File size: 10,193 Bytes
c5d3e8d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 | from dataclasses import dataclass, field
from functools import reduce
from typing import Callable, Dict, List, Optional, Tuple, Union, Any
import numpy as np
import torch
import wandb
import torch.nn as nn
from torch.utils.data import Dataset
from transformers import Trainer, Seq2SeqTrainingArguments
from transformers.data.data_collator import DataCollator
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
from transformers.trainer import (
EvalPrediction,
PreTrainedModel,
PreTrainedTokenizerBase,
TrainerCallback,
)
from transformers.trainer_pt_utils import get_parameter_names
from transformers.utils import is_sagemaker_mp_enabled, logging
from peft.tuners.lora.layer import Linear as LoraLinear
# include_keywords = ["block.0", "block.4"]
include_keywords = ["encoder.block.2","encoder.block.3","encoder.block.4"] # for T5
# include_keywords = ["layers.27", "layers.6"] # for Llama
do_log = False
def get_forward_hook(name):
def hook(module, input, output):
wandb.log(
{
f"{name}/input_mean": input[0].mean().item(),
f"{name}/input_std": input[0].std().item(),
f"{name}/output_mean": output.mean().item(),
f"{name}/output_std": output.std().item(),
},
commit=False,
)
return hook
class LogTrainer(Trainer):
def __init__(
self,
model: Union[PreTrainedModel, nn.Module] = None,
args: Seq2SeqTrainingArguments = None,
data_collator: Optional[DataCollator] = None,
train_dataset: Optional[Dataset] = None,
eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None,
tokenizer: Optional[PreTrainedTokenizerBase] = None,
model_init: Optional[Callable[[], PreTrainedModel]] = None,
compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,
callbacks: Optional[List[TrainerCallback]] = None,
optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (
None,
None,
),
preprocess_logits_for_metrics: Optional[
Callable[[torch.Tensor, torch.Tensor], torch.Tensor]
] = None,
):
super().__init__(
model,
args,
data_collator,
train_dataset,
eval_dataset,
tokenizer,
model_init,
compute_metrics,
callbacks,
optimizers,
preprocess_logits_for_metrics,
)
self.is_peft = "PeftModel" in type(model).__name__
if self.is_peft:
for name, module in model.named_modules():
if isinstance(module, LoraLinear):
self.scaling = module.scaling["default"]
break
self.orig_A = None
self.orig_B = None
self.orig_W = None
self.gradient_accumulation_counter = 0
def training_step(
self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]
) -> torch.Tensor:
if not do_log:
return super().training_step(model, inputs)
if self.is_peft:
if self.orig_A is None:
self.orig_A = {}
self.orig_B = {}
for name, param in model.named_parameters():
if param.requires_grad and any(
[kw in name for kw in include_keywords]
):
if "lora_A" in name:
self.orig_A[name.split("lora_A.")[0]] = (
param.detach().clone()
)
elif "lora_B" in name:
self.orig_B[name.split("lora_B.")[0]] = (
param.detach().clone()
)
for name, module in model.named_modules():
if any([kw in name for kw in include_keywords]) and isinstance(module, LoraLinear):
breakpoint()
hook = get_forward_hook(name)
module.register_forward_hook(hook)
else:
if self.orig_W is None:
self.orig_W = {}
for name, param in model.named_parameters():
if param.requires_grad and any(
[kw in name for kw in include_keywords]
):
self.orig_W[name] = param.detach().clone()
model.train()
inputs = self._prepare_inputs(inputs)
with self.compute_loss_context_manager():
loss = self.compute_loss(model, inputs)
if self.args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
self.accelerator.backward(loss)
with torch.no_grad():
if (
self.gradient_accumulation_counter
% self.args.gradient_accumulation_steps
== self.args.gradient_accumulation_steps - 1
):
if self.is_peft:
A_dict = {}
B_dict = {}
for name, param in model.named_parameters():
if param.requires_grad and any(
[kw in name for kw in include_keywords]
):
if "lora_A" in name:
A_dict[name.split("lora_A.")[0]] = param
elif "lora_B" in name:
B_dict[name.split("lora_B.")[0]] = param
assert (
len(A_dict)
== len(self.orig_A)
== len(B_dict)
== len(self.orig_B)
), (
len(A_dict),
len(self.orig_A),
len(B_dict),
len(self.orig_B),
)
for key in A_dict.keys():
A = A_dict[key]
B = B_dict[key]
lora_r = A.shape[0]
A_grad = A_dict[key].grad
B_grad = B_dict[key].grad
A_0 = self.orig_A[key]
B_0 = self.orig_B[key]
A_diff = A - A_0
B_diff = B - B_0
BA = torch.matmul(B, A)
BA_0 = torch.matmul(B_0, A_0)
BA_diff = BA - BA_0
BA_diff_norm = torch.norm(BA_diff).item()
A_diff_norm = torch.norm(A_diff).item()
B_diff_norm = torch.norm(B_diff).item()
A_norm = torch.norm(A).item()
B_norm = torch.norm(B).item()
A_grad_norm = torch.norm(A_grad).item()
B_grad_norm = torch.norm(B_grad).item()
# BA_singular_values = torch.svd(BA_diff.float(), compute_uv=False).S[:lora_r]
BA_singular_values = torch.svd_lowrank(
BA_diff.float(), q=2 * lora_r
)[1][:lora_r]
top_1_ratio = (
BA_singular_values[0] / BA_singular_values.sum()
).item()
top_4_ratio = (
BA_singular_values[:4].sum() / BA_singular_values.sum()
).item()
wandb.log(
{
f"A_norm/{key}": A_norm,
f"B_norm/{key}": B_norm,
f"A_grad_norm/{key}": A_grad_norm,
f"B_grad_norm/{key}": B_grad_norm,
f"A_diff_norm/{key}": A_diff_norm,
f"B_diff_norm/{key}": B_diff_norm,
f"BA_diff_norm/{key}": BA_diff_norm,
f"scaled_BA_diff_norm/{key}": self.scaling
* BA_diff_norm,
f"BA_top_1_ratio/{key}": top_1_ratio,
f"BA_top_4_ratio/{key}": top_4_ratio,
"train/global_step": self.state.global_step,
}
)
else:
W_dict = {}
for name, param in model.named_parameters():
if (
param.requires_grad
and any([kw in name for kw in include_keywords])
and len(param.shape) == 2
):
W_dict[name] = param
for key in W_dict.keys():
W = W_dict[key]
W_grad = W.grad
W_0 = self.orig_W[key]
W_diff = W - W_0
W_diff_norm = torch.norm(W_diff).item()
W_norm = torch.norm(W).item()
W_grad_norm = torch.norm(W_grad).item()
U, S, V = torch.svd(W_diff.float())
top_1_ratio = S[0] / S.sum()
top_4_ratio = S[:4].sum() / S.sum()
wandb.log(
{
f"W_norm/{key}": W_norm,
f"W_grad_norm/{key}": W_grad_norm,
f"W_diff_norm/{key}": W_diff_norm,
"train/global_step": self.state.global_step,
f"W_top_1_ratio/{key}": top_1_ratio.item(),
f"W_top_4_ratio/{key}": top_4_ratio.item(),
}
)
self.gradient_accumulation_counter += 1
return loss.detach() / self.args.gradient_accumulation_steps
|