Spaces:
Runtime error
Runtime error
| import os | |
| import torch | |
| from torch import nn | |
| from torch.utils.data import Sampler | |
| from transformers import Trainer | |
| from transformers.trainer import ( | |
| is_sagemaker_mp_enabled, | |
| get_parameter_names, | |
| has_length, | |
| ALL_LAYERNORM_LAYERS, | |
| # ShardedDDPOption, | |
| logger, | |
| ) | |
| from typing import List, Optional | |
| def maybe_zero_3(param, ignore_status=False, name=None): | |
| from deepspeed import zero | |
| from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus | |
| if hasattr(param, "ds_id"): | |
| if param.ds_status == ZeroParamStatus.NOT_AVAILABLE: | |
| if not ignore_status: | |
| print(name, 'no ignore status') | |
| with zero.GatheredParameters([param]): | |
| param = param.data.detach().cpu().clone() | |
| else: | |
| param = param.detach().cpu().clone() | |
| return param | |
| def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match): | |
| to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)} | |
| to_return = {k: maybe_zero_3(v, ignore_status=True, name=k).cpu() for k, v in to_return.items()} | |
| return to_return | |
| def split_to_even_chunks(indices, lengths, num_chunks): | |
| """ | |
| Split a list of indices into `chunks` chunks of roughly equal lengths. | |
| """ | |
| if len(indices) % num_chunks != 0: | |
| return [indices[i::num_chunks] for i in range(num_chunks)] | |
| num_indices_per_chunk = len(indices) // num_chunks | |
| chunks = [[] for _ in range(num_chunks)] | |
| chunks_lengths = [0 for _ in range(num_chunks)] | |
| for index in indices: | |
| shortest_chunk = chunks_lengths.index(min(chunks_lengths)) | |
| chunks[shortest_chunk].append(index) | |
| chunks_lengths[shortest_chunk] += lengths[index] | |
| if len(chunks[shortest_chunk]) == num_indices_per_chunk: | |
| chunks_lengths[shortest_chunk] = float("inf") | |
| return chunks | |
| def get_modality_length_grouped_indices(lengths, batch_size, world_size, generator=None): | |
| # We need to use torch for the random part as a distributed sampler will set the random seed for torch. | |
| assert all(l != 0 for l in lengths), "Should not have zero length." | |
| mm_indices, mm_lengths = zip(*[(i, l) for i, l in enumerate(lengths) if l > 0]) | |
| lang_indices, lang_lengths = zip(*[(i, -l) for i, l in enumerate(lengths) if l < 0]) | |
| assert len(mm_indices) > 0, "Should have at least one multimodal sample." | |
| assert len(lang_indices) > 0, "Should have at least one language sample." | |
| mm_shuffle = [mm_indices[i] for i in get_length_grouped_indices(mm_lengths, batch_size, world_size, generator=None)] | |
| lang_shuffle = [lang_indices[i] for i in get_length_grouped_indices(lang_lengths, batch_size, world_size, generator=None)] | |
| megabatch_size = world_size * batch_size | |
| mm_megabatches = [mm_shuffle[i : i + megabatch_size] for i in range(0, len(mm_shuffle), megabatch_size)] | |
| lang_megabatches = [lang_shuffle[i : i + megabatch_size] for i in range(0, len(lang_shuffle), megabatch_size)] | |
| last_mm = mm_megabatches[-1] | |
| last_lang = lang_megabatches[-1] | |
| additional_batch = last_mm + last_lang | |
| megabatches = mm_megabatches[:-1] + lang_megabatches[:-1] | |
| megabatch_indices = torch.randperm(len(megabatches), generator=generator) | |
| megabatches = [megabatches[i] for i in megabatch_indices] | |
| if len(additional_batch) >= megabatch_size: | |
| megabatches = [additional_batch[:megabatch_size]] + megabatches | |
| additional_batch = additional_batch[megabatch_size:] | |
| if len(additional_batch) > 0: | |
| megabatches.append(additional_batch) | |
| return [i for megabatch in megabatches for i in megabatch] | |
| def get_length_grouped_indices(lengths, batch_size, world_size, generator=None, merge=True): | |
| # We need to use torch for the random part as a distributed sampler will set the random seed for torch. | |
| indices = torch.randperm(len(lengths), generator=generator) | |
| megabatch_size = world_size * batch_size | |
| megabatches = [indices[i : i + megabatch_size].tolist() for i in range(0, len(lengths), megabatch_size)] | |
| megabatches = [sorted(megabatch, key=lambda i: lengths[i], reverse=True) for megabatch in megabatches] | |
| megabatches = [split_to_even_chunks(megabatch, lengths, world_size) for megabatch in megabatches] | |
| return [i for megabatch in megabatches for batch in megabatch for i in batch] | |
| class LengthGroupedSampler(Sampler): | |
| r""" | |
| Sampler that samples indices in a way that groups together features of the dataset of roughly the same length while | |
| keeping a bit of randomness. | |
| """ | |
| def __init__( | |
| self, | |
| batch_size: int, | |
| world_size: int, | |
| lengths: Optional[List[int]] = None, | |
| generator=None, | |
| group_by_modality: bool = False, | |
| ): | |
| if lengths is None: | |
| raise ValueError("Lengths must be provided.") | |
| self.batch_size = batch_size | |
| self.world_size = world_size | |
| self.lengths = lengths | |
| self.generator = generator | |
| self.group_by_modality = group_by_modality | |
| def __len__(self): | |
| return len(self.lengths) | |
| def __iter__(self): | |
| if self.group_by_modality: | |
| indices = get_modality_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator) | |
| else: | |
| indices = get_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator) | |
| return iter(indices) | |
| class LLaVATrainer(Trainer): | |
| def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]: | |
| if self.train_dataset is None or not has_length(self.train_dataset): | |
| return None | |
| if self.args.group_by_modality_length: | |
| lengths = self.train_dataset.modality_lengths | |
| return LengthGroupedSampler( | |
| # self.args.train_batch_size * self.args.gradient_accumulation_steps, # TODO: seems that we should not have gradient_accumulation_steps | |
| self.args.train_batch_size, | |
| world_size=self.args.world_size, | |
| lengths=lengths, | |
| group_by_modality=True, | |
| ) | |
| else: | |
| return super()._get_train_sampler() | |
| def create_optimizer(self): | |
| """ | |
| Setup the optimizer. | |
| We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the | |
| Trainer's init through `optimizers`, or subclass and override this method in a subclass. | |
| """ | |
| if is_sagemaker_mp_enabled(): | |
| return super().create_optimizer() | |
| # if self.sharded_ddp == ShardedDDPOption.SIMPLE: | |
| # return super().create_optimizer() | |
| opt_model = self.model | |
| if self.optimizer is None: | |
| decay_parameters = get_parameter_names(opt_model, ALL_LAYERNORM_LAYERS) | |
| decay_parameters = [name for name in decay_parameters if "bias" not in name] | |
| if self.args.mm_projector_lr is not None: | |
| projector_parameters = [name for name, _ in opt_model.named_parameters() if "mm_projector" in name] | |
| optimizer_grouped_parameters = [ | |
| { | |
| "params": [ | |
| p for n, p in opt_model.named_parameters() if (n in decay_parameters and n not in projector_parameters and p.requires_grad) | |
| ], | |
| "weight_decay": self.args.weight_decay, | |
| "name": "decay_no_proj_parameters" | |
| }, | |
| { | |
| "params": [ | |
| p for n, p in opt_model.named_parameters() if (n not in decay_parameters and n not in projector_parameters and p.requires_grad) | |
| ], | |
| "weight_decay": 0.0, | |
| "name": "no_decay_no_proj_parameters" | |
| }, | |
| { | |
| "params": [ | |
| p for n, p in opt_model.named_parameters() if (n in decay_parameters and n in projector_parameters and p.requires_grad) | |
| ], | |
| "weight_decay": self.args.weight_decay, | |
| "lr": self.args.mm_projector_lr, | |
| "name": "decay_proj_parameters" | |
| }, | |
| { | |
| "params": [ | |
| p for n, p in opt_model.named_parameters() if (n not in decay_parameters and n in projector_parameters and p.requires_grad) | |
| ], | |
| "weight_decay": 0.0, | |
| "lr": self.args.mm_projector_lr, | |
| "name": "no_decay_proj_parameters" | |
| }, | |
| ] | |
| else: | |
| optimizer_grouped_parameters = [ | |
| { | |
| "params": [ | |
| p for n, p in opt_model.named_parameters() if (n in decay_parameters and p.requires_grad) | |
| ], | |
| "weight_decay": self.args.weight_decay, | |
| "name": "decay_parameters" | |
| }, | |
| { | |
| "params": [ | |
| p for n, p in opt_model.named_parameters() if (n not in decay_parameters and p.requires_grad) | |
| ], | |
| "weight_decay": 0.0, | |
| "name": "no_decay_parameters" | |
| }, | |
| ] | |
| if getattr(self.args, "moe_enable", False): | |
| from deepspeed.moe.utils import split_params_into_different_moe_groups_for_optimizer | |
| optimizer_grouped_parameters = split_params_into_different_moe_groups_for_optimizer(optimizer_grouped_parameters) | |
| optimizer_cls, optimizer_kwargs = self.get_optimizer_cls_and_kwargs(self.args) | |
| self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs) | |
| if optimizer_cls.__name__ == "Adam8bit": | |
| import bitsandbytes | |
| manager = bitsandbytes.optim.GlobalOptimManager.get_instance() | |
| skipped = 0 | |
| for module in opt_model.modules(): | |
| if isinstance(module, nn.Embedding): | |
| skipped += sum({p.data_ptr(): p.numel() for p in module.parameters()}.values()) | |
| logger.info(f"skipped {module}: {skipped/2**20}M params") | |
| manager.register_module_override(module, "weight", {"optim_bits": 32}) | |
| logger.debug(f"bitsandbytes: will optimize {module} in fp32") | |
| logger.info(f"skipped: {skipped/2**20}M params") | |
| return self.optimizer | |
| def _save_checkpoint(self, model, trial, metrics=None): | |
| if getattr(self.args, 'tune_mm_mlp_adapter', False): | |
| from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR | |
| checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}" | |
| run_dir = self._get_output_dir(trial=trial) | |
| output_dir = os.path.join(run_dir, checkpoint_folder) | |
| # Only save Adapter | |
| keys_to_match = ['mm_projector', 'vision_resampler'] | |
| if getattr(self.args, "use_im_start_end", False): | |
| keys_to_match.extend(['embed_tokens', 'embed_in']) | |
| weight_to_save = get_mm_adapter_state_maybe_zero_3(self.model.named_parameters(), keys_to_match) | |
| if self.args.local_rank == 0 or self.args.local_rank == -1: | |
| self.model.config.save_pretrained(output_dir) | |
| torch.save(weight_to_save, os.path.join(output_dir, f'mm_projector.bin')) | |
| else: | |
| super(LLaVATrainer, self)._save_checkpoint(model, trial, metrics) | |
| def _save(self, output_dir: Optional[str] = None, state_dict=None): | |
| if getattr(self.args, 'tune_mm_mlp_adapter', False): | |
| pass | |
| else: | |
| super(LLaVATrainer, self)._save(output_dir, state_dict) | |