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194 lines
8.8 KiB
Python
194 lines
8.8 KiB
Python
import torch, os, argparse, accelerate
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from diffsynth.core import UnifiedDataset
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from diffsynth.pipelines.flux_image import FluxImagePipeline, ModelConfig
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from diffsynth.diffusion import *
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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class FluxTrainingModule(DiffusionTrainingModule):
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def __init__(
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self,
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model_paths=None, model_id_with_origin_paths=None,
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tokenizer_1_path=None, tokenizer_2_path=None,
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trainable_models=None,
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lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None,
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preset_lora_path=None, preset_lora_model=None,
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use_gradient_checkpointing=True,
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use_gradient_checkpointing_offload=False,
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extra_inputs=None,
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fp8_models=None,
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offload_models=None,
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device="cpu",
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task="sft",
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):
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super().__init__()
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# Load models
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model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, device=device)
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tokenizer_1_config = ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="tokenizer/") if tokenizer_1_path is None else ModelConfig(tokenizer_1_path)
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tokenizer_2_config = ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="tokenizer_2/") if tokenizer_2_path is None else ModelConfig(tokenizer_2_path)
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self.pipe = FluxImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, tokenizer_1_config=tokenizer_1_config, tokenizer_2_config=tokenizer_2_config)
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self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model)
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# Training mode
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self.switch_pipe_to_training_mode(
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self.pipe, trainable_models,
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lora_base_model, lora_target_modules, lora_rank, lora_checkpoint,
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preset_lora_path, preset_lora_model,
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task=task,
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)
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# Other configs
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self.use_gradient_checkpointing = use_gradient_checkpointing
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self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
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self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else []
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self.fp8_models = fp8_models
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self.task = task
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self.task_to_loss = {
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"sft:data_process": lambda pipe, *args: args,
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"direct_distill:data_process": lambda pipe, *args: args,
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"sft": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi),
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"sft:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi),
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"direct_distill": lambda pipe, inputs_shared, inputs_posi, inputs_nega: DirectDistillLoss(pipe, **inputs_shared, **inputs_posi),
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"direct_distill:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: DirectDistillLoss(pipe, **inputs_shared, **inputs_posi),
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}
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def get_pipeline_inputs(self, data):
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inputs_posi = {"prompt": data["prompt"]}
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inputs_nega = {"negative_prompt": ""}
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inputs_shared = {
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# Assume you are using this pipeline for inference,
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# please fill in the input parameters.
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"input_image": data["image"],
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"height": data["image"].size[1],
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"width": data["image"].size[0],
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# Please do not modify the following parameters
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# unless you clearly know what this will cause.
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"cfg_scale": 1,
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"embedded_guidance": 1,
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"t5_sequence_length": 512,
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"tiled": False,
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"rand_device": self.pipe.device,
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"use_gradient_checkpointing": self.use_gradient_checkpointing,
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"use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload,
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}
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inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared)
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return inputs_shared, inputs_posi, inputs_nega
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def forward(self, data, inputs=None):
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if inputs is None: inputs = self.get_pipeline_inputs(data)
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inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype)
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for unit in self.pipe.units:
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inputs = self.pipe.unit_runner(unit, self.pipe, *inputs)
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loss = self.task_to_loss[self.task](self.pipe, *inputs)
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return loss
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def flux_parser():
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parser = argparse.ArgumentParser(description="Simple example of a training script.")
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parser = add_general_config(parser)
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parser = add_image_size_config(parser)
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parser.add_argument("--tokenizer_1_path", type=str, default=None, help="Path to CLIP tokenizer.")
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parser.add_argument("--tokenizer_2_path", type=str, default=None, help="Path to T5 tokenizer.")
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parser.add_argument("--align_to_opensource_format", default=False, action="store_true", help="Whether to align the lora format to opensource format. Only for DiT's LoRA.")
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return parser
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def convert_lora_format(state_dict, alpha=None):
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prefix_rename_dict = {
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"single_blocks": "lora_unet_single_blocks",
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"blocks": "lora_unet_double_blocks",
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}
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middle_rename_dict = {
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"norm.linear": "modulation_lin",
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"to_qkv_mlp": "linear1",
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"proj_out": "linear2",
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"norm1_a.linear": "img_mod_lin",
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"norm1_b.linear": "txt_mod_lin",
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"attn.a_to_qkv": "img_attn_qkv",
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"attn.b_to_qkv": "txt_attn_qkv",
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"attn.a_to_out": "img_attn_proj",
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"attn.b_to_out": "txt_attn_proj",
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"ff_a.0": "img_mlp_0",
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"ff_a.2": "img_mlp_2",
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"ff_b.0": "txt_mlp_0",
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"ff_b.2": "txt_mlp_2",
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}
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suffix_rename_dict = {
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"lora_B.weight": "lora_up.weight",
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"lora_A.weight": "lora_down.weight",
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}
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state_dict_ = {}
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for name, param in state_dict.items():
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names = name.split(".")
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if names[-2] != "lora_A" and names[-2] != "lora_B":
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names.pop(-2)
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prefix = names[0]
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middle = ".".join(names[2:-2])
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suffix = ".".join(names[-2:])
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block_id = names[1]
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if middle not in middle_rename_dict:
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continue
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rename = prefix_rename_dict[prefix] + "_" + block_id + "_" + middle_rename_dict[middle] + "." + suffix_rename_dict[suffix]
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state_dict_[rename] = param
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if rename.endswith("lora_up.weight"):
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lora_alpha = alpha if alpha is not None else param.shape[-1]
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state_dict_[rename.replace("lora_up.weight", "alpha")] = torch.tensor((lora_alpha,))[0]
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return state_dict_
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if __name__ == "__main__":
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parser = flux_parser()
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args = parser.parse_args()
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accelerator = accelerate.Accelerator(
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gradient_accumulation_steps=args.gradient_accumulation_steps,
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kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=args.find_unused_parameters)],
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)
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dataset = UnifiedDataset(
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base_path=args.dataset_base_path,
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metadata_path=args.dataset_metadata_path,
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repeat=args.dataset_repeat,
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data_file_keys=args.data_file_keys.split(","),
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main_data_operator=UnifiedDataset.default_image_operator(
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base_path=args.dataset_base_path,
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max_pixels=args.max_pixels,
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height=args.height,
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width=args.width,
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height_division_factor=16,
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width_division_factor=16,
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)
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)
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model = FluxTrainingModule(
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model_paths=args.model_paths,
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model_id_with_origin_paths=args.model_id_with_origin_paths,
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tokenizer_1_path=args.tokenizer_1_path,
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tokenizer_2_path=args.tokenizer_2_path,
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trainable_models=args.trainable_models,
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lora_base_model=args.lora_base_model,
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lora_target_modules=args.lora_target_modules,
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lora_rank=args.lora_rank,
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lora_checkpoint=args.lora_checkpoint,
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preset_lora_path=args.preset_lora_path,
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preset_lora_model=args.preset_lora_model,
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use_gradient_checkpointing=args.use_gradient_checkpointing,
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use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
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extra_inputs=args.extra_inputs,
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fp8_models=args.fp8_models,
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offload_models=args.offload_models,
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task=args.task,
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device=accelerator.device,
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)
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model_logger = ModelLogger(
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args.output_path,
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remove_prefix_in_ckpt=args.remove_prefix_in_ckpt,
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state_dict_converter=convert_lora_format if args.align_to_opensource_format else lambda x:x,
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)
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launcher_map = {
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"sft:data_process": launch_data_process_task,
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"direct_distill:data_process": launch_data_process_task,
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"sft": launch_training_task,
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"sft:train": launch_training_task,
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"direct_distill": launch_training_task,
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"direct_distill:train": launch_training_task,
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}
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launcher_map[args.task](accelerator, dataset, model, model_logger, args=args)
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