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fix: grad accum loss calc

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Zach 2023-04-06 12:11:10 +00:00
parent e4e88dff33
commit 8f2eb1a583
1 changed files with 13 additions and 9 deletions
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22
train.py
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@ -1,8 +1,9 @@
import os import os
from transformers import AutoModelForCausalLM, AutoTokenizer, AdamW, get_scheduler from transformers import AutoModelForCausalLM, AutoTokenizer, get_scheduler
from transformers.trainer_pt_utils import get_parameter_names from transformers.trainer_pt_utils import get_parameter_names
import torch import torch
import torch.nn as nn import torch.nn as nn
from torch.optim import AdamW
from argparse import ArgumentParser from argparse import ArgumentParser
from read import read_config from read import read_config
from accelerate import Accelerator from accelerate import Accelerator
@ -24,7 +25,7 @@ def format_metrics(metrics, split, prefix=""):
def evaluate(model, val_dataloader): def evaluate(model, val_dataloader):
model.eval() model.eval()
val_loss = MeanMetric().to(model.device) val_loss = MeanMetric(nan_strategy="error").to(model.device)
with torch.no_grad(): with torch.no_grad():
for batch in tqdm(val_dataloader): for batch in tqdm(val_dataloader):
@ -43,7 +44,7 @@ def train(accelerator, config):
accelerator.print(config) accelerator.print(config)
accelerator.print(f"Using {accelerator.num_processes} GPUs") accelerator.print(f"Using {accelerator.num_processes} GPUs")
tokenizer = AutoTokenizer.from_pretrained(config['tokenizer_name']) tokenizer = AutoTokenizer.from_pretrained(config['tokenizer_name'], model_max_length=config['max_length'])
# llama has no pad token, set it to new token # llama has no pad token, set it to new token
if tokenizer.pad_token is None: if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token tokenizer.pad_token = tokenizer.eos_token
@ -51,7 +52,7 @@ def train(accelerator, config):
with accelerator.main_process_first(): with accelerator.main_process_first():
train_dataloader, val_dataloader = load_data(config, tokenizer) train_dataloader, val_dataloader = load_data(config, tokenizer)
checkpoint = config["gradient_checkpointing"] checkpoint = config["gradient_checkpointing"]
model = AutoModelForCausalLM.from_pretrained(config["model_name"], model = AutoModelForCausalLM.from_pretrained(config["model_name"],
@ -139,17 +140,22 @@ def train(accelerator, config):
# log gradients # log gradients
if accelerator.is_main_process and config["wandb"]: if accelerator.is_main_process and config["wandb"]:
wandb.watch(model, log_freq=config["log_grads_every"]) wandb.watch(model, log_freq=config["log_grads_every"], log="all")
for epoch in range(config["num_epochs"]): for epoch in range(config["num_epochs"]):
train_loss = MeanMetric().to(model.device) train_loss = MeanMetric(nan_strategy="error").to(model.device)
for step, batch in enumerate(tqdm(train_dataloader)): for step, batch in enumerate(tqdm(train_dataloader)):
model.train() model.train()
outputs = model(**batch) outputs = model(**batch)
loss = outputs.loss loss = outputs.loss
loss = loss / gradient_accumulation_steps
# gather loss before backprop in case of gradient accumulation
loss_values = accelerator.gather_for_metrics({"loss": loss.detach().float()})
train_loss.update(loss_values["loss"])
loss = loss / gradient_accumulation_steps
accelerator.backward(loss) accelerator.backward(loss)
# get gradient norm of all params
# log LR in case something weird happens # log LR in case something weird happens
if step > 0 and step % (config["eval_every"] // 10) == 0: if step > 0 and step % (config["eval_every"] // 10) == 0:
@ -162,8 +168,6 @@ def train(accelerator, config):
scheduler.step() scheduler.step()
optimizer.zero_grad() optimizer.zero_grad()
loss_values = accelerator.gather_for_metrics({"loss": loss.detach()})
train_loss.update(loss_values["loss"])
if step > 0 and step % config["save_every"] == 0: if step > 0 and step % config["save_every"] == 0:
curr_step = step + epoch * len(train_dataloader) curr_step = step + epoch * len(train_dataloader)