forked from mindspore-Ecosystem/mindspore
191 lines
7.4 KiB
Python
191 lines
7.4 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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import numpy as np
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import pytest
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import mindspore.nn as nn
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import mindspore.ops.operations as P
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from mindspore import Tensor, context
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from mindspore.ops.functional import vmap
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from mindspore.ops.operations.math_ops import Lerp
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class LerpNet(nn.Cell):
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def __init__(self):
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super(LerpNet, self).__init__()
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self.lerp = Lerp()
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def construct(self, start, end, weight):
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output = self.lerp(start, end, weight)
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return output
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class DynamicShapeNet(nn.Cell):
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def __init__(self, axis=0):
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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.lerp = LerpNet()
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self.axis = axis
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def construct(self, start, end, weight, indices):
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unique_indices, _ = self.unique(indices)
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real_start = self.gather(start, unique_indices, self.axis)
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real_end = self.gather(end, unique_indices, self.axis)
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return real_start, real_end, self.lerp(real_start, real_end, weight)
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class LerpVMapNet(nn.Cell):
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def __init__(self, forward_net, in_axes, out_axes):
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super(LerpVMapNet, self).__init__()
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self.net = forward_net
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self.in_axes = in_axes
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self.out_axes = out_axes
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def construct(self, start, end, weight):
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return vmap(self.net, self.in_axes, self.out_axes)(start, end, weight)
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def lerp_vmap_case():
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"""
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Feature: test lerp vamp feature.
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Description: test special case.
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Expectation: match to mindspore.ops.Lerp.
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"""
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# Case 1: in_axes start batch remains 0, other remains None.
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start = Tensor(np.array([[1., 2., 3., 4.], [1., 3., 5., 7.]]).astype(np.float32))
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end = Tensor(np.array([10., 10., 10., 10.]).astype(np.float32))
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weight = 0.5
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benchmark_output = np.array([[5.5, 6., 6.5, 7.], [5.5, 6.5, 7.5, 8.5]]).astype(np.float32)
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in_axes = (0, None, None)
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out_axes = 0
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output = LerpVMapNet(LerpNet(), in_axes, out_axes)(start, end, weight)
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assert np.allclose(output.asnumpy(), benchmark_output)
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# Case 2: start remains 0 batch, end remains 1 batch, weight remains None.
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start = Tensor(np.array([[1., 2., 3., 4.], [1., 3., 5., 7.]]).astype(np.float32))
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end = Tensor(np.array([[10., 4.], [10., 4.], [10., 4.], [10., 4.]]).astype(np.float32))
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weight = 0.5
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benchmark_output = np.array([[5.5, 6., 6.5, 7.], [2.5, 3.5, 4.5, 5.5]]).astype(np.float32)
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in_axes = (0, 1, None)
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out_axes = 0
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output = LerpVMapNet(LerpNet(), in_axes, out_axes)(start, end, weight)
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assert np.allclose(output.asnumpy(), benchmark_output)
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# Case 3: start remains 1 batch,end remains 1 batch, weight remains 0 batch.
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start = Tensor(np.array([[1., 1], [2., 3.], [3., 5.], [4., 7.]]).astype(np.float32))
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end = Tensor(np.array([[10., 4.], [10., 4.], [10., 4.], [10., 4.]]).astype(np.float32))
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weight = Tensor(np.array([0.5, 0.4]).astype(np.float32))
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benchmark_output = np.array([[5.5, 6., 6.5, 7.], [2.2, 3.4, 4.6, 5.8]]).astype(np.float32)
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in_axes = (1, 1, 0)
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out_axes = 0
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output = LerpVMapNet(LerpNet(), in_axes, out_axes)(start, end, weight)
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assert np.allclose(output.asnumpy(), benchmark_output)
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# Case 4: start remain None, end remains 1 batch, weight remains 0 batch.
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start = Tensor(np.array([1., 2., 3., 4.]).astype(np.float32))
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end = Tensor(np.array([[10., 4.], [10., 4.], [10., 4.], [10., 4.]]).astype(np.float32))
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weight = Tensor(np.array([0.5, 0.4]).astype(np.float32))
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benchmark_output = np.array([[5.5, 6., 6.5, 7.], [2.2, 2.8, 3.4, 4.]]).astype(np.float32)
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in_axes = (None, 1, 0)
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out_axes = 0
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output = LerpVMapNet(LerpNet(), in_axes, out_axes)(start, end, weight)
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assert np.allclose(output.asnumpy(), benchmark_output)
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def lerp_np_bencmark(start, end, weight):
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"""
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Feature: generate a lerp numpy benchmark.
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Description: The input shape may need to broadcast.
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Expectation: match to np mindspore lerp.
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"""
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end = np.broadcast_to(end, start.shape)
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weight = np.broadcast_to(weight, start.shape)
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result = start + weight * (end - start)
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return result
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_cpu
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@pytest.mark.parametrize("data_shape", [(4,), (3, 4), (4, 5, 7)])
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@pytest.mark.parametrize("data_type", [np.float32, np.float16])
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def test_lerp(data_shape, data_type):
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"""
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Feature: Test Lerp.
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Description: The input shape may need to broadcast.
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Expectation: match to np benchmark.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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start = np.random.random(data_shape).astype(data_type)
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end = np.ones(data_shape).astype(data_type)
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error = 1e-6
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if data_type == np.float16:
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error = 1e-3
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weight = 0.5
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benchmark_output = lerp_np_bencmark(start, end, weight)
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lerp = LerpNet()
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output = lerp(Tensor(start), Tensor(end), Tensor(np.array(weight, dtype=data_type)))
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np.testing.assert_allclose(output.asnumpy(), benchmark_output, rtol=error, atol=error)
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context.set_context(mode=context.PYNATIVE_MODE)
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output = lerp(Tensor(start), Tensor(end), Tensor(np.array(weight, dtype=data_type)))
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np.testing.assert_allclose(output.asnumpy(), benchmark_output, rtol=error, atol=error)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_cpu
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@pytest.mark.parametrize("data_type", [np.float32, np.float16])
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def test_lerp_dy_shape(data_type):
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"""
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Feature: Test Lerp DyNamicShape.
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Description: The input shape may need to broadcast.
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Expectation: match to np benchmark.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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np.random.seed(1)
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data_shape = (4, 5, 7)
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start = np.random.random(data_shape).astype(data_type)
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end = np.ones(data_shape).astype(data_type)
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indices = np.random.randint(0, 4, size=4)
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weight = 0.5
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loss = 1e-6
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if data_type == np.float16:
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loss = 1e-3
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net = DynamicShapeNet()
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real_start, real_end, ms_result = net(Tensor(start), Tensor(end), weight, Tensor(indices))
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np_result = lerp_np_bencmark(real_start.asnumpy(), real_end.asnumpy(), weight)
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np.testing.assert_allclose(np_result, ms_result.asnumpy(), rtol=loss, atol=loss)
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context.set_context(mode=context.PYNATIVE_MODE)
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net = DynamicShapeNet()
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real_start, real_end, ms_result = net(Tensor(start), Tensor(end), weight, Tensor(indices))
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np_result = lerp_np_bencmark(real_start.asnumpy(), real_end.asnumpy(), weight)
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np.testing.assert_allclose(np_result, ms_result.asnumpy(), rtol=loss, atol=loss)
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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_lerp_vmap_cpu():
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"""
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Feature: test Lerp vmap on CPU.
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Description: inputs(start, end, weight) with batch.
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Expectation: the result match with expect
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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lerp_vmap_case()
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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lerp_vmap_case()
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