from functools import partial
from time import time
import psutil
import torch
import torch.nn as nn
from packaging import version
import colossalai
from colossalai.logging import disable_existing_loggers, get_dist_logger
from colossalai.nn.optimizer import HybridAdam
from colossalai.nn.parallel import ZeroDDP
from colossalai.tensor import ColoParameter, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
from colossalai.utils import get_current_device
from colossalai.utils.model.colo_init_context import ColoInitContext
from colossalai.zero import ZeroOptimizer
from transformers import GPT2Config, GPT2LMHeadModel
def parse_args():
parser = colossalai.get_default_parser()
parser.add_argument(
"--tp_degree",
type=int,
default=1,
help="Tensor Parallelism Degree.",
)
parser.add_argument(
"--placement",
type=str,
default='cpu',
help="Placement Policy for Gemini.",
)
args = parser.parse_args()
return args
## Parameter Sharding Strategies for Tensor Parallelism
def split_param_single_dim_tp1d(dim: int, param: ColoParameter, pg: ProcessGroup):
spec = (ShardSpec([dim], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
if param.process_group.tp_world_size() == 1:
param.set_process_group(pg)
param.set_tensor_spec(*spec)
def split_param_row_tp1d(param: ColoParameter, pg: ProcessGroup):
split_param_single_dim_tp1d(0, param, pg)
def split_param_col_tp1d(param: ColoParameter, pg: ProcessGroup):
split_param_single_dim_tp1d(-1, param, pg)
## 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.model = GPT2LMHeadModel(
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))
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]
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 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 get_cpu_mem():
return psutil.Process().memory_info().rss / 1024**2
def get_gpu_mem():
return torch.cuda.memory_allocated() / 1024**2
def get_mem_info(prefix=''):
return f'{prefix}GPU memory usage: {get_gpu_mem():.2f} MB, CPU memory usage: {get_cpu_mem():.2f} MB'
def get_tflops(model_numel, batch_size, seq_len, step_time):
return model_numel * batch_size * seq_len * 8 / 1e12 / (step_time + 1e-12)
# Tensor Parallel
def tensor_parallelize(model: torch.nn.Module, pg: ProcessGroup):
"""tensor_parallelize
Sharding the Model Parameters.
Args:
model (torch.nn.Module): a torch module to be sharded
"""
for mn, module in model.named_modules():
for pn, param in module.named_parameters(recurse=False):
# set process group for all parameters
param.set_process_group(pg)
if 'mlp.c_fc' in mn:
if 'weight' in pn or 'bias' in pn:
split_param_col_tp1d(param, pg) # colmn slice
# keep the shape of the output from c_fc
param.compute_spec.set_output_replicate(False)
elif 'mlp.c_proj' in mn:
if 'weight' in pn:
split_param_row_tp1d(param, pg) # row slice
elif 'wte' in mn or 'wpe' in mn:
split_param_col_tp1d(param, pg) # colmn slice
elif 'c_attn' in mn or 'c_proj' in mn:
split_param_col_tp1d(param, pg) # colmn slice
# Gemini + ZeRO DDP
def gemini_zero_dpp(model: torch.nn.Module, pg: ProcessGroup, placememt_policy: str = "auto"):
cai_version = colossalai.__version__
if version.parse(cai_version) > version.parse("0.1.10"):
from colossalai.nn.parallel import GeminiDDP
model = GeminiDDP(model,
device=get_current_device(),
placement_policy=placememt_policy,
pin_memory=True,
search_range_mb=32)
elif version.parse(cai_version) <= version.parse("0.1.10") and version.parse(cai_version) >= version.parse("0.1.9"):
from colossalai.gemini import ChunkManager, GeminiManager
chunk_size = ChunkManager.search_chunk_size(model, 64 * 1024**2, 32)
gemini_manager = GeminiManager(placememt_policy, chunk_manager)
chunk_manager = ChunkManager(chunk_size,
pg,
enable_distributed_storage=True,
init_device=GeminiManager.get_default_device(placememt_policy))
model = ZeroDDP(model, gemini_manager)
else:
raise NotImplemented(f"CAI version {cai_version} is not supported")
return model
def main():
args = parse_args()
BATCH_SIZE = 8
SEQ_LEN = 1024
VOCAB_SIZE = 50257
NUM_STEPS = 10
disable_existing_loggers()
colossalai.launch_from_torch(config={})
pg = ProcessGroup(tp_degree=args.tp_degree)
logger = get_dist_logger()
logger.info(get_mem_info(), ranks=[0])
# build GPT model
with ColoInitContext(device=get_current_device()):
model = gpt2_medium(checkpoint=True)
numel = sum([p.numel() for p in model.parameters()])
logger.info(f'Model numel: {numel}', ranks=[0])
get_tflops_func = partial(get_tflops, numel, BATCH_SIZE, SEQ_LEN)
# Tensor Parallelism (TP)
tensor_parallelize(model, pg)
# Gemini + ZeRO DP, Note it must be used after TP
model = gemini_zero_dpp(model, pg, args.placement)
logger.info(get_mem_info(prefix='After init model, '), ranks=[0])
# build criterion
criterion = GPTLMLoss()
# build optimizer
optimizer = HybridAdam(model.parameters(), lr=1e-3)
optimizer = ZeroOptimizer(optimizer, model, initial_scale=2**5)
logger.info(get_mem_info(prefix='After init optim, '), ranks=[0])
torch.cuda.synchronize()
model.train()
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)
optimizer.zero_grad()
start = time()
outputs = model(input_ids, attn_mask)
loss = criterion(outputs, input_ids)
logger.info(get_mem_info(prefix=f'[{n+1}/{NUM_STEPS}] Forward '), ranks=[0])
optimizer.backward(loss)
logger.info(get_mem_info(prefix=f'[{n+1}/{NUM_STEPS}] Backward '), ranks=[0])
optimizer.step()
logger.info(get_mem_info(prefix=f'[{n+1}/{NUM_STEPS}] Optimizer step '), ranks=[0])
step_time = time() - start
logger.info(
f'[{n+1}/{NUM_STEPS}] Loss:{loss.item():.3f}, Step time: {step_time:.3f}s, TFLOPS: {get_tflops_func(step_time):.3f}',
ranks=[0])
torch.cuda.synchronize()
if __name__ == '__main__':
main()