wehub-resource-sync
151 lines
4.9 KiB
Python
151 lines
4.9 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 os
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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.framework import core
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def get_coord(mesh_list, rank):
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x = 0
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y = 0
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for sub_list in mesh_list:
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if rank in sub_list:
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y = sub_list.index(rank)
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return x, y
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x += 1
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return -1, -1
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class TestReshardSameStatus:
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def __init__(self):
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self._shape = eval(os.getenv("shape"))
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self._dtype = os.getenv("dtype")
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self._seeds = eval(os.getenv("seeds"))
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self._backend = os.getenv("backend")
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def test_diff_1d_mesh_shard(self, dev_ctx):
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paddle.seed(self._seeds)
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in_mesh_list = [0]
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out_mesh_list = [1]
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in_mesh = dist.ProcessMesh(in_mesh_list, dim_names=["x"])
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value = paddle.uniform(self._shape, self._dtype)
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in_expected_local_tensor_list = paddle.split(
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value, num_or_sections=in_mesh.shape[0], axis=0
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)
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if dist.get_rank() in in_mesh_list:
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index = in_mesh_list.index(dist.get_rank()) % in_mesh.shape[0]
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elif dist.get_rank() in out_mesh_list:
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index = out_mesh_list.index(dist.get_rank()) % in_mesh.shape[0]
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input_tensor = dist.shard_tensor(value, in_mesh, [dist.Shard(0)])
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if dist.get_rank() in in_mesh_list:
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# check the value of input tensor
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in_expected_local_tensor_list = paddle.split(
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value, num_or_sections=in_mesh.shape[0], axis=0
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)
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np.testing.assert_equal(
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input_tensor._local_value().numpy(),
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in_expected_local_tensor_list[index].numpy(),
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)
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out_mesh = dist.ProcessMesh(out_mesh_list, dim_names=["x"])
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out = dist.reshard(input_tensor, out_mesh, [dist.Shard(0)])
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if dist.get_rank() in out_mesh_list:
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np.testing.assert_equal(
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out._local_value().numpy(),
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in_expected_local_tensor_list[index].numpy(),
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)
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def test_diff_nd_mesh_shard_partial(self, dev_ctx):
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paddle.seed(self._seeds)
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in_mesh_list = [[0], [1]]
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out_mesh_list = [[1], [0]]
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in_mesh = dist.ProcessMesh(in_mesh_list, dim_names=["x", "y"])
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value = paddle.uniform(self._shape, self._dtype)
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input_tensor = dist.shard_tensor(
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value, in_mesh, [dist.Shard(0), dist.Partial()]
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)
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in_expected_local_tensor_list = paddle.split(
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value, num_or_sections=in_mesh.shape[0], axis=0
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)
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in_flatten_list = [
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item for sub_list in in_mesh_list for item in sub_list
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]
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out_flatten_list = [
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item for sub_list in out_mesh_list for item in sub_list
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]
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in_x, in_y = get_coord(in_mesh_list, dist.get_rank())
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out_x, out_y = get_coord(out_mesh_list, dist.get_rank())
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if dist.get_rank() in in_flatten_list:
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if in_y == 0:
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np.testing.assert_equal(
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input_tensor._local_value().numpy(),
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in_expected_local_tensor_list[in_x].numpy(),
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)
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else:
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zeros = paddle.zeros(input_tensor._local_shape)
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np.testing.assert_equal(
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input_tensor._local_value().numpy(),
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zeros.numpy(),
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)
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out_mesh = dist.ProcessMesh(out_mesh_list, dim_names=["x", "y"])
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out = dist.reshard(
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input_tensor, out_mesh, [dist.Shard(0), dist.Partial()]
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)
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if dist.get_rank() in out_flatten_list:
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if out_y == 0:
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np.testing.assert_equal(
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out._local_value().numpy(),
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in_expected_local_tensor_list[out_x].numpy(),
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)
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else:
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zeros = paddle.zeros(out._local_shape)
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np.testing.assert_equal(
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out._local_value().numpy(),
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zeros.numpy(),
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)
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def run_test_case(self):
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if self._backend == "cpu":
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paddle.set_device("cpu")
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place = paddle.CPUPlace()
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elif self._backend == "gpu":
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place = paddle.CUDAPlace(dist.get_rank())
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dev_ctx = core.DeviceContext.create(place)
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self.test_diff_1d_mesh_shard(dev_ctx)
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self.test_diff_nd_mesh_shard_partial(dev_ctx)
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if __name__ == '__main__':
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TestReshardSameStatus().run_test_case()
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