wehub-resource-sync
263 lines
8.9 KiB
Python
263 lines
8.9 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 os
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import random
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import unittest
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import numpy as np
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from test_to_static_pir_program import create_data_loader
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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.base.framework import auto_complete_op_role
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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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from paddle.distributed.auto_parallel.static.utils import set_all_ops_op_role
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from paddle.distributed.fleet.meta_optimizers.common import OpRole
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BATCH_SIZE = 4
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BATCH_NUM = 10
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IMAGE_SIZE = 16
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CLASS_NUM = 8
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class HybridParallelDemoNet(nn.Layer):
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def __init__(self, mesh1, mesh2):
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super().__init__()
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self._mesh1 = mesh1
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self._mesh2 = mesh2
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self.linear_0 = nn.Linear(IMAGE_SIZE, IMAGE_SIZE, bias_attr=False)
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self.linear_1 = nn.Linear(IMAGE_SIZE, CLASS_NUM, bias_attr=False)
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self.relu_0 = nn.ReLU()
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self.relu_1 = nn.ReLU()
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self.relu_2 = nn.ReLU()
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# shard the weights of this layer
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self.linear_0.weight = dist.shard_tensor(
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self.linear_0.weight,
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self._mesh1,
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[dist.Replicate(), dist.Shard(1)],
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stop_gradient=False,
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)
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self.linear_1.weight = dist.shard_tensor(
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self.linear_1.weight,
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self._mesh2,
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[dist.Replicate(), dist.Shard(0)],
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stop_gradient=False,
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)
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def forward(self, x):
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x.stop_gradient = False
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out = self.relu_0(x) # trigger backward partial allreduce
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out = self.linear_0(out)
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out = self.relu_1(out)
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out = dist.reshard(out, self._mesh2, [dist.Shard(0), dist.Shard(1)])
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out = self.linear_1(out)
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out = self.relu_2(out) # trigger forward partial allreduce
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return out
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class TestML3DParallel(unittest.TestCase):
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def __init__(self):
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self._seed = eval(os.getenv("seed"))
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self.mesh1 = dist.ProcessMesh([[0, 1], [2, 3]], dim_names=["x", "y"])
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self.mesh2 = dist.ProcessMesh([[4, 5], [6, 7]], dim_names=["x", "y"])
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def set_random_seed(self, seed):
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random.seed(seed)
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np.random.seed(seed)
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paddle.seed(seed)
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def test_to_static_program(self):
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paddle.base.set_flags({'FLAGS_enable_pir_api': 1})
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mesh1 = self.mesh1
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mesh2 = self.mesh2
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threeD_layer = HybridParallelDemoNet(mesh1, mesh2)
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opt = paddle.optimizer.SGD(
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learning_rate=0.1, parameters=threeD_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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batch_size=BATCH_SIZE,
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batch_num=BATCH_NUM,
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image_size=IMAGE_SIZE,
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class_num=CLASS_NUM,
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)
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dist_loader = dist.shard_dataloader(
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loader, shard_dims="x", meshes=[mesh1, mesh2]
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)
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dist_model = dist.to_static(threeD_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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set_all_ops_op_role(dist_program.global_block(), OpRole.Forward)
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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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with auto_complete_op_role(dist_program, OpRole.Backward):
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params_grads = paddle.autograd.ir_backward.append_backward(loss)
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with auto_complete_op_role(dist_program, OpRole.Optimize):
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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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from paddle.distributed.auto_parallel.static.pir_pass import (
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apply_partition_pass,
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)
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apply_partition_pass(dist_program)
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rank = paddle.distributed.get_rank()
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ops = dist_program.global_block().ops
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op_names = [op.name() for op in ops]
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std_ops = [
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'pd_op.data',
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'pd_op.data',
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'builtin.parameter',
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'builtin.parameter',
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'pd_op.data',
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'pd_op.data',
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'pd_op.relu',
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'pd_op.matmul',
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'pd_op.relu',
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'dist_op.reshard',
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'pd_op.matmul',
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'dist_op.reshard',
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'pd_op.relu',
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'pd_op.subtract',
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'pd_op.square',
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'pd_op.full_int_array',
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'pd_op.mean',
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'builtin.shadow_output',
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'pd_op.full',
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'pd_op.full_like',
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'dist_op.reshard',
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'pd_op.mean_grad',
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'dist_op.reshard',
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'pd_op.square_grad',
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'pd_op.subtract_grad',
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'pd_op.relu_grad',
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'pd_op.matmul_grad',
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'dist_op.reshard',
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'pd_op.relu_grad',
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'pd_op.matmul_grad',
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'dist_op.reshard',
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'pd_op.relu_grad',
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'dist_op.reshard',
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'pd_op.sgd_',
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'dist_op.reshard',
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'pd_op.sgd_',
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]
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assert op_names == std_ops
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def test_loss_value(self):
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paddle.disable_static()
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paddle.base.set_flags({'FLAGS_enable_pir_api': 1})
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self.set_random_seed(self._seed)
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data_loader = create_data_loader(
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batch_size=BATCH_SIZE,
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batch_num=BATCH_NUM,
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image_size=IMAGE_SIZE,
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class_num=CLASS_NUM,
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)
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self.set_random_seed(self._seed)
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dy_layer = HybridParallelDemoNet(self.mesh1, self.mesh2)
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dy_opt = paddle.optimizer.SGD(
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learning_rate=0.1, parameters=dy_layer.parameters()
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)
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self.set_random_seed(self._seed)
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loss_fn = nn.MSELoss()
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dy2st_layer = HybridParallelDemoNet(self.mesh1, self.mesh2)
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dy2st_opt = paddle.optimizer.SGD(
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learning_rate=0.1, parameters=dy2st_layer.parameters()
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)
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dist_loader = dist.shard_dataloader(
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data_loader, shard_dims="x", meshes=[self.mesh1, self.mesh2]
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)
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dy_losses = self.run_dynamic(dy_layer, dy_opt, dist_loader)
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dy2st_losses = self.run_dy2static(dy2st_layer, dy2st_opt, dist_loader)
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paddle.disable_static()
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rank_id = dist.get_rank()
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if rank_id in self.mesh2.process_ids:
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pd_loss_dy2st = paddle.to_tensor(dy2st_losses)
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pd_loss_dy2st = dist.auto_parallel.api.dtensor_from_local(
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pd_loss_dy2st,
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self.mesh2,
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[dist.Partial(dist.ReduceType.kRedAvg), dist.Replicate()],
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)
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pd_loss_dy2st = dist.reshard(
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pd_loss_dy2st, self.mesh2, [dist.Replicate(), dist.Replicate()]
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)
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dy2st_losses = pd_loss_dy2st.numpy()
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np.testing.assert_equal(dy_losses, dy2st_losses)
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def run_dy2static(self, layer, opt, dist_loader):
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loss_fn = nn.MSELoss()
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dist_model = dist.to_static(layer, dist_loader, loss_fn, opt)
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dist_model.train()
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mode = "train"
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dist_program = dist_model._engine.main_program
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dist_model._fetch_value(
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dist_program.global_block().ops[4].result(0), "fetch_value"
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)
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loss_list = []
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for batch_id, data in enumerate(dist_loader()):
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if isinstance(data, dict):
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image = data['image']
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label = data['label']
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else:
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image, label = data
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loss = dist_model(image, label)
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assert "fetch_value" in dist_model.outs
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loss_list.append(loss)
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return np.array(loss_list)
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def run_dynamic(self, layer, opt, dist_loader, is_recompute=False):
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# create loss
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loss_fn = nn.MSELoss()
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loss_list = []
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for batch_id, data in enumerate(dist_loader()):
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if isinstance(data, dict):
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image = data['image']
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label = data['label']
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else:
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image, label = data
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if is_recompute:
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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_list.append(loss.numpy())
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loss.backward()
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opt.step()
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opt.clear_grad()
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return np.array(loss_list)
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def run_test_cases(self):
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self.test_to_static_program()
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self.test_loss_value()
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if __name__ == "__main__":
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TestML3DParallel().run_test_cases()
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