训练速度慢，GPU利用率低

[dayunyan]

Describe the bug/ 问题描述 (Mandatory / 必填)
LoRA微调Qwen2.5-3B模型时，训练阶段前10个step的速度比较快，能达到1~2s/step，随后逐渐减慢到10s/step以上，并且GPU的利用率在前期能达到100%，但在100个step之后就长时间地停在2%。

• Hardware Environment(Ascend/GPU/CPU) / 硬件环境:

GPU

• Software Environment / 软件环境 (Mandatory / 必填):
  -- MindSpore version (e.g., 1.7.0.Bxxx) : 2.2.14
  -- Python version (e.g., Python 3.7.5) : 3.9
  -- OS platform and distribution (e.g., Linux Ubuntu 16.04): 22.04
  -- GCC/Compiler version (if compiled from source):

• Excute Mode / 执行模式 (Mandatory / 必填)(PyNative/Graph):

/mode pynative

To Reproduce / 重现步骤 (Mandatory / 必填)
Steps to reproduce the behavior:

    def forward_fn(input_ids, attention_mask, labels):
        output = model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            labels=labels,
        )
        # loss = compute_ce_loss(output.logits, labels)
        return output.loss, output.logits

    grad_fn = ms.value_and_grad(
        forward_fn, None, model.trainable_params(), has_aux=True
    )

    def train_step(input_ids, attention_mask, labels):
        (loss, logits), grads = grad_fn(input_ids, attention_mask, labels)
        optimizer.step(grads)
        return loss, logits

    for epoch in tqdm(range(num_epochs), desc="Epoch"):
        model.set_train(True)
        total_loss, total_step = 0, 0
        with tqdm(total=num_batches, leave=False, position=1, desc="train_step") as t:
            for step, pack in enumerate(train_dataset.create_dict_iterator()):
                input_ids = pack["input_ids"]
                attention_mask = pack["attention_mask"]
                labels = pack["labels"]
                loss, logits = train_step(
                    input_ids=input_ids, attention_mask=attention_mask, labels=labels
                )
                total_loss += loss.asnumpy()
                lr_scheduler.step()
                total_step += 1
                curr_loss = total_loss / total_step
                t.set_postfix({"train-loss": f"{curr_loss:.2f}"})
                t.update(1)
                # if profiler is not None:
                #     if step == 10:
                #         profiler.start()
                #     if step == 100:
                #         profiler.stop()

        model.set_train(False)
        eval_loss = 0
        total_step = 0
        eval_preds = []
        total_text_labels = []
        with tqdm(
            total=num_batches_eval, leave=False, position=1, desc="eval_step"
        ) as t:
            for step, pack in enumerate(eval_dataset.create_dict_iterator()):
                input_ids = pack["input_ids"]
                attention_mask = pack["attention_mask"]
                labels = pack["labels"]
                text_inputs = pack["text_inputs"]
                text_labels = pack["text_labels"]
                with ms._no_grad():
                    outputs = model(
                        input_ids=input_ids,
                        attention_mask=attention_mask,
                    )
                loss = compute_ce_loss(outputs.logits, labels)
                eval_loss += loss.asnumpy()
                total_step += 1
                curr_eval_loss = eval_loss / total_step
                eval_preds.extend(
                    tokenizer.batch_decode(
                        outputs.logits.argmax(axis=-1).asnumpy(),
                        skip_special_tokens=True,
                    )
                )

                total_text_labels.extend(text_labels.tolist())
                t.set_postfix({"eval-loss": f"{curr_eval_loss:.2f}"})
                t.update(1)
        bleu_avg = compute_bleu_metrics(eval_preds, total_text_labels)
        # accuracy = correct / total * 100
        # print(f"{accuracy=} % on the evaluation dataset")
        eval_epoch_loss = eval_loss / eval_dataset.get_dataset_size()
        eval_ppl = np.exp(eval_epoch_loss)
        train_epoch_loss = total_loss / train_dataset.get_dataset_size()
        train_ppl = np.exp(train_epoch_loss)
        tqdm.write(
            f"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=} {bleu_avg=}"
        )

Expected behavior / 预期结果 (Mandatory / 必填)
训练速度保持稳定且快速，GPU利用率能稳定且不能过低。

Screenshots/ 日志 / 截图 (Mandatory / 必填)

Additional context / 备注 (Optional / 选填)
Add any other context about the problem here.

[lvyufeng]

试试用mindspore高版本？

[lvyufeng]

完整代码也用附件传一下，我看看能不能复现

[dayunyan]

完整代码也用附件传一下，我看看能不能复现

我暂时还没有Ascend环境，应该用不了2.3版本吧。代码已上传，麻烦您了。
Code.zip

[lvyufeng]

加一下QQ群，给你申请点代金券 721548151

[dayunyan]

加一下QQ群，给你申请点代金券 721548151

昨晚刚加😀

[EdwinWang37]

@lvyufeng @dayunyan 请问一下大家这个问题换高版本之后解决没，我也是这个问题呜呜（_）