
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grad_1 = jvp_fn(v_1)
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assert isinstance(primal, tuple)
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assert len(primal) == 2
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assert np.allclose(primal[0].asnumpy(), expect_primal[0].asnumpy())
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assert np.allclose(primal[1].asnumpy(), expect_primal[1].asnumpy())
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assert isinstance(grad_0, tuple)
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assert len(grad_0) == 2
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assert np.allclose(grad_0[0].asnumpy(), expect_grad_0[0].asnumpy())
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assert np.allclose(grad_0[1].asnumpy(), expect_grad_0[1].asnumpy())
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assert isinstance(grad_1, tuple)
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assert len(grad_1) == 2
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assert np.allclose(grad_1[0].asnumpy(), expect_grad_1[0].asnumpy())
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assert np.allclose(grad_1[1].asnumpy(), expect_grad_1[1].asnumpy())
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_linearize_multiple_inputs_single_output_diverse_v_graph():
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"""
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Features: Function linearize
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Description: Test linearize with multiple inputs, single output and diverse v in graph mode.
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Expectation: No exception.
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"""
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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v_0 = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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v_1 = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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net = MultipleInputSingleOutputNet()
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expect_primal, expect_grad_0 = jvp(net, (x, y), (v_0, v_0))
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expect_primal, expect_grad_1 = jvp(net, (x, y), (v_0, v_1))
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primal, jvp_fn = linearize(net, (x, y))
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grad_0 = jvp_fn((v_0, v_0))
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grad_1 = jvp_fn((v_0, v_1))
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assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
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assert np.allclose(grad_0.asnumpy(), expect_grad_0.asnumpy())
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assert np.allclose(grad_1.asnumpy(), expect_grad_1.asnumpy())
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_linearize_multiple_inputs_multiple_outputs_diverse_v_graph():
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"""
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Features: Function linearize
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Description: Test linearize with multiple inputs, multiple outputs and diverse v in graph mode.
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Expectation: No exception.
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"""
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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v_0 = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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v_1 = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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net = MultipleInputMultipleOutputNet()
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expect_primal, expect_grad_0 = jvp(net, (x, y), (v_0, v_0))
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expect_primal, expect_grad_1 = jvp(net, (x, y), (v_0, v_1))
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primal, jvp_fn = linearize(net, (x, y))
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grad_0 = jvp_fn((v_0, v_0))
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grad_1 = jvp_fn((v_0, v_1))
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assert isinstance(primal, tuple)
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assert len(primal) == 2
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assert np.allclose(primal[0].asnumpy(), expect_primal[0].asnumpy())
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assert np.allclose(primal[1].asnumpy(), expect_primal[1].asnumpy())
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assert isinstance(grad_0, tuple)
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assert len(grad_0) == 2
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assert np.allclose(grad_0[0].asnumpy(), expect_grad_0[0].asnumpy())
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assert np.allclose(grad_0[1].asnumpy(), expect_grad_0[1].asnumpy())
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assert isinstance(grad_1, tuple)
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assert len(grad_1) == 2
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assert np.allclose(grad_1[0].asnumpy(), expect_grad_1[0].asnumpy())
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assert np.allclose(grad_1[1].asnumpy(), expect_grad_1[1].asnumpy())
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_linearize_input_function_single_input_single_output_diverse_v_graph():
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"""
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Features: Function linearize
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Description: Test linearize with function, single input, single output and default v in graph mode.
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Expectation: No exception.
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"""
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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v_0 = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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v_1 = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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def test_function(inputs):
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return inputs**3
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expect_primal, expect_grad_0 = jvp(test_function, x, v_0)
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expect_primal, expect_grad_1 = jvp(test_function, x, v_1)
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primal, jvp_fn = linearize(test_function, x)
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grad_0 = jvp_fn(v_0)
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grad_1 = jvp_fn(v_1)
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assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
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assert np.allclose(grad_0.asnumpy(), expect_grad_0.asnumpy())
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assert np.allclose(grad_1.asnumpy(), expect_grad_1.asnumpy())
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_linearize_jit_function_single_input_single_output_diverse_v_graph():
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"""
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Features: Function linearize
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Description: Test linearize with @jit decorated function, single input, single output and diverse v in graph mode.
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Expectation: No exception.
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"""
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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v_0 = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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v_1 = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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net = SingleInputSingleOutputNet()
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@jit
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def linearize_with_jit_function(inputs, v_0, v_1):
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output, jvp_fn = linearize(net, inputs)
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grad_0 = jvp_fn(v_0)
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grad_1 = jvp_fn(v_1)
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return output, grad_0, grad_1
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expect_primal, expect_grad_0 = jvp(net, x, v_0)
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expect_primal, expect_grad_1 = jvp(net, x, v_1)
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primal, grad_0, grad_1 = linearize_with_jit_function(x, v_0, v_1)
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assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
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assert np.allclose(grad_0.asnumpy(), expect_grad_0.asnumpy())
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assert np.allclose(grad_1.asnumpy(), expect_grad_1.asnumpy())
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_linearize_construct_single_input_single_output_diverse_v_graph():
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"""
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Features: Function linearize
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Description: Test linearize with construct, single input, single output and diverse v in graph mode.
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Expectation: No exception.
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"""
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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v_0 = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
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v_1 = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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net = SingleInputSingleOutputNet()
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class Net(nn.Cell):
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def __init__(self, network):
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super(Net, self).__init__()
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self.net = network
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def construct(self, inputs, v_0, v_1):
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output, jvp_fn = linearize(net, inputs)
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grad_0 = jvp_fn(v_0)
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grad_1 = jvp_fn(v_1)
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return output, grad_0, grad_1
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test_net = Net(SingleInputSingleOutputNet())
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expect_primal, expect_grad_0 = jvp(net, x, v_0)
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expect_primal, expect_grad_1 = jvp(net, x, v_1)
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primal, grad_0, grad_1 = test_net(x, v_0, v_1)
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assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
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assert np.allclose(grad_0.asnumpy(), expect_grad_0.asnumpy())
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assert np.allclose(grad_1.asnumpy(), expect_grad_1.asnumpy())
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