wehub-resource-sync
126 lines
3.9 KiB
Python
Executable File
126 lines
3.9 KiB
Python
Executable File
# Copyright (c) Microsoft Corporation.
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# SPDX-License-Identifier: Apache-2.0
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# DeepSpeed Team
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import os
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import json
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import argparse
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import torch
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import deepspeed
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from torch.utils.data.distributed import DistributedSampler
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import deepspeed.comm as dist
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class SimpleModel(torch.nn.Module):
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def __init__(self, hidden_dim, empty_grad=False):
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super(SimpleModel, self).__init__()
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self.linear = torch.nn.Linear(hidden_dim, hidden_dim, bias=True)
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self.linear = torch.nn.Linear(hidden_dim, hidden_dim, bias=False)
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if empty_grad:
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self.layers2 = torch.nn.ModuleList([torch.nn.Linear(hidden_dim,
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hidden_dim)]) #QuantizeLinear(hidden_dim, hidden_dim)
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self.cross_entropy_loss = torch.nn.CrossEntropyLoss()
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def forward(self, x, y):
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hidden = x
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hidden1 = self.linear(hidden)
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hidden2 = self.linear(hidden1)
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return self.cross_entropy_loss(hidden2, y)
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def create_config_from_dict(tmpdir, config_dict):
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config_path = os.path.join(tmpdir, 'temp_config.json')
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with open(config_path, 'w') as fd:
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json.dump(config_dict, fd)
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return config_path
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def get_data_loader(model, total_samples, hidden_dim, device):
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batch_size = model.train_micro_batch_size_per_gpu()
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train_data = torch.randn(total_samples, hidden_dim, device=device, dtype=torch.half)
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train_label = torch.empty(total_samples, dtype=torch.long, device=device).random_(hidden_dim)
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train_dataset = torch.utils.data.TensorDataset(train_data, train_label)
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sampler = DistributedSampler(train_dataset)
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train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, sampler=sampler)
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return train_loader
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def get_args(tmpdir, config_dict):
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parser = argparse.ArgumentParser()
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parser.add_argument("--local_rank", type=int, default=0)
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parser.add_argument('--zero', type=int, default=0)
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parser.add_argument('--zero_hpz_partition_size', type=int, default=1)
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args = parser.parse_args() #args=''
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config_dict["zero_optimization"]["stage"] = args.zero
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config_dict["zero_optimization"]["zero_hpz_partition_size"] = args.zero_hpz_partition_size
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print('config_dict["zero_optimization"]', config_dict["zero_optimization"])
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config_path = create_config_from_dict(tmpdir, config_dict)
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args.deepspeed_config = config_path
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return args
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def print0(msg):
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if dist.get_rank() == 0:
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print(msg, flush=True)
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rank = int(os.environ['RANK'])
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print('seed:', 2222 + rank)
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torch.random.manual_seed(2222 + rank)
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config_dict = {
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"train_batch_size": 256,
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"steps_per_print": 1,
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"optimizer": {
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"type": "Adam",
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"params": {
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"lr": 0.00015,
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}
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},
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"fp16": {
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"enabled": True,
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"initial_scale_power": 8
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},
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"zero_optimization": {
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"stage": 0,
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"reduce_bucket_size": 20,
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"zero_hpz_partition_size": 1,
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"reduce_scatter": True,
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"zero_quantized_weights": False,
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"zero_quantized_gradients": False
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}
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}
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# "initial_scale_power": 15
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args = get_args('/tmp/', config_dict)
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hidden_dim = 4 * 1024
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model = SimpleModel(hidden_dim, empty_grad=False)
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model, _, _, _ = deepspeed.initialize(args=args,
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model=model,
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model_parameters=model.parameters(),
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dist_init_required=True)
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def print_params(tag, model):
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if dist.get_rank() == 0:
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for n, p in model.named_parameters():
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print0("{} {}:{}".format(tag, n, p))
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data_loader = get_data_loader(model=model, total_samples=256, hidden_dim=hidden_dim, device=model.device)
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#print_params('pre-train', model)
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for n, batch in enumerate(data_loader):
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loss = model(batch[0], batch[1])
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if dist.get_rank() == 0:
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print("LOSS:", loss.item())
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model.backward(loss)
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model.step()
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#print_params('step={}'.format(n), model)
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#if n == 5: break
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