wehub-resource-sync
192 lines
6.5 KiB
Python
192 lines
6.5 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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from semi_auto_parallel_simple_net import (
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DemoNet,
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TestSimpleNetForSemiAutoParallel,
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)
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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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class TestSimpleNetWithRecomputeForSemiAutoParallel(
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TestSimpleNetForSemiAutoParallel
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):
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def __init__(self):
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self._dtype = os.getenv("dtype")
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self._backend = os.getenv("backend")
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self._seed = eval(os.getenv("seed"))
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self._mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
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paddle.set_device(self._backend)
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self.init_single_card_net_result()
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def run_dynamic_recompute(self, layer, shard_input=False):
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# create loss
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loss_fn = nn.MSELoss()
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opt = paddle.optimizer.SGD(
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learning_rate=0.001, parameters=layer.parameters()
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)
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# run forward and backward
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for _ in range(1):
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image, label = self.init_input_data()
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if shard_input:
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image = dist.shard_tensor(image, self._mesh, [dist.Shard(0)])
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image.stop_gradient = False
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out = layer(image)
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loss = loss_fn(out, label)
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loss.backward()
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opt.step()
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opt.clear_grad()
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return loss, layer.parameters()
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def init_single_card_net_result(self):
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self.set_random_seed(self._seed)
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(
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self.base_loss,
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self.base_parameters,
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) = self.run_dynamic_recompute(
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DemoNet("recompute_demo", is_recompute=True)
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)
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def test_dp_demo_net(self):
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self.set_random_seed(self._seed)
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(
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self.dp_loss,
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self.dp_parameters,
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) = self.run_dynamic_recompute(
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DemoNet("recompute_dp_demo", is_recompute=True),
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shard_input=True,
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)
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self.check_tensor_eq(self.dp_loss, self.base_loss)
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self.check_tensor_eq(self.dp_loss, self.base_loss)
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for param, param_base in zip(self.dp_parameters, self.base_parameters):
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self.check_tensor_eq(param, param_base, rtol=1e-4)
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self.check_tensor_eq(param.grad, param_base.grad)
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def test_dp_demo_net_use_reentrant_false(self):
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self.set_random_seed(self._seed)
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(
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self.dp_loss,
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self.dp_parameters,
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) = self.run_dynamic_recompute(
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DemoNet(
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"recompute_use_reentrant_false_dp_demo",
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is_recompute=True,
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recompute_use_reentrant=False,
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),
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shard_input=True,
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)
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self.check_tensor_eq(self.dp_loss, self.base_loss)
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self.check_tensor_eq(self.dp_loss, self.base_loss)
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for param, param_base in zip(self.dp_parameters, self.base_parameters):
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self.check_tensor_eq(param, param_base, rtol=1e-4)
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self.check_tensor_eq(param.grad, param_base.grad)
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def test_dp_demo_net_offload_inputs(self):
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self.set_random_seed(self._seed)
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(
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self.dp_loss,
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self.dp_parameters,
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) = self.run_dynamic_recompute(
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DemoNet(
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"recompute_dp_demo_offload_inputs",
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is_recompute=True,
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offload_recompute_inputs=True,
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),
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shard_input=True,
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)
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self.check_tensor_eq(self.dp_loss, self.base_loss)
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self.check_tensor_eq(self.dp_loss, self.base_loss)
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for param, param_base in zip(self.dp_parameters, self.base_parameters):
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self.check_tensor_eq(param, param_base, rtol=1e-4)
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self.check_tensor_eq(param.grad, param_base.grad)
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def test_mp_demo_net(self):
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self.set_random_seed(self._seed)
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mp_layer = dist.shard_layer(
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DemoNet("recompute_mp_demo", is_recompute=True),
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self._mesh,
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self.shard_fn,
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)
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(
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self.mp_loss,
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self.mp_parameters,
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) = self.run_dynamic_recompute(mp_layer)
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self.check_tensor_eq(self.mp_loss, self.base_loss)
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for param, param_base in zip(self.mp_parameters, self.base_parameters):
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self.check_tensor_eq(param, param_base)
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self.check_tensor_eq(param.grad, param_base.grad)
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def test_mp_demo_net_use_reentrant_false(self):
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self.set_random_seed(self._seed)
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mp_layer = dist.shard_layer(
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DemoNet(
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"recompute_use_reentrant_false_mp_demo",
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is_recompute=True,
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recompute_use_reentrant=False,
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),
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self._mesh,
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self.shard_fn,
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)
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(
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self.mp_loss,
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self.mp_parameters,
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) = self.run_dynamic_recompute(mp_layer)
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self.check_tensor_eq(self.mp_loss, self.base_loss)
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for param, param_base in zip(self.mp_parameters, self.base_parameters):
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self.check_tensor_eq(param, param_base)
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self.check_tensor_eq(param.grad, param_base.grad)
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def test_mp_demo_net_offload_inputs(self):
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self.set_random_seed(self._seed)
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mp_layer = dist.shard_layer(
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DemoNet(
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"recompute_mp_demo_offload_inputs",
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is_recompute=True,
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offload_recompute_inputs=True,
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),
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self._mesh,
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self.shard_fn,
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)
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(
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self.mp_loss,
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self.mp_parameters,
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) = self.run_dynamic_recompute(mp_layer)
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self.check_tensor_eq(self.mp_loss, self.base_loss)
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for param, param_base in zip(self.mp_parameters, self.base_parameters):
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self.check_tensor_eq(param, param_base)
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self.check_tensor_eq(param.grad, param_base.grad)
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def run_test_case(self):
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self.test_dp_demo_net()
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self.test_dp_demo_net_use_reentrant_false()
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self.test_dp_demo_net_offload_inputs()
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self.test_mp_demo_net()
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self.test_mp_demo_net_use_reentrant_false()
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self.test_mp_demo_net_offload_inputs()
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if __name__ == '__main__':
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TestSimpleNetWithRecomputeForSemiAutoParallel().run_test_case()
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