wehub-resource-sync
103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
# Copyright (c) 2024 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 pathlib
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import sys
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import unittest
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import paddle
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import paddle.distributed as dist
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from paddle import nn
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from paddle.distributed.auto_parallel.static.mix_to_dist_pass import (
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apply_mix2dist_pass,
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)
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sys.path.append(str(pathlib.Path(__file__).resolve().parents[0]))
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from test_to_static_pir_program import (
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BATCH_SIZE,
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CLASS_NUM,
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DemoNet,
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create_data_loader,
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)
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class ReshardDemoNet(DemoNet):
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def __init__(self, mesh, shard=True):
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super().__init__(mesh, shard=True)
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def forward(self, x):
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out = DemoNet.forward(self, x)
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out = dist.reshard(out, self._mesh, [dist.Shard(0)])
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return out
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class TestToStaticPirProgramTrain(unittest.TestCase):
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def test_to_static_program(self):
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mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
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layer = ReshardDemoNet(mesh)
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opt = paddle.optimizer.SGD(
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learning_rate=0.1, parameters=layer.parameters()
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)
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loss_fn = nn.MSELoss()
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loader = create_data_loader()
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dist_loader = dist.shard_dataloader(loader, meshes=[mesh])
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dist_model = dist.to_static(layer, dist_loader, loss_fn, opt)
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engine = dist_model._engine
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engine._build("train")
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dist_program = engine._fwd_main_progs["train"]
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apply_mix2dist_pass(dist_program)
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loss = dist_program.get_output_value_by_name(engine._loss_names[0])
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with paddle.static.program_guard(dist_program):
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params_grads = paddle.autograd.ir_backward.append_backward(loss)
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engine._optimizer._apply_optimize(
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loss, startup_program=None, params_grads=params_grads
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)
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index = 0
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for op in dist_program.global_block().ops:
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if op.name() == 'dist_op.reshard':
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if index == 0:
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# forward reshard op
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self.fwd_input = op.operand_source(0)
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self.assertEqual(
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self.fwd_input.dist_attr().dims_mapping, [-1, -1]
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)
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self.assertEqual(
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self.fwd_input.dist_attr().partial_dims, set()
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)
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self.assertEqual(
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self.fwd_input._local_shape,
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[BATCH_SIZE, CLASS_NUM],
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)
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self.fwd_output = op.result(0)
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self.assertEqual(
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self.fwd_output.dist_attr().dims_mapping, [0, -1]
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)
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self.assertEqual(
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self.fwd_output.dist_attr().partial_dims, set()
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)
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self.assertEqual(
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self.fwd_output._local_shape,
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[BATCH_SIZE / 2, CLASS_NUM],
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)
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elif index == 1:
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# backward reshard op
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self.assertEqual(op.result(0).type(), self.fwd_input.type())
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index += 1
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self.assertEqual(index, 2)
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if __name__ == "__main__":
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unittest.main()
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