From d81209bf497f55132abd14deb9c109d4d13087ca Mon Sep 17 00:00:00 2001
From: Aryan <aryan@huggingface.co>
Date: Fri, 4 Oct 2024 18:18:54 +0530
Subject: [PATCH] Low-bit memory optimizers, CpuOffloadOptimizer, Memory
 Reports (#3)

* update

* update

* update

* update

* update

* update

* update

* update

* update
---
 .gitignore                               |   2 +
 README.md                                | 234 +++++++++++++++++++----
 assets/slaying-ooms.png                  | Bin 0 -> 168410 bytes
 metrics.sh                               | 155 ---------------
 requirements.txt                         |  13 ++
 train_text_to_video_lora.sh              |  42 ++--
 train_text_to_video_sft.sh               |  35 ++--
 training/args.py                         |  22 ++-
 training/cogvideox_text_to_video_lora.py |  69 ++++---
 training/cogvideox_text_to_video_sft.py  |  65 ++++---
 training/utils.py                        | 111 ++++++++---
 11 files changed, 434 insertions(+), 314 deletions(-)
 create mode 100644 assets/slaying-ooms.png
 delete mode 100755 metrics.sh
 create mode 100644 requirements.txt

diff --git a/.gitignore b/.gitignore
index 1a89f04..e652a10 100644
--- a/.gitignore
+++ b/.gitignore
@@ -165,3 +165,5 @@ cython_debug/
 wandb/
 *.txt
 dump*
+
+!requirements.txt
diff --git a/README.md b/README.md
index 487d435..c1e585f 100644
--- a/README.md
+++ b/README.md
@@ -43,11 +43,6 @@ As an example, let's use [this](https://huggingface.co/datasets/Wild-Heart/Disne
 huggingface-cli download --repo-type dataset Wild-Heart/Disney-VideoGeneration-Dataset --local-dir video-dataset-disney
 ```
 
-#### Rough notes and TODOs:
-
-- Uncompiled SFT works end-to-end on dummy example. Need to test on larger dataset (not priority at the moment)
-- Compiled SFT fails with `THUDM/CogVideoX-2b` throwing the following error (by error, it's more of a graph break situation due to mixin numpy/cpu device when getting sincos positional embeddings).
-
 ## Training
 
 TODO
@@ -58,44 +53,205 @@ Note: Untested on MPS
 
 ## Memory requirements
 
-|       model        | lora rank | optimizer | gradient_checkpointing | memory_before_training | memory_after_validation | memory_after_testing |
-|:------------------:|:---------:|:---------:|:----------------------:|:----------------------:|:-----------------------:|:--------------------:|
-| THUDM/CogVideoX-2b |    16     |  adamw    |          False         |         12.945         |         39.553          |       23.148         |
-| THUDM/CogVideoX-2b |    16     |  adamw    |          True          |         12.946         |         18.436          |       23.160         |
-| THUDM/CogVideoX-2b |    64     |  adamw    |          False         |         13.035         |         40.051          |       23.430         |
-| THUDM/CogVideoX-2b |    64     |  adamw    |          True          |         13.035         |         18.883          |       23.414         |
-| THUDM/CogVideoX-2b |    256    |  adamw    |          False         |         13.095         |         42.004          |       24.385         |
-| THUDM/CogVideoX-2b |    256    |  adamw    |          True          |         13.095         |         19.307          |       24.381         |
+<table align="center">
+<tr>
+  <td align="center"><a href="https://www.youtube.com/watch?v=UvRl4ansfCg"> Slaying OOMs with PyTorch</a></td>
+</tr>
+<tr>
+  <td align="center"><img src="assets/slaying-ooms.png" style="width: 480px; height: 480px;"></td>
+</tr>
+</table>
 
-**Note:** `memory_after_validation` is indicative of the peak memory required for training.
+Supported and verified memory optimizations for training include:
+- `CPUOffloadOptimizer` from [TorchAO](https://github.com/pytorch/ao). You can read about its capabilities and limitations [here](https://github.com/pytorch/ao/tree/main/torchao/prototype/low_bit_optim#optimizer-cpu-offload). In short, it allows you to use the CPU for storing trainable parameters and gradients. This results in the optimizer step happening on the CPU, which requires a fast CPU optimizer, such as `torch.AdamW(fused=True)` or applying `torch.compile` on the optimizer step. Additionally, it is recommended to not `torch.compile` your model for training. Gradient clipping and accumulation is not supported yet either.
+- Low-bit optimizers from [bitsandbytes](https://huggingface.co/docs/bitsandbytes/optimizers). TODO: to test and make [TorchAO](https://github.com/pytorch/ao/tree/main/torchao/prototype/low_bit_optim) ones work
+- TODO: DeepSpeed ZeRO
+
+> [!IMPORTANT]
+> The memory requirements are reported after running the `training/prepare_dataset.py`, which converts the videos and captions to latents and embeddings. During training, we directly load the latents and embeddings, and do not require the VAE or the T5 text encoder. However, if you perform validation/testing, these must be loaded and increase the amount of required memory. Not performing validation/testing saves a significant amount of memory, which can be used to focus solely on training if you're on smaller VRAM GPUs.
+
+### LoRA finetuning
 
 <details>
-<summary> stack trace </summary>
+<summary> AdamW </summary>
 
-```
-skipping cudagraphs due to skipping cudagraphs due to cpu device (cat_3). Found from : 
-   File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/accelerate/utils/operations.py", line 820, in forward
-    return model_forward(*args, **kwargs)
-  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/accelerate/utils/operations.py", line 808, in __call__
-    return convert_to_fp32(self.model_forward(*args, **kwargs))
-  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/amp/autocast_mode.py", line 44, in decorate_autocast
-    return func(*args, **kwargs)
-  File "/home/aryan/work/diffusers/src/diffusers/models/transformers/cogvideox_transformer_3d.py", line 446, in forward
-    hidden_states = self.patch_embed(encoder_hidden_states, hidden_states)
-  File "/home/aryan/work/diffusers/src/diffusers/models/embeddings.py", line 435, in forward
-    pos_embedding = self._get_positional_embeddings(height, width, pre_time_compression_frames)
-  File "/home/aryan/work/diffusers/src/diffusers/models/embeddings.py", line 385, in _get_positional_embeddings
-    pos_embedding = get_3d_sincos_pos_embed(
-  File "/home/aryan/work/diffusers/src/diffusers/models/embeddings.py", line 108, in get_3d_sincos_pos_embed
-    grid = np.stack(grid, axis=0)
+With `train_batch_size = 1`:
+
+|       model        | lora rank | gradient_checkpointing | memory_before_training | memory_before_validation | memory_after_validation | memory_after_testing |
+|:------------------:|:---------:|:----------------------:|:----------------------:|:------------------------:|:-----------------------:|:--------------------:|
+| THUDM/CogVideoX-2b |    16     |          False         |         12.945         |          43.764          |         46.918          |       24.234         |
+| THUDM/CogVideoX-2b |    16     |          True          |         12.945         |          12.945          |         21.121          |       24.234         |
+| THUDM/CogVideoX-2b |    64     |          False         |         13.035         |          44.314          |         47.469          |       24.469         |
+| THUDM/CogVideoX-2b |    64     |          True          |         13.036         |          13.035          |         21.564          |       24.500         |
+| THUDM/CogVideoX-2b |    256    |          False         |         13.095         |          45.826          |         48.990          |       25.543         |
+| THUDM/CogVideoX-2b |    256    |          True          |         13.094         |          13.095          |         22.344          |       25.537         |
+| THUDM/CogVideoX-5b |    16     |          True          |         19.742         |          19.742          |         28.746          |       38.123         |
+| THUDM/CogVideoX-5b |    64     |          True          |         20.006         |          20.818          |         30.338          |       38.738         |
+| THUDM/CogVideoX-5b |    256    |          True          |         20.771         |          22.119          |         31.939          |       41.537         |
+
+With `train_batch_size = 4`:
+
+|       model        | lora rank | gradient_checkpointing | memory_before_training | memory_before_validation | memory_after_validation | memory_after_testing |
+|:------------------:|:---------:|:----------------------:|:----------------------:|:------------------------:|:-----------------------:|:--------------------:|
+| THUDM/CogVideoX-2b |    16     |          True          |         12.945         |          21.803          |         21.814          |       24.322         |
+| THUDM/CogVideoX-2b |    64     |          True          |         13.035         |          22.254          |         22.254          |       24.572         |
+| THUDM/CogVideoX-2b |    256    |          True          |         13.094         |          22.020          |         22.033          |       25.574         |
+| THUDM/CogVideoX-5b |    16     |          True          |         19.742         |          46.492          |         46.492          |       38.197         |
+| THUDM/CogVideoX-5b |    64     |          True          |         20.006         |          47.805          |         47.805          |       39.365         |
+| THUDM/CogVideoX-5b |    256    |          True          |         20.771         |          47.268          |         47.332          |       41.008         |
+
+> [!NOTE]
+> Trying to run CogVideoX-5b without gradient checkpointing OOMs even on an A100 (80 GB), so the memory measurements have not been specified.
+
+</details>
+
+<details>
+<summary> AdamW (8-bit bitsandbytes) </summary>
+
+With `train_batch_size = 1`:
+
+|       model        | lora rank | gradient_checkpointing | memory_before_training | memory_before_validation | memory_after_validation | memory_after_testing |
+|:------------------:|:---------:|:----------------------:|:----------------------:|:------------------------:|:-----------------------:|:--------------------:|
+| THUDM/CogVideoX-2b |    16     |          False         |         12.945         |          43.732          |         46.887          |        24.195        |
+| THUDM/CogVideoX-2b |    16     |          True          |         12.945         |          12.945          |         21.430          |        24.195        |
+| THUDM/CogVideoX-2b |    64     |          False         |         13.035         |          44.004          |         47.158          |        24.369        |
+| THUDM/CogVideoX-2b |    64     |          True          |         13.035         |          13.035          |         21.297          |        24.357        |
+| THUDM/CogVideoX-2b |    256    |          False         |         13.035         |          45.291          |         48.455          |        24.836        |
+| THUDM/CogVideoX-2b |    256    |          True          |         13.035         |          13.035          |         21.625          |        24.869        |
+| THUDM/CogVideoX-5b |    16     |          True          |         19.742         |          19.742          |         28.602          |        38.049        |
+| THUDM/CogVideoX-5b |    64     |          True          |         20.006         |          20.818          |         29.359          |        38.520        |
+| THUDM/CogVideoX-5b |    256    |          True          |         20.771         |          21.352          |         30.727          |        39.596        |
+
+With `train_batch_size = 4`:
+
+|       model        | lora rank | gradient_checkpointing | memory_before_training | memory_before_validation | memory_after_validation | memory_after_testing |
+|:------------------:|:---------:|:----------------------:|:----------------------:|:------------------------:|:-----------------------:|:--------------------:|
+| THUDM/CogVideoX-2b |    16     |          True          |         12.945         |          21.734          |         21.775          |       24.281         |
+| THUDM/CogVideoX-2b |    64     |          True          |         13.036         |          21.941          |         21.941          |       24.445         |
+| THUDM/CogVideoX-2b |    256    |          True          |         13.094         |          22.020          |         22.266          |       24.943         |
+| THUDM/CogVideoX-5b |    16     |          True          |         19.742         |          46.320          |         46.326          |       38.104         |
+| THUDM/CogVideoX-5b |    64     |          True          |         20.006         |          46.820          |         46.820          |       38.588         |
+| THUDM/CogVideoX-5b |    256    |          True          |         20.771         |          47.920          |         47.980          |       40.002         |
+
+</details>
+
+<details>
+<summary> AdamW (8-bit torchao) </summary>
+
+Currently, errors out with following stack-trace:
+
+```python
+Traceback (most recent call last):               
+  File "/raid/aryan/cogvideox-distillation/training/cogvideox_text_to_video_lora.py", line 915, in <module>                                                                                         
+    main(args)                                   
+  File "/raid/aryan/cogvideox-distillation/training/cogvideox_text_to_video_lora.py", line 719, in main                                                                                             
+    optimizer.step()                                                                              
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/accelerate/optimizer.py", line 159, in step                                                                                           
+    self.scaler.step(self.optimizer, closure)    
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/amp/grad_scaler.py", line 457, in step                                                                                          
+    retval = self._maybe_opt_step(optimizer, optimizer_state, *args, **kwargs)                    
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/amp/grad_scaler.py", line 352, in _maybe_opt_step                                                                               
+    retval = optimizer.step(*args, **kwargs)     
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/accelerate/optimizer.py", line 214, in patched_step                                                                                   
+    return method(*args, **kwargs)               
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 137, in wrapper                                                                                    
+    return func.__get__(opt, opt.__class__)(*args, **kwargs)                                      
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/optim/optimizer.py", line 487, in wrapper                                                                                       
+    out = func(*args, **kwargs)                  
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context                                                                            
+    return func(*args, **kwargs)                 
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torchao/prototype/low_bit_optim/adam.py", line 87, in step                                                                            
+    raise RuntimeError(                          
+RuntimeError: lr was changed to a non-Tensor object. If you want to update lr, please use optim.param_groups[0]['lr'].fill_(new_lr)
 ```
 </details>
 
-- Make T2V LoRA script up-to-date
-- Make I2V LoRA script up-to-date
-- Make scripts compatible with DDP
-- Make scripts compatible with FSDP
-- Make scripts compatible with DeepSpeed
-- Test scripts with memory-efficient optimizer
-- Test scripts with quantization using torchao, CPUOffloadOptimizer, etc.
-- Make 5B lora finetuning work in under 24GB
+<details>
+<summary> AdamW (4-bit torchao) </summary>
+
+Same error as AdamW (8-bit torchao)
+
+</details>
+
+<details>
+<summary> AdamW + CPUOffloadOptimizer (with gradient offloading) </summary>
+
+With `train_batch_size = 1`:
+
+|       model        | lora rank | gradient_checkpointing | memory_before_training | memory_before_validation | memory_after_validation | memory_after_testing |
+|:------------------:|:---------:|:----------------------:|:----------------------:|:------------------------:|:-----------------------:|:--------------------:|
+| THUDM/CogVideoX-2b |    16     |          False         |         12.945         |          43.705          |         46.859          |       24.180         |
+| THUDM/CogVideoX-2b |    16     |          True          |         12.945         |          12.945          |         21.395          |       24.180         |
+| THUDM/CogVideoX-2b |    64     |          False         |         13.035         |          43.916          |         47.070          |       24.234         |
+| THUDM/CogVideoX-2b |    64     |          True          |         13.035         |          13.035          |         20.887          |       24.266         |
+| THUDM/CogVideoX-2b |    256    |          False         |         13.095         |          44.947          |         48.111          |       24.607         |
+| THUDM/CogVideoX-2b |    256    |          True          |         13.095         |          13.095          |         21.391          |       24.635         |
+| THUDM/CogVideoX-5b |    16     |          True          |         19.742         |          19.742          |         28.533          |       38.002         |
+| THUDM/CogVideoX-5b |    64     |          True          |         20.006         |          20.006          |         29.107          |       38.785         |
+| THUDM/CogVideoX-5b |    256    |          True          |         20.771         |          20.771          |         30.078          |       39.559         |
+
+With `train_batch_size = 4`:
+
+|       model        | lora rank | gradient_checkpointing | memory_before_training | memory_before_validation | memory_after_validation | memory_after_testing |
+|:------------------:|:---------:|:----------------------:|:----------------------:|:------------------------:|:-----------------------:|:--------------------:|
+| THUDM/CogVideoX-2b |    16     |          True          |         12.945         |          21.709          |         21.762          |       24.254         |
+| THUDM/CogVideoX-2b |    64     |          True          |         13.035         |          21.844          |         21.855          |       24.338         |
+| THUDM/CogVideoX-2b |    256    |          True          |         13.094         |          22.020          |         22.031          |       24.709         |
+| THUDM/CogVideoX-5b |    16     |          True          |         19.742         |          46.262          |         46.297          |       38.400         |
+| THUDM/CogVideoX-5b |    64     |          True          |         20.006         |          46.561          |         46.574          |       38.840         |
+| THUDM/CogVideoX-5b |    256    |          True          |         20.771         |          47.268          |         47.332          |       39.623         |
+
+> [!NOTE]
+> Trying to run CogVideoX-5b without gradient checkpointing OOMs even on an A100 (80 GB), so the memory measurements have not been specified.
+
+</details>
+
+<details>
+<summary> AdamW (8-bit bitsandbytes) + CPUOffloadOptimizer (with gradient offloading) </summary>
+
+Currently, errors out with the following stack-trace:
+
+```python
+  File "/raid/aryan/cogvideox-distillation/training/cogvideox_text_to_video_lora.py", line 925, in <module>                                                                                         
+    main(args)                                                                                                                                                                                      
+  File "/raid/aryan/cogvideox-distillation/training/cogvideox_text_to_video_lora.py", line 727, in main                                                                                             
+    optimizer.step()                                                                                                                                                                                
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context                                                                            
+    return func(*args, **kwargs)                                                                                                                                                                    
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torchao/prototype/low_bit_optim/cpu_offload.py", line 87, in step                                                                     
+    self.optim_dict[p_cuda].step()                                                                                                                                                                  
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/optim/optimizer.py", line 487, in wrapper                                                                                       
+    out = func(*args, **kwargs)                                                                                                                                                                     
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context                                                                            
+    return func(*args, **kwargs)                                                                                                                                                                    
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/bitsandbytes/optim/optimizer.py", line 287, in step                                                                                   
+    self.update_step(group, p, gindex, pindex)                                                                                                                                                      
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context                                                                            
+    return func(*args, **kwargs)                                                                                                                                                                    
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/bitsandbytes/optim/optimizer.py", line 546, in update_step                                                                            
+    F.optimizer_update_8bit_blockwise(                                                                                                                                                              
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/bitsandbytes/functional.py", line 1774, in optimizer_update_8bit_blockwise                                                            
+    prev_device = pre_call(g.device)                                                                                                                                                                
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/bitsandbytes/functional.py", line 463, in pre_call                                                                                    
+    torch.cuda.set_device(device)                                                                                                                                                                   
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/cuda/__init__.py", line 476, in set_device                                                                                      
+    device = _get_device_index(device)           
+  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/cuda/_utils.py", line 34, in _get_device_index                                                                                  
+    raise ValueError(f"Expected a cuda device, but got: {device}")                                
+ValueError: Expected a cuda device, but got: cpu
+```
+
+</details>
+
+### Full finetuning
+
+> [!NOTE]
+> `memory_after_validation` is indicative of the peak memory required for training. This is because apart from the activations, parameters and gradients stored for training, you also need to load the vae and text encoder in memory and spend some memory to perform inference. In order to reduce total memory required to perform training, one can choose to not perform validation/testing as part of the training script.
+
+- [ ] Make scripts compatible with DDP
+- [ ] Make scripts compatible with FSDP
+- [ ] Make scripts compatible with DeepSpeed
+- [x] Test scripts with memory-efficient optimizer from bitsandbytes
+- [x] Test scripts with CPUOffloadOptimizer, etc.
+- [ ] Test scripts with torchao quantization, and low bit memory optimizers, etc.
+- [x] Make 5B lora finetuning work in under 24GB
diff --git a/assets/slaying-ooms.png b/assets/slaying-ooms.png
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z&=5iTm6}OFE$R#_UQ8>1PEJQh2a*{fFftN-)wStGk94-4x}6mXDXEqN8({wrxeCe$
zOw4owehOLvV5Z!Xl5xNtx@fyMkZxK?VNt@rXp3vVS>psWoAXuMkFha4;3j}U33wxr
z1$G4#j*5!PSHJ5s#`&)in^)DpZ#W-x?{_d8A&0ln<`;c>>hX7X_v4iPms6aRQv^%p
zaUrNqfbSLypP*oPz|9KPL+-JX?(gj_gIOXV4bt=SdcmvL2D*!w$w~%A$TC!_?AjSW
zN5(0XQD{}h>1JOsxWv0n4Okcuot>W_0>9t!%^N_{KXYU3dXfRWdGK_XmkTo0bvOk9
zfBg^nEU{12CI~^FeRqE+4xIA`f6u!1i?w8;taPxi9~Z!fi@WRn!+Ebo52PPF750e`
z#R<aPjW4%bk9;MizqOIpl#8*mst=sNctXFl3C-z1FxP!>d4!tgoeP2b+@XWZ^k{9e
zie=SG;f<aC^Hflxuw(^nFfh7sW91)%O@jv0+1VMC>7r;z13^d)?F0Pb@Z!({kRyZ~
zOqdeuhnf3VCuAL7S^Z39tlI4+*;nF~64T$s82!fhXVfM2XlVl<Oj>Lb@e3mBjxrq6
z$|-wWfh+*r4^0p=78?i0!pEoCVzl<z;Lf8HD+l;>>(E_0%9EX1KC2u8BfJR3-UqV>
zMY(Y{)!+jRwd+U_J^7SF&Y(=lX7|_uqu><M_hSb7ztwUVisTh{g1LwZc^#%()Np(f
zx<?}-+64ZEbV<oPpApNN%I}h6HvMsGH$0Qj*!mkX9^OOhHaAq-b8!PQw7s(xs3a@v
zF0D!!5#eL;;EI4H<Dm*4{1=7?YywMi<iml1#p+*Q{{Ov7@%<xuK~b_^Bs_Zruqer?
K%T_)&3;sVo>hI<N

literal 0
HcmV?d00001

diff --git a/metrics.sh b/metrics.sh
deleted file mode 100755
index 4341cbe..0000000
--- a/metrics.sh
+++ /dev/null
@@ -1,155 +0,0 @@
-# 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
-
-# GPU_IDS="1"
-# LEARNING_RATES=("1e-4")
-# LR_SCHEDULES=("cosine_with_restarts")
-# OPTIMIZERS=("adamw")
-# MAX_TRAIN_STEPS=("2")
-# RANK=("16" "64" "256")
-# GRADIENT_CHECKPOINTING=("" "--gradient_checkpointing")
-
-# DATA_ROOT="/raid/aryan/video-dataset-disney/"
-# CAPTION_COLUMN="prompts.txt"
-# VIDEO_COLUMN="videos.txt"
-
-# for learning_rate in "${LEARNING_RATES[@]}"; do
-#   for lr_schedule in "${LR_SCHEDULES[@]}"; do
-#     for optimizer in "${OPTIMIZERS[@]}"; do
-#       for steps in "${MAX_TRAIN_STEPS[@]}"; do
-#         for rank in "${RANK[@]}"; do
-#           for gradient_checkpointing in "${GRADIENT_CHECKPOINTING[@]}"; do
-#             cache_dir="/raid/aryan/cogvideox-lora/"
-#             output_dir="/raid/aryan/cogvideox-lora__optimizer_${optimizer}__steps_${steps}__lr-schedule_${lr_schedule}__learning-rate_${learning_rate}/"
-
-#             cmd="accelerate launch --config_file accelerate_configs/uncompiled_1.yaml --gpu_ids $GPU_IDS training/cogvideox_text_to_video_lora.py \
-#               --pretrained_model_name_or_path THUDM/CogVideoX-2b \
-#               --cache_dir $cache_dir \
-#               --data_root $DATA_ROOT \
-#               --caption_column $CAPTION_COLUMN \
-#               --video_column $VIDEO_COLUMN \
-#               --id_token BW_STYLE \
-#               --height_buckets 480 \
-#               --width_buckets 720 \
-#               --frame_buckets 49 \
-#               --validation_prompt \"BW_STYLE A black and white animated scene unfolds with an anthropomorphic goat surrounded by musical notes and symbols, suggesting a playful environment. Mickey Mouse appears, leaning forward in curiosity as the goat remains still. The goat then engages with Mickey, who bends down to converse or react. The dynamics shift as Mickey grabs the goat, potentially in surprise or playfulness, amidst a minimalistic background. The scene captures the evolving relationship between the two characters in a whimsical, animated setting, emphasizing their interactions and emotions:::BW_STYLE A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance\" \
-#               --validation_prompt_separator ::: \
-#               --num_validation_videos 1 \
-#               --validation_epochs 1 \
-#               --seed 42 \
-#               --rank $rank \
-#               --lora_alpha 64 \
-#               --mixed_precision fp16 \
-#               --output_dir $output_dir \
-#               --max_num_frames 49 \
-#               --train_batch_size 1 \
-#               --max_train_steps $steps \
-#               --checkpointing_steps 1000 \
-#               --gradient_accumulation_steps 1 \
-#               $gradient_checkpointing \
-#               --learning_rate $learning_rate \
-#               --lr_scheduler $lr_schedule \
-#               --lr_warmup_steps 200 \
-#               --lr_num_cycles 1 \
-#               --enable_slicing \
-#               --enable_tiling \
-#               --optimizer $optimizer \
-#               --beta1 0.9 \
-#               --beta2 0.95 \
-#               --weight_decay 0.001 \
-#               --max_grad_norm 1.0 \
-#               --allow_tf32 \
-#               --report_to wandb \
-#               --nccl_timeout 1800"
-            
-#             echo "Running command: $cmd"
-#             eval $cmd
-#             echo -ne "-------------------- Finished executing script --------------------\n\n"
-#           done
-#         done
-#       done
-#     done
-#   done
-# done
-
-# For testing load from tensor data
-export TORCH_LOGS="+dynamo,recompiles,graph_breaks"
-export TORCHDYNAMO_VERBOSE=1
-export WANDB_MODE="offline"
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-
-GPU_IDS="1"
-LEARNING_RATES=("1e-4")
-LR_SCHEDULES=("cosine_with_restarts")
-OPTIMIZERS=("adamw")
-MAX_TRAIN_STEPS=("2")
-RANK=("16" "64" "256")
-GRADIENT_CHECKPOINTING=("" "--gradient_checkpointing")
-
-DATA_ROOT="training/dump"
-CAPTION_COLUMN="prompts.txt"
-VIDEO_COLUMN="videos.txt"
-
-for learning_rate in "${LEARNING_RATES[@]}"; do
-  for lr_schedule in "${LR_SCHEDULES[@]}"; do
-    for optimizer in "${OPTIMIZERS[@]}"; do
-      for steps in "${MAX_TRAIN_STEPS[@]}"; do
-        for rank in "${RANK[@]}"; do
-          for gradient_checkpointing in "${GRADIENT_CHECKPOINTING[@]}"; do
-            cache_dir="/raid/aryan/cogvideox-lora/"
-            output_dir="/raid/aryan/cogvideox-lora__optimizer_${optimizer}__steps_${steps}__lr-schedule_${lr_schedule}__learning-rate_${learning_rate}/"
-
-            cmd="accelerate launch --config_file accelerate_configs/uncompiled_1.yaml --gpu_ids $GPU_IDS training/cogvideox_text_to_video_lora.py \
-              --pretrained_model_name_or_path THUDM/CogVideoX-2b \
-              --cache_dir $cache_dir \
-              --data_root $DATA_ROOT \
-              --caption_column $CAPTION_COLUMN \
-              --video_column $VIDEO_COLUMN \
-              --id_token BW_STYLE \
-              --height_buckets 480 \
-              --width_buckets 720 \
-              --frame_buckets 49 \
-              --load_tensors \
-              --validation_prompt \"BW_STYLE A black and white animated scene unfolds with an anthropomorphic goat surrounded by musical notes and symbols, suggesting a playful environment. Mickey Mouse appears, leaning forward in curiosity as the goat remains still. The goat then engages with Mickey, who bends down to converse or react. The dynamics shift as Mickey grabs the goat, potentially in surprise or playfulness, amidst a minimalistic background. The scene captures the evolving relationship between the two characters in a whimsical, animated setting, emphasizing their interactions and emotions\" \
-              --validation_prompt_separator ::: \
-              --num_validation_videos 1 \
-              --validation_epochs 2 \
-              --seed 42 \
-              --rank $rank \
-              --lora_alpha 64 \
-              --mixed_precision fp16 \
-              --output_dir $output_dir \
-              --max_num_frames 49 \
-              --train_batch_size 1 \
-              --max_train_steps $steps \
-              --checkpointing_steps 1000 \
-              --gradient_accumulation_steps 1 \
-              $gradient_checkpointing \
-              --learning_rate $learning_rate \
-              --lr_scheduler $lr_schedule \
-              --lr_warmup_steps 200 \
-              --lr_num_cycles 1 \
-              --enable_slicing \
-              --enable_tiling \
-              --optimizer $optimizer \
-              --beta1 0.9 \
-              --beta2 0.95 \
-              --weight_decay 0.001 \
-              --max_grad_norm 1.0 \
-              --allow_tf32 \
-              --report_to wandb \
-              --nccl_timeout 1800"
-            
-            echo "Running command: $cmd"
-            eval $cmd
-            echo -ne "-------------------- Finished executing script --------------------\n\n"
-          done
-        done
-      done
-    done
-  done
-done
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..e5ddcf3
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,13 @@
+accelerate
+bitsandbytes
+diffusers
+transformers
+huggingface_hub
+hf_transfer
+peft
+decord
+wandb
+pandas
+torch
+torchvision
+torchao
diff --git a/train_text_to_video_lora.sh b/train_text_to_video_lora.sh
index 6e9931b..9af8187 100755
--- a/train_text_to_video_lora.sh
+++ b/train_text_to_video_lora.sh
@@ -4,26 +4,34 @@ export WANDB_MODE="offline"
 export NCCL_P2P_DISABLE=1
 export TORCH_NCCL_ENABLE_MONITORING=0
 
-GPU_IDS="2"
-LEARNING_RATES=("1e-4")
-LR_SCHEDULES=("cosine_with_restarts")
-OPTIMIZERS=("adamw")
-MAX_TRAIN_STEPS=("2")
+GPU_IDS="0"
 
-DATA_ROOT="dump"
-CAPTION_COLUMN="prompts.txt"
+# Training Configurations
+# Experiment with as many hyperparameters as you want!
+LEARNING_RATES=("1e-4" "1e-3")
+LR_SCHEDULES=("cosine_with_restarts")
+OPTIMIZERS=("adamw", "adam")
+MAX_TRAIN_STEPS=("3000")
+
+# Single GPU uncompiled training
+ACCELERATE_CONFIG_FILE="accelerate_configs/uncompiled_1.yaml"
+
+# Absolute path to where the data is located. Make sure to have read the README for how to prepare data.
+# This example assumes you downloaded an already prepared dataset from HF CLI as follows:
+#   huggingface-cli download --repo-type dataset Wild-Heart/Disney-VideoGeneration-Dataset --local-dir /path/to/my/datasets/disney-dataset
+DATA_ROOT="/path/to/my/datasets/disney-dataset"
+CAPTION_COLUMN="prompt.txt"
 VIDEO_COLUMN="videos.txt"
 
+# Launch experiments with different hyperparameters
 for learning_rate in "${LEARNING_RATES[@]}"; do
   for lr_schedule in "${LR_SCHEDULES[@]}"; do
     for optimizer in "${OPTIMIZERS[@]}"; do
       for steps in "${MAX_TRAIN_STEPS[@]}"; do
-        cache_dir="/raid/aryan/cogvideox-lora/"
-        output_dir="/raid/aryan/cogvideox-lora__optimizer_${optimizer}__steps_${steps}__lr-schedule_${lr_schedule}__learning-rate_${learning_rate}/"
+        output_dir="/path/to/my/models/cogvideox-lora__optimizer_${optimizer}__steps_${steps}__lr-schedule_${lr_schedule}__learning-rate_${learning_rate}/"
 
-        cmd="accelerate launch --config_file accelerate_configs/uncompiled_1.yaml --gpu_ids $GPU_IDS training/cogvideox_text_to_video_lora.py \
-          --pretrained_model_name_or_path THUDM/CogVideoX-2b \
-          --cache_dir $cache_dir \
+        cmd="accelerate launch --config_file $ACCELERATE_CONFIG_FILE --gpu_ids $GPU_IDS training/cogvideox_text_to_video_lora.py \
+          --pretrained_model_name_or_path THUDM/CogVideoX-5b \
           --data_root $DATA_ROOT \
           --caption_column $CAPTION_COLUMN \
           --video_column $VIDEO_COLUMN \
@@ -31,14 +39,14 @@ for learning_rate in "${LEARNING_RATES[@]}"; do
           --height_buckets 480 \
           --width_buckets 720 \
           --frame_buckets 49 \
-          --validation_prompt \"BW_STYLE A black and white animated scene unfolds with an anthropomorphic goat surrounded by musical notes and symbols, suggesting a playful environment. Mickey Mouse appears, leaning forward in curiosity as the goat remains still. The goat then engages with Mickey, who bends down to converse or react. The dynamics shift as Mickey grabs the goat, potentially in surprise or playfulness, amidst a minimalistic background. The scene captures the evolving relationship between the two characters in a whimsical, animated setting, emphasizing their interactions and emotions::: BW_STYLE A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance\" \
+          --validation_prompt \"BW_STYLE A black and white animated scene unfolds with an anthropomorphic goat surrounded by musical notes and symbols, suggesting a playful environment. Mickey Mouse appears, leaning forward in curiosity as the goat remains still. The goat then engages with Mickey, who bends down to converse or react. The dynamics shift as Mickey grabs the goat, potentially in surprise or playfulness, amidst a minimalistic background. The scene captures the evolving relationship between the two characters in a whimsical, animated setting, emphasizing their interactions and emotions:::BW_STYLE A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance\" \
           --validation_prompt_separator ::: \
           --num_validation_videos 1 \
           --validation_epochs 10 \
           --seed 42 \
-          --rank 64 \
-          --lora_alpha 64 \
-          --mixed_precision fp16 \
+          --rank 128 \
+          --lora_alpha 128 \
+          --mixed_precision bf16 \
           --output_dir $output_dir \
           --max_num_frames 49 \
           --train_batch_size 1 \
@@ -48,7 +56,7 @@ for learning_rate in "${LEARNING_RATES[@]}"; do
           --gradient_checkpointing \
           --learning_rate $learning_rate \
           --lr_scheduler $lr_schedule \
-          --lr_warmup_steps 200 \
+          --lr_warmup_steps 400 \
           --lr_num_cycles 1 \
           --enable_slicing \
           --enable_tiling \
diff --git a/train_text_to_video_sft.sh b/train_text_to_video_sft.sh
index 22dabc3..8419a9b 100755
--- a/train_text_to_video_sft.sh
+++ b/train_text_to_video_sft.sh
@@ -1,48 +1,55 @@
-# export TORCH_LOGS="+dynamo,recompiles,graph_breaks"
-# export TORCHDYNAMO_VERBOSE=1
+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
 
-GPU_IDS="3"
+GPU_IDS="0"
+
+# Training Configurations
+# Experiment with as many hyperparameters as you want!
 LEARNING_RATES=("1e-4")
 LR_SCHEDULES=("cosine_with_restarts")
 OPTIMIZERS=("adamw")
 MAX_TRAIN_STEPS=("20000")
 
-# DATA_ROOT="/raid/aryan/dataset-cogvideox/"
-DATA_ROOT="/raid/aryan/openvid-1m"
-CAPTION_COLUMN="prompts.txt"
+# Single GPU uncompiled training
+ACCELERATE_CONFIG_FILE="accelerate_configs/uncompiled_1.yaml"
+
+# Absolute path to where the data is located. Make sure to have read the README for how to prepare data.
+# This example assumes you downloaded an already prepared dataset from HF CLI as follows:
+#   huggingface-cli download --repo-type dataset Wild-Heart/Tom-and-Jerry-VideoGeneration-Dataset --local-dir /path/to/my/datasets/tom-and-jerry-dataset
+DATA_ROOT="/path/to/my/datasets/tom-and-jerry-dataset"
+CAPTION_COLUMN="captions.txt"
 VIDEO_COLUMN="videos.txt"
 
+# Launch experiments with different hyperparameters
 for learning_rate in "${LEARNING_RATES[@]}"; do
   for lr_schedule in "${LR_SCHEDULES[@]}"; do
     for optimizer in "${OPTIMIZERS[@]}"; do
       for steps in "${MAX_TRAIN_STEPS[@]}"; do
-        cache_dir="/raid/aryan/cogvideox-sft/"
-        output_dir="/raid/aryan/cogvideox-sft__optimizer_${optimizer}__steps_${steps}__lr-schedule_${lr_schedule}__learning-rate_${learning_rate}/"
+        output_dir="/path/to/my/models/cogvideox-sft__optimizer_${optimizer}__steps_${steps}__lr-schedule_${lr_schedule}__learning-rate_${learning_rate}/"
 
-        cmd="accelerate launch --config_file accelerate_configs/uncompiled_1.yaml --gpu_ids $GPU_IDS training/cogvideox_text_to_video_sft.py \
-          --pretrained_model_name_or_path THUDM/CogVideoX-2b \
-          --cache_dir $cache_dir \
+        cmd="accelerate launch --config_file $ACCELERATE_CONFIG_FILE --gpu_ids $GPU_IDS training/cogvideox_text_to_video_sft.py \
+          --pretrained_model_name_or_path THUDM/CogVideoX-5b \
           --data_root $DATA_ROOT \
           --caption_column $CAPTION_COLUMN \
           --video_column $VIDEO_COLUMN \
           --height_buckets 480 \
           --width_buckets 720 \
           --frame_buckets 49 \
-          --validation_prompt \"a man wearing a bicycle helmet, riding a bike through a forested area. The man is wearing a black t-shirt and appears to be in motion, as suggested by the slight blur of the background. The forest is lush and green, with trees and foliage filling the background. The man's helmet is white with a black visor, and he is looking directly at the camera with a slight smile on his face. The style of the video is casual and candid, capturing a moment of outdoor activity:::A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance\" \
+          --validation_prompt \"Tom, the mischievous gray cat, is sprawled out on a vibrant red pillow, his body relaxed and his eyes half-closed, as if he's just woken up or is about to doze off. His white paws are stretched out in front of him, and his tail is casually draped over the edge of the pillow. The setting appears to be a cozy corner of a room, with a warm yellow wall in the background and a hint of a wooden floor. The scene captures a rare moment of tranquility for Tom, contrasting with his usual energetic and playful demeanor:::A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance\" \
           --validation_prompt_separator ::: \
           --num_validation_videos 1 \
           --validation_epochs 1 \
           --seed 42 \
-          --mixed_precision fp16 \
+          --mixed_precision bf16 \
           --output_dir $output_dir \
           --max_num_frames 49 \
           --train_batch_size 1 \
           --max_train_steps $steps \
           --checkpointing_steps 2000 \
-          --gradient_accumulation_steps 1 \
+          --gradient_accumulation_steps 4 \
           --gradient_checkpointing \
           --learning_rate $learning_rate \
           --lr_scheduler $lr_schedule \
diff --git a/training/args.py b/training/args.py
index fa8d6d4..5cf16e4 100644
--- a/training/args.py
+++ b/training/args.py
@@ -301,13 +301,21 @@ def _get_optimizer_args(parser: argparse.ArgumentParser) -> None:
         "--optimizer",
         type=lambda s: s.lower(),
         default="adam",
-        choices=["adam", "adamw", "prodigy"],
+        choices=["adam", "adamw", "prodigy", "came"],
         help=("The optimizer type to use."),
     )
     parser.add_argument(
         "--use_8bit",
         action="store_true",
-        help="Whether or not to use 8-bit optimizers from `bitsandbytes`. Ignored if incompatible optimzer selected.",
+        help="Whether or not to use 8-bit optimizers from `bitsandbytes` or `bitsandbytes`.",
+    )
+    parser.add_argument(
+        "--use_4bit",
+        action="store_true",
+        help="Whether or not to use 4-bit optimizers from `torchao`.",
+    )
+    parser.add_argument(
+        "--use_torchao", action="store_true", help="Whether or not to use the `torchao` backend for optimizers."
     )
     parser.add_argument(
         "--beta1",
@@ -355,6 +363,16 @@ def _get_optimizer_args(parser: argparse.ArgumentParser) -> None:
         action="store_true",
         help="Remove lr from the denominator of D estimate to avoid issues during warm-up stage.",
     )
+    parser.add_argument(
+        "--use_cpu_offload_optimizer",
+        action="store_true",
+        help="Whether or not to use the CPUOffloadOptimizer from TorchAO to perform optimization step and maintain parameters on the CPU.",
+    )
+    parser.add_argument(
+        "--offload_gradients",
+        action="store_true",
+        help="Whether or not to offload the gradients to CPU when using the CPUOffloadOptimizer from TorchAO.",
+    )
 
 
 def _get_configuration_args(parser: argparse.ArgumentParser) -> None:
diff --git a/training/cogvideox_text_to_video_lora.py b/training/cogvideox_text_to_video_lora.py
index 9d52149..2d258b1 100644
--- a/training/cogvideox_text_to_video_lora.py
+++ b/training/cogvideox_text_to_video_lora.py
@@ -25,7 +25,6 @@ from typing import Any, Dict
 import diffusers
 import torch
 import transformers
-import wandb
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import (
@@ -43,11 +42,7 @@ from diffusers import (
 from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import cast_training_params
-from diffusers.utils import (
-    convert_unet_state_dict_to_peft,
-    export_to_video,
-    is_wandb_available,
-)
+from diffusers.utils import convert_unet_state_dict_to_peft, export_to_video
 from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
 from diffusers.utils.torch_utils import is_compiled_module
 from huggingface_hub import create_repo, upload_folder
@@ -56,6 +51,8 @@ from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 from transformers import AutoTokenizer, T5EncoderModel
 
+import wandb
+
 
 from args import get_args  # isort:skip
 from dataset import BucketSampler, VideoDatasetWithResizing  # isort:skip
@@ -230,10 +227,6 @@ def main(args):
     if torch.backends.mps.is_available():
         accelerator.native_amp = False
 
-    if args.report_to == "wandb":
-        if not is_wandb_available():
-            raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
-
     # Make one log on every process with the configuration for debugging.
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
@@ -436,6 +429,7 @@ def main(args):
         "lr": args.learning_rate,
     }
     params_to_optimize = [transformer_parameters_with_lr]
+    num_trainable_parameters = sum(param.numel() for model in params_to_optimize for param in model["params"])
 
     use_deepspeed_optimizer = (
         accelerator.state.deepspeed_plugin is not None
@@ -459,7 +453,11 @@ def main(args):
         prodigy_use_bias_correction=args.prodigy_use_bias_correction,
         prodigy_safeguard_warmup=args.prodigy_safeguard_warmup,
         use_8bit=args.use_8bit,
+        use_4bit=args.use_4bit,
+        use_torchao=args.use_torchao,
         use_deepspeed=use_deepspeed_optimizer,
+        use_cpu_offload_optimizer=args.use_cpu_offload_optimizer,
+        offload_gradients=args.offload_gradients,
     )
 
     # Dataset and DataLoader
@@ -524,24 +522,31 @@ def main(args):
         args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
         overrode_max_train_steps = True
 
-    if use_deepspeed_scheduler:
-        from accelerate.utils import DummyScheduler
-
-        lr_scheduler = DummyScheduler(
-            name=args.lr_scheduler,
-            optimizer=optimizer,
-            total_num_steps=args.max_train_steps * accelerator.num_processes,
-            num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+    if args.use_cpu_offload_optimizer:
+        lr_scheduler = None
+        accelerator.print(
+            "CPU Offload Optimizer cannot be used with DeepSpeed or builtin PyTorch LR Schedulers. If "
+            "you are training with those settings, they will be ignored."
         )
     else:
-        lr_scheduler = get_scheduler(
-            args.lr_scheduler,
-            optimizer=optimizer,
-            num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
-            num_training_steps=args.max_train_steps * accelerator.num_processes,
-            num_cycles=args.lr_num_cycles,
-            power=args.lr_power,
-        )
+        if use_deepspeed_scheduler:
+            from accelerate.utils import DummyScheduler
+
+            lr_scheduler = DummyScheduler(
+                name=args.lr_scheduler,
+                optimizer=optimizer,
+                total_num_steps=args.max_train_steps * accelerator.num_processes,
+                num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+            )
+        else:
+            lr_scheduler = get_scheduler(
+                args.lr_scheduler,
+                optimizer=optimizer,
+                num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+                num_training_steps=args.max_train_steps * accelerator.num_processes,
+                num_cycles=args.lr_num_cycles,
+                power=args.lr_power,
+            )
 
     # Prepare everything with our `accelerator`.
     transformer, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
@@ -567,7 +572,6 @@ def main(args):
 
     # Train!
     total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-    num_trainable_parameters = sum(param.numel() for model in params_to_optimize for param in model["params"])
 
     accelerator.print("***** Running training *****")
     accelerator.print(f"  Num trainable parameters = {num_trainable_parameters}")
@@ -624,6 +628,8 @@ def main(args):
         torch.cuda.empty_cache()
         torch.cuda.synchronize(accelerator.device)
 
+    alphas_cumprod = scheduler.alphas_cumprod.to(accelerator.device, dtype=torch.float32)
+
     for epoch in range(first_epoch, args.num_train_epochs):
         transformer.train()
 
@@ -691,8 +697,7 @@ def main(args):
 
                 model_pred = scheduler.get_velocity(model_output, noisy_model_input, timesteps)
 
-                alphas_cumprod = scheduler.alphas_cumprod[timesteps]
-                weights = 1 / (1 - alphas_cumprod)
+                weights = 1 / (1 - alphas_cumprod[timesteps])
                 while len(weights.shape) < len(model_pred.shape):
                     weights = weights.unsqueeze(-1)
 
@@ -714,7 +719,8 @@ def main(args):
                     optimizer.step()
                     optimizer.zero_grad()
 
-                lr_scheduler.step()
+                if not args.use_cpu_offload_optimizer:
+                    lr_scheduler.step()
 
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
@@ -747,9 +753,10 @@ def main(args):
                         accelerator.save_state(save_path)
                         logger.info(f"Saved state to {save_path}")
 
+            last_lr = lr_scheduler.get_last_lr()[0] if lr_scheduler is not None else args.learning_rate
             logs = {
                 "loss": loss.detach().item(),
-                "lr": lr_scheduler.get_last_lr()[0],
+                "lr": last_lr,
                 "gradient_norm_before_clip": gradient_norm_before_clip,
                 "gradient_norm_after_clip": gradient_norm_after_clip,
             }
diff --git a/training/cogvideox_text_to_video_sft.py b/training/cogvideox_text_to_video_sft.py
index c2a9d8b..b10fd6e 100644
--- a/training/cogvideox_text_to_video_sft.py
+++ b/training/cogvideox_text_to_video_sft.py
@@ -25,7 +25,6 @@ from typing import Any, Dict
 import diffusers
 import torch
 import transformers
-import wandb
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import (
@@ -43,7 +42,7 @@ from diffusers import (
 from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import cast_training_params
-from diffusers.utils import export_to_video, is_wandb_available
+from diffusers.utils import export_to_video
 from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
 from diffusers.utils.torch_utils import is_compiled_module
 from huggingface_hub import create_repo, upload_folder
@@ -51,6 +50,8 @@ from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 from transformers import AutoTokenizer, T5EncoderModel
 
+import wandb
+
 
 from args import get_args  # isort:skip
 from dataset import BucketSampler, VideoDatasetWithResizing  # isort:skip
@@ -183,10 +184,6 @@ def main(args):
     if torch.backends.mps.is_available():
         accelerator.native_amp = False
 
-    if args.report_to == "wandb":
-        if not is_wandb_available():
-            raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
-
     # Make one log on every process with the configuration for debugging.
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
@@ -363,6 +360,7 @@ def main(args):
         "lr": args.learning_rate,
     }
     params_to_optimize = [transformer_parameters_with_lr]
+    num_trainable_parameters = sum(param.numel() for model in params_to_optimize for param in model["params"])
 
     use_deepspeed_optimizer = (
         accelerator.state.deepspeed_plugin is not None
@@ -386,7 +384,11 @@ def main(args):
         prodigy_use_bias_correction=args.prodigy_use_bias_correction,
         prodigy_safeguard_warmup=args.prodigy_safeguard_warmup,
         use_8bit=args.use_8bit,
+        use_4bit=args.use_4bit,
+        use_torchao=args.use_torchao,
         use_deepspeed=use_deepspeed_optimizer,
+        use_cpu_offload_optimizer=args.use_cpu_offload_optimizer,
+        offload_gradients=args.offload_gradients,
     )
 
     # Dataset and DataLoader
@@ -451,24 +453,31 @@ def main(args):
         args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
         overrode_max_train_steps = True
 
-    if use_deepspeed_scheduler:
-        from accelerate.utils import DummyScheduler
-
-        lr_scheduler = DummyScheduler(
-            name=args.lr_scheduler,
-            optimizer=optimizer,
-            total_num_steps=args.max_train_steps * accelerator.num_processes,
-            num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+    if args.use_cpu_offload_optimizer:
+        lr_scheduler = None
+        accelerator.print(
+            "CPU Offload Optimizer cannot be used with DeepSpeed or builtin PyTorch LR Schedulers. If "
+            "you are training with those settings, they will be ignored."
         )
     else:
-        lr_scheduler = get_scheduler(
-            args.lr_scheduler,
-            optimizer=optimizer,
-            num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
-            num_training_steps=args.max_train_steps * accelerator.num_processes,
-            num_cycles=args.lr_num_cycles,
-            power=args.lr_power,
-        )
+        if use_deepspeed_scheduler:
+            from accelerate.utils import DummyScheduler
+
+            lr_scheduler = DummyScheduler(
+                name=args.lr_scheduler,
+                optimizer=optimizer,
+                total_num_steps=args.max_train_steps * accelerator.num_processes,
+                num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+            )
+        else:
+            lr_scheduler = get_scheduler(
+                args.lr_scheduler,
+                optimizer=optimizer,
+                num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+                num_training_steps=args.max_train_steps * accelerator.num_processes,
+                num_cycles=args.lr_num_cycles,
+                power=args.lr_power,
+            )
 
     # Prepare everything with our `accelerator`.
     transformer, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
@@ -494,7 +503,6 @@ def main(args):
 
     # Train!
     total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-    num_trainable_parameters = sum(param.numel() for model in params_to_optimize for param in model["params"])
 
     accelerator.print("***** Running training *****")
     accelerator.print(f"  Num trainable parameters = {num_trainable_parameters}")
@@ -551,6 +559,8 @@ def main(args):
         torch.cuda.empty_cache()
         torch.cuda.synchronize(accelerator.device)
 
+    alphas_cumprod = scheduler.alphas_cumprod.to(accelerator.device, dtype=torch.float32)
+
     for epoch in range(first_epoch, args.num_train_epochs):
         transformer.train()
 
@@ -618,8 +628,7 @@ def main(args):
 
                 model_pred = scheduler.get_velocity(model_output, noisy_model_input, timesteps)
 
-                alphas_cumprod = scheduler.alphas_cumprod[timesteps]
-                weights = 1 / (1 - alphas_cumprod)
+                weights = 1 / (1 - alphas_cumprod[timesteps])
                 while len(weights.shape) < len(model_pred.shape):
                     weights = weights.unsqueeze(-1)
 
@@ -641,7 +650,8 @@ def main(args):
                     optimizer.step()
                     optimizer.zero_grad()
 
-                lr_scheduler.step()
+                if not args.use_cpu_offload_optimizer:
+                    lr_scheduler.step()
 
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
@@ -674,9 +684,10 @@ def main(args):
                         accelerator.save_state(save_path)
                         logger.info(f"Saved state to {save_path}")
 
+            last_lr = lr_scheduler.get_last_lr()[0] if lr_scheduler is not None else args.learning_rate
             logs = {
                 "loss": loss.detach().item(),
-                "lr": lr_scheduler.get_last_lr()[0],
+                "lr": last_lr,
                 "gradient_norm_before_clip": gradient_norm_before_clip,
                 "gradient_norm_after_clip": gradient_norm_after_clip,
             }
diff --git a/training/utils.py b/training/utils.py
index 609045f..72cdfc3 100644
--- a/training/utils.py
+++ b/training/utils.py
@@ -1,4 +1,5 @@
 import gc
+import inspect
 from typing import Optional, Tuple, Union
 
 import torch
@@ -22,7 +23,11 @@ def get_optimizer(
     prodigy_use_bias_correction: bool = False,
     prodigy_safeguard_warmup: bool = False,
     use_8bit: bool = False,
+    use_4bit: bool = False,
+    use_torchao: bool = False,
     use_deepspeed: bool = False,
+    use_cpu_offload_optimizer: bool = False,
+    offload_gradients: bool = False,
 ) -> torch.optim.Optimizer:
     optimizer_name = optimizer_name.lower()
 
@@ -38,18 +43,32 @@ def get_optimizer(
             weight_decay=weight_decay,
         )
 
+    if use_8bit and use_4bit:
+        raise ValueError("Cannot set both `use_8bit` and `use_4bit` to True.")
+
+    if (use_torchao and (use_8bit or use_4bit)) or use_cpu_offload_optimizer:
+        try:
+            import torchao
+
+            torchao.__version__
+        except ImportError:
+            raise ImportError(
+                "To use optimizers from torchao, please install the torchao library: `USE_CPP=0 pip install torchao`."
+            )
+
+    if not use_torchao and use_4bit:
+        raise ValueError("4-bit Optimizers are only supported with torchao.")
+
     # Optimizer creation
-    supported_optimizers = ["adam", "adamw", "prodigy"]
+    supported_optimizers = ["adam", "adamw", "prodigy", "came"]
     if optimizer_name not in supported_optimizers:
         logger.warning(
             f"Unsupported choice of optimizer: {optimizer_name}. Supported optimizers include {supported_optimizers}. Defaulting to `AdamW`."
         )
         optimizer_name = "adamw"
 
-    if use_8bit and optimizer_name not in ["adam", "adamw"]:
-        logger.warning(
-            f"use_8bit_adam is ignored when optimizer is not set to 'Adam' or 'AdamW'. Optimizer was set to {optimizer_name}."
-        )
+    if (use_8bit or use_4bit) and optimizer_name not in ["adam", "adamw"]:
+        raise ValueError("`use_8bit` and `use_4bit` can only be used with the Adam and AdamW optimizers.")
 
     if use_8bit:
         try:
@@ -60,24 +79,32 @@ def get_optimizer(
             )
 
     if optimizer_name == "adamw":
-        optimizer_class = bnb.optim.AdamW8bit if use_8bit else torch.optim.AdamW
+        if use_torchao:
+            from torchao.prototype.low_bit_optim import AdamW4bit, AdamW8bit
 
-        optimizer = optimizer_class(
-            params_to_optimize,
-            betas=(beta1, beta2),
-            eps=epsilon,
-            weight_decay=weight_decay,
-        )
+            optimizer_class = AdamW8bit if use_8bit else AdamW4bit if use_4bit else torch.optim.AdamW
+        else:
+            optimizer_class = bnb.optim.AdamW8bit if use_8bit else torch.optim.AdamW
+
+        init_kwargs = {
+            "betas": (beta1, beta2),
+            "eps": epsilon,
+            "weight_decay": weight_decay,
+        }
 
     elif optimizer_name == "adam":
-        optimizer_class = bnb.optim.Adam8bit if use_8bit else torch.optim.Adam
+        if use_torchao:
+            from torchao.prototype.low_bit_optim import Adam4bit, Adam8bit
 
-        optimizer = optimizer_class(
-            params_to_optimize,
-            betas=(beta1, beta2),
-            eps=epsilon,
-            weight_decay=weight_decay,
-        )
+            optimizer_class = Adam8bit if use_8bit else Adam4bit if use_4bit else torch.optim.Adam
+        else:
+            optimizer_class = bnb.optim.Adam8bit if use_8bit else torch.optim.Adam
+
+        init_kwargs = {
+            "betas": (beta1, beta2),
+            "eps": epsilon,
+            "weight_decay": weight_decay,
+        }
 
     elif optimizer_name == "prodigy":
         try:
@@ -92,17 +119,43 @@ def get_optimizer(
                 "Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0"
             )
 
-        optimizer = optimizer_class(
-            params_to_optimize,
-            lr=learning_rate,
-            betas=(beta1, beta2),
-            beta3=beta3,
-            weight_decay=weight_decay,
-            eps=epsilon,
-            decouple=prodigy_decouple,
-            use_bias_correction=prodigy_use_bias_correction,
-            safeguard_warmup=prodigy_safeguard_warmup,
+        init_kwargs = {
+            "lr": learning_rate,
+            "betas": (beta1, beta2),
+            "beta3": beta3,
+            "eps": epsilon,
+            "weight_decay": weight_decay,
+            "decouple": prodigy_decouple,
+            "use_bias_correction": prodigy_use_bias_correction,
+            "safeguard_warmup": prodigy_safeguard_warmup,
+        }
+
+    elif optimizer_name == "came":
+        try:
+            import came_pytorch
+        except ImportError:
+            raise ImportError("To use CAME, please install the came-pytorch library: `pip install came-pytorch`")
+
+        optimizer_class = came_pytorch.CAME
+
+        init_kwargs = {
+            "lr": learning_rate,
+            "eps": (1e-30, 1e-16),
+            "betas": (beta1, beta2, beta3),
+            "weight_decay": weight_decay,
+        }
+
+    if use_cpu_offload_optimizer:
+        from torchao.prototype.low_bit_optim import CPUOffloadOptimizer
+
+        if "fused" in inspect.signature(optimizer_class.__init__).parameters:
+            init_kwargs.update({"fused": True})
+
+        optimizer = CPUOffloadOptimizer(
+            params_to_optimize, optimizer_class=optimizer_class, offload_gradients=offload_gradients, **init_kwargs
         )
+    else:
+        optimizer = optimizer_class(params_to_optimize, **init_kwargs)
 
     return optimizer