Add st and ut for jvp vjp and grad

tests/st/gradient/test_vjp_pynative.py

@@ -0,0 +1,71 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""test jvp in pynative mode"""
+import numpy as np
+import pytest
+import mindspore.nn as nn
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.nn.grad import Vjp
+
+context.set_context(mode=context.PYNATIVE_MODE)
+
+
+class SingleInputNet(nn.Cell):
+    def construct(self, x):
+        return x**3
+
+
+class MultipleInputsOutputNet(nn.Cell):
+    def construct(self, x, y):
+        return 2*x, y**3
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_vjp_single_input_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputNet()
+    expect_primal = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
+    expect_grad = Tensor(np.array([[3, 12], [27, 48]]).astype(np.float32))
+    primal, grad = Vjp(net)(x, v)
+    assert np.allclose(primal.asnumpy(), expect_primal.asnumpy())
+    assert np.allclose(grad.asnumpy(), expect_grad.asnumpy())
+
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_vjp_multiple_inputs_default_v_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = MultipleInputsOutputNet()
+    expect_primal_0 = Tensor(np.array([[2, 4], [6, 8]]).astype(np.float32))
+    expect_primal_1 = Tensor(np.array([[1, 8], [27, 64]]).astype(np.float32))
+    expect_grad_0 = Tensor(np.array([[2, 2], [2, 2]]).astype(np.float32))
+    expect_grad_1 = Tensor(np.array([[3, 12], [27, 48]]).astype(np.float32))
+    primal, grad = Vjp(net)(x, y, (v, v))
+    assert isinstance(primal, tuple)
+    assert len(primal) == 2
+    assert np.allclose(primal[0].asnumpy(), expect_primal_0.asnumpy())
+    assert np.allclose(primal[1].asnumpy(), expect_primal_1.asnumpy())
+    assert isinstance(grad, tuple)
+    assert len(grad) == 2
+    assert np.allclose(grad[0].asnumpy(), expect_grad_0.asnumpy())
+    assert np.allclose(grad[1].asnumpy(), expect_grad_1.asnumpy())

tests/ut/python/nn/gradient/test_jvp_graph.py

@@ -0,0 +1,148 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""test jvp in graph mode"""
+
+import numpy as np
+import pytest
+import mindspore.nn as nn
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.nn.grad import Jvp
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+class SingleInputSingleOutputNet(nn.Cell):
+    def construct(self, x):
+        return x**3
+
+
+class SingleInputMultipleOutputNet(nn.Cell):
+    def construct(self, x):
+        return x**3, 2*x
+
+
+class MultipleInputSingleOutputNet(nn.Cell):
+    def construct(self, x, y):
+        return 2*x + 3*y
+
+
+class MultipleInputMultipleOutputNet(nn.Cell):
+    def construct(self, x, y):
+        return 2*x, y**3
+
+
+def test_jvp_single_input_single_output_default_v_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputSingleOutputNet()
+    Jvp(net)(x, v)
+
+
+def test_jvp_single_input_single_output_custom_v_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    net = SingleInputSingleOutputNet()
+    Jvp(net)(x, v)
+
+
+def test_jvp_single_input_multiple_outputs_default_v_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputMultipleOutputNet()
+    Jvp(net)(x, v)
+
+
+def test_jvp_single_input_multiple_outputs_custom_v_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    net = SingleInputMultipleOutputNet()
+    Jvp(net)(x, v)
+
+
+def test_jvp_multiple_inputs_single_output_default_v_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = MultipleInputSingleOutputNet()
+    Jvp(net)(x, y, (v, v))
+
+
+def test_jvp_multiple_inputs_single_output_custom_v_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v1 = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    v2 = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    net = MultipleInputSingleOutputNet()
+    Jvp(net)(x, y, (v1, v2))
+
+
+def test_jvp_multiple_inputs_multiple_outputs_default_v_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = MultipleInputMultipleOutputNet()
+    Jvp(net)(x, y, (v, v))
+
+
+def test_jvp_multiple_inputs_multiple_outputs_custom_v_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v1 = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    v2 = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    net = MultipleInputMultipleOutputNet()
+    Jvp(net)(x, y, (v1, v2))
+
+
+def test_jvp_wrong_input_v_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputSingleOutputNet()
+    with pytest.raises(TypeError):
+        Jvp(net)(x, (v, v))
+
+
+def test_jvp_wrong_input_v_2_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputSingleOutputNet()
+    with pytest.raises(TypeError):
+        Jvp(net)(x, (v,))
+
+
+def test_jvp_wrong_input_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputSingleOutputNet()
+    with pytest.raises(TypeError):
+        Jvp(net)(x, x, v)
+
+
+def test_jvp_wrong_input_2_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = MultipleInputSingleOutputNet()
+    with pytest.raises(TypeError):
+        Jvp(net)((x, y), (v, v))
+
+
+def test_jvp_wrong_input_3_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = MultipleInputSingleOutputNet()
+    with pytest.raises(TypeError):
+        Jvp(net)(x, y, v)

tests/ut/python/nn/gradient/test_jvp_pynative.py

@@ -0,0 +1,147 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""test jvp in pynative mode """
+
+import numpy as np
+import pytest
+import mindspore.nn as nn
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.nn.grad import Jvp
+
+context.set_context(mode=context.PYNATIVE_MODE)
+
+class SingleInputSingleOutputNet(nn.Cell):
+    def construct(self, x):
+        return x**3
+
+
+class SingleInputMultipleOutputNet(nn.Cell):
+    def construct(self, x):
+        return x**3, 2*x
+
+
+class MultipleInputSingleOutputNet(nn.Cell):
+    def construct(self, x, y):
+        return 2*x + 3*y
+
+
+class MultipleInputMultipleOutputNet(nn.Cell):
+    def construct(self, x, y):
+        return 2*x, y**3
+
+
+def test_jvp_single_input_single_output_default_v_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputSingleOutputNet()
+    Jvp(net)(x, v)
+
+
+def test_jvp_single_input_single_output_custom_v_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    net = SingleInputSingleOutputNet()
+    Jvp(net)(x, v)
+
+
+def test_jvp_single_input_multiple_outputs_default_v_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputMultipleOutputNet()
+    Jvp(net)(x, v)
+
+
+def test_jvp_single_input_multiple_outputs_custom_v_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    net = SingleInputMultipleOutputNet()
+    Jvp(net)(x, v)
+
+
+def test_jvp_multiple_inputs_multiple_outputs_default_v_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = MultipleInputMultipleOutputNet()
+    Jvp(net)(x, y, (v, v))
+
+
+def test_jvp_multiple_inputs_multiple_outputs_custom_v_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v1 = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    v2 = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    net = MultipleInputMultipleOutputNet()
+    Jvp(net)(x, y, (v1, v2))
+
+
+def test_jvp_multiple_inputs_single_output_default_v_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = MultipleInputSingleOutputNet()
+    Jvp(net)(x, y, (v, v))
+
+
+def test_jvp_multiple_inputs_single_output_custom_v_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v1 = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    v2 = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    net = MultipleInputSingleOutputNet()
+    Jvp(net)(x, y, (v1, v2))
+
+
+def test_jvp_wrong_input_v_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputSingleOutputNet()
+    with pytest.raises(TypeError):
+        Jvp(net)(x, (v, v))
+
+
+def test_jvp_wrong_input_v_2_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputSingleOutputNet()
+    with pytest.raises(TypeError):
+        Jvp(net)(x, (v,))
+
+
+def test_jvp_wrong_input_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputSingleOutputNet()
+    with pytest.raises(TypeError):
+        Jvp(net)(x, x, v)
+
+
+def test_jvp_wrong_input_2_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = MultipleInputSingleOutputNet()
+    with pytest.raises(TypeError):
+        Jvp(net)((x, y), (v, v))
+
+
+def test_jvp_wrong_input_3_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = MultipleInputSingleOutputNet()
+    with pytest.raises(TypeError):
+        Jvp(net)(x, y, v)

tests/ut/python/nn/gradient/test_vjp_graph.py

@@ -0,0 +1,82 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""test jvp in graph mode"""
+import numpy as np
+import pytest
+import mindspore.nn as nn
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.nn.grad import Vjp
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+class SingleInputNet(nn.Cell):
+    def construct(self, x):
+        return x**3
+
+
+class MultipleInputsOutputNet(nn.Cell):
+    def construct(self, x, y):
+        return 2*x, y**3
+
+
+def test_vjp_single_input_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputNet()
+    Vjp(net)(x, v)
+
+
+def test_vjp_multiple_inputs_default_v_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = MultipleInputsOutputNet()
+    Vjp(net)(x, y, (v, v))
+
+
+def test_vjp_wrong_input_v_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputNet()
+    with pytest.raises(TypeError):
+        Vjp(net)(x, (v, v))
+
+
+def test_vjp_wrong_input_v_2_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputNet()
+    with pytest.raises(TypeError):
+        Vjp(net)(x, (v,))
+
+
+def test_vjp_wrong_input_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputNet()
+    with pytest.raises(TypeError):
+        Vjp(net)(x, y, v)
+
+
+def test_vjp_wrong_input_2_graph():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = MultipleInputsOutputNet()
+    with pytest.raises(TypeError):
+        Vjp(net)((x, y), (v, v))

tests/ut/python/nn/gradient/test_vjp_pynative.py

@@ -0,0 +1,82 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""test jvp in pynative mode"""
+import numpy as np
+import pytest
+import mindspore.nn as nn
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.nn.grad import Vjp
+
+context.set_context(mode=context.PYNATIVE_MODE)
+
+
+class SingleInputNet(nn.Cell):
+    def construct(self, x):
+        return x**3
+
+
+class MultipleInputsOutputNet(nn.Cell):
+    def construct(self, x, y):
+        return 2*x, y**3
+
+
+def test_vjp_single_input_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputNet()
+    Vjp(net)(x, v)
+
+
+def test_vjp_multiple_inputs_default_v_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = MultipleInputsOutputNet()
+    Vjp(net)(x, y, (v, v))
+
+
+def test_vjp_wrong_input_v_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputNet()
+    with pytest.raises(TypeError):
+        Vjp(net)(x, (v, v))
+
+
+def test_vjp_wrong_input_v_2_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputNet()
+    with pytest.raises(TypeError):
+        Vjp(net)(x, (v,))
+
+
+def test_vjp_wrong_input_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = SingleInputNet()
+    with pytest.raises(TypeError):
+        Vjp(net)(x, y, v)
+
+
+def test_vjp_wrong_input_2_pynative():
+    x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
+    v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32))
+    net = MultipleInputsOutputNet()
+    with pytest.raises(TypeError):
+        Vjp(net)((x, y), (v, v))