Great, can you provide an example of data processing based on sequence paralleler? thanks

The dataset and sampler are handled in the Trainer

https://github.com/huggingface/transformers/pull/31525/files#diff-ed55888e6665791fe92cc8fc0c499da54f4ace6738551cd9a2591881cda076deR847-R855

The data collator example is accidentally deleted when editing

            seq_parallel_world_size = mpu.get_sequence_parallel_world_size()
            seq_parallel_world_rank = mpu.get_sequence_parallel_rank()

            seq_length = input_ids.size(1)
            sub_seq_length = seq_length // seq_parallel_world_size
            sub_seq_start = seq_parallel_world_rank * sub_seq_length
            sub_seq_end = (seq_parallel_world_rank + 1) * sub_seq_length

            # There is no kv cache when training
            past_key_values_length = 0

            position_ids = torch.arange(
                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long,
            )
            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)

            batch = dict(
                input_ids=input_ids[:, sub_seq_start:sub_seq_end],
                labels=labels[:, sub_seq_start:sub_seq_end],
                position_ids=position_ids[:, sub_seq_start:sub_seq_end],
                attention_mask=(input_ids != self.tokenizer.pad_token_id),
            )

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how long time this pr merge, when can it finish ? ...

cc @SunMarc if you have the bandwidth to take a look!

@zeyugao I carefully read your pull requests for transformers and accelerate, and pulled your code to try training. Now I have encountered a problem: when entering DistributedAttention, the q, k, v before _SeqAllToAll.apply are not [b, s/p, n, h], but still [b, s, n, h]. I checked the modified parts of the data processing, such as accelerate/data_loader.py and transformers/trainer.py, but did not find any relevant processing code. So, may I ask where the sequence splitting is done?

@glowwormX It is in the pr description

@zeyugao My God, I missed it, I thought there was this code in pr. Thank you for replying.

@zeyugao Have you compared the loss of sequence parallel? After a fixed seed is added to DistributedSampler, the training data is the same. Modify the trainer.py:

        if is_accelerate_available() and mpu.sequence_parallel_is_enabled():
            assert self.args.group_by_length is False, "Group by length is not supported with sequence parallel."
            return DistributedSampler(
                dataset=self.train_dataset,
                num_replicas=mpu.get_data_parallel_world_size(),
                rank=mpu.get_data_parallel_rank(),
                shuffle=True,
                seed=42
            )

However, when the same data is calculated, the average loss value after sequence parallel is different from the loss value without sequence parallel.

In addition, what is the reason why starcoder does not support sdpa? I am trying to modify qwen2 and I do not know if it does not support sdpa.

@glowwormX The main reason should be that it need to use custom loss calculation, otherwise there are some tokens (in the head and tail of each subsequence) not contributing to the final loss: https://github.com/microsoft/DeepSpeed/pull/5774/files#diff-13f25bb51b0f4019d8cb09c07204a33510dca5dccfae736baf10134f893704d5

the reason why starcoder does not support sdpa

I do not have much spare time to make the shape correct when using sdpa for startcoder2 at that time

@zeyugao: Your implementation does not use this loss function right? It still works ok even so?