File size: 8,959 Bytes
15e8d2f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 |
from transformers import AutoModel, AutoTokenizer, AutoConfig, AdamW, get_linear_schedule_with_warmup
from torch.utils.data import DataLoader
import transformers
from sklearn.model_selection import train_test_split
from datasets import load_dataset, DatasetDict
import torch.nn as nn
import torch
import wandb
from tqdm import tqdm
args_max_epoch = 1
args_batch_size = 64
args_learning_rate = 3e-5
args_num_warmup_steps = 100
args_gradient_accumulation_steps_default = 2
adapter_hidden_dim = 4096
device = 'cuda'
def main():
wandb.init(project="MappingAdapater_training_v6", name="training_run")
model = MappingStructure(checkpointE = "sentence-transformers/stsb-roberta-large",
checkpointD = "mistralai/Mistral-7B-Instruct-v0.1",
hidden_dim = adapter_hidden_dim,
torch_dtype = torch.float16,
flash_attn = True,
).to(device)
for n,p in model.named_parameters():
if 'mapping' not in n:
p.requires_grad = False
else:
p.requires_grad = True
dataset = load_dataset("sade-adrien/redpajama_v2_sample_10M")['train']
train_dataset, val_dataset = split_dataset(dataset, train_size=.989333)
datasets = DatasetDict({
'train': train_dataset,
'val': val_dataset
})
train_dataloader = DataLoader(datasets['train'], batch_size=args_batch_size, shuffle=True)
val_dataloader = DataLoader(datasets['val'], batch_size=args_batch_size, shuffle=False)
optimizer = AdamW(model.parameters(), lr=args_learning_rate)
scheduler = get_linear_schedule_with_warmup(optimizer, args_num_warmup_steps, args_max_epoch*len(train_dataloader))
global_step = 0
for epoch in range(args_max_epoch):
train_dataloader = DataLoader(datasets['train'], batch_size=args_batch_size, shuffle=True, worker_init_fn=lambda _: torch.manual_seed(epoch))
for batch in tqdm(train_dataloader):
input_prompt = batch['raw_content']
outputs = model(input_prompt=input_prompt, compute_loss=True)
loss = outputs['loss']
# Gradient accumulation
loss = loss / args_gradient_accumulation_steps_default
loss.backward()
if (global_step + 1) % args_gradient_accumulation_steps_default == 0:
optimizer.step()
optimizer.zero_grad()
scheduler.step()
if (global_step + 1) % 2000 == 0:
torch.save({
'epoch': epoch,
'mapping_state_dict': model.mapping.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'scheduler_state_dict': scheduler.state_dict(),
'global_step': global_step,
}, f'models/mapping_adapter_checkpoint_{global_step + 1}steps.pth')
global_step += 1
val_loss = None
if (global_step + 1) % 8000 == 0:
model.eval()
val_loss = 0.0
with torch.no_grad():
for val_batch in tqdm(val_dataloader):
val_inputs = val_batch['raw_content']
val_outputs = model(input_prompt=val_inputs, compute_loss=True)
val_loss += val_outputs['loss']
val_loss /= len(val_dataloader)
model.train()
wandb.log({
'step': global_step + 1,
'learning_rate': scheduler.get_last_lr()[0],
'train_loss': loss.item() * args_gradient_accumulation_steps_default,
'val_loss': val_loss.item() if val_loss else None
})
def split_dataset(dataset, train_size=.9):
index = int(len(dataset) * train_size)
return dataset.select(range(index)), dataset.select(range(index, len(dataset)))
class MappingAdapter(nn.Module):
def __init__(self, input_dim, output_dim, hidden_dim):
super(MappingAdapter, self).__init__()
self.layer1 = nn.Linear(input_dim, hidden_dim)
self.layer2 = nn.Linear(hidden_dim, output_dim)
self.activation = nn.LeakyReLU(.01)
def forward(self, x):
x = self.layer1(x)
x = self.activation(x)
x = self.layer2(x)
return x
class MappingStructure(nn.Module):
def __init__(self, checkpointE, checkpointD, hidden_dim=2048, torch_dtype=torch.float32, flash_attn=False):
super(MappingStructure, self).__init__()
self.configE = AutoConfig.from_pretrained(checkpointE)
self.Encoder = AutoModel.from_pretrained(checkpointE,
low_cpu_mem_usage = True,
torch_dtype = torch_dtype,
config = self.configE
)
self.configD = AutoConfig.from_pretrained(checkpointD)
if flash_attn:
self.configD.update({'_flash_attn_2_enabled' : True})
self.Decoder = AutoModel.from_pretrained(checkpointD,
low_cpu_mem_usage = True,
torch_dtype = torch_dtype,
config = self.configD
)
self.mapping = MappingAdapter(self.configD.hidden_size, self.configE.hidden_size, hidden_dim=hidden_dim).to(torch_dtype)
self._init_tokenizers(checkpointE, checkpointD)
def _init_tokenizers(self, checkpointE, checkpointD):
self.tokenizerE = AutoTokenizer.from_pretrained(checkpointE, use_fast = False, revision = 'main', config = self.configE, padding_side='left')
self.tokenizerD = AutoTokenizer.from_pretrained(checkpointD, use_fast = False, revision = 'main', config = self.configD, padding_side='left')
self.tokenizerD.pad_token_id = self.tokenizerD.unk_token_id
def cosine_sim(self, u, v):
assert u.shape == v.shape, "u and v must have the same shape"
u_normalized = u / torch.norm(u, dim=1, keepdim=True)
v_normalized = v / torch.norm(v, dim=1, keepdim=True)
# Compute cosine similarity using dot product
return torch.sum(u_normalized * v_normalized, dim=1)
def mean_pooling(self, hidden_state, attention_mask):
token_embeddings = hidden_state
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
def build_batch(self, input_prompt):
size = torch.randint(1, self.configE.max_position_embeddings-2, (1,)).item()
targets = []
for prompt in input_prompt:
tokenized_input = self.tokenizerE(prompt)
tokenized_input = {'input_ids': tokenized_input['input_ids'][:size],
'attention_mask': tokenized_input['attention_mask'][:size],
}
targets.append(tokenized_input)
targets = self.tokenizerE.pad(targets, padding=True, return_tensors='pt')
return targets
def forward(self, input_prompt, compute_loss=False):
loss = None
# Slice prompt of needed to fit encoder max position embeddings (hard constraint)
if not compute_loss:
inputs = self.tokenizerD(input_prompt, return_tensors='pt', padding=True).to(device)
hidden_state_D = self.Decoder(**inputs).last_hidden_state
hidden_state_D_mapped = self.mapping(hidden_state_D)
else:
targets = self.build_batch(input_prompt).to(device)
input_prompt_sliced = self.tokenizerE.batch_decode(targets['input_ids'], skip_special_tokens=True)
inputs = self.tokenizerD(input_prompt_sliced, return_tensors='pt', padding=True).to(device)
hidden_state_D = self.Decoder(**inputs).last_hidden_state
hidden_state_D_mapped = self.mapping(hidden_state_D)
hidden_state_E = self.Encoder(**targets).last_hidden_state
proj_E = self.mean_pooling(hidden_state_E, targets['attention_mask'])
proj_D = self.mean_pooling(hidden_state_D_mapped, inputs['attention_mask'])
loss = 1 - torch.mean(self.cosine_sim(proj_E, proj_D))
del inputs
del targets
del input_prompt_sliced
del hidden_state_E
del proj_E
del proj_D
torch.cuda.empty_cache()
return {'loss': loss,
'last_hidden_state': hidden_state_D,
'last_hidden_state_mapped': hidden_state_D_mapped,
}
if __name__ == '__main__':
main() |