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228 lines
7.1 KiB
228 lines
7.1 KiB
# 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 NetBatchDot(nn.Cell):
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def __init__(self, axes):
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super(NetBatchDot, self).__init__()
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self.axes = axes
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def construct(self, x, y):
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return C.batch_dot(x, y, self.axes)
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# Implementation with numpy in tensorflow
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def _reference_batch_dot(x, y, axes):
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if isinstance(axes, int):
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axes = [axes, axes]
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elif isinstance(axes, tuple):
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axes = list(axes)
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if axes is None:
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if y.ndim == 2:
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axes = [x.ndim - 1, y.ndim - 1]
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else:
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axes = [x.ndim - 1, y.ndim - 2]
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if axes[0] < 0:
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axes[0] += x.ndim
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if axes[1] < 0:
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axes[1] += y.ndim
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result = []
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axes = [axes[0] - 1, axes[1] - 1]
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for xi, yi in zip(x, y):
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result.append(np.tensordot(xi, yi, axes))
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result = np.array(result)
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if result.ndim == 1:
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result = np.expand_dims(result, -1)
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return result
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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_batch_dot_fp32():
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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np.random.seed(12876)
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# case 1
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shape_x1 = (3, 12, 5, 2, 3)
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shape_x2 = (3, 1, 7, 3, 2)
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axes = (-1, -2)
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x1 = np.ones(shape=shape_x1).astype(np.float32)
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x2 = np.ones(shape=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 = NetBatchDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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tf_result = _reference_batch_dot(x1, x2, axes)
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assert np.allclose(ms_result_np, tf_result)
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# case 2
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shape_x1 = (4, 3, 7, 5)
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shape_x2 = (4, 1, 7, 1)
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axes = 2
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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 = NetBatchDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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tf_result = _reference_batch_dot(x1, x2, axes)
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assert np.allclose(ms_result_np, tf_result)
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# case 3
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shape_x1 = (18, 3, 5, 7)
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shape_x2 = (18, 1, 3, 7)
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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 = NetBatchDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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tf_result = _reference_batch_dot(x1, x2, axes)
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assert np.allclose(ms_result_np, tf_result)
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# case 4
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shape_x1 = (2, 11, 3, 9)
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shape_x2 = (2, 7, 9, 3)
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axes = None
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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 = NetBatchDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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tf_result = _reference_batch_dot(x1, x2, axes)
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assert np.allclose(ms_result_np, tf_result)
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# case 5
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shape_x1 = (7, 5)
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shape_x2 = (7, 5)
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axes = None
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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 = NetBatchDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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tf_result = _reference_batch_dot(x1, x2, axes)
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assert np.allclose(ms_result_np, tf_result)
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# case 6
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shape_x1 = (7, 3, 5)
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shape_x2 = (7, 5)
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axes = None
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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 = NetBatchDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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tf_result = _reference_batch_dot(x1, x2, axes)
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assert np.allclose(ms_result_np, tf_result)
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# case 7
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shape_x1 = (7, 5)
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shape_x2 = (7, 5, 3)
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axes = None
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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 = NetBatchDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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tf_result = _reference_batch_dot(x1, x2, axes)
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assert np.allclose(ms_result_np, tf_result)
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# case 8
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shape_x1 = (39, 6)
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shape_x2 = (39, 6)
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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 = NetBatchDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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tf_result = _reference_batch_dot(x1, x2, axes)
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assert np.allclose(ms_result_np, tf_result)
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# case 9
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shape_x1 = (21, 2, 3)
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shape_x2 = (21, 3, 2)
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axes = (-1, -2)
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x1 = np.ones(shape=shape_x1).astype(np.float32)
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x2 = np.ones(shape=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 = NetBatchDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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tf_result = _reference_batch_dot(x1, x2, axes)
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assert np.allclose(ms_result_np, tf_result)
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# case 10
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shape_x1 = (4, 3, 2, 1, 7, 5)
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shape_x2 = (4, 5, 7, 1)
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axes = -2
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x1 = np.ones(shape=shape_x1).astype(np.float32)
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x2 = np.ones(shape=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 = NetBatchDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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tf_result = _reference_batch_dot(x1, x2, axes)
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assert np.allclose(ms_result_np, tf_result)
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# case 10
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shape_x1 = (4, 3, 2, 1, 7, 5)
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shape_x2 = (4, 5, 7, 1)
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axes = -2
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x1 = np.ones(shape=shape_x1).astype(np.float16)
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x2 = np.ones(shape=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 = NetBatchDot(axes)
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ms_result_np = network(x1_tensor, x2_tensor).asnumpy()
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tf_result = _reference_batch_dot(x1, x2, axes)
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assert np.allclose(ms_result_np, tf_result)
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