175 lines
7.2 KiB
Python
175 lines
7.2 KiB
Python
import os
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import torch
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from torch.utils.data import Sampler
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from transformers import Trainer
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from transformers.trainer import (
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has_length,
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)
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from typing import List, Optional
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def maybe_zero_3(param, ignore_status=False, name=None):
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from deepspeed import zero
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from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
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if hasattr(param, "ds_id"):
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if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
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if not ignore_status:
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print(name, 'no ignore status')
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with zero.GatheredParameters([param]):
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param = param.data.detach().cpu().clone()
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else:
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param = param.detach().cpu().clone()
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return param
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def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
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to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)}
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to_return = {k: maybe_zero_3(v, ignore_status=True, name=k).cpu() for k, v in to_return.items()}
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return to_return
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def split_to_even_chunks(indices, lengths, num_chunks):
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"""
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Split a list of indices into `chunks` chunks of roughly equal lengths.
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"""
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if len(indices) % num_chunks != 0:
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return [indices[i::num_chunks] for i in range(num_chunks)]
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num_indices_per_chunk = len(indices) // num_chunks
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chunks = [[] for _ in range(num_chunks)]
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chunks_lengths = [0 for _ in range(num_chunks)]
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for index in indices:
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shortest_chunk = chunks_lengths.index(min(chunks_lengths))
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chunks[shortest_chunk].append(index)
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chunks_lengths[shortest_chunk] += lengths[index]
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if len(chunks[shortest_chunk]) == num_indices_per_chunk:
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chunks_lengths[shortest_chunk] = float("inf")
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return chunks
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def get_modality_length_grouped_indices(lengths, batch_size, world_size, generator=None):
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# We need to use torch for the random part as a distributed sampler will set the random seed for torch.
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assert all(l != 0 for l in lengths), "Should not have zero length."
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mm_indices, mm_lengths = zip(*[(i, l) for i, l in enumerate(lengths) if l > 0])
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lang_indices, lang_lengths = zip(*[(i, -l) for i, l in enumerate(lengths) if l < 0])
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assert len(mm_indices) > 0, "Should have at least one multimodal sample."
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assert len(lang_indices) > 0, "Should have at least one language sample."
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mm_shuffle = [mm_indices[i] for i in get_length_grouped_indices(mm_lengths, batch_size, world_size, generator=None)]
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lang_shuffle = [lang_indices[i] for i in get_length_grouped_indices(lang_lengths, batch_size, world_size, generator=None)]
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megabatch_size = world_size * batch_size
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mm_megabatches = [mm_shuffle[i : i + megabatch_size] for i in range(0, len(mm_shuffle), megabatch_size)]
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lang_megabatches = [lang_shuffle[i : i + megabatch_size] for i in range(0, len(lang_shuffle), megabatch_size)]
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last_mm = mm_megabatches[-1]
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last_lang = lang_megabatches[-1]
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additional_batch = last_mm + last_lang
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megabatches = mm_megabatches[:-1] + lang_megabatches[:-1]
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megabatch_indices = torch.randperm(len(megabatches), generator=generator)
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megabatches = [megabatches[i] for i in megabatch_indices]
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if len(additional_batch) >= megabatch_size:
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megabatches = [additional_batch[:megabatch_size]] + megabatches
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additional_batch = additional_batch[megabatch_size:]
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if len(additional_batch) > 0:
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megabatches.append(additional_batch)
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return [i for megabatch in megabatches for i in megabatch]
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def get_length_grouped_indices(lengths, batch_size, world_size, generator=None, merge=True):
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# We need to use torch for the random part as a distributed sampler will set the random seed for torch.
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indices = torch.randperm(len(lengths), generator=generator)
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megabatch_size = world_size * batch_size
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megabatches = [indices[i : i + megabatch_size].tolist() for i in range(0, len(lengths), megabatch_size)]
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megabatches = [sorted(megabatch, key=lambda i: lengths[i], reverse=True) for megabatch in megabatches]
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megabatches = [split_to_even_chunks(megabatch, lengths, world_size) for megabatch in megabatches]
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return [i for megabatch in megabatches for batch in megabatch for i in batch]
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class LengthGroupedSampler(Sampler):
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r"""
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Sampler that samples indices in a way that groups together features of the dataset of roughly the same length while
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keeping a bit of randomness.
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"""
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def __init__(
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self,
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batch_size: int,
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world_size: int,
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lengths: Optional[List[int]] = None,
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generator=None,
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group_by_modality: bool = False,
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):
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if lengths is None:
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raise ValueError("Lengths must be provided.")
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self.batch_size = batch_size
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self.world_size = world_size
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self.lengths = lengths
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self.generator = generator
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self.group_by_modality = group_by_modality
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def __len__(self):
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return len(self.lengths)
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def __iter__(self):
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if self.group_by_modality:
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indices = get_modality_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator)
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else:
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indices = get_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator)
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return iter(indices)
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class LLaVATrainer(Trainer):
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def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
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if self.train_dataset is None or not has_length(self.train_dataset):
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return None
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if self.args.group_by_modality_length:
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lengths = self.train_dataset.modality_lengths
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return LengthGroupedSampler(
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# self.args.train_batch_size * self.args.gradient_accumulation_steps, # TODO: seems that we should not have gradient_accumulation_steps
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self.args.train_batch_size,
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world_size=self.args.world_size,
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lengths=lengths,
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group_by_modality=True,
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)
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else:
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return super()._get_train_sampler()
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def _save_checkpoint(self, model, trial, metrics=None):
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if getattr(self.args, 'tune_mm_mlp_adapter', False):
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from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
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checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
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run_dir = self._get_output_dir(trial=trial)
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output_dir = os.path.join(run_dir, checkpoint_folder)
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# Only save Adapter
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keys_to_match = ['mm_projector', 'vision_resampler']
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if getattr(self.args, "use_im_start_end", False):
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keys_to_match.extend(['embed_tokens', 'embed_in'])
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weight_to_save = get_mm_adapter_state_maybe_zero_3(self.model.named_parameters(), keys_to_match)
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if self.args.local_rank == 0 or self.args.local_rank == -1:
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self.model.config.save_pretrained(output_dir)
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torch.save(weight_to_save, os.path.join(output_dir, f'mm_projector.bin'))
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else:
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super(LLaVATrainer, self)._save_checkpoint(model, trial, metrics)
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def _save(self, output_dir: Optional[str] = None, state_dict=None):
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if getattr(self.args, 'tune_mm_mlp_adapter', False):
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pass
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else:
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super(LLaVATrainer, self)._save(output_dir, state_dict)
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