fp8

.buildkite/pipeline.yml

@@ -61,7 +61,7 @@ steps:
                 - "pyproject.toml"
                 - "docker/Dockerfile.python3.12"
               config:
-                command: "timeout 90m .buildkite/scripts/pr_test.sh"
+                command: "timeout 45m .buildkite/scripts/pr_test.sh"
                 label: "SSIM Tests"
                 env:
                   - TEST_TYPE=ssim
@@ -76,7 +76,7 @@ steps:
                 - "pyproject.toml"
                 - "docker/Dockerfile.python3.12"
               config:
-                command: "timeout 20m .buildkite/scripts/pr_test.sh"
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
                 label: "LoRA Inference Tests"
                 env:
                   - TEST_TYPE=inference_lora

.github/workflows/fastvideo-kernel-publish.yml

@@ -156,23 +156,8 @@ jobs:
           
           # Fix the wheel to be manylinux compliant
           pip install auditwheel
-          # Point auditwheel at torch libs, but do not vendor them into the wheel.
-          TORCH_LIB_DIR=$(python - <<'PY'
-          import os
-          import torch
-
-          print(os.path.join(os.path.dirname(torch.__file__), "lib"))
-          PY
-          )
-          export LD_LIBRARY_PATH="${TORCH_LIB_DIR}:${LD_LIBRARY_PATH}"
           # Target manylinux_2_35 (Ubuntu 22.04 native)
-          auditwheel repair dist/*.whl --plat manylinux_2_35_x86_64 -w fixed_dist \
-            --exclude libtorch_cuda.so \
-            --exclude libtorch_cpu.so \
-            --exclude libtorch.so \
-            --exclude libc10.so \
-            --exclude libc10_cuda.so \
-            --exclude libtorch_python.so
+          auditwheel repair dist/*.whl --plat manylinux_2_35_x86_64 -w fixed_dist
           # Move fixed wheels back to dist for upload consistency
           rm dist/*.whl
           mv fixed_dist/*.whl dist/

.pre-commit-config.yaml

@@ -68,7 +68,7 @@ repos:
     entry: bash
     args:
       - -c
-      - 'git ls-files | grep -v "^\"*fastvideo/tests/ssim/" | grep -v "^\"*fastvideo/tests/inference/lora/L40S_reference_videos/" | grep " " && echo "Filenames should not contain spaces!" && exit 1 || exit 0'
+      - 'git ls-files | grep -v "^fastvideo/tests/ssim/" | grep -v "^fastvideo/tests/inference/lora/L40S_reference_videos/" | grep " " && echo "Filenames should not contain spaces!" && exit 1 || exit 0'
     language: system
     always_run: true
     pass_filenames: false

README.md

@@ -1,47 +1,41 @@
 <div align="center">
 <img src=assets/logos/logo.svg width="30%"/>
 </div>
+**FastVideo is a unified post-training and inference framework for accelerated video generation.**
+
+FastVideo features an end-to-end unified pipeline for accelerating diffusion models, starting from data preprocessing to model training, finetuning, distillation, and inference. FastVideo is designed to be modular and extensible, allowing users to easily add new optimizations and techniques. Whether it is training-free optimizations or post-training optimizations, FastVideo has you covered.
 
 <p align="center">
-     | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/"><b> Quick Start</b></a> | <a href="https://github.com/hao-ai-lab/FastVideo/discussions/982"  target="_blank"><b>Weekly Dev Meeting</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/sv3MMKyv" target="_blank"> <b> WeChat </b> </a> |
+    | 🕹️ <a href="https://fastwan.fastvideo.org/"<b>Online Demo</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/"><b> Quick Start</b></a> | 🤗 <a href="https://huggingface.co/collections/FastVideo/fastwan-6886a305d9799c8cd1496408"  target="_blank"><b>FastWan</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/c7g1qdD" target="_blank"> <b> WeChat </b> </a> |
 </p>
 
-**FastVideo is a unified post-training and inference framework for accelerated video generation.**
+<div align="center">
+<img src=assets/fastwan.png width="90%"/>
+</div>
 
 ## NEWS
 - ```2025/11/19```: Release [CausalWan2.2 I2V A14B Preview](https://huggingface.co/FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers) models, [Blog](https://hao-ai-lab.github.io/blogs/fastvideo_causalwan_preview/) and [Inference Code!](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py)
 - ```2025/08/04```: Release [FastWan](https://hao-ai-lab.github.io/FastVideo/distillation/dmd) models and [Sparse-Distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
-
-<details>
-<summary>More</summary>
-
 - ```2025/06/14```: Release finetuning and inference code for [VSA](https://arxiv.org/pdf/2505.13389)
 - ```2025/04/24```: [FastVideo V1](https://hao-ai-lab.github.io/blogs/fastvideo/) is released!
 - ```2025/02/18```: Release the inference code for [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/).
 
-</details>
-
 ## Key Features
 
 FastVideo has the following features:
-- End-to-end post-training support for bidirectional and autoregressive models:
+- End-to-end post-training support:
+  - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) for Wan2.1 and Wan2.2 to achineve >50x denoising speedup
+  - Data preprocessing pipeline for video data
   - Support full finetuning and LoRA finetuning for state-of-the-art open video DiTs
-  - Data preprocessing pipeline for video, image, and text data
-  - Distribution Matching Distillation (DMD2) stepwise distillation.
-  - Sparse attention with [Video Sparse Attention](https://arxiv.org/pdf/2505.13389)
-  - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) to achineve >50x denoising speedup
-  - Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing.
-  - Causal distillation through Self-Forcing
-  - See this [page](https://hao-ai-lab.github.io/FastVideo/training/overview/) for full list of supported models and recipes.
+  - Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing, with near linear scaling to 64 GPUs
 - State-of-the-art performance optimizations for inference
-  - Sequence Parallelism for distributed inference
-  - Multiple state-of-the-art attention backends
-  - User-friendly CLI and Python API
-  - See this [page](https://hao-ai-lab.github.io/FastVideo/inference/optimizations/) for full list of supported optimizations.
+  - [Video Sparse Attention](https://arxiv.org/pdf/2505.13389)
+  - [Sliding Tile Attention](https://arxiv.org/pdf/2502.04507)
+  - [TeaCache](https://arxiv.org/pdf/2411.19108)
+  - [Sage Attention](https://arxiv.org/abs/2410.02367)
 - Diverse hardware and OS support
   - Support H100, A100, 4090
   - Support Linux, Windows, MacOS
-  - See this [page](https://hao-ai-lab.github.io/FastVideo/inference/hardware_support/) for full list of supported hardware and OS.
 
 ## Getting Started
 We recommend using an environment manager such as `Conda` to create a clean environment:

docs/attention/developer/index.md

@@ -50,25 +50,21 @@ class MyNewAttnBackend(AttentionBackend):
 FastVideo uses a `ForwardContext` to pass global metadata (like current timestep, batch info, or custom attention configurations) to attention backends without changing the `forward` signature of every layer. **This is optional and only required if your backend needs dynamic per-step information.**
 
 To use this:
-
 1. **Set Context**: In your pipeline or generation loop, use the `set_forward_context` context manager.
 2. **Access Context**: Inside your attention backend, use `get_forward_context()`.
 
-See [`docs/attention/sta/index.md`](../sta/index.md) (Sliding Tile Attention) for an example of how complex configuration (window sizes) is passed this way.
+See `docs/attention/sta/index.md` (Sliding Tile Attention) for an example of how complex configuration (window sizes) is passed this way.
 
 ## 3. Adding Compiled Kernels (C++/CUDA)
 
 If your backend requires custom CUDA kernels, you need to add them to the `fastvideo-kernel` package.
 
 ### A. Add Source Files
-
 Place your kernel implementation files in `fastvideo-kernel/csrc/attention/`.
-
 * `mynew_attn.cu` (CUDA implementation)
 * `mynew_attn.h` (Optional headers)
 
 ### B. Register in Extension
-
 Update `fastvideo-kernel/csrc/common_extension.cpp` to expose your function to Python.
 
 ```cpp
@@ -88,7 +84,6 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
 ```
 
 ### C. Update CMakeLists.txt
-
 Update `fastvideo-kernel/CMakeLists.txt` to compile your new files.
 
 **Case 1: General CUDA Kernel (Runs on all GPUs)**
@@ -116,7 +111,6 @@ endif()
 ```
 
 ### D. Expose in Python Ops
-
 Update `fastvideo-kernel/python/fastvideo_kernel/ops.py` to make the function importable and handle fallbacks gracefully.
 
 ```python
@@ -139,7 +133,6 @@ def my_compiled_attn_func(q, k, v):
 ```
 
 ### E. Expose in Package Init
-
 Update `fastvideo-kernel/python/fastvideo_kernel/__init__.py` to export the function.
 
 ```python

docs/attention/index.md

@@ -41,7 +41,7 @@ Clone the repository and build the kernel:
 
 ```bash
 # Clone recursively to get ThunderKittens submodule
-git clone https://github.com/hao-ai-lab/FastVideo.git
+git clone --recursive https://github.com/hao-ai-lab/FastVideo.git
 cd FastVideo/fastvideo-kernel
 
 # Build and install

docs/attention/vsa/index.md

@@ -13,13 +13,11 @@ from fastvideo_kernel import video_sparse_attn
 
 # q, k, v: [batch_size, num_heads, seq_len, head_dim]
 # variable_block_sizes: Number of valid tokens per block
-# q_variable_block_sizes: Number of valid tokens per q block (can differ from KV for q/k of different lengths)
 # topk: Number of blocks to attend
 
 output = video_sparse_attn(
     q, k, v, 
-    block_sizes,
-    block_sizes,
+    variable_block_sizes=block_sizes, 
     topk=32
 )
 ```

docs/design/overview.md

@@ -4,26 +4,25 @@ This document outlines FastVideo's architecture for developers interested in fra
 
 ## Table of Contents - Directory Structure and Files
 
-- [`fastvideo/pipelines/`](#pipeline-system) - Core diffusion pipeline components
-- [`fastvideo/models/`](#model-components) - Model implementations
-  - [`dits/`](#transformer-models) - Transformer-based diffusion models
-  - [`vaes/`](#vae-variational-auto-encoder) - Variational autoencoders
-  - [`encoders/`](#text-and-image-encoders) - Text and image encoders
-  - [`schedulers/`](#schedulers) - Diffusion schedulers
-- [`fastvideo/attention/`](#optimized-attention) - Optimized attention implementations
-- [`fastvideo/distributed/`](#distributed-processing) - Distributed computing utilities
-- [`fastvideo/layers/`](#tensor-parallelism) - Custom neural network layers
-- [`fastvideo/platforms/`](#platforms) - Hardware platform abstractions
-- [`fastvideo/worker/`](#executor-and-worker-system) - Multi-GPU process management
-- [`fastvideo/fastvideo_args.py`](#fastvideoargs) - Argument handling
-- [`fastvideo/forward_context.py`](#forward-context-management) - Forward pass context management
+- [`fastvideo/pipelines/`](#design-pipeline-system) - Core diffusion pipeline components
+- [`fastvideo/models/`](#design-model-components) - Model implementations
+  - [`dits/`](#design-transformer-models) - Transformer-based diffusion models
+  - [`vaes/`](#design-vae-variational-auto-encoder) - Variational autoencoders
+  - [`encoders/`](#design-text-and-image-encoders) - Text and image encoders
+  - [`schedulers/`](#design-schedulers) - Diffusion schedulers
+- [`fastvideo/attention/`](#design-optimized-attention) - Optimized attention implementations
+- [`fastvideo/distributed/`](#design-distributed-processing) - Distributed computing utilities
+- [`fastvideo/layers/`](#design-tensor-parallelism) - Custom neural network layers
+- [`fastvideo/platforms/`](#design-platforms) - Hardware platform abstractions
+- [`fastvideo/worker/`](#design-executor-and-worker-abstractions) - Multi-GPU process management
+- [`fastvideo/fastvideo_args.py`](#design-fastvideo-args) - Argument handling
+- [`fastvideo/forward_context.py`](#design-forwardcontext) - Forward pass context management
 - `fastvideo/utils.py` - Utility functions
-- [`fastvideo/logger.py`](#logger) - Logging infrastructure
+- [`fastvideo/logger.py`](#design-logger) - Logging infrastructure
 
 ## Core Architecture
 
 FastVideo separates model components from execution logic with these principles:
-
 - **Component Isolation**: Models (encoders, VAEs, transformers) are isolated from execution (pipelines, stages, distributed processing)
 - **Modular Design**: Components can be independently replaced
 - **Distributed Execution**: Supports various parallelism strategies (Tensor, Sequence)
@@ -35,14 +34,12 @@ FastVideo separates model components from execution logic with these principles:
 The `FastVideoArgs` class in `fastvideo/fastvideo_args.py` serves as the central configuration system for FastVideo. It contains all parameters needed to control model loading, inference configuration, performance optimization settings, and more.
 
 Key features include:
-
 - **Command-line Interface**: Automatic conversion between CLI arguments and dataclass fields
 - **Configuration Groups**: Organized by functional areas (model loading, video params, optimization settings)
 - **Context Management**: Global access to current settings via `get_current_fastvideo_args()`
 - **Parameter Validation**: Ensures valid combinations of settings
 
 Common configuration areas:
-
 - **Model paths and loading options**: `model_path`, `trust_remote_code`, `revision`
 - **Distributed execution settings**: `num_gpus`, `tp_size`, `sp_size`
 - **Video generation parameters**: `height`, `width`, `num_frames`, `num_inference_steps`
@@ -93,9 +90,7 @@ class MyCustomPipeline(ComposedPipelineBase):
 ```
 
 ### Pipeline Stages
-
 Each stage handles a specific diffusion process component:
-
 - **Input Validation**: Parameter verification
 - **Text Encoding**: CLIP, LLaMA, or T5-based encoding
 - **Image Encoding**: Image input processing
@@ -138,7 +133,6 @@ Transformer networks perform the actual denoising during diffusion:
   - `HunyuanVideoTransformer3DModel`
 
 Features include:
-
 - Text/image conditioning
 - Standardized interface for model-specific optimizations
 
@@ -167,7 +161,6 @@ VAEs handle conversion between pixel space and latent space:
 These models compress image/video data to a more efficient latent representation (typically 4x-8x smaller in each dimension).
 
 FastVideo's VAE implementations include:
-
 - Efficient video batch processing
 - Memory optimization
 - Optional tiling for large frames
@@ -186,7 +179,6 @@ Encoders process conditioning inputs into embeddings:
   - `CLIPVisionModel`
 
 FastVideo implements optimizations such as:
-
 - Vocab parallelism for distributed processing
 - Caching for common prompts
 - Precision-tuned computation
@@ -201,7 +193,6 @@ Schedulers manage the diffusion sampling process:
   - `FlowMatchEulerDiscreteScheduler`
 
 These components control:
-
 - Diffusion timestep sequences
 - Noise prediction to latent update conversions
 - Quality/speed trade-offs
@@ -228,9 +219,7 @@ This diagram shows how models are discovered, validated, and loaded across entry
 The `fastvideo/attention/` directory contains optimized attention implementations crucial for efficient video diffusion:
 
 ### Attention Backends
-
 Multiple implementations with automatic selection:
-
 - **FLASH_ATTN**: Optimized for supporting hardware
 - **TORCH_SDPA**: Built-in PyTorch scaled dot-product attention
 - **SLIDING_TILE_ATTN**: For very long sequences
@@ -251,9 +240,7 @@ self.attn = LocalAttention(
 ![Attention backend selector design](../assets/images/attention_backend.png)
 
 ### Attention Patterns
-
 Supports various patterns with memory optimization techniques:
-
 - **Cross/Self/Temporal/Global-Local Attention**
 - Chunking, progressive computation, optimized masking
 
@@ -309,7 +296,6 @@ self.attn = DistributedAttention(
 ```
 
 ### Communication Primitives
-
 Efficient distributed operations via AllGather, AllReduce, and synchronization mechanisms.
 
 Efficient communication primitives minimize distributed overhead:
@@ -328,7 +314,6 @@ Defined in `fastvideo/forward_context.py`, `ForwardContext` manages execution-sp
 - **Profiling Data**: Potential hooks for performance metrics collection
 
 This context-based approach enables:
-
 - Dynamic optimization based on execution state (e.g., attention backend selection)
 - Step-specific customizations within model components
 
@@ -354,14 +339,12 @@ FastVideo implements a flexible execution model for distributed processing:
 - **GPU Workers**: Handle actual model execution on individual GPUs
 
 The MultiProcExecutor implementation:
-
 1. Spawns worker processes for each GPU
 2. Establishes communication channels via pipes
 3. Coordinates distributed operations across workers
 4. Handles graceful startup and shutdown of the process group
 
 Each GPU worker:
-
 1. Initializes the distributed environment
 2. Builds the pipeline for the specified model
 3. Executes requested operations on its assigned GPU
@@ -376,13 +359,11 @@ The `fastvideo/platforms/` directory provides hardware platform abstractions tha
 ### Platform Abstraction
 
 FastVideo's platform abstraction layer enables:
-
 - **Hardware Detection**: Automatic detection of available hardware
 - **Backend Selection**: Appropriate selection of compute kernels
 - **Memory Management**: Efficient utilization of hardware-specific memory features
 
 The primary components include:
-
 - **Platform Interface**: Defines the common API for all platform implementations
 - **CUDA Platform**: Optimized implementation for NVIDIA GPUs
 - **Backend Enum**: Used throughout the codebase for feature selection
@@ -402,7 +383,6 @@ else:
 The platform system is designed to be extensible for future hardware targets.
 
 ## Logger
-
 See [PR](https://github.com/hao-ai-lab/FastVideo/pull/356)
 
 *TODO*: (help wanted) Add an environment variable that disables process-aware logging.
@@ -417,7 +397,6 @@ If you're a new contributor, here are some common areas to explore:
 4. **Hardware support**: Extend the `platforms` module for new hardware targets
 
 When adding code, follow these practices:
-
 - Use type hints for better code readability
 - Add appropriate docstrings
 - Maintain the separation between model components and execution logic

docs/distillation/dmd.md

@@ -13,7 +13,7 @@ We provide two distilled models:
 Both models are trained on **61×448×832** resolution but support generating videos with **any resolution** (1.3B  model mainly support 480P, 14B model support 480P and 720P, quality may degrade for different resolutions).
 
 ## ⚙️ Inference
-First install [VSA](../attention/vsa/index.md). Set `MODEL_BASE` to your own model path and run:
+First install [VSA](https://hao-ai-lab.github.io/FastVideo/video_sparse_attention/installation). Set `MODEL_BASE` to your own model path and run:
 
 ```bash
 bash scripts/inference/v1_inference_wan_dmd.sh

docs/getting_started/installation.md

@@ -11,13 +11,15 @@ FastVideo supports the following hardware platforms:
 ### Using pip
 
 ```bash
-# Create and activate a new conda environment
-conda create -n fastvideo python=3.12
-conda activate fastvideo
-
 pip install fastvideo
 ```
 
+### Using conda
+
+```bash
+conda install -c conda-forge fastvideo
+```
+
 ### From source
 
 ```bash
@@ -26,12 +28,6 @@ cd FastVideo
 pip install -e .
 ```
 
-Also optionally install flash-attn:
-
-```bash
-pip install flash-attn --no-build-isolation
-```
-
 ## Hardware Requirements
 
 - **NVIDIA GPUs**: CUDA 11.8+ with compute capability 7.0+
@@ -42,4 +38,4 @@ pip install flash-attn --no-build-isolation
 
 - [Quick Start Guide](quick_start.md) - Get started with your first video generation
 - [Configuration](../inference/configuration.md) - Learn about configuration options
-- [Examples](../inference/examples/examples_inference_index.md) - Explore example scripts and notebooks
+- [Examples](../inference/examples/) - Explore example scripts and notebooks

docs/getting_started/installation/gpu.md

@@ -84,12 +84,12 @@ pip install flash-attn --no-build-isolation
 
 ## Set up using Docker
 We also have prebuilt docker images with FastVideo dependencies pre-installed:
-[Docker Images](../../contributing/developer_env/docker.md)
+[Docker Images](#docker)
 
 ## Development Environment Setup
 
 If you're planning to contribute to FastVideo please see the following page:
-[Contributor Guide](../../contributing/overview.md)
+[Contributor Guide](#developer-overview)
 
 ## Hardware Requirements
 

docs/getting_started/installation/mps.md

@@ -78,7 +78,7 @@ uv pip install -e .
 ## Development Environment Setup
 
 If you're planning to contribute to FastVideo please see the following page:
-[Contributor Guide](../../contributing/overview.md)
+[Contributor Guide](#developer-overview)
 
 ## Hardware Requirements
 

docs/getting_started/quick_start.md

@@ -15,12 +15,6 @@ conda activate fastvideo
 pip install fastvideo
 ```
 
-Also optionally install flash-attn:
-
-```bash
-pip install flash-attn --no-build-isolation
-```
-
 ## Basic Usage
 
 ### Text-to-Video Generation
@@ -81,3 +75,4 @@ if __name__ == '__main__':
 - [Configuration](../inference/configuration.md) - Learn about configuration options
 - [Examples](../inference/examples/) - Explore more examples
 - [Optimizations](../inference/optimizations.md) - Performance optimization tips
+- [Low VRAM Inference](../inference/low_vram_inference.md) - Memory-saving settings (CPU offload, sharded loading, etc.)

docs/inference/add_pipeline.md

@@ -45,7 +45,6 @@ FastVideo uses the Hugging Face Diffusers format for model organization:
 ### Implementing Modules
 
 Place new modules in the appropriate directories:
-
 - Encoders: `fastvideo/models/encoders/`
 - VAEs: `fastvideo/models/vaes/`
 - Transformer models: `fastvideo/models/dits/`
@@ -54,15 +53,12 @@ Place new modules in the appropriate directories:
 ### Adapting Model Layers
 
 #### Layer Replacements
-
 Replace standard PyTorch layers with FastVideo optimized versions:
-
 - nn.LayerNorm → fastvideo.layers.layernorm.RMSNorm
 - Embedding layers → fastvideo.layers.vocab_parallel_embedding modules
 - Activation functions → versions from fastvideo.layers.activation
 
 #### Distributed Linear Layers
-
 Use appropriate parallel layers for distribution:
 
 ```python
@@ -95,7 +91,6 @@ self.out_proj = RowParallelLinear(
 ```
 
 ### Attention Layers
-
 Replace standard attention with FastVideo's optimized attention:
 
 ```python
@@ -309,7 +304,6 @@ EntryClass = [MyCustomPipeline, MyOtherPipeline]
 ```
 
 The registry will automatically:
-
 1. Scan all packages under `fastvideo/pipelines/`
 2. Look for `EntryClass` variables
 3. Register pipelines using their class names as identifiers

docs/inference/cli.md

@@ -1,7 +1,7 @@
 # FastVideo CLI Inference
 
 The FastVideo CLI provides a quick way to access the FastVideo inference pipeline for video generation. For more advanced usage,
-see the Python interface [here](examples/basic.md).
+see the Python interface [here](https://hao-ai-lab.github.io/FastVideo/inference/examples/basic.html).
 
 ## Basic Usage
 

docs/inference/configuration.md

@@ -74,4 +74,4 @@ if __name__ == '__main__':
 
 ## Performance Optimization
 
-For configuring optimizations, please see our [optimizations guide](optimizations.md)
+For configuring optimizations, please see our [optimizations guide](#inference-optimizations)

docs/inference/inference_quick_start.md

@@ -3,7 +3,6 @@
 This page contains step-by-step instructions to get you quickly started with video generation using FastVideo.
 
 ## Requirements
-
 - **OS**: Linux (Tested on Ubuntu 22.04+)
 - **Python**: 3.10-3.12
 - **CUDA**: 12.8
@@ -22,10 +21,9 @@ conda activate fastvideo
 pip install fastvideo
 ```
 
-For advanced installation options, see the [Installation Guide](../getting_started/installation.md).
+For advanced installation options, see the [Installation Guide](installation.md).
 
 ## Generating Your First Video
-
 Here's a minimal example to generate a video using the default settings. Create a file called `example.py` with the following code:
 
 ```python
@@ -62,10 +60,9 @@ python example.py
 The generated video will be saved in the current directory under `my_videos/`  
 
 More inference example scripts can be found in `scripts/inference/`
-
 ## Available Models
 
-Please see the [support matrix](support_matrix.md) for the list of supported models and their available optimizations.
+Please see the [support matrix](#support-matrix) for the list of supported models and their available optimizations.
 
 ## Image-to-Video Generation
 
@@ -99,28 +96,20 @@ if __name__ == '__main__':
 Common issues and their solutions:
 
 ### Out of Memory Errors
-
 If you encounter CUDA out of memory errors:
-
 - Reduce `num_frames` or video resolution
-- Enable memory optimization with `enable_model_cpu_offload`
+- Enable memory optimization with CPU-offload and sharded loading flags (see [Low VRAM Inference](low_vram_inference.md))
 - Try a smaller model or use distilled versions
 - Use `num_gpus` > 1 if multiple GPUs are available
-- Try enabling FSDP inference with `use_fsdp_inference=True` (may slow down generation)
-- Try enabling DiT layerwise offload with `dit_layerwise_offload=True` (now only a few models support this, but may introduce less overhead than FSDP)
 
 ### Slow Generation
-
 To speed up generation:
-
 - Reduce `num_inference_steps` (20-30 is usually sufficient)
 - Use half precision (`fp16`) for the VAE
 - Use multiple GPUs if available
 
 ### Unexpected Results
-
 If the generated video doesn't match your prompt:
-
 - Try increasing `guidance_scale` (7.0-9.0 works well)
 - Make your prompt more detailed and specific
 - Experiment with different random seeds
@@ -128,8 +117,8 @@ If the generated video doesn't match your prompt:
 
 ## Next Steps
 
-- Learn about [Advanced Inference Configurations](configuration.md)
-- Learn about using [Optimizations](optimizations.md)
-- See [Examples](examples/examples_inference_index.md) for more usage scenarios
+- Learn about [Advanced Inference Configurations](#inference-configuration)
+- Learn about using [Optimizations](#inference-optimizations)
+- See [Examples](../examples/examples_inference_index.md) for more usage scenarios
 - Join our [Community Discord](https://discord.gg/JA7cksDz86).
 - Join our [Community Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ).

docs/inference/optimizations.md

@@ -7,13 +7,13 @@ This page describes the various options for speeding up generation times in Fast
 
 - Optimized Attention Backends
 
-  - [Flash Attention](#flash-attention)
-  - [Sliding Tile Attention](#sliding-tile-attention)
-  - [Sage Attention](#sage-attention)
-  - [Sage Attention 3](#sage-attention-3)
+  - [Flash Attention](#optimizations-flash)
+  - [Sliding Tile Attention](#optimizations-sta)
+  - [Sage Attention](#optimizations-sage)
+  - [Sage Attention 3](#optimizations-sage3)
 
 - Caching Techniques
-  - [TeaCache](#teacache)
+  - [TeaCache](#optimizations-teacache)
 
 ## Attention Backends
 
@@ -74,7 +74,7 @@ python setup.py install
 pip install st_attn==0.0.4
 ```
 
-Please see [this page](../attention/sta/index.md) for more installation instructions.
+Please see [this page](#sta-installation) for more installation instructions.
 
 ### Video Sparse Attention
 
@@ -85,7 +85,7 @@ git submodule update --init --recursive
 python setup_vsa.py install
 ```
 
-Please see [this page](../attention/vsa/index.md) for more installation instructions.
+Please see [this page](#vsa-installation) for more installation instructions.
 
 ### Sage Attention
 

docs/inference/support_matrix.md

@@ -40,26 +40,20 @@ The `HuggingFace Model ID` can be directly pass to `from_pretrained()` methods a
   }
 </style>
 
-| Model Name | HuggingFace Model ID | Resolutions | TeaCache | Sliding Tile Attn | Sage Attn | VSA | BSA |
-|------------|---------------------|-------------|----------|-------------------|-----------|-----|-----|
-| FastWan2.1 T2V 1.3B | `FastVideo/FastWan2.1-T2V-1.3B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ✅ | ⭕ |
-| FastWan2.2 TI2V 5B Full Attn* | `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers` | 720P | ⭕ | ⭕ | ⭕ | ✅ | ⭕ |
-| Wan2.2 TI2V 5B | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | 720P | ⭕ | ⭕ | ✅ | ⭕ | ⭕ |
-| Wan2.2 T2V A14B | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ | ⭕ |
-| Wan2.2 I2V A14B | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ | ⭕ |
-| HunyuanVideo | `hunyuanvideo-community/HunyuanVideo` | 720px1280p<br>544px960p | ❌ | ✅ | ✅ | ⭕ | ⭕ |
-| FastHunyuan | `FastVideo/FastHunyuan-diffusers` | 720px1280p<br>544px960p | ❌ | ✅ | ✅ | ⭕ | ⭕ |
-| Wan2.1 T2V 1.3B | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` | 480P | ✅ | ✅* | ✅ | ⭕ | ⭕ |
-| Wan2.1 T2V 14B | `Wan-AI/Wan2.1-T2V-14B-Diffusers` | 480P, 720P | ✅ | ✅* | ✅ | ⭕ | ⭕ |
-| Wan2.1 I2V 480P | `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` | 480P | ✅ | ✅* | ✅ | ⭕ | ⭕ |
-| Wan2.1 I2V 720P | `Wan-AI/Wan2.1-I2V-14B-720P-Diffusers` | 720P | ✅ | ✅ | ✅ | ⭕ | ⭕ |
-| StepVideo T2V | `FastVideo/stepvideo-t2v-diffusers` | 768px768px204f<br>544px992px204f<br>544px992px136f | ❌ | ❌ | ✅ | ⭕ | ⭕ |
-| TurboWan2.1 T2V 1.3B | `loayrashid/TurboWan2.1-T2V-1.3B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| TurboWan2.1 T2V 14B | `loayrashid/TurboWan2.1-T2V-14B-Diffusers` | 480P, 720P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| LongCat T2V 13.6B | See note** | 480P<br>720P | ❌ | ❌ | ❌ | ⭕ | ✅ |
-| Matrix Game 2.0 Base | `FastVideo/Matrix-Game-2.0-Base-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| Matrix Game 2.0 GTA | `FastVideo/Matrix-Game-2.0-GTA-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| Matrix Game 2.0 TempleRun | `FastVideo/Matrix-Game-2.0-TempleRun-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
+| Model Name | HuggingFace Model ID | Resolutions | TeaCache | Sliding Tile Attn | Sage Attn | VSA |
+|------------|---------------------|-------------|----------|-------------------|-----------|-----|
+| FastWan2.1 T2V 1.3B | `FastVideo/FastWan2.1-T2V-1.3B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ✅ |
+| FastWan2.2 TI2V 5B Full Attn* | `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers` | 720P | ⭕ | ⭕ | ⭕ | ✅ |
+| Wan2.2 TI2V 5B | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | 720P | ⭕ | ⭕ | ✅ | ⭕ |
+| Wan2.2 T2V A14B | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ |
+| Wan2.2 I2V A14B | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ |
+| HunyuanVideo | `hunyuanvideo-community/HunyuanVideo` | 720px1280p<br>544px960p | ❌ | ✅ | ✅ | ⭕ |
+| FastHunyuan | `FastVideo/FastHunyuan-diffusers` | 720px1280p<br>544px960p | ❌ | ✅ | ✅ | ⭕ |
+| Wan2.1 T2V 1.3B | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` | 480P | ✅ | ✅* | ✅ | ⭕ |
+| Wan2.1 T2V 14B | `Wan-AI/Wan2.1-T2V-14B-Diffusers` | 480P, 720P | ✅ | ✅* | ✅ | ⭕ |
+| Wan2.1 I2V 480P | `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` | 480P | ✅ | ✅* | ✅ | ⭕ |
+| Wan2.1 I2V 720P | `Wan-AI/Wan2.1-I2V-14B-720P-Diffusers` | 720P | ✅ | ✅ | ✅ | ⭕ |
+| StepVideo T2V | `FastVideo/stepvideo-t2v-diffusers` | 768px768px204f<br>544px992px204f<br>544px992px136f | ❌ | ❌ | ✅ | ⭕ |
 
 **Note**: Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue.
 
@@ -70,13 +64,3 @@ The `HuggingFace Model ID` can be directly pass to `from_pretrained()` methods a
 
 ### Sliding Tile Attention
 - Currently only Hopper GPUs (H100s) are supported.
-
-### TurboWan2.1 (TurboDiffusion)
-- Uses TurboDiffusionPipeline with RCM scheduler for 1-4 step generation
-- Requires SLA attention backend: `export FASTVIDEO_ATTENTION_BACKEND=SLA_ATTN`
-- Uses `guidance_scale=1.0` (no classifier-free guidance)
-
-### Matrix Game 2.0
-- Image-to-video game world models with keyboard/mouse control input
-- Three variants available: Base (universal), GTA, and TempleRun
-- Each variant has different keyboard dimensions for control inputs

docs/training/data_preprocess.md

@@ -1,130 +1,45 @@
-# 🧱 Data Preprocessing
 
-To save GPU memory during training, FastVideo precomputes text embeddings and VAE latents. This eliminates the need to load the text encoder and VAE during training.
+# 🧱 Data Preprocess
 
-## Quick Start
+To save GPU memory, we precompute text embeddings and VAE latents to eliminate the need to load the text encoder and VAE during training.
 
-Download the sample dataset and run preprocessing:
+We provide a sample dataset to help you get started. Download the source media using the following command:
 
 ```bash
-# Download the crush-smol dataset
-python scripts/huggingface/download_hf.py \
-    --repo_id "wlsaidhi/crush-smol-merged" \
-    --local_dir "data/crush-smol" \
-    --repo_type "dataset"
-
-# Run preprocessing
-bash examples/training/finetune/wan_t2v_1.3B/crush_smol/preprocess_wan_data_t2v_new.sh
+python scripts/huggingface/download_hf.py --repo_id=FastVideo/mini_i2v_dataset --local_dir=data/mini_i2v_dataset --repo_type=dataset
 ```
 
-## Preprocessing Pipeline
+The folder `crush-smol_raw/` contains raw videos and captions for testing preprocessing, while `crush-smol_preprocessed/` contains latents prepared for testing training.
 
-The new preprocessing pipeline supports multiple dataset formats and video loaders:
-
-```bash
-GPU_NUM=2
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-DATASET_PATH="data/crush-smol/"
-OUTPUT_DIR="data/crush-smol_processed_t2v/"
-
-torchrun --nproc_per_node=$GPU_NUM \
-    -m fastvideo.pipelines.preprocess.v1_preprocessing_new \
-    --model_path $MODEL_PATH \
-    --mode preprocess \
-    --workload_type t2v \
-    --preprocess.video_loader_type torchvision \
-    --preprocess.dataset_type merged \
-    --preprocess.dataset_path $DATASET_PATH \
-    --preprocess.dataset_output_dir $OUTPUT_DIR \
-    --preprocess.preprocess_video_batch_size 2 \
-    --preprocess.dataloader_num_workers 0 \
-    --preprocess.max_height 480 \
-    --preprocess.max_width 832 \
-    --preprocess.num_frames 77 \
-    --preprocess.train_fps 16 \
-    --preprocess.samples_per_file 8 \
-    --preprocess.flush_frequency 8 \
-    --preprocess.video_length_tolerance_range 5
-```
-
-### Key Parameters
-
-| Parameter | Description |
-|-----------|-------------|
-| `--workload_type` | Task type: `t2v` (text-to-video) or `i2v` (image-to-video) |
-| `--preprocess.dataset_type` | Input format: `hf` (HuggingFace) or `merged` (local folder) |
-| `--preprocess.dataset_path` | Path to dataset (HF repo ID or local folder) |
-| `--preprocess.dataset_output_dir` | Output directory for Parquet files |
-| `--preprocess.video_loader_type` | Video decoder: `torchcodec` or `torchvision` |
-| `--preprocess.max_height` / `max_width` | Target resolution for videos |
-| `--preprocess.num_frames` | Number of frames to extract per video |
-| `--preprocess.train_fps` | Target FPS for frame extraction |
-
-## Dataset Formats
-
-### Merged Dataset (Local Folder)
-
-Structure your dataset as follows:
+To preprocess the dataset for fine-tuning or distillation, run:
 
 ```
-your_dataset/
-├── videos/
-│   ├── video_001.mp4
-│   ├── video_002.mp4
-│   └── ...
-└── videos2caption.json
+bash scripts/preprocess/v1_preprocess_wan_data_t2v # for wan
 ```
 
-The `videos2caption.json` maps video filenames to captions:
+## Process your own dataset
 
-```json
-[
-  {"path": "video_001.mp4", "cap": "A cat playing with yarn..."},
-  {"path": "video_002.mp4", "cap": "Ocean waves at sunset..."}
-]
-```
-
-### HuggingFace Dataset
-
-Use `--preprocess.dataset_type hf` and point `--preprocess.dataset_path` to a HuggingFace dataset with `video` and `caption` columns.
-
-## Creating Your Own Dataset
-
-If you have raw videos and captions in separate files, generate the `videos2caption.json`:
-
-```bash
-python scripts/dataset_preparation/prepare_json_file.py \
-    --data_folder path/to/your_raw_data/ \
-    --output path/to/output_folder
-```
-
-Your raw data folder should contain:
+If you wish to create your own dataset for finetuning or distillation, please refer `mini_i2v_dataset/crush-smol_raw/` to structure you video dataset in the following format:
 
 ```
-your_raw_data/
+path_to_your_dataset_folder/
 ├── videos/
 │   ├── 0.mp4
 │   ├── 1.mp4
-│   └── ...
-├── videos.txt    # list of video filenames
-└── prompt.txt    # corresponding captions (one per line)
+├── videos.txt
+└── prompt.txt
 ```
 
-## Output Format
+To generate the `videos2caption.json` and `merge.txt`, run
 
-Preprocessing outputs Parquet files in the `combined_parquet_dataset/` subdirectory containing:
+``` python
+python scripts/dataset_preparation/prepare_json_file.py --data_folder mini_i2v_dataset/crush-smol_raw/ --output your_output_folder
+```
 
-- `vae_latent_bytes` — VAE-encoded video latent
-- `text_embedding_bytes` — text encoder output
-- `clip_feature_bytes` — CLIP image features (I2V only)
-- `first_frame_latent_bytes` — first frame latent (I2V only)
-- Metadata: shapes, dtypes, and sample identifiers
+Adjust the `DATA_MERGE_PATH` and `OUTPUT_DIR` in `scripts/preprocess/v1_preprocess_****.sh` accordingly and run:
 
-## Examples
+```
+bash scripts/preprocess/v1_preprocess_****.sh
+```
 
-See ready-to-run preprocessing scripts in the training examples:
-
-- **T2V**: `examples/training/finetune/wan_t2v_1.3B/crush_smol/preprocess_wan_data_t2v_new.sh`
-- **I2V**: `examples/training/finetune/wan_i2v_14B_480p/crush_smol/preprocess_wan_data_i2v_new.sh`
-
-**→ [Browse all training examples](examples/examples_training_index.md)**
+The preprocessed data will be put into the `OUTPUT_DIR` and the `videos2caption.json` can be used in finetune and distill scripts.

docs/training/finetune.md

@@ -1,176 +1,78 @@
-# 🧠 Finetuning
 
-This guide covers finetuning video diffusion models with FastVideo, including full finetuning and LoRA.
-
-## Training Arguments
-
-FastVideo training scripts use several argument groups:
-
-### Training Arguments
-
-| Argument | Description |
-|----------|-------------|
-| `--max_train_steps` | Total training steps |
-| `--train_batch_size` | Batch size per GPU |
-| `--gradient_accumulation_steps` | Steps to accumulate before optimizer update |
-| `--num_latent_t` | Temporal latent dimension (reduce to save memory) |
-| `--num_height` / `--num_width` | Video resolution |
-| `--num_frames` | Number of frames per video |
-| `--output_dir` | Directory for checkpoints |
-
-### Parallelism Arguments
-
-| Argument | Description |
-|----------|-------------|
-| `--num_gpus` | Total number of GPUs |
-| `--sp_size` | Sequence parallel size (increase to reduce memory per GPU) |
-| `--tp_size` | Tensor parallel size |
-| `--hsdp_replicate_dim` | HSDP replication dimension |
-| `--hsdp_shard_dim` | HSDP sharding dimension |
-
-### Optimizer Arguments
-
-| Argument | Description |
-|----------|-------------|
-| `--learning_rate` | Base learning rate |
-| `--mixed_precision` | Precision mode (`bf16` recommended) |
-| `--weight_decay` | Weight decay for regularization |
-| `--max_grad_norm` | Gradient clipping threshold |
-
-### Validation Arguments
-
-| Argument | Description |
-|----------|-------------|
-| `--log_validation` | Enable validation logging |
-| `--validation_dataset_file` | JSON file with validation prompts |
-| `--validation_steps` | Run validation every N steps |
-| `--validation_sampling_steps` | Inference steps for validation |
-| `--validation_guidance_scale` | CFG scale for validation |
-
-## Full Finetuning
-
-Full finetuning updates all model weights. This provides the best quality but requires more GPU memory.
+# 🧠 Finetune
+## ⚡ Full Finetune
+Ensure your data is prepared and preprocessed in the format specified in [data_preprocess.md](#v0-data-preprocess). For convenience, we also provide a mochi preprocessed Black Myth Wukong data that can be downloaded directly:
 
 ```bash
-# Example: Wan2.1 T2V 1.3B full finetune (4 GPUs)
-bash examples/training/finetune/wan_t2v_1.3B/crush_smol/finetune_t2v.sh
+python scripts/huggingface/download_hf.py --repo_id=FastVideo/Mochi-Black-Myth --local_dir=data/Mochi-Black-Myth --repo_type=dataset
 ```
 
-**Typical settings:**
+Download the original model weights as specified in the [Distillation Section](../distillation/dmd.md):
 
-- Learning rate: `1e-5` to `5e-5`
-- Gradient checkpointing: `--enable_gradient_checkpointing_type "full"`
-- Memory scaling: Increase `--sp_size` or reduce `--num_latent_t` to fit in memory
+Then you can run the finetune with:
 
-## LoRA Finetuning
+```
+bash scripts/finetune/finetune_mochi.sh # for mochi
+```
 
-LoRA (Low-Rank Adaptation) trains lightweight adapters while keeping the base model frozen. This significantly reduces memory usage and training time.
-
-### LoRA-Specific Arguments
-
-| Argument | Description |
-|----------|-------------|
-| `--lora_training True` | Enable LoRA mode |
-| `--lora_rank` | Rank of LoRA adapters (16, 32, 64, 128) |
-
-### Learning Rate for LoRA
-
-**Important:** LoRA typically requires a **10–20× higher learning rate** than full finetuning because only the low-rank adapters are being trained while the base model is frozen.
-
-| Training Mode | Recommended Learning Rate |
-|---------------|---------------------------|
-| Full finetune | `1e-5` to `5e-5` |
-| LoRA | `1e-4` to `2e-4` |
-
-### Example LoRA Training
+**Note that for finetuning, we did not tune the hyperparameters in the provided script.**
+## ⚡ Finetune with VSA
+Follow [data_preprocess.md](#v0-data-preprocess) to get parquet files for preproccessed latent, and then run:
 
 ```bash
-# Example: Wan2.1 T2V 1.3B LoRA finetune (1 GPU)
-bash examples/training/finetune/wan_t2v_1.3B/crush_smol/finetune_t2v_lora.sh
+bash scripts/finetune/finetune_v1_VSA.sh
 ```
 
-Key differences from full finetune:
+## ⚡ Lora Finetune
 
-- Add `--lora_training True --lora_rank 32`
-- Use higher learning rate (10–20× full finetune)
-- Can run on fewer GPUs (even single GPU)
-- Outputs adapter weights instead of full model
-
-## LoRA Extraction and Merging
-
-FastVideo provides tools to extract LoRA adapters from finetuned models and merge them back.
-
-### Extract LoRA Adapter
-
-Extract a LoRA adapter by comparing a finetuned model to its base:
+Hunyuan supports Lora fine-tuning of videos up to 720p. Demos and prompts of Black-Myth-Wukong can be found in [here](https://huggingface.co/FastVideo/Hunyuan-Black-Myth-Wukong-lora-weight). You can download the Lora weight through:
 
 ```bash
-python scripts/lora_extraction/extract_lora.py \
-  --base Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
-  --ft path/to/your/finetuned_model \
-  --out adapter_r32.safetensors \
-  --rank 32
+python scripts/huggingface/download_hf.py --repo_id=FastVideo/Hunyuan-Black-Myth-Wukong-lora-weight --local_dir=data/Hunyuan-Black-Myth-Wukong-lora-weight --repo_type=model
 ```
 
-| Argument | Description |
-|----------|-------------|
-| `--base` | Base model (HuggingFace ID or local path) |
-| `--ft` | Finetuned model path |
-| `--out` | Output adapter file (.safetensors) |
-| `--rank` | LoRA rank (16, 32, 64, 128) |
-| `--full-rank` | Extract full-rank adapter (optional) |
+### Minimum Hardware Requirement
+- 40 GB GPU memory each for 2 GPUs with lora.
+- 30 GB GPU memory each for 2 GPUs with CPU offload and lora.
 
-### Merge LoRA Adapter
+Currently, both Mochi and Hunyuan models support Lora finetuning through diffusers. To generate personalized videos from your own dataset, you'll need to follow three main steps: dataset preparation, finetuning, and inference.
 
-Merge an adapter back into a base model:
+### Dataset Preparation
+We provide scripts to better help you get started to train on your own characters!
+You can run this to organize your dataset to get the videos2caption.json before preprocess. Specify your video folder and corresponding caption folder (caption files should be .txt files and have the same name with its video):
+
+```
+python scripts/dataset_preparation/prepare_json_file.py --video_dir data/input_videos/ --prompt_dir data/captions/ --output_path data/output_folder/videos2caption.json --verbose
+```
+
+Also, we provide script to resize your videos:
+
+```
+python scripts/data_preprocess/resize_videos.py
+```
+
+### Finetuning
+After basic dataset preparation and preprocess, you can start to finetune your model using Lora:
+
+```
+bash scripts/finetune/finetune_hunyuan_hf_lora.sh
+```
+
+### Inference
+For inference with Lora checkpoint, you can run the following scripts with additional parameter `--lora_checkpoint_dir`:
+
+```
+bash scripts/inference/inference_hunyuan_hf.sh
+```
+
+**We also provide scripts for Mochi in the same directory.**
+
+### Finetune with Both Image and Video
+Our codebase support finetuning with both image and video.
 
 ```bash
-python scripts/lora_extraction/merge_lora.py \
-  --base Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
-  --adapter adapter_r32.safetensors \
-  --ft path/to/your/finetuned_model \
-  --output merged_model
+bash scripts/finetune/finetune_hunyuan.sh
+bash scripts/finetune/finetune_mochi_lora_mix.sh
 ```
 
-| Argument | Description |
-|----------|-------------|
-| `--base` | Base model path |
-| `--adapter` | LoRA adapter file |
-| `--ft` | Finetuned model (for config reference) |
-| `--output` | Output directory for merged model |
-
-### Validate Merged Model
-
-Compare the merged model against the original finetuned model:
-
-```bash
-python scripts/lora_extraction/lora_inference_comparison.py \
-  --base merged_model \
-  --ft path/to/your/finetuned_model \
-  --adapter NONE \
-  --output-dir results \
-  --prompt "A cat sitting on a windowsill" \
-  --compute-ssim \
-  --compute-lpips
-```
-
-## Training Examples
-
-Ready-to-run training scripts are available for multiple models:
-
-**→ [Browse all training examples](examples/examples_training_index.md)**
-
-| Model | Type | Example |
-|-------|------|---------|
-| Wan2.1 T2V 1.3B | T2V | `examples/training/finetune/wan_t2v_1.3B/crush_smol/` |
-| Wan2.1 I2V 14B | I2V | `examples/training/finetune/wan_i2v_14B_480p/crush_smol/` |
-| Wan2.1-Fun 1.3B InP | I2V | `examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/` |
-| Wan2.1 VSA | T2V/I2V | `examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/` |
-
-Each example includes:
-
-- `download_dataset.sh` — download sample data
-- `preprocess_*.sh` — run preprocessing
-- `finetune_*.sh` — full finetune launcher
-- `finetune_*_lora.sh` — LoRA finetune launcher
-- `validation.json` — validation prompts
+For Image-Video Mixture Fine-tuning, make sure to enable the `--group_frame` option in your script.

docs/training/overview.md

@@ -1,67 +0,0 @@
-# Training Overview
-
-FastVideo supports finetuning video diffusion models on custom datasets. This page explains what data you need and how to get started.
-
-## Data Requirements
-
-To save GPU memory during training, FastVideo precomputes embeddings and latents ahead of time. This eliminates the need to load the text encoder and VAE during training, significantly reducing memory usage.
-
-### Text-to-Video (T2V) Finetuning
-
-For T2V models, you need:
-
-| Component | Description |
-|-----------|-------------|
-| **Text embeddings** | Precomputed embeddings from the model's text encoder (e.g., T5 or LLaMA). Stored as numpy arrays in Parquet files. |
-| **Video latents** | VAE-encoded representations of your training videos. Each video is encoded into a compressed latent tensor. |
-
-### Image-to-Video (I2V) Finetuning
-
-For I2V models, you need everything from T2V plus additional image conditioning. Note that not all I2V architectures require encoded images—this depends on how the model conditions on the input frame. Wan2.1 and Wan2.2 A14B I2V models do require these additional components:
-
-| Component | Description |
-|-----------|-------------|
-| **Text embeddings** | Same as T2V—precomputed from the text encoder. |
-| **Video latents** | Same as T2V—VAE-encoded video representations. |
-| **First frame latent** | VAE-encoded representation of the first frame, used as the conditioning image. |
-| **CLIP features** | Image embeddings from a CLIP vision encoder for the conditioning frame. |
-
-## Preprocessing
-
-Before training, you need to preprocess your raw videos and captions into Parquet files containing precomputed latents and embeddings.
-
-FastVideo supports two input formats:
-
-- **HuggingFace datasets** — load directly from HF Hub or local HF datasets
-- **Merged datasets** — local folder with videos and a `videos2caption.json` metadata file
-
-**→ See [Data Preprocessing](data_preprocess.md) for full details and examples.**
-
-## Training Examples
-
-Ready-to-run examples with preprocessing scripts, training launchers, and validation configs are available for multiple models and datasets:
-
-**→ [Browse all training examples](examples/examples_training_index.md)**
-
-Each example includes:
-
-- `download_dataset.sh` — download sample data
-- `preprocess_*.sh` — run preprocessing
-- `finetune_*.sh` — launch training (full finetune or LoRA)
-- `validation.json` — validation prompts for checkpoints
-
-## Training Methods
-
-FastVideo supports several training approaches:
-
-| Method | Use Case |
-|--------|----------|
-| **Full finetune** | Adapt entire model to a new domain or style |
-| **LoRA finetune** | Lightweight adaptation with frozen base weights |
-| **VSA finetune** | Finetune with Variable Sparse Attention for efficiency |
-
-## Next Steps
-
-1. **Get started**: Pick an example from the [training examples index](examples/examples_training_index.md)
-2. **Prepare data**: Follow [data preprocessing](data_preprocess.md) for your own dataset
-3. **Run inference**: After training, see [inference examples](../inference/examples/examples_inference_index.md)

docs/utilities/lora.md

@@ -1,71 +0,0 @@
-# LoRA Extraction and Merging
-
-Tools for extracting and merging LoRA adapters for FastVideo models.
-
-## Extract LoRA Adapter
-
-```bash
-python scripts/lora_extraction/extract_lora.py \
-  --base Wan-AI/Wan2.2-TI2V-5B-Diffusers \
-  --ft FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers \
-  --out adapter_r32.safetensors \
-  --rank 32
-```
-
-**Options:**
-
-- `--base`: Base model (HuggingFace ID or local path)
-- `--ft`: Fine-tuned model (HuggingFace ID or local path)
-- `--out`: Output adapter file
-- `--rank`: LoRA rank (16, 32, 64, 128)
-- `--full-rank`: Extract full-rank adapter (optional)
-
-## Merge Adapter
-
-```bash
-python scripts/lora_extraction/merge_lora.py \
-  --base Wan-AI/Wan2.2-TI2V-5B-Diffusers \
-  --adapter adapter_r32.safetensors \
-  --ft FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers \
-  --output merged_model
-```
-
-**Options:**
-
-- `--base`: Base model (HuggingFace ID or local path)
-- `--adapter`: LoRA adapter file (.safetensors)
-- `--ft`: Fine-tuned model (for configuration)
-- `--output`: Output directory
-
-## Validate Quality (Optional)
-
-```bash
-python scripts/lora_extraction/lora_inference_comparison.py \
-  --base merged_model \
-  --ft FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers \
-  --adapter NONE \
-  --output-dir results \
-  --prompt "A cat sitting on a windowsill" \
-  --seed 42 \
-  --height 480 \
-  --width 480 \
-  --num-frames 49 \
-  --num-inference-steps 32 \
-  --compute-ssim \
-  --compute-lpips
-```
-
-**Options:**
-
-- `--base`: Merged model or base model path
-- `--ft`: Fine-tuned model (reference)
-- `--adapter`: Path to adapter or NONE
-- `--output-dir`: Output directory
-- `--prompt`: Text prompt (default: "A cat sitting on a windowsill")
-- `--seed`: Random seed (default: 42)
-- `--height`: Video height (default: 480)
-- `--width`: Video width (default: 832)
-- `--num-frames`: Number of frames (default: 49)
-- `--num-inference-steps`: Inference steps (default: 32)
-- `--compute-ssim`: Compute SSIM metric
-- `--compute-lpips`: Compute LPIPS metric

examples/distill/SFWan2.1-T2V/README.md

@@ -1,19 +0,0 @@
-# Self-Forcing Distillation for SFWan2.1 T2V 1.3B
-
-These scripts demonstrate self-forcing distillation (SFwan) for the causal Wan2.1 T2V 1.3B model. The workflow mirrors DMD2 while injecting self-forcing blocks so the student can autoregressively refine later frames.
-
-## Run the recipe
-1. Download the preprocessed text-video dataset:
-   ```bash
-   bash examples/distill/SFWan2.1-T2V/download_dataset.sh
-   ```
-2. (Optional) Regenerate parquet shards locally:
-   ```bash
-   bash examples/distill/SFWan2.1-T2V/preprocess_data.sh
-   ```
-3. Launch self-forcing distillation with your cluster settings:
-   ```bash
-   sbatch examples/distill/SFWan2.1-T2V/distill_dmd_t2v_1.3B.sh
-   ```
-
-Update the dataset paths and wandb credentials inside the script before running on your environment.

examples/inference/basic/basic.py

@@ -12,7 +12,7 @@ def main():
         "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         dit_cpu_offload=False,
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_cosmos2_5_t2w.py

@@ -1,42 +0,0 @@
-from fastvideo import VideoGenerator
-
-
-def main():
-    # Point this to your local diffusers model dir (or replace with a HF model ID).
-    model_path = "KyleShao/Cosmos-Predict2.5-2B-Diffusers"
-
-    generator = VideoGenerator.from_pretrained(
-        model_path,
-        num_gpus=1,
-        use_fsdp_inference=False,  # set True if GPU is out of memory
-        dit_cpu_offload=False,
-        vae_cpu_offload=False,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=True,
-    )
-
-    prompt = (
-       "A high-definition video captures the precision of robotic welding in an industrial setting. The first frame showcases a robotic arm, equipped with a welding torch, positioned over a large metal structure. The welding process is in full swing, with bright sparks and intense light illuminating the scene, creating a vivid display of blue and white hues. A significant amount of smoke billows around the welding area, partially obscuring the view but emphasizing the heat and activity. The background reveals parts of the workshop environment, including a ventilation system and various pieces of machinery, indicating a busy and functional industrial workspace. As the video progresses, the robotic arm maintains its steady position, continuing the welding process and moving to its left. The welding torch consistently emits sparks and light, and the smoke continues to rise, diffusing slightly as it moves upward. The metal surface beneath the torch shows ongoing signs of heating and melting. The scene retains its industrial ambiance, with the welding sparks and smoke dominating the visual field, underscoring the ongoing nature of the welding operation."
-    )
-
-    video = generator.generate_video(
-        prompt,
-        negative_prompt="",
-        height=704,
-        width=1280,
-        num_frames=77,
-        num_inference_steps=35,
-        guidance_scale=7.0,
-        fps=24,
-        output_path="outputs_video/cosmos2_5_t2w.mp4",
-        save_video=True,
-    )
-
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
-
-
-

examples/inference/basic/basic_dmd.py

@@ -14,7 +14,7 @@ def main():
         model_name,
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         # Adjust these offload parameters if you have < 32GB of VRAM
         text_encoder_cpu_offload=True,
         pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"

examples/inference/basic/basic_hy15.py

@@ -12,7 +12,7 @@ def main():
         "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         dit_cpu_offload=True,
         vae_cpu_offload=True,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_longcat_i2v.py

@@ -1,210 +0,0 @@
-"""
-LongCat Image-to-Video (I2V) Example Script
-
-This script demonstrates LongCat I2V inference using the FastVideo Python API.
-LongCat I2V takes an input image and generates a video from it.
-
-It runs both basic generation (50 steps) and distill+refine generation
-(16 steps distill + 50 steps refinement to 720p with BSA).
-
-Usage:
-    python examples/inference/basic/basic_longcat_i2v.py
-
-Note:
-    Refinement uses 768x768 dimensions where latent (48x48) is divisible by 8,
-    compatible with BSA chunks [4, 4, 8].
-"""
-
-import glob
-import os
-
-from fastvideo import VideoGenerator
-
-# Common prompts and settings matching the shell script examples
-PROMPT = (
-    "A woman sits at a wooden table by the window in a cozy café. She reaches out "
-    "with her right hand, picks up the white coffee cup from the saucer, and gently "
-    "brings it to her lips to take a sip. After drinking, she places the cup back on "
-    "the table and looks out the window, enjoying the peaceful atmosphere."
-)
-
-NEGATIVE_PROMPT = (
-    "Bright tones, overexposed, static, blurred details, subtitles, style, works, "
-    "paintings, images, static, overall gray, worst quality, low quality, JPEG compression "
-    "residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, "
-    "deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, "
-    "three legs, many people in the background, walking backwards"
-)
-
-# Input image path
-IMAGE_PATH = "assets/girl.png"
-
-SEED = 42
-
-
-def basic_generation():
-    """
-    Run basic LongCat I2V generation (50 steps at 480p).
-    
-    This uses the full 50-step denoising process for highest quality.
-    """
-    print("=" * 60)
-    print("LongCat I2V: Basic Generation (50 steps, 480p)")
-    print("=" * 60)
-    
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-I2V-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=False,
-    )
-    
-    output_path = "outputs_video/longcat_i2v_basic"
-    
-    generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        image_path=IMAGE_PATH,
-        output_path=output_path,
-        save_video=True,
-        height=480,
-        width=480,  # Square
-        num_frames=93,
-        num_inference_steps=50,
-        fps=15,
-        guidance_scale=4.0,
-        seed=SEED,
-    )
-    
-    print(f"\nBasic generation complete! Video saved to: {output_path}")
-    generator.shutdown()
-
-
-def distill_refine_generation():
-    """
-    Run LongCat I2V with distill+refine pipeline (16 steps + refinement to 768p).
-    
-    This uses the distilled LoRA for fast 480p generation (16 steps),
-    then refines to 768p using the refinement LoRA with BSA enabled.
-    """
-    print("\n" + "=" * 60)
-    print("LongCat I2V: Distill + Refine Pipeline")
-    print("=" * 60)
-    
-    # Stage 1: Distilled generation (16 steps at 480p)
-    print("\n[Stage 1] Distilled generation (16 steps, 480p)")
-    print("-" * 40)
-    
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-I2V-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=False,
-        lora_path="FastVideo/LongCat-Video-T2V-Distilled-LoRA",
-        lora_nickname="distilled",
-    )
-    
-    distill_output_path = "outputs_video/longcat_i2v_distill"
-    
-    generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        image_path=IMAGE_PATH,
-        output_path=distill_output_path,
-        save_video=True,
-        height=480,
-        width=480,  # Square
-        num_frames=93,
-        num_inference_steps=16,
-        fps=15,
-        guidance_scale=1.0,
-        seed=SEED,
-    )
-    
-    print(f"Distilled generation complete! Video saved to: {distill_output_path}")
-    generator.shutdown()
-    
-    # Stage 2: Refinement (480p -> 768p)
-    print("\n[Stage 2] Refinement (480p -> 768p with BSA)")
-    print("-" * 40)
-    
-    # Find the actual saved video file from stage 1
-    video_files = glob.glob(os.path.join(distill_output_path, "*.mp4"))
-    if not video_files:
-        raise FileNotFoundError(f"No video file found in {distill_output_path}")
-    # Use the most recently created video file
-    distill_video_path = max(video_files, key=os.path.getmtime)
-    print(f"Using stage 1 video: {distill_video_path}")
-    
-    # Create a new generator with refinement LoRA and BSA enabled
-    # Note: Refinement uses the T2V model (not I2V) since it's upscaling the generated video
-    # For BSA [4, 4, 8]: latent must be divisible by 8
-    # 768x768: latent 48x48, 48%8=0 ✓
-    refine_generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-T2V-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=True,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=True,
-        bsa_sparsity=0.875,
-        bsa_chunk_q=[4, 4, 4],
-        bsa_chunk_k=[4, 4, 4],
-        lora_path="FastVideo/LongCat-Video-T2V-Refinement-LoRA",
-        lora_nickname="refinement",
-    )
-    
-    refine_output_path = "outputs_video/longcat_i2v_refine_720p"
-    
-    refine_generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        output_path=refine_output_path,
-        save_video=True,
-        refine_from=distill_video_path,
-        t_thresh=0.5,
-        spatial_refine_only=False,
-        num_cond_frames=0,
-        height=720,
-        width=720,
-        num_inference_steps=50,
-        fps=30,
-        guidance_scale=1.0,
-        seed=SEED,
-    )
-    
-    print(f"Refinement complete! Video saved to: {refine_output_path}")
-    refine_generator.shutdown()
-
-
-def main():
-    """Run both basic and distill+refine generation pipelines."""
-    print("\n" + "=" * 60)
-    print("LongCat Image-to-Video Example")
-    print("=" * 60 + "\n")
-    
-    # Run basic generation
-    basic_generation()
-    
-    # Run distill+refine pipeline
-    distill_refine_generation()
-    
-    print("\n" + "=" * 60)
-    print("All generations complete!")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    main()
-
-

examples/inference/basic/basic_longcat_t2v.py

@@ -1,198 +0,0 @@
-"""
-LongCat Text-to-Video (T2V) Example Script
-
-This script demonstrates LongCat T2V inference using the FastVideo Python API.
-It runs both basic generation (50 steps) and distill+refine generation
-(16 steps distill + 50 steps refinement to 720p).
-
-Usage:
-    python examples/inference/basic/basic_longcat_t2v.py
-"""
-
-import glob
-import os
-
-from fastvideo import VideoGenerator
-
-# Common prompts and settings matching the shell script examples
-PROMPT = (
-    "In a realistic photography style, a white boy around seven or eight years old "
-    "sits on a park bench, wearing a light blue T-shirt, denim shorts, and white sneakers. "
-    "He holds an ice cream cone with vanilla and chocolate flavors, and beside him is a "
-    "medium-sized golden Labrador. Smiling, the boy offers the ice cream to the dog, "
-    "who eagerly licks it with its tongue. The sun is shining brightly, and the background "
-    "features a green lawn and several tall trees, creating a warm and loving scene."
-)
-
-NEGATIVE_PROMPT = (
-    "Bright tones, overexposed, static, blurred details, subtitles, style, works, "
-    "paintings, images, static, overall gray, worst quality, low quality, JPEG compression "
-    "residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, "
-    "deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, "
-    "three legs, many people in the background, walking backwards"
-)
-
-SEED = 42
-
-
-def basic_generation():
-    """
-    Run basic LongCat T2V generation (50 steps at 480p).
-    
-    This uses the full 50-step denoising process for highest quality.
-    """
-    print("=" * 60)
-    print("LongCat T2V: Basic Generation (50 steps, 480p)")
-    print("=" * 60)
-    
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-T2V-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=False,
-    )
-    
-    output_path = "outputs_video/longcat_t2v_basic"
-    
-    generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        output_path=output_path,
-        save_video=True,
-        height=480,
-        width=832,
-        num_frames=93,
-        num_inference_steps=50,
-        fps=15,
-        guidance_scale=4.0,
-        seed=SEED,
-    )
-    
-    print(f"\nBasic generation complete! Video saved to: {output_path}")
-    generator.shutdown()
-
-
-def distill_refine_generation():
-    """
-    Run LongCat T2V with distill+refine pipeline (16 steps + refinement to 720p).
-    
-    This uses the distilled LoRA for fast 480p generation (16 steps),
-    then refines to 720p using the refinement LoRA with BSA enabled.
-    """
-    print("\n" + "=" * 60)
-    print("LongCat T2V: Distill + Refine Pipeline")
-    print("=" * 60)
-    
-    # Stage 1: Distilled generation (16 steps at 480p)
-    print("\n[Stage 1] Distilled generation (16 steps, 480p)")
-    print("-" * 40)
-    
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-T2V-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=False,
-        lora_path="FastVideo/LongCat-Video-T2V-Distilled-LoRA",
-        lora_nickname="distilled",
-    )
-    
-    distill_output_path = "outputs_video/longcat_t2v_distill"
-    
-    generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        output_path=distill_output_path,
-        save_video=True,
-        height=480,
-        width=832,
-        num_frames=93,
-        num_inference_steps=16,
-        fps=15,
-        guidance_scale=1.0,
-        seed=SEED,
-    )
-    
-    print(f"Distilled generation complete! Video saved to: {distill_output_path}")
-    generator.shutdown()
-    
-    # Stage 2: Refinement (480p -> 720p)
-    print("\n[Stage 2] Refinement (480p -> 720p with BSA)")
-    print("-" * 40)
-    
-    # Find the actual saved video file from stage 1
-    video_files = glob.glob(os.path.join(distill_output_path, "*.mp4"))
-    if not video_files:
-        raise FileNotFoundError(f"No video file found in {distill_output_path}")
-    # Use the most recently created video file
-    distill_video_path = max(video_files, key=os.path.getmtime)
-    print(f"Using stage 1 video: {distill_video_path}")
-    
-    # Create a new generator with refinement LoRA and BSA enabled
-    refine_generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-T2V-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=True,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=True,
-        bsa_sparsity=0.875,
-        bsa_chunk_q=[4, 4, 8],
-        bsa_chunk_k=[4, 4, 8],
-        lora_path="FastVideo/LongCat-Video-T2V-Refinement-LoRA",
-        lora_nickname="refinement",
-    )
-    
-    refine_output_path = "outputs_video/longcat_t2v_refine_720p"
-    
-    refine_generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        output_path=refine_output_path,
-        save_video=True,
-        refine_from=distill_video_path,
-        t_thresh=0.5,
-        spatial_refine_only=False,
-        num_cond_frames=0,
-        height=720,
-        width=1280,
-        num_inference_steps=50,
-        fps=30,
-        guidance_scale=1.0,
-        seed=SEED,
-    )
-    
-    print(f"Refinement complete! Video saved to: {refine_output_path}")
-    refine_generator.shutdown()
-
-
-def main():
-    """Run both basic and distill+refine generation pipelines."""
-    print("\n" + "=" * 60)
-    print("LongCat Text-to-Video Example")
-    print("=" * 60 + "\n")
-    
-    # Run basic generation
-    basic_generation()
-    
-    # Run distill+refine pipeline
-    distill_refine_generation()
-    
-    print("\n" + "=" * 60)
-    print("All generations complete!")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    main()
-
-

examples/inference/basic/basic_longcat_vc.py

@@ -1,228 +0,0 @@
-"""
-LongCat Video Continuation (VC) Example Script
-
-This script demonstrates LongCat VC inference using the FastVideo Python API.
-LongCat VC takes an input video and generates a continuation of it.
-
-It runs both basic generation (50 steps) and distill+refine generation
-(16 steps distill + 50 steps refinement to 720p).
-
-Usage:
-    python examples/inference/basic/basic_longcat_vc.py
-
-Prerequisites:
-    - Ensure the input video exists at assets/motorcycle.mp4
-      (or provide your own video)
-"""
-
-import glob
-import os
-
-from fastvideo import VideoGenerator
-
-# Common prompts and settings matching the shell script examples
-PROMPT = (
-    "A person rides a motorcycle along a long, straight road that stretches between "
-    "a body of water and a forested hillside. The rider steadily accelerates, keeping "
-    "the motorcycle centered between the guardrails, while the scenery passes by on "
-    "both sides. The video captures the journey from the rider's perspective, emphasizing "
-    "the sense of motion and adventure."
-)
-
-NEGATIVE_PROMPT = (
-    "Bright tones, overexposed, static, blurred details, subtitles, style, works, "
-    "paintings, images, static, overall gray, worst quality, low quality, JPEG compression "
-    "residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, "
-    "deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, "
-    "three legs, many people in the background, walking backwards"
-)
-
-# Input video path
-VIDEO_PATH = "assets/motorcycle.mp4"
-
-# Number of conditioning frames from the input video
-NUM_COND_FRAMES = 13
-
-SEED = 42
-
-
-def basic_generation():
-    """
-    Run basic LongCat VC generation (50 steps at 480p).
-    
-    This uses the full 50-step denoising process for highest quality.
-    """
-    print("=" * 60)
-    print("LongCat VC: Basic Generation (50 steps, 480p)")
-    print("=" * 60)
-    
-    # Check if video exists
-    if not os.path.exists(VIDEO_PATH):
-        raise FileNotFoundError(
-            f"Video not found at {VIDEO_PATH}. "
-            "Please provide a valid video path."
-        )
-    
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-VC-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=False,
-    )
-    
-    output_path = "outputs_video/longcat_vc_basic"
-    
-    generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        video_path=VIDEO_PATH,
-        num_cond_frames=NUM_COND_FRAMES,
-        output_path=output_path,
-        save_video=True,
-        height=480,
-        width=832,
-        num_frames=93,
-        num_inference_steps=50,
-        fps=15,
-        guidance_scale=4.0,
-        seed=SEED,
-    )
-    
-    print(f"\nBasic generation complete! Video saved to: {output_path}")
-    generator.shutdown()
-
-
-def distill_refine_generation():
-    """
-    Run LongCat VC with distill+refine pipeline (16 steps + refinement to 720p).
-    
-    This uses the distilled LoRA for fast 480p generation (16 steps),
-    then refines to 720p using the refinement LoRA with BSA enabled.
-    """
-    print("\n" + "=" * 60)
-    print("LongCat VC: Distill + Refine Pipeline")
-    print("=" * 60)
-    
-    # Check if video exists
-    if not os.path.exists(VIDEO_PATH):
-        raise FileNotFoundError(
-            f"Video not found at {VIDEO_PATH}. "
-            "Please provide a valid video path."
-        )
-    
-    # Stage 1: Distilled generation (16 steps at 480p)
-    print("\n[Stage 1] Distilled generation (16 steps, 480p)")
-    print("-" * 40)
-    
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-VC-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=False,
-        lora_path="FastVideo/LongCat-Video-T2V-Distilled-LoRA",
-        lora_nickname="distilled",
-    )
-    
-    distill_output_path = "outputs_video/longcat_vc_distill"
-    
-    generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        video_path=VIDEO_PATH,
-        num_cond_frames=NUM_COND_FRAMES,
-        output_path=distill_output_path,
-        save_video=True,
-        height=480,
-        width=832,
-        num_frames=93,
-        num_inference_steps=16,
-        fps=15,
-        guidance_scale=1.0,
-        seed=SEED,
-    )
-    
-    print(f"Distilled generation complete! Video saved to: {distill_output_path}")
-    generator.shutdown()
-    
-    # Stage 2: Refinement (480p -> 720p)
-    print("\n[Stage 2] Refinement (480p -> 720p with BSA)")
-    print("-" * 40)
-    
-    # Find the actual saved video file from stage 1
-    video_files = glob.glob(os.path.join(distill_output_path, "*.mp4"))
-    if not video_files:
-        raise FileNotFoundError(f"No video file found in {distill_output_path}")
-    # Use the most recently created video file
-    distill_video_path = max(video_files, key=os.path.getmtime)
-    print(f"Using stage 1 video: {distill_video_path}")
-    
-    # Create a new generator with refinement LoRA and BSA enabled
-    # Note: Refinement uses the T2V model (not VC) since it's upscaling the generated video
-    refine_generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-T2V-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=True,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=True,
-        bsa_sparsity=0.875,
-        bsa_chunk_q=[4, 4, 8],
-        bsa_chunk_k=[4, 4, 8],
-        lora_path="FastVideo/LongCat-Video-T2V-Refinement-LoRA",
-        lora_nickname="refinement",
-    )
-    
-    refine_output_path = "outputs_video/longcat_vc_refine_720p"
-    
-    refine_generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        output_path=refine_output_path,
-        save_video=True,
-        refine_from=distill_video_path,
-        t_thresh=0.5,
-        spatial_refine_only=False,
-        num_cond_frames=0,  # For refinement, no conditioning frames
-        height=720,
-        width=1280,
-        num_inference_steps=50,
-        fps=30,
-        guidance_scale=1.0,
-        seed=SEED,
-    )
-    
-    print(f"Refinement complete! Video saved to: {refine_output_path}")
-    refine_generator.shutdown()
-
-
-def main():
-    """Run both basic and distill+refine generation pipelines."""
-    print("\n" + "=" * 60)
-    print("LongCat Video Continuation Example")
-    print("=" * 60 + "\n")
-    
-    # Run basic generation
-    basic_generation()
-    
-    # Run distill+refine pipeline
-    distill_refine_generation()
-    
-    print("\n" + "=" * 60)
-    print("All generations complete!")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    main()
-
-

examples/inference/basic/basic_matrixgame.py

@@ -43,7 +43,7 @@ def main():
         config["model_path"],
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_matrixgame_streaming.py

@@ -1,98 +0,0 @@
-from fastvideo.entrypoints.streaming_generator import StreamingVideoGenerator
-from fastvideo.models.dits.matrix_game.utils import get_current_action_async, expand_action_to_frames
-
-import torch
-import asyncio
-
-# Available variants: "base_distilled_model", "gta_distilled_model", "templerun_distilled_model"
-# Each variant has different keyboard_dim:
-#   - base_distilled_model: keyboard_dim=4
-#   - gta_distilled_model: keyboard_dim=2
-#   - templerun_distilled_model: keyboard_dim=7 (keyboard only, no mouse)
-MODEL_VARIANT = "base_distilled_model"
-
-# Variant-specific settings
-VARIANT_CONFIG = {
-    "base_distilled_model": {
-        "model_path": "FastVideo/Matrix-Game-2.0-Base-Diffusers",
-        "keyboard_dim": 4,
-        "mode": "universal",
-        "image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/universal/0000.png",
-    },
-    "gta_distilled_model": {
-        "model_path": "FastVideo/Matrix-Game-2.0-GTA-Diffusers",
-        "keyboard_dim": 2,
-        "mode": "gta_drive",
-        "image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/gta_drive/0000.png",
-    },
-    "templerun_distilled_model": {
-        "model_path": "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers",
-        "keyboard_dim": 7,
-        "mode": "templerun",
-        "image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/temple_run/0000.png",
-    },
-}
-
-
-OUTPUT_PATH = "video_samples_matrixgame2"
-async def main():
-    # FastVideo will automatically use the optimal default arguments for the
-    # model.
-    # If a local path is provided, FastVideo will make a best effort
-    # attempt to identify the optimal arguments.
-    config = VARIANT_CONFIG[MODEL_VARIANT]
-
-    generator = StreamingVideoGenerator.from_pretrained(
-        config["model_path"],
-        # FastVideo will automatically handle distributed setup
-        num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
-        dit_cpu_offload=True, # DiT need to be offloaded for MoE
-        vae_cpu_offload=False,
-        text_encoder_cpu_offload=True,
-        # Set pin_cpu_memory to false if CPU RAM is limited and there're no frequent CPU-GPU transfer
-        pin_cpu_memory=True,
-        # image_encoder_cpu_offload=False,
-    )
-
-    max_blocks = 50
-    num_frames = 597    
-    actions = {
-        "keyboard": torch.zeros((num_frames, config["keyboard_dim"])),
-        "mouse": torch.zeros((num_frames, 2))
-    }
-    grid_sizes = torch.tensor([150, 44, 80])
-    mode = config["mode"]
-
-    generator.reset(
-        prompt="",
-        image_path=config["image_url"],
-        mouse_cond=actions["mouse"].unsqueeze(0),
-        keyboard_cond=actions["keyboard"].unsqueeze(0),
-        grid_sizes=grid_sizes,
-        num_frames=num_frames,
-        height=352,
-        width=640,
-        num_inference_steps=50,
-        output_path=OUTPUT_PATH,
-        save_video=True,
-    )
-    print("Initialization complete.")
-
-    for block_id in range(max_blocks):
-        print(f"\n=== Block {block_id + 1}/{max_blocks} ===")
-        
-        action = await get_current_action_async(mode)
-        keyboard_cond, mouse_cond = expand_action_to_frames(action, 12)
-        await generator.step_async(keyboard_cond, mouse_cond)
-        
-        if (await asyncio.to_thread(input, "\nContinue? (y/n): ")).lower() == 'n':
-            break
-
-    # Save final video
-    generator.finalize()
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/inference/basic/basic_self_forcing_causal.py

@@ -13,7 +13,7 @@ def main():
         model_name,
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         text_encoder_cpu_offload=False,
         dit_cpu_offload=False,
     )

examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py

@@ -14,7 +14,7 @@ def main():
         "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         dit_precision="fp32",
         vae_cpu_offload=False,

examples/inference/basic/basic_self_forcing_causal_wan2_2_t2v.py

@@ -14,7 +14,7 @@ def main():
         "rand0nmr/SFWan2.2-T2V-A14B-Diffusers",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_turbodiffusion.py

@@ -1,55 +0,0 @@
-import os
-
-# Set SLA attention backend BEFORE fastvideo imports
-os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLA_ATTN"
-
-from fastvideo import VideoGenerator
-
-OUTPUT_PATH = "video_samples_turbodiffusion"
-
-
-def main() -> None:
-    # TurboDiffusion: 1-4 step video generation using RCM scheduler + SLA attention
-    # FastVideo will automatically use TurboDiffusionPipeline when specified
-    generator = VideoGenerator.from_pretrained(
-        "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers",
-        # FastVideo will automatically handle distributed setup
-        num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
-
-        # set to false if using RTX 4090 
-        # pin_cpu_memory=False,
-    )
-
-    # Generate videos with the same simple API, regardless of GPU count
-    # TurboDiffusion defaults: guidance_scale=1.0 and num_inference_steps=4 (from config)
-    prompt = (
-        "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
-        "wide with interest. The playful yet serene atmosphere is complemented by soft "
-        "natural light filtering through the petals. Mid-shot, warm and cheerful tones."
-    )
-    video = generator.generate_video(
-        prompt,
-        output_path=OUTPUT_PATH,
-        save_video=True,
-        seed=42,
-    )
-
-    # Generate another video with a different prompt, without reloading the model!
-    prompt2 = (
-        "A majestic lion strides across the golden savanna, its powerful frame "
-        "glistening under the warm afternoon sun. The tall grass ripples gently in "
-        "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
-        "embodying the raw energy of the wild. Low angle, steady tracking shot, "
-        "cinematic."
-    )
-    video2 = generator.generate_video(
-        prompt2,
-        output_path=OUTPUT_PATH,
-        save_video=True,
-        seed=42,
-    )
-
-
-if __name__ == "__main__":
-    main()

examples/inference/basic/basic_turbodiffusion_14b.py

@@ -1,49 +0,0 @@
-import os
-
-# Set SLA attention backend BEFORE fastvideo imports
-os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLA_ATTN"
-
-from fastvideo import VideoGenerator
-
-OUTPUT_PATH = "video_samples_turbodiffusion_14B"
-
-
-def main() -> None:
-    # TurboDiffusion 14B: 1-4 step video generation using RCM scheduler + SLA attention
-    # FastVideo will automatically use TurboDiffusionPipeline when specified
-    generator = VideoGenerator.from_pretrained(
-        "loayrashid/TurboWan2.1-T2V-14B-Diffusers",
-        # 14B model needs more GPUs
-        num_gpus=2,
-    )
-
-    prompt = (
-        "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
-        "wide with interest. The playful yet serene atmosphere is complemented by soft "
-        "natural light filtering through the petals. Mid-shot, warm and cheerful tones."
-    )
-    video = generator.generate_video(
-        prompt,
-        output_path=OUTPUT_PATH,
-        save_video=True,
-        seed=42,
-    )
-
-    # Generate another video with a different prompt, without reloading the model!
-    prompt2 = (
-        "A majestic lion strides across the golden savanna, its powerful frame "
-        "glistening under the warm afternoon sun. The tall grass ripples gently in "
-        "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
-        "embodying the raw energy of the wild. Low angle, steady tracking shot, "
-        "cinematic."
-    )
-    video2 = generator.generate_video(
-        prompt2,
-        output_path=OUTPUT_PATH,
-        save_video=True,
-        seed=42,
-    )
-
-
-if __name__ == "__main__":
-    main()

examples/inference/basic/basic_turbodiffusion_i2v.py

@@ -1,37 +0,0 @@
-import os
-
-# Set SLA attention backend BEFORE fastvideo imports
-os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLA_ATTN"
-
-from fastvideo import VideoGenerator
-
-# Use local model path
-MODEL_PATH = "loayrashid/TurboWan2.2-I2V-A14B-Diffusers"
-OUTPUT_PATH = "video_samples_turbodiffusion_i2v"
-
-
-def main() -> None:
-    # TurboDiffusion I2V: 1-4 step image-to-video generation
-    generator = VideoGenerator.from_pretrained(
-        MODEL_PATH,
-        num_gpus=2,
-    )
-
-    # Example prompt and image for I2V
-    prompt = ("Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside.")
-
-    # Use an example image path
-    image_path = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG"
-
-
-    video = generator.generate_video(
-        prompt,
-        image_path=image_path,
-        output_path=OUTPUT_PATH,
-        save_video=True,
-        seed=42,
-    )
-
-
-if __name__ == "__main__":
-    main()

examples/inference/basic/basic_wan2_2.py

@@ -12,7 +12,7 @@ def main():
         "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory 
+        use_fsdp_inference=True,
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_wan2_2_Fun.py

@@ -14,7 +14,7 @@ def main():
         # "alibaba-pai/Wan2.2-Fun-A14B-Control",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_wan2_2_i2v.py

@@ -12,7 +12,7 @@ def main():
         "Wan-AI/Wan2.2-I2V-A14B-Diffusers",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_wan2_2_ti2v.py

@@ -11,7 +11,7 @@ def main():
         model_name,
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         dit_cpu_offload=True,
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/gradio/local/gradio_local_demo_matrixgame.py

@@ -1,684 +0,0 @@
-import argparse
-import asyncio
-import os
-import time
-
-import gradio as gr
-import torch
-import uvicorn
-from fastapi import FastAPI, Request, HTTPException
-from fastapi.responses import HTMLResponse, FileResponse
-
-from fastvideo.entrypoints.streaming_generator import StreamingVideoGenerator
-from fastvideo.models.dits.matrix_game.utils import expand_action_to_frames
-
-
-VARIANT_CONFIG = {
-    "Matrix-Game-2.0-Base": {
-        "model_path": "FastVideo/Matrix-Game-2.0-Base-Diffusers",
-        "keyboard_dim": 4,
-        "mode": "universal",
-        "image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/universal/0000.png",
-    },
-    "Matrix-Game-2.0-GTA": {
-        "model_path": "FastVideo/Matrix-Game-2.0-GTA-Diffusers",
-        "keyboard_dim": 2,
-        "mode": "gta_drive",
-        "image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/gta_drive/0000.png",
-    },
-    "Matrix-Game-2.0-TempleRun": {
-        "model_path": "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers",
-        "keyboard_dim": 7,
-        "mode": "templerun",
-        "image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/temple_run/0000.png",
-    },
-}
-
-MODEL_PATH_MAPPING = {
-    name: config["model_path"] for name, config in VARIANT_CONFIG.items()
-}
-
-
-CAM_VALUE = 0.1
-KEYBOARD_MAP_UNIVERSAL = {
-    "W (Forward)": [1, 0, 0, 0],
-    "S (Back)": [0, 1, 0, 0],
-    "A (Left)": [0, 0, 1, 0],
-    "D (Right)": [0, 0, 0, 1],
-    "Q (Stop)": [0, 0, 0, 0],
-}
-KEYBOARD_MAP_GTA = {
-    "W (Forward)": [1, 0],
-    "S (Back)": [0, 1],
-    "Q (Stop)": [0, 0],
-}
-KEYBOARD_MAP_TEMPLERUN = {
-    "Q (Run)": [1, 0, 0, 0, 0, 0, 0],
-    "W (Jump)": [0, 1, 0, 0, 0, 0, 0],
-    "S (Slide)": [0, 0, 1, 0, 0, 0, 0],
-    "Z (Turn Left)": [0, 0, 0, 1, 0, 0, 0],
-    "C (Turn Right)": [0, 0, 0, 0, 1, 0, 0],
-    "A (Left)": [0, 0, 0, 0, 0, 1, 0],
-    "D (Right)": [0, 0, 0, 0, 0, 0, 1],
-}
-
-
-CAMERA_MAP_UNIVERSAL = {
-    "U (Center)": [0, 0],
-    "I (Up)": [CAM_VALUE, 0],
-    "K (Down)": [-CAM_VALUE, 0],
-    "J (Left)": [0, -CAM_VALUE],
-    "L (Right)": [0, CAM_VALUE],
-}
-CAMERA_MAP_GTA = {
-    "Q (Straight)": [0, 0],
-    "A (Steer Left)": [0, -CAM_VALUE],
-    "D (Steer Right)": [0, CAM_VALUE],
-}
-
-def setup_model_environment(model_path: str) -> None:
-    # if "fullattn" in model_path.lower():
-    #     os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "FLASH_ATTN"
-    # else:
-    #     os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
-    os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "FLASH_ATTN"
-    os.environ["FASTVIDEO_STAGE_LOGGING"] = "1"
-
-def create_timing_display(inference_time, total_time, stage_execution_times, num_frames):
-    dit_denoising_time = f"{stage_execution_times[5]:.2f}s" if len(stage_execution_times) > 5 else "N/A"
-    
-    timing_html = f"""
-    <div style="margin: 10px 0;">
-        <h3 style="text-align: center; margin-bottom: 10px;">⏱️ Timing Breakdown</h3>
-        <div style="display: grid; grid-template-columns: repeat(5, 1fr); gap: 10px; margin-bottom: 10px;">
-            <div class="timing-card timing-card-highlight">
-                <div style="font-size: 20px;">🚀</div>
-                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">DiT Denoising</div>
-                <div style="font-size: 16px; color: #ffa200; font-weight: bold;">{dit_denoising_time}</div>
-            </div>
-            <div class="timing-card">
-                <div style="font-size: 20px;">🧠</div>
-                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">E2E (w. vae/text encoder)</div>
-                <div style="font-size: 16px; color: #2563eb;">{inference_time:.2f}s</div>
-            </div>
-            <div class="timing-card">
-                <div style="font-size: 20px;">🎬</div>
-                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Video Encoding</div>
-                <div style="font-size: 16px; color: #dc2626;">N/A</div>
-            </div>
-            <div class="timing-card">
-                <div style="font-size: 20px;">🌐</div>
-                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Network Transfer</div>
-                <div style="font-size: 16px; color: #059669;">N/A</div>
-            </div>
-            <div class="timing-card">
-                <div style="font-size: 20px;">📊</div>
-                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Total Processing</div>
-                <div style="font-size: 18px; color: #0277bd;">{total_time:.2f}s</div>
-            </div>
-        </div>"""
-    
-    if inference_time > 0:
-        fps = num_frames / inference_time
-        timing_html += f"""
-        <div class="performance-card" style="margin-top: 15px;">
-            <span style="font-weight: bold;">Generation Speed: </span>
-            <span style="font-size: 18px; color: #6366f1; font-weight: bold;">{fps:.1f} frames/second</span>
-        </div>"""
-    
-    return timing_html + "</div>"
-
-def get_action_tensors(mode: str, keyboard_key: str, mouse_key: str | None):
-    if mode == "universal":
-        keyboard = torch.tensor(KEYBOARD_MAP_UNIVERSAL.get(keyboard_key, [0, 0, 0, 0])).cuda()
-        mouse = torch.tensor(CAMERA_MAP_UNIVERSAL.get(mouse_key, [0, 0])).cuda()
-    elif mode == "gta_drive":
-        keyboard = torch.tensor(KEYBOARD_MAP_GTA.get(keyboard_key, [0, 0])).cuda()
-        mouse = torch.tensor(CAMERA_MAP_GTA.get(mouse_key, [0, 0])).cuda()
-    elif mode == "templerun":
-        keyboard = torch.tensor(KEYBOARD_MAP_TEMPLERUN.get(keyboard_key, [1, 0, 0, 0, 0, 0, 0])).cuda()
-        mouse = None
-    else:
-        raise ValueError(f"Unknown mode: {mode}")
-    
-    return {"keyboard": keyboard, "mouse": mouse}
-
-def create_gradio_interface(generators: dict[str, StreamingVideoGenerator], loaded_model_name: str):
-    initial_config = VARIANT_CONFIG.get(loaded_model_name, VARIANT_CONFIG["Matrix-Game-2.0-Base"])
-    initial_mode = initial_config["mode"]
-    
-    if initial_mode == "universal":
-        initial_kb_choices = list(KEYBOARD_MAP_UNIVERSAL.keys())
-        initial_mouse_choices = list(CAMERA_MAP_UNIVERSAL.keys())
-        initial_mouse_visible = True
-    elif initial_mode == "gta_drive":
-        initial_kb_choices = list(KEYBOARD_MAP_GTA.keys())
-        initial_mouse_choices = list(CAMERA_MAP_GTA.keys())
-        initial_mouse_visible = True
-    else:  # templerun
-        initial_kb_choices = list(KEYBOARD_MAP_TEMPLERUN.keys())
-        initial_mouse_choices = []
-        initial_mouse_visible = False
-    
-    theme = gr.themes.Base().set(
-        button_primary_background_fill="#2563eb",
-        button_primary_background_fill_hover="#1d4ed8",
-        button_primary_text_color="white",
-        slider_color="#2563eb",
-        checkbox_background_color_selected="#2563eb",
-    )
-    
-    with gr.Blocks(title="FastVideo - Matrix Game 2.0", theme=theme) as demo:
-        game_state = gr.State({
-            "initialized": False,
-            "current_model": None,
-            "block_idx": 0,
-            "max_blocks": 50,
-        })
-        
-        # Header
-        gr.Image("assets/full.svg", show_label=False, container=False, height=80)
-        
-        gr.HTML("""
-        <div style="text-align: center; margin-bottom: 10px;">
-            <p style="font-size: 18px;"> Make Video Generation Go Blurrrrrrr </p>
-            <p style="font-size: 18px;"> <a href="https://github.com/hao-ai-lab/FastVideo/tree/main" target="_blank">Code</a> | <a href="https://hao-ai-lab.github.io/blogs/fastvideo_post_training/" target="_blank">Blog</a> | <a href="https://hao-ai-lab.github.io/FastVideo/" target="_blank">Docs</a>  </p>
-        </div>
-        """)
-        
-        with gr.Accordion("🎥 What Is FastVideo?", open=False):
-            gr.HTML("""
-            <div style="padding: 20px; line-height: 1.6;">
-                <p style="font-size: 16px; margin-bottom: 15px;">
-                    FastVideo is an inference and post-training framework for diffusion models. It features an end-to-end unified pipeline for accelerating diffusion models, starting from data preprocessing to model training, finetuning, distillation, and inference. FastVideo is designed to be modular and extensible, allowing users to easily add new optimizations and techniques. Whether it is training-free optimizations or post-training optimizations, FastVideo has you covered.
-                </p>
-            </div>
-            """)
-        
-        # Model Selection
-        with gr.Row():
-            model_selection = gr.Dropdown(
-                choices=[loaded_model_name],
-                value=loaded_model_name,
-                label="Select Model",
-                interactive=False
-            )
-        
-
-        # Main Layout
-        with gr.Row(equal_height=True, elem_classes="main-content-row"):
-            with gr.Column(scale=1, elem_classes="advanced-options-column"):
-                with gr.Group():
-                    gr.HTML("<div style='margin: 0 0 15px 0; text-align: center; font-size: 16px;'>Game Controls</div>")
-                    
-                    with gr.Group():
-                        gr.HTML("<div style='font-size: 14px; margin-bottom: 5px; font-weight: bold;'>🎮 Keyboard Control</div>")
-                        keyboard_action = gr.Radio(
-                            choices=initial_kb_choices,
-                            value=initial_kb_choices[0] if initial_kb_choices else None,
-                            label="Movement",
-                            show_label=False,
-                            interactive=True
-                        )
-                    
-                    with gr.Group(visible=initial_mouse_visible) as mouse_group:
-                        gr.HTML("<div style='font-size: 14px; margin-bottom: 5px; font-weight: bold;'>🖱️ Mouse/Camera Control</div>")
-                        mouse_action = gr.Radio(
-                            choices=initial_mouse_choices if initial_mouse_visible else [],
-                            value=initial_mouse_choices[0] if initial_mouse_choices else None,
-                            label="Camera",
-                            show_label=False,
-                            interactive=True
-                        )
-                    
-                    with gr.Row():
-                        action_btn = gr.Button("Start", variant="primary")
-                        stop_btn = gr.Button("Stop", variant="stop")
-                    
-                    gr.HTML("<div style='margin-top: 15px;'></div>")
-                    
-                    seed = gr.Slider(
-                        label="Seed",
-                        minimum=0,
-                        maximum=1000000,
-                        step=1,
-                        value=1024,
-                    )
-                    randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
-                    seed_output = gr.Number(label="Used Seed")
-                    
-                    block_counter = gr.Textbox(label="Progress", value="Block: 0 / 50", interactive=False, lines=1)
-
-
-            # Right Column: Video Output
-            with gr.Column(scale=1, elem_classes="video-column"):
-                video_output = gr.Video(
-                    label="Generated Video",
-                    show_label=True,
-                    height=466,
-                    width=600,
-                    container=True,
-                    elem_classes="video-component",
-                    autoplay=True
-                )
-
-        # Styles
-        gr.HTML("""
-        <style>
-        .center-button {
-            display: flex !important;
-            justify-content: center !important;
-            height: 100% !important;
-            padding-top: 1.4em !important;
-        }
-        
-        .gradio-container {
-            max-width: 1200px !important;
-            margin: 0 auto !important;
-        }
-        
-        .main {
-            max-width: 1200px !important;
-            margin: 0 auto !important;
-        }
-        
-        .gr-form, .gr-box, .gr-group {
-            max-width: 1200px !important;
-        }
-        
-        .gr-video {
-            max-width: 500px !important;
-            margin: 0 auto !important;
-        }
-        
-        .main-content-row {
-            display: flex !important;
-            align-items: flex-start !important;
-            min-height: 500px !important;
-            gap: 20px !important;
-        }
-        
-        .advanced-options-column,
-        .video-column {
-            display: flex !important;
-            flex-direction: column !important;
-            flex: 1 !important;
-            min-height: 400px !important;
-            align-items: stretch !important;
-        }
-        
-        .video-column > * {
-            margin-top: 0 !important;
-        }
-        
-        .video-column .gr-video,
-        .video-component {
-            margin-top: 0 !important;
-            padding-top: 0 !important;
-        }
-        
-        .video-column .gr-video .gr-form {
-            margin-top: 0 !important;
-        }
-        
-        .advanced-options-column .gr-group,
-        .video-column .gr-video {
-            margin-top: 0 !important;
-            vertical-align: top !important;
-        }
-        
-        .advanced-options-column > *:last-child,
-        .video-column > *:last-child {
-            flex-grow: 0 !important;
-        }
-        
-        @media (max-width: 1400px) {
-            .main-content-row {
-                min-height: 600px !important;
-            }
-            
-            .advanced-options-column,
-            .video-column {
-                min-height: 600px !important;
-            }
-        }
-        
-        @media (max-width: 1200px) {
-            .main-content-row {
-                flex-direction: column !important;
-                align-items: stretch !important;
-            }
-            
-            .advanced-options-column,
-            .video-column {
-                min-height: auto !important;
-                width: 100% !important;
-            }
-        }
-        
-        .timing-card {
-            background: var(--background-fill-secondary) !important;
-            border: 1px solid var(--border-color-primary) !important;
-            color: var(--body-text-color) !important;
-            padding: 10px;
-            border-radius: 8px;
-            text-align: center;
-            min-height: 80px;
-            display: flex;
-            flex-direction: column;
-            justify-content: center;
-        }
-        
-        .timing-card-highlight {
-            background: var(--background-fill-primary) !important;
-            border: 2px solid var(--color-accent) !important;
-        }
-        
-        .performance-card {
-            background: var(--background-fill-secondary) !important;
-            border: 1px solid var(--border-color-primary) !important;
-            color: var(--body-text-color) !important;
-            padding: 10px;
-            border-radius: 6px;
-            text-align: center;
-        }
-
-        .gr-number input[readonly] {
-            background-color: var(--background-fill-secondary) !important;
-            border: 1px solid var(--border-color-primary) !important;
-            color: var(--body-text-color-subdued) !important;
-            cursor: default !important;
-            text-align: center !important;
-            font-weight: 500 !important;
-        }
-        </style>
-        """)
-        
-        # UI update based on model selection
-        def on_model_change(model_name):
-            config = VARIANT_CONFIG.get(model_name, VARIANT_CONFIG["Matrix-Game-2.0-Base"])
-            mode = config["mode"]
-            
-            if mode == "universal":
-                kb_choices = list(KEYBOARD_MAP_UNIVERSAL.keys())
-                mouse_choices = list(CAMERA_MAP_UNIVERSAL.keys())
-                mouse_visible = True
-            elif mode == "gta_drive":
-                kb_choices = list(KEYBOARD_MAP_GTA.keys())
-                mouse_choices = list(CAMERA_MAP_GTA.keys())
-                mouse_visible = True
-            else:  # templerun
-                kb_choices = list(KEYBOARD_MAP_TEMPLERUN.keys())
-                mouse_choices = []
-                mouse_visible = False
-            
-            return (
-                gr.update(choices=kb_choices, value=kb_choices[0] if kb_choices else None),
-                gr.update(choices=mouse_choices, value=mouse_choices[0] if mouse_choices else None, visible=mouse_visible),
-                gr.update(visible=mouse_visible),
-            )
-        
-        model_selection.change(
-            fn=on_model_change,
-            inputs=model_selection,
-            outputs=[keyboard_action, mouse_action, mouse_group]
-        )
-
-        def start_game(model_name, seed_val, randomize, state):
-            if randomize:
-                seed_val = torch.randint(0, 1000000, (1,)).item()
-            
-            config = VARIANT_CONFIG.get(model_name)
-            if not config:
-                return state, seed_val, "Block: 0 / 50", None, "", gr.update(), gr.update()
-            
-            generator = generators.get(config["model_path"])
-            if not generator:
-                return state, seed_val, "Block: 0 / 50", None, "", gr.update(), gr.update()
-            
-            # If already initialized, clean up first
-            if state.get("initialized"):
-                try:
-                    # Clear accumulated frames without saving
-                    generator.accumulated_frames = []
-                    generator.executor.execute_streaming_clear()
-                except Exception as e:
-                    print(f"Warning: cleanup error: {e}")
-            
-            # Streaming parameters
-            num_latent_frames_per_block = 3
-            max_blocks = 50
-            total_latent_frames = num_latent_frames_per_block * max_blocks
-            num_frames = (total_latent_frames - 1) * 4 + 1
-            
-            actions = {
-                "keyboard": torch.zeros((num_frames, config["keyboard_dim"])),
-                "mouse": torch.zeros((num_frames, 2))
-            }
-            grid_sizes = torch.tensor([150, 44, 80])
-            
-            output_dir = os.path.abspath("outputs/matrixgame")
-            os.makedirs(output_dir, exist_ok=True)
-            video_path = os.path.join(output_dir, f"video_{int(time.time())}.mp4")
-            
-            generator.reset(
-                prompt="",
-                image_path=config["image_url"],
-                mouse_cond=actions["mouse"].unsqueeze(0),
-                keyboard_cond=actions["keyboard"].unsqueeze(0),
-                grid_sizes=grid_sizes,
-                num_frames=num_frames,
-                height=352,
-                width=640,
-                num_inference_steps=50,
-                output_path=video_path,
-            )
-            
-            new_state = {
-                "initialized": True,
-                "current_model": model_name,
-                "block_idx": 0,
-                "max_blocks": max_blocks,
-                "video_path": video_path,
-                "frames_per_block": num_latent_frames_per_block * 4,
-                "mode": config["mode"],
-                "seed": seed_val,
-            }
-            
-            return new_state, seed_val, "Block: 0 / 50", None, gr.update(value="Step"), gr.update(interactive=True)
-        
-        async def step_game(keyboard_key, mouse_key, model_name, state):
-            if not state.get("initialized"):
-                return state, state.get("seed", 0), "Block: 0 / 50", None, gr.update(), gr.update()
-            
-            # total_start_time = time.time()
-            config = VARIANT_CONFIG.get(model_name)
-            generator = generators.get(config["model_path"])
-            mode = state["mode"]
-            frames_per_block = state["frames_per_block"]
-
-            # Parse inputs to tensors
-            action = get_action_tensors(mode, keyboard_key, mouse_key)
-            keyboard_cond, mouse_cond = expand_action_to_frames(action, frames_per_block)
-            
-            # run step async
-            # inference_start_time = time.time()
-            frames, block_future = await generator.step_async(keyboard_cond, mouse_cond)
-            # inference_time = time.time() - inference_start_time
-            
-            # wait for block file to be written
-            block_path = await asyncio.to_thread(block_future.result) if block_future else None
-            state["block_idx"] = generator.block_idx
-            block_str = f"Block: {state['block_idx']} / {state['max_blocks']}"
-            
-            # total_time = time.time() - total_start_time
-            
-            # Timing breakdown
-            # timing_html = create_timing_display(inference_time, total_time, [], frames_per_block)
-
-            return state, state.get("seed", 0), block_str, block_path, gr.update(), gr.update()
-
-        def stop_game(model_name, state):
-            if not state.get("initialized"):
-                return {"initialized": False}, 0, "Block: 0 / 50", None, gr.update(value="Start"), gr.update(interactive=False)
-            
-            config = VARIANT_CONFIG.get(model_name)
-            generator = generators.get(config["model_path"])
-            
-            final_path = state.get("video_path")
-            generator.finalize(final_path)
-            
-            return {"initialized": False}, state.get("seed", 0), "Block: 0 / 50", final_path, gr.update(value="Start"), gr.update(interactive=False)
-        
-        async def handle_action(keyboard_key, mouse_key, model_name, seed_val, randomize, state):
-            if not state.get("initialized"):
-                return start_game(model_name, seed_val, randomize, state)
-            else:
-                return await step_game(keyboard_key, mouse_key, model_name, state)
-        
-        action_btn.click(
-            fn=handle_action,
-            inputs=[keyboard_action, mouse_action, model_selection, seed, randomize_seed, game_state],
-            outputs=[game_state, seed_output, block_counter, video_output, action_btn, stop_btn]
-        )
-        
-        stop_btn.click(
-            fn=stop_game,
-            inputs=[model_selection, game_state],
-            outputs=[game_state, seed_output, block_counter, video_output, action_btn, stop_btn]
-        )
-        
-        gr.HTML("""
-        <div style="text-align: center; margin-top: 10px; margin-bottom: 15px;">
-            <p style="font-size: 16px; margin: 0;">Note that this demo is meant to showcase Matrix Game's quality and that under a large number of requests, generation speed may be affected.</p>
-        </div>
-        """)
-    
-    return demo
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Matrix Game Gradio Demo")
-    parser.add_argument("--model", type=str, default="Matrix-Game-2.0-Base",
-                        choices=list(VARIANT_CONFIG.keys()),
-                        help="Model variant to load")
-    parser.add_argument("--host", type=str, default="0.0.0.0")
-    parser.add_argument("--port", type=int, default=7860)
-    args = parser.parse_args()
-    
-    # Load the selected model
-    config = VARIANT_CONFIG[args.model]
-    model_path = config["model_path"]
-    
-    print(f"Loading model: {model_path}")
-    setup_model_environment(model_path)
-    generator = StreamingVideoGenerator.from_pretrained(
-        model_path,
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=True,
-        vae_cpu_offload=False,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=True,
-    )
-    
-    generators = {model_path: generator}
-    
-    demo = create_gradio_interface(generators, args.model)
-    
-    print(f"Starting Gradio at http://{args.host}:{args.port}")
-    
-    # FastAPI Wrapper
-    app = FastAPI()
-    
-    @app.get("/logo.png")
-    def get_logo():
-        return FileResponse(
-            "assets/full.svg",
-            media_type="image/svg+xml",
-            headers={
-                "Cache-Control": "public, max-age=3600",
-                "Access-Control-Allow-Origin": "*"
-            }
-        )
-
-    @app.get("/favicon.ico")
-    def get_favicon():
-        favicon_path = "assets/icon-simple.svg"
-        
-        if os.path.exists(favicon_path):
-            return FileResponse(
-                favicon_path, 
-                media_type="image/svg+xml",
-                headers={
-                    "Cache-Control": "public, max-age=3600",
-                    "Access-Control-Allow-Origin": "*"
-                }
-            )
-        else:
-            raise HTTPException(status_code=404, detail="Favicon not found")
-    
-    @app.get("/", response_class=HTMLResponse)
-    def index(request: Request):
-        base_url = str(request.base_url).rstrip('/')
-        return f"""
-        <!DOCTYPE html>
-        <html lang="en">
-        <head>
-            <meta charset="UTF-8" />
-            <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-            
-            <title>FastVideo - Matrix Game 2.0</title>
-            <meta name="title" content="MatrixGame2.0">
-            <meta name="description" content="Make video generation go blurrrrrrr">
-            <meta name="keywords" content="FastVideo, video generation, AI, machine learning, Matrix Game 2.0">
-            
-            <meta property="og:type" content="website">
-            <meta property="og:url" content="{base_url}/">
-            <meta property="og:title" content="FastVideo - Matrix Game 2.0">
-            <meta property="og:description" content="Make video generation go blurrrrrrr">
-            <meta property="og:image" content="{base_url}/logo.png">
-            <meta property="og:image:width" content="1200">
-            <meta property="og:image:height" content="630">
-            <meta property="og:site_name" content="MatrixGame2.0">
-            
-            <meta property="twitter:card" content="summary_large_image">
-            <meta property="twitter:url" content="{base_url}/">
-            <meta property="twitter:title" content="MatrixGame2.0">
-            <meta property="twitter:description" content="Make video generation go blurrrrrrr">
-            <meta property="twitter:image" content="{base_url}/logo.png">
-            <link rel="icon" type="image/png" sizes="32x32" href="/favicon.ico">
-            <link rel="icon" type="image/png" sizes="16x16" href="/favicon.ico">
-            <link rel="apple-touch-icon" href="/favicon.ico">
-            <style>
-                body, html {{
-                    margin: 0;
-                    padding: 0;
-                    height: 100%;
-                    overflow: hidden;
-                }}
-                iframe {{
-                    width: 100%;
-                    height: 100vh;
-                    border: none;
-                }}
-            </style>
-        </head>
-        <body>
-            <iframe src="/gradio" width="100%" height="100%" style="border: none;"></iframe>
-        </body>
-        </html>
-        """
-
-    app = gr.mount_gradio_app(
-        app, 
-        demo, 
-        path="/gradio",
-        allowed_paths=[os.path.abspath("outputs"), os.path.abspath("fastvideo-logos")]
-    )
-    
-    uvicorn.run(app, host=args.host, port=args.port)
-
-
-if __name__ == "__main__":
-    main()

examples/inference/optimizations/text_encoder_quant_example.py

@@ -1,46 +0,0 @@
-from fastvideo import VideoGenerator
-import argparse
-
-OUTPUT_PATH = "video_samples_wan2_2_5B_ti2v"
-
-
-def main(text_encoder_path: str):
-    # FastVideo will automatically use the optimal default arguments for the
-    # model.
-    # If a local path is provided, FastVideo will make a best effort
-    # attempt to identify the optimal arguments.
-    model_name = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
-    generator = VideoGenerator.from_pretrained(
-        model_name,
-        # FastVideo will automatically handle distributed setup
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=True,
-        vae_cpu_offload=False,
-        text_encoder_cpu_offload=False,
-        # AbsMaxFP8 is the quantization method used by ComfyUI; 
-        # check fastvideo/layers/quantization/* for more quantization methods
-        override_text_encoder_quant="AbsMaxFP8",
-        # for Wan 2.2, this is the path to "umt5_xxl_fp8_e4m3fn_scaled.safetensors"
-        override_text_encoder_safetensors=text_encoder_path,
-        pin_cpu_memory=True,  # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
-    )
-
-    # I2V is triggered just by passing in an image_path argument
-    prompt = "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
-    image_path = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG"
-    video = generator.generate_video(
-        prompt, output_path=OUTPUT_PATH, save_video=True, image_path=image_path
-    )
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--text_encoder_path",
-        type=str,
-        required=True,
-        help="Path to the quantized text encoder safetensors file.",
-    )
-    args = parser.parse_args()
-    main(args.text_encoder_path)

examples/training/rl/finetune_t2v_grpo.sh

@@ -1,129 +0,0 @@
-#!/bin/bash
-
-# Change to FastVideo root directory (3 levels up from this script)
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-FASTVIDEO_ROOT="$(cd "$SCRIPT_DIR/../../.." && pwd)"
-cd "$FASTVIDEO_ROOT"
-
-# Add FastVideo root to PYTHONPATH so Python can find the fastvideo package
-export PYTHONPATH="$FASTVIDEO_ROOT${PYTHONPATH:+:$PYTHONPATH}"
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-RL_DATASET_DIR="data/ocr/"  # Path to RL prompt dataset directory (should contain train.txt and test.txt)
-VALIDATION_DATASET_FILE="$SCRIPT_DIR/validation.json"
-NUM_GPUS=1
-
-# use GPU 3
-export CUDA_VISIBLE_DEVICES=3
-
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_t2v_grpo"
-  --output_dir "checkpoints/wan_t2v_grpo"
-  --max_train_steps 5000
-  --train_batch_size 4
-  # --train_sp_batch_size 4
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 5
-  --num_height 240
-  --num_width 416
-  --num_frames 33
-  --lora_rank 32
-  --lora_training True
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS 
-  --sp_size $NUM_GPUS 
-  --tp_size $NUM_GPUS
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim $NUM_GPUS
-  # --use-fsdp-inference False
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments (for RL prompt dataset)
-dataset_args=(
-  --data_path $RL_DATASET_DIR  # Used as fallback if rl_dataset_path not set
-  --rl_dataset_path $RL_DATASET_DIR  # RL prompt dataset directory
-  --rl_dataset_type "text"  # "text" or "geneval"
-  --rl_num_image_per_prompt 4  # k parameter (number of samples per prompt)
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation True
-  --validation_dataset_file $VALIDATION_DATASET_FILE
-  --validation_steps 5
-  --validation_sampling_steps "50" 
-  --validation_guidance_scale "6.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 5e-5
-  --mixed_precision "bf16"
-  --weight_only_checkpointing_steps 10
-  --training_state_checkpointing_steps 10
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# RL-specific arguments
-rl_args=(
-  --inference_mode False
-  --rl_mode True
-  --rl_algorithm "grpo"
-  --rl_kl_beta 0.004  # KL regularization coefficient
-  --rl_policy_clip_range 0.2  # Policy clipping range for GRPO
-  --rl_kl_reward 0.0  # KL reward coefficient (typically 0)
-  --rl_global_std False  # Use per-prompt std (recommended for GRPO)
-  --rl_per_prompt_stat_tracking True  # Enable per-prompt stat tracking
-  --rl_warmup_steps 0  # Number of warmup steps (SFT before RL)
-  --reward-models "{\"paddle_ocr\": 1.0}" # use video_ocr reward function
-)
-
-# CFG arguments
-cfg_args=(
-  --guidance_scale 1.0 # use guidance_scale > 1.0 to enable CFG
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0  # No CFG during training (CFG used in sampling)
-  --dit_precision "fp32"
-  # --dit_precision "bf16"
-  --num_euler_timesteps 50
-  --ema_start_step 0
-  # --resume_from_checkpoint "checkpoints/wan_t2v_grpo/checkpoint-XXX"
-  --enable-gradient-checkpointing-type "full"
-)
-
-torchrun \
-  --nnodes 1 \
-  --nproc_per_node $NUM_GPUS \
-  --master_port 29501 \
-    "$FASTVIDEO_ROOT/fastvideo/training/wan_rl_training_pipeline.py" \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${rl_args[@]}" \
-    "${miscellaneous_args[@]}"

fastvideo-kernel/CMakeLists.txt

@@ -10,8 +10,6 @@ if(GPU_BACKEND STREQUAL "ROCM")
     enable_language(HIP)
 else()
     enable_language(CUDA)
-    # Ensure CUDA toolkit targets (CUDA::cudart, CUDA::cuda_driver, etc.) are available.
-    find_package(CUDAToolkit REQUIRED)
 endif()
 
 # Import common utils if needed, but we keep it simple for now
@@ -155,30 +153,6 @@ if(BUILD_CXX_KERNELS)
         $<$<COMPILE_LANGUAGE:CUDA>:${CUDA_FLAGS}>
     )
 
-    # Link against Torch libraries to avoid undefined symbols at import time
-    # (e.g., torch::autograd vtables) when loading the extension module.
-    target_link_libraries(fastvideo_kernel_ops PRIVATE ${TORCH_LIBRARIES})
-
-    # Also link against libtorch_python to satisfy Python-binding symbols
-    # (e.g., torch::PyWarningHandler) required by torch/extension.h.
-    execute_process(
-        COMMAND "${Python_EXECUTABLE}" -c "import torch; from pathlib import Path; p=Path(torch.__file__).parent/'lib'; m=sorted(p.glob('libtorch_python*')); print(str(m[0]) if m else '')"
-        OUTPUT_VARIABLE TORCH_PYTHON_LIBRARY_PATH
-        OUTPUT_STRIP_TRAILING_WHITESPACE
-        ERROR_QUIET
-    )
-    if(TORCH_PYTHON_LIBRARY_PATH)
-        message(STATUS "TORCH_PYTHON_LIBRARY_PATH: ${TORCH_PYTHON_LIBRARY_PATH}")
-        target_link_libraries(fastvideo_kernel_ops PRIVATE "${TORCH_PYTHON_LIBRARY_PATH}")
-    else()
-        message(WARNING "Could not locate libtorch_python; fastvideo_kernel_ops may fail to import.")
-    endif()
-
-    # Link CUDA runtime + driver explicitly (fixes missing symbols like cuGetErrorString at import time)
-    if(NOT GPU_BACKEND STREQUAL "ROCM")
-        target_link_libraries(fastvideo_kernel_ops PRIVATE CUDA::cudart CUDA::cuda_driver)
-    endif()
-
     # We install it to fastvideo_kernel/_C so we can load it to register the ops
     install(TARGETS fastvideo_kernel_ops LIBRARY DESTINATION fastvideo_kernel/_C)
 endif()

fastvideo-kernel/README.md

@@ -34,7 +34,7 @@ from fastvideo_kernel import sliding_tile_attention, video_sparse_attn, moba_att
 out = sliding_tile_attention(q, k, v, window_sizes, text_len)
 
 # Example: Video Sparse Attention (with Triton fallback)
-out = video_sparse_attn(q, k, v, block_sizes, block_sizes, topk=5)
+out = video_sparse_attn(q, k, v, block_sizes, topk=5)
 
 # Example: VMoBA
 out = moba_attn_varlen(q, k, v, cu_seqlens_q, cu_seqlens_k, ...)

fastvideo-kernel/csrc/attention/block_sparse_h100.cu

@@ -639,8 +639,7 @@ void bwd_attend_ker(const __grid_constant__ bwd_globals<D> g) {
 
     // store kq and vq
 
-    // ! the following two line seems unnecessary.
-    // tma::store_async_wait(); // ensure qg is finished
+    // ensuring all writes are finished
     __syncthreads();
 
     warpgroup::store(kg_smem[0], kg_reg);
@@ -661,6 +660,145 @@ void bwd_attend_ker(const __grid_constant__ bwd_globals<D> g) {
     tma::store_async_wait(); 
 }
 
+
+template<int D>
+void block_sparse_attention_forward_impl(
+    bf16* d_q, bf16* d_k, bf16* d_v, float* d_l, bf16* d_o,
+    int batch, int qo_heads, int kv_heads, int seq_len, int hr,
+    int max_kv_blocks_per_q, 
+    int32_t* q2k_block_sparse_index_ptr,
+    int32_t* q2k_block_sparse_num_ptr,
+    int32_t* block_size_ptr,
+    cudaStream_t stream
+) {
+    using K = fwd_attend_ker_tile_dims<D>;
+    using q_tile    =         st_bf<K::qo_height, K::tile_width>;
+    using k_tile    =         st_bf<K::kv_height, K::tile_width>;
+    using v_tile    =         st_bf<K::kv_height, K::tile_width>;
+    using l_col_vec = col_vec<st_fl<K::qo_height, K::tile_width>>;
+    using o_tile    =         st_bf<K::qo_height, K::tile_width>;
+
+    using q_global = gl<bf16,  -1, -1, -1, -1, q_tile>;
+    using k_global = gl<bf16,  -1, -1, -1, -1, k_tile>;
+    using v_global = gl<bf16,  -1, -1, -1, -1, v_tile>;
+    using l_global = gl<float, -1, -1, -1, -1, l_col_vec>;
+    using o_global = gl<bf16,  -1, -1, -1, -1, o_tile>;
+
+    using globals      = fwd_globals<D>;
+
+    q_global qg_arg{d_q, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    k_global kg_arg{d_k, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    v_global vg_arg{d_v, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    l_global lg_arg{d_l, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,   static_cast<unsigned int>(seq_len)};
+    o_global og_arg{d_o, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+
+    globals g{
+        qg_arg, kg_arg, vg_arg, lg_arg, og_arg, 
+        static_cast<int>(seq_len), static_cast<int>(hr), static_cast<int>(max_kv_blocks_per_q), 
+        q2k_block_sparse_index_ptr, q2k_block_sparse_num_ptr, block_size_ptr
+    };
+
+    // Shared memory size for the kernel
+    // 54000 bytes is calibrated for H100 shared memory constraints for these tile sizes
+    constexpr int mem_size = 54000;
+
+    dim3 grid(seq_len/(64), qo_heads, batch);
+
+    cudaFuncSetAttribute(
+        fwd_attend_ker<D>,
+        cudaFuncAttributeMaxDynamicSharedMemorySize,
+        mem_size
+    );
+
+    fwd_attend_ker<D><<<grid, (128), mem_size, stream>>>(g);
+}
+
+template<int D>
+void block_sparse_attention_backward_impl(
+    bf16* d_q, bf16* d_k, bf16* d_v, bf16* d_o, bf16* d_og, float* d_l, float* d_d, float* d_qg, float* d_kg, float* d_vg,
+    int batch, int qo_heads, int kv_heads, int seq_len, int hr, int max_q_blocks_per_kv,
+    int32_t* k2q_block_sparse_index_ptr,
+    int32_t* k2q_block_sparse_num_ptr,
+    int32_t* block_size_ptr,
+    cudaStream_t stream
+) {
+    using G = bwd_attend_ker_tile_dims<D>;
+    using og_tile = st_bf<4*16, D>;
+    using o_tile  = st_bf<4*16, D>;
+    using d_tile  = col_vec<st_fl<4*16, D>>;
+
+    using og_global = gl<bf16,  -1, -1, -1, -1, og_tile>;
+    using o_global  = gl<bf16,  -1, -1, -1, -1, o_tile>;
+    using d_global  = gl<float, -1, -1, -1, -1, d_tile>;
+
+    using prep_globals = bwd_prep_globals<D>;
+
+    constexpr int mem_size_prep = kittens::MAX_SHARED_MEMORY;
+    int threads_prep = PREP_NUM_WARPS * kittens::WARP_THREADS;
+    dim3 grid_bwd_prep(seq_len/(PREP_NUM_WARPS*kittens::TILE_ROW_DIM<bf16>*4), qo_heads, batch);
+
+    og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
+
+    prep_globals bwd_g{prep_og_arg, prep_o_arg, prep_d_arg};
+
+    cudaFuncSetAttribute(
+        bwd_attend_prep_ker<D>,
+        cudaFuncAttributeMaxDynamicSharedMemorySize,
+        mem_size_prep
+    );
+    bwd_attend_prep_ker<D><<<grid_bwd_prep, threads_prep, mem_size_prep, stream>>>(bwd_g);
+
+    using bwd_q_tile    =         st_bf<G::tile_h_qo, G::tile_width>;
+    using bwd_k_tile    =         st_bf<G::tile_h,    G::tile_width>;
+    using bwd_v_tile    =         st_bf<G::tile_h,    G::tile_width>;
+    using bwd_og_tile   =         st_bf<G::tile_h_qo, G::tile_width>;
+    using bwd_qg_tile   =         st_fl<G::tile_h_qo, G::tile_width>;
+    using bwd_kg_tile   =         st_fl<G::tile_h,    G::tile_width>;
+    using bwd_vg_tile   =         st_fl<G::tile_h,    G::tile_width>;
+    using bwd_l_tile    = row_vec<st_fl<G::tile_h_qo, G::tile_h>>;
+    using bwd_d_tile    = row_vec<st_fl<G::tile_h_qo, G::tile_h>>;
+
+    using bwd_q_global  = gl<bf16,  -1, -1, -1, -1, bwd_q_tile>;
+    using bwd_k_global  = gl<bf16,  -1, -1, -1, -1, bwd_k_tile>;
+    using bwd_v_global  = gl<bf16,  -1, -1, -1, -1, bwd_v_tile>;
+    using bwd_og_global = gl<bf16,  -1, -1, -1, -1, bwd_og_tile>;
+    using bwd_qg_global = gl<float, -1, -1, -1, -1, bwd_qg_tile>;
+    using bwd_kg_global = gl<float, -1, -1, -1, -1, bwd_kg_tile>;
+    using bwd_vg_global = gl<float, -1, -1, -1, -1, bwd_vg_tile>;
+    using bwd_l_global  = gl<float, -1, -1, -1, -1, bwd_l_tile>;
+    using bwd_d_global  = gl<float, -1, -1, -1, -1, bwd_d_tile>;
+
+    using bwd_global_args = bwd_globals<D>;
+
+    bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
+    bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
+
+    bwd_global_args bwd_global{bwd_q_arg, bwd_k_arg, bwd_v_arg, bwd_og_arg, bwd_qg_arg, bwd_kg_arg, bwd_vg_arg, bwd_l_arg, bwd_d_arg, 
+                        static_cast<int>(seq_len), static_cast<int>(hr), static_cast<int>(max_q_blocks_per_kv),
+                        k2q_block_sparse_index_ptr, k2q_block_sparse_num_ptr, block_size_ptr};
+
+    dim3 grid_bwd_main(seq_len/64, qo_heads, batch);
+    int threads_main = 128;
+    // Calibrated shared memory sizes for different head dimensions
+    int bwd_mem_size = (D == 64) ? 72000 : 113000;
+
+    cudaFuncSetAttribute(
+        bwd_attend_ker<D>,
+        cudaFuncAttributeMaxDynamicSharedMemorySize,
+        bwd_mem_size
+    );
+    bwd_attend_ker<D><<<grid_bwd_main, threads_main, bwd_mem_size, stream>>>(bwd_global);
+}
+
 #include "pyutils/torch_helpers.cuh"
 #include <ATen/cuda/CUDAContext.h>
 #include <iostream>
@@ -672,32 +810,23 @@ block_sparse_attention_forward(
     torch::Tensor v, 
     torch::Tensor q2k_block_sparse_index, 
     torch::Tensor q2k_block_sparse_num, 
-    torch::Tensor kv_block_size
+    torch::Tensor block_size
 )
 {
     CHECK_INPUT(q);
     CHECK_INPUT(k);
     CHECK_INPUT(v);
 
-    // q shape: (batch, qo_heads, q_seq_len, head_dim)
-    // k shape: (batch, kv_heads, kv_seq_len, head_dim)
-    // v shape: (batch, kv_heads, kv_seq_len, head_dim)
-    // q2k_block_sparse_index shape: (batch, qo_heads, num_q_blocks, max_kv_blocks_per_q)
-    // q2k_block_sparse_num shape: (batch, qo_heads, num_q_blocks)
-    // kv_block_size shape: (num_kv_blocks) This does not need other dimensions because across all batch/heads the padding is the same.
-    
     auto batch    = q.size(0);
-    auto q_seq_len  = q.size(2);
-    auto kv_seq_len = k.size(2);
+    auto seq_len  = q.size(2); 
     auto head_dim = q.size(3); 
     auto qo_heads = q.size(1);
     auto kv_heads = k.size(1);
     auto max_kv_blocks_per_q = q2k_block_sparse_index.size(3);
-    auto num_q_blocks = q2k_block_sparse_index.size(2);
-    auto num_kv_blocks = kv_block_size.size(0);
+    auto num_q_blocks = block_size.size(0);
     TORCH_CHECK(batch==1, "Batch size dim will be removed in the future, please set batch to 1");
-    TORCH_CHECK(num_q_blocks * BLOCK_M == q_seq_len, "This kernel supports variable q block size, but it assumes the input sequence is properly padded.");
-    TORCH_CHECK(num_kv_blocks * BLOCK_M == kv_seq_len, "This kernel supports variable kv block size, but it assumes the input sequence is properly padded.");
+    TORCH_CHECK(num_q_blocks * 64 == seq_len, "This kernel supports variable block size, but it assumes the input sequence is properly padded.");
+    TORCH_CHECK(num_q_blocks == q2k_block_sparse_index.size(2), "Number of Q blocks does not match between q2k_block_sparse_index and block_size");
     // check to see that these dimensions match for all inputs
     TORCH_CHECK(q.size(0) == batch, "Q batch dimension - idx 0 - must match for all inputs");
     TORCH_CHECK(k.size(0) == batch, "K batch dimension - idx 0 - must match for all inputs");
@@ -705,9 +834,11 @@ block_sparse_attention_forward(
     TORCH_CHECK(q2k_block_sparse_index.size(0) == batch, "q2k_block_sparse_index batch dimension - idx 0 - must match for all inputs");
     TORCH_CHECK(q2k_block_sparse_num.size(0) == batch, "q2k_block_sparse_num batch dimension - idx 0 - must match for all inputs");
 
-    TORCH_CHECK(v.size(2) == kv_seq_len, "V sequence length dimension - idx 2 - must match K inputs");
-    TORCH_CHECK(q2k_block_sparse_num.size(2) == num_q_blocks, "q2k_block_sparse_num idx 2 - must match num_q_blocks");
-
+    TORCH_CHECK(q.size(2) == seq_len, "Q sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(k.size(2) == seq_len, "K sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(v.size(2) == seq_len, "V sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(q2k_block_sparse_index.size(2) == seq_len / BLOCK_M, "q2k_block_sparse_index idx 2 - must match seq_len / BLOCK_M");
+    TORCH_CHECK(q2k_block_sparse_num.size(2) == seq_len / BLOCK_M, "q2k_block_sparse_num idx 2 - must match seq_len / BLOCK_M");
 
     TORCH_CHECK(q.size(3) == head_dim, "Q head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(k.size(3) == head_dim, "K head dimension - idx 3 - must match for all non-vector inputs");
@@ -733,12 +864,12 @@ block_sparse_attention_forward(
     // for the returned outputs
     torch::Tensor o     = torch::empty({static_cast<const uint>(batch), 
                                         static_cast<const uint>(qo_heads), 
-                                        static_cast<const uint>(q_seq_len), 
+                                        static_cast<const uint>(seq_len), 
                                         static_cast<const uint>(head_dim)}, v.options());
     
     torch::Tensor l_vec = torch::empty({static_cast<const uint>(batch), 
                                         static_cast<const uint>(qo_heads), 
-                                        static_cast<const uint>(q_seq_len), 
+                                        static_cast<const uint>(seq_len), 
                                         static_cast<const uint>(1)}, 
                                         torch::TensorOptions().dtype(torch::kFloat).device(q.device()).memory_format(at::MemoryFormat::Contiguous));
         
@@ -749,110 +880,32 @@ block_sparse_attention_forward(
     float* l_ptr = reinterpret_cast<float*>(l_vec.data_ptr<float>());
     float* d_l   = reinterpret_cast<float*>(l_ptr);
 
-    //cudadevicesynchronize();
     const c10::cuda::OptionalCUDAGuard device_guard(q.device());    
     const cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream(); 
 
+    // Temporated implementation to avoid code duplication between head_dim=64 and 128
     if (head_dim == 64) {
-        using q_tile    =         st_bf<fwd_attend_ker_tile_dims<64>::qo_height, fwd_attend_ker_tile_dims<64>::tile_width>;
-        using k_tile    =         st_bf<fwd_attend_ker_tile_dims<64>::kv_height, fwd_attend_ker_tile_dims<64>::tile_width>;
-        using v_tile    =         st_bf<fwd_attend_ker_tile_dims<64>::kv_height, fwd_attend_ker_tile_dims<64>::tile_width>;
-        using l_col_vec = col_vec<st_fl<fwd_attend_ker_tile_dims<64>::qo_height, fwd_attend_ker_tile_dims<64>::tile_width>>;
-        using o_tile    =         st_bf<fwd_attend_ker_tile_dims<64>::qo_height, fwd_attend_ker_tile_dims<64>::tile_width>;
-
-        using q_global = gl<bf16,  -1, -1, -1, -1, q_tile>;
-        using k_global = gl<bf16,  -1, -1, -1, -1, k_tile>;
-        using v_global = gl<bf16,  -1, -1, -1, -1, v_tile>;
-        using l_global = gl<float, -1, -1, -1, -1, l_col_vec>;
-        using o_global = gl<bf16,  -1, -1, -1, -1, o_tile>;
-
-        using globals      = fwd_globals<64>;
-
-        q_global qg_arg{d_q, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        k_global kg_arg{d_k, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        v_global vg_arg{d_v, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        l_global lg_arg{d_l, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,   static_cast<unsigned int>(q_seq_len)};
-        o_global og_arg{d_o, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-
-        globals g{
-            qg_arg, 
-            kg_arg, 
-            vg_arg, 
-            lg_arg, 
-            og_arg, 
-            static_cast<int>(q_seq_len), 
-            static_cast<int>(hr), 
-            static_cast<int>(max_kv_blocks_per_q), 
-            reinterpret_cast<int32_t*>(q2k_block_sparse_index.data_ptr()), 
+        block_sparse_attention_forward_impl<64>(
+            d_q, d_k, d_v, d_l, d_o,
+            batch, qo_heads, kv_heads, seq_len, hr,
+            max_kv_blocks_per_q,
+            reinterpret_cast<int32_t*>(q2k_block_sparse_index.data_ptr()),
             reinterpret_cast<int32_t*>(q2k_block_sparse_num.data_ptr()),
-            reinterpret_cast<int32_t*>(kv_block_size.data_ptr())
-        };
-
-        constexpr int mem_size = 54000;
-
-        dim3 grid(q_seq_len/(BLOCK_M), qo_heads, batch);
-
-        cudaFuncSetAttribute(
-            fwd_attend_ker<64>,
-            cudaFuncAttributeMaxDynamicSharedMemorySize,
-            mem_size
+            reinterpret_cast<int32_t*>(block_size.data_ptr()),
+            stream
         );
-
-        fwd_attend_ker<64><<<grid, (128), mem_size, stream>>>(g);
-
-        CHECK_CUDA_ERROR(cudaGetLastError());
-        // cudaStreamSynchronize(stream);
-    }
-
-    if (head_dim == 128) {
-        using q_tile    =         st_bf<fwd_attend_ker_tile_dims<128>::qo_height, fwd_attend_ker_tile_dims<128>::tile_width>;
-        using k_tile    =         st_bf<fwd_attend_ker_tile_dims<128>::kv_height, fwd_attend_ker_tile_dims<128>::tile_width>;
-        using v_tile    =         st_bf<fwd_attend_ker_tile_dims<128>::kv_height, fwd_attend_ker_tile_dims<128>::tile_width>;
-        using l_col_vec = col_vec<st_fl<fwd_attend_ker_tile_dims<128>::qo_height, fwd_attend_ker_tile_dims<128>::tile_width>>;
-        using o_tile    =         st_bf<fwd_attend_ker_tile_dims<128>::qo_height, fwd_attend_ker_tile_dims<128>::tile_width>;
-
-        using q_global = gl<bf16,  -1, -1, -1, -1, q_tile>;
-        using k_global = gl<bf16,  -1, -1, -1, -1, k_tile>;
-        using v_global = gl<bf16,  -1, -1, -1, -1, v_tile>;
-        using l_global = gl<float, -1, -1, -1, -1, l_col_vec>;
-        using o_global = gl<bf16,  -1, -1, -1, -1, o_tile>;
-
-        using globals      = fwd_globals<128>;
-
-        q_global qg_arg{d_q, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        k_global kg_arg{d_k, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        v_global vg_arg{d_v, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        l_global lg_arg{d_l, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,   static_cast<unsigned int>(q_seq_len)};
-        o_global og_arg{d_o, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-
-        globals g{
-            qg_arg, 
-            kg_arg, 
-            vg_arg, 
-            lg_arg, 
-            og_arg, 
-            static_cast<int>(q_seq_len), 
-            static_cast<int>(hr), 
-            static_cast<int>(max_kv_blocks_per_q), 
-            reinterpret_cast<int32_t*>(q2k_block_sparse_index.data_ptr()), 
+    } else if (head_dim == 128) {
+        block_sparse_attention_forward_impl<128>(
+            d_q, d_k, d_v, d_l, d_o,
+            batch, qo_heads, kv_heads, seq_len, hr,
+            max_kv_blocks_per_q,
+            reinterpret_cast<int32_t*>(q2k_block_sparse_index.data_ptr()),
             reinterpret_cast<int32_t*>(q2k_block_sparse_num.data_ptr()),
-            reinterpret_cast<int32_t*>(kv_block_size.data_ptr())
-        };
-
-        constexpr int mem_size = 54000;
-
-        dim3 grid(q_seq_len/(BLOCK_M), qo_heads, batch);
-
-        cudaFuncSetAttribute(
-            fwd_attend_ker<128>,
-            cudaFuncAttributeMaxDynamicSharedMemorySize,
-            mem_size
+            reinterpret_cast<int32_t*>(block_size.data_ptr()),
+            stream
         );
-
-        fwd_attend_ker<128><<<grid, (128), mem_size, stream>>>(g);
-
-        CHECK_CUDA_ERROR(cudaGetLastError());
-        // cudaStreamSynchronize(stream);
+    } else {
+        TORCH_CHECK(false, "Unsupported head_dim: ", head_dim, ". Only 64 and 128 are supported.");
     }
 
     return {o, l_vec};
@@ -868,7 +921,7 @@ block_sparse_attention_backward(torch::Tensor q,
                    torch::Tensor og,
                    torch::Tensor k2q_block_sparse_index,
                    torch::Tensor k2q_block_sparse_num,
-                   torch::Tensor kv_block_size)
+                   torch::Tensor block_size)
 {
     CHECK_INPUT(q);
     CHECK_INPUT(k);
@@ -877,23 +930,11 @@ block_sparse_attention_backward(torch::Tensor q,
     CHECK_INPUT(o);
     CHECK_INPUT(og);
 
-    // q: [batch, qo_heads, q_seq_len, head_dim]
-    // k: [batch, kv_heads, kv_seq_len, head_dim]
-    // v: [batch, kv_heads, kv_seq_len, head_dim]
-    // o: [batch, qo_heads, q_seq_len, head_dim]
-    // l_vec: [batch, qo_heads, q_seq_len, 1]
-    // og: [batch, qo_heads, q_seq_len, head_dim]
-    // k2q_block_sparse_index: [batch, kv_heads, num_kv_blocks, max_num_q_blocks]
-    // k2q_block_sparse_num: [batch, kv_heads, num_kv_blocks]
-    // kv_block_size: [num_kv_blocks]
-
     auto batch    = q.size(0);
-    auto q_seq_len  = q.size(2);
-    auto kv_seq_len = k.size(2);
+    auto seq_len  = q.size(2);
     auto head_dim = q.size(3);
     auto max_q_blocks_per_kv = k2q_block_sparse_index.size(3);
-    auto num_kv_blocks = kv_block_size.size(0);
-    TORCH_CHECK(k2q_block_sparse_index.size(2) == num_kv_blocks, "k2q_block_sparse_index.size(2) must match num_kv_blocks (kv_block_size.size(0))");
+    TORCH_CHECK(k2q_block_sparse_index.size(2) == block_size.size(0), "k2q_block_sparse_index.size(2) must match block_size.size(0)");
     // check to see that these dimensions match for all inputs
     TORCH_CHECK(q.size(0)     == batch, "Q  batch dimension - idx 0 - must match for all inputs");
     TORCH_CHECK(k.size(0)     == batch, "K  batch dimension - idx 0 - must match for all inputs");
@@ -904,18 +945,23 @@ block_sparse_attention_backward(torch::Tensor q,
     TORCH_CHECK(k2q_block_sparse_index.size(0) == batch, "k2q_block_sparse_index batch dimension - idx 0 - must match for all inputs");
     TORCH_CHECK(k2q_block_sparse_num.size(0) == batch, "k2q_block_sparse_num batch dimension - idx 0 - must match for all inputs");
 
-    TORCH_CHECK(v.size(2)     == kv_seq_len, "V  sequence length dimension - idx 2 - must match K sequence length");
-    TORCH_CHECK(l_vec.size(2) == q_seq_len, "L  sequence length dimension - idx 2 - must match Q sequence length");
-    TORCH_CHECK(o.size(2)     == q_seq_len, "O  sequence length dimension - idx 2 - must match Q sequence length");
-    TORCH_CHECK(og.size(2)    == q_seq_len, "OG sequence length dimension - idx 2 - must match Q sequence length");
-    TORCH_CHECK(k2q_block_sparse_index.size(2) == num_kv_blocks, "k2q_block_sparse_index idx 2 - must match num_kv_blocks (kv_block_size.size(0))");
-    TORCH_CHECK(k2q_block_sparse_num.size(2) == num_kv_blocks, "k2q_block_sparse_num idx 2 - must match num_kv_blocks (kv_block_size.size(0))");
+
+    TORCH_CHECK(q.size(2)     == seq_len, "Q  sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(k.size(2)     == seq_len, "K  sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(v.size(2)     == seq_len, "V  sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(l_vec.size(2) == seq_len, "L  sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(o.size(2)     == seq_len, "O  sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(og.size(2)    == seq_len, "OG sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(k2q_block_sparse_index.size(2) == seq_len / BLOCK_N, "k2q_block_sparse_index idx 2 - must match seq_len / BLOCK_N");
+    TORCH_CHECK(k2q_block_sparse_num.size(2) == seq_len / BLOCK_N, "k2q_block_sparse_num idx 2 - must match seq_len / BLOCK_N");
 
     TORCH_CHECK(q.size(3)  == head_dim, "Q  head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(k.size(3)  == head_dim, "K  head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(v.size(3)  == head_dim, "V  head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(o.size(3)  == head_dim, "O  head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(og.size(3) == head_dim, "OG head dimension - idx 3 - must match for all non-vector inputs");
+    
+
 
     auto qo_heads = q.size(1);
     auto kv_heads = k.size(1);
@@ -942,20 +988,20 @@ block_sparse_attention_backward(torch::Tensor q,
 
     torch::Tensor qg = torch::zeros({static_cast<const uint>(batch), 
                                      static_cast<const uint>(qo_heads), 
-                                     static_cast<const uint>(q_seq_len), 
+                                     static_cast<const uint>(seq_len), 
                                      static_cast<const uint>(head_dim)},   l_vec.options());
     torch::Tensor kg = torch::zeros({static_cast<const uint>(batch), 
                                      static_cast<const uint>(kv_heads), 
-                                     static_cast<const uint>(kv_seq_len), 
+                                     static_cast<const uint>(seq_len), 
                                      static_cast<const uint>(head_dim)},   l_vec.options());
     torch::Tensor vg = torch::zeros({static_cast<const uint>(batch), 
                                      static_cast<const uint>(kv_heads), 
-                                     static_cast<const uint>(kv_seq_len), 
+                                     static_cast<const uint>(seq_len), 
                                      static_cast<const uint>(head_dim)},   l_vec.options());
     
     torch::Tensor d_vec = torch::empty({static_cast<const uint>(batch), 
                                         static_cast<const uint>(qo_heads), 
-                                        static_cast<const uint>(q_seq_len), 
+                                        static_cast<const uint>(seq_len), 
                                         static_cast<const uint>(1)},       l_vec.options());
 
     float*         qg_ptr = qg.data_ptr<float>();
@@ -984,7 +1030,7 @@ block_sparse_attention_backward(torch::Tensor q,
     //  cudaStreamSynchronize(stream);
 
     // TORCH_CHECK(seq_len % (4*kittens::TILE_DIM*4) == 0, "sequence length must be divisible by 256");
-    dim3 grid_bwd(q_seq_len/(PREP_NUM_WARPS*kittens::TILE_ROW_DIM<bf16>*4), qo_heads, batch);
+    dim3 grid_bwd(seq_len/(PREP_NUM_WARPS*kittens::TILE_ROW_DIM<bf16>*4), qo_heads, batch);
 
     if (head_dim == 64)  {
         using og_tile = st_bf<4*16, 64>;
@@ -997,9 +1043,9 @@ block_sparse_attention_backward(torch::Tensor q,
 
         using bwd_prep_globals = bwd_prep_globals<64>;
 
-        og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
+        og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
+        o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
+        d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
 
         bwd_prep_globals bwd_g{prep_og_arg, prep_o_arg, prep_d_arg};
 
@@ -1036,15 +1082,15 @@ block_sparse_attention_backward(torch::Tensor q,
 
         using bwd_global_args = bwd_globals<64>;
 
-        bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
-        bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
+        bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
+        bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
 
         bwd_global_args bwd_global{bwd_q_arg, 
                         bwd_k_arg, 
@@ -1055,14 +1101,14 @@ block_sparse_attention_backward(torch::Tensor q,
                         bwd_vg_arg, 
                         bwd_l_arg, 
                         bwd_d_arg, 
-                        static_cast<int>(kv_seq_len), // N is not used in the kernel
+                        static_cast<int>(seq_len), 
                         static_cast<int>(hr),
                         static_cast<int>(max_q_blocks_per_kv),
                         reinterpret_cast<int32_t*>(k2q_block_sparse_index.data_ptr()),
                         reinterpret_cast<int32_t*>(k2q_block_sparse_num.data_ptr()),
-                        reinterpret_cast<int32_t*>(kv_block_size.data_ptr())};
+                        reinterpret_cast<int32_t*>(block_size.data_ptr())};
 
-        dim3 grid_bwd_2(kv_seq_len/BLOCK_N, qo_heads, batch);
+        dim3 grid_bwd_2(seq_len/64, qo_heads, batch);
         threads = 128;
 
         //cudadevicesynchronize();
@@ -1101,9 +1147,9 @@ block_sparse_attention_backward(torch::Tensor q,
 
         using bwd_prep_globals = bwd_prep_globals<128>;
 
-        og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
+        og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
+        o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
+        d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
 
         bwd_prep_globals bwd_g{prep_og_arg, prep_o_arg, prep_d_arg};
 
@@ -1140,15 +1186,15 @@ block_sparse_attention_backward(torch::Tensor q,
 
         using bwd_global_args = bwd_globals<128>;
 
-        bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
-        bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
+        bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
+        bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
 
         bwd_global_args bwd_global{bwd_q_arg, 
                         bwd_k_arg, 
@@ -1159,14 +1205,14 @@ block_sparse_attention_backward(torch::Tensor q,
                         bwd_vg_arg, 
                         bwd_l_arg, 
                         bwd_d_arg, 
-                        static_cast<int>(kv_seq_len), // N is not used in the kernel
+                        static_cast<int>(seq_len), 
                         static_cast<int>(hr),
                         static_cast<int>(max_q_blocks_per_kv),
                         reinterpret_cast<int32_t*>(k2q_block_sparse_index.data_ptr()),
                         reinterpret_cast<int32_t*>(k2q_block_sparse_num.data_ptr()),
-                        reinterpret_cast<int32_t*>(kv_block_size.data_ptr())};
+                        reinterpret_cast<int32_t*>(block_size.data_ptr())};
 
-        dim3 grid_bwd_2(kv_seq_len/BLOCK_N, qo_heads, batch);
+        dim3 grid_bwd_2(seq_len/64, qo_heads, batch);
         threads = 128;
 
         //cudadevicesynchronize();
@@ -1187,4 +1233,4 @@ block_sparse_attention_backward(torch::Tensor q,
 
     return {qg, kg, vg};
     //cudadevicesynchronize();
-}
+}

fastvideo-kernel/csrc/turbodiffusion/norm/layernorm.cu

@@ -4,7 +4,6 @@
 #include <torch/all.h>
 #include <torch/python.h>
 #include <cutlass/cutlass.h>
-#include <cutlass/numeric_types.h>
 #include "common/common.hpp"
 #include "norm/layernorm.hpp"
 
@@ -15,6 +14,10 @@ auto layer_norm(
   std::optional<at::Tensor const> const B,
   std::optional<at::Tensor> Output
 ) {
+  using ElementIn = float;
+  using ElementOut = float;
+  using ElementWeight = float;
+
   int64_t const m = Input.size(0);
   int64_t const n = Input.size(1);
   torch::Device const input_device = Input.device();
@@ -23,70 +26,31 @@ auto layer_norm(
     Output.emplace(
       torch::empty(
         {m, n},
-        torch::TensorOptions().device(input_device).dtype(Input.scalar_type())
+        torch::TensorOptions().device(input_device).dtype(torch::kFloat32)
       )
     );
   }
 
-  TORCH_CHECK(Output.value().scalar_type() == Input.scalar_type(),
-              "Output dtype must match Input dtype. Got Output=",
-              Output.value().scalar_type(), ", Input=", Input.scalar_type());
-  if (W.has_value()) {
-    TORCH_CHECK(W.value().scalar_type() == Input.scalar_type(),
-                "W dtype must match Input dtype. Got W=",
-                W.value().scalar_type(), ", Input=", Input.scalar_type());
-  }
-  if (B.has_value()) {
-    TORCH_CHECK(B.value().scalar_type() == Input.scalar_type(),
-                "B dtype must match Input dtype. Got B=",
-                B.value().scalar_type(), ", Input=", Input.scalar_type());
-  }
 
   void *Iptr = Input.data_ptr();
   void *Wptr = W.has_value() ? W.value().data_ptr() : nullptr;
   void *Bptr = B.has_value() ? B.value().data_ptr() : nullptr;
   void *Optr = Output.value().data_ptr();
 
-  auto stream = at::cuda::getCurrentCUDAStream().stream();
-  if (Input.scalar_type() == at::kHalf) {
-    using ElementIn = cutlass::half_t;
-    using ElementOut = cutlass::half_t;
-    using ElementWeight = cutlass::half_t;
-    BOOL_SWITCH(B.has_value(), BIAS, [&]{
-      BOOL_SWITCH(W.has_value(), AFFINE, [&]{
-        CONFIG_SWITCH(n, [&]{
-          layernorm<ElementIn, ElementOut, ElementWeight, AFFINE, BIAS, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-              Iptr, Wptr, Bptr, Optr, eps, m, n, stream);
-        });
+  BOOL_SWITCH(B.has_value(), BIAS, [&]{
+    BOOL_SWITCH(W.has_value(), AFFINE, [&]{
+    CONFIG_SWITCH(n, [&]{
+    layernorm<
+    ElementIn, ElementOut, ElementWeight,
+    AFFINE, BIAS,
+    MAX_HIDDEN_SIZE, NUM_THR_PER_CTA> (
+      Iptr, Wptr, Bptr,
+      Optr, eps, m, n,
+      at::cuda::getCurrentCUDAStream().stream()
+        );
       });
     });
-  } else if (Input.scalar_type() == at::kBFloat16) {
-    using ElementIn = cutlass::bfloat16_t;
-    using ElementOut = cutlass::bfloat16_t;
-    using ElementWeight = cutlass::bfloat16_t;
-    BOOL_SWITCH(B.has_value(), BIAS, [&]{
-      BOOL_SWITCH(W.has_value(), AFFINE, [&]{
-        CONFIG_SWITCH(n, [&]{
-          layernorm<ElementIn, ElementOut, ElementWeight, AFFINE, BIAS, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-              Iptr, Wptr, Bptr, Optr, eps, m, n, stream);
-        });
-      });
-    });
-  } else if (Input.scalar_type() == at::kFloat) {
-    using ElementIn = float;
-    using ElementOut = float;
-    using ElementWeight = float;
-    BOOL_SWITCH(B.has_value(), BIAS, [&]{
-      BOOL_SWITCH(W.has_value(), AFFINE, [&]{
-        CONFIG_SWITCH(n, [&]{
-          layernorm<ElementIn, ElementOut, ElementWeight, AFFINE, BIAS, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-              Iptr, Wptr, Bptr, Optr, eps, m, n, stream);
-        });
-      });
-    });
-  } else {
-    TORCH_CHECK(false, "Unsupported dtype for layer_norm_cuda: ", Input.scalar_type());
-  }
+  });
   
     
 

fastvideo-kernel/csrc/turbodiffusion/norm/layernorm.hpp

@@ -68,22 +68,9 @@ public:
     // mean reduction
     float u = _reduce_sum(x, shared_data) / params.n;
 
-    // IMPORTANT:
-    // Loader pads out-of-range lanes with 0. That is OK for the sum, but after
-    // subtracting mean, those padded lanes become -u and would incorrectly
-    // contribute to the variance. Mask them back to 0 before variance reduction.
-    // We launch exactly NumThrPerCta threads for a 1xMaxHiddenSize tile,
-    // so each thread is responsible for a contiguous chunk in N.
-    int thr_n_offset = tidx * NumElementPerThread;
     CUTLASS_PRAGMA_UNROLL
-    for (int i = 0; i < NumElementPerThread; ++i) {
-      int idx = thr_n_offset + i;
-      if (idx < params.n) {
-        x[i] -= u;
-      } else {
-        x[i] = 0.f;
-      }
-    }
+    for (int i = 0; i < NumElementPerThread; ++i)
+      x[i] -= u;
 
     __syncthreads();
     // var reduction

fastvideo-kernel/csrc/turbodiffusion/norm/rmsnorm.cu

@@ -4,7 +4,6 @@
 #include <torch/all.h>
 #include <torch/python.h>
 #include <cutlass/cutlass.h>
-#include <cutlass/numeric_types.h>
 #include <pybind11/pybind11.h>
 
 #include "common/common.hpp"
@@ -17,6 +16,10 @@ auto rms_norm(
   std::optional<at::Tensor>& Output
 ) {
   
+  using ElementIn = float;
+  using ElementOut = float;
+  using ElementWeight = float;
+  
   int64_t const m = Input.size(0);
   int64_t const n = Input.size(1);
   torch::Device const input_device = Input.device();
@@ -25,51 +28,27 @@ auto rms_norm(
     Output.emplace(
       torch::empty(
         {m, n},
-        torch::TensorOptions().device(input_device).dtype(Input.scalar_type())
+        torch::TensorOptions().device(input_device).dtype(torch::kFloat32)
       )
     );
   }
 
-  TORCH_CHECK(Output.value().scalar_type() == Input.scalar_type(),
-              "Output dtype must match Input dtype. Got Output=",
-              Output.value().scalar_type(), ", Input=", Input.scalar_type());
-  if (Weight.has_value()) {
-    TORCH_CHECK(Weight.value().scalar_type() == Input.scalar_type(),
-                "Weight dtype must match Input dtype. Got Weight=",
-                Weight.value().scalar_type(), ", Input=", Input.scalar_type());
-  }
-
   void *Iptr = Input.data_ptr();
   void *Wptr = Weight.has_value() ? Weight.value().data_ptr() : nullptr;
   void *Optr = Output.value().data_ptr();
 
-  if (Input.scalar_type() == at::kHalf) {
-    using ElementIn = cutlass::half_t;
-    using ElementOut = cutlass::half_t;
-    using ElementWeight = cutlass::half_t;
-    CONFIG_SWITCH(n, [&]{
-      rmsnorm<ElementIn, ElementOut, ElementWeight, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-          Iptr, Wptr, Optr, eps, m, n, at::cuda::getCurrentCUDAStream().stream());
-    });
-  } else if (Input.scalar_type() == at::kBFloat16) {
-    using ElementIn = cutlass::bfloat16_t;
-    using ElementOut = cutlass::bfloat16_t;
-    using ElementWeight = cutlass::bfloat16_t;
-    CONFIG_SWITCH(n, [&]{
-      rmsnorm<ElementIn, ElementOut, ElementWeight, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-          Iptr, Wptr, Optr, eps, m, n, at::cuda::getCurrentCUDAStream().stream());
-    });
-  } else if (Input.scalar_type() == at::kFloat) {
-    using ElementIn = float;
-    using ElementOut = float;
-    using ElementWeight = float;
-    CONFIG_SWITCH(n, [&]{
-      rmsnorm<ElementIn, ElementOut, ElementWeight, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-          Iptr, Wptr, Optr, eps, m, n, at::cuda::getCurrentCUDAStream().stream());
-    });
-  } else {
-    TORCH_CHECK(false, "Unsupported dtype for rms_norm_cuda: ", Input.scalar_type());
-  }
+
+  CONFIG_SWITCH(n, [&]{
+    rmsnorm<
+    ElementIn, ElementOut, ElementWeight,
+    MAX_HIDDEN_SIZE, NUM_THR_PER_CTA
+    > (
+      Iptr, Wptr,
+      Optr, 
+      eps, m, n, 
+      at::cuda::getCurrentCUDAStream().stream()
+    );
+});
   
 
   return Output;

fastvideo-kernel/pyproject.toml

@@ -9,7 +9,7 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "fastvideo-kernel"
-version = "0.2.4"
+version = "0.2.1"
 description = "Unified CUDA kernels for FastVideo"
 readme = "README.md"
 requires-python = ">=3.10"

fastvideo-kernel/python/fastvideo_kernel/block_sparse_attn.py

@@ -1,298 +0,0 @@
-from __future__ import annotations
-
-import os
-from typing import Tuple
-
-import torch
-
-
-def _get_sm90_ops():
-    try:
-        from fastvideo_kernel._C import fastvideo_kernel_ops  # type: ignore
-    except Exception:
-        return None, None
-    return (
-        getattr(fastvideo_kernel_ops, "block_sparse_fwd", None),
-        getattr(fastvideo_kernel_ops, "block_sparse_bwd", None),
-    )
-
-
-def _is_sm90() -> bool:
-    if not torch.cuda.is_available():
-        return False
-    major, minor = torch.cuda.get_device_capability(0)
-    return major == 9 and minor == 0
-
-
-def _force_triton() -> bool:
-    # Force Triton even on SM90 and even if the compiled extension is available.
-    # Useful for CI / debugging / parity testing.
-    return os.environ.get("FASTVIDEO_KERNEL_VSA_FORCE_TRITON", "0") == "1"
-
-
-def _map_to_index_torch(block_map: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    Pure-torch (no triton) conversion:
-      block_map: [B, H, Q, KV] bool  (or [H, Q, KV] which will be treated as B=1)
-      returns:
-        index: [B, H, Q, KV] int32 (packed KV indices, -1 padding)
-        num:   [B, H, Q] int32 (#kv blocks per q block)
-    """
-    if block_map.dim() == 3:
-        block_map = block_map.unsqueeze(0)
-    if block_map.dim() != 4:
-        raise ValueError(f"block_map must be [B,H,Q,KV] (or [H,Q,KV]), got shape={tuple(block_map.shape)}")
-    if block_map.dtype != torch.bool:
-        block_map = block_map.to(torch.bool)
-
-    B, H, Q, KV = block_map.shape
-    index = torch.full((B, H, Q, KV), -1, dtype=torch.int32, device=block_map.device)
-    num = torch.zeros((B, H, Q), dtype=torch.int32, device=block_map.device)
-
-    # Small sizes in practice (B=1, H<=16, Q/KV<=64), so a Python loop is fine.
-    for b in range(B):
-        for h in range(H):
-            for q in range(Q):
-                kv_idx = torch.nonzero(block_map[b, h, q], as_tuple=False).flatten().to(torch.int32)
-                n = int(kv_idx.numel())
-                if n:
-                    index[b, h, q, :n] = kv_idx
-                num[b, h, q] = n
-    return index, num
-
-
-@torch.library.custom_op(
-    "fastvideo_kernel::block_sparse_attn_triton",
-    mutates_args=(),
-    device_types="cuda",
-)
-def block_sparse_attn_triton(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    q = q.contiguous()
-    k = k.contiguous()
-    v = v.contiguous()
-    block_map = block_map.to(torch.bool)
-    q2k_idx, q2k_num = _map_to_index_torch(block_map)
-
-    from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import (  # local import
-        triton_block_sparse_attn_forward,
-    )
-
-    o, M = triton_block_sparse_attn_forward(q, k, v, q2k_idx, q2k_num, variable_block_sizes)
-    return o, M
-
-
-@torch.library.register_fake("fastvideo_kernel::block_sparse_attn_triton")
-def _block_sparse_attn_triton_fake(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    o = torch.empty_like(q)
-    M = torch.empty((q.shape[0], q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
-    return o, M
-
-
-@torch.library.custom_op(
-    "fastvideo_kernel::block_sparse_attn_backward_triton",
-    mutates_args=(),
-    device_types="cuda",
-)
-def block_sparse_attn_backward_triton(
-    grad_output: torch.Tensor,
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    o: torch.Tensor,
-    M: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    grad_output = grad_output.contiguous()
-    block_map = block_map.to(torch.bool)
-    q2k_idx, q2k_num = _map_to_index_torch(block_map)
-    k2q_idx, k2q_num = _map_to_index_torch(block_map.transpose(-1, -2).contiguous())
-
-    from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import (  # local import
-        triton_block_sparse_attn_backward,
-    )
-
-    dq, dk, dv = triton_block_sparse_attn_backward(
-        grad_output, q, k, v, o, M, q2k_idx, q2k_num, k2q_idx, k2q_num, variable_block_sizes
-    )
-    return dq, dk, dv
-
-
-@torch.library.register_fake("fastvideo_kernel::block_sparse_attn_backward_triton")
-def _block_sparse_attn_backward_triton_fake(
-    grad_output: torch.Tensor,
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    o: torch.Tensor,
-    M: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    dq = torch.empty_like(q)
-    dk = torch.empty_like(k)
-    dv = torch.empty_like(v)
-    return dq, dk, dv
-
-
-def _backward_triton(ctx, grad_o, grad_M):
-    q, k, v, o, M, block_map, variable_block_sizes = ctx.saved_tensors
-    dq, dk, dv = block_sparse_attn_backward_triton(grad_o, q, k, v, o, M, block_map, variable_block_sizes)
-    return dq, dk, dv, None, None
-
-
-def _setup_context_triton(ctx, inputs, output):
-    q, k, v, block_map, variable_block_sizes = inputs
-    o, M = output
-    ctx.save_for_backward(q, k, v, o, M, block_map, variable_block_sizes)
-
-
-block_sparse_attn_triton.register_autograd(_backward_triton, setup_context=_setup_context_triton)
-
-
-@torch.library.custom_op(
-    "fastvideo_kernel::block_sparse_attn_sm90",
-    mutates_args=(),
-    device_types="cuda",
-)
-def block_sparse_attn_sm90(
-    q_padded: torch.Tensor,
-    k_padded: torch.Tensor,
-    v_padded: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    block_sparse_fwd, _ = _get_sm90_ops()
-    if block_sparse_fwd is None:
-        raise ImportError("fastvideo_kernel_ops.block_sparse_fwd is not available")
-
-    q_padded = q_padded.contiguous()
-    k_padded = k_padded.contiguous()
-    v_padded = v_padded.contiguous()
-    block_map = block_map.to(torch.bool)
-    q2k_idx, q2k_num = _map_to_index_torch(block_map)
-
-    o_padded, lse_padded = block_sparse_fwd(
-        q_padded, k_padded, v_padded, q2k_idx, q2k_num, variable_block_sizes.int()
-    )
-    return o_padded, lse_padded
-
-
-@torch.library.register_fake("fastvideo_kernel::block_sparse_attn_sm90")
-def _block_sparse_attn_sm90_fake(
-    q_padded: torch.Tensor,
-    k_padded: torch.Tensor,
-    v_padded: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    o = torch.empty_like(q_padded)
-    lse = torch.empty((q_padded.shape[0], q_padded.shape[1], q_padded.shape[2], 1), device=q_padded.device, dtype=torch.float32)
-    return o, lse
-
-
-@torch.library.custom_op(
-    "fastvideo_kernel::block_sparse_attn_backward_sm90",
-    mutates_args=(),
-    device_types="cuda",
-)
-def block_sparse_attn_backward_sm90(
-    grad_output_padded: torch.Tensor,
-    q_padded: torch.Tensor,
-    k_padded: torch.Tensor,
-    v_padded: torch.Tensor,
-    o_padded: torch.Tensor,
-    lse_padded: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    _, block_sparse_bwd = _get_sm90_ops()
-    if block_sparse_bwd is None:
-        raise ImportError("fastvideo_kernel_ops.block_sparse_bwd is not available")
-
-    grad_output_padded = grad_output_padded.contiguous()
-    block_map = block_map.to(torch.bool)
-    k2q_idx, k2q_num = _map_to_index_torch(block_map.transpose(-1, -2).contiguous())
-
-    dq, dk, dv = block_sparse_bwd(
-        q_padded,
-        k_padded,
-        v_padded,
-        o_padded,
-        lse_padded,
-        grad_output_padded,
-        k2q_idx,
-        k2q_num,
-        variable_block_sizes.int(),
-    )
-    # C++ kernel returns fp32 grads; cast back to match PyTorch convention if needed
-    return dq.to(grad_output_padded.dtype), dk.to(grad_output_padded.dtype), dv.to(grad_output_padded.dtype)
-
-
-@torch.library.register_fake("fastvideo_kernel::block_sparse_attn_backward_sm90")
-def _block_sparse_attn_backward_sm90_fake(
-    grad_output_padded: torch.Tensor,
-    q_padded: torch.Tensor,
-    k_padded: torch.Tensor,
-    v_padded: torch.Tensor,
-    o_padded: torch.Tensor,
-    lse_padded: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    dq = torch.empty_like(q_padded)
-    dk = torch.empty_like(k_padded)
-    dv = torch.empty_like(v_padded)
-    return dq, dk, dv
-
-
-def _backward_sm90(ctx, grad_o, grad_lse):
-    q, k, v, o, lse, block_map, variable_block_sizes = ctx.saved_tensors
-    dq, dk, dv = block_sparse_attn_backward_sm90(
-        grad_o, q, k, v, o, lse, block_map, variable_block_sizes
-    )
-    return dq, dk, dv, None, None
-
-
-def _setup_context_sm90(ctx, inputs, output):
-    q, k, v, block_map, variable_block_sizes = inputs
-    o, lse = output
-    ctx.save_for_backward(q, k, v, o, lse, block_map, variable_block_sizes)
-
-
-block_sparse_attn_sm90.register_autograd(_backward_sm90, setup_context=_setup_context_sm90)
-
-
-def block_sparse_attn(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    Unified block-sparse attention op with autograd support.
-    - On SM90 with compiled extension present: uses fastvideo_kernel_ops.block_sparse_fwd/bwd.
-    - Otherwise: uses Triton implementation (requires q/k/v to have same padded length today).
-    """
-    block_sparse_fwd, block_sparse_bwd = _get_sm90_ops()
-    if (not _force_triton()) and _is_sm90() and (block_sparse_fwd is not None) and (block_sparse_bwd is not None):
-        return block_sparse_attn_sm90(q, k, v, block_map, variable_block_sizes)
-    # Triton path: generally assumes q/k/v share the same padded length
-    if q.shape[2] != k.shape[2] or q.shape[2] != v.shape[2]:
-        raise RuntimeError("Triton fallback requires q/k/v to have the same padded length.")
-    return block_sparse_attn_triton(q, k, v, block_map, variable_block_sizes)
-
-

fastvideo-kernel/python/fastvideo_kernel/ops.py

@@ -1,6 +1,5 @@
 import math
 import torch
-from .block_sparse_attn import block_sparse_attn
 from .triton_kernels.block_sparse_attn_triton import triton_block_sparse_attn_forward
 from .triton_kernels.st_attn_triton import sliding_tile_attention_triton
 from .triton_kernels.index import map_to_index
@@ -46,22 +45,14 @@ def sliding_tile_attention(
     flag = shape_map[seq_shape]
 
     for head_idx, (t, h, w) in enumerate(window_size):
-        # Per-head slices are not contiguous in the batch dimension when batch>1
-        # (they keep the original head-stride). The TK kernel assumes contiguous
-        # [B, H, S, D] layout, so we materialize a contiguous [B,1,S,D] view.
-        q_h = q[:, head_idx:head_idx + 1].contiguous()
-        k_h = k[:, head_idx:head_idx + 1].contiguous()
-        v_h = v[:, head_idx:head_idx + 1].contiguous()
-        o_h = torch.empty_like(q_h)
         sta_fwd(
-            q_h, k_h,
-            v_h, o_h,
+            q[:, head_idx:head_idx + 1], k[:, head_idx:head_idx + 1],
+            v[:, head_idx:head_idx + 1], output[:, head_idx:head_idx + 1],
             t, h, w, text_length, False, has_text, flag
         )
-        output[:, head_idx:head_idx + 1] = o_h
 
     if has_text:
-        sta_fwd(q.contiguous(), k.contiguous(), v.contiguous(), output, 3, 3, 3, text_length, True, True, flag)
+        sta_fwd(q, k, v, output, 3, 3, 3, text_length, True, True, flag)
 
     return output[:, :, :seq_length]
 
@@ -71,7 +62,6 @@ def video_sparse_attn(
     k: torch.Tensor,
     v: torch.Tensor,
     variable_block_sizes: torch.Tensor,
-    q_variable_block_sizes: torch.Tensor,
     topk: int,
     block_size: int | tuple = 64,
     compress_attn_weight: torch.Tensor = None,
@@ -80,42 +70,14 @@ def video_sparse_attn(
         block_size = (block_size, block_size, block_size)
 
     block_elements = block_size[0] * block_size[1] * block_size[2]
-    batch, heads, q_seq_len, dim = q.shape
-    kv_seq_len = k.shape[2]
-    if v.shape[2] != kv_seq_len:
-        raise ValueError(
-            f"Expected k and v to have the same sequence length, got "
-            f"k.shape[2]={kv_seq_len}, v.shape[2]={v.shape[2]}"
-        )
-    if k.shape[0] != batch or v.shape[0] != batch or k.shape[1] != heads or v.shape[1] != heads:
-        raise ValueError("Expected q/k/v to have the same batch and head dimensions.")
-
-    if q_seq_len % block_elements != 0 or kv_seq_len % block_elements != 0:
-        raise ValueError(
-            f"q_seq_len and kv_seq_len must be divisible by block_elements={block_elements}, "
-            f"got q_seq_len={q_seq_len}, kv_seq_len={kv_seq_len}"
-        )
-    q_num_blocks = q_seq_len // block_elements
-    kv_num_blocks = kv_seq_len // block_elements
-
-    if variable_block_sizes.numel() != kv_num_blocks:
-        raise ValueError(
-            f"variable_block_sizes must have length kv_num_blocks={kv_num_blocks}, "
-            f"got {variable_block_sizes.numel()}"
-        )
-
-    if q_variable_block_sizes.numel() != q_num_blocks:
-        raise ValueError(
-            f"q_variable_block_sizes must have length q_num_blocks={q_num_blocks}, "
-            f"got {q_variable_block_sizes.numel()}"
-        )
+    batch, heads, seq_len, dim = q.shape
 
     # Compression branch
-    q_c = q.view(batch, heads, q_num_blocks, block_elements, dim)
-    k_c = k.view(batch, heads, kv_num_blocks, block_elements, dim)
-    v_c = v.view(batch, heads, kv_num_blocks, block_elements, dim)
+    q_c = q.view(batch, heads, seq_len // block_elements, block_elements, dim)
+    k_c = k.view(batch, heads, seq_len // block_elements, block_elements, dim)
+    v_c = v.view(batch, heads, seq_len // block_elements, block_elements, dim)
 
-    q_c = (q_c.float().sum(dim=3) / q_variable_block_sizes.view(1, 1, -1, 1)).to(
+    q_c = (q_c.float().sum(dim=3) / variable_block_sizes.view(1, 1, -1, 1)).to(
         q.dtype)
     k_c = (k_c.float().sum(dim=3) / variable_block_sizes.view(1, 1, -1, 1)).to(
         k.dtype)
@@ -126,9 +88,9 @@ def video_sparse_attn(
     attn = torch.softmax(scores, dim=-1)
     out_c = torch.matmul(attn, v_c)
 
-    out_c = out_c.view(batch, heads, q_num_blocks, 1, dim)
+    out_c = out_c.view(batch, heads, seq_len // block_elements, 1, dim)
     out_c = out_c.repeat(1, 1, 1, block_elements,
-                         1).view(batch, heads, q_seq_len, dim)
+                         1).view(batch, heads, seq_len, dim)
 
     # Sparse branch
     topk_idx = torch.topk(scores, topk, dim=-1).indices
@@ -138,17 +100,12 @@ def video_sparse_attn(
     idx, num = map_to_index(mask)
     
     if block_sparse_fwd is not None:
-        # Use autograd-enabled wrapper so backward works (and still uses SM90 kernel when available)
-        out_s = block_sparse_attn(q, k, v, mask, variable_block_sizes)[0]
+        out_s = block_sparse_fwd(
+            q, k, v, idx, num, variable_block_sizes.int()
+        )[0] # block_sparse_fwd returns vector<Tensor>
     else:
-        if q_seq_len != kv_seq_len:
-            raise RuntimeError(
-                "q/k have different lengths, but the compiled CUDA kernel (block_sparse_fwd) "
-                "is not available. The Triton fallback currently requires q and k/v to have "
-                "the same padded length."
-            )
-        # Triton-only forward (kept for environments without the wrapper deps)
-        out_s, _ = triton_block_sparse_attn_forward(q, k, v, idx, num, variable_block_sizes)
+        out_s, _ = triton_block_sparse_attn_forward(q, k, v, idx, num,
+                                                    variable_block_sizes)
 
     if compress_attn_weight is not None:
         return out_c * compress_attn_weight + out_s

fastvideo-kernel/python/fastvideo_kernel/triton_kernels/sla_triton.py

@@ -1,311 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Adapted from TurboDiffusion SLA implementation
-# Copyright (c) 2025 by SLA team.
-#
-# Citation:
-# @article{zhang2025sla,
-#   title={SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse-Linear Attention},
-#   author={Jintao Zhang and Haoxu Wang and Kai Jiang and Shuo Yang and Kaiwen Zheng and
-#           Haocheng Xi and Ziteng Wang and Hongzhou Zhu and Min Zhao and Ion Stoica and
-#           Joseph E. Gonzalez and Jun Zhu and Jianfei Chen},
-#   journal={arXiv preprint arXiv:2509.24006},
-#   year={2025}
-# }
-
-import torch
-import triton
-import triton.language as tl
-
-
-@triton.jit
-def _attn_fwd(
-    Q, K, V,
-    qk_scale: tl.constexpr,
-    topk: tl.constexpr,
-    LUT, LSE, OS,
-    L: tl.constexpr,
-    M_BLOCKS: tl.constexpr,
-    D: tl.constexpr,
-    BLOCK_M: tl.constexpr,
-    BLOCK_N: tl.constexpr,
-):
-    idx_m = tl.program_id(0).to(tl.int64)
-    idx_bh = tl.program_id(1).to(tl.int64)
-
-    qkv_offset = idx_bh * L * D
-    lut_offset = (idx_bh * M_BLOCKS + idx_m) * topk
-    lse_offset = idx_bh * L
-    offs_m = idx_m * BLOCK_M + tl.arange(0, BLOCK_M)
-    offs_n = tl.arange(0, BLOCK_N)
-    offs_d = tl.arange(0, D)
-
-    Q_ptrs = Q + qkv_offset + offs_m[:, None] * D + offs_d[None, :]
-    K_ptrs = K + qkv_offset + offs_n[None, :] * D + offs_d[:, None]
-    V_ptrs = V + qkv_offset + offs_n[:, None] * D + offs_d[None, :]
-    OS_ptrs = OS + qkv_offset + offs_m[:, None] * D + offs_d[None, :]
-    LUT_ptr = LUT + lut_offset
-    LSE_ptrs = LSE + lse_offset + offs_m
-    
-    m_i = tl.full([BLOCK_M], -float('inf'), dtype=tl.float32)
-    l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
-    o_s = tl.zeros([BLOCK_M, D], dtype=tl.float32)
-
-    q = tl.load(Q_ptrs, mask=offs_m[:, None] < L)
-    for block_idx in tl.range(topk):
-        idx_n = tl.load(LUT_ptr + block_idx)
-        n_mask = offs_n < L - idx_n * BLOCK_N
-        
-        k = tl.load(K_ptrs + idx_n * BLOCK_N * D, mask=n_mask[None, :])
-        qk = tl.dot(q, k) * (qk_scale * 1.4426950408889634)  # = 1 / ln(2)
-        if L - idx_n * BLOCK_N < BLOCK_N:
-            qk = tl.where(n_mask[None, :], qk, float("-inf"))
-
-        v = tl.load(V_ptrs + idx_n * BLOCK_N * D, mask=n_mask[:, None])
-        local_m = tl.max(qk, 1)
-        new_m = tl.maximum(m_i, local_m)
-        qk = qk - new_m[:, None]
-
-        p = tl.math.exp2(qk)
-        l_ij = tl.sum(p, 1)
-        alpha = tl.math.exp2(m_i - new_m)
-        o_s = o_s * alpha[:, None]
-        o_s += tl.dot(p.to(v.dtype), v)
-
-        l_i = l_i * alpha + l_ij
-        m_i = new_m
-
-    o_s = o_s / l_i[:, None]
-    tl.store(OS_ptrs, o_s.to(OS.type.element_ty), mask=offs_m[:, None] < L)
-    
-    m_i += tl.math.log2(l_i)
-    tl.store(LSE_ptrs, m_i, mask=offs_m < L)
-
-
-@triton.jit
-def _attn_bwd_preprocess(
-    OS, DOS, DELTAS,
-    L,
-    D: tl.constexpr,
-    BLOCK_M: tl.constexpr,
-):
-    idx_m = tl.program_id(0).to(tl.int64)
-    idx_bh = tl.program_id(1).to(tl.int64)
-
-    OS += idx_bh * L * D
-    DOS += idx_bh * L * D
-    DELTAS += idx_bh * L
-
-    offs_m = idx_m * BLOCK_M + tl.arange(0, BLOCK_M)
-    offs_d = tl.arange(0, D)
-
-    o_s = tl.load(OS + offs_m[:, None] * D + offs_d[None, :], mask=offs_m[:, None] < L)
-    do_s = tl.load(DOS + offs_m[:, None] * D + offs_d[None, :], mask=offs_m[:, None] < L)
-    
-    delta_s = tl.sum(o_s * do_s, axis=1).to(DELTAS.type.element_ty)
-    tl.store(DELTAS + offs_m, delta_s, mask=offs_m < L)
-
-
-@triton.jit
-def _attn_bwd_dq(
-    Q, K, V, LSE, DELTAS,
-    DOS, DQ, LUT,
-    qk_scale: tl.constexpr,
-    topk: tl.constexpr,
-    L: tl.constexpr,
-    M_BLOCKS: tl.constexpr,
-    D: tl.constexpr,
-    BLOCK_M: tl.constexpr,
-    BLOCK_N: tl.constexpr,
-):
-    idx_m = tl.program_id(0).to(tl.int64)
-    idx_bh = tl.program_id(1).to(tl.int64)
-
-    offs_m = idx_m * BLOCK_M + tl.arange(0, BLOCK_M)
-    offs_n = tl.arange(0, BLOCK_N)
-    offs_d = tl.arange(0, D)
-
-    qkv_offset = idx_bh * L * D
-    lse_offset = idx_bh * L
-    lut_offset = (idx_bh * M_BLOCKS + idx_m) * topk
-
-    Q_ptrs = Q + qkv_offset + offs_m[:, None] * D + offs_d[None, :]
-    K_ptrs = K + qkv_offset + offs_n[:, None] * D + offs_d[None, :]
-    V_ptrs = V + qkv_offset + offs_n[:, None] * D + offs_d[None, :]
-    DQ_ptrs = DQ + qkv_offset + offs_m[:, None] * D + offs_d[None, :]
-    DOS_ptrs = DOS + qkv_offset + offs_m[:, None] * D + offs_d[None, :]
-    LSE_ptrs = LSE + lse_offset + offs_m
-    DELTAS_ptrs = DELTAS + lse_offset + offs_m
-    LUT_ptr = LUT + lut_offset
-
-    q = tl.load(Q_ptrs, mask=offs_m[:, None] < L)
-    do_s = tl.load(DOS_ptrs, mask=offs_m[:, None] < L)
-    delta_s = tl.load(DELTAS_ptrs, mask=offs_m < L)
-    lse = tl.load(LSE_ptrs, mask=offs_m < L, other=float("inf"))
-    
-    dq = tl.zeros([BLOCK_M, D], dtype=tl.float32)
-    for block_idx in tl.range(topk, num_stages=2):
-        idx_n = tl.load(LUT_ptr + block_idx)
-        n_mask = offs_n < L - idx_n * BLOCK_N
-        
-        k = tl.load(K_ptrs + idx_n * BLOCK_N * D, mask=n_mask[:, None])
-        v = tl.load(V_ptrs + idx_n * BLOCK_N * D, mask=n_mask[:, None])
-        qk = tl.dot(q, k.T) * (qk_scale * 1.4426950408889634)
-        p = tl.math.exp2(qk - lse[:, None])
-        p = tl.where(n_mask[None, :], p, 0.0)
-        
-        dp = tl.dot(do_s, v.T).to(tl.float32)
-        ds = p * (dp - delta_s[:, None])
-        dq += tl.dot(ds.to(k.dtype), k)
-    tl.store(DQ_ptrs, dq * qk_scale, mask=offs_m[:, None] < L)
-    
-
-@triton.jit
-def _attn_bwd_dkdv(
-    Q, K, V, DOS, DK, DV,
-    qk_scale, KBID, LSE, DELTAS,
-    L: tl.constexpr,
-    M_BLOCKS: tl.constexpr,
-    N_BLOCKS: tl.constexpr,
-    D: tl.constexpr,
-    BLOCK_M: tl.constexpr,
-    BLOCK_N: tl.constexpr,
-    BLOCK_SLICE_FACTOR: tl.constexpr,
-):
-    BLOCK_M2: tl.constexpr = BLOCK_M // BLOCK_SLICE_FACTOR
-
-    idx_n = tl.program_id(0).to(tl.int64)
-    idx_bh = tl.program_id(1).to(tl.int64)
-
-    offs_n = idx_n * BLOCK_N + tl.arange(0, BLOCK_N)
-    offs_m = tl.arange(0, BLOCK_M2)
-    offs_d = tl.arange(0, D)
-
-    qkv_offset = idx_bh * L * D
-    kbid_offset = idx_bh * M_BLOCKS * N_BLOCKS
-    lse_offset = idx_bh * L
-
-    Q_ptrs = Q + qkv_offset + offs_m[:, None] * D + offs_d[None, :]
-    K_ptrs = K + qkv_offset + offs_n[:, None] * D + offs_d[None, :]
-    V_ptrs = V + qkv_offset + offs_n[:, None] * D + offs_d[None, :]
-    DOS_ptrs = DOS + qkv_offset + offs_m[:, None] * D + offs_d[None, :]
-    DK_ptrs = DK + qkv_offset + offs_n[:, None] * D + offs_d[None, :]
-    DV_ptrs = DV + qkv_offset + offs_n[:, None] * D + offs_d[None, :]
-    LSE_ptrs = LSE + lse_offset + offs_m
-    DELTAS_ptrs = DELTAS + lse_offset + offs_m
-    KBID_ptr = KBID + kbid_offset + idx_n
-
-    k = tl.load(K_ptrs, mask=offs_n[:, None] < L)
-    v = tl.load(V_ptrs, mask=offs_n[:, None] < L)
-        
-    dk = tl.zeros([BLOCK_N, D], dtype=tl.float32)
-    dv = tl.zeros([BLOCK_N, D], dtype=tl.float32)
-    for idx_m in tl.range(0, L, BLOCK_M2):
-        kbid = tl.load(KBID_ptr)
-        if kbid == 1:
-            m_mask = offs_m < L - idx_m
-            q = tl.load(Q_ptrs, mask=m_mask[:, None])
-            lse = tl.load(LSE_ptrs, mask=m_mask, other=float("inf"))
-            qkT = tl.dot(k, q.T) * (qk_scale * 1.4426950408889634)
-            pT = tl.math.exp2(qkT - lse[None, :])
-            pT = tl.where(offs_n[:, None] < L, pT, 0.0)
-
-            do = tl.load(DOS_ptrs, mask=m_mask[:, None])
-            dv += tl.dot(pT.to(do.dtype), do)
-            delta = tl.load(DELTAS_ptrs, mask=m_mask)
-            dpT = tl.dot(v, tl.trans(do))
-            dsT = pT * (dpT - delta[None, :])
-            dk += tl.dot(dsT.to(q.dtype), q)
-        
-        Q_ptrs += BLOCK_M2 * D
-        DOS_ptrs += BLOCK_M2 * D
-        LSE_ptrs += BLOCK_M2
-        DELTAS_ptrs += BLOCK_M2
-        if (idx_m + BLOCK_M2) % BLOCK_M == 0:
-            KBID_ptr += N_BLOCKS
-
-    tl.store(DK_ptrs, dk * qk_scale, mask=offs_n[:, None] < L)
-    tl.store(DV_ptrs, dv, mask=offs_n[:, None] < L)
-    
-
-class _attention(torch.autograd.Function):
-    """Sparse attention forward/backward with autograd support."""
-    
-    @staticmethod
-    def forward(ctx, q, k, v, k_block_id, lut, topk, BLOCK_M, BLOCK_N, qk_scale=None):
-        assert q.is_contiguous() and k.is_contiguous() and v.is_contiguous()
-        assert k_block_id.is_contiguous() and lut.is_contiguous()
-
-        assert BLOCK_M == 64 or BLOCK_M == 128
-        assert BLOCK_N == 64
-
-        B, H, L, D = q.shape
-        if qk_scale is None:
-            qk_scale = D**-0.5
-
-        M_BLOCKS = triton.cdiv(L, BLOCK_M)
-
-        o_s = torch.empty_like(v)
-        lse = torch.empty(q.shape[:-1], device=q.device, dtype=torch.float32)
-
-        grid = (M_BLOCKS, B * H)
-        _attn_fwd[grid](
-            q, k, v, qk_scale, topk,
-            lut, lse, o_s,
-            L, M_BLOCKS,
-            D, BLOCK_M, BLOCK_N,
-            num_warps=4 if q.shape[-1] == 64 else 8,
-            num_stages=3
-        )
-        
-        ctx.save_for_backward(q, k, v, k_block_id, lut, lse, o_s)
-        ctx.qk_scale = qk_scale
-        ctx.topk = topk
-        ctx.BLOCK_M = BLOCK_M
-        ctx.BLOCK_N = BLOCK_N
-        return o_s
-
-    @staticmethod
-    def backward(ctx, do_s):
-        q, k, v, k_block_id, lut, lse, o_s = ctx.saved_tensors
-        do_s = do_s.contiguous()
-
-        BLOCK_M, BLOCK_N = ctx.BLOCK_M, ctx.BLOCK_N
-        B, H, L, D = q.shape
-
-        M_BLOCKS = triton.cdiv(L, BLOCK_M)
-        N_BLOCKS = triton.cdiv(L, BLOCK_N)
-
-        dq = torch.empty_like(q)
-        dk = torch.empty_like(k)
-        dv = torch.empty_like(v)
-        delta_s = torch.empty_like(lse)
-
-        grid = (M_BLOCKS, B * H)
-        _attn_bwd_preprocess[grid](
-            o_s, do_s, delta_s,
-            L, D, BLOCK_M,
-        )
-
-        grid = (M_BLOCKS, B * H)
-        _attn_bwd_dq[grid](
-            q, k, v, lse, delta_s,
-            do_s, dq, lut,
-            ctx.qk_scale, ctx.topk,
-            L, M_BLOCKS,
-            D, BLOCK_M, BLOCK_N,
-            num_warps=4 if q.shape[-1] == 64 else 8,
-            num_stages=4 if q.shape[-1] == 64 else 5
-        )
-
-        grid = (N_BLOCKS, B * H)
-        _attn_bwd_dkdv[grid](
-            q, k, v, do_s, dk, dv,
-            ctx.qk_scale, k_block_id, lse, delta_s,
-            L, M_BLOCKS, N_BLOCKS,
-            D, BLOCK_M, BLOCK_N,
-            BLOCK_SLICE_FACTOR=BLOCK_M // 64,
-            num_warps=4 if q.shape[-1] == 64 else 8,
-            num_stages=4 if q.shape[-1] == 64 else 5
-        )
-
-        return dq, dk, dv, None, None, None, None, None, None

fastvideo-kernel/python/fastvideo_kernel/version.py

@@ -1 +1 @@
-__version__ = "0.2.4"
+__version__ = "0.2.1"

fastvideo-kernel/tests/test_vsa.py

@@ -42,13 +42,37 @@ def block_sparse_kernel_test(Q, K, V, block_sparse_mask, variable_block_sizes, q
     q_padded = vsa_pad(Q, q_non_pad_index, q_num_blocks, BLOCK_M)
     k_padded = vsa_pad(K, kv_non_pad_index, kv_num_blocks, BLOCK_M)
     v_padded = vsa_pad(V, kv_non_pad_index, kv_num_blocks, BLOCK_M)
-    # Use autograd-enabled wrapper (internally dispatches to SM90 kernel or Triton)
-    from fastvideo_kernel.block_sparse_attn import block_sparse_attn
-    output_padded, _aux = block_sparse_attn(
-        q_padded, k_padded, v_padded, block_sparse_mask, variable_block_sizes
-    )
+    # Use raw kernel or triton
+    try:
+        from fastvideo_kernel._C import fastvideo_kernel_ops
+        raw_kernel = getattr(fastvideo_kernel_ops, "block_sparse_fwd", None)
+    except ImportError:
+        raw_kernel = None
 
-    output = output_padded[:, :, q_non_pad_index, :]
+    from fastvideo_kernel.triton_kernels.index import map_to_index
+
+    
+    # Convert mask to indices
+    # block_sparse_mask is [H, M, N] bool
+    # We need to map it to index.
+    # block_sparse_mask needs to be expanded/reshaped? 
+    # generate_block_sparse_mask_for_function returns [H, NumBlocksQ, NumBlocksKV]
+    
+    # Ops.py logic:
+    # mask = torch.zeros_like(scores, dtype=torch.bool).scatter_(-1, topk_idx, True)
+    # idx, num = map_to_index(mask)
+    
+    idx, num = map_to_index(block_sparse_mask.unsqueeze(0)) # Add batch dim [1, H, M, N]
+    
+    if raw_kernel:
+        out_s = raw_kernel(q_padded, k_padded, v_padded, idx, num, variable_block_sizes.int())
+        output = out_s[0]
+    else:
+        # Fallback to triton testing if C++ not available
+        from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import triton_block_sparse_attn_forward
+        output, _ = triton_block_sparse_attn_forward(q_padded, k_padded, v_padded, idx, num, variable_block_sizes)
+
+    output = output[:, :, q_non_pad_index, :]
     output.backward(dO)
     return output, Q.grad, K.grad, V.grad
 
@@ -240,6 +264,7 @@ def generate_error_graphs_qkdiff(h, d, error_mode='all'):
 
     print("-" * 150)
 
+@pytest.mark.skip()
 def test_video_sparse_attention_backward():
     if not torch.cuda.is_available():
         return

fastvideo-kernel/tests/test_vsa_forward.py

@@ -3,7 +3,6 @@ import sys
 from typing import Tuple
 
 import torch
-import pytest
 
 from .utils import (
     generate_block_sparse_mask_for_function,
@@ -58,14 +57,23 @@ def block_sparse_forward_test(
     k_padded = ref.vsa_pad(K, kv_non_pad_index, kv_num_blocks, BLOCK_M)
     v_padded = ref.vsa_pad(V, kv_non_pad_index, kv_num_blocks, BLOCK_M)
 
-    # Use autograd-enabled wrapper (internally dispatches SM90 C++ vs Triton)
-    from fastvideo_kernel.block_sparse_attn import block_sparse_attn
+    # Use raw kernel or triton
     try:
-        out_padded, _aux = block_sparse_attn(
-            q_padded, k_padded, v_padded, block_sparse_mask, variable_block_sizes
+        from fastvideo_kernel._C import fastvideo_kernel_ops
+        raw_kernel = getattr(fastvideo_kernel_ops, "block_sparse_fwd", None)
+    except ImportError:
+        raw_kernel = None
+        
+    from fastvideo_kernel.triton_kernels.index import map_to_index
+    idx, num = map_to_index(block_sparse_mask)
+    
+    if raw_kernel:
+        out_padded = raw_kernel(q_padded, k_padded, v_padded, idx, num, variable_block_sizes.int())[0]
+    else:
+        from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import triton_block_sparse_attn_forward
+        out_padded, _ = triton_block_sparse_attn_forward(
+            q_padded, k_padded, v_padded, idx, num, variable_block_sizes
         )
-    except RuntimeError as e:
-        pytest.skip(str(e))
 
     # Remove padding on the query side
     out = out_padded[:, :, q_non_pad_index, :]
@@ -148,6 +156,11 @@ def run_forward_qk_diff(
 ) -> Tuple[float, float]:
     """
     Forward-only correctness test for the case S_q != S_kv.
+
+    NOTE:
+      - The Triton backend supports different Q/KV logical lengths via padding.
+      - The SM90 (H100) CUDA backend currently assumes the same number of blocks
+        for Q and KV, so we skip this test there.
     """
     assert torch.cuda.is_available(), "VSA kernels require CUDA"
 

fastvideo/attention/backends/sla.py

@@ -1,587 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SLA (Sparse-Linear Attention) backend for FastVideo
-# Adapted from TurboDiffusion SLA implementation
-#
-# Copyright (c) 2025 by SLA team.
-# Citation:
-# @article{zhang2025sla,
-#   title={SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse-Linear Attention},
-#   author={Jintao Zhang and Haoxu Wang and Kai Jiang and Shuo Yang and Kaiwen Zheng and
-#           Haocheng Xi and Ziteng Wang and Hongzhou Zhu and Min Zhao and Ion Stoica and
-#           Joseph E. Gonzalez and Jun Zhu and Jianfei Chen},
-#   journal={arXiv preprint arXiv:2509.24006},
-#   year={2025}
-# }
-
-from dataclasses import dataclass
-from typing import Any
-from collections.abc import Callable
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import triton
-import triton.language as tl
-
-from fastvideo.attention.backends.abstract import (
-    AttentionBackend,
-    AttentionImpl,
-    AttentionMetadata,
-    AttentionMetadataBuilder,
-)
-from fastvideo_kernel.triton_kernels.sla_triton import _attention
-from fastvideo.logger import init_logger
-
-logger = init_logger(__name__)
-
-# ============================================================================
-# SLA Utility functions (moved from sla_kernels/utils.py)
-# ============================================================================
-
-
-@triton.jit
-def compress_kernel(
-    X,
-    XM,
-    L: tl.constexpr,
-    D: tl.constexpr,
-    BLOCK_L: tl.constexpr,
-):
-    idx_l = tl.program_id(0)
-    idx_bh = tl.program_id(1)
-
-    offs_l = idx_l * BLOCK_L + tl.arange(0, BLOCK_L)
-    offs_d = tl.arange(0, D)
-
-    x_offset = idx_bh * L * D
-    xm_offset = idx_bh * ((L + BLOCK_L - 1) // BLOCK_L) * D
-    x = tl.load(X + x_offset + offs_l[:, None] * D + offs_d[None, :],
-                mask=offs_l[:, None] < L)
-
-    nx = min(BLOCK_L, L - idx_l * BLOCK_L)
-    x_mean = tl.sum(x, axis=0, dtype=tl.float32) / nx
-    tl.store(XM + xm_offset + idx_l * D + offs_d,
-             x_mean.to(XM.dtype.element_ty))
-
-
-def mean_pool(x: torch.Tensor, BLK: int) -> torch.Tensor:
-    """Mean pool tensor along sequence dimension with block size BLK."""
-    assert x.is_contiguous()
-
-    B, H, L, D = x.shape
-    L_BLOCKS = (L + BLK - 1) // BLK
-    x_mean = torch.empty((B, H, L_BLOCKS, D), device=x.device, dtype=x.dtype)
-
-    grid = (L_BLOCKS, B * H)
-    compress_kernel[grid](x, x_mean, L, D, BLK)
-    return x_mean
-
-
-def get_block_map(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    topk_ratio: float,
-    BLKQ: int = 64,
-    BLKK: int = 64,
-) -> tuple[torch.Tensor, torch.Tensor, int]:
-    """Compute sparse block map for attention based on QK similarity.
-    
-    Args:
-        q: Query tensor of shape (B, H, L, D)
-        k: Key tensor of shape (B, H, L, D)
-        topk_ratio: Ratio of key blocks to attend to (0-1)
-        BLKQ: Query block size
-        BLKK: Key block size
-        
-    Returns:
-        sparse_map: Binary mask of shape (B, H, num_q_blocks, num_k_blocks)
-        lut: Top-k indices of shape (B, H, num_q_blocks, topk)
-        topk: Number of key blocks selected
-    """
-    arg_k = k - torch.mean(
-        k, dim=-2, keepdim=True)  # smooth-k technique from SageAttention
-    pooled_qblocks = mean_pool(q, BLKQ)
-    pooled_kblocks = mean_pool(arg_k, BLKK)
-    pooled_score = pooled_qblocks @ pooled_kblocks.transpose(-1, -2)
-
-    K = pooled_score.shape[-1]
-    topk = min(K, int(topk_ratio * K))
-    lut = torch.topk(pooled_score, topk, dim=-1, sorted=False).indices
-
-    sparse_map = torch.zeros_like(pooled_score, dtype=torch.int8)
-    sparse_map.scatter_(-1, lut, 1)
-    return sparse_map, lut, topk
-
-
-# ============================================================================
-# SLA Backend classes
-# ============================================================================
-
-
-class SLAAttentionBackend(AttentionBackend):
-    """Sparse-Linear Attention backend."""
-
-    accept_output_buffer: bool = True
-
-    @staticmethod
-    def get_supported_head_sizes() -> list[int]:
-        return [64, 128]
-
-    @staticmethod
-    def get_name() -> str:
-        return "SLA_ATTN"
-
-    @staticmethod
-    def get_impl_cls() -> type["SLAAttentionImpl"]:
-        return SLAAttentionImpl
-
-    @staticmethod
-    def get_metadata_cls() -> type["SLAAttentionMetadata"]:
-        return SLAAttentionMetadata
-
-    @staticmethod
-    def get_builder_cls() -> type["SLAAttentionMetadataBuilder"]:
-        return SLAAttentionMetadataBuilder
-
-
-@dataclass
-class SLAAttentionMetadata(AttentionMetadata):
-    """Metadata for SLA attention."""
-    current_timestep: int
-    topk_ratio: float = 0.5  # Ratio of key blocks to attend to
-
-
-class SLAAttentionMetadataBuilder(AttentionMetadataBuilder):
-    """Builder for SLA attention metadata."""
-
-    def __init__(self) -> None:
-        pass
-
-    def prepare(self) -> None:
-        pass
-
-    def build(
-        self,
-        current_timestep: int,
-        topk_ratio: float = 0.5,
-        **kwargs: dict[str, Any],
-    ) -> SLAAttentionMetadata:
-        return SLAAttentionMetadata(
-            current_timestep=current_timestep,
-            topk_ratio=topk_ratio,
-        )
-
-
-class SLAAttentionImpl(AttentionImpl, nn.Module):
-    """SLA attention implementation with learnable linear projection.
-    
-    This implementation combines sparse attention with linear attention,
-    using a learnable projection to blend the outputs. The sparse attention
-    uses a block-sparse pattern determined by QK similarity.
-    
-    Args:
-        num_heads: Number of attention heads
-        head_size: Dimension of each head
-        topk_ratio: Ratio of key blocks to attend to (0-1), default 0.5
-        feature_map: Feature map for linear attention ('softmax', 'elu', 'relu')
-        BLKQ: Query block size for sparse attention
-        BLKK: Key block size for sparse attention
-        use_bf16: Whether to use bfloat16 for computation
-    """
-
-    def __init__(
-        self,
-        num_heads: int,
-        head_size: int,
-        causal: bool = False,
-        softmax_scale: float | None = None,
-        num_kv_heads: int | None = None,
-        prefix: str = "",
-        # SLA-specific parameters - matched to TurboDiffusion defaults
-        topk_ratio: float = 0.1,  # TurboDiffusion uses topk=0.1
-        feature_map: str = "softmax",
-        BLKQ: int = 128,  # TurboDiffusion uses BLKQ=128
-        BLKK: int = 64,  # TurboDiffusion uses BLKK=64
-        use_bf16: bool = True,
-        **extra_impl_args,
-    ) -> None:
-        nn.Module.__init__(self)
-
-        self.num_heads = num_heads
-        self.head_size = head_size
-        self.softmax_scale = softmax_scale if softmax_scale else head_size**-0.5
-        self.causal = causal
-        self.prefix = prefix
-
-        # SLA-specific config
-        self.topk_ratio = topk_ratio
-        self.BLKQ = BLKQ
-        self.BLKK = BLKK
-        self.dtype = torch.bfloat16 if use_bf16 else torch.float16
-
-        # Learnable linear projection for combining sparse + linear attention
-        self.proj_l = nn.Linear(head_size, head_size, dtype=torch.float32)
-
-        # Feature map for linear attention
-        # Type annotation for callables
-        self.feature_map_q: Callable[[torch.Tensor], torch.Tensor]
-        self.feature_map_k: Callable[[torch.Tensor], torch.Tensor]
-        if feature_map == "elu":
-            self.feature_map_q = lambda x: F.elu(x) + 1
-            self.feature_map_k = lambda x: F.elu(x) + 1
-        elif feature_map == "relu":
-            self.feature_map_q = F.relu
-            self.feature_map_k = F.relu
-        elif feature_map == "softmax":
-            self.feature_map_q = lambda x: F.softmax(x, dim=-1)
-            self.feature_map_k = lambda x: F.softmax(x, dim=-1)
-        else:
-            raise ValueError(f"Unknown feature map: {feature_map}")
-
-        self._init_weights()
-
-    def _init_weights(self) -> None:
-        """Initialize projection weights to zero for residual-like behavior."""
-        with torch.no_grad():
-            nn.init.zeros_(self.proj_l.weight)
-            nn.init.zeros_(self.proj_l.bias)  # type: ignore[arg-type]
-
-    def _calc_linear_attention(
-        self,
-        q: torch.Tensor,
-        k: torch.Tensor,
-        v: torch.Tensor,
-    ) -> torch.Tensor:
-        """Compute linear attention: (Q @ K^T @ V) / normalizer.
-        
-        Args:
-            q: Query tensor (B, H, L, D) after feature map
-            k: Key tensor (B, H, L, D) after feature map
-            v: Value tensor (B, H, L, D)
-            
-        Returns:
-            Linear attention output (B, H, L, D)
-        """
-        kvsum = k.transpose(-1, -2) @ v  # (B, H, D, D)
-        ksum = torch.sum(k, dim=-2, keepdim=True)  # (B, H, 1, D)
-        return (q @ kvsum) / (1e-5 + (q * ksum).sum(dim=-1, keepdim=True))
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        attn_metadata: AttentionMetadata,
-    ) -> torch.Tensor:
-        """Forward pass for SLA attention.
-        
-        Input tensors are in FastVideo format: (B, L, H, D)
-        Internally converted to SLA format: (B, H, L, D)
-        
-        Args:
-            query: Query tensor (B, L, H, D)
-            key: Key tensor (B, L, H, D)
-            value: Value tensor (B, L, H, D)
-            attn_metadata: Attention metadata
-            
-        Returns:
-            Output tensor (B, L, H, D)
-        """
-        original_dtype = query.dtype
-
-        # Convert from FastVideo format (B, L, H, D) to SLA format (B, H, L, D)
-        q = query.transpose(1, 2).contiguous()
-        k = key.transpose(1, 2).contiguous()
-        v = value.transpose(1, 2).contiguous()
-
-        # Get topk ratio from metadata if available
-        topk_ratio = self.topk_ratio
-        if hasattr(attn_metadata, 'topk_ratio'):
-            topk_ratio = attn_metadata.topk_ratio  # type: ignore[union-attr]
-
-        # Compute block-sparse attention pattern
-        sparse_map, lut, real_topk = get_block_map(q,
-                                                   k,
-                                                   topk_ratio=topk_ratio,
-                                                   BLKQ=self.BLKQ,
-                                                   BLKK=self.BLKK)
-
-        # Convert to compute dtype
-        q = q.to(self.dtype)
-        k = k.to(self.dtype)
-        v = v.to(self.dtype)
-
-        # Sparse attention
-        o_s = _attention.apply(q, k, v, sparse_map, lut, real_topk, self.BLKQ,
-                               self.BLKK)
-
-        # Linear attention with feature maps
-        q_linear = self.feature_map_q(q).contiguous().to(self.dtype)
-        k_linear = self.feature_map_k(k).contiguous().to(self.dtype)
-        o_l = self._calc_linear_attention(q_linear, k_linear, v)
-
-        # Project linear attention output and combine
-        with torch.amp.autocast('cuda', dtype=self.dtype):
-            o_l = self.proj_l(o_l)
-
-        # Combine sparse and linear outputs
-        output = (o_s + o_l).to(original_dtype)
-
-        # Convert back to FastVideo format (B, L, H, D)
-        output = output.transpose(1, 2)
-
-        return output
-
-
-# Check if spas_sage_attn is available for SageSLA
-SAGESLA_ENABLED = True
-try:
-    import spas_sage_attn._qattn as qattn
-    import spas_sage_attn._fused as fused
-    from spas_sage_attn.utils import get_vanilla_qk_quant, block_map_lut_triton
-except ImportError:
-    SAGESLA_ENABLED = False
-
-SAGE2PP_ENABLED = True
-try:
-    from spas_sage_attn._qattn import qk_int8_sv_f8_accum_f16_block_sparse_attn_inst_buf_fuse_v_scale_with_pv_threshold
-except ImportError:
-    SAGE2PP_ENABLED = False
-
-
-class SageSLAAttentionBackend(AttentionBackend):
-    """Quantized Sparse-Linear Attention backend using SageAttention kernels."""
-
-    accept_output_buffer: bool = True
-
-    @staticmethod
-    def get_supported_head_sizes() -> list[int]:
-        return [64, 128]
-
-    @staticmethod
-    def get_name() -> str:
-        return "SAGE_SLA_ATTN"
-
-    @staticmethod
-    def get_impl_cls() -> type["SageSLAAttentionImpl"]:
-        return SageSLAAttentionImpl
-
-    @staticmethod
-    def get_metadata_cls() -> type["SLAAttentionMetadata"]:
-        return SLAAttentionMetadata
-
-    @staticmethod
-    def get_builder_cls() -> type["SLAAttentionMetadataBuilder"]:
-        return SLAAttentionMetadataBuilder
-
-
-def _get_cuda_arch(device_index: int) -> str:
-    """Get CUDA architecture string for the given device."""
-    major, minor = torch.cuda.get_device_capability(device_index)
-    return f"sm{major}{minor}"
-
-
-class SageSLAAttentionImpl(AttentionImpl, nn.Module):
-    """SageSLA attention implementation using quantized SageAttention kernels.
-    
-    This uses INT8 quantization for Q/K and FP8 for V to achieve better performance
-    while maintaining accuracy. Requires spas_sage_attn package.
-    
-    Args:
-        num_heads: Number of attention heads
-        head_size: Dimension of each head (must be 64 or 128)
-        topk_ratio: Ratio of key blocks to attend to (0-1), default 0.5
-        feature_map: Feature map for linear attention ('softmax', 'elu', 'relu')
-        use_bf16: Whether to use bfloat16 for computation
-    """
-
-    def __init__(
-        self,
-        num_heads: int,
-        head_size: int,
-        causal: bool = False,
-        softmax_scale: float | None = None,
-        num_kv_heads: int | None = None,
-        prefix: str = "",
-        # SageSLA-specific parameters
-        topk_ratio: float = 0.5,
-        feature_map: str = "softmax",
-        use_bf16: bool = True,
-        **extra_impl_args,
-    ) -> None:
-        nn.Module.__init__(self)
-
-        if not SAGESLA_ENABLED:
-            raise ImportError(
-                "SageSLA requires spas_sage_attn. "
-                "Install with: pip install git+https://github.com/thu-ml/SpargeAttn.git"
-            )
-
-        assert head_size in [
-            64, 128
-        ], f"SageSLA requires head_size in [64, 128], got {head_size}"
-
-        self.num_heads = num_heads
-        self.head_size = head_size
-        self.softmax_scale = softmax_scale if softmax_scale else head_size**-0.5
-        self.causal = causal
-        self.prefix = prefix
-
-        # SageSLA-specific config
-        self.topk_ratio = topk_ratio
-        self.dtype = torch.bfloat16 if use_bf16 else torch.float16
-
-        # Learnable linear projection for combining sparse + linear attention
-        self.proj_l = nn.Linear(head_size, head_size, dtype=torch.float32)
-
-        # Feature map for linear attention
-        # Type annotation for callables
-        self.feature_map_q: Callable[[torch.Tensor], torch.Tensor]
-        self.feature_map_k: Callable[[torch.Tensor], torch.Tensor]
-        if feature_map == "elu":
-            self.feature_map_q = lambda x: F.elu(x) + 1
-            self.feature_map_k = lambda x: F.elu(x) + 1
-        elif feature_map == "relu":
-            self.feature_map_q = F.relu
-            self.feature_map_k = F.relu
-        elif feature_map == "softmax":
-            self.feature_map_q = lambda x: F.softmax(x, dim=-1)
-            self.feature_map_k = lambda x: F.softmax(x, dim=-1)
-        else:
-            raise ValueError(f"Unknown feature map: {feature_map}")
-
-        self._init_weights()
-
-    def _init_weights(self) -> None:
-        """Initialize projection weights to zero for residual-like behavior."""
-        with torch.no_grad():
-            nn.init.zeros_(self.proj_l.weight)
-            nn.init.zeros_(self.proj_l.bias)  # type: ignore[arg-type]
-
-    def _calc_linear_attention(
-        self,
-        q: torch.Tensor,
-        k: torch.Tensor,
-        v: torch.Tensor,
-    ) -> torch.Tensor:
-        """Compute linear attention: (Q @ K^T @ V) / normalizer."""
-        kvsum = k.transpose(-1, -2) @ v
-        ksum = torch.sum(k, dim=-2, keepdim=True)
-        return (q @ kvsum) / (1e-5 + (q * ksum).sum(dim=-1, keepdim=True))
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        attn_metadata: AttentionMetadata,
-    ) -> torch.Tensor:
-        """Forward pass for SageSLA attention with quantized kernels.
-        
-        Input tensors are in FastVideo format: (B, L, H, D)
-        
-        Args:
-            query: Query tensor (B, L, H, D)
-            key: Key tensor (B, L, H, D)
-            value: Value tensor (B, L, H, D)
-            attn_metadata: Attention metadata
-            
-        Returns:
-            Output tensor (B, L, H, D)
-        """
-        original_dtype = query.dtype
-
-        # Convert from FastVideo format (B, L, H, D) to SLA format (B, H, L, D)
-        q = query.transpose(1, 2).contiguous()
-        k = key.transpose(1, 2).contiguous()
-        v = value.transpose(1, 2).contiguous()
-
-        # Get topk ratio from metadata if available
-        topk_ratio = self.topk_ratio
-        if hasattr(attn_metadata, 'topk_ratio'):
-            topk_ratio = attn_metadata.topk_ratio  # type: ignore[union-attr]
-
-        # Determine block sizes based on GPU architecture
-        arch = _get_cuda_arch(q.device.index)
-        if arch == "sm90":
-            BLKQ, BLKK = 64, 128
-        else:
-            BLKQ, BLKK = 128, 64
-
-        # Compute block-sparse attention pattern
-        sparse_map, lut, real_topk = get_block_map(q,
-                                                   k,
-                                                   topk_ratio=topk_ratio,
-                                                   BLKQ=BLKQ,
-                                                   BLKK=BLKK)
-
-        # Convert to compute dtype
-        q = q.to(self.dtype)
-        k = k.to(self.dtype)
-        v = v.to(self.dtype)
-
-        # ========== SPARGE QUANTIZED ATTENTION ==========
-        km = k.mean(dim=-2, keepdim=True)
-        headdim = q.size(-1)
-        scale = 1.0 / (headdim**0.5)
-
-        # Quantize Q, K to INT8
-        q_int8, q_scale, k_int8, k_scale = get_vanilla_qk_quant(
-            q, k, km, BLKQ, BLKK)
-        lut_triton, valid_block_num = block_map_lut_triton(sparse_map)
-
-        # Quantize V to FP8
-        b, h_kv, kv_len, head_dim = v.shape
-        padded_len = (kv_len + 127) // 128 * 128
-        v_transposed_permutted = torch.empty((b, h_kv, head_dim, padded_len),
-                                             dtype=v.dtype,
-                                             device=v.device)
-        fused.transpose_pad_permute_cuda(v, v_transposed_permutted, 1)
-        v_fp8 = torch.empty(v_transposed_permutted.shape,
-                            dtype=torch.float8_e4m3fn,
-                            device=v.device)
-        v_scale = torch.empty((b, h_kv, head_dim),
-                              dtype=torch.float32,
-                              device=v.device)
-        fused.scale_fuse_quant_cuda(v_transposed_permutted, v_fp8, v_scale,
-                                    kv_len, 2.25, 1)
-
-        # Sparse attention with quantized kernels
-        o_s = torch.empty_like(q)
-        if arch == "sm90":
-            qattn.qk_int8_sv_f8_accum_f32_block_sparse_attn_inst_buf_fuse_v_scale_sm90(
-                q_int8, k_int8, v_fp8, o_s, lut_triton, valid_block_num,
-                q_scale, k_scale, v_scale, 1, False, 1, scale)
-        else:
-            pvthreshold = torch.full((q.shape[-3], ),
-                                     1e6,
-                                     dtype=torch.float32,
-                                     device=q.device)
-            if SAGE2PP_ENABLED:
-                qk_int8_sv_f8_accum_f16_block_sparse_attn_inst_buf_fuse_v_scale_with_pv_threshold(
-                    q_int8, k_int8, v_fp8, o_s, lut_triton, valid_block_num,
-                    pvthreshold, q_scale, k_scale, v_scale, 1, False, 1, scale,
-                    0)
-            else:
-                qattn.qk_int8_sv_f8_accum_f32_block_sparse_attn_inst_buf_fuse_v_scale_with_pv_threshold(
-                    q_int8, k_int8, v_fp8, o_s, lut_triton, valid_block_num,
-                    pvthreshold, q_scale, k_scale, v_scale, 1, False, 1, scale,
-                    0)
-        # ========== END SPARGE ==========
-
-        # Linear attention with feature maps
-        q_linear = self.feature_map_q(q).contiguous().to(self.dtype)
-        k_linear = self.feature_map_k(k).contiguous().to(self.dtype)
-        o_l = self._calc_linear_attention(q_linear, k_linear, v)
-
-        # Project linear attention output and combine
-        with torch.amp.autocast('cuda', dtype=self.dtype):
-            o_l = self.proj_l(o_l)
-
-        # Combine sparse and linear outputs
-        output = (o_s + o_l).to(original_dtype)
-
-        # Convert back to FastVideo format (B, L, H, D)
-        output = output.transpose(1, 2)
-
-        return output

fastvideo/attention/backends/video_sparse_attn.py

@@ -276,9 +276,8 @@ class VideoSparseAttentionImpl(AttentionImpl):
             query,
             key,
             value,
-            attn_metadata.variable_block_sizes,
-            attn_metadata.variable_block_sizes,
-            cur_topk,
+            variable_block_sizes=attn_metadata.variable_block_sizes,
+            topk=cur_topk,
             block_size=VSA_TILE_SIZE,
             compress_attn_weight=gate_compress).transpose(1, 2)
 

fastvideo/attention/layer.py

@@ -50,10 +50,6 @@ class DistributedAttention(nn.Module):
                                   num_kv_heads=num_kv_heads,
                                   prefix=f"{prefix}.impl",
                                   **extra_impl_args)
-        # Register attn_impl as submodule if it has learnable parameters (e.g., SLA's proj_l)
-        # This ensures its parameters are included in state_dict() for saving/loading
-        if isinstance(self.attn_impl, nn.Module):
-            self.add_module('attn_impl', self.attn_impl)
         self.num_heads = num_heads
         self.head_size = head_size
         self.num_kv_heads = num_kv_heads

fastvideo/configs/models/dits/base.py

@@ -18,8 +18,7 @@ class DiTArchConfig(ArchConfig):
         AttentionBackendEnum.SLIDING_TILE_ATTN, AttentionBackendEnum.SAGE_ATTN,
         AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA,
         AttentionBackendEnum.VIDEO_SPARSE_ATTN, AttentionBackendEnum.VMOBA_ATTN,
-        AttentionBackendEnum.SAGE_ATTN_THREE, AttentionBackendEnum.SLA_ATTN,
-        AttentionBackendEnum.SAGE_SLA_ATTN)
+        AttentionBackendEnum.SAGE_ATTN_THREE)
 
     hidden_size: int = 0
     num_attention_heads: int = 0

fastvideo/configs/models/dits/longcat.py

@@ -26,6 +26,7 @@ class LongCatVideoArchConfig(DiTArchConfig):
         default_factory=lambda: [is_longcat_blocks])
 
     # Parameter name mapping for weight conversion
+    # Maps original LongCat third_party names -> native FastVideo names
     param_names_mapping: dict = field(
         default_factory=lambda: {
             # Embedders

fastvideo/configs/models/encoders/__init__.py

@@ -7,11 +7,10 @@ from fastvideo.configs.models.encoders.clip import (
 from fastvideo.configs.models.encoders.llama import LlamaConfig
 from fastvideo.configs.models.encoders.t5 import T5Config, T5LargeConfig
 from fastvideo.configs.models.encoders.qwen2_5 import Qwen2_5_VLConfig
-from fastvideo.configs.models.encoders.reason1 import Reason1ArchConfig, Reason1Config
 
 __all__ = [
     "EncoderConfig", "TextEncoderConfig", "ImageEncoderConfig",
     "BaseEncoderOutput", "CLIPTextConfig", "CLIPVisionConfig",
     "WAN2_1ControlCLIPVisionConfig", "LlamaConfig", "T5Config", "T5LargeConfig",
-    "Qwen2_5_VLConfig", "Reason1ArchConfig", "Reason1Config"
+    "Qwen2_5_VLConfig"
 ]

fastvideo/configs/models/encoders/reason1.py

@@ -1,72 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Config for Reason1 (Qwen2.5-VL) text encoder."""
-
-from dataclasses import dataclass, field
-from typing import Any
-
-from fastvideo.configs.models.encoders.base import TextEncoderArchConfig, TextEncoderConfig
-
-
-@dataclass
-class Reason1ArchConfig(TextEncoderArchConfig):
-    """Architecture settings (defaults match Qwen2.5-VL-7B-Instruct)."""
-
-    architectures: list[str] = field(
-        default_factory=lambda: ["Qwen2_5_VLForConditionalGeneration"])
-    model_type: str = "qwen2_5_vl"
-
-    vocab_size: int = 152064
-    hidden_size: int = 3584
-    num_hidden_layers: int = 28
-    num_attention_heads: int = 28
-    num_key_value_heads: int = 4
-    intermediate_size: int = 18944
-
-    text_len: int = 512
-    hidden_state_skip_layer: int = 0
-    bos_token_id: int = 151643
-    pad_token_id: int = 151643
-    eos_token_id: int = 151645
-
-    image_token_id: int = 151655
-    video_token_id: int = 151656
-    vision_token_id: int = 151654
-    vision_start_token_id: int = 151652
-    vision_end_token_id: int = 151653
-
-    vision_config: dict[str, Any] | None = None
-
-    rope_theta: float = 1000000.0
-    rope_scaling: dict[str, Any] | None = field(default_factory=lambda: {
-        "type": "mrope",
-        "mrope_section": [16, 24, 24]
-    })
-    max_position_embeddings: int = 128000
-    max_window_layers: int = 28
-
-    embedding_concat_strategy: str = "mean_pooling"
-    n_layers_per_group: int = 5
-    num_embedding_padding_tokens: int = 512
-
-    attention_dropout: float = 0.0
-    hidden_act: str = "silu"
-    initializer_range: float = 0.02
-    rms_norm_eps: float = 1e-6
-
-    use_sliding_window: bool = False
-    sliding_window: int = 32768
-
-    tie_word_embeddings: bool = False
-    use_cache: bool = False
-    output_hidden_states: bool = True
-
-    torch_dtype: str = "bfloat16"
-    _attn_implementation: str = "flash_attention_2"
-
-
-@dataclass
-class Reason1Config(TextEncoderConfig):
-    """Reason1 text encoder config."""
-
-    arch_config: Reason1ArchConfig = field(default_factory=Reason1ArchConfig)
-    tokenizer_type: str = "Qwen/Qwen2.5-VL-7B-Instruct"

fastvideo/configs/models/vaes/__init__.py

@@ -1,5 +1,4 @@
 from fastvideo.configs.models.vaes.cosmosvae import CosmosVAEConfig
-from fastvideo.configs.models.vaes.cosmos2_5vae import Cosmos25VAEConfig
 from fastvideo.configs.models.vaes.hunyuanvae import HunyuanVAEConfig
 from fastvideo.configs.models.vaes.hunyuan15vae import Hunyuan15VAEConfig
 from fastvideo.configs.models.vaes.stepvideovae import StepVideoVAEConfig
@@ -10,6 +9,5 @@ __all__ = [
     "WanVAEConfig",
     "StepVideoVAEConfig",
     "CosmosVAEConfig",
-    "Cosmos25VAEConfig",
     "Hunyuan15VAEConfig",
 ]

fastvideo/configs/models/vaes/cosmos2_5vae.py

@@ -1,223 +0,0 @@
-"""Cosmos 2.5 (Wan2.1-style) VAE config and checkpoint-key mapping."""
-
-from __future__ import annotations
-
-import re
-from dataclasses import dataclass, field
-
-import torch
-
-from fastvideo.configs.models.vaes.base import VAEArchConfig, VAEConfig
-
-
-@dataclass
-class Cosmos25VAEArchConfig(VAEArchConfig):
-    _name_or_path: str = ""
-    base_dim: int = 96
-    decoder_base_dim: int | None = None
-    z_dim: int = 16
-    dim_mult: tuple[int, ...] = (1, 2, 4, 4)
-    num_res_blocks: int = 2
-    attn_scales: tuple[float, ...] = ()
-    temperal_downsample: tuple[bool, ...] = (False, True, True)
-    dropout: float = 0.0
-    is_residual: bool = False
-    in_channels: int = 3
-    out_channels: int = 3
-    patch_size: int | None = None
-    scale_factor_temporal: int = 4
-    scale_factor_spatial: int = 8
-    clip_output: bool = True
-
-    latents_mean: tuple[float, ...] = (
-        -0.7571,
-        -0.7089,
-        -0.9113,
-        0.1075,
-        -0.1745,
-        0.9653,
-        -0.1517,
-        1.5508,
-        0.4134,
-        -0.0715,
-        0.5517,
-        -0.3632,
-        -0.1922,
-        -0.9497,
-        0.2503,
-        -0.2921,
-    )
-    latents_std: tuple[float, ...] = (
-        2.8184,
-        1.4541,
-        2.3275,
-        2.6558,
-        1.2196,
-        1.7708,
-        2.6052,
-        2.0743,
-        3.2687,
-        2.1526,
-        2.8652,
-        1.5579,
-        1.6382,
-        1.1253,
-        2.8251,
-        1.9160,
-    )
-
-    # Simple 1:1 renames. More complex decoder remapping is handled by
-    # `map_official_key()`.
-    param_names_mapping: dict[str, str] = field(
-        default_factory=lambda: {
-            r"^conv1\.(.*)$": r"quant_conv.\1",
-            r"^conv2\.(.*)$": r"post_quant_conv.\1",
-            r"^encoder\.conv1\.(.*)$": r"encoder.conv_in.\1",
-            r"^decoder\.conv1\.(.*)$": r"decoder.conv_in.\1",
-            r"^encoder\.head\.0\.gamma$": r"encoder.norm_out.gamma",
-            r"^encoder\.head\.2\.(.*)$": r"encoder.conv_out.\1",
-            r"^decoder\.head\.0\.gamma$": r"decoder.norm_out.gamma",
-            r"^decoder\.head\.2\.(.*)$": r"decoder.conv_out.\1",
-        })
-
-    @staticmethod
-    def map_official_key(key: str) -> str | None:
-        """Map a single official checkpoint key into FastVideo key space."""
-
-        def map_residual_subkey(prefix: str, sub: str) -> str | None:
-            if re.match(r"^residual\.0\.gamma$", sub):
-                return f"{prefix}.norm1.gamma"
-            m = re.match(r"^residual\.2\.(weight|bias)$", sub)
-            if m:
-                return f"{prefix}.conv1.{m.group(1)}"
-            if re.match(r"^residual\.3\.gamma$", sub):
-                return f"{prefix}.norm2.gamma"
-            m = re.match(r"^residual\.6\.(weight|bias)$", sub)
-            if m:
-                return f"{prefix}.conv2.{m.group(1)}"
-            m = re.match(r"^shortcut\.(weight|bias)$", sub)
-            if m:
-                return f"{prefix}.conv_shortcut.{m.group(1)}"
-            return None
-
-        def map_attn_subkey(prefix: str, sub: str) -> str | None:
-            if re.match(r"^norm\.gamma$", sub):
-                return f"{prefix}.norm.gamma"
-            m = re.match(r"^to_qkv\.(weight|bias)$", sub)
-            if m:
-                return f"{prefix}.to_qkv.{m.group(1)}"
-            m = re.match(r"^proj\.(weight|bias)$", sub)
-            if m:
-                return f"{prefix}.proj.{m.group(1)}"
-            return None
-
-        def map_resample_subkey(prefix: str, sub: str) -> str | None:
-            m = re.match(r"^resample\.1\.(weight|bias)$", sub)
-            if m:
-                return f"{prefix}.resample.1.{m.group(1)}"
-            m = re.match(r"^time_conv\.(weight|bias)$", sub)
-            if m:
-                return f"{prefix}.time_conv.{m.group(1)}"
-            return None
-
-        m = re.match(r"^conv1\.(weight|bias)$", key)
-        if m:
-            return f"quant_conv.{m.group(1)}"
-        m = re.match(r"^conv2\.(weight|bias)$", key)
-        if m:
-            return f"post_quant_conv.{m.group(1)}"
-        m = re.match(r"^(encoder|decoder)\.conv1\.(weight|bias)$", key)
-        if m:
-            return f"{m.group(1)}.conv_in.{m.group(2)}"
-        m = re.match(r"^(encoder|decoder)\.head\.0\.gamma$", key)
-        if m:
-            return f"{m.group(1)}.norm_out.gamma"
-        m = re.match(r"^(encoder|decoder)\.head\.2\.(weight|bias)$", key)
-        if m:
-            return f"{m.group(1)}.conv_out.{m.group(2)}"
-
-        m = re.match(r"^(encoder|decoder)\.middle\.0\.(.*)$", key)
-        if m:
-            return map_residual_subkey(f"{m.group(1)}.mid_block.resnets.0",
-                                       m.group(2))
-        m = re.match(r"^(encoder|decoder)\.middle\.1\.(.*)$", key)
-        if m:
-            return map_attn_subkey(f"{m.group(1)}.mid_block.attentions.0",
-                                   m.group(2))
-        m = re.match(r"^(encoder|decoder)\.middle\.2\.(.*)$", key)
-        if m:
-            return map_residual_subkey(f"{m.group(1)}.mid_block.resnets.1",
-                                       m.group(2))
-
-        m = re.match(r"^encoder\.downsamples\.(\d+)\.(.*)$", key)
-        if m:
-            idx = int(m.group(1))
-            sub = m.group(2)
-            if sub.startswith("residual.") or sub.startswith("shortcut."):
-                return map_residual_subkey(f"encoder.down_blocks.{idx}", sub)
-            if sub.startswith("resample.") or sub.startswith("time_conv."):
-                return map_resample_subkey(f"encoder.down_blocks.{idx}", sub)
-            return None
-
-        m = re.match(r"^decoder\.upsamples\.(\d+)\.(.*)$", key)
-        if m:
-            uidx = int(m.group(1))
-            sub = m.group(2)
-
-            if uidx in (0, 1, 2):
-                block_i, res_i = 0, uidx
-            elif uidx == 3:
-                block_i, res_i = 0, None
-            elif uidx in (4, 5, 6):
-                block_i, res_i = 1, uidx - 4
-            elif uidx == 7:
-                block_i, res_i = 1, None
-            elif uidx in (8, 9, 10):
-                block_i, res_i = 2, uidx - 8
-            elif uidx == 11:
-                block_i, res_i = 2, None
-            elif uidx in (12, 13, 14):
-                block_i, res_i = 3, uidx - 12
-            else:
-                return None
-
-            if res_i is None:
-                return map_resample_subkey(
-                    f"decoder.up_blocks.{block_i}.upsamplers.0",
-                    sub,
-                )
-
-            return map_residual_subkey(
-                f"decoder.up_blocks.{block_i}.resnets.{res_i}",
-                sub,
-            )
-
-        return None
-
-    temporal_compression_ratio: int = 4
-    spatial_compression_ratio: int = 8
-
-    def __post_init__(self):
-        self.scaling_factor: torch.Tensor = 1.0 / torch.tensor(
-            self.latents_std).view(1, self.z_dim, 1, 1, 1)
-        self.shift_factor: torch.Tensor = torch.tensor(self.latents_mean).view(
-            1, self.z_dim, 1, 1, 1)
-        self.temporal_compression_ratio = self.scale_factor_temporal
-        self.spatial_compression_ratio = self.scale_factor_spatial
-
-
-@dataclass
-class Cosmos25VAEConfig(VAEConfig):
-    """Cosmos2.5 VAE config."""
-
-    arch_config: Cosmos25VAEArchConfig = field(
-        default_factory=Cosmos25VAEArchConfig)
-
-    use_feature_cache: bool = True
-    use_tiling: bool = False
-    use_temporal_tiling: bool = False
-    use_parallel_tiling: bool = False
-
-    def __post_init__(self):
-        self.blend_num_frames = (self.tile_sample_min_num_frames -
-                                 self.tile_sample_stride_num_frames) * 2

fastvideo/configs/pipelines/__init__.py

@@ -1,7 +1,6 @@
 from fastvideo.configs.pipelines.base import (PipelineConfig,
                                               SlidingTileAttnConfig)
 from fastvideo.configs.pipelines.cosmos import CosmosConfig
-from fastvideo.configs.pipelines.cosmos2_5 import Cosmos25Config
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
 from fastvideo.configs.pipelines.registry import (
@@ -16,5 +15,5 @@ __all__ = [
     "Hunyuan15T2V480PConfig", "Hunyuan15T2V720PConfig", "SlidingTileAttnConfig",
     "WanT2V480PConfig", "WanI2V480PConfig", "WanT2V720PConfig",
     "WanI2V720PConfig", "StepVideoT2VConfig", "SelfForcingWanT2V480PConfig",
-    "CosmosConfig", "Cosmos25Config", "get_pipeline_config_cls_from_name"
+    "CosmosConfig", "get_pipeline_config_cls_from_name"
 ]

fastvideo/configs/pipelines/base.py

@@ -12,7 +12,8 @@ from fastvideo.configs.models import (DiTConfig, EncoderConfig, ModelConfig,
 from fastvideo.configs.models.encoders import BaseEncoderOutput
 from fastvideo.configs.utils import update_config_from_args
 from fastvideo.logger import init_logger
-from fastvideo.utils import FlexibleArgumentParser, StoreBoolean, shallow_asdict
+from fastvideo.utils import (FlexibleArgumentParser, StoreBoolean,
+                             PRECISION_TO_TYPE, shallow_asdict)
 
 logger = init_logger(__name__)
 
@@ -311,6 +312,21 @@ class PipelineConfig:
                 f"Length of text encoder configs ({len(self.text_encoder_configs)}) must be equal to length of text encoder precisions ({len(self.text_encoder_precisions)})"
             )
 
+        unsupported_precisions = [
+            precision for precision in self.text_encoder_precisions
+            if precision not in PRECISION_TO_TYPE
+            and not precision.startswith("fp8")
+        ]
+        if unsupported_precisions:
+            supported = ", ".join(PRECISION_TO_TYPE.keys())
+            logger.warning(
+                "Unsupported text encoder precision(s) detected in config: %s. "
+                "FastVideo will attempt to load them with transformers AutoModel when possible. "
+                "Supported fast paths: %s.",
+                unsupported_precisions,
+                supported,
+            )
+
         if len(self.text_encoder_configs) != len(self.preprocess_text_funcs):
             raise ValueError(
                 f"Length of text encoder configs ({len(self.text_encoder_configs)}) must be equal to length of text preprocessing functions ({len(self.preprocess_text_funcs)})"

fastvideo/configs/pipelines/cosmos2_5.py

@@ -1,89 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from collections.abc import Callable
-from dataclasses import dataclass, field
-
-import torch
-
-from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
-from fastvideo.configs.models.dits import Cosmos25VideoConfig
-from fastvideo.configs.models.dits.cosmos2_5 import Cosmos25ArchConfig
-from fastvideo.configs.models.encoders import BaseEncoderOutput
-from fastvideo.configs.models.encoders.reason1 import Reason1Config, Reason1ArchConfig
-from fastvideo.configs.models.vaes import Cosmos25VAEConfig
-from fastvideo.configs.pipelines.base import PipelineConfig, STA_Mode
-
-
-def _identity_preprocess_text(prompt: str) -> str:
-    return prompt
-
-
-def reason1_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
-    hidden_states = getattr(outputs, "hidden_states", None)
-    if hidden_states is None:
-        raise ValueError("Reason1 postprocess requires outputs.hidden_states")
-
-    hs = list(hidden_states)[1:]
-    normed = []
-    for h in hs:
-        h = h.float()
-        h = (h - h.mean(dim=-1, keepdim=True)) / (h.std(dim=-1, keepdim=True) +
-                                                  1e-8)
-        normed.append(h)
-    return torch.cat(normed, dim=-1).to(hidden_states[0].dtype)
-
-
-@dataclass
-class Cosmos25Config(PipelineConfig):
-    """Configuration for Cosmos 2.5 (Predict2.5) video generation pipeline."""
-
-    dit_config: DiTConfig = field(default_factory=lambda: Cosmos25VideoConfig(
-        arch_config=Cosmos25ArchConfig(
-            num_attention_heads=16,
-            attention_head_dim=128,
-            in_channels=16,
-            out_channels=16,
-            num_layers=28,
-            patch_size=[1, 2, 2],
-            max_size=[128, 240, 240],
-            rope_scale=[1.0, 3.0, 3.0],
-            text_embed_dim=1024,
-            mlp_ratio=4.0,
-            adaln_lora_dim=256,
-            use_adaln_lora=True,
-            concat_padding_mask=True,
-            extra_pos_embed_type=None,
-            use_crossattn_projection=True,
-            rope_enable_fps_modulation=False,
-            qk_norm="rms_norm",
-        )))
-
-    vae_config: VAEConfig = field(default_factory=Cosmos25VAEConfig)
-
-    text_encoder_configs: tuple[EncoderConfig, ...] = field(
-        default_factory=lambda: (Reason1Config(arch_config=Reason1ArchConfig(
-            embedding_concat_strategy="full_concat")), ))
-
-    preprocess_text_funcs: tuple[Callable[[str], str], ...] = field(
-        default_factory=lambda: (_identity_preprocess_text, ))
-    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
-                                  ...] = field(default_factory=lambda:
-                                               (reason1_postprocess_text, ))
-
-    dit_precision: str = "bf16"
-    vae_precision: str = "bf16"
-    text_encoder_precisions: tuple[str, ...] = field(
-        default_factory=lambda: ("bf16", ))
-
-    embedded_cfg_scale: float = 0.0
-    flow_shift: float = 5.0
-
-    vae_tiling: bool = False
-    vae_sp: bool = False
-
-    STA_mode: STA_Mode = STA_Mode.NONE
-    skip_time_steps: int = 0
-
-    def __post_init__(self):
-        self.vae_config.load_encoder = True
-        self.vae_config.load_decoder = True
-        self._vae_latent_dim = 16

fastvideo/configs/pipelines/longcat.py

@@ -17,7 +17,11 @@ from fastvideo.configs.pipelines.base import PipelineConfig
 
 @dataclass
 class LongCatDiTArchConfig(DiTArchConfig):
-    """Extended DiTArchConfig with LongCat-specific fields."""
+    """Extended DiTArchConfig with LongCat-specific fields.
+    
+    NOTE: This is for Phase 1 wrapper compatibility. For native model (Phase 2),
+    use LongCatVideoConfig from fastvideo.configs.models.dits.longcat instead.
+    """
     # LongCat-specific architecture parameters
     adaln_tembed_dim: int = 512
     caption_channels: int = 4096
@@ -84,16 +88,20 @@ def umt5_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
 
 @dataclass
 class LongCatT2V480PConfig(PipelineConfig):
-    """Configuration for LongCat pipeline (480p).
+    """Configuration for LongCat pipeline (480p) aligned to LongCat-Video modules.
 
     Components expected by loaders:
       - tokenizer: AutoTokenizer
       - text_encoder: UMT5EncoderModel
-      - transformer: LongCatTransformer3DModel
+      - transformer: LongCatVideoTransformer3DModel (Phase 1 wrapper)
+                  OR LongCatTransformer3DModel (Phase 2 native)
       - vae: AutoencoderKLWan (Wan VAE, 4x8 compression)
       - scheduler: FlowMatchEulerDiscreteScheduler
     """
 
+    # DiT config with LongCat-specific arch_config
+    # NOTE: For Phase 1 wrapper, uses LongCatDiTArchConfig
+    # For Phase 2 native model, can use LongCatVideoConfig directly
     dit_config: DiTConfig = field(
         default_factory=lambda: DiTConfig(arch_config=LongCatDiTArchConfig()))
 

fastvideo/configs/pipelines/registry.py

@@ -6,14 +6,10 @@ from collections.abc import Callable
 
 from fastvideo.configs.pipelines.base import PipelineConfig
 from fastvideo.configs.pipelines.cosmos import CosmosConfig
-from fastvideo.configs.pipelines.cosmos2_5 import Cosmos25Config
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
 from fastvideo.configs.pipelines.longcat import LongCatT2V480PConfig
-from fastvideo.configs.pipelines.turbodiffusion import (
-    TurboDiffusionT2V_1_3B_Config, TurboDiffusionT2V_14B_Config,
-    TurboDiffusionI2V_A14B_Config)
 
 # isort: off
 from fastvideo.configs.pipelines.wan import (
@@ -56,29 +52,14 @@ PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
     "Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_Config,
     "Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_Config,
     "nvidia/Cosmos-Predict2-2B-Video2World": CosmosConfig,
-    "KyleShao/Cosmos-Predict2.5-2B-Diffusers": Cosmos25Config,
     "FastVideo/Matrix-Game-2.0-Base-Diffusers": MatrixGameI2V480PConfig,
     "FastVideo/Matrix-Game-2.0-GTA-Diffusers": MatrixGameI2V480PConfig,
     "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers": MatrixGameI2V480PConfig,
-    # LongCat Video models
-    "FastVideo/LongCat-Video-T2V-Diffusers": LongCatT2V480PConfig,
-    "FastVideo/LongCat-Video-I2V-Diffusers": LongCatT2V480PConfig,
-    "FastVideo/LongCat-Video-VC-Diffusers": LongCatT2V480PConfig,
-    # TurboDiffusion models
-    "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers": TurboDiffusionT2V_1_3B_Config,
-    "loayrashid/TurboWan2.1-T2V-14B-Diffusers": TurboDiffusionT2V_14B_Config,
-    "loayrashid/TurboWan2.2-I2V-A14B-Diffusers": TurboDiffusionI2V_A14B_Config,
     # Add other specific weight variants
 }
 
 # For determining pipeline type from model ID
 PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
-    "longcatimagetovideo":
-    lambda id: "longcatimagetovideo" in id.lower(),
-    "longcatvideocontinuation":
-    lambda id: "longcatvideocontinuation" in id.lower(),
-    "longcat":
-    lambda id: "longcat" in id.lower(),
     "hunyuan":
     lambda id: "hunyuan" in id.lower(),
     "hunyuan15":
@@ -96,21 +77,15 @@ PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
     "stepvideo":
     lambda id: "stepvideo" in id.lower(),
     "cosmos":
-    lambda id: "cosmos" in id.lower() and ("2.5" not in id.lower(
-    ) and "2_5" not in id.lower() and "25" not in id.lower()),
-    "cosmos25":
-    lambda id: "cosmos25" in id.lower(),
-    "turbodiffusion":
-    lambda id: "turbodiffusion" in id.lower() or "turbowan" in id.lower(),
+    lambda id: "cosmos" in id.lower(),
+    "longcat":
+    lambda id: "longcat" in id.lower(),
     # Add other pipeline architecture detectors
 }
 
 # Fallback configs when exact match isn't found but architecture is detected
 PIPELINE_FALLBACK_CONFIG: dict[str, type[PipelineConfig]] = {
-    "longcatimagetovideo": LongCatT2V480PConfig,
-    "longcatvideocontinuation": LongCatT2V480PConfig,
     "longcat": LongCatT2V480PConfig,
-    "cosmos25": Cosmos25Config,
     "hunyuan":
     HunyuanConfig,  # Base Hunyuan config as fallback for any Hunyuan variant
     "matrixgame": MatrixGameI2V480PConfig,
@@ -121,8 +96,7 @@ PIPELINE_FALLBACK_CONFIG: dict[str, type[PipelineConfig]] = {
     "wanimagetovideo": WanI2V480PConfig,
     "wandmdpipeline": FastWan2_1_T2V_480P_Config,
     "wancausaldmdpipeline": SelfForcingWanT2V480PConfig,
-    "stepvideo": StepVideoT2VConfig,
-    "turbodiffusion": TurboDiffusionT2V_1_3B_Config,
+    "stepvideo": StepVideoT2VConfig
     # Other fallbacks by architecture
 }
 

fastvideo/configs/pipelines/turbodiffusion.py

@@ -1,128 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-TurboDiffusion pipeline configurations.
-
-TurboDiffusion uses RCM (recurrent Consistency Model) scheduler with
-SLA (Sparse-Linear Attention) for fast 1-4 step video generation.
-"""
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
-from fastvideo.configs.models.dits import WanVideoConfig
-from fastvideo.configs.models.encoders import CLIPVisionConfig
-from fastvideo.configs.models.vaes import WanVAEConfig
-from fastvideo.configs.pipelines.base import PipelineConfig
-from fastvideo.configs.pipelines.wan import t5_postprocess_text, T5Config, BaseEncoderOutput
-
-import torch
-from collections.abc import Callable
-
-
-@dataclass
-class TurboDiffusionT2VConfig(PipelineConfig):
-    """Base configuration for TurboDiffusion T2V pipeline.
-    
-    Uses RCM scheduler with sigma_max=80 for 1-4 step generation.
-    No boundary_ratio (single model, no switching).
-    """
-    # DiT
-    dit_config: DiTConfig = field(default_factory=WanVideoConfig)
-    # VAE
-    vae_config: VAEConfig = field(default_factory=WanVAEConfig)
-    vae_tiling: bool = False
-    vae_sp: bool = False
-
-    # Denoising stage
-    flow_shift: float | None = 3.0
-
-    # No boundary_ratio for T2V (single model)
-    boundary_ratio: float | None = None
-
-    # Text encoding stage
-    text_encoder_configs: tuple[EncoderConfig, ...] = field(
-        default_factory=lambda: (T5Config(), ))
-    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
-                                  ...] = field(default_factory=lambda:
-                                               (t5_postprocess_text, ))
-
-    # Precision for each component
-    precision: str = "bf16"
-    vae_precision: str = "fp32"
-    text_encoder_precisions: tuple[str, ...] = field(
-        default_factory=lambda: ("fp32", ))
-
-    # self-forcing params
-    warp_denoising_step: bool = True
-
-    def __post_init__(self):
-        self.vae_config.load_encoder = False
-        self.vae_config.load_decoder = True
-        # Ensure no boundary_ratio is set in dit_config
-        self.dit_config.boundary_ratio = None
-
-
-@dataclass
-class TurboDiffusionT2V_1_3B_Config(TurboDiffusionT2VConfig):
-    """Configuration for TurboDiffusion T2V 1.3B model."""
-    pass
-
-
-@dataclass
-class TurboDiffusionT2V_14B_Config(TurboDiffusionT2VConfig):
-    """Configuration for TurboDiffusion T2V 14B model.
-    
-    Uses same config as 1.3B but with higher flow_shift for 14B model.
-    """
-    flow_shift: float | None = 5.0
-
-
-@dataclass
-class TurboDiffusionI2VConfig(PipelineConfig):
-    """Base configuration for TurboDiffusion I2V pipeline.
-    
-    Uses RCM scheduler with sigma_max=200 for 1-4 step generation.
-    Uses boundary_ratio=0.9 for high-noise to low-noise model switching.
-    """
-    # DiT
-    dit_config: DiTConfig = field(default_factory=WanVideoConfig)
-    # VAE
-    vae_config: VAEConfig = field(default_factory=WanVAEConfig)
-    vae_tiling: bool = False
-    vae_sp: bool = False
-
-    # Denoising stage
-    flow_shift: float | None = 5.0
-
-    boundary_ratio: float | None = 0.9
-
-    # Text encoding stage
-    text_encoder_configs: tuple[EncoderConfig, ...] = field(
-        default_factory=lambda: (T5Config(), ))
-    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
-                                  ...] = field(default_factory=lambda:
-                                               (t5_postprocess_text, ))
-
-    # Image encoder for I2V
-    image_encoder_config: EncoderConfig = field(
-        default_factory=CLIPVisionConfig)
-    image_encoder_precision: str = "fp32"
-
-    # Precision for each component
-    precision: str = "bf16"
-    vae_precision: str = "fp32"
-    text_encoder_precisions: tuple[str, ...] = field(
-        default_factory=lambda: ("fp32", ))
-
-    # self-forcing params
-    warp_denoising_step: bool = True
-
-    def __post_init__(self):
-        self.vae_config.load_encoder = True
-        self.vae_config.load_decoder = True
-        self.dit_config.boundary_ratio = self.boundary_ratio
-
-
-@dataclass
-class TurboDiffusionI2V_A14B_Config(TurboDiffusionI2VConfig):
-    """Configuration for TurboDiffusion I2V A14B model."""
-    pass

fastvideo/configs/sample/base.py

@@ -223,7 +223,7 @@ class SamplingParam:
             help="Path to input image for image-to-video generation",
         )
         parser.add_argument(
-            "--video-path",
+            "--video_path",
             type=str,
             default=SamplingParam.video_path,
             help="Path to input video for video-to-video generation",

fastvideo/configs/sample/cosmos2_5.py

@@ -1,19 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass
-
-from fastvideo.configs.sample.base import SamplingParam
-
-
-@dataclass
-class Cosmos_Predict2_5_2B_Diffusers_SamplingParam(SamplingParam):
-    """Defaults for Cosmos 2.5 (Predict2.5) text-to-video diffusers-format model."""
-
-    height: int = 480
-    width: int = 832
-    num_frames: int = 121
-    fps: int = 24
-
-    guidance_scale: float = 7.0
-    # Official Cosmos2.5 sampling uses empty string as unconditional.
-    negative_prompt: str = ""
-    num_inference_steps: int = 35

fastvideo/configs/sample/registry.py

@@ -9,7 +9,6 @@ from fastvideo.configs.sample.hunyuan15 import Hunyuan15_480P_SamplingParam, Hun
 from fastvideo.configs.sample.stepvideo import StepVideoT2VSamplingParam
 
 from fastvideo.configs.sample.cosmos import Cosmos_Predict2_2B_Video2World_SamplingParam
-from fastvideo.configs.sample.cosmos2_5 import Cosmos_Predict2_5_2B_Diffusers_SamplingParam
 
 # isort: off
 from fastvideo.configs.sample.wan import (
@@ -27,11 +26,6 @@ from fastvideo.configs.sample.wan import (
     SelfForcingWan2_2_T2V_A14B_480P_SamplingParam,
     MatrixGame2_SamplingParam,
 )
-from fastvideo.configs.sample.turbodiffusion import (
-    TurboDiffusionT2V_1_3B_SamplingParam,
-    TurboDiffusionT2V_14B_SamplingParam,
-    TurboDiffusionI2V_A14B_SamplingParam,
-)
 # isort: on
 from fastvideo.logger import init_logger
 from fastvideo.utils import (maybe_download_model_index,
@@ -40,48 +34,36 @@ from fastvideo.utils import (maybe_download_model_index,
 logger = init_logger(__name__)
 # Registry maps specific model weights to their config classes
 SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
-    "FastVideo/FastHunyuan-diffusers":
-    FastHunyuanSamplingParam,
-    "hunyuanvideo-community/HunyuanVideo":
-    HunyuanSamplingParam,
+    "FastVideo/FastHunyuan-diffusers": FastHunyuanSamplingParam,
+    "hunyuanvideo-community/HunyuanVideo": HunyuanSamplingParam,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v":
     Hunyuan15_480P_SamplingParam,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v":
     Hunyuan15_720P_SamplingParam,
-    "FastVideo/stepvideo-t2v-diffusers":
-    StepVideoT2VSamplingParam,
+    "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VSamplingParam,
 
     # Wan2.1
-    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers":
-    WanT2V_1_3B_SamplingParam,
-    "Wan-AI/Wan2.1-T2V-14B-Diffusers":
-    WanT2V_14B_SamplingParam,
-    "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers":
-    WanI2V_14B_480P_SamplingParam,
-    "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers":
-    WanI2V_14B_720P_SamplingParam,
+    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V_1_3B_SamplingParam,
+    "Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V_14B_SamplingParam,
+    "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V_14B_480P_SamplingParam,
+    "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V_14B_720P_SamplingParam,
     "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers":
     Wan2_1_Fun_1_3B_InP_SamplingParam,
     "IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers":
     Wan2_1_Fun_1_3B_Control_SamplingParam,
 
     # Wan2.2
-    "Wan-AI/Wan2.2-TI2V-5B-Diffusers":
-    Wan2_2_TI2V_5B_SamplingParam,
+    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
     "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers":
     Wan2_2_TI2V_5B_SamplingParam,
-    "Wan-AI/Wan2.2-T2V-A14B-Diffusers":
-    Wan2_2_T2V_A14B_SamplingParam,
-    "Wan-AI/Wan2.2-I2V-A14B-Diffusers":
-    Wan2_2_I2V_A14B_SamplingParam,
+    "Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_SamplingParam,
+    "Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_SamplingParam,
 
     # FastWan2.1
-    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers":
-    FastWanT2V480P_SamplingParam,
+    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWanT2V480P_SamplingParam,
 
     # FastWan2.2
-    "FastVideo/FastWan2.2-TI2V-5B-Diffusers":
-    Wan2_2_TI2V_5B_SamplingParam,
+    "FastVideo/FastWan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
 
     # Causal Self-Forcing Wan2.1
     "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers":
@@ -97,25 +79,10 @@ SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
     "nvidia/Cosmos-Predict2-2B-Video2World":
     Cosmos_Predict2_2B_Video2World_SamplingParam,
 
-    # Cosmos2.5
-    "KyleShao/Cosmos-Predict2.5-2B-Diffusers":
-    Cosmos_Predict2_5_2B_Diffusers_SamplingParam,
-
     # MatrixGame2.0 models
-    "FastVideo/Matrix-Game-2.0-Base-Diffusers":
-    MatrixGame2_SamplingParam,
-    "FastVideo/Matrix-Game-2.0-GTA-Diffusers":
-    MatrixGame2_SamplingParam,
-    "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers":
-    MatrixGame2_SamplingParam,
-
-    # TurboDiffusion models
-    "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers":
-    TurboDiffusionT2V_1_3B_SamplingParam,
-    "loayrashid/TurboWan2.1-T2V-14B-Diffusers":
-    TurboDiffusionT2V_14B_SamplingParam,
-    "loayrashid/TurboWan2.2-I2V-A14B-Diffusers":
-    TurboDiffusionI2V_A14B_SamplingParam,
+    "FastVideo/Matrix-Game-2.0-Base-Diffusers": MatrixGame2_SamplingParam,
+    "FastVideo/Matrix-Game-2.0-GTA-Diffusers": MatrixGame2_SamplingParam,
+    "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers": MatrixGame2_SamplingParam,
 
     # Add other specific weight variants
 }
@@ -138,12 +105,6 @@ SAMPLING_PARAM_DETECTOR: dict[str, Callable[[str], bool]] = {
     lambda id: "wancausaldmdpipeline" in id.lower(),
     "matrixgame":
     lambda id: "matrixgame" in id.lower() or "matrix-game" in id.lower(),
-    "turbodiffusion":
-    lambda id: "turbodiffusion" in id.lower() or "turbowan" in id.lower(),
-    "cosmos25":
-    lambda id: "cosmos2_5" in id.lower(),
-    "cosmos":
-    lambda id: "cosmos" in id.lower() and "2_5" not in id.lower(),
     # Add other pipeline architecture detectors
 }
 
@@ -160,10 +121,6 @@ SAMPLING_FALLBACK_PARAM: dict[str, Any] = {
     "wancausaldmdpipeline": SelfForcingWan2_1_T2V_1_3B_480P_SamplingParam,
     "stepvideo": StepVideoT2VSamplingParam,
     "matrixgame": MatrixGame2_SamplingParam,
-    "turbodiffusion":
-    TurboDiffusionT2V_1_3B_SamplingParam,  # Default to T2V for fallback
-    "cosmos25": Cosmos_Predict2_5_2B_Diffusers_SamplingParam,
-    "cosmos": Cosmos_Predict2_2B_Video2World_SamplingParam,
     # Other fallbacks by architecture
 }
 
@@ -187,6 +144,9 @@ def get_sampling_param_cls_for_name(pipeline_name_or_path: str) -> Any | None:
 
     if os.path.exists(pipeline_name_or_path):
         config = verify_model_config_and_directory(pipeline_name_or_path)
+        logger.warning(
+            "FastVideo may not correctly identify the optimal sampling param for this model, as the local directory may have been renamed."
+        )
     else:
         config = maybe_download_model_index(pipeline_name_or_path)
 

fastvideo/configs/sample/turbodiffusion.py

@@ -1,73 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-TurboDiffusion sampling parameters.
-
-TurboDiffusion uses RCM (recurrent Consistency Model) scheduler for
-1-4 step video generation with no classifier-free guidance.
-"""
-from dataclasses import dataclass
-
-from fastvideo.configs.sample.base import SamplingParam
-
-
-@dataclass
-class TurboDiffusionT2V_1_3B_SamplingParam(SamplingParam):
-    """Sampling parameters for TurboDiffusion T2V 1.3B model.
-    
-    Uses 4-step RCM sampling with guidance_scale=1.0 (no CFG).
-    """
-    # Video parameters
-    height: int = 480
-    width: int = 832
-    num_frames: int = 81
-    fps: int = 16
-
-    # Denoising stage - TurboDiffusion uses 1-4 steps with no CFG
-    guidance_scale: float = 1.0
-    num_inference_steps: int = 4
-
-    # No negative prompt needed for TurboDiffusion (no CFG)
-    negative_prompt: str | None = None
-
-
-@dataclass
-class TurboDiffusionT2V_14B_SamplingParam(SamplingParam):
-    """Sampling parameters for TurboDiffusion T2V 14B model.
-    
-    Uses 4-step RCM sampling with guidance_scale=1.0 (no CFG).
-    """
-    # Video parameters (720p for 14B)
-    height: int = 720
-    width: int = 1280
-    num_frames: int = 81
-    fps: int = 16
-
-    # Denoising stage - TurboDiffusion uses 1-4 steps with no CFG
-    guidance_scale: float = 1.0
-    num_inference_steps: int = 4
-
-    # No negative prompt needed for TurboDiffusion (no CFG)
-    negative_prompt: str | None = None
-
-
-@dataclass
-class TurboDiffusionI2V_A14B_SamplingParam(SamplingParam):
-    """Sampling parameters for TurboDiffusion I2V A14B model.
-    
-    Uses 4-step RCM sampling with dual-model switching (high/low noise).
-    """
-    # Video parameters (720p for A14B I2V)
-    height: int = 720
-    width: int = 1280
-    num_frames: int = 81
-    fps: int = 16
-
-    # Denoising stage - TurboDiffusion uses 1-4 steps with no CFG
-    guidance_scale: float = 1.0
-    num_inference_steps: int = 4
-
-    # Note: boundary_ratio is set in the pipeline config (TurboDiffusionI2VConfig),
-    # not here. This keeps sampling params and pipeline config separate.
-
-    # No negative prompt needed for TurboDiffusion (no CFG)
-    negative_prompt: str | None = None

fastvideo/dataset/__init__.py

@@ -8,7 +8,6 @@ from fastvideo.dataset.preprocessing_datasets import VideoCaptionMergedDataset,
 from fastvideo.dataset.transform import (CenterCropResizeVideo, Normalize255,
                                          TemporalRandomCrop)
 from fastvideo.dataset.validation_dataset import ValidationDataset
-from fastvideo.dataset.rl_prompt_dataset import build_rl_prompt_dataloader
 
 
 def getdataset(args) -> VideoCaptionMergedDataset:
@@ -48,6 +47,5 @@ def gettextdataset(args) -> TextDataset:
 
 __all__ = [
     "build_parquet_map_style_dataloader", "ValidationDataset",
-    "VideoCaptionMergedDataset", "TextDataset",
-    "build_rl_prompt_dataloader"
+    "VideoCaptionMergedDataset", "TextDataset"
 ]

fastvideo/dataset/rl_prompt_dataset.py

@@ -1,174 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import torch
-from torch.utils.data import Dataset, DataLoader, Sampler
-import json
-import os
-
-
-class TextPromptDataset(Dataset):
-    """Dataset for loading text prompts from a simple text file (one prompt per line)."""
-    
-    def __init__(self, dataset, split='train'):
-        self.file_path = os.path.join(dataset, f'{split}.txt')
-        with open(self.file_path, 'r') as f:
-            self.prompts = [line.strip() for line in f.readlines()]
-        
-    def __len__(self):
-        return len(self.prompts)
-    
-    def __getitem__(self, idx):
-        return {"prompt": self.prompts[idx], "metadata": {}}
-
-    @staticmethod
-    def collate_fn(examples):
-        prompts = [example["prompt"] for example in examples]
-        metadatas = [example["metadata"] for example in examples]
-        return prompts, metadatas
-
-
-class GenevalPromptDataset(Dataset):
-    """Dataset for loading prompts with metadata from JSONL files (e.g., GenEval format)."""
-    
-    def __init__(self, dataset, split='train'):
-        self.file_path = os.path.join(dataset, f'{split}_metadata.jsonl')
-        with open(self.file_path, 'r', encoding='utf-8') as f:
-            self.metadatas = [json.loads(line) for line in f]
-            self.prompts = [item['prompt'] for item in self.metadatas]
-        
-    def __len__(self):
-        return len(self.prompts)
-    
-    def __getitem__(self, idx):
-        return {"prompt": self.prompts[idx], "metadata": self.metadatas[idx]}
-
-    @staticmethod
-    def collate_fn(examples):
-        prompts = [example["prompt"] for example in examples]
-        metadatas = [example["metadata"] for example in examples]
-        return prompts, metadatas
-
-
-class KRepeatSampler(Sampler):
-    """Sampler that repeats each sample k times, ensuring synchronized random selection. For single-node training, set num_replicas=1 and rank=0."""
-    
-    def __init__(self, dataset, batch_size, k, num_replicas, rank, seed=0):
-        self.dataset = dataset
-        self.batch_size = batch_size        # Batch size per GPU/card
-        self.k = k                          # Number of repetitions per sample
-        self.num_replicas = num_replicas    # Total number of GPUs/cards
-        self.rank = rank                    # Current GPU/card rank
-        self.seed = seed                    # Random seed for synchronization
-        
-        # Calculate the number of unique samples needed for each iteration
-        self.total_samples = self.num_replicas * self.batch_size
-        assert self.total_samples % self.k == 0, f"k can not div n*b, k{k}-num_replicas{num_replicas}-batch_size{batch_size}"
-        self.m = self.total_samples // self.k  # different number of samples
-        self.step = 0
-
-    def __iter__(self):
-        while True:
-            # Generate a deterministic random sequence to ensure all cards are synchronized
-            g = torch.Generator()
-            g.manual_seed(self.seed + self.step)
-            
-            # Randomly select m unique samples
-            indices = torch.randperm(len(self.dataset), generator=g)[:self.m].tolist()
-            
-            # Repeat each sample k times to generate a total of n*b samples
-            repeated_indices = [idx for idx in indices for _ in range(self.k)]
-            
-            # Shuffle the order to ensure even distribution
-            shuffled_indices = torch.randperm(len(repeated_indices), generator=g).tolist()
-            shuffled_samples = [repeated_indices[i] for i in shuffled_indices]
-            
-            # Split samples among all cards
-            per_card_samples = []
-            for i in range(self.num_replicas):
-                start = i * self.batch_size
-                end = start + self.batch_size
-                per_card_samples.append(shuffled_samples[start:end])
-            
-            # Return the sample indices for the current card
-            yield per_card_samples[self.rank]
-        
-    def __len__(self):
-        return len(self.dataset) // self.batch_size
-    
-    def set_step(self, step):
-        """Used to synchronize the random state for different epochs."""
-        self.step = step
-
-
-def build_rl_prompt_dataloader(
-    dataset_path: str,
-    dataset_type: str = "text",
-    split: str = "train",
-    train_batch_size: int = 8,
-    test_batch_size: int = 8,
-    k: int = 1,
-    seed: int = 42,
-    train_num_workers: int = 1,
-    test_num_workers: int = 8,
-    num_replicas: int = 1,
-    rank: int = 0,
-) -> tuple[DataLoader, DataLoader]:
-    """
-    Factory function to create train and test dataloaders for RL prompt datasets.
-    
-    Args:
-        dataset_path: Path to dataset directory
-        dataset_type: "text" for TextPromptDataset or "geneval" for GenevalPromptDataset
-        split: Dataset split ("train" or "test")
-        train_batch_size: Batch size per GPU for training
-        test_batch_size: Batch size for testing
-        k: Number of times to repeat each sample (num_image_per_prompt)
-        seed: Random seed for sampler synchronization
-        train_num_workers: Number of workers for training dataloader
-        test_num_workers: Number of workers for test dataloader
-        num_replicas: Number of replicas (default 1 for single-node)
-        rank: Rank of current process (default 0 for single-node)
-    
-    Returns:
-        Tuple of (train_dataloader, test_dataloader)
-    """
-    # Create datasets based on type
-    if dataset_type == "text":
-        train_dataset = TextPromptDataset(dataset_path, 'train')
-        test_dataset = TextPromptDataset(dataset_path, 'test')
-        collate_fn = TextPromptDataset.collate_fn
-    elif dataset_type == "geneval":
-        train_dataset = GenevalPromptDataset(dataset_path, 'train')
-        test_dataset = GenevalPromptDataset(dataset_path, 'test')
-        collate_fn = GenevalPromptDataset.collate_fn
-    else:
-        raise ValueError(f"Unknown dataset_type: {dataset_type}. Must be 'text' or 'geneval'")
-    
-    # Create infinite-loop training sampler
-    train_sampler = KRepeatSampler(
-        dataset=train_dataset,
-        batch_size=train_batch_size,
-        k=k,
-        num_replicas=num_replicas,
-        rank=rank,
-        seed=seed
-    )
-    
-    # Create training dataloader with batch_sampler (infinite loop)
-    train_dataloader = DataLoader(
-        train_dataset,
-        batch_sampler=train_sampler,
-        num_workers=train_num_workers,
-        collate_fn=collate_fn,
-    )
-    
-    # Create standard test dataloader
-    test_dataloader = DataLoader(
-        test_dataset,
-        batch_size=test_batch_size,
-        collate_fn=collate_fn,
-        shuffle=False,
-        num_workers=test_num_workers,
-    )
-    
-    return train_dataloader, test_dataloader, train_dataset, test_dataset
-

fastvideo/entrypoints/streaming_generator.py

@@ -1,288 +0,0 @@
-import asyncio
-import os
-from concurrent.futures import Future, ThreadPoolExecutor
-
-import imageio
-import numpy as np
-import torch
-import torchvision
-from einops import rearrange
-
-from fastvideo.configs.sample import SamplingParam
-from fastvideo.entrypoints.video_generator import VideoGenerator
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines import ForwardBatch
-from fastvideo.utils import align_to, shallow_asdict
-from fastvideo.worker.executor import Executor
-from fastvideo.worker.multiproc_executor import MultiprocExecutor
-
-logger = init_logger(__name__)
-
-
-class IncrementalVideoWriter:
-
-    def __init__(self, path: str, fps: int = 24, block_dir: str | None = None):
-        self._executor = ThreadPoolExecutor(max_workers=2,
-                                            thread_name_prefix="video_write_")
-        self._path = path
-        self._writer = imageio.get_writer(path, fps=fps, format="mp4")
-        self._pending_main: Future | None = None
-        self._block_dir = block_dir
-        self._block_idx = 0
-        self._fps = fps
-
-    @property
-    def path(self) -> str:
-        return self._path
-
-    def add_frames(self, frames: list[np.ndarray]) -> Future | None:
-        # Wait for previous main video write to complete
-        if self._pending_main is not None:
-            self._pending_main.result()
-
-        # Copy frames to avoid race conditions
-        frames_copy = [f.copy() for f in frames]
-        self._pending_main = self._executor.submit(self._write_frames,
-                                                   frames_copy)
-
-        # Write block file if block_dir is set
-        block_future = None
-        if self._block_dir:
-            self._block_idx += 1
-            block_path = os.path.join(self._block_dir,
-                                      f"b{self._block_idx}.mp4")
-            block_future = self._executor.submit(self._write_block, frames_copy,
-                                                 block_path)
-        return block_future
-
-    def _write_frames(self, frames: list[np.ndarray]) -> None:
-        for frame in frames:
-            self._writer.append_data(frame)
-
-    def _write_block(self, frames: list[np.ndarray], path: str) -> str:
-        imageio.mimsave(path, frames, fps=self._fps)
-        return path
-
-    def close(self) -> None:
-        if self._pending_main is not None:
-            self._pending_main.result()
-            self._pending_main = None
-        if self._writer:
-            self._writer.close()
-            self._writer = None
-        self._executor.shutdown(wait=True)
-
-
-class StreamingVideoGenerator(VideoGenerator):
-    """
-    This class extends VideoGenerator with streaming capabilities,
-    allowing incremental video generation with step-by-step control.
-    """
-
-    def __init__(self,
-                 fastvideo_args: FastVideoArgs,
-                 executor_class: type[Executor],
-                 log_stats: bool,
-                 use_queue_mode: bool = True):
-        super().__init__(fastvideo_args, executor_class, log_stats)
-        self.accumulated_frames: list[np.ndarray] = []
-        self.sampling_param: SamplingParam | None = None
-        self.batch: ForwardBatch | None = None
-        self._use_queue_mode = use_queue_mode and isinstance(
-            self.executor, MultiprocExecutor)
-        self.writer: IncrementalVideoWriter | None = None
-        self.block_dir: str | None = None
-        self.block_idx: int = 0
-
-    @classmethod
-    def from_fastvideo_args(
-            cls, fastvideo_args: FastVideoArgs) -> "StreamingVideoGenerator":
-        executor_class = Executor.get_class(fastvideo_args)
-        return cls(
-            fastvideo_args=fastvideo_args,
-            executor_class=executor_class,
-            log_stats=False,
-        )
-
-    def reset(
-            self,
-            prompt: str = "A gameplay video of a cyberpunk city",
-            image_path: str | None = None,
-            num_frames: int = 120,  # Default max frames
-            **kwargs):
-        self.accumulated_frames = []
-        self.block_idx = 0
-        self.block_dir = None
-        if self.writer:
-            self.writer.close()
-            self.writer = None
-        self.executor.execute_streaming_clear()
-
-        # Handle batch processing from text file
-        if self.sampling_param is None:
-            self.sampling_param = SamplingParam.from_pretrained(
-                self.fastvideo_args.model_path)
-
-        self.sampling_param.update(kwargs)
-        self.sampling_param.prompt = prompt
-        if image_path:
-            self.sampling_param.image_path = image_path
-        self.sampling_param.num_frames = num_frames
-
-        if "output_path" in kwargs:
-            output_path = self._prepare_output_path(kwargs["output_path"],
-                                                    prompt)
-            # Create block directory for individual block files
-            block_dir = output_path.replace(".mp4", "")
-            os.makedirs(block_dir, exist_ok=True)
-            self.block_dir = block_dir
-            self.writer = IncrementalVideoWriter(output_path,
-                                                 fps=24,
-                                                 block_dir=block_dir)
-
-        fastvideo_args = self.fastvideo_args
-
-        self.sampling_param.height = align_to(self.sampling_param.height, 16)
-        self.sampling_param.width = align_to(self.sampling_param.width, 16)
-
-        latents_size = [(self.sampling_param.num_frames - 1) // 4 + 1,
-                        self.sampling_param.height // 8,
-                        self.sampling_param.width // 8]
-        n_tokens = latents_size[0] * latents_size[1] * latents_size[2]
-
-        self.sampling_param.return_frames = True
-        self.sampling_param.save_video = False
-
-        self.batch = ForwardBatch(
-            **shallow_asdict(self.sampling_param),
-            eta=0.0,
-            n_tokens=n_tokens,
-            VSA_sparsity=fastvideo_args.VSA_sparsity,
-        )
-
-        if self._use_queue_mode:
-            self.executor.submit_reset(self.batch, fastvideo_args)
-            result = self.executor.wait_result()
-            if result.error:
-                raise result.error
-        else:
-            self.executor.execute_streaming_reset(self.batch, fastvideo_args)
-
-    def step(
-            self, keyboard_cond: torch.Tensor,
-            mouse_cond: torch.Tensor) -> tuple[list[np.ndarray], Future | None]:
-        if self.batch is None:
-            raise RuntimeError("Call reset() before step()")
-
-        if self._use_queue_mode and self.executor._streaming_enabled:
-            self.executor.submit_step(keyboard_cond, mouse_cond)
-            result = self.executor.wait_result()
-            if result.error:
-                raise result.error
-            output_batch = result.output_batch
-        else:
-            # Fallback to RPC-based
-            output_batch = self.executor.execute_streaming_step(
-                keyboard_action=keyboard_cond, mouse_action=mouse_cond)
-
-        frames = self._process_output_batch(output_batch)
-        block_future = None
-        if len(frames) > 0:
-            self.accumulated_frames.extend(frames)
-            self.block_idx += 1
-            if self.writer:
-                # Returns Future for block file, or None if no block_dir
-                block_future = self.writer.add_frames(frames)
-
-        return frames, block_future
-
-    async def step_async(
-            self, keyboard_cond: torch.Tensor,
-            mouse_cond: torch.Tensor) -> tuple[list[np.ndarray], Future | None]:
-        if self.batch is None:
-            raise RuntimeError("Call reset() before step_async()")
-
-        if self._use_queue_mode and self.executor._streaming_enabled:
-            self.executor.submit_step(keyboard_cond, mouse_cond)
-
-            loop = asyncio.get_running_loop()
-            result = await loop.run_in_executor(None, self.executor.wait_result)
-
-            if result.error:
-                raise result.error
-            output_batch = result.output_batch
-        else:
-            # Fallback to RPC-based
-            output_batch = await self.executor.execute_streaming_step_async(
-                keyboard_action=keyboard_cond,
-                mouse_action=mouse_cond,
-            )
-
-        frames = self._process_output_batch(output_batch)
-        block_future = None
-        if len(frames) > 0:
-            self.accumulated_frames.extend(frames)
-            self.block_idx += 1
-            if self.writer:
-                block_future = self.writer.add_frames(frames)
-
-        return frames, block_future
-
-    def finalize(self,
-                 output_path: str = "streaming_output.mp4",
-                 fps: int = 24) -> str:
-        if not self.accumulated_frames:
-            logger.warning("No frames to save.")
-            return ""
-
-        if self.writer:
-            output_path = self.writer.path
-            self.writer.close()
-            self.writer = None
-            logger.info("Saved video to %s", output_path)
-        else:
-            imageio.mimsave(output_path,
-                            self.accumulated_frames,
-                            fps=fps,
-                            format="mp4")
-            logger.info("Saved video to %s", output_path)
-
-        if self._use_queue_mode and self.executor._streaming_enabled:
-            self.executor.submit_clear()
-        else:
-            self.executor.execute_streaming_clear()
-        self.accumulated_frames = []
-        return output_path
-
-    def _process_output_batch(self,
-                              output_batch: ForwardBatch) -> list[np.ndarray]:
-        if output_batch.output is None:
-            return []
-
-        samples = output_batch.output
-        # [B, C, T, H, W] or [1, C, T, H, W]
-        if len(samples.shape) == 5:
-            # Rearrange to [T, B, C, H, W] for processing loop
-            videos = rearrange(samples, "b c t h w -> t b c h w")
-        else:
-            logger.warning("Unexpected output shape: %s", samples.shape)
-            return []
-
-        frames = []
-        for x in videos:
-            x = torchvision.utils.make_grid(x, nrow=1)
-            x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
-            frames.append((x * 255).cpu().numpy().astype(np.uint8))
-
-        return frames
-
-    def shutdown(self):
-        if self.writer:
-            self.writer.close()
-            self.writer = None
-
-        if self._use_queue_mode and self.executor._streaming_enabled:
-            self.executor.disable_streaming()
-
-        super().shutdown()

fastvideo/fastvideo_args.py

@@ -12,7 +12,6 @@ from typing import Any, TYPE_CHECKING
 from fastvideo.configs.configs import PreprocessConfig
 from fastvideo.configs.pipelines.base import PipelineConfig, STA_Mode
 from fastvideo.configs.utils import clean_cli_args
-from fastvideo.layers.quantization import QUANTIZATION_METHODS, QuantizationMethods
 from fastvideo.logger import init_logger
 from fastvideo.utils import FlexibleArgumentParser, StoreBoolean
 
@@ -132,8 +131,7 @@ class FastVideoArgs:
 
     # CPU offload parameters
     dit_cpu_offload: bool = True
-    use_fsdp_inference: bool = False
-    dit_layerwise_offload: bool = True
+    use_fsdp_inference: bool = True
     text_encoder_cpu_offload: bool = True
     image_encoder_cpu_offload: bool = True
     vae_cpu_offload: bool = True
@@ -172,10 +170,9 @@ class FastVideoArgs:
         "transformer": True,
         "vae": True,
     })
-
-    override_text_encoder_safetensors: str | None = None  # path to safetensors file for text encoder override
-    override_text_encoder_quant: QuantizationMethods = None
-
+    text_encoder_override: str | None = None
+    text_encoder_override_path: str | None = None
+    text_encoder_dtype: str | None = None
     override_transformer_cls_name: str | None = None
     init_weights_from_safetensors: str = ""  # path to safetensors file for initial weight loading
     init_weights_from_safetensors_2: str = ""  # path to safetensors file for initial weight loading for transformer_2
@@ -422,18 +419,11 @@ class FastVideoArgs:
             help=
             "Use CPU offload for DiT inference. Enable if run out of memory with FSDP.",
         )
-        parser.add_argument(
-            "--dit-layerwise-offload",
-            action=StoreBoolean,
-            help="Enable layerwise CPU offload with async H2D prefetch overlap.",
-        )
         parser.add_argument(
             "--use-fsdp-inference",
             action=StoreBoolean,
             help=
-            "Use FSDP for inference by sharding the model weights. FSDP helps reduce GPU memory usage but may introduce"
-            +
-            " weight transfer overhead depending on the specific setup. Enable if run out of memory.",
+            "Use FSDP for inference by sharding the model weights. Latency is very low due to prefetch--enable if run out of memory.",
         )
         parser.add_argument(
             "--text-encoder-cpu-offload",
@@ -441,6 +431,31 @@ class FastVideoArgs:
             help=
             "Use CPU offload for text encoder. Enable if run out of memory.",
         )
+        parser.add_argument(
+            "--text-encoder-override",
+            type=str,
+            default=FastVideoArgs.text_encoder_override,
+            help=
+            ("Load text encoder weights from a different local path or HF repo "
+             "instead of the main diffusers snapshot."),
+        )
+        parser.add_argument(
+            "--text-encoder-override-path",
+            type=str,
+            default=FastVideoArgs.text_encoder_override_path,
+            help=
+            ("Optional relative path to the text encoder inside the override repository "
+             "(defaults to the module name)."),
+        )
+        parser.add_argument(
+            "--text-encoder-dtype",
+            type=str,
+            default=FastVideoArgs.text_encoder_dtype,
+            help=
+            ("Torch dtype string for loading text encoders with transformers AutoModel "
+             "(e.g., fp16, bf16, fp32, fp8). If set, overrides pipeline-config precisions."
+             ),
+        )
         parser.add_argument(
             "--image-encoder-cpu-offload",
             action=StoreBoolean,
@@ -489,19 +504,6 @@ class FastVideoArgs:
             default=FastVideoArgs.enable_stage_verification,
             help="Enable input/output verification for pipeline stages",
         )
-        parser.add_argument(
-            "--override-text-encoder-safetensors",
-            type=str,
-            default=FastVideoArgs.override_text_encoder_safetensors,
-            help="Path to safetensors file for text encoder override",
-        )
-        parser.add_argument(
-            "--override-text-encoder-quant",
-            type=str,
-            choices=QUANTIZATION_METHODS,
-            default=FastVideoArgs.override_text_encoder_quant,
-            help="Quantization method for text encoder override",
-        )
         parser.add_argument(
             "--override-transformer-cls-name",
             type=str,
@@ -605,25 +607,22 @@ class FastVideoArgs:
         kwargs['preprocess_config'] = PreprocessConfig.from_kwargs(kwargs)
         return cls(**kwargs)
 
+    def get_component_override(
+            self, module_name: str) -> tuple[str | None, str | None]:
+        """Return override repo/path for a given module if configured."""
+
+        if module_name.startswith(
+                "text_encoder") and self.text_encoder_override:
+            return self.text_encoder_override, self.text_encoder_override_path
+
+        return None, None
+
     def check_fastvideo_args(self) -> None:
         """Validate inference arguments for consistency"""
         from fastvideo.platforms import current_platform
 
         if current_platform.is_mps():
             self.use_fsdp_inference = False
-            self.dit_layerwise_offload = False
-
-        if self.dit_layerwise_offload:
-            if self.use_fsdp_inference:
-                logger.warning(
-                    "dit_layerwise_offload is enabled, automatically disabling use_fsdp_inference."
-                )
-                self.use_fsdp_inference = False
-            if self.dit_cpu_offload:
-                logger.warning(
-                    "dit_layerwise_offload is enabled, automatically disabling dit_cpu_offload."
-                )
-                self.dit_cpu_offload = False
 
         # Validate mode and inference_mode consistency
         assert isinstance(
@@ -740,271 +739,6 @@ def get_current_fastvideo_args() -> FastVideoArgs:
     return _current_fastvideo_args
 
 
-@dataclasses.dataclass
-class RLArgs:
-    """
-    Reinforcement Learning (RL) specific arguments
-    """
-    # ============================================================================
-    # SHARED RL CONFIGURATION
-    rl_mode: bool = False # Enable RL training mode
-    rl_algorithm: str = "grpo"  # RL algorithm to use: "grpo", "ppo", "dpo"
-
-    # Trajectory collection
-    num_rollouts: int = 4  # Number of rollouts to collect per training step
-    rollout_steps: str = "20,30"  # Random intermediate steps for sampling (comma-separated)
-    noise_injection_min: int = 10  # Minimum timestep for noise injection
-    noise_injection_max: int = 40  # Maximum timestep for noise injection
-    use_sde_sampling: bool = True  # Use SDE sampling (Flow-GRPO-Fast)
-    num_denoising_steps: int = 2  # Number of denoising steps per trajectory (1-2 for fast)
-
-    # Advantage estimation
-    gamma: float = 0.99  # Discount factor for returns
-    lambda_param: float = 0.95  # GAE lambda parameter
-    use_gae: bool = True  # Use Generalized Advantage Estimation
-    normalize_advantages: bool = True  # Normalize advantages before policy update
-
-    # Reward models
-    reward_models: dict[str, float] = field(default_factory=lambda: {"dummy": 1.0})  # reward models (names, weight)
-    value_model_path: str = ""  # Path to value model (can be empty to train from scratch)
-    value_model_share_backbone: bool = False  # Share transformer backbone between policy and value
-
-    # Training schedule
-    warmup_steps: int = 1000  # Collect SFT-style data before starting RL
-    collect_on_policy: bool = True  # Collect fresh rollouts each step (on-policy)
-    timestep_fraction: float = 0.99  # Fraction of timesteps to train on
-    num_inner_epochs: int = 1  # Number of inner epochs per outer epoch
-
-    # KL regularization
-    kl_beta: float = 0.004  # KL loss coefficient (GRPO uses KL loss, DPO uses larger beta)
-    kl_reward: float = 0.0  # KL reward coefficient (alternative to KL loss, typically 0)
-
-    # SFT integration
-    sft_weight: float = 0.0  # SFT loss weight for supervised learning in RL training
-    sft_batch_size: int = 3  # Batch size for SFT data
-
-    # CFG
-    guidance_scale = 1.0 # use guidance_scale > 1.0 to enable CFG
-
-    # Statistics tracking
-    global_std: bool = False  # Use global std across all samples vs per-group std
-    per_prompt_stat_tracking: bool = True  # Track statistics per prompt
-
-    # Training options
-    use_diffusion_loss: bool = True  # Use diffusion loss in training
-
-    # ============================================================================
-    # GRPO-SPECIFIC CONFIGURATION
-
-    # Policy optimization
-    grpo_policy_clip_range: float = 0.001  # PPO-style clipping range for policy ratio
-    grpo_value_clip_range: float = 0.2  # Value function clipping range
-    grpo_num_policy_epochs: int = 1  # Number of policy update epochs (GRPO typically uses 1)
-    grpo_num_value_epochs: int = 1  # Number of value function update epochs
-    grpo_target_kl: float = 0.01  # Target KL divergence for early stopping
-    grpo_entropy_coef: float = 0.0  # Entropy coefficient for exploration
-    grpo_value_loss_coef: float = 0.5  # Value loss coefficient
-
-    # GRPO-Guard safety mechanisms
-    grpo_use_grpo_guard: bool = True  # Enable GRPO-Guard safety mechanisms
-    grpo_ratio_norm_correction: bool = True  # RatioNorm: correct importance ratio bias
-    grpo_gradient_reweighting: bool = True  # Reweight gradients across denoising steps
-    grpo_max_importance_ratio: float = 10.0  # Clip importance ratios above this value
-
-    # ============================================================================
-    # DPO-SPECIFIC CONFIGURATION
-
-    dpo_beta: float = 100.0  # DPO regularization parameter (typically much larger than GRPO beta)
-    dpo_ref_update_step: int = 10000000  # Reference model update frequency for OnlineDPO
-    dpo_label_smoothing: float = 0.0  # Label smoothing for DPO loss
-
-    @staticmethod
-    def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
-        """Add RL-specific CLI arguments to the parser."""
-        # RL (Reinforcement Learning) arguments
-        parser.add_argument("--rl-mode",
-                            action=StoreBoolean,
-                            help="Enable RL training mode")
-        parser.add_argument("--rl-algorithm",
-                            type=str,
-                            default=RLArgs.rl_algorithm,
-                            choices=["grpo", "ppo", "dpo"],
-                            help="RL algorithm to use (grpo, ppo, dpo)")
-
-        # Trajectory collection (Flow-GRPO-Fast)
-        parser.add_argument("--rl-num-rollouts",
-                            type=int,
-                            default=RLArgs.num_rollouts,
-                            help="Number of rollouts to collect per training step")
-        parser.add_argument("--rl-rollout-steps",
-                            type=str,
-                            default=RLArgs.rollout_steps,
-                            help="Random intermediate steps for sampling (comma-separated)")
-        parser.add_argument("--rl-noise-injection-min",
-                            type=int,
-                            default=RLArgs.noise_injection_min,
-                            help="Minimum timestep for noise injection")
-        parser.add_argument("--rl-noise-injection-max",
-                            type=int,
-                            default=RLArgs.noise_injection_max,
-                            help="Maximum timestep for noise injection")
-        parser.add_argument("--rl-use-sde-sampling",
-                            action=StoreBoolean,
-                            help="Use SDE sampling (Flow-GRPO-Fast)")
-        parser.add_argument("--rl-num-denoising-steps",
-                            type=int,
-                            default=RLArgs.num_denoising_steps,
-                            help="Number of denoising steps per trajectory (1-2 for fast)")
-
-        # Advantage estimation
-        parser.add_argument("--rl-gamma",
-                            type=float,
-                            default=RLArgs.gamma,
-                            help="Discount factor for returns")
-        parser.add_argument("--rl-lambda",
-                            type=float,
-                            default=RLArgs.lambda_param,
-                            help="GAE lambda parameter")
-        parser.add_argument("--rl-use-gae",
-                            action=StoreBoolean,
-                            help="Use Generalized Advantage Estimation")
-        parser.add_argument("--rl-normalize-advantages",
-                            action=StoreBoolean,
-                            help="Normalize advantages before policy update")
-
-        # Policy optimization (GRPO/PPO)
-        parser.add_argument("--rl-policy-clip-range",
-                            type=float,
-                            default=RLArgs.grpo_policy_clip_range,
-                            dest="grpo_policy_clip_range",  # Map to RLArgs field name
-                            help="PPO-style clipping range for policy ratio")
-        parser.add_argument("--rl-value-clip-range",
-                            type=float,
-                            default=RLArgs.grpo_value_clip_range,
-                            help="Value function clipping range")
-        parser.add_argument("--rl-num-policy-epochs",
-                            type=int,
-                            default=RLArgs.grpo_num_policy_epochs,
-                            help="Number of policy update epochs (GRPO typically uses 1)")
-        parser.add_argument("--rl-num-value-epochs",
-                            type=int,
-                            default=RLArgs.grpo_num_value_epochs,
-                            help="Number of value function update epochs")
-        parser.add_argument("--rl-target-kl",
-                            type=float,
-                            default=RLArgs.grpo_target_kl,
-                            help="Target KL divergence for early stopping")
-        parser.add_argument("--rl-entropy-coef",
-                            type=float,
-                            default=RLArgs.grpo_entropy_coef,
-                            help="Entropy coefficient for exploration")
-        parser.add_argument("--rl-value-loss-coef",
-                            type=float,
-                            default=RLArgs.grpo_value_loss_coef,
-                            help="Value loss coefficient")
-
-        # GRPO-Guard (safety mechanisms)
-        parser.add_argument("--rl-use-grpo-guard",
-                            action=StoreBoolean,
-                            help="Enable GRPO-Guard safety mechanisms")
-        parser.add_argument("--rl-ratio-norm-correction",
-                            action=StoreBoolean,
-                            help="RatioNorm: correct importance ratio bias")
-        parser.add_argument("--rl-gradient-reweighting",
-                            action=StoreBoolean,
-                            help="Reweight gradients across denoising steps")
-        parser.add_argument("--rl-max-importance-ratio",
-                            type=float,
-                            default=RLArgs.grpo_max_importance_ratio,
-                            help="Clip importance ratios above this value")
-
-        # Reward models
-        parser.add_argument("--reward-models",
-                            type=str,
-                            default='{"dummy": 1.0}',
-                            help="Reward models as JSON dict (e.g., '{\"video_ocr\": 1.0, \"pickscore\": 0.5}')")
-        parser.add_argument("--value-model-path",
-                            type=str,
-                            default=RLArgs.value_model_path,
-                            help="Path to value model (can be empty to train from scratch)")
-        parser.add_argument("--value-model-share-backbone",
-                            action=StoreBoolean,
-                            help="Share transformer backbone between policy and value")
-
-        # Training schedule
-        parser.add_argument("--rl-warmup-steps",
-                            type=int,
-                            default=RLArgs.warmup_steps,
-                            help="Collect SFT-style data before starting RL")
-        parser.add_argument("--rl-collect-on-policy",
-                            action=StoreBoolean,
-                            help="Collect fresh rollouts each step (on-policy)")
-        parser.add_argument("--rl-timestep-fraction",
-                            type=float,
-                            default=RLArgs.timestep_fraction,
-                            help="Fraction of timesteps to train on")
-        parser.add_argument("--rl-num-inner-epochs",
-                            type=int,
-                            default=RLArgs.num_inner_epochs,
-                            help="Number of inner epochs per outer epoch")
-
-        # KL regularization
-        parser.add_argument("--rl-kl-beta",
-                            type=float,
-                            default=RLArgs.kl_beta,
-                            dest="kl_beta",  # Map CLI arg to RLArgs field name
-                            help="KL loss coefficient (GRPO uses KL loss, DPO uses larger beta)")
-        parser.add_argument("--rl-kl-reward",
-                            type=float,
-                            default=RLArgs.kl_reward,
-                            help="KL reward coefficient (alternative to KL loss, typically 0)")
-
-        # SFT integration
-        parser.add_argument("--rl-sft-weight",
-                            type=float,
-                            default=RLArgs.sft_weight,
-                            help="SFT loss weight for supervised learning in RL training")
-        parser.add_argument("--rl-sft-batch-size",
-                            type=int,
-                            default=RLArgs.sft_batch_size,
-                            help="Batch size for SFT data")
-
-        # CFG settings
-        parser.add_argument("--guidance-scale",
-                            type=float,
-                            default=1.0,
-                            help="Guidance scale for CFG")
-
-        # Statistics tracking
-        parser.add_argument("--rl-global-std",
-                            action=StoreBoolean,
-                            help="Use global std across all samples vs per-group std")
-        parser.add_argument("--rl-per-prompt-stat-tracking",
-                            action=StoreBoolean,
-                            help="Track statistics per prompt")
-
-        # Training options
-        parser.add_argument("--rl-use-diffusion-loss",
-                            action=StoreBoolean,
-                            help="Use diffusion loss in training")
-
-        # DPO-specific
-        parser.add_argument("--dpo-beta",
-                            type=float,
-                            default=RLArgs.dpo_beta,
-                            help="DPO regularization parameter (typically much larger than GRPO beta)")
-        parser.add_argument("--dpo-ref-update-step",
-                            type=int,
-                            default=RLArgs.dpo_ref_update_step,
-                            help="Reference model update frequency for OnlineDPO")
-        parser.add_argument("--dpo-label-smoothing",
-                            type=float,
-                            default=RLArgs.dpo_label_smoothing,
-                            help="Label smoothing for DPO loss")
-
-        return parser
-
-
 @dataclasses.dataclass
 class TrainingArgs(FastVideoArgs):
     """
@@ -1017,11 +751,6 @@ class TrainingArgs(FastVideoArgs):
     num_height: int = 0
     num_width: int = 0
     num_frames: int = 0
-    
-    # RL dataset configuration (for RL prompt datasets)
-    rl_dataset_path: str = ""  # Path to RL prompt dataset directory (defaults to data_path if not set)
-    rl_dataset_type: str = "text"  # "text" or "geneval"
-    rl_num_image_per_prompt: int = 4  # k parameter for KRepeatSampler (num_image_per_prompt)
 
     train_batch_size: int = 0
     num_latent_t: int = 0
@@ -1132,9 +861,6 @@ class TrainingArgs(FastVideoArgs):
     last_step_only: bool = False  # Only use the last timestep for training
     context_noise: int = 0  # Context noise level for cache updates
 
-    # Nested RL configuration
-    rl_args: RLArgs = dataclasses.field(default_factory=RLArgs)
-
     @classmethod
     def from_cli_args(cls, args: argparse.Namespace) -> "TrainingArgs":
         provided_args = clean_cli_args(args)
@@ -1159,25 +885,6 @@ class TrainingArgs(FastVideoArgs):
                 kwargs[attr] = WorkloadType.from_string(
                     workload_type_value) if isinstance(
                         workload_type_value, str) else workload_type_value
-            elif attr == 'rl_args':
-                # Construct nested RLArgs from CLI arguments
-                rl_kwargs = {}
-                for rl_field in dataclasses.fields(RLArgs):
-                    rl_attr = rl_field.name
-                    if hasattr(args, rl_attr):
-                        value = getattr(args, rl_attr)
-                        # Special handling for reward_models: parse JSON string to dict
-                        if rl_attr == 'reward_models' and isinstance(value, str):
-                            rl_kwargs[rl_attr] = json.loads(value) if value else {}
-                        else:
-                            rl_kwargs[rl_attr] = value
-                    else:
-                        # Use default value from RLArgs
-                        if rl_field.default_factory is not dataclasses.MISSING:
-                            rl_kwargs[rl_attr] = rl_field.default_factory()
-                        elif rl_field.default is not dataclasses.MISSING:
-                            rl_kwargs[rl_attr] = rl_field.default
-                kwargs[attr] = RLArgs(**rl_kwargs)
             # Use getattr with default value from the dataclass for potentially missing attributes
             else:
                 # Get the field to check its default value
@@ -1207,26 +914,11 @@ class TrainingArgs(FastVideoArgs):
         parser.add_argument("--data-path",
                             type=str,
                             required=True,
-                            help="Path to parquet files (or RL prompt dataset directory for RL training)")
+                            help="Path to parquet files")
         parser.add_argument("--dataloader-num-workers",
                             type=int,
                             required=True,
                             help="Number of workers for dataloader")
-        
-        # RL dataset arguments (optional, defaults to data_path)
-        parser.add_argument("--rl-dataset-path",
-                            type=str,
-                            default="",
-                            help="Path to RL prompt dataset directory (defaults to --data-path if not set)")
-        parser.add_argument("--rl-dataset-type",
-                            type=str,
-                            default="text",
-                            choices=["text", "geneval"],
-                            help="RL dataset type: 'text' for TextPromptDataset or 'geneval' for GenevalPromptDataset")
-        parser.add_argument("--rl-num-image-per-prompt",
-                            type=int,
-                            default=4,
-                            help="Number of times to repeat each prompt (k parameter for KRepeatSampler)")
         parser.add_argument("--num-height",
                             type=int,
                             required=True,
@@ -1591,9 +1283,6 @@ class TrainingArgs(FastVideoArgs):
                             default=TrainingArgs.context_noise,
                             help="Context noise level for cache updates")
 
-        # RL (Reinforcement Learning) arguments
-        RLArgs.add_cli_args(parser)
-
         return parser
 
 

fastvideo/hooks/hooks.py

@@ -1,122 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-import functools
-from typing import Any
-from torch import nn
-
-
-class ForwardHook:
-    """
-    Base class for forward hooks.
-    Hooks are used in the way:
-        modified_args, modified_kwargs = hook.pre_forward(module, *args, **kwargs)
-        output = module.forward(*modified_args, **modified_kwargs)
-        modified_output = hook.post_forward(module, output)
-    """
-
-    @classmethod
-    def name(cls) -> str:
-        raise NotImplementedError
-
-    def on_attach(self, module: nn.Module):  # noqa: B027
-        """Called once when the hook is attached to the module."""
-        pass
-
-    def on_detach(self, module: nn.Module):  # noqa: B027
-        """
-        Called once when the hook is detached from the module.
-        Note: this function is not guaranteed to be called if the module is
-              deleted before the hook is detached.
-        """
-        pass
-
-    def pre_forward(self, module: nn.Module, *args,
-                    **kwargs) -> tuple[tuple[Any, ...], dict[str, Any]]:
-        """Called before the module's forward method is executed."""
-        return args, kwargs
-
-    def post_forward(self, module: nn.Module, output: Any) -> Any:
-        """Called after the module's forward method is executed."""
-        return output
-
-
-class ModuleHookManager:
-    module_hook_attribute = "_hook_manager"
-
-    def __init__(self, module: nn.Module):
-        self.module = module
-        self.forward_hooks: dict[str, ForwardHook] = {}
-        self.original_forward = module.forward
-
-    @classmethod
-    def get_from(cls, module: nn.Module) -> "ModuleHookManager | None":
-        if hasattr(module, cls.module_hook_attribute):
-            return getattr(module, cls.module_hook_attribute)
-        return None
-
-    @classmethod
-    def get_from_or_default(cls, module: nn.Module) -> "ModuleHookManager":
-        if not hasattr(module, cls.module_hook_attribute):
-            setattr(module, cls.module_hook_attribute, cls(module))
-
-            def forward_hook_wrapper(mod: nn.Module, *args, **kwargs):
-                manager: ModuleHookManager = getattr(mod,
-                                                     cls.module_hook_attribute)
-                for hook in manager.forward_hooks.values():
-                    args, kwargs = hook.pre_forward(mod, *args, **kwargs)
-                output = manager.original_forward(*args, **kwargs)
-                for hook in reversed(manager.forward_hooks.values()):
-                    output = hook.post_forward(mod, output)
-                return output
-
-            module.forward = functools.partial(forward_hook_wrapper, module)
-
-        return getattr(module, cls.module_hook_attribute)
-
-    @staticmethod
-    def remove_from_manager(module: nn.Module) -> None:
-        if hasattr(module, ModuleHookManager.module_hook_attribute):
-            manager: ModuleHookManager = getattr(
-                module, ModuleHookManager.module_hook_attribute)
-            module.forward = manager.original_forward
-            delattr(module, ModuleHookManager.module_hook_attribute)
-
-    def _check_manager_attached(self) -> None:
-        if not hasattr(self.module, self.module_hook_attribute):
-            raise ValueError("ModuleHookManager is not attached to the module.")
-        if getattr(self.module, self.module_hook_attribute) is not self:
-            raise ValueError(
-                "ModuleHookManager attached to the module is different.")
-
-    def append_forward_hook(self, hook: ForwardHook):
-        self._check_manager_attached()
-        if hook.name() in self.forward_hooks:
-            raise ValueError(
-                f"Hook with name {hook.name()} is already registered.")
-        # after python 3.7, dicts maintain insertion order
-        self.forward_hooks[hook.name()] = hook
-        hook.on_attach(self.module)
-
-    def replace_forward_hook(self,
-                             hook_name: str,
-                             new_hook: ForwardHook,
-                             run_on_attach: bool = True):
-        self._check_manager_attached()
-        if hook_name not in self.forward_hooks:
-            raise ValueError(f"No hook with name {hook_name} found.")
-        old_hook = self.forward_hooks[hook_name]
-        if run_on_attach:
-            old_hook.on_detach(self.module)
-        self.forward_hooks[hook_name] = new_hook
-        new_hook.on_attach(self.module)
-
-    def remove_forward_hook(self, hook_name: str, run_detach: bool = True):
-        self._check_manager_attached()
-        if hook_name not in self.forward_hooks:
-            raise ValueError(f"No hook with name {hook_name} found.")
-        if run_detach:
-            self.forward_hooks[hook_name].on_detach(self.module)
-        del self.forward_hooks[hook_name]
-
-    def get_forward_hook(self, hook_name: str) -> ForwardHook | None:
-        return self.forward_hooks.get(hook_name, None)

fastvideo/hooks/layerwise_offload.py

@@ -1,164 +0,0 @@
-from contextlib import contextmanager
-from typing import Any
-import torch
-from torch import nn
-from fastvideo.hooks.hooks import ForwardHook, ModuleHookManager
-from fastvideo.logger import init_logger
-
-logger = init_logger(__name__)
-
-
-def _tensor_placeholder(tensor: torch.Tensor,
-                        device: torch.device) -> torch.Tensor:
-    """Create a rank-preserving empty placeholder on the specified device."""
-    shape = (0, ) if tensor.ndim <= 0 else (0, ) * tensor.ndim
-    return torch.empty(shape, device=device, dtype=tensor.dtype)
-
-
-class LayerwiseOffloadState:
-
-    def __init__(
-        self,
-        async_copy_stream: torch.cuda.Stream,
-        device: torch.device,
-        next_state: "LayerwiseOffloadState | None" = None,
-    ) -> None:
-        self.async_copy_stream = async_copy_stream
-        self.next_state = next_state
-        self.gpu_named_parameters: dict[str, torch.Tensor] = {}
-        self.cpu_named_parameters: dict[str, torch.Tensor] = {}
-        self.module_ref: nn.Module = None  # type: ignore
-        self.device: torch.device = device
-
-    def _will_offload(self, name: str) -> bool:
-        return True
-
-    @torch.compiler.disable
-    def on_init(self, module: nn.Module):
-        self.module_ref = module
-        for name, param in self.module_ref.named_parameters():
-            if self._will_offload(name):
-                self.cpu_named_parameters[name] = (
-                    param.data.detach().to("cpu").pin_memory())
-                param.data = _tensor_placeholder(param.data, self.device)
-
-    @torch.compiler.disable
-    def wait_and_replace_params(self):
-        torch.cuda.current_stream().wait_stream(self.async_copy_stream)
-        # now gpu_named_parameters are ready
-        for name, param in self.module_ref.named_parameters():
-            if not self._will_offload(name):
-                continue
-            if name not in self.gpu_named_parameters:
-                # first load with blocking load
-                self.gpu_named_parameters[name] = self.cpu_named_parameters[
-                    name].to(self.device)
-            param.data = self.gpu_named_parameters[name]
-
-    @torch.compiler.disable
-    def prefetch_params(self):
-        compute_stream = torch.cuda.current_stream()
-        with torch.cuda.stream(self.async_copy_stream):
-            for name, param in self.module_ref.named_parameters():
-                if not self._will_offload(name):
-                    continue
-                assert name not in self.gpu_named_parameters
-                gpu_param = self.cpu_named_parameters[name].to(
-                    self.device, non_blocking=True)
-                gpu_param.record_stream(
-                    compute_stream
-                )  # ensure tensor will not be freed until forward is completed
-                self.gpu_named_parameters[name] = gpu_param
-
-    @torch.compiler.disable
-    def release_gpu_params(self):
-        for name, param in self.module_ref.named_parameters():
-            if self._will_offload(name):
-                param.data = _tensor_placeholder(param.data, self.device)
-                del self.gpu_named_parameters[name]
-        assert len(self.gpu_named_parameters) == 0
-
-
-class LayerwiseOffloadHook(ForwardHook):
-    """A hook that enables layerwise CPU offloading during forward pass."""
-
-    def __init__(self, state: LayerwiseOffloadState) -> None:
-        self.state = state
-
-    def on_attach(self, module: nn.Module):
-        self.state.on_init(module)  # pyright: ignore
-
-    def on_detach(self, module: nn.Module):
-        named_parameters = dict(module.named_parameters())
-        for name, cpu_tensor in self.state.cpu_named_parameters.items():
-            if name not in self.state.gpu_named_parameters:
-                if name in named_parameters:
-                    named_parameters[name].data = cpu_tensor.to(
-                        device=self.state.device)
-                else:
-                    logger.warning(
-                        "Parameter {} not found in module during detachment.",
-                        name,
-                    )
-
-    @classmethod
-    def name(cls) -> str:
-        return "LayerwiseOffloadHook"
-
-    def pre_forward(self, module: nn.Module, *args, **kwargs):
-        self.state.wait_and_replace_params()  # pyright: ignore
-        if self.state.next_state is not None:
-            self.state.next_state.prefetch_params()  # pyright: ignore
-        return args, kwargs
-
-    def post_forward(self, module: torch.nn.Module, output: Any):
-        self.state.release_gpu_params()  # pyright: ignore
-        return output
-
-    @contextmanager
-    def mutate_params_scope(self):
-        try:
-            # load params to GPU and keep them there
-            self.state.wait_and_replace_params()  # pyright: ignore
-            yield
-        finally:
-            # instead of releasing, we should overwrite the original params since they have been modified
-            self.state.cpu_named_parameters.clear()
-            self.state.gpu_named_parameters.clear()
-            self.state.on_init(self.state.module_ref)  # pyright: ignore
-
-
-def enable_layerwise_offload(model: nn.Module, is_replace: bool = False):
-    if torch.cuda.is_available():
-        device = torch.device("cuda", torch.cuda.current_device())
-    else:
-        logger.warning(
-            "CUDA is not available. Layerwise offloading is disabled.")
-        return
-    state_list = []
-    async_stream = torch.cuda.Stream()
-    for name, submodule in model.named_children():
-        if isinstance(submodule, nn.ModuleList):
-            for idx, module_entry in enumerate(submodule):
-                state = LayerwiseOffloadState(async_copy_stream=async_stream,
-                                              device=device)
-                state_list.append(state)
-                hook_mgr = ModuleHookManager.get_from_or_default(module_entry)
-                hook = LayerwiseOffloadHook(state)
-                if is_replace:
-                    existing_hook = hook_mgr.forward_hooks.get(hook.name())
-                    if existing_hook is not None:
-                        hook_mgr.replace_forward_hook(hook.name(), hook)
-                    else:
-                        raise AssertionError(
-                            f"Expect hook exists in {name} for replacement.")
-                else:
-                    hook_mgr.append_forward_hook(hook)
-            break
-    if len(state_list) == 0:
-        raise ValueError(
-            "No nn.ModuleList found in the model for layerwise offloading.")
-
-    # circular linking of states
-    for i in range(len(state_list)):
-        state_list[i].next_state = state_list[(i + 1) % len(state_list)]

fastvideo/layers/linear.py

@@ -7,20 +7,13 @@ import torch
 import torch.nn.functional as F
 from torch.nn.parameter import Parameter
 
-from fastvideo.distributed import (
-    divide,
-    get_tp_rank,
-    get_tp_world_size,
-    split_tensor_along_last_dim,
-    tensor_model_parallel_all_gather,
-    tensor_model_parallel_all_reduce,
-)
-from fastvideo.layers.quantization.base_config import (
-    QuantizationConfig,
-    QuantizeMethodBase,
-)
+from fastvideo.distributed import (divide, get_tp_rank, get_tp_world_size,
+                                   split_tensor_along_last_dim,
+                                   tensor_model_parallel_all_gather,
+                                   tensor_model_parallel_all_reduce)
+from fastvideo.layers.quantization.base_config import (QuantizationConfig,
+                                                       QuantizeMethodBase)
 from fastvideo.logger import init_logger
-
 # yapf: disable
 from fastvideo.models.parameter import (BasevLLMParameter,
                                         BlockQuantScaleParameter,
@@ -34,22 +27,12 @@ from fastvideo.models.utils import set_weight_attrs
 logger = init_logger(__name__)
 
 WEIGHT_LOADER_V2_SUPPORTED = [
-    "CompressedTensorsLinearMethod",
-    "AWQMarlinLinearMethod",
-    "AWQLinearMethod",
-    "GPTQMarlinLinearMethod",
-    "Fp8LinearMethod",
-    "MarlinLinearMethod",
-    "QQQLinearMethod",
-    "GPTQMarlin24LinearMethod",
-    "TPUInt8LinearMethod",
-    "GPTQLinearMethod",
-    "FBGEMMFp8LinearMethod",
-    "ModelOptFp8LinearMethod",
-    "IPEXAWQLinearMethod",
-    "IPEXGPTQLinearMethod",
-    "HQQMarlinMethod",
-    "QuarkLinearMethod",
+    "CompressedTensorsLinearMethod", "AWQMarlinLinearMethod", "AWQLinearMethod",
+    "GPTQMarlinLinearMethod", "Fp8LinearMethod", "MarlinLinearMethod",
+    "QQQLinearMethod", "GPTQMarlin24LinearMethod", "TPUInt8LinearMethod",
+    "GPTQLinearMethod", "FBGEMMFp8LinearMethod", "ModelOptFp8LinearMethod",
+    "IPEXAWQLinearMethod", "IPEXGPTQLinearMethod", "HQQMarlinMethod",
+    "QuarkLinearMethod"
 ]
 
 
@@ -58,8 +41,8 @@ def adjust_scalar_to_fused_array(
         shard_id: str | int) -> tuple[torch.Tensor, torch.Tensor]:
     """For fused modules (QKV and MLP) we have an array of length
     N that holds 1 scale for each "logical" matrix. So the param
-    is an array of length N. The loaded_weight corresponds to
-    one of the shards on disk. Here, we slice the param based on
+    is an array of length N. The loaded_weight corresponds to 
+    one of the shards on disk. Here, we slice the param based on 
     the shard_id for loading.
     """
     qkv_idxs = {"q": 0, "k": 1, "v": 2}
@@ -82,23 +65,18 @@ class LinearMethodBase(QuantizeMethodBase):
     """Base class for different (maybe quantized) linear methods."""
 
     @abstractmethod
-    def create_weights(
-        self,
-        layer: torch.nn.Module,
-        input_size_per_partition: int,
-        output_partition_sizes: list[int],
-        input_size: int,
-        output_size: int,
-        params_dtype: torch.dtype,
-        **extra_weight_attrs,
-    ) -> None:
-        """Create weights for a linear layer.
+    def create_weights(self, layer: torch.nn.Module,
+                       input_size_per_partition: int,
+                       output_partition_sizes: list[int], input_size: int,
+                       output_size: int, params_dtype: torch.dtype,
+                       **extra_weight_attrs) -> None:
+        """Create weights for a linear layer. 
            The weights will be set as attributes of the layer.
 
         Args:
             layer: The layer that is using the LinearMethodBase factory.
             input_size_per_partition: Size of the weight input dim on rank X.
-            output_partition_sizes: Sizes of the output dim of each logical
+            output_partition_sizes: Sizes of the output dim of each logical 
                 weight on rank X. E.g., output_partition_sizes for QKVLinear
                 is a list contains the width of Wq, Wk, Wv on rank X.
             input_size: Size of the input dim of the weight across all ranks.
@@ -108,12 +86,10 @@ class LinearMethodBase(QuantizeMethodBase):
         raise NotImplementedError
 
     @abstractmethod
-    def apply(
-        self,
-        layer: torch.nn.Module,
-        x: torch.Tensor,
-        bias: torch.Tensor | None = None,
-    ) -> torch.Tensor:
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: torch.Tensor | None = None) -> torch.Tensor:
         """Apply the weights in layer to the input tensor.
         Expects create_weights to have been called before on the layer."""
         raise NotImplementedError
@@ -122,37 +98,28 @@ class LinearMethodBase(QuantizeMethodBase):
 class UnquantizedLinearMethod(LinearMethodBase):
     """Linear method without quantization."""
 
-    def create_weights(
-        self,
-        layer: torch.nn.Module,
-        input_size_per_partition: int,
-        output_partition_sizes: list[int],
-        input_size: int,
-        output_size: int,
-        params_dtype: torch.dtype,
-        **extra_weight_attrs,
-    ) -> None:
-        weight = Parameter(
-            torch.empty(
-                sum(output_partition_sizes),
-                input_size_per_partition,
-                dtype=params_dtype,
-            ),
-            requires_grad=False,
-        )
+    def create_weights(self, layer: torch.nn.Module,
+                       input_size_per_partition: int,
+                       output_partition_sizes: list[int], input_size: int,
+                       output_size: int, params_dtype: torch.dtype,
+                       **extra_weight_attrs) -> None:
+        weight = Parameter(torch.empty(
+            sum(output_partition_sizes),
+            input_size_per_partition,
+            dtype=params_dtype,
+        ),
+                           requires_grad=False)
         set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
         layer.register_parameter("weight", weight)
         set_weight_attrs(weight, extra_weight_attrs)
 
-    def apply(
-        self,
-        layer: torch.nn.Module,
-        x: torch.Tensor,
-        bias: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        output = (
-            F.linear(x, layer.weight, bias) if torch.cuda.is_available()
-            or bias is None else F.linear(x, layer.weight, bias.to(x.dtype))
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: torch.Tensor | None = None) -> torch.Tensor:
+        output = F.linear(x, layer.weight, bias) if torch.cuda.is_available(
+        ) or bias is None else F.linear(
+            x, layer.weight, bias.to(x.dtype)
         )  # NOTE: this line assumes that we are using amp when using cuda and is needed to account for the fact that amp isn't supported in mps
         return output
 
@@ -190,8 +157,8 @@ class LinearBase(torch.nn.Module):
         self.quant_config = quant_config
         self.prefix = prefix
         if quant_config is None:
-            self.quant_method: QuantizeMethodBase | None = (
-                UnquantizedLinearMethod())
+            self.quant_method: QuantizeMethodBase | None = UnquantizedLinearMethod(
+            )
         else:
             self.quant_method = quant_config.get_quant_method(self,
                                                               prefix=prefix)
@@ -214,36 +181,29 @@ class ReplicatedLinear(LinearBase):
                         (e.g. model.layers.0.qkv_proj)
     """
 
-    def __init__(
-        self,
-        input_size: int,
-        output_size: int,
-        bias: bool = True,
-        skip_bias_add: bool = False,
-        params_dtype: torch.dtype | None = None,
-        quant_config: QuantizationConfig | None = None,
-        prefix: str = "",
-    ):
-        super().__init__(
-            input_size,
-            output_size,
-            skip_bias_add,
-            params_dtype,
-            quant_config,
-            prefix=prefix,
-        )
+    def __init__(self,
+                 input_size: int,
+                 output_size: int,
+                 bias: bool = True,
+                 skip_bias_add: bool = False,
+                 params_dtype: torch.dtype | None = None,
+                 quant_config: QuantizationConfig | None = None,
+                 prefix: str = ""):
+        super().__init__(input_size,
+                         output_size,
+                         skip_bias_add,
+                         params_dtype,
+                         quant_config,
+                         prefix=prefix)
 
         # All the linear layer supports quant method.
         assert self.quant_method is not None
-        self.quant_method.create_weights(
-            self,
-            self.input_size,
-            [self.output_size],
-            self.input_size,
-            self.output_size,
-            self.params_dtype,
-            weight_loader=self.weight_loader,
-        )
+        self.quant_method.create_weights(self,
+                                         self.input_size, [self.output_size],
+                                         self.input_size,
+                                         self.output_size,
+                                         self.params_dtype,
+                                         weight_loader=self.weight_loader)
 
         if bias:
             self.bias = Parameter(
@@ -251,13 +211,10 @@ class ReplicatedLinear(LinearBase):
                     self.output_size,
                     dtype=self.params_dtype,
                 ))
-            set_weight_attrs(
-                self.bias,
-                {
-                    "output_dim": 0,
-                    "weight_loader": self.weight_loader,
-                },
-            )
+            set_weight_attrs(self.bias, {
+                "output_dim": 0,
+                "weight_loader": self.weight_loader,
+            })
         else:
             self.register_parameter("bias", None)
 
@@ -308,21 +265,19 @@ class ColumnParallelLinear(LinearBase):
         output_sizes: list of output sizes packed into one output, like for QKV
                        the list would be size 3.
         prefix: The name of the layer in the state dict, including all parents
-                        (e.g. model.layers.0.qkv_proj)
+                        (e.g. model.layers.0.qkv_proj) 
     """
 
-    def __init__(
-        self,
-        input_size: int,
-        output_size: int,
-        bias: bool = True,
-        gather_output: bool = False,
-        skip_bias_add: bool = False,
-        params_dtype: torch.dtype | None = None,
-        quant_config: QuantizationConfig | None = None,
-        output_sizes: list[int] | None = None,
-        prefix: str = "",
-    ):
+    def __init__(self,
+                 input_size: int,
+                 output_size: int,
+                 bias: bool = True,
+                 gather_output: bool = False,
+                 skip_bias_add: bool = False,
+                 params_dtype: torch.dtype | None = None,
+                 quant_config: QuantizationConfig | None = None,
+                 output_sizes: list[int] | None = None,
+                 prefix: str = ""):
         # Divide the weight matrix along the last dimension.
         self.tp_size = get_tp_world_size()
         self.input_size_per_partition = input_size
@@ -335,14 +290,8 @@ class ColumnParallelLinear(LinearBase):
                 for output_size in self.output_sizes
             ]
 
-        super().__init__(
-            input_size,
-            output_size,
-            skip_bias_add,
-            params_dtype,
-            quant_config,
-            prefix,
-        )
+        super().__init__(input_size, output_size, skip_bias_add, params_dtype,
+                         quant_config, prefix)
 
         self.gather_output = gather_output
 
@@ -359,21 +308,17 @@ class ColumnParallelLinear(LinearBase):
             params_dtype=self.params_dtype,
             weight_loader=(
                 self.weight_loader_v2 if self.quant_method.__class__.__name__
-                in WEIGHT_LOADER_V2_SUPPORTED else self.weight_loader),
-        )
+                in WEIGHT_LOADER_V2_SUPPORTED else self.weight_loader))
         if bias:
             self.bias = Parameter(
                 torch.empty(
                     self.output_size_per_partition,
                     dtype=params_dtype,
                 ))
-            set_weight_attrs(
-                self.bias,
-                {
-                    "output_dim": 0,
-                    "weight_loader": self.weight_loader,
-                },
-            )
+            set_weight_attrs(self.bias, {
+                "output_dim": 0,
+                "weight_loader": self.weight_loader,
+            })
         else:
             self.register_parameter("bias", None)
 
@@ -456,37 +401,32 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
                         (e.g. model.layers.0.qkv_proj)
     """
 
-    def __init__(
-        self,
-        input_size: int,
-        output_sizes: list[int],
-        bias: bool = True,
-        gather_output: bool = False,
-        skip_bias_add: bool = False,
-        params_dtype: torch.dtype | None = None,
-        quant_config: QuantizationConfig | None = None,
-        prefix: str = "",
-    ):
+    def __init__(self,
+                 input_size: int,
+                 output_sizes: list[int],
+                 bias: bool = True,
+                 gather_output: bool = False,
+                 skip_bias_add: bool = False,
+                 params_dtype: torch.dtype | None = None,
+                 quant_config: QuantizationConfig | None = None,
+                 prefix: str = ""):
         self.output_sizes = output_sizes
         tp_size = get_tp_world_size()
         assert all(output_size % tp_size == 0 for output_size in output_sizes)
-        super().__init__(
-            input_size=input_size,
-            output_size=sum(output_sizes),
-            bias=bias,
-            gather_output=gather_output,
-            skip_bias_add=skip_bias_add,
-            params_dtype=params_dtype,
-            quant_config=quant_config,
-            prefix=prefix,
-        )
+        super().__init__(input_size=input_size,
+                         output_size=sum(output_sizes),
+                         bias=bias,
+                         gather_output=gather_output,
+                         skip_bias_add=skip_bias_add,
+                         params_dtype=params_dtype,
+                         quant_config=quant_config,
+                         prefix=prefix)
+
+    def weight_loader(self,
+                      param: Parameter,
+                      loaded_weight: torch.Tensor,
+                      loaded_shard_id: int | None = None) -> None:
 
-    def weight_loader(
-        self,
-        param: Parameter,
-        loaded_weight: torch.Tensor,
-        loaded_shard_id: int | None = None,
-    ) -> None:
         param_data = param.data
         output_dim = getattr(param, "output_dim", None)
         # Special case for AQLM codebooks.
@@ -578,22 +518,20 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
             # Special case for Quantization.
             # If quantized, we need to adjust the offset and size to account
             # for the packing.
-            if (isinstance(param, PackedColumnParameter | PackedvLLMParameter)
-                    and param.packed_dim == param.output_dim):
-                shard_size, shard_offset = (
+            if isinstance(param, PackedColumnParameter | PackedvLLMParameter
+                          ) and param.packed_dim == param.output_dim:
+                shard_size, shard_offset = \
                     param.adjust_shard_indexes_for_packing(
-                        shard_size=shard_size, shard_offset=shard_offset))
+                    shard_size=shard_size, shard_offset=shard_offset)
 
             loaded_weight_shard = loaded_weight.narrow(param.output_dim,
                                                        shard_offset, shard_size)
             self.weight_loader_v2(param, loaded_weight_shard, shard_id)
 
-    def weight_loader_v2(
-        self,
-        param: BasevLLMParameter,
-        loaded_weight: torch.Tensor,
-        loaded_shard_id: int | None = None,
-    ) -> None:
+    def weight_loader_v2(self,
+                         param: BasevLLMParameter,
+                         loaded_weight: torch.Tensor,
+                         loaded_shard_id: int | None = None) -> None:
         if loaded_shard_id is None:
             if isinstance(param, PerTensorScaleParameter):
                 param.load_merged_column_weight(loaded_weight=loaded_weight,
@@ -630,12 +568,10 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
             shard_offset = sum(self.output_sizes[:loaded_shard_id]) // tp_size
             shard_size = self.output_sizes[loaded_shard_id] // tp_size
 
-        param.load_merged_column_weight(
-            loaded_weight=loaded_weight,
-            shard_id=loaded_shard_id,
-            shard_offset=shard_offset,
-            shard_size=shard_size,
-        )
+        param.load_merged_column_weight(loaded_weight=loaded_weight,
+                                        shard_id=loaded_shard_id,
+                                        shard_offset=shard_offset,
+                                        shard_size=shard_size)
 
 
 class QKVParallelLinear(ColumnParallelLinear):
@@ -664,18 +600,16 @@ class QKVParallelLinear(ColumnParallelLinear):
                         (e.g. model.layers.0.qkv_proj)
     """
 
-    def __init__(
-        self,
-        hidden_size: int,
-        head_size: int,
-        total_num_heads: int,
-        total_num_kv_heads: int | None = None,
-        bias: bool = True,
-        skip_bias_add: bool = False,
-        params_dtype: torch.dtype | None = None,
-        quant_config: QuantizationConfig | None = None,
-        prefix: str = "",
-    ):
+    def __init__(self,
+                 hidden_size: int,
+                 head_size: int,
+                 total_num_heads: int,
+                 total_num_kv_heads: int | None = None,
+                 bias: bool = True,
+                 skip_bias_add: bool = False,
+                 params_dtype: torch.dtype | None = None,
+                 quant_config: QuantizationConfig | None = None,
+                 prefix: str = ""):
         self.hidden_size = hidden_size
         self.head_size = head_size
         self.total_num_heads = total_num_heads
@@ -692,31 +626,29 @@ class QKVParallelLinear(ColumnParallelLinear):
             self.num_kv_heads = divide(self.total_num_kv_heads, tp_size)
             self.num_kv_head_replicas = 1
         input_size = self.hidden_size
-        output_size = ((self.num_heads + 2 * self.num_kv_heads) * tp_size *
-                       self.head_size)
+        output_size = (self.num_heads +
+                       2 * self.num_kv_heads) * tp_size * self.head_size
         self.output_sizes = [
             self.num_heads * self.head_size * tp_size,  # q_proj
             self.num_kv_heads * self.head_size * tp_size,  # k_proj
-            self.num_kv_heads * self.head_size * tp_size,  # v_proj
+            self.num_kv_heads * self.head_size * tp_size,  # v_proj 
         ]
 
-        super().__init__(
-            input_size=input_size,
-            output_size=output_size,
-            bias=bias,
-            gather_output=False,
-            skip_bias_add=skip_bias_add,
-            params_dtype=params_dtype,
-            quant_config=quant_config,
-            prefix=prefix,
-        )
+        super().__init__(input_size=input_size,
+                         output_size=output_size,
+                         bias=bias,
+                         gather_output=False,
+                         skip_bias_add=skip_bias_add,
+                         params_dtype=params_dtype,
+                         quant_config=quant_config,
+                         prefix=prefix)
 
     def _get_shard_offset_mapping(self, loaded_shard_id: str) -> int | None:
         shard_offset_mapping = {
             "q": 0,
             "k": self.num_heads * self.head_size,
             "v": (self.num_heads + self.num_kv_heads) * self.head_size,
-            "total": (self.num_heads + 2 * self.num_kv_heads) * self.head_size,
+            "total": (self.num_heads + 2 * self.num_kv_heads) * self.head_size
         }
         return shard_offset_mapping.get(loaded_shard_id)
 
@@ -731,7 +663,7 @@ class QKVParallelLinear(ColumnParallelLinear):
     def _load_fused_module_from_checkpoint(self, param: BasevLLMParameter,
                                            loaded_weight: torch.Tensor):
         """
-        Handle special case for models where QKV layers are already
+        Handle special case for models where QKV layers are already 
         fused on disk. In this case, we have no shard id. This function
         determmines the shard id by splitting these layers and then calls
         the weight loader using the shard id.
@@ -742,39 +674,31 @@ class QKVParallelLinear(ColumnParallelLinear):
         shard_offsets = [
             # (shard_id, shard_offset, shard_size)
             ("q", 0, self.total_num_heads * self.head_size),
-            (
-                "k",
-                self.total_num_heads * self.head_size,
-                self.total_num_kv_heads * self.head_size,
-            ),
-            (
-                "v",
-                (self.total_num_heads + self.total_num_kv_heads) *
-                self.head_size,
-                self.total_num_kv_heads * self.head_size,
-            ),
+            ("k", self.total_num_heads * self.head_size,
+             self.total_num_kv_heads * self.head_size),
+            ("v",
+             (self.total_num_heads + self.total_num_kv_heads) * self.head_size,
+             self.total_num_kv_heads * self.head_size),
         ]
 
         for shard_id, shard_offset, shard_size in shard_offsets:
             # Special case for Quantization.
             # If quantized, we need to adjust the offset and size to account
             # for the packing.
-            if (isinstance(param, PackedColumnParameter | PackedvLLMParameter)
-                    and param.packed_dim == param.output_dim):
-                shard_size, shard_offset = (
+            if isinstance(param, PackedColumnParameter | PackedvLLMParameter
+                          ) and param.packed_dim == param.output_dim:
+                shard_size, shard_offset = \
                     param.adjust_shard_indexes_for_packing(
-                        shard_size=shard_size, shard_offset=shard_offset))
+                    shard_size=shard_size, shard_offset=shard_offset)
 
             loaded_weight_shard = loaded_weight.narrow(param.output_dim,
                                                        shard_offset, shard_size)
             self.weight_loader_v2(param, loaded_weight_shard, shard_id)
 
-    def weight_loader_v2(
-        self,
-        param: BasevLLMParameter,
-        loaded_weight: torch.Tensor,
-        loaded_shard_id: str | None = None,
-    ):
+    def weight_loader_v2(self,
+                         param: BasevLLMParameter,
+                         loaded_weight: torch.Tensor,
+                         loaded_shard_id: str | None = None):
         if loaded_shard_id is None:  # special case for certain models
             if isinstance(param, PerTensorScaleParameter):
                 param.load_qkv_weight(loaded_weight=loaded_weight, shard_id=0)
@@ -791,20 +715,17 @@ class QKVParallelLinear(ColumnParallelLinear):
         shard_offset = self._get_shard_offset_mapping(loaded_shard_id)
         shard_size = self._get_shard_size_mapping(loaded_shard_id)
 
-        param.load_qkv_weight(
-            loaded_weight=loaded_weight,
-            num_heads=self.num_kv_head_replicas,
-            shard_id=loaded_shard_id,
-            shard_offset=shard_offset,
-            shard_size=shard_size,
-        )
+        param.load_qkv_weight(loaded_weight=loaded_weight,
+                              num_heads=self.num_kv_head_replicas,
+                              shard_id=loaded_shard_id,
+                              shard_offset=shard_offset,
+                              shard_size=shard_size)
+
+    def weight_loader(self,
+                      param: Parameter,
+                      loaded_weight: torch.Tensor,
+                      loaded_shard_id: str | None = None):
 
-    def weight_loader(
-        self,
-        param: Parameter,
-        loaded_weight: torch.Tensor,
-        loaded_shard_id: str | None = None,
-    ):
         param_data = param.data
         output_dim = getattr(param, "output_dim", None)
         # Special case for AQLM codebooks.
@@ -827,20 +748,14 @@ class QKVParallelLinear(ColumnParallelLinear):
             shard_offsets = [
                 # (shard_id, shard_offset, shard_size)
                 ("q", 0, self.total_num_heads * self.head_size),
-                (
-                    "k",
-                    self.total_num_heads * self.head_size,
-                    self.total_num_kv_heads * self.head_size,
-                ),
-                (
-                    "v",
-                    (self.total_num_heads + self.total_num_kv_heads) *
-                    self.head_size,
-                    self.total_num_kv_heads * self.head_size,
-                ),
+                ("k", self.total_num_heads * self.head_size,
+                 self.total_num_kv_heads * self.head_size),
+                ("v", (self.total_num_heads + self.total_num_kv_heads) *
+                 self.head_size, self.total_num_kv_heads * self.head_size),
             ]
 
             for shard_id, shard_offset, shard_size in shard_offsets:
+
                 loaded_weight_shard = loaded_weight.narrow(
                     output_dim, shard_offset, shard_size)
                 self.weight_loader(param, loaded_weight_shard, shard_id)
@@ -928,18 +843,16 @@ class RowParallelLinear(LinearBase):
         quant_config: Quantization configure.
     """
 
-    def __init__(
-        self,
-        input_size: int,
-        output_size: int,
-        bias: bool = True,
-        input_is_parallel: bool = True,
-        skip_bias_add: bool = False,
-        params_dtype: torch.dtype | None = None,
-        reduce_results: bool = True,
-        quant_config: QuantizationConfig | None = None,
-        prefix: str = "",
-    ):
+    def __init__(self,
+                 input_size: int,
+                 output_size: int,
+                 bias: bool = True,
+                 input_is_parallel: bool = True,
+                 skip_bias_add: bool = False,
+                 params_dtype: torch.dtype | None = None,
+                 reduce_results: bool = True,
+                 quant_config: QuantizationConfig | None = None,
+                 prefix: str = ""):
         # Divide the weight matrix along the first dimension.
         self.tp_rank = get_tp_rank()
         self.tp_size = get_tp_world_size()
@@ -947,14 +860,8 @@ class RowParallelLinear(LinearBase):
         self.output_size_per_partition = output_size
         self.output_partition_sizes = [output_size]
 
-        super().__init__(
-            input_size,
-            output_size,
-            skip_bias_add,
-            params_dtype,
-            quant_config,
-            prefix,
-        )
+        super().__init__(input_size, output_size, skip_bias_add, params_dtype,
+                         quant_config, prefix)
 
         self.input_is_parallel = input_is_parallel
         self.reduce_results = reduce_results
@@ -969,8 +876,7 @@ class RowParallelLinear(LinearBase):
             params_dtype=self.params_dtype,
             weight_loader=(
                 self.weight_loader_v2 if self.quant_method.__class__.__name__
-                in WEIGHT_LOADER_V2_SUPPORTED else self.weight_loader),
-        )
+                in WEIGHT_LOADER_V2_SUPPORTED else self.weight_loader))
         if not reduce_results and (bias and not skip_bias_add):
             raise ValueError("When not reduce the results, adding bias to the "
                              "results can lead to incorrect results")
@@ -978,13 +884,10 @@ class RowParallelLinear(LinearBase):
         if bias:
             self.bias = Parameter(
                 torch.empty(self.output_size, dtype=params_dtype))
-            set_weight_attrs(
-                self.bias,
-                {
-                    "output_dim": 0,
-                    "weight_loader": self.weight_loader,
-                },
-            )
+            set_weight_attrs(self.bias, {
+                "output_dim": 0,
+                "weight_loader": self.weight_loader,
+            })
         else:
             self.register_parameter("bias", None)
 
@@ -1013,6 +916,7 @@ class RowParallelLinear(LinearBase):
 
     def weight_loader_v2(self, param: BasevLLMParameter,
                          loaded_weight: torch.Tensor):
+
         # Special case for loading scales off disk, which often do not
         # have a shape (such as in the case of AutoFP8).
         if len(loaded_weight.shape) == 0:

fastvideo/layers/quantization/__init__.py

@@ -2,7 +2,7 @@ from typing import Literal, get_args
 
 from fastvideo.layers.quantization.base_config import QuantizationConfig
 
-QuantizationMethods = Literal[None, "AbsMaxFP8"]
+QuantizationMethods = Literal[None]
 
 QUANTIZATION_METHODS: list[str] = list(get_args(QuantizationMethods))
 
@@ -50,12 +50,7 @@ def get_quantization_config(quantization: str) -> type[QuantizationConfig]:
     if quantization not in QUANTIZATION_METHODS:
         raise ValueError(f"Invalid quantization method: {quantization}")
 
-    # lazy import to avoid triggering `torch.compile` too early
-    from .absmax_fp8 import AbsMaxFP8Config
-
-    method_to_config: dict[str, type[QuantizationConfig]] = {
-        "AbsMaxFP8": AbsMaxFP8Config,
-    }
+    method_to_config: dict[str, type[QuantizationConfig]] = {}
     # Update the `method_to_config` with customized quantization methods.
     method_to_config.update(_CUSTOMIZED_METHOD_TO_QUANT_CONFIG)
 

fastvideo/layers/quantization/absmax_fp8.py

@@ -1,193 +0,0 @@
-from typing import Any
-import torch
-from fastvideo.distributed.parallel_state import get_tp_world_size
-from fastvideo.layers.linear import (
-    LinearBase,
-    LinearMethodBase,
-    MergedColumnParallelLinear,
-    QKVParallelLinear,
-)
-from fastvideo.layers.quantization import QuantizationMethods
-from fastvideo.layers.quantization.base_config import (
-    QuantizationConfig,
-    QuantizeMethodBase,
-)
-from fastvideo.models.utils import set_weight_attrs
-import torch.nn as nn
-
-
-class AbsMaxFP8Config(QuantizationConfig):
-    """
-    Config class for absmax float8_e4m3fn quantization.
-    Currently only support per-tensor quantization.
-    """
-
-    @staticmethod
-    def get_config_filenames() -> list[str]:
-        return []
-
-    @classmethod
-    def from_config(cls, config: dict[str, Any]) -> "QuantizationConfig":
-        return cls()
-
-    def get_name(self) -> QuantizationMethods:
-        return "AbsMaxFP8"
-
-    def get_supported_act_dtypes(self) -> list[torch.dtype]:
-        return [torch.bfloat16, torch.float16, torch.float32]
-
-    @classmethod
-    def get_min_capability(cls) -> int:
-        return 75
-
-    def get_quant_method(self, layer: torch.nn.Module,
-                         prefix: str) -> QuantizeMethodBase | None:
-        if isinstance(layer, LinearBase):
-            return AbsMaxFP8LinearMethod()
-        return None
-
-
-class AbsMaxFP8Parameter(nn.Parameter):
-
-    def weight_loader(
-        self,
-        param: nn.Parameter,
-        loaded_weight: torch.Tensor,
-        _share_id: str | None = None,
-    ) -> None:
-        if len(loaded_weight.shape) == 0:
-            loaded_weight = loaded_weight.reshape(1)
-
-        assert param.size() == loaded_weight.size(), (
-            f"Tried to load weights of size {loaded_weight.size()}"
-            f"to a parameter of size {param.size()}")
-        param.data.copy_(loaded_weight)
-
-
-class AbsMaxFP8MergedParameter(nn.Parameter):
-
-    def weight_loader(
-        self,
-        param: nn.Parameter,
-        loaded_weight: torch.Tensor,
-        share_id: str | int | None = None,
-    ) -> None:
-        # currently only support QKVParallelLinear and MergedColumnParallelLinear
-        output_partition_sizes: list[int] = self.output_partition_sizes
-        if share_id is None:
-            share_id = 0
-        if isinstance(share_id, str) and share_id in ["q", "k", "v"]:
-            # QKVParallelLinear case
-            share_idx = ["q", "k", "v"].index(share_id)
-            start_idx = sum(output_partition_sizes[:share_idx])
-            end_idx = start_idx + output_partition_sizes[share_idx]
-        elif isinstance(share_id, int):
-            # MergedColumnParallelLinear case
-            tp_size = get_tp_world_size()
-            if tp_size > 1:
-                # TODO: support this case
-                raise NotImplementedError(
-                    "AbsMaxFP8MergedParameter with integer share_id is not supported in tensor parallelism greater than 1 yet."
-                )
-            start_idx = sum(output_partition_sizes[:share_id])
-            end_idx = start_idx + output_partition_sizes[share_id]
-        else:
-            raise ValueError(
-                f"AbsMaxFP8MergedParameter requires share_id to be ['q', 'k', 'v'] or int, got {share_id}."
-            )
-        if len(loaded_weight.shape) == 0:
-            loaded_weight = loaded_weight.reshape(1)
-        assert loaded_weight.numel() == 1
-        # fill in the corresponding partition by repeating the val
-        param.data[start_idx:end_idx].fill_(loaded_weight.item())
-
-
-class AbsMaxFP8LinearMethod(LinearMethodBase):
-    """Linear method with AbsMax FP8 quantization."""
-
-    @staticmethod
-    def _convert_scale(scale: Any) -> torch.nn.Parameter:
-        if scale is None:
-            scale = torch.tensor([1.0], dtype=torch.float32)
-        if not isinstance(scale, torch.Tensor):
-            scale = torch.tensor([scale], dtype=torch.float32)
-        if scale.dtype != torch.float32:
-            raise NotImplementedError("Only float32 scale is supported")
-        return AbsMaxFP8Parameter(scale, requires_grad=False)
-
-    @staticmethod
-    def _merged_placeholder(
-        output_partition_sizes: list[int], ) -> torch.nn.Parameter:
-        scale = torch.ones(
-            sum(output_partition_sizes),
-            dtype=torch.float32,
-        )
-        para = AbsMaxFP8MergedParameter(
-            scale,
-            False,
-        )
-        set_weight_attrs(
-            para,
-            {
-                "output_partition_sizes": output_partition_sizes,
-            },
-        )
-        return para
-
-    def create_weights(
-        self,
-        layer: torch.nn.Module,
-        input_size_per_partition: int,
-        output_partition_sizes: list[int],
-        input_size: int,
-        output_size: int,
-        params_dtype: torch.dtype,
-        **extra_weight_attrs,
-    ) -> None:
-        assert params_dtype in [
-            torch.bfloat16, torch.float16, torch.float32
-        ], (f"AbsMaxFP8LinearMethod only supports bfloat16, float16, or float32 original dtype, got {params_dtype}."
-            )
-        weight = nn.Parameter(
-            torch.empty(
-                sum(output_partition_sizes),
-                input_size_per_partition,
-                dtype=torch.float8_e4m3fn,
-            ),
-            requires_grad=False,
-        )
-        if isinstance(layer, QKVParallelLinear | MergedColumnParallelLinear):
-            scale_weight = self._merged_placeholder(output_partition_sizes, )
-        else:
-            scale_weight = self._convert_scale(
-                extra_weight_attrs.get("scale_weight"))
-        scale_input = self._convert_scale(extra_weight_attrs.get("scale_input"))
-
-        set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
-        layer.register_parameter("weight", weight)
-        layer.register_parameter("scale_weight", scale_weight)
-        layer.register_parameter("scale_input", scale_input)
-        set_weight_attrs(
-            weight,
-            {
-                "output_dtype": params_dtype,
-            },
-        )
-        set_weight_attrs(weight, extra_weight_attrs)
-
-    def apply(
-        self,
-        layer: torch.nn.Module,
-        x: torch.Tensor,
-        bias: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        weight_quant = layer.weight
-        output_dtype: torch.dtype = weight_quant.output_dtype
-        scale_weight: torch.Tensor = layer.scale_weight.data.to(output_dtype)
-        scale_input: torch.Tensor = layer.scale_input.data.to(output_dtype)
-        weight_output_type = weight_quant.to(dtype=output_dtype)
-        weight_final = weight_output_type * scale_weight.unsqueeze(1)
-        x_final = x.to(dtype=output_dtype) * scale_input
-
-        return nn.functional.linear(x_final, weight_final,
-                                    bias=bias).to(dtype=output_dtype)

fastvideo/models/dits/longcat.py

@@ -1,6 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 """
 Native LongCat Video DiT implementation using FastVideo conventions.
+
+This is a Phase 2 reimplementation that replaces the third_party wrapper
+with native FastVideo layers for better performance and integration.
 """
 
 from typing import Any
@@ -126,7 +129,7 @@ class TimestepEmbedder(nn.Module):
         # Sinusoidal embedding in FP32
         t_freq = self.timestep_embedding(t.flatten(), self.frequency_embedding_size)
         
-        # Cast to model dtype before MLP (matching original LongCat)
+        # Cast to model dtype before MLP
         # Handle LoRA wrapper if present
         linear_layer = self.linear_1.base_layer if hasattr(self.linear_1, 'base_layer') else self.linear_1
         target_dtype = linear_layer.weight.dtype
@@ -163,14 +166,13 @@ class CaptionEmbedder(nn.Module):
         self.text_tokens_zero_pad = text_tokens_zero_pad
 
         # Two-layer MLP using ReplicatedLinear
-        # CRITICAL: Original LongCat uses GELU(approximate="tanh"), NOT SiLU!
         self.linear_1 = ReplicatedLinear(
             caption_channels,
             hidden_size,
             bias=True,
             params_dtype=dtype,
         )
-        self.act = nn.GELU(approximate="tanh")  # Match original LongCat
+        self.act = nn.SiLU()
         self.linear_2 = ReplicatedLinear(
             hidden_size,
             hidden_size,
@@ -266,19 +268,10 @@ class LongCatSelfAttention(nn.Module):
         self,
         x: torch.Tensor,              # [B, N, C]
         latent_shape: tuple,          # (T, H, W)
-        num_cond_latents: int = 0,    # Number of conditioning latent frames (for I2V)
-        return_kv: bool = False,      # Return K/V for caching
         **kwargs
-    ) -> torch.Tensor | tuple:
+    ) -> torch.Tensor:
         """
         Forward pass with 3D RoPE and optional BSA.
-        
-        For I2V mode (num_cond_latents > 0):
-        - Conditioned tokens only attend to themselves
-        - Noise tokens attend to ALL tokens (cond + noise)
-        
-        Args:
-            return_kv: If True, return (output, (k_cache, v_cache)) for KV caching
         """
         B, N, C = x.shape
         T, H, W = latent_shape
@@ -297,12 +290,6 @@ class LongCatSelfAttention(nn.Module):
         q = self.q_norm(q)
         k = self.k_norm(k)
 
-        # Save pre-RoPE K/V for cache if requested (before RoPE is applied)
-        if return_kv:
-            # [B, N, num_heads, head_dim] -> [B, num_heads, N, head_dim]
-            k_cache = k.transpose(1, 2).clone()
-            v_cache = v.transpose(1, 2).clone()
-
         # For RoPE: need [B, num_heads, N, head_dim]
         q_rope = q.transpose(1, 2)
         k_rope = k.transpose(1, 2)
@@ -314,50 +301,6 @@ class LongCatSelfAttention(nn.Module):
         q = q_rope.transpose(1, 2)
         k = k_rope.transpose(1, 2)
 
-        # === I2V Split Attention ===
-        # For I2V, conditioned tokens and noise tokens are processed separately
-        if num_cond_latents > 0:
-            # Calculate number of conditioned tokens (cond_latents * spatial_tokens_per_frame)
-            num_cond_tokens = num_cond_latents * (N // T)
-            
-            # Conditioned tokens: only attend to themselves (same seq length, use self.attn)
-            q_cond = q[:, :num_cond_tokens].contiguous()
-            k_cond = k[:, :num_cond_tokens].contiguous()
-            v_cond = v[:, :num_cond_tokens].contiguous()
-            out_cond, _ = self.attn(q_cond, k_cond, v_cond)
-            
-            # Noise tokens: attend to ALL tokens (different seq lengths!)
-            # Need to use flash attention directly since q has different length than k/v
-            q_noise = q[:, num_cond_tokens:].contiguous()  # [B, N_noise, num_heads, head_dim]
-            # k, v are full: [B, N, num_heads, head_dim]
-            
-            # Transpose for flash attention: [B, num_heads, seq, head_dim]
-            q_noise_t = q_noise.transpose(1, 2)
-            k_t = k.transpose(1, 2)
-            v_t = v.transpose(1, 2)
-            
-            # Use scaled dot product attention (handles different q/kv lengths)
-            out_noise_t = torch.nn.functional.scaled_dot_product_attention(
-                q_noise_t, k_t, v_t, 
-                attn_mask=None,
-                dropout_p=0.0,
-                is_causal=False
-            )  # [B, num_heads, N_noise, head_dim]
-            
-            # Transpose back: [B, N_noise, num_heads, head_dim]
-            out_noise = out_noise_t.transpose(1, 2)
-            
-            # Merge conditioned and noise outputs
-            out = torch.cat([out_cond, out_noise], dim=1)
-            
-            # Reshape and project out
-            out = out.reshape(B, N, C)
-            out, _ = self.to_out(out)
-            
-            if return_kv:
-                return out, (k_cache, v_cache)
-            return out
-
         # === Attention: BSA or standard ===
         if self.enable_bsa and T > 1:  # Only use BSA for multi-frame videos
             # BSA expects [B, H, S, D] format
@@ -405,96 +348,6 @@ class LongCatSelfAttention(nn.Module):
         out = out.reshape(B, N, C)
         out, _ = self.to_out(out)
 
-        if return_kv:
-            return out, (k_cache, v_cache)
-        return out
-
-    def forward_with_kv_cache(
-        self,
-        x: torch.Tensor,              # [B, N_noise, C] - only noise tokens
-        latent_shape: tuple,          # (T_noise, H, W) - shape for noise only
-        num_cond_latents: int,        # Number of conditioning latent frames
-        kv_cache: tuple,              # (k_cond, v_cond) - [B, heads, N_cond, head_dim]
-    ) -> torch.Tensor:
-        """
-        Forward using cached K/V from conditioning frames.
-        
-        x contains only NOISE tokens.
-        kv_cache contains pre-computed K/V for CONDITIONING tokens.
-        
-        CRITICAL: RoPE positions for noise tokens must start AFTER conditioning.
-        We achieve this by padding Q with dummy tokens for conditioning positions,
-        applying RoPE to the full sequence, then extracting only noise token Q.
-        """
-        B, N, C = x.shape
-        T, H, W = latent_shape
-
-        k_cache, v_cache = kv_cache
-
-        # Handle batch size mismatch (cache might be smaller for CFG)
-        # When using CFG, latent_model_input is doubled [neg, pos], but cache is for original batch
-        if k_cache.shape[0] != B:
-            # Expand cache to match input batch size
-            # For CFG: repeat the cache for both negative and positive branches
-            repeat_factor = B // k_cache.shape[0]
-            k_cache = k_cache.repeat(repeat_factor, 1, 1, 1)
-            v_cache = v_cache.repeat(repeat_factor, 1, 1, 1)
-
-        # Project to Q/K/V for noise tokens
-        q, _ = self.to_q(x)
-        k, _ = self.to_k(x)
-        v, _ = self.to_v(x)
-
-        # Reshape to heads: [B, N, num_heads, head_dim]
-        q = q.view(B, N, self.num_heads, self.head_dim)
-        k = k.view(B, N, self.num_heads, self.head_dim)
-        v = v.view(B, N, self.num_heads, self.head_dim)
-
-        # Per-head RMS normalization
-        q = self.q_norm(q)
-        k = self.k_norm(k)
-
-        # Transpose for RoPE: [B, heads, N, head_dim]
-        q_rope = q.transpose(1, 2)
-        k_rope = k.transpose(1, 2)
-        v = v.transpose(1, 2)
-
-        # CRITICAL: Apply RoPE with correct positional offset
-        # Noise frame queries need positions starting from num_cond_latents
-        # Following the original LongCat approach:
-        # 1. Pad Q with dummy tokens matching k_cache shape
-        # 2. Apply RoPE to full sequence (T_cond + T_noise)
-        # 3. Extract only the noise portion of Q
-        
-        # Create dummy Q padding to fill conditioning positions
-        # k_cache shape: [B, heads, N_cond, head_dim]
-        q_padding = torch.cat([torch.empty_like(k_cache), q_rope], dim=2).contiguous()
-        
-        # Concatenate cached K with noise K for RoPE
-        k_full = torch.cat([k_cache, k_rope], dim=2)
-        v_full = torch.cat([v_cache, v], dim=2)
-        
-        # Apply RoPE to full sequence (includes both cond and noise positions)
-        # Grid size: (T_cond + T_noise, H, W)
-        full_T = num_cond_latents + T
-        q_padding, k_full = self.rope_3d(q_padding, k_full, grid_size=(full_T, H, W))
-        
-        # Extract only the noise portion of Q (last N tokens)
-        q_rope = q_padding[:, :, -N:].contiguous()
-
-        # Run attention: Q_noise attends to full K/V (cond + noise)
-        out = torch.nn.functional.scaled_dot_product_attention(
-            q_rope, k_full, v_full,
-            attn_mask=None,
-            dropout_p=0.0,
-            is_causal=False
-        )  # [B, heads, N_noise, head_dim]
-
-        # Transpose back: [B, N_noise, heads, head_dim]
-        out = out.transpose(1, 2)
-        out = out.reshape(B, N, C)
-        out, _ = self.to_out(out)
-
         return out
 
 
@@ -541,8 +394,6 @@ class LongCatCrossAttention(nn.Module):
         self,
         x: torch.Tensor,              # [B, N_img, C]
         context: torch.Tensor,        # [B, N_text, C]
-        latent_shape: tuple = None,   # (T, H, W) - needed for I2V
-        num_cond_latents: int = 0,    # Number of conditioning latent frames (for I2V)
         **kwargs
     ) -> torch.Tensor:
         """
@@ -551,57 +402,9 @@ class LongCatCrossAttention(nn.Module):
         Args:
             x: Image tokens [B, N_img, C]
             context: Text tokens [B, N_text, C] (standard padded format)
-            latent_shape: (T, H, W) - needed for calculating num_cond_tokens
-            num_cond_latents: Number of conditioning latent frames (for I2V)
-            
-        For I2V mode (num_cond_latents > 0):
-        - Conditioned tokens get ZERO cross-attention output
-        - Only noise tokens get cross-attention with text
         """
         B, N_img, C = x.shape
 
-        # === I2V: Only noise tokens get cross-attention ===
-        if num_cond_latents > 0 and latent_shape is not None:
-            T, H, W = latent_shape
-            num_cond_tokens = num_cond_latents * (N_img // T)
-            
-            # Only process noise tokens
-            x_noise = x[:, num_cond_tokens:]  # [B, N_noise, C]
-            
-            # Project Q, K, V for noise tokens only
-            q, _ = self.to_q(x_noise)
-            k, _ = self.to_k(context)
-            v, _ = self.to_v(context)
-            
-            N_text = context.shape[1]
-            N_noise = x_noise.shape[1]
-
-            # Reshape to heads
-            q = q.view(B, N_noise, self.num_heads, self.head_dim)
-            k = k.view(B, N_text, self.num_heads, self.head_dim)
-            v = v.view(B, N_text, self.num_heads, self.head_dim)
-
-            # Per-head RMS normalization
-            q = self.q_norm(q)
-            k = self.k_norm(k)
-
-            # Run cross-attention
-            out_noise = self.attn(q, k, v)  # [B, N_noise, num_heads, head_dim]
-            out_noise = out_noise.reshape(B, N_noise, C)
-            out_noise, _ = self.to_out(out_noise)
-            
-            # Conditioned tokens get zero output
-            out_cond = torch.zeros(
-                (B, num_cond_tokens, C), 
-                dtype=out_noise.dtype, 
-                device=out_noise.device
-            )
-            
-            # Merge
-            out = torch.cat([out_cond, out_noise], dim=1)
-            return out
-
-        # === Standard cross-attention ===
         # Project Q, K, V (standard cross-attention like WanVideo/StepVideo/Cosmos)
         q, _ = self.to_q(x)
         k, _ = self.to_k(context)
@@ -672,19 +475,19 @@ class LongCatSwiGLUFFN(nn.Module):
 
 def modulate_fp32(norm: nn.Module, x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
     """
-    Apply modulation in FP32 for numerical stability.
+    Apply modulation in FP32 for numerical stability (matching original LongCat).
     
-    Converts inputs to FP32 for the modulation operation, then casts back.
+    shift and scale should already be FP32 from torch.amp.autocast context.
     """
-    orig_dtype = x.dtype
+    # Ensure modulation params are FP32 (should be from autocast)
+    assert shift.dtype == torch.float32 and scale.dtype == torch.float32, \
+        f"shift and scale must be FP32, got {shift.dtype} and {scale.dtype}"
     
-    # Convert to FP32 for numerical stability
-    shift_fp32 = shift.float()
-    scale_fp32 = scale.float()
+    orig_dtype = x.dtype
     
     # Normalize and modulate in FP32
     x_norm = norm(x.to(torch.float32))
-    x_mod = x_norm * (scale_fp32 + 1) + shift_fp32
+    x_mod = x_norm * (scale + 1) + shift
     
     return x_mod.to(orig_dtype)
 
@@ -765,21 +568,10 @@ class LongCatTransformerBlock(nn.Module):
         context: torch.Tensor,        # [B, N_text, C]
         t: torch.Tensor,              # [B, T, C_t]
         latent_shape: tuple,          # (T, H, W)
-        num_cond_latents: int = 0,    # Number of conditioning latent frames (for I2V)
-        return_kv: bool = False,      # Return K/V for caching
-        kv_cache: tuple | None = None,  # Pre-computed K/V cache
-        skip_crs_attn: bool = False,  # Skip cross-attention (for cache init)
         **kwargs
-    ) -> torch.Tensor | tuple:
+    ) -> torch.Tensor:
         """
         Forward pass with AdaLN modulation.
-        
-        Args:
-            num_cond_latents: For I2V, number of conditioning latent frames.
-                              These frames use split attention behavior.
-            return_kv: If True, return (x, (k_cache, v_cache))
-            kv_cache: Pre-computed K/V from conditioning frames
-            skip_crs_attn: If True, skip cross-attention (used during cache init)
         """
         B, N, C = x.shape
         T, H, W = latent_shape
@@ -800,47 +592,17 @@ class LongCatTransformerBlock(nn.Module):
         x_norm = modulate_fp32(self.norm_attn, x.view(B, T, -1, C), shift_msa, scale_msa)
         x_norm = x_norm.view(B, N, C)
 
-        # Handle KV cache
-        if kv_cache is not None:
-            # Move cache to device if offloaded
-            kv_cache = (kv_cache[0].to(x.device), kv_cache[1].to(x.device))
-            attn_out = self.self_attn.forward_with_kv_cache(
-                x_norm,
-                latent_shape=latent_shape,
-                num_cond_latents=num_cond_latents,
-                kv_cache=kv_cache,
-            )
-            kv_cache_new = None  # Don't return cache when using cache
-        else:
-            attn_result = self.self_attn(
-                x_norm, 
-                latent_shape=latent_shape,
-                num_cond_latents=num_cond_latents,
-                return_kv=return_kv,
-            )
-            if return_kv:
-                attn_out, kv_cache_new = attn_result
-            else:
-                attn_out = attn_result
-                kv_cache_new = None
+        attn_out = self.self_attn(x_norm, latent_shape=latent_shape)
 
         # Residual with gating (CRITICAL: FP32 like original, then cast back)
         with torch.amp.autocast(device_type='cuda', dtype=torch.float32):
             x = x + (gate_msa * attn_out.view(B, T, -1, C)).view(B, N, C)
         x = x.to(x_orig_dtype)
 
-        # === Cross-Attention (skip if requested) ===
-        if not skip_crs_attn:
-            x_norm_cross = self.norm_cross(x)
-            # When using KV cache, no need for num_cond_latents in cross-attn
-            cross_num_cond = 0 if kv_cache is not None else num_cond_latents
-            cross_out = self.cross_attn(
-                x_norm_cross, 
-                context,
-                latent_shape=latent_shape,
-                num_cond_latents=cross_num_cond
-            )
-            x = x + cross_out
+        # === Cross-Attention ===
+        x_norm_cross = self.norm_cross(x)
+        cross_out = self.cross_attn(x_norm_cross, context)
+        x = x + cross_out
 
         # === FFN ===
         x_norm_ffn = modulate_fp32(self.norm_ffn, x.view(B, T, -1, C), shift_mlp, scale_mlp)
@@ -853,8 +615,6 @@ class LongCatTransformerBlock(nn.Module):
             x = x + (gate_mlp * ffn_out.view(B, T, -1, C)).view(B, N, C)
         x = x.to(x_orig_dtype)
 
-        if return_kv:
-            return x, kv_cache_new
         return x
 
 
@@ -910,12 +670,16 @@ class FinalLayer(nn.Module):
         B, N, C = x.shape
         T, _, _ = latent_shape
 
-        # AdaLN modulation
-        t_mod = self.adaln_act(t)
-        mod_params, _ = self.adaln_linear(t_mod)
-        shift, scale = mod_params.unsqueeze(2).chunk(2, dim=-1)
+        # AdaLN modulation (FP32 for stability like original)
+        with torch.amp.autocast(device_type='cuda', dtype=torch.float32):
+            t_mod = self.adaln_act(t)
+            mod_params, _ = self.adaln_linear(t_mod)
+            # Ensure FP32 output (needed when LoRA is applied)
+            if mod_params.dtype != torch.float32:
+                mod_params = mod_params.float()
+            shift, scale = mod_params.unsqueeze(2).chunk(2, dim=-1)
 
-        # Modulate (converts to FP32 internally for stability)
+        # Modulate
         x = modulate_fp32(self.norm, x.view(B, T, -1, C), shift, scale)
         x = x.reshape(B, N, C)
 
@@ -932,6 +696,8 @@ class FinalLayer(nn.Module):
 class LongCatTransformer3DModel(CachableDiT):
     """
     Native LongCat Video Transformer using FastVideo layers.
+    
+    This is a Phase 2 implementation that replaces third_party dependencies.
     """
 
     # FSDP sharding: shard at each transformer block
@@ -1023,28 +789,13 @@ class LongCatTransformer3DModel(CachableDiT):
         encoder_attention_mask: torch.Tensor | None = None,  # [B, N_text]
         encoder_hidden_states_image: torch.Tensor | list[torch.Tensor] | None = None,
         guidance: float | None = None,         # Unused, for API compatibility
-        num_cond_latents: int = 0,             # For I2V: number of conditioning latent frames
-        # === KV Cache Parameters ===
-        return_kv: bool = False,               # If True, return (output, kv_cache_dict)
-        kv_cache_dict: dict | None = None,     # Pre-computed {block_idx: (k, v)}
-        skip_crs_attn: bool = False,           # Skip cross-attention (for cache init)
-        offload_kv_cache: bool = False,        # Move cache to CPU after compute
         **kwargs
-    ) -> torch.Tensor | tuple[torch.Tensor, dict]:
+    ) -> torch.Tensor:
         """
         Forward pass with FastVideo parameter ordering.
         
         NOTE: This follows FastVideo convention:
               (hidden_states, encoder_hidden_states, timestep)
-              
-        Args:
-            num_cond_latents: For I2V, number of conditioning latent frames.
-                              These frames are treated as "clean" (timestep=0)
-                              and use split attention behavior.
-            return_kv: If True, return (output, kv_cache_dict)
-            kv_cache_dict: Pre-computed K/V cache {block_idx: (k, v)}
-            skip_crs_attn: If True, skip cross-attention (for cache init)
-            offload_kv_cache: If True, move cache to CPU after compute
         """
         B, _, T, H, W = hidden_states.shape
 
@@ -1074,31 +825,12 @@ class LongCatTransformer3DModel(CachableDiT):
             encoder_attention_mask=encoder_attention_mask
         )  # [B, N_text, C]
 
-        # 4. Transformer blocks with optional KV cache
-        kv_cache_dict_ret = {} if return_kv else None
-        
+        # 4. Transformer blocks
         for i, block in enumerate(self.blocks):
-            # Get cache for this block if available
-            block_kv_cache = kv_cache_dict.get(i, None) if kv_cache_dict else None
-            
-            block_out = block(
+            x = block(
                 x, context, t,
-                latent_shape=(N_t, N_h, N_w),
-                num_cond_latents=num_cond_latents,
-                return_kv=return_kv,
-                kv_cache=block_kv_cache,
-                skip_crs_attn=skip_crs_attn,
+                latent_shape=(N_t, N_h, N_w)
             )
-            
-            if return_kv:
-                x, kv_cache = block_out
-                # Store cache
-                if offload_kv_cache:
-                    kv_cache_dict_ret[i] = (kv_cache[0].cpu(), kv_cache[1].cpu())
-                else:
-                    kv_cache_dict_ret[i] = (kv_cache[0].contiguous(), kv_cache[1].contiguous())
-            else:
-                x = block_out
 
         # 5. Output projection
         output = self.final_layer(x, t, latent_shape=(N_t, N_h, N_w))
@@ -1109,8 +841,6 @@ class LongCatTransformer3DModel(CachableDiT):
         # Cast to float32 for better accuracy (as per original)
         output = output.to(torch.float32)
 
-        if return_kv:
-            return output, kv_cache_dict_ret
         return output
 
     def unpatchify(self, x: torch.Tensor, N_t: int, N_h: int, N_w: int) -> torch.Tensor:

fastvideo/models/dits/matrix_game/utils.py

@@ -1,13 +1,14 @@
 from __future__ import annotations
 
-import asyncio
 import os
 import random
 
+# import cv2
+import numpy as np
 import torch
+from diffusers.utils import export_to_video
 from PIL import Image
 
-from fastvideo.distributed.parallel_state import get_local_torch_device
 from fastvideo.utils import logger
 
 
@@ -35,120 +36,6 @@ KEYBOARD_MAP_7 = {  # templerun_distilled_model: still/w/s/left/right/a/d
 }
 KEYBOARD_MAP = KEYBOARD_MAP_4  # Default for backward compatibility
 
-def expand_action_to_frames(action: dict, num_frames: int) -> tuple[torch.Tensor, torch.Tensor]:
-    result = {}
-    for key, tensor in action.items():
-        if tensor is not None:
-             # Expand to [num_frames, D] then unsqueeze to [1, num_frames, D]
-            result[key] = tensor.unsqueeze(0).repeat(num_frames, 1).unsqueeze(0)
-        else:
-            result[key] = None
-
-    if "mouse" not in result or result["mouse"] is None:
-         # keyboard device if available, otherwise default
-         device = result.get("keyboard", torch.tensor([])).device if result.get("keyboard") is not None else get_local_torch_device()
-         result["mouse"] = torch.zeros(1, num_frames, 2, device=device)
-    
-    return result["keyboard"], result["mouse"]
-
-def get_current_action(mode="universal"):
-
-    CAM_VALUE = 0.1
-    if mode == 'universal':
-        logger.info("")
-        logger.info('-'*30)
-        logger.info("PRESS [I, K, J, L, U] FOR CAMERA TRANSFORM\n (I: up, K: down, J: left, L: right, U: no move)")
-        logger.info("PRESS [W, S, A, D, Q] FOR MOVEMENT\n (W: forward, S: back, A: left, D: right, Q: no move)")
-        logger.info('-'*30)
-        CAMERA_VALUE_MAP = {
-            "i":  [CAM_VALUE, 0],
-            "k":  [-CAM_VALUE, 0],
-            "j":  [0, -CAM_VALUE],
-            "l":  [0, CAM_VALUE],
-            "u":  [0, 0]
-        }
-        KEYBOARD_IDX = { 
-            "w": [1, 0, 0, 0], "s": [0, 1, 0, 0], "a": [0, 0, 1, 0], "d": [0, 0, 0, 1],
-            "q": [0, 0, 0, 0]
-        }
-        flag = 0
-        while flag != 1:
-            try:
-                idx_mouse = input('Please input the mouse action (e.g. `U`):\n').strip().lower()
-                idx_keyboard = input('Please input the keyboard action (e.g. `W`):\n').strip().lower()
-                if idx_mouse in CAMERA_VALUE_MAP and idx_keyboard in KEYBOARD_IDX:
-                    flag = 1
-            except Exception:
-                pass
-        mouse_cond = torch.tensor(CAMERA_VALUE_MAP[idx_mouse]).cuda()
-        keyboard_cond = torch.tensor(KEYBOARD_IDX[idx_keyboard]).cuda()
-    elif mode == 'gta_drive':
-        logger.info("")
-        logger.info('-'*30)
-        logger.info("PRESS [W, S, A, D, Q] FOR MOVEMENT\n (W: forward, S: back, A: left, D: right, Q: no move)")
-        logger.info('-'*30)
-        CAMERA_VALUE_MAP = {
-            "a":  [0, -CAM_VALUE],
-            "d":  [0, CAM_VALUE],
-            "q":  [0, 0]
-        }
-        KEYBOARD_IDX = { 
-            "w": [1, 0], "s": [0, 1],
-            "q": [0, 0]
-        }
-        flag = 0
-        while flag != 1:
-            try:
-                indexes = input('Please input the actions (split with ` `):\n(e.g. `W` for forward, `W A` for forward and left)\n').strip().lower().split(' ')
-                idx_mouse = []
-                idx_keyboard = []
-                for i in indexes:
-                    if i in CAMERA_VALUE_MAP.keys():
-                        idx_mouse += [i]
-                    elif i in KEYBOARD_IDX.keys():
-                        idx_keyboard += [i]
-                if len(idx_mouse) == 0:
-                    idx_mouse += ['q']
-                if len(idx_keyboard) == 0:
-                    idx_keyboard += ['q']
-                assert idx_mouse in [['a'], ['d'], ['q']] and idx_keyboard in [['q'], ['w'], ['s']]
-                flag = 1
-            except Exception:
-                pass
-        mouse_cond = torch.tensor(CAMERA_VALUE_MAP[idx_mouse[0]]).cuda()
-        keyboard_cond = torch.tensor(KEYBOARD_IDX[idx_keyboard[0]]).cuda()
-    elif mode == 'templerun':
-        logger.info("")
-        logger.info('-'*30)
-        logger.info("PRESS [W, S, A, D, Z, C, Q] FOR ACTIONS\n (W: jump, S: slide, A: left side, D: right side, Z: turn left, C: turn right, Q: no move)")
-        logger.info('-'*30)
-        KEYBOARD_IDX = { 
-            "w": [0, 1, 0, 0, 0, 0, 0], "s": [0, 0, 1, 0, 0, 0, 0],
-            "a": [0, 0, 0, 0, 0, 1, 0], "d": [0, 0, 0, 0, 0, 0, 1],
-            "z": [0, 0, 0, 1, 0, 0, 0], "c": [0, 0, 0, 0, 1, 0, 0],
-            "q": [1, 0, 0, 0, 0, 0, 0]
-        }
-        flag = 0
-        while flag != 1:
-            try:
-                idx_keyboard = input('Please input the action: \n(e.g. `W` for forward, `Z` for turning left)\n').strip().lower()
-                if idx_keyboard in KEYBOARD_IDX.keys():
-                    flag = 1
-            except Exception:
-                pass
-        keyboard_cond = torch.tensor(KEYBOARD_IDX[idx_keyboard]).cuda()
-    
-    if mode != 'templerun':
-        return {
-            "mouse": mouse_cond,
-            "keyboard": keyboard_cond
-        }
-    return {
-        "keyboard": keyboard_cond
-    }
-
-async def get_current_action_async(mode="universal"):
-    return await asyncio.to_thread(get_current_action, mode)
 
 def load_initial_image(image_path: str = None) -> Image.Image:
     if image_path and os.path.exists(image_path):
@@ -156,6 +43,7 @@ def load_initial_image(image_path: str = None) -> Image.Image:
     logger.warning("No image provided, creating placeholder...")
     return Image.new("RGB", (640, 352), (128, 128, 128))
 
+
 def create_action_presets(num_frames: int, keyboard_dim: int = 4, seed: int = None):
     if keyboard_dim not in (2, 4, 7):
         raise ValueError(f"keyboard_dim must be 2, 4, or 7, got {keyboard_dim}")
@@ -257,6 +145,7 @@ def create_action_presets(num_frames: int, keyboard_dim: int = 4, seed: int = No
 
     return {"keyboard": keyboard_condition, "mouse": mouse_condition}
 
+
 def parse_config(config, mode="universal"):
     assert mode in ['universal', 'gta_drive', 'templerun']
     key_data = {}
@@ -294,6 +183,7 @@ def parse_config(config, mode="universal"):
                 )
     return key_data, mouse_data
 
+
 # NOTE: drawing functions are commented out to avoid cv2/libGL dependency.
 #
 # def draw_rounded_rectangle(image, top_left, bottom_right, color, radius=10, alpha=0.5):
@@ -309,6 +199,7 @@ def parse_config(config, mode="universal"):
 #     cv2.ellipse(overlay, (x2 - radius, y2 - radius), (radius, radius), 0, 0, 90, color, -1)
 #     cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0, image)
 #
+#
 # def draw_keys_on_frame(frame, keys, key_size=(80, 50), spacing=20, bottom_margin=30, mode='universal'):
 #     h, w, _ = frame.shape
 #     horison_shift = 90
@@ -353,6 +244,7 @@ def parse_config(config, mode="universal"):
 #         text_y = y + (key_size[1] + text_size[1]) // 2
 #         cv2.putText(frame, key_icon[key], (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 0), 2)
 #
+#
 # def overlay_icon(frame, icon, position, scale=1.0, rotation=0):
 #     x, y = position
 #     h, w, _ = icon.shape
@@ -390,6 +282,7 @@ def parse_config(config, mode="universal"):
 #         frame_region[:, :, c] = (1 - alpha) * frame_region[:, :, c] + alpha * icon_rgb[:, :, c]
 #     frame[top_left_y:bottom_right_y, top_left_x:bottom_right_x] = frame_region
 #
+#
 # def process_video(input_video, output_video, config, mouse_icon_path, 
 #                   mouse_scale=1.0, mouse_rotation=0, process_icon=True, mode='universal'):
 #     key_data, mouse_data = parse_config(config, mode=mode)

fastvideo/models/dits/wanvideo.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import math
-from contextlib import nullcontext
 from typing import Any
 
 import numpy as np
@@ -736,7 +735,7 @@ class WanTransformer3DModel(CachableDiT):
             else:
                 for block in self.blocks:
                     hidden_states = block(hidden_states, encoder_hidden_states,
-                                              timestep_proj, freqs_cis, attention_mask)
+                                          timestep_proj, freqs_cis, attention_mask)
             # if teacache is enabled, we need to cache the original hidden states
 
             if enable_teacache:

fastvideo/models/encoders/reason1.py

@@ -1,353 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Reason1 (Qwen2.5-VL) text encoder."""
-
-import os
-from dataclasses import dataclass
-from collections.abc import Iterable
-
-import torch
-from transformers import AutoProcessor
-
-from fastvideo.configs.models.encoders import BaseEncoderOutput, Reason1Config
-from fastvideo.logger import init_logger
-from fastvideo.models.encoders.base import TextEncoder
-from fastvideo.models.loader.weight_utils import default_weight_loader
-from fastvideo.platforms import AttentionBackendEnum
-
-from fastvideo.models.encoders.qwen2_5_vl_custom import (
-    Qwen2_5_VLForConditionalGenerationSimple,
-    Qwen2_5_VLConfig,
-    get_rope_index,
-)
-
-logger = init_logger(__name__)
-
-
-@dataclass(frozen=True)
-class _WeightsSource:
-    """Mimic `TextEncoderLoader.Source` (avoid import cycles)."""
-
-    model_or_path: str
-    prefix: str = ""
-    fall_back_to_pt: bool = True
-    allow_patterns_overrides: list[str] | None = None
-
-
-
-
-class Reason1TextEncoder(TextEncoder):
-    """Reason1 (Qwen2.5-VL) text encoder."""
-
-    _supported_attention_backends: tuple[AttentionBackendEnum, ...] = (
-        AttentionBackendEnum.FLASH_ATTN,
-        AttentionBackendEnum.TORCH_SDPA,
-    )
-    
-    def __init__(self, config: Reason1Config, prefix: str = "", checkpoint_path: str | None = None):
-        super().__init__(config)
-        
-        self.prefix = prefix
-        self.quant_config = None  # For future quantization support
-        
-        self.embedding_concat_strategy = config.arch_config.embedding_concat_strategy
-        self.n_layers_per_group = config.arch_config.n_layers_per_group
-        self.num_embedding_padding_tokens = config.arch_config.num_embedding_padding_tokens
-        
-        config_path = checkpoint_path if checkpoint_path else config.tokenizer_type
-        
-        logger.info("Initializing Reason1TextEncoder (Qwen2.5-VL) from %s", config_path)
-        try:
-            from transformers import AutoConfig as HFAutoConfig
-            hf_config = HFAutoConfig.from_pretrained(
-                config_path,
-                trust_remote_code=True,
-            )
-        except Exception as e:
-            logger.warning("Failed to load HF config from %s (%s). Using default Qwen2.5-VL-7B config.",
-                           config_path, e)
-            hf_config = Qwen2_5_VLConfig(
-                    hidden_size=3584,
-                    intermediate_size=18944,
-                    max_window_layers=28,
-                    num_attention_heads=28,
-                    num_hidden_layers=28,
-                    num_key_value_heads=4,
-                    tie_word_embeddings=False,
-                    vocab_size=152064,
-            )
-        
-        hf_config.output_hidden_states = True
-        
-        if hasattr(config.arch_config, '_attn_implementation') and config.arch_config._attn_implementation:
-            hf_config._attn_implementation = config.arch_config._attn_implementation
-        else:
-            hf_config._attn_implementation = "flash_attention_2"
-        logger.info("Reason1 attention implementation: %s", getattr(hf_config, "_attn_implementation", None))
-        
-        with torch.device("meta"):
-            self.model = Qwen2_5_VLForConditionalGenerationSimple(hf_config)
-        
-        self.processor = AutoProcessor.from_pretrained(
-            config_path,
-            trust_remote_code=True,
-        )
-
-        weights_override = os.getenv("FASTVIDEO_REASON1_WEIGHTS_PATH")
-        if weights_override:
-            self.secondary_weights = (
-                _WeightsSource(
-                    model_or_path=weights_override,
-                    prefix="",
-                    fall_back_to_pt=True,
-                    allow_patterns_overrides=None,
-                ),
-            )
-            logger.info("Reason1TextEncoder: overlaying weights from %s", weights_override)
-        
-        self._weights_loaded = False
-    
-    def forward(
-        self,
-        input_ids: torch.Tensor | None,
-        position_ids: torch.Tensor | None = None,
-        attention_mask: torch.Tensor | None = None,
-        inputs_embeds: torch.Tensor | None = None,
-        output_hidden_states: bool | None = None,
-        **kwargs,
-    ) -> BaseEncoderOutput:
-        # Cosmos2.5 alignment: keep attention_mask=None.
-        outputs = self.model(
-            input_ids=input_ids,
-            attention_mask=None,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            output_hidden_states=True,
-            return_dict=True,
-            pixel_values=kwargs.get('pixel_values', None),
-            pixel_values_videos=kwargs.get('pixel_values_videos', None),
-            image_grid_thw=kwargs.get('image_grid_thw', None),
-            video_grid_thw=kwargs.get('video_grid_thw', None),
-        )
-        
-        hidden_states = outputs.hidden_states
-        last_hidden_state = hidden_states[-1]
-        
-        return BaseEncoderOutput(
-            last_hidden_state=last_hidden_state,
-            hidden_states=hidden_states if output_hidden_states else None,
-            attention_mask=None,
-        )
-    
-    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
-        first_weight = None
-        weights_list = []
-        for name, weight in weights:
-            if first_weight is None:
-                first_weight = weight
-                self.model = self.model.to_empty(device=weight.device)
-                self.model.init_weights(buffer_device=weight.device)
-            weights_list.append((name, weight))
-        
-        params_dict = dict(self.model.named_parameters())
-        loaded_params: set[str] = set()
-        skipped_weights = {"lm_head": 0, "visual": 0, "decoder": 0}
-        
-        for name, loaded_weight in weights_list:
-            if "lm_head" in name:
-                skipped_weights["lm_head"] += 1
-                continue
-            if "visual" in name:
-                skipped_weights["visual"] += 1
-                continue
-            if "decoder" in name:
-                skipped_weights["decoder"] += 1
-                continue
-            
-            # Handle stacked params mapping (for quantized models)
-            for param_name, weight_name, shard_id in self.config.arch_config.stacked_params_mapping:
-                if weight_name not in name:
-                    continue
-                name = name.replace(weight_name, param_name)
-                if name.endswith(".bias") and name not in params_dict:
-                    continue
-                
-                if name not in params_dict:
-                    continue
-                
-                param = params_dict[name]
-                weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
-                param_name_with_prefix = f"model.{name}" if self.prefix == "" else f"{self.prefix}.{name}"
-                loaded_params.add(param_name_with_prefix)
-                break
-            else:
-                if name.endswith(".bias") and name not in params_dict:
-                    continue
-                
-                if name not in params_dict:
-                    continue
-                
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader", default_weight_loader)
-                weight_loader(param, loaded_weight)
-                param_name_with_prefix = f"model.{name}" if self.prefix == "" else f"{self.prefix}.{name}"
-                loaded_params.add(param_name_with_prefix)
-        if first_weight is not None:
-            self.model = self.model.to(first_weight.device)
-        
-        all_params = set(f"model.{name}" if self.prefix == "" else f"{self.prefix}.{name}" 
-                        for name in params_dict.keys())
-        loaded_params.update(all_params)
-        
-        # Mark weights as loaded
-        self._weights_loaded = True
-        return loaded_params
-    
-    def compute_text_embeddings_online(
-        self,
-        data_batch: dict[str, list[str]],
-        input_caption_key: str,
-    ) -> torch.Tensor:
-        prompts = data_batch[input_caption_key]
-        return self.compute_text_embeddings(prompts)
-    
-    def compute_text_embeddings(
-        self,
-        prompts: list[str],
-        device: str | torch.device = "cuda",
-    ) -> torch.Tensor:
-        """Compute embeddings for a list of prompts."""
-        input_ids_batch = []
-        
-        tok = getattr(self.processor, "tokenizer", None)
-        if tok is None:
-            raise RuntimeError("Reason1TextEncoder requires processor.tokenizer")
-        pad_id = getattr(tok, "pad_id", None)
-        if pad_id is None:
-            pad_id = getattr(tok, "pad_token_id", None)
-        if pad_id is None:
-            pad_id = getattr(self.model.config, "pad_token_id", None)
-        if pad_id is None:
-            pad_id = 0
-        
-        for prompt in prompts:
-            conversations = [
-                {
-                    "role": "system",
-                    "content": [
-                        {
-                            "type": "text",
-                            "text": "You are a helpful assistant who will provide prompts to an image generator.",
-                        }
-                    ],
-                },
-                {
-                    "role": "user",
-                    "content": [
-                        {
-                            "type": "text",
-                            "text": prompt,
-                        }
-                    ],
-                },
-            ]
-            
-            try:
-                tokenizer_output = tok.apply_chat_template(
-                    conversations,
-                    tokenize=True,
-                    add_generation_prompt=False,
-                    add_vision_id=False,
-                )
-            except TypeError:
-                tokenizer_output = tok.apply_chat_template(
-                conversations,
-                tokenize=True,
-                add_generation_prompt=False,
-            )
-
-            if isinstance(tokenizer_output, dict) and "input_ids" in tokenizer_output:
-                input_ids = tokenizer_output["input_ids"]
-                if hasattr(input_ids, "tolist"):
-                    input_ids = input_ids.tolist()
-            else:
-                input_ids = tokenizer_output
-                if hasattr(input_ids, "tolist"):
-                    input_ids = input_ids.tolist()
-                if isinstance(input_ids, list) and len(input_ids) == 1 and isinstance(
-                        input_ids[0], list):
-                    input_ids = input_ids[0]
-                if not isinstance(input_ids, list):
-                    raise RuntimeError(
-                        f"Unexpected chat_template output type: {type(tokenizer_output)}"
-                    )
-            
-            if self.num_embedding_padding_tokens > len(input_ids):
-                pad_len = self.num_embedding_padding_tokens - len(input_ids)
-                input_ids = input_ids + [pad_id] * pad_len
-            else:
-                input_ids = input_ids[:self.num_embedding_padding_tokens]
-            
-            input_ids = torch.LongTensor(input_ids).to(device=device)
-            input_ids_batch.append(input_ids)
-        
-        input_ids_batch = torch.stack(input_ids_batch, dim=0)
-
-        # Cosmos2.5 alignment: keep attention_mask=None.
-        target_device = input_ids_batch.device
-        try:
-            embed_device = self.model.model.embed_tokens.weight.device  # type: ignore[attr-defined]
-        except Exception:
-            embed_device = None
-        if embed_device is not None and embed_device != target_device:
-            self.model = self.model.to(target_device)
-        
-        with torch.no_grad():
-            position_ids, _ = get_rope_index(
-                self.model.config,
-                input_ids_batch,
-                image_grid_thw=None,
-                video_grid_thw=None,
-                second_per_grid_ts=None,
-                attention_mask=None,
-            )
-            position_ids = position_ids.to(target_device)
-                
-            outputs = self.model.model(
-                input_ids=input_ids_batch,
-                position_ids=position_ids,
-                attention_mask=None,
-                output_hidden_states=True,
-                return_dict=True,
-                use_cache=False,
-            )
-            hidden_states = outputs.hidden_states
-        
-        normalized_hidden_states = []
-        for layer_idx in range(1, len(hidden_states)):
-            normalized_state = self._mean_normalize(hidden_states[layer_idx])
-            normalized_hidden_states.append(normalized_state)
-        
-        if self.embedding_concat_strategy == "full_concat":
-            text_embeddings = torch.cat(normalized_hidden_states, dim=-1)
-        elif self.embedding_concat_strategy == "mean_pooling":
-            text_embeddings = torch.stack(normalized_hidden_states).mean(dim=0)
-        elif self.embedding_concat_strategy == "pool_every_n_layers_and_concat":
-            pooled_embeddings = []
-            for i in range(0, len(normalized_hidden_states), self.n_layers_per_group):
-                group = normalized_hidden_states[i : i + self.n_layers_per_group]
-                pooled = torch.stack(group).mean(dim=0)
-                pooled_embeddings.append(pooled)
-            text_embeddings = torch.cat(pooled_embeddings, dim=-1)
-        else:
-            raise ValueError(
-                f"Unknown embedding_concat_strategy: {self.embedding_concat_strategy}"
-            )
-        
-        return text_embeddings
-    @staticmethod
-    def _mean_normalize(tensor: torch.Tensor) -> torch.Tensor:
-        return (tensor - tensor.mean(dim=-1, keepdim=True)) / (
-            tensor.std(dim=-1, keepdim=True) + 1e-8
-        )
-

fastvideo/models/encoders/t5.py

@@ -31,11 +31,8 @@ from fastvideo.configs.models.encoders import BaseEncoderOutput, T5Config
 from fastvideo.distributed import get_tp_rank, get_tp_world_size
 from fastvideo.layers.activation import get_act_fn
 from fastvideo.layers.layernorm import RMSNorm
-from fastvideo.layers.linear import (
-    MergedColumnParallelLinear,
-    QKVParallelLinear,
-    RowParallelLinear,
-)
+from fastvideo.layers.linear import (MergedColumnParallelLinear,
+                                     QKVParallelLinear, RowParallelLinear)
 from fastvideo.layers.quantization import QuantizationConfig
 from fastvideo.layers.vocab_parallel_embedding import VocabParallelEmbedding
 from fastvideo.models.encoders.base import TextEncoder
@@ -47,7 +44,6 @@ class AttentionType:
     Attention type.
     Use string to be compatible with `torch.compile`.
     """
-
     # Decoder attention between previous layer Q/K/V
     DECODER = "decoder"
     # Encoder attention between previous layer Q/K/V for encoder-decoder
@@ -64,16 +60,17 @@ class AttentionMetadata:
 
 
 class T5DenseActDense(nn.Module):
-    def __init__(
-        self, config: T5Config, quant_config: QuantizationConfig | None = None
-    ):
+
+    def __init__(self,
+                 config: T5Config,
+                 quant_config: QuantizationConfig | None = None):
         super().__init__()
-        self.wi = MergedColumnParallelLinear(
-            config.d_model, [config.d_ff], bias=False
-        )
-        self.wo = RowParallelLinear(
-            config.d_ff, config.d_model, bias=False, quant_config=quant_config
-        )
+        self.wi = MergedColumnParallelLinear(config.d_model, [config.d_ff],
+                                             bias=False)
+        self.wo = RowParallelLinear(config.d_ff,
+                                    config.d_model,
+                                    bias=False,
+                                    quant_config=quant_config)
         self.act = get_act_fn(config.dense_act_fn)
 
     def forward(self, hidden_states) -> torch.Tensor:
@@ -84,21 +81,23 @@ class T5DenseActDense(nn.Module):
 
 
 class T5DenseGatedActDense(nn.Module):
-    def __init__(
-        self, config: T5Config, quant_config: QuantizationConfig | None = None
-    ):
+
+    def __init__(self,
+                 config: T5Config,
+                 quant_config: QuantizationConfig | None = None):
         super().__init__()
-        self.wi_0 = MergedColumnParallelLinear(
-            config.d_model, [config.d_ff], bias=False, quant_config=quant_config
-        )
-        self.wi_1 = MergedColumnParallelLinear(
-            config.d_model, [config.d_ff], bias=False, quant_config=quant_config
-        )
+        self.wi_0 = MergedColumnParallelLinear(config.d_model, [config.d_ff],
+                                               bias=False,
+                                               quant_config=quant_config)
+        self.wi_1 = MergedColumnParallelLinear(config.d_model, [config.d_ff],
+                                               bias=False,
+                                               quant_config=quant_config)
         # Should not run in fp16 unless mixed-precision is used,
         # see https://github.com/huggingface/transformers/issues/20287.
-        self.wo = RowParallelLinear(
-            config.d_ff, config.d_model, bias=False, quant_config=quant_config
-        )
+        self.wo = RowParallelLinear(config.d_ff,
+                                    config.d_model,
+                                    bias=False,
+                                    quant_config=quant_config)
         self.act = get_act_fn(config.dense_act_fn)
 
     def forward(self, hidden_states) -> torch.Tensor:
@@ -110,18 +109,17 @@ class T5DenseGatedActDense(nn.Module):
 
 
 class T5LayerFF(nn.Module):
-    def __init__(
-        self, config: T5Config, quant_config: QuantizationConfig | None = None
-    ):
+
+    def __init__(self,
+                 config: T5Config,
+                 quant_config: QuantizationConfig | None = None):
         super().__init__()
         if config.is_gated_act:
             self.DenseReluDense = T5DenseGatedActDense(
-                config, quant_config=quant_config
-            )
+                config, quant_config=quant_config)
         else:
-            self.DenseReluDense = T5DenseActDense(
-                config, quant_config=quant_config
-            )
+            self.DenseReluDense = T5DenseActDense(config,
+                                                  quant_config=quant_config)
 
         self.layer_norm = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
 
@@ -134,41 +132,39 @@ class T5LayerFF(nn.Module):
 
 # T5 has attn_bias and does not use softmax scaling
 class T5MultiHeadAttention(nn.Module):
+
     def __init__(self) -> None:
         super().__init__()
 
     def forward(self, q, k, v, attn_bias=None):
         b, _, n, c = q.shape
-        attn = torch.einsum("binc,bjnc->bnij", q, k)
+        attn = torch.einsum('binc,bjnc->bnij', q, k)
         if attn_bias is not None:
             attn += attn_bias
 
         attn = F.softmax(attn.float(), dim=-1).type_as(attn)
-        x = torch.einsum("bnij,bjnc->binc", attn, v)
+        x = torch.einsum('bnij,bjnc->binc', attn, v)
         x = x.reshape(b, -1, n * c)
         return x
 
 
 class T5Attention(nn.Module):
-    def __init__(
-        self,
-        config: T5Config,
-        attn_type: str,
-        has_relative_attention_bias=False,
-        quant_config: QuantizationConfig | None = None,
-        prefix: str = "",
-    ):
+
+    def __init__(self,
+                 config: T5Config,
+                 attn_type: str,
+                 has_relative_attention_bias=False,
+                 quant_config: QuantizationConfig | None = None,
+                 prefix: str = ""):
         super().__init__()
         self.attn_type = attn_type
         # Cross-attention has no relative pos encoding anyway
         self.is_decoder = attn_type == AttentionType.DECODER
         self.has_relative_attention_bias = has_relative_attention_bias
-        self.relative_attention_num_buckets = (
+        self.relative_attention_num_buckets = \
             config.relative_attention_num_buckets
-        )
-        self.relative_attention_max_distance = (
+        self.relative_attention_max_distance = \
             config.relative_attention_max_distance
-        )
         self.d_model = config.d_model
         self.key_value_proj_dim = config.d_kv
         self.total_num_heads = self.total_num_kv_heads = config.num_heads
@@ -195,13 +191,12 @@ class T5Attention(nn.Module):
         self.attn = T5MultiHeadAttention()
 
         if self.has_relative_attention_bias:
-            self.relative_attention_bias = VocabParallelEmbedding(
-                self.relative_attention_num_buckets,
-                self.total_num_heads,
-                org_num_embeddings=self.relative_attention_num_buckets,
-                padding_size=self.relative_attention_num_buckets,
-                quant_config=quant_config,
-            )
+            self.relative_attention_bias = \
+                VocabParallelEmbedding(self.relative_attention_num_buckets,
+                                       self.total_num_heads,
+                                       org_num_embeddings=self.relative_attention_num_buckets,
+                                       padding_size=self.relative_attention_num_buckets,
+                                       quant_config=quant_config)
         self.o = RowParallelLinear(
             self.total_num_heads * self.key_value_proj_dim,
             self.d_model,
@@ -211,20 +206,21 @@ class T5Attention(nn.Module):
         )
 
     @staticmethod
-    def _relative_position_bucket(
-        relative_position, bidirectional=True, num_buckets=32, max_distance=128
-    ) -> torch.Tensor:
+    def _relative_position_bucket(relative_position,
+                                  bidirectional=True,
+                                  num_buckets=32,
+                                  max_distance=128) -> torch.Tensor:
         """
         Adapted from Mesh Tensorflow:
         https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
-        Translate relative position to a bucket number for relative attention.
-        The relative position is defined as memory_position - query_position,
-        i.e. the distance in tokens from the attending position to the
-        attended-to position. If bidirectional=False, then positive relative
-        positions are invalid. We use smaller buckets for small absolute
-        relative_position and larger buckets for larger absolute
+        Translate relative position to a bucket number for relative attention. 
+        The relative position is defined as memory_position - query_position, 
+        i.e. the distance in tokens from the attending position to the 
+        attended-to position. If bidirectional=False, then positive relative 
+        positions are invalid. We use smaller buckets for small absolute 
+        relative_position and larger buckets for larger absolute 
         relative_positions. All relative positions >=max_distance map to the
-        same bucket. All relative positions <=-max_distance map to the same
+        same bucket. All relative positions <=-max_distance map to the same 
         bucket. This should allow for more graceful generalization to longer
         sequences than the model has been trained on
         Args:
@@ -235,18 +231,16 @@ class T5Attention(nn.Module):
         Returns:
             a Tensor with the same shape as relative_position, containing int32
             values in the range [0, num_buckets)
-        """  # noqa: E501
+        """# noqa: E501
         relative_buckets = 0
         if bidirectional:
             num_buckets //= 2
             relative_buckets += (relative_position > 0).to(
-                torch.long
-            ) * num_buckets
+                torch.long) * num_buckets
             relative_position = torch.abs(relative_position)
         else:
-            relative_position = -torch.min(
-                relative_position, torch.zeros_like(relative_position)
-            )
+            relative_position = -torch.min(relative_position,
+                                           torch.zeros_like(relative_position))
         # now relative_position is in the range [0, inf)
 
         # half of the buckets are for exact increments in positions
@@ -256,32 +250,30 @@ class T5Attention(nn.Module):
         # The other half of the buckets are for logarithmically bigger bins
         # in positions up to max_distance
         relative_position_if_large = max_exact + (
-            torch.log(relative_position.float() / max_exact)
-            / math.log(max_distance / max_exact)
-            * (num_buckets - max_exact)
-        ).to(torch.long)
+            torch.log(relative_position.float() / max_exact) /
+            math.log(max_distance / max_exact) *
+            (num_buckets - max_exact)).to(torch.long)
         relative_position_if_large = torch.min(
             relative_position_if_large,
-            torch.full_like(relative_position_if_large, num_buckets - 1),
-        )
+            torch.full_like(relative_position_if_large, num_buckets - 1))
 
-        relative_buckets += torch.where(
-            is_small, relative_position, relative_position_if_large
-        )
+        relative_buckets += torch.where(is_small, relative_position,
+                                        relative_position_if_large)
         return relative_buckets
 
-    def compute_bias(
-        self, query_length, key_length, device=None
-    ) -> torch.Tensor:
+    def compute_bias(self,
+                     query_length,
+                     key_length,
+                     device=None) -> torch.Tensor:
         """Compute binned relative position bias"""
         if device is None:
             device = self.relative_attention_bias.weight.device
-        context_position = torch.arange(
-            query_length, dtype=torch.long, device=device
-        )[:, None]
-        memory_position = torch.arange(
-            key_length, dtype=torch.long, device=device
-        )[None, :]
+        context_position = torch.arange(query_length,
+                                        dtype=torch.long,
+                                        device=device)[:, None]
+        memory_position = torch.arange(key_length,
+                                       dtype=torch.long,
+                                       device=device)[None, :]
         # max_seq_len, nh
         relative_position = memory_position - context_position
         relative_position_bucket = self._relative_position_bucket(
@@ -294,8 +286,7 @@ class T5Attention(nn.Module):
             relative_position_bucket
         )  # shape (query_length, key_length, num_heads)
         x = values.permute([2, 0, 1]).unsqueeze(
-            0
-        )  # shape (1, num_heads, query_length, key_length)
+            0)  # shape (1, num_heads, query_length, key_length)
         return x
 
     def forward(
@@ -323,9 +314,8 @@ class T5Attention(nn.Module):
             # The bias term is computed on longest sequence in batch. Biases
             # for shorter sequences are slices of the longest.
             assert self.attn_type == AttentionType.ENCODER
-            attn_bias = self.compute_bias(seq_len, seq_len).repeat(
-                num_seqs, 1, 1, 1
-            )
+            attn_bias = self.compute_bias(seq_len,
+                                          seq_len).repeat(num_seqs, 1, 1, 1)
             attn_metadata.attn_bias = attn_bias
         else:
             # Encoder/Decoder Self-Attention Layer, attn bias already cached.
@@ -334,27 +324,24 @@ class T5Attention(nn.Module):
         from fastvideo.platforms import current_platform
 
         if attention_mask is not None:
-            attention_mask = (
-                attention_mask.view(bs, 1, 1, -1)
-                if attention_mask.ndim == 2
-                else attention_mask.unsqueeze(1)
-            )
-            mask_val = (
-                -1e4 if current_platform.is_mps() else torch.finfo(q.dtype).min
-            )
+            attention_mask = attention_mask.view(
+                bs, 1, 1,
+                -1) if attention_mask.ndim == 2 else attention_mask.unsqueeze(1)
+            mask_val = -1e4 if current_platform.is_mps() else torch.finfo(
+                q.dtype).min
             attn_bias.masked_fill_(attention_mask == 0, mask_val)
 
         if get_tp_world_size() > 1:
             rank = get_tp_rank()
-            attn_bias = attn_bias[
-                :, rank * self.n_heads : (rank + 1) * self.n_heads, :, :
-            ]
+            attn_bias = attn_bias[:, rank * self.n_heads:(rank + 1) *
+                                  self.n_heads, :, :]
         attn_output = self.attn(q, k, v, attn_bias)
         output, _ = self.o(attn_output)
         return output
 
 
 class T5LayerSelfAttention(nn.Module):
+
     def __init__(
         self,
         config,
@@ -366,12 +353,10 @@ class T5LayerSelfAttention(nn.Module):
         self.SelfAttention = T5Attention(
             config,
             AttentionType.DECODER
-            if "decoder" in prefix
-            else AttentionType.ENCODER,
+            if "decoder" in prefix else AttentionType.ENCODER,
             has_relative_attention_bias=has_relative_attention_bias,
             quant_config=quant_config,
-            prefix=f"{prefix}.SelfAttention",
-        )
+            prefix=f"{prefix}.SelfAttention")
         self.layer_norm = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
 
     def forward(
@@ -391,20 +376,17 @@ class T5LayerSelfAttention(nn.Module):
 
 
 class T5LayerCrossAttention(nn.Module):
-    def __init__(
-        self,
-        config,
-        quant_config: QuantizationConfig | None = None,
-        prefix: str = "",
-    ):
+
+    def __init__(self,
+                 config,
+                 quant_config: QuantizationConfig | None = None,
+                 prefix: str = ""):
         super().__init__()
-        self.EncDecAttention = T5Attention(
-            config,
-            AttentionType.ENCODER_DECODER,
-            has_relative_attention_bias=False,
-            quant_config=quant_config,
-            prefix=f"{prefix}.EncDecAttention",
-        )
+        self.EncDecAttention = T5Attention(config,
+                                           AttentionType.ENCODER_DECODER,
+                                           has_relative_attention_bias=False,
+                                           quant_config=quant_config,
+                                           prefix=f"{prefix}.EncDecAttention")
         self.layer_norm = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
 
     def forward(
@@ -422,14 +404,13 @@ class T5LayerCrossAttention(nn.Module):
 
 
 class T5Block(nn.Module):
-    def __init__(
-        self,
-        config: T5Config,
-        is_decoder: bool,
-        has_relative_attention_bias=False,
-        quant_config: QuantizationConfig | None = None,
-        prefix: str = "",
-    ):
+
+    def __init__(self,
+                 config: T5Config,
+                 is_decoder: bool,
+                 has_relative_attention_bias=False,
+                 quant_config: QuantizationConfig | None = None,
+                 prefix: str = ""):
         super().__init__()
         self.is_decoder = is_decoder
         self.layer = nn.ModuleList()
@@ -438,18 +419,13 @@ class T5Block(nn.Module):
                 config,
                 has_relative_attention_bias=has_relative_attention_bias,
                 quant_config=quant_config,
-                prefix=f"{prefix}.self_attn",
-            )
-        )
+                prefix=f"{prefix}.self_attn"))
 
         if self.is_decoder:
             self.layer.append(
-                T5LayerCrossAttention(
-                    config,
-                    quant_config=quant_config,
-                    prefix=f"{prefix}.cross_attn",
-                )
-            )
+                T5LayerCrossAttention(config,
+                                      quant_config=quant_config,
+                                      prefix=f"{prefix}.cross_attn"))
 
         self.layer.append(T5LayerFF(config, quant_config=quant_config))
 
@@ -459,15 +435,13 @@ class T5Block(nn.Module):
         attention_mask: torch.Tensor,
         attn_metadata: AttentionMetadata | None = None,
     ) -> torch.Tensor:
-        hidden_states = self.layer[0](
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            attn_metadata=attn_metadata,
-        )
+
+        hidden_states = self.layer[0](hidden_states=hidden_states,
+                                      attention_mask=attention_mask,
+                                      attn_metadata=attn_metadata)
         if self.is_decoder:
-            hidden_states = self.layer[1](
-                hidden_states=hidden_states, attn_metadata=attn_metadata
-            )
+            hidden_states = self.layer[1](hidden_states=hidden_states,
+                                          attn_metadata=attn_metadata)
 
             # Apply Feed Forward layer
             hidden_states = self.layer[2](hidden_states)
@@ -477,49 +451,37 @@ class T5Block(nn.Module):
 
 
 class T5Stack(nn.Module):
-    def __init__(
-        self,
-        config: T5Config,
-        is_decoder: bool,
-        n_layers: int,
-        embed_tokens=None,
-        quant_config: QuantizationConfig | None = None,
-        prefix: str = "",
-        is_umt5: bool = False,
-    ):
+
+    def __init__(self,
+                 config: T5Config,
+                 is_decoder: bool,
+                 n_layers: int,
+                 embed_tokens=None,
+                 quant_config: QuantizationConfig | None = None,
+                 prefix: str = "",
+                 is_umt5: bool = False):
         super().__init__()
         self.embed_tokens = embed_tokens
         self.is_umt5 = is_umt5
         if is_umt5:
-            self.block = nn.ModuleList(
-                [
-                    T5Block(
-                        config,
+            self.block = nn.ModuleList([
+                T5Block(config,
                         is_decoder=is_decoder,
                         has_relative_attention_bias=True,
                         quant_config=quant_config,
-                        prefix=f"{prefix}.blocks.{i}",
-                    )
-                    for i in range(n_layers)
-                ]
-            )
+                        prefix=f"{prefix}.blocks.{i}") for i in range(n_layers)
+            ])
         else:
             # Only the first block has relative positional encoding.
-            self.block = nn.ModuleList(
-                [
-                    T5Block(
-                        config,
+            self.block = nn.ModuleList([
+                T5Block(config,
                         is_decoder=is_decoder,
                         has_relative_attention_bias=i == 0,
                         quant_config=quant_config,
-                        prefix=f"{prefix}.blocks.{i}",
-                    )
-                    for i in range(n_layers)
-                ]
-            )
-        self.final_layer_norm = RMSNorm(
-            config.d_model, eps=config.layer_norm_epsilon
-        )
+                        prefix=f"{prefix}.blocks.{i}") for i in range(n_layers)
+            ])
+        self.final_layer_norm = RMSNorm(config.d_model,
+                                        eps=config.layer_norm_epsilon)
 
     def forward(
         self,
@@ -540,24 +502,24 @@ class T5Stack(nn.Module):
 
 
 class T5EncoderModel(TextEncoder):
+
     def __init__(self, config: T5Config, prefix: str = ""):
         super().__init__(config)
 
+        quant_config = None
+
         self.shared = VocabParallelEmbedding(
             config.vocab_size,
             config.d_model,
-            org_num_embeddings=config.vocab_size,
-        )
+            org_num_embeddings=config.vocab_size)
 
-        self.encoder = T5Stack(
-            config,
-            False,
-            config.num_layers,
-            self.shared,
-            quant_config=config.quant_config,
-            prefix=f"{prefix}.encoder",
-            is_umt5=False,
-        )
+        self.encoder = T5Stack(config,
+                               False,
+                               config.num_layers,
+                               self.shared,
+                               quant_config=quant_config,
+                               prefix=f"{prefix}.encoder",
+                               is_umt5=False)
 
     def get_input_embeddings(self):
         return self.shared
@@ -583,9 +545,8 @@ class T5EncoderModel(TextEncoder):
             attention_mask=attention_mask,
         )
 
-    def load_weights(
-        self, weights: Iterable[tuple[str, torch.Tensor]]
-    ) -> set[str]:
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
         stacked_params_mapping = [
             # (param_name, shard_name, shard_id)
             (".qkv_proj", ".q", "q"),
@@ -623,33 +584,32 @@ class T5EncoderModel(TextEncoder):
                     continue
 
                 param = params_dict[name]
-                weight_loader = getattr(
-                    param, "weight_loader", default_weight_loader
-                )
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
                 weight_loader(param, loaded_weight)
             loaded_params.add(name)
         return loaded_params
 
 
 class UMT5EncoderModel(TextEncoder):
+
     def __init__(self, config: T5Config, prefix: str = ""):
         super().__init__(config)
 
+        quant_config = None
+
         self.shared = VocabParallelEmbedding(
             config.vocab_size,
             config.d_model,
-            org_num_embeddings=config.vocab_size,
-        )
+            org_num_embeddings=config.vocab_size)
 
-        self.encoder = T5Stack(
-            config,
-            False,
-            config.num_layers,
-            self.shared,
-            quant_config=config.quant_config,
-            prefix=f"{prefix}.encoder",
-            is_umt5=True,
-        )
+        self.encoder = T5Stack(config,
+                               False,
+                               config.num_layers,
+                               self.shared,
+                               quant_config=quant_config,
+                               prefix=f"{prefix}.encoder",
+                               is_umt5=True)
 
     def get_input_embeddings(self):
         return self.shared
@@ -675,20 +635,15 @@ class UMT5EncoderModel(TextEncoder):
             attention_mask=attention_mask,
         )
 
-    def load_weights(
-        self, weights: Iterable[tuple[str, torch.Tensor]]
-    ) -> set[str]:
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
         for name, loaded_weight in weights:
             loaded = False
             if "decoder" in name or "lm_head" in name:
                 continue
-            for (
-                param_name,
-                weight_name,
-                shard_id,
-            ) in self.config.arch_config.stacked_params_mapping:
+            for param_name, weight_name, shard_id in self.config.arch_config.stacked_params_mapping:
                 if weight_name not in name:
                     continue
                 name = name.replace(weight_name, param_name)
@@ -713,9 +668,8 @@ class UMT5EncoderModel(TextEncoder):
                     continue
 
                 param = params_dict[name]
-                weight_loader = getattr(
-                    param, "weight_loader", default_weight_loader
-                )
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
                 weight_loader(param, loaded_weight)
             loaded_params.add(name)
         return loaded_params

fastvideo/models/layerwise_offload.py

@@ -1,224 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# Inspired by SGLang's layerwise offload implementation:
-# https://github.com/sgl-project/sglang/pull/15511
-#
-# This implementation provides a lightweight layerwise CPU offload manager
-# with async H2D prefetch using a dedicated CUDA stream, following SGLang's design.
-
-import re
-from contextlib import contextmanager
-from typing import Dict, Set, Optional, Tuple
-
-import torch
-
-
-class LayerwiseOffloadManager:
-    """A lightweight layerwise CPU offload manager.
-
-    Offloads per-layer parameters/buffers from GPU to CPU, and supports async H2D
-    prefetch using a dedicated CUDA stream.
-    """
-
-    def __init__(
-        self,
-        model: torch.nn.Module,
-        *,
-        module_list_attr: str,
-        num_layers: int,
-        enabled: bool,
-        pin_cpu_memory: bool = True,
-        auto_initialize: bool = False,
-    ) -> None:
-        self.model = model
-        self.module_list_attr = module_list_attr
-        self.num_layers = int(num_layers)
-        self.pin_cpu_memory = bool(pin_cpu_memory)
-
-        self.enabled = bool(enabled and torch.cuda.is_available())
-        self.device = (
-            torch.device("cuda", torch.cuda.current_device()) if self.enabled else None
-        )
-        self.copy_stream = torch.cuda.Stream() if self.enabled else None
-
-        self._layer_name_re = re.compile(
-            rf"(^|\.){re.escape(module_list_attr)}\.(\d+)(\.|$)"
-        )
-
-        self._cpu_weights: Dict[int, Dict[str, torch.Tensor]] = {}
-        self._cpu_dtypes: Dict[int, Dict[str, torch.dtype]] = {}
-
-        self._gpu_layers: Dict[int, Set[str]] = {}
-
-        self._named_parameters: Dict[str, torch.nn.Parameter] = {}
-        self._named_buffers: Dict[str, torch.Tensor] = {}
-
-        self._meta: Dict[str, Tuple[int, torch.dtype]] = {}
-
-        if auto_initialize:
-            self.initialize()
-
-    def _match_layer_idx(self, name: str) -> Optional[int]:
-        m = self._layer_name_re.search(name)
-        if not m:
-            return None
-        try:
-            return int(m.group(2))
-        except Exception:
-            return None
-
-    def _record_meta(self, name: str, t: torch.Tensor) -> None:
-        if name not in self._meta:
-            self._meta[name] = (int(t.ndim), t.dtype)
-
-    def _make_placeholder(self, name: str) -> torch.Tensor:
-        """Rank-preserving empty placeholder on GPU."""
-        assert self.device is not None
-        ndim, dtype = self._meta[name]
-        shape = (0,) if ndim <= 0 else (0,) * ndim
-        return torch.empty(shape, device=self.device, dtype=dtype)
-
-    def _get_target(self, name: str) -> torch.Tensor:
-        if name in self._named_parameters:
-            return self._named_parameters[name]
-        return self._named_buffers[name]
-
-    def _offload_tensor(self, name: str, tensor: torch.Tensor, layer_idx: int) -> None:
-        if layer_idx not in self._cpu_weights:
-            self._cpu_weights[layer_idx] = {}
-            self._cpu_dtypes[layer_idx] = {}
-
-        self._record_meta(name, tensor)
-
-        cpu_weight = tensor.detach().to("cpu")
-        if self.pin_cpu_memory:
-            cpu_weight = cpu_weight.pin_memory()
-
-        self._cpu_weights[layer_idx][name] = cpu_weight
-        self._cpu_dtypes[layer_idx][name] = tensor.dtype
-
-        if self.device is not None:
-            tensor.data = self._make_placeholder(name)
-
-    @torch.compiler.disable
-    def initialize(self) -> None:
-        """Offload all matched layer tensors to CPU and prefetch layer 0 (sync)."""
-        if not self.enabled:
-            return
-
-        self._named_parameters = dict(self.model.named_parameters())
-        self._named_buffers = dict(self.model.named_buffers())
-
-        for name, param in self._named_parameters.items():
-            layer_idx = self._match_layer_idx(name)
-            if layer_idx is None or layer_idx >= self.num_layers:
-                continue
-            self._offload_tensor(name, param, layer_idx)
-
-        for name, buf in self._named_buffers.items():
-            layer_idx = self._match_layer_idx(name)
-            if layer_idx is None or layer_idx >= self.num_layers:
-                continue
-            self._offload_tensor(name, buf, layer_idx)
-
-        self.prefetch_layer(0, non_blocking=False)
-        if self.copy_stream is not None:
-            torch.cuda.current_stream().wait_stream(self.copy_stream)
-
-    @torch.compiler.disable
-    def prefetch_layer(self, layer_idx: int, non_blocking: bool = True) -> None:
-        """Prefetch a layer's tensors from CPU to GPU (async on copy_stream)."""
-        if not self.enabled or self.device is None or self.copy_stream is None:
-            return
-        if layer_idx < 0 or layer_idx >= self.num_layers:
-            return
-        if layer_idx in self._gpu_layers:
-            return
-        if layer_idx not in self._cpu_weights:
-            return
-
-        self.copy_stream.wait_stream(torch.cuda.current_stream())
-
-        param_names: Set[str] = set()
-        with torch.cuda.stream(self.copy_stream):
-            for name, cpu_weight in self._cpu_weights[layer_idx].items():
-                target = self._get_target(name)
-
-                gpu_weight = torch.empty(
-                    cpu_weight.shape,
-                    dtype=self._cpu_dtypes[layer_idx][name],
-                    device=self.device,
-                )
-                gpu_weight.copy_(cpu_weight, non_blocking=non_blocking)
-
-                target.data = gpu_weight
-                param_names.add(name)
-
-        self._gpu_layers[layer_idx] = param_names
-
-    @contextmanager
-    def layer_scope(
-        self,
-        *,
-        prefetch_layer_idx: Optional[int],
-        release_layer_idx: Optional[int],
-        non_blocking: bool = True,
-    ):
-        if self.enabled and release_layer_idx is not None:
-            cur = release_layer_idx
-            if (
-                cur not in self._gpu_layers
-                and cur in self._cpu_weights
-                and self.device is not None
-                and self.copy_stream is not None
-            ):
-                self.prefetch_layer(cur, non_blocking=False)
-                torch.cuda.current_stream().wait_stream(self.copy_stream)
-
-        if self.enabled and prefetch_layer_idx is not None:
-            self.prefetch_layer(prefetch_layer_idx, non_blocking=non_blocking)
-
-        try:
-            yield
-        finally:
-            if self.enabled and self.copy_stream is not None:
-                torch.cuda.current_stream().wait_stream(self.copy_stream)
-            if self.enabled and release_layer_idx is not None:
-                self.release_layer(release_layer_idx)
-
-
-    @torch.compiler.disable
-    def release_layer(self, layer_idx: int) -> None:
-        """Release a layer's tensors back to placeholders (free VRAM)."""
-        if not self.enabled or self.device is None:
-            return
-
-        if layer_idx < 0:
-            return
-
-        param_names = self._gpu_layers.pop(layer_idx, None)
-        if not param_names:
-            return
-
-        for name in param_names:
-            target = self._get_target(name)
-            # Ensure meta exists even if something unexpected happened
-            self._record_meta(name, target)
-            target.data = self._make_placeholder(name)
-
-    @torch.compiler.disable
-    def release_all(self) -> None:
-        """Release all currently-resident layers back to placeholders."""
-        if not self.enabled or self.device is None:
-            return
-
-        if self.copy_stream is not None:
-            torch.cuda.current_stream().wait_stream(self.copy_stream)
-
-        for layer_idx in list(self._gpu_layers.keys()):
-            param_names = self._gpu_layers.pop(layer_idx, None)
-            if not param_names:
-                continue
-            for name in param_names:
-                target = self._get_target(name)
-                self._record_meta(name, target)
-                target.data = self._make_placeholder(name)

fastvideo/models/loader/component_loader.py

@@ -15,27 +15,22 @@ import torch.distributed as dist
 import torch.nn as nn
 from safetensors.torch import load_file as safetensors_load_file
 from torch.distributed import init_device_mesh
-from transformers import AutoImageProcessor, AutoProcessor, AutoTokenizer
+from transformers import AutoImageProcessor, AutoModel, AutoTokenizer
+from transformers import UMT5EncoderModel
 from transformers.utils import SAFE_WEIGHTS_INDEX_NAME
 
 from fastvideo.configs.models import EncoderConfig
 from fastvideo.distributed import get_local_torch_device
 from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.layers.quantization import get_quantization_config
 from fastvideo.logger import init_logger
-from fastvideo.models.encoders.base import TextEncoder
 from fastvideo.models.hf_transformer_utils import get_diffusers_config
 from fastvideo.models.loader.fsdp_load import maybe_load_fsdp_model, shard_model
 from fastvideo.models.loader.utils import set_default_torch_dtype
 from fastvideo.models.loader.weight_utils import (
-    filter_duplicate_safetensors_files,
-    filter_files_not_needed_for_inference,
-    pt_weights_iterator,
-    safetensors_weights_iterator,
-)
+    filter_duplicate_safetensors_files, filter_files_not_needed_for_inference,
+    pt_weights_iterator, safetensors_weights_iterator)
 from fastvideo.models.registry import ModelRegistry
-from fastvideo.utils import PRECISION_TO_TYPE, is_pin_memory_available
-from fastvideo.hooks.layerwise_offload import enable_layerwise_offload
+from fastvideo.utils import PRECISION_TO_TYPE
 
 logger = init_logger(__name__)
 
@@ -50,27 +45,26 @@ class ComponentLoader(ABC):
     def load(self, model_path: str, fastvideo_args: FastVideoArgs):
         """
         Load the component based on the model path, architecture, and inference args.
-
+        
         Args:
             model_path: Path to the component model
             fastvideo_args: FastVideoArgs
-
+            
         Returns:
             The loaded component
         """
         raise NotImplementedError
 
     @classmethod
-    def for_module_type(
-        cls, module_type: str, transformers_or_diffusers: str
-    ) -> "ComponentLoader":
+    def for_module_type(cls, module_type: str,
+                        transformers_or_diffusers: str) -> 'ComponentLoader':
         """
         Factory method to create a component loader for a specific module type.
-
+        
         Args:
             module_type: Type of module (e.g., "vae", "text_encoder", "transformer", "scheduler")
             transformers_or_diffusers: Whether the module is from transformers or diffusers
-
+            
         Returns:
             A component loader for the specified module type
         """
@@ -90,19 +84,14 @@ class ComponentLoader(ABC):
 
         if module_type in module_loaders:
             loader_cls, expected_library = module_loaders[module_type]
-            # Allow fastvideo.* libraries for custom implementations (e.g. Cosmos2_5Pipeline)
-            # that aren't available in diffusers/transformers yet
-            is_fastvideo_module = transformers_or_diffusers.startswith("fastvideo.")
-            if not is_fastvideo_module:
-                # Assert that the library matches what's expected for this module type
-                assert transformers_or_diffusers == expected_library, f"{module_type} must be loaded from {expected_library}, got {transformers_or_diffusers}"
+            # Assert that the library matches what's expected for this module type
+            assert transformers_or_diffusers == expected_library, f"{module_type} must be loaded from {expected_library}, got {transformers_or_diffusers}"
             return loader_cls()
 
         # For unknown module types, use a generic loader
         logger.warning(
             "No specific loader found for module type: %s. Using generic loader.",
-            module_type,
-        )
+            module_type)
         return GenericComponentLoader(transformers_or_diffusers)
 
 
@@ -165,45 +154,36 @@ class TextEncoderLoader(ComponentLoader):
 
         if use_safetensors:
             hf_weights_files = filter_duplicate_safetensors_files(
-                hf_weights_files, hf_folder, index_file
-            )
+                hf_weights_files, hf_folder, index_file)
         else:
             hf_weights_files = filter_files_not_needed_for_inference(
-                hf_weights_files
-            )
+                hf_weights_files)
 
         if len(hf_weights_files) == 0:
             raise RuntimeError(
-                f"Cannot find any model weights with `{model_name_or_path}`"
-            )
+                f"Cannot find any model weights with `{model_name_or_path}`")
 
         return hf_folder, hf_weights_files, use_safetensors
 
     def _get_weights_iterator(
-        self, source: "Source", to_cpu: bool
-    ) -> Generator[tuple[str, torch.Tensor], None, None]:
+            self, source: "Source",
+            to_cpu: bool) -> Generator[tuple[str, torch.Tensor], None, None]:
         """Get an iterator for the model weights based on the load format."""
         hf_folder, hf_weights_files, use_safetensors = self._prepare_weights(
-            source.model_or_path,
-            source.fall_back_to_pt,
-            source.allow_patterns_overrides,
-        )
+            source.model_or_path, source.fall_back_to_pt,
+            source.allow_patterns_overrides)
         if use_safetensors:
-            weights_iterator = safetensors_weights_iterator(
-                hf_weights_files, to_cpu=to_cpu
-            )
+            weights_iterator = safetensors_weights_iterator(hf_weights_files,
+                                                            to_cpu=to_cpu)
         else:
-            weights_iterator = pt_weights_iterator(
-                hf_weights_files, to_cpu=to_cpu
-            )
+            weights_iterator = pt_weights_iterator(hf_weights_files,
+                                                   to_cpu=to_cpu)
 
         if self.counter_before_loading_weights == 0.0:
             self.counter_before_loading_weights = time.perf_counter()
         # Apply the prefix.
-        return (
-            (source.prefix + name, tensor)
-            for (name, tensor) in weights_iterator
-        )
+        return ((source.prefix + name, tensor)
+                for (name, tensor) in weights_iterator)
 
     def _get_all_weights(
         self,
@@ -215,9 +195,8 @@ class TextEncoderLoader(ComponentLoader):
             model_path,
             prefix="",
             fall_back_to_pt=getattr(model, "fall_back_to_pt_during_load", True),
-            allow_patterns_overrides=getattr(
-                model, "allow_patterns_overrides", None
-            ),
+            allow_patterns_overrides=getattr(model, "allow_patterns_overrides",
+                                             None),
         )
         yield from self._get_weights_iterator(primary_weights, to_cpu)
 
@@ -246,100 +225,78 @@ class TextEncoderLoader(ComponentLoader):
 
         # @TODO(Wei): Better way to handle this?
         try:
-            encoder_config = (
-                fastvideo_args.pipeline_config.text_encoder_configs[0]
-            )
+            encoder_config = fastvideo_args.pipeline_config.text_encoder_configs[
+                0]
             encoder_config.update_model_arch(model_config)
-            encoder_precision = (
-                fastvideo_args.pipeline_config.text_encoder_precisions[0]
-            )
+            encoder_precision = fastvideo_args.pipeline_config.text_encoder_precisions[
+                0]
         except Exception:
-            encoder_config = (
-                fastvideo_args.pipeline_config.text_encoder_configs[1]
-            )
+            encoder_config = fastvideo_args.pipeline_config.text_encoder_configs[
+                1]
             encoder_config.update_model_arch(model_config)
-            encoder_precision = (
-                fastvideo_args.pipeline_config.text_encoder_precisions[1]
-            )
+            encoder_precision = fastvideo_args.pipeline_config.text_encoder_precisions[
+                1]
+
+        requested_dtype = fastvideo_args.text_encoder_dtype or encoder_precision
 
         target_device = get_local_torch_device()
-        # TODO(will): add support for other dtypes
-        return self.load_model(
-            model_path,
-            encoder_config,
-            target_device,
-            fastvideo_args,
-            encoder_precision,
-            use_text_encoder_override=True,
-        )
+        if requested_dtype not in PRECISION_TO_TYPE:
+            logger.info(
+                "Loading text encoder via transformers AutoModel with dtype=%s",
+                requested_dtype,
+            )
+            return self._load_with_transformers(
+                model_path,
+                requested_dtype,
+                fastvideo_args,
+                target_device,
+            )
 
-    def load_model(
-        self,
-        model_path: str,
-        model_config: EncoderConfig,
-        target_device: torch.device,
-        fastvideo_args: FastVideoArgs,
-        dtype: str = "fp16",
-        use_text_encoder_override: bool = False,  # prevent subclasses from misusing
-    ):
-        use_cpu_offload = (
-            fastvideo_args.text_encoder_cpu_offload
-            and len(getattr(model_config, "_fsdp_shard_conditions", [])) > 0
-        )
+        return self.load_model(model_path, encoder_config, target_device,
+                               fastvideo_args, requested_dtype)
+
+    def load_model(self,
+                   model_path: str,
+                   model_config: EncoderConfig,
+                   target_device: torch.device,
+                   fastvideo_args: FastVideoArgs,
+                   dtype: str = "fp16"):
+        use_cpu_offload = fastvideo_args.text_encoder_cpu_offload and len(
+            getattr(model_config, "_fsdp_shard_conditions", [])) > 0
 
         from fastvideo.platforms import current_platform
 
         if fastvideo_args.text_encoder_cpu_offload:
-            target_device = (
-                torch.device("mps")
-                if current_platform.is_mps()
-                else torch.device("cpu")
+            target_device = torch.device(
+                "mps") if current_platform.is_mps() else torch.device("cpu")
+
+        if dtype not in PRECISION_TO_TYPE:
+            supported = ", ".join(PRECISION_TO_TYPE.keys())
+            raise ValueError(
+                f"Unsupported text encoder precision '{dtype}'. "
+                f"Supported precisions: {supported}. "
+                "FP8 checkpoints will currently be materialized in a supported dtype, "
+                "so they will not reduce VRAM usage."
             )
 
-        # Set quantization config if specified
-        if (
-            use_text_encoder_override
-            and fastvideo_args.override_text_encoder_quant is not None
-        ):
-            if fastvideo_args.override_text_encoder_safetensors is None:
-                raise ValueError(
-                    "override_text_encoder_quant is set but override_text_encoder_safetensors is None"
-                )
-            quant_cls = get_quantization_config(
-                fastvideo_args.override_text_encoder_quant
-            )
-            model_config.quant_config = quant_cls()
+        logger.info("Loading text encoder with precision=%s (%s)",
+                    dtype, PRECISION_TO_TYPE[dtype])
 
         with set_default_torch_dtype(PRECISION_TO_TYPE[dtype]):
             with target_device:
                 architectures = getattr(model_config, "architectures", [])
                 model_cls, _ = ModelRegistry.resolve_model_cls(architectures)
-                model: TextEncoder = model_cls(model_config)  # type: ignore
+                model = model_cls(model_config)
 
             weights_to_load = {name for name, _ in model.named_parameters()}
-            if (
-                use_text_encoder_override
-                and fastvideo_args.override_text_encoder_safetensors is not None
-            ):
-                loaded_weights: set[str] = model.load_weights(
-                    safetensors_weights_iterator(
-                        [fastvideo_args.override_text_encoder_safetensors],
-                        to_cpu=use_cpu_offload,
-                    )
-                )  # type: ignore
-            else:
-                loaded_weights: set[str] = model.load_weights(
-                    self._get_all_weights(
-                        model, model_path, to_cpu=use_cpu_offload
-                    )
-                )  # type: ignore
-
+            loaded_weights = model.load_weights(
+                self._get_all_weights(model, model_path,
+                                      to_cpu=use_cpu_offload))
             self.counter_after_loading_weights = time.perf_counter()
             logger.info(
                 "Loading weights took %.2f seconds",
-                self.counter_after_loading_weights
-                - self.counter_before_loading_weights,
-            )
+                self.counter_after_loading_weights -
+                self.counter_before_loading_weights)
 
             # Explicitly move model to target device after loading weights
             model = model.to(target_device)
@@ -347,7 +304,6 @@ class TextEncoderLoader(ComponentLoader):
             from fastvideo.platforms import current_platform
 
             if use_cpu_offload:
-                pin_cpu_memory = fastvideo_args.pin_cpu_memory and is_pin_memory_available()
                 # Disable FSDP for MPS as it's not compatible
                 if current_platform.is_mps():
                     logger.info(
@@ -365,8 +321,7 @@ class TextEncoderLoader(ComponentLoader):
                         reshard_after_forward=True,
                         mesh=mesh["offload"],
                         fsdp_shard_conditions=model._fsdp_shard_conditions,
-                        pin_cpu_memory=pin_cpu_memory,
-                    )
+                        pin_cpu_memory=fastvideo_args.pin_cpu_memory)
                 else:
                     mesh = init_device_mesh(
                         "cuda",
@@ -379,22 +334,65 @@ class TextEncoderLoader(ComponentLoader):
                         reshard_after_forward=True,
                         mesh=mesh["offload"],
                         fsdp_shard_conditions=model._fsdp_shard_conditions,
-                        pin_cpu_memory=pin_cpu_memory,
-                    )
+                        pin_cpu_memory=fastvideo_args.pin_cpu_memory)
             # We only enable strict check for non-quantized models
             # that have loaded weights tracking currently.
             # if loaded_weights is not None:
             weights_not_loaded = weights_to_load - loaded_weights
-            if weights_not_loaded and model_config.quant_config is None:
+            if weights_not_loaded:
+                raise ValueError("Following weights were not initialized from "
+                                 f"checkpoint: {weights_not_loaded}")
+
+        return model.eval()
+
+    def _resolve_torch_dtype(self, dtype: str) -> torch.dtype:
+        if dtype in PRECISION_TO_TYPE:
+            return PRECISION_TO_TYPE[dtype]
+
+        if dtype.startswith("fp8"):
+            torch_dtype = getattr(torch, "float8_e4m3fn", None)
+            if torch_dtype is None:
+                torch_dtype = getattr(torch, "float8_e4m3fnuz", None)
+
+            if torch_dtype is None:
                 raise ValueError(
-                    "Following weights were not initialized from "
-                    f"checkpoint: {weights_not_loaded}"
+                    "FP8 requested for text encoder loading, but the current "
+                    "PyTorch build does not expose float8 dtypes. Upgrade PyTorch "
+                    "or choose a supported dtype (fp16/bf16/fp32)."
                 )
+            return torch_dtype
+
+        raise ValueError(
+            f"Unsupported text encoder dtype '{dtype}'. "
+            "Pass a torch dtype string such as fp16, bf16, fp32, or fp8."
+        )
+
+    def _load_with_transformers(
+        self,
+        model_path: str,
+        dtype: str,
+        fastvideo_args: FastVideoArgs,
+        target_device: torch.device,
+    ) -> nn.Module:
+        torch_dtype = self._resolve_torch_dtype(dtype)
+        device_map = "auto" if fastvideo_args.text_encoder_cpu_offload else None
+
+        model = AutoModel.from_pretrained(
+            model_path,
+            trust_remote_code=fastvideo_args.trust_remote_code,
+            revision=fastvideo_args.revision,
+            torch_dtype=torch_dtype,
+            device_map=device_map,
+        )
+
+        if device_map is None:
+            model = model.to(target_device)
 
         return model.eval()
 
 
 class ImageEncoderLoader(TextEncoderLoader):
+
     def load(self, model_path: str, fastvideo_args: FastVideoArgs):
         """Load the text encoders based on the model path, and inference args."""
         # model_config: PretrainedConfig = get_hf_config(
@@ -417,21 +415,13 @@ class ImageEncoderLoader(TextEncoderLoader):
         from fastvideo.platforms import current_platform
 
         if fastvideo_args.image_encoder_cpu_offload:
-            target_device = (
-                torch.device("mps")
-                if current_platform.is_mps()
-                else torch.device("cpu")
-            )
+            target_device = torch.device("mps") if current_platform.is_mps() else torch.device("cpu")
         else:
             target_device = get_local_torch_device()
         # TODO(will): add support for other dtypes
         return self.load_model(
-            model_path,
-            encoder_config,
-            target_device,
-            fastvideo_args,
-            fastvideo_args.pipeline_config.image_encoder_precision,
-        )
+            model_path, encoder_config, target_device, fastvideo_args,
+            fastvideo_args.pipeline_config.image_encoder_precision)
 
 
 class ImageProcessorLoader(ComponentLoader):
@@ -441,12 +431,9 @@ class ImageProcessorLoader(ComponentLoader):
         """Load the image processor based on the model path, and inference args."""
         logger.info("Loading image processor from %s", model_path)
 
-        image_processor = AutoImageProcessor.from_pretrained(
-            model_path,
-        )
-        logger.info(
-            "Loaded image processor: %s", image_processor.__class__.__name__
-        )
+        image_processor = AutoImageProcessor.from_pretrained(model_path, )
+        logger.info("Loaded image processor: %s",
+                    image_processor.__class__.__name__)
         return image_processor
 
 
@@ -457,39 +444,12 @@ class TokenizerLoader(ComponentLoader):
         """Load the tokenizer based on the model path, and inference args."""
         logger.info("Loading tokenizer from %s", model_path)
 
-        # Cosmos2.5 stores an AutoProcessor config in `tokenizer/config.json` (not a tokenizer
-        # config). Use its `_name_or_path` (e.g. Qwen/Qwen2.5-VL-7B-Instruct) as the source.
-        tokenizer_cfg_path = os.path.join(model_path, "config.json")
-        if os.path.exists(tokenizer_cfg_path):
-            try:
-                with open(tokenizer_cfg_path, "r") as f:
-                    tokenizer_cfg = json.load(f)
-                if isinstance(tokenizer_cfg, dict) and (
-                    tokenizer_cfg.get("_class_name") == "AutoProcessor"
-                    or "processor_type" in tokenizer_cfg
-                ):
-                    src = tokenizer_cfg.get("_name_or_path", "")
-                    if isinstance(src, str) and src.strip():
-                        processor = AutoProcessor.from_pretrained(
-                            src.strip(),
-                            trust_remote_code=True,
-                        )
-                        logger.info(
-                            "Loaded tokenizer/processor from %s: %s",
-                            src,
-                            processor.__class__.__name__,
-                        )
-                        return processor
-            except Exception:
-                # If parsing fails, fall through to AutoTokenizer below.
-                pass
-
         tokenizer = AutoTokenizer.from_pretrained(
             model_path,  # "<path to model>/tokenizer"
             # in v0, this was same string as encoder_name "ClipTextModel"
             # TODO(will): pass these tokenizer kwargs from inference args? Maybe
             # other method of config?
-            padding_size="right",
+            padding_size='right',
         )
         logger.info("Loaded tokenizer: %s", tokenizer.__class__.__name__)
         return tokenizer
@@ -502,9 +462,7 @@ class VAELoader(ComponentLoader):
         """Load the VAE based on the model path, and inference args."""
         config = get_diffusers_config(model=model_path)
         class_name = config.pop("_class_name")
-        assert class_name is not None, (
-            "Model config does not contain a _class_name attribute. Only diffusers format is supported."
-        )
+        assert class_name is not None, "Model config does not contain a _class_name attribute. Only diffusers format is supported."
         fastvideo_args.model_paths["vae"] = model_path
 
         vae_config = fastvideo_args.pipeline_config.vae_config
@@ -513,53 +471,23 @@ class VAELoader(ComponentLoader):
         from fastvideo.platforms import current_platform
 
         if fastvideo_args.vae_cpu_offload:
-            target_device = (
-                torch.device("mps")
-                if current_platform.is_mps()
-                else torch.device("cpu")
-            )
+            target_device = torch.device("mps") if current_platform.is_mps() else torch.device("cpu")
         else:
             target_device = get_local_torch_device()
 
-        with set_default_torch_dtype(
-            PRECISION_TO_TYPE[fastvideo_args.pipeline_config.vae_precision]
-            if fastvideo_args.pipeline_config.vae_precision
-            else torch.bfloat16
-        ):
-            # Cosmos2.5 uses a Wan2.1 VAE stored as `tokenizer.safetensors` under the VAE folder.
-            is_cosmos25 = fastvideo_args.pipeline_config.__class__.__name__ == "Cosmos25Config"
-            if class_name == "AutoencoderKLWan" and is_cosmos25:
-                from fastvideo.models.vaes.cosmos25wanvae import Cosmos25WanVAE
-
-                dtype = PRECISION_TO_TYPE[fastvideo_args.pipeline_config.vae_precision]
-                vae = Cosmos25WanVAE(device=target_device, dtype=dtype)
-
-                weight_path = os.path.join(model_path, "tokenizer.safetensors")
-                if not os.path.exists(weight_path):
-                    raise FileNotFoundError(
-                        f"Missing Cosmos2.5 VAE weights: {weight_path}"
-                    )
-                sd = safetensors_load_file(weight_path)
-                vae.load_state_dict(sd, strict=False)
-                return vae.eval()
-
+        with set_default_torch_dtype(PRECISION_TO_TYPE[
+                fastvideo_args.pipeline_config.vae_precision] if fastvideo_args.pipeline_config.vae_precision else torch.bfloat16):
             vae_cls, _ = ModelRegistry.resolve_model_cls(class_name)
             vae = vae_cls(vae_config).to(target_device)
 
         # Find all safetensors files
         safetensors_list = glob.glob(
             os.path.join(str(model_path), "*.safetensors"))
-        if not safetensors_list:
-            raise ValueError(f"No safetensors files found in {model_path}")
-        # Common case: a single `.safetensors` checkpoint file.
-        # Some models may be sharded into multiple files; in that case we merge.
-        if len(safetensors_list) == 1:
-            loaded = safetensors_load_file(safetensors_list[0])
-        else:
-            loaded = {}
-            for sf_file in safetensors_list:
-                loaded.update(safetensors_load_file(sf_file))
-        vae.load_state_dict(loaded, strict=False)
+        loaded = {}
+        for sf_file in safetensors_list:
+            loaded.update(safetensors_load_file(sf_file))
+        vae.load_state_dict(
+            loaded, strict=False)  # We might only load encoder or decoder
 
         return vae.eval()
 
@@ -575,8 +503,7 @@ class TransformerLoader(ComponentLoader):
         if cls_name is None:
             raise ValueError(
                 "Model config does not contain a _class_name attribute. "
-                "Only diffusers format is supported."
-            )
+                "Only diffusers format is supported.")
 
         logger.info("transformer cls_name: %s", cls_name)
         if fastvideo_args.override_transformer_cls_name is not None:
@@ -593,69 +520,44 @@ class TransformerLoader(ComponentLoader):
 
         # Find all safetensors files
         safetensors_list = glob.glob(
-            os.path.join(str(model_path), "*.safetensors")
-        )
+            os.path.join(str(model_path), "*.safetensors"))
         if not safetensors_list:
             raise ValueError(f"No safetensors files found in {model_path}")
 
         # Check if we should use custom initialization weights
-        custom_weights_path = getattr(
-            fastvideo_args, "init_weights_from_safetensors", None
-        )
-        use_custom_weights = (
-            custom_weights_path
-            and os.path.exists(custom_weights_path)
-            and not hasattr(fastvideo_args, "_loading_teacher_critic_model")
-        )
+        custom_weights_path = getattr(fastvideo_args, 'init_weights_from_safetensors', None)
+        use_custom_weights = (custom_weights_path and os.path.exists(custom_weights_path) and 
+                            not hasattr(fastvideo_args, '_loading_teacher_critic_model'))
 
         if use_custom_weights:
-            if "transformer_2" in model_path:
-                custom_weights_path = getattr(
-                    fastvideo_args, "init_weights_from_safetensors_2", None
-                )
-            assert custom_weights_path is not None, (
-                "Custom initialization weights must be provided"
-            )
+            if 'transformer_2' in model_path:
+                custom_weights_path = getattr(fastvideo_args, 'init_weights_from_safetensors_2', None)
+            assert custom_weights_path is not None, "Custom initialization weights must be provided"
             if os.path.isdir(custom_weights_path):
                 safetensors_list = glob.glob(
-                    os.path.join(str(custom_weights_path), "*.safetensors")
-                )
+                    os.path.join(str(custom_weights_path), "*.safetensors"))
             else:
-                assert custom_weights_path.endswith(".safetensors"), (
-                    "Custom initialization weights must be a safetensors file"
-                )
+                assert custom_weights_path.endswith(".safetensors"), "Custom initialization weights must be a safetensors file"
                 safetensors_list = [custom_weights_path]
 
-        logger.info(
-            "Loading model from %s safetensors files: %s",
-            len(safetensors_list),
-            safetensors_list,
-        )
+        logger.info("Loading model from %s safetensors files: %s",
+                    len(safetensors_list), safetensors_list)
 
         default_dtype = PRECISION_TO_TYPE[
-            fastvideo_args.pipeline_config.dit_precision
-        ]
+            fastvideo_args.pipeline_config.dit_precision]
 
         # Load the model using FSDP loader
-        logger.info("Loading model from %s, default_dtype: %s", cls_name,
-                    default_dtype)
         assert fastvideo_args.hsdp_shard_dim is not None
-        # Cosmos2.5 checkpoints can include extra entries not present in the
-        # instantiated model (e.g. pos_embedder ranges / *_extra_state). Load
-        # non-strictly for Cosmos2.5 only; keep upstream strict behavior for others.
-        strict_load = not (
-            cls_name.startswith("Cosmos25")
-            or cls_name == "Cosmos25Transformer3DModel"
-            or getattr(fastvideo_args.pipeline_config, "prefix", "") == "Cosmos25"
-        )
         model = maybe_load_fsdp_model(
             model_cls=model_cls,
-            init_params={"config": dit_config, "hf_config": hf_config},
+            init_params={
+                "config": dit_config,
+                "hf_config": hf_config
+            },
             weight_dir_list=safetensors_list,
             device=get_local_torch_device(),
             hsdp_replicate_dim=fastvideo_args.hsdp_replicate_dim,
             hsdp_shard_dim=fastvideo_args.hsdp_shard_dim,
-            strict=strict_load,
             cpu_offload=fastvideo_args.dit_cpu_offload,
             pin_cpu_memory=fastvideo_args.pin_cpu_memory,
             fsdp_inference=fastvideo_args.use_fsdp_inference,
@@ -666,20 +568,15 @@ class TransformerLoader(ComponentLoader):
             output_dtype=None,
             training_mode=fastvideo_args.training_mode,
             enable_torch_compile=fastvideo_args.enable_torch_compile,
-            torch_compile_kwargs=fastvideo_args.torch_compile_kwargs,
-        )
+            torch_compile_kwargs=fastvideo_args.torch_compile_kwargs)
+
 
         total_params = sum(p.numel() for p in model.parameters())
         logger.info("Loaded model with %.2fB parameters", total_params / 1e9)
 
-        assert next(model.parameters()).dtype == default_dtype, (
-            "Model dtype does not match default dtype"
-        )
+        assert next(model.parameters()).dtype == default_dtype, "Model dtype does not match default dtype"
 
         model = model.eval()
-
-        if fastvideo_args.inference_mode and fastvideo_args.dit_layerwise_offload:
-            enable_layerwise_offload(model)
         return model
 
 
@@ -691,9 +588,7 @@ class SchedulerLoader(ComponentLoader):
         config = get_diffusers_config(model=model_path)
 
         class_name = config.pop("_class_name")
-        assert class_name is not None, (
-            "Model config does not contain a _class_name attribute. Only diffusers format is supported."
-        )
+        assert class_name is not None, "Model config does not contain a _class_name attribute. Only diffusers format is supported."
 
         scheduler_cls, _ = ModelRegistry.resolve_model_cls(class_name)
 
@@ -702,8 +597,7 @@ class SchedulerLoader(ComponentLoader):
             scheduler.set_shift(fastvideo_args.pipeline_config.flow_shift)
         if fastvideo_args.pipeline_config.timesteps_scale is not None:
             scheduler.set_timesteps_scale(
-                fastvideo_args.pipeline_config.timesteps_scale
-            )
+                fastvideo_args.pipeline_config.timesteps_scale)
         return scheduler
 
 
@@ -716,11 +610,8 @@ class GenericComponentLoader(ComponentLoader):
 
     def load(self, model_path: str, fastvideo_args: FastVideoArgs):
         """Load a generic component based on the model path, and inference args."""
-        logger.warning(
-            "Using generic loader for %s with library %s",
-            model_path,
-            self.library,
-        )
+        logger.warning("Using generic loader for %s with library %s",
+                       model_path, self.library)
 
         if self.library == "transformers":
             from transformers import AutoModel
@@ -730,10 +621,8 @@ class GenericComponentLoader(ComponentLoader):
                 trust_remote_code=fastvideo_args.trust_remote_code,
                 revision=fastvideo_args.revision,
             )
-            logger.info(
-                "Loaded generic transformers model: %s",
-                model.__class__.__name__,
-            )
+            logger.info("Loaded generic transformers model: %s",
+                        model.__class__.__name__)
             return model
         elif self.library == "diffusers":
             logger.warning(
@@ -755,21 +644,18 @@ class PipelineComponentLoader:
     """
 
     @staticmethod
-    def load_module(
-        module_name: str,
-        component_model_path: str,
-        transformers_or_diffusers: str,
-        fastvideo_args: FastVideoArgs,
-    ):
+    def load_module(module_name: str, component_model_path: str,
+                    transformers_or_diffusers: str,
+                    fastvideo_args: FastVideoArgs):
         """
         Load a pipeline module.
-
+        
         Args:
             module_name: Name of the module (e.g., "vae", "text_encoder", "transformer", "scheduler")
             component_model_path: Path to the component model
             transformers_or_diffusers: Whether the module is from transformers or diffusers
             pipeline_args: Inference arguments
-
+            
         Returns:
             The loaded module
         """
@@ -781,9 +667,8 @@ class PipelineComponentLoader:
         )
 
         # Get the appropriate loader for this module type
-        loader = ComponentLoader.for_module_type(
-            module_name, transformers_or_diffusers
-        )
+        loader = ComponentLoader.for_module_type(module_name,
+                                                 transformers_or_diffusers)
 
         # Load the module
         return loader.load(component_model_path, fastvideo_args)

fastvideo/models/loader/fsdp_load.py

@@ -23,7 +23,7 @@ from fastvideo.logger import init_logger
 from fastvideo.models.loader.utils import (get_param_names_mapping,
                                            hf_to_custom_state_dict)
 from fastvideo.models.loader.weight_utils import safetensors_weights_iterator
-from fastvideo.utils import set_mixed_precision_policy, is_pin_memory_available
+from fastvideo.utils import set_mixed_precision_policy
 
 logger = init_logger(__name__)
 
@@ -67,7 +67,6 @@ def maybe_load_fsdp_model(
     default_dtype: torch.dtype,
     param_dtype: torch.dtype,
     reduce_dtype: torch.dtype,
-    strict: bool = True,
     cpu_offload: bool = False,
     fsdp_inference: bool = False,
     output_dtype: torch.dtype | None = None,
@@ -107,7 +106,6 @@ def maybe_load_fsdp_model(
         logger.info("Disabling FSDP for MPS platform as it's not compatible")
 
     if use_fsdp:
-        pin_cpu_memory = pin_cpu_memory and is_pin_memory_available()
         world_size = hsdp_replicate_dim * hsdp_shard_dim
         if not training_mode and not fsdp_inference:
             hsdp_replicate_dim = world_size
@@ -143,7 +141,7 @@ def maybe_load_fsdp_model(
         weight_iterator,
         device,
         default_dtype,
-        strict=strict,
+        strict=True,
         cpu_offload=cpu_offload,
         param_names_mapping=param_names_mapping_fn,
     )
@@ -153,7 +151,6 @@ def maybe_load_fsdp_model(
                 f"Unexpected param or buffer {n} on meta device.")
         # Avoid unintended computation graph accumulation during inference
         if isinstance(p, torch.nn.Parameter):
-            
             p.requires_grad = False
 
     compile_in_loader = enable_torch_compile and training_mode
@@ -296,26 +293,6 @@ def load_model_from_full_model_state_dict(
     for target_param_name, full_tensor in custom_param_sd.items():
         meta_sharded_param = meta_sd.get(target_param_name)
         if meta_sharded_param is None:
-            # Some checkpoints include extra entries that are not part of the
-            # instantiated model's state_dict (e.g. `_extra_state` keys from
-            # some FSDP checkpoint formats). These can be safely skipped.
-            if (target_param_name.endswith("._extra_state")
-                    or target_param_name.endswith("_extra_state")):
-                logger.warning(
-                    "Skipping non-parameter checkpoint key: %s",
-                    target_param_name,
-                )
-                continue
-
-            # For non-strict loads, treat this as an "unexpected key" and skip it
-            # (mirrors torch.nn.Module.load_state_dict(strict=False)).
-            if not strict:
-                logger.warning(
-                    "Skipping unexpected checkpoint key (not present in model): %s",
-                    target_param_name,
-                )
-                continue
-
             raise ValueError(
                 f"Parameter {target_param_name} not found in custom model state dict. The hf to custom mapping may be incorrect."
             )
@@ -341,7 +318,7 @@ def load_model_from_full_model_state_dict(
                        unused_keys)
 
     # List of allowed parameter name patterns
-    ALLOWED_NEW_PARAM_PATTERNS = ["gate_compress", "proj_l"]  # Can be extended as needed
+    ALLOWED_NEW_PARAM_PATTERNS = ["gate_compress"]  # Can be extended as needed
     for new_param_name in unused_keys:
         if not any(pattern in new_param_name
                    for pattern in ALLOWED_NEW_PARAM_PATTERNS):

fastvideo/models/registry.py

@@ -30,7 +30,6 @@ _TEXT_TO_VIDEO_DIT_MODELS = {
     "CausalWanTransformer3DModel": ("dits", "causal_wanvideo", "CausalWanTransformer3DModel"),
     "StepVideoModel": ("dits", "stepvideo", "StepVideoModel"),
     "CosmosTransformer3DModel": ("dits", "cosmos", "CosmosTransformer3DModel"),
-    "Cosmos25Transformer3DModel": ("dits", "cosmos2_5", "Cosmos25Transformer3DModel"),
     "LongCatVideoTransformer3DModel": ("dits", "longcat_video_dit", "LongCatVideoTransformer3DModel"),  # Wrapper (Phase 1)
     "LongCatTransformer3DModel": ("dits", "longcat", "LongCatTransformer3DModel"),  # Native (Phase 2)
 }
@@ -51,9 +50,6 @@ _TEXT_ENCODER_MODELS = {
     "STEP1TextEncoder": ("encoders", "stepllm", "STEP1TextEncoder"),
     "BertModel": ("encoders", "clip", "CLIPTextModel"),
     "Qwen2_5_VLTextModel": ("encoders", "qwen2_5", "Qwen2_5_VLTextModel"),
-    "Reason1TextEncoder": ("encoders", "reason1", "Reason1TextEncoder"),
-    "Qwen2_5_VLForConditionalGeneration":
-    ("encoders", "reason1", "Reason1TextEncoder"),
 }
 
 _IMAGE_ENCODER_MODELS: dict[str, tuple] = {
@@ -76,13 +72,9 @@ _SCHEDULERS = {
      "FlowMatchEulerDiscreteScheduler"),
     "UniPCMultistepScheduler":
     ("schedulers", "scheduling_unipc_multistep", "UniPCMultistepScheduler"),
-    "FlowUniPCMultistepScheduler":
-    ("schedulers", "scheduling_flow_unipc_multistep", "FlowUniPCMultistepScheduler"),
     "SelfForcingFlowMatchScheduler":
     ("schedulers", "scheduling_self_forcing_flow_match",
      "SelfForcingFlowMatchScheduler"),
-    "RCMScheduler":
-    ("schedulers", "scheduling_rcm", "RCMScheduler"),
 }
 
 _FAST_VIDEO_MODELS = {