forked from mindspore-Ecosystem/mindspore
134 lines
4.3 KiB
Python
134 lines
4.3 KiB
Python
# Copyright 2021-2022 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 numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore as ms
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from mindspore import Tensor
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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from mindspore.ops.functional import vmap
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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class Net(nn.Cell):
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def __init__(self, sample, replacement, seed=0):
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super(Net, self).__init__()
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self.sample = sample
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self.replacement = replacement
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self.seed = seed
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def construct(self, x):
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return C.multinomial(x, self.sample, self.replacement, self.seed)
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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_multinomial_net():
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x0 = Tensor(np.array([0.9, 0.2]).astype(np.float32))
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x1 = Tensor(np.array([[0.9, 0.2], [0.9, 0.2]]).astype(np.float32))
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net0 = Net(1, True, 20)
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net1 = Net(2, True, 20)
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net2 = Net(6, True, 20)
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out0 = net0(x0)
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out1 = net1(x0)
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out2 = net2(x1)
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assert out0.asnumpy().shape == (1,)
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assert out1.asnumpy().shape == (2,)
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assert out2.asnumpy().shape == (2, 6)
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class DynamicShapeNet(nn.Cell):
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"""
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Inputs:
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- **x** (Tensor) - the input tensor containing the cumsum of probabilities, must be 1 or 2
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dimensions. Must be one of the following types: float16, float32, float64. CPU and GPU
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supports x 1 or 2 dimensions and Ascend only supports 2 dimensions.
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- **num_samples** (int) - number of samples to draw, must be a nonnegative number.
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"""
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def __init__(self):
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super(DynamicShapeNet, self).__init__()
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self.unique = P.Unique()
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self.gather = P.Gather()
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self.multinomial = P.Multinomial()
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def construct(self, x, indices):
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unique_indices, _ = self.unique(indices)
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x = self.gather(x, unique_indices, 0)
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return self.multinomial(x, 2)
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@pytest.mark.level2
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_multinomial_dynamic_shape():
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"""
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Feature: test Multinomial dynamic_shape feature.
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Description: test Multinomial dynamic_shape feature. Only support GRAPH_MODE.
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Expectation: success.
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"""
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# dynamic inputs
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indices_np = np.random.randint(0, 3, size=6)
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indices_ms = Tensor(indices_np)
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# data preparation
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x = Tensor(np.arange(20).reshape(4, 5).astype(np.float32) / 10)
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# dynamic shape
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x_dyn = Tensor(shape=[None for _ in x.shape], dtype=x.dtype)
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dynamic_shape_net = DynamicShapeNet()
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dynamic_shape_net.set_inputs(x_dyn, indices_ms)
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# run in graph mode
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outputs = dynamic_shape_net(x, indices_ms)
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expect_shape = (len(np.unique(indices_np)), 2)
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assert outputs.asnumpy().shape == expect_shape
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class BatchedMultinomial(nn.Cell):
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def __init__(self):
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super().__init__()
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self.multinomial = P.Multinomial(seed=5, seed2=6)
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def construct(self, prob, num_sample):
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return self.multinomial(prob, num_sample)
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def multinomial(prob, num_sample):
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return P.Multinomial(seed=5, seed2=6)(prob, num_sample)
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@pytest.mark.level2
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_multinomial_vmap():
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"""
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Feature: test Multinomial vmap feature.
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Description: test Multinomial vmap feature.
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Expectation: success.
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"""
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prob = Tensor([[0.5, 0.5], [0.5, 0.5], [0.5, 0.5]], ms.float32)
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num_sample = 3
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batched_multinomial = BatchedMultinomial()
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batched_out = batched_multinomial(prob, num_sample)
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vmap_out = vmap(multinomial, in_axes=(0, None), out_axes=0)(prob, num_sample)
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assert (batched_out.asnumpy() == vmap_out.asnumpy()).all()
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