CogView4 ModelSpec

examples/training/sft/cogview4/raider_white_tarot/train.sh

@@ -0,0 +1,162 @@
+#!/bin/bash
+
+set -e -x
+
+# export TORCH_LOGS="+dynamo,recompiles,graph_breaks"
+# export TORCHDYNAMO_VERBOSE=1
+export WANDB_MODE="offline"
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+export FINETRAINERS_LOG_LEVEL="DEBUG"
+
+# Finetrainers supports multiple backends for distributed training. Select your favourite and benchmark the differences!
+# BACKEND="accelerate"
+BACKEND="ptd"
+
+# In this setting, I'm using 2 GPUs on a 4-GPU node for training
+NUM_GPUS=2
+CUDA_VISIBLE_DEVICES="2,3"
+
+# Check the JSON files for the expected JSON format
+TRAINING_DATASET_CONFIG="examples/training/sft/cogview4/raider_white_tarot/training.json"
+VALIDATION_DATASET_FILE="examples/training/sft/cogview4/raider_white_tarot/validation.json"
+
+# Depending on how many GPUs you have available, choose your degree of parallelism and technique!
+DDP_1="--parallel_backend $BACKEND --pp_degree 1 --dp_degree 1 --dp_shards 1 --cp_degree 1 --tp_degree 1"
+DDP_2="--parallel_backend $BACKEND --pp_degree 1 --dp_degree 2 --dp_shards 1 --cp_degree 1 --tp_degree 1"
+DDP_4="--parallel_backend $BACKEND --pp_degree 1 --dp_degree 4 --dp_shards 1 --cp_degree 1 --tp_degree 1"
+FSDP_2="--parallel_backend $BACKEND --pp_degree 1 --dp_degree 1 --dp_shards 2 --cp_degree 1 --tp_degree 1"
+FSDP_4="--parallel_backend $BACKEND --pp_degree 1 --dp_degree 1 --dp_shards 4 --cp_degree 1 --tp_degree 1"
+HSDP_2_2="--parallel_backend $BACKEND --pp_degree 1 --dp_degree 2 --dp_shards 2 --cp_degree 1 --tp_degree 1"
+
+# Parallel arguments
+parallel_cmd=(
+  $DDP_2
+)
+
+# Model arguments
+model_cmd=(
+  --model_name "cogview4"
+  --pretrained_model_name_or_path "THUDM/CogView4-6B"
+)
+
+# Dataset arguments
+# Here, we know that the dataset size if about ~80 images. In `training.json`, we duplicate the same
+# dataset 3 times for multi-resolution training. This gives us a total of about 240 images. Since
+# we're using 2 GPUs for training, we can split the data into 120 images per GPU and precompute
+# all embeddings at once, instead of doing it on-the-fly which would be slower (the ideal usecase
+# of not using `--precomputation_once` is when you're training on large datasets)
+dataset_cmd=(
+  --dataset_config $TRAINING_DATASET_CONFIG
+  --dataset_shuffle_buffer_size 32
+  --precomputation_items 120
+  --precomputation_once
+)
+
+# Dataloader arguments
+dataloader_cmd=(
+  --dataloader_num_workers 0
+)
+
+# Diffusion arguments
+diffusion_cmd=(
+  --flow_weighting_scheme "logit_normal"
+)
+
+# Training arguments
+# We target just the attention projections layers for LoRA training here.
+# You can modify as you please and target any layer (regex is supported)
+training_cmd=(
+  --training_type "lora"
+  --seed 42
+  --batch_size 1
+  --train_steps 5000
+  --rank 32
+  --lora_alpha 32
+  --target_modules "transformer_blocks.*(to_q|to_k|to_v|to_out.0)"
+  --gradient_accumulation_steps 1
+  --gradient_checkpointing
+  --checkpointing_steps 1000
+  --checkpointing_limit 2
+  # --resume_from_checkpoint 3000
+  --enable_slicing
+  --enable_tiling
+)
+
+# Optimizer arguments
+optimizer_cmd=(
+  --optimizer "adamw"
+  --lr 3e-5
+  --lr_scheduler "constant_with_warmup"
+  --lr_warmup_steps 1000
+  --lr_num_cycles 1
+  --beta1 0.9
+  --beta2 0.99
+  --weight_decay 1e-4
+  --epsilon 1e-8
+  --max_grad_norm 1.0
+)
+
+# Validation arguments
+validation_cmd=(
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 500
+)
+
+# Miscellaneous arguments
+miscellaneous_cmd=(
+  --tracker_name "finetrainers-cogview4"
+  --output_dir "/raid/aryan/cogview4"
+  --init_timeout 600
+  --nccl_timeout 600
+  --report_to "wandb"
+)
+
+# Execute the training script
+if [ "$BACKEND" == "accelerate" ]; then
+
+  ACCELERATE_CONFIG_FILE=""
+  if [ "$NUM_GPUS" == 1 ]; then
+    ACCELERATE_CONFIG_FILE="accelerate_configs/uncompiled_1.yaml"
+  elif [ "$NUM_GPUS" == 2 ]; then
+    ACCELERATE_CONFIG_FILE="accelerate_configs/uncompiled_2.yaml"
+  elif [ "$NUM_GPUS" == 4 ]; then
+    ACCELERATE_CONFIG_FILE="accelerate_configs/uncompiled_4.yaml"
+  elif [ "$NUM_GPUS" == 8 ]; then
+    ACCELERATE_CONFIG_FILE="accelerate_configs/uncompiled_8.yaml"
+  fi
+
+  accelerate launch --config_file "$ACCELERATE_CONFIG_FILE" --gpu_ids $CUDA_VISIBLE_DEVICES train.py \
+    "${parallel_cmd[@]}" \
+    "${model_cmd[@]}" \
+    "${dataset_cmd[@]}" \
+    "${dataloader_cmd[@]}" \
+    "${diffusion_cmd[@]}" \
+    "${training_cmd[@]}" \
+    "${optimizer_cmd[@]}" \
+    "${validation_cmd[@]}" \
+    "${miscellaneous_cmd[@]}"
+
+elif [ "$BACKEND" == "ptd" ]; then
+
+  export CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES
+
+  torchrun \
+    --standalone \
+    --nnodes=1 \
+    --nproc_per_node=$NUM_GPUS \
+    --rdzv_backend c10d \
+    --rdzv_endpoint="localhost:0" \
+    train.py \
+      "${parallel_cmd[@]}" \
+      "${model_cmd[@]}" \
+      "${dataset_cmd[@]}" \
+      "${dataloader_cmd[@]}" \
+      "${diffusion_cmd[@]}" \
+      "${training_cmd[@]}" \
+      "${optimizer_cmd[@]}" \
+      "${validation_cmd[@]}" \
+      "${miscellaneous_cmd[@]}"
+fi
+
+echo -ne "-------------------- Finished executing script --------------------\n\n"

examples/training/sft/cogview4/raider_white_tarot/training.json

@@ -0,0 +1,34 @@
+{
+  "datasets": [
+    {
+      "data_root": "multimodalart/1920-raider-waite-tarot-public-domain",
+      "dataset_type": "image",
+      "id_token": "TRTCRD",
+      "image_resolution_buckets": [
+        [1280, 720]
+      ],
+      "reshape_mode": "bicubic",
+      "remove_common_llm_caption_prefixes": true
+    },
+    {
+      "data_root": "multimodalart/1920-raider-waite-tarot-public-domain",
+      "dataset_type": "image",
+      "id_token": "TRTCRD",
+      "image_resolution_buckets": [
+        [512, 512]
+      ],
+      "reshape_mode": "center_crop",
+      "remove_common_llm_caption_prefixes": true
+    },
+    {
+      "data_root": "multimodalart/1920-raider-waite-tarot-public-domain",
+      "dataset_type": "image",
+      "id_token": "TRTCRD",
+      "image_resolution_buckets": [
+        [768, 768]
+      ],
+      "reshape_mode": "center_crop",
+      "remove_common_llm_caption_prefixes": true
+    }
+  ]
+}

examples/training/sft/cogview4/raider_white_tarot/validation.json

@@ -0,0 +1,68 @@
+{
+  "data": [
+    {
+      "caption": "TRTCRD a trtcrd of a knight mounting a running horse wearing an armor and holding a staff, \"knight of wands\"",
+      "image_path": null,
+      "video_path": null,
+      "num_inference_steps": 50,
+      "height": 1280,
+      "width": 720
+    },
+    {
+      "caption": "TRTCRD a trtcrd of a woman sitting on a throne, wearing a crown and holding a trophee, \"queen of cups\"",
+      "image_path": null,
+      "video_path": null,
+      "num_inference_steps": 50,
+      "height": 1280,
+      "width": 720
+    },
+    {
+      "caption": "TRTCRD a trtcrd of a knight holding the cup while mounts on a stationary horse",
+      "image_path": null,
+      "video_path": null,
+      "num_inference_steps": 50,
+      "height": 1280,
+      "width": 720
+    },
+    {
+      "caption": "TRTCRD a trtcrd of a person in a red robe holding a scale and giving coins to two kneeling figures, surrounded by six pentacles",
+      "image_path": null,
+      "video_path": null,
+      "num_inference_steps": 50,
+      "height": 1280,
+      "width": 720
+    },
+    {
+      "caption": "TRTCRD a trtcrd of a knight holding the cup while mounts on a stationary horse",
+      "image_path": null,
+      "video_path": null,
+      "num_inference_steps": 50,
+      "height": 512,
+      "width": 512
+    },
+    {
+      "caption": "TRTCRD a trtcrd of a person in a red robe holding a scale and giving coins to two kneeling figures, surrounded by six pentacles",
+      "image_path": null,
+      "video_path": null,
+      "num_inference_steps": 50,
+      "height": 512,
+      "width": 512
+    },
+    {
+      "caption": "TRTCRD a trtcrd of a knight holding the cup while mounts on a stationary horse",
+      "image_path": null,
+      "video_path": null,
+      "num_inference_steps": 50,
+      "height": 768,
+      "width": 768
+    },
+    {
+      "caption": "TRTCRD a trtcrd of a person in a red robe holding a scale and giving coins to two kneeling figures, surrounded by six pentacles",
+      "image_path": null,
+      "video_path": null,
+      "num_inference_steps": 50,
+      "height": 768,
+      "width": 768
+    }
+  ]
+}

finetrainers/config.py

@@ -3,13 +3,15 @@
 
 from .models import ModelSpecification
 from .models.cogvideox import CogVideoXModelSpecification
+from .models.cogview4 import CogView4ModelSpecification
 from .models.hunyuan_video import HunyuanVideoModelSpecification
 from .models.ltx_video import LTXVideoModelSpecification
 from .models.wan import WanModelSpecification
 
 
 class ModelType(str, Enum):
     COGVIDEOX = "cogvideox"
+    COGVIEW4 = "cogview4"
     HUNYUAN_VIDEO = "hunyuan_video"
     LTX_VIDEO = "ltx_video"
     WAN = "wan"
@@ -21,6 +23,14 @@ class TrainingType(str, Enum):
 
 
 SUPPORTED_MODEL_CONFIGS = {
+    ModelType.COGVIDEOX: {
+        TrainingType.LORA: CogVideoXModelSpecification,
+        TrainingType.FULL_FINETUNE: CogVideoXModelSpecification,
+    },
+    ModelType.COGVIEW4: {
+        TrainingType.LORA: CogView4ModelSpecification,
+        TrainingType.FULL_FINETUNE: CogView4ModelSpecification,
+    },
     ModelType.HUNYUAN_VIDEO: {
         TrainingType.LORA: HunyuanVideoModelSpecification,
         TrainingType.FULL_FINETUNE: HunyuanVideoModelSpecification,
@@ -29,10 +39,6 @@ class TrainingType(str, Enum):
         TrainingType.LORA: LTXVideoModelSpecification,
         TrainingType.FULL_FINETUNE: LTXVideoModelSpecification,
     },
-    ModelType.COGVIDEOX: {
-        TrainingType.LORA: CogVideoXModelSpecification,
-        TrainingType.FULL_FINETUNE: CogVideoXModelSpecification,
-    },
     ModelType.WAN: {
         TrainingType.LORA: WanModelSpecification,
         TrainingType.FULL_FINETUNE: WanModelSpecification,

finetrainers/data/dataset.py

@@ -600,11 +600,17 @@ def __iter__(self):
         for sample in iter(self.dataset):
             if self.dataset_type == "image":
                 if self.image_resolution_buckets:
+                    sample["_original_num_frames"] = 1
+                    sample["_original_height"] = sample["image"].size(1)
+                    sample["_original_width"] = sample["image"].size(2)
                     sample["image"] = FF.resize_to_nearest_bucket_image(
                         sample["image"], self.image_resolution_buckets, self.reshape_mode
                     )
             elif self.dataset_type == "video":
                 if self.video_resolution_buckets:
+                    sample["_original_num_frames"] = sample["video"].size(0)
+                    sample["_original_height"] = sample["video"].size(2)
+                    sample["_original_width"] = sample["video"].size(3)
                     sample["video"], _first_frame_only = FF.resize_to_nearest_bucket_video(
                         sample["video"], self.video_resolution_buckets, self.reshape_mode
                     )
@@ -751,9 +757,27 @@ def _initialize_hub_dataset(dataset_name: str, dataset_type: str, infinite: bool
         return _initialize_data_caption_file_dataset_from_hub(dataset_name, dataset_type, infinite)
     elif _has_data_file_caption_file_lists(repo_file_list, remote=True):
         return _initialize_data_file_caption_file_dataset_from_hub(dataset_name, dataset_type, infinite)
-    else:
+
+    has_tar_files = any(file.endswith(".tar") for file in repo_file_list)
+    if has_tar_files:
         return _initialize_webdataset(dataset_name, dataset_type, infinite)
 
+    # TODO(aryan): handle parquet
+    # TODO(aryan): This should be improved
+    caption_files = [pathlib.Path(file).name for file in repo_file_list if file.endswith(".txt")]
+    if len(caption_files) < MAX_PRECOMPUTABLE_ITEMS_LIMIT:
+        try:
+            dataset_root = snapshot_download(dataset_name, repo_type="dataset")
+            if dataset_type == "image":
+                dataset = ImageFolderDataset(dataset_root, infinite=infinite)
+            else:
+                dataset = VideoFolderDataset(dataset_root, infinite=infinite)
+            return dataset
+        except Exception:
+            pass
+
+    raise ValueError(f"Could not load dataset {dataset_name} from the HF Hub")
+
 
 def _initialize_data_caption_file_dataset_from_hub(
     dataset_name: str, dataset_type: str, infinite: bool = False

finetrainers/functional/image.py

@@ -22,7 +22,7 @@ def resize_crop_image(image: torch.Tensor, size: Tuple[int, int]) -> torch.Tenso
 
 
 def bicubic_resize_image(image: torch.Tensor, size: Tuple[int, int]) -> torch.Tensor:
-    return F.interpolate(image, size=size, mode="bicubic", align_corners=False)
+    return F.interpolate(image.unsqueeze(0), size=size, mode="bicubic", align_corners=False)[0]
 
 
 def find_nearest_resolution_image(image: torch.Tensor, resolution_buckets: List[Tuple[int, int]]) -> Tuple[int, int]:

finetrainers/models/cogview4/__init__.py

@@ -0,0 +1 @@
+from .base_specification import CogView4ModelSpecification