wehub-resource-sync
510 lines
20 KiB
Python
510 lines
20 KiB
Python
# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# coding: utf-8
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import warnings
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warnings.filterwarnings("ignore", message=".*pkg_resources is deprecated.*", category=UserWarning)
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warnings.filterwarnings("ignore", category=FutureWarning, module="diffusers.models.transformers.transformer_2d")
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import os
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
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os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
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import os.path as osp
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from copy import deepcopy
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from typing import Tuple, cast, Optional
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import torch
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import torch.distributed as dist
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from torch.utils.data import DataLoader
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from transformers import HfArgumentParser, set_seed
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from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import Qwen2_5_VLVisionConfig
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from safetensors.torch import load_file
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from PIL import Image
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from torchvision.utils import make_grid
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import numpy as np
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from tqdm import trange
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from data.dataset_base import DataConfig, simple_custom_collate
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from data.data_utils import add_special_tokens
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from modeling.vae.wan.model import WanVideoVAE
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from modeling.lance import LanceConfig, Lance, Qwen2ForCausalLM
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from modeling.qwen2 import Qwen2Tokenizer
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from modeling.qwen2.modeling_qwen2 import Qwen2Config
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from modeling.vit.qwen2_5_vl_vit import Qwen2_5_VisionTransformerPretrainedModel
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from common.utils.misc import tuple_mul, AutoEncoderParams
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from common.utils.logging import get_logger
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from common.val.utils import make_padded_latent
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from data.datasets_custom import ValidationDataset
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from config.config_factory import ModelArguments, DataArguments, TrainingArguments, EvaluationArguments, get_model_path
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def init_from_vlm_checkpoint(model: Qwen2ForCausalLM, model_args: ModelArguments, log_rank0):
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# NOTE: VLM initialization loads through this path.
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def load_safetensors_state_dict(folder_path):
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# Select safetensors files only and sort by filename for deterministic order.
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safetensor_files = sorted(
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f for f in os.listdir(folder_path) if f.endswith(".safetensors")
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)
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state_dict = {}
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for filename in safetensor_files:
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file_path = osp.join(folder_path, filename)
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state_dict.update(load_file(file_path))
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return state_dict
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state_dict = load_safetensors_state_dict(model_args.llm_path)
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# Rename parameters to match Lance parameter names.
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for k in list(state_dict.keys()):
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if "visual" in k: # ViT and connector
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state_dict[k.replace("visual", "vit_model")] = state_dict.pop(k)
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else:
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# Add the language_model prefix.
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state_dict["language_model." + k] = state_dict.pop(k)
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result = model.load_state_dict(state_dict, strict=False)
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clean_memory(state_dict)
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def init_from_model_path_if_needed(model: Qwen2ForCausalLM, model_args: ModelArguments):
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# Always load the trained Lance checkpoint from model_path.
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path_dir = model_args.model_path
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ema_path = osp.join(path_dir, "ema.safetensors")
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model_path = osp.join(path_dir, "model.safetensors")
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model_path_ft = None
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if osp.exists(model_path):
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model_path_ft = model_path
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elif osp.exists(ema_path):
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model_path_ft = ema_path
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if model_path_ft:
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model_state_dict = load_file(model_path_ft, device="cpu")
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else:
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raise FileNotFoundError(
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f"Fine-tuning failed: No valid checkpoint ('ema.safetensors' or 'model.safetensors') found in {path_dir}"
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)
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# NOTE: position embeds are fixed sinusoidal embeddings, so we can just pop it off,
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# which makes it easier to adapt to different resolutions.
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if 'latent_pos_embed.pos_embed' in model_state_dict:
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model_state_dict.pop('latent_pos_embed.pos_embed')
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msg = model.load_state_dict(model_state_dict, strict=False)
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clean_memory(model_state_dict)
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return msg
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def clean_memory(*objects):
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"""Clear memory and release the GPU cache."""
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for obj in objects:
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del obj
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import gc
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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def decode_video_tensor_for_dpg(v_list):
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"""
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Decode video tensors for DPG while preserving the original save format.
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"""
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N_target = len(v_list)
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if N_target != 1:
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from einops import rearrange
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padded_videos_latent = [v.permute(1, 0, 2, 3) for v in v_list]
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v_tc_hw = rearrange(padded_videos_latent, "n t c h w -> t c h (n w)")
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else:
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v_tc_hw = v_list[0].permute(1, 0, 2, 3)
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v_tc_hw = v_tc_hw.float().clip(-1, 1).mul_(0.5).add_(0.5).mul_(255).round().clamp(0, 255).to(torch.uint8)
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return v_tc_hw
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def resolve_dpg_paths(
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model_args: ModelArguments,
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data_args: DataArguments,
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) -> None:
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if not model_args.model_path:
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raise ValueError("DPG requires --model_path to be provided explicitly.")
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if not model_args.llm_path:
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model_args.llm_path = model_args.model_path
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if not model_args.vit_path:
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model_args.vit_path = get_model_path("vit.qwen2_5_vl")
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if not data_args.val_dataset_config_file:
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data_args.val_dataset_config_file = get_model_path("dpg.data")
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def validate_on_fixed_batch(
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fsdp_model: Lance,
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vae_model: Optional[WanVideoVAE],
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tokenizer: Qwen2Tokenizer,
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val_data_cpu: dict,
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training_args: TrainingArguments,
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model_args: ModelArguments,
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data_args: DataArguments,
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inference_args: EvaluationArguments,
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curr_step: int,
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logger,
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new_token_ids,
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image_token_id: int,
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device: int,
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save_source_video: bool = False,
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save_path_gen: str = "",
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save_path_gt: str = "",
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sample_num_per_prompt: int = 1,
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):
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"""
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Validation logic that preserves the same save format as the original file.
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"""
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# Check whether distributed execution has been initialized.
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if dist.is_initialized():
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is_rank0 = (dist.get_rank() == 0)
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else:
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is_rank0 = True
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log_rank0 = logger.info if is_rank0 else (lambda *_: None)
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val_data = val_data_cpu.cuda(device).to_dict()
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with torch.no_grad(), torch.amp.autocast("cuda", enabled=True, dtype=torch.bfloat16):
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# Compute padded_latent.
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if "padded_videos" in val_data.keys():
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val_data["padded_latent"] = make_padded_latent(val_data["padded_videos"], val_data["vae_data_mode"], vae_model)
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# -------------------- GEN branch --------------------
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tensor_list_for_grid = []
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loop_iterator = trange(sample_num_per_prompt) if is_rank0 else range(sample_num_per_prompt)
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# Support resumable generation.
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save_name = f"{save_path_gen}/{val_data['index']}.png"
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if os.path.exists(save_name):
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return None
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for sample_num_per_prompt_index in loop_iterator:
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# Sample generations with the original parameters.
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params = {
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"val_packed_text_ids": val_data["packed_text_ids"],
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"val_packed_text_indexes": val_data["packed_text_indexes"],
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"val_sample_lens": val_data["sample_lens"],
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"val_packed_position_ids": val_data["packed_position_ids"],
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"val_split_lens": val_data["split_lens"],
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"val_attn_modes": val_data["attn_modes"],
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"val_sample_N_target": val_data["sample_N_target"],
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"val_packed_vae_token_indexes": val_data["packed_vae_token_indexes"],
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"timestep_shift": training_args.validation_timestep_shift,
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"num_timesteps": training_args.validation_num_timesteps,
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"val_mse_loss_indexes": val_data.get("mse_loss_indexes", None),
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"val_padded_latent": val_data["padded_latent"],
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"video_sizes": val_data["video_sizes"],
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"cfg_text_scale": model_args.cfg_text_scale,
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"cfg_interval": training_args.cfg_interval,
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"cfg_renorm_min": training_args.cfg_renorm_min,
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"cfg_renorm_type": training_args.cfg_renorm_type,
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"device": device,
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"dtype": torch.bfloat16,
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"new_token_ids": new_token_ids,
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"max_samples": training_args.validation_max_samples,
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"validation_noise_seed": training_args.validation_noise_seed + sample_num_per_prompt_index,
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"apply_chat_template": training_args.apply_chat_template,
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"apply_qwen_2_5_vl_pos_emb": training_args.apply_qwen_2_5_vl_pos_emb,
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"image_token_id": image_token_id,
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"val_packed_vit_token_indexes": val_data.get("packed_vit_token_indexes", None),
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"val_packed_vit_tokens": val_data.get("packed_vit_tokens", None),
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"vit_video_grid_thw": val_data.get("vit_video_grid_thw", None),
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"vae_video_grid_thw": val_data["vae_video_grid_thw"],
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"video_grid_thw": val_data.get("video_grid_thw", None),
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"caption": val_data.get("caption", None),
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"sample_task": val_data["sample_task"],
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"sample_modality": val_data["sample_modality"],
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"cfg_type": training_args.cfg_type,
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"cfg_uncond_token_id": training_args.cfg_uncond_token_id,
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"index": val_data["index"],
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"val_padded_videos": val_data["padded_videos"] if save_source_video else None,
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}
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if training_args.use_KVcache:
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denoise_latent, captions, padded_videos, index = fsdp_model.validation_gen_KVcache(**params)
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else:
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denoise_latent, captions, padded_videos, index = fsdp_model.validation_gen(**params)
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# Decode and save.
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for i_val, latent in enumerate(denoise_latent):
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v_list = [vae_model.vae_decode([latent_])[0] for latent_ in latent]
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# Keep the original save format.
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v_thwc = decode_video_tensor_for_dpg(v_list)
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# Use frame 0 directly.
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if v_thwc.shape[0] == 1:
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tensor_list_for_grid.append(v_thwc.squeeze(0).cpu())
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else:
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raise NotImplementedError("Image saving is required")
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# Keep the original save format.
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grid_tensor = make_grid(tensor_list_for_grid, nrow=int(np.sqrt(sample_num_per_prompt)), padding=0, pad_value=255)
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grid_numpy = grid_tensor.permute(1, 2, 0).numpy()
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Image.fromarray(grid_numpy).save(save_name)
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def main():
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# ========================= Env setup ==============================
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assert torch.cuda.is_available()
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if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
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dist.init_process_group("nccl")
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GLOBAL_RANK = dist.get_rank()
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WORLD_SIZE = dist.get_world_size()
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else:
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GLOBAL_RANK = 0
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WORLD_SIZE = 1
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LOCAL_RANK = GLOBAL_RANK % torch.cuda.device_count()
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DEVICE = LOCAL_RANK
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torch.cuda.set_device(DEVICE)
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# ========================= Args and logger setup ==============================
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parser = HfArgumentParser((ModelArguments, DataArguments, EvaluationArguments))
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model_args, data_args, inference_args = cast(
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Tuple[ModelArguments, DataArguments, EvaluationArguments],
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parser.parse_args_into_dataclasses(),
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)
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training_args = inference_args
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# ========================= DPG path resolution ==============================
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resolve_dpg_paths(model_args, data_args)
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# NOTE: validation_noise_seed matches validation_data_seed.
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training_args.validation_noise_seed = inference_args.evaluation_seed
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training_args.validation_data_seed = inference_args.evaluation_seed
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logger = get_logger()
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log_rank0 = print if GLOBAL_RANK == 0 else (lambda *_: None)
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# Set seed:
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seed = training_args.global_seed * WORLD_SIZE + GLOBAL_RANK
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set_seed(seed)
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# ========================= LLM model setup ==============================
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llm_config: Qwen2Config = Qwen2Config.from_json_file(osp.join(model_args.model_path, "llm_config.json"))
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llm_config.layer_module = model_args.layer_module
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llm_config.qk_norm = model_args.llm_qk_norm
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llm_config.qk_norm_und = model_args.llm_qk_norm_und
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llm_config.qk_norm_gen = model_args.llm_qk_norm_gen
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llm_config.tie_word_embeddings = model_args.tie_word_embeddings
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llm_config.freeze_und = training_args.freeze_und
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llm_config.apply_qwen_2_5_vl_pos_emb = training_args.apply_qwen_2_5_vl_pos_emb
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language_model: Qwen2ForCausalLM = Qwen2ForCausalLM(llm_config)
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if training_args.visual_und:
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if model_args.vit_type in ("qwen2_5_vl", "qwen_2_5_vl_original"):
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vit_config = Qwen2_5_VLVisionConfig.from_pretrained(model_args.vit_path)
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vit_model = Qwen2_5_VisionTransformerPretrainedModel(vit_config)
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vit_weights = load_file(osp.join(model_args.vit_path, "vit.safetensors"))
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vit_model.load_state_dict(vit_weights, strict=True)
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else:
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raise ValueError(f"Unsupported vit_type: {model_args.vit_type}")
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clean_memory(vit_weights)
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if training_args.visual_gen:
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vae_model = WanVideoVAE()
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vae_config: AutoEncoderParams = deepcopy(vae_model.vae_config)
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else:
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vae_model = None
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vae_config = None
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# Lance config.
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config = LanceConfig(
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visual_gen=training_args.visual_gen,
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visual_und=training_args.visual_und,
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llm_config=llm_config,
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vit_config=vit_config if training_args.visual_und else None,
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vae_config=vae_config if training_args.visual_gen else None,
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latent_patch_size=model_args.latent_patch_size,
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max_num_frames=model_args.max_num_frames,
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max_latent_size=model_args.max_latent_size,
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vit_max_num_patch_per_side=model_args.vit_max_num_patch_per_side,
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connector_act=model_args.connector_act,
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interpolate_pos=model_args.interpolate_pos,
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timestep_shift=training_args.timestep_shift,
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)
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model: Lance = Lance(
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language_model=language_model,
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vit_model=vit_model if training_args.visual_und else None,
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vit_type=model_args.vit_type,
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config=config,
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training_args=training_args,
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)
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model = model.to(DEVICE)
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# Setup tokenizer for model:
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tokenizer: Qwen2Tokenizer = Qwen2Tokenizer.from_pretrained(model_args.model_path)
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tokenizer, new_token_ids, num_new_tokens = add_special_tokens(tokenizer)
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# Initialize MoE before loading the checkpoint.
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if training_args.copy_init_moe:
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language_model.init_moe()
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init_from_model_path_if_needed(model, model_args)
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# Resize after loading the checkpoint.
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if num_new_tokens > 0:
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model.language_model.resize_token_embeddings(len(tokenizer))
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model.config.llm_config.vocab_size = len(tokenizer)
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model.language_model.config.vocab_size = len(tokenizer)
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if model_args.vit_type.lower() == "qwen2_5_vl":
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from common.model.hacks import hack_qwen2_5_vl_config
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language_model = hack_qwen2_5_vl_config(language_model)
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image_token_id = language_model.config.video_token_id
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new_token_ids.update({"image_token_id": image_token_id})
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model.update_tokenizer(tokenizer=tokenizer)
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if model_args.tie_word_embeddings:
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model.language_model.untie_lm_head()
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model.language_model.copy_new_token_rows_to_lm_head(num_new_tokens)
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model_args.tie_word_embeddings = False
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llm_config.tie_word_embeddings = False
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else:
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assert model.language_model.get_input_embeddings().weight.data.data_ptr() != model.language_model.get_output_embeddings().weight.data.data_ptr(), 'tie_word_embeddings conflict'
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model = model.to(device=DEVICE, dtype=torch.bfloat16)
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model.eval()
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if vae_model is not None and hasattr(vae_model, "eval"):
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vae_model.eval()
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# Setup packed dataloader with a simple DataConfig instance.
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dataset_config = DataConfig(grouped_datasets={})
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# Configure basic parameters.
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dataset_config.num_frames = inference_args.num_frames
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dataset_config.H = inference_args.video_height
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dataset_config.W = inference_args.video_width
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dataset_config.task = inference_args.task
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dataset_config.resolution = inference_args.resolution
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dataset_config.text_template = inference_args.text_template
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# Configure VIT parameters.
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if training_args.visual_und:
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dataset_config.vit_patch_size = model_args.vit_patch_size
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dataset_config.vit_patch_size_temporal = model_args.vit_patch_size_temporal
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dataset_config.vit_max_num_patch_per_side = model_args.vit_max_num_patch_per_side
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# Configure VAE parameters.
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if training_args.visual_gen and vae_config:
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assert len(model_args.latent_patch_size) == 3, "len(latent_patch_size) must be 3"
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vae_downsample = tuple_mul(
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model_args.latent_patch_size, (vae_config.downsample_temporal, vae_config.downsample_spatial, vae_config.downsample_spatial)
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)
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dataset_config.latent_patch_size = model_args.latent_patch_size
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dataset_config.vae_downsample = vae_downsample
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dataset_config.max_latent_size = model_args.max_latent_size
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dataset_config.max_num_frames = model_args.max_num_frames
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# Share dropout settings.
|
|
dataset_config.text_cond_dropout_prob = model_args.text_cond_dropout_prob
|
|
dataset_config.vae_cond_dropout_prob = model_args.vae_cond_dropout_prob
|
|
dataset_config.vit_cond_dropout_prob = model_args.vit_cond_dropout_prob
|
|
|
|
# Create dataset.
|
|
val_dataset = ValidationDataset(
|
|
jsonl_path= data_args.val_dataset_config_file,
|
|
tokenizer=tokenizer,
|
|
data_args=data_args,
|
|
model_args=model_args,
|
|
training_args=training_args,
|
|
new_token_ids=new_token_ids,
|
|
dataset_config=dataset_config,
|
|
local_rank=GLOBAL_RANK,
|
|
world_size=WORLD_SIZE,
|
|
)
|
|
|
|
val_loader = DataLoader(
|
|
val_dataset,
|
|
batch_size=1,
|
|
num_workers=0,
|
|
pin_memory=True,
|
|
collate_fn=simple_custom_collate,
|
|
drop_last=True,
|
|
prefetch_factor=None,
|
|
persistent_workers=False,
|
|
multiprocessing_context=None,
|
|
)
|
|
|
|
val_loader_iter = iter(val_loader)
|
|
|
|
if not os.path.exists(inference_args.save_path_gen):
|
|
os.makedirs(inference_args.save_path_gen, exist_ok=True)
|
|
|
|
# Main loop.
|
|
from tqdm import tqdm
|
|
import time
|
|
from datetime import datetime, timedelta
|
|
|
|
total_batches = len(val_loader)
|
|
pbar = tqdm(total=total_batches, desc="Validating", unit="batch", leave=True, ncols=120, disable=(GLOBAL_RANK != 0))
|
|
start_time = time.time()
|
|
|
|
for i in range(total_batches):
|
|
val_data_cpu = next(val_loader_iter)
|
|
|
|
validate_on_fixed_batch(
|
|
fsdp_model=model,
|
|
vae_model=vae_model,
|
|
tokenizer=tokenizer,
|
|
val_data_cpu=val_data_cpu,
|
|
training_args=training_args,
|
|
model_args=model_args,
|
|
data_args=data_args,
|
|
inference_args=inference_args,
|
|
curr_step=0,
|
|
logger=logger,
|
|
new_token_ids=new_token_ids,
|
|
image_token_id=image_token_id,
|
|
device=DEVICE,
|
|
save_source_video=False,
|
|
save_path_gen=inference_args.save_path_gen,
|
|
save_path_gt="",
|
|
sample_num_per_prompt=inference_args.sample_num_per_prompt,
|
|
)
|
|
|
|
if GLOBAL_RANK == 0:
|
|
elapsed = time.time() - start_time
|
|
avg_time = elapsed / (i + 1)
|
|
eta_seconds = avg_time * (total_batches - i - 1)
|
|
expected_finish = datetime.now() + timedelta(seconds=eta_seconds)
|
|
finish_str = expected_finish.strftime('%Y-%m-%d %H:%M:%S')
|
|
|
|
pbar.set_postfix_str(f"ETA: {timedelta(seconds=int(eta_seconds))} | Finish: {finish_str}")
|
|
pbar.update(1)
|
|
|
|
if GLOBAL_RANK == 0:
|
|
pbar.close()
|
|
|
|
if dist.is_initialized():
|
|
dist.destroy_process_group()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|