wehub-resource-sync
419 lines
12 KiB
Python
419 lines
12 KiB
Python
# Copyright (c) 2022 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 argparse
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import os
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import shutil
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import subprocess
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import sys
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import tempfile
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import numpy as np
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import paddle
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from paddle import distributed as dist
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from paddle.distributed import fleet
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from paddle.distributed.fleet.meta_parallel.sharding.group_sharded_optimizer_stage2 import (
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GroupShardedOptimizerStage2,
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)
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from paddle.distributed.fleet.meta_parallel.sharding.group_sharded_stage2 import (
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GroupShardedStage2,
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)
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from paddle.incubate.distributed.utils.io import load, save
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from paddle.nn import Linear
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print(load)
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epoch = 2
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linear_size = 1000
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class MLP(paddle.nn.Layer):
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def __init__(self, linear_size=1000, param_attr=None, bias_attr=None):
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super().__init__()
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self._linear1 = Linear(linear_size, linear_size)
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self._linear2 = Linear(linear_size, linear_size)
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self._linear3 = Linear(linear_size, 10)
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def forward(self, inputs):
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y = self._linear1(inputs)
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y = self._linear2(y)
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y = self._linear3(y)
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return y
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class RandomDataset(paddle.io.Dataset):
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def __init__(self, num_samples=2000, linear_size=1000):
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self.num_samples = num_samples
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self.linear_size = linear_size
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def __getitem__(self, idx):
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img = np.random.rand(self.linear_size).astype('float32')
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label = np.ones(1).astype('int64')
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return img, label
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def __len__(self):
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return self.num_samples
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def optimizer_setting(model, use_pure_fp16, opt_group=False):
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clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0)
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optimizer = paddle.optimizer.AdamW(
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parameters=(
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[
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{
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"params": model.parameters(),
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}
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]
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if opt_group
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else model.parameters()
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),
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learning_rate=0.001,
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weight_decay=0.00001,
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grad_clip=clip,
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multi_precision=use_pure_fp16,
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)
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return optimizer
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def train_mlp(
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model,
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sharding_stage,
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batch_size=100,
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use_pure_fp16=False,
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accumulate_grad=False,
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opt_group=False,
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save_model=False,
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test_minimize=False,
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opt_state=None,
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):
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if sharding_stage != "dp":
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group = paddle.distributed.new_group([0, 1], backend="nccl")
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if opt_group:
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optimizer = optimizer_setting(
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model=model, use_pure_fp16=use_pure_fp16, opt_group=opt_group
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)
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else:
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optimizer = optimizer_setting(model=model, use_pure_fp16=use_pure_fp16)
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if sharding_stage == 2:
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optimizer = GroupShardedOptimizerStage2(
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params=optimizer._parameter_list, optim=optimizer, group=group
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)
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model = GroupShardedStage2(
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model, optimizer, group=group, buffer_max_size=2**21
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)
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model._set_reduce_overlap(True)
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optimizer._set_broadcast_overlap(True, model)
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else:
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model = paddle.DataParallel(model)
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# check optimizer.minimize() error
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if test_minimize:
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try:
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optimizer.minimize()
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except:
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print(
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"====== Find sharding_stage2_optimizer.minimize() error ======"
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)
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return
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paddle.seed(2023)
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np.random.seed(2023)
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train_loader = paddle.io.DataLoader(
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RandomDataset(),
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batch_size=batch_size,
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shuffle=False,
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drop_last=True,
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num_workers=0,
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)
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if sharding_stage == 2:
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model.to(device="gpu")
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if opt_state is not None:
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optimizer.set_state_dict(opt_state)
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for eop in range(epoch):
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model.train()
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for batch_id, data in enumerate(train_loader()):
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img, label = data
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label.stop_gradient = True
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img.stop_gradient = True
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out = model(img)
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loss = paddle.nn.functional.cross_entropy(input=out, label=label)
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avg_loss = paddle.mean(x=loss.cast(dtype=paddle.float32))
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if batch_size == 20:
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avg_loss = avg_loss / 5
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avg_loss.backward()
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if not accumulate_grad:
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optimizer.step()
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optimizer.clear_grad()
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if accumulate_grad:
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optimizer.step()
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optimizer.clear_grad()
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paddle.device.cuda.synchronize()
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if save_model:
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return model, optimizer
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return model.parameters()
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def save_model(model, output_dir, **configs):
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configs["save_model"] = True
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model, opt = train_mlp(model, **configs)
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model_file = os.path.join(
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output_dir, f"rank{dist.get_rank()}model.pdparams"
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)
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opt_file = os.path.join(output_dir, f"rank{dist.get_rank()}model.pdopt")
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g_model_file = os.path.join(
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output_dir, f"rank{dist.get_rank()}g_model.pdparams"
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)
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g_opt_file = os.path.join(output_dir, f"rank{dist.get_rank()}g_model.pdopt")
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paddle.save(model.state_dict(), model_file)
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paddle.save(opt.state_dict(), opt_file)
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save(
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model.state_dict(), g_model_file, gather_to=[0, 1], state_type="params"
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)
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save(opt.state_dict(), g_opt_file, gather_to=[0, 1], state_type="opt")
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def load_mode(model, model_state_dict, output_param_path, **configs):
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configs["save_model"] = False
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model.set_state_dict(model_state_dict)
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params = train_mlp(model, **configs)
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paddle.save(params, output_param_path)
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def step_check(path1, path2):
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m1 = paddle.load(path1)
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m2 = paddle.load(path2)
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for v1, v2 in zip(m1, m2):
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np.testing.assert_allclose(v1.numpy(), v2.numpy())
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print(f"value same: {v1.name}")
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def step_save(strategy, output_dir, seed):
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python_exe = sys.executable
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# save data
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os.makedirs(output_dir + "/logs", exist_ok=True)
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filename = os.path.basename(__file__)
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cmd = (
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f"{python_exe} -m paddle.distributed.launch --log_dir {output_dir}/logs"
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f" --gpus 0,1 {filename} --cmd save --strategy {strategy} --output_dir {output_dir} --seed {seed}"
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)
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p = subprocess.Popen(cmd.split())
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p.communicate()
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assert p.poll() == 0
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def step_load(
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saved_strategy, current_strategy, saved_dir, load_way, output_path, seed
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):
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python_exe = sys.executable
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os.makedirs(f"{saved_dir}/load/logs", exist_ok=True)
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filename = os.path.basename(__file__)
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# load dp
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cmd = (
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f"{python_exe} -m paddle.distributed.launch --log_dir {saved_dir}/load/logs"
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f" --gpus 0,1 {filename} --cmd load --strategy {current_strategy} --output_dir {saved_dir} --load_dir {saved_dir}/{saved_strategy}/save --load_way {load_way}"
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f" --output_param_path {output_path} --seed {seed}"
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)
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p = subprocess.Popen(cmd.split())
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p.communicate()
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assert p.poll() == 0
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def test_save_load(args):
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np.random.seed(args.seed)
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paddle.seed(args.seed)
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if args.cmd == "main":
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run_case(args)
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return
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paddle.distributed.init_parallel_env()
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strategy = fleet.DistributedStrategy()
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if args.strategy == "dp":
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strategy.hybrid_configs = {
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"dp_degree": 2,
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"mp_degree": 1,
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"pp_degree": 1,
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"sharding_degree": 1,
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}
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elif args.strategy == "sharding_stage2":
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strategy.hybrid_configs = {
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"dp_degree": 1,
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"mp_degree": 1,
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"pp_degree": 1,
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"sharding_degree": 2,
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}
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else:
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raise ValueError(f"Not supported strategy: {args.strategy}")
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fleet.init(is_collective=True, strategy=strategy)
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fleet.set_log_level("DEBUG")
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mlp1 = MLP()
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output_dir = os.path.join(args.output_dir, args.strategy, args.cmd)
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os.makedirs(output_dir, exist_ok=True)
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if args.cmd.lower() == "save":
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if args.strategy == "dp":
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# DP VS stage2
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save_model(
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mlp1,
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output_dir,
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sharding_stage="dp",
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use_pure_fp16=False,
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opt_group=False,
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save_model=True,
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)
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elif args.strategy == "sharding_stage2":
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save_model(
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mlp1,
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output_dir,
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sharding_stage=2,
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use_pure_fp16=False,
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opt_group=False,
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save_model=True,
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)
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else:
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raise ValueError(f"Not supported {args.strategy}")
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elif args.cmd.lower() == "load":
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output_dir = args.load_dir
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model_file = os.path.join(
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output_dir, f"rank{dist.get_rank()}model.pdparams"
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)
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opt_file = os.path.join(output_dir, f"rank{dist.get_rank()}model.pdopt")
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g_model_file = os.path.join(
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output_dir, f"rank{args.gather_to}g_model.pdparams"
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)
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g_opt_file = os.path.join(
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output_dir, f"rank{args.gather_to}g_model.pdopt"
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)
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if args.load_way == "load":
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model_file = g_model_file
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opt_file = g_opt_file
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load_ = lambda x: eval(args.load_way)(x, place='cpu')
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else:
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load_ = eval(args.load_way)
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model = load_(model_file)
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opt = load_(opt_file)
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for k in opt.keys():
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print("opt k:", k)
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if args.strategy == "dp":
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load_mode(
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mlp1,
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model,
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args.output_param_path,
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sharding_stage="dp",
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use_pure_fp16=False,
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opt_group=False,
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save_model=False,
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opt_state=opt,
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)
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elif args.strategy == "sharding_stage2":
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load_mode(
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mlp1,
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model,
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args.output_param_path,
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sharding_stage=2,
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use_pure_fp16=False,
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opt_group=False,
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save_model=False,
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opt_state=opt,
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)
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else:
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raise ValueError(f"Not supported strategy {args.strategy}")
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else:
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raise ValueError(f"Not supported cmd: {args.cmd}")
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def run_case(args):
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saving_strategy = args.test_case.split(":")[0]
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loading_strategy = args.test_case.split(":")[1]
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output_dir = tempfile.mkdtemp()
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print("output dir:", output_dir)
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os.makedirs(output_dir + "/load_save", exist_ok=True)
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# save dp
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step_save(saving_strategy, output_dir, args.seed)
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# return
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# load dp
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p1 = os.path.join(output_dir, "m1.pdparams")
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p2 = os.path.join(output_dir, "m2.pdparams")
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step_load(
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saving_strategy,
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saving_strategy,
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output_dir,
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"paddle.load",
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p1,
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args.seed + 1,
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)
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step_load(
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saving_strategy, loading_strategy, output_dir, "load", p2, args.seed + 2
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)
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# check
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step_check(p1, p2)
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shutil.rmtree(output_dir)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--cmd", default="main", choices=["main", "save", "load"]
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)
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parser.add_argument(
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"--strategy", required=False, choices=["dp", "sharding_stage2"]
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)
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parser.add_argument(
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"--load_way", choices=["paddle.load", "load"], required=False
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)
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parser.add_argument("--load_dir", required=False)
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parser.add_argument("--output_dir", required=False)
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parser.add_argument("--output_param_path", required=False)
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parser.add_argument(
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"--test_case",
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required=False,
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choices=[
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"dp:dp",
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"dp:sharding_stage2",
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"sharding_stage2:dp",
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"sharding_stage2:sharding_stage2",
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],
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)
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parser.add_argument("--gather_to", required=False, default=0)
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parser.add_argument("--seed", type=int, default=2022)
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args = parser.parse_args()
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test_save_load(args)
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