wehub-resource-sync
98 lines
3.9 KiB
Python
98 lines
3.9 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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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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import unittest
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from collections import OrderedDict
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from paddle.distributed.auto_parallel.static.dist_attribute import (
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DistTensorSpec,
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TensorDistAttr,
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)
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from paddle.distributed.fleet import auto
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from paddle.framework import convert_nptype_to_datatype_or_vartype, core
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class TestUniqueSPMDRule(unittest.TestCase):
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def setUp(self):
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self.rule = core.get_phi_spmd_rule("unique")
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x_shape = [4, 8]
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process_mesh = auto.ProcessMesh(mesh=[[0, 1], [2, 3]])
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x_tensor_dist_attr = TensorDistAttr()
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x_tensor_dist_attr.dims_mapping = [1, 0]
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x_tensor_dist_attr.process_mesh = process_mesh
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self.x_dist_tensor_spec = DistTensorSpec(x_shape, x_tensor_dist_attr)
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self.attrs = OrderedDict()
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self.attrs["return_index"] = True
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self.attrs["return_inverse"] = True
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self.attrs["return_counts"] = True
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self.attrs["axis"] = []
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self.attrs['dtype'] = convert_nptype_to_datatype_or_vartype("int32")
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def test_infer_forward(self):
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# return_index=True, return_inverse=True, return_counts=True, axis={}
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# [0, -1] --> [-1,-1], [-1], [-1], [-1], [-1]
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self.x_dist_tensor_spec.set_dims_mapping([0, -1])
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result_dist_attrs = self.rule.infer_forward(
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self.x_dist_tensor_spec,
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self.attrs["return_index"],
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self.attrs["return_inverse"],
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self.attrs["return_counts"],
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self.attrs["axis"],
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self.attrs['dtype'],
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)
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self.assertEqual(len(result_dist_attrs), 2)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(len(inferred_input_dist_attrs), 1)
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self.assertEqual(len(inferred_output_dist_attrs), 4)
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [-1, -1])
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self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [-1])
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self.assertEqual(inferred_output_dist_attrs[1].dims_mapping, [-1])
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self.assertEqual(inferred_output_dist_attrs[2].dims_mapping, [-1])
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self.assertEqual(inferred_output_dist_attrs[3].dims_mapping, [-1])
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# return_index=True, return_inverse=True, return_counts=True, axis={0}
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# [0, -1] --> [-1,-1], [-1，-1], [-1], [-1], [-1]
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self.x_dist_tensor_spec.set_dims_mapping([0, -1])
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self.attrs["axis"] = [0]
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result_dist_attrs = self.rule.infer_forward(
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self.x_dist_tensor_spec,
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self.attrs["return_index"],
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self.attrs["return_inverse"],
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self.attrs["return_counts"],
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self.attrs["axis"],
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self.attrs['dtype'],
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)
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self.assertEqual(len(result_dist_attrs), 2)
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inferred_input_dist_attrs = result_dist_attrs[0]
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inferred_output_dist_attrs = result_dist_attrs[1]
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self.assertEqual(len(inferred_input_dist_attrs), 1)
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self.assertEqual(len(inferred_output_dist_attrs), 4)
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self.assertEqual(inferred_input_dist_attrs[0].dims_mapping, [-1, -1])
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self.assertEqual(inferred_output_dist_attrs[0].dims_mapping, [-1, -1])
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self.assertEqual(inferred_output_dist_attrs[1].dims_mapping, [-1])
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self.assertEqual(inferred_output_dist_attrs[2].dims_mapping, [-1])
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self.assertEqual(inferred_output_dist_attrs[3].dims_mapping, [-1])
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if __name__ == "__main__":
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unittest.main()
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