[Training] Use inference pipeline for training validation (#585)

Author: SolitaryThinker

examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/README.md

@@ -0,0 +1,10 @@
+This directory contain e2e examples scripts for finetuning Wan2.1 I2V. 
+
+Execute the following commands from `FastVideo/` to run training:
+
+- Download crush-smol dataset:
+`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/download_dataset.sh`
+- Preprocess the videos and captions into latents:
+`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/preprocess_wan_data_i2v.sh`
+- Edit the following file and run finetuning:
+`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/finetune_i2v.sh`

examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/download_dataset.sh

@@ -0,0 +1,3 @@
+#!/bin/bash
+
+python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"

examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/finetune_i2v.sh

@@ -0,0 +1,93 @@
+#!/bin/bash
+
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export TOKENIZERS_PARALLELISM=false
+# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
+
+MODEL_PATH="weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers"
+DATA_DIR="data/crush-smol_processed_i2v_1_3b_inp/combined_parquet_dataset/"
+VALIDATION_DATASET_FILE="examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/validation.json"
+NUM_GPUS=8
+# export CUDA_VISIBLE_DEVICES=4,5
+# IP=[MASTER NODE IP]
+
+# Training arguments
+training_args=(
+  --tracker_project_name "wan_i2v_finetune"
+  --output_dir "$DATA_DIR/outputs/wan_i2v_finetune"
+  --max_train_steps 2000
+  --train_batch_size 4
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 1
+  --num_latent_t 8
+  --num_height 480
+  --num_width 832
+  --num_frames 77
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_GPUS
+  --sp_size 4
+  --tp_size 4
+  --hsdp_replicate_dim 2
+  --hsdp_shard_dim 4
+)
+
+# Model arguments
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path "$DATA_DIR"
+  --dataloader_num_workers 1
+)
+
+# Validation arguments
+validation_args=(
+  --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 100
+  --validation_sampling_steps "40"
+  --validation_guidance_scale "1.0"
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --learning_rate 2e-5
+  --mixed_precision "bf16"
+  --checkpointing_steps 2000
+  --weight_decay 1e-4
+  --max_grad_norm 1.0
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --allow_tf32
+  --checkpoints_total_limit 3
+  --training_cfg_rate 0.1
+  --multi_phased_distill_schedule "4000-1"
+  --not_apply_cfg_solver
+  --dit_precision "fp32"
+  --num_euler_timesteps 50
+  --ema_start_step 0
+  --enable_gradient_checkpointing_type "full"
+)
+
+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
+torchrun \
+  --nnodes 1 \
+  --nproc_per_node $NUM_GPUS \
+    fastvideo/v1/training/wan_i2v_training_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"

examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/finetune_i2v.slurm

@@ -0,0 +1,131 @@
+#!/bin/bash
+#SBATCH --job-name=i2v
+#SBATCH --partition=main
+#SBATCH --qos=hao
+#SBATCH --nodes=4
+#SBATCH --ntasks=4
+#SBATCH --ntasks-per-node=1
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=128
+#SBATCH --nodelist=fs-mbz-gpu-[100-850]
+#SBATCH --mem=1440G
+#SBATCH --output=i2v_output/i2v_%j.out
+#SBATCH --error=i2v_output/i2v_%j.err
+#SBATCH --exclusive
+set -e -x
+
+# Environment Setup
+source ~/conda/miniconda/bin/activate
+conda activate will-fv
+
+# Basic Info
+export WANDB_MODE="online"
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+# different cache dir for different processes
+export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
+export MASTER_PORT=29500
+export NODE_RANK=$SLURM_PROCID
+nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
+export MASTER_ADDR=${nodes[0]}
+export CUDA_VISIBLE_DEVICES=$SLURM_LOCALID
+export TOKENIZERS_PARALLELISM=false
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
+
+echo "MASTER_ADDR: $MASTER_ADDR"
+echo "NODE_RANK: $NODE_RANK"
+
+# Configs
+NUM_GPUS=8
+
+MODEL_PATH="weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers"
+DATA_DIR="data/crush-smol_processed_i2v_1_3b_inp/combined_parquet_dataset/"
+VALIDATION_DATASET_FILE="examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/validation.json"
+# export CUDA_VISIBLE_DEVICES=4,5
+# IP=[MASTER NODE IP]
+
+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
+
+# Training arguments
+training_args=(
+  --tracker_project_name wan_i2v_finetune
+  --output_dir="$DATA_DIR/outputs/wan_i2v_finetune_2n"
+  --max_train_steps=2000
+  --train_batch_size=2
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps=1
+  --num_latent_t 8
+  --num_height 480
+  --num_width 832
+  --num_frames 77
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_GPUS
+  --sp_size $NUM_GPUS
+  --tp_size $NUM_GPUS
+  --hsdp_replicate_dim $SLURM_JOB_NUM_NODES
+  --hsdp_shard_dim $NUM_GPUS
+)
+
+# Model arguments
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path "$DATA_DIR"
+  --dataloader_num_workers 10
+)
+
+# Validation arguments
+validation_args=(
+  --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 100
+  --validation_sampling_steps "40"
+  --validation_guidance_scale "1.0"
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --learning_rate=1e-5
+  --mixed_precision="bf16"
+  --checkpointing_steps=1000
+  --weight_decay 1e-4
+  --max_grad_norm 1.0
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --allow_tf32
+  --checkpoints_total_limit 3
+  --training_cfg_rate 0.1
+  --multi_phased_distill_schedule "4000-1"
+  --not_apply_cfg_solver
+  --dit_precision "fp32"
+  --num_euler_timesteps 50
+  --ema_start_step 0
+  --enable_gradient_checkpointing_type "full"
+)
+
+srun torchrun \
+--nnodes $SLURM_JOB_NUM_NODES \
+--nproc_per_node $NUM_GPUS \
+--node_rank $SLURM_PROCID \
+--rdzv_backend=c10d \
+--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
+    fastvideo/v1/training/wan_i2v_training_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"

examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/preprocess_wan_data_i2v.sh

@@ -0,0 +1,26 @@
+#!/bin/bash
+
+GPU_NUM=1 # 2,4,8
+MODEL_PATH="weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers"
+MODEL_TYPE="wan"
+DATA_MERGE_PATH="data/crush-smol/merge.txt"
+OUTPUT_DIR="data/crush-smol_processed_i2v_1_3b_inp/"
+VALIDATION_PATH="examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/validation.json"
+
+torchrun --nproc_per_node=$GPU_NUM \
+    fastvideo/v1/pipelines/preprocess/v1_preprocess.py \
+    --model_path $MODEL_PATH \
+    --data_merge_path $DATA_MERGE_PATH \
+    --preprocess_video_batch_size 8 \
+    --max_height 480 \
+    --max_width 832 \
+    --num_frames 77 \
+    --dataloader_num_workers 0 \
+    --output_dir=$OUTPUT_DIR \
+    --model_type $MODEL_TYPE \
+    --train_fps 16 \
+    --validation_dataset_file $VALIDATION_PATH \
+    --samples_per_file 8 \
+    --flush_frequency 8 \
+    --video_length_tolerance_range 5 \
+    --preprocess_task "i2v" 

examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/validation.json

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+{
+  "data": [
+    {
+      "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
+      "image_path": null,
+      "video_path": "validation_dataset/yYcK4nANZz4-Scene-034.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.",
+      "image_path": null,
+      "video_path": "validation_dataset/yYcK4nANZz4-Scene-027.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table.",
+      "image_path": null,
+      "video_path": "validation_dataset/yYcK4nANZz4-Scene-030.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    }
+  ]
+}

examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/validation_dataset/yYcK4nANZz4-Scene-027.mp4

@@ -2,11 +2,12 @@
 
 export WANDB_BASE_URL="https://api.wandb.ai"
 export WANDB_MODE=online
+export TOKENIZERS_PARALLELISM=false
 # export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
 
 MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
 DATA_DIR="data/crush-smol_processed_i2v/combined_parquet_dataset/"
-VALIDATION_DIR="data/crush-smol_processed_i2v/validation_parquet_dataset/"
+VALIDATION_DATASET_FILE="examples/training/finetune/wan_i2v_14b_480p/crush_smol/validation.json"
 NUM_GPUS=8
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]
@@ -49,7 +50,7 @@ dataset_args=(
 # Validation arguments
 validation_args=(
   --log_validation
-  --validation_preprocessed_path "$VALIDATION_DIR"
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
   --validation_steps 100
   --validation_sampling_steps "40"
   --validation_guidance_scale "1.0"
@@ -75,6 +76,7 @@ miscellaneous_args=(
   --dit_precision "fp32"
   --num_euler_timesteps 50
   --ema_start_step 0
+  --enable_gradient_checkpointing_type "full"
 )
 
 # If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t