tjruwase
commented
1 year ago @Muggle666, can you please share repro steps including full script and command line? Thanks!
Open Muggle666 opened 1 year ago
tjruwase
commented
1 year ago @Muggle666, can you please share repro steps including full script and command line? Thanks!
Muggle666
commented
1 year ago The LLAMA model is from https://github.com/juncongmoo/pyllama/tree/main. And I just try to add lora on it. The full model code is:
from typing import Optional, Tuple
from dataclasses import dataclass
import math
import torch
from torch import nn
import torch.nn.functional as F
from enum import IntEnum
from torch.backends.cuda import sdp_kernel
import loralib as lora
import deepspeed
@dataclass
class ModelArgs:
dim: int = 512
n_layers: int = 8
n_heads: int = 8
vocab_size: int = -1 # defined later by tokenizer
multiple_of: int = 256 # make SwiGLU hidden layer size multiple of large power of 2
norm_eps: float = 1e-5
max_batch_size: int = 32
max_seq_len: int = 2048
class RMSNorm(torch.nn.Module):
def __init__(self, dim: int, eps: float = 1e-6, name=''):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
self.name = name
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
output = self._norm(x.float()).type_as(x)
return output * self.weight
def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
t = torch.arange(end, device=freqs.device) # type: ignore
freqs = torch.outer(t, freqs).float() # type: ignore
freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64
return freqs_cis
def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
ndim = x.ndim
assert 0 <= 1 < ndim
#assert freqs_cis.shape == (x.shape[1], x.shape[-1])
shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
return freqs_cis.view(*shape)
def apply_rotary_emb(
xq: torch.Tensor,
xk: torch.Tensor,
freqs_cis: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
return xq_out.type_as(xq), xk_out.type_as(xk)
class SDPBackend(IntEnum):
r"""
Enum class for the scaled dot product attention backends.
"""
ERROR = -1
MATH = 0
FLASH_ATTENTION = 1
EFFICIENT_ATTENTION = 2
backend_map = {
SDPBackend.MATH: {
"enable_math": True,
"enable_flash": False,
"enable_mem_efficient": False},
SDPBackend.FLASH_ATTENTION: {
"enable_math": False,
"enable_flash": True,
"enable_mem_efficient": False},
SDPBackend.EFFICIENT_ATTENTION: {
"enable_math": False,
"enable_flash": False,
"enable_mem_efficient": True}
}
class Attention(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.n_local_heads = args.n_heads // 1
self.head_dim = args.dim // args.n_heads
self.wq = lora.Linear(args.dim,
args.n_heads * self.head_dim,
r=32, lora_alpha=64, lora_dropout=0.05)
# self.wq = nn.Linear(args.dim,
# args.n_heads * self.head_dim,
# bias=False)
# self.wk = nn.Linear(args.dim,
# args.n_heads * self.head_dim,
# bias=False)
self.wk = lora.Linear(args.dim,
args.n_heads * self.head_dim,
r=32, lora_alpha=64, lora_dropout=0.05)
# self.wk = lora.Linear(args.dim,
# args.n_heads * self.head_dim,
# r=8, lora_alpha=16)
self.wv = lora.Linear(args.dim,
args.n_heads * self.head_dim,
r=32, lora_alpha=64, lora_dropout=0.05)
# self.wv = nn.Linear(args.dim,
# args.n_heads * self.head_dim,
# bias=False)
self.wo = lora.Linear(args.dim,
args.n_heads * self.head_dim,
r=32, lora_alpha=64, lora_dropout=0.05)
# self.wo = nn.Linear(args.n_heads * self.head_dim,
# args.dim,
# bias=False)
def forward(self, x: torch.Tensor, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor]):
bsz, seqlen, _ = x.shape
xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
xk = xk.view(bsz, seqlen, self.n_local_heads, self.head_dim)
xv = xv.view(bsz, seqlen, self.n_local_heads, self.head_dim)
xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis)
keys = xk
values = xv
xq = xq.transpose(1, 2)
keys = keys.transpose(1, 2)
values = values.transpose(1, 2)
with sdp_kernel(**backend_map[SDPBackend.EFFICIENT_ATTENTION]):
output = F.scaled_dot_product_attention(xq, keys, values, attn_mask=None, dropout_p=0.0, is_causal=True)
output = output.transpose(
1, 2
).contiguous().view(bsz, seqlen, -1)
return self.wo(output)
class FeedForward(nn.Module):
def __init__(
self,
dim: int,
hidden_dim: int,
multiple_of: int,
):
super().__init__()
hidden_dim = int(2 * hidden_dim / 3)
hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
# self.w1 = nn.Linear(dim, hidden_dim, bias=False)
self.w1 = lora.Linear(dim,
hidden_dim,
r=32, lora_alpha=64, lora_dropout=0.05)
# self.w2 = nn.Linear(hidden_dim, dim, bias=False)
self.w2 = lora.Linear(hidden_dim,
dim,
r=32, lora_alpha=64, lora_dropout=0.05)
# self.w3 = nn.Linear(dim, hidden_dim, bias=False)
self.w3 = lora.Linear(dim,
hidden_dim,
r=32, lora_alpha=64, lora_dropout=0.05)
def forward(self, x):
return self.w2(F.silu(self.w1(x)) * self.w3(x))
class TransformerBlock(nn.Module):
def __init__(self, layer_id: int, args: ModelArgs):
super().__init__()
self.n_heads = args.n_heads
self.dim = args.dim
self.head_dim = args.dim // args.n_heads
self.attention = Attention(args)
self.feed_forward = FeedForward(
dim=args.dim, hidden_dim=4 * args.dim, multiple_of=args.multiple_of
)
self.layer_id = layer_id
self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps, name='attn_norm')
self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps, name='ffn_norm')
def forward(self, x: torch.Tensor, freqs_cis=None, mask=None):
h = x + self.attention.forward(self.attention_norm(x), freqs_cis, mask)
out = h + self.feed_forward.forward(self.ffn_norm(h))
return out, freqs_cis, mask
class Transformer(nn.Module):
def __init__(self, params: ModelArgs, checkpoint_activations=False, checkpoint_num_layers=5):
super().__init__()
self.params = params
self.vocab_size = params.vocab_size
self.n_layers = params.n_layers
self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
self.layers = torch.nn.ModuleList()
for layer_id in range(params.n_layers):
self.layers.append(TransformerBlock(layer_id, params))
self.norm = RMSNorm(params.dim, eps=params.norm_eps, name='last_norm')
self.output = nn.Linear(params.dim, params.vocab_size, bias=False)
self.freqs_cis = precompute_freqs_cis(
self.params.dim // self.params.n_heads, self.params.max_seq_len * 2
)
self.checkpoint_activations = checkpoint_activations
self.checkpoint_num_layers = checkpoint_num_layers
if deepspeed.checkpointing.is_configured():
self.get_cuda_rng_tracker = deepspeed.checkpointing.get_cuda_rng_tracker
self.checkpoint = deepspeed.checkpointing.checkpoint
# @torch.inference_mode()
def forward(self, tokens: torch.Tensor):
def custom(start, end):
def custom_forward(*inputs):
layers_ = self.layers[start:end]
x_ = inputs[0]
for layer in layers_:
x_, o1, o2 = layer(x_, inputs[1], inputs[2])
return x_
return custom_forward
_bsz, seqlen = tokens.shape
start_pos = 0
h = self.tok_embeddings(tokens)
self.freqs_cis = self.freqs_cis.to(h.device)
freqs_cis = self.freqs_cis[start_pos: start_pos + seqlen]
mask = None
if seqlen > 1:
mask = torch.full((1, 1, seqlen, seqlen), float("-inf"), device=tokens.device)
mask = torch.triu(mask, diagonal=start_pos + 1).type_as(h)
if self.checkpoint_activations:
l = 0
num_layers = len(self.layers)
chunk_length = self.checkpoint_num_layers
while l < num_layers:
h = self.checkpoint(custom(l, l + chunk_length),
h, freqs_cis, mask)
l += chunk_length
else:
for layer in self.layers:
h, freqs_cis, mask = layer(h, freqs_cis, mask)
h = self.norm(h)
# (bsz, seq_len, vocab_size)
output = self.output(h)
return output.float()My main training script is as:
# -- coding:utf-8 -
import torch
import argparse
import torch.multiprocessing as mp
from torch.utils.data.distributed import DistributedSampler
import torch.distributed as dist
from torch.utils.data import Dataset
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader
from torch.distributed import init_process_group, destroy_process_group
from llama.model_train import Transformer, ModelArgs
from llama.tokenizer import Tokenizer
import numpy as np
import random
from utils import CustomDataset
import deepspeed
import loralib as lora
import h5py
import pickle
import json
import os
import logging
import mpu
from deepspeed.runtime.zero.stage_1_and_2 import estimate_zero2_model_states_mem_needs_all_live
from deepspeed.runtime.zero.stage3 import estimate_zero3_model_states_mem_needs_all_live
# # os.environ['DEEPSPEED_LOG_LEVEL'] = 'DEBUG'
# os.environ["BITSANDBYTES_NOWELCOME"] = "1"
# os.environ["TORCH_CPP_LOG_LEVEL"] = "INFO"
# os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"
# os.environ["NCCL_DEBUG"] = "DETAIL"
# os.environ['CUDA_LAUNCH_BLOCKING'] = "1"
# ==============config==============
PAD_ID = 2
IGNORE_INDEX = -1
EPOCHS = 10
BATCH_SIZE = 1
CUTOFF_LEN = 256
LEARNING_RATE = 3e-4
accumulation_steps = 16
device = 'cuda'
# initialize the distributed group
def ddp_setup():
#init_process_group(backend="nccl")
deepspeed.init_distributed(dist_backend='nccl')
def load_dataset_hdf5(filename):
with h5py.File(filename, 'r') as f:
data = f['data'][:]
labels = f['labels'][:]
return CustomDataset(data, labels)
def set_random_seeds(random_seed=0):
torch.manual_seed(random_seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(random_seed)
random.seed(random_seed)
def get_model(ckpt_path, tokenizer_path, model_params, args):
tokenizer = Tokenizer(model_path=tokenizer_path)
with open(model_params, "r") as f:
params = json.loads(f.read())
checkpoint_activations = params.pop("checkpointing")
checkpoint_num_layers = params.pop("checkpoint_layers")
if checkpoint_activations:
# mpu.initialize_model_parallel(int(os.environ["WORLD_SIZE"]))
mpu.initialize_model_parallel(1)
deepspeed.checkpointing.configure(mpu, deepspeed_config=args.deepspeed_config, num_checkpoints=params['n_layers'])
mpu.checkpoint = deepspeed.checkpointing.checkpoint
mpu.get_cuda_rng_tracker = deepspeed.checkpointing.get_cuda_rng_tracker
mpu.model_parallel_cuda_manual_seed = deepspeed.checkpointing.model_parallel_cuda_manual_seed
model_args: ModelArgs = ModelArgs(
max_seq_len=CUTOFF_LEN, max_batch_size=BATCH_SIZE, **params
)
model_args.vocab_size = tokenizer.n_words
# torch.set_default_tensor_type(torch.cuda.HalfTensor)
if args.deepspeed_config['zero_optimization']['stage'] == 3:
if int(os.environ["RANK"]) == 0:
weights = torch.load(ckpt_path, map_location=torch.device('cpu'))
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(weights, "_metadata", None)
weights = weights.copy()
if metadata is not None:
weights._metadata = metadata
error_msgs = []
else:
weights = None
metadata = None
error_msgs = []
else:
weights = torch.load(ckpt_path, map_location=torch.device('cpu'))
def load(module: torch.nn.Module, prefix=""):
# because zero3 puts placeholders in model params, this context
# manager gathers (unpartitions) the params of the current layer, then loads from
# the state dict and then re-partitions them again
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
args = (weights, prefix, local_metadata, True, [], [], error_msgs)
with deepspeed.zero.GatheredParameters(list(module.parameters(recurse=False)), modifier_rank=0):
if deepspeed.comm.get_rank() == 0:
module._load_from_state_dict(*args)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + ".")
if args.deepspeed_config['zero_optimization']['stage'] == 3:
with deepspeed.zero.Init():
model = Transformer(model_args, checkpoint_activations=checkpoint_activations,
checkpoint_num_layers=checkpoint_num_layers)
load(model, prefix="")
print("load finished!!!")
else:
model = Transformer(model_args, checkpoint_activations=checkpoint_activations,
checkpoint_num_layers=checkpoint_num_layers)
# estimate_zero2_model_states_mem_needs_all_live(model, num_gpus_per_node=8, num_nodes=1)
# estimate_zero3_model_states_mem_needs_all_live(model, num_gpus_per_node=8, num_nodes=1)
model.load_state_dict(weights, strict=False)
# weights = torch.load(ckpt_path, map_location=torch.device('cpu'))
# model.load_state_dict(weights, strict=False)
# model = model.half()
del weights
if checkpoint_activations:
def make_inputs_require_grad(module, input, output):
output.requires_grad_(True)
module = next(getattr(model, 'tok_embeddings').named_modules())[-1]
module.register_forward_hook(make_inputs_require_grad)
lora.mark_only_lora_as_trainable(model)
return model
class Trainer:
def __init__(
self,
model: torch.nn.Module,
train_data: DataLoader,
args
):
self.local_rank = int(os.environ["LOCAL_RANK"])
self.global_rank = int(os.environ["RANK"])
self.vocab_size = model.vocab_size
print('check vocab size', self.vocab_size)
# model.to(self.local_rank)
self.train_data = train_data
self.model = model
del model
param_group = [p for p in self.model.parameters() if p.requires_grad]
print('trainable params:', [n for n, p in self.model.named_parameters() if p.requires_grad])
# Calculate the total number of parameters in the model
total_params = sum(p.numel() for p in self.model.parameters())
# Calculate the total number of trainable parameters in the model
trainable_params = sum(p.numel() for p in self.model.parameters() if p.requires_grad)
print(f'Trainable Parameters: {trainable_params}')
# Calculate the percentage of trainable parameters
# percentage_trainable = (trainable_params / total_params) * 100
# print(f'Trainable Parameters: {trainable_params} ({percentage_trainable:.2f}% of total parameters)')
print('start initialize')
self.model, self.optimizer, _, _ = deepspeed.initialize(
args=args, model=self.model, model_parameters=param_group
)
print('=============model initialized===================')
def train(self, max_epoch):
loss_fct = torch.nn.CrossEntropyLoss(ignore_index=-1)
step = 0
gradient_accumulation_steps = 10
train_losses = []
validation_losses = []
for e in range(max_epoch):
self.model.train()
accumulated_loss = 0
num_micro_batches = 0
for source, label in self.train_data:
source = source.to(self.local_rank)
label = label.to(self.local_rank)
self.optimizer.zero_grad()
logits = self.model(source)
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = label[..., 1:].contiguous()
loss = loss_fct(shift_logits.view(-1, self.vocab_size), shift_labels.view(-1))
accumulated_loss += loss.item()
num_micro_batches += 1
if num_micro_batches == gradient_accumulation_steps:
# Calculate and print the average loss for the full batch
avg_loss = torch.tensor([(accumulated_loss / num_micro_batches)],
device=self.local_rank)
dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
avg_loss = avg_loss.item() / dist.get_world_size()
train_losses.append(avg_loss)
if self.global_rank == 0:
print(f'epoch {e} global mean loss:', avg_loss)
# Reset the accumulated loss and micro-batch counter
accumulated_loss = 0
num_micro_batches = 0
self.model.backward(loss)
self.model.step()
step += 1
torch.distributed.barrier()
if self.global_rank == 0:
self.model.eval()
save_path = f"/home/LM_new/save_model/consolidated_tele_768finetune_v2_epoch{e}.pth"
original_model = self.model.module
torch.save(lora.lora_state_dict(original_model), save_path)
print('saved')
return train_losses, validation_losses
def get_train_obj(args, ckpt_path='/home/LM_new//model/merged_state_dict.pth',
tokenizer_path='/home/LM_new//model/tokenizer.model',
model_params='/home/LM_new/model/params.json',
data_path='/home/LM_new/data/combine_tune_tele_data.pkl'):
with open(data_path, "rb") as file:
dataset = pickle.load(file)
print('data size', len(dataset))
dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False,
sampler=DistributedSampler(dataset))
print('data loaded')
model = get_model(ckpt_path, tokenizer_path, model_params, args)
return dataloader, model
def main():
ddp_setup()
set_random_seeds()
ds_config = '/home/LM_new//model/deepspeed_config.json'
parser = argparse.ArgumentParser()
parser.add_argument('--local_rank', type=int, default=int(os.environ["LOCAL_RANK"]),
help='Local rank for distributed training')
parser.add_argument('--config-file', default=ds_config, type=str)
parser = deepspeed.add_config_arguments(parser)
args = parser.parse_args()
args.deepspeed_config = json.load(open(args.config_file, 'r', encoding='utf-8'))
dataset, model = get_train_obj(args)
trainer = Trainer(model, dataset, args)
trainer.train(EPOCHS)
destroy_process_group()
if __name__ == "__main__":
os.system('echo $DEEPSPEED_LOG_LEVEL')
main()
I use torchrun to run it as : torchrun --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr=xx.xx.xx.xx --master_port=8080 train_ds_parallel.py
tjruwase
commented
1 year ago Thanks for sharing these. However, I am running into this import error.
Traceback (most recent call last):
File "train_ds_parallel.py", line 15, in <module>
from utils import CustomDataset
ModuleNotFoundError: No module named 'utils'
[2023-06-07 18:29:56,799] [INFO] [launch.py:315:sigkill_handler] Killing subprocess 79135
[2023-06-07 18:29:56,799] [ERROR] [launch.py:321:sigkill_handler] ['/opt/conda/bin/python', '-u', 'train_ds_parallel.py', '--local_rank=0'] exits with return code = 1class CustomDataset(Dataset):
def __init__(self, data, labels):
self.data = data
self.labels = labels
def __len__(self):
return len(self.data)
def __getitem__(self, index):
sample = self.data[index]
label = self.labels[index]
return torch.tensor(sample, dtype=torch.long), torch.tensor(label, dtype=torch.long)This is the custom dataset class, you can try to feed random tensors to the model just for reproducing. I think it should be same.
tjruwase
commented
1 year ago @Muggle666, apologies for the delay on this. I have some bandwidth to investigate but I am stuck on these file paths
If you still need this investigated, can you please share the sample content to unblock me? Thanks!
OsebeSammi
commented
7 months ago Faced similar issue when offloading to CPU using config
"zero_optimization": { "stage": 3, "offload_param": { "device": "cpu", "buffer_count": 5, "buffer_size": 1e8, "max_in_cpu": 1e9 }, "offload_optimizer": { "device": "cpu" } } }
I removed deepspeed version 0.14 and installed 0.13 and it worked fine. Please investigate issue when offloading parameters to CPU
chchenhui
commented
6 months ago I encountered the same problem on deepspeed version 0.14.2.
wentinghome
commented
6 months ago I encountered the same problem on deepspeed version 0.14.2.
same here
tjruwase
commented
6 months ago @wentinghome, @chchenhui, @OsebeSammi since this is a long running issue, it would be great if you could provide a repro from your own failure. Thanks!
Yangr116
commented
1 week ago Any update?
I am trying to enable cpu activation offload when training my custom LLAMA model. However, an error occur:
It seems like some inputs of the attention operation is offloaded in cpu but others are not. I thinks this is automatically handled by deepspeed, so do I use this feature in a wrong way?
My model code is like:
And the deepspeed config is:
Hope someone can help me out, thank you!