github
/
ColossalAI
mirror of https://github.com/hpcaitech/ColossalAI
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

Move GPT PP Example

Browse Source
pull/2373/head
Ziyue Jiang 2 years ago
parent 48d33b1b17
commit 3a15b20421
5 changed files with 290 additions and 0 deletions
Show Stats Download Patch File Download Diff File

37
examples/language/gpt/experiments/pipeline_parallel/README.md
Unescape View File

@ -0,0 +1,37 @@
# Auto-Parallelism with GPT2
## Requirements
Before you can launch training, you need to install the following requirements.
### Install PyTorch
```bash
#conda
conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch
#pip
pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113
```
### Install [Colossal-AI v0.2.0](https://colossalai.org/download/) From Official Website
```bash
pip install colossalai==0.2.0+torch1.12cu11.3 -f https://release.colossalai.org
```
### Install transformers
```bash
pip install transformers
```
## Dataset
For simplicity, the input data is randonly generated here.
## Training
```bash
#Run the Pipeline Parallel on GPT with default setting and a dummy dataset.
bash run.sh
```

73
examples/language/gpt/experiments/pipeline_parallel/model_zoo.py
Unescape View File

@ -0,0 +1,73 @@
from torch import nn
from transformers import GPT2Config, GPT2LMHeadModel
## Define the Model and Loss Based on Huggingface transformers GPT2LMHeadModel
class GPTLMModel(nn.Module):
def __init__(self,
hidden_size=768,
num_layers=12,
num_attention_heads=12,
max_seq_len=1024,
vocab_size=50257,
checkpoint=False):
super().__init__()
self.checkpoint = checkpoint
self.config = GPT2Config(n_embd=hidden_size,
n_layer=num_layers,
n_head=num_attention_heads,
n_positions=max_seq_len,
n_ctx=max_seq_len,
vocab_size=vocab_size)
self.model = GPT2LMHeadModel(self.config)
if checkpoint:
self.model.gradient_checkpointing_enable()
def forward(self, input_ids, attention_mask):
# Only return lm_logits
return self.model(input_ids=input_ids, attention_mask=attention_mask, use_cache=not self.checkpoint)[0]
def gpt2_medium(checkpoint=False):
return GPTLMModel(hidden_size=1024, num_layers=24, num_attention_heads=16, checkpoint=checkpoint)
def gpt2_xl(checkpoint=True):
return GPTLMModel(hidden_size=1600, num_layers=48, num_attention_heads=32, checkpoint=checkpoint)
def gpt2_10b(checkpoint=True):
return GPTLMModel(hidden_size=4096, num_layers=50, num_attention_heads=16, checkpoint=checkpoint)
def gpt2_14b(checkpoint=True):
return GPTLMModel(hidden_size=4096, num_layers=70, num_attention_heads=16, checkpoint=checkpoint)
def gpt2_20b(checkpoint=True):
return GPTLMModel(hidden_size=8192, num_layers=25, num_attention_heads=16, checkpoint=checkpoint)
def gpt2_24b(checkpoint=True):
return GPTLMModel(hidden_size=8192, num_layers=30, num_attention_heads=16, checkpoint=checkpoint)
def model_builder(model_size: str) -> callable:
if model_size == "gpt2_medium":
return gpt2_medium
elif model_size == "gpt2_xl":
return gpt2_xl
elif model_size == "gpt2_10b":
return gpt2_10b
elif model_size == "gpt2_14b":
return gpt2_14b
elif model_size == "gpt2_20b":
return gpt2_20b
elif model_size == "gpt2_24b":
return gpt2_24b
else:
raise TypeError(f"model_builder {model_size}")
__all__ = ['model_builder']

7
examples/language/gpt/experiments/pipeline_parallel/run.sh
Unescape View File

@ -0,0 +1,7 @@
export GPUNUM=${GPUNUM:-4}
export BATCH_SIZE=${BATCH_SIZE:-16}
export MODEL_TYPE=${MODEL_TYPE:-"gpt2_medium"}
export NUM_MICROBATCH=${NUM_MICROBATCH:-8}
mkdir -p pp_logs
python train_gpt_pp.py --device="cuda" --model_type=${MODEL_TYPE} --num_microbatches=${NUM_MICROBATCH} --world_size=${GPUNUM} --batch_size=${BATCH_SIZE} 2>&1 | tee ./pp_logs/${MODEL_TYPE}_gpu_${GPUNUM}_bs_${BATCH_SIZE}_nm_${NUM_MICROBATCH}.log

161
examples/language/gpt/experiments/pipeline_parallel/train_gpt_pp.py
Unescape View File

@ -0,0 +1,161 @@
import argparse
import time
from functools import partial
import torch
from model_zoo import model_builder
from torch import nn
from tqdm import tqdm
from colossalai.fx import ColoTracer
from colossalai.fx.passes.adding_split_node_pass import avgnode_split_pass, split_with_split_nodes_pass
from colossalai.logging import disable_existing_loggers, get_dist_logger
from colossalai.nn.optimizer import HybridAdam
from colossalai.pipeline.middleware.adaptor import get_fx_topology
from colossalai.pipeline.rpc._pipeline_schedule import OneFOneBPipelineEngine
from colossalai.pipeline.rpc.utils import rpc_run
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--model_type', type=str, default="gpt2_medium")
parser.add_argument('--world_size', type=int, default=2)
parser.add_argument('--batch_size', type=int, default=16)
parser.add_argument('--dp_degree', type=int, default=1)
parser.add_argument('--tp_degree', type=int, default=1)
parser.add_argument('--num_microbatches', type=int, default=2)
parser.add_argument('--device', type=str, choices=['cpu', 'cuda'], default='cuda')
parser.add_argument('--master_addr', type=str, default='localhost')
parser.add_argument('--master_port', type=str, default='29011')
parser.add_argument('--num_worker_threads', type=int, default=128)
return parser.parse_args()
class GPTLMLoss(nn.Module):
def __init__(self):
super().__init__()
self.loss_fn = nn.CrossEntropyLoss()
def forward(self, logits, labels):
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
return self.loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
# Randomly Generated Data
def get_data(batch_size, seq_len, vocab_size):
input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=torch.cuda.current_device())
attention_mask = torch.ones_like(input_ids)
return input_ids, attention_mask
def get_tflops(model_numel, batch_size, seq_len, step_time):
return model_numel * batch_size * seq_len * 8 / 1e12 / (step_time + 1e-12)
def create_partition_module(pp_rank: int, stage_num: int, model, data_kwargs):
tracer = ColoTracer()
meta_args = {k: v.to('meta') for k, v in data_kwargs.items()}
graph = tracer.trace(root=model, meta_args=meta_args)
gm = torch.fx.GraphModule(model, graph, model.__class__.__name__)
annotated_model = avgnode_split_pass(gm, stage_num)
top_module, split_submodules = split_with_split_nodes_pass(annotated_model, merge_output=True)
topo = get_fx_topology(top_module)
for submodule in split_submodules:
if isinstance(submodule, torch.fx.GraphModule):
setattr(submodule, '_topo', topo)
return split_submodules[pp_rank + 1]
def partition(model, data_kwargs, pp_rank: int, chunk: int, stage_num: int):
module = create_partition_module(pp_rank, stage_num, model, data_kwargs)
return module
def run_master(args):
batch_size = args.batch_size
device = args.device
world_size = args.world_size
stage_num = world_size
num_microbatches = args.num_microbatches
model_type = args.model_type
# batch size per DP degree
SEQ_LEN = 1024
VOCAB_SIZE = 50257
NUM_STEPS = 10
WARMUP_STEPS = 1
disable_existing_loggers()
logger = get_dist_logger()
logger.info(f"{args.model_type}, batch size {batch_size}, num stage {stage_num}, num microbatch {num_microbatches}",
ranks=[0])
torch.manual_seed(123)
# build criterion
criterion = GPTLMLoss()
# warm up pipeline fx partition
input_ids, attn_mask = get_data(batch_size, SEQ_LEN, VOCAB_SIZE)
warmup_data_kwargs = {'input_ids': input_ids, 'attention_mask': attn_mask}
# create model
model = model_builder(model_type)(checkpoint=False)
# set 1f1b pipeline engine
pp_engine = OneFOneBPipelineEngine(partition_fn=partial(partition, model, warmup_data_kwargs),
stage_num=stage_num,
num_microbatches=num_microbatches,
device=device,
chunk=1,
criterion=criterion,
metric=None,
checkpoint=False)
partition_numels = pp_engine.remote_numels()
for rank, numel in partition_numels.items():
logger.info(f'{rank=} numel in the partition:{numel}')
# build optim
pp_engine.initialize_optimizer(HybridAdam, lr=1e-3)
ranks_tflops = {}
for n in range(NUM_STEPS):
# we just use randomly generated data here
input_ids, attn_mask = get_data(batch_size, SEQ_LEN, VOCAB_SIZE)
batch = {'input_ids': input_ids, 'attention_mask': attn_mask}
start = time.time()
outputs = pp_engine.forward_backward(batch=batch, labels=input_ids, forward_only=False)
step_time = time.time() - start
for rank, numel in partition_numels.items():
if rank not in ranks_tflops:
ranks_tflops[rank] = []
step_tflops = get_tflops(numel, batch_size, SEQ_LEN, step_time)
logger.info(
f"Rank{rank} , [{n + 1}/{NUM_STEPS}] , Step time: {step_time:.3f}s, TFLOPS: {get_tflops(numel, batch_size, SEQ_LEN, step_time):.3f}",
ranks=[0],
)
if n >= WARMUP_STEPS:
ranks_tflops[rank].append(step_tflops)
median_index = ((NUM_STEPS - WARMUP_STEPS) >> 1) + WARMUP_STEPS
gpu_tflops = []
for rank, tflops_list in ranks_tflops.items():
tflops_list.sort()
gpu_tflops.append(tflops_list[median_index])
logger.info(f"GPU{rank} Median TFLOPS is {tflops_list[median_index]:.3f}")
logger.info(f"Total TFLOPS is {sum(gpu_tflops):.3f}")
logger.info(f"Avg TFLOPS per GPU is {sum(gpu_tflops) / world_size:.3f}")
if __name__ == '__main__':
args = parse_args()
rpc_run(args, run_master)

12
examples/language/gpt/experiments/pipeline_parallel/utils.py
Unescape View File

@ -0,0 +1,12 @@
import torch
# Randomly Generated Data
def get_data(batch_size, seq_len, vocab_size):
input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=torch.cuda.current_device())
attention_mask = torch.ones_like(input_ids)
return input_ids, attention_mask
def get_tflops(model_numel, batch_size, seq_len, step_time):
return model_numel * batch_size * seq_len * 8 / 1e12 / (step_time + 1e-12)
Write Preview
Loading…
Cancel
Save