wehub-resource-sync
149 lines
5.4 KiB
Python
149 lines
5.4 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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import numpy as np
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import paddle
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import paddle.distributed as dist
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from paddle.distributed import Replicate
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class TestDistTensor(unittest.TestCase):
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def test_dist_tensor_creation(self):
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shape = [10, 5]
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mesh = dist.ProcessMesh([[0, 1], [2, 3]], dim_names=["x", "y"])
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placements = [Replicate(), Replicate()]
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# create dist tensor using numpy
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dist_tensor_with_numpy = dist.shard_tensor(
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np.ones(shape, dtype=np.float32), mesh, placements
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)
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# create dist tensor using tensor
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dist_tensor_with_tensor = dist.shard_tensor(
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paddle.ones(shape), mesh, placements
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)
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# create normal tensor
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tensor = paddle.ones(shape)
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# test dist tensor properties
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self.assertEqual(dist_tensor_with_numpy.shape, shape)
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self.assertEqual(dist_tensor_with_tensor.shape, shape)
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self.assertEqual(dist_tensor_with_numpy.is_dist(), True)
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self.assertEqual(dist_tensor_with_tensor.is_dist(), True)
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self.assertEqual(tensor.is_dist(), False)
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self.assertEqual(
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str(dist_tensor_with_numpy), str(dist_tensor_with_tensor)
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)
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self.assertEqual(dist_tensor_with_numpy.placements, placements)
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self.assertEqual(dist_tensor_with_tensor.placements, placements)
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def test_dist_parameter(self):
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mesh = dist.ProcessMesh([[0, 1], [2, 3]], dim_names=["x", "y"])
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placements = [Replicate(), Replicate()]
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dense_param = paddle.create_parameter(
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[10, 5], name="linear_1.weight", dtype='float32'
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)
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dist_param = dist.shard_tensor(dense_param, mesh, placements)
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self.assertEqual(dense_param.name + ".dist", dist_param.name)
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class TestDistTensorFromFn(unittest.TestCase):
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def run_dtensor_from_fn(self):
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# Create a dist_attr
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mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
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placements = [Replicate()]
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# for static graph here.
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dist_attr = dist.DistAttr(mesh=mesh, sharding_specs=[None])
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# Call the function dtensor_from_fn with dist_attr parameter
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result = dist.dtensor_from_fn(paddle.ones, mesh, placements, shape=[16])
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# Verify the result
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if paddle.in_dynamic_mode():
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self.assertIsInstance(result, paddle.Tensor)
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self.assertEqual(result.shape, [16])
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self.assertEqual(result.placements, placements)
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else:
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dist_attr.dynamic_dims = [0]
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dist_attr.chunk_id = 0
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self.assertIsInstance(result, paddle.base.libpaddle.pir.Value)
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self.assertEqual(result.shape, [16])
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self.assertEqual(
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result.dist_attr().dims_mapping, dist_attr.dims_mapping
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)
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self.assertEqual(
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result.dist_attr().process_mesh, dist_attr.process_mesh
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)
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result_zeros = dist.dtensor_from_fn(
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paddle.zeros, mesh, placements, shape=[16]
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)
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if paddle.in_dynamic_mode():
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dist_attr.dynamic_dims = []
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self.assertIsInstance(result_zeros, paddle.Tensor)
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self.assertEqual(result_zeros.shape, [16])
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self.assertEqual(result_zeros.placements, placements)
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else:
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dist_attr.dynamic_dims = [0]
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dist_attr.chunk_id = 0
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self.assertIsInstance(result_zeros, paddle.base.libpaddle.pir.Value)
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self.assertEqual(result_zeros.shape, [16])
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self.assertEqual(
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result_zeros.dist_attr().dims_mapping, dist_attr.dims_mapping
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)
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self.assertEqual(
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result_zeros.dist_attr().process_mesh, dist_attr.process_mesh
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)
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result_random = dist.dtensor_from_fn(
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paddle.rand, mesh, placements, shape=[16]
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)
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if paddle.in_dynamic_mode():
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dist_attr.dynamic_dims = []
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self.assertIsInstance(result_random, paddle.Tensor)
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self.assertEqual(result_random.shape, [16])
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self.assertEqual(result_random.placements, placements)
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else:
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dist_attr.dynamic_dims = [0]
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dist_attr.chunk_id = 0
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self.assertIsInstance(
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result_random, paddle.base.libpaddle.pir.Value
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)
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self.assertEqual(result_random.shape, [16])
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self.assertEqual(
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result_random.dist_attr().dims_mapping, dist_attr.dims_mapping
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)
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self.assertEqual(
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result_random.dist_attr().process_mesh, dist_attr.process_mesh
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)
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def test_dynamic_mode(self):
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self.run_dtensor_from_fn()
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# Test exceptions when running in static mode
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def test_static_mode(self):
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paddle.enable_static()
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self.run_dtensor_from_fn()
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paddle.disable_static()
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if __name__ == "__main__":
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unittest.main()
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