wehub-resource-sync
132 lines
5.4 KiB
Python
132 lines
5.4 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import unittest
|
|
|
|
from paddle.distributed.auto_parallel.static.dist_attribute import (
|
|
DistTensorSpec,
|
|
TensorDistAttr,
|
|
)
|
|
from paddle.distributed.fleet import auto
|
|
from paddle.framework import core
|
|
|
|
|
|
class TestReplicatedSPMDRule(unittest.TestCase):
|
|
def setUp(self):
|
|
# After replaced all spmd rules by phi impl, we can recover the
|
|
# api name to `get_spmd_rule`
|
|
self.rule = core.get_phi_spmd_rule("replicated")
|
|
|
|
x_shape = [10, 10, 32, 48]
|
|
y_shape = [32, 48]
|
|
out1_shape = [10, 10, 32, 48]
|
|
out2_shape = [10, 32, 48]
|
|
process_mesh = auto.ProcessMesh(mesh=[[0, 1, 2], [3, 4, 5]])
|
|
|
|
x_tensor_dist_attr = TensorDistAttr()
|
|
x_tensor_dist_attr.dims_mapping = [-1, 1, -1, -1]
|
|
x_tensor_dist_attr.process_mesh = process_mesh
|
|
self.x_dist_tensor_spec = DistTensorSpec(x_shape, x_tensor_dist_attr)
|
|
|
|
y_tensor_dist_attr = TensorDistAttr()
|
|
y_tensor_dist_attr.dims_mapping = [0, -1]
|
|
y_tensor_dist_attr.process_mesh = process_mesh
|
|
self.y_dist_tensor_spec = DistTensorSpec(y_shape, y_tensor_dist_attr)
|
|
|
|
out1_tensor_dist_attr = TensorDistAttr()
|
|
# out1_tensor_dist_attr.dims_mapping = [-1, 1, 0, -1] // unset on purpose, inferforward only need shape infer of output
|
|
# out1_tensor_dist_attr.process_mesh = process_mesh
|
|
self.out1_dist_tensor_spec = DistTensorSpec(
|
|
out1_shape, out1_tensor_dist_attr
|
|
)
|
|
out2_tensor_dist_attr = TensorDistAttr()
|
|
self.out2_dist_tensor_spec = DistTensorSpec(
|
|
out2_shape, out2_tensor_dist_attr
|
|
)
|
|
|
|
def test_replicated_infer_forward(self):
|
|
# return all -1
|
|
# 2 inputs 2 outputs
|
|
in_vec = [self.x_dist_tensor_spec, self.y_dist_tensor_spec]
|
|
out_vec = [self.out1_dist_tensor_spec, self.out2_dist_tensor_spec]
|
|
|
|
result_dist_attrs = self.rule.infer_forward(in_vec, out_vec)
|
|
|
|
self.assertEqual(len(result_dist_attrs), 2)
|
|
self.assertEqual(len(result_dist_attrs[0]), 2)
|
|
self.assertEqual(len(result_dist_attrs[1]), 2)
|
|
|
|
self.assertEqual(result_dist_attrs[0][0].dims_mapping, [-1, -1, -1, -1])
|
|
self.assertEqual(result_dist_attrs[0][1].dims_mapping, [-1, -1])
|
|
self.assertEqual(result_dist_attrs[1][0].dims_mapping, [-1, -1, -1, -1])
|
|
self.assertEqual(result_dist_attrs[1][1].dims_mapping, [-1, -1, -1])
|
|
|
|
# 1 inputs 2 outputs
|
|
in_vec = [self.y_dist_tensor_spec]
|
|
out_vec = [self.out1_dist_tensor_spec, self.out2_dist_tensor_spec]
|
|
|
|
result_dist_attrs = self.rule.infer_forward(in_vec, out_vec)
|
|
|
|
self.assertEqual(len(result_dist_attrs), 2)
|
|
self.assertEqual(len(result_dist_attrs[0]), 1)
|
|
self.assertEqual(len(result_dist_attrs[1]), 2)
|
|
|
|
self.assertEqual(result_dist_attrs[0][0].dims_mapping, [-1, -1])
|
|
self.assertEqual(result_dist_attrs[1][0].dims_mapping, [-1, -1, -1, -1])
|
|
self.assertEqual(result_dist_attrs[1][1].dims_mapping, [-1, -1, -1])
|
|
|
|
def test_replicated_infer_backward(self):
|
|
process_mesh = auto.ProcessMesh(mesh=[[0, 1, 2], [3, 4, 5]])
|
|
self.out1_dist_tensor_spec.set_dims_mapping([-1, 1, 0, -1])
|
|
self.out1_dist_tensor_spec.set_process_mesh(process_mesh)
|
|
self.out2_dist_tensor_spec.set_dims_mapping([1, -1, 0])
|
|
self.out2_dist_tensor_spec.set_process_mesh(process_mesh)
|
|
|
|
in_vec = [self.x_dist_tensor_spec, self.y_dist_tensor_spec]
|
|
out_vec = [self.out1_dist_tensor_spec, self.out2_dist_tensor_spec]
|
|
|
|
result_dist_attrs = self.rule.infer_backward(in_vec, out_vec)
|
|
|
|
self.assertEqual(len(result_dist_attrs), 2)
|
|
self.assertEqual(len(result_dist_attrs[0]), 2)
|
|
self.assertEqual(len(result_dist_attrs[1]), 2)
|
|
|
|
self.assertEqual(result_dist_attrs[0][0].dims_mapping, [-1, -1, -1, -1])
|
|
self.assertEqual(result_dist_attrs[0][1].dims_mapping, [-1, -1])
|
|
self.assertEqual(result_dist_attrs[1][0].dims_mapping, [-1, -1, -1, -1])
|
|
self.assertEqual(result_dist_attrs[1][1].dims_mapping, [-1, -1, -1])
|
|
|
|
# 1 inputs 3 outputs
|
|
in_vec = [self.y_dist_tensor_spec]
|
|
out_vec = [
|
|
self.x_dist_tensor_spec,
|
|
self.out1_dist_tensor_spec,
|
|
self.out2_dist_tensor_spec,
|
|
]
|
|
|
|
result_dist_attrs = self.rule.infer_backward(in_vec, out_vec)
|
|
|
|
self.assertEqual(len(result_dist_attrs), 2)
|
|
self.assertEqual(len(result_dist_attrs[0]), 1)
|
|
self.assertEqual(len(result_dist_attrs[1]), 3)
|
|
|
|
self.assertEqual(result_dist_attrs[0][0].dims_mapping, [-1, -1])
|
|
self.assertEqual(result_dist_attrs[1][0].dims_mapping, [-1, -1, -1, -1])
|
|
self.assertEqual(result_dist_attrs[1][1].dims_mapping, [-1, -1, -1, -1])
|
|
self.assertEqual(result_dist_attrs[1][2].dims_mapping, [-1, -1, -1])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|