forked from mindspore-Ecosystem/mindspore
340 lines
15 KiB
Python
340 lines
15 KiB
Python
# Copyright 2020 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
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from mindspore import Tensor
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import mindspore.nn as nn
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import mindspore.context as context
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from mindspore.ops import composite as C
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class NetTensorDot(nn.Cell):
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def __init__(self, axes):
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super(NetTensorDot, self).__init__()
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self.axes = axes
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def construct(self, x, y):
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return C.tensor_dot(x, y, self.axes)
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class GradNetwork(nn.Cell):
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def __init__(self, network):
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super(GradNetwork, self).__init__()
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self.grad = C.GradOperation(get_all=True, sens_param=True)
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self.network = network
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def construct(self, input_data_a, input_data_b, sens):
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gout = self.grad(self.network)(input_data_a, input_data_b, sens)
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return gout
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tensor_dot_fp32():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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np.random.seed(12876)
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shape_x1 = (1, 3, 9, 7)
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shape_x2 = (9, 7, 3, 1)
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axes = ((1, 3), (2, 1))
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x1 = np.random.random(shape_x1).astype(np.float32)
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x2 = np.random.random(shape_x2).astype(np.float32)
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x1_tensor = Tensor(x1, dtype=mindspore.float32)
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x2_tensor = Tensor(x2, dtype=mindspore.float32)
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network = NetTensorDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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np_result = np.tensordot(x1, x2, axes)
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np.testing.assert_array_almost_equal(ms_result_np, np_result)
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# 1D
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shape_x1 = (200)
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shape_x2 = (200)
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axes = 1
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x1 = np.random.random(shape_x1).astype(np.float32)
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x2 = np.random.random(shape_x2).astype(np.float32)
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x1_tensor = Tensor(x1, dtype=mindspore.float32)
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x2_tensor = Tensor(x2, dtype=mindspore.float32)
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network = NetTensorDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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np_result = np.tensordot(x1, x2, axes)
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np.allclose(ms_result_np, np_result)
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# 2D
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shape_x1 = (100, 300)
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shape_x2 = (300, 700)
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axes = ([1], [0])
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x1 = np.random.random(shape_x1).astype(np.float32)
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x2 = np.random.random(shape_x2).astype(np.float32)
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x1_tensor = Tensor(x1, dtype=mindspore.float32)
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x2_tensor = Tensor(x2, dtype=mindspore.float32)
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network = NetTensorDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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np_result = np.tensordot(x1, x2, axes)
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np.allclose(ms_result_np, np_result)
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# 3D
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shape_x1 = (110, 30, 900)
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shape_x2 = (900, 70, 30)
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axes = ((1, 2), (2, 0))
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x1 = np.random.random(shape_x1).astype(np.float32)
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x2 = np.random.random(shape_x2).astype(np.float32)
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x1_tensor = Tensor(x1, dtype=mindspore.float32)
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x2_tensor = Tensor(x2, dtype=mindspore.float32)
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network = NetTensorDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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np_result = np.tensordot(x1, x2, axes)
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np.allclose(ms_result_np, np_result)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tensor_dot_fp16():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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np.random.seed(41329)
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shape_x1 = (1, 3, 4, 1)
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shape_x2 = (4, 1, 7, 5)
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axes = 2 # select first N from
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x1 = np.random.random(shape_x1).astype(np.float16)
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x2 = np.random.random(shape_x2).astype(np.float16)
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x1_tensor = Tensor(x1, dtype=mindspore.float16)
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x2_tensor = Tensor(x2, dtype=mindspore.float16)
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network = NetTensorDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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np_result = np.tensordot(x1, x2, axes)
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np.testing.assert_array_almost_equal(ms_result_np, np_result)
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# 1D
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shape_x1 = (300)
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shape_x2 = (300)
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axes = 1
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x1 = np.random.random(shape_x1).astype(np.float16)
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x2 = np.random.random(shape_x2).astype(np.float16)
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x1_tensor = Tensor(x1, dtype=mindspore.float16)
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x2_tensor = Tensor(x2, dtype=mindspore.float16)
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network = NetTensorDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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np_result = np.tensordot(x1, x2, axes)
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np.testing.assert_array_almost_equal(ms_result_np, np_result)
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# 2D
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shape_x1 = (100, 300)
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shape_x2 = (300, 100)
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axes = ([1], [0])
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x1 = np.random.random(shape_x1).astype(np.float16)
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x2 = np.random.random(shape_x2).astype(np.float16)
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x1_tensor = Tensor(x1, dtype=mindspore.float16)
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x2_tensor = Tensor(x2, dtype=mindspore.float16)
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network = NetTensorDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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np_result = np.tensordot(x1, x2, axes)
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np.testing.assert_array_almost_equal(ms_result_np, np_result)
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# 3D
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shape_x1 = (60, 30, 450)
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shape_x2 = (450, 90, 30)
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axes = ((1, 2), (2, 0))
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x1 = np.random.random(shape_x1).astype(np.float16)
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x2 = np.random.random(shape_x2).astype(np.float16)
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x1_tensor = Tensor(x1, dtype=mindspore.float16)
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x2_tensor = Tensor(x2, dtype=mindspore.float16)
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network = NetTensorDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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np_result = np.tensordot(x1, x2, axes)
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np.testing.assert_array_almost_equal(ms_result_np, np_result)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tensor_dot_outer():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(2746)
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shape_x1 = (1, 2, 3) # incompatable dims for x1 and x2
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shape_x2 = (4, 5, 6)
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axes = 0 # outer product does not require multiplicable dims
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x1 = np.random.random(shape_x1).astype(np.float32)
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x2 = np.random.random(shape_x2).astype(np.float32)
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x1_tensor = Tensor(x1, dtype=mindspore.float32)
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x2_tensor = Tensor(x2, dtype=mindspore.float32)
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network = NetTensorDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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np_result = np.tensordot(x1, x2, axes)
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np.testing.assert_array_almost_equal(ms_result_np, np_result)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tensor_dot_backprop():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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# TEST 1
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shape_x1 = (2, 4, 2)
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shape_x2 = (3, 2, 3)
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axes = ((0,), (1,)) # select first N from
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network = NetTensorDot(axes)
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np.random.seed(115)
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x1 = np.random.random(shape_x1).astype(np.float16)
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np.random.seed(1467)
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x2 = np.random.random(shape_x2).astype(np.float16)
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x1_tensor = Tensor(x1, dtype=mindspore.float16)
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x2_tensor = Tensor(x2, dtype=mindspore.float16)
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np.random.seed(157)
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grad = np.random.random((4, 2, 3, 3))
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grad_tensor = Tensor(grad, dtype=mindspore.float16)
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grad_network = GradNetwork(network)
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dx1, dx2 = grad_network(x1_tensor, x2_tensor, grad_tensor)
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dx1, dx2 = dx1.asnumpy(), dx2.asnumpy()
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# precomputed
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expect_dx1 = np.array([[[2.0293, 2.4473],
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[2.9727, 1.4873],
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[1.7910, 3.4727],
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[2.4160, 1.7227]],
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[[2.5547, 2.5039],
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[3.4062, 2.3320],
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[2.6270, 3.1543],
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[2.1406, 1.7666]]])
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expect_dx2 = np.array([[[2.1523, 2.9199, 0.8350],
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[2.0254, 2.7734, 1.3213]],
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[[2.6836, 2.4707, 1.0156],
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[2.9746, 3.0254, 1.9199]],
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[[1.8545, 1.7803, 1.3457],
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[2.2676, 2.1797, 1.2764]]])
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np.allclose(dx1, expect_dx1)
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np.allclose(dx2, expect_dx2)
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# TEST 2
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shape_x1 = (10, 35)
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shape_x2 = (20, 10)
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axes = ((0,), (1,)) # select first N from
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network = NetTensorDot(axes)
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np.random.seed(215)
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x1 = np.random.random(shape_x1).astype(np.float16)
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np.random.seed(2467)
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x2 = np.random.random(shape_x2).astype(np.float16)
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x1_tensor = Tensor(x1, dtype=mindspore.float16)
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x2_tensor = Tensor(x2, dtype=mindspore.float16)
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np.random.seed(257)
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grad = np.random.random((35, 20))
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grad_tensor = Tensor(grad, dtype=mindspore.float16)
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grad_network = GradNetwork(network)
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dx1, dx2 = grad_network(x1_tensor, x2_tensor, grad_tensor)
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dx1, dx2 = dx1.asnumpy(), dx2.asnumpy()
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# precomputed
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expect_dx1 = np.array([[5.9727, 4.6484, 5.1836, 4.3906, 5.1641, 5.1406, 5.1211, 6.5352, 4.9922,
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4.4297, 4.4648, 6.5469, 6.2305, 4.8789, 6.8320, 5.3906, 4.7383, 6.0352,
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4.7383, 4.4844, 5.3711, 6.2617, 4.6484, 5.8672, 4.7500, 6.0234, 3.6387,
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5.3789, 5.9727, 5.7227, 6.0234, 4.9609, 5.0117, 5.4141, 5.1406],
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[5.2305, 4.0078, 4.6328, 3.9238, 4.2773, 4.2539, 4.6797, 5.1289, 3.7910,
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3.8887, 3.2930, 5.5898, 5.4219, 3.6211, 5.5234, 3.5391, 4.8516, 4.7539,
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4.2500, 2.9785, 4.8867, 5.4648, 5.0195, 6.0195, 4.7109, 3.9727, 3.4922,
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4.1484, 4.7969, 5.3555, 4.9414, 5.2969, 3.1992, 5.2031, 4.4648],
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[5.2266, 5.2617, 5.3750, 4.7930, 4.9062, 5.4102, 4.9336, 6.9414, 4.4961,
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4.4023, 4.7344, 5.8125, 4.9180, 4.7891, 5.9805, 5.2383, 4.6445, 6.1172,
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4.8477, 3.7578, 4.3047, 5.7969, 4.5859, 6.0273, 4.3438, 4.7305, 4.0938,
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4.8398, 5.8320, 5.3438, 5.3281, 4.8320, 4.0938, 4.9375, 5.3281],
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[7.4297, 5.1484, 6.3477, 5.4844, 5.7852, 6.3906, 5.5234, 7.2383, 5.2969,
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4.9844, 4.5625, 7.3047, 7.3789, 6.4453, 8.2266, 6.6172, 5.5547, 7.0234,
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4.8594, 4.9531, 6.0469, 6.9258, 6.1055, 6.7539, 6.6953, 6.0430, 4.5117,
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5.7344, 7.4297, 6.4219, 6.8125, 6.4141, 5.2773, 6.8828, 6.0430],
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[5.7969, 4.7109, 5.8281, 4.5703, 5.5078, 6.4219, 4.8359, 7.1484, 4.2617,
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4.8477, 4.2539, 5.6016, 6.4414, 5.7305, 6.4766, 5.4648, 4.5859, 6.5547,
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5.5156, 3.3848, 5.1523, 5.5352, 4.9531, 6.5938, 5.2969, 4.6055, 5.2109,
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4.4961, 5.8984, 5.4531, 5.8086, 5.7930, 5.0742, 5.4102, 4.9453],
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[7.2188, 5.8789, 6.9453, 6.0039, 6.7188, 7.3359, 6.7695, 8.6172, 5.6680,
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6.4219, 6.1836, 7.7695, 7.5391, 6.5312, 8.2812, 7.5352, 5.8867, 7.7070,
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6.0039, 5.1172, 6.4844, 7.4297, 5.9219, 7.5078, 6.3125, 6.9805, 5.3750,
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5.9805, 7.2148, 7.6484, 7.8828, 6.7695, 5.7109, 6.8828, 6.9023],
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[5.7656, 4.3633, 4.5039, 4.4375, 4.3867, 5.4336, 4.3672, 5.5469, 3.5742,
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4.0508, 3.7402, 5.9141, 5.7734, 4.5781, 5.6719, 4.5625, 4.5391, 5.1719,
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4.3945, 3.4844, 4.9297, 5.7227, 4.8203, 5.8125, 4.8633, 4.3125, 3.6641,
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4.3789, 5.6133, 5.1758, 4.9141, 5.8008, 4.0391, 5.8984, 4.3594],
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[4.7734, 3.4238, 4.3477, 3.6270, 4.4883, 5.2031, 3.9023, 5.0078, 2.9355,
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3.8477, 3.4648, 5.1445, 4.8398, 4.4297, 5.1641, 4.2422, 4.2695, 4.6992,
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4.5039, 2.5176, 4.2500, 5.6680, 4.1875, 5.4141, 3.6094, 3.1758, 3.8398,
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3.9180, 5.3320, 4.6523, 3.9531, 4.8281, 3.9863, 4.8867, 4.3711],
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[6.7578, 5.3164, 6.0000, 4.4531, 5.8789, 6.3750, 5.1094, 7.0391, 4.5781,
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4.8633, 4.5156, 6.6641, 6.3594, 5.5664, 6.9453, 5.5820, 5.1992, 6.9570,
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5.3242, 3.8574, 5.1445, 6.0547, 5.0273, 6.9180, 5.1914, 4.6914, 4.6445,
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5.1289, 5.8711, 6.2070, 6.1953, 5.7695, 4.7617, 5.5898, 4.9492],
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[4.9180, 4.0117, 4.1211, 3.4629, 3.6445, 4.6602, 3.7031, 4.9062, 4.1133,
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3.0020, 3.2246, 4.6562, 4.4727, 3.3828, 5.2695, 4.0078, 3.2559, 4.9688,
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3.5742, 3.1133, 3.8223, 4.7578, 3.7949, 4.8438, 4.0664, 4.4336, 3.0957,
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4.4375, 4.2969, 4.1758, 4.5234, 4.2930, 3.9434, 4.8281, 3.0703]])
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expect_dx2 = np.array([[6.7930, 7.0000, 8.8203, 9.7031, 8.1250,
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6.7422, 8.4844, 8.7031, 7.2891, 10.1484],
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[8.5781, 8.1641, 9.9609, 9.2344, 9.3281,
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8.1484, 9.8984, 9.0391, 7.9805, 11.0469],
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[8.1016, 7.0781, 8.9688, 10.0938, 9.6641,
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7.1523, 8.2969, 8.8594, 8.3047, 10.2578],
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[7.0938, 7.3477, 9.3594, 8.2422, 7.9141,
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6.5156, 8.2812, 8.2266, 6.9766, 8.5703],
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[9.2891, 9.2500, 11.6875, 9.5234, 10.1172,
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8.8125, 9.5781, 9.5547, 8.9688, 11.2266],
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[9.3594, 7.7539, 9.2500, 9.2500, 8.1094,
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8.0859, 8.7344, 8.2031, 8.5859, 10.3203],
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[8.7344, 7.7227, 10.2578, 10.1641, 9.3984,
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8.1719, 8.0156, 8.6953, 8.6797, 10.6875],
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[8.8750, 7.9922, 10.2422, 10.3984, 9.5234,
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8.5156, 8.7266, 8.8125, 8.2578, 10.2578],
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[9.5703, 8.9844, 10.0547, 10.3047, 10.4062,
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8.2422, 10.7031, 9.7891, 9.2969, 11.0078],
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[9.2891, 9.5391, 10.5938, 10.5078, 9.8203,
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8.5156, 9.0859, 9.0703, 8.7812, 10.8750],
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[8.6094, 8.2734, 10.2734, 9.7891, 9.4531,
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7.5820, 8.4609, 8.6094, 7.7578, 10.3438],
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[8.2891, 8.7578, 9.3906, 9.6016, 9.4375,
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7.1016, 8.6875, 8.1875, 8.2188, 9.3672],
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[7.2969, 6.6953, 9.3984, 8.2422, 8.3438,
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7.5547, 7.6445, 7.5820, 7.5156, 9.0781],
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[8.3906, 7.3516, 8.5938, 9.2422, 8.7734,
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8.0781, 9.1250, 7.8359, 7.7891, 10.9375],
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[9.9219, 8.8281, 9.4141, 10.2500, 9.8047,
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8.5234, 8.5391, 8.4609, 8.5859, 11.2422],
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[6.8984, 6.4570, 8.0000, 6.4688, 7.4609,
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6.6016, 7.0352, 6.6797, 6.5586, 7.7070],
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[8.0625, 7.4805, 8.7578, 8.3281, 8.2188,
|
|
7.4023, 8.5312, 7.5312, 7.1445, 10.3750],
|
|
[7.7773, 6.6484, 9.1094, 8.0078, 7.8281,
|
|
7.1016, 8.2422, 8.1562, 6.8828, 10.3281],
|
|
[8.3281, 8.3672, 9.7656, 10.4922, 8.2500,
|
|
7.5625, 8.4922, 8.9844, 8.0703, 10.3438],
|
|
[7.5195, 7.0430, 7.9453, 8.4375, 7.6641,
|
|
6.9688, 7.7734, 8.7734, 6.3672, 9.4766]])
|
|
np.allclose(dx1, expect_dx1)
|
|
np.allclose(dx2, expect_dx2)
|